diff --git a/Bin/Config/Common/ModelParaDefinesNM_cn.dat b/Bin/Config/Common/ModelParaDefinesNM_cn.dat
index 3b97d08..4ad2fde 100644
--- a/Bin/Config/Common/ModelParaDefinesNM_cn.dat
+++ b/Bin/Config/Common/ModelParaDefinesNM_cn.dat
@@ -1 +1 @@
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
\ No newline at end of file
+PD94bWwgdmVycup_mpac2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4NCg0KPCEtLSoqKioqKioqKioqKiwzyoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKiotLT4NCjwhLS0gICAg5pys5paH5qGj55qE55uu55qE5piv5bu656uLV1RBSeaVsOWAvOinoyhOTSnmqKHlnZfnmoTlj4LmlbDlrprkuYkgICAgICAgICAgICAgICAgLS0+DQo8IS0tICAgIOS7heWMheWQq1BFQknmsYLop6PlmajmiYDpnIDnmoTlgqjlsYLjgIHkupXlkozlh6DkvZXlr7nosaHlj4LmlbAgICAgICAgICAgICAgICAgICAtLT4NCjwhLS0gICAgRGF0ZTogMjAyNjA2MTUgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIC0tPg0KPCEtLSoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKiotLT4NCg0KPCEtLQ0KICAgIOWPguaVsOiMg+WbtO+8mlBFQknmsYLop6PlmajmiYDpnIDnmoTlgqjlsYLjgIHkupXlkozlh6DkvZXlr7nosaHlj4LmlbANCiAgICDlr7nlupQgSFhfTldUTV9NT0RFTF9JTlBVVCDnu5PmnoTkvZPlrZfmrrUNCiAgICDpgJrov4cgX3BhcmFIZWxwZXItPmxvYWRQYXJhRGVmaW5lcygpIOWKoOi9ve+8jHNLZXkg5qCH6K+G5Li6IHNfTm1fU2VyaWUNCi0tPg0KDQo8Um9vdD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IuWCqOWxguWPguaVsCI+DQogICAgPFBhcmFJdGVtIE5hbWU9ImgiICAgQWxpYXM9IuWCqOWxguWOmuW6piIgICAgICAgVW5pdD0ibSIgICAgIERlZmF1bHQ9IjEwIiAgICBNYXg9IjEwMDAwIiAgTWluPSIxZS0wNSIgIERpZ2l0PSI2IiAgRGVzYz0icmVzZXJ2b2lyIHRoaWNrbmVzcyIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUGkiICBBbGlhcz0i5Yid5aeL5Y6L5YqbIiAgICAgICBVbml0PSJNUGEiICAgRGVmYXVsdD0iNDAiICAgIE1heD0iNTAwIiAgICBNaW49IjAuMSIgICAgRGlnaXQ9IjYiICBEZXNjPSJpbml0aWFsIGZvcm1hdGlvbiBwcmVzc3VyZSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iSyIgICBBbGlhcz0i5riX6YCP546HIiAgICAgICAgIFVuaXQ9IkRhcmN5IiBEZWZhdWx0PSIwLjAwMSIgTWF4PSIxMCIgICAgIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9InBlcm1lYWJpbGl0eSIgLz4KICAgIDxQYXJhSXRlbSBOYW1lPSJwaGkiIEFsaWFzPSLlrZTpmpnluqYiICAgICAgICAgVW5pdD0iIiAgICAgIERlZmF1bHQ9IjAuMSIgICBNYXg9IjEiICAgICAgTWluPSIxZS0wNSIgIERpZ2l0PSI2IiAgRGVzYz0icG9yb3NpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkN0aSIgQWxpYXM9Iue7vOWQiOWOi+e8qeezu+aVsCIgICBVbml0PSIxL01QYSIgRGVmYXVsdD0iMC4wMDEiIE1heD0iMTAiICAgICBNaW49IjFlLTMwIiAgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJ0b3RhbCBjb21wcmVzc2liaWxpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkNmIiAgQWxpYXM9IuWyqeefs+WOi+e8qeezu+aVsCIgICBVbml0PSIxL01QYSIgRGVmYXVsdD0iMC4wMDAxIiBNYXg9IjEwIiAgICBNaW49IjFlLTMwIiAgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyb2NrIGNvbXByZXNzaWJpbGl0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iU29pIiBBbGlhcz0i5Yid5aeL5ZCr5rK56aWx5ZKM5bqmIiBVbml0PSIiICAgICAgRGVmYXVsdD0iMC44IiAgIE1heD0iMSIgICAgICBNaW49IjAiICAgICAgRGlnaXQ9IjYiICBEZXNjPSJpbml0aWFsIG9pbCBzYXR1cmF0aW9uIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJTd2kiIEFsaWFzPSLliJ3lp4vlkKvmsLTppbHlkozluqYiIFVuaXQ9IiIgICAgICBEZWZhdWx0PSIwLjIiICAgTWF4PSIxIiAgICAgIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9ImluaXRpYWwgd2F0ZXIgc2F0dXJhdGlvbiIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSLkupXlj4LmlbAiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX1giICAgIEFsaWFzPSJY5Z2Q5qCHIiAgICAgICBVbml0PSJtIiAgICBEZWZhdWx0PSIwIiAgICAgTWF4PSIxMDAwMCIgIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgIERlc2M9IndlbGwgWCBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX1kiICAgIEFsaWFzPSJZ5Z2Q5qCHIiAgICAgICBVbml0PSJtIiAgICBEZWZhdWx0PSIwIiAgICAgTWF4PSIxMDAwMCIgIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgIERlc2M9IndlbGwgWSBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX1J3IiAgIEFsaWFzPSLkupXnrZLljYrlvoQiICAgIFVuaXQ9Im0iICAgIERlZmF1bHQ9IjAuMSIgICBNYXg9IjEwIiAgICAgTWluPSIwLjAwMSIgIERpZ2l0PSI2IiAgRGVzYz0id2VsbGJvcmUgcmFkaXVzIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX1NraW4iIEFsaWFzPSLooajnmq7ns7vmlbAiICAgIFVuaXQ9IiIgICAgIERlZmF1bHQ9IjAiICAgICBNYXg9IjEwMDAiICAgTWluPSItMTAwIiAgIERpZ2l0PSI2IiAgRGVzYz0ic2tpbiBmYWN0b3IiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IldfQyIgICAgQWxpYXM9IuS6leetkuWCqOmbhuezu+aVsCIgVW5pdD0ibV4zL01QYSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwIiAgIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9IndlbGxib3JlIHN0b3JhZ2UgY29lZmZpY2llbnQiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IldfV2VsbExlbmd0aCIgICAgICAgICBBbGlhcz0i5rC05bmz5q616ZW/5bqmIiAgIFVuaXQ9Im0iICAgIERlZmF1bHQ9IjIwMCIgIE1heD0iMTAwMDAiIE1pbj0iMWUtMDUiIERpZ2l0PSI2IiBEZXNjPSJob3Jpem9udGFsIHdlbGwgbGVuZ3RoIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX0RyYWluQW5nbGUiICAgICAgICAgQWxpYXM9IuaOkuawtOinkuW6piIgICAgIFVuaXQ9Im8iICAgIERlZmF1bHQ9IjAiICAgIE1heD0iMzYwIiAgIE1pbj0iLTM2MCIgIERpZ2l0PSI2IiBEZXNjPSJob3Jpem9udGFsIHdlbGwgZHJhaW4gYW5nbGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IldfTnVtYmVyT2ZGcmFjdHVyZXMiICBBbGlhcz0i6KOC57yd5pWw6YePIiAgICAgVW5pdD0iIiAgICAgRGVmYXVsdD0iMTUiICAgTWF4PSIxMDAwIiAgTWluPSIyIiAgICAgRGlnaXQ9IjAiIERlc2M9Im51bWJlciBvZiBmcmFjdHVyZXMiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IldfRnJhY3R1cmVIYWxmTGVuZ3RoIiBBbGlhcz0i6KOC57yd5Y2K6ZW/IiAgICAgVW5pdD0ibSIgICAgRGVmYXVsdD0iNTAiICAgTWF4PSIxMDAwMCIgTWluPSIxZS0wNSIgRGlnaXQ9IjYiIERlc2M9ImZyYWN0dXJlIGhhbGYtbGVuZ3RoIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJXX0ZyYWN0dXJlQW5nbGUiICAgICAgQWxpYXM9Iuijgue8neinkuW6piIgICAgIFVuaXQ9Im8iICAgIERlZmF1bHQ9IjkwIiAgIE1heD0iMzYwIiAgIE1pbj0iLTM2MCIgIERpZ2l0PSI2IiBEZXNjPSJmcmFjdHVyZSBhbmdsZSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iV19EZmMiICAgICAgICAgICAgICAgIEFsaWFzPSLoo4LnvJ3lr7zmtYHog73lipsiIFVuaXQ9Im1kLm0iIERlZmF1bHQ9IjEwMDAiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgIERpZ2l0PSI2IiBEZXNjPSJmcmFjdHVyZSBjb25kdWN0aXZpdHkiIC8+DQogIDwvUGFyYUdyb3VwPg0KDQogIDxQYXJhR3JvdXAgTmFtZT0iIiBBbGlhcz0i54us56uL6KOC57yd5Y+C5pWwIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iRl9YMCIgQWxpYXM9Iui1t+eCuVgwIiAgIFVuaXQ9Im0iICAgIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iZnJhY3R1cmUgc3RhcnQgWCBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGX1kwIiBBbGlhcz0i6LW354K5WTAiICAgVW5pdD0ibSIgICAgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmcmFjdHVyZSBzdGFydCBZIGNvb3JkaW5hdGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZfWDEiIEFsaWFzPSLnu4jngrlYMSIgICBVbml0PSJtIiAgICBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImZyYWN0dXJlIGVuZCBYIGNvb3JkaW5hdGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZfWTEiIEFsaWFzPSLnu4jngrlZMSIgICBVbml0PSJtIiAgICBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImZyYWN0dXJlIGVuZCBZIGNvb3JkaW5hdGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZfRkMiIEFsaWFzPSLlr7zmtYHog73lipsiIFVuaXQ9Im1kLm0iIERlZmF1bHQ9IjEiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgRGVzYz0iZnJhY3R1cmUgY29uZHVjdGl2aXR5IiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IuaWreWxguWPguaVsCI+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZUX1gwIiBBbGlhcz0i6LW354K5WDAiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iZmF1bHQgc3RhcnQgWCBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGVF9ZMCIgQWxpYXM9Iui1t+eCuVkwIiBVbml0PSJtIiBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImZhdWx0IHN0YXJ0IFkgY29vcmRpbmF0ZSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iRlRfWDEiIEFsaWFzPSLnu4jngrlYMSIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmYXVsdCBlbmQgWCBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGVF9ZMSIgQWxpYXM9Iue7iOeCuVkxIiBVbml0PSJtIiBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImZhdWx0IGVuZCBZIGNvb3JkaW5hdGUiIC8+DQogIDwvUGFyYUdyb3VwPg0KDQogIDxQYXJhR3JvdXAgTmFtZT0iIiBBbGlhcz0i55+p5b2i6L6555WM5Y+C5pWwIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWE1pbiIgQWxpYXM9IuW3pui+ueeVjFgiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1pbmltdW0gWCIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWU1pbiIgQWxpYXM9IuS4i+i+ueeVjFkiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1pbmltdW0gWSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWE1heCIgQWxpYXM9IuWPs+i+ueeVjFgiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1heGltdW0gWCIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWU1heCIgQWxpYXM9IuS4iui+ueeVjFkiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1heGltdW0gWSIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSLlnIblvaLovrnnlYzlj4LmlbAiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCQ19DZW50ZXJYIiBBbGlhcz0i5ZyG5b+DWCIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJjaXJjbGUgYm91bmRhcnkgY2VudGVyIFgiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkJDX0NlbnRlclkiIEFsaWFzPSLlnIblv4NZIiBVbml0PSJtIiBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImNpcmNsZSBib3VuZGFyeSBjZW50ZXIgWSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQkNfUmFkaXVzIiAgQWxpYXM9IuWNiuW+hCIgIFVuaXQ9Im0iIERlZmF1bHQ9IjEiIE1heD0iMTAwMDAiIE1pbj0iMWUtMDUiICBEaWdpdD0iNiIgRGVzYz0iY2lyY2xlIGJvdW5kYXJ5IHJhZGl1cyIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSLlpJrovrnlvaLovrnnlYzngrnlj4LmlbAiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCUF9YIiBBbGlhcz0iWOWdkOaghyIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJwb2x5Z29uIGJvdW5kYXJ5IHZlcnRleCBYIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCUF9ZIiBBbGlhcz0iWeWdkOaghyIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJwb2x5Z29uIGJvdW5kYXJ5IHZlcnRleCBZIiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IuWMuuWfn+WPguaVsCI+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJfTGVha2FnZSIgQWxpYXM9IuazhOmcsuezu+aVsCIgVW5pdD0iIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiIERpZ2l0PSI2IiBEZXNjPSJyZWdpb24gbGVha2FnZSBjb2VmZmljaWVudCIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSLmoIforrDljLrln5/lj4LmlbAiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSTV9Db21XIiAgICAgICBBbGlhcz0i5YKo6IO95q+UIiBVbml0PSIiIERlZmF1bHQ9IjEiIE1heD0iMSIgICAgTWluPSIwIiAgICAgRGlnaXQ9IjYiIERlc2M9InN0b3JhZ2UgcmF0aW8iIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJNX0NvbUtyIiAgICAgIEFsaWFzPSLmtYHluqbmr5QiIFVuaXQ9IiIgRGVmYXVsdD0iMSIgTWF4PSIxMDAwIiBNaW49IjAuMDAxIiBEaWdpdD0iNiIgRGVzYz0ibW9iaWxpdHkgcmF0aW8iIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJNX05ldFRvR3Jvc3MiIEFsaWFzPSLlh4Dmr5vmr5QiIFVuaXQ9IiIgRGVmYXVsdD0iMSIgTWF4PSIxIiAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgRGVzYz0ibmV0LXRvLWdyb3NzIiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8IS0tIOe7k+aenOmdouadv+WPguaVsOWPqueUsSByZWJ1aWxkUmVzdWx0UGFyYXMoKSDpgInmi6nvvIzkuI3kvJrlh7rnjrDlnKjlt6bkvqfpnaLmnb/kuK3jgIIgLS0+DQogIDxQYXJhR3JvdXAgTmFtZT0iIiBBbGlhcz0i57uT5p6c5Y+C5pWwIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X1dfUnciICAgQWxpYXM9IuS6leW+hCIgICAgICAgICBVbml0PSJtIiAgICAgIERlZmF1bHQ9IjAuMSIgICAgTWF4PSIxMCIgICAgTWluPSIwLjAwMSIgIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IHdlbGxib3JlIHJhZGl1cyIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X1dfRGZjIiAgQWxpYXM9Iuijgue8neWvvOa1geiDveWKmyIgVW5pdD0ibWQubSIgICBEZWZhdWx0PSIxMDAwIiAgIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBmcmFjdHVyZSBjb25kdWN0aXZpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9XX1NraW4iIEFsaWFzPSLooajnmq7ns7vmlbAiICAgICBVbml0PSIiICAgICAgIERlZmF1bHQ9IjAiICAgICAgTWF4PSIxMDAwIiAgTWluPSItMTAwIiAgIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IHNraW4gZmFjdG9yIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfV19DIiAgICBBbGlhcz0i5LqV562S5YKo6ZuG57O75pWwIiBVbml0PSJtXjMvTVBhIiBEZWZhdWx0PSIwIiAgICAgIE1heD0iMTAwMCIgIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCB3ZWxsYm9yZSBzdG9yYWdlIGNvZWZmaWNpZW50IiAvPg0KDQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9QaSIgIEFsaWFzPSLliJ3lp4vlnLDlsYLljovlipsiICAgVW5pdD0iTVBhIiAgIERlZmF1bHQ9IjQwIiAgICAgTWF4PSI1MDAiICAgTWluPSIwLjEiICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgaW5pdGlhbCBmb3JtYXRpb24gcHJlc3N1cmUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9LIiAgIEFsaWFzPSLmuJfpgI/njociICAgICAgICAgVW5pdD0iRGFyY3kiIERlZmF1bHQ9IjAuMDAxIiAgTWF4PSIxMCIgICAgTWluPSIwIiAgICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgcGVybWVhYmlsaXR5IiAvPgogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9oIiAgIEFsaWFzPSLlgqjlsYLljprluqYiICAgICAgIFVuaXQ9Im0iICAgICBEZWZhdWx0PSIxMCIgICAgIE1heD0iMTAwMDAiIE1pbj0iMWUtMDUiIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IHJlc2Vydm9pciB0aGlja25lc3MiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9waGkiIEFsaWFzPSLlrZTpmpnluqYiICAgICAgICAgVW5pdD0iIiAgICAgIERlZmF1bHQ9IjAuMSIgICAgTWF4PSIxIiAgICAgTWluPSIxZS0wNSIgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgcG9yb3NpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9DdGkiIEFsaWFzPSLnu7zlkIjljovnvKnns7vmlbAiICAgVW5pdD0iMS9NUGEiIERlZmF1bHQ9IjAuMDAxIiAgTWF4PSIxMCIgICAgTWluPSIxZS0zMCIgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyZXN1bHQgdG90YWwgY29tcHJlc3NpYmlsaXR5IiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfQ2YiICBBbGlhcz0i5bKp55+z5Y6L57yp57O75pWwIiAgIFVuaXQ9IjEvTVBhIiBEZWZhdWx0PSIwLjAwMDEiIE1heD0iMTAiICAgIE1pbj0iMWUtMzAiIERpZ2l0PSIxMCIgU2NpZW50aWZpYz0iMSIgRGVzYz0icmVzdWx0IHJvY2sgY29tcHJlc3NpYmlsaXR5IiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfU29pIiBBbGlhcz0i5Yid5aeL5ZCr5rK56aWx5ZKM5bqmIiBVbml0PSIiICAgICAgRGVmYXVsdD0iMC44IiAgICBNYXg9IjEiICAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBpbml0aWFsIG9pbCBzYXR1cmF0aW9uIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfU2dpIiBBbGlhcz0i5Yid5aeL5ZCr5rCU6aWx5ZKM5bqmIiBVbml0PSIiICAgICAgRGVmYXVsdD0iMCIgICAgICBNYXg9IjEiICAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBpbml0aWFsIGdhcyBzYXR1cmF0aW9uIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfU3dpIiBBbGlhcz0i5Yid5aeL5ZCr5rC06aWx5ZKM5bqmIiBVbml0PSIiICAgICAgRGVmYXVsdD0iMC4yIiAgICBNYXg9IjEiICAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBpbml0aWFsIHdhdGVyIHNhdHVyYXRpb24iIC8+DQoNCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0JvIiAgIEFsaWFzPSLmsrnkvZPnp6/ns7vmlbAiIFVuaXQ9Im0zL20zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBvaWwgZm9ybWF0aW9uIHZvbHVtZSBmYWN0b3IiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9NaXVvIiBBbGlhcz0i5rK555u46buP5bqmIiAgIFVuaXQ9Im1QYS5zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjFlLTMwIiBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBvaWwgdmlzY29zaXR5IiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfQ28iICAgQWxpYXM9IuayueWOi+e8qeezu+aVsCIgVW5pdD0iMS9NUGEiIERlZmF1bHQ9IjAiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyZXN1bHQgb2lsIGNvbXByZXNzaWJpbGl0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0J3IiAgIEFsaWFzPSLmsLTkvZPnp6/ns7vmlbAiIFVuaXQ9Im0zL20zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCB3YXRlciBmb3JtYXRpb24gdm9sdW1lIGZhY3RvciIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X01pdXciIEFsaWFzPSLmsLTnm7jpu4/luqYiICAgVW5pdD0ibVBhLnMiIERlZmF1bHQ9IjEiIE1heD0iMWUrMzAiIE1pbj0iMWUtMzAiIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IHdhdGVyIHZpc2Nvc2l0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0N3IiAgIEFsaWFzPSLmsLTljovnvKnns7vmlbAiIFVuaXQ9IjEvTVBhIiBEZWZhdWx0PSIwIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgIERpZ2l0PSIxMCIgU2NpZW50aWZpYz0iMSIgRGVzYz0icmVzdWx0IHdhdGVyIGNvbXByZXNzaWJpbGl0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0JnIiAgIEFsaWFzPSLmsJTkvZPnp6/ns7vmlbAiIFVuaXQ9Im0zL20zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBnYXMgZm9ybWF0aW9uIHZvbHVtZSBmYWN0b3IiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9NaXVnIiBBbGlhcz0i5rCU55u46buP5bqmIiAgIFVuaXQ9Im1QYS5zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjFlLTMwIiBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBnYXMgdmlzY29zaXR5IiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfQ2ciICAgQWxpYXM9IuawlOWOi+e8qeezu+aVsCIgVW5pdD0iMS9NUGEiIERlZmF1bHQ9IjAiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyZXN1bHQgZ2FzIGNvbXByZXNzaWJpbGl0eSIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCjwvUm9vdD4NCg==
\ No newline at end of file
diff --git a/Bin/Config/Common/ModelParaDefinesNM_en.dat b/Bin/Config/Common/ModelParaDefinesNM_en.dat
index a1226c0..cac7ac4 100644
--- a/Bin/Config/Common/ModelParaDefinesNM_en.dat
+++ b/Bin/Config/Common/ModelParaDefinesNM_en.dat
@@ -1 +1 @@
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
\ No newline at end of file
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
\ No newline at end of file
diff --git a/Bin/Config/Common/UnitDefault_cn.dat b/Bin/Config/Common/UnitDefault_cn.dat
index 7e3dd09..6271b6b 100644
--- a/Bin/Config/Common/UnitDefault_cn.dat
+++ b/Bin/Config/Common/UnitDefault_cn.dat
@@ -1 +1 @@
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
\ No newline at end of file
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
\ No newline at end of file
diff --git a/Bin/Config/Common/UnitDefault_en.dat b/Bin/Config/Common/UnitDefault_en.dat
index 1d2140c..a988b43 100644
--- a/Bin/Config/Common/UnitDefault_en.dat
+++ b/Bin/Config/Common/UnitDefault_en.dat
@@ -1 +1 @@
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
\ No newline at end of file
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
\ No newline at end of file
diff --git a/Bin/Config/Lang/cn/MPA_cn.qm b/Bin/Config/Lang/cn/MPA_cn.qm
index 496f6ae..3d5457b 100644
Binary files a/Bin/Config/Lang/cn/MPA_cn.qm and b/Bin/Config/Lang/cn/MPA_cn.qm differ
diff --git a/Bin/Config/Lang/cn/MPA_cn.ts b/Bin/Config/Lang/cn/MPA_cn.ts
index c9930a5..970e255 100644
--- a/Bin/Config/Lang/cn/MPA_cn.ts
+++ b/Bin/Config/Lang/cn/MPA_cn.ts
@@ -2129,12 +2129,6 @@
         <source>Normalized Gauss Newton</source>
         <translation>规则化高斯-牛顿</translation>
     </message>
-    <message>
-        <location filename="../../../../Src/iGui/iSubWxs/iWxAutoFit.cpp" line="71"/>
-        <location filename="../../../../Src/iGui/iSubWxs/iWxAutoFit.cpp" line="311"/>
-        <source>Genetic Algorithm</source>
-        <translation>遗传算法</translation>
-    </message>
     <message>
         <location filename="../../../../Src/iGui/iSubWxs/iWxAutoFit.cpp" line="138"/>
         <source>Parameter</source>
diff --git a/Bin/Config/Lang/cn/WTAI_cn.ts b/Bin/Config/Lang/cn/WTAI_cn.ts
index 2fd73d5..46e180b 100644
--- a/Bin/Config/Lang/cn/WTAI_cn.ts
+++ b/Bin/Config/Lang/cn/WTAI_cn.ts
@@ -4715,10 +4715,6 @@ MethodID:%1</source>
         <source>Normalized Gauss Newton</source>
         <translation>规则化高斯-牛顿</translation>
     </message>
-    <message>
-        <source>Genetic Algorithm</source>
-        <translation type="obsolete">遗传算法</translation>
-    </message>
     <message>
         <location filename="../../../../Src/mGui/mSubWxs/iWxAutoFit.cpp" line="272"/>
         <source>Parameter</source>
diff --git a/Bin/Config/Lang/cn/nmNum_cn.qm b/Bin/Config/Lang/cn/nmNum_cn.qm
index 32bc151..9df8a26 100644
Binary files a/Bin/Config/Lang/cn/nmNum_cn.qm and b/Bin/Config/Lang/cn/nmNum_cn.qm differ
diff --git a/Bin/Config/Lang/cn/nmNum_cn.ts b/Bin/Config/Lang/cn/nmNum_cn.ts
index e5a7886..efd37e2 100644
--- a/Bin/Config/Lang/cn/nmNum_cn.ts
+++ b/Bin/Config/Lang/cn/nmNum_cn.ts
@@ -48,346 +48,19 @@ Reason: %1</source>
     </message>
 </context>
 <context>
-    <name>nmCalculationAutoFitGA</name>
-    <message>
-        <source>=== GA Automatic Fitting Started ===</source>
-        <translation>=== GA自动拟合开始 ===</translation>
-    </message>
-    <message>
-        <source>Algorithm: Genetic Algorithm</source>
-        <translation>算法：遗传算法</translation>
-    </message>
-    <message>
-        <source>ERROR: Failed to load configuration from data manager</source>
-        <translation>错误：从数据管理器加载配置失败</translation>
-    </message>
-    <message>
-        <source>Enabled parameters count: %1</source>
-        <translation>启用参数数量：%1</translation>
-    </message>
-    <message>
-        <source>ERROR: No parameters enabled for optimization</source>
-        <translation>错误：没有启用优化参数</translation>
-    </message>
-    <message>
-        <source>ERROR: Target LogLog data is empty or insufficient</source>
-        <translation>错误：目标双对数数据为空或不足</translation>
-    </message>
-    <message>
-        <source>ERROR: Target LogLog data arrays have inconsistent sizes</source>
-        <translation>错误：目标双对数数据数组大小不一致</translation>
-    </message>
-    <message>
-        <source>Target data validation passed (%1 data points)</source>
-        <translation>目标数据验证通过（%1 个数据点）</translation>
-    </message>
-    <message>
-        <source>=== Evaluating Initial Solution (Elite Protection) ===</source>
-        <translation>=== 评估初始解 ===</translation>
-    </message>
-    <message>
-        <source>Initial parameters: </source>
-        <translation>初始参数：</translation>
-    </message>
-    <message>
-        <source>Starting initial solution evaluation...</source>
-        <translation>开始初始解评估...</translation>
-    </message>
-    <message>
-        <source>ERROR: m_userInitialSolution is empty!</source>
-        <translation>错误：初始解为空！</translation>
-    </message>
-    <message>
-        <source>Initial param[%1] = %2</source>
-        <translation>初始参数[%1] = %2</translation>
-    </message>
-    <message>
-        <source>evaluateGenes returned: %1</source>
-        <translation>评估基因 返回：%1</translation>
-    </message>
-    <message>
-        <source>Taking SUCCESS branch (fitness &lt; 1e9)</source>
-        <translation>进入成功分支</translation>
-    </message>
-    <message>
-        <source>Initial solution evaluation successful</source>
-        <translation>初始解评估成功</translation>
-    </message>
-    <message>
-        <source>Initial fitness (error): %1</source>
-        <translation>初始误差：%1</translation>
-    </message>
-    <message>
-        <source>Elite protection activated - initial solution will be preserved if no significant improvement found</source>
-        <translation>如果未找到显著改进，将保留初始解.</translation>
-    </message>
-    <message>
-        <source>Taking FAILURE branch (fitness &gt;= 1e9)</source>
-        <translation>进入失败分支</translation>
-    </message>
-    <message>
-        <source>Initial solution evaluation failed - starting with random initialization</source>
-        <translation>初始解评估失败 - 使用随机初始化开始</translation>
-    </message>
-    <message>
-        <source>Exception during initial solution evaluation</source>
-        <translation>初始解评估期间出现异常</translation>
-    </message>
-    <message>
-        <source>Population initialized: %1 individuals, %2 dimensions</source>
-        <translation>种群初始化：%1 个个体，%2 个维度</translation>
-    </message>
-    <message>
-        <source>=== Starting GA Main Loop ===</source>
-        <translation>=== 开始GA主循环 ===</translation>
-    </message>
-    <message>
-        <source>--- Generation %1/%2 ---</source>
-        <translation>--- 第 %1/%2 代 ---</translation>
-    </message>
-    <message>
-        <source>Current best error: %1</source>
-        <translation>当前最佳误差：%1</translation>
-    </message>
-    <message>
-        <source>Total evaluations: %1 (successful: %2, failures: %3)</source>
-        <translation>总评估次数：%1（成功：%2，失败：%3）</translation>
-    </message>
-    <message>
-        <source>Optimization stopped by user request</source>
-        <translation>优化因用户请求而停止</translation>
-    </message>
-    <message>
-        <source>Generation %1 completed: best = %2, avg = %3, worst = %4</source>
-        <translation>第 %1 代完成：最佳 = %2，平均 = %3，最差 = %4</translation>
-    </message>
-    <message>
-        <source>=== TARGET ACHIEVED ===</source>
-        <translation>=== 达到目标 ===</translation>
-    </message>
-    <message>
-        <source>Target error achieved! Current error: %1 &lt; Target: %2</source>
-        <translation>达到目标误差！当前误差：%1 &lt; 目标：%2</translation>
-    </message>
-    <message>
-        <source>Optimization completed successfully after %1 generations</source>
-        <translation>达到目标误差！当前误差：%1 &lt; 目标：%2</translation>
-    </message>
-    <message>
-        <source>=== TRUE CONVERGENCE DETECTED ===</source>
-        <translation>=== 检测到真正收敛 ===</translation>
-    </message>
-    <message>
-        <source>Algorithm has converged to a stable solution</source>
-        <translation>算法已收敛到稳定解</translation>
-    </message>
-    <message>
-        <source>Final error: %1 after %2 generations</source>
-        <translation>最终误差：%1，经过 %2 代</translation>
-    </message>
-    <message>
-        <source>Solution quality: %1 (1.0 = target achieved)</source>
-        <translation>解质量：%1</translation>
-    </message>
-    <message>
-        <source>=== LOCAL OPTIMUM DETECTED ===</source>
-        <translation>=== 检测到局部最优 ===</translation>
-    </message>
-    <message>
-        <source>Algorithm appears to be trapped in local optimum</source>
-        <translation>算法似乎陷入局部最优</translation>
-    </message>
-    <message>
-        <source>Current error: %1 after %2 generations</source>
-        <translation>当前误差：%1，经过 %2 代</translation>
-    </message>
-    <message>
-        <source>Suggestion: Try restarting with different parameters or larger search space</source>
-        <translation>建议：尝试使用不同参数或更大搜索空间重新开始</translation>
-    </message>
-    <message>
-        <source>=== CONSECUTIVE FAILURES ===</source>
-        <translation>=== 连续失败 ===</translation>
-    </message>
-    <message>
-        <source>Too many consecutive failed generations (%1/%2)</source>
-        <translation>连续失败代数过多（%1/%2）</translation>
-    </message>
-    <message>
-        <source>  Optimization status: diversity=%1</source>
-        <translation>优化状态：多样性=%1</translation>
-    </message>
-    <message>
-        <source>Generation %1 completed - Current best: %2</source>
-        <translation>第 %1 代完成 - 当前最佳：%2</translation>
-    </message>
-    <message>
-        <source>CRITICAL ERROR: %1</source>
-        <translation>严重错误: %1</translation>
-    </message>
-    <message>
-        <source>CRITICAL ERROR: Unknown exception in GA main loop</source>
-        <translation>严重错误：GA主循环中的未知异常</translation>
-    </message>
-    <message>
-        <source>Applying optimized parameters to model...</source>
-        <translation>正在将优化参数应用到模型...</translation>
-    </message>
-    <message>
-        <source>=== Optimization Results ===</source>
-        <translation>=== 优化结果 ===</translation>
-    </message>
-    <message>
-        <source>Final error: %1</source>
-        <translation>最终误差: %1</translation>
-    </message>
-    <message>
-        <source>Total generations: %1</source>
-        <translation>总代数：%1</translation>
-    </message>
-    <message>
-        <source>Total evaluations: %1 (successful: %2)</source>
-        <translation>总评估次数: %1 (成功: %2)</translation>
-    </message>
-    <message>
-        <source>Optimized parameters: </source>
-        <translation>优化参数:</translation>
-    </message>
-    <message>
-        <source>Parameters applied successfully to data manager</source>
-        <translation>参数已成功应用到数据管理器</translation>
-    </message>
-    <message>
-        <source>ERROR: Failed to apply final parameters: %1</source>
-        <translation>错误: 应用最终参数失败: %1</translation>
-    </message>
-    <message>
-        <source>ERROR: Unknown error applying final parameters</source>
-        <translation>错误: 应用最终参数时出现未知错误</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION SUCCESSFUL ===</source>
-        <translation>=== GA优化成功 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION CONVERGED ===</source>
-        <translation>=== GA优化收敛 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION - LOCAL OPTIMUM ===</source>
-        <translation>=== GA优化 - 局部最优 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION - MAX GENERATIONS ===</source>
-        <translation>=== GA优化 - 达到最大代数 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION STOPPED BY USER ===</source>
-        <translation>=== GA优化 - 用户停止 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION FAILED ===</source>
-        <translation>=== GA优化失败 ===</translation>
-    </message>
-    <message>
-        <source>=== GA OPTIMIZATION - UNKNOWN END ===</source>
-        <translation>=== GA优化 - 未知结束 ===</translation>
-    </message>
-    <message>
-        <source>Result: %1</source>
-        <translation>结果: %1</translation>
-    </message>
-    <message>
-        <source>=== User Stop Request Received ===</source>
-        <translation>=== 用户停止请求已接收 ===</translation>
-    </message>
-    <message>
-        <source>Gracefully stopping GA optimization...</source>
-        <translation>在停止GA优化...</translation>
-    </message>
-    <message>
-        <source>Force stopping current evaluation...</source>
-        <translation>强制停止当前评估...</translation>
-    </message>
-    <message>
-        <source>GA optimization stop request processed</source>
-        <translation>GA优化停止请求已处理</translation>
-    </message>
-    <message>
-        <source>Stop request received but optimization is not running</source>
-        <translation>收到停止请求但优化未运行</translation>
-    </message>
-    <message>
-        <source>  Individual %1 improved: %2 -&gt; %3</source>
-        <translation>个体 %1 改进：%2 -&gt; %3</translation>
-    </message>
-    <message>
-        <source>  Individual %1: evaluation failed</source>
-        <translation>个体 %1：评估失败</translation>
-    </message>
-    <message>
-        <source>  Individual %1: Exception: %2</source>
-        <translation>个体 %1：异常：%2</translation>
-    </message>
-    <message>
-        <source>  Individual %1: Unknown exception</source>
-        <translation>个体 %1：未知异常</translation>
-    </message>
-    <message>
-        <source>WARNING: No successful evaluations in generation %1 (consecutive failures: %2)</source>
-        <translation>警告：第 %1 代中没有成功评估（连续失败：%2）</translation>
-    </message>
-    <message>
-        <source>Current generation stats: %1 successful, %2 failed out of %3 individuals (success rate: %4%)</source>
-        <translation>当前代统计：%3 个个体中 %1 个成功，%2 个失败（成功率：%4%）</translation>
-    </message>
-    <message>
-        <source>ERROR: Too many consecutive failed generations (%1/%2) - stopping optimization</source>
-        <translation>错误：连续失败代数过多（%1/%2）- 停止优化</translation>
-    </message>
-    <message>
-        <source>WARNING: Low success rate (%1%) in generation %2, but continuing optimization</source>
-        <translation>警告：第 %2 代中成功率低（%1%），但继续优化</translation>
-    </message>
-    <message>
-        <source>No initial solution for elite protection</source>
-        <translation>没有初始解用于迭代</translation>
-    </message>
-    <message>
-        <source>=== Final Result Validation (Elite Protection) ===</source>
-        <translation>=== 最终结果验证 ===</translation>
-    </message>
-    <message>
-        <source>Comparing results: Initial=%1, Final=%2</source>
-        <translation>比较结果: 初始=%1, 最终=%2</translation>
-    </message>
-    <message>
-        <source>Improvement: %1 (%2%)</source>
-        <translation>改进: %1 (%2%)</translation>
-    </message>
+    <name>nmCalculationAutoFitPSO</name>
     <message>
-        <source>Elite protection triggered: insufficient improvement</source>
-        <translation>改进不足</translation>
+        <source>Effective improvement threshold: max(%1, %2% of baseline error); %3 consecutive ineffective steps trigger convergence confirmation</source>
+        <translation>有效改善阈值：取 %1 与基准误差的 %2% 中较大值；连续 %3 次无有效改善后进行收敛确认</translation>
     </message>
     <message>
-        <source>Threshold: %1%, Actual: %2%</source>
-        <translation>阈值: %1%, 实际: %2%</translation>
+        <source>No effective improvement for %1 consecutive steps; rebuilding sensitivity model for confirmation</source>
+        <translation>连续 %1 次无有效改善，正在重建灵敏度模型进行确认</translation>
     </message>
     <message>
-        <source>Restoring initial solution as final result</source>
-        <translation>恢复初始解作为最终结果</translation>
+        <source>Sensitivity rebuild produced no effective improvement; local convergence detected</source>
+        <translation>灵敏度重建后仍无有效改善，判定为局部收敛</translation>
     </message>
-    <message>
-        <source>Initial solution restored successfully</source>
-        <translation>初始解恢复成功</translation>
-    </message>
-    <message>
-        <source>Final result validated - significant improvement achieved</source>
-        <translation>最终结果已验证 - 实现显著改进</translation>
-    </message>
-</context>
-<context>
-    <name>nmCalculationAutoFitPSO</name>
     <message>
         <source>=== User Stop Request Received ===</source>
         <translation>=== 用户停止请求已接收 ===</translation>
@@ -3698,6 +3371,38 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
         <source>Warning</source>
         <translation>警告</translation>
     </message>
+    <message>
+        <source>Invalid parameter range</source>
+        <translation>参数范围无效</translation>
+    </message>
+    <message>
+        <source>The parameter table is unavailable.</source>
+        <translation>参数表不可用。</translation>
+    </message>
+    <message>
+        <source>The parameter row is invalid.</source>
+        <translation>参数行无效。</translation>
+    </message>
+    <message>
+        <source>The range values for %1 are incomplete.</source>
+        <translation>%1 的范围值不完整。</translation>
+    </message>
+    <message>
+        <source>The minimum value, initial value, and maximum value of %1 must be finite numbers.</source>
+        <translation>%1 的最小值、初始值和最大值必须是有限数值。</translation>
+    </message>
+    <message>
+        <source>The physical range of %1 is invalid.</source>
+        <translation>%1 的物理范围无效。</translation>
+    </message>
+    <message>
+        <source>The values of %1 exceed the physical range [%2, %3].</source>
+        <translation>%1 的参数值超出物理范围 [%2, %3]。</translation>
+    </message>
+    <message>
+        <source>The values of %1 must satisfy: minimum &lt;= initial value &lt;= maximum.</source>
+        <translation>%1 的参数值必须满足：最小值 &lt;= 初始值 &lt;= 最大值。</translation>
+    </message>
     <message>
         <source>Please select a target well!</source>
         <translation>请选择一口目标井！</translation>
@@ -3740,11 +3445,6 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
         <source>Optimized parameters have been applied to the model.</source>
         <translation>拟合参数已应用到模型。</translation>
     </message>
-    <message>
-        <source>GA Optimization completed:
-</source>
-        <translation>GA求解完成：</translation>
-    </message>
     <message>
         <source>Optimization Completed</source>
         <translation>拟合完成</translation>
@@ -3774,27 +3474,14 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
         <source>Optimization Stopped</source>
         <translation>拟合停止</translation>
     </message>
-    <message>
-        <source>GA Optimization stopped by user:
-</source>
-        <translation>GA拟合被用户强行终止：</translation>
-    </message>
     <message>
         <source>PSO algorithm selected.</source>
         <translation>用户选择PSO。</translation>
     </message>
-    <message>
-        <source>GA algorithm selected.</source>
-        <translation>用户选择GA</translation>
-    </message>
     <message>
         <source>PSO (Particle Swarm)</source>
         <translation>PSO</translation>
     </message>
-    <message>
-        <source>GA (Genetic Algorithm)</source>
-        <translation>GA</translation>
-    </message>
     <message>
         <source>Darcy</source>
         <translation type="unfinished"></translation>
@@ -3811,6 +3498,18 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
         <source>On</source>
         <translation>开启</translation>
     </message>
+    <message>
+        <source>Fracture conductivity</source>
+        <translation>裂缝导流能力</translation>
+    </message>
+    <message>
+        <source>Fracture half length</source>
+        <translation>裂缝半长</translation>
+    </message>
+    <message>
+        <source>The minimum value of %1 must be greater than zero for automatic fitting.</source>
+        <translation>自动拟合时，%1 的最小值和初始值必须大于零。</translation>
+    </message>
 </context>
 <context>
     <name>nmWxAutomaticfitting</name>
diff --git a/Bin/Config/Unit/UnitDefault.dat b/Bin/Config/Unit/UnitDefault.dat
index 1d2140c..7637503 100644
--- a/Bin/Config/Unit/UnitDefault.dat
+++ b/Bin/Config/Unit/UnitDefault.dat
@@ -1 +1 @@
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
\ No newline at end of file
+77u/PD94bWwgcup_mpadmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4NCjxSb290Pg0KICAgIDxUeXwzyBlT2Y+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0iY29tcHJlc3NpYmlsaXR5IiBOYW1lPSJjb21wcmVzc2liaWxpdHkiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSIxL01QYSIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSIxL1BhIiBrPSIxZTYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iMS9wc2kiIGs9IjE0NS4wMzgiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iMS9hdG0iIGs9IjkuODY5MjMiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0iZGVuc2l0eSIgTmFtZT0iZGVuc2l0eSI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImcvY21eMyIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJrZy9tXjMiIGs9IjAuMDAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImxiL2Z0XjMiIGs9IjAuMDE2MDIiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibGIvZ2FsKFVTKSIgaz0iMC4xMTk4MjYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibGIvZ2FsKFVLKSIgaz0iMC4wOTk3NiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJsYi9iYmwiIGs9IjAuMDAyODUzIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImcvY2MiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZy9jZiIgaz0iMy41MzE0N2UtMDUiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZy9mdF4zIiBrPSIzLjUzMTQ3ZS0wNSIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCiAgICAgICAgPFVuaXRHcm91cCBUeXBlPSJnZW90aGVybWFsZ3JhZGllbnQiIE5hbWU9Imdlb3RoZXJtYWxncmFkaWVudCI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImRlZy9tIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkYvaW4iIGs9IjEuOCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJkZWcva20iIGs9IjAuMDk5Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkYvbWlsZSIgaz0iMC4wMzQ0ODI4Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkYvZnQiIGs9IjE4MS44MTgiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0ibGVuZ3RoIiBOYW1lPSJsZW5ndGgiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJpbiIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtIiBrPSIzOS4zNzAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNtIiBrPSIwLjM5MzcwMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtbSIgaz0iMC4wMzkzNzAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImttIiBrPSIzOTM3MC4xIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImZ0IiBrPSIxMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtaSIgaz0iNjMzNDYuNSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJ5ZCIgaz0iMzUuOTg3MyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJubWlsZSIgaz0iNzI5MTMuNCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJubSIgaz0iMy45MzcwMWUtMDgiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0icmFkIiBrPSI3LjgyODMxIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InBlcmNlbnQiIE5hbWU9InBlcmNlbnQiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSIlIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InB1IiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1eMy9tXjMiIGs9IjEwMCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJ2L3YiIGs9IjEwMCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJmcmFjdGlvbiIgaz0iMTAwIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InBlcm1lYWJpbGl0eSIgTmFtZT0icGVybWVhYmlsaXR5Ij4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iRGFyY3kiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibUQiIGs9IjAuMDAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNtXi0yIiBrPSI5LjgxZSswNyIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCiAgICAgICAgPFVuaXRHcm91cCBUeXBlPSJwb3dlciIgTmFtZT0icG93ZXIiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJXIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkJ0dS9oIiBrPSIwLjI5MzA3MSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJrZ2YubS9zIiBrPSI5LjgwNjY1Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNhbC9zIiBrPSI0LjE4NjgiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iaHAiIGs9IjczNS40OTkiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0icHJlc3N1cmUiIE5hbWU9InByZXNzdXJlIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iUGEiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iTVBhIiBrPSIxZSswNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJLUGEiIGs9IjEwMDAiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iYmFyIiBrPSIxMDAwMDAiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iVG9yciIgaz0iMTMzLjMyMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJkeW4vY21eMiIgaz0iMC4xIi8+DQogICAgICAgICAgICA8VW5pdCBVbml0PSJtbUhnIiBrPSIxMzMuMzIyIi8+DQogICAgICAgICAgICA8VW5pdCBVbml0PSJtbUgyTyIgaz0iOS44MDY2NSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJhdCIgaz0iOTgwNjYuNSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJwc2kiIGs9IjY4OTYuNTUiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iYXRtIiBrPSIxMDEzMjUiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia2dmL2NtXjIiIGs9Ijk4MDc5LjciLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0icHJvZHVjdGlvbiIgTmFtZT0icHJvZHVjdGlvbiI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1eMy9kIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InRjZi9kIiBrPSIwLjAyODMyIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1eMy9hIiBrPSIwLjAwMjc0MDA3Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImJjZi9kIiBrPSIwLjAyODMyIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1tY2YvZCIgaz0iMjgzMjAiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibWNmL2QiIGs9IjI4LjMyIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InN0Yi9kIiBrPSIwLjE1OSIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCiAgICAgICAgPFVuaXRHcm91cCBUeXBlPSJwcm9kZ2FzIiBOYW1lPSJwcm9kZ2FzIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibV4zL2QiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iMTBeNCptXjMvZCIgaz0iMTAwMDAiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0idGNmL2QiIGs9IjAuMDI4MzIiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibV4zL2EiIGs9IjAuMDAyNzQwMDciLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iYmNmL2QiIGs9IjAuMDI4MzIiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibW1jZi9kIiBrPSIyODMyMCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtY2YvZCIgaz0iMjguMzIiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ic3RiL2QiIGs9IjAuMTU5Ii8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InByb2RvaWwiIE5hbWU9InByb2RvaWwiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjMvZCIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJ0Y2YvZCIgaz0iMC4wMjgzMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjMvYSIgaz0iMC4wMDI3NDAwNyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJiY2YvZCIgaz0iMC4wMjgzMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtbWNmL2QiIGs9IjI4MzIwIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1jZi9kIiBrPSIyOC4zMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJzdGIvZCIgaz0iMC4xNTkiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0icHJvZHdhdGVyIiBOYW1lPSJwcm9kd2F0ZXIiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjMvZCIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJ0Y2YvZCIgaz0iMC4wMjgzMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjMvYSIgaz0iMC4wMDI3NDAwNyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJiY2YvZCIgaz0iMC4wMjgzMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtbWNmL2QiIGs9IjI4MzIwIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1jZi9kIiBrPSIyOC4zMiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJzdGIvZCIgaz0iMC4xNTkiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0icmFkaW9hY3Rpdml0eSIgTmFtZT0icmFkaW9hY3Rpdml0eSI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImdBUEkiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iQVBJIiBrPSIxIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InNsb3duZXNzIiBOYW1lPSJzbG93bmVzcyI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InVzL20iIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0idXMvZnQiIGs9IjMuMjgwODQiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0ic3F1YXJlIiBOYW1lPSJzcXVhcmUiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjIiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia21eMiIgaz0iMWUrMDYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iaW5eMiIgaz0iMC4wMDA2NDUyIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNtXjIiIGs9IjAuMDAwMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJhY3JlIiBrPSI0MDQ3Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1pbGVeMiIgaz0iMi41ODg4OGUrMDYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZnReMiIgaz0iMC4wOTMiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iaGEiIGs9IjEwMDAwIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InlkXjIiIGs9IjAuODM2MSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJyYWQyIiBrPSIwLjAzOTUzNjkiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0ic3RyZW5ndGgiIE5hbWU9InN0cmVuZ3RoIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iTiIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJsYmYiIGs9IjQuNDQ0NDQiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia2dmIiBrPSI5LjgxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImR5biIgaz0iMWUtMDUiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idGVtcGVyYXR1cmUiIE5hbWU9InRlbXBlcmF0dXJlIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZGVnIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IksiIGs9IjEiIGI9IjI3My4xNSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJGIiBrPSIxLjgiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0i4oSDIiBrPSIxIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InRoZW1hbCBwb3dlciIgTmFtZT0idGhlbWFsIHBvd2VyIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iSiIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJjYWwiIGs9IjQuMTg2OCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJrZ2YubSIgaz0iOS44MDY2NSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJCdHUiIGs9IjEwNTUuMDYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia1cuaCIgaz0iMy42ZSswNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJmdC5sYmYiIGs9IjEuMzU1ODIiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iaHAuaChVUykiIGs9IjIuNjQ3NzllKzA2Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkhwLmgoVUspIiBrPSIyLjY4NDUyZSswNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJrZy5tIiBrPSI5LjgwMDA4Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImtjYWwiIGs9IjQxODUuODUiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iQlRVIiBrPSIxMDU0Ljg1Ii8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9InRpbWUiIE5hbWU9InRpbWUiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJ1cyIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtcyIgaz0iMTAwMCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJzIiBrPSIxZSswNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtaW4iIGs9IjZlKzA3Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImhyIiBrPSIzLjZlKzA5Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkRheSIgaz0iOC42NGUrMTAiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idmVsb2NpdHkiIE5hbWU9InZlbG9jaXR5Ij4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibS9zIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1pbGUvaCIgaz0iMC40NDcwNCIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJmdC9zIiBrPSIwLjMwNDgiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idmlzY29zaXR5IiBOYW1lPSJ2aXNjb3NpdHkiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJQYS5zIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1QYS5zIiBrPSIwLjAwMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJQIiBrPSIwLjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iY1AiIGs9IjAuMDAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImJmLnMvZnReMiIgaz0iNDcuODgwMyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJrZ2Yucy9tXjMiIGs9IjkuODA2NjUiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idmlzY29zaXR5IGFjdCIgTmFtZT0idmlzY29zaXR5IGFjdCI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1eMi9zIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNtXjIvcyIgaz0iMC4wMDAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImZ0XjIvcyIgaz0iMC4wOTI5MDMiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iU3QiIGs9IjAuMDAwMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJjU3QiIGs9IjFlLTA2Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1tXjIvcyIgaz0iMWUtMDYiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idm9sdGFnZSIgTmFtZT0idm9sdGFnZSI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im12IiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InYiIGs9IjEwMDAiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0idm9sdW1lIiBOYW1lPSJ2b2x1bWUiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtXjMiIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibWwiIGs9IjFlLTA2Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IkwiIGs9IjAuMDAxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImluXjMiIGs9IjEuNjM5ZS0wNSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJjbV4zIiBrPSIxZS0wNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJwdCIgaz0iMC4wMDA0NzMiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZ2FsKFVTKSIgaz0iMC4wMDM3ODUiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iYmJsIiBrPSIwLjE1OSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJiY2YiIGs9IjIuODMxN2UrMDYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0idGNmIiBrPSIyLjgzMTdlKzEwIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1tY2YiIGs9IjI4MzE3Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1jZiIgaz0iMjguMzE3Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImZ0XjMiIGs9IjAuMDI4MyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJnYWwoVUspIiBrPSIwLjAwNDU0NiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJnaSIgaz0iMC4wMDAxMTgiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0icXQiIGs9IjAuMDAwOTQ2Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9ImNjIiBrPSIxZS0wNiIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJhY3JlLmZ0IiBrPSIxMjM0Ii8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9IndlaWdodCIgTmFtZT0id2VpZ2h0Ij4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia2ciIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0idG9uIiBrPSIxMDAwIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Imx0b24iIGs9IjEwMTYiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibGIiIGs9IjAuNDU0Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im96IiBrPSIwLjAyODM1Ii8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9InN0b24iIGs9IjkwNyIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJnIiBrPSIwLjAwMSIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9IndzIiBOYW1lPSLnm5DluqYiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtZy9MIiBrPSIxIi8+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Is68Zy9tTCIgaz0iMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJnL0wiIGs9IjEwMDAiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ia2cvbV4zIiBrPSIwLjAwMSIvPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJwcG0iIGs9IjEiLz4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iS3BwbSIgaz0iMTAwMCIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9IlRvRG8xIiBOYW1lPSJUb0RvMSI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9IjEvbV4zL2QiIGs9IjEiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0iVG9EbzIiIE5hbWU9IlRvRG8yIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iwrAiIGs9IjEiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0iVG9EbzMiIE5hbWU9IlRvRG8zIj4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0ibV4zL01QYSIgaz0iMSIvPg0KICAgICAgICA8L1VuaXRHcm91cD4NCiAgICAgICAgPFVuaXRHcm91cCBUeXBlPSJUb0RvNCIgTmFtZT0iVG9EbzQiPg0KICAgICAgICAgICAgPFVuaXQgUmVtYXJrPSIgIiBVbml0PSJtRC8obVBhLnMpIiBrPSIxIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9IlRvRG81IiBOYW1lPSJUb0RvNSI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Ik1QYS9tIiBrPSIxIi8+DQogICAgICAgIDwvVW5pdEdyb3VwPg0KICAgICAgICA8VW5pdEdyb3VwIFR5cGU9IlRvRG82IiBOYW1lPSJUb0RvNiI+DQogICAgICAgICAgICA8VW5pdCBSZW1hcms9IiAiIFVuaXQ9Im1ELm0iIGs9IjEiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQogICAgICAgIDxVbml0R3JvdXAgVHlwZT0iVG9EbzciIE5hbWU9IlRvRG83Ij4NCiAgICAgICAgICAgIDxVbml0IFJlbWFyaz0iICIgVW5pdD0iZy9tb2wiIGs9IjEiLz4NCiAgICAgICAgPC9Vbml0R3JvdXA+DQoNCg0KICAgIDwvVHlwZU9mPg0KPC9Sb290Pg0K
\ No newline at end of file
diff --git a/Bin/XmlFiles/ModelParaDefinesNM_cn.xml b/Bin/XmlFiles/ModelParaDefinesNM_cn.xml
index 367fb1c..f26f2ef 100644
--- a/Bin/XmlFiles/ModelParaDefinesNM_cn.xml
+++ b/Bin/XmlFiles/ModelParaDefinesNM_cn.xml
@@ -17,7 +17,7 @@
   <ParaGroup Name="" Alias="储层参数">
     <ParaItem Name="h"   Alias="储层厚度"       Unit="m"     Default="10"    Max="10000"  Min="1e-05"  Digit="6"  Desc="reservoir thickness" />
     <ParaItem Name="Pi"  Alias="初始压力"       Unit="MPa"   Default="40"    Max="500"    Min="0.1"    Digit="6"  Desc="initial formation pressure" />
-    <ParaItem Name="K"   Alias="渗透率"         Unit="mD"    Default="1"     Max="10000"  Min="0"      Digit="6"  Desc="permeability" />
+    <ParaItem Name="K"   Alias="渗透率"         Unit="Darcy" Default="0.001" Max="10"     Min="0"      Digit="6"  Desc="permeability" />
     <ParaItem Name="phi" Alias="孔隙度"         Unit=""      Default="0.1"   Max="1"      Min="1e-05"  Digit="6"  Desc="porosity" />
     <ParaItem Name="Cti" Alias="综合压缩系数"   Unit="1/MPa" Default="0.001" Max="10"     Min="1e-30"  Digit="10" Scientific="1" Desc="total compressibility" />
     <ParaItem Name="Cf"  Alias="岩石压缩系数"   Unit="1/MPa" Default="0.0001" Max="10"    Min="1e-30"  Digit="10" Scientific="1" Desc="rock compressibility" />
@@ -90,7 +90,7 @@
     <ParaItem Name="Result_W_C"    Alias="井筒储集系数" Unit="m^3/MPa" Default="0"      Max="1000"  Min="0"      Digit="6"  Desc="result wellbore storage coefficient" />
 
     <ParaItem Name="Result_Pi"  Alias="初始地层压力"   Unit="MPa"   Default="40"     Max="500"   Min="0.1"   Digit="6"  Desc="result initial formation pressure" />
-    <ParaItem Name="Result_K"   Alias="渗透率"         Unit="mD"    Default="1"      Max="10000" Min="0"     Digit="6"  Desc="result permeability" />
+    <ParaItem Name="Result_K"   Alias="渗透率"         Unit="Darcy" Default="0.001"  Max="10"    Min="0"     Digit="6"  Desc="result permeability" />
     <ParaItem Name="Result_h"   Alias="储层厚度"       Unit="m"     Default="10"     Max="10000" Min="1e-05" Digit="6"  Desc="result reservoir thickness" />
     <ParaItem Name="Result_phi" Alias="孔隙度"         Unit=""      Default="0.1"    Max="1"     Min="1e-05" Digit="6"  Desc="result porosity" />
     <ParaItem Name="Result_Cti" Alias="综合压缩系数"   Unit="1/MPa" Default="0.001"  Max="10"    Min="1e-30" Digit="10" Scientific="1" Desc="result total compressibility" />
diff --git a/Bin/XmlFiles/ModelParaDefinesNM_en.xml b/Bin/XmlFiles/ModelParaDefinesNM_en.xml
index fb7e17f..ad27365 100644
--- a/Bin/XmlFiles/ModelParaDefinesNM_en.xml
+++ b/Bin/XmlFiles/ModelParaDefinesNM_en.xml
@@ -18,7 +18,7 @@
   <ParaGroup Name="" Alias="Reservoir Parameters">
     <ParaItem Name="h"   Alias="Reservoir Thickness"         Unit="m"     Default="10"    Max="10000"  Min="1e-05"  Digit="3"  Desc="reservoir thickness" />
     <ParaItem Name="Pi"  Alias="Initial Pressure"            Unit="MPa"   Default="40"    Max="500"    Min="0.1"    Digit="5"  Desc="initial formation pressure" />
-    <ParaItem Name="K"   Alias="Permeability"                Unit="mD"    Default="1"     Max="10000"  Min="0"      Digit="6"  Desc="permeability" />
+    <ParaItem Name="K"   Alias="Permeability"                Unit="Darcy" Default="0.001" Max="10"     Min="0"      Digit="6"  Desc="permeability" />
     <ParaItem Name="phi" Alias="Porosity"                    Unit=""      Default="0.1"   Max="1"      Min="1e-05"  Digit="4"  Desc="porosity" />
     <ParaItem Name="Cti" Alias="Total Compressibility"       Unit="1/MPa" Default="0.001" Max="10"     Min="1e-30"  Digit="10" Scientific="1" Desc="total compressibility" />
     <ParaItem Name="Cf"  Alias="Rock Compressibility"        Unit="1/MPa" Default="0.0001" Max="10"    Min="1e-30"  Digit="10" Scientific="1" Desc="rock compressibility" />
@@ -91,7 +91,7 @@
     <ParaItem Name="Result_W_C"    Alias="Wellbore Storage Coeff" Unit="m^3/MPa" Default="0"      Max="1000"  Min="0"      Digit="6"  Desc="result wellbore storage coefficient" />
 
     <ParaItem Name="Result_Pi"  Alias="Initial Formation Pressure" Unit="MPa"   Default="40"     Max="500"   Min="0.1"   Digit="6"  Desc="result initial formation pressure" />
-    <ParaItem Name="Result_K"   Alias="Permeability"               Unit="mD"    Default="1"      Max="10000" Min="0"     Digit="6"  Desc="result permeability" />
+    <ParaItem Name="Result_K"   Alias="Permeability"               Unit="Darcy" Default="0.001"  Max="10"    Min="0"     Digit="6"  Desc="result permeability" />
     <ParaItem Name="Result_h"   Alias="Reservoir Thickness"        Unit="m"     Default="10"     Max="10000" Min="1e-05" Digit="6"  Desc="result reservoir thickness" />
     <ParaItem Name="Result_phi" Alias="Porosity"                   Unit=""      Default="0.1"    Max="1"     Min="1e-05" Digit="6"  Desc="result porosity" />
     <ParaItem Name="Result_Cti" Alias="Total Compressibility"      Unit="1/MPa" Default="0.001"  Max="10"    Min="1e-30" Digit="10" Scientific="1" Desc="result total compressibility" />
diff --git a/Bin/XmlFiles/UnitDefault_cn.xml b/Bin/XmlFiles/UnitDefault_cn.xml
index e96401b..28882e7 100644
--- a/Bin/XmlFiles/UnitDefault_cn.xml
+++ b/Bin/XmlFiles/UnitDefault_cn.xml
@@ -166,7 +166,7 @@
         <!-- ==================== 第15组：渗透率 ==================== -->
         <UnitGroup Type="permeability" Name="permeability">
 
-            <Unit ID="U1501" Unit="D" k="1" Digit="4" Remark="base unit"/>
+            <Unit ID="U1501" Unit="Darcy" k="1" Digit="4" Remark="base unit"/>
             <Unit ID="U1502" Unit="mD" k="0.001" Digit="4" />
             <Unit ID="U1503" Unit="1/cm²" k="9.81e+07" Digit="2"/>
 
diff --git a/Bin/XmlFiles/UnitDefault_en.xml b/Bin/XmlFiles/UnitDefault_en.xml
index b6fa119..3c4111b 100644
--- a/Bin/XmlFiles/UnitDefault_en.xml
+++ b/Bin/XmlFiles/UnitDefault_en.xml
@@ -46,7 +46,7 @@
             <Unit Remark=" " Unit="fraction" k="100"/>
         </UnitGroup>
         <UnitGroup Type="permeability" Name="permeability">
-            <Unit Remark=" " Unit="D" k="1"/>
+            <Unit Remark=" " Unit="Darcy" k="1"/>
             <Unit Remark=" " Unit="mD" k="0.001"/>
             <Unit Remark=" " Unit="cm^-2" k="9.81e+07"/>
         </UnitGroup>
diff --git a/Include/mAlg/mAlgDefines/mAlgDefines.h b/Include/mAlg/mAlgDefines/mAlgDefines.h
index 7c96b83..4eeaa12 100644
--- a/Include/mAlg/mAlgDefines/mAlgDefines.h
+++ b/Include/mAlg/mAlgDefines/mAlgDefines.h
@@ -292,8 +292,7 @@ enum Fit_Method
 {
 	FM_GaussNewton = 0, //高斯牛顿
     FM_GaussNewtonEx,   //归一化高斯牛顿
-    FM_Genetic,         //遗传算法
-    FM_ParticleSwarm,   //粒子群算法(Particle Swarm Optimization)
+    FM_ParticleSwarm = 3,   //粒子群算法(Particle Swarm Optimization)，保留原有枚举值
     FM_Unknown
 };
 
diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h
deleted file mode 100644
index 64f9e4d..0000000
--- a/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h
+++ /dev/null
@@ -1,267 +0,0 @@
-#ifndef NMCALCULATIONAUTOFITGA_H
-#define NMCALCULATIONAUTOFITGA_H
-
-#include <QObject>
-#include <QVector>
-#include <QPointF>
-#include <QString>
-#include <QTimer>
-#include <QApplication>
-#include <QDateTime>
-#include <QCoreApplication>
-#include <QDir>
-#include <QDebug>
-#include <QTime>
-
-#include "nmCalculation_global.h"
-
-class nmDataWellBase;
-
-enum StopReasonGA {
-	GA_CONTINUE_OPTIMIZATION = 0,
-	GA_TARGET_ACHIEVED,
-	GA_TRUE_CONVERGENCE,
-	GA_LOCAL_OPTIMUM,
-	GA_MAX_ITERATIONS,
-	GA_USER_STOPPED,
-	GA_CONSECUTIVE_FAILURES,
-	GA_OPTIMIZATION_FAILED
-};
-
-struct GAIndividual
-{
-	QVector<double> genes;          // 基因（参数值）
-	double fitness;                 // 适应度值
-	bool isEvaluated;              // 是否已评估
-
-	GAIndividual() : fitness(1e10), isEvaluated(false) {}
-};
-
-class NMCALCULATION_EXPORT nmCalculationAutoFitGA : public QObject
-{
-	Q_OBJECT
-
-public:
-	explicit nmCalculationAutoFitGA(QObject* parent = nullptr);
-	virtual ~nmCalculationAutoFitGA();
-
-	// ==================== 公共接口方法 ====================
-	void setTargetLogLogData(const QVector<QVector<double>>& targetData);
-	bool startAutoFitting();
-	void stopFitting();
-	bool isRunning() const;
-	int getCurrentGeneration() const;
-	QVector<double> getBestSolution() const;
-	double getBestFitness() const;
-	QString getLastError() const;
-	void resetOptimizer();
-
-	void setGATargetWellName(const QString& wellName);
-
-signals:
-	void progressUpdated(int generation, double bestFitness);
-	void fittingFinished(bool success, const QString& message);
-	void logMessageGenerated(const QString& message);
-
-	private slots:
-		void updateProgress();
-
-private:
-
-	// 临时目录管理
-	void initializeTemporaryDirectory();
-	void cleanupTemporaryDirectory();
-	bool removeDirectoryRecursively(const QString& path);
-
-	// ==================== 数据加载方法 ====================
-	// 从数据管理器加载所有配置
-	bool loadAllConfigFromDataManager();
-	// 加载优化配置
-	void loadOptimizationConfig();
-	// 加载参数边界
-	void loadParameterBounds();
-	// 提取用户初始值
-	void extractUserInitialValues();
-	// ==================== 遗传算法核心方法 ====================
-	// 初始化种群
-	void initializePopulation();
-	// 评估基因
-	double evaluateGenes(const QVector<double>& genes);
-	// 评估个体
-	double evaluateIndividual(GAIndividual& individual);
-	// 评估种群
-	void evaluatePopulation();
-	// 选择操作
-	int tournamentSelection();
-	int rouletteWheelSelection();
-
-	// 交叉操作
-	void crossover(const GAIndividual& parent1, const GAIndividual& parent2,
-		GAIndividual& offspring1, GAIndividual& offspring2);
-	void singlePointCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
-		GAIndividual& offspring1, GAIndividual& offspring2);
-	void uniformCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
-		GAIndividual& offspring1, GAIndividual& offspring2);
-
-	// 变异操作
-	void mutate(GAIndividual& individual);
-	void gaussianMutation(GAIndividual& individual);
-	void polynomialMutation(GAIndividual& individual);
-
-	// 精英保留
-	void applyElitism(QVector<GAIndividual>& newPopulation);
-	// 更新种群统计
-	void updatePopulationStatistics();
-	// 收敛检查
-	bool checkConvergence();
-	// 自适应参数更新
-	void adaptiveParameterUpdate(int generation);
-
-	// ==================== 智能收敛判断方法 ====================
-	// 分析优化状态
-	StopReasonGA analyzeOptimizationStatus();
-	// 检查真收敛
-	bool checkTrueConvergence() const;
-	// 检查局部最优陷阱
-	bool checkLocalOptimumTrap() const;
-	// 计算种群多样性
-	double calculatePopulationDiversity() const;
-	// 计算适应度方差
-	double calculateFitnessVariance(int windowSize) const;
-	// 计算长期改进
-	double calculateLongTermImprovement(int windowSize) const;
-	// 更新收敛指标
-	void updateConvergenceMetrics();
-	// 最终结果验证和保护
-	void validateAndProtectFinalResult();
-
-	// ==================== 参数处理方法 ====================
-	// 参数验证
-	bool validateParameters(const QVector<double>& parameters) const;
-	// 双对数数据验证
-	bool validateLogLogData(const QVector<QVector<double>>& logLogData) const;
-	// 初始值验证
-	bool validateInitialValues() const;
-	// 应用参数到数据管理器
-	void applyParametersToDataManager(const QVector<double>& parameters);
-	// 更新储层参数
-	void updateReservoirParameters(const QVector<double>& parameters);
-	// 更新井参数
-	void updateWellParameters(const QVector<double>& parameters);
-	// 更新井到数据管理器
-	void updateWellToDataManager(nmDataWellBase* pWell);
-	// 参数边界约束
-	void clampToLimits(QVector<double>& parameters) const;
-
-	// ==================== 求解器相关方法 ====================
-	// 运行求解器
-	QVector<QVector<double>> runSolver();
-	// 运行EXE求解器
-	QVector<QVector<double>> runSolverExe();
-	// 运行Dll求解器
-	QVector<QVector<double>> runSolverDll();
-	// 验证求解器结果
-	bool validateSolverResult(const QVector<QVector<double>>& result) const;
-
-	// ==================== 数据处理方法 ====================
-	// 插值数据
-	QVector<QPointF> interpolateData(const QVector<QPointF>& source, 
-		const QVector<double>& targetX) const;
-	// 计算双对数曲线误差
-	double calculateLogLogCurveError(const QVector<QVector<double>>& target, 
-		const QVector<QVector<double>>& result) const;
-	// 计算曲线误差
-	double calculateCurveError(const QVector<QPointF>& curve1, 
-		const QVector<QPointF>& curve2) const;
-
-	// ==================== 工具方法 ====================
-	// 生成0-1随机数
-	double random01() const;
-	// 高斯随机数
-	double gaussianRandom(double mean, double stddev) const;
-	// 获取启用参数数量
-	int getEnabledParameterCount() const;
-	// 保存优化结果
-	void saveOptimizationResult();
-
-
-private:
-	// ==================== 常量定义 ====================
-	static const double MIN_FITNESS_IMPROVEMENT;
-	static const double MUTATION_STRENGTH;
-	static const int CONVERGENCE_CHECK_INTERVAL;
-	static const int MAX_STAGNATION_GENERATIONS;
-
-	// ==================== 核心状态变量 ====================
-	bool m_isRunning;                    // 是否正在运行
-	bool m_shouldStop;                   // 是否应该停止
-	bool m_isPaused;                     // 是否暂停
-	int m_currentGeneration;             // 当前代数
-
-	// 适应度统计
-	double m_bestFitness;                // 最优适应度
-	double m_worstFitness;               // 最差适应度
-	double m_averageFitness;             // 平均适应度
-	double m_previousBestFitness;        // 上一代最优适应度
-
-	// ==================== GA算法参数 ====================
-	int m_populationSize;                // 种群大小
-	int m_maxGenerations;                // 最大代数
-	double m_targetError;                // 目标误差
-	double m_crossoverRate;              // 交叉概率
-	double m_mutationRate;               // 变异概率
-	double m_elitismRate;                // 精英保留比例
-	int m_tournamentSize;                // 锦标赛选择大小
-	bool m_useUniformCrossover;          // 是否使用均匀交叉
-
-	// ==================== 种群和个体 ====================
-	QVector<GAIndividual> m_population;  // 当前种群
-	QVector<GAIndividual> m_eliteIndividuals; // 精英个体
-	GAIndividual m_bestIndividual;       // 全局最优个体
-
-	// ==================== 评估统计 ====================
-	int m_totalEvaluations;              // 总评估次数
-	int m_successfulEvaluations;         // 成功评估次数
-	int m_evaluationInProgress;          // 正在进行的评估计数
-	int m_consecutiveFailures;           // 连续失败次数
-
-	// ==================== 精英保护相关 ====================
-	QVector<double> m_initialValues;     // 用户初始参数值
-	QVector<double> m_userInitialSolution; // 用户初始解
-	double m_userInitialFitness;          // 用户初始适应度
-	int m_consecutiveFailedGenerations;   // 连续失败代数
-	int m_maxConsecutiveFailures;         // 最大允许连续失败数
-	bool m_hasValidUserSolution;          // 是否有有效的用户解
-	double m_improvementThreshold;        // 改进阈值
-
-	// ==================== 收敛判断相关 ====================
-	double m_diversityThreshold;         // 多样性阈值
-	double m_convergenceVarianceThreshold; // 收敛方差阈值
-	int m_trueConvergenceWindow;          // 真收敛判断窗口
-	int m_localOptimumWindow;             // 局部最优判断窗口
-	double m_nearTargetFactor;            // 接近目标的因子
-	double m_farTargetFactor;             // 远离目标的因子
-
-	// ==================== 历史记录 ====================
-	QVector<double> m_convergenceHistory; // 收敛历史
-	QVector<double> m_diversityHistory;   // 多样性历史
-
-	// ==================== 参数配置 ====================
-	QVector<bool> m_parameterSelected;    // 参数选择状态
-	QVector<double> m_parameterLower;     // 参数下界
-	QVector<double> m_parameterUpper;     // 参数上界
-	QVector<int> m_enabledParamIndices;   // 启用参数索引
-
-	// ==================== 目标数据 ====================
-	QVector<QVector<double>> m_targetLogLogData; // 目标双对数数据
-
-	// ==================== 其他 ====================
-	QString m_lastError;                  // 最后错误信息
-	QTimer* m_progressTimer;              // 进度更新定时器
-	// DLL求解器需要的临时目录
-	QString m_tempDirectory;
-
-	QString m_targetWellName;// 目标井名称
-};
-
-#endif // NMCALCULATIONAUTOFITGA_H
\ No newline at end of file
diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h
index e59db09..9731259 100644
--- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h
+++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h
@@ -9,6 +9,7 @@
 #include <QMutex>
 #include <QDateTime>
 #include <QFile>
+#include <limits>
 
 #include "nmCalculation_global.h"
 
@@ -19,6 +20,69 @@ class nmDataWellBase;
 class QTimer;
 class QProcess;
 
+// 双对数曲线误差分解。该结构同时保存用于候选排序的主目标，以及用于判断
+// 曲线上下、左右和形状偏差的诊断量。total 是唯一的接受和排序依据，诊断量
+// 只参与信赖域选参，不能再次叠加到 total，否则会重复计算同一批曲线残差。
+struct AutoFitObjectiveBreakdown {
+	// valid 表示本次曲线评价完整有效；无效评价统一保留 total=1e10。
+	// pressureLoss 和 derivativeLoss 均在 log(value) 空间按固定网格计算。
+	bool valid;
+	double total;
+	double pressureLoss;
+	double derivativeLoss;
+	// 固定目标网格上的普通对数残差。非代理搜索使用它建立完整 Jacobian，
+	// 向量平方和与 total 的平方一致。
+	QVector<double> residualVector;
+
+	// 上下偏差使用压力和导数残差共享的算术平均中心。
+	// verticalCommonBias 为正表示模拟曲线整体偏高，为负表示整体偏低；
+	// verticalReliable=false 时仍保留数值，但不能据此确定参数调整方向。
+	double verticalCommonBias;
+	double verticalLoss;
+	bool verticalReliable;
+
+	// 水平偏差在 log(time) 坐标中计算。physicalShift 为正表示模拟曲线相对
+	// 目标偏右，即相同曲线特征在模拟结果中出现得更晚。
+	double horizontalPhysicalShift;
+	double horizontalLoss;
+	bool horizontalReliable;
+	// true 表示当前曲线无法可靠区分上下和左右误差；此时禁止使用两类有符号
+	// 诊断量选参，但去除公共中心后的 shapeLoss 仍可用于局部选参。
+	bool registrationAmbiguous;
+
+	// 去除公共均值中心和可信左右偏差后剩余的整体形状误差；verticalReliable
+	// 只控制能否把公共中心解释为上下参数方向，不改变 shape 的中心化公式。
+	double shapeLoss;
+
+	// 兼容现有 trace 列。当前非代理搜索不再单独识别或调度晚期分量。
+	double lateDerivativeSlopeBias;
+	double lateDerivativeTrendLoss;
+	bool lateDerivativeTrendReliable;
+
+	// 模拟曲线对目标固定网格的有效覆盖率，取覆盖点比例与连续 log-time
+	// 跨度比例中的较小值。低于损失函数门槛时本次评价直接无效。
+	double coverage;
+
+	AutoFitObjectiveBreakdown()
+		: valid(false)
+		, total(1.0e10)
+		, pressureLoss(std::numeric_limits<double>::quiet_NaN())
+		, derivativeLoss(std::numeric_limits<double>::quiet_NaN())
+		, verticalCommonBias(std::numeric_limits<double>::quiet_NaN())
+		, verticalLoss(std::numeric_limits<double>::quiet_NaN())
+		, verticalReliable(false)
+		, horizontalPhysicalShift(std::numeric_limits<double>::quiet_NaN())
+		, horizontalLoss(std::numeric_limits<double>::quiet_NaN())
+		, horizontalReliable(false)
+		, registrationAmbiguous(false)
+		, shapeLoss(std::numeric_limits<double>::quiet_NaN())
+		, lateDerivativeSlopeBias(std::numeric_limits<double>::quiet_NaN())
+		, lateDerivativeTrendLoss(std::numeric_limits<double>::quiet_NaN())
+		, lateDerivativeTrendReliable(false)
+		, coverage(std::numeric_limits<double>::quiet_NaN())
+	{}
+};
+
 // PSO粒子结构
 // 这里的 position / velocity / bestPosition 只保存“用户勾选参与拟合的参数”，
 // 不是完整的 11 个储层/井筒参数。完整参数向量会在写 trace 或调用代理模型时
@@ -33,6 +97,8 @@ struct AutoFitParticle {
 	QVector<double> velocity;      // 速度
 	QVector<double> bestPosition;  // 真实求解器确认的个体最优位置
 	QVector<double> guideBestPosition; // 仅用于速度更新的引导位置；不会参与真实 gbest/最终结果
+	AutoFitObjectiveBreakdown currentObjectiveBreakdown; // 当前真实评价对应的误差分解
+	AutoFitObjectiveBreakdown bestObjectiveBreakdown;    // pbest 对应的误差分解
 	double fitness;                // 当前适应度
 	double bestFitness;            // 真实求解器确认的个体最优适应度
 	double guideBestObjective;     // guideBestPosition 对应的真实或代理目标值
@@ -96,6 +162,7 @@ public:
 	void stopFitting();
 	QVector<double> getBestSolution() const;
 	double getBestFitness() const;
+	AutoFitObjectiveBreakdown getLastObjectiveBreakdown() const;
 	QString getLastError() const;
 	bool isRunning() const;
 	int getCurrentIteration() const;
@@ -137,20 +204,26 @@ private:
 	void loadOptimizationConfig();
 	void loadParameterBounds();
 
-	// ===== PSO核心算法 =====
+	// ===== 自动拟合核心算法 =====
 	//
-	// 主流程：
-	// 1. extractUserInitialValues(): 从当前项目数据中取用户已有初始解；
-	// 2. initializeSwarm(): 根据初始解和上下界生成粒子群；
-	// 3. updateParticle(): 对单个粒子跑真实求解器并计算误差；
-	// 4. updateGlobalBest(): 只用真实求解器误差更新全局最优；
-	// 5. updateVelocityAndPosition(): 按 PSO 公式推进下一代粒子。
+	// 代理开启时保留原 PSO 筛选流程；代理关闭时使用真实求解器驱动的
+	// 诊断灵敏度信赖域搜索，不依赖 pbest/gbest 速度公式。
 	void extractUserInitialValues();
 	void initializeSwarm();
 	void updateVelocityAndPosition();
 	double evaluateFitness(const QVector<double>& parameters);
 	void updateGlobalBest();
 	void updateParticle(int particleIndex);
+	// 非代理拟合入口：建立有限差分灵敏度，按诊断分量选择参数，再用有界
+	// LM/信赖域产生候选；所有候选最终都由真实求解器总误差决定是否接受。
+	StopReasonPSO runTrustRegionFitting();
+	// 对一个信赖域候选执行完整真实评价，并一次性返回误差、诊断量、曲线和耗时。
+	// 返回 false 表示求解失败、损失无效或用户已请求停止。
+	bool evaluateTrustRegionPoint(const QVector<double>& parameters,
+			double* fitness,
+			AutoFitObjectiveBreakdown* breakdown,
+			QVector<QVector<double> >* curve,
+			int* elapsedMs);
 
 	// ===== 参数应用方法 =====
 	//
@@ -166,6 +239,7 @@ private:
 	// ===== 求解器相关 =====
 	QVector<QVector<double> > runSolver();
 	QVector<QVector<double>> runSolverDll();
+	bool runFinalFullSolver();
 	QVector<QVector<double>> runSolverExe();
 
 	// ===== 数据处理 =====
@@ -220,7 +294,8 @@ private:
 		double surrogateObjective,
 		const QString& screeningDecision,
 		const QVector<double>& pbestPosition,
-		double pbestObjective);
+		double pbestObjective,
+		const AutoFitObjectiveBreakdown* objectiveBreakdown = nullptr);
 	void writeIterationTraceRows();
 	QVector<double> buildTraceParameterVector(const QVector<double>& selectedParameters) const;
 	void resetRunSummary();
@@ -276,30 +351,34 @@ private:
 	bool m_isRunning;        // 当前是否有一次自动拟合正在运行。
 	bool m_shouldStop;       // 用户停止标志；主循环和求解器等待循环会定期检查它。
 	bool m_isPaused;         // 预留暂停标志；主循环中有暂停等待逻辑。
-	int m_currentIteration;  // 当前 PSO 迭代序号，从 0 开始。
+	int m_currentIteration;  // 当前自动拟合迭代序号，从 0 开始。
 	QString m_lastError;     // 最近一次失败原因，供 UI 展示或日志排查。
 
-	// ===== PSO数据 =====
+	// ===== 优化状态数据 =====
 	QVector<double> m_initialValues;              // 当前模型中提取的用户初始值，顺序与 m_enabledParamIndices 一致。
 	QVector<AutoFitParticle> m_swarm;             // 粒子群，每个粒子只保存启用参数维度。
-	QVector<double> m_globalBestPosition;         // 全局最优参数，仍是启用参数向量。
+	QVector<double> m_globalBestPosition;         // 真实求解器确认的当前最优参数。
 	double m_globalBestFitness;                   // 全局最优真实误差，越小越好。
 	double m_previousBestFitness;                 // 上一轮全局最优误差，用于自适应参数更新。
+	AutoFitObjectiveBreakdown m_globalBestObjectiveBreakdown; // 真实 gbest 对应的误差分解。
 	QVector<QVector<double> > m_lastEvaluatedLogLogData; // 最近一次真实求解得到的 result log-log 曲线。
 	QVector<QVector<double> > m_globalBestLogLogData;    // 当前全局最优对应的 result log-log 曲线。
+	mutable AutoFitObjectiveBreakdown m_lastObjectiveBreakdown; // 最近一次损失评价的误差分解。
 	QVector<QVector<double> > m_userInitialLogLogData;   // 用户初始解对应的 result log-log 曲线，用于精英保护。
+	AutoFitObjectiveBreakdown m_userInitialObjectiveBreakdown; // 用户初始解对应的误差分解。
 
 	// ===== 优化配置 =====
 	//
 	// 参数索引约定：
 	// 0 k 渗透率；1 skin 表皮系数；2 wellboreC 井筒储集；
 	// 3 phi 孔隙度；4 h 储层厚度；5 Ct 综合压缩系数；
-	// 6 Cf 岩石压缩系数；7 Swi 初始含水饱和度。
+	// 6 Cf 岩石压缩系数；7 Swi 初始含水饱和度；
+	// 8 Dfc 裂缝导流能力；9 fractureHalfLength 裂缝半长。
 	// m_enabledParamIndices 保存被用户勾选的参数索引，粒子的 position 维度与它一致。
-	QVector<bool> m_parameterSelected;      // 完整 8 个参数是否被用户勾选参与拟合。
-	QVector<double> m_parameterLower;       // 完整 8 个参数的搜索下界。
-	QVector<double> m_parameterUpper;       // 完整 8 个参数的搜索上界。
-	QVector<int> m_enabledParamIndices;     // 被勾选参数在完整 8 维体系中的索引。
+	QVector<bool> m_parameterSelected;      // 完整 10 个参数是否被用户勾选参与拟合。
+	QVector<double> m_parameterLower;       // 完整 10 个参数的搜索下界。
+	QVector<double> m_parameterUpper;       // 完整 10 个参数的搜索上界。
+	QVector<int> m_enabledParamIndices;     // 被勾选参数在完整 10 维体系中的索引。
 	QVector<QVector<double> > m_targetLogLogData; // 目标井 history log-log 曲线：time/pressure/derivative。
 	QString m_targetWellName;               // 目标井名称；读写井参数和读取模拟曲线都依赖它。
 
@@ -312,7 +391,7 @@ private:
 	double m_socialParam;        // 群体学习因子，控制粒子靠近全局 gbest 的程度。
 
 	// ===== 统计信息 =====
-	int m_totalEvaluations;        // 已调用真实求解器评价的粒子总数。
+	int m_totalEvaluations;        // 真实求解器评价总次数，包含粒子评价和方向试算。
 	int m_successfulEvaluations;   // 真实求解器成功且误差有效的评价次数。
 	QVector<double> m_convergenceHistory; // 每代全局最优误差历史，用于收敛判断。
 
@@ -328,7 +407,7 @@ private:
 	// ===== 精英保护 =====  
 	QVector<double> m_userInitialSolution;      // 用户初始解参数，若最终改进不足会恢复它。
 	double m_userInitialFitness;                // 用户初始解真实误差。
-    double m_improvementThreshold;              // 最终结果相对初始解至少需要达到的改进阈值。
+    double m_improvementThreshold;              // 仅用于日志区分显著改进和微小改进。
 	bool m_hasValidUserSolution;                // 初始解是否成功跑过真实求解器。
 
 	int m_consecutiveFailedIterations;    // 连续失败迭代次数
@@ -357,8 +436,8 @@ private:
 	// 这些字段只描述代理筛选和运行复盘，不参与 PSO 数学更新。
 	bool m_traceEnabled;                 // 是否写出 trace CSV/meta 文件。
 	QString m_traceRunId;                // 本次运行 ID，作为 trace/candidate/score 文件名的一部分。
-	QString m_traceFilePath;             // pso_baseline_trace_<run_id>.csv 完整路径。
-	QString m_traceMetaFilePath;         // pso_baseline_trace_<run_id>.meta.json 完整路径。
+	QString m_traceFilePath;             // 本次自动拟合 trace CSV 的完整路径。
+	QString m_traceMetaFilePath;         // 与 trace 匹配的 meta JSON 完整路径。
 	QFile m_traceFile;                   // trace CSV 文件句柄。
 	bool m_surrogateScreeningEnabled;    // 用户配置中的 PSO acceleration 开关。
 	unsigned int m_psoRandomSeed;        // PSO 随机种子，也用于可复现 random audit。
diff --git a/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h b/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h
index 1de9f71..3efc33f 100644
--- a/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h
+++ b/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h
@@ -2,6 +2,8 @@
 #define NMCALCULATIONDLLPEBISOLVERTASK_H
 
 #include <QThread>
+#include <QString>
+#include <QVector>
 #include <iostream>
 #include <vector>
 #include <Windows.h>
@@ -22,6 +24,13 @@ class NMCALCULATION_EXPORT nmCalculationDllPebiSolverTask : public QThread {
     // 返回 false 时调用方会丢弃本次结果, 防止复用上一粒子留下的旧曲线.
     bool wasSuccessful() const;
 
+    // 自动拟合粒子评价只提取目标井曲线，不写回共享数据和网格压力场。
+    // 井名为空时保持原有完整结果保存模式。
+    void setAutoFitTargetWell(const QString& wellName);
+    QVector<QVector<double> > getAutoFitResultPressure() const;
+    QVector<QVector<double> > getAutoFitResultLogLog() const;
+    QVector<QVector<double> > getAutoFitResultSemiLog() const;
+
   private:
     bool execute();
 
@@ -37,6 +46,10 @@ class NMCALCULATION_EXPORT nmCalculationDllPebiSolverTask : public QThread {
     QString m_sPostprocessingDir;
     // run() 在线程内保存 execute() 结果, 等待线程结束的调用方只读取该状态.
     bool m_lastRunSucceeded;
+    QString m_autoFitTargetWellName;
+    QVector<QVector<double> > m_autoFitResultPressure;
+    QVector<QVector<double> > m_autoFitResultLogLog;
+    QVector<QVector<double> > m_autoFitResultSemiLog;
 
   private slots:
     //void slotTaskUpdateProgress();
diff --git a/Include/nmNum/nmData/nmDataAutomaticFitting.h b/Include/nmNum/nmData/nmDataAutomaticFitting.h
index b2da223..98829eb 100644
--- a/Include/nmNum/nmData/nmDataAutomaticFitting.h
+++ b/Include/nmNum/nmData/nmDataAutomaticFitting.h
@@ -75,6 +75,20 @@ public:
 	nmDataAttribute& getSwiMin();
 	void setSwiMin(const nmDataAttribute& swiMin);
 
+	// Getter and Setter for fractureConductivityMax
+	nmDataAttribute& getFractureConductivityMax();
+	void setFractureConductivityMax(const nmDataAttribute& fractureConductivityMax);
+	// Getter and Setter for fractureConductivityMin
+	nmDataAttribute& getFractureConductivityMin();
+	void setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin);
+
+	// Getter and Setter for fractureHalfLengthMax
+	nmDataAttribute& getFractureHalfLengthMax();
+	void setFractureHalfLengthMax(const nmDataAttribute& fractureHalfLengthMax);
+	// Getter and Setter for fractureHalfLengthMin
+	nmDataAttribute& getFractureHalfLengthMin();
+	void setFractureHalfLengthMin(const nmDataAttribute& fractureHalfLengthMin);
+
 	// Getter and Setter for iteration count
 	nmDataAttribute& getIterationCount();
 	void setIterationCount(const nmDataAttribute& iterationCount);
@@ -114,6 +128,12 @@ public:
 	bool getSwiSelected() const;
 	void setSwiSelected(bool selected);
 
+	bool getFractureConductivitySelected() const;
+	void setFractureConductivitySelected(bool selected);
+
+	bool getFractureHalfLengthSelected() const;
+	void setFractureHalfLengthSelected(bool selected);
+
 private:
 	// 参数最大值
 	nmDataAttribute m_permeabilityMax;
@@ -124,6 +144,8 @@ private:
 	nmDataAttribute m_ctMax;
 	nmDataAttribute m_cfMax;
 	nmDataAttribute m_swiMax;
+	nmDataAttribute m_fractureConductivityMax;
+	nmDataAttribute m_fractureHalfLengthMax;
 
 	// 参数最小值
 	nmDataAttribute m_permeabilityMin;
@@ -134,6 +156,8 @@ private:
 	nmDataAttribute m_ctMin;
 	nmDataAttribute m_cfMin;
 	nmDataAttribute m_swiMin;
+	nmDataAttribute m_fractureConductivityMin;
+	nmDataAttribute m_fractureHalfLengthMin;
 
 	// 迭代参数
 	nmDataAttribute m_iterationCount;      // 迭代步数
@@ -150,6 +174,8 @@ private:
 	bool m_ctSelected;                     // 是否选择综合压缩系数进行拟合
 	bool m_cfSelected;                     // 是否选择岩石压缩系数进行拟合
     bool m_swiSelected;					   // 是否选择初始含水饱和度进行拟合
+	bool m_fractureConductivitySelected; // 是否选择裂缝导流能力进行拟合
+	bool m_fractureHalfLengthSelected;  // 是否选择裂缝半长进行拟合
 };
 
 #endif // NMDATAAUTOMATICFITTING_H
diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h
index a245aac..a8c4f76 100644
--- a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h
+++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h
@@ -21,17 +21,10 @@
 #include "nmDataWellBase.h"
 #include "nmDataAutomaticFitting.h"
 #include "nmCalculationAutoFitPSO.h"
-#include "nmCalculationAutoFitGA.h"
 #include "nmWxAutomaticfittingStart.h"
 
 #include "nmSubWxs_global.h"
 
-// 算法类型枚举
-enum OptimizationAlgorithm {
-	ALGORITHM_PSO = 0,
-	ALGORITHM_GA = 1
-};
-
 class NM_SUB_WXS_EXPORT nmWxAutomaticFitting : public iDlgBase
 {
 	Q_OBJECT
@@ -46,7 +39,7 @@ public:
 		void onAccept();
 		void onReject();
 		void onWellSelected(int index);
-		void onAlgorithmChanged(int index);
+		void onParameterTableItemChanged(QTableWidgetItem* item);
 
 		// 自动拟合相关槽函数
 		void runAutoFitting();
@@ -63,6 +56,12 @@ private:
 	void setParameterRowVisible(QTableWidget* table, int row, bool visible);
 	void renumberVisibleParameterRows(QTableWidget* table);
 	void updateParameterVisibility(QTableWidget* table, NM_SOLVER_MODEL_TYPE eType);
+	void initializeSuggestedParameterRanges();
+	void updateRangeForParameter(int parameterIndex, double centerValue);
+	void setParameterRange(int parameterIndex, double minValue, double maxValue);
+	bool getPhysicalParameterRange(int parameterIndex, double& minValue, double& maxValue);
+	void normalizeSavedParameterRanges();
+	bool validateParameterTable(QString& errorMessage, int parameterIndex = -1);
 
 	void startAutoFitting(const QVector<QVector<double>>& targetData, const QStringList& selectedParams, const QString& targetWellName);
 	void cleanupFitting();
@@ -92,6 +91,8 @@ private:
 	QCheckBox* m_ctCheckBox;       // 综合压缩系数
 	QCheckBox* m_cfCheckBox;       // 岩石压缩系数
 	QCheckBox* m_swiCheckBox;	   // 初始含水饱和度
+	QCheckBox* m_dfcCheckBox;      // 裂缝导流能力
+	QCheckBox* m_fractureHalfLengthCheckBox; // 裂缝半长
 
 	// 按钮
 	QPushButton* m_reverseBtn;
@@ -108,10 +109,10 @@ private:
 
 	// 自动拟合相关成员
     nmCalculationAutoFitPSO* m_autoFitterPSO;
-	nmCalculationAutoFitGA* m_autoFitterGA;
 	QProgressDialog* m_progressDialog;
 	QTimer* m_progressTimer;
-	OptimizationAlgorithm m_selectedAlgorithm;  // 选中的算法类型
+	bool m_autoParameterRanges;
+	bool m_updatingParameterRanges;
 
 	// 拟合开始界面
 	nmWxAutomaticfittingStart* m_progressMonitor;
diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h
index eb37eeb..3e9067e 100644
--- a/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h
+++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h
@@ -22,12 +22,10 @@
 #include <QRectF>
 #include <QSplitter>
 
-#include "nmCalculationAutoFitGA.h"
 #include "nmCalculationAutoFitPSO.h"
 
 // 前向声明
 class nmCalculationAutoFitPSO;
-class nmCalculationAutoFitGA;
 class QPainter;
 class QColor;
 class QPaintEvent;
@@ -68,12 +66,6 @@ private:
 	bool m_pseudoPressureMode;
 };
 
-// 算法类型枚举
-enum FittingAlgorithmType {
-	FITTING_ALGORITHM_PSO = 0,
-	FITTING_ALGORITHM_GA = 1
-};
-
 class nmWxAutomaticfittingStart : public iDlgBase
 {
 	Q_OBJECT
@@ -85,8 +77,6 @@ public:
 	// PSO算法接口
 	void setAutoFitter(nmCalculationAutoFitPSO* autoFitter);
 
-	// GA算法接口
-	void setAutoFitterGA(nmCalculationAutoFitGA* autoFitter);
 
 	// 通用设置接口
 	void setFittingParameters(int maxIterations, double targetError, const QString& wellName);
@@ -161,8 +151,6 @@ private:
 
 	// 算法实例
 	nmCalculationAutoFitPSO* m_autoFitterPSO;
-	nmCalculationAutoFitGA* m_autoFitterGA;
-	FittingAlgorithmType m_algorithmType;
 
 	// 拟合参数
 	int m_maxIterations;
diff --git a/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h b/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h
index 720d0bb..8a9728b 100644
--- a/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h
+++ b/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h
@@ -5,6 +5,7 @@
 
 class nmGridRowUtils;
 class nmDataAnalyzeManager;
+class QTimer;
 
 /// @brief PEBI求解器专用的参数输入/编辑属性对话框窗体
 /// @note  与 nmWxParaProperty 结构一致，区别在于内部使用 nmGridRowUtils（NM子类）
@@ -76,6 +77,8 @@ public slots:
     void slotWellRemoved(QString wellCode, QString wellName);
     /// @brief 几何对象新增、删除或改名后重建参数面板
     void slotGeometryListChanged();
+    /// @brief 合并同一轮事件中的多次井列表变化，只重建一次参数面板
+    void slotDeferredWellListRebuild();
 
 protected:
 
@@ -93,6 +96,13 @@ protected:
     // 数据管理器引用（用于全量重建面板时获取井和几何对象列表）
     nmDataAnalyzeManager* m_pDataManager;
 
+    // 批量增删井时合并参数面板刷新
+    QTimer* m_pWellListRebuildTimer;
+
+private:
+
+    void scheduleWellListRebuild();
+
 signals:
 
     /// @brief 参数值变更信号（由 slotParaCtrlValueChanged 转发）
diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp
deleted file mode 100644
index 45eda30..0000000
--- a/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp
+++ /dev/null
@@ -1,2911 +0,0 @@
-#include "nmCalculationAutoFitGA.h"
-//#include "nmCalculationExeSolverTask.h" 
-#include "nmCalculationDllPebiSolverTask.h"
-#include "nmDataAnalyzeManager.h"
-#include "nmDataWellBase.h"
-#include "nmDataVerticalWell.h"
-#include "nmDataVerticalFracturedWell.h"
-#include "nmDataHorizontalFracturedWell.h"
-#include "nmDataReservoir.h"
-#include "nmDataAutomaticFitting.h"
-
-#include <QApplication>
-#include <QDebug>
-#include <QTime>
-#include <QDir>
-#include <QtCore/qmath.h>
-#include <cmath>
-#include <algorithm>
-
-#ifdef Q_OS_WIN
-#include <windows.h>
-#include <float.h>
-#define DEBUG_OUT(msg) OutputDebugStringA(QString("[AutoFit] %1\n").arg(msg).toLocal8Bit().data())
-#endif
-
-// 常量定义
-const double nmCalculationAutoFitGA::MIN_FITNESS_IMPROVEMENT = 1e-8;
-const double nmCalculationAutoFitGA::MUTATION_STRENGTH = 0.1;
-const int nmCalculationAutoFitGA::CONVERGENCE_CHECK_INTERVAL = 10;
-const int nmCalculationAutoFitGA::MAX_STAGNATION_GENERATIONS = 20;
-
-// 无穷大和NaN检查
-static inline bool isFiniteNumber(double value)
-{
-#ifdef Q_OS_WIN
-	return _finite(value) != 0 && !_isnan(value);
-#else
-	return std::isfinite(value);
-#endif
-}
-
-// sleep函数
-static inline void msleep(int ms)
-{
-#ifdef Q_OS_WIN
-	Sleep(ms);
-#endif
-}
-
-// 构造函数
-nmCalculationAutoFitGA::nmCalculationAutoFitGA(QObject* parent)
-	: QObject(parent)
-	, m_isRunning(false)
-	, m_shouldStop(false)
-	, m_isPaused(false)
-	, m_currentGeneration(0)
-	, m_bestFitness(1e10)
-	, m_worstFitness(-1e10)
-	, m_averageFitness(1e10)
-	, m_previousBestFitness(1e10)
-	, m_populationSize(40)
-	, m_maxGenerations(100)
-	, m_targetError(0.001)
-	, m_crossoverRate(0.8)
-	, m_mutationRate(0.1)
-	, m_elitismRate(0.1)
-	, m_tournamentSize(3)
-	, m_useUniformCrossover(true)
-	, m_totalEvaluations(0)
-	, m_successfulEvaluations(0)
-	, m_evaluationInProgress(0)
-	, m_consecutiveFailures(0)
-	, m_progressTimer(0)
-	, m_userInitialFitness(1e10)
-	, m_consecutiveFailedGenerations(0)
-	, m_maxConsecutiveFailures(3)
-	, m_hasValidUserSolution(false)
-	, m_improvementThreshold(0.05)
-	, m_diversityThreshold(0.05)
-	, m_convergenceVarianceThreshold(1e-8)
-	, m_trueConvergenceWindow(15)
-	, m_localOptimumWindow(8)
-	, m_nearTargetFactor(2.0)
-	, m_farTargetFactor(10.0)
-	, m_targetWellName("")
-{
-	DEBUG_OUT(QString("GA Constructor: this=0x%1").arg((quintptr)this, 0, 16));
-
-	// 初始化随机数种子
-	qsrand(QTime::currentTime().msec());
-
-	// 初始化临时目录用于DLL求解器
-	initializeTemporaryDirectory();
-
-	// 初始化最优个体
-	m_bestIndividual.fitness = 1e10;
-	m_bestIndividual.isEvaluated = false;
-
-	// 创建进度更新定时器
-	m_progressTimer = new QTimer(this);
-	connect(m_progressTimer, SIGNAL(timeout()), this, SLOT(updateProgress()));
-
-	DEBUG_OUT("AutoFit GA calculator initialized (data-driven mode)");
-	DEBUG_OUT("GA Constructor completed");
-}
-
-// 析构函数
-nmCalculationAutoFitGA::~nmCalculationAutoFitGA()
-{
-	DEBUG_OUT(QString("GA Destructor: this=0x%1").arg((quintptr)this, 0, 16));
-
-	// 首先停止算法
-	if(m_isRunning) {
-		m_shouldStop = true;  // 立即设置停止标志
-
-		// 等待当前操作完成，增加超时时间
-		int waitCount = 0;
-		while(m_isRunning && waitCount < 100) {
-			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50);
-			msleep(50);
-			waitCount++;
-		}
-
-		// 如果仍在运行则强制停止
-		if(m_isRunning) {
-			DEBUG_OUT("Force stopping GA - timeout reached");
-			m_isRunning = false;
-		}
-	}
-
-	// 清理临时目录
-	cleanupTemporaryDirectory();
-
-	// 断开所有信号连接，防止回调已销毁的对象
-	disconnect(this, nullptr, nullptr, nullptr);
-
-	DEBUG_OUT("GA Destructor completed");
-}
-
-// ==================== 目录处理方法 ====================
-
-void nmCalculationAutoFitGA::initializeTemporaryDirectory()
-{
-	QString timestamp = QDateTime::currentDateTime().toString("yyyyMMdd_hhmmss_zzz");
-	QString processId = QString::number(QCoreApplication::applicationPid());
-
-	m_tempDirectory = QApplication::applicationDirPath() + 
-		"/autofit_temp_" + processId + "_" + timestamp;
-
-	// 确保目录不存在
-	int counter = 0;
-	QString originalPath = m_tempDirectory;
-
-	while(QDir(m_tempDirectory).exists() && counter < 100) {
-		m_tempDirectory = originalPath + "_" + QString::number(counter);
-		counter++;
-	}
-
-	if(QDir().mkpath(m_tempDirectory)) {
-		DEBUG_OUT(QString("Initialized temp directory: %1").arg(m_tempDirectory));
-	} else {
-		DEBUG_OUT(QString("Warning: Failed to create temp directory: %1").arg(m_tempDirectory));
-		m_tempDirectory = QApplication::applicationDirPath();
-	}
-}
-
-void nmCalculationAutoFitGA::cleanupTemporaryDirectory()
-{
-	if(QDir(m_tempDirectory).exists()) {
-		if(removeDirectoryRecursively(m_tempDirectory)) {
-			DEBUG_OUT("Temp directory cleaned up successfully");
-		} else {
-			DEBUG_OUT("Warning: Failed to clean up temp directory completely");
-		}
-	}
-}
-
-bool nmCalculationAutoFitGA::removeDirectoryRecursively(const QString& path)
-{
-	QDir dir(path);
-
-	if(!dir.exists()) {
-		return true;
-	}
-
-	// 递归删除子目录和文件
-	QFileInfoList entries = dir.entryInfoList(QDir::NoDotAndDotDot | QDir::AllEntries | QDir::Hidden);
-	bool allRemoved = true;
-
-	for(int i = 0; i < entries.size(); ++i) {
-		const QFileInfo& entry = entries[i];
-
-		if(entry.isDir()) {
-			if(!removeDirectoryRecursively(entry.absoluteFilePath())) {
-				allRemoved = false;
-			}
-		} else {
-			QFile file(entry.absoluteFilePath());
-
-			// 处理只读文件
-			if(!file.permissions().testFlag(QFile::WriteUser)) {
-				file.setPermissions(file.permissions() | QFile::WriteUser);
-			}
-
-			if(!file.remove()) {
-				DEBUG_OUT(QString("Failed to remove file: %1").arg(entry.absoluteFilePath()));
-				allRemoved = false;
-			}
-		}
-	}
-
-	// 删除目录本身
-	if(allRemoved) {
-		return dir.rmdir(path);
-	}
-
-	return false;
-}
-
-// ==================== 公共接口方法 ====================
-
-void nmCalculationAutoFitGA::setTargetLogLogData(const QVector<QVector<double>>& targetData)
-{
-	m_targetLogLogData = targetData;
-	DEBUG_OUT(QString("Target LogLog data set: %1 arrays").arg(targetData.size()));
-
-	if(targetData.size() >= 3) {
-		DEBUG_OUT(QString("LogLog data points: X=%1, Y1=%2, Y2=%3")
-			.arg(targetData[0].size())
-			.arg(targetData[1].size())
-			.arg(targetData[2].size()));
-	}
-}
-
-bool nmCalculationAutoFitGA::startAutoFitting()
-{
-	if(m_isRunning) {
-		m_lastError = "GA fitting is already running";
-		return false;
-	}
-
-	DEBUG_OUT("=== GA AUTO FITTING START ===");
-
-	// 发送初始化日志
-	emit logMessageGenerated(tr("=== GA Automatic Fitting Started ==="));
-	emit logMessageGenerated(tr("Algorithm: Genetic Algorithm"));
-
-	try {
-		// 从数据管理器加载所有配置
-		if(!loadAllConfigFromDataManager()) {
-			emit logMessageGenerated(tr("ERROR: Failed to load configuration from data manager"));
-			return false;
-		}
-
-		int enabledParams = getEnabledParameterCount();
-		emit logMessageGenerated(tr("Enabled parameters count: %1").arg(enabledParams));
-
-		if(enabledParams == 0) {
-			m_lastError = "No parameters enabled for optimization";
-			emit logMessageGenerated(tr("ERROR: No parameters enabled for optimization"));
-			return false;
-		}
-
-		if(m_targetLogLogData.isEmpty() || m_targetLogLogData.size() < 3) {
-			m_lastError = "Target LogLog data is empty or insufficient";
-			emit logMessageGenerated(tr("ERROR: Target LogLog data is empty or insufficient"));
-			return false;
-		}
-
-		// 检查数据一致性
-		if(m_targetLogLogData[0].size() != m_targetLogLogData[1].size() ||
-			m_targetLogLogData[0].size() != m_targetLogLogData[2].size()) {
-				m_lastError = "Target LogLog data arrays have inconsistent sizes";
-				emit logMessageGenerated(tr("ERROR: Target LogLog data arrays have inconsistent sizes"));
-				return false;
-		}
-
-		emit logMessageGenerated(tr("Target data validation passed (%1 data points)").arg(m_targetLogLogData[0].size()));
-
-		// 使用保存的初始值进行精英保护
-		QVector<double> savedInitialValues = m_initialValues;
-
-		// 重置状态
-		resetOptimizer();
-		m_isRunning = true;
-		m_shouldStop = false;
-		m_isPaused = false;
-		m_currentGeneration = 0;
-		m_consecutiveFailures = 0;
-		m_consecutiveFailedGenerations = 0;
-
-		// 精英保护：评估用户初始解
-		if(!savedInitialValues.isEmpty()) {
-			m_userInitialSolution = savedInitialValues;
-			emit logMessageGenerated(tr("=== Evaluating Initial Solution (Elite Protection) ==="));
-
-			// 输出初始参数值
-			QString paramStr = tr("Initial parameters: ");
-			for(int i = 0; i < m_userInitialSolution.size(); ++i) {
-				paramStr += QString("[%1]=%2 ").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6);
-			}
-			emit logMessageGenerated(paramStr);
-
-			try {
-				emit logMessageGenerated(tr("Starting initial solution evaluation..."));
-				DEBUG_OUT(QString("Before evaluateFitness: m_userInitialSolution size = %1").arg(m_userInitialSolution.size()));
-
-				if(m_userInitialSolution.isEmpty()) {
-					emit logMessageGenerated(tr("ERROR: m_userInitialSolution is empty!"));
-					return false;
-				}
-
-				for(int i = 0; i < m_userInitialSolution.size(); ++i) {
-					emit logMessageGenerated(tr("Initial param[%1] = %2").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6));
-				}
-
-				m_userInitialFitness = evaluateGenes(m_userInitialSolution);
-
-				emit logMessageGenerated(tr("evaluateGenes returned: %1").arg(m_userInitialFitness, 0, 'e', 10));
-
-				if(m_userInitialFitness < 1e9) {
-					emit logMessageGenerated(tr("Taking SUCCESS branch (fitness < 1e9)"));
-					m_hasValidUserSolution = true;
-					m_bestFitness = m_userInitialFitness;
-					m_bestIndividual.genes = m_userInitialSolution;
-					m_bestIndividual.fitness = m_userInitialFitness;
-					m_bestIndividual.isEvaluated = true;
-
-					emit logMessageGenerated(tr("Initial solution evaluation successful"));
-					emit logMessageGenerated(tr("Initial fitness (error): %1").arg(m_userInitialFitness, 0, 'e', 4));
-					emit logMessageGenerated(tr("Elite protection activated - initial solution will be preserved if no significant improvement found"));
-				} else {
-					emit logMessageGenerated(tr("Taking FAILURE branch (fitness >= 1e9)"));
-					m_hasValidUserSolution = false;
-					emit logMessageGenerated(tr("Initial solution evaluation failed - starting with random initialization"));
-				}
-			} catch(...) {
-				m_hasValidUserSolution = false;
-				emit logMessageGenerated(tr("Exception during initial solution evaluation"));
-			}
-
-			// 恢复初始值供种群初始化使用
-			m_initialValues = savedInitialValues;
-		}
-
-		// 初始化种群
-		initializePopulation();
-		emit logMessageGenerated(tr("Population initialized: %1 individuals, %2 dimensions").arg(m_populationSize).arg(getEnabledParameterCount()));
-
-		// GA主循环
-		emit logMessageGenerated(tr("=== Starting GA Main Loop ==="));
-
-		for(m_currentGeneration = 0; m_currentGeneration < m_maxGenerations && !m_shouldStop; ++m_currentGeneration) {
-
-			// 每次迭代都输出标题，或者只在重要迭代输出详细信息
-			bool shouldOutputDetail = (m_currentGeneration % qMax(1, m_maxGenerations / 10) == 0) ||
-				(m_currentGeneration < 5) ||
-				(m_currentGeneration >= m_maxGenerations - 2);
-
-			// 每次迭代都输出标题
-			emit logMessageGenerated(tr("--- Generation %1/%2 ---").arg(m_currentGeneration + 1).arg(m_maxGenerations));
-
-			// 只在特定迭代输出详细统计信息
-			if(shouldOutputDetail) {
-				emit logMessageGenerated(tr("Current best error: %1").arg(m_bestFitness, 0, 'e', 4));
-				emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2, failures: %3)")
-					.arg(m_totalEvaluations).arg(m_successfulEvaluations).arg(m_totalEvaluations - m_successfulEvaluations));
-			}
-
-			// 检查暂停状态
-			while(m_isPaused && !m_shouldStop) {
-				QApplication::processEvents();
-				//msleep(100);
-			}
-
-			if(m_shouldStop) {
-				emit logMessageGenerated(tr("Optimization stopped by user request"));
-				break;
-			}
-
-			// 1. 评估种群
-			evaluatePopulation();
-
-			if(m_shouldStop) break;
-
-			// 2. 更新统计信息
-			updatePopulationStatistics();
-
-			// 3. 发射进度信号
-			emit progressUpdated(m_currentGeneration, m_bestFitness);
-
-			if(shouldOutputDetail) {
-				emit logMessageGenerated(tr("Generation %1 completed: best = %2, avg = %3, worst = %4")
-					.arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4)
-					.arg(m_averageFitness, 0, 'e', 4).arg(m_worstFitness, 0, 'e', 4));
-			}
-
-			// 记录收敛历史
-			m_convergenceHistory.append(m_bestFitness);
-
-			// 更新收敛指标
-			updateConvergenceMetrics();
-
-			// 智能收敛判断
-			StopReasonGA stopReason = analyzeOptimizationStatus();
-
-			if(stopReason == GA_TARGET_ACHIEVED) {
-				emit logMessageGenerated(tr("=== TARGET ACHIEVED ==="));
-				emit logMessageGenerated(tr("Target error achieved! Current error: %1 < Target: %2")
-					.arg(m_bestFitness, 0, 'e', 4).arg(m_targetError, 0, 'e', 4));
-				emit logMessageGenerated(tr("Optimization completed successfully after %1 generations")
-					.arg(m_currentGeneration + 1));
-				break;
-			}
-			else if(stopReason == GA_TRUE_CONVERGENCE) {
-				emit logMessageGenerated(tr("=== TRUE CONVERGENCE DETECTED ==="));
-				emit logMessageGenerated(tr("Algorithm has converged to a stable solution"));
-				emit logMessageGenerated(tr("Final error: %1 after %2 generations")
-					.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1));
-				emit logMessageGenerated(tr("Solution quality: %1 (1.0 = target achieved)")
-					.arg(m_targetError / qMax(1e-10, m_bestFitness), 0, 'f', 3));
-				break;
-			}
-			else if(stopReason == GA_LOCAL_OPTIMUM) {
-				emit logMessageGenerated(tr("=== LOCAL OPTIMUM DETECTED ==="));
-				emit logMessageGenerated(tr("Algorithm appears to be trapped in local optimum"));
-				emit logMessageGenerated(tr("Current error: %1 after %2 generations")
-					.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1));
-				emit logMessageGenerated(tr("Suggestion: Try restarting with different parameters or larger search space"));
-				break;
-			}
-			else if(stopReason == GA_CONSECUTIVE_FAILURES) {
-				emit logMessageGenerated(tr("=== CONSECUTIVE FAILURES ==="));
-				emit logMessageGenerated(tr("Too many consecutive failed generations (%1/%2)")
-					.arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures));
-				break;
-			}
-			else if(stopReason == GA_CONTINUE_OPTIMIZATION) {
-				// 继续优化，每10次迭代输出一次状态
-				if(m_currentGeneration > 0 && m_currentGeneration % 10 == 0) {
-					double diversity = calculatePopulationDiversity();
-
-					emit logMessageGenerated(tr("  Optimization status: diversity=%1")
-						.arg(diversity, 0, 'f', 4));
-				}
-			}
-
-			// 4. 创建新一代种群
-			if(m_currentGeneration < m_maxGenerations - 1)
-			{
-				QVector<GAIndividual> newPopulation;
-				newPopulation.reserve(m_populationSize);
-
-				// 精英保留
-				applyElitism(newPopulation);
-
-				// 生成新个体直到填满种群
-				while(newPopulation.size() < m_populationSize && !m_shouldStop) {
-					// 选择父代
-					int parent1Index = tournamentSelection();
-					int parent2Index = tournamentSelection();
-
-					// 确保父代不同
-					while(parent1Index == parent2Index && m_population.size() > 1) {
-						parent2Index = tournamentSelection();
-					}
-
-					GAIndividual offspring1, offspring2;
-
-					// 交叉
-					if(random01() < m_crossoverRate) {
-						crossover(m_population[parent1Index], m_population[parent2Index],
-							offspring1, offspring2);
-					} else {
-						offspring1 = m_population[parent1Index];
-						offspring2 = m_population[parent2Index];
-					}
-
-					// 变异
-					if(random01() < m_mutationRate) {
-						mutate(offspring1);
-					}
-					if(random01() < m_mutationRate) {
-						mutate(offspring2);
-					}
-
-					// 边界约束
-					clampToLimits(offspring1.genes);
-					clampToLimits(offspring2.genes);
-
-					// 添加到新种群
-					if(newPopulation.size() < m_populationSize) {
-						newPopulation.append(offspring1);
-					}
-					if(newPopulation.size() < m_populationSize) {
-						newPopulation.append(offspring2);
-					}
-				}
-
-				// 替换种群
-				m_population = newPopulation;
-
-				// 自适应参数调整
-				adaptiveParameterUpdate(m_currentGeneration);
-			}
-
-			// 输出迭代结束标记
-			if(shouldOutputDetail) {
-				emit logMessageGenerated(tr("Generation %1 completed - Current best: %2")
-					.arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4));
-			}
-
-			// 强制处理事件，保持界面响应
-			QApplication::processEvents();
-
-			// 在世代间稍作停顿，减少系统负载
-			if(m_currentGeneration % 3 == 2) {
-				//msleep(200);
-			}
-		}
-
-		// 最终结果验证和保护
-		validateAndProtectFinalResult();
-
-	} catch(const std::exception& e) {
-		m_lastError = QString("Critical exception in GA main loop: %1").arg(e.what());
-		emit logMessageGenerated(tr("CRITICAL ERROR: %1").arg(e.what()));
-		cleanupTemporaryDirectory();
-		m_isRunning = false;
-		if(m_progressTimer) m_progressTimer->stop();
-		emit fittingFinished(false, m_lastError);
-		return false;
-	} catch(...) {
-		m_lastError = "Unknown critical exception in GA main loop";
-		emit logMessageGenerated(tr("CRITICAL ERROR: Unknown exception in GA main loop"));
-		cleanupTemporaryDirectory();
-		m_isRunning = false;
-		if(m_progressTimer) m_progressTimer->stop();
-		emit fittingFinished(false, m_lastError);
-		return false;
-	}
-
-	m_isRunning = false;
-	if(m_progressTimer) m_progressTimer->stop();
-
-	// 应用最终参数
-	if(!m_bestIndividual.genes.isEmpty()) {
-		try {
-			emit logMessageGenerated(tr("Applying optimized parameters to model..."));
-			applyParametersToDataManager(m_bestIndividual.genes);
-			saveOptimizationResult();
-
-			// 输出最终优化结果
-			emit logMessageGenerated(tr("=== Optimization Results ==="));
-			emit logMessageGenerated(tr("Final error: %1").arg(m_bestFitness, 0, 'e', 4));
-			emit logMessageGenerated(tr("Total generations: %1").arg(m_currentGeneration + 1));
-			emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2)")
-				.arg(m_totalEvaluations).arg(m_successfulEvaluations));
-
-			// 输出最优参数值
-			QString finalParams = tr("Optimized parameters: ");
-			for(int i = 0; i < m_bestIndividual.genes.size(); ++i) {
-				finalParams += QString("[%1]=%2 ").arg(i).arg(m_bestIndividual.genes[i], 0, 'f', 6);
-			}
-			emit logMessageGenerated(finalParams);
-
-			emit logMessageGenerated(tr("Parameters applied successfully to data manager"));
-		} catch(const std::exception& e) {
-			emit logMessageGenerated(tr("ERROR: Failed to apply final parameters: %1").arg(e.what()));
-			m_lastError = QString("Failed to apply final parameters: %1").arg(e.what());
-		} catch(...) {
-			emit logMessageGenerated(tr("ERROR: Unknown error applying final parameters"));
-			m_lastError = "Failed to apply final parameters due to unknown error";
-		}
-	}
-
-	// 判断系统确定最终结果
-	bool success;
-	QString message;
-	StopReasonGA finalReason = analyzeOptimizationStatus();
-
-	if(finalReason == GA_TARGET_ACHIEVED) {
-		success = true;
-		//message = QString("GA optimization completed successfully. Target achieved. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION SUCCESSFUL ==="));
-	}
-	else if(finalReason == GA_TRUE_CONVERGENCE) {
-		success = true;
-		//message = QString("GA optimization converged to stable solution. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION CONVERGED ==="));
-	}
-	else if(finalReason == GA_LOCAL_OPTIMUM) {
-		success = true;
-		//message = QString("GA optimization trapped in local optimum. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION - LOCAL OPTIMUM ==="));
-	}
-	else if(finalReason == GA_MAX_ITERATIONS) {
-		success = true;
-		//message = QString("GA optimization completed. Max generations reached. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION - MAX GENERATIONS ==="));
-	}
-	else if(finalReason == GA_USER_STOPPED) {
-		success = true;
-		//message = QString("GA optimization stopped by user. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION STOPPED BY USER ==="));
-	}
-	else if(finalReason == GA_CONSECUTIVE_FAILURES) {
-		success = false;
-		//message = QString("GA optimization failed due to consecutive failures. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION FAILED ==="));
-	}
-	else {
-		success = false;
-		//message = QString("GA optimization ended unexpectedly. Best error: %1, Generations: %2")
-		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
-		emit logMessageGenerated(tr("=== GA OPTIMIZATION - UNKNOWN END ==="));
-	}
-
-	emit logMessageGenerated(tr("Result: %1").arg(success ? "SUCCESS" : "FAILED"));
-
-	emit fittingFinished(success, message);
-	cleanupTemporaryDirectory();
-	return success;
-}
-
-void nmCalculationAutoFitGA::stopFitting()
-{
-	if(m_isRunning) {
-		DEBUG_OUT("Stop request received, setting stop flag...");
-
-		// 添加停止日志
-		emit logMessageGenerated(tr("=== User Stop Request Received ==="));
-		emit logMessageGenerated(tr("Gracefully stopping GA optimization..."));
-
-		m_shouldStop = true;
-
-		if(m_progressTimer) {
-			m_progressTimer->stop();
-		}
-
-		// 等待当前评估完成，缩短超时时间
-		int waitCount = 0;
-		while(m_evaluationInProgress > 0 && waitCount < 30) {  // 减少等待时间
-			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50);
-			msleep(50);
-			waitCount++;
-		}
-
-		// 超时时强制重置
-		if(m_evaluationInProgress > 0) {
-			DEBUG_OUT("Force resetting evaluation counter");
-			emit logMessageGenerated(tr("Force stopping current evaluation..."));
-			m_evaluationInProgress = 0;
-		}
-
-		// 确保运行标志被清除
-		m_isRunning = false;
-
-		emit logMessageGenerated(tr("GA optimization stop request processed"));
-		DEBUG_OUT("Stop request processed");
-	} else {
-		DEBUG_OUT("Stop request received but GA is not running");
-		emit logMessageGenerated(tr("Stop request received but optimization is not running"));
-	}
-	cleanupTemporaryDirectory();
-}
-
-bool nmCalculationAutoFitGA::isRunning() const
-{
-	return m_isRunning;
-}
-
-int nmCalculationAutoFitGA::getCurrentGeneration() const
-{
-	return m_currentGeneration;
-}
-
-QVector<double> nmCalculationAutoFitGA::getBestSolution() const
-{
-	return m_bestIndividual.genes;
-}
-
-double nmCalculationAutoFitGA::getBestFitness() const
-{
-	return m_bestFitness;
-}
-
-QString nmCalculationAutoFitGA::getLastError() const
-{
-	return m_lastError;
-}
-
-void nmCalculationAutoFitGA::resetOptimizer()
-{
-	m_population.clear();
-	m_eliteIndividuals.clear();
-	m_bestIndividual = GAIndividual();
-	m_bestFitness = 1e10;
-	m_worstFitness = -1e10;
-	m_averageFitness = 1e10;
-	m_previousBestFitness = 1e10;
-	m_currentGeneration = 0;
-	m_totalEvaluations = 0;
-	m_successfulEvaluations = 0;
-	m_convergenceHistory.clear();
-	m_lastError.clear();
-	m_initialValues.clear();
-	m_userInitialSolution.clear();
-	m_userInitialFitness = 1e10;
-	m_hasValidUserSolution = false;
-	m_diversityHistory.clear();
-
-	DEBUG_OUT("GA optimizer reset");
-}
-
-void nmCalculationAutoFitGA::setGATargetWellName(const QString& wellName)
-{
-	m_targetWellName = wellName;
-}
-
-void nmCalculationAutoFitGA::updateProgress()
-{
-	// 这个槽函数在定时器触发时被调用，可以用来更新界面或执行周期性任务
-	if(m_isRunning) {
-		QApplication::processEvents();
-	}
-}
-
-// ==================== 数据加载方法 ====================
-bool nmCalculationAutoFitGA::loadAllConfigFromDataManager()
-{
-	try {
-		loadOptimizationConfig();
-		loadParameterBounds();
-		extractUserInitialValues();
-		return true;
-	} catch(...) {
-		m_lastError = "Failed to load configuration from data manager";
-		return false;
-	}
-}
-
-void nmCalculationAutoFitGA::loadOptimizationConfig()
-{
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-	nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy();
-
-	// 加载基本配置
-	m_maxGenerations = fittingData.getIterationCount().getValue().toInt();
-	m_targetError = fittingData.getErrorTolerance().getValue().toDouble();
-
-	// 设置GA默认参数
-	m_populationSize = 40;
-	m_crossoverRate = 0.8;
-	m_mutationRate = 0.1;
-	m_elitismRate = 0.15;
-	m_tournamentSize = 3;
-	m_useUniformCrossover = true;
-
-	DEBUG_OUT(QString("Loaded GA optimization config: generations=%1, error=%2, population=%3")
-		.arg(m_maxGenerations).arg(m_targetError).arg(m_populationSize));
-}
-
-void nmCalculationAutoFitGA::loadParameterBounds()
-{
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-	nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy();
-
-	// 获取参数选择状态
-	m_parameterSelected.resize(8);
-	m_parameterSelected[0] = fittingData.getPermeabilitySelected();
-	m_parameterSelected[1] = fittingData.getSkinSelected();
-	m_parameterSelected[2] = fittingData.getWellboreStorageSelected();
-	m_parameterSelected[3] = fittingData.getPorositySelected();
-	m_parameterSelected[4] = fittingData.getThicknessSelected();
-	m_parameterSelected[5] = fittingData.getCtSelected();
-	m_parameterSelected[6] = fittingData.getCfSelected();
-	m_parameterSelected[7] = fittingData.getSwiSelected();
-
-	// 获取参数边界
-	m_parameterLower.resize(8);
-	m_parameterUpper.resize(8);
-
-	m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble();
-	m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble();
-
-	m_parameterLower[1] = fittingData.getSkinMin().getValue().toDouble();
-	m_parameterUpper[1] = fittingData.getSkinMax().getValue().toDouble();
-
-	m_parameterLower[2] = fittingData.getWellboreStorageMin().getValue().toDouble();
-	m_parameterUpper[2] = fittingData.getWellboreStorageMax().getValue().toDouble();
-
-	m_parameterLower[3] = fittingData.getPorosityMin().getValue().toDouble();
-	m_parameterUpper[3] = fittingData.getPorosityMax().getValue().toDouble();
-
-	m_parameterLower[4] = fittingData.getThicknessMin().getValue().toDouble();
-	m_parameterUpper[4] = fittingData.getThicknessMax().getValue().toDouble();
-
-	m_parameterLower[5] = fittingData.getCtMin().getValue().toDouble();
-	m_parameterUpper[5] = fittingData.getCtMax().getValue().toDouble();
-
-	m_parameterLower[6] = fittingData.getCfMin().getValue().toDouble();
-	m_parameterUpper[6] = fittingData.getCfMax().getValue().toDouble();
-
-	m_parameterLower[7] = fittingData.getSwiMin().getValue().toDouble();
-	m_parameterUpper[7] = fittingData.getSwiMax().getValue().toDouble();
-
-	// 更新启用参数索引
-	m_enabledParamIndices.clear();
-	for(int i = 0; i < m_parameterSelected.size(); ++i) {
-		if(m_parameterSelected[i]) {
-			m_enabledParamIndices.append(i);
-		}
-	}
-	DEBUG_OUT(QString("Loaded parameter bounds: %1 enabled parameters")
-		.arg(m_enabledParamIndices.size()));
-}
-
-void nmCalculationAutoFitGA::extractUserInitialValues()
-{
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-	nmDataReservoir reservoirData = dataManager->getReservoirDataCopy();
-	//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
-	nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
-
-	m_initialValues.clear();
-
-	// 按照启用参数的顺序提取初始值
-	for(int i = 0; i < m_enabledParamIndices.size(); ++i) {
-		int paramIndex = m_enabledParamIndices[i];
-		double initialValue = 0.0;
-
-		switch(paramIndex) {
-		case 0: // 渗透率
-			initialValue = reservoirData.getPermeability().getValue().toDouble();
-			break;
-
-		case 1: // 表皮系数
-			if(pTargetWell) {
-				initialValue = pTargetWell->getPerforation(0)->getSkin().getValue().toDouble();
-			}
-
-			break;
-
-		case 2: // 井筒储集系数
-			if(pTargetWell) {
-				initialValue = pTargetWell->getWellboreStorage().getValue().toDouble();
-			}
-
-			break;
-
-		case 3: // 孔隙度
-			initialValue = reservoirData.getPorosity().getValue().toDouble();
-			break;
-
-		case 4: // 储层厚度
-			initialValue = reservoirData.getThickness().getValue().toDouble();
-			break;
-
-		case 5: // 综合压缩系数
-			initialValue = reservoirData.getCt().getValue().toDouble();
-			break;
-
-		case 6: // 岩石压缩系数
-			initialValue = reservoirData.getCf().getValue().toDouble();
-			break;
-
-		case 7: // 初始含水饱和度
-			initialValue = reservoirData.getSwi().getValue().toDouble();
-			break;
-		}
-
-		m_initialValues.append(initialValue);
-	}
-
-	DEBUG_OUT(QString("Extracted %1 user initial values").arg(m_initialValues.size()));
-
-	for(int i = 0; i < m_initialValues.size(); ++i) {
-		DEBUG_OUT(QString("  Initial[%1] = %2").arg(i).arg(m_initialValues[i], 0, 'e', 3));
-	}
-
-	// 验证初始值
-	if(!validateInitialValues()) {
-		DEBUG_OUT("Warning: Some initial values are outside parameter bounds");
-	}
-}
-
-// ==================== 遗传算法核心方法 ====================
-
-void nmCalculationAutoFitGA::initializePopulation()
-{
-	int dimensions = getEnabledParameterCount();
-	if(dimensions == 0) return;
-
-	m_population.clear();
-	m_population.resize(m_populationSize);
-
-	bool hasValidInitials = !m_initialValues.isEmpty() && m_initialValues.size() >= dimensions;
-	int guidedCount = hasValidInitials ? qMax(2, m_populationSize / 2) : qMax(1, m_populationSize / 3);
-
-	DEBUG_OUT(QString("Enhanced population initialization: %1 individuals, %2 guided, %3 random")
-		.arg(m_populationSize).arg(guidedCount).arg(m_populationSize - guidedCount));
-
-	for(int i = 0; i < m_populationSize; ++i) {
-		GAIndividual& individual = m_population[i];
-		individual.genes.resize(dimensions);
-		individual.fitness = 1e10;
-		individual.isEvaluated = false;
-
-		// 基因初始化
-        for(int j = 0; j < dimensions; ++j) {
-			int paramIndex = m_enabledParamIndices[j];
-			double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
-
-			if(i == 0 && hasValidInitials) {
-				// 第一个个体：使用用户初始值
-				individual.genes[j] = m_initialValues[j];
-			} else if(i < guidedCount && hasValidInitials) {
-				// 引导搜索策略
-				double searchRadius;
-				if(i <= guidedCount / 3) {
-					searchRadius = range * 0.03;
-				} else if(i <= guidedCount * 2 / 3) {
-					searchRadius = range * 0.08;
-				} else {
-					searchRadius = range * 0.15;
-				}
-				double offset = (random01() - 0.5) * searchRadius;
-				individual.genes[j] = m_initialValues[j] + offset;
-			} else {
-				// 随机初始化
-				individual.genes[j] = m_parameterLower[paramIndex] + random01() * range;
-			}
-		}
-
-		// 边界约束
-		clampToLimits(individual.genes);
-	}
-}
-
-double nmCalculationAutoFitGA::evaluateGenes(const QVector<double>& genes)
-{
-	const QString funcName = QString("evaluateGenes[Gen%1]").arg(m_currentGeneration);
-	static int callCount = 0;
-	callCount++;
-
-	try {
-		DEBUG_OUT(QString("%1: Call #%2 - Starting evaluation with %3 genes")
-			.arg(funcName).arg(callCount).arg(genes.size()));
-
-		// 打印参数值用于对比
-		QString paramStr = "Parameters: ";
-
-		for(int i = 0; i < genes.size(); ++i) {
-			paramStr += QString("[%1]=%2 ").arg(i).arg(genes[i], 0, 'f', 6);
-		}
-
-		DEBUG_OUT(QString("%1: %2").arg(funcName).arg(paramStr));
-
-		// 1. 参数有效性检查
-		if(!validateParameters(genes)) {
-			DEBUG_OUT(QString("%1: Call #%2 - Invalid parameters").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		// 2. 检查数据管理器状态
-		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-
-		if(!dataManager) {
-			DEBUG_OUT(QString("%1: Call #%2 - DataManager is null").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		// 3. 应用参数到数据管理器
-		try {
-			DEBUG_OUT(QString("%1: Call #%2 - Applying parameters to DataManager").arg(funcName).arg(callCount));
-			applyParametersToDataManager(genes);
-			DEBUG_OUT(QString("%1: Call #%2 - Parameters applied successfully").arg(funcName).arg(callCount));
-		} catch(const std::exception& e) {
-			DEBUG_OUT(QString("%1: Call #%2 - Failed to apply parameters: %3").arg(funcName).arg(callCount).arg(e.what()));
-			return 1e10;
-		} catch(...) {
-			DEBUG_OUT(QString("%1: Call #%2 - Unknown error applying parameters").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		// 4. 运行求解器
-		QVector<QVector<double> > solverResult;
-		const int maxRetries = 2;
-		bool solverSuccess = false;
-
-		for(int retry = 0; retry <= maxRetries; ++retry) {
-			if(m_shouldStop) return 1e10;
-
-			try {
-				DEBUG_OUT(QString("%1: Call #%2 - Solver attempt %3/%4")
-					.arg(funcName).arg(callCount).arg(retry + 1).arg(maxRetries + 1));
-
-				// 在求解器调用前添加短暂延迟，确保状态稳定
-				if(retry > 0) {
-					DEBUG_OUT(QString("%1: Call #%2 - Retry delay before solver attempt")
-						.arg(funcName).arg(callCount));
-					msleep(1000); // 增加延迟时间
-				}
-
-				solverResult = runSolver();
-
-				if(!solverResult.isEmpty() && validateSolverResult(solverResult)) {
-					DEBUG_OUT(QString("%1: Call #%2 - Solver successful on attempt %3, result size: %4")
-						.arg(funcName).arg(callCount).arg(retry + 1).arg(solverResult[0].size()));
-					solverSuccess = true;
-					break;
-				} else {
-					DEBUG_OUT(QString("%1: Call #%2 - Solver failed on attempt %3 - empty or invalid result")
-						.arg(funcName).arg(callCount).arg(retry + 1));
-
-					if(retry < maxRetries) {
-						DEBUG_OUT(QString("%1: Call #%2 - Will retry solver").arg(funcName).arg(callCount));
-					}
-				}
-
-			} catch(const std::exception& e) {
-				DEBUG_OUT(QString("%1: Call #%2 - Solver exception on attempt %3: %4")
-					.arg(funcName).arg(callCount).arg(retry + 1).arg(e.what()));
-			} catch(...) {
-				DEBUG_OUT(QString("%1: Call #%2 - Unknown solver exception on attempt %3")
-					.arg(funcName).arg(callCount).arg(retry + 1));
-			}
-		}
-
-		if(!solverSuccess) {
-			DEBUG_OUT(QString("%1: Call #%2 - All solver attempts failed").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		// 5. 获取双对数结果数据
-		QVector<QVector<double> > resultLogLogData;
-
-		try {
-
-			nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
-
-			if(pTargetWell) {
-				resultLogLogData = pTargetWell->getResultLogLog();
-
-				if(!validateLogLogData(resultLogLogData)) {
-					DEBUG_OUT(QString("%1: Call #%2 - Invalid result LogLog data").arg(funcName).arg(callCount));
-					return 1e10;
-				}
-
-				DEBUG_OUT(QString("%1: Call #%2 - LogLog result data obtained, size: %3")
-					.arg(funcName).arg(callCount).arg(resultLogLogData[0].size()));
-			} else {
-				DEBUG_OUT(QString("%1: Call #%2 - No wells found after solver").arg(funcName).arg(callCount));
-				return 1e10;
-			}
-		} catch(const std::exception& e) {
-			DEBUG_OUT(QString("%1: Call #%2 - Error getting LogLog result: %3").arg(funcName).arg(callCount).arg(e.what()));
-			return 1e10;
-		} catch(...) {
-			DEBUG_OUT(QString("%1: Call #%2 - Unknown error getting LogLog result").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		// 6. 双对数曲线对齐和误差计算
-		double error;
-
-		try {
-			error = calculateLogLogCurveError(m_targetLogLogData, resultLogLogData);
-
-			if(!isFiniteNumber(error) || error < 0) {
-				DEBUG_OUT(QString("%1: Call #%2 - Invalid error value: %3").arg(funcName).arg(callCount).arg(error));
-				return 1e10;
-			}
-
-			DEBUG_OUT(QString("%1: Call #%2 - Evaluation successful, error = %3")
-				.arg(funcName).arg(callCount).arg(error, 0, 'e', 6));
-
-		} catch(const std::exception& e) {
-			DEBUG_OUT(QString("%1: Call #%2 - LogLog error calculation failed: %3").arg(funcName).arg(callCount).arg(e.what()));
-			return 1e10;
-		} catch(...) {
-			DEBUG_OUT(QString("%1: Call #%2 - Unknown error in LogLog error calculation").arg(funcName).arg(callCount));
-			return 1e10;
-		}
-
-		return error;
-
-	} catch(const std::exception& e) {
-		DEBUG_OUT(QString("%1: Call #%2 - Top-level exception: %3").arg(funcName).arg(callCount).arg(e.what()));
-		return 1e10;
-	} catch(...) {
-		DEBUG_OUT(QString("%1: Call #%2 - Unknown top-level exception").arg(funcName).arg(callCount));
-		return 1e10;
-	}
-}
-
-double nmCalculationAutoFitGA::evaluateIndividual(GAIndividual& individual)
-{
-	// 如果已经评估过，直接返回
-	if(individual.isEvaluated) {
-		return individual.fitness;
-	}
-
-	// 调用新的评估函数
-	individual.fitness = evaluateGenes(individual.genes);
-	individual.isEvaluated = true;
-
-	return individual.fitness;
-}
-
-void nmCalculationAutoFitGA::evaluatePopulation()
-{
-	int successfulEvaluations = 0;
-	int totalEvaluations = 0;
-	int currentGenerationFailed = 0;
-
-	for(int i = 0; i < m_population.size() && !m_shouldStop; ++i) {
-		GAIndividual& individual = m_population[i];
-
-		if(!individual.isEvaluated) {
-			try {
-				double previousFitness = individual.fitness;
-				individual.fitness = evaluateGenes(individual.genes);
-				individual.isEvaluated = true;
-				totalEvaluations++;
-				m_totalEvaluations++;
-
-				if(individual.fitness < 1e9) {
-					successfulEvaluations++;
-					m_successfulEvaluations++;
-
-					// 更新全局最优
-					if(individual.fitness < m_bestFitness) {
-						m_bestFitness = individual.fitness;
-						m_bestIndividual = individual;
-
-						emit logMessageGenerated(tr("  Individual %1 improved: %2 -> %3")
-							.arg(i + 1).arg(previousFitness, 0, 'e', 3).arg(individual.fitness, 0, 'e', 3));
-					}
-				} else {
-					currentGenerationFailed++;
-					emit logMessageGenerated(tr("  Individual %1: evaluation failed").arg(i + 1));
-				}
-
-				// 每评估2个个体处理一次事件
-				if(i % 2 == 0) {
-					QApplication::processEvents();
-				}
-
-			} catch(const std::exception& e) {
-				emit logMessageGenerated(tr("  Individual %1: Exception: %2").arg(i + 1).arg(e.what()));
-				individual.fitness = 1e10;
-				individual.isEvaluated = true;
-				totalEvaluations++;
-				currentGenerationFailed++;
-				m_totalEvaluations++;
-			} catch(...) {
-				emit logMessageGenerated(tr("  Individual %1: Unknown exception").arg(i + 1));
-				individual.fitness = 1e10;
-				individual.isEvaluated = true;
-				totalEvaluations++;
-				currentGenerationFailed++;
-				m_totalEvaluations++;
-			}
-		}
-	}
-
-	if(m_shouldStop) return;
-
-	double currentSuccessRate = totalEvaluations > 0 ?
-		(double)successfulEvaluations / totalEvaluations : 0.0;
-
-	// 更新连续失败代数计数
-	if(successfulEvaluations == 0) {
-		m_consecutiveFailedGenerations++;
-		emit logMessageGenerated(tr("WARNING: No successful evaluations in generation %1 (consecutive failures: %2)")
-			.arg(m_currentGeneration + 1).arg(m_consecutiveFailedGenerations));
-	} else {
-		m_consecutiveFailedGenerations = 0; // 重置连续失败计数
-	}
-
-	// 输出当前代统计
-	emit logMessageGenerated(tr("Current generation stats: %1 successful, %2 failed out of %3 individuals (success rate: %4%)")
-		.arg(successfulEvaluations).arg(currentGenerationFailed)
-		.arg(totalEvaluations).arg(currentSuccessRate * 100, 0, 'f', 1));
-
-	// 检查是否需要停止优化
-	if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) {
-		m_lastError = QString("Too many consecutive failed generations (%1)").arg(m_consecutiveFailedGenerations);
-		emit logMessageGenerated(tr("ERROR: Too many consecutive failed generations (%1/%2) - stopping optimization")
-			.arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures));
-		return;
-	}
-
-	// 警告低成功率但不立即停止
-	if(currentSuccessRate < 0.5 && m_currentGeneration > 3) {
-		emit logMessageGenerated(tr("WARNING: Low success rate (%1%) in generation %2, but continuing optimization")
-			.arg(currentSuccessRate * 100, 0, 'f', 1).arg(m_currentGeneration + 1));
-	}
-}
-
-int nmCalculationAutoFitGA::tournamentSelection()
-{
-	int bestIndex = qrand() % m_population.size();
-	double bestFitness = m_population[bestIndex].fitness;
-
-	for(int i = 1; i < m_tournamentSize; ++i) {
-		int candidateIndex = qrand() % m_population.size();
-		double candidateFitness = m_population[candidateIndex].fitness;
-
-		if(candidateFitness < bestFitness) {
-			bestIndex = candidateIndex;
-			bestFitness = candidateFitness;
-		}
-	}
-
-	return bestIndex;
-}
-
-int nmCalculationAutoFitGA::rouletteWheelSelection()
-{
-	// 计算适应度总和（使用倒数，因为我们要最小化）
-	double totalFitness = 0.0;
-	double maxFitness = -1e10;
-
-	// 找到最大适应度值
-	for(int i = 0; i < m_population.size(); ++i) {
-		if(m_population[i].fitness > maxFitness) {
-			maxFitness = m_population[i].fitness;
-		}
-	}
-
-	// 计算转换后的适应度总和
-	for(int i = 0; i < m_population.size(); ++i) {
-		double transformedFitness = maxFitness - m_population[i].fitness + 1e-6;
-		totalFitness += transformedFitness;
-	}
-
-	// 轮盘赌选择
-	double randomValue = random01() * totalFitness;
-	double cumulativeFitness = 0.0;
-
-	for(int i = 0; i < m_population.size(); ++i) {
-		double transformedFitness = maxFitness - m_population[i].fitness + 1e-6;
-		cumulativeFitness += transformedFitness;
-
-		if(cumulativeFitness >= randomValue) {
-			return i;
-		}
-	}
-
-	return m_population.size() - 1; // 备用选择
-}
-
-void nmCalculationAutoFitGA::crossover(const GAIndividual& parent1, const GAIndividual& parent2,
-	GAIndividual& offspring1, GAIndividual& offspring2)
-{
-	if(m_useUniformCrossover) {
-		uniformCrossover(parent1, parent2, offspring1, offspring2);
-	} else {
-		singlePointCrossover(parent1, parent2, offspring1, offspring2);
-	}
-}
-
-void nmCalculationAutoFitGA::singlePointCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
-	GAIndividual& offspring1, GAIndividual& offspring2)
-{
-	int dimensions = parent1.genes.size();
-
-	if(dimensions == 0) return;
-
-	// 初始化子代
-	offspring1.genes.resize(dimensions);
-	offspring2.genes.resize(dimensions);
-	offspring1.fitness = 1e10;
-	offspring2.fitness = 1e10;
-	offspring1.isEvaluated = false;
-	offspring2.isEvaluated = false;
-
-	// 选择交叉点
-	int crossoverPoint = qrand() % dimensions;
-
-	// 执行交叉
-	for(int i = 0; i < dimensions; ++i) {
-		if(i < crossoverPoint) {
-			offspring1.genes[i] = parent1.genes[i];
-			offspring2.genes[i] = parent2.genes[i];
-		} else {
-			offspring1.genes[i] = parent2.genes[i];
-			offspring2.genes[i] = parent1.genes[i];
-		}
-	}
-}
-
-void nmCalculationAutoFitGA::uniformCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
-	GAIndividual& offspring1, GAIndividual& offspring2)
-{
-	int dimensions = parent1.genes.size();
-
-	if(dimensions == 0) return;
-
-	// 初始化子代
-	offspring1.genes.resize(dimensions);
-	offspring2.genes.resize(dimensions);
-	offspring1.fitness = 1e10;
-	offspring2.fitness = 1e10;
-	offspring1.isEvaluated = false;
-	offspring2.isEvaluated = false;
-
-	// 均匀交叉
-	for(int i = 0; i < dimensions; ++i) {
-		if(random01() < 0.5) {
-			offspring1.genes[i] = parent1.genes[i];
-			offspring2.genes[i] = parent2.genes[i];
-		} else {
-			offspring1.genes[i] = parent2.genes[i];
-			offspring2.genes[i] = parent1.genes[i];
-		}
-	}
-}
-
-void nmCalculationAutoFitGA::mutate(GAIndividual& individual)
-{
-	// 使用高斯变异作为主要方式
-	gaussianMutation(individual);
-}
-
-void nmCalculationAutoFitGA::gaussianMutation(GAIndividual& individual)
-{
-	for(int i = 0; i < individual.genes.size(); ++i) {
-		if(random01() < m_mutationRate) {
-			int paramIndex = m_enabledParamIndices[i];
-			double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
-			double sigma = range * MUTATION_STRENGTH;
-
-			// 高斯变异
-			double mutation = gaussianRandom(0.0, sigma);
-			individual.genes[i] += mutation;
-
-			// 边界处理
-			individual.genes[i] = qMax(m_parameterLower[paramIndex],
-				qMin(m_parameterUpper[paramIndex], individual.genes[i]));
-		}
-	}
-
-	// 标记为未评估
-	individual.isEvaluated = false;
-}
-
-void nmCalculationAutoFitGA::polynomialMutation(GAIndividual& individual)
-{
-	const double eta = 20.0; // 分布指数
-
-	for(int i = 0; i < individual.genes.size(); ++i) {
-		if(random01() < m_mutationRate) {
-			int paramIndex = m_enabledParamIndices[i];
-			double lower = m_parameterLower[paramIndex];
-			double upper = m_parameterUpper[paramIndex];
-			double y = individual.genes[i];
-
-			double delta1 = (y - lower) / (upper - lower);
-			double delta2 = (upper - y) / (upper - lower);
-
-			double rnd = random01();
-			double mut_pow = 1.0 / (eta + 1.0);
-
-			double deltaq;
-
-			if(rnd <= 0.5) {
-				double xy = 1.0 - delta1;
-				double val = 2.0 * rnd + (1.0 - 2.0 * rnd) * pow(xy, eta + 1.0);
-				deltaq = pow(val, mut_pow) - 1.0;
-			} else {
-				double xy = 1.0 - delta2;
-				double val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * pow(xy, eta + 1.0);
-				deltaq = 1.0 - pow(val, mut_pow);
-			}
-
-			y += deltaq * (upper - lower);
-			individual.genes[i] = qMax(lower, qMin(upper, y));
-		}
-	}
-
-	// 标记为未评估
-	individual.isEvaluated = false;
-}
-
-void nmCalculationAutoFitGA::applyElitism(QVector<GAIndividual>& newPopulation)
-{
-	int eliteCount = static_cast<int>(m_populationSize * m_elitismRate);
-
-	if(eliteCount == 0) return;
-
-	// 对种群按适应度排序
-	QVector<GAIndividual> sortedPopulation = m_population;
-
-	// 冒泡排序（适应度从小到大）
-	for(int i = 0; i < sortedPopulation.size() - 1; ++i) {
-		for(int j = 0; j < sortedPopulation.size() - 1 - i; ++j) {
-			if(sortedPopulation[j].fitness > sortedPopulation[j + 1].fitness) {
-				GAIndividual temp = sortedPopulation[j];
-				sortedPopulation[j] = sortedPopulation[j + 1];
-				sortedPopulation[j + 1] = temp;
-			}
-		}
-	}
-
-	// 复制精英个体到新种群
-	for(int i = 0; i < eliteCount && i < sortedPopulation.size(); ++i) {
-		newPopulation.append(sortedPopulation[i]);
-	}
-
-	DEBUG_OUT(QString("Applied elitism: %1 elite individuals preserved").arg(eliteCount));
-}
-
-void nmCalculationAutoFitGA::updatePopulationStatistics()
-{
-	if(m_population.isEmpty()) return;
-
-	m_previousBestFitness = m_bestFitness;
-
-	double sum = 0.0;
-	double minFitness = 1e10;
-	double maxFitness = -1e10;
-	int validCount = 0;
-
-	for(int i = 0; i < m_population.size(); ++i) {
-		const GAIndividual& individual = m_population[i];
-
-		if(individual.isEvaluated && individual.fitness < 1e9) {
-			sum += individual.fitness;
-			validCount++;
-
-			if(individual.fitness < minFitness) {
-				minFitness = individual.fitness;
-			}
-
-			if(individual.fitness > maxFitness) {
-				maxFitness = individual.fitness;
-			}
-
-			// 更新全局最优
-			if(individual.fitness < m_bestFitness) {
-				// 只有显著改进时才更新
-				double improvement = (m_bestFitness - individual.fitness);
-				double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness));
-
-				if(relativeImprovement > m_improvementThreshold) {
-					m_bestFitness = individual.fitness;
-					m_bestIndividual = individual;
-
-					DEBUG_OUT(QString("Global best updated with %1% improvement: %2")
-						.arg(relativeImprovement * 100, 0, 'f', 3)
-						.arg(m_bestFitness, 0, 'e', 4));
-				}
-			}
-		}
-	}
-
-	if(validCount > 0) {
-		m_averageFitness = sum / validCount;
-		m_worstFitness = maxFitness;
-	} else {
-		m_averageFitness = 1e10;
-		m_worstFitness = 1e10;
-	}
-
-	// 在没有找到更好解时才检查精英保护
-	if(m_hasValidUserSolution && m_userInitialFitness < m_bestFitness) {
-		DEBUG_OUT("Elite protection: No significant improvement found, checking initial solution");
-
-		// 检查初始解是否仍然是最优的
-		double improvement = m_bestFitness - m_userInitialFitness;
-		double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness));
-
-		if(relativeImprovement > m_improvementThreshold * 0.5) { // 使用更宽松的阈值
-			DEBUG_OUT("Elite protection: Restoring user initial solution");
-			m_bestFitness = m_userInitialFitness;
-			m_bestIndividual.genes = m_userInitialSolution;
-			m_bestIndividual.fitness = m_userInitialFitness;
-			m_bestIndividual.isEvaluated = true;
-		}
-	}
-}
-
-bool nmCalculationAutoFitGA::checkConvergence()
-{
-	if(m_convergenceHistory.size() < CONVERGENCE_CHECK_INTERVAL) {
-		return false;
-	}
-
-	// 检查最近几次世代的改进
-	double recentBest = m_convergenceHistory.last();
-	double oldBest = m_convergenceHistory[m_convergenceHistory.size() - CONVERGENCE_CHECK_INTERVAL];
-
-	double improvement = oldBest - recentBest;
-
-	// 如果连续多代没有显著改进，认为已收敛
-	if(improvement < MIN_FITNESS_IMPROVEMENT) {
-		// 检查是否连续停滞
-		int stagnationCount = 0;
-
-		for(int i = m_convergenceHistory.size() - 1; i >= qMax(0, m_convergenceHistory.size() - MAX_STAGNATION_GENERATIONS); --i) {
-			if(i > 0) {
-				double diff = m_convergenceHistory[i - 1] - m_convergenceHistory[i];
-
-				if(diff < MIN_FITNESS_IMPROVEMENT) {
-					stagnationCount++;
-				} else {
-					break;
-				}
-			}
-		}
-
-		return stagnationCount >= MAX_STAGNATION_GENERATIONS;
-	}
-
-	return false;
-}
-
-void nmCalculationAutoFitGA::adaptiveParameterUpdate(int generation)
-{
-	// 自适应调整变异率
-	double progress = static_cast<double>(generation) / m_maxGenerations;
-
-	// 早期探索，后期开发
-	m_mutationRate = 0.2 * (1.0 - progress) + 0.05 * progress;
-
-	// 自适应调整交叉率
-	if(generation > 0) {
-		double improvement = m_previousBestFitness - m_bestFitness;
-
-		if(improvement < MIN_FITNESS_IMPROVEMENT) {
-			// 如果改进很小，增加探索性
-			m_mutationRate = qMin(0.3, m_mutationRate * 1.1);
-			m_crossoverRate = qMax(0.6, m_crossoverRate * 0.95);
-		} else {
-			// 如果有明显改进，增加开发性
-			m_mutationRate = qMax(0.05, m_mutationRate * 0.9);
-			m_crossoverRate = qMin(0.9, m_crossoverRate * 1.05);
-		}
-	}
-}
-
-void nmCalculationAutoFitGA::validateAndProtectFinalResult()
-{
-	if(!m_hasValidUserSolution) {
-		emit logMessageGenerated(tr("No initial solution for elite protection"));
-		return;
-	}
-
-	emit logMessageGenerated(tr("=== Final Result Validation (Elite Protection) ==="));
-
-	// 使用已有的评估结果
-	double finalFitness = m_bestFitness;
-	double initialFitness = m_userInitialFitness;
-
-	emit logMessageGenerated(tr("Comparing results: Initial=%1, Final=%2")
-		.arg(initialFitness, 0, 'e', 4).arg(finalFitness, 0, 'e', 4));
-
-	// 计算改进程度
-	double improvement = initialFitness - finalFitness;
-	double relativeImprovement = improvement / qMax(1e-10, qAbs(initialFitness));
-
-	emit logMessageGenerated(tr("Improvement: %1 (%2%)")
-		.arg(improvement, 0, 'e', 4).arg(relativeImprovement * 100, 0, 'f', 2));
-
-	if(relativeImprovement < m_improvementThreshold) {
-		emit logMessageGenerated(tr("Elite protection triggered: insufficient improvement"));
-		emit logMessageGenerated(tr("Threshold: %1%, Actual: %2%")
-			.arg(m_improvementThreshold * 100, 0, 'f', 2)
-			.arg(relativeImprovement * 100, 0, 'f', 4));
-		emit logMessageGenerated(tr("Restoring initial solution as final result"));
-
-		m_bestFitness = initialFitness;
-		m_bestIndividual.genes = m_userInitialSolution;
-		m_bestIndividual.fitness = initialFitness;
-		m_bestIndividual.isEvaluated = true;
-
-		emit logMessageGenerated(tr("Initial solution restored successfully"));
-	} else {
-		emit logMessageGenerated(tr("Final result validated - significant improvement achieved"));
-	}
-}
-
-StopReasonGA nmCalculationAutoFitGA::analyzeOptimizationStatus()
-{
-	// 1. 检查用户停止
-	if(m_shouldStop) {
-		return GA_USER_STOPPED;
-	}
-
-	// 2. 检查连续失败
-	if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) {
-		return GA_CONSECUTIVE_FAILURES;
-	}
-
-	// 3. 检查是否达到目标精度
-	if(m_bestFitness < m_targetError) {
-		return GA_TARGET_ACHIEVED;
-	}
-
-	// 4. 检查是否达到最大迭代数
-	if(m_currentGeneration >= m_maxGenerations - 1) {
-		return GA_MAX_ITERATIONS;
-	}
-
-	// 5. 需要足够的历史数据才能判断收敛
-	if(m_convergenceHistory.size() < m_localOptimumWindow) {
-		return GA_CONTINUE_OPTIMIZATION;
-	}
-
-	// 6. 检查真正的收敛
-	if(m_convergenceHistory.size() >= m_trueConvergenceWindow && checkTrueConvergence()) {
-		return GA_TRUE_CONVERGENCE;
-	}
-
-	// 7. 检查局部最优陷阱
-	if(checkLocalOptimumTrap()) {
-		return GA_LOCAL_OPTIMUM;
-	}
-
-	return GA_CONTINUE_OPTIMIZATION;
-}
-
-bool nmCalculationAutoFitGA::checkTrueConvergence() const
-{
-	if(m_convergenceHistory.size() < m_trueConvergenceWindow) {
-		return false;
-	}
-
-	// 1. 检查解质量 - 如果已经接近目标，小改进可能是真收敛
-	bool nearTarget = (m_bestFitness < m_targetError * m_nearTargetFactor);
-
-	// 2. 检查适应度稳定性 - 长期小幅波动
-	double recentVariance = calculateFitnessVariance(10);
-	double recentMean = 0.0;
-	int windowSize = qMin(10, m_convergenceHistory.size());
-
-	// 计算最近窗口的均值
-	for(int i = m_convergenceHistory.size() - windowSize; i < m_convergenceHistory.size(); ++i) {
-		recentMean += m_convergenceHistory[i];
-	}
-
-	recentMean /= windowSize;
-
-	double relativeVariance = recentVariance / qMax(1e-10, recentMean * recentMean);
-	bool stableError = (relativeVariance < m_convergenceVarianceThreshold);
-
-	// 3. 检查种群多样性 - 应该收敛到同一区域
-	double currentDiversity = calculatePopulationDiversity();
-	bool lowDiversity = (currentDiversity < m_diversityThreshold);
-
-	// 4. 检查长期改进趋势
-	double longTermImprovement = calculateLongTermImprovement(m_trueConvergenceWindow);
-	bool minimalLongTermImprovement = (longTermImprovement < 1e-4); // 0.01%
-
-	// 真收敛的判断条件
-	bool isConverged = nearTarget ||
-		(stableError && lowDiversity && minimalLongTermImprovement);
-
-	if(isConverged) {
-		DEBUG_OUT("=== TRUE CONVERGENCE ANALYSIS ===");
-		DEBUG_OUT(QString("nearTarget=%1 (fitness=%2, target*factor=%3)")
-			.arg(nearTarget).arg(m_bestFitness, 0, 'e', 4)
-			.arg(m_targetError * m_nearTargetFactor, 0, 'e', 4));
-		DEBUG_OUT(QString("stableError=%1 (relativeVariance=%2)")
-			.arg(stableError).arg(relativeVariance, 0, 'e', 6));
-		DEBUG_OUT(QString("lowDiversity=%1 (diversity=%2, threshold=%3)")
-			.arg(lowDiversity).arg(currentDiversity, 0, 'f', 6).arg(m_diversityThreshold));
-		DEBUG_OUT(QString("longTermImprovement=%1%")
-			.arg(longTermImprovement * 100, 0, 'f', 4));
-	}
-
-	return isConverged;
-}
-
-bool nmCalculationAutoFitGA::checkLocalOptimumTrap() const
-{
-	if(m_convergenceHistory.size() < m_localOptimumWindow) {
-		return false;
-	}
-
-	// 1. 检查解质量 - 如果距离目标还很远，停滞就可能是局部最优
-	bool farFromTarget = (m_bestFitness > m_targetError * m_farTargetFactor);
-
-	// 2. 检查短期改进 - 近期改进非常小
-	double shortTermImprovement = calculateLongTermImprovement(m_localOptimumWindow);
-	bool poorShortTermImprovement = (shortTermImprovement < 1e-5); // 0.001%
-
-	// 3. 检查种群多样性 - 可能过早聚集或无效分散
-	double currentDiversity = calculatePopulationDiversity();
-	bool problematicDiversity = (currentDiversity < m_diversityThreshold * 0.1) ||
-		(currentDiversity > m_diversityThreshold * 5.0);
-
-	// 4. 检查适应度方差 - 可能卡在平坦区域
-	double fitnessVariance = calculateFitnessVariance(m_localOptimumWindow);
-	bool flatFitnessLandscape = (fitnessVariance < m_convergenceVarianceThreshold * 0.1);
-
-	// 局部最优的判断条件
-	bool isLocalOptimum = farFromTarget && poorShortTermImprovement &&
-		(problematicDiversity || flatFitnessLandscape);
-
-	if(isLocalOptimum) {
-		DEBUG_OUT("=== LOCAL OPTIMUM ANALYSIS ===");
-		DEBUG_OUT(QString("farFromTarget=%1 (fitness=%2, target*factor=%3)")
-			.arg(farFromTarget).arg(m_bestFitness, 0, 'e', 4)
-			.arg(m_targetError * m_farTargetFactor, 0, 'e', 4));
-		DEBUG_OUT(QString("poorShortTermImprovement=%1 (improvement=%2%)")
-			.arg(poorShortTermImprovement).arg(shortTermImprovement * 100, 0, 'f', 4));
-		DEBUG_OUT(QString("problematicDiversity=%1 (diversity=%2)")
-			.arg(problematicDiversity).arg(currentDiversity, 0, 'f', 6));
-		DEBUG_OUT(QString("fitnessVariance=%1")
-			.arg(fitnessVariance, 0, 'e', 6));
-	}
-
-	return isLocalOptimum;
-}
-
-double nmCalculationAutoFitGA::calculatePopulationDiversity() const
-{
-	if(m_population.size() < 2) return 0.0;
-
-	int dimensions = getEnabledParameterCount();
-
-	if(dimensions == 0) return 0.0;
-
-	double totalDiversity = 0.0;
-
-	for(int dim = 0; dim < dimensions; ++dim) {
-		// 计算该维度上所有个体的均值
-		double mean = 0.0;
-
-		for(int i = 0; i < m_population.size(); ++i) {
-			mean += m_population[i].genes[dim];
-		}
-
-		mean /= m_population.size();
-
-		// 计算该维度上的方差
-		double variance = 0.0;
-
-		for(int i = 0; i < m_population.size(); ++i) {
-			double diff = m_population[i].genes[dim] - mean;
-			variance += diff * diff;
-		}
-
-		variance /= m_population.size();
-
-		// 归一化到参数范围
-		int paramIndex = m_enabledParamIndices[dim];
-		double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
-		double normalizedStd = sqrt(variance) / qMax(1e-10, range);
-
-		totalDiversity += normalizedStd;
-	}
-
-	return totalDiversity / dimensions;
-}
-
-double nmCalculationAutoFitGA::calculateFitnessVariance(int windowSize) const
-{
-	if(m_convergenceHistory.size() < windowSize) {
-		return 1e10; // 数据不足，返回大值
-	}
-
-	// 计算最近windowSize次迭代的方差
-	double mean = 0.0;
-	int startIdx = m_convergenceHistory.size() - windowSize;
-
-	for(int i = startIdx; i < m_convergenceHistory.size(); ++i) {
-		mean += m_convergenceHistory[i];
-	}
-
-	mean /= windowSize;
-
-	double variance = 0.0;
-
-	for(int i = startIdx; i < m_convergenceHistory.size(); ++i) {
-		double diff = m_convergenceHistory[i] - mean;
-		variance += diff * diff;
-	}
-
-	variance /= windowSize;
-
-	return variance;
-}
-
-double nmCalculationAutoFitGA::calculateLongTermImprovement(int windowSize) const
-{
-	if(m_convergenceHistory.size() < windowSize) {
-		return 1.0; // 数据不足，假设有改进
-	}
-
-	double oldFitness = m_convergenceHistory[m_convergenceHistory.size() - windowSize];
-	double improvement = (oldFitness - m_bestFitness) / qMax(1e-10, qAbs(oldFitness));
-
-	return improvement;
-}
-
-void nmCalculationAutoFitGA::updateConvergenceMetrics()
-{
-	// 更新多样性历史
-	m_diversityHistory.append(calculatePopulationDiversity());
-
-	// 保持历史长度合理（最多保留50个数据点）
-	const int maxHistorySize = 50;
-
-	while(m_diversityHistory.size() > maxHistorySize) {
-		m_diversityHistory.remove(0);
-	}
-}
-
-// ==================== 参数应用方法 ====================
-
-bool nmCalculationAutoFitGA::validateParameters(const QVector<double>& parameters) const
-{
-	if(parameters.size() != getEnabledParameterCount()) {
-		return false;
-	}
-
-	for(int i = 0; i < parameters.size(); ++i) {
-		if(!isFiniteNumber(parameters[i])) {
-			return false;
-		}
-
-		// 检查参数范围
-		if(i < m_enabledParamIndices.size()) {
-			int paramIndex = m_enabledParamIndices[i];
-
-			if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) {
-				if(parameters[i] < m_parameterLower[paramIndex] ||
-					parameters[i] > m_parameterUpper[paramIndex]) {
-						return false;
-				}
-			}
-		}
-	}
-
-	// 直接拦截会导致求解器数值崩溃的参数值
-	for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) {
-		int paramIndex = m_enabledParamIndices[i];
-		double value = parameters[i];
-
-		switch(paramIndex) {
-		case 0: // 渗透率：必须大于零
-			if(value <= 1e-8) {
-				DEBUG_OUT(QString("Rejecting near-zero permeability: %1").arg(value));
-				return false;
-			}
-
-			break;
-
-		case 2: // 井筒储集系数：必须大于零
-			if(value <= 1e-10) {
-				DEBUG_OUT(QString("Rejecting near-zero wellbore storage: %1").arg(value));
-				return false;
-			}
-
-			break;
-
-		case 3: // 孔隙度：必须在合理范围
-			if(value <= 1e-6 || value >= 0.99) {
-				DEBUG_OUT(QString("Rejecting unrealistic porosity: %1").arg(value));
-				return false;
-			}
-
-			break;
-
-		case 5: // 综合压缩系数：必须大于零
-			if(value <= 1e-8) {
-				DEBUG_OUT(QString("Rejecting near-zero total compressibility: %1").arg(value));
-				return false;
-			}
-
-			break;
-		}
-	}
-
-	return true;
-}
-
-bool nmCalculationAutoFitGA::validateLogLogData(const QVector<QVector<double>>& logLogData) const
-{
-	// 检查基本结构
-	if(logLogData.size() < 3) {
-		DEBUG_OUT("LogLog data has less than 3 arrays");
-		return false;
-	}
-
-	// 检查数组大小一致性
-	int size = logLogData[0].size();
-
-	if(size == 0) {
-		DEBUG_OUT("Empty LogLog data");
-		return false;
-	}
-
-	if(logLogData[1].size() != size || logLogData[2].size() != size) {
-		DEBUG_OUT(QString("LogLog data size mismatch: X=%1, Y1=%2, Y2=%3")
-			.arg(logLogData[0].size())
-			.arg(logLogData[1].size())
-			.arg(logLogData[2].size()));
-		return false;
-	}
-
-	// 检查最小数据点数
-	if(size < 5) {
-		DEBUG_OUT(QString("Too few LogLog data points: %1").arg(size));
-		return false;
-	}
-
-	// 数据有效性检查
-	for(int i = 0; i < size; ++i) {
-		if(!isFiniteNumber(logLogData[0][i]) ||
-			!isFiniteNumber(logLogData[1][i]) ||
-			!isFiniteNumber(logLogData[2][i])) {
-				DEBUG_OUT(QString("Invalid LogLog data at index %1").arg(i));
-				return false;
-		}
-	}
-
-	return true;
-}
-
-bool nmCalculationAutoFitGA::validateInitialValues() const
-{
-	if(m_initialValues.size() != m_enabledParamIndices.size()) {
-		DEBUG_OUT("Initial values count mismatch with enabled parameters");
-		return false;
-	}
-
-	bool allValid = true;
-
-	for(int i = 0; i < m_initialValues.size(); ++i) {
-		int paramIndex = m_enabledParamIndices[i];
-		double value = m_initialValues[i];
-
-		if(!isFiniteNumber(value)) {
-			DEBUG_OUT(QString("Initial value[%1] is not finite: %2").arg(i).arg(value));
-			allValid = false;
-			continue;
-		}
-
-		if(paramIndex < m_parameterLower.size() && paramIndex < m_parameterUpper.size()) {
-			double minVal = m_parameterLower[paramIndex];
-			double maxVal = m_parameterUpper[paramIndex];
-
-			if(value < minVal || value > maxVal) {
-				DEBUG_OUT(QString("Initial value[%1] = %2 is outside bounds [%3, %4]")
-					.arg(i).arg(value).arg(minVal).arg(maxVal));
-				allValid = false;
-			}
-		}
-	}
-
-	return allValid;
-}
-
-void nmCalculationAutoFitGA::applyParametersToDataManager(const QVector<double>& parameters)
-{
-	if(parameters.size() != getEnabledParameterCount()) {
-		return;
-	}
-
-	updateReservoirParameters(parameters);
-	updateWellParameters(parameters);
-}
-
-void nmCalculationAutoFitGA::updateReservoirParameters(const QVector<double>& parameters)
-{
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-	nmDataReservoir reservoirData = dataManager->getReservoirDataCopy();
-
-	int paramIndex = 0;
-	for(int i = 0; i < m_parameterSelected.size(); ++i) {
-		if(m_parameterSelected[i] && paramIndex < parameters.size()) {
-			double value = parameters[paramIndex];
-
-			switch(i) {
-			case 0: // 渗透率
-				reservoirData.getPermeability().setValue(value);
-				break;
-			case 3: // 孔隙度
-				reservoirData.getPorosity().setValue(value);
-				break;
-			case 4: // 储层厚度
-				reservoirData.getThickness().setValue(value);
-				break;
-			case 5: // 综合压缩系数
-				reservoirData.getCt().setValue(value);
-				break;
-			case 6: // 岩石压缩系数
-				reservoirData.getCf().setValue(value);
-				break;
-			case 7: // 初始含水饱和度
-				reservoirData.getSwi().setValue(value);
-				break;
-			}
-			paramIndex++;
-		}
-	}
-
-	// 更新数据管理器
-	dataManager->updateReservoirData(reservoirData);
-}
-
-void nmCalculationAutoFitGA::updateWellParameters(const QVector<double>& parameters)
-{
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-
-	// 获取井列表，更新第一口井的参数
-	//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
-	nmDataWellBase* pWell = dataManager->findWellByName(m_targetWellName);
-	if(!pWell) return;
-
-	//nmDataWellBase* pWell = wells[0]; // 使用第一口井
-
-	int paramIndex = 0;
-	for(int i = 0; i < m_parameterSelected.size(); ++i) {
-		if(m_parameterSelected[i] && paramIndex < parameters.size()) {
-			double value = parameters[paramIndex];
-
-			switch(i) {
-			case 1: { // 表皮系数
-                nmDataAttribute skinAttr = pWell->getPerforation(0)->getSkin();
-				skinAttr.setValue(value);
-                pWell->setRateDependentSkin(skinAttr);
-					}
-					break;
-			case 2: { // 井筒储集系数
-				nmDataAttribute wellboreAttr = pWell->getWellboreStorage();
-				wellboreAttr.setValue(value);
-				pWell->setWellboreStorage(wellboreAttr);
-					}
-					break;
-			}
-			paramIndex++;
-		}
-	}
-
-	// 根据井类型更新到数据管理器
-	updateWellToDataManager(pWell);
-}
-
-void nmCalculationAutoFitGA::updateWellToDataManager(nmDataWellBase* pWell)
-{
-	if(!pWell) return;
-
-	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-	NM_WELL_MODEL wellType = pWell->getWellType();
-
-	switch(wellType) {
-	case NM_WELL_MODEL::Vertical_Well: {
-		nmDataVerticalWell* pVerticalWell = dynamic_cast<nmDataVerticalWell*>(pWell);
-		if(pVerticalWell) {
-			QVector<nmDataVerticalWell> wells;
-			wells.append(*pVerticalWell);
-			dataManager->updateVerticalWells(wells);
-		}
-		break;
-									   }
-	case NM_WELL_MODEL::Vertical_Fractured_Well: {
-		nmDataVerticalFracturedWell* pVFracturedWell = dynamic_cast<nmDataVerticalFracturedWell*>(pWell);
-		if(pVFracturedWell) {
-			QVector<nmDataVerticalFracturedWell> wells;
-			wells.append(*pVFracturedWell);
-			dataManager->updateVerticalFracturedWells(wells);
-		}
-		break;
-												 }
-	case NM_WELL_MODEL::Horizontal_Fractured_Well: {
-		nmDataHorizontalFracturedWell* pHFracturedWell = dynamic_cast<nmDataHorizontalFracturedWell*>(pWell);
-		if(pHFracturedWell) {
-			QVector<nmDataHorizontalFracturedWell> wells;
-			wells.append(*pHFracturedWell);
-			dataManager->updateHorizontalFracturedWells(wells);
-		}
-		break;
-												   }
-	default:
-		break;
-	}
-}
-
-void nmCalculationAutoFitGA::clampToLimits(QVector<double>& parameters) const
-{
-	for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) {
-		int paramIndex = m_enabledParamIndices[i];
-		if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) {
-			parameters[i] = qMax(m_parameterLower[paramIndex],
-				qMin(m_parameterUpper[paramIndex], parameters[i]));
-		}
-	}
-}
-
-// ==================== 求解器相关方法 ====================
-
-QVector<QVector<double>> nmCalculationAutoFitGA::runSolver()
-{
-	//return runSolverExe();
-	return runSolverDll();
-}
-
-bool nmCalculationAutoFitGA::validateSolverResult(const QVector<QVector<double>>& result) const
-{
-	// 基本检查
-	if(result.size() < 2) {
-		DEBUG_OUT("Solver result has less than 2 arrays");
-		return false;
-	}
-
-	if(result[0].size() != result[1].size()) {
-		DEBUG_OUT(QString("Size mismatch: X=%1, Y=%2").arg(result[0].size()).arg(result[1].size()));
-		return false;
-	}
-
-	if(result[0].size() == 0) {
-		DEBUG_OUT("Empty solver result");
-		return false;
-	}
-
-	// 检查最小数据点数
-	if(result[0].size() < 10) {
-		DEBUG_OUT(QString("Too few data points: %1").arg(result[0].size()));
-		return false;
-	}
-
-	// 数据有效性检查
-	for(int i = 0; i < result[0].size(); ++i) {
-		if(!isFiniteNumber(result[0][i]) || !isFiniteNumber(result[1][i])) {
-			DEBUG_OUT(QString("Invalid data at index %1: X=%2, Y=%3")
-				.arg(i).arg(result[0][i]).arg(result[1][i]));
-			return false;
-		}
-	}
-
-	return true;
-}
-
-QVector<QVector<double>> nmCalculationAutoFitGA::runSolverDll()
-{
-	DEBUG_OUT("SOLVER DLL START");
-
-	if(m_evaluationInProgress > 0) {
-		DEBUG_OUT("DLL Solver already running, skipping");
-		return QVector<QVector<double>>();
-	}
-
-	++m_evaluationInProgress;
-	QVector<QVector<double>> result;
-	nmCalculationDllPebiSolverTask* dllTask = nullptr;
-
-	try {
-		DEBUG_OUT("Creating DLL solver task");
-		dllTask = new nmCalculationDllPebiSolverTask(m_tempDirectory);
-
-		if(m_shouldStop) {
-			DEBUG_OUT("Should stop - cleaning up and returning empty result");
-			delete dllTask;
-			--m_evaluationInProgress;
-			return result;
-		}
-
-		DEBUG_OUT("Starting DLL solver execution...");
-
-		// 异步执行
-		dllTask->start();
-
-		// 等待完成
-		int waitTime = 0;
-		const int maxWait = 30000;  // 30秒超时
-		const int checkInterval = 500;
-
-		while(waitTime < maxWait) {
-			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 100);
-
-			if(!dllTask->isRunning()) {
-				DEBUG_OUT("DLL solver task completed");
-				break;
-			}
-
-			if(m_shouldStop) {
-				DEBUG_OUT("DLL solver task terminated by user");
-				dllTask->terminate();
-				break;
-			}
-
-			msleep(checkInterval);
-			waitTime += checkInterval;
-		}
-
-		// 超时处理
-		if(dllTask->isRunning()) {
-			DEBUG_OUT("DLL solver task timeout, terminating...");
-			dllTask->terminate();
-			dllTask->wait(2000);
-
-			delete dllTask;
-			dllTask = nullptr;
-			--m_evaluationInProgress;
-			m_consecutiveFailures++;
-			return result;
-		}
-
-		// 验证结果数据是否已更新
-		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-		//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
-		nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
-
-		if(!pTargetWell) {
-			DEBUG_OUT("No wells found in data manager after DLL execution");
-			delete dllTask;
-			--m_evaluationInProgress;
-			return result;
-		}
-
-		// 验证结果数据
-		QVector<QVector<double>> pressureResult = pTargetWell->getResultPressure();
-		QVector<QVector<double>> logLogResult = pTargetWell->getResultLogLog();
-
-		DEBUG_OUT(QString("DLL result verification - Pressure arrays: %1, LogLog arrays: %2")
-			.arg(pressureResult.size()).arg(logLogResult.size()));
-
-		if(pressureResult.size() >= 2) {
-			DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2")
-				.arg(pressureResult[0].size()).arg(pressureResult[1].size()));
-
-			if(pressureResult[0].size() > 0) {
-				DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4")
-					.arg(pressureResult[0][0])
-					.arg(pressureResult[0][pressureResult[0].size()-1])
-					.arg(pressureResult[1][0])
-					.arg(pressureResult[1][pressureResult[1].size()-1]));
-			}
-		}
-
-		// 数据有效性检查
-		if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) {
-			result = pressureResult;
-			DEBUG_OUT(QString("Got DLL solver result: %1 points").arg(result[0].size()));
-			m_consecutiveFailures = 0;
-
-			// 检查结果是否与之前不同
-			static QVector<double> lastPressureResult;
-			bool isDifferentFromLast = false;
-
-			if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) {
-				isDifferentFromLast = true;
-			} else {
-				for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) {
-					if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) {
-						isDifferentFromLast = true;
-						break;
-					}
-				}
-			}
-
-			if(isDifferentFromLast) {
-				DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from DLL");
-				lastPressureResult = pressureResult[1];
-			} else {
-				DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!");
-			}
-
-		} else {
-			DEBUG_OUT("DLL solver result is empty or invalid");
-			DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3")
-				.arg(pressureResult.size())
-				.arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0)
-				.arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0));
-			m_consecutiveFailures++;
-		}
-
-	} catch(const std::bad_alloc& e) {
-		DEBUG_OUT(QString("Memory allocation failed in DLL solver: %1").arg(e.what()));
-		m_consecutiveFailures++;
-	} catch(const std::exception& e) {
-		DEBUG_OUT(QString("Exception in DLL solver: %1").arg(e.what()));
-		m_consecutiveFailures++;
-	} catch(...) {
-		DEBUG_OUT("Unknown exception in DLL solver");
-		m_consecutiveFailures++;
-	}
-
-	// 清理DLL任务
-	if(dllTask) {
-		if(dllTask->isRunning()) {
-			dllTask->terminate();
-			dllTask->wait(3000);
-		}
-
-		DEBUG_OUT("Cleaning up DLL solver task...");
-		delete dllTask;
-		dllTask = nullptr;
-	}
-
-	QApplication::processEvents(QEventLoop::AllEvents, 100);
-	--m_evaluationInProgress;
-
-	DEBUG_OUT(QString("SOLVER DLL END - ResultPoints: %1")
-		.arg(result.isEmpty() ? 0 : result[0].size()));
-
-	return result;
-}
-
-//QVector<QVector<double>> nmCalculationAutoFitGA::runSolverExe()
-//{
-//	DEBUG_OUT("SOLVER EXE START");
-//
-//	if(m_evaluationInProgress > 0) {
-//		DEBUG_OUT("EXE Solver already running, skipping");
-//		return QVector<QVector<double>>();
-//	}
-//
-//	++m_evaluationInProgress;
-//	QVector<QVector<double>> result;
-//	nmCalculationExeSolverTask* exeTask = nullptr;
-//
-//	try {
-//		DEBUG_OUT("Creating EXE solver task");
-//		exeTask = new nmCalculationExeSolverTask(QString());
-//
-//		if(m_shouldStop) {
-//			DEBUG_OUT("Should stop - cleaning up and returning empty result");
-//			delete exeTask;
-//			--m_evaluationInProgress;
-//			return result;
-//		}
-//
-//		DEBUG_OUT("Starting EXE solver execution...");
-//
-//		// 启动
-//		bool executeSuccess = exeTask->execute();
-//
-//		// 检查执行状态
-//		if(!executeSuccess) {
-//			DEBUG_OUT(QString("EXE solver execution failed: %1").arg(exeTask->getLastError()));
-//			DEBUG_OUT(QString("EXE solver exit code: %1").arg(exeTask->getExitCode()));
-//
-//			// 清理并返回空结果
-//			delete exeTask;
-//			exeTask = nullptr;
-//			--m_evaluationInProgress;
-//			m_consecutiveFailures++;
-//			return result;
-//		}
-//
-//		DEBUG_OUT("EXE solver execution completed successfully");
-//
-//		// 验证结果数据是否已更新
-//		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-//		QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
-//
-//		if(wells.isEmpty()) {
-//			DEBUG_OUT("No wells found in data manager after EXE execution");
-//			delete exeTask;
-//			--m_evaluationInProgress;
-//			return result;
-//		}
-//
-//		// 验证结果数据的时间戳或唯一性
-//		QVector<QVector<double>> pressureResult = wells[0]->getResultPressure();
-//		QVector<QVector<double>> logLogResult = wells[0]->getResultLogLog();
-//
-//		DEBUG_OUT(QString("Raw result verification - Pressure arrays: %1, LogLog arrays: %2")
-//			.arg(pressureResult.size()).arg(logLogResult.size()));
-//
-//		if(pressureResult.size() >= 2) {
-//			DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2")
-//				.arg(pressureResult[0].size()).arg(pressureResult[1].size()));
-//
-//			if(pressureResult[0].size() > 0) {
-//				DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4")
-//					.arg(pressureResult[0][0])
-//					.arg(pressureResult[0][pressureResult[0].size() - 1])
-//					.arg(pressureResult[1][0])
-//					.arg(pressureResult[1][pressureResult[1].size() - 1]));
-//			}
-//		}
-//
-//		if(logLogResult.size() >= 3) {
-//			DEBUG_OUT(QString("LogLog result - X points: %1, Y1 points: %2, Y2 points: %3")
-//				.arg(logLogResult[0].size()).arg(logLogResult[1].size()).arg(logLogResult[2].size()));
-//
-//			if(logLogResult[0].size() > 0) {
-//				DEBUG_OUT(QString("Sample LogLog data - X[0]: %1, X[last]: %2, Y1[0]: %3, Y2[0]: %4")
-//					.arg(logLogResult[0][0])
-//					.arg(logLogResult[0][logLogResult[0].size() - 1])
-//					.arg(logLogResult[1][0])
-//					.arg(logLogResult[2][0]));
-//			}
-//		}
-//
-//		// 数据有效性检查
-//		if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) {
-//			result = pressureResult;
-//			DEBUG_OUT(QString("Got EXE solver result: %1 points").arg(result[0].size()));
-//			m_consecutiveFailures = 0;
-//
-//			// 检查结果是否与之前不同
-//			static QVector<double> lastPressureResult;
-//			bool isDifferentFromLast = false;
-//
-//			if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) {
-//				isDifferentFromLast = true;
-//			} else {
-//				for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) {
-//					if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) {
-//						isDifferentFromLast = true;
-//						break;
-//					}
-//				}
-//			}
-//
-//			if(isDifferentFromLast) {
-//				DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from EXE");
-//				lastPressureResult = pressureResult[1];
-//			} else {
-//				DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!");
-//			}
-//
-//		} else {
-//			DEBUG_OUT("EXE solver result is empty or invalid");
-//			DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3")
-//				.arg(pressureResult.size())
-//				.arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0)
-//				.arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0));
-//			m_consecutiveFailures++;
-//		}
-//
-//	} catch(const std::exception& e) {
-//		DEBUG_OUT(QString("Exception in EXE solver: %1").arg(e.what()));
-//		m_consecutiveFailures++;
-//	} catch(...) {
-//		DEBUG_OUT("Unknown exception in EXE solver");
-//		m_consecutiveFailures++;
-//	}
-//
-//	// 清理EXE任务
-//	if(exeTask) {
-//		DEBUG_OUT("Cleaning up EXE solver task...");
-//		delete exeTask;
-//		exeTask = nullptr;
-//	}
-//
-//	QApplication::processEvents(QEventLoop::AllEvents, 100);
-//	--m_evaluationInProgress;
-//
-//	DEBUG_OUT(QString("SOLVER EXE END - ResultPoints: %1")
-//		.arg(result.isEmpty() ? 0 : result[0].size()));
-//
-//	return result;
-//}
-
-// ==================== 数据处理方法 ====================
-QVector<QPointF> nmCalculationAutoFitGA::interpolateData(
-	const QVector<QPointF>& source, const QVector<double>& targetX) const
-{
-	QVector<QPointF> result;
-
-	if(source.isEmpty() || targetX.isEmpty()) {
-		DEBUG_OUT("Warning: Empty data in interpolation");
-		return result;
-	}
-
-	// 数据清理和验证合并
-	QVector<QPointF> validSource;
-	const double MAX_REASONABLE_VALUE = 1e12;
-
-	for(int i = 0; i < source.size(); ++i) {
-		const QPointF& point = source[i];
-
-		// 检查数值有效性
-		if(!isFiniteNumber(point.x()) || !isFiniteNumber(point.y())) {
-			DEBUG_OUT(QString("Skipping invalid data point at index %1: X=%2, Y=%3")
-				.arg(i).arg(point.x()).arg(point.y()));
-			continue;
-		}
-
-		// 检查极大值 - 跳过而不是失败
-		if(qAbs(point.y()) > MAX_REASONABLE_VALUE) {
-			DEBUG_OUT(QString("Skipping extremely large Y value at index %1: %2")
-				.arg(i).arg(point.y()));
-			continue;
-		}
-
-		// 只保留有效的数据点
-		validSource.append(point);
-	}
-
-	// 检查清理后的数据是否足够
-	if(validSource.size() < 3) {
-		DEBUG_OUT(QString("Insufficient valid data points after cleaning: %1")
-			.arg(validSource.size()));
-		return result; // 返回空结果，但不算失败
-	}
-
-	DEBUG_OUT(QString("Data cleaning: %1 -> %2 valid points")
-		.arg(source.size()).arg(validSource.size()));
-
-	// 对清理后的数据进行排序
-	QVector<QPointF> sortedSource = validSource;
-
-	for(int i = 0; i < sortedSource.size() - 1; ++i) {
-		for(int j = 0; j < sortedSource.size() - 1 - i; ++j) {
-			if(sortedSource[j].x() > sortedSource[j + 1].x()) {
-				QPointF temp = sortedSource[j];
-				sortedSource[j] = sortedSource[j + 1];
-				sortedSource[j + 1] = temp;
-			}
-		}
-	}
-
-	double sourceMinX = sortedSource.first().x();
-	double sourceMaxX = sortedSource.last().x();
-
-	double targetMinX = targetX[0];
-	double targetMaxX = targetX[0];
-
-	for(int i = 1; i < targetX.size(); ++i) {
-		if(targetX[i] < targetMinX) targetMinX = targetX[i];
-
-		if(targetX[i] > targetMaxX) targetMaxX = targetX[i];
-	}
-
-	DEBUG_OUT(QString("Source X range: [%1, %2], Target X range: [%3, %4]")
-		.arg(sourceMinX).arg(sourceMaxX).arg(targetMinX).arg(targetMaxX));
-
-	// 安全插值算法
-	for(int i = 0; i < targetX.size(); ++i) {
-		double x = targetX[i];
-
-		if(!isFiniteNumber(x)) continue;
-
-		double y = 0.0;
-
-		// 插值逻辑（保持原有逻辑，但使用sortedSource）
-		if(x <= sourceMinX) {
-			if(sortedSource.size() >= 2) {
-				double dx = sortedSource[1].x() - sortedSource[0].x();
-
-				if(qAbs(dx) > 1e-10) {
-					double slope = (sortedSource[1].y() - sortedSource[0].y()) / dx;
-					slope = qMax(-1e6, qMin(1e6, slope));
-					y = sortedSource[0].y() + slope * (x - sortedSource[0].x());
-				} else {
-					y = sortedSource[0].y();
-				}
-			} else {
-				y = sortedSource[0].y();
-			}
-		} else if(x >= sourceMaxX) {
-			if(sortedSource.size() >= 2) {
-				int lastIdx = sortedSource.size() - 1;
-				double dx = sortedSource[lastIdx].x() - sortedSource[lastIdx - 1].x();
-
-				if(qAbs(dx) > 1e-10) {
-					double slope = (sortedSource[lastIdx].y() - sortedSource[lastIdx - 1].y()) / dx;
-					slope = qMax(-1e6, qMin(1e6, slope));
-					y = sortedSource[lastIdx].y() + slope * (x - sortedSource[lastIdx].x());
-				} else {
-					y = sortedSource[lastIdx].y();
-				}
-			} else {
-				y = sortedSource.last().y();
-			}
-		} else {
-			// 内插
-			bool found = false;
-
-			for(int j = 0; j < sortedSource.size() - 1; ++j) {
-				if(x >= sortedSource[j].x() && x <= sortedSource[j + 1].x()) {
-					double dx = sortedSource[j + 1].x() - sortedSource[j].x();
-
-					if(qAbs(dx) > 1e-10) {
-						double ratio = (x - sortedSource[j].x()) / dx;
-						y = sortedSource[j].y() + ratio * (sortedSource[j + 1].y() - sortedSource[j].y());
-					} else {
-						y = sortedSource[j].y();
-					}
-
-					found = true;
-					break;
-				}
-			}
-
-			if(!found) {
-				// 使用最近点
-				double minDist = 1e10;
-
-				for(int k = 0; k < sortedSource.size(); ++k) {
-					double dist = qAbs(sortedSource[k].x() - x);
-
-					if(dist < minDist) {
-						minDist = dist;
-						y = sortedSource[k].y();
-					}
-				}
-			}
-		}
-
-		// 最终数值检查
-		if(!isFiniteNumber(y)) {
-			y = sortedSource.size() > 0 ? sortedSource[0].y() : 1.0;
-		}
-
-		y = qMax(-1e12, qMin(1e12, y));
-
-		result.append(QPointF(x, y));
-	}
-
-	if(result.isEmpty()) {
-		DEBUG_OUT("LogLog interpolation failed");
-		return result;
-	}
-
-	DEBUG_OUT(QString("Interpolation completed: %1 -> %2 points")
-		.arg(validSource.size()).arg(result.size()));
-
-	return result;
-}
-
-double nmCalculationAutoFitGA::calculateLogLogCurveError(
-	const QVector<QVector<double>>& target,
-	const QVector<QVector<double>>& result) const
-{
-	// 验证数据
-	if(!validateLogLogData(target) || !validateLogLogData(result)) {
-		return 1e10;
-	}
-
-	try {
-		// 数据对齐：找到X值的重叠区域
-		double targetMinX = target[0][0];
-		double targetMaxX = target[0][0];
-
-		for(int i = 1; i < target[0].size(); ++i) {
-			if(target[0][i] < targetMinX) targetMinX = target[0][i];
-
-			if(target[0][i] > targetMaxX) targetMaxX = target[0][i];
-		}
-
-		double resultMinX = result[0][0];
-		double resultMaxX = result[0][0];
-
-		for(int i = 1; i < result[0].size(); ++i) {
-			if(result[0][i] < resultMinX) resultMinX = result[0][i];
-
-			if(result[0][i] > resultMaxX) resultMaxX = result[0][i];
-		}
-
-		double overlapMinX = qMax(targetMinX, resultMinX);
-		double overlapMaxX = qMin(targetMaxX, resultMaxX);
-
-		if(overlapMinX >= overlapMaxX) {
-			DEBUG_OUT("No overlap between target and result LogLog curves");
-			return 1e10;
-		}
-
-		// 生成公共X网格进行插值
-		QVector<double> commonX;
-		int numPoints = 50;
-
-		if(overlapMinX > 0 && overlapMaxX > 0) {
-			// 对数空间均匀分布
-			double logMin = qLn(overlapMinX);
-			double logMax = qLn(overlapMaxX);
-
-			for(int i = 0; i < numPoints; ++i) {
-				double logX = logMin + i * (logMax - logMin) / (numPoints - 1);
-				double x = qExp(logX);
-
-				// 数值保护
-				if(!isFiniteNumber(x) || x <= 0) {
-					continue;
-				}
-
-				commonX.append(x);
-			}
-
-			DEBUG_OUT("Using log-uniform grid for better early-time coverage");
-		}
-
-		if(commonX.isEmpty()) {
-			DEBUG_OUT("Failed to generate common X grid");
-			return 1e10;
-		}
-
-		// 插值目标曲线
-		QVector<QPointF> targetCurve1, targetCurve2;
-
-		for(int i = 0; i < target[0].size(); ++i) {
-			// 检查数据有效性
-			if(isFiniteNumber(target[0][i]) && isFiniteNumber(target[1][i]) &&
-				isFiniteNumber(target[2][i])) {
-					targetCurve1.append(QPointF(target[0][i], target[1][i]));
-					targetCurve2.append(QPointF(target[0][i], target[2][i]));
-			}
-		}
-
-		if(targetCurve1.isEmpty() || targetCurve2.isEmpty()) {
-			DEBUG_OUT("Target curves are empty after filtering");
-			return 1e10;
-		}
-
-		QVector<QPointF> alignedTarget1 = interpolateData(targetCurve1, commonX);
-		QVector<QPointF> alignedTarget2 = interpolateData(targetCurve2, commonX);
-
-		// 插值结果曲线
-		QVector<QPointF> resultCurve1, resultCurve2;
-
-		for(int i = 0; i < result[0].size(); ++i) {
-			// 检查数据有效性
-			if(isFiniteNumber(result[0][i]) && isFiniteNumber(result[1][i]) &&
-				isFiniteNumber(result[2][i])) {
-					resultCurve1.append(QPointF(result[0][i], result[1][i]));
-					resultCurve2.append(QPointF(result[0][i], result[2][i]));
-			}
-		}
-
-		if(resultCurve1.isEmpty() || resultCurve2.isEmpty()) {
-			DEBUG_OUT("Result curves are empty after filtering");
-			return 1e10;
-		}
-
-		QVector<QPointF> alignedResult1 = interpolateData(resultCurve1, commonX);
-		QVector<QPointF> alignedResult2 = interpolateData(resultCurve2, commonX);
-
-		// 检查插值结果
-		if(alignedTarget1.isEmpty() || alignedTarget2.isEmpty() ||
-			alignedResult1.isEmpty() || alignedResult2.isEmpty()) {
-				DEBUG_OUT("LogLog interpolation failed");
-				return 1e10;
-		}
-
-		if(alignedTarget1.size() != alignedResult1.size() ||
-			alignedTarget2.size() != alignedResult2.size()) {
-				DEBUG_OUT("LogLog interpolation size mismatch");
-				return 1e10;
-		}
-
-		// 计算两条曲线的误差
-		double error1 = calculateCurveError(alignedTarget1, alignedResult1);
-		double error2 = calculateCurveError(alignedTarget2, alignedResult2);
-
-		// 检查个别误差是否有效
-		if(!isFiniteNumber(error1) || error1 > 1e9) {
-			DEBUG_OUT(QString("Curve1 error is invalid: %1").arg(error1));
-			error1 = 1e10;
-		}
-
-		if(!isFiniteNumber(error2) || error2 > 1e9) {
-			DEBUG_OUT(QString("Curve2 error is invalid: %1").arg(error2));
-			error2 = 1e10;
-		}
-
-		// 组合误差 - 添加保护
-		double combinedError;
-
-		if(error1 > 1e9 && error2 > 1e9) {
-			combinedError = 1e10;
-		} else if(error1 > 1e9) {
-			combinedError = error2;
-		} else if(error2 > 1e9) {
-			combinedError = error1;
-		} else {
-			combinedError = 0.5 * error1 + 0.5 * error2;
-		}
-
-		DEBUG_OUT(QString("LogLog errors: Curve1=%1, Curve2=%2, Combined=%3")
-			.arg(error1, 0, 'e', 4).arg(error2, 0, 'e', 4).arg(combinedError, 0, 'e', 4));
-
-		return qMin(1e9, combinedError);
-
-	} catch(const std::exception& e) {
-		DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what()));
-		return 1e10;
-	} catch(...) {
-		DEBUG_OUT("Unknown exception in LogLog error calculation");
-		return 1e10;
-	}
-}
-
-double nmCalculationAutoFitGA::calculateCurveError(
-	const QVector<QPointF>& curve1, const QVector<QPointF>& curve2) const
-{
-	if(curve1.size() != curve2.size() || curve1.isEmpty()) {
-		return 1e10;
-	}
-
-	// 预检查：确保没有无穷大值
-	for(int i = 0; i < curve1.size(); ++i) {
-		if(!isFiniteNumber(curve1[i].y()) || !isFiniteNumber(curve2[i].y())) {
-			DEBUG_OUT(QString("Infinite value detected at index %1: Y1=%2, Y2=%3")
-				.arg(i).arg(curve1[i].y()).arg(curve2[i].y()));
-			return 1e10;
-		}
-
-		if(qAbs(curve1[i].y()) > 1e12 || qAbs(curve2[i].y()) > 1e12) {
-			DEBUG_OUT(QString("Extremely large value detected at index %1")
-				.arg(i));
-			return 1e10;
-		}
-	}
-
-	double totalError = 0.0;
-	double totalWeight = 0.0;
-	int validPoints = 0;
-
-	for(int i = 0; i < curve1.size(); ++i) {
-		double y1 = curve1[i].y();
-		double y2 = curve2[i].y();
-
-		// 跳过异常值
-		if(!isFiniteNumber(y1) || !isFiniteNumber(y2)) {
-			continue;
-		}
-
-		// 自适应权重：根据Y值大小调整，添加上限
-		double weightFactor = qMin(100.0, qAbs(y1) * 0.01);
-		double weight = 1.0 / (1.0 + weightFactor);
-
-		// 相对误差和绝对误差的组合
-		double yMax = qMax(qAbs(y1), qAbs(y2));
-		yMax = qMax(1e-12, yMax); // 防止除零
-
-		double relativeError = qAbs(y1 - y2) / yMax;
-		double absoluteError = qAbs(y1 - y2);
-
-		// 限制误差值
-		relativeError = qMin(1e6, relativeError);
-		absoluteError = qMin(1e6, absoluteError);
-
-		// 误差组合：相对误差为主，绝对误差为辅
-		double pointError = 0.7 * relativeError + 0.3 * absoluteError;
-
-		if(isFiniteNumber(pointError) && pointError < 1e10) {
-			totalError += weight * pointError * pointError;
-			totalWeight += weight;
-			validPoints++;
-		}
-	}
-
-	if(totalWeight > 0 && validPoints > 0) {
-		double result = sqrt(totalError / totalWeight);
-
-		// 最终检查
-		if(!isFiniteNumber(result)) {
-			DEBUG_OUT("Final error calculation produced infinite result");
-			return 1e10;
-		}
-
-		return qMin(1e9, result); // 限制最大误差值
-	} else {
-		DEBUG_OUT(QString("No valid points for error calculation: validPoints=%1")
-			.arg(validPoints));
-		return 1e10;
-	}
-}
-// ==================== 工具方法 ====================
-
-double nmCalculationAutoFitGA::random01() const
-{
-	return static_cast<double>(qrand()) / RAND_MAX;
-}
-
-double nmCalculationAutoFitGA::gaussianRandom(double mean, double stddev) const
-{
-	static bool hasSpare = false;
-	static double spare;
-
-	if(hasSpare) {
-		hasSpare = false;
-		return spare * stddev + mean;
-	}
-
-	hasSpare = true;
-	double u = qMax(random01(), 1e-12);
-	double v = random01();
-	double mag = stddev * sqrt(-2.0 * log(u));
-	spare = mag * cos(2.0 * 3.14159265359 * v);
-
-	return mag * sin(2.0 * 3.14159265359 * v) + mean;
-}
-
-int nmCalculationAutoFitGA::getEnabledParameterCount() const
-{
-	int count = 0;
-
-	for(int i = 0; i < m_parameterSelected.size(); ++i) {
-		if(m_parameterSelected[i]) count++;
-	}
-
-	return count;
-}
-
-void nmCalculationAutoFitGA::saveOptimizationResult()
-{
-	DEBUG_OUT(QString("GA optimization result: fitness=%1, evaluations=%2/%3")
-		.arg(m_bestFitness, 0, 'e', 4)
-		.arg(m_successfulEvaluations)
-		.arg(m_totalEvaluations));
-}
diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp
index d0deda2..cb510f6 100644
--- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp
+++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp
@@ -104,6 +104,17 @@ static inline bool isFiniteNumber(double value)
 #endif
 }
 
+// 两个 RMSE 的差不能直接解释为被消除的独立误差。RMSE 的平方才对应
+// 均方能量，因此先计算 reduced^2-full^2，再开方恢复原量纲。这里用于分别
+// 提取“消除公共上下偏差”和“消除水平位移”实际减少的误差贡献。
+static double nestedRmsContribution(double reducedModelLoss,
+                                    double fullModelLoss)
+{
+    return qSqrt(qMax(0.0,
+                      reducedModelLoss * reducedModelLoss -
+                      fullModelLoss * fullModelLoss));
+}
+
 static inline bool isInClosedRange(double value, double lower, double upper)
 {
     return isFiniteNumber(value) && value >= lower && value <= upper;
@@ -421,7 +432,7 @@ static QString findExecutableInPath(const QString& executableName)
 static QStringList traceParameterNames()
 {
     // trace 和 trace meta 使用的完整参数名顺序。
-    // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-7 索引一致。
+    // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-9 索引一致。
     QStringList names;
     names << "k"
           << "skin"
@@ -430,7 +441,9 @@ static QStringList traceParameterNames()
           << "h"
           << "Ct"
           << "Cf"
-          << "Swi";
+          << "Swi"
+          << "Dfc"
+          << "fractureHalfLength";
     return names;
 }
 
@@ -721,8 +734,8 @@ void nmCalculationAutoFitPSO::setTargetLogLogData(const QVector<QVector<double>
 void nmCalculationAutoFitPSO::stopFitting()
 {
     // 用户点击停止时走这里。停止策略是“请求式停止”：
-    // 先置 m_shouldStop，让主循环/求解器等待逻辑自然退出；短时间内还在评价时再重置计数。
-    // 这样可以减少 DLL 任务被硬中断导致的数据状态残留。
+    // 只置 m_shouldStop，让主循环/求解器等待逻辑退出并自行维护任务计数。
+    // 不在这里清理临时目录或强制清零计数，避免与正在返回的 DLL 任务竞争。
     if(m_simulationMode && m_simulationTimer) {
         m_simulationTimer->stop();
     }
@@ -736,25 +749,28 @@ void nmCalculationAutoFitPSO::stopFitting()
 
         m_shouldStop = true;
 
-        // 等待当前评估完成，缩短超时时间
+        if(m_simulationMode) {
+            m_isRunning = false;
+            closeTraceFile();
+            cleanupTemporaryDirectory();
+            emit logMessageGenerated(tr("PSO simulation stop request processed"));
+            return;
+        }
+
+        // 给当前评价一个短暂的自然退出时间。若仍在运行，
+        // runSolverDll() 会在下一个等待周期检查 m_shouldStop 并结束任务。
         int waitCount = 0;
 
-        while(m_evaluationInProgress > 0 && waitCount < 30) {  // 减少等待时间
+        while(m_evaluationInProgress > 0 && waitCount < 30) {
             QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50);
             msleep(50);
             waitCount++;
         }
 
-        // 超时时强制重置
         if(m_evaluationInProgress > 0) {
-            DEBUG_OUT("Force resetting evaluation counter");
-            emit logMessageGenerated(tr("Force stopping current evaluation..."));
-            m_evaluationInProgress = 0;
+            emit logMessageGenerated(tr("Waiting for current solver evaluation to stop..."));
         }
 
-        // 确保运行标志被清除
-        m_isRunning = false;
-
         emit logMessageGenerated(tr("PSO optimization stop request processed"));
         DEBUG_OUT("Stop request processed");
     } else {
@@ -762,8 +778,6 @@ void nmCalculationAutoFitPSO::stopFitting()
         emit logMessageGenerated(tr("Stop request received but optimization is not running"));
     }
 
-    closeTraceFile();
-    cleanupTemporaryDirectory();
 }
 
 bool nmCalculationAutoFitPSO::isRunning() const
@@ -790,6 +804,12 @@ double nmCalculationAutoFitPSO::getBestFitness() const
     return m_globalBestFitness;
 }
 
+AutoFitObjectiveBreakdown nmCalculationAutoFitPSO::getLastObjectiveBreakdown() const
+{
+    // 返回最近一次损失评价的误差分解，供界面或后续优化逻辑读取。
+    return m_lastObjectiveBreakdown;
+}
+
 QString nmCalculationAutoFitPSO::getLastError() const
 {
     // 上一次失败的人类可读错误信息，主要给 UI 层弹窗或日志使用。
@@ -806,9 +826,12 @@ void nmCalculationAutoFitPSO::resetOptimizer()
     m_globalBestPosition.clear();
     m_globalBestFitness = 1e10;
     m_previousBestFitness = 1e10;
+    m_globalBestObjectiveBreakdown = AutoFitObjectiveBreakdown();
     m_lastEvaluatedLogLogData.clear();
     m_globalBestLogLogData.clear();
+    m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown();
     m_userInitialLogLogData.clear();
+    m_userInitialObjectiveBreakdown = AutoFitObjectiveBreakdown();
     m_currentIteration = 0;
     m_totalEvaluations = 0;
     m_successfulEvaluations = 0;
@@ -836,9 +859,8 @@ void nmCalculationAutoFitPSO::setPSOTargetWellName(const QString& wellName)
 
 void nmCalculationAutoFitPSO::initializeTraceFile()
 {
-    // 创建本次 PSO 的可复盘文件：
-    // - pso_baseline_trace_<run_id>.csv：逐代逐粒子的参数、真实误差、代理误差和筛选决策；
-    // - pso_baseline_trace_<run_id>.meta.json：目标曲线、流量制度、参数上下界、PSO/代理配置。
+    // 创建本次自动拟合的可复盘文件。代理 PSO 与非代理信赖域使用不同前缀，
+    // 防止代理回放脚本把信赖域记录误当成最新 PSO 粒子记录。
     //
     // 代理模型评分脚本也会读取 meta.json，因此 trace meta 不是单纯日志，而是C++ 与 Python 代理模型之间的运行上下文契约。
     if(!m_traceEnabled) {
@@ -857,8 +879,13 @@ void nmCalculationAutoFitPSO::initializeTraceFile()
         return;
     }
 
-    m_traceFilePath = traceDir.absoluteFilePath(QString("pso_baseline_trace_%1.csv").arg(m_traceRunId));
-    m_traceMetaFilePath = traceDir.absoluteFilePath(QString("pso_baseline_trace_%1.meta.json").arg(m_traceRunId));
+    QString tracePrefix = isSurrogateScreeningEnabled()
+                          ? "pso_baseline_trace"
+                          : "trust_region_trace";
+    m_traceFilePath = traceDir.absoluteFilePath(
+        QString("%1_%2.csv").arg(tracePrefix).arg(m_traceRunId));
+    m_traceMetaFilePath = traceDir.absoluteFilePath(
+        QString("%1_%2.meta.json").arg(tracePrefix).arg(m_traceRunId));
     m_traceFile.setFileName(m_traceFilePath);
 
     if(!m_traceFile.open(QIODevice::WriteOnly | QIODevice::Text)) {
@@ -870,11 +897,12 @@ void nmCalculationAutoFitPSO::initializeTraceFile()
 
     writeTraceHeader();
     writeTraceMetaFile();
-    DEBUG_OUT(QString("PSO baseline trace initialized: %1").arg(m_traceFilePath));
-    emit logMessageGenerated(tr("PSO baseline trace: %1").arg(m_traceFilePath));
+    DEBUG_OUT(QString("Automatic fitting trace initialized: %1").arg(m_traceFilePath));
+    emit logMessageGenerated(tr("Automatic fitting trace: %1").arg(m_traceFilePath));
 
     if(!m_traceMetaFilePath.isEmpty()) {
-        emit logMessageGenerated(tr("PSO baseline trace meta: %1").arg(m_traceMetaFilePath));
+        emit logMessageGenerated(
+            tr("Automatic fitting trace meta: %1").arg(m_traceMetaFilePath));
     }
 
     emit logMessageGenerated(tr("PSO surrogate screening: %1, model=%2, keep=%3, audit=%4, warmup=%5, min_solver=%6")
@@ -985,11 +1013,13 @@ void nmCalculationAutoFitPSO::closeTraceFile()
 void nmCalculationAutoFitPSO::writeTraceHeader()
 {
     // trace CSV 字段说明：
-    // - 当前粒子参数只记录代理模型关心的 k/skin/wellboreC/phi/h/Ct/Cf；
+    // - 代理模式保持原 k/skin/wellboreC/phi/h/Ct/Cf 契约；
+    // - 非代理模式额外记录 Swi/Dfc/裂缝半长，便于复盘信赖域调整；
     // - solver_objective 是真实求解器误差；
     // - surrogate_objective 是 Python 代理评分；
     // - screening_decision 说明该粒子为什么跑/不跑真实求解器；
-    // - pbest/gbest 字段用于离线复盘 PSO 更新是否只依赖真实误差。
+    // - pbest/gbest 字段用于离线复盘 PSO 更新是否只依赖真实误差；
+    // - 末尾诊断字段记录同一次真实评价的分量误差，便于核对引导方向和接受结果。
     if(!m_traceFile.isOpen()) {
         return;
     }
@@ -1005,8 +1035,13 @@ void nmCalculationAutoFitPSO::writeTraceHeader()
          << "phi"
          << "h"
          << "Ct"
-         << "Cf"
-         << "solver_objective"
+         << "Cf";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "Swi"
+             << "Dfc"
+             << "fractureHalfLength";
+    }
+    cols << "solver_objective"
          << "solver_success"
          << "elapsed_ms"
          << "surrogate_objective"
@@ -1018,16 +1053,45 @@ void nmCalculationAutoFitPSO::writeTraceHeader()
          << "pbest_phi"
          << "pbest_h"
          << "pbest_Ct"
-         << "pbest_Cf"
-         << "gbest_objective"
+         << "pbest_Cf";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "pbest_Swi"
+             << "pbest_Dfc"
+             << "pbest_fractureHalfLength";
+    }
+    cols << "gbest_objective"
          << "gbest_k"
          << "gbest_skin"
          << "gbest_wellboreC"
          << "gbest_phi"
          << "gbest_h"
          << "gbest_Ct"
-         << "gbest_Cf"
-         << "enabled_param_indices";
+         << "gbest_Cf";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "gbest_Swi"
+             << "gbest_Dfc"
+             << "gbest_fractureHalfLength";
+    }
+    cols << "enabled_param_indices"
+         << "pressure_loss"
+         << "derivative_loss";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "vertical_common_bias";
+    }
+    cols << "vertical_loss";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "vertical_reliable"
+             << "horizontal_physical_shift";
+    }
+    cols << "horizontal_loss";
+    if(!isSurrogateScreeningEnabled()) {
+        cols << "horizontal_reliable";
+    }
+    cols << "shape_loss"
+         << "late_trend_loss"
+         << "late_slope_bias"
+         << "late_trend_reliable"
+         << "registration_ambiguous";
 
     QTextStream out(&m_traceFile);
     out << cols.join(",") << "\n";
@@ -1069,8 +1133,14 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile()
 
     QTextStream out(&metaFile);
     out << "{\n";
-    out << "  \"schema_version\": 1,\n";
-    out << "  \"trace_type\": \"pso_baseline_replay_meta\",\n";
+    // 非代理 v5 增加带符号诊断列；代理 PSO 保留原 v3 字段和目标，避免改变
+    // 已有模型的训练和回放契约。
+    out << "  \"schema_version\": "
+        << (isSurrogateScreeningEnabled() ? 3 : 7) << ",\n";
+    out << "  \"trace_type\": "
+        << jsonEscape(isSurrogateScreeningEnabled()
+                      ? "pso_baseline_replay_meta"
+                      : "diagnostic_trust_region_meta") << ",\n";
     out << "  \"run_id\": " << jsonEscape(m_traceRunId) << ",\n";
     out << "  \"created_at\": " << jsonEscape(QDateTime::currentDateTime().toString(Qt::ISODate)) << ",\n";
     out << "  \"trace_csv\": " << jsonEscape(QFileInfo(m_traceFilePath).fileName()) << ",\n";
@@ -1127,10 +1197,10 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile()
 
 QVector<double> nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector<double>& selectedParameters) const
 {
-    // 将粒子内部使用的“启用参数向量”还原成完整 8 维参数向量。
+    // 将粒子内部使用的“启用参数向量”还原成完整 10 维参数向量。
     // 未启用的参数从当前 DataManager 读取，启用的参数用 selectedParameters 覆盖。
     // trace CSV、候选 CSV、代理训练域检查都需要这个完整向量。
-    QVector<double> fullParams(8, 0.0);
+    QVector<double> fullParams(10, 0.0);
 
     nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
 
@@ -1146,8 +1216,28 @@ QVector<double> nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector
         nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
 
         if(pTargetWell) {
-            fullParams[1] = pTargetWell->getPerforation(0)->getSkin().getValue().toDouble();
+            nmDataPerforation* perforation = pTargetWell->getPerforation(0);
+            if(perforation) {
+                fullParams[1] = perforation->getSkin().getValue().toDouble();
+            }
             fullParams[2] = pTargetWell->getWellboreStorage().getValue().toDouble();
+
+            // Dfc 只存在于两类压裂井，普通井在完整向量中保持为 0。
+            if(pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) {
+                nmDataVerticalFracturedWell* fracturedWell =
+                    dynamic_cast<nmDataVerticalFracturedWell*>(pTargetWell);
+                if(fracturedWell) {
+                    fullParams[8] = fracturedWell->getDfc().getValue().toDouble();
+                    fullParams[9] = fracturedWell->getFractureHalfLength().getValue().toDouble();
+                }
+            } else if(pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+                nmDataHorizontalFracturedWell* fracturedWell =
+                    dynamic_cast<nmDataHorizontalFracturedWell*>(pTargetWell);
+                if(fracturedWell) {
+                    fullParams[8] = fracturedWell->getDfc().getValue().toDouble();
+                    fullParams[9] = fracturedWell->getFractureHalfLength().getValue().toDouble();
+                }
+            }
         }
     }
 
@@ -1172,7 +1262,8 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation,
         double surrogateObjective,
         const QString& screeningDecision,
         const QVector<double>& pbestPosition,
-        double pbestObjective)
+        double pbestObjective,
+        const AutoFitObjectiveBreakdown* objectiveBreakdown)
 {
     // 写一行 trace。generation=-1/particleIndex=-1 表示用户初始解；
     // 普通粒子行的 phase 为 particle_solver、particle_verified_cache 或 particle_not_evaluated。
@@ -1202,8 +1293,13 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation,
          << traceParamAt(currentParams, 3)
          << traceParamAt(currentParams, 4)
          << traceParamAt(currentParams, 5)
-         << traceParamAt(currentParams, 6)
-         << traceNumber(solverObjective)
+         << traceParamAt(currentParams, 6);
+    if(!isSurrogateScreeningEnabled()) {
+        cols << traceParamAt(currentParams, 7)
+             << traceParamAt(currentParams, 8)
+             << traceParamAt(currentParams, 9);
+    }
+    cols << traceNumber(solverObjective)
          << QString::number(solverSuccess ? 1 : 0)
          << QString::number(elapsedMs)
          << traceNumber(surrogateObjective)
@@ -1215,16 +1311,55 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation,
          << traceParamAt(pbestParams, 3)
          << traceParamAt(pbestParams, 4)
          << traceParamAt(pbestParams, 5)
-         << traceParamAt(pbestParams, 6)
-         << traceNumber(m_globalBestFitness)
+         << traceParamAt(pbestParams, 6);
+    if(!isSurrogateScreeningEnabled()) {
+        cols << traceParamAt(pbestParams, 7)
+             << traceParamAt(pbestParams, 8)
+             << traceParamAt(pbestParams, 9);
+    }
+    cols << traceNumber(m_globalBestFitness)
          << traceParamAt(gbestParams, 0)
          << traceParamAt(gbestParams, 1)
          << traceParamAt(gbestParams, 2)
          << traceParamAt(gbestParams, 3)
          << traceParamAt(gbestParams, 4)
          << traceParamAt(gbestParams, 5)
-         << traceParamAt(gbestParams, 6)
-         << csvEscape(enabledIndices.join(";"));
+         << traceParamAt(gbestParams, 6);
+    if(!isSurrogateScreeningEnabled()) {
+        cols << traceParamAt(gbestParams, 7)
+             << traceParamAt(gbestParams, 8)
+             << traceParamAt(gbestParams, 9);
+    }
+    cols << csvEscape(enabledIndices.join(";"));
+
+    if(objectiveBreakdown && objectiveBreakdown->valid) {
+        cols << traceNumber(objectiveBreakdown->pressureLoss)
+             << traceNumber(objectiveBreakdown->derivativeLoss);
+        if(!isSurrogateScreeningEnabled()) {
+            cols << traceNumber(objectiveBreakdown->verticalCommonBias);
+        }
+        cols << traceNumber(objectiveBreakdown->verticalLoss);
+        if(!isSurrogateScreeningEnabled()) {
+            cols << QString::number(objectiveBreakdown->verticalReliable ? 1 : 0)
+                 << traceNumber(objectiveBreakdown->horizontalPhysicalShift);
+        }
+        cols << traceNumber(objectiveBreakdown->horizontalLoss);
+        if(!isSurrogateScreeningEnabled()) {
+            cols << QString::number(objectiveBreakdown->horizontalReliable ? 1 : 0);
+        }
+        cols << traceNumber(objectiveBreakdown->shapeLoss)
+             << traceNumber(objectiveBreakdown->lateDerivativeTrendLoss)
+             << traceNumber(objectiveBreakdown->lateDerivativeSlopeBias)
+             << QString::number(objectiveBreakdown->lateDerivativeTrendReliable ? 1 : 0)
+             << QString::number(objectiveBreakdown->registrationAmbiguous ? 1 : 0);
+    } else {
+        // 未运行真实求解器或评价无效时保持列数一致，诊断字段写空值。
+        int diagnosticColumnCount =
+            isSurrogateScreeningEnabled() ? 9 : 13;
+        for(int i = 0; i < diagnosticColumnCount; ++i) {
+            cols << QString();
+        }
+    }
 
     QTextStream out(&m_traceFile);
     out << cols.join(",") << "\n";
@@ -1257,9 +1392,11 @@ void nmCalculationAutoFitPSO::writeIterationTraceRows()
                       particle.lastEvaluationSuccess,
                       particle.lastEvaluationElapsedMs,
                       particle.surrogateObjective,
-                      particle.screeningDecision,
-                      particle.bestPosition,
-                      particle.bestFitness);
+                       particle.screeningDecision,
+                       particle.bestPosition,
+                       particle.bestFitness,
+                       particle.evaluatedThisIteration
+                       ? &particle.currentObjectiveBreakdown : nullptr);
     }
 }
 
@@ -2789,14 +2926,14 @@ void nmCalculationAutoFitPSO::loadParameterBounds()
     // 读取用户勾选的拟合参数及上下界。
     //
     // 这里构建三个核心数组：
-    // - m_parameterSelected[8]：完整参数体系中每个参数是否参与拟合；
-    // - m_parameterLower/Upper[8]：完整参数体系的搜索上下界；
+    // - m_parameterSelected[10]：完整参数体系中每个参数是否参与拟合；
+    // - m_parameterLower/Upper[10]：完整参数体系的搜索上下界；
     // - m_enabledParamIndices：把粒子内部紧凑向量映射回完整参数索引。
     nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
     nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy();
 
     // 获取参数选择状态
-    m_parameterSelected.resize(8);
+    m_parameterSelected.resize(10);
     m_parameterSelected[0] = fittingData.getPermeabilitySelected();
     m_parameterSelected[1] = fittingData.getSkinSelected();
     m_parameterSelected[2] = fittingData.getWellboreStorageSelected();
@@ -2805,10 +2942,12 @@ void nmCalculationAutoFitPSO::loadParameterBounds()
     m_parameterSelected[5] = fittingData.getCtSelected();
     m_parameterSelected[6] = fittingData.getCfSelected();
     m_parameterSelected[7] = fittingData.getSwiSelected();
+    m_parameterSelected[8] = fittingData.getFractureConductivitySelected();
+    m_parameterSelected[9] = fittingData.getFractureHalfLengthSelected();
 
     // 获取参数边界
-    m_parameterLower.resize(8);
-    m_parameterUpper.resize(8);
+    m_parameterLower.resize(10);
+    m_parameterUpper.resize(10);
 
     m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble();
     m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble();
@@ -2834,6 +2973,12 @@ void nmCalculationAutoFitPSO::loadParameterBounds()
     m_parameterLower[7] = fittingData.getSwiMin().getValue().toDouble();
     m_parameterUpper[7] = fittingData.getSwiMax().getValue().toDouble();
 
+    m_parameterLower[8] = fittingData.getFractureConductivityMin().getValue().toDouble();
+    m_parameterUpper[8] = fittingData.getFractureConductivityMax().getValue().toDouble();
+
+    m_parameterLower[9] = fittingData.getFractureHalfLengthMin().getValue().toDouble();
+    m_parameterUpper[9] = fittingData.getFractureHalfLengthMax().getValue().toDouble();
+
     // 更新启用参数索引
     m_enabledParamIndices.clear();
 
@@ -2847,21 +2992,19 @@ void nmCalculationAutoFitPSO::loadParameterBounds()
               .arg(m_enabledParamIndices.size()));
 }
 
-// ==================== PSO算法核心方法 ====================
+// ==================== 自动拟合核心方法 ====================
 bool nmCalculationAutoFitPSO::startAutoFitting()
 {
-    // 自动拟合的总入口。可以把这个函数当成 PSO 的“运行剧本”：
-    // 读取配置 -> 校验输入 -> 评价用户初始解 -> 初始化粒子群 ->
-    // 按代循环评价粒子 -> 更新全局最优 -> 判断停止 -> 保存结果。
+    // 自动拟合总入口：代理开启时保留原 PSO 筛选流程；代理关闭时改走
+    // 诊断灵敏度信赖域搜索。两条路径共用初始解评价、真实求解器和结果写回。
+    StopReasonPSO finalReason = PSO_CONTINUE_OPTIMIZATION;
+    bool useParticleSwarm = false;
+
     if(m_isRunning) {
         m_lastError = "Auto fitting is already running";
         return false;
     }
 
-    // 发送初始化日志
-    //emit logMessageGenerated(tr("=== PSO Automatic Fitting Started ==="));
-    emit logMessageGenerated(tr("Algorithm: Particle Swarm Optimization"));
-
     try {
         // 从 DataManager 读取界面保存的自动拟合配置。
         // 本类不直接依赖 UI 控件，便于后续从脚本或其他入口复用。
@@ -2870,6 +3013,11 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
             return false;
         }
 
+        useParticleSwarm = isSurrogateScreeningEnabled();
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("Algorithm: Particle Swarm Optimization")
+                                 : tr("Algorithm: Diagnostic Trust-Region Search"));
+
         if(m_simulationMode) {
             // 调试/演示用快速路径，不调用真实求解器。正式工况通常不走这里。
             DEBUG_OUT("=== SIMULATION MODE ACTIVATED ===");
@@ -2903,6 +3051,15 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
 
         emit logMessageGenerated(tr("Target data validation passed (%1 data points)").arg(m_targetLogLogData[0].size()));
 
+        if(m_targetWellName.isEmpty()) {
+            m_lastError = "Target well name is empty";
+            emit logMessageGenerated(tr("ERROR: Target well name is empty"));
+            return false;
+        }
+
+        emit logMessageGenerated(tr("Particle evaluation mode: solve all wells, retain target well '%1' only")
+                                 .arg(m_targetWellName));
+
         // 使用保存的初始值进行精英保护。resetOptimizer() 会清空部分运行状态，
         // 所以先把用户当前模型参数缓存下来，后面再恢复用于初始解评价和粒子初始化。
         QVector<double> savedInitialValues = m_initialValues;
@@ -2990,6 +3147,8 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
                     m_globalBestPosition = m_userInitialSolution;
                     m_userInitialLogLogData = m_lastEvaluatedLogLogData;
                     m_globalBestLogLogData = m_userInitialLogLogData;
+                    m_userInitialObjectiveBreakdown = m_lastObjectiveBreakdown;
+                    m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown;
 
                     emit logMessageGenerated(tr("Initial solution evaluation successful"));
                     emit logMessageGenerated(tr("Initial Error: %1").arg(m_userInitialFitness, 0, 'e', 4));
@@ -3008,9 +3167,11 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
                               m_userInitialFitness < 1e9,
                               initialEvalElapsedMs,
                               std::numeric_limits<double>::quiet_NaN(),
-                              "initial_solution",
-                              m_hasValidUserSolution ? m_userInitialSolution : QVector<double>(),
-                              m_userInitialFitness);
+                               "initial_solution",
+                               m_hasValidUserSolution ? m_userInitialSolution : QVector<double>(),
+                               m_userInitialFitness,
+                               m_hasValidUserSolution
+                               ? &m_userInitialObjectiveBreakdown : nullptr);
             } catch(...) {
                 m_hasValidUserSolution = false;
                 emit logMessageGenerated(tr("Exception during initial solution evaluation"));
@@ -3020,12 +3181,13 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
             m_initialValues = savedInitialValues;
         }
 
-        // 初始化粒子群。粒子维度等于用户勾选的参数数量，而不是固定 11 维。
-        if(kUseFixedPsoSeed) {
-            emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed));
-        } else {
-            emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed));
-        }
+        if(useParticleSwarm) {
+            // 初始化粒子群。粒子维度等于用户勾选的参数数量，而不是固定 11 维。
+            if(kUseFixedPsoSeed) {
+                emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed));
+            } else {
+                emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed));
+            }
 
         initializeSwarm();
         emit logMessageGenerated(tr("Swarm initialized: %1 particles, %2 dimensions").arg(m_swarmSize).arg(getEnabledParameterCount()));
@@ -3174,9 +3336,7 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
                                          .arg(currentSuccessRate * 100, 0, 'f', 1).arg(m_currentIteration + 1));
             }
 
-            // 更新全局最优。updateGlobalBest() 只读取粒子的真实 bestFitness，
-            // 不使用代理模型的 surrogateObjective。
-            //double previousGlobalBest = m_globalBestFitness;
+            // 只从真实求解器确认的粒子 bestFitness 更新全局最优，不使用代理分数。
             updateGlobalBest();
             // 记录本代所有粒子的真实/代理误差和筛选决策，用于复盘和排障。
             writeIterationTraceRows();
@@ -3265,11 +3425,40 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
             }
         }
 
+        finalReason = analyzeOptimizationStatus();
+        } else {
+            // 非代理路径不初始化粒子，也不使用 pbest/gbest 速度更新。
+            finalReason = runTrustRegionFitting();
+        }
+
         // 最终结果验证和保护
         validateAndProtectFinalResult();
 
+        if(!useParticleSwarm && !m_globalBestPosition.isEmpty() &&
+                m_globalBestObjectiveBreakdown.valid) {
+            // 精英保护可能恢复用户初始解，最终行必须在保护之后写入，确保 trace
+            // 中最后记录的就是实际回写 DataManager 的参数，而非最后一次接受候选。
+            writeTraceRow(m_currentIteration, -1,
+                          "trust_region_final",
+                          m_globalBestPosition,
+                          m_globalBestFitness,
+                          m_globalBestFitness < 1.0e9,
+                          -1,
+                          std::numeric_limits<double>::quiet_NaN(),
+                          "final_result",
+                          m_globalBestPosition,
+                          m_globalBestFitness,
+                          &m_globalBestObjectiveBreakdown);
+        }
+
+        if(m_globalBestFitness < m_targetError) {
+            finalReason = PSO_TARGET_ACHIEVED;
+        }
+
     } catch(const std::exception& e) {
-        m_lastError = QString(tr("Critical exception in PSO main loop: %1")).arg(e.what());
+        m_lastError = useParticleSwarm
+                      ? QString(tr("Critical exception in PSO main loop: %1")).arg(e.what())
+                      : QString(tr("Critical exception in automatic fitting: %1")).arg(e.what());
         emit logMessageGenerated(tr("CRITICAL ERROR: %1").arg(e.what()));
         closeTraceFile();
         cleanupTemporaryDirectory();
@@ -3277,7 +3466,9 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
         emit fittingFinished(false, m_lastError);
         return false;
     } catch(...) {
-        m_lastError = QString(tr("Unknown critical exception in PSO main loop"));
+        m_lastError = useParticleSwarm
+                      ? QString(tr("Unknown critical exception in PSO main loop"))
+                      : QString(tr("Unknown critical exception in automatic fitting"));
         emit logMessageGenerated(tr("CRITICAL ERROR: Unknown exception in PSO main loop"));
         closeTraceFile();
         cleanupTemporaryDirectory();
@@ -3286,13 +3477,49 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
         return false;
     }
 
-    m_isRunning = false;
+    bool finalFullSolverSucceeded = true;
+    bool finalFullSolverExecuted = false;
 
     // 应用最终参数
     if(!m_globalBestPosition.isEmpty()) {
         try {
             emit logMessageGenerated(tr("Applying optimized parameters to model..."));
             applyParametersToDataManager(m_globalBestPosition);
+
+			// 即使用户此时停止、不再执行最终完整计算，也要把 PEBI 缓存恢复为
+			// 最终已接受的裂缝参数，避免缓存仍停留在最后一个被拒绝的候选值。
+			const bool fractureGridParameterSelected =
+				(m_parameterSelected.size() > 8 && m_parameterSelected[8]) ||
+				(m_parameterSelected.size() > 9 && m_parameterSelected[9]);
+			if(fractureGridParameterSelected) {
+				nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance();
+				if(!pebiGrid || !pebiGrid->generateOutputPara()) {
+					throw std::runtime_error("Failed to refresh final fracture parameters");
+				}
+			}
+
+            if(m_shouldStop) {
+                // 手动停止优先保持快速返回，仅写回已确认的最优参数。
+                emit logMessageGenerated(tr("Final full-field calculation skipped after user stop"));
+            } else {
+                // 粒子阶段只保留目标井临时曲线。正常结束后用最优参数完整计算一次，
+                // 将全部井曲线和网格压力场写回项目，该次不计入 PSO 粒子评价数。
+                emit logMessageGenerated(tr("Running final full-field calculation with optimized parameters..."));
+                finalFullSolverExecuted = true;
+                finalFullSolverSucceeded = runFinalFullSolver();
+
+                if(finalFullSolverSucceeded) {
+                    emit logMessageGenerated(tr("Final full-field calculation completed successfully"));
+                } else if(m_shouldStop) {
+                    finalFullSolverExecuted = false;
+                    finalFullSolverSucceeded = true;
+                    emit logMessageGenerated(tr("Final full-field calculation stopped by user"));
+                } else {
+                    emit logMessageGenerated(tr("ERROR: Final full-field calculation failed"));
+                    m_lastError = tr("Optimized parameters were found, but the final full-field calculation failed");
+                }
+            }
+
             saveOptimizationResult();
 
             // 输出最终优化结果
@@ -3311,56 +3538,94 @@ bool nmCalculationAutoFitPSO::startAutoFitting()
 
             emit logMessageGenerated(finalParams);
 
-            emit logMessageGenerated(tr("Parameters applied successfully to data manager"));
+            if(finalFullSolverExecuted && finalFullSolverSucceeded) {
+                emit logMessageGenerated(tr("Parameters and full-field results applied successfully to data manager"));
+            } else if(!finalFullSolverExecuted) {
+                emit logMessageGenerated(tr("Optimized parameters applied to data manager"));
+            }
         } catch(const std::exception& e) {
+            finalFullSolverSucceeded = false;
             emit logMessageGenerated(tr("ERROR: Failed to apply final parameters: %1").arg(e.what()));
             m_lastError = QString("Failed to apply final parameters: %1").arg(e.what());
         } catch(...) {
+            finalFullSolverSucceeded = false;
             emit logMessageGenerated(tr("ERROR: Unknown error applying final parameters"));
             m_lastError = "Failed to apply final parameters due to unknown error";
         }
     }
 
+    m_isRunning = false;
+
     // 判断系统确定最终结果
     bool success;
     QString message;
-    StopReasonPSO finalReason = analyzeOptimizationStatus();
 
     if(finalReason == PSO_TARGET_ACHIEVED) {
         success = true;
         message = QString(tr("Target achieved. Best error: %1, Iterations: %2"))
                   .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION SUCCESSFUL ==="));
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION SUCCESSFUL ===")
+                                 : tr("=== AUTOMATIC FITTING SUCCESSFUL ==="));
     } else if(finalReason == PSO_TRUE_CONVERGENCE) {
         success = true;
-        message = QString(tr("PSO optimization converged to stable solution. Best error: %1, Iterations: %2"))
-                  .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION CONVERGED ==="));
+        message = useParticleSwarm
+                  ? QString(tr("PSO optimization converged to stable solution. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1)
+                  : QString(tr("Automatic fitting converged to a stable solution. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION CONVERGED ===")
+                                 : tr("=== AUTOMATIC FITTING CONVERGED ==="));
     } else if(finalReason == PSO_LOCAL_OPTIMUM) {
         success = true;
-        message = QString(tr("PSO optimization trapped in local optimum. Best error: %1, Iterations: %2"))
-                  .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION - LOCAL OPTIMUM ==="));
+        message = useParticleSwarm
+                  ? QString(tr("PSO optimization trapped in local optimum. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1)
+                  : QString(tr("Automatic fitting reached a local optimum. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION - LOCAL OPTIMUM ===")
+                                 : tr("=== AUTOMATIC FITTING - LOCAL OPTIMUM ==="));
     } else if(finalReason == PSO_MAX_ITERATIONS) {
         success = true;
         message = QString(tr("Max iterations reached. Best error: %1, Iterations: %2"))
                   .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION - MAX ITERATIONS ==="));
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION - MAX ITERATIONS ===")
+                                 : tr("=== AUTOMATIC FITTING - MAX ITERATIONS ==="));
     } else if(finalReason == PSO_USER_STOPPED) {
         success = true;
         message = QString(tr("Best error: %1, Iterations: %2"))
                   .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION STOPPED BY USER ==="));
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION STOPPED BY USER ===")
+                                 : tr("=== AUTOMATIC FITTING STOPPED BY USER ==="));
     } else if(finalReason == PSO_CONSECUTIVE_FAILURES) {
         success = false;
-        message = QString(tr("PSO optimization failed due to consecutive failures. Best error: %1, Iterations: %2"))
-                  .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION FAILED ==="));
+        message = useParticleSwarm
+                  ? QString(tr("PSO optimization failed due to consecutive failures. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1)
+                  : QString(tr("Automatic fitting failed due to consecutive failures. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION FAILED ===")
+                                 : tr("=== AUTOMATIC FITTING FAILED ==="));
     } else {
         success = false;
-        message = QString(tr("PSO optimization ended unexpectedly. Best error: %1, Iterations: %2"))
-                  .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
-        emit logMessageGenerated(tr("=== PSO OPTIMIZATION - UNKNOWN END ==="));
+        message = useParticleSwarm
+                  ? QString(tr("PSO optimization ended unexpectedly. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1)
+                  : QString(tr("Automatic fitting ended unexpectedly. Best error: %1, Iterations: %2"))
+                    .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1);
+        emit logMessageGenerated(useParticleSwarm
+                                 ? tr("=== PSO OPTIMIZATION - UNKNOWN END ===")
+                                 : tr("=== AUTOMATIC FITTING - UNKNOWN END ==="));
+    }
+
+    if(!finalFullSolverSucceeded) {
+        success = false;
+        message = m_lastError;
     }
 
     emitRunSummary(success, finalReason);
@@ -3446,6 +3711,38 @@ void nmCalculationAutoFitPSO::extractUserInitialValues()
             case 7: // 初始含水饱和度
                 initialValue = reservoirData.getSwi().getValue().toDouble();
                 break;
+
+            case 8: // 裂缝导流能力
+                if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) {
+                    nmDataVerticalFracturedWell* fracturedWell =
+                        dynamic_cast<nmDataVerticalFracturedWell*>(pTargetWell);
+                    if(fracturedWell) {
+                        initialValue = fracturedWell->getDfc().getValue().toDouble();
+                    }
+                } else if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+                    nmDataHorizontalFracturedWell* fracturedWell =
+                        dynamic_cast<nmDataHorizontalFracturedWell*>(pTargetWell);
+                    if(fracturedWell) {
+                        initialValue = fracturedWell->getDfc().getValue().toDouble();
+                    }
+                }
+                break;
+
+            case 9: // 裂缝半长
+                if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) {
+                    nmDataVerticalFracturedWell* fracturedWell =
+                        dynamic_cast<nmDataVerticalFracturedWell*>(pTargetWell);
+                    if(fracturedWell) {
+                        initialValue = fracturedWell->getFractureHalfLength().getValue().toDouble();
+                    }
+                } else if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+                    nmDataHorizontalFracturedWell* fracturedWell =
+                        dynamic_cast<nmDataHorizontalFracturedWell*>(pTargetWell);
+                    if(fracturedWell) {
+                        initialValue = fracturedWell->getFractureHalfLength().getValue().toDouble();
+                    }
+                }
+                break;
         }
 
         m_initialValues.append(initialValue);
@@ -3490,6 +3787,8 @@ void nmCalculationAutoFitPSO::initializeSwarm()
         particle.velocity.resize(dimensions);
         particle.bestPosition.resize(dimensions);
         particle.guideBestPosition.resize(dimensions);
+        particle.currentObjectiveBreakdown = AutoFitObjectiveBreakdown();
+        particle.bestObjectiveBreakdown = AutoFitObjectiveBreakdown();
         particle.bestFitness = 1e10;
         particle.guideBestObjective = 1e10;
         particle.guideBestFromSurrogate = false;
@@ -3555,162 +3854,1451 @@ void nmCalculationAutoFitPSO::initializeSwarm()
 
         particle.bestPosition = particle.position;
         particle.guideBestPosition = particle.position;
+
+        // 第一个粒子可能直接复用用户初始解，因此同步保存初始解的误差分解，
+        // 后续误差引导只能使用真实求解器确认过的 breakdown。
+        if(i == 0 && m_hasValidUserSolution) {
+            particle.currentObjectiveBreakdown = m_userInitialObjectiveBreakdown;
+            particle.bestObjectiveBreakdown = m_userInitialObjectiveBreakdown;
+        }
     }
 }
 
-void nmCalculationAutoFitPSO::updateParticle(int particleIndex)
+// 信赖域搜索统一在 [0, 1] 内部坐标工作。正值参数使用对数坐标，使内部相同步长
+// 表示近似相同的相对变化，避免 k、C、Ct、Cf 等跨数量级参数被线性尺度支配；
+// skin 可为负数、Swi 的物理意义是线性比例，因此二者保持有界线性坐标。
+static bool useTrustRegionLogScale(int parameterIndex, double lower, double upper)
 {
-    if(particleIndex < 0 || particleIndex >= m_swarm.size()) return;
-
-    AutoFitParticle& particle = m_swarm[particleIndex];
+    return parameterIndex != 1 && parameterIndex != 7 &&
+           lower > 0.0 && upper > lower;
+}
 
-    // 单粒子真实评价入口。
-    // 这里调用 evaluateFitness()，因此会真实写 DataManager、调用求解器、计算误差。
-    // 被代理模型筛掉的粒子不会进入这个函数。
-    particle.evaluatedThisIteration = false;
-    particle.lastEvaluationSuccess = false;
-    particle.lastEvaluationElapsedMs = -1;
-    particle.pbestRelativeImprovementThisIteration = 0.0;
-    particle.selectedForSolver = true;
+static double toTrustRegionCoordinate(double value,
+                                      int parameterIndex,
+                                      double lower,
+                                      double upper)
+{
+    // 所有进入优化器的物理值先投影到用户上下界，再转换成无量纲坐标。
+    // 这样有限差分步长、信赖半径和参数间相关性可以在统一尺度上比较。
+    value = qMax(lower, qMin(upper, value));
 
-    if(particle.screeningDecision.isEmpty()) {
-        particle.screeningDecision = "full_solver";
+    if(useTrustRegionLogScale(parameterIndex, lower, upper)) {
+        return (qLn(value) - qLn(lower)) / (qLn(upper) - qLn(lower));
     }
 
-    bool reuseInitialSolution = m_currentIteration == 0 &&
-                                particleIndex == 0 &&
-                                m_hasValidUserSolution &&
-                                m_userInitialFitness < 1e9 &&
-                                particle.position.size() == m_userInitialSolution.size();
-
-    for(int i = 0; reuseInitialSolution && i < particle.position.size(); ++i) {
-        double tolerance = qMax(1.0e-12, qAbs(m_userInitialSolution[i]) * 1.0e-12);
-        reuseInitialSolution = qAbs(particle.position[i] - m_userInitialSolution[i]) <= tolerance;
-    }
+    return upper > lower ? (value - lower) / (upper - lower) : 0.0;
+}
 
-    if(reuseInitialSolution) {
-        // 初始解在进入粒子群前已经真实求解过。第一代第0号粒子位置完全相同，
-        // 直接复用真实误差和曲线，避免一次重复 DLL 调用且不改变 PSO 数学状态。
-        particle.fitness = m_userInitialFitness;
-        particle.currentLogLogData = m_userInitialLogLogData;
-        particle.lastEvaluationElapsedMs = 0;
-        particle.evaluatedThisIteration = true;
-        particle.lastEvaluationSuccess = true;
-        particle.screeningDecision = "initial_solution_cache";
-        emit logMessageGenerated(tr("Particle 1 reused the verified initial solution"));
-    } else {
-        QTime evalTimer;
-        evalTimer.start();
-        particle.fitness = evaluateFitness(particle.position);
-        particle.currentLogLogData = m_lastEvaluatedLogLogData;
-        particle.lastEvaluationElapsedMs = evalTimer.elapsed();
-        particle.evaluatedThisIteration = true;
-        particle.lastEvaluationSuccess = (particle.fitness < 1e9);
-        m_totalEvaluations++;
+static double fromTrustRegionCoordinate(double coordinate,
+                                        int parameterIndex,
+                                        double lower,
+                                        double upper)
+{
+    // 候选内部坐标先限制在 [0,1]，再执行上述映射的逆变换，保证写回
+    // DataManager 的参数始终位于用户设置的物理范围内。
+    coordinate = qMax(0.0, qMin(1.0, coordinate));
 
-        if(particle.fitness < 1e9) {
-            m_successfulEvaluations++;
-        }
+    if(useTrustRegionLogScale(parameterIndex, lower, upper)) {
+        return qExp(qLn(lower) + coordinate * (qLn(upper) - qLn(lower)));
     }
 
-    // 更新个体最优 pbest。这里使用的是真实求解器误差 particle.fitness，
-    // 不是代理模型给出的 surrogateObjective。
-    double previousBestFitness = particle.bestFitness;
+    return lower + coordinate * (upper - lower);
+}
 
-    if(particle.fitness < previousBestFitness) {
-        particle.pbestRelativeImprovementThisIteration = previousBestFitness >= 1e9
-                ? 1.0
-                : (previousBestFitness - particle.fitness) /
-                  qMax(1e-10, qAbs(previousBestFitness));
+enum TrustRegionErrorComponent
+{
+    TRUST_REGION_VERTICAL_COMPONENT = 0,
+    TRUST_REGION_HORIZONTAL_COMPONENT,
+    TRUST_REGION_SHAPE_COMPONENT,
+    TRUST_REGION_TOTAL_COMPONENT
+};
 
-        // 如果当前位置尚未成为新的真实全局最优，而上一代存在尚未真实验证、且代理
-        // 仍判断更优的 guide，就保留它继续引导速度；真实 pbest 仍照常更新。
-        bool preserveSurrogateGuide = false;
+// 一次真实求解的完整快照。除了参数和总误差，还保存内部坐标、诊断分量和
+// 双对数曲线，因此拒绝候选后可以完整恢复上一个已接受工作点。
+struct TrustRegionEvaluation
+{
+    QVector<double> parameters;
+    QVector<double> coordinates;
+    AutoFitObjectiveBreakdown breakdown;
+    QVector<QVector<double> > curve;
+    double fitness;
+    int elapsedMs;
+    bool valid;
+
+    TrustRegionEvaluation()
+        : fitness(1.0e10)
+        , elapsedMs(-1)
+        , valid(false)
+    {}
+};
 
-        bool currentBeatsGlobalBest = particle.fitness < m_globalBestFitness;
+// LM 只使用固定长度、全部有限的普通残差。代理路径不会进入本搜索器。
+static bool trustRegionResidualsValid(
+        const AutoFitObjectiveBreakdown& breakdown)
+{
+    // 损失函数固定使用 80 个压力点和 80 个导数点。严格校验长度，避免
+    // Jacobian 沿用旧维度后访问另一候选的短残差向量。
+    if(!breakdown.valid || breakdown.residualVector.size() != 160) {
+        return false;
+    }
 
-        if(!currentBeatsGlobalBest &&
-                isSurrogateScreeningEnabled() &&
-                particle.guideBestFromSurrogate &&
-                isFiniteNumber(particle.guideBestObjective) &&
-                isFiniteNumber(particle.surrogateObjective)) {
-            double requiredImprovement = qMax(1.0e-10,
-                                               qAbs(particle.guideBestObjective) *
-                                               kSurrogateGuidePbestMinRelativeImprovement);
-            preserveSurrogateGuide = particle.surrogateObjective -
-                                     particle.guideBestObjective > requiredImprovement;
+    for(int i = 0; i < breakdown.residualVector.size(); ++i) {
+        if(!isFiniteNumber(breakdown.residualVector[i])) {
+            return false;
         }
+    }
+    return true;
+}
 
-        particle.bestFitness = particle.fitness;
-        particle.bestPosition = particle.position;
-        particle.bestLogLogData = particle.currentLogLogData;
+// 计算向量二范数的平方，避免在只比较能量或计算正规方程时反复开方。
+static double trustRegionSquaredNorm(const QVector<double>& values)
+{
+    double sum = 0.0;
+    for(int i = 0; i < values.size(); ++i) {
+        sum += values[i] * values[i];
+    }
+    return sum;
+}
 
-        if(!preserveSurrogateGuide) {
-            particle.guideBestPosition = particle.position;
-            particle.guideBestObjective = isFiniteNumber(particle.surrogateObjective)
-                                          ? particle.surrogateObjective
-                                          : particle.fitness;
-            particle.guideBestFromSurrogate = false;
-        }
+// 计算同维向量内积；维度不一致表示局部模型无效，返回零让调用方放弃修正。
+static double trustRegionDotProduct(const QVector<double>& left,
+                                    const QVector<double>& right)
+{
+    if(left.size() != right.size()) {
+        return 0.0;
+    }
 
-        DEBUG_OUT(QString("Particle %1 improved: error = %2")
-                  .arg(particleIndex).arg(particle.fitness, 0, 'e', 4));
+    double sum = 0.0;
+    for(int i = 0; i < left.size(); ++i) {
+        sum += left[i] * right[i];
     }
+    return sum;
 }
 
-void nmCalculationAutoFitPSO::updateGlobalBest()
+// trace 和运行日志使用稳定的英文标识，便于现有离线脚本继续按字段筛选。
+static QString trustRegionComponentName(int component)
 {
-    // 保存上一轮的全局最优，用于后续自适应参数调整等
-    m_previousBestFitness = m_globalBestFitness;
-    bool globalBestUpdated = false;
+    if(component == TRUST_REGION_VERTICAL_COMPONENT) {
+        return "vertical";
+    }
+    if(component == TRUST_REGION_HORIZONTAL_COMPONENT) {
+        return "horizontal";
+    }
+    if(component == TRUST_REGION_SHAPE_COMPONENT) {
+        return "shape";
+    }
+    return "total";
+}
 
-    // 遍历所有粒子，寻找比当前 global best 更好的个体最优。
-    // 注意：particle.bestFitness 只有在真实求解器评价成功后才会更新。
-    for(int i = 0; i < m_swarm.size(); ++i) {
-        const AutoFitParticle& particle = m_swarm[i];
+// 三类损失量纲一致，直接选择当前最大的可靠分量；都很小时退回总残差梯度。
+static int trustRegionDominantComponent(
+        const AutoFitObjectiveBreakdown& breakdown,
+        double diagnosisThreshold)
+{
+    int component = TRUST_REGION_TOTAL_COMPONENT;
+    double largestLoss = diagnosisThreshold;
 
-        // 只要个体最优比当前全局最优小，就认为是更好的解
-        if(particle.bestFitness < m_globalBestFitness) {
+    if(breakdown.verticalReliable &&
+            isFiniteNumber(breakdown.verticalLoss) &&
+            breakdown.verticalLoss > largestLoss) {
+        component = TRUST_REGION_VERTICAL_COMPONENT;
+        largestLoss = breakdown.verticalLoss;
+    }
+    if(breakdown.horizontalReliable &&
+            !breakdown.registrationAmbiguous &&
+            isFiniteNumber(breakdown.horizontalLoss) &&
+            breakdown.horizontalLoss > largestLoss) {
+        component = TRUST_REGION_HORIZONTAL_COMPONENT;
+        largestLoss = breakdown.horizontalLoss;
+    }
+    if(isFiniteNumber(breakdown.shapeLoss) &&
+            breakdown.shapeLoss > largestLoss) {
+        component = TRUST_REGION_SHAPE_COMPONENT;
+    }
 
-            double improvement = m_globalBestFitness - particle.bestFitness;
-            double relativeImprovement =
-                improvement / qMax(1e-10, qAbs(m_globalBestFitness));
+    return component;
+}
 
-            // 区分显著改进和微小改进，但无论如何都会更新全局最优
-            if(relativeImprovement > m_improvementThreshold) {
-                DEBUG_OUT(QString(tr("Global best updated with %1% improvement: %2"))
-                          .arg(relativeImprovement * 100.0, 0, 'f', 3)
-                          .arg(particle.bestFitness, 0, 'e', 4));
-            } else {
-                DEBUG_OUT(QString(tr("Global best updated (minor improvement %1% < %2%) to %3"))
-                          .arg(relativeImprovement * 100.0, 0, 'f', 3)
-                          .arg(m_improvementThreshold * 100.0, 0, 'f', 2)
-                          .arg(particle.bestFitness, 0, 'e', 4));
-            }
+// 求解选中参数对应的阻尼正规方程。上下和左右诊断量保留方向；形状没有
+// 天然正负，因此使用 shapeLoss 对参数的局部导数。参数最多八维，使用带
+// 部分主元的高斯消元即可处理该小矩阵，并在主元退化时明确返回失败。
+static bool solveTrustRegionLinearSystem(
+        QVector<QVector<double> > matrix,
+        QVector<double> rightHandSide,
+        QVector<double>* solution)
+{
+    if(!solution || matrix.isEmpty() ||
+            matrix.size() != rightHandSide.size()) {
+        return false;
+    }
 
-            // 无论相对改进是否超过阈值，都要更新全局最优
-            m_globalBestFitness  = particle.bestFitness;
-            m_globalBestPosition = particle.bestPosition;
-            m_globalBestLogLogData = particle.bestLogLogData;
-            globalBestUpdated    = true;
-            emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness);
+    const int size = matrix.size();
+    for(int i = 0; i < size; ++i) {
+        if(matrix[i].size() != size) {
+            return false;
         }
     }
 
-    // 只有在本轮没有找到任何更好的解时才考虑恢复用户初始解
-    if(!globalBestUpdated && m_hasValidUserSolution && m_userInitialFitness < m_globalBestFitness) {
-
-        double improvement = m_globalBestFitness - m_userInitialFitness;
-        double relativeImprovement =
-            improvement / qMax(1e-10, qAbs(m_globalBestFitness));
+    for(int column = 0; column < size; ++column) {
+        int pivotRow = column;
+        double pivotMagnitude = qAbs(matrix[column][column]);
+        for(int row = column + 1; row < size; ++row) {
+            double magnitude = qAbs(matrix[row][column]);
+            if(magnitude > pivotMagnitude) {
+                pivotMagnitude = magnitude;
+                pivotRow = row;
+            }
+        }
+        if(pivotMagnitude <= 1.0e-14) {
+            return false;
+        }
 
-        DEBUG_OUT("Elite protection: Restoring user initial solution as global best");
-        DEBUG_OUT(QString("Elite solution is better than current best by %1%")
-                  .arg(relativeImprovement * 100.0, 0, 'f', 3));
+        if(pivotRow != column) {
+            qSwap(matrix[pivotRow], matrix[column]);
+            qSwap(rightHandSide[pivotRow], rightHandSide[column]);
+        }
 
-        m_globalBestFitness  = m_userInitialFitness;
+        for(int row = column + 1; row < size; ++row) {
+            double factor = matrix[row][column] /
+                            matrix[column][column];
+            matrix[row][column] = 0.0;
+            for(int nextColumn = column + 1;
+                    nextColumn < size; ++nextColumn) {
+                matrix[row][nextColumn] -=
+                    factor * matrix[column][nextColumn];
+            }
+            rightHandSide[row] -= factor * rightHandSide[column];
+        }
+    }
+
+    solution->fill(0.0, size);
+    for(int row = size - 1; row >= 0; --row) {
+        double value = rightHandSide[row];
+        for(int column = row + 1; column < size; ++column) {
+            value -= matrix[row][column] * (*solution)[column];
+        }
+        double pivot = matrix[row][row];
+        if(qAbs(pivot) <= 1.0e-14) {
+            return false;
+        }
+        (*solution)[row] = value / pivot;
+        if(!isFiniteNumber((*solution)[row])) {
+            return false;
+        }
+    }
+    return true;
+}
+
+// 计算两个 Jacobian 列向量的绝对余弦相似度。接近 1 表示两个参数在当前
+// 工作点对曲线的影响几乎相同，联合调整容易产生不可辨识方向。
+static double trustRegionJacobianColumnCorrelation(
+        const QVector<QVector<double> >& jacobian,
+        int leftColumn,
+        int rightColumn)
+{
+    double product = 0.0;
+    double leftNorm = 0.0;
+    double rightNorm = 0.0;
+    for(int row = 0; row < jacobian.size(); ++row) {
+        if(leftColumn >= jacobian[row].size() ||
+                rightColumn >= jacobian[row].size()) {
+            return 1.0;
+        }
+        double left = jacobian[row][leftColumn];
+        double right = jacobian[row][rightColumn];
+        product += left * right;
+        leftNorm += left * left;
+        rightNorm += right * right;
+    }
+
+    if(leftNorm <= 1.0e-20 || rightNorm <= 1.0e-20) {
+        return 0.0;
+    }
+    return qAbs(product) / qSqrt(leftNorm * rightNorm);
+}
+
+// 每次接受一个真实候选后，使用满足最新割线条件的秩一修正更新完整残差
+// Jacobian。这样模型吸收了刚得到的真实变化，又不必立即逐参数重新试算。
+static void updateTrustRegionJacobian(
+        QVector<QVector<double> >* jacobian,
+        const QVector<double>& oldResidual,
+        const QVector<double>& newResidual,
+        const QVector<double>& coordinateStep)
+{
+    if(!jacobian || jacobian->size() != oldResidual.size() ||
+            oldResidual.size() != newResidual.size()) {
+        return;
+    }
+
+    double denominator = trustRegionSquaredNorm(coordinateStep);
+    if(denominator <= 1.0e-12) {
+        return;
+    }
+
+    for(int row = 0; row < jacobian->size(); ++row) {
+        if((*jacobian)[row].size() != coordinateStep.size()) {
+            return;
+        }
+
+        double predictedChange = 0.0;
+        for(int column = 0; column < coordinateStep.size(); ++column) {
+            predictedChange +=
+                (*jacobian)[row][column] * coordinateStep[column];
+        }
+        double correction =
+            (newResidual[row] - oldResidual[row] - predictedChange) /
+            denominator;
+        for(int column = 0; column < coordinateStep.size(); ++column) {
+            (*jacobian)[row][column] +=
+                correction * coordinateStep[column];
+        }
+    }
+}
+
+// 对上下偏差、左右偏差和形状损失的梯度执行同样的割线秩一修正，使诊断
+// 选参模型与完整残差 Jacobian 保持在同一个已接受工作点。
+static void updateTrustRegionScalarGradient(
+        QVector<double>* gradient,
+        double oldValue,
+        double newValue,
+        const QVector<double>& coordinateStep)
+{
+    if(!gradient || gradient->size() != coordinateStep.size() ||
+            !isFiniteNumber(oldValue) || !isFiniteNumber(newValue)) {
+        return;
+    }
+
+    double denominator = trustRegionSquaredNorm(coordinateStep);
+    if(denominator <= 1.0e-12) {
+        return;
+    }
+
+    double predictedChange = trustRegionDotProduct(
+        *gradient, coordinateStep);
+    double correction =
+        (newValue - oldValue - predictedChange) / denominator;
+    for(int i = 0; i < gradient->size(); ++i) {
+        (*gradient)[i] += correction * coordinateStep[i];
+    }
+}
+
+bool nmCalculationAutoFitPSO::evaluateTrustRegionPoint(
+        const QVector<double>& parameters,
+        double* fitness,
+        AutoFitObjectiveBreakdown* breakdown,
+        QVector<QVector<double> >* curve,
+        int* elapsedMs)
+{
+    if(!fitness || !breakdown || !curve || !elapsedMs || m_shouldStop) {
+        return false;
+    }
+
+    // evaluateFitness() 会写入 DataManager 并调用真实求解器。这里统一统计
+    // 真实评价次数和耗时，同时严格要求固定残差、诊断结构和结果曲线均有效。
+    QTime timer;
+    timer.start();
+    *fitness = evaluateFitness(parameters);
+    *elapsedMs = timer.elapsed();
+    *breakdown = m_lastObjectiveBreakdown;
+    *curve = m_lastEvaluatedLogLogData;
+    ++m_totalEvaluations;
+
+    bool valid = isFiniteNumber(*fitness) && *fitness < 1.0e9 &&
+                 breakdown->valid &&
+                 trustRegionResidualsValid(*breakdown) &&
+                 !curve->isEmpty();
+    if(valid) {
+        ++m_successfulEvaluations;
+    }
+
+    return valid;
+}
+
+StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting()
+{
+    const int dimensions = getEnabledParameterCount();
+    if(dimensions <= 0 || m_enabledParamIndices.size() != dimensions) {
+        m_lastError = tr("No valid parameters are available for trust-region fitting");
+        return PSO_OPTIMIZATION_FAILED;
+    }
+
+    // 真实求解次数比“外层迭代次数”更能反映耗时。预算至少允许完成一次全参数
+    // 灵敏度和两次候选评价，同时避免连续重建 Jacobian 导致运行时间失控。
+    const int maximumEvaluations = qMax(
+        m_totalEvaluations + dimensions + 2,
+        qMax(20, m_maxIterations * 3));
+    // 下列步长均位于归一化内部坐标：0.04 表示参数范围的 4%，信赖半径
+    // 限制一次联合移动的二范数，相关性门槛用于排除响应近乎共线的参数。
+    const double sensitivityStep = 0.04;
+    const double minimumCoordinateStep = 1.0e-5;
+    const double minimumTrustRadius = 2.0e-3;
+    const double maximumTrustRadius = 0.30;
+    const double columnCorrelationLimit = 0.995;
+    const double diagnosisThreshold = 1.0e-5;
+    // 误差下降至少达到绝对 1e-5 且相对当前有效基准 0.2% 才算有效改善。
+    // 更小的下降仍保留为最佳解，但不能反复清除停滞状态、延长拟合时间。
+    const double effectiveRelativeImprovement = 2.0e-3;
+    const double effectiveAbsoluteImprovement = 1.0e-5;
+    const int maximumIneffectiveSteps = 3;
+
+    // damping 是 LM 阻尼；拒绝或预测失准时增大，真实下降与预测一致时减小。
+    // 两组累计量控制 Jacobian 重建，避免长期使用已偏离当前工作点的局部模型。
+    double trustRadius = 0.12;
+    double damping = 1.0e-2;
+    int consecutiveRejectedSteps = 0;
+    int consecutiveSolverFailures = 0;
+    int acceptedSinceRebuild = 0;
+    int consecutiveIneffectiveSteps = 0;
+    double movementSinceRebuild = 0.0;
+    bool rebuildRequested = true;
+    bool modelRebuiltAtMinimumRadius = false;
+    bool stagnationConfirmationRequested = false;
+    StopReasonPSO stopReason = PSO_MAX_ITERATIONS;
+
+    // jacobian 的行对应固定 160 维残差，列对应用户勾选的参数。
+    // 三个 gradient 单独描述诊断分量对参数的局部变化，只用于本轮选参。
+    QVector<QVector<double> > jacobian;
+    QVector<double> verticalGradient(dimensions, 0.0);
+    QVector<double> horizontalGradient(dimensions, 0.0);
+    QVector<double> shapeGradient(dimensions, 0.0);
+    QVector<bool> jacobianColumnValid(dimensions, false);
+
+    // 参数向量的顺序始终与 m_enabledParamIndices 一致，不能按完整参数索引
+    // 直接访问；下面两个转换函数集中维护这层映射关系。
+    auto coordinatesFromParameters = [&](const QVector<double>& parameters)
+            -> QVector<double> {
+        QVector<double> coordinates(dimensions, 0.0);
+        for(int i = 0; i < dimensions; ++i) {
+            int parameterIndex = m_enabledParamIndices[i];
+            coordinates[i] = toTrustRegionCoordinate(
+                parameters[i], parameterIndex,
+                m_parameterLower[parameterIndex],
+                m_parameterUpper[parameterIndex]);
+        }
+        return coordinates;
+    };
+
+    auto parametersFromCoordinates = [&](const QVector<double>& coordinates)
+            -> QVector<double> {
+        QVector<double> parameters(dimensions, 0.0);
+        for(int i = 0; i < dimensions; ++i) {
+            int parameterIndex = m_enabledParamIndices[i];
+            parameters[i] = fromTrustRegionCoordinate(
+                coordinates[i], parameterIndex,
+                m_parameterLower[parameterIndex],
+                m_parameterUpper[parameterIndex]);
+        }
+        return parameters;
+    };
+
+    auto restoreEvaluationState = [&](const TrustRegionEvaluation& evaluation) {
+        // evaluateFitness() 会把试算参数写入 DataManager。无论候选是否接受，
+        // 下一次计算前都恢复到唯一的已接受工作点，防止失败试算污染后续求解。
+        applyParametersToDataManager(evaluation.parameters);
+        m_lastObjectiveBreakdown = evaluation.breakdown;
+        m_lastEvaluatedLogLogData = evaluation.curve;
+    };
+
+    // 只有真实总误差更小的工作点才能发布为全局最优；曲线和诊断快照必须
+    // 与参数同步更新，防止界面显示或最终精英保护使用错配的数据。
+    auto publishAcceptedPoint = [&](const TrustRegionEvaluation& evaluation) {
+        m_previousBestFitness = m_globalBestFitness;
+        m_globalBestPosition = evaluation.parameters;
+        m_globalBestFitness = evaluation.fitness;
+        m_globalBestObjectiveBreakdown = evaluation.breakdown;
+        m_globalBestLogLogData = evaluation.curve;
+        emit bestCurveUpdated(m_targetLogLogData,
+                              m_globalBestLogLogData,
+                              m_currentIteration + 1,
+                              m_globalBestFitness);
+    };
+
+    auto processPauseAndStop = [&]() -> bool {
+        while(m_isPaused && !m_shouldStop) {
+            QApplication::processEvents();
+            msleep(100);
+        }
+        QApplication::processEvents();
+        return !m_shouldStop;
+    };
+
+    // current 始终代表唯一已接受工作点。优先复用启动阶段已经真实验证的
+    // 用户初始解，避免在信赖域入口重复调用一次昂贵求解器。
+    TrustRegionEvaluation current;
+    if(m_hasValidUserSolution &&
+            m_globalBestPosition.size() == dimensions &&
+            trustRegionResidualsValid(m_globalBestObjectiveBreakdown) &&
+            !m_globalBestLogLogData.isEmpty()) {
+        current.parameters = m_globalBestPosition;
+        current.coordinates = coordinatesFromParameters(current.parameters);
+        current.breakdown = m_globalBestObjectiveBreakdown;
+        current.curve = m_globalBestLogLogData;
+        current.fitness = m_globalBestFitness;
+        current.elapsedMs = 0;
+        current.valid = true;
+    } else {
+        // 用户初始解无效时只做一次确定性的范围中点回退；所有正值参数在对数
+        // 坐标取中点，避免线性中点过分偏向跨数量级范围的上界。
+        current.coordinates.fill(0.5, dimensions);
+        current.parameters = parametersFromCoordinates(current.coordinates);
+        current.valid = evaluateTrustRegionPoint(
+            current.parameters,
+            &current.fitness,
+            &current.breakdown,
+            &current.curve,
+            &current.elapsedMs);
+        writeTraceRow(-1, -1,
+                      "trust_region_midpoint",
+                      current.parameters,
+                      current.fitness,
+                      current.valid,
+                      current.elapsedMs,
+                      std::numeric_limits<double>::quiet_NaN(),
+                      current.valid ? "midpoint_valid" : "midpoint_invalid",
+                      QVector<double>(),
+                      1.0e10,
+                      current.valid ? &current.breakdown : nullptr);
+        if(!current.valid) {
+            m_lastError = tr("The initial solution and parameter-range midpoint are both invalid");
+            return m_shouldStop
+                   ? PSO_USER_STOPPED
+                   : PSO_OPTIMIZATION_FAILED;
+        }
+        publishAcceptedPoint(current);
+    }
+
+    restoreEvaluationState(current);
+    m_convergenceHistory.append(current.fitness);
+    emit logMessageGenerated(
+        tr("Trust-region initial error: %1; evaluation budget: %2")
+        .arg(current.fitness, 0, 'e', 4)
+        .arg(maximumEvaluations));
+
+    // 有效改善始终相对“上一次有效改善后的误差”累计判断，避免一连串微小
+    // 下降每次都清零计数；累计达到门槛后才开始新的有效改善基准。
+    double effectiveImprovementBaseline = current.fitness;
+    auto registerEffectiveImprovement = [&](double fitness) -> bool {
+        const double requiredImprovement = qMax(
+            effectiveAbsoluteImprovement,
+            qAbs(effectiveImprovementBaseline) *
+            effectiveRelativeImprovement);
+        const double improvement = effectiveImprovementBaseline - fitness;
+        if(improvement < requiredImprovement) {
+            return false;
+        }
+
+        effectiveImprovementBaseline = fitness;
+        consecutiveIneffectiveSteps = 0;
+        stagnationConfirmationRequested = false;
+        return true;
+    };
+
+    // 连续三次没有有效改善时只请求一次灵敏度重建。重建完成后由主循环
+    // 直接检查累计改善，仍达不到门槛就判定局部收敛，不再继续微小试探。
+    auto recordIneffectiveStep = [&]() -> bool {
+        ++consecutiveIneffectiveSteps;
+        if(consecutiveIneffectiveSteps < maximumIneffectiveSteps) {
+            return false;
+        }
+
+        if(stagnationConfirmationRequested) {
+            return true;
+        }
+
+        consecutiveIneffectiveSteps = 0;
+        stagnationConfirmationRequested = true;
+        rebuildRequested = true;
+        emit logMessageGenerated(
+            tr("No effective improvement for %1 consecutive steps; "
+               "rebuilding sensitivity model for confirmation")
+            .arg(maximumIneffectiveSteps));
+        return false;
+    };
+
+    emit logMessageGenerated(
+        tr("Effective improvement threshold: max(%1, %2% of baseline error); "
+           "%3 consecutive ineffective steps trigger convergence confirmation")
+        .arg(effectiveAbsoluteImprovement, 0, 'e', 2)
+        .arg(effectiveRelativeImprovement * 100.0, 0, 'f', 2)
+        .arg(maximumIneffectiveSteps));
+
+    if(current.fitness < m_targetError) {
+        return PSO_TARGET_ACHIEVED;
+    }
+
+    // 在同一个真实工作点逐参数做单边差分。首选可用空间更大的方向；只有该方向
+    // 求解失败时才补算反方向，因此初次建模通常每个参数只增加一次真实求解。
+    auto rebuildSensitivity = [&]() -> bool {
+        const TrustRegionEvaluation base = current;
+        const int residualCount = base.breakdown.residualVector.size();
+        if(residualCount <= 0) {
+            return false;
+        }
+
+        jacobian = QVector<QVector<double> >(
+            residualCount, QVector<double>(dimensions, 0.0));
+        verticalGradient.fill(0.0, dimensions);
+        horizontalGradient.fill(0.0, dimensions);
+        shapeGradient.fill(0.0, dimensions);
+        jacobianColumnValid.fill(false, dimensions);
+
+        TrustRegionEvaluation bestProbe;
+        int bestProbeColumn = -1;
+        double bestProbeDelta = 0.0;
+        // 差分步长不超过参数范围的 4%，信赖域收缩后同步减小，但保留 0.5%
+        // 下限，避免步长太小使求解器数值噪声淹没真实灵敏度。
+        const double finiteDifferenceStep = qMin(
+            sensitivityStep,
+            qMax(5.0e-3, trustRadius * 0.5));
+
+        for(int column = 0;
+                column < dimensions &&
+                m_totalEvaluations < maximumEvaluations &&
+                processPauseAndStop();
+                ++column) {
+            // 单边差分优先选择离边界空间更大的方向；首方向求解无效时才反向
+            // 补算，因此正常情况下每个参数只消耗一次真实求解。
+            double positiveRoom = 1.0 - base.coordinates[column];
+            double negativeRoom = base.coordinates[column];
+            double preferredSign = positiveRoom >= negativeRoom ? 1.0 : -1.0;
+            bool columnBuilt = false;
+
+            for(int directionAttempt = 0;
+                    directionAttempt < 2 &&
+                    !columnBuilt &&
+                    m_totalEvaluations < maximumEvaluations;
+                    ++directionAttempt) {
+                double direction = directionAttempt == 0
+                                   ? preferredSign : -preferredSign;
+                double availableRoom = direction > 0.0
+                                       ? positiveRoom : negativeRoom;
+                double deltaMagnitude = qMin(
+                    finiteDifferenceStep, availableRoom);
+                if(deltaMagnitude < minimumCoordinateStep) {
+                    continue;
+                }
+
+                TrustRegionEvaluation probe;
+                probe.coordinates = base.coordinates;
+                probe.coordinates[column] += direction * deltaMagnitude;
+                probe.parameters = parametersFromCoordinates(probe.coordinates);
+                probe.valid = evaluateTrustRegionPoint(
+                    probe.parameters,
+                    &probe.fitness,
+                    &probe.breakdown,
+                    &probe.curve,
+                    &probe.elapsedMs);
+
+                QString decision = probe.valid
+                                   ? "sensitivity_valid"
+                                   : (directionAttempt == 0
+                                      ? "sensitivity_retry_opposite"
+                                      : "sensitivity_invalid");
+                writeTraceRow(m_currentIteration,
+                              column,
+                              "trust_region_sensitivity",
+                              probe.parameters,
+                              probe.fitness,
+                              probe.valid,
+                              probe.elapsedMs,
+                              std::numeric_limits<double>::quiet_NaN(),
+                              decision,
+                              base.parameters,
+                              base.fitness,
+                              probe.valid ? &probe.breakdown : nullptr);
+
+                if(!probe.valid) {
+                    restoreEvaluationState(base);
+                    continue;
+                }
+
+                double delta = probe.coordinates[column] -
+                               base.coordinates[column];
+                if(qAbs(delta) < minimumCoordinateStep ||
+                        probe.breakdown.residualVector.size() != residualCount) {
+                    restoreEvaluationState(base);
+                    continue;
+                }
+
+                // 第 column 列是固定残差向量相对内部参数坐标的有限差分：
+                // J[:,column] = (r_probe-r_base)/delta。
+                for(int row = 0; row < residualCount; ++row) {
+                    jacobian[row][column] =
+                        (probe.breakdown.residualVector[row] -
+                         base.breakdown.residualVector[row]) / delta;
+                }
+
+                // 有符号诊断量只有在基点和试算点都可靠时才能计算方向梯度；
+                // shapeLoss 无方向可靠性标志，始终记录其局部变化率。
+                if(base.breakdown.verticalReliable &&
+                        probe.breakdown.verticalReliable &&
+                        !base.breakdown.registrationAmbiguous &&
+                        !probe.breakdown.registrationAmbiguous) {
+                    verticalGradient[column] =
+                        (probe.breakdown.verticalCommonBias -
+                         base.breakdown.verticalCommonBias) / delta;
+                }
+                if(base.breakdown.horizontalReliable &&
+                        probe.breakdown.horizontalReliable &&
+                        !base.breakdown.registrationAmbiguous &&
+                        !probe.breakdown.registrationAmbiguous) {
+                    horizontalGradient[column] =
+                        (probe.breakdown.horizontalPhysicalShift -
+                         base.breakdown.horizontalPhysicalShift) / delta;
+                }
+                shapeGradient[column] =
+                    (probe.breakdown.shapeLoss -
+                     base.breakdown.shapeLoss) / delta;
+                jacobianColumnValid[column] = true;
+                columnBuilt = true;
+
+                if(probe.fitness < base.fitness &&
+                        (!bestProbe.valid ||
+                         probe.fitness < bestProbe.fitness)) {
+                    bestProbe = probe;
+                    bestProbeColumn = column;
+                    bestProbeDelta = delta;
+                }
+                restoreEvaluationState(base);
+            }
+        }
+
+        int validColumnCount = 0;
+        for(int i = 0; i < jacobianColumnValid.size(); ++i) {
+            if(jacobianColumnValid[i]) {
+                ++validColumnCount;
+            }
+        }
+        if(validColumnCount == 0 || m_shouldStop) {
+            restoreEvaluationState(base);
+            return false;
+        }
+
+        // 灵敏度试算本身若找到更优真实解也应保留。所有列先基于同一个 base
+        // 建完，再用该已知割线把 Jacobian 平移到新工作点，避免边算边移动基点。
+        if(bestProbe.valid && bestProbeColumn >= 0) {
+            QVector<double> acceptedStep(dimensions, 0.0);
+            acceptedStep[bestProbeColumn] = bestProbeDelta;
+            updateTrustRegionJacobian(
+                &jacobian,
+                base.breakdown.residualVector,
+                bestProbe.breakdown.residualVector,
+                acceptedStep);
+            if(base.breakdown.verticalReliable &&
+                    bestProbe.breakdown.verticalReliable) {
+                updateTrustRegionScalarGradient(
+                    &verticalGradient,
+                    base.breakdown.verticalCommonBias,
+                    bestProbe.breakdown.verticalCommonBias,
+                    acceptedStep);
+            }
+            if(base.breakdown.horizontalReliable &&
+                    bestProbe.breakdown.horizontalReliable) {
+                updateTrustRegionScalarGradient(
+                    &horizontalGradient,
+                    base.breakdown.horizontalPhysicalShift,
+                    bestProbe.breakdown.horizontalPhysicalShift,
+                    acceptedStep);
+            }
+            updateTrustRegionScalarGradient(
+                &shapeGradient,
+                base.breakdown.shapeLoss,
+                bestProbe.breakdown.shapeLoss,
+                acceptedStep);
+
+            current = bestProbe;
+            publishAcceptedPoint(current);
+            restoreEvaluationState(current);
+            m_convergenceHistory.append(current.fitness);
+            writeTraceRow(m_currentIteration,
+                          bestProbeColumn,
+                          "trust_region_sensitivity_accept",
+                          current.parameters,
+                          current.fitness,
+                          true,
+                          0,
+                          std::numeric_limits<double>::quiet_NaN(),
+                          "accepted_cached_probe",
+                          current.parameters,
+                          current.fitness,
+                          &current.breakdown);
+            emit logMessageGenerated(
+                tr("Sensitivity probe accepted: error reduced to %1")
+                .arg(current.fitness, 0, 'e', 4));
+        } else {
+            restoreEvaluationState(current);
+        }
+
+        acceptedSinceRebuild = 0;
+        movementSinceRebuild = 0.0;
+        consecutiveRejectedSteps = 0;
+        rebuildRequested = false;
+        // 若重建过程中接受了试算点，当前模型已通过割线平移而不是在新点完整
+        // 重算；再遇到最小半径停滞时仍允许做一次真正的新点重建。
+        modelRebuiltAtMinimumRadius =
+            trustRadius <= minimumTrustRadius * 1.01 &&
+            !bestProbe.valid;
+        emit logMessageGenerated(
+            tr("Sensitivity model rebuilt: %1/%2 parameter columns valid")
+            .arg(validColumnCount)
+            .arg(dimensions));
+        return true;
+    };
+
+    int completedIterations = 0;
+    for(int iteration = 0;
+            iteration < m_maxIterations &&
+            m_totalEvaluations < maximumEvaluations &&
+            !m_shouldStop;
+            ++iteration) {
+        m_currentIteration = iteration;
+        completedIterations = iteration + 1;
+
+        if(!processPauseAndStop()) {
+            break;
+        }
+        if(rebuildRequested) {
+            const bool confirmingStagnation =
+                stagnationConfirmationRequested;
+            if(!rebuildSensitivity()) {
+                stopReason = m_shouldStop
+                             ? PSO_USER_STOPPED
+                             : PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            if(current.fitness < m_targetError) {
+                stopReason = PSO_TARGET_ACHIEVED;
+                break;
+            }
+            if(m_totalEvaluations >= maximumEvaluations) {
+                stopReason = PSO_MAX_ITERATIONS;
+                break;
+            }
+            const bool rebuildEffective =
+                registerEffectiveImprovement(current.fitness);
+            if(confirmingStagnation && !rebuildEffective) {
+                emit logMessageGenerated(
+                    tr("Sensitivity rebuild produced no effective improvement; "
+                       "local convergence detected"));
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+        }
+
+        // 先确定当前最突出的可靠诊断误差，用其梯度回答“哪些参数最能改善
+        // 当前问题”；实际 LM 方向仍由完整残差梯度和 Jacobian 共同计算。
+        int dominantComponent = trustRegionDominantComponent(
+            current.breakdown, diagnosisThreshold);
+        const QVector<double>* componentGradient = nullptr;
+        if(dominantComponent == TRUST_REGION_VERTICAL_COMPONENT) {
+            componentGradient = &verticalGradient;
+        } else if(dominantComponent == TRUST_REGION_HORIZONTAL_COMPONENT) {
+            componentGradient = &horizontalGradient;
+        } else if(dominantComponent == TRUST_REGION_SHAPE_COMPONENT) {
+            componentGradient = &shapeGradient;
+        }
+
+        // 主目标采用 0.5*||r||^2，其对参数的梯度为 J^T*r。这里不再叠加
+        // vertical/horizontal/shape，保证诊断分量不会改变真实接受目标。
+        QVector<double> totalGradient(dimensions, 0.0);
+        for(int column = 0; column < dimensions; ++column) {
+            if(!jacobianColumnValid[column]) {
+                continue;
+            }
+            for(int row = 0; row < jacobian.size(); ++row) {
+                totalGradient[column] +=
+                    jacobian[row][column] *
+                    current.breakdown.residualVector[row];
+            }
+        }
+
+        // 每轮最多联合调整三个灵敏参数。按当前诊断梯度绝对值由大到小选取，
+        // 并剔除 Jacobian 响应过度共线的列，降低弱可辨识参数互相补偿的风险。
+        QVector<int> selectedColumns;
+        QVector<bool> alreadyConsidered(dimensions, false);
+        for(int selection = 0; selection < qMin(3, dimensions); ++selection) {
+            int bestColumn = -1;
+            double bestScore = 0.0;
+            for(int column = 0; column < dimensions; ++column) {
+                if(alreadyConsidered[column] ||
+                        !jacobianColumnValid[column]) {
+                    continue;
+                }
+
+                double score = componentGradient
+                               ? qAbs((*componentGradient)[column])
+                               : qAbs(totalGradient[column]);
+                if(!isFiniteNumber(score) || score <= bestScore) {
+                    continue;
+                }
+
+                bool excessivelyCorrelated = false;
+                for(int selectedIndex = 0;
+                        selectedIndex < selectedColumns.size();
+                        ++selectedIndex) {
+                    if(trustRegionJacobianColumnCorrelation(
+                                jacobian,
+                                column,
+                                selectedColumns[selectedIndex]) >
+                            columnCorrelationLimit) {
+                        excessivelyCorrelated = true;
+                        break;
+                    }
+                }
+                if(!excessivelyCorrelated) {
+                    bestColumn = column;
+                    bestScore = score;
+                }
+            }
+            if(bestColumn < 0 || bestScore <= 1.0e-12) {
+                break;
+            }
+            selectedColumns.append(bestColumn);
+            alreadyConsidered[bestColumn] = true;
+        }
+
+        // 诊断梯度接近零时，说明该分量在当前局部无法可靠选参，退回完整残差
+        // 梯度，但接受标准仍然只有真实 total，诊断值不会重复计入目标函数。
+        if(selectedColumns.isEmpty() && componentGradient) {
+            dominantComponent = TRUST_REGION_TOTAL_COMPONENT;
+            componentGradient = nullptr;
+            alreadyConsidered.fill(false, dimensions);
+            for(int selection = 0;
+                    selection < qMin(3, dimensions);
+                    ++selection) {
+                int bestColumn = -1;
+                double bestScore = 0.0;
+                for(int column = 0; column < dimensions; ++column) {
+                    if(alreadyConsidered[column] ||
+                            !jacobianColumnValid[column]) {
+                        continue;
+                    }
+                    double score = qAbs(totalGradient[column]);
+                    if(score <= bestScore) {
+                        continue;
+                    }
+                    bool excessivelyCorrelated = false;
+                    for(int selectedIndex = 0;
+                            selectedIndex < selectedColumns.size();
+                            ++selectedIndex) {
+                        if(trustRegionJacobianColumnCorrelation(
+                                    jacobian,
+                                    column,
+                                    selectedColumns[selectedIndex]) >
+                                columnCorrelationLimit) {
+                            excessivelyCorrelated = true;
+                            break;
+                        }
+                    }
+                    if(!excessivelyCorrelated) {
+                        bestColumn = column;
+                        bestScore = score;
+                    }
+                }
+                if(bestColumn < 0 || bestScore <= 1.0e-12) {
+                    break;
+                }
+                selectedColumns.append(bestColumn);
+                alreadyConsidered[bestColumn] = true;
+            }
+        }
+
+        // 当前局部没有可用方向时先缩小半径并重建灵敏度；只有已经在最小
+        // 半径完整重建后仍无方向，才把它判定为局部最优。
+        if(selectedColumns.isEmpty()) {
+            if(trustRadius <= minimumTrustRadius * 1.01 &&
+                    modelRebuiltAtMinimumRadius) {
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
+            damping = qMin(1.0e8, damping * 4.0);
+            rebuildRequested = true;
+            if(recordIneffectiveStep()) {
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            continue;
+        }
+
+        // 在选中参数子空间构造 LM 正规方程：
+        // (J^T*J + damping*diag(J^T*J))*step = -J^T*r。
+        // 对角缩放使不同参数列的灵敏度量级差异不会直接改变阻尼强弱。
+        const int selectedCount = selectedColumns.size();
+        QVector<QVector<double> > normalMatrix(
+            selectedCount, QVector<double>(selectedCount, 0.0));
+        QVector<double> rightHandSide(selectedCount, 0.0);
+        for(int left = 0; left < selectedCount; ++left) {
+            int leftColumn = selectedColumns[left];
+            rightHandSide[left] = -totalGradient[leftColumn];
+            for(int right = 0; right < selectedCount; ++right) {
+                int rightColumn = selectedColumns[right];
+                for(int row = 0; row < jacobian.size(); ++row) {
+                    normalMatrix[left][right] +=
+                        jacobian[row][leftColumn] *
+                        jacobian[row][rightColumn];
+                }
+            }
+            double diagonalScale = qMax(
+                1.0e-10, normalMatrix[left][left]);
+            normalMatrix[left][left] += damping * diagonalScale;
+        }
+
+        QVector<double> selectedStep;
+        bool solved = solveTrustRegionLinearSystem(
+            normalMatrix, rightHandSide, &selectedStep);
+        QVector<double> coordinateStep(dimensions, 0.0);
+        if(solved) {
+            for(int i = 0; i < selectedCount; ++i) {
+                coordinateStep[selectedColumns[i]] = selectedStep[i];
+            }
+        }
+
+        double stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep));
+        if(!solved || !isFiniteNumber(stepNorm) ||
+                stepNorm < minimumCoordinateStep) {
+            // 正规方程退化时使用投影最速下降方向，仍只移动本轮已选择的参数。
+            coordinateStep.fill(0.0, dimensions);
+            double gradientNormSquared = 0.0;
+            for(int i = 0; i < selectedCount; ++i) {
+                int column = selectedColumns[i];
+                double stepDirection = -totalGradient[column];
+                if((current.coordinates[column] <= minimumCoordinateStep &&
+                        stepDirection < 0.0) ||
+                        (current.coordinates[column] >=
+                         1.0 - minimumCoordinateStep &&
+                         stepDirection > 0.0)) {
+                    stepDirection = 0.0;
+                }
+                coordinateStep[column] = stepDirection;
+                gradientNormSquared += stepDirection * stepDirection;
+            }
+            double gradientNorm = qSqrt(gradientNormSquared);
+            if(gradientNorm > minimumCoordinateStep) {
+                double scale = trustRadius / gradientNorm;
+                for(int i = 0; i < selectedCount; ++i) {
+                    int column = selectedColumns[i];
+                    coordinateStep[column] *= scale;
+                }
+            }
+            stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep));
+        }
+
+        // LM 解只给出局部模型建议方向；若超出当前信赖半径，保持方向不变并
+        // 等比例截短，避免一次试算离开 Jacobian 有效的局部区域。
+        if(stepNorm > trustRadius && stepNorm > 0.0) {
+            double scale = trustRadius / stepNorm;
+            for(int i = 0; i < coordinateStep.size(); ++i) {
+                coordinateStep[i] *= scale;
+            }
+        }
+
+        // 将 LM 步长投影到用户给定的参数范围，实际用于预测下降的也是投影后步长。
+        QVector<double> candidateCoordinates = current.coordinates;
+        for(int i = 0; i < dimensions; ++i) {
+            candidateCoordinates[i] = qBound(
+                0.0,
+                current.coordinates[i] + coordinateStep[i],
+                1.0);
+            coordinateStep[i] = candidateCoordinates[i] -
+                                current.coordinates[i];
+        }
+        stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep));
+
+        // 用线性模型 r_new ~= r_current + J*step 预测残差，再用平方能量
+        // 的下降量与真实候选下降量比较，作为调整阻尼和半径的依据。
+        QVector<double> predictedResidual =
+            current.breakdown.residualVector;
+        for(int row = 0; row < jacobian.size(); ++row) {
+            for(int column = 0; column < dimensions; ++column) {
+                predictedResidual[row] +=
+                    jacobian[row][column] * coordinateStep[column];
+            }
+        }
+        double predictedReduction = 0.5 *
+            (trustRegionSquaredNorm(current.breakdown.residualVector) -
+             trustRegionSquaredNorm(predictedResidual));
+
+        // 无实际移动或模型预测不下降时没有必要调用昂贵求解器。将它按一次
+        // 拒绝处理，并在连续发生后重建灵敏度，防止继续沿失效模型试算。
+        if(stepNorm < minimumCoordinateStep ||
+                !isFiniteNumber(predictedReduction) ||
+                predictedReduction <= 1.0e-14) {
+            trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
+            damping = qMin(1.0e8, damping * 4.0);
+            ++consecutiveRejectedSteps;
+            if(consecutiveRejectedSteps >= 2) {
+                if(trustRadius <= minimumTrustRadius * 1.01 &&
+                        modelRebuiltAtMinimumRadius) {
+                    stopReason = PSO_LOCAL_OPTIMUM;
+                    break;
+                }
+                rebuildRequested = true;
+            }
+            if(recordIneffectiveStep()) {
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            continue;
+        }
+
+        TrustRegionEvaluation candidate;
+        candidate.coordinates = candidateCoordinates;
+        candidate.parameters = parametersFromCoordinates(candidate.coordinates);
+        candidate.valid = evaluateTrustRegionPoint(
+            candidate.parameters,
+            &candidate.fitness,
+            &candidate.breakdown,
+            &candidate.curve,
+            &candidate.elapsedMs);
+
+        if(!candidate.valid) {
+            // 求解失败的候选不能改变 current。先完整恢复上一个已接受参数和
+            // 对应误差快照，再缩小信赖域；连续失败达到上限才终止整个拟合。
+            ++consecutiveSolverFailures;
+            ++consecutiveRejectedSteps;
+            trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
+            damping = qMin(1.0e8, damping * 4.0);
+            writeTraceRow(m_currentIteration,
+                          -1,
+                          "trust_region_candidate",
+                          candidate.parameters,
+                          candidate.fitness,
+                          false,
+                          candidate.elapsedMs,
+                          std::numeric_limits<double>::quiet_NaN(),
+                          "solver_invalid",
+                          current.parameters,
+                          current.fitness,
+                          nullptr);
+            restoreEvaluationState(current);
+            if(consecutiveRejectedSteps >= 2) {
+                rebuildRequested = true;
+            }
+            if(consecutiveSolverFailures >= m_maxConsecutiveFailures) {
+                stopReason = PSO_CONSECUTIVE_FAILURES;
+                break;
+            }
+            if(recordIneffectiveStep()) {
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            continue;
+        }
+
+        consecutiveSolverFailures = 0;
+        // 有效候选即使最终被拒绝，也提供了一条真实割线，可用于修正下一轮
+        // 局部模型；是否成为新工作点仍只由下面的 total 严格比较决定。
+        const AutoFitObjectiveBreakdown oldBreakdown = current.breakdown;
+        updateTrustRegionJacobian(
+            &jacobian,
+            oldBreakdown.residualVector,
+            candidate.breakdown.residualVector,
+            coordinateStep);
+        if(oldBreakdown.verticalReliable &&
+                candidate.breakdown.verticalReliable &&
+                !oldBreakdown.registrationAmbiguous &&
+                !candidate.breakdown.registrationAmbiguous) {
+            updateTrustRegionScalarGradient(
+                &verticalGradient,
+                oldBreakdown.verticalCommonBias,
+                candidate.breakdown.verticalCommonBias,
+                coordinateStep);
+        }
+        if(oldBreakdown.horizontalReliable &&
+                candidate.breakdown.horizontalReliable &&
+                !oldBreakdown.registrationAmbiguous &&
+                !candidate.breakdown.registrationAmbiguous) {
+            updateTrustRegionScalarGradient(
+                &horizontalGradient,
+                oldBreakdown.horizontalPhysicalShift,
+                candidate.breakdown.horizontalPhysicalShift,
+                coordinateStep);
+        }
+        updateTrustRegionScalarGradient(
+            &shapeGradient,
+            oldBreakdown.shapeLoss,
+            candidate.breakdown.shapeLoss,
+            coordinateStep);
+
+        // reductionRatio 衡量局部线性模型的可信度：接近 1 表示预测准确；
+        // 值较小表示虽然可能下降，但模型低估了非线性，需要收紧下一步。
+        double actualReduction = 0.5 *
+            (current.fitness * current.fitness -
+             candidate.fitness * candidate.fitness);
+        double reductionRatio = actualReduction / predictedReduction;
+        bool accepted = candidate.fitness < current.fitness;
+        QString componentName = trustRegionComponentName(dominantComponent);
+
+        if(accepted) {
+            // 真实总误差下降后才正式替换 current，并同步发布参数、曲线和诊断。
+            // 模型预测可靠时减小阻尼并可扩大半径，预测较差时保守收缩。
+            current = candidate;
+            publishAcceptedPoint(current);
+            restoreEvaluationState(current);
+            ++acceptedSinceRebuild;
+            movementSinceRebuild += stepNorm;
+            consecutiveRejectedSteps = 0;
+            m_convergenceHistory.append(current.fitness);
+
+            if(reductionRatio > 0.75) {
+                damping = qMax(1.0e-8, damping * 0.5);
+                if(stepNorm >= trustRadius * 0.8) {
+                    trustRadius = qMin(
+                        maximumTrustRadius, trustRadius * 1.6);
+                }
+            } else if(reductionRatio > 0.25) {
+                damping = qMax(1.0e-8, damping * 0.8);
+            } else {
+                damping = qMin(1.0e8, damping * 2.0);
+                trustRadius = qMax(
+                    minimumTrustRadius, trustRadius * 0.75);
+            }
+
+            if(acceptedSinceRebuild >= 6 ||
+                    movementSinceRebuild >= 0.30) {
+                rebuildRequested = true;
+            }
+            modelRebuiltAtMinimumRadius = false;
+        } else {
+            // 拒绝时 candidate 只保留在 trace 中，DataManager 和内存状态都恢复
+            // 到 current。连续拒绝说明割线模型可能失真，因此请求重新试算灵敏度。
+            ++consecutiveRejectedSteps;
+            damping = qMin(1.0e8, damping * 4.0);
+            trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
+            restoreEvaluationState(current);
+            if(consecutiveRejectedSteps >= 2) {
+                rebuildRequested = true;
+            }
+        }
+
+        // 候选只要更优就继续作为 current 保存；是否足以解除停滞，则统一
+        // 相对上一次有效改善基准判断。拒绝和微小改善都会累计无效次数。
+        const bool effectiveImprovement =
+            registerEffectiveImprovement(current.fitness);
+        if(!effectiveImprovement && recordIneffectiveStep()) {
+            stopReason = PSO_LOCAL_OPTIMUM;
+        }
+
+        writeTraceRow(m_currentIteration,
+                      -1,
+                      "trust_region_candidate",
+                      candidate.parameters,
+                      candidate.fitness,
+                      true,
+                      candidate.elapsedMs,
+                      std::numeric_limits<double>::quiet_NaN(),
+                      accepted
+                      ? QString("accepted_%1").arg(componentName)
+                      : QString("rejected_%1").arg(componentName),
+                      current.parameters,
+                      current.fitness,
+                      &candidate.breakdown);
+
+        emit logMessageGenerated(
+            tr("Iteration %1: focus=%2, parameters=%3, error=%4, result=%5")
+            .arg(iteration + 1)
+            .arg(componentName)
+            .arg(selectedColumns.size())
+            .arg(candidate.fitness, 0, 'e', 4)
+            .arg(accepted ? tr("accepted") : tr("rejected")));
+        emit progressUpdated(iteration + 1, m_globalBestFitness);
+
+        if(stopReason == PSO_LOCAL_OPTIMUM) {
+            break;
+        }
+
+        if(current.fitness < m_targetError) {
+            stopReason = PSO_TARGET_ACHIEVED;
+            break;
+        }
+        if(trustRadius <= minimumTrustRadius * 1.01 &&
+                consecutiveRejectedSteps >= 2) {
+            if(modelRebuiltAtMinimumRadius) {
+                stopReason = PSO_LOCAL_OPTIMUM;
+                break;
+            }
+            rebuildRequested = true;
+        }
+    }
+
+    if(completedIterations > 0) {
+        m_currentIteration = completedIterations - 1;
+    }
+    restoreEvaluationState(current);
+
+    if(m_shouldStop) {
+        return PSO_USER_STOPPED;
+    }
+    if(current.fitness < m_targetError) {
+        return PSO_TARGET_ACHIEVED;
+    }
+    if(stopReason == PSO_CONSECUTIVE_FAILURES ||
+            stopReason == PSO_LOCAL_OPTIMUM ||
+            stopReason == PSO_OPTIMIZATION_FAILED) {
+        return stopReason;
+    }
+    return PSO_MAX_ITERATIONS;
+}
+
+void nmCalculationAutoFitPSO::updateParticle(int particleIndex)
+{
+    if(particleIndex < 0 || particleIndex >= m_swarm.size()) return;
+
+    AutoFitParticle& particle = m_swarm[particleIndex];
+
+    // 单粒子真实评价入口。
+    // 这里调用 evaluateFitness()，因此会真实写 DataManager、调用求解器、计算误差。
+    // 被代理模型筛掉的粒子不会进入这个函数。
+    particle.evaluatedThisIteration = false;
+    particle.lastEvaluationSuccess = false;
+    particle.lastEvaluationElapsedMs = -1;
+    particle.pbestRelativeImprovementThisIteration = 0.0;
+    particle.selectedForSolver = true;
+
+    if(particle.screeningDecision.isEmpty()) {
+        particle.screeningDecision = "full_solver";
+    }
+
+    bool reuseInitialSolution = m_currentIteration == 0 &&
+                                particleIndex == 0 &&
+                                m_hasValidUserSolution &&
+                                m_userInitialFitness < 1e9 &&
+                                particle.position.size() == m_userInitialSolution.size();
+
+    for(int i = 0; reuseInitialSolution && i < particle.position.size(); ++i) {
+        double tolerance = qMax(1.0e-12, qAbs(m_userInitialSolution[i]) * 1.0e-12);
+        reuseInitialSolution = qAbs(particle.position[i] - m_userInitialSolution[i]) <= tolerance;
+    }
+
+    if(reuseInitialSolution) {
+        // 初始解在进入粒子群前已经真实求解过。第一代第0号粒子位置完全相同，
+        // 直接复用真实误差和曲线，避免一次重复 DLL 调用且不改变 PSO 数学状态。
+        particle.fitness = m_userInitialFitness;
+        particle.currentLogLogData = m_userInitialLogLogData;
+        particle.currentObjectiveBreakdown = m_userInitialObjectiveBreakdown;
+        particle.lastEvaluationElapsedMs = 0;
+        particle.evaluatedThisIteration = true;
+        particle.lastEvaluationSuccess = true;
+        particle.screeningDecision = "initial_solution_cache";
+        emit logMessageGenerated(tr("Particle 1 reused the verified initial solution"));
+    } else {
+        QTime evalTimer;
+        evalTimer.start();
+        particle.fitness = evaluateFitness(particle.position);
+        particle.currentLogLogData = m_lastEvaluatedLogLogData;
+        particle.currentObjectiveBreakdown = m_lastObjectiveBreakdown;
+        particle.lastEvaluationElapsedMs = evalTimer.elapsed();
+        particle.evaluatedThisIteration = true;
+        particle.lastEvaluationSuccess = (particle.fitness < 1e9);
+        m_totalEvaluations++;
+
+        if(particle.fitness < 1e9) {
+            m_successfulEvaluations++;
+        }
+    }
+
+    // 更新个体最优 pbest。这里使用的是真实求解器误差 particle.fitness，
+    // 不是代理模型给出的 surrogateObjective。
+    double previousBestFitness = particle.bestFitness;
+
+    if(particle.fitness < previousBestFitness) {
+        particle.pbestRelativeImprovementThisIteration = previousBestFitness >= 1e9
+                ? 1.0
+                : (previousBestFitness - particle.fitness) /
+                  qMax(1e-10, qAbs(previousBestFitness));
+
+        // 如果当前位置尚未成为新的真实全局最优，而上一代存在尚未真实验证、且代理
+        // 仍判断更优的 guide，就保留它继续引导速度；真实 pbest 仍照常更新。
+        bool preserveSurrogateGuide = false;
+
+        bool currentBeatsGlobalBest = particle.fitness < m_globalBestFitness;
+
+        if(!currentBeatsGlobalBest &&
+                isSurrogateScreeningEnabled() &&
+                particle.guideBestFromSurrogate &&
+                isFiniteNumber(particle.guideBestObjective) &&
+                isFiniteNumber(particle.surrogateObjective)) {
+            double requiredImprovement = qMax(1.0e-10,
+                                               qAbs(particle.guideBestObjective) *
+                                               kSurrogateGuidePbestMinRelativeImprovement);
+            preserveSurrogateGuide = particle.surrogateObjective -
+                                     particle.guideBestObjective > requiredImprovement;
+        }
+
+        particle.bestFitness = particle.fitness;
+        particle.bestPosition = particle.position;
+        particle.bestLogLogData = particle.currentLogLogData;
+        particle.bestObjectiveBreakdown = particle.currentObjectiveBreakdown;
+
+        if(!preserveSurrogateGuide) {
+            particle.guideBestPosition = particle.position;
+            particle.guideBestObjective = isFiniteNumber(particle.surrogateObjective)
+                                          ? particle.surrogateObjective
+                                          : particle.fitness;
+            particle.guideBestFromSurrogate = false;
+        }
+
+        DEBUG_OUT(QString("Particle %1 improved: error = %2")
+                  .arg(particleIndex).arg(particle.fitness, 0, 'e', 4));
+    }
+}
+
+void nmCalculationAutoFitPSO::updateGlobalBest()
+{
+    // 保存上一轮的全局最优，用于后续自适应参数调整等
+    m_previousBestFitness = m_globalBestFitness;
+    bool globalBestUpdated = false;
+
+    // 遍历所有粒子，寻找比当前 global best 更好的个体最优。
+    // 注意：particle.bestFitness 只有在真实求解器评价成功后才会更新。
+    for(int i = 0; i < m_swarm.size(); ++i) {
+        const AutoFitParticle& particle = m_swarm[i];
+
+        // 只要个体最优比当前全局最优小，就认为是更好的解
+        if(particle.bestFitness < m_globalBestFitness) {
+
+            double improvement = m_globalBestFitness - particle.bestFitness;
+            double relativeImprovement =
+                improvement / qMax(1e-10, qAbs(m_globalBestFitness));
+
+            // 区分显著改进和微小改进，但无论如何都会更新全局最优
+            if(relativeImprovement > m_improvementThreshold) {
+                DEBUG_OUT(QString(tr("Global best updated with %1% improvement: %2"))
+                          .arg(relativeImprovement * 100.0, 0, 'f', 3)
+                          .arg(particle.bestFitness, 0, 'e', 4));
+            } else {
+                DEBUG_OUT(QString(tr("Global best updated (minor improvement %1% < %2%) to %3"))
+                          .arg(relativeImprovement * 100.0, 0, 'f', 3)
+                          .arg(m_improvementThreshold * 100.0, 0, 'f', 2)
+                          .arg(particle.bestFitness, 0, 'e', 4));
+            }
+
+            // 无论相对改进是否超过阈值，都要更新全局最优
+            m_globalBestFitness  = particle.bestFitness;
+            m_globalBestPosition = particle.bestPosition;
+            m_globalBestLogLogData = particle.bestLogLogData;
+            m_globalBestObjectiveBreakdown = particle.bestObjectiveBreakdown;
+            globalBestUpdated    = true;
+            emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness);
+        }
+    }
+
+    // 只有在本轮没有找到任何更好的解时才考虑恢复用户初始解
+    if(!globalBestUpdated && m_hasValidUserSolution && m_userInitialFitness < m_globalBestFitness) {
+
+        double improvement = m_globalBestFitness - m_userInitialFitness;
+        double relativeImprovement =
+            improvement / qMax(1e-10, qAbs(m_globalBestFitness));
+
+        DEBUG_OUT("Elite protection: Restoring user initial solution as global best");
+        DEBUG_OUT(QString("Elite solution is better than current best by %1%")
+                  .arg(relativeImprovement * 100.0, 0, 'f', 3));
+
+        m_globalBestFitness  = m_userInitialFitness;
         m_globalBestPosition = m_userInitialSolution;
         m_globalBestLogLogData = m_userInitialLogLogData;
+        m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown;
         emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness);
     }
 }
@@ -3787,6 +5375,7 @@ void nmCalculationAutoFitPSO::updateVelocityAndPosition()
             int paramIndex = m_enabledParamIndices[j];
             double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
             double maxVel = range * VELOCITY_LIMIT_FACTOR;
+
             particle.velocity[j] = qMax(-maxVel, qMin(maxVel, particle.velocity[j]));
 
             // 更新位置
@@ -3961,7 +5550,7 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector<double>& parameter
     // 1. 校验粒子参数是否在用户设置的上下界和基本物理范围内；
     // 2. 将参数写入 DataManager 的储层/目标井对象；
     // 3. 调用真实数值求解器，生成模拟结果；
-    // 4. 从目标井读取模拟后的 result log-log 曲线；
+    // 4. 从本次求解任务读取目标井 result log-log 曲线；
     // 5. 与目标 history log-log 曲线计算误差，误差越小代表拟合越好。
     //
     // 返回 1e10 表示该粒子评价失败或结果不可用。PSO 会把它当成很差的解。
@@ -3969,6 +5558,7 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector<double>& parameter
     static int callCount = 0;
     callCount++;
     m_lastEvaluatedLogLogData.clear();
+    m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown();
 
     try {
         DEBUG_OUT(QString("%1: Call #%2 - Starting evaluation with %3 parameters")
@@ -4066,6 +5656,20 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector<double>& parameter
             return 1e10;
         }
 
+		// Dfc 和裂缝半长都通过 PEBI 裂缝数组传入，不属于每次求解都会重新组装的 Base/CS 参数。
+		// 勾选任一裂缝参数时刷新网格输出，保证本次真实试算使用新的导流能力和端点坐标。
+		const bool fractureGridParameterSelected =
+			(m_parameterSelected.size() > 8 && m_parameterSelected[8]) ||
+			(m_parameterSelected.size() > 9 && m_parameterSelected[9]);
+		if(fractureGridParameterSelected) {
+			nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance();
+			if(!pebiGrid || !pebiGrid->generateOutputPara()) {
+				DEBUG_OUT(QString("%1: Call #%2 - Failed to refresh PEBI fracture parameters")
+				          .arg(funcName).arg(callCount));
+				return 1e10;
+			}
+		}
+
         // 4. 运行求解器。真实求解器偶发失败时允许重试，避免一次 DLL 调用异常
         // 直接让整个粒子评价失败。
         QVector<QVector<double>> solverResult;
@@ -4122,24 +5726,11 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector<double>& parameter
             return 1e10;
         }
 
-        // 5. 获取 LogLog 数据。runSolver() 会更新 DataManager 中目标井的计算结果，
-        // 这里再从目标井读取 resultLogLogData 作为模拟曲线。
-        QVector<QVector<double>> resultLogLogData;
+        // 5. 获取 LogLog 数据。runSolverDll() 直接从求解任务复制目标井曲线，
+        // 不再依赖 DataManager 中可能被其它井或上一粒子改写的共享结果。
+        QVector<QVector<double>> resultLogLogData = m_lastEvaluatedLogLogData;
 
         try {
-            nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
-
-            if(!pTargetWell) {
-                DEBUG_OUT(QString("%1: Call #%2 - Target well '%3' NOT FOUND")
-                          .arg(funcName).arg(callCount).arg(m_targetWellName));
-                return 1e10;
-            }
-
-            DEBUG_OUT(QString("%1: Call #%2 - Target well found: %3")
-                      .arg(funcName).arg(callCount).arg(m_targetWellName));
-
-            resultLogLogData = pTargetWell->getResultLogLog();
-
             if(!validateLogLogData(resultLogLogData)) {
                 DEBUG_OUT(QString("%1: Call #%2 - LogLog data VALIDATION FAILED")
                           .arg(funcName).arg(callCount));
@@ -4241,7 +5832,7 @@ void nmCalculationAutoFitPSO::updateReservoirParameters(const QVector<double>& p
     nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
     nmDataReservoir reservoirData = dataManager->getReservoirDataCopy();
 
-    // paramIndex 是粒子 position 中的索引；i 是完整 8 个参数体系中的索引。
+    // paramIndex 是粒子 position 中的索引；i 是完整 10 个参数体系中的索引。
     // 只有 m_parameterSelected[i] 为 true 时，才从 parameters 中消费一个值。
     int paramIndex = 0;
 
@@ -4287,7 +5878,8 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector<double>& parame
 {
     // 更新目标井上的拟合参数。目前井级可拟合参数主要是：
     // - skin：写入第一个 perforation；
-    // - wellboreC：写入井筒储集系数。
+    // - wellboreC：写入井筒储集系数；
+    // - Dfc/裂缝半长：只写入垂直压裂井或多段压裂水平井。
     // 如果目标井不存在或没有射孔数据，这里只记录 debug，不抛异常。
     nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
 
@@ -4323,6 +5915,45 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector<double>& parame
                         pWell->setWellboreStorage(wellboreAttr);
                     }
                     break;
+
+                case 8: { // 裂缝导流能力
+                        if(pWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) {
+                            nmDataVerticalFracturedWell* fracturedWell =
+                                dynamic_cast<nmDataVerticalFracturedWell*>(pWell);
+                            if(fracturedWell) {
+                                nmDataAttribute dfc = fracturedWell->getDfc();
+                                dfc.setValue(value);
+                                fracturedWell->setDfc(dfc);
+                            }
+                        } else if(pWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+                            nmDataHorizontalFracturedWell* fracturedWell =
+                                dynamic_cast<nmDataHorizontalFracturedWell*>(pWell);
+                            if(fracturedWell) {
+                                nmDataAttribute dfc = fracturedWell->getDfc();
+                                dfc.setValue(value);
+                                fracturedWell->setDfc(dfc);
+                            }
+                        }
+                    }
+                    break;
+
+                case 9: { // 裂缝半长
+                        // 直接修改井对象中的属性，复用已有信号重算裂缝端点。
+                        if(pWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) {
+                            nmDataVerticalFracturedWell* fracturedWell =
+                                dynamic_cast<nmDataVerticalFracturedWell*>(pWell);
+                            if(fracturedWell) {
+                                fracturedWell->getFractureHalfLength().setValue(value);
+                            }
+                        } else if(pWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+                            nmDataHorizontalFracturedWell* fracturedWell =
+                                dynamic_cast<nmDataHorizontalFracturedWell*>(pWell);
+                            if(fracturedWell) {
+                                fracturedWell->getFractureHalfLength().setValue(value);
+                            }
+                        }
+                    }
+                    break;
             }
 
             paramIndex++;
@@ -4608,10 +6239,8 @@ void nmCalculationAutoFitPSO::saveOptimizationResult()
 
 void nmCalculationAutoFitPSO::validateAndProtectFinalResult()
 {
-    // 最终精英保护。
-    // PSO 是随机启发式算法，某些工况下可能没有找到比用户初始模型更好的结果。
-    // 这里用初始解误差与最终全局最优误差做比较，如果改进不足，就恢复初始解，
-    // 避免“自动拟合”把已有模型调坏。
+    // 最终精英保护只阻止无效结果或真正变差的结果。任何真实误差下降都应保留，
+    // 不能再用固定百分比门槛把已经找到的更优解恢复成初始值。
     if(!m_hasValidUserSolution) {
         emit logMessageGenerated(tr("No initial solution for elite protection"));
         return;
@@ -4626,28 +6255,36 @@ void nmCalculationAutoFitPSO::validateAndProtectFinalResult()
     emit logMessageGenerated(tr("Comparing results: Initial=%1, Final=%2")
                              .arg(initialFitness, 0, 'e', 4).arg(finalFitness, 0, 'e', 4));
 
-    // 计算改进程度
-    double improvement = initialFitness - finalFitness;
-    double relativeImprovement = improvement / qMax(1e-10, qAbs(initialFitness));
-
-    emit logMessageGenerated(tr("Improvement: %1 (%2%)")
-                             .arg(improvement, 0, 'e', 4).arg(relativeImprovement * 100, 0, 'f', 2));
+    bool finalValid = isFiniteNumber(finalFitness) &&
+                      finalFitness < 1.0e9 &&
+                      m_globalBestPosition.size() ==
+                      m_userInitialSolution.size() &&
+                      !m_globalBestLogLogData.isEmpty() &&
+                      m_globalBestObjectiveBreakdown.valid;
+    if(finalValid) {
+        double improvement = initialFitness - finalFitness;
+        double relativeImprovement =
+            improvement / qMax(1.0e-10, qAbs(initialFitness));
+        emit logMessageGenerated(tr("Improvement: %1 (%2%)")
+                                 .arg(improvement, 0, 'e', 4)
+                                 .arg(relativeImprovement * 100, 0, 'f', 2));
+    }
 
-    if(relativeImprovement < m_improvementThreshold) {
-        emit logMessageGenerated(tr("Elite protection triggered: insufficient improvement"));
-        emit logMessageGenerated(tr("Threshold: %1%, Actual: %2%")
-                                 .arg(m_improvementThreshold * 100, 0, 'f', 2)
-                                 .arg(relativeImprovement * 100, 0, 'f', 4));
+    if(!finalValid || finalFitness > initialFitness) {
+        emit logMessageGenerated(
+            tr("Elite protection triggered: final result is invalid or worse than initial"));
         emit logMessageGenerated(tr("Restoring initial solution as final result"));
 
         m_globalBestFitness = initialFitness;
         m_globalBestPosition = m_userInitialSolution;
         m_globalBestLogLogData = m_userInitialLogLogData;
+        m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown;
         emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness);
 
         emit logMessageGenerated(tr("Initial solution restored successfully"));
     } else {
-        emit logMessageGenerated(tr("Final result validated - significant improvement achieved"));
+        emit logMessageGenerated(
+            tr("Final result validated - solution is not worse than initial"));
     }
 }
 
@@ -4690,9 +6327,8 @@ int nmCalculationAutoFitPSO::getEnabledParameterCount() const
 
 bool nmCalculationAutoFitPSO::validateParameters(const QVector<double>& parameters) const
 {
-    // 参数合法性检查分两层：
-    // 1. 与用户界面设置一致：维度、有限数、上下界；
-    // 2. 求解器保护：拦截会导致数值崩溃或明显无物理意义的极端值。
+    // 参数物理范围已由拟合窗口统一校验；候选评价只检查维度、有限数和
+    // 用户设置的上下界，避免另一套硬编码阈值与实际搜索范围冲突。
     if(parameters.size() != getEnabledParameterCount()) {
         return false;
     }
@@ -4715,46 +6351,6 @@ bool nmCalculationAutoFitPSO::validateParameters(const QVector<double>& paramete
         }
     }
 
-    // 直接拦截会导致求解器数值崩溃的参数值
-    for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) {
-        int paramIndex = m_enabledParamIndices[i];
-        double value = parameters[i];
-
-        switch(paramIndex) {
-            case 0: // 渗透率：必须大于零
-                if(value <= 1e-8) {
-                    DEBUG_OUT(QString("Rejecting near-zero permeability: %1").arg(value));
-                    return false;
-                }
-
-                break;
-
-            case 2: // 井筒储集系数：必须大于零
-                if(value <= 1e-10) {
-                    DEBUG_OUT(QString("Rejecting near-zero wellbore storage: %1").arg(value));
-                    return false;
-                }
-
-                break;
-
-            case 3: // 孔隙度：必须在合理范围
-                if(value <= 1e-6 || value >= 0.99) {
-                    DEBUG_OUT(QString("Rejecting unrealistic porosity: %1").arg(value));
-                    return false;
-                }
-
-                break;
-
-            case 5: // 综合压缩系数：必须大于零
-                if(value <= 1e-8) {
-                    DEBUG_OUT(QString("Rejecting near-zero total compressibility: %1").arg(value));
-                    return false;
-                }
-
-                break;
-        }
-    }
-
     return true;
 }
 
@@ -4955,170 +6551,865 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError(
     const QVector<QVector<double> >& target,
     const QVector<QVector<double> >& result) const
 {
-    // 双对数曲线误差计算。
-    //
-    // target 通常来自目标井历史曲线，result 来自当前粒子参数下的模拟曲线。
-    // 两条曲线的时间点往往不完全一致，所以这里先取两者时间范围的重叠区间，
-    // 再在公共时间网格上插值对齐，最后分别计算压力曲线和压力导数曲线误差。
-    //
-    // 返回值越小表示拟合越好；返回 1e10 表示曲线无效或无法比较。
-    // 验证数据
+    // 主目标只比较固定网格上的压力和导数残差；上下、左右和形状只负责诊断
+    // 误差来源和选择参数，避免同一残差在 total 中被重复计算。整个计算过程均
+    // 位于 log(time)-log(value) 坐标，因此得到的是相对尺度偏差而非原始压力量纲。
+    const double invalidLoss = 1.0e10;
+    const double valueFloor = 1.0e-12;
+    const double minimumCoverage = 0.95;
+    const int numPoints = 80;
+    m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown();
+
     if(!validateLogLogData(target) || !validateLogLogData(result)) {
-        return 1e10;
+        return invalidLoss;
     }
 
     try {
-        // 数据对齐：找到目标曲线与模拟曲线 time 轴的重叠区域。
-        // 不在重叠区域内的点不参与误差，避免外推导致误差失真。
-        double targetMinX = target[0][0];
-        double targetMaxX = target[0][0];
-
-        for(int i = 1; i < target[0].size(); ++i) {
-            if(target[0][i] < targetMinX) targetMinX = target[0][i];
-
-            if(target[0][i] > targetMaxX) targetMaxX = target[0][i];
-        }
+        // 无法比较的采样行先跳过；有限但非正的导数无法进入双对数空间，
+        // 当前数据又没有逐点有效掩码，因此遇到这种导数时判本次评价无效。
+        auto prepareCurve = [valueFloor](const QVector<QVector<double> >& data,
+                                         QVector<QPointF>* pressure,
+                                         QVector<QPointF>* derivative) -> bool {
+            if(!pressure || !derivative || data.size() < 3 ||
+                    data[0].size() != data[1].size() ||
+                    data[0].size() != data[2].size()) {
+                return false;
+            }
 
-        double resultMinX = result[0][0];
-        double resultMaxX = result[0][0];
+            for(int i = 0; i < data[0].size(); ++i) {
+                if(!isFiniteNumber(data[0][i]) ||
+                        !isFiniteNumber(data[1][i]) ||
+                        !isFiniteNumber(data[2][i]) ||
+                        data[0][i] <= 0.0 ||
+                        data[1][i] <= 0.0) {
+                    continue;
+                }
+                if(data[2][i] <= 0.0) {
+                    return false;
+                }
 
-        for(int i = 1; i < result[0].size(); ++i) {
-            if(result[0][i] < resultMinX) resultMinX = result[0][i];
+                pressure->append(QPointF(data[0][i], data[1][i]));
+                derivative->append(
+                    QPointF(data[0][i], qMax(data[2][i], valueFloor)));
+            }
 
-            if(result[0][i] > resultMaxX) resultMaxX = result[0][i];
-        }
+            // 求解器输出可能不是严格升序，且同一时刻可能出现重复记录。
+            // 插值前统一排序并让后出现的记录覆盖同时间旧值，保证横坐标严格递增。
+            auto sortAndUnique = [](QVector<QPointF>* curve) {
+                std::stable_sort(
+                    curve->begin(), curve->end(),
+                    [](const QPointF& left, const QPointF& right) {
+                        return left.x() < right.x();
+                    });
+
+                QVector<QPointF> unique;
+                unique.reserve(curve->size());
+                for(int i = 0; i < curve->size(); ++i) {
+                    if(unique.isEmpty() ||
+                            curve->at(i).x() > unique.last().x()) {
+                        unique.append(curve->at(i));
+                    } else {
+                        unique[unique.size() - 1] = curve->at(i);
+                    }
+                }
+                *curve = unique;
+            };
+
+            sortAndUnique(pressure);
+            sortAndUnique(derivative);
+            return pressure->size() >= 3 && derivative->size() >= 3;
+        };
+
+        QVector<QPointF> targetPressure;
+        QVector<QPointF> targetDerivative;
+        QVector<QPointF> resultPressure;
+        QVector<QPointF> resultDerivative;
+        if(!prepareCurve(target, &targetPressure, &targetDerivative) ||
+                !prepareCurve(result, &resultPressure, &resultDerivative)) {
+            return invalidLoss;
+        }
+
+        // 在双对数坐标中插值。二分定位用于后面的多次水平配准试算。
+        auto interpolateLogValue = [valueFloor](
+                const QVector<QPointF>& curve,
+                double x,
+                double* value) -> bool {
+            if(!value || curve.size() < 2 || x <= 0.0 ||
+                    x < curve.first().x() || x > curve.last().x()) {
+                return false;
+            }
 
-        double overlapMinX = qMax(targetMinX, resultMinX);
-        double overlapMaxX = qMin(targetMaxX, resultMaxX);
+            int low = 0;
+            int high = curve.size() - 1;
+            while(low < high) {
+                int middle = low + (high - low) / 2;
+                if(curve[middle].x() < x) {
+                    low = middle + 1;
+                } else {
+                    high = middle;
+                }
+            }
 
-        if(overlapMinX >= overlapMaxX) {
-            DEBUG_OUT("No overlap between target and result LogLog curves");
-            return 1e10;
+            int right = qBound(1, low, curve.size() - 1);
+            int left = right - 1;
+            double leftLogX = qLn(curve[left].x());
+            double rightLogX = qLn(curve[right].x());
+            double denominator = rightLogX - leftLogX;
+            double leftLogY =
+                qLn(qMax(qAbs(curve[left].y()), valueFloor));
+            double rightLogY =
+                qLn(qMax(qAbs(curve[right].y()), valueFloor));
+
+            if(qAbs(denominator) <= 1.0e-12) {
+                *value = leftLogY;
+            } else {
+                double ratio = (qLn(x) - leftLogX) / denominator;
+                *value = leftLogY +
+                         ratio * (rightLogY - leftLogY);
+            }
+            return isFiniteNumber(*value);
+        };
+
+        // 覆盖率通过后若只缺少首尾少量点，用模拟曲线自身的端点斜率作短距离
+        // 双对数外推。该外推只用于主损失的固定网格，不参与水平配准搜索。
+        auto extrapolateEndpointLogValue = [valueFloor](
+                const QVector<QPointF>& curve,
+                double x,
+                double* value) -> bool {
+            if(!value || curve.size() < 2 || x <= 0.0) {
+                return false;
+            }
+            int left = x < curve.first().x()
+                       ? 0
+                       : curve.size() - 2;
+            int right = left + 1;
+            double leftLogX = qLn(curve[left].x());
+            double rightLogX = qLn(curve[right].x());
+            double denominator = rightLogX - leftLogX;
+            if(qAbs(denominator) <= 1.0e-12) {
+                return false;
+            }
+            double leftLogY =
+                qLn(qMax(qAbs(curve[left].y()), valueFloor));
+            double rightLogY =
+                qLn(qMax(qAbs(curve[right].y()), valueFloor));
+            double ratio = (qLn(x) - leftLogX) / denominator;
+            *value = leftLogY + ratio * (rightLogY - leftLogY);
+            return isFiniteNumber(*value);
+        };
+
+        const double targetMinX = targetPressure.first().x();
+        const double targetMaxX = targetPressure.last().x();
+        const double resultMinX = resultPressure.first().x();
+        const double resultMaxX = resultPressure.last().x();
+        if(targetMinX <= 0.0 || targetMaxX <= targetMinX ||
+                resultMinX <= 0.0 || resultMaxX <= resultMinX) {
+            return invalidLoss;
+        }
+
+        QVector<double> commonX(numPoints);
+        QVector<double> commonLogX(numPoints);
+        QVector<double> targetLogPressure(numPoints);
+        QVector<double> targetLogDerivative(numPoints);
+        const double targetLogMinX = qLn(targetMinX);
+        const double targetLogMaxX = qLn(targetMaxX);
+
+        // 固定使用目标曲线的完整 log-time 网格，候选之间不会因采样点不同而失去可比性。
+        for(int i = 0; i < numPoints; ++i) {
+            double logX = targetLogMinX +
+                          static_cast<double>(i) *
+                          (targetLogMaxX - targetLogMinX) /
+                          (numPoints - 1);
+            commonLogX[i] = logX;
+            commonX[i] = qExp(logX);
+
+            if(!interpolateLogValue(
+                        targetPressure, commonX[i],
+                        &targetLogPressure[i]) ||
+                    !interpolateLogValue(
+                        targetDerivative, commonX[i],
+                        &targetLogDerivative[i])) {
+                return invalidLoss;
+            }
         }
 
-        // 生成公共 X 网格进行插值。使用对数均匀网格，是为了给早期时间段
-        // 更多分辨率；试井双对数曲线的早期形态通常对参数识别很敏感。
-        QVector<double> commonX;
-        int numPoints = 50;
+        QVector<double> pressureResidual(
+            numPoints, std::numeric_limits<double>::quiet_NaN());
+        QVector<double> derivativeResidual(
+            numPoints, std::numeric_limits<double>::quiet_NaN());
+        int firstSupported = -1;
+        int lastSupported = -1;
+        int supportedCount = 0;
 
-        if(overlapMinX > 0 && overlapMaxX > 0) {
-            // 对数空间均匀分布
-            double logMin = qLn(overlapMinX);
-            double logMax = qLn(overlapMaxX);
+        // 残差定义为“模拟减目标”：正值表示模拟曲线偏高，负值表示偏低。
+        for(int i = 0; i < numPoints; ++i) {
+            if(commonX[i] < resultMinX || commonX[i] > resultMaxX) {
+                continue;
+            }
 
-            for(int i = 0; i < numPoints; ++i) {
-                double logX = logMin + i * (logMax - logMin) / (numPoints - 1);
-                double x = qExp(logX);
+            double resultLogPressure = 0.0;
+            double resultLogDerivative = 0.0;
+            if(!interpolateLogValue(
+                        resultPressure, commonX[i],
+                        &resultLogPressure) ||
+                    !interpolateLogValue(
+                        resultDerivative, commonX[i],
+                        &resultLogDerivative)) {
+                // 已位于结果时间范围内却无法插值说明数据存在内部断点，不能补线。
+                return invalidLoss;
+            }
 
-                // 数值保护
-                if(!isFiniteNumber(x) || x <= 0) {
+            pressureResidual[i] =
+                resultLogPressure - targetLogPressure[i];
+            derivativeResidual[i] =
+                resultLogDerivative - targetLogDerivative[i];
+            ++supportedCount;
+            if(firstSupported < 0) {
+                firstSupported = i;
+            }
+            lastSupported = i;
+        }
+
+        // 覆盖率同时约束“有效点数量”和“连续时间跨度”。取两者较小值可避免
+        // 点数很多但只集中在局部时段的候选被误认为覆盖充分。
+        AutoFitObjectiveBreakdown breakdown;
+        breakdown.coverage = supportedCount > 0
+                             ? static_cast<double>(supportedCount) /
+                               numPoints
+                             : 0.0;
+        if(firstSupported >= 0 && lastSupported >= firstSupported) {
+            double targetSpan =
+                qMax(1.0e-12, targetLogMaxX - targetLogMinX);
+            double coveredSpan =
+                commonLogX[lastSupported] - commonLogX[firstSupported];
+            breakdown.coverage = qMin(
+                breakdown.coverage,
+                qMax(0.0, coveredSpan / targetSpan));
+        }
+
+        // 覆盖率只判断候选是否有效，不再加入固定惩罚，避免所有误差被整体抬高。
+        if(breakdown.coverage < minimumCoverage) {
+            breakdown.total = invalidLoss;
+            m_lastObjectiveBreakdown = breakdown;
+            return invalidLoss;
+        }
+
+        // 通过门槛后最多只缺少首尾少量目标点。按模拟曲线端点趋势补齐后，
+        // 每个候选仍在固定 80 点上计算均方根误差，不能靠少算难拟合端点获益。
+        for(int i = 0; i < numPoints; ++i) {
+            if(isFiniteNumber(pressureResidual[i]) &&
+                    isFiniteNumber(derivativeResidual[i])) {
+                continue;
+            }
+            double resultLogPressure = 0.0;
+            double resultLogDerivative = 0.0;
+            if(!extrapolateEndpointLogValue(
+                        resultPressure, commonX[i],
+                        &resultLogPressure) ||
+                    !extrapolateEndpointLogValue(
+                        resultDerivative, commonX[i],
+                        &resultLogDerivative)) {
+                return invalidLoss;
+            }
+            pressureResidual[i] =
+                resultLogPressure - targetLogPressure[i];
+            derivativeResidual[i] =
+                resultLogDerivative - targetLogDerivative[i];
+        }
+
+        // 在指定中心附近计算普通均方根误差。
+        auto rmseAround = [](
+                const QVector<double>& values,
+                int begin,
+                int end,
+                double center) -> double {
+            double sum = 0.0;
+            int count = 0;
+            int validBegin = qMax(0, begin);
+            int validEnd =
+                qMin(end, static_cast<int>(values.size()));
+
+            for(int i = validBegin; i < validEnd; ++i) {
+                if(!isFiniteNumber(values[i])) {
                     continue;
                 }
 
-                commonX.append(x);
+                double difference = values[i] - center;
+                sum += difference * difference;
+                ++count;
             }
 
-            DEBUG_OUT("Using log-uniform grid for better early-time coverage");
-        }
-
-        if(commonX.isEmpty()) {
-            DEBUG_OUT("Failed to generate common X grid");
-            return 1e10;
-        }
-
-        // 插值目标曲线。target[1] 是压力，target[2] 是压力导数。
-        QVector<QPointF> targetCurve1, targetCurve2;
-
-        for(int i = 0; i < target[0].size(); ++i) {
-            // 检查数据有效性
-            if(isFiniteNumber(target[0][i]) && isFiniteNumber(target[1][i]) &&
-                    isFiniteNumber(target[2][i])) {
-                targetCurve1.append(QPointF(target[0][i], target[1][i]));
-                targetCurve2.append(QPointF(target[0][i], target[2][i]));
+            return count > 0
+                   ? qSqrt(sum / count)
+                   : std::numeric_limits<double>::quiet_NaN();
+        };
+
+        auto rmse = [&rmseAround](
+                const QVector<double>& values,
+                int begin,
+                int end) -> double {
+            return rmseAround(values, begin, end, 0.0);
+        };
+
+        // 普通算术平均中心保留上下偏差的符号。
+        auto meanCenterRange = [](
+                const QVector<double>& values,
+                int begin,
+                int end) -> double {
+            int validBegin = qMax(0, begin);
+            int validEnd =
+                qMin(end, static_cast<int>(values.size()));
+            double center = 0.0;
+            int count = 0;
+
+            for(int i = validBegin; i < validEnd; ++i) {
+                if(isFiniteNumber(values[i])) {
+                    center += values[i];
+                    ++count;
+                }
+            }
+            if(count == 0) {
+                return std::numeric_limits<double>::quiet_NaN();
+            }
+            return center / count;
+        };
+
+        // 压力和导数合并后只求一个公共中心，表示两条曲线共同的上下位移。
+        // 分别去中心会把压力与导数之间真实的相对形状差异一并消除。
+        auto commonMeanCenterRange = [&meanCenterRange](
+                const QVector<double>& pressureValues,
+                const QVector<double>& derivativeValues,
+                int begin,
+                int end) -> double {
+            QVector<double> combined;
+            int validBegin = qMax(0, begin);
+            int validEnd = qMin(
+                end,
+                qMin(static_cast<int>(pressureValues.size()),
+                     static_cast<int>(derivativeValues.size())));
+            combined.reserve(2 * qMax(0, validEnd - validBegin));
+
+            for(int i = validBegin; i < validEnd; ++i) {
+                if(isFiniteNumber(pressureValues[i])) {
+                    combined.append(pressureValues[i]);
+                }
+                if(isFiniteNumber(derivativeValues[i])) {
+                    combined.append(derivativeValues[i]);
+                }
+            }
+            return meanCenterRange(combined, 0, combined.size());
+        };
+
+        // 两个通道按能量等权合并，返回值与单通道 RMSE 保持同一量纲。
+        auto jointRmseAround = [&rmseAround](
+                const QVector<double>& pressureValues,
+                const QVector<double>& derivativeValues,
+                int begin,
+                int end,
+                double center) -> double {
+            double pressureLoss = rmseAround(
+                pressureValues, begin, end, center);
+            double derivativeLoss = rmseAround(
+                derivativeValues, begin, end, center);
+            if(!isFiniteNumber(pressureLoss) ||
+                    !isFiniteNumber(derivativeLoss)) {
+                return std::numeric_limits<double>::quiet_NaN();
+            }
+            return qSqrt(0.5 *
+                         (pressureLoss * pressureLoss +
+                          derivativeLoss * derivativeLoss));
+        };
+
+        // 主目标始终使用未做上下或左右校正的完整曲线误差。
+        breakdown.pressureLoss =
+            rmse(pressureResidual, 0, numPoints);
+        breakdown.derivativeLoss =
+            rmse(derivativeResidual, 0, numPoints);
+
+        // 压力和导数各占一半权重。缩放后 residualVector 的二范数就是
+        // sqrt(0.5 * pressureLoss^2 + 0.5 * derivativeLoss^2)。
+        const double residualScale = qSqrt(0.5 / numPoints);
+        breakdown.residualVector.reserve(2 * numPoints);
+        for(int i = 0; i < numPoints; ++i) {
+            breakdown.residualVector.append(
+                residualScale *
+                pressureResidual[i]);
+        }
+        for(int i = 0; i < numPoints; ++i) {
+            breakdown.residualVector.append(
+                residualScale *
+                derivativeResidual[i]);
+        }
+
+        const double logGridStep =
+            (targetLogMaxX - targetLogMinX) /
+            (numPoints - 1);
+        const double resultLogMinX = qLn(resultMinX);
+        const double resultLogMaxX = qLn(resultMaxX);
+        // 左右配准只在所有候选位移都共同覆盖的固定区间比较，至少保留 80%
+        // 目标点；每个 log-time 网格间隔再细分为 8 份，提高位移分辨率。
+        const int minimumRegistrationPoints =
+            (numPoints * 4) / 5;
+        const int shiftSubdivisions = 8;
+        int maximumShiftIntervals = 4;
+        int registrationBegin = 0;
+        int registrationEnd = numPoints;
+        double maximumPhysicalShift =
+            maximumShiftIntervals * logGridStep;
+
+        // 所有位移候选使用同一组目标点。结果范围不足时逐步缩小最大位移，
+        // 但用于配准的固定公共区间不得少于目标网格的 80%。
+        auto updateRegistrationRange = [&](double maximumShift) {
+            registrationBegin = 0;
+            registrationEnd = numPoints;
+            while(registrationBegin < registrationEnd &&
+                    commonLogX[registrationBegin] - maximumShift <
+                    resultLogMinX - 1.0e-12) {
+                ++registrationBegin;
+            }
+            while(registrationEnd > registrationBegin &&
+                    commonLogX[registrationEnd - 1] + maximumShift >
+                    resultLogMaxX + 1.0e-12) {
+                --registrationEnd;
+            }
+        };
+
+        updateRegistrationRange(maximumPhysicalShift);
+        while(maximumShiftIntervals > 0 &&
+                registrationEnd - registrationBegin <
+                minimumRegistrationPoints) {
+            --maximumShiftIntervals;
+            maximumPhysicalShift =
+                maximumShiftIntervals * logGridStep;
+            updateRegistrationRange(maximumPhysicalShift);
+        }
+        if(registrationEnd - registrationBegin <
+                minimumRegistrationPoints) {
+            return invalidLoss;
+        }
+
+        // physicalShift 为正表示模拟曲线偏右；对齐时在目标时刻右侧读取模拟值。
+        auto buildShiftResidual = [&](
+                double physicalShift,
+                int compareBegin,
+                int compareEnd,
+                QVector<double>* shiftedPressure,
+                QVector<double>* shiftedDerivative) -> bool {
+            if(!shiftedPressure || !shiftedDerivative) {
+                return false;
             }
-        }
-
-        if(targetCurve1.isEmpty() || targetCurve2.isEmpty()) {
-            DEBUG_OUT("Target curves are empty after filtering");
-            return 1e10;
-        }
 
-        QVector<QPointF> alignedTarget1 = interpolateData(targetCurve1, commonX);
-        QVector<QPointF> alignedTarget2 = interpolateData(targetCurve2, commonX);
+            shiftedPressure->fill(
+                std::numeric_limits<double>::quiet_NaN(),
+                numPoints);
+            shiftedDerivative->fill(
+                std::numeric_limits<double>::quiet_NaN(),
+                numPoints);
+
+            int validBegin = qMax(0, compareBegin);
+            int validEnd = qMin(numPoints, compareEnd);
+            for(int i = validBegin; i < validEnd; ++i) {
+                double shiftedLogX =
+                    commonLogX[i] + physicalShift;
+                if(shiftedLogX < resultLogMinX - 1.0e-12 ||
+                        shiftedLogX >
+                        resultLogMaxX + 1.0e-12) {
+                    return false;
+                }
 
-        // 插值结果曲线。result 与 target 使用同一 commonX，保证逐点可比。
-        QVector<QPointF> resultCurve1, resultCurve2;
+                double shiftedX = qExp(
+                    qBound(resultLogMinX,
+                           shiftedLogX,
+                           resultLogMaxX));
+                double resultLogPressure = 0.0;
+                double resultLogDerivative = 0.0;
+                if(!interpolateLogValue(
+                            resultPressure, shiftedX,
+                            &resultLogPressure) ||
+                        !interpolateLogValue(
+                            resultDerivative, shiftedX,
+                            &resultLogDerivative)) {
+                    return false;
+                }
 
-        for(int i = 0; i < result[0].size(); ++i) {
-            // 检查数据有效性
-            if(isFiniteNumber(result[0][i]) && isFiniteNumber(result[1][i]) &&
-                    isFiniteNumber(result[2][i])) {
-                resultCurve1.append(QPointF(result[0][i], result[1][i]));
-                resultCurve2.append(QPointF(result[0][i], result[2][i]));
+                (*shiftedPressure)[i] =
+                    resultLogPressure - targetLogPressure[i];
+                (*shiftedDerivative)[i] =
+                    resultLogDerivative - targetLogDerivative[i];
+            }
+            return true;
+        };
+
+        // 损失相同时优先选择绝对位移更小的候选，避免平坦 profile 在数值噪声
+        // 下无故偏向搜索边界。
+        auto isBetterProfileValue = [](
+                double loss,
+                double shift,
+                double bestLoss,
+                double bestShift) -> bool {
+            const double tolerance = 1.0e-12;
+            return loss < bestLoss - tolerance ||
+                   (qAbs(loss - bestLoss) <= tolerance &&
+                    qAbs(shift) < qAbs(bestShift));
+        };
+
+        const int halfShiftStepCount =
+            maximumShiftIntervals * shiftSubdivisions;
+        const double physicalShiftStep =
+            logGridStep / shiftSubdivisions;
+        double zeroShiftCenteredLoss =
+            std::numeric_limits<double>::quiet_NaN();
+        double bestCenteredLoss =
+            std::numeric_limits<double>::infinity();
+        double bestPhysicalShift = 0.0;
+        int bestShiftStep = 0;
+        double bestPressureLoss =
+            std::numeric_limits<double>::infinity();
+        double bestPressureShift = 0.0;
+        double bestDerivativeLoss =
+            std::numeric_limits<double>::infinity();
+        double bestDerivativeShift = 0.0;
+        QVector<double> profileLosses(
+            2 * halfShiftStepCount + 1,
+            std::numeric_limits<double>::quiet_NaN());
+        QVector<double> profileCommonBiases(
+            2 * halfShiftStepCount + 1,
+            std::numeric_limits<double>::quiet_NaN());
+        QVector<double> shiftedPressureResidual;
+        QVector<double> shiftedDerivativeResidual;
+
+        // 位移和公共上下偏移联合求解，避免“先扣上下还是先扣左右”的顺序依赖。
+        for(int shiftStep = -halfShiftStepCount;
+                shiftStep <= halfShiftStepCount;
+                ++shiftStep) {
+            double physicalShift =
+                shiftStep * physicalShiftStep;
+            if(!buildShiftResidual(
+                        physicalShift,
+                        registrationBegin,
+                        registrationEnd,
+                        &shiftedPressureResidual,
+                        &shiftedDerivativeResidual)) {
+                continue;
             }
-        }
-
-        if(resultCurve1.isEmpty() || resultCurve2.isEmpty()) {
-            DEBUG_OUT("Result curves are empty after filtering");
-            return 1e10;
-        }
-
-        QVector<QPointF> alignedResult1 = interpolateData(resultCurve1, commonX);
-        QVector<QPointF> alignedResult2 = interpolateData(resultCurve2, commonX);
 
-        // 检查插值结果
-        if(alignedTarget1.isEmpty() || alignedTarget2.isEmpty() ||
-                alignedResult1.isEmpty() || alignedResult2.isEmpty()) {
-            DEBUG_OUT("LogLog interpolation failed");
-            return 1e10;
+            double commonBias = commonMeanCenterRange(
+                shiftedPressureResidual,
+                shiftedDerivativeResidual,
+                registrationBegin,
+                registrationEnd);
+            double centeredLoss = jointRmseAround(
+                shiftedPressureResidual,
+                shiftedDerivativeResidual,
+                registrationBegin,
+                registrationEnd,
+                commonBias);
+            double pressureBias = meanCenterRange(
+                shiftedPressureResidual,
+                registrationBegin,
+                registrationEnd);
+            double derivativeBias = meanCenterRange(
+                shiftedDerivativeResidual,
+                registrationBegin,
+                registrationEnd);
+            double pressureLoss = rmseAround(
+                shiftedPressureResidual,
+                registrationBegin,
+                registrationEnd,
+                pressureBias);
+            double derivativeLoss = rmseAround(
+                shiftedDerivativeResidual,
+                registrationBegin,
+                registrationEnd,
+                derivativeBias);
+
+            if(!isFiniteNumber(centeredLoss) ||
+                    !isFiniteNumber(pressureLoss) ||
+                    !isFiniteNumber(derivativeLoss)) {
+                continue;
+            }
+            int profileIndex = shiftStep + halfShiftStepCount;
+            profileLosses[profileIndex] = centeredLoss;
+            profileCommonBiases[profileIndex] = commonBias;
+            if(shiftStep == 0) {
+                zeroShiftCenteredLoss = centeredLoss;
+            }
+            if(isBetterProfileValue(
+                        centeredLoss,
+                        physicalShift,
+                        bestCenteredLoss,
+                        bestPhysicalShift)) {
+                bestCenteredLoss = centeredLoss;
+                bestPhysicalShift = physicalShift;
+                bestShiftStep = shiftStep;
+            }
+            if(isBetterProfileValue(
+                        pressureLoss,
+                        physicalShift,
+                        bestPressureLoss,
+                        bestPressureShift)) {
+                bestPressureLoss = pressureLoss;
+                bestPressureShift = physicalShift;
+            }
+            if(isBetterProfileValue(
+                        derivativeLoss,
+                        physicalShift,
+                        bestDerivativeLoss,
+                        bestDerivativeShift)) {
+                bestDerivativeLoss = derivativeLoss;
+                bestDerivativeShift = physicalShift;
+            }
         }
 
-        if(alignedTarget1.size() != alignedResult1.size() ||
-                alignedTarget2.size() != alignedResult2.size()) {
-            DEBUG_OUT("LogLog interpolation size mismatch");
-            return 1e10;
+        if(!isFiniteNumber(zeroShiftCenteredLoss) ||
+                !isFiniteNumber(bestCenteredLoss) ||
+                !isFiniteNumber(bestPressureLoss) ||
+                !isFiniteNumber(bestDerivativeLoss)) {
+            return invalidLoss;
+        }
+
+        // horizontalGain 是“允许水平位移”相对“固定零位移”减少的均方能量。
+        // 只有改善足够明显且最优点不是边界，才把位移解释为可靠左右偏差。
+        double horizontalGain = nestedRmsContribution(
+            zeroShiftCenteredLoss, bestCenteredLoss);
+        double horizontalSignalThreshold =
+            qMax(1.0e-5, zeroShiftCenteredLoss * 0.02);
+        int bestProfileIndex = bestShiftStep + halfShiftStepCount;
+        double nearbyProfileLoss =
+            std::numeric_limits<double>::infinity();
+        int leftProfileIndex =
+            bestProfileIndex - shiftSubdivisions;
+        int rightProfileIndex =
+            bestProfileIndex + shiftSubdivisions;
+        if(leftProfileIndex >= 0 &&
+                leftProfileIndex < profileLosses.size() &&
+                isFiniteNumber(profileLosses[leftProfileIndex])) {
+            nearbyProfileLoss = qMin(
+                nearbyProfileLoss,
+                profileLosses[leftProfileIndex]);
+        }
+        if(rightProfileIndex >= 0 &&
+                rightProfileIndex < profileLosses.size() &&
+                isFiniteNumber(profileLosses[rightProfileIndex])) {
+            nearbyProfileLoss = qMin(
+                nearbyProfileLoss,
+                profileLosses[rightProfileIndex]);
+        }
+        double profileContrast = isFiniteNumber(nearbyProfileLoss)
+                                 ? nestedRmsContribution(
+                                       nearbyProfileLoss,
+                                       bestCenteredLoss)
+                                 : 0.0;
+        bool flatRegistrationProfile =
+            profileContrast <= horizontalSignalThreshold;
+
+        // 平台曲线的 profile 也可能很平，但公共 bias 在各个位移下保持稳定，
+        // 此时仍能可靠判断上下。只有近优位移会明显改变 bias 才说明上下/左右不可辨识。
+        double minimumNearOptimalBias =
+            std::numeric_limits<double>::infinity();
+        double maximumNearOptimalBias =
+            -std::numeric_limits<double>::infinity();
+        for(int i = 0; i < profileLosses.size(); ++i) {
+            if(isFiniteNumber(profileLosses[i]) &&
+                    isFiniteNumber(profileCommonBiases[i]) &&
+                    profileLosses[i] <=
+                    bestCenteredLoss + horizontalSignalThreshold) {
+                minimumNearOptimalBias = qMin(
+                    minimumNearOptimalBias,
+                    profileCommonBiases[i]);
+                maximumNearOptimalBias = qMax(
+                    maximumNearOptimalBias,
+                    profileCommonBiases[i]);
+            }
         }
-
-        // 计算两条曲线的误差。当前压力和导数各占 50%。
-        // 如果后续要让导数形态更重要，可以从这里调整权重。
-        double error1 = calculateCurveError(alignedTarget1, alignedResult1);
-        double error2 = calculateCurveError(alignedTarget2, alignedResult2);
-
-        // 检查个别误差是否有效
-        if(!isFiniteNumber(error1) || error1 > 1e9) {
-            DEBUG_OUT(QString("Curve1 error is invalid: %1").arg(error1));
-            error1 = 1e10;
+        double nearOptimalBiasSpread =
+            isFiniteNumber(minimumNearOptimalBias) &&
+            isFiniteNumber(maximumNearOptimalBias)
+            ? maximumNearOptimalBias - minimumNearOptimalBias
+            : std::numeric_limits<double>::infinity();
+        bool commonBiasStable = nearOptimalBiasSpread <= 1.0e-2;
+        bool horizontalAtBoundary =
+            halfShiftStepCount > 0 &&
+            qAbs(bestShiftStep) == halfShiftStepCount;
+        // 压力和导数通道分别求出的最佳位移若方向相反或相差过大，说明一个
+        // 单一水平平移无法解释两条曲线，此时标记配准歧义并禁用左右引导。
+        bool pressureShiftDetected =
+            qAbs(bestPressureShift) >=
+            0.5 * physicalShiftStep;
+        bool derivativeShiftDetected =
+            qAbs(bestDerivativeShift) >=
+            0.5 * physicalShiftStep;
+        bool channelShiftConflict =
+            pressureShiftDetected &&
+            derivativeShiftDetected &&
+            (bestPressureShift * bestDerivativeShift < 0.0 ||
+             qAbs(bestPressureShift - bestDerivativeShift) >
+             2.0 * logGridStep);
+
+        breakdown.horizontalLoss = horizontalGain;
+        breakdown.horizontalReliable =
+            maximumShiftIntervals > 0 &&
+            !horizontalAtBoundary &&
+            !channelShiftConflict &&
+            !flatRegistrationProfile &&
+            horizontalGain > horizontalSignalThreshold &&
+            qAbs(bestPhysicalShift) >=
+            0.5 * physicalShiftStep;
+        breakdown.registrationAmbiguous =
+            channelShiftConflict ||
+            (qAbs(bestPhysicalShift) >=
+             0.5 * physicalShiftStep &&
+             !breakdown.horizontalReliable) ||
+            (flatRegistrationProfile && !commonBiasStable);
+        breakdown.horizontalPhysicalShift =
+            breakdown.horizontalReliable
+            ? bestPhysicalShift
+            : 0.0;
+
+        // 可信水平位移确定后，在该位移实际覆盖的最大区间重新计算上下和形状。
+        int diagnosticBegin = 0;
+        int diagnosticEnd = numPoints;
+        while(diagnosticBegin < diagnosticEnd &&
+                commonLogX[diagnosticBegin] +
+                breakdown.horizontalPhysicalShift <
+                resultLogMinX - 1.0e-12) {
+            ++diagnosticBegin;
+        }
+        while(diagnosticEnd > diagnosticBegin &&
+                commonLogX[diagnosticEnd - 1] +
+                breakdown.horizontalPhysicalShift >
+                resultLogMaxX + 1.0e-12) {
+            --diagnosticEnd;
+        }
+        if(diagnosticEnd - diagnosticBegin <
+                minimumRegistrationPoints ||
+                !buildShiftResidual(
+                    breakdown.horizontalPhysicalShift,
+                    diagnosticBegin,
+                    diagnosticEnd,
+                    &shiftedPressureResidual,
+                    &shiftedDerivativeResidual)) {
+            return invalidLoss;
+        }
+
+        double commonBias = commonMeanCenterRange(
+            shiftedPressureResidual,
+            shiftedDerivativeResidual,
+            diagnosticBegin,
+            diagnosticEnd);
+        double rawAlignedLoss = jointRmseAround(
+            shiftedPressureResidual,
+            shiftedDerivativeResidual,
+            diagnosticBegin,
+            diagnosticEnd,
+            0.0);
+        double centeredAlignedLoss = jointRmseAround(
+            shiftedPressureResidual,
+            shiftedDerivativeResidual,
+            diagnosticBegin,
+            diagnosticEnd,
+            commonBias);
+        if(!isFiniteNumber(commonBias) ||
+                !isFiniteNumber(rawAlignedLoss) ||
+                !isFiniteNumber(centeredAlignedLoss)) {
+            return invalidLoss;
+        }
+
+        // 原始对齐误差减去公共中心后的能量差定义为上下误差贡献。只有它相对
+        // 当前对齐误差足够明显，且配准无歧义时，公共 bias 才可用于有符号选参。
+        breakdown.verticalCommonBias = commonBias;
+        breakdown.verticalLoss = nestedRmsContribution(
+            rawAlignedLoss, centeredAlignedLoss);
+        breakdown.verticalReliable =
+            !breakdown.registrationAmbiguous &&
+            breakdown.verticalLoss >
+            qMax(1.0e-5, rawAlignedLoss * 0.02);
+
+        QVector<double> shapePressure(
+            numPoints, std::numeric_limits<double>::quiet_NaN());
+        QVector<double> shapeDerivative(
+            numPoints, std::numeric_limits<double>::quiet_NaN());
+        for(int i = diagnosticBegin; i < diagnosticEnd; ++i) {
+            if(isFiniteNumber(shiftedPressureResidual[i])) {
+                shapePressure[i] =
+                    shiftedPressureResidual[i] - commonBias;
+            }
+            if(isFiniteNumber(shiftedDerivativeResidual[i])) {
+                shapeDerivative[i] =
+                    shiftedDerivativeResidual[i] - commonBias;
+            }
         }
 
-        if(!isFiniteNumber(error2) || error2 > 1e9) {
-            DEBUG_OUT(QString("Curve2 error is invalid: %1").arg(error2));
-            error2 = 1e10;
+        // shapeLoss 是去除可信左右位移和公共均值中心后的剩余误差。
+        double shapePressureLoss = rmse(
+            shapePressure, diagnosticBegin, diagnosticEnd);
+        double shapeDerivativeLoss = rmse(
+            shapeDerivative, diagnosticBegin, diagnosticEnd);
+        if(!isFiniteNumber(shapePressureLoss) ||
+                !isFiniteNumber(shapeDerivativeLoss)) {
+            return invalidLoss;
+        }
+        breakdown.shapeLoss = qSqrt(
+            0.5 *
+            (shapePressureLoss * shapePressureLoss +
+             shapeDerivativeLoss * shapeDerivativeLoss));
+
+        // 现阶段不识别或单独调度晚期流动段；保留字段只为了维持现有 trace 列。
+        breakdown.lateDerivativeSlopeBias = 0.0;
+        breakdown.lateDerivativeTrendLoss = 0.0;
+        breakdown.lateDerivativeTrendReliable = false;
+
+        if(!isFiniteNumber(breakdown.pressureLoss) ||
+                !isFiniteNumber(breakdown.derivativeLoss) ||
+                !isFiniteNumber(breakdown.verticalCommonBias) ||
+                !isFiniteNumber(breakdown.verticalLoss) ||
+                !isFiniteNumber(breakdown.horizontalPhysicalShift) ||
+                !isFiniteNumber(breakdown.horizontalLoss) ||
+                !isFiniteNumber(breakdown.shapeLoss) ||
+                breakdown.residualVector.size() != 2 * numPoints) {
+            return invalidLoss;
         }
 
-        // 组合误差 - 添加保护
-        double combinedError;
-
-        if(error1 > 1e9 && error2 > 1e9) {
-            combinedError = 1e10;
-        } else if(error1 > 1e9) {
-            combinedError = error2;
-        } else if(error2 > 1e9) {
-            combinedError = error1;
+        if(isSurrogateScreeningEnabled()) {
+            // 代理模型仍按原目标比较，不能用未参与训练的新损失改变候选排序。
+            breakdown.total =
+                0.5 * breakdown.pressureLoss +
+                0.5 * breakdown.derivativeLoss;
         } else {
-            combinedError = 0.5 * error1 + 0.5 * error2;
-        }
-
-        DEBUG_OUT(QString("LogLog errors: Curve1=%1, Curve2=%2, Combined=%3")
-                  .arg(error1, 0, 'e', 4).arg(error2, 0, 'e', 4).arg(combinedError, 0, 'e', 4));
-
-        return qMin(1e9, combinedError);
-
+            // 非代理总目标等于固定 160 维普通残差的二范数；压力和
+            // 导数各占一半能量。上下、左右和形状分量不参与候选排序与接受。
+            breakdown.total = qSqrt(
+                0.5 * breakdown.pressureLoss * breakdown.pressureLoss +
+                0.5 * breakdown.derivativeLoss * breakdown.derivativeLoss);
+        }
+        breakdown.valid =
+            isFiniteNumber(breakdown.total) &&
+            breakdown.total >= 0.0;
+        m_lastObjectiveBreakdown = breakdown;
+
+        DEBUG_OUT(
+            QString("LogLog objective: pressure=%1, derivative=%2, vertical=%3, horizontal=%4, shape=%5, ambiguous=%6, shift=%7, coverage=%8, total=%9")
+            .arg(breakdown.pressureLoss, 0, 'e', 4)
+            .arg(breakdown.derivativeLoss, 0, 'e', 4)
+            .arg(breakdown.verticalLoss, 0, 'e', 4)
+            .arg(breakdown.horizontalLoss, 0, 'e', 4)
+            .arg(breakdown.shapeLoss, 0, 'e', 4)
+            .arg(breakdown.registrationAmbiguous)
+            .arg(breakdown.horizontalPhysicalShift, 0, 'e', 4)
+            .arg(breakdown.coverage, 0, 'f', 4)
+            .arg(breakdown.total, 0, 'e', 4));
+
+        return breakdown.valid
+               ? qMin(1.0e9, breakdown.total)
+               : invalidLoss;
     } catch(const std::exception& e) {
-        DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what()));
-        return 1e10;
+        DEBUG_OUT(
+            QString("Exception in LogLog error calculation: %1")
+            .arg(e.what()));
+        return invalidLoss;
     } catch(...) {
         DEBUG_OUT("Unknown exception in LogLog error calculation");
-        return 1e10;
+        return invalidLoss;
     }
 }
 
@@ -5145,7 +7436,7 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
 {
     // DLL 求解器路径。
     // 这个函数负责把当前 DataManager 中的项目状态交给底层数值求解器，
-    // 并让目标井生成 result log-log 数据。evaluateFitness() 后续会从目标井读取该结果。
+    // 数值求解仍包含全部计算井，以保留井间干扰；后处理只提取目标井曲线。
     //
     // 如果这里失败，通常需要优先检查：HX_NWTM.dll、license、网格/井数据是否完整、
     // 目标井是否存在，以及 DataManager 中刚写入的参数是否导致求解器异常。
@@ -5157,6 +7448,7 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
     }
 
     ++m_evaluationInProgress;
+    m_lastEvaluatedLogLogData.clear();
     QVector<QVector<double>> result;
     nmCalculationDllPebiSolverTask* dllTask = nullptr;
 
@@ -5164,6 +7456,15 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
         DEBUG_OUT("Creating DLL solver task");
         dllTask = new nmCalculationDllPebiSolverTask(m_tempDirectory);
 
+        if(m_targetWellName.isEmpty()) {
+            DEBUG_OUT("Target well name is empty - target-only solver cannot start");
+            delete dllTask;
+            --m_evaluationInProgress;
+            return result;
+        }
+
+        dllTask->setAutoFitTargetWell(m_targetWellName);
+
         if(m_shouldStop) {
             DEBUG_OUT("Should stop - cleaning up and returning empty result");
             delete dllTask;
@@ -5223,22 +7524,9 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
             return result;
         }
 
-        // 验证结果数据是否已更新。DLL 任务会把结果写回 DataManager 中的目标井对象。
-        nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
-        //QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
-        nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);
-
-        if(!pTargetWell) {
-            DEBUG_OUT("No wells found in data manager after DLL execution");
-            delete dllTask;
-            --m_evaluationInProgress;
-            return result;
-        }
-
-        // 验证结果数据。evaluateFitness() 最终用的是 logLogResult，
-        // 但这里返回 pressureResult 给 validateSolverResult() 做基本求解成功判断。
-        QVector<QVector<double>> pressureResult = pTargetWell->getResultPressure();
-        QVector<QVector<double>> logLogResult = pTargetWell->getResultLogLog();
+        // 任务结束后复制其局部结果，删除任务前不再持有任务内部引用。
+        QVector<QVector<double>> pressureResult = dllTask->getAutoFitResultPressure();
+        QVector<QVector<double>> logLogResult = dllTask->getAutoFitResultLogLog();
 
         DEBUG_OUT(QString("DLL result verification - Pressure arrays: %1, LogLog arrays: %2")
                   .arg(pressureResult.size()).arg(logLogResult.size()));
@@ -5257,8 +7545,12 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
         }
 
         // 数据有效性检查
-        if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) {
+        if(pressureResult.size() >= 2
+                && pressureResult[0].size() > 0
+                && pressureResult[1].size() > 0
+                && validateLogLogData(logLogResult)) {
             result = pressureResult;
+            m_lastEvaluatedLogLogData = logLogResult;
             DEBUG_OUT(QString("Got DLL solver result: %1 points").arg(result[0].size()));
             m_consecutiveFailures = 0;
 
@@ -5326,6 +7618,58 @@ QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverDll()
     return result;
 }
 
+bool nmCalculationAutoFitPSO::runFinalFullSolver()
+{
+    // 不设置目标井名，任务按原完整模式保存全部井和网格结果。
+    if(m_evaluationInProgress > 0) {
+        DEBUG_OUT("Cannot start final full-field solver while another evaluation is running");
+        return false;
+    }
+
+    ++m_evaluationInProgress;
+    nmCalculationDllPebiSolverTask dllTask(m_tempDirectory);
+    dllTask.start();
+
+    const int maxWait = 3600000;
+    const int checkInterval = 50;
+    QTime waitTimer;
+    waitTimer.start();
+
+    while(waitTimer.elapsed() < maxWait) {
+        if(dllTask.wait(checkInterval)) {
+            break;
+        }
+
+        // 最终完整计算可能持续较长时间，此处需处理停止按钮事件。
+        QApplication::processEvents(QEventLoop::AllEvents, checkInterval);
+
+        if(!dllTask.isRunning()) {
+            break;
+        }
+
+        if(m_shouldStop) {
+            DEBUG_OUT("Final full-field solver terminated by user");
+            dllTask.terminate();
+            dllTask.wait(2000);
+            --m_evaluationInProgress;
+            return false;
+        }
+    }
+
+    if(dllTask.isRunning()) {
+        DEBUG_OUT("Final full-field solver timeout, terminating task");
+        dllTask.terminate();
+        dllTask.wait(2000);
+        --m_evaluationInProgress;
+        return false;
+    }
+
+    dllTask.wait();
+    const bool succeeded = dllTask.wasSuccessful();
+    --m_evaluationInProgress;
+    return succeeded;
+}
+
 //QVector<QVector<double>> nmCalculationAutoFitPSO::runSolverExe()
 //{
 //	DEBUG_OUT("SOLVER EXE START");
diff --git a/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp b/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp
index 7951652..d8d9ce5 100644
--- a/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp
+++ b/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp
@@ -333,6 +333,26 @@ bool nmCalculationDllPebiSolverTask::wasSuccessful() const
     return m_lastRunSucceeded;
 }
 
+void nmCalculationDllPebiSolverTask::setAutoFitTargetWell(const QString& wellName)
+{
+    m_autoFitTargetWellName = wellName;
+}
+
+QVector<QVector<double> > nmCalculationDllPebiSolverTask::getAutoFitResultPressure() const
+{
+    return m_autoFitResultPressure;
+}
+
+QVector<QVector<double> > nmCalculationDllPebiSolverTask::getAutoFitResultLogLog() const
+{
+    return m_autoFitResultLogLog;
+}
+
+QVector<QVector<double> > nmCalculationDllPebiSolverTask::getAutoFitResultSemiLog() const
+{
+    return m_autoFitResultSemiLog;
+}
+
 bool nmCalculationDllPebiSolverTask::execute()
 {
     return this->execPebiMode();
@@ -703,6 +723,15 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult(
     int modelType)
 {
     nmDataAnalyzeManager* pDataInstance = nmDataAnalyzeManager::getCurrentInstance();
+    if(pDataInstance == nullptr) {
+        return false;
+    }
+
+    const bool autoFitTargetOnly = !m_autoFitTargetWellName.isEmpty();
+    bool autoFitTargetFound = false;
+    m_autoFitResultPressure.clear();
+    m_autoFitResultLogLog.clear();
+    m_autoFitResultSemiLog.clear();
 
     QVector<QVector<double>> vvecPressure;
     QVector<QVector<double>> vvecLogLog;
@@ -720,16 +749,22 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult(
     }
 
     // 获取参与求解的井的顺序
-    QVector<QPair<NM_WELL_MODEL, QString>> vecWellsOrder = nmDataAnalyzeManager::getCurrentInstance()->getCalculationWells();
+    QVector<QPair<NM_WELL_MODEL, QString>> vecWellsOrder = pDataInstance->getCalculationWells();
 
-    // 清空井名和二维位置的映射
-    pDataInstance->clearWellLocations();
+    // 粒子评价不修改全局井位置，完整求解仍按原流程重建映射.
+    if(!autoFitTargetOnly) {
+        pDataInstance->clearWellLocations();
+    }
 
     // 遍历每口井，处理其数据
     for(int wellIdx = 0; wellIdx < vecWellsOrder.size(); ++wellIdx) {
         NM_WELL_MODEL eWellType = vecWellsOrder[wellIdx].first;   // 获取井的类型
         QString sWellName = vecWellsOrder[wellIdx].second;        // 获取井的名称
 
+        if(autoFitTargetOnly && sWellName != m_autoFitTargetWellName) {
+            continue;
+        }
+
         // 跳过裂缝（或未知井类型）
         if(eWellType == NM_WELL_MODEL::Unknow_Well) {
             continue;
@@ -881,17 +916,34 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult(
             }
         }
 
-        // 将计算结果保存到对应的井数据里
-        // 压力
-        pWellData->setResultPressure(vvecPressure);
-        // 双对数
-        pWellData->setResultLogLog(vvecLogLog);
-        // 半对数
-        pWellData->setResultSemiLog(vvecSemiLog);
-
-        // 存储当前井名称和二维位置到映射
-        QPointF ptWellCoords(pWellData->getX().getValue().toDouble(), pWellData->getY().getValue().toDouble());
-        pDataInstance->addWellLocation(sWellName, ptWellCoords);
+        if(autoFitTargetOnly) {
+            // 粒子评价结果保存在任务对象内，避免反复改写 DataManager.
+            m_autoFitResultPressure = vvecPressure;
+            m_autoFitResultLogLog = vvecLogLog;
+            m_autoFitResultSemiLog = vvecSemiLog;
+            autoFitTargetFound = true;
+            break;
+        } else {
+            // 完整模式保持原行为：写入全部井曲线和井位置.
+            pWellData->setResultPressure(vvecPressure);
+            pWellData->setResultLogLog(vvecLogLog);
+            pWellData->setResultSemiLog(vvecSemiLog);
+
+            QPointF ptWellCoords(pWellData->getX().getValue().toDouble(), pWellData->getY().getValue().toDouble());
+            pDataInstance->addWellLocation(sWellName, ptWellCoords);
+        }
+    }
+
+    if(autoFitTargetOnly) {
+        const bool pressureValid = autoFitTargetFound
+                                   && m_autoFitResultPressure.size() >= 2
+                                   && !m_autoFitResultPressure[0].isEmpty()
+                                   && m_autoFitResultPressure[0].size() == m_autoFitResultPressure[1].size();
+        const bool logLogValid = m_autoFitResultLogLog.size() >= 3
+                                 && !m_autoFitResultLogLog[0].isEmpty()
+                                 && m_autoFitResultLogLog[0].size() == m_autoFitResultLogLog[1].size()
+                                 && m_autoFitResultLogLog[0].size() == m_autoFitResultLogLog[2].size();
+        return pressureValid && logLogValid;
     }
 
     // 计算有效单元数量
diff --git a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp
index a4876ed..af01ce0 100644
--- a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp
+++ b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp
@@ -11,26 +11,31 @@ nmDataAutomaticFitting::nmDataAutomaticFitting()
 	m_ctSelected = true;                   // 默认选中
 	m_cfSelected = false;                  // 默认不选中
     m_swiSelected = false;				  // 默认不选中
-
-	// 初始化参数最大值
-	m_permeabilityMax = nmDataAttribute("Permeability Max", 10.0, "Darcy");  // 1000 mD
-	m_skinMax = nmDataAttribute("Skin Max", 10.0, "");                        // 100
-	m_wellboreStorageMax = nmDataAttribute("Wellbore Storage Max", 2.0, "m^3/MPa");
-	m_porosityMax = nmDataAttribute("Porosity Max", 0.5, "");             // 50%
-	m_thicknessMax = nmDataAttribute("Thickness Max", 50.0, "m");
-	m_ctMax = nmDataAttribute("Ct Max", 0.1, "");                           // 1/MPa
-	m_cfMax = nmDataAttribute("Cf Max", 0.01, "");                             // 1/MPa
-	m_swiMax = nmDataAttribute("Swi Max", 1.0, "");
-
-	// 初始化参数最小值
-	m_permeabilityMin = nmDataAttribute("Permeability Min", 0.001, "Darcy");   // 0.001 mD
-	m_skinMin = nmDataAttribute("Skin Min", -10.0, "");                        // 允许负表皮
-	m_wellboreStorageMin = nmDataAttribute("Wellbore Storage Min", 1e-4, "m^3/MPa");
-	m_porosityMin = nmDataAttribute("Porosity Min", 0.01, "");       // 1%
-	m_thicknessMin = nmDataAttribute("Thickness Min", 2.0, "m");
-	m_ctMin = nmDataAttribute("Ct Min", 1e-3, "");                           // 小正值
-	m_cfMin = nmDataAttribute("Cf Min", 1e-5, "");                             // 小正值
-	m_swiMin = nmDataAttribute("Swi Min", 0.0, "");
+	m_fractureConductivitySelected = false; // 仅压裂井可用，默认不选中
+	m_fractureHalfLengthSelected = false;  // 仅压裂井可用，默认不选中
+
+	// 拟合上下界不再使用固定默认值，由自动拟合窗口按数据对象初值和物理边界生成。
+	m_permeabilityMax = nmDataAttribute("Permeability Max", QVariant(), "Darcy");
+	m_skinMax = nmDataAttribute("Skin Max", QVariant(), "");
+	m_wellboreStorageMax = nmDataAttribute("Wellbore Storage Max", QVariant(), "m^3/MPa");
+	m_porosityMax = nmDataAttribute("Porosity Max", QVariant(), "");
+	m_thicknessMax = nmDataAttribute("Thickness Max", QVariant(), "m");
+	m_ctMax = nmDataAttribute("Ct Max", QVariant(), "");
+	m_cfMax = nmDataAttribute("Cf Max", QVariant(), "");
+	m_swiMax = nmDataAttribute("Swi Max", QVariant(), "");
+	m_fractureConductivityMax = nmDataAttribute("Fracture Conductivity Max", QVariant(), "md.m");
+	m_fractureHalfLengthMax = nmDataAttribute("Fracture Half Length Max", QVariant(), "m");
+
+	m_permeabilityMin = nmDataAttribute("Permeability Min", QVariant(), "Darcy");
+	m_skinMin = nmDataAttribute("Skin Min", QVariant(), "");
+	m_wellboreStorageMin = nmDataAttribute("Wellbore Storage Min", QVariant(), "m^3/MPa");
+	m_porosityMin = nmDataAttribute("Porosity Min", QVariant(), "");
+	m_thicknessMin = nmDataAttribute("Thickness Min", QVariant(), "m");
+	m_ctMin = nmDataAttribute("Ct Min", QVariant(), "");
+	m_cfMin = nmDataAttribute("Cf Min", QVariant(), "");
+	m_swiMin = nmDataAttribute("Swi Min", QVariant(), "");
+	m_fractureConductivityMin = nmDataAttribute("Fracture Conductivity Min", QVariant(), "md.m");
+	m_fractureHalfLengthMin = nmDataAttribute("Fracture Half Length Min", QVariant(), "m");
 
 	// 初始化迭代参数
 	m_iterationCount = nmDataAttribute("Iteration Count", 20, "");
@@ -60,6 +65,8 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF
 		m_ctSelected = other.m_ctSelected;
 		m_cfSelected = other.m_cfSelected;
 		m_swiSelected = other.m_swiSelected;
+		m_fractureConductivitySelected = other.m_fractureConductivitySelected;
+		m_fractureHalfLengthSelected = other.m_fractureHalfLengthSelected;
 
 		// 复制参数最大值
 		m_permeabilityMax = other.m_permeabilityMax;
@@ -70,6 +77,8 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF
 		m_ctMax = other.m_ctMax;
 		m_cfMax = other.m_cfMax;
 		m_swiMax = other.m_swiMax;
+		m_fractureConductivityMax = other.m_fractureConductivityMax;
+		m_fractureHalfLengthMax = other.m_fractureHalfLengthMax;
 
 		// 复制参数最小值
 		m_permeabilityMin = other.m_permeabilityMin;
@@ -80,6 +89,8 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF
 		m_ctMin = other.m_ctMin;
 		m_cfMin = other.m_cfMin;
 		m_swiMin = other.m_swiMin;
+		m_fractureConductivityMin = other.m_fractureConductivityMin;
+		m_fractureHalfLengthMin = other.m_fractureHalfLengthMin;
 
 		// 复制迭代参数
 		m_iterationCount = other.m_iterationCount;
@@ -103,6 +114,8 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca
 	fittingObject.AddMember("CtSelected", m_ctSelected, allocator);
 	fittingObject.AddMember("CfSelected", m_cfSelected, allocator);
     fittingObject.AddMember("SwiSelected", m_swiSelected, allocator);
+	fittingObject.AddMember("FractureConductivitySelected", m_fractureConductivitySelected, allocator);
+	fittingObject.AddMember("FractureHalfLengthSelected", m_fractureHalfLengthSelected, allocator);
 
 	// 序列化参数最大值
 	fittingObject.AddMember("PermeabilityMax", m_permeabilityMax.ToJsonValue(allocator), allocator);
@@ -113,6 +126,8 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca
 	fittingObject.AddMember("CtMax", m_ctMax.ToJsonValue(allocator), allocator);
 	fittingObject.AddMember("CfMax", m_cfMax.ToJsonValue(allocator), allocator);
 	fittingObject.AddMember("SwiMax", m_swiMax.ToJsonValue(allocator), allocator);
+	fittingObject.AddMember("FractureConductivityMax", m_fractureConductivityMax.ToJsonValue(allocator), allocator);
+	fittingObject.AddMember("FractureHalfLengthMax", m_fractureHalfLengthMax.ToJsonValue(allocator), allocator);
 
 	// 序列化参数最小值
 	fittingObject.AddMember("PermeabilityMin", m_permeabilityMin.ToJsonValue(allocator), allocator);
@@ -123,6 +138,8 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca
 	fittingObject.AddMember("CtMin", m_ctMin.ToJsonValue(allocator), allocator);
 	fittingObject.AddMember("CfMin", m_cfMin.ToJsonValue(allocator), allocator);
 	fittingObject.AddMember("SwiMin", m_swiMin.ToJsonValue(allocator), allocator);
+	fittingObject.AddMember("FractureConductivityMin", m_fractureConductivityMin.ToJsonValue(allocator), allocator);
+	fittingObject.AddMember("FractureHalfLengthMin", m_fractureHalfLengthMin.ToJsonValue(allocator), allocator);
 
 	// 序列化迭代参数
 	fittingObject.AddMember("IterationCount", m_iterationCount.ToJsonValue(allocator), allocator);
@@ -160,6 +177,12 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue)
 	if (jsonValue.HasMember("SwiSelected") && jsonValue["SwiSelected"].IsBool()) {
 		m_swiSelected = jsonValue["SwiSelected"].GetBool();
 	}
+	if (jsonValue.HasMember("FractureConductivitySelected") && jsonValue["FractureConductivitySelected"].IsBool()) {
+		m_fractureConductivitySelected = jsonValue["FractureConductivitySelected"].GetBool();
+	}
+	if (jsonValue.HasMember("FractureHalfLengthSelected") && jsonValue["FractureHalfLengthSelected"].IsBool()) {
+		m_fractureHalfLengthSelected = jsonValue["FractureHalfLengthSelected"].GetBool();
+	}
 
 	// 反序列化参数最大值
 	if (jsonValue.HasMember("PermeabilityMax") && jsonValue["PermeabilityMax"].IsObject()) {
@@ -186,6 +209,12 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue)
 	if (jsonValue.HasMember("SwiMax") && jsonValue["SwiMax"].IsObject()) {
 		m_swiMax.FromJsonValue(jsonValue["SwiMax"]);
 	}
+	if (jsonValue.HasMember("FractureConductivityMax") && jsonValue["FractureConductivityMax"].IsObject()) {
+		m_fractureConductivityMax.FromJsonValue(jsonValue["FractureConductivityMax"]);
+	}
+	if (jsonValue.HasMember("FractureHalfLengthMax") && jsonValue["FractureHalfLengthMax"].IsObject()) {
+		m_fractureHalfLengthMax.FromJsonValue(jsonValue["FractureHalfLengthMax"]);
+	}
 
 	// 反序列化参数最小值
 	if (jsonValue.HasMember("PermeabilityMin") && jsonValue["PermeabilityMin"].IsObject()) {
@@ -212,6 +241,12 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue)
 	if (jsonValue.HasMember("SwiMin") && jsonValue["SwiMin"].IsObject()) {
 		m_swiMin.FromJsonValue(jsonValue["SwiMin"]);
 	}
+	if (jsonValue.HasMember("FractureConductivityMin") && jsonValue["FractureConductivityMin"].IsObject()) {
+		m_fractureConductivityMin.FromJsonValue(jsonValue["FractureConductivityMin"]);
+	}
+	if (jsonValue.HasMember("FractureHalfLengthMin") && jsonValue["FractureHalfLengthMin"].IsObject()) {
+		m_fractureHalfLengthMin.FromJsonValue(jsonValue["FractureHalfLengthMin"]);
+	}
 
 	// 反序列化迭代参数
 	if (jsonValue.HasMember("IterationCount") && jsonValue["IterationCount"].IsObject()) {
@@ -254,6 +289,12 @@ void nmDataAutomaticFitting::setCfSelected(bool selected) { m_cfSelected = selec
 bool nmDataAutomaticFitting::getSwiSelected() const { return m_swiSelected; }
 void nmDataAutomaticFitting::setSwiSelected(bool selected) { m_swiSelected = selected; }
 
+bool nmDataAutomaticFitting::getFractureConductivitySelected() const { return m_fractureConductivitySelected; }
+void nmDataAutomaticFitting::setFractureConductivitySelected(bool selected) { m_fractureConductivitySelected = selected; }
+
+bool nmDataAutomaticFitting::getFractureHalfLengthSelected() const { return m_fractureHalfLengthSelected; }
+void nmDataAutomaticFitting::setFractureHalfLengthSelected(bool selected) { m_fractureHalfLengthSelected = selected; }
+
 
 // Getter and Setter implementations for Max values
 nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMax() { return m_permeabilityMax; }
@@ -280,6 +321,12 @@ void nmDataAutomaticFitting::setCfMax(const nmDataAttribute& cfMax) { m_cfMax =
 nmDataAttribute& nmDataAutomaticFitting::getSwiMax() { return m_swiMax; }
 void nmDataAutomaticFitting::setSwiMax(const nmDataAttribute& swiMax) { m_swiMax = swiMax; }
 
+nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMax() { return m_fractureConductivityMax; }
+void nmDataAutomaticFitting::setFractureConductivityMax(const nmDataAttribute& fractureConductivityMax) { m_fractureConductivityMax = fractureConductivityMax; }
+
+nmDataAttribute& nmDataAutomaticFitting::getFractureHalfLengthMax() { return m_fractureHalfLengthMax; }
+void nmDataAutomaticFitting::setFractureHalfLengthMax(const nmDataAttribute& fractureHalfLengthMax) { m_fractureHalfLengthMax = fractureHalfLengthMax; }
+
 // Getter and Setter implementations for Min values
 nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMin() { return m_permeabilityMin; }
 void nmDataAutomaticFitting::setPermeabilityMin(const nmDataAttribute& permeabilityMin) { m_permeabilityMin = permeabilityMin; }
@@ -305,6 +352,12 @@ void nmDataAutomaticFitting::setCfMin(const nmDataAttribute& cfMin) { m_cfMin =
 nmDataAttribute& nmDataAutomaticFitting::getSwiMin() { return m_swiMin; }
 void nmDataAutomaticFitting::setSwiMin(const nmDataAttribute& swiMin) { m_swiMin = swiMin; }
 
+nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMin() { return m_fractureConductivityMin; }
+void nmDataAutomaticFitting::setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin) { m_fractureConductivityMin = fractureConductivityMin; }
+
+nmDataAttribute& nmDataAutomaticFitting::getFractureHalfLengthMin() { return m_fractureHalfLengthMin; }
+void nmDataAutomaticFitting::setFractureHalfLengthMin(const nmDataAttribute& fractureHalfLengthMin) { m_fractureHalfLengthMin = fractureHalfLengthMin; }
+
 // Getter and Setter implementations for iteration parameters
 nmDataAttribute& nmDataAutomaticFitting::getIterationCount() { return m_iterationCount; }
 void nmDataAutomaticFitting::setIterationCount(const nmDataAttribute& iterationCount) { m_iterationCount = iterationCount; }
diff --git a/Src/nmNum/nmSubWnd/nmSubWndMain.cpp b/Src/nmNum/nmSubWnd/nmSubWndMain.cpp
index 1f2b236..4574dbc 100644
--- a/Src/nmNum/nmSubWnd/nmSubWndMain.cpp
+++ b/Src/nmNum/nmSubWnd/nmSubWndMain.cpp
@@ -65,6 +65,7 @@
 #include "iAnalRun.h"
 
 #include <QVector>
+#include <QSet>
 
 #include "nmSingalCenter.h"
 
@@ -995,8 +996,18 @@ void nmSubWndMain::updateSelectedWells(QList<ZxDataWell *> wellObjList)
 		}
 	}
 
-	// 当前在画布上的所有 ZxDataWell* 数据列表 (Key Set)
+	// 用集合保存当前井和选中井，避免井数增加后反复调用 QList::contains 造成平方级查找。
 	QList<ZxDataWell*> pWellPlotDataList = pWellDataToPlotMap.keys();
+	QSet<ZxDataWell*> pWellPlotDataSet;
+	for(int i = 0; i < pWellPlotDataList.count(); ++i) {
+		pWellPlotDataSet.insert(pWellPlotDataList[i]);
+	}
+	QSet<ZxDataWell*> selectedWellDataSet;
+	for(int i = 0; i < wellObjList.count(); ++i) {
+		if(wellObjList[i] != nullptr) {
+			selectedWellDataSet.insert(wellObjList[i]);
+		}
+	}
 
 	// setp 2，找出需要 REMOVE (删除) 的图元
 	QVector<nmObjPointWell*> vDeleteWellPlotList;
@@ -1005,7 +1016,7 @@ void nmSubWndMain::updateSelectedWells(QList<ZxDataWell *> wellObjList)
 	for(int i = 0; i < pWellPlotDataList.count(); ++i) {
 		ZxDataWell *pWellDataOnPlot = pWellPlotDataList[i];
 		// 如果该井数据不在用户选中的列表 wellObjList 中，则需要删除
-		if (!wellObjList.contains(pWellDataOnPlot)) {
+		if (!selectedWellDataSet.contains(pWellDataOnPlot)) {
 			// 从 Map 中获取对应的图元对象
 			vDeleteWellPlotList.append(pWellDataToPlotMap.value(pWellDataOnPlot));
 		}
@@ -1018,12 +1029,16 @@ void nmSubWndMain::updateSelectedWells(QList<ZxDataWell *> wellObjList)
 	for(int i = 0; i < wellObjList.count(); ++i) {
 		ZxDataWell *pWellDataSelected = wellObjList[i];
 		// 确保井数据有效
-		if (pWellDataSelected && !pWellPlotDataList.contains(pWellDataSelected)) {
+		if (pWellDataSelected && !pWellPlotDataSet.contains(pWellDataSelected)) {
 			// 如果该井数据不在当前画布上的数据列表中，则需要添加
 			vAddWellPlotList.append(pWellDataSelected);
 		}
 	}
 
+	// 单井增删过程中会多次请求重绘；批量阶段先暂停，全部处理完成后再统一刷新。
+	const bool updatesEnabled = m_pWxPlot->updatesEnabled();
+	m_pWxPlot->setUpdatesEnabled(false);
+
 	// setp 4，执行删除操作
 	for(int i = 0; i < vDeleteWellPlotList.count(); ++i) {
 		nmObjPointWell* pWellPlot = vDeleteWellPlotList[i];
@@ -1065,7 +1080,10 @@ void nmSubWndMain::updateSelectedWells(QList<ZxDataWell *> wellObjList)
 
 
     // 添加完毕后，重新渲染
-    m_pWxPlot->update();
+    m_pWxPlot->setUpdatesEnabled(updatesEnabled);
+    if(updatesEnabled) {
+        m_pWxPlot->update();
+    }
 }
 
 // 地质图导入
diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp
index 202d14c..6bd7312 100644
--- a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp
+++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp
@@ -4,6 +4,9 @@
 #include "nmWxParameterProperty.h"
 #include "nmDataAnalyzeManager.h"
 #include "iSubWndFitting.h"
+#include "iSysParaHelper.h"
+#include "iParameter.h"
+#include "mModuleDefines.h"
 #include "mGui/mGuiAnal/iAnalRun.h"
 #include "iGuiPlot.h"
 #include "ZxObjCurve.h"
@@ -32,6 +35,35 @@ bool nmAutoFitUiIsFinite(double value)
 #endif
 }
 
+// 物理边界可能由多个浮点配置量计算得到，界面十进制文本再转回 double 后会有
+// 末位舍入差。这里只放宽约 8 个机器精度，不放宽实际物理范围。
+bool nmAutoFitUiNearlyEqual(double left, double right)
+{
+	const double scale = qMax(std::fabs(left), std::fabs(right));
+	return std::fabs(left - right) <= DBL_EPSILON * 8.0 * scale;
+}
+
+// 从系统参数表读取物理边界，所有自动拟合参数统一以参数表配置为准。
+bool nmAutoFitReadPhysicalRange(const char* parameterName,
+	double& minValue, double& maxValue)
+{
+	iSysParaHelper* paraHelper = _paraHelper;
+	if(!paraHelper) {
+		return false;
+	}
+
+	iParameter* parameter = paraHelper->getPara(QString::fromLatin1(parameterName), s_Nm_Serie);
+	if(!parameter || !nmAutoFitUiIsFinite(parameter->m_dMin)
+	   || !nmAutoFitUiIsFinite(parameter->m_dMax)
+	   || parameter->m_dMin >= parameter->m_dMax) {
+		return false;
+	}
+
+	minValue = parameter->m_dMin;
+	maxValue = parameter->m_dMax;
+	return true;
+}
+
 // 主界面气井双对数中的源曲线和导数曲线已经采用拟压力量纲.
 // 自动拟合直接读取这两条显示曲线, 避免井对象中的原始压力历史数据与结果曲线量纲不一致.
 // 两条曲线可能因无效点过滤而长度不同, 因此按时间坐标匹配共同有效点.
@@ -155,7 +187,7 @@ void nmWxAutomaticFitting::renumberVisibleParameterRows(QTableWidget* table)
 	}
 }
 
-// 根据当前模型类型控制Ct/Cf/Swi参数显示。
+// 根据当前模型类型控制 Ct/Cf/Swi，并根据目标井类型控制裂缝导流能力显示。
 void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOLVER_MODEL_TYPE eType)
 {
 	if(!table) {
@@ -196,17 +228,377 @@ void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOL
 	setParameterRowVisible(table, 6, showCf);   // Cf
 	setParameterRowVisible(table, 7, showSwi);  // Swi
 
+	// Dfc 只属于垂直压裂井和多段压裂水平井。普通井隐藏并取消勾选，
+	// 防止切换目标井后不可见的裂缝参数仍进入拟合参数向量。
+	bool showFractureConductivity = false;
+	nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance();
+	if(manager && m_targetWellCombo) {
+		nmDataWellBase* targetWell = manager->findWellByName(m_targetWellCombo->currentText());
+		if(targetWell) {
+			NM_WELL_MODEL wellType = targetWell->getWellType();
+			showFractureConductivity =
+				wellType == NM_WELL_MODEL::Vertical_Fractured_Well ||
+				wellType == NM_WELL_MODEL::Horizontal_Fractured_Well;
+		}
+	}
+	setParameterRowVisible(table, 8, showFractureConductivity); // Dfc
+	setParameterRowVisible(table, 9, showFractureConductivity); // 裂缝半长
+
 	renumberVisibleParameterRows(table);
 }
 
+// 获取参数的系统物理边界，并为 Swi 叠加当前储层饱和度约束。
+bool nmWxAutomaticFitting::getPhysicalParameterRange(int parameterIndex,
+	double& minValue, double& maxValue)
+{
+	static const char* parameterNames[] = {
+		"Result_K", "Result_W_Skin", "Result_W_C", "Result_phi",
+		"Result_h", "Result_Cti", "Result_Cf", "Result_Swi", "Result_W_Dfc",
+		"W_FractureHalfLength"
+	};
+	if(parameterIndex < 0 || parameterIndex >= 10) {
+		return false;
+	}
+
+	if(!nmAutoFitReadPhysicalRange(parameterNames[parameterIndex], minValue, maxValue)) {
+		return false;
+	}
+
+	if(parameterIndex == 7) {
+		double soi = reservoirData.getSoi().getValue().toDouble();
+		double sgi = reservoirData.getSgi().getValue().toDouble();
+		if(nmAutoFitUiIsFinite(soi) && nmAutoFitUiIsFinite(sgi)) {
+			if(soi < 0.0 || sgi < 0.0 || soi + sgi > 1.0) {
+				// Soi+Sgi 超过 1 时没有可行的 Swi，固定到物理下限，避免继续搜索非法区间。
+				minValue = 0.0;
+				maxValue = 0.0;
+			} else {
+				maxValue = qMin(maxValue, 1.0 - soi - sgi);
+			}
+		}
+	}
+
+	return true;
+}
+
+// 将一组上下界同步到表格和自动拟合数据，保证 PSO 读取到同一份配置。
+void nmWxAutomaticFitting::setParameterRange(int parameterIndex,
+	double minValue, double maxValue)
+{
+	if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= m_parameterTable->rowCount()
+	   || !nmAutoFitUiIsFinite(minValue) || !nmAutoFitUiIsFinite(maxValue)) {
+		return;
+	}
+
+	if(maxValue < minValue) {
+		qSwap(minValue, maxValue);
+	}
+
+	const bool wasUpdatingRanges = m_updatingParameterRanges;
+	m_updatingParameterRanges = true;
+
+	if(m_parameterTable->item(parameterIndex, 2)) {
+		m_parameterTable->item(parameterIndex, 2)->setText(QString::number(minValue, 'g', 10));
+	}
+	if(m_parameterTable->item(parameterIndex, 4)) {
+		m_parameterTable->item(parameterIndex, 4)->setText(QString::number(maxValue, 'g', 10));
+	}
+
+	switch(parameterIndex) {
+	case 0:
+		automaticFittingData.getPermeabilityMin().setValue(minValue);
+		automaticFittingData.getPermeabilityMax().setValue(maxValue);
+		break;
+	case 1:
+		automaticFittingData.getSkinMin().setValue(minValue);
+		automaticFittingData.getSkinMax().setValue(maxValue);
+		break;
+	case 2:
+		automaticFittingData.getWellboreStorageMin().setValue(minValue);
+		automaticFittingData.getWellboreStorageMax().setValue(maxValue);
+		break;
+	case 3:
+		automaticFittingData.getPorosityMin().setValue(minValue);
+		automaticFittingData.getPorosityMax().setValue(maxValue);
+		break;
+	case 4:
+		automaticFittingData.getThicknessMin().setValue(minValue);
+		automaticFittingData.getThicknessMax().setValue(maxValue);
+		break;
+	case 5:
+		automaticFittingData.getCtMin().setValue(minValue);
+		automaticFittingData.getCtMax().setValue(maxValue);
+		break;
+	case 6:
+		automaticFittingData.getCfMin().setValue(minValue);
+		automaticFittingData.getCfMax().setValue(maxValue);
+		break;
+	case 7:
+		automaticFittingData.getSwiMin().setValue(minValue);
+		automaticFittingData.getSwiMax().setValue(maxValue);
+		break;
+	case 8:
+		automaticFittingData.getFractureConductivityMin().setValue(minValue);
+		automaticFittingData.getFractureConductivityMax().setValue(maxValue);
+		break;
+	case 9:
+		automaticFittingData.getFractureHalfLengthMin().setValue(minValue);
+		automaticFittingData.getFractureHalfLengthMax().setValue(maxValue);
+		break;
+	default:
+		break;
+	}
+
+	m_updatingParameterRanges = wasUpdatingRanges;
+}
+
+// 根据当前初值生成建议搜索范围，并始终截断在系统物理边界内。skin 使用
+// 加减固定宽度，其余正值参数使用倍率范围；该规则在首次加载和拟合完成后复用。
+void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex,
+	double centerValue)
+{
+	if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 10
+	   || !nmAutoFitUiIsFinite(centerValue)) {
+		return;
+	}
+
+	double physicalMin = 0.0;
+	double physicalMax = 0.0;
+	if(!getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax)) {
+		return;
+	}
+
+	double reference = centerValue;
+	const bool positiveParameter = parameterIndex != 1;
+
+	// 正值参数初值为 0 时不再借用旧的界面范围，直接从物理边界选取搜索尺度。
+	if(positiveParameter && reference <= 0.0) {
+		if(physicalMin > 0.0) {
+			reference = physicalMin;
+		} else {
+			reference = qMax(physicalMax * 0.01, 1.0e-12);
+		}
+	}
+
+	if(physicalMax < physicalMin) {
+		return;
+	}
+	reference = qBound(physicalMin, reference, physicalMax);
+	const double boundedCenterValue = qBound(physicalMin, centerValue, physicalMax);
+
+	double newMin = physicalMin;
+	double newMax = physicalMax;
+	if(parameterIndex == 1) {
+		const double skinHalfRange = 10.0;
+		newMin = qMax(physicalMin, reference - skinHalfRange);
+		newMax = qMin(physicalMax, reference + skinHalfRange);
+	} else if(reference > 0.0
+	          && !(parameterIndex == 7 && centerValue <= 0.0)
+	          && !(parameterIndex == 8 && centerValue <= 0.0)) {
+		const double lowerFactor = 0.1;
+		const double upperFactor = 10.0;
+		newMin = qMax(physicalMin, reference * lowerFactor);
+		newMax = qMin(physicalMax, reference * upperFactor);
+	} else if(parameterIndex == 7) {
+		// 没有可靠 Swi 初值时，不把搜索范围压缩到零附近。
+		newMin = physicalMin;
+		newMax = physicalMax;
+	} else if(parameterIndex == 8) {
+		// Dfc=0 表示无限导流，不存在以零为中心的连续倍率范围。
+		newMin = physicalMin;
+		newMax = physicalMax;
+	}
+
+	// 任何自动范围都必须包含本次使用的中心值，并且不能越过物理边界。
+	newMin = qMin(newMin, boundedCenterValue);
+	newMax = qMax(newMax, boundedCenterValue);
+	newMin = qMax(newMin, physicalMin);
+	newMax = qMin(newMax, physicalMax);
+
+	if(newMax >= newMin) {
+		setParameterRange(parameterIndex, newMin, newMax);
+	}
+}
+
+// 首次进入自动范围模式时，按当前表格中的初值为所有参数建立建议范围。
+void nmWxAutomaticFitting::initializeSuggestedParameterRanges()
+{
+	if(!m_parameterTable) {
+		return;
+	}
+
+	for(int parameterIndex = 0; parameterIndex < 10; ++parameterIndex) {
+		QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3);
+		if(initialItem) {
+			bool initialOk = false;
+			const double initialValue = initialItem->text().toDouble(&initialOk);
+			if(initialOk && nmAutoFitUiIsFinite(initialValue)) {
+				updateRangeForParameter(parameterIndex, initialValue);
+			} else {
+				// 数据对象没有提供该初值时使用完整物理区间，不回退到旧的默认范围。
+				double physicalMin = 0.0;
+				double physicalMax = 0.0;
+				if(getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax)
+				   && physicalMax >= physicalMin) {
+					setParameterRange(parameterIndex, physicalMin, physicalMax);
+				}
+			}
+		}
+	}
+}
+
+// 校正已保存的范围：保留物理边界内的用户区间，无交集时按当前初值生成兜底区间。
+void nmWxAutomaticFitting::normalizeSavedParameterRanges()
+{
+	if(!m_parameterTable) {
+		return;
+	}
+
+	for(int parameterIndex = 0; parameterIndex < 10; ++parameterIndex) {
+		QTableWidgetItem* minItem = m_parameterTable->item(parameterIndex, 2);
+		QTableWidgetItem* maxItem = m_parameterTable->item(parameterIndex, 4);
+		QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3);
+		if(!minItem || !maxItem || !initialItem) {
+			continue;
+		}
+
+		double physicalMin = 0.0;
+		double physicalMax = 0.0;
+		if(!getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax)) {
+			continue;
+		}
+		bool savedMinOk = false;
+		bool savedMaxOk = false;
+		const double savedMin = minItem->text().toDouble(&savedMinOk);
+		const double savedMax = maxItem->text().toDouble(&savedMaxOk);
+		// 旧项目没有 Dfc 字段时，nmDataAttribute 会表现为 0~0。Dfc=0 在求解器中
+		// 表示无限导流，不能作为连续拟合区间，因此按当前压裂井初值重新建范围。
+		const bool savedRangeValid = savedMinOk && savedMaxOk
+			&& nmAutoFitUiIsFinite(savedMin) && nmAutoFitUiIsFinite(savedMax)
+			&& savedMax >= savedMin
+			&& !(parameterIndex == 8 && savedMax <= 1.0e-10);
+
+		if(savedRangeValid && physicalMax >= physicalMin) {
+			const double clippedMin = qMax(savedMin, physicalMin);
+			const double clippedMax = qMin(savedMax, physicalMax);
+			if(clippedMax >= clippedMin) {
+				setParameterRange(parameterIndex, clippedMin, clippedMax);
+				continue;
+			}
+		}
+
+		// 已保存范围无效或与物理边界无交集时，按数据对象初值重新生成。
+		bool initialOk = false;
+		const double initialValue = initialItem->text().toDouble(&initialOk);
+		if(initialOk && nmAutoFitUiIsFinite(initialValue)) {
+			updateRangeForParameter(parameterIndex, initialValue);
+		} else if(physicalMax >= physicalMin) {
+			setParameterRange(parameterIndex, physicalMin, physicalMax);
+		}
+	}
+}
+
+// 校验当前表格中的参数范围；parameterIndex 为 -1 时检查所有可见参数行。
+bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int parameterIndex)
+{
+	if(!m_parameterTable) {
+		errorMessage = tr("The parameter table is unavailable.");
+		return false;
+	}
+	if(parameterIndex < -1 || parameterIndex >= 10) {
+		errorMessage = tr("The parameter row is invalid.");
+		return false;
+	}
+
+	static const char* parameterNames[] = {
+		"Permeability", "Skin", "Wellbore storage", "Porosity",
+		"Thickness", "Ct", "Cf", "Swi", "Fracture conductivity",
+		"Fracture half length"
+	};
+
+	const int firstParameterIndex = parameterIndex < 0 ? 0 : parameterIndex;
+	const int lastParameterIndex = parameterIndex < 0 ? 10 : parameterIndex + 1;
+	for(int currentParameterIndex = firstParameterIndex;
+		currentParameterIndex < lastParameterIndex; ++currentParameterIndex) {
+		// 隐藏参数不参与当前模型拟合，不用它们的历史值阻塞当前设置。
+		if(m_parameterTable->isRowHidden(currentParameterIndex)) {
+			continue;
+		}
+
+		QTableWidgetItem* minItem = m_parameterTable->item(currentParameterIndex, 2);
+		QTableWidgetItem* initialItem = m_parameterTable->item(currentParameterIndex, 3);
+		QTableWidgetItem* maxItem = m_parameterTable->item(currentParameterIndex, 4);
+		if(!minItem || !initialItem || !maxItem) {
+			errorMessage = tr("The range values for %1 are incomplete.")
+				.arg(tr(parameterNames[currentParameterIndex]));
+			return false;
+		}
+
+		bool minOk = false;
+		bool initialOk = false;
+		bool maxOk = false;
+		const double minValue = minItem->text().toDouble(&minOk);
+		const double initialValue = initialItem->text().toDouble(&initialOk);
+		const double maxValue = maxItem->text().toDouble(&maxOk);
+		if(!minOk || !initialOk || !maxOk
+		   || !nmAutoFitUiIsFinite(minValue)
+		   || !nmAutoFitUiIsFinite(initialValue)
+		   || !nmAutoFitUiIsFinite(maxValue)) {
+			errorMessage = tr("The minimum value, initial value, and maximum value of %1 must be finite numbers.")
+				.arg(tr(parameterNames[currentParameterIndex]));
+			return false;
+		}
+
+		double physicalMin = 0.0;
+		double physicalMax = 0.0;
+		if(!getPhysicalParameterRange(currentParameterIndex, physicalMin, physicalMax)
+		   || !nmAutoFitUiIsFinite(physicalMin) || !nmAutoFitUiIsFinite(physicalMax)
+		   || physicalMax < physicalMin) {
+			errorMessage = tr("The physical range of %1 is invalid.")
+				.arg(tr(parameterNames[currentParameterIndex]));
+			return false;
+		}
+
+		if((minValue < physicalMin && !nmAutoFitUiNearlyEqual(minValue, physicalMin))
+		   || (minValue > physicalMax && !nmAutoFitUiNearlyEqual(minValue, physicalMax))
+		   || (initialValue < physicalMin && !nmAutoFitUiNearlyEqual(initialValue, physicalMin))
+		   || (initialValue > physicalMax && !nmAutoFitUiNearlyEqual(initialValue, physicalMax))
+		   || (maxValue < physicalMin && !nmAutoFitUiNearlyEqual(maxValue, physicalMin))
+		   || (maxValue > physicalMax && !nmAutoFitUiNearlyEqual(maxValue, physicalMax))) {
+			errorMessage = tr("The values of %1 exceed the physical range [%2, %3].")
+				.arg(tr(parameterNames[currentParameterIndex]))
+				.arg(QString::number(physicalMin, 'g', 10))
+				.arg(QString::number(physicalMax, 'g', 10));
+			return false;
+		}
+
+		if(minValue > maxValue || minValue > initialValue || initialValue > maxValue) {
+			errorMessage = tr("The values of %1 must satisfy: minimum <= initial value <= maximum.")
+				.arg(tr(parameterNames[currentParameterIndex]));
+			return false;
+		}
+
+		// 底层用 Dfc=0 表示无限导流，这是离散模型选项，不属于有限导流拟合域。
+		if(currentParameterIndex == 8 && m_dfcCheckBox->isChecked()
+		   && (minValue <= 1.0e-10 || initialValue <= 1.0e-10)) {
+			errorMessage = tr("The minimum value of %1 must be greater than zero for automatic fitting.")
+				.arg(tr(parameterNames[currentParameterIndex]));
+			return false;
+		}
+	}
+
+	return true;
+}
+
 nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent)
 	: iDlgBase(parent)
 	, m_autoFitterPSO(nullptr)
-	, m_autoFitterGA(nullptr)
 	, m_progressDialog(nullptr)
 	, m_progressTimer(nullptr)
 	, m_progressMonitor(nullptr)
-	, m_selectedAlgorithm(ALGORITHM_PSO)  // 默认选择PSO算法
+	, m_autoParameterRanges(true)
+	// 构造期间先禁止即时校验，避免初始值写入和范围生成之间出现短暂的不一致。
+	, m_updatingParameterRanges(true)
 {
 	DEBUG_UI(QString("AutoFitting Constructor: this=0x%1").arg((quintptr)this, 0, 16));
 
@@ -233,32 +625,13 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent)
 	nmDataAnalyzeManager* pManager = nmDataAnalyzeManager::getCurrentInstance();
 	reservoirData = pManager->getReservoirDataCopy();
 	automaticFittingData = pManager->getAutomaticFittingDataCopy();
+	const bool hasSavedFittingData = pManager && pManager->getAutomaticFittingData() != nullptr;
+	// 自动范围始终开启；用户在表格中修改上下限后，itemChanged 会临时切换为手工范围。
+	m_autoParameterRanges = true;
 
 	NM_SOLVER_MODEL_TYPE solverModelType = pManager->getSolverModelType();
-	// 只有尚未点击确定保存过配置时才使用相态默认值；重新打开时保留已保存的数据。
-	if(pManager->getAutomaticFittingData() == nullptr) {
-		bool isOilOrWaterSinglePhase = solverModelType == SMT_Oil_ConstPvt ||
-		                                   solverModelType == SMT_Oil_VariablePvt ||
-		                                   solverModelType == SMT_Water_ConstPvt ||
-		                                   solverModelType == SMT_Water_VariablePvt;
-		if(isOilOrWaterSinglePhase) {
-			reservoirData.getPermeability().setValue(2.5e-2);
-			automaticFittingData.getPermeabilityMin().setValue(1.0e-3);
-			automaticFittingData.getPermeabilityMax().setValue(10.0);
-			automaticFittingData.getSkinMin().setValue(-10.0);
-			automaticFittingData.getSkinMax().setValue(10.0);
-			automaticFittingData.getWellboreStorageMin().setValue(1.0e-4);
-			automaticFittingData.getWellboreStorageMax().setValue(2.0);
-			automaticFittingData.getPorosityMin().setValue(1.0e-2);
-			automaticFittingData.getPorosityMax().setValue(5.0e-1);
-			automaticFittingData.getThicknessMin().setValue(2.0);
-			automaticFittingData.getThicknessMax().setValue(50.0);
-			automaticFittingData.getCtMin().setValue(1.0e-3);
-			automaticFittingData.getCtMax().setValue(1.0e-1);
-			automaticFittingData.getCfMin().setValue(1.0e-5);
-			automaticFittingData.getCfMax().setValue(1.0e-2);
-		}
-
+	// 未保存过配置时只保留参数选择的相态默认值，不再覆盖数据对象中的初值或范围。
+	if(!hasSavedFittingData) {
 		if(solverModelType == SMT_Oil_ConstPvt ||
 		   solverModelType == SMT_Water_ConstPvt) {
 			// T1/T3 的综合压缩系数默认不参与拟合。
@@ -268,43 +641,6 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent)
 			// T2/T4 的岩石压缩系数默认参与拟合。
 			automaticFittingData.setCfSelected(true);
 		}
-
-		// 气单相变化 PVT 使用 T5 数据集对应的初始值和拟合范围。
-		if(solverModelType == SMT_Gas_VariablePvt) {
-			reservoirData.getPermeability().setValue(5.5e-6);
-			reservoirData.getPorosity().setValue(3.5e-2);
-			reservoirData.getThickness().setValue(8.0);
-			reservoirData.getCf().setValue(3.0e-4);
-
-			automaticFittingData.getPermeabilityMin().setValue(4.5e-6);
-			automaticFittingData.getPermeabilityMax().setValue(6.2e-6);
-			automaticFittingData.getSkinMin().setValue(0.0);
-			automaticFittingData.getSkinMax().setValue(4.5);
-			automaticFittingData.getWellboreStorageMin().setValue(5.0e-5);
-			automaticFittingData.getWellboreStorageMax().setValue(1.6e-4);
-			automaticFittingData.getPorosityMin().setValue(8.0e-3);
-			automaticFittingData.getPorosityMax().setValue(4.0e-2);
-			automaticFittingData.getThicknessMin().setValue(7.0);
-			automaticFittingData.getThicknessMax().setValue(11.0);
-			automaticFittingData.getCfMin().setValue(1.0e-4);
-			automaticFittingData.getCfMax().setValue(2.0e-3);
-		} else if(solverModelType == SMT_Oil_ConstPvt ||
-		          solverModelType == SMT_Water_ConstPvt) {
-			// T1/T3 使用固定初值，不继承当前储层参数。
-			reservoirData.getPorosity().setValue(2.45e-2);
-			reservoirData.getThickness().setValue(9.144);
-			reservoirData.getCt().setValue(1.0e-2);
-		} else if(solverModelType == SMT_Oil_VariablePvt) {
-			// 油单相变化 PVT 使用固定初值。
-			reservoirData.getPorosity().setValue(6.0e-2);
-			reservoirData.getThickness().setValue(10.5);
-			reservoirData.getCf().setValue(1.0e-3);
-		} else if(solverModelType == SMT_Water_VariablePvt) {
-			// 水单相变化 PVT 使用固定初值。
-			reservoirData.getPorosity().setValue(6.0e-2);
-			reservoirData.getThickness().setValue(10.5);
-			reservoirData.getCf().setValue(1.5e-3);
-		}
 	}
 
 	setupUI();
@@ -315,6 +651,12 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent)
 	if(m_targetWellCombo->count() > 0) {
 		onWellSelected(0); // 默认选中第一口井
 	}
+	if(!hasSavedFittingData) {
+		initializeSuggestedParameterRanges();
+	} else {
+		normalizeSavedParameterRanges();
+	}
+	m_updatingParameterRanges = false;
 
 	DEBUG_UI("AutoFitting Constructor completed");
 }
@@ -333,9 +675,6 @@ nmWxAutomaticFitting::~nmWxAutomaticFitting()
 	if (m_autoFitterPSO) {
 		disconnect(m_autoFitterPSO, nullptr, this, nullptr);
 	}
-	if (m_autoFitterGA) {
-		disconnect(m_autoFitterGA, nullptr, this, nullptr);
-	}
 
 	DEBUG_UI("AutoFitting destructor - completed");
 }
@@ -371,7 +710,7 @@ void nmWxAutomaticFitting::setupUI()
 void nmWxAutomaticFitting::setupParameterTable()
 {
 	// 创建表格
-	m_parameterTable = new QTableWidget(8, 6, this);
+	m_parameterTable = new QTableWidget(10, 6, this);
 
 	// 设置表头
 	QStringList headers;
@@ -480,6 +819,27 @@ void nmWxAutomaticFitting::setupParameterTable()
 	m_parameterTable->setItem(7, 4, new QTableWidgetItem(QString::number(automaticFittingData.getSwiMax().getValue().toDouble())));
 	m_parameterTable->setItem(7, 5, new QTableWidgetItem(""));
 
+	// 裂缝导流能力 (Dfc)。该行只对压裂井显示，初值在 onWellSelected() 中
+	// 从当前目标井读取，其他裂缝几何参数保持固定，不进入自动拟合。
+	m_parameterTable->setItem(8, 0, new QTableWidgetItem("9"));
+	m_dfcCheckBox = new QCheckBox(tr("Fracture conductivity"));
+	m_dfcCheckBox->setChecked(automaticFittingData.getFractureConductivitySelected());
+	m_parameterTable->setCellWidget(8, 1, m_dfcCheckBox);
+	m_parameterTable->setItem(8, 2, new QTableWidgetItem(QString::number(automaticFittingData.getFractureConductivityMin().getValue().toDouble())));
+	m_parameterTable->setItem(8, 3, new QTableWidgetItem());
+	m_parameterTable->setItem(8, 4, new QTableWidgetItem(QString::number(automaticFittingData.getFractureConductivityMax().getValue().toDouble())));
+	m_parameterTable->setItem(8, 5, new QTableWidgetItem(tr("md.m")));
+
+	// 裂缝半长。该行与 Dfc 一样只对压裂井显示，初值从当前目标井读取。
+	m_parameterTable->setItem(9, 0, new QTableWidgetItem("10"));
+	m_fractureHalfLengthCheckBox = new QCheckBox(tr("Fracture half length"));
+	m_fractureHalfLengthCheckBox->setChecked(automaticFittingData.getFractureHalfLengthSelected());
+	m_parameterTable->setCellWidget(9, 1, m_fractureHalfLengthCheckBox);
+	m_parameterTable->setItem(9, 2, new QTableWidgetItem(QString::number(automaticFittingData.getFractureHalfLengthMin().getValue().toDouble())));
+	m_parameterTable->setItem(9, 3, new QTableWidgetItem());
+	m_parameterTable->setItem(9, 4, new QTableWidgetItem(QString::number(automaticFittingData.getFractureHalfLengthMax().getValue().toDouble())));
+	m_parameterTable->setItem(9, 5, new QTableWidgetItem(tr("m")));
+
 	// 设置表格行为
 	for(int i = 0; i < m_parameterTable->rowCount(); ++i) {
 		for(int j = 0; j < 6; ++j) {
@@ -514,6 +874,8 @@ void nmWxAutomaticFitting::setupParameterTable()
 	// 连接选择改变信号
 	connect(m_parameterTable, SIGNAL(currentCellChanged(int, int, int, int)),
 		this, SLOT(onCellSelectionChanged(int, int, int, int)));
+	connect(m_parameterTable, SIGNAL(itemChanged(QTableWidgetItem*)),
+		this, SLOT(onParameterTableItemChanged(QTableWidgetItem*)));
 }
 
 void nmWxAutomaticFitting::setupControlPanel()
@@ -528,9 +890,7 @@ void nmWxAutomaticFitting::setupControlPanel()
 	QLabel* algorithmLabel = new QLabel(tr("Algorithm:"));
 	m_algorithmCombo = new QComboBox();
 	m_algorithmCombo->addItem(tr("PSO (Particle Swarm)"));
-	m_algorithmCombo->addItem(tr("GA (Genetic Algorithm)"));
-	m_algorithmCombo->setCurrentIndex(0); // 默认选择PSO
-	connect(m_algorithmCombo, SIGNAL(currentIndexChanged(int)), this, SLOT(onAlgorithmChanged(int)));
+	m_algorithmCombo->setCurrentIndex(0);
 	m_algorithmCombo->setMaximumWidth(160);
 	m_algorithmCombo->setMinimumWidth(160);
 
@@ -679,37 +1039,41 @@ void nmWxAutomaticFitting::onReverseSelection()
 	if(!m_parameterTable->isRowHidden(5)) m_ctCheckBox->setChecked(!m_ctCheckBox->isChecked());
 	if(!m_parameterTable->isRowHidden(6)) m_cfCheckBox->setChecked(!m_cfCheckBox->isChecked());
 	if(!m_parameterTable->isRowHidden(7)) m_swiCheckBox->setChecked(!m_swiCheckBox->isChecked());
+	if(!m_parameterTable->isRowHidden(8)) m_dfcCheckBox->setChecked(!m_dfcCheckBox->isChecked());
+	if(!m_parameterTable->isRowHidden(9)) m_fractureHalfLengthCheckBox->setChecked(!m_fractureHalfLengthCheckBox->isChecked());
 }
 
-void nmWxAutomaticFitting::onAlgorithmChanged(int index)
+void nmWxAutomaticFitting::onParameterTableItemChanged(QTableWidgetItem* item)
 {
-	m_selectedAlgorithm = static_cast<OptimizationAlgorithm>(index);
-
-	// 根据算法类型调整界面提示或参数
-	QString algorithmInfo;
-	switch(m_selectedAlgorithm) {
-	case ALGORITHM_PSO:
-		algorithmInfo = tr("PSO algorithm selected.");
-		if(m_surrogateCombo) {
-			m_surrogateCombo->setEnabled(true);
-		}
-		break;
-	case ALGORITHM_GA:
-		algorithmInfo = tr("GA algorithm selected.");
-		if(m_surrogateCombo) {
-			m_surrogateCombo->setEnabled(false);
-		}
-		break;
+	if(!item || m_updatingParameterRanges) {
+		return;
+	}
+
+	// 用户改动范围后，切井和拟合结果不再自动覆盖这组手工范围。
+	if(item->column() == 2 || item->column() == 4) {
+		m_autoParameterRanges = false;
 	}
 
-	// 在状态栏或工具提示中显示算法信息
-	m_algorithmCombo->setToolTip(algorithmInfo);
+	if(item->column() >= 2 && item->column() <= 4
+	   && !m_parameterTable->isRowHidden(item->row())) {
+		QString validationError;
+		if(!validateParameterTable(validationError, item->row())) {
+			// 表格编辑提交后立即提示，用户不需要先点击“确定”才发现错误。
+			QMessageBox::warning(this, tr("Invalid parameter range"), validationError);
+		}
+	}
 }
 
 void nmWxAutomaticFitting::onAccept()
 {
-    // 首先保存参数设置
-    setAutomaticFittingValue();
+	QString validationError;
+	if(!validateParameterTable(validationError)) {
+		QMessageBox::warning(this, tr("Invalid parameter range"), validationError);
+		return;
+	}
+
+	// 校验通过后再保存参数设置，非法输入不会被静默裁剪。
+	setAutomaticFittingValue();
 	// 更新完成后，通知参数界面刷新
 	nmWxParameterProperty::notifyUpdateTable();
 
@@ -795,7 +1159,8 @@ void nmWxAutomaticFitting::onAccept()
 		m_cCheckBox->isChecked() || m_phiCheckBox->isChecked() ||
 		m_hCheckBox->isChecked() ||
 		m_ctCheckBox->isChecked() || m_cfCheckBox->isChecked() ||
-		m_swiCheckBox->isChecked();
+		m_swiCheckBox->isChecked() || m_dfcCheckBox->isChecked() ||
+		m_fractureHalfLengthCheckBox->isChecked();
 
 	if(!hasSelectedParams) {
 		QMessageBox::warning(this, tr("Warning"), tr("Please select at least one parameter for optimization!"));
@@ -813,6 +1178,8 @@ void nmWxAutomaticFitting::onAccept()
 	if(m_ctCheckBox->isChecked()) selectedParameterNames << tr("Ct");
 	if(m_cfCheckBox->isChecked()) selectedParameterNames << tr("Cf");
 	if(m_swiCheckBox->isChecked()) selectedParameterNames << tr("Swi");
+	if(m_dfcCheckBox->isChecked()) selectedParameterNames << tr("Fracture conductivity");
+	if(m_fractureHalfLengthCheckBox->isChecked()) selectedParameterNames << tr("Fracture half length");
 
 	// 启动自动拟合 - 传递双对数历史数据
 	startAutoFitting(targetLogLogData, selectedParameterNames, selectedWellName);
@@ -829,8 +1196,11 @@ void nmWxAutomaticFitting::onWellSelected(int index)
 
 	// 在分类的井数据中查找匹配的井
 	bool found = false;
+	bool fracturedWell = false;
 	double skinValue = 0.0;
 	double wellboreStorageValue = 0.0;
+	double fractureConductivityValue = 0.0;
+	double fractureHalfLengthValue = 0.0;
 
 	// 查找垂直井
 	for(int i = 0; i < m_verticalWells.size(); ++i) {
@@ -860,6 +1230,9 @@ void nmWxAutomaticFitting::onWellSelected(int index)
 			if(m_verticalFracturedWells[i].getWellName() == selectedWellName) {
 				skinValue = m_verticalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble();
 				wellboreStorageValue = m_verticalFracturedWells[i].getWellboreStorage().getValue().toDouble();
+				fractureConductivityValue = m_verticalFracturedWells[i].getDfc().getValue().toDouble();
+				fractureHalfLengthValue = m_verticalFracturedWells[i].getFractureHalfLength().getValue().toDouble();
+				fracturedWell = true;
 				found = true;
 				break;
 			}
@@ -872,35 +1245,21 @@ void nmWxAutomaticFitting::onWellSelected(int index)
 			if(m_horizontalFracturedWells[i].getWellName() == selectedWellName) {
 				skinValue = m_horizontalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble();
 				wellboreStorageValue = m_horizontalFracturedWells[i].getWellboreStorage().getValue().toDouble();
+				fractureConductivityValue = m_horizontalFracturedWells[i].getDfc().getValue().toDouble();
+				fractureHalfLengthValue = m_horizontalFracturedWells[i].getFractureHalfLength().getValue().toDouble();
+				fracturedWell = true;
 				found = true;
 				break;
 			}
 		}
 	}
 
-	// 尚未保存拟合配置时使用相态默认值；已保存时保留井上的 Skin 和井筒储集系数。
-	nmDataAnalyzeManager* pManager = nmDataAnalyzeManager::getCurrentInstance();
-	bool useDefaultFittingValues = pManager && pManager->getAutomaticFittingData() == nullptr;
-	if(useDefaultFittingValues && (pManager->getSolverModelType() == SMT_Oil_ConstPvt ||
-	                               pManager->getSolverModelType() == SMT_Water_ConstPvt)) {
-		skinValue = 0.0;
-		wellboreStorageValue = 1.0e-2;
-		found = true;
-	} else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Gas_VariablePvt) {
-		skinValue = 1.0;
-		wellboreStorageValue = 6.0e-5;
-		found = true;
-	} else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Oil_VariablePvt) {
-		skinValue = -0.2;
-		wellboreStorageValue = 1.5e-2;
-		found = true;
-	} else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Water_VariablePvt) {
-		skinValue = -0.2;
-		wellboreStorageValue = 1.0e-2;
-		found = true;
-	}
-
 	if (found) {
+		// 先按目标井类型刷新可见行，再写入当前井的井级初值。
+		nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance();
+		if(manager) {
+			updateParameterVisibility(m_parameterTable, manager->getSolverModelType());
+		}
 		// 更新表格数据
 		// 确保表格项存在
 		if(!m_parameterTable->item(1, 3)) {
@@ -915,6 +1274,21 @@ void nmWxAutomaticFitting::onWellSelected(int index)
 
 		// 设置井筒储集系数（Wellbore storage）
 		m_parameterTable->item(2, 3)->setText(QString::number(wellboreStorageValue));
+		if(fracturedWell && m_parameterTable->item(8, 3)) {
+			m_parameterTable->item(8, 3)->setText(QString::number(fractureConductivityValue));
+		}
+		if(fracturedWell && m_parameterTable->item(9, 3)) {
+			m_parameterTable->item(9, 3)->setText(QString::number(fractureHalfLengthValue));
+		}
+
+		if(m_autoParameterRanges) {
+			updateRangeForParameter(1, skinValue);
+			updateRangeForParameter(2, wellboreStorageValue);
+			if(fracturedWell) {
+				updateRangeForParameter(8, fractureConductivityValue);
+				updateRangeForParameter(9, fractureHalfLengthValue);
+			}
+		}
 	}
 }
 
@@ -929,6 +1303,8 @@ void nmWxAutomaticFitting::setAutomaticFittingValue()
 	automaticFittingData.setCtSelected(m_ctCheckBox->isChecked());
 	automaticFittingData.setCfSelected(m_cfCheckBox->isChecked());
 	automaticFittingData.setSwiSelected(m_swiCheckBox->isChecked());
+	automaticFittingData.setFractureConductivitySelected(m_dfcCheckBox->isChecked());
+	automaticFittingData.setFractureHalfLengthSelected(m_fractureHalfLengthCheckBox->isChecked());
 	automaticFittingData.setSurrogateScreeningEnabled(m_surrogateCombo && m_surrogateCombo->currentIndex() == 1);
 
 	// 保存渗透率的最小值和最大值
@@ -963,6 +1339,14 @@ void nmWxAutomaticFitting::setAutomaticFittingValue()
 	automaticFittingData.getSwiMin().setValue(m_parameterTable->item(7, 2)->text().toDouble());
 	automaticFittingData.getSwiMax().setValue(m_parameterTable->item(7, 4)->text().toDouble());
 
+	// 保存裂缝导流能力的最小值和最大值
+	automaticFittingData.getFractureConductivityMin().setValue(m_parameterTable->item(8, 2)->text().toDouble());
+	automaticFittingData.getFractureConductivityMax().setValue(m_parameterTable->item(8, 4)->text().toDouble());
+
+	// 保存裂缝半长的最小值和最大值
+	automaticFittingData.getFractureHalfLengthMin().setValue(m_parameterTable->item(9, 2)->text().toDouble());
+	automaticFittingData.getFractureHalfLengthMax().setValue(m_parameterTable->item(9, 4)->text().toDouble());
+
 	// 保存迭代参数
 	automaticFittingData.getIterationCount().setValue(m_iterationEdit->text().toInt());
 	automaticFittingData.getErrorTolerance().setValue(m_errorLimitEdit->text().toDouble());
@@ -986,6 +1370,8 @@ void nmWxAutomaticFitting::setAutomaticFittingValue()
 	if(!selectedWellName.isEmpty()) {
 		double newSkinValue = m_parameterTable->item(1, 3)->text().toDouble();
 		double newWellboreStorageValue = m_parameterTable->item(2, 3)->text().toDouble();
+		double newFractureConductivityValue = m_parameterTable->item(8, 3)->text().toDouble();
+		double newFractureHalfLengthValue = m_parameterTable->item(9, 3)->text().toDouble();
 
 		// 直接从数据管理器获取目标井
 		nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance();
@@ -1008,6 +1394,25 @@ void nmWxAutomaticFitting::setAutomaticFittingValue()
 			// 根据井类型单独更新这一口井
 			NM_WELL_MODEL wellType = pTargetWell->getWellType();
 
+			// Dfc 属于压裂井对象；只修改这一项，裂缝位置、长度和段数保持原值。
+			if(wellType == NM_WELL_MODEL::Vertical_Fractured_Well) {
+				nmDataVerticalFracturedWell* fracturedWell = dynamic_cast<nmDataVerticalFracturedWell*>(pTargetWell);
+				if(fracturedWell) {
+					nmDataAttribute dfc = fracturedWell->getDfc();
+					dfc.setValue(newFractureConductivityValue);
+					fracturedWell->setDfc(dfc);
+					fracturedWell->getFractureHalfLength().setValue(newFractureHalfLengthValue);
+				}
+			} else if(wellType == NM_WELL_MODEL::Horizontal_Fractured_Well) {
+				nmDataHorizontalFracturedWell* fracturedWell = dynamic_cast<nmDataHorizontalFracturedWell*>(pTargetWell);
+				if(fracturedWell) {
+					nmDataAttribute dfc = fracturedWell->getDfc();
+					dfc.setValue(newFractureConductivityValue);
+					fracturedWell->setDfc(dfc);
+					fracturedWell->getFractureHalfLength().setValue(newFractureHalfLengthValue);
+				}
+			}
+
 			if(wellType == NM_WELL_MODEL::Vertical_Well) {
 				nmDataVerticalWell* pVerticalWell = dynamic_cast<nmDataVerticalWell*>(pTargetWell);
 				if(pVerticalWell != nullptr) {
@@ -1041,11 +1446,10 @@ void nmWxAutomaticFitting::startAutoFitting(const QVector<QVector<double>>& targ
 	// 先清理之前的实例
 	cleanupFitting();
 
-	if (m_selectedAlgorithm == ALGORITHM_PSO) {
-		DEBUG_UI("Creating PSO auto fitter");
-		m_autoFitterPSO = new nmCalculationAutoFitPSO(this);
-		m_autoFitterPSO->setTargetLogLogData(targetData);
-		m_autoFitterPSO->setPSOTargetWellName(targetWellName);
+	DEBUG_UI("Creating PSO auto fitter");
+	m_autoFitterPSO = new nmCalculationAutoFitPSO(this);
+	m_autoFitterPSO->setTargetLogLogData(targetData);
+	m_autoFitterPSO->setPSOTargetWellName(targetWellName);
 
 		//// 特定井名时使用快速路径
 		//if (targetWellName == "VerticalWell1") {
@@ -1080,46 +1484,23 @@ void nmWxAutomaticFitting::startAutoFitting(const QVector<QVector<double>>& targ
 		//}
 
 
-		m_progressMonitor = new nmWxAutomaticfittingStart(this);
-		m_progressMonitor->setAutoFitter(m_autoFitterPSO);
-		m_progressMonitor->setPseudoPressureMode(
-			nmDataAnalyzeManager::getCurrentInstance()->getSolverModelType() == SMT_Gas_VariablePvt);
-		m_progressMonitor->setTargetLogLogData(targetData);
-
-		connect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)),
-			this, SLOT(onFittingFinished(bool, QString)));
-
-		int maxIterations = m_iterationEdit->text().toInt();
-		double targetError = m_errorLimitEdit->text().toDouble();
-		QString wellName = m_targetWellCombo->currentText();
-		m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName);
-		m_progressMonitor->setSelectedParameters(selectedParams);
-
-		m_progressMonitor->show();
-		QTimer::singleShot(100, this, SLOT(runAutoFitting()));
-
-	} else if (m_selectedAlgorithm == ALGORITHM_GA) {
-		DEBUG_UI("Creating GA auto fitter");
-		m_autoFitterGA = new nmCalculationAutoFitGA(this);
-		m_autoFitterGA->setTargetLogLogData(targetData);
-		m_autoFitterGA->setGATargetWellName(targetWellName);
+	m_progressMonitor = new nmWxAutomaticfittingStart(this);
+	m_progressMonitor->setAutoFitter(m_autoFitterPSO);
+	m_progressMonitor->setPseudoPressureMode(
+		nmDataAnalyzeManager::getCurrentInstance()->getSolverModelType() == SMT_Gas_VariablePvt);
+	m_progressMonitor->setTargetLogLogData(targetData);
 
-		m_progressMonitor = new nmWxAutomaticfittingStart(this);
-		m_progressMonitor->setAutoFitterGA(m_autoFitterGA);
-		m_progressMonitor->setTargetLogLogData(targetData);
-
-		connect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)),
-			this, SLOT(onFittingFinished(bool, QString)));
+	connect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)),
+		this, SLOT(onFittingFinished(bool, QString)));
 
-		int maxIterations = m_iterationEdit->text().toInt();
-		double targetError = m_errorLimitEdit->text().toDouble();
-		QString wellName = m_targetWellCombo->currentText();
-		m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName);
-		m_progressMonitor->setSelectedParameters(selectedParams);
+	int maxIterations = m_iterationEdit->text().toInt();
+	double targetError = m_errorLimitEdit->text().toDouble();
+	QString wellName = m_targetWellCombo->currentText();
+	m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName);
+	m_progressMonitor->setSelectedParameters(selectedParams);
 
-		m_progressMonitor->show();
-		QTimer::singleShot(100, this, SLOT(runAutoFitting()));
-	}
+	m_progressMonitor->show();
+	QTimer::singleShot(100, this, SLOT(runAutoFitting()));
 }
 
 void nmWxAutomaticFitting::runAutoFitting()
@@ -1128,10 +1509,8 @@ void nmWxAutomaticFitting::runAutoFitting()
 		m_progressMonitor->markFittingStarted();
 	}
 
-	if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) {
+	if(m_autoFitterPSO) {
 		m_autoFitterPSO->startAutoFitting();
-	} else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) {
-		m_autoFitterGA->startAutoFitting();
 	}
 }
 
@@ -1147,18 +1526,13 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag
 		disconnect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)),
 			this, SLOT(onFittingFinished(bool, QString)));
 	}
-	if (m_autoFitterGA) {
-		disconnect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)),
-			this, SLOT(onFittingFinished(bool, QString)));
-	}
-
-	// 更新最佳参数到表格
-	updateBestParametersToTable();
-
 	if(success) {
+		// 只有成功拟合的结果才用于生成下一轮范围，失败结果不污染当前配置。
+		updateBestParametersToTable();
+
 		QString resultInfo;
 
-		if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) {
+		if(m_autoFitterPSO) {
 			double bestFitness = m_autoFitterPSO->getBestFitness();
 
 			// 检查是否是用户停止的情况
@@ -1173,23 +1547,6 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag
 				resultInfo += tr("Optimized parameters have been applied to the model.");
 				QMessageBox::information(this, tr("Optimization Completed"), resultInfo);
 			}
-
-		} else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) {
-			// GA的类似处理
-
-			double bestFitness = m_autoFitterGA->getBestFitness();
-
-			if(message.contains("stopped by user", Qt::CaseInsensitive)) {
-				resultInfo = tr("GA Optimization stopped by user:\n");
-				resultInfo += tr("Best Error: %1\n").arg(bestFitness, 0, 'e', 4);
-				resultInfo += tr("Current parameters have been applied to the model.");
-				QMessageBox::information(this, tr("Optimization Stopped"), resultInfo);
-			} else {
-				resultInfo = tr("GA Optimization completed:\n");
-				resultInfo += tr("Best Error: %1\n").arg(bestFitness, 0, 'e', 4);
-				resultInfo += tr("Optimized parameters have been applied to the model.");
-				QMessageBox::information(this, tr("Optimization Completed"), resultInfo);
-			}
 		}
 	} else {
 		// 只有真正失败的情况才显示警告
@@ -1199,11 +1556,9 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag
 
 void nmWxAutomaticFitting::onStopFitting()
 {
-	if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO && m_autoFitterPSO->isRunning()) {
+	if(m_autoFitterPSO && m_autoFitterPSO->isRunning()) {
 		m_autoFitterPSO->stopFitting();
-	} else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA && m_autoFitterGA->isRunning()) {
-	  m_autoFitterGA->stopFitting();
-	  }
+	}
 }
 
 void nmWxAutomaticFitting::cleanupFitting()
@@ -1246,31 +1601,6 @@ void nmWxAutomaticFitting::cleanupFitting()
 		DEBUG_UI("PSO fitter cleaned up");
 	}
 
-	if (m_autoFitterGA) {
-		DEBUG_UI("Stopping and disconnecting GA fitter");
-
-		disconnect(m_autoFitterGA, nullptr, nullptr, nullptr);
-
-		if (m_autoFitterGA->isRunning()) {
-			m_autoFitterGA->stopFitting();
-
-			int waitCount = 0;
-			while (m_autoFitterGA->isRunning() && waitCount < 50) {
-				QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 100);
-				waitCount++;
-			}
-
-			// 如果仍在运行则强制停止
-			if (m_autoFitterGA->isRunning()) {
-				DEBUG_UI("Force stopping GA - timeout reached");
-			}
-		}
-
-		delete m_autoFitterGA;
-		m_autoFitterGA = nullptr;
-		DEBUG_UI("GA fitter cleaned up");
-	}
-
 	// 清理进度监控
 	if (m_progressMonitor) {
 		// 先断开进度监控的信号连接
@@ -1296,10 +1626,8 @@ void nmWxAutomaticFitting::updateBestParametersToTable()
 	QVector<double> bestSolution;
 
 	// 获取最佳解决方案
-	if (m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) {
+	if (m_autoFitterPSO) {
 		bestSolution = m_autoFitterPSO->getBestSolution();
-	} else if (m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) {
-		bestSolution = m_autoFitterGA->getBestSolution();
 	}
 
 	if (bestSolution.isEmpty()) return;
@@ -1314,79 +1642,22 @@ void nmWxAutomaticFitting::updateBestParametersToTable()
 	if(m_ctCheckBox->isChecked()) enabledParams.append(5);     // 综合压缩系数
 	if(m_cfCheckBox->isChecked()) enabledParams.append(6);     // 岩石压缩系数
 	if(m_swiCheckBox->isChecked()) enabledParams.append(7);    // 初始含水饱和度
-
-	// 范围收缩比例
-	double shrinkFactor = 0.3;
+	if(m_dfcCheckBox->isChecked()) enabledParams.append(8);    // 裂缝导流能力
+	if(m_fractureHalfLengthCheckBox->isChecked()) enabledParams.append(9); // 裂缝半长
 
 	// 更新参数值和范围
 	for (int i = 0; i < bestSolution.size() && i < enabledParams.size(); ++i) {
 		int paramIndex = enabledParams[i];
 		double bestValue = bestSolution[i];
+		if(!nmAutoFitUiIsFinite(bestValue)) {
+			continue;
+		}
 
 		// 更新初始值
 		m_parameterTable->item(paramIndex, 3)->setText(QString::number(bestValue, 'g', 4));
-
-		// 获取当前的最小值和最大值
-		double currentMin = m_parameterTable->item(paramIndex, 2)->text().toDouble();
-		double currentMax = m_parameterTable->item(paramIndex, 4)->text().toDouble();
-		double currentRange = currentMax - currentMin;
-
-		// 计算新的范围
-		double newHalfRange = currentRange * shrinkFactor * 0.5;
-		double newMin = bestValue - newHalfRange;
-		double newMax = bestValue + newHalfRange;
-
-		// 确保某些参数不为负数
-		if (paramIndex == 0 || paramIndex == 2 || paramIndex == 3 || paramIndex == 4) {
-			newMin = qMax(newMin, 0.0);
-		}
-
-		// 确保最小范围，避免范围过小
-		double minAllowedRange = currentRange * 0.05;
-		if ((newMax - newMin) < minAllowedRange) {
-			double center = (newMax + newMin) * 0.5;
-			newMin = center - minAllowedRange * 0.5;
-			newMax = center + minAllowedRange * 0.5;
-		}
-
-		// 更新表格中的最小值和最大值
-		m_parameterTable->item(paramIndex, 2)->setText(QString::number(newMin, 'g', 4));
-		m_parameterTable->item(paramIndex, 4)->setText(QString::number(newMax, 'g', 4));
-
-		// 保存收缩后的范围
-		switch(paramIndex) {
-		case 0: // 渗透率
-			automaticFittingData.getPermeabilityMin().setValue(newMin);
-			automaticFittingData.getPermeabilityMax().setValue(newMax);
-			break;
-		case 1: // 表皮系数
-			automaticFittingData.getSkinMin().setValue(newMin);
-			automaticFittingData.getSkinMax().setValue(newMax);
-			break;
-		case 2: // 井筒储集系数
-			automaticFittingData.getWellboreStorageMin().setValue(newMin);
-			automaticFittingData.getWellboreStorageMax().setValue(newMax);
-			break;
-		case 3: // 孔隙度
-			automaticFittingData.getPorosityMin().setValue(newMin);
-			automaticFittingData.getPorosityMax().setValue(newMax);
-			break;
-		case 4: // 储层厚度
-			automaticFittingData.getThicknessMin().setValue(newMin);
-			automaticFittingData.getThicknessMax().setValue(newMax);
-			break;
-		case 5: // 综合压缩系数
-			automaticFittingData.getCtMin().setValue(newMin);
-			automaticFittingData.getCtMax().setValue(newMax);
-			break;
-		case 6: // 岩石压缩系数
-			automaticFittingData.getCfMin().setValue(newMin);
-			automaticFittingData.getCfMax().setValue(newMax);
-			break;
-		case 7: // 初始含水饱和度
-			automaticFittingData.getSwiMin().setValue(newMin);
-			automaticFittingData.getSwiMax().setValue(newMax);
-			break;
+		// 自动范围模式下，以拟合结果为中心复用首次建范围的规则；手工范围由用户保留。
+		if(m_autoParameterRanges) {
+			updateRangeForParameter(paramIndex, bestValue);
 		}
 	}
 
diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp
index 6e0bd2b..acb1a3f 100644
--- a/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp
+++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp
@@ -383,8 +383,6 @@ nmWxAutomaticfittingStart::nmWxAutomaticfittingStart(QWidget *parent)
 	, chartGroup(nullptr)
 	, curveChart(nullptr)
 	, m_autoFitterPSO(nullptr)
-	, m_autoFitterGA(nullptr)
-	, m_algorithmType(FITTING_ALGORITHM_PSO)
 	, m_maxIterations(100)
 	, m_targetError(0.001)
 	, m_wellName("")
@@ -580,8 +578,6 @@ void nmWxAutomaticfittingStart::setupControlArea()
 void nmWxAutomaticfittingStart::setAutoFitter(nmCalculationAutoFitPSO* autoFitter)
 {
 	m_autoFitterPSO = autoFitter;
-	m_autoFitterGA = nullptr;  // 清空GA实例
-	m_algorithmType = FITTING_ALGORITHM_PSO;
 
 	// 更新算法类型显示
 	algorithmTypeValue->setText("PSO");
@@ -604,31 +600,6 @@ void nmWxAutomaticfittingStart::setAutoFitter(nmCalculationAutoFitPSO* autoFitte
 	}
 }
 
-void nmWxAutomaticfittingStart::setAutoFitterGA(nmCalculationAutoFitGA* autoFitter)
-{
-	m_autoFitterGA = autoFitter;
-	m_autoFitterPSO = nullptr;  // 清空PSO实例
-	m_algorithmType = FITTING_ALGORITHM_GA;
-
-	// 更新算法类型显示
-	algorithmTypeValue->setText("GA");
-	algorithmTypeValue->setStyleSheet("QLabel { color: red; font-weight: bold; }");
-
-	if (m_autoFitterGA) {
-		connect(m_autoFitterGA, SIGNAL(progressUpdated(int, double)),
-			this, SLOT(onFittingProgress(int, double)));
-		connect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)),
-			this, SLOT(onFittingFinished(bool, QString)));
-		connect(m_autoFitterGA, SIGNAL(logMessageGenerated(QString)),
-			this, SLOT(onLogMessageReceived(QString)));
-
-		// 启用停止按钮
-		stopButton->setEnabled(true);
-
-		addLogMessage(tr("GA auto fitting started"));
-	}
-}
-
 void nmWxAutomaticfittingStart::setFittingParameters(int maxIterations, double targetError, const QString& wellName)
 {
 	m_maxIterations = maxIterations;
@@ -641,7 +612,7 @@ void nmWxAutomaticfittingStart::setFittingParameters(int maxIterations, double t
 
 	progressBar->setRange(0, maxIterations);
 
-	QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA";
+	const QString algorithmName = "PSO";
 	addLogMessage(tr("%1 fitting parameters set: MaxIterations=%2, TargetAccuracy=%3, TargetWell=%4")
 		.arg(algorithmName).arg(maxIterations).arg(formatScientific(targetError)).arg(wellName));
 }
@@ -650,7 +621,7 @@ void nmWxAutomaticfittingStart::markFittingStarted()
 {
 	m_startTime = QDateTime::currentDateTime();
 
-	QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA";
+	const QString algorithmName = "PSO";
 	QString timestamp = m_startTime.toString("yyyy-MM-dd hh:mm:ss");
 
 	addLogMessage(tr("=== %1 Fitting Session Started at %2 ===")
@@ -665,7 +636,7 @@ void nmWxAutomaticfittingStart::setSelectedParameters(const QStringList& paramet
 	// 立即更新参数表格
 	updateParameterTable();
 
-	QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA";
+	const QString algorithmName = "PSO";
 	addLogMessage(tr("%1 selected parameters: %2").arg(algorithmName).arg(parameterNames.join(", ")));
 }
 
@@ -716,7 +687,7 @@ void nmWxAutomaticfittingStart::onFittingFinished(bool success, const QString& m
 {
 	m_isFinished = true;
 
-	QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA";
+	const QString algorithmName = "PSO";
 
 	// 更新状态
 	if (success) {
@@ -800,25 +771,18 @@ void nmWxAutomaticfittingStart::onFittingFinished(bool success, const QString& m
 
 void nmWxAutomaticfittingStart::onStopButtonClicked()
 {
-	bool isRunning = false;
-	if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) {
-		isRunning = m_autoFitterPSO->isRunning();
-	} else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) {
-        isRunning = m_autoFitterGA->isRunning();
-	}
+	const bool isRunning = m_autoFitterPSO && m_autoFitterPSO->isRunning();
 
 	if (isRunning) {
-		QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA";
+		const QString algorithmName = "PSO";
 		int ret = QMessageBox::question(this, tr("Confirm Stop"),
 			tr("Are you sure you want to stop the %1 fitting process?").arg(algorithmName),
 			QMessageBox::Yes | QMessageBox::No,
 			QMessageBox::No);
 
 		if (ret == QMessageBox::Yes) {
-			if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) {
+			if (m_autoFitterPSO) {
 				m_autoFitterPSO->stopFitting();
-			} else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) {
-				m_autoFitterGA->stopFitting();
 			}
 			addLogMessage(tr("User requested to stop %1 fitting").arg(algorithmName));
 		}
@@ -840,16 +804,11 @@ void nmWxAutomaticfittingStart::updateParameterTable()
 		// 最优参数值
 		QString valueText = "N/A";
 
-		if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) {
+		if (m_autoFitterPSO) {
 			QVector<double> bestSolution = m_autoFitterPSO->getBestSolution();
 			if (i < bestSolution.size()) {
 				valueText = formatScientific(bestSolution[i]);
 			}
-		} else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) {
-			QVector<double> bestSolution = m_autoFitterGA->getBestSolution();
-			if (i < bestSolution.size()) {
-				valueText = formatScientific(bestSolution[i]);
-			}
 		}
 
 		QTableWidgetItem* valueItem = new QTableWidgetItem(valueText);
@@ -883,16 +842,8 @@ QString nmWxAutomaticfittingStart::formatScientific(double value)
 
 void nmWxAutomaticfittingStart::closeEvent(QCloseEvent *event)
 {
-	bool isRunning = false;
-	QString algorithmName;
-
-	if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) {
-		isRunning = m_autoFitterPSO->isRunning();
-		algorithmName = "PSO";
-	} else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) {
-		isRunning = m_autoFitterGA->isRunning();
-		algorithmName = "GA";
-	}
+	const bool isRunning = m_autoFitterPSO && m_autoFitterPSO->isRunning();
+	const QString algorithmName = "PSO";
 
 	if (isRunning && !m_isFinished) {
 		int ret = QMessageBox::question(this, tr("Confirm Close"),
@@ -901,10 +852,8 @@ void nmWxAutomaticfittingStart::closeEvent(QCloseEvent *event)
 			QMessageBox::No);
 
 		if (ret == QMessageBox::Yes) {
-			if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) {
+			if (m_autoFitterPSO) {
 				m_autoFitterPSO->stopFitting();
-			} else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) {
-				m_autoFitterGA->stopFitting();
 			}
 			event->accept();
 		} else {
diff --git a/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp b/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp
index 54071eb..1788fa0 100644
--- a/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp
+++ b/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp
@@ -23,6 +23,7 @@
 
 #include <QCoreApplication>
 #include <QSet>
+#include <QTimer>
 
 #include "nmWxParaPropertyPebi.h"
 
@@ -194,6 +195,11 @@ nmWxParaPropertyPebi::nmWxParaPropertyPebi(QWidget* parent) :
     m_listParas.clear();
     m_pGridItemUtils = nullptr;
     m_pDataManager = nullptr;
+    // 0ms 单次定时器用于合并同一轮事件中的多次井增删通知。
+    m_pWellListRebuildTimer = new QTimer(this);
+    m_pWellListRebuildTimer->setSingleShot(true);
+    connect(m_pWellListRebuildTimer, SIGNAL(timeout()),
+            this, SLOT(slotDeferredWellListRebuild()));
 
     setWindowTitle(tr("Numerical para property"));
 }
@@ -357,6 +363,12 @@ void nmWxParaPropertyPebi::slotParaCtrlValueChanged(QString sPara, QVariant o)
 
 void nmWxParaPropertyPebi::rebuildAllParas()
 {
+    // 已有显式全量重建时，取消尚未执行的延迟刷新，避免重复重建面板。
+    if (m_pWellListRebuildTimer != nullptr && m_pWellListRebuildTimer->isActive())
+    {
+        m_pWellListRebuildTimer->stop();
+    }
+
     if (m_pMainLayout == nullptr || m_pDataManager == nullptr)
         return;
 
@@ -746,13 +758,27 @@ void nmWxParaPropertyPebi::slotWellAdded(QString wellCode, QString wellName, QSt
     Q_UNUSED(wellCode);
     Q_UNUSED(wellName);
     Q_UNUSED(paras);
-    rebuildAllParas();
+    scheduleWellListRebuild();
 }
 
 void nmWxParaPropertyPebi::slotWellRemoved(QString wellCode, QString wellName)
 {
     Q_UNUSED(wellCode);
     Q_UNUSED(wellName);
+    scheduleWellListRebuild();
+}
+
+void nmWxParaPropertyPebi::scheduleWellListRebuild()
+{
+    if (m_pWellListRebuildTimer != nullptr)
+    {
+        // 批量循环内重复 start 会重置同一个单次定时器，返回事件循环后只触发一次。
+        m_pWellListRebuildTimer->start(0);
+    }
+}
+
+void nmWxParaPropertyPebi::slotDeferredWellListRebuild()
+{
     rebuildAllParas();
 }