From cd8bd9bafedb4ee0fe909af6c6fc52a6b7f040cf Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Thu, 17 Sep 2026 17:06:08 +0800 Subject: [PATCH] =?UTF-8?q?perf(nmNum):=20=E4=BC=98=E5=8C=96=20LM=20?= =?UTF-8?q?=E7=81=B5=E6=95=8F=E5=BA=A6=E9=87=8D=E5=BB=BA=E6=97=B6=E6=9C=BA?= =?UTF-8?q?=E4=B8=8E=E9=87=87=E6=A0=B7=E5=8A=A0=E5=AF=86=E8=A1=94=E6=8E=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 取消累计步长触发重建，连续两步实际改善不足预测的 25% 时刷新模型 - 将兜底刷新间隔调整为接受 10 步，避免微小预测量和单纯约束拒绝触发重建 - 合并临近的采样加密与灵敏度重建，提前考虑剩余求解预算 - 阶段切换时清除旧目标的预测失准计数，保留必要的失败恢复与停滞确认 - 补充重建原因日志、跟踪记录及中文翻译 --- Bin/Config/Lang/cn/nmNum_cn.qm | Bin 142167 -> 143515 bytes Bin/Config/Lang/cn/nmNum_cn.ts | 48 +++++++++++ .../nmCalculation/nmCalculationAutoFitLM.cpp | 77 +++++++++++++----- 3 files changed, 105 insertions(+), 20 deletions(-) diff --git a/Bin/Config/Lang/cn/nmNum_cn.qm b/Bin/Config/Lang/cn/nmNum_cn.qm index e279b9ee2932fcddafd619e093e3993f4f18a347..5408661a05b73dd27fc1d2ae8321f279271634af 100644 GIT binary patch delta 14371 zcmZv@cU+Hq{Qv)ay|4H6-owmJ)`@H)9kbH0DwI_cEfuL$=ApMJg+#JPL}p}!Y$X{X zAsHE!z4u<<$NPGHZs+s6{l4En&beRjt82Vo&)0LmE;mY5YbsQxZifPD7rt4$>{6rp zCzw1b3t0JOdM8`705k%)#aNcm0$ZExMCT`-)9|C&fx;)MsQzWuldpQ&Da^@$3 zUU+U97);D&36Y5=5bIeNCqVBfoF5v2;Y4Rnao%*6$m+i%lK)HWrZ<>ClyehYM07~Y znTz|(zn?{L{?dWViE;;W{*IBzT5aa+`4R7zV|Isn;DnhNv3x!F5X``hn5mh6Hb`Wx z?r`>M32r4iT#HETO0;FWMAl&zr$Z6vP=RwqeNOLgoI#KR?qi`pP5dAlBQ>8`_=AQ!1u)J;69}-vHIPK z+8c;@Vr~0m5Zm^hv-~#a=ek6FTM^sdiqmu+qVAhTY#7$iOs@%?qp&3A-vLQPu5*a} z70H>3H5}ZY*lAloS(B`MYD<3hJps~0;hub`>y$%XIwaMKo@YH)oI5$4@x^O2TRz3D8dBM)vZDl zRgEbBxI|VLYZV2($bZUtW`RWZ0d|h_>?>v*1qtoK?`hc}CeacazcQI*Z9WiN6+nXX zd!lcpBu|7?c2DB`Sw`|L)u4(9d#jbWdX72-#KS);*8&B;s;CQoJ(hNo_WgorZPf~0$E8hr-toAQboLxrjP!m!rra(EnkHISvPoI3k*x zqKP%wLQ3-t&$T60YXi~Ut)xE#<(u^n8Gi1?NUM->I6T>w?o@O6J)(Ehsn*pVL|xxe zov}L+D<)F?Rggf;F3$b!s9Ep|Vg)m)ZK!a!=(C7D>K<|ue3E2rb8mN z{sfo$wt(86nL{)tjoN9S6B}5UY&+Q!eX$|i-Z0ZQ6R9(m5VdH>8MK}L-qsCDwU2Yd zVsgyzA=WjSoCXCD^_WP`ec>N&7Slg2dBp6SQQs-{#KyaDW@)JJJQM8oLVp~9PgYR> zmbl^YSnA&yOE922=i*VEJN9uNX~3C(jQaaEhCLT@-d@W2`vCP%L`3{^gIrem!KE+b z40um2PasLJd>Zr%A^dQEayK={3zgf+eLuWW-ZY6!F^b#|;rEFqoW;vIZ{6xCnKPf*u?iZ#7;7Ihi8K2hjfh8Zs&bY_q?QvM7$uQaolYjtlRiY& z^-1Dbxa`k6AdxEMCHqe7RA=%Ef+WVtY2?_B(DI(-J!m>yaSD0QIY2By%egIrGrul* zuO3RQSw4+&gd=M!qfzruK?xghF5N++@;ef3`y`P~SZU%1IgdHd37j{Z(5N5VpezG8 z&lovx;6XUgYVq^TvpfHt=ubYYQi)y8C4X&< z*35rGi4|vXJ~PrT`yTLot>|D!Ct@GH=)~q8#75{qwky8u0H2XTy&VMh0 z#BRmXtwariQy$&XY$kTGHQm2vHrprkz$%$&RS3N(>j@7#&`j8WjqUVq#Xe%r7wGe4 zIG!z68QqQ}c5oI`cv(VZqc|tFU{zD>iMBW6+~LW2+JW=p8qO=5C9=y=tZD_W-`SkA z-~{KTQqF62K@%Lp)t;>W7Q86^6Kj}@eD1CbYjoHSVRs|5ZVM$^JdfGj!ct7F#ad1H zgqXjdb)Ve^!Q=+(S>{HpT@vfnGzLnxhxM9@>oR+Bp1#9*b+kk#uflq7*+}fc7S?-v z7}3(6%-!S&`+wh-jWp+PRok)AmiLH_PGo_f(_n7DS&&;Qk+6sbnUI|P^OOZ|4Iwsv z2%EXi1~2ZzVyX@&YM#mBk7q$QM2XDsk|mtXA{Ot)7IZs6RQ8A^Z9<-KCWNi@3M95U zf~}g2rC7a}nbr>qBi15}t>4v}$n6+QUynbmxW+cVt4Zv;70YmdR@*OPn>YC(NX?bV z>V~i#%ZS+h^=y|vu8&#Ic6-MVJ+8~Lby(|%$JyyRIm9+)u`5Zvh^-D{r5{cZ^RL1x zRwtoAS<5QrKE(E&FtG=duOUNu%pRP=NQ7Y84^>?lv(r}L+q@ZtjX{Q zViU*8TBc5eLmMk=vkns8XnHT}RE$(~|tcx4|{=B-Z=c+cue4fjk8b>2ojgbA5_?l?RTUq}x7^uS+nd>SBmzyGyRr8d& zYY}Y+FOUtnn~eA$T_W?l)CMlNjB{+R%>S8zXz@|m#4C`@b&V|Yb`g}bvn={!BNP_T zWRpIfAbPz>He&(I^nR3V)?5{KoA<;<+Q{N}4kfxd{(o4;d1r=fZaX}0!wuQe z926^`>d97)^oRXlh?lLYx{yd6CR_WuBe9G~+4{0mL|;8+>mQhTMrL+AZ0;P{PG6Yk z)*-T;yOB;`3zTJ5q!HO)tB+r1r1+(FA3IbF8T5!bCJ*->R6(f$C5%or;>I`k!e zr<)1;H|ENYj=`GrzbDHbzMELYZ&_X;P!*V_6Bwh>R;` zrB=I86@8K2tbx*OX)jsX1xRK^RoT5PS0e2lS!L{bVlJh!hxuxvmjM!)?gVG&`k+Zo z%&{XsaeSlfQN2%yY71nKe!+9yvz0wD|9-Vh_Qd@A^&{EyZU`D9x5z#PV8G3kvLBOQ zW35+lmJbwU%b=8FiUdWvJ3OJippJw@OCJf^URc74JA(eI8&Og_!SKBY5}htW71M)x z2(!_GEIN@TL0oPkAx1&a~TiFyTs#i(mU-`@%rV{pDc6D<7w5#06)CW|Rp!|z#wMI}b|X0uSMRdrM@ zDMFK0bBIOT3QcWJNfa_tus!@69`KFO z?Gdznl3M7I1SR}(U+7uwC9!l%!R&Hb?0vzZ3zRfzBIoMHf|H^N98Z?e+dP4yx}3M1 z1?N?^m|#Z`68L8F6aGQkOg}mZeJ$|fl~F?9LMWHb1);CmoW|A^`mKk3?;IoaPlEEz zZXpbO3ninEoR4-1gUl{`+a|%?p$@UK{es61{0^Nb4Ecpjsl(r#4vE5uGjM2g&kLT{ zF|qn_g7@)s*#Cj6g0Ig$qOcZ%?_xNV%R0ed;uj(C`+1^yav{7UTxZ!IA##5|B3Y5J zJk^INElOAsj~Qkh6Vlhfvz?wPY#g{5t(YKT+ucntyS_rE0(rrjy29RkDDi-3VgFTl z!pJScp`F)>F8q{eVkskp<8Q){*BgYBAE2f09tyeNe2FEW74o)2o9|l*c{@_U0HI(M zbmZ*{;cP()G3!j>+|Fa@3xo=Vn>P|YH3=8Paoy7^!nG2(){gfC$)#UDCcNnJn%Jcq z!mDfdh%Ja0-n!y_PwNVw2L28E?-FZ1Am-%G>7FQjb9;}3VX~ZEISdciPOiKH(dLm{ z-2(A^wx3)#)CFPtJm<4axgmBhl39W1Z;VlYwL_wx1=A>psJ zGM6)TAm?@;&a6G0d+a$+yUR_|$O`N76Bna7%ZG4Q4CQ?CoAXW0|KWSi4_7%qd&*aS z{7tO8AYc10B)4apJS`d46!T8L{&);guM+vDllx(whvXS|Hy|8Wm2ckCoS1izNxr#! 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阶段切换前发生求解失败 + + + 10 accepted steps since last rebuild + 距上次重建已接受 10 步调整，进行兜底刷新 + + + confirm stagnation after ineffective steps + 连续无有效改善，重新确认停滞 + + + sampling refinement merged with pending rebuild + 提前加密采样，与本次重建合并处理 + + + no feasible descent step + 未找到可行的下降方向 + + + 2 consecutive solver failures + 连续 2 次候选求解失败 + + + 2 consecutive steps with actual improvement below 25% of prediction + 连续 2 步实际改善不足预测的 25% + + + inaccurate model at minimum trust radius + 最小信赖半径下仍连续预测失准 + nmCalculationSolver diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp index 68f683d9..b932eaa7 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp @@ -1041,7 +1041,7 @@ void nmCalculationAutoFitLM::writeTraceMetaFile() QTextStream out(&metaFile); out << "{\n"; - out << " \"schema_version\": 9,\n"; + out << " \"schema_version\": 10,\n"; out << " \"strategy\": \"permeability_height_then_shape_then_original_lm\",\n"; out << " \"shape_metric\": \"pressure_and_derivative_log_slopes_81_points_lag_8\",\n"; out << " \"shape_stage_total_tolerance\": \"max(0.02, 25% of stage entry total)\",\n"; @@ -1819,15 +1819,17 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() 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。 + // 按连续预测失准刷新 Jacobian；接受步数只作兜底，不因累计移动距离强制重建。 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; + int consecutivePoorPredictions = 0; bool rebuildRequested = true; + // 跟踪文件保留稳定英文原因，界面输出时再翻译，避免受本地编码影响。 + const char* rebuildReason = QT_TR_NOOP("initial sensitivity model"); bool modelRebuiltAtMinimumRadius = false; bool stagnationConfirmationRequested = false; QVector attemptedWindows(kAutoFitTimeWindowCount, false); @@ -2061,12 +2063,13 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() jacobian.clear(); jacobianColumnValid.fill(false); rebuildRequested = true; + rebuildReason = QT_TR_NOOP("sampling layer changed"); trustRadius = 0.12; damping = 1.0e-2; consecutiveRejectedSteps = 0; consecutiveIneffectiveSteps = 0; acceptedSinceRebuild = 0; - movementSinceRebuild = 0.0; + consecutivePoorPredictions = 0; modelRebuiltAtMinimumRadius = false; stagnationConfirmationRequested = false; attemptedWindows.fill(false); @@ -2088,7 +2091,12 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() auto enterTotalStage = [&](const QString& reason) { // 保留当前曲线和完整 J，只重置阶段停滞状态、阻尼及信赖半径。 - rebuildRequested = jacobian.isEmpty() || consecutiveSolverFailures > 0; + rebuildRequested = jacobian.isEmpty() || consecutiveSolverFailures > 0 || acceptedSinceRebuild >= 10; + if(jacobian.isEmpty()) rebuildReason = QT_TR_NOOP("no valid sensitivity model"); + else if(consecutiveSolverFailures > 0) rebuildReason = QT_TR_NOOP("solver failure before stage switch"); + else if(acceptedSinceRebuild >= 10) rebuildReason = QT_TR_NOOP("10 accepted steps since last rebuild"); + // 阶段目标已改变，不把形状目标下的预测失准累计到整体阶段。 + consecutivePoorPredictions = 0; modelRebuiltAtMinimumRadius = false; shapeStage = false; effectiveImprovementBaseline = current.fitness; @@ -2148,6 +2156,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() stagnationConfirmationRequested = true; rebuildRequested = true; + rebuildReason = QT_TR_NOOP("confirm stagnation after ineffective steps"); emit logMessageGenerated( tr("No effective improvement for %1 consecutive steps; " "rebuilding sensitivity model for confirmation") @@ -2324,7 +2333,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() } acceptedSinceRebuild = 0; - movementSinceRebuild = 0.0; + consecutivePoorPredictions = 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 @@ -2356,18 +2365,34 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() enterTotalStage(tr("reserve remaining iterations and evaluations for total fitting")); } if(!shapeStage && m_layeredSampling && m_samplingStride > 1) { - const bool reserveFinalBudget = maximumEvaluations - m_totalEvaluations <= 2 * (dimensions + 1); + const int remainingEvaluations = maximumEvaluations - m_totalEvaluations; + // 已准备重建时，提前计入本轮差分及候选的开销，避免紧接着因预算再加密。 + const bool reserveFinalBudget = remainingEvaluations <= 2 * (dimensions + 1) || + (rebuildRequested && remainingEvaluations <= 3 * (dimensions + 1)); const int layerDeadline = qMax(1, m_maxIterations * (m_samplingStride == 4 ? 1 : 2) / 3); - if(reserveFinalBudget || iteration >= layerDeadline) { - promoteSampling(reserveFinalBudget); + const bool mergeRefinement = rebuildRequested && iteration < layerDeadline && + (iteration + 1 >= layerDeadline || reserveFinalBudget); + if(reserveFinalBudget || iteration >= layerDeadline || mergeRefinement) { + const bool promoted = promoteSampling(reserveFinalBudget); if(samplingRefreshFailed) break; + if(promoted && mergeRefinement) { + rebuildReason = QT_TR_NOOP("sampling refinement merged with pending rebuild"); + } } } if(rebuildRequested) { + emit logMessageGenerated(tr("Rebuilding sensitivity model: %1").arg(tr(rebuildReason))); + writeTraceRow(m_currentIteration, -1, "sensitivity_rebuild", current.parameters, + current.fitness, true, 0, rebuildReason, ¤t.breakdown); const bool confirmingStagnation = stagnationConfirmationRequested; if(!rebuildSensitivity()) { - if(shapeStage && !m_shouldStop) { enterTotalStage(tr("no valid shape sensitivity model")); rebuildRequested = true; continue; } + if(shapeStage && !m_shouldStop) { + enterTotalStage(tr("no valid shape sensitivity model")); + rebuildRequested = true; + rebuildReason = QT_TR_NOOP("no valid sensitivity model"); + continue; + } if(promoteSampling(false)) continue; stopReason = m_shouldStop ? LM_USER_STOPPED @@ -2507,6 +2532,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); damping = qMin(1.0e8, damping * 4.0); rebuildRequested = true; + rebuildReason = QT_TR_NOOP("no feasible descent step"); if(recordIneffectiveStep()) { if(promoteSampling(false)) continue; stopReason = LM_LOCAL_OPTIMUM; @@ -2544,6 +2570,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() // 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; + consecutivePoorPredictions = 0; trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); damping = qMin(1.0e8, damping * 4.0); writeTraceRow(m_currentIteration, @@ -2556,8 +2583,9 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() "solver_invalid_" + selectionName, nullptr); restoreEvaluationState(current); - if(consecutiveRejectedSteps >= 2) { + if(consecutiveSolverFailures >= 2) { rebuildRequested = true; + rebuildReason = QT_TR_NOOP("2 consecutive solver failures"); } if(consecutiveSolverFailures >= m_maxConsecutiveFailures) { stopReason = LM_CONSECUTIVE_FAILURES; @@ -2582,9 +2610,20 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() 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 * (trustRegionSquaredNorm(objectiveResidual) - + const double objectiveEnergy = 0.5 * trustRegionSquaredNorm(objectiveResidual); + double actualReduction = objectiveEnergy - 0.5 * ( trustRegionSquaredNorm(shapeStage ? candidate.breakdown.shapeResiduals : candidate.breakdown.residualVector)); double reductionRatio = actualReduction / predictedReduction; + // 使用同一采样层、同一阶段目标比较预测和实际改善。接近收敛时的微小 + // 预测量交给停滞逻辑处理，避免比例数值波动反复触发昂贵的全参数重建。 + const double predictionFloor = qMax(1.0e-14, objectiveEnergy * 1.0e-8); + const bool poorPrediction = predictedReduction > predictionFloor && + (!isFiniteNumber(reductionRatio) || reductionRatio < 0.25); + consecutivePoorPredictions = poorPrediction ? consecutivePoorPredictions + 1 : 0; + if(consecutivePoorPredictions >= 2) { + rebuildRequested = true; + rebuildReason = QT_TR_NOOP("2 consecutive steps with actual improvement below 25% of prediction"); + } bool accepted = acceptable(candidate, current); // 粗层认为下降而完整数据不认可时累计，连续两次就提前加密。 if(!shapeStage && m_layeredSampling && m_samplingStride > 1 && !accepted && actualReduction > 0.0) { @@ -2599,7 +2638,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() // 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。 acceptPoint(candidate); ++acceptedSinceRebuild; - movementSinceRebuild += stepNorm; consecutiveRejectedSteps = 0; if(reductionRatio > 0.75) { @@ -2616,21 +2654,19 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() minimumTrustRadius, trustRadius * 0.75); } - if(acceptedSinceRebuild >= 6 || - movementSinceRebuild >= 0.30) { + if(acceptedSinceRebuild >= 10 && !rebuildRequested) { rebuildRequested = true; + rebuildReason = QT_TR_NOOP("10 accepted steps since last rebuild"); } 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。 + // 到 current。形状上限或全目标点验收也可能拒绝候选，不能仅凭拒绝 + // 次数认定模型失准；重建由上面的预测质量判断，约束冲突先缩步。 ++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 @@ -2687,13 +2723,14 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting() break; } if(selectedWindow < 0 && trustRadius <= minimumTrustRadius * 1.01 && - consecutiveRejectedSteps >= 2) { + consecutiveRejectedSteps >= 2 && consecutivePoorPredictions >= 2) { if(modelRebuiltAtMinimumRadius) { if(promoteSampling(false)) continue; stopReason = LM_LOCAL_OPTIMUM; break; } rebuildRequested = true; + rebuildReason = QT_TR_NOOP("inaccurate model at minimum trust radius"); } }