From 135b762a29900bb0bf3db7477e0b082aff9b4054 Mon Sep 17 00:00:00 2001 From: AgentLabCn <130165633+AgentLabCn@users.noreply.github.com> Date: Mon, 1 Dec 2025 20:05:31 +0800 Subject: [PATCH] debug average vs max --- app.py | 2 +- data/2025/ProductionLine.csv | 226 +++++++++++++++--------------- data/2025/factory_data - 副本.csv | 28 ---- data/2025/factory_data.csv | 56 ++++---- ga_calibration_by_one_factory.py | 28 ++-- genetic_calibration.py | 2 +- simulation_model.py | 97 ++++++------- 7 files changed, 212 insertions(+), 227 deletions(-) delete mode 100644 data/2025/factory_data - 副本.csv diff --git a/app.py b/app.py index bc859f8..3a2743a 100644 --- a/app.py +++ b/app.py @@ -219,7 +219,7 @@ def Page(): return solara.Markdown(f"**累计产量（机组）：{model.cumulative_production:.2f}**") def get_mean_abs_error(model): - return solara.Markdown(f"**工厂年产量均值绝对误差：{model.mean_abs_error:.2f}**") + return solara.Markdown(f"**工厂年产量均值绝对误差：{model.error:.2f}**") with solara.Column() as main: with solara.Card("产品筛选"): diff --git a/data/2025/ProductionLine.csv b/data/2025/ProductionLine.csv index 2852312..71964bd 100644 --- a/data/2025/ProductionLine.csv +++ b/data/2025/ProductionLine.csv @@ -1,113 +1,113 @@ -,,Ƿ¹,ID,ͺ,ʼ·,·,ĥϵ,ϵСֵ,ϵֵ -,,,1,GWBD-A2,1,12,0.8032053657286623,0.5,3 -,,,2,GWBD-A2,1,12,2.242946843455525,0.5,3 -,ű,,ű1,GWBD-A2,1,12,1.0739881298814442,0.5,3 -,ű,,ű2,GWBD-A2,1,12,0.9913016076339164,0.5,3 -,ʱ,,ʱ1,GWBD-A2,2,12,3.0,0.5,3 -,ʱ,,ʱ2,GWBD-A2,2,12,2.311832265915291,0.5,3 -,ʱ,,ʱ3,GW99A,1,4,2.9868087839892805,0.5,3 -,ʱ,,ʱ4,GW99A,1,9,2.0715004199346243,0.5,3 -,ʱɫ,,ʱɫ1,GWBD-A3,2,12,0.9813943031647696,0.5,3 -,ʱɫ,,ʱɫ2,GWBD-A3,2,12,2.4817092745783658,0.5,3 -ϱ,ʱ,,ʱ1,GW130,6,12,2.958234128852966,0.5,3 -ϱ,ʱ,,ʱ2,GW130,8,12,0.9790769771894252,0.5,3 -,ʱԭ,,ʱԭ1,GWBD-A3,4,12,2.359322651601647,0.5,3 -,ʱԭ,,ʱԭ2,GWBD-A3,4,12,0.970824759034235,0.5,3 -ϲ,ʱ,,ʱ1,GW99A,2,12,1.06880969505422,0.5,3 -ϲ,ʱ,,ʱ2,GW99A,2,12,1.0574518379243223,0.5,3 -ϱ,˫,,˫1,GW110.5,2,11,0.6498736198951686,0.5,3 -ϱ,˫,,˫2,GW110.5,2,11,0.6602103773366866,0.5,3 -ϱ,˫,,˫3,GW93,1,1,1.7074438854550105,0.5,3 -ϱ,˫Ӫ,,˫Ӫ1,GWBD-A2,6,12,2.658522392353532,0.5,3 -ϱ,˫Ӫ,,˫Ӫ2,GWBD-A2,6,12,1.6326535653359566,0.5,3 -ϱ,˫Ӫ,,˫Ӫ3,GWBD-A2,7,12,2.7884634092284912,0.5,3 -ϱ,˫Ӫ,,˫Ӫ4,GWBD-A2,7,12,1.4990532841241448,0.5,3 -ϱ,˳,,˳1,GW99A,8,12,1.0494581838329766,0.5,3 -ϱ,˳,,˳2,GW99A,8,12,1.0687053812272254,0.5,3 -ϱ,˳ɳ,,˳ɳ1,GW93,2,2,1.0145139485188388,0.5,3 -ϱ,˳ɳ,,˳ɳ2,GW93,5,5,0.9187771973071808,0.5,3 -ϱ,˳ɳ,,˳ɳ3,GW93,11,11,1.0227677050731363,0.5,3 -ϱ,˳ɳ,,˳ɳ4,GW99,10,11,1.0906123896403612,0.5,3 -ϱ,˳ɳ,,˳ɳ5,GW99A,5,12,1.0000157318056455,0.5,3 -ϱ,˳ɳ,,˳ɳ6,GW99A,5,12,1.0792176556312845,0.5,3 -,вİ׳,,вİ׳1,GWBD-A3,8,8,1.84178390427543,0.5,3 -ϱ,вĸ,,вĸ1,GW110.5,1,11,1.912016897604764,0.5,3 -ϱ,вĸ,,вĸ2,GW110.5,1,11,0.8370206795410767,0.5,3 -ϱ,вĸ,,вĸ3,GW110.5,1,11,1.1182849464210354,0.5,3 -ϱ,вĸ,,вĸ4,GW83.4,1,11,1.4119792797149264,0.5,3 -ϱ,вĸ,,вĸ5,GW93,6,8,1.2755620923409356,0.5,3 -ϱ,вĸ,,вĸ6,GW99A,1,7,1.4570004301708643,0.5,3 -ϱ,вĸ,,вĸ7,GW99A,1,7,2.2014135700869284,0.5,3 -ϱ,вĸ,,вĸ8,SI90.2,1,12,2.1614685169948693,0.5,3 -ϱ,вĸ,,вĸ9,SI90.2,1,12,1.275196649549683,0.5,3 -ϱ,вĸ,,вĸ10,SI90.2,1,12,1.5271673397917631,0.5,3 -ϱ,вĸ,,вĸ11,SI90.2,1,12,0.7423712157410889,0.5,3 -ϱ,вĸ,,вĸ12,SI90.2,2,12,1.2646324802629867,0.5,3 -ϱ,вĸ,,вĸ13,SI90.2,2,12,0.9554400175665158,0.5,3 -,вĹ,,вĹ1,GWBD-A3,7,11,0.998747932696368,0.5,3 -,вĹ,,вĹ2,GWBD-A3,7,11,2.9767751702801672,0.5,3 -,вĺ,,вĺ1,GW99A,1,12,1.4830831491291596,0.5,3 -,вĺ,,вĺ2,GW99A,1,12,1.0888969811545297,0.5,3 -,вĺ,,вĺ3,GWBD-A2,2,12,1.5023258418752827,0.5,3 -,вĺ,,вĺ4,GWBD-A2,3,12,1.102775747560624,0.5,3 -,вľȪ,,вľȪ1,GW99,1,12,0.5622969570539172,0.5,3 -,вľȪ,,вľȪ2,GW99,1,12,1.150024482747415,0.5,3 -,вľȪ,,вľȪ3,GWBD-A2,1,12,1.5459022834883542,0.5,3 -,вľȪ,,вľȪ4,GWBD-A2,1,12,2.9639734937057667,0.5,3 -,вľȪ,,вľȪ5,GWBD-A2,2,12,2.212176654543076,0.5,3 -,вľȪ,,вľȪ6,GWBD-A2,3,12,1.0356930471233745,0.5,3 -,вľȪ,,вľȪ7,SI90.2,1,1,2.4273446212774297,0.5,3 -,вľȪ,,вľȪ8,SI90.2,1,4,1.0649603727762178,0.5,3 -,вľȪ,,вľȪ9,SI90.2,3,4,0.6376416250041546,0.5,3 -,вľȪ,,вľȪ10,SI90.2,9,9,2.0072777075728943,0.5,3 -,вľȪ,,вľȪ11,SI90.2,4,12,1.3664189491081575,0.5,3 -,вľȪ,,вľȪ12,SI90.2,4,12,2.7585455100963725,0.5,3 -ϱ,вƸ,,вƸ1,GW93,1,4,0.6380193777126062,0.5,3 -ϱ,вƸ,,вƸ2,GW93,1,12,2.4506758131580946,0.5,3 -ϱ,вƸ,,вƸ3,GW93,1,12,2.669431356239481,0.5,3 -ϲ,вƼ,,вƼ1,GW83.4,1,3,1.9110812265524788,0.5,3 -ϲ,вƼ,,вƼ2,GW93,1,10,2.6507295870466416,0.5,3 -ϲ,вƼ,,вƼ3,GW93,1,10,1.687082980580373,0.5,3 -ϲ,вƼ,,вƼ4,GWBD-A3,1,12,0.6783033734311962,0.5,3 -ϲ,вƼ,,вƼ5,GWBD-A3,1,12,1.3047442992624676,0.5,3 -,в,,в1,GW99,1,12,1.0622480731614574,0.5,3 -,в,,в2,GW99,1,12,0.9848399267309024,0.5,3 -,в,,в3,GWBD-A2,1,12,2.254156012076419,0.5,3 -,в,,в4,GWBD-A2,1,12,1.7254468138843502,0.5,3 -,в,,в5,GWBD-A2,2,12,0.5705179617507162,0.5,3 -,в,,в6,SI90.2,1,1,0.8214144687810874,0.5,3 -,в,,в7,SI90.2,1,1,2.545528482349731,0.5,3 -,в˰,,в˰1,GW93,1,9,1.0695212200904811,0.5,3 -,в˰,,в˰2,GW93,1,9,1.0912804475566316,0.5,3 -,в,,в1,GW130,1,12,1.814520488259726,0.5,3 -,в,,в2,GW130,11,12,2.838458381081099,0.5,3 -,в,,в3,SI122,1,12,2.830586835154425,0.5,3 -,в,,в4,SI122,1,12,1.3363257879855754,0.5,3 -,в,,в1,GWBD-A2,1,12,2.4869571005924014,0.5,3 -,в,,в2,GWBD-A2,1,12,2.4698950834768234,0.5,3 -,в,,в3,GWBD-A3,7,9,2.927579772268298,0.5,3 -,вϪ,,вϪ1,GW93,3,3,1.7651093767774086,0.5,3 -,ͨ,,ͨ1,GW99,11,11,0.7831592691610867,0.5,3 -,ͨ,,ͨ2,GW99A,1,10,1.540926708239259,0.5,3 -,ͨ,,ͨ3,GWBD-A2,1,12,1.931780035552089,0.5,3 -,ͨ,,ͨ4,GWBD-A2,1,12,1.0164084233944286,0.5,3 -,ͨ,,ͨ5,GWBD-A2,1,12,0.840555078914054,0.5,3 -,ͨ,,ͨ1,GWBD-A2,1,12,2.087343566138866,0.5,3 -,ͨ,,ͨ2,GWBD-A2,2,12,1.0704526164218118,0.5,3 -ϱ,ͨ綫,,ͨ綫1,GW110.5,6,10,1.3241403875683502,0.5,3 -ϱ,ͨ綫,,ͨ綫2,GW110.5,6,10,1.0779044534267077,0.5,3 -ϱ,ͨ綫,,ͨ綫3,GW76,1,6,1.2634384670468408,0.5,3 -ϱ,ͨ綫,,ͨ綫4,GW76,11,12,1.086720532076798,0.5,3 -ϱ,ͨ綫,,ͨ綫5,GW76,11,12,0.9580780681635088,0.5,3 -ϱ,ͨ綫,,ͨ綫6,GW81,1,12,1.4244065199658889,0.5,3 -ϱ,ͨ綫,,ͨ綫7,GW81,1,12,1.1125186832972436,0.5,3 -ϱ,ͨ綫,,ͨ綫8,GW81,1,12,1.0880193045165334,0.5,3 -ϱ,ͨ綫,,ͨ綫9,GW86,10,12,1.0228512727112231,0.5,3 -ϱ,ͨ綫,,ͨ綫10,GWBD-D,4,10,1.0275145272690325,0.5,3 -ϱ,ͨ綫,,ͨ綫11,GW83.3,7,9,2.7419468504868334,0.5,3 -,ͨ,,ͨ1,GW83.4,1,1,1.796533172065262,0.5,3 -,ͨ,,ͨ2,GW99A,1,1,1.0773042029829227,0.5,3 -,ͨ,,ͨ3,GWBD-A2,1,12,0.8633209522768549,0.5,3 -,ͨ,,ͨ4,GWBD-A2,1,12,0.8421534665300137,0.5,3 -,ͨ,,ͨ5,GWBD-A3,2,12,0.9182717456417385,0.5,3 -,ͨ,,ͨ6,GWBD-A3,2,12,0.9284823666140081,0.5,3 +区域名,工厂名,是否新工厂,产线ID,生产型号,开始月份,结束月份,磨合系数,系数最小值,系数最大值 +西北东部,艾郎玉门,是,艾郎玉门1,GWBD-A2,1,12,2.573832299130555,0.5,3 +西北东部,艾郎玉门,是,艾郎玉门2,GWBD-A2,1,12,0.7551349273834977,0.5,3 +华北,艾郎张北,是,艾郎张北1,GWBD-A2,1,12,2.115080130961469,0.5,3 +华北,艾郎张北,是,艾郎张北2,GWBD-A2,1,12,0.6797069684688172,0.5,3 +西北西部,时代巴里坤,是,时代巴里坤1,GWBD-A2,2,12,1.8810616541608631,0.5,3 +西北西部,时代巴里坤,是,时代巴里坤2,GWBD-A2,2,12,2.987097262834335,0.5,3 +西北西部,时代巴里坤,是,时代巴里坤3,GW99A,1,4,2.488871403042081,0.5,3 +西北西部,时代巴里坤,是,时代巴里坤4,GW99A,1,9,2.953943233688721,0.5,3 +华南,时代百色,是,时代百色1,GWBD-A3,2,12,1.6690292523195245,0.5,3 +华南,时代百色,是,时代百色2,GWBD-A3,2,12,2.4659503577446857,0.5,3 +华东南北部,时代射阳,是,时代射阳1,GW130,6,12,2.2076058205327764,0.5,3 +华东南北部,时代射阳,是,时代射阳2,GW130,8,12,1.430132739097293,0.5,3 +东北,时代松原,是,时代松原1,GWBD-A3,4,12,1.0363142371846124,0.5,3 +东北,时代松原,是,时代松原2,GWBD-A3,4,12,2.1701304430049246,0.5,3 +华东南南部,时代株洲,是,时代株洲1,GW99A,2,12,1.081678858112704,0.5,3 +华东南南部,时代株洲,是,时代株洲2,GW99A,2,12,1.0913646502654977,0.5,3 +华东南北部,双瑞大丰,是,双瑞大丰1,GW110.5,2,11,0.7663884592433219,0.5,3 +华东南北部,双瑞大丰,是,双瑞大丰2,GW110.5,2,11,0.5106326560706359,0.5,3 +华东南北部,双瑞大丰,否,双瑞大丰3,GW93,1,1,1.3145870292666928,0.5,3 +华东南北部,双瑞东营,是,双瑞东营1,GWBD-A2,6,12,2.543085429687747,0.5,3 +华东南北部,双瑞东营,是,双瑞东营2,GWBD-A2,6,12,2.79268044357321,0.5,3 +华东南北部,双瑞东营,是,双瑞东营3,GWBD-A2,7,12,2.927074556394514,0.5,3 +华东南北部,双瑞东营,是,双瑞东营4,GWBD-A2,7,12,2.892880738588988,0.5,3 +华东南北部,天顺濮阳,否,天顺濮阳1,GW99A,8,12,1.0527500393509173,0.5,3 +华东南北部,天顺濮阳,否,天顺濮阳2,GW99A,8,12,1.069042487726414,0.5,3 +华东南北部,天顺沙洋,否,天顺沙洋1,GW93,2,2,2.32043479739694,0.5,3 +华东南北部,天顺沙洋,是,天顺沙洋2,GW93,5,5,0.6973591106929395,0.5,3 +华东南北部,天顺沙洋,否,天顺沙洋3,GW93,11,11,1.1607811390472684,0.5,3 +华东南北部,天顺沙洋,否,天顺沙洋4,GW99,10,11,2.9663458837234176,0.5,3 +华东南北部,天顺沙洋,否,天顺沙洋5,GW99A,5,12,0.913690176263654,0.5,3 +华东南北部,天顺沙洋,否,天顺沙洋6,GW99A,5,12,0.8787050723740802,0.5,3 +东北,中材白城,否,中材白城1,GWBD-A3,8,8,2.1402649111425927,0.5,3 +华东南北部,中材阜宁,否,中材阜宁1,GW110.5,1,11,1.5459138854823482,0.5,3 +华东南北部,中材阜宁,否,中材阜宁2,GW110.5,1,11,0.5729502079816026,0.5,3 +华东南北部,中材阜宁,否,中材阜宁3,GW110.5,1,11,1.5694540282848988,0.5,3 +华东南北部,中材阜宁,否,中材阜宁4,GW83.4,1,11,1.382119991468305,0.5,3 +华东南北部,中材阜宁,否,中材阜宁5,GW93,6,8,0.8827086544315473,0.5,3 +华东南北部,中材阜宁,否,中材阜宁6,GW99A,1,7,2.830733081902044,0.5,3 +华东南北部,中材阜宁,否,中材阜宁7,GW99A,1,7,2.0523054358827126,0.5,3 +华东南北部,中材阜宁,否,中材阜宁8,SI90.2,1,12,1.6546434532678769,0.5,3 +华东南北部,中材阜宁,否,中材阜宁9,SI90.2,1,12,2.736515935954447,0.5,3 +华东南北部,中材阜宁,是,中材阜宁10,SI90.2,1,12,1.056360739447041,0.5,3 +华东南北部,中材阜宁,是,中材阜宁11,SI90.2,1,12,2.938099080422176,0.5,3 +华东南北部,中材阜宁,是,中材阜宁12,SI90.2,2,12,0.7522973434359188,0.5,3 +华东南北部,中材阜宁,是,中材阜宁13,SI90.2,2,12,1.455419784069309,0.5,3 +西北西部,中材哈密,否,中材哈密1,GWBD-A3,7,11,1.0178664652883072,0.5,3 +西北西部,中材哈密,否,中材哈密2,GWBD-A3,7,11,2.6355971342265194,0.5,3 +华北,中材邯郸,否,中材邯郸1,GW99A,1,12,1.1728678581197811,0.5,3 +华北,中材邯郸,否,中材邯郸2,GW99A,1,12,1.0447043893191337,0.5,3 +华北,中材邯郸,是,中材邯郸3,GWBD-A2,2,12,1.443120583655738,0.5,3 +华北,中材邯郸,是,中材邯郸4,GWBD-A2,3,12,1.9378786142508244,0.5,3 +西北东部,中材酒泉,否,中材酒泉1,GW99,1,12,1.126932621978494,0.5,3 +西北东部,中材酒泉,否,中材酒泉2,GW99,1,12,1.144783324686626,0.5,3 +西北东部,中材酒泉,否,中材酒泉3,GWBD-A2,1,12,0.8063768405900967,0.5,3 +西北东部,中材酒泉,是,中材酒泉4,GWBD-A2,1,12,0.990324308056816,0.5,3 +西北东部,中材酒泉,是,中材酒泉5,GWBD-A2,2,12,2.265886526545412,0.5,3 +西北东部,中材酒泉,是,中材酒泉6,GWBD-A2,3,12,1.839298288914216,0.5,3 +西北东部,中材酒泉,否,中材酒泉7,SI90.2,1,1,1.2621204611288224,0.5,3 +西北东部,中材酒泉,否,中材酒泉8,SI90.2,1,4,0.8199844980580712,0.5,3 +西北东部,中材酒泉,否,中材酒泉9,SI90.2,3,4,2.762280541648126,0.5,3 +西北东部,中材酒泉,否,中材酒泉10,SI90.2,9,9,0.593157493625113,0.5,3 +西北东部,中材酒泉,否,中材酒泉11,SI90.2,4,12,1.1027785967994126,0.5,3 +西北东部,中材酒泉,否,中材酒泉12,SI90.2,4,12,0.6872334261934359,0.5,3 +华东南北部,中材连云港,否,中材连云港1,GW93,1,4,0.947630481798518,0.5,3 +华东南北部,中材连云港,否,中材连云港2,GW93,1,12,1.3688662392144466,0.5,3 +华东南北部,中材连云港,否,中材连云港3,GW93,1,12,2.797094312017575,0.5,3 +华东南南部,中材萍乡,否,中材萍乡1,GW83.4,1,3,2.0773221443454304,0.5,3 +华东南南部,中材萍乡,否,中材萍乡2,GW93,1,10,1.3802802554963542,0.5,3 +华东南南部,中材萍乡,否,中材萍乡3,GW93,1,10,1.4002226284151378,0.5,3 +华东南南部,中材萍乡,否,中材萍乡4,GWBD-A3,1,12,0.7708097434812091,0.5,3 +华东南南部,中材萍乡,否,中材萍乡5,GWBD-A3,1,12,2.592994930030489,0.5,3 +东北,中材锡林,否,中材锡林1,GW99,1,12,1.149507908747532,0.5,3 +东北,中材锡林,否,中材锡林2,GW99,1,12,2.559150953717717,0.5,3 +东北,中材锡林,是,中材锡林3,GWBD-A2,1,12,0.7655680581592179,0.5,3 +东北,中材锡林,是,中材锡林4,GWBD-A2,1,12,1.2703315998965434,0.5,3 +东北,中材锡林,是,中材锡林5,GWBD-A2,2,12,0.8143637605841937,0.5,3 +东北,中材锡林,否,中材锡林6,SI90.2,1,1,2.306341573772662,0.5,3 +东北,中材锡林,否,中材锡林7,SI90.2,1,1,1.78433681769329,0.5,3 +东北,中材兴安盟,否,中材兴安盟1,GW93,1,9,1.102017941748625,0.5,3 +东北,中材兴安盟,否,中材兴安盟2,GW93,1,9,1.1319603805540963,0.5,3 +华南,中材阳江,是,中材阳江1,GW130,1,12,1.1786952000344315,0.5,3 +华南,中材阳江,是,中材阳江2,GW130,11,12,2.401570055570436,0.5,3 +华南,中材阳江,否,中材阳江3,SI122,1,12,2.9892600685463484,0.5,3 +华南,中材阳江,否,中材阳江4,SI122,1,12,2.736186842293012,0.5,3 +西北西部,中材伊吾,否,中材伊吾1,GWBD-A2,1,12,1.3930072691073077,0.5,3 +西北西部,中材伊吾,否,中材伊吾2,GWBD-A2,1,12,1.977477183020509,0.5,3 +西北西部,中材伊吾,是,中材伊吾3,GWBD-A3,7,9,0.5529804495458042,0.5,3 +华南,中材玉溪,否,中材玉溪1,GW93,3,3,1.959546411962172,0.5,3 +西北西部,重通昌吉,否,重通昌吉1,GW99,11,11,0.5,0.5,3 +西北西部,重通昌吉,是,重通昌吉2,GW99A,1,10,2.051928553788,0.5,3 +西北西部,重通昌吉,否,重通昌吉3,GWBD-A2,1,12,2.6658563724837165,0.5,3 +西北西部,重通昌吉,否,重通昌吉4,GWBD-A2,1,12,0.5338053591905982,0.5,3 +西北西部,重通昌吉,是,重通昌吉5,GWBD-A2,1,12,1.6278097723685112,0.5,3 +东北,重通大安,是,重通大安1,GWBD-A2,1,12,1.581903910413776,0.5,3 +东北,重通大安,是,重通大安2,GWBD-A2,2,12,1.3105714103279738,0.5,3 +华东南北部,重通如东,否,重通如东1,GW110.5,6,10,2.662169226944904,0.5,3 +华东南北部,重通如东,否,重通如东2,GW110.5,6,10,2.82368855931312,0.5,3 +华东南北部,重通如东,否,重通如东3,GW76,1,6,1.8825731353509496,0.5,3 +华东南北部,重通如东,否,重通如东4,GW76,11,12,0.9278020901772052,0.5,3 +华东南北部,重通如东,否,重通如东5,GW76,11,12,1.0234023740731988,0.5,3 +华东南北部,重通如东,否,重通如东6,GW81,1,12,1.0689261905119656,0.5,3 +华东南北部,重通如东,否,重通如东7,GW81,1,12,1.0733442879188466,0.5,3 +华东南北部,重通如东,否,重通如东8,GW81,1,12,1.0894565180344418,0.5,3 +华东南北部,重通如东,是,重通如东9,GW86,10,12,0.9369533470121776,0.5,3 +华东南北部,重通如东,是,重通如东10,GWBD-D,4,10,1.16385774999936,0.5,3 +华东南北部,重通如东,是,重通如东11,GW83.3,7,9,1.0150864337534389,0.5,3 +西北东部,重通武威,否,重通武威1,GW83.4,1,1,1.0249345530326432,0.5,3 +西北东部,重通武威,否,重通武威2,GW99A,1,1,1.4390978913290176,0.5,3 +西北东部,重通武威,否,重通武威3,GWBD-A2,1,12,2.2770043222733705,0.5,3 +西北东部,重通武威,否,重通武威4,GWBD-A2,1,12,0.7021776323779197,0.5,3 +西北东部,重通武威,是,重通武威5,GWBD-A3,2,12,0.9008060819850424,0.5,3 +西北东部,重通武威,是,重通武威6,GWBD-A3,2,12,2.1363106837251777,0.5,3 diff --git a/data/2025/factory_data - 副本.csv b/data/2025/factory_data - 副本.csv deleted file mode 100644 index 4b54fee..0000000 --- a/data/2025/factory_data - 副本.csv +++ /dev/null @@ -1,28 +0,0 @@ -,Ӣ,ƽĥϵ,С -,AilangYumen,1.5230761045920935,0.3881128436694239 -ű,AilangZhangbei,1.0326448687576804,0.3875473927222936 -ʱ,ShidaiBalikun,2.592535367459799,0.3447525809589239 -ʱɫ,ShidaiBaise,1.7315517888715677,0.3810874444018801 -ʱ,ShidaiSheyang,1.9686555530211955,0.3907170103360906 -ʱԭ,ShidaiSongyuan,1.665073705317941,0.3762141500549016 -ʱ,ShidaiZhuzhou,1.063130766489271,0.3441028083793268 -˫,ShuangruiDafeng,1.0058426275622885,0.2548982526287585 -˫Ӫ,ShuangruiDongying,2.144673162760532,0.2654149421577937 -˳,TianshunPuyang,1.059081782530101,0.3420826063591247 -˳ɳ,TianshunShayang,1.0209841046627413,0.3431195242102018 -вİ׳,ZhongcaiBaicheng,1.84178390427543,0.3040622851529628 -вĸ,ZhongcaiFuning,1.3953503165990335,0.2224680588438837 -вĹ,ZhongcaiHami,1.987761551488268,0.2359049003020729 -вĺ,ZhongcaiHandan,1.294270429929899,0.2269262246567306 -вľȪ,ZhongcaiJiuquan,1.6443546420414277,0.2408385393820534 -вƸ,ZhongcaiLianyungang,1.919375515703394,0.1895613222334434 -вƼ,ZhongcaiPingxiang,1.6463882933746317,0.2156093482814695 -в,ZhongcaiXilin,1.4234502483906664,0.186874092416876 -в˰,ZhongcaiXinganmeng,1.0804008338235565,0.1888690411673305 -в,ZhongcaiYangjiang,2.204972873120206,0.1585609701706 -в,ZhongcaiYiwu,2.6281439854458406,0.1592597821901667 -вϪ,ZhongcaiYuxi,1.7651093767774086,0.1215239331335629 -ͨ,ChongtongChangji,1.2225659030521836,0.1187457988190405 -ͨ,ChongtongDaan,1.578898091280339,0.1099638209648977 -ͨ綫,ChongtongRudong,1.2843217333208146,0.10536445035245522 -ͨ,ChongtongWuwei,1.0,10000.0 diff --git a/data/2025/factory_data.csv b/data/2025/factory_data.csv index 89bab4c..ba28ab9 100644 --- a/data/2025/factory_data.csv +++ b/data/2025/factory_data.csv @@ -1,28 +1,28 @@ -,Ӣ,ƽĥϵ,С -,AilangYumen,1.5230761045920935,0.3881128436694239 -ű,AilangZhangbei,1.0326448687576804,0.3875473927222936 -ʱ,ShidaiBalikun,2.592535367459799,0.3447525809589239 -ʱɫ,ShidaiBaise,1.7315517888715677,0.3810874444018801 -ʱ,ShidaiSheyang,1.9686555530211955,0.3907170103360906 -ʱԭ,ShidaiSongyuan,1.665073705317941,0.3762141500549016 -ʱ,ShidaiZhuzhou,1.063130766489271,0.3441028083793268 -˫,ShuangruiDafeng,1.0058426275622885,0.2548982526287585 -˫Ӫ,ShuangruiDongying,2.144673162760532,0.2654149421577937 -˳,TianshunPuyang,1.059081782530101,0.3420826063591247 -˳ɳ,TianshunShayang,1.0209841046627413,0.3431195242102018 -вİ׳,ZhongcaiBaicheng,1.84178390427543,0.3040622851529628 -вĸ,ZhongcaiFuning,1.3953503165990335,0.2224680588438837 -вĹ,ZhongcaiHami,1.987761551488268,0.2359049003020729 -вĺ,ZhongcaiHandan,1.294270429929899,0.2269262246567306 -вľȪ,ZhongcaiJiuquan,1.6443546420414277,0.2408385393820534 -вƸ,ZhongcaiLianyungang,1.919375515703394,0.1895613222334434 -вƼ,ZhongcaiPingxiang,1.6463882933746317,0.2156093482814695 -в,ZhongcaiXilin,1.4234502483906664,0.186874092416876 -в˰,ZhongcaiXinganmeng,1.0804008338235565,0.1888690411673305 -в,ZhongcaiYangjiang,2.204972873120206,0.1585609701706 -в,ZhongcaiYiwu,2.6281439854458406,0.1592597821901667 -вϪ,ZhongcaiYuxi,1.7651093767774086,0.1215239331335629 -ͨ,ChongtongChangji,1.2225659030521836,0.1187457988190405 -ͨ,ChongtongDaan,1.578898091280339,0.1099638209648977 -ͨ綫,ChongtongRudong,1.2843217333208146,0.1053644503524552 -ͨ,ChongtongWuwei,1.0710109843518,0.10157518253678476 +工厂中文名,工厂英文名,工厂平均磨合系数,最小误差 +艾郎玉门,AilangYumen,1.6644836132570262,0.4109048664614467 +艾郎张北,AilangZhangbei,1.397393549715143,0.3875473927222936 +时代巴里坤,ShidaiBalikun,2.5777433884315,0.1084387587668106 +时代百色,ShidaiBaise,2.067489805032105,0.3822434351539541 +时代射阳,ShidaiSheyang,1.818869279815035,0.3907170103360906 +时代松原,ShidaiSongyuan,1.6032223400947685,0.3826740983753151 +时代株洲,ShidaiZhuzhou,1.0865217541891008,0.381593662574357 +双瑞大丰,ShuangruiDafeng,0.8638693815268835,0.3331325041383269 +双瑞东营,ShuangruiDongying,2.788930292061115,0.09394600514362228 +天顺濮阳,TianshunPuyang,1.0608962635386656,0.342536423517118 +天顺沙洋,TianshunShayang,1.48955269658305,0.3452121483167365 +中材白城,ZhongcaiBaicheng,2.1402649111425927,0.34455662553732 +中材阜宁,ZhongcaiFuning,1.6484247401561711,0.2904939733696281 +中材哈密,ZhongcaiHami,1.8267317997574133,0.2724502886592768 +中材邯郸,ZhongcaiHandan,1.3996428613363694,0.282263520694731 +中材酒泉,ZhongcaiJiuquan,1.28342974401872,0.2568142301144558 +中材连云港,ZhongcaiLianyungang,1.7045303443435131,0.2307662040664297 +中材萍乡,ZhongcaiPingxiang,1.644325940353724,0.2829493917509724 +中材锡林,ZhongcaiXilin,1.5213715246530224,0.2281958103288435 +中材兴安盟,ZhongcaiXinganmeng,1.116989161151361,0.2285419508619 +中材阳江,ZhongcaiYangjiang,2.3264280416110568,0.1893196457297771 +中材伊吾,ZhongcaiYiwu,1.307821633891207,0.2163127405194821 +中材玉溪,ZhongcaiYuxi,1.959546411962172,0.1522826086927401 +重通昌吉,ChongtongChangji,1.4758800115661652,0.1486544907789079 +重通大安,ChongtongDaan,1.4462376603708749,0.1364186176846021 +重通如东,ChongtongRudong,1.424296355735419,0.1440551201664654 +重通武威,ChongtongWuwei,1.413388527453862,0.1406968232829397 diff --git a/ga_calibration_by_one_factory.py b/ga_calibration_by_one_factory.py index ebff21d..4696a7a 100644 --- a/ga_calibration_by_one_factory.py +++ b/ga_calibration_by_one_factory.py @@ -23,12 +23,24 @@ def clip(val: float, bounds: Tuple[float, float]) -> float: return max(lo, min(hi, val)) +def _match_factory_row(df: pd.DataFrame, cols: dict, factory_code: str) -> pd.Series: + """Match factory by English name first, fall back to Chinese name.""" + eng_col = cols["工厂英文名"] + cn_col = cols["工厂中文名"] + code_norm = factory_code.strip().lower() + mask_eng = df[eng_col].astype(str).str.strip().str.lower() == code_norm + if mask_eng.any(): + return df.loc[mask_eng].iloc[0] + mask_cn = df[cn_col].astype(str).str.strip() == factory_code.strip() + if mask_cn.any(): + return df.loc[mask_cn].iloc[0] + raise ValueError(f"在{eng_col}/{cn_col} 中找不到工厂 {factory_code}") + + def load_factory_row(csv_path: Path, factory_code: str) -> tuple[pd.Series, pd.DataFrame, dict]: df, cols, _ = read_csv_with_encoding(csv_path, required={"工厂中文名", "工厂英文名", "工厂平均磨合系数", "最小误差"}) - row = df.loc[df[cols["工厂英文名"]].astype(str).str.strip() == factory_code] - if row.empty: - raise ValueError(f"在 {csv_path} 中找不到工厂英文名: {factory_code}") - return row.iloc[0], df, cols + row = _match_factory_row(df, cols, factory_code) + return row, df, cols def _csv_lock_path(csv_path: Path) -> Path: @@ -64,16 +76,14 @@ def update_factory_csv(csv_path: Path, factory_code: str, new_factor: float, new fd = _acquire_lock(lock_path) try: df, cols, enc = read_csv_with_encoding(csv_path, required={"工厂中文名", "工厂英文名", "工厂平均磨合系数", "最小误差"}) - mask = df[cols["工厂英文名"]].astype(str).str.strip() == factory_code - if not mask.any(): - raise ValueError(f"在 {csv_path} 中找不到工厂英文名: {factory_code}") + row = _match_factory_row(df, cols, factory_code) + mask = df.index == row.name df.loc[mask, cols["工厂平均磨合系数"]] = float(new_factor) df.loc[mask, cols["最小误差"]] = float(new_error) df.to_csv(csv_path, index=False, encoding=enc) finally: _release_lock(lock_path, fd) - def update_production_line_csv(csv_path: Path, factory_name_cn: str, line_ids: List[str], best_genes: List[float]) -> None: lock_path = _csv_lock_path(csv_path) fd = _acquire_lock(lock_path) @@ -105,7 +115,7 @@ def evaluate(factory_code: str, line_ids: List[str], genes: List[float]) -> floa model.line_factor[lid] = float(val) while model.running: model.step() - return model.mean_abs_error + return model.error def mutate(genes: List[float]) -> List[float]: diff --git a/genetic_calibration.py b/genetic_calibration.py index cf34a97..993bce6 100644 --- a/genetic_calibration.py +++ b/genetic_calibration.py @@ -45,7 +45,7 @@ def evaluate(genes: List[float]) -> float: ) while model.running: model.step() - return model.mean_abs_error + return model.error except PermissionError as e: print(f"文件访问冲突: {e}. 正在重试...") return float('inf') # 返回高值以惩罚该个体 diff --git a/simulation_model.py b/simulation_model.py index 97d429c..ed5a5f8 100644 --- a/simulation_model.py +++ b/simulation_model.py @@ -89,7 +89,7 @@ class SimulationModel(Model): self.cumulative_production = 0 self.monthly_totals = {} self.production_log = [] - self.mean_abs_error = 0.0 + self.error = 0.0 # Allow explicit product_set override; otherwise fall back to config. self.product_set = self._parse_product_set(product_set if product_set is not None else cfg.get("product_set")) self.output_enabled = output_enabled @@ -156,7 +156,7 @@ class SimulationModel(Model): { "cumulative_production": lambda m: m.cumulative_production, "monthly_total": lambda m: m.monthly_totals.get(m.current_month, 0), - "mean_abs_error": lambda m: m.mean_abs_error, + "error": lambda m: m.error, "fulfill_pct_overall": lambda m: m.overall_fulfill_pct, "transport_cost": lambda m: m.monthly_transport_cost, } @@ -218,20 +218,9 @@ class SimulationModel(Model): use_max: bool = True, ) -> dict: """ - Compute error metrics based on cumulative monthly deviation. - For each factory: - - cumulative monthly production vs benchmark - - deviation ratio per month (can be negative) - - factory error = max or mean absolute deviation ratio across 12 months - Returns: - { - "mean_abs_error": float, - "factory_max_ratio": {factory_name: value}, - "factory_cum_ratio": {factory_name: pd.Series of length 12 (ratios)} - } + Compute cumulative deviation ratios per factory (no aggregation). Ratios are unitless (e.g., 0.02 == 2%). """ - errors = [] factory_max_ratio = {} factory_cum_ratio = {} month_cols = [f"{m}月" for m in range(1, 13)] @@ -250,14 +239,10 @@ class SimulationModel(Model): if actual_total <= 0: continue pct_ratio = (prod_cum - bench_cum) / actual_total # signed ratio - agg_val = pct_ratio.abs().max() if use_max else pct_ratio.abs().mean() - errors.append(float(agg_val)) - factory_max_ratio[fname] = float(agg_val) factory_cum_ratio[fname] = pct_ratio + factory_max_ratio[fname] = float(pct_ratio.abs().max()) - mean_abs_error = float("inf") if not errors else sum(errors) / len(errors) return { - "mean_abs_error": mean_abs_error, "factory_max_ratio": factory_max_ratio, "factory_cum_ratio": factory_cum_ratio, } @@ -864,7 +849,7 @@ class SimulationModel(Model): def _finalize_factory_errors(self, write_files: bool): if not self.production_log: - self.mean_abs_error = float("inf") + self.error = float("inf") return df = pd.DataFrame(self.production_log) factory_pivot = ( @@ -887,18 +872,33 @@ class SimulationModel(Model): benchmark = pd.read_csv(f"data/{filename}/benchmark.csv", encoding="utf-8-sig") except UnicodeDecodeError: benchmark = pd.read_csv(f"data/{filename}/benchmark.csv", encoding="gbk") + benchmark = benchmark.rename(columns=lambda c: str(c).strip()) + month_cols = [col for col in benchmark.columns if str(col).strip().endswith("月") and str(col).strip()[:-1].isdigit()] + month_cols = sorted(month_cols, key=lambda x: int(str(x).strip()[:-1])) + if len(month_cols) < 12: + raise KeyError(f"benchmark.csv 缺少月份列，找到: {month_cols}") benchmark_sorted = benchmark.sort_values(by=benchmark.columns[0]) total_col = "总计" - month_cols = [f"{m}月" for m in range(1, 13)] if total_col not in benchmark_sorted.columns: benchmark_sorted[total_col] = benchmark_sorted[month_cols].sum(axis=1) - stats = self._compute_factory_error_stats(factory_pivot, benchmark_sorted, total_col=total_col, use_max=self.use_error_max) - cum_pct_by_factory = {k: v * 100.0 for k, v in stats["factory_cum_ratio"].items()} # to % - if stats["mean_abs_error"] == float("inf"): - self.mean_abs_error = float("inf") - return - self.mean_abs_error = stats["mean_abs_error"] + stats = self._compute_factory_error_stats( + factory_pivot, + benchmark_sorted, + total_col=total_col, + use_max=self.use_error_max, + ) + cum_ratio_by_factory = stats["factory_cum_ratio"] + # Aggregate factory-level errors (mean across factories of max/mean per-month absolute ratio) + factory_errors = [] + for ratio_series in cum_ratio_by_factory.values(): + metric_val = ratio_series.abs().max() if self.use_error_max else ratio_series.abs().mean() + factory_errors.append(float(metric_val)) + if not factory_errors: + self.error = float("inf") + else: + # Aggregate across factories: max when use_error_max=True, otherwise mean. + self.error = max(factory_errors) if self.use_error_max else sum(factory_errors) / len(factory_errors) if not write_files: return @@ -909,28 +909,30 @@ class SimulationModel(Model): factory_report_path = os.path.join(output_dir, f"factory_report_{timestamp}.csv") factory_pivot.to_csv(factory_report_path, index=False, encoding="utf-8-sig") - # Error summary based on cumulative deviation percentage (max abs per factory) + # Error summary based on cumulative deviation ratio (unitless, consistent with GA) err_rows = [] - for fname, pct_series in cum_pct_by_factory.items(): - max_abs_pct = pct_series.abs().max() if self.use_error_max else pct_series.abs().mean() - err_rows.append({"工厂名称": fname, "最大累积偏差[%]" if self.use_error_max else "平均累积偏差[%]": float(max_abs_pct)}) - error_df = pd.DataFrame(err_rows) + metric_col = "最大累计偏差" if self.use_error_max else "平均累计偏差" + for fname, ratio_series in cum_ratio_by_factory.items(): + metric_val = ratio_series.abs().max() if self.use_error_max else ratio_series.abs().mean() + err_rows.append({"工厂名称": fname, metric_col: float(metric_val)}) + error_df = pd.DataFrame(err_rows) if err_rows else pd.DataFrame(columns=["工厂名称", metric_col]) error_path = os.path.join(output_dir, f"factory_error_{timestamp}.csv") error_df.to_csv(error_path, index=False, encoding="utf-8-sig") # Error bar charts by month using cumulative percentage deviations - if cum_pct_by_factory: + if cum_ratio_by_factory: month_labels = [f"{m}月" for m in range(1, 13)] - month_pct_df = pd.DataFrame( - {fname: pct.reset_index(drop=True) for fname, pct in cum_pct_by_factory.items()} + month_ratio_df = pd.DataFrame( + {fname: pct.reset_index(drop=True) for fname, pct in cum_ratio_by_factory.items()} ).transpose() - month_abs_means = month_pct_df.abs().mean(axis=0) + month_abs_means = month_ratio_df.abs().mean(axis=0) month_abs_means.index = month_labels[: len(month_abs_means)] + month_abs_means_pct = month_abs_means * 100 plt.figure(figsize=(10, 5)) - month_abs_means.plot(kind="bar") - plt.ylabel("月度累积偏差平均值[%]") - plt.title("按月份的累积偏差（取绝对值后平均）") + month_abs_means_pct.plot(kind="bar") + plt.ylabel("月度累计偏差平均值[%]") + plt.title("按月份的累计偏差（取绝对值后平均）") plt.tight_layout() month_plot_path = os.path.join(output_dir, f"error_by_month_{timestamp}.png") plt.savefig(month_plot_path) @@ -938,18 +940,20 @@ class SimulationModel(Model): factory_df = pd.DataFrame( { - "name": list(cum_pct_by_factory.keys()), - "error_pct": [ - (pct.abs().max() if self.use_error_max else pct.abs().mean()) for pct in cum_pct_by_factory.values() + "name": list(cum_ratio_by_factory.keys()), + "error_ratio": [ + (pct.abs().max() if self.use_error_max else pct.abs().mean()) for pct in cum_ratio_by_factory.values() ], } - ).sort_values(by="error_pct") + ) + factory_df = factory_df.sort_values(by="error_ratio") + factory_df["error_pct"] = factory_df["error_ratio"] * 100 self.factory_error_df = factory_df.reset_index(drop=True) plt.figure(figsize=(12, 6)) ax = factory_df["error_pct"].reset_index(drop=True).plot(kind="bar") metric_label = "最大" if self.use_error_max else "平均" - plt.ylabel(f"{metric_label}累积偏差[%]") - plt.title(f"按工厂的{metric_label}累积偏差（百分比）") + plt.ylabel(f"{metric_label}????[%]") + plt.title(f"????{metric_label}?????????") plt.tight_layout() for idx, row in factory_df.reset_index(drop=True).iterrows(): val = row["error_pct"] @@ -966,8 +970,7 @@ class SimulationModel(Model): factory_plot_path = os.path.join(output_dir, f"error_by_factory_{timestamp}.png") plt.savefig(factory_plot_path) plt.close() - - # Save histories for inventory, fulfillment pct, unmet backlog +# Save histories for inventory, fulfillment pct, unmet backlog inv_path = os.path.join(output_dir, f"inventory_history_{timestamp}.csv") fulfill_path = os.path.join(output_dir, f"fulfill_history_{timestamp}.csv") unmet_path = os.path.join(output_dir, f"unmet_history_{timestamp}.csv")