20251209Ver8（精密电流源温漂特性测试报告）

一、背景

在昨日的测试中，我们对数据进行了初步汇总与分析。基于昨日测试结果，今日我们进一步开展更为细致的评测工作。本次测试将温度增量设定为 5℃，继续采用全量程扫描测试方法。误差定义保持不变，仍为电流输出误差 = 设定值 - DM3068 示值（单位：mA）。

1、测试条件

校准基准：25±1℃环境下完成全量程电流校准
误差定义：电流输出误差 = 设定值 - DM3068示值（单位：mA）
测试方法：全量程扫描测试

2、宽温域精度数据

环境温度 (℃)	平均误差 (mA)	误差波动幅度 (mA)	全量程误差分布范围 (mA)	▲mA/▲℃
45	+0.15	±0.05	+0.10 ~ +0.20	0.012
35	+0.03	±0.05	-0.02 ~ +0.08	0.003
25	0.00	±0.05	±0.05	0.003
15	-0.03	±0.05	-0.08 ~ +0.02	0.003
5	0.00	±0.075	±0.075	-

▲ 图1 平均误差与环境温度线性建模

3、核心结论

温漂系数 ：约 0.003mA/℃（折算至输出级）
极性拐点：35℃以上由负漂转正漂
线性度：在5℃~45℃范围内呈现良好线性
幅度稳定性：除5℃极端低温外，误差波动均保持±0.05mA
最优温区：25℃校准点精度最高，误差带±0.05mA

二、复测

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0330,5.0197,10.0241,15.0566,20.0727,25.0460,30.0367,35.0556,40.0363,45.0479,50.0270,55.0423,60.0512,65.0515,70.0757,75.0560,80.0480,85.0211,90.0469,95.0475,100.0397,105.0477,110.0319,115.0502,120.0476,125.0366,130.0105,135.0214,140.0380,145.0498,150.0396,155.0284,160.0389,165.0074,170.0225,175.0236,180.0127,185.0130,189.9918,195.0161,200.0073,205.0068,209.9888,215.0049,219.9885,225.0040,230.0135,235.0152,240.0161,245.0084,249.9912,254.9820,259.9975,264.9814,269.9567,274.9903,279.9941,284.9727,289.9687,294.9873,299.9725,304.9833,309.9913,314.9726,319.9607,324.9828,329.9904,334.9710,339.9861,344.9423,349.9752,354.9524,359.9621,364.9426,369.9764,374.9645,379.9749,384.9474,389.9674,394.9521,399.9330,404.9437,409.9418,414.9277,419.9463,424.9465,429.9502,434.9307,439.9416,444.9408,449.9308,454.9254,459.9268,464.9198,469.9092,474.9447,479.9338,484.9198,489.9136,494.9284,499.9206]

▲ 图2 8-2-5

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0259,5.0141,9.9972,15.0219,20.0220,25.0331,30.0010,34.9912,39.9793,44.9774,49.9927,54.9809,60.0107,65.0140,70.0034,74.9966,80.0201,84.9994,90.0229,95.0215,100.0237,105.0146,110.0073,115.0077,119.9957,124.9867,129.9991,134.9999,139.9523,144.9973,149.9857,154.9955,160.0037,164.9881,169.9909,174.9742,179.9848,184.9596,189.9942,194.9767,199.9626,204.9642,209.9537,214.9727,219.9682,224.9837,229.9841,234.9672,239.9702,244.9738,249.9735,254.9587,259.9598,264.9803,269.9423,274.9655,279.9513,284.9507,289.9525,294.9316,299.9540,304.9492,309.9435,314.9634,319.9620,324.9728,329.9752,334.9489,339.9585,344.9431,349.9564,354.9522,359.9586,364.9473,369.9383,374.9426,379.9289,384.9364,389.9350,394.9344,399.9278,404.9304,409.9381,414.9251,419.9462,424.9405,429.9462,434.9510,439.9454,444.9314,449.9361,454.9175,459.9292,464.9298,469.9163,474.9331,479.9213,484.9398,489.9414,494.9387,499.9470]

▲ 图3 8-2-10

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0147,5.0272,10.0038,15.0074,19.9817,25.0132,30.0116,35.0120,40.0092,44.9886,50.0050,55.0143,59.9939,65.0159,70.0138,75.0032,80.0148,84.9882,89.9861,94.9986,99.9668,105.0012,109.9828,114.9804,119.9773,124.9510,129.9715,134.9655,139.9643,144.9886,149.9647,154.9650,159.9769,164.9773,169.9547,174.9709,179.9802,184.9784,189.9936,194.9700,199.9813,204.9653,209.9835,214.9603,219.9765,224.9773,229.9735,234.9829,239.9719,244.9682,249.9523,254.9582,259.9752,264.9672,269.9503,274.9400,279.9558,284.9813,289.9426,294.9408,299.9552,304.9500,309.9318,314.9500,319.9571,324.9249,329.9438,334.9302,339.9195,344.9369,349.9505,354.9559,359.9503,364.9288,369.9550,374.9543,379.9349,384.9340,389.9378,394.9730,399.9490,404.9354,409.9641,414.9379,419.9614,424.9306,429.9496,434.9370,439.9388,444.9229,449.9425,454.9500,459.9352,464.9429,469.9290,474.9338,479.9181,484.9221,489.9340,494.9341,499.9192]

▲ 图4 8-2-15

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0187,4.9763,9.9496,14.9833,19.9902,24.9888,29.9915,34.9867,39.9881,44.9929,49.9844,54.9813,59.9740,65.0059,70.0083,74.9799,79.9738,84.9668,89.9924,94.9888,99.9667,105.0114,109.9773,114.9702,119.9678,124.9853,129.9717,134.9783,139.9798,144.9930,149.9626,154.9785,159.9862,164.9745,169.9555,174.9484,179.9544,184.9840,189.9877,194.9820,199.9664,204.9709,209.9721,214.9531,219.9638,224.9566,229.9704,234.9809,239.9661,244.9674,249.9548,254.9787,259.9823,264.9750,269.9709,274.9643,279.9694,284.9514,289.9715,294.9563,299.9490,304.9663,309.9449,314.9755,319.9731,324.9552,329.9831,334.9646,339.9792,344.9770,349.9634,354.9776,359.9474,364.9663,369.9449,374.9681,379.9547,384.9510,389.9413,394.9266,399.9699,404.9502,409.9642,414.9672,419.9644,424.9656,429.9575,434.9516,439.9724,444.9549,449.9737,454.9464,459.9625,464.9611,469.9575,474.9553,479.9421,484.9690,489.9556,494.9484,499.9643]

▲ 图5 8-2-20

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0202,4.9959,9.9980,14.9912,20.0056,24.9949,30.0219,35.0179,40.0093,45.0039,50.0119,55.0217,59.9891,65.0067,69.9971,74.9940,80.0086,85.0070,90.0206,95.0077,100.0055,105.0125,109.9936,115.0191,120.0253,124.9955,130.0071,135.0127,140.0003,144.9951,149.9766,155.0059,160.0001,165.0151,169.9980,174.9827,180.0066,184.9985,189.9908,195.0103,199.9810,204.9978,209.9830,214.9832,219.9843,224.9875,229.9838,234.9781,239.9782,244.9885,249.9593,254.9853,259.9795,264.9901,269.9787,275.0060,279.9774,284.9789,289.9810,294.9872,299.9924,304.9887,309.9834,314.9973,319.9699,324.9998,329.9834,334.9565,339.9836,344.9903,349.9712,354.9538,359.9627,364.9719,369.9668,374.9735,379.9459,384.9706,389.9697,394.9724,399.9774,404.9632,409.9626,414.9714,419.9767,424.9806,429.9860,435.0053,439.9822,444.9668,449.9589,454.9962,459.9862,464.9982,469.9642,474.9874,479.9849,484.9731,489.9956,494.9590,499.9610]

▲ 图6 8-2-25

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0034,4.9164,9.9230,14.9341,19.9442,24.9594,29.9457,34.9413,39.9473,44.9376,49.9538,54.9319,59.9711,64.9747,69.9702,74.9648,79.9626,84.9694,89.9680,94.9593,99.9510,104.9615,109.9452,114.9541,119.9331,124.9478,129.9579,134.9426,139.9497,144.9560,149.9311,154.9720,159.9516,164.9533,169.9511,174.9471,179.9527,184.9589,189.9618,194.9531,199.9596,204.9436,209.9309,214.9575,219.9448,224.9375,229.9793,234.9629,239.9619,244.9529,249.9466,254.9521,259.9442,264.9457,269.9582,274.9711,279.9468,284.9470,289.9336,294.9532,299.9567,304.9378,309.9200,314.9316,319.9300,324.9270,329.9385,334.9536,339.9446,344.9531,349.9539,354.9658,359.9497,364.9408,369.9293,374.9644,379.9386,384.9421,389.9392,394.9388,399.9427,404.9379,409.9518,414.9408,419.9483,424.9379,429.9492,434.9434,439.9504,444.9328,449.9236,454.9639,459.9623,464.9366,469.9406,474.9592,479.9411,484.9311,489.9531,494.9430,499.9360]

▲ 图7 8-2-30

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0142,4.8778,9.8732,14.8801,19.8972,24.9084,29.8842,34.8840,39.8799,44.8812,49.8940,54.9053,59.8839,64.9102,69.8941,74.9075,79.8974,84.9039,89.9202,94.9117,99.8852,104.9092,109.8945,114.9283,119.9130,124.8895,129.9189,134.9104,139.9069,144.9011,149.9029,154.9073,159.9140,164.9162,169.8750,174.9078,179.8933,184.9085,189.8982,194.9132,199.9091,204.8869,209.8897,214.8902,219.8910,224.9018,229.9063,234.8913,239.9098,244.9231,249.9095,254.9053,259.9274,264.9151,269.8939,274.9099,279.8944,284.9048,289.9026,294.9056,299.8967,304.9171,309.9163,314.9012,319.9229,324.9188,329.9260,334.8944,339.9008,344.8908,349.8948,354.9121,359.8891,364.8990,369.9042,374.9184,379.8969,384.8924,389.8818,394.8801,399.8781,404.8853,409.8873,414.8842,419.9050,424.8967,429.8880,434.8925,439.8885,444.9109,449.9129,454.8878,459.8712,464.8795,469.8858,474.8895,479.8599,484.8910,489.8927,494.8870,499.8850]

▲ 图8 8-2-35

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0165,4.8217,9.8328,14.8443,19.8324,24.8386,29.8268,34.8179,39.8055,44.8153,49.8119,54.8276,59.8289,64.8365,69.8339,74.8504,79.8117,84.8266,89.8469,94.8347,99.8355,104.8553,109.8305,114.8603,119.8513,124.8214,129.8544,134.8575,139.8535,144.8660,149.8470,154.8510,159.8398,164.8510,169.8400,174.8298,179.8413,184.8538,189.8645,194.8665,199.8438,204.8412,209.8200,214.8409,219.8452,224.8602,229.8522,234.8589,239.8416,244.8357,249.8404,254.8461,259.8604,264.8597,269.8509,274.8706,279.8419,284.8440,289.8645,294.8413,299.8649,304.8654,309.8423,314.8617,319.8818,324.8646,329.8521,334.8226,339.8605,344.8639,349.8567,354.8906,359.8495,364.8679,369.8606,374.8544,379.8560,384.8414,389.8594,394.8563,399.8536,404.8545,409.8416,414.8647,419.8718,424.8564,429.8417,434.8811,439.8637,444.8791,449.8605,454.8653,459.8837,464.8788,469.8793,474.8736,479.8687,484.8548,489.9049,494.8650,499.8712]

▲ 图9 8-2-40

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0161,4.7430,9.7321,14.7517,19.7402,24.7627,29.7396,34.7269,39.7331,44.7563,49.7476,54.7495,59.7648,64.7584,69.7599,74.7704,79.7371,84.7636,89.7654,94.7495,99.7734,104.7662,109.7375,114.7643,119.7665,124.7664,129.7633,134.7742,139.7508,144.7844,149.7566,154.7804,159.7864,164.7920,169.7813,174.7686,179.8066,184.8081,189.8107,194.7650,199.7745,204.7799,209.7679,214.8044,219.7971,224.7955,229.7875,234.7701,239.7986,244.8039,249.7910,254.7835,259.7739,264.7838,269.7943,274.8140,279.7866,284.7703,289.7974,294.7914,299.8063,304.7830,309.7828,314.7954,319.8087,324.7814,329.7968,334.7834,339.8094,344.8094,349.8047,354.8067,359.7698,364.7991,369.8034,374.8197,379.8025,384.8113,389.7871,394.8090,399.8079,404.8080,409.8152,414.8065,419.8002,424.7827,429.8211,434.8177,439.8027,444.7925,449.7882,454.8001,459.8237,464.8164,469.8075,474.7780,479.8008,484.8106,489.8022,494.8087,499.7964]

▲ 图10 8-2-45

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0243,4.7151,9.6996,14.6793,19.6918,24.7138,29.7070,34.7087,39.6833,44.7076,49.7285,54.7135,59.7109,64.7091,69.6910,74.7074,79.7167,84.7046,89.7120,94.7013,99.7196,104.7039,109.7143,114.7195,119.7222,124.7024,129.7327,134.7096,139.7113,144.7188,149.7084,154.7306,159.7525,164.7446,169.7178,174.7359,179.7330,184.7256,189.7284,194.7317,199.7232,204.7191,209.7015,214.7356,219.7240,224.7324,229.7520,234.7293,239.7353,244.7345,249.7586,254.7319,259.7455,264.7368,269.7469,274.7431,279.7463,284.7445,289.7463,294.7410,299.7594,304.7684,309.7335,314.7428,319.7483,324.7637,329.7649,334.7490,339.7551,344.7428,349.7444,354.7497,359.7454,364.7620,369.7447,374.7341,379.7354,384.7423,389.7511,394.7528,399.7391,404.7621,409.7678,414.7747,419.7404,424.7541,429.7457,434.7684,439.7786,444.7645,449.7600,454.7818,459.7842,464.7592,469.7830,474.7760,479.7822,484.7660,489.7854,494.7590,499.7593]

▲ 图11 8-2-50

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0247,4.6436,9.6201,14.6127,19.6351,24.6527,29.6229,34.6423,39.6319,44.6544,49.6277,54.6089,59.6506,64.6277,69.6494,74.6335,79.6419,84.6288,89.6358,94.6374,99.6218,104.6569,109.6317,114.6340,119.6316,124.6311,129.6246,134.6518,139.6198,144.6154,149.6285,154.6141,159.6243,164.6258,169.6253,174.6265,179.6402,184.6351,189.6436,194.6605,199.6531,204.6194,209.6283,214.6376,219.6485,224.6508,229.6525,234.6511,239.6358,244.6506,249.6561,254.6605,259.6514,264.6664,269.6391,274.6572,279.6574,284.6506,289.6771,294.6553,299.6623,304.6639,309.6505,314.6680,319.6746,324.6415,329.6550,334.6488,339.6689,344.6662,349.6292,354.6676,359.6596,364.6813,369.6673,374.6817,379.6840,384.6712,389.6708,394.6547,399.6689,404.6815,409.6790,414.6589,419.6675,424.6611,429.6943,434.6685,439.6638,444.6804,449.6680,454.6928,459.6787,464.6683,469.6669,474.6790,479.6917,484.6735,489.6921,494.6928,499.6838]

▲ 图12 8-2-55

python 复制代码

idim=[0.0000,5.0000,10.0000,15.0000,20.0000,25.0000,30.0000,35.0000,40.0000,45.0000,50.0000,55.0000,60.0000,65.0000,70.0000,75.0000,80.0000,85.0000,90.0000,95.0000,100.0000,105.0000,110.0000,115.0000,120.0000,125.0000,130.0000,135.0000,140.0000,145.0000,150.0000,155.0000,160.0000,165.0000,170.0000,175.0000,180.0000,185.0000,190.0000,195.0000,200.0000,205.0000,210.0000,215.0000,220.0000,225.0000,230.0000,235.0000,240.0000,245.0000,250.0000,255.0000,260.0000,265.0000,270.0000,275.0000,280.0000,285.0000,290.0000,295.0000,300.0000,305.0000,310.0000,315.0000,320.0000,325.0000,330.0000,335.0000,340.0000,345.0000,350.0000,355.0000,360.0000,365.0000,370.0000,375.0000,380.0000,385.0000,390.0000,395.0000,400.0000,405.0000,410.0000,415.0000,420.0000,425.0000,430.0000,435.0000,440.0000,445.0000,450.0000,455.0000,460.0000,465.0000,470.0000,475.0000,480.0000,485.0000,490.0000,495.0000,500.0000]
mdim=[-0.0326,4.5340,9.5187,14.5345,19.5176,24.5504,29.5560,34.5459,39.5320,44.5553,49.5443,54.5249,59.5574,64.5515,69.5492,74.5477,79.5260,84.5378,89.5478,94.5665,99.5470,104.5514,109.5584,114.5522,119.5526,124.5241,129.5548,134.5538,139.5406,144.5505,149.5333,154.5522,159.5698,164.5465,169.5547,174.5415,179.5410,184.5511,189.5508,194.5703,199.5265,204.5413,209.5376,214.5471,219.5403,224.5503,229.5456,234.5298,239.5402,244.5512,249.5119,254.5211,259.5078,264.5095,269.5061,274.5056,279.5362,284.5120,289.5323,294.5343,299.5538,304.5438,309.5255,314.5455,319.5297,324.5232,329.5462,334.5091,339.5344,344.5400,349.5064,354.5277,359.5163,364.5209,369.5237,374.5328,379.5150,384.5079,389.5189,394.5153,399.5180,404.5254,409.4889,414.5112,419.5438,424.5133,429.5100,434.4993,439.5159,444.5347,449.5043,454.5037,459.5251,464.5149,469.5230,474.5017,479.5128,484.5420,489.5307,494.5465,499.5290]

▲ 图13 8-2-60

三、测试程序

python 复制代码

import os
import glob
import numpy as np
import pandas as pd
from headm import tspload
import matplotlib.pyplot as plt
import matplotlib
import traceback
import sys

# 强制立即输出
sys.stdout = sys.__stdout__
sys.stdout.reconfigure(encoding='utf-8')

try:
    # 设置中文字体，防止乱码
    matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS', 'DejaVu Sans']
    matplotlib.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

    print('程序开始运行...')
    # ==============================================================================
    # 第一部分：数据加载与初步处理
    # ==============================================================================

    # 查找所有err-8-2-*.npz文件
    npz_files = sorted(glob.glob('err-8-2-*.npz'))
    print(f'找到 {len(npz_files)} 个NPZ文件: {npz_files}')

    # 创建数据字典
    data_dict = {'set_current': []}

    # 加载每个NPZ文件
    data_length = None
    skipped_files = []
    for npz_file in npz_files:
        filename = os.path.basename(npz_file)
        temp_str = filename.split('-')[-1].replace('.npz', '')
        temperature = int(temp_str)
        print(f'  加载: {filename} (温度: {temperature}°C)', flush=True)
        idim, mdim = tspload(filename.replace('.npz', ''), 'idim', 'mdim')
        print(f'    idim长度: {len(idim)}, mdim长度: {len(mdim)}', flush=True)

        # 检查数据长度是否一致
        if data_length is None:
            data_length = len(mdim)
            print(f'    设置基准长度为: {data_length}', flush=True)
        elif len(mdim) != data_length:
            print(f'    警告: {filename} 的数据长度 {len(mdim)} 与基准长度 {data_length} 不一致，跳过该文件!', flush=True)
            skipped_files.append(filename)
            continue

        column_name = f'{temperature}C'
        data_dict[column_name] = mdim
        if len(data_dict['set_current']) == 0:
            data_dict['set_current'] = idim

    if skipped_files:
        print(f'\n已跳过 {len(skipped_files)} 个不完整的数据文件: {skipped_files}')

    # 创建DataFrame
    df = pd.DataFrame(data_dict)
    print(f'\n创建DataFrame，列名: {df.columns.tolist()}')
    print(f'数据形状: {df.shape}')

    # 动态获取所有温度列并按数值大小排序
    temp_cols = [col for col in df.columns if col != 'set_current']
    temp_values = [int(col[:-1]) for col in temp_cols]  # 提取温度数值
    sorted_indices = sorted(range(len(temp_values)), key=lambda i: temp_values[i])
    available_temps = [temp_cols[i] for i in sorted_indices]

    # 按温度顺序重新排列列
    df = df[['set_current'] + available_temps]

    # 标记校准温度（25°C，如果存在则作为基准，否则取最接近25°C的温度）
    ref_temp = '25C'
    if ref_temp not in available_temps:
        # 如果没有25°C，选择最接近25°C的温度作为基准
        temp_diffs = [(abs(int(t[:-1]) - 25), t) for t in available_temps]
        ref_temp = min(temp_diffs, key=lambda x: x[0])[1]
        print(f'警告：25°C数据不存在，使用 {ref_temp} 作为校准基准')

    # 保存原始测量数据到CSV
    csv_filename = 'merged_temperature_drift.csv'
    df.to_csv(csv_filename, index=False)
    print(f'原始测量数据已保存至 {csv_filename}')
    print(f'可用温度点: {available_temps}')
    print(f'电流范围: {df["set_current"].min()} ~ {df["set_current"].max()} mA (间隔 {df["set_current"].iloc[1] - df["set_current"].iloc[0]} mA)')

    # ==============================================================================
    # 第二部分：温度漂移科学计算方法
    # ==============================================================================
    # 科学方法说明：
    # 1. 温漂系数计算：根据温度变化量ΔT和漂移量ΔI计算温漂系数 TC = ΔI / ΔT (mA/°C)
    # 2. 温度稳定性分析：计算不同电流点下温漂系数的一致性
    # 3. 分段分析：分别分析低温区(5-15°C)、温区(15-25°C)、高温区(25-30°C)的漂移特性

    # ref_temp已在上面定义
    print(f'\n校准温度: {ref_temp}')
    print(f'ref_temp是否在df.columns中: {ref_temp in df.columns}')
    print(f'df.columns: {df.columns.tolist()}')

    if ref_temp in df.columns:
        print(f'\n{"="*80}')
        print(f'温度漂移分析（以{ref_temp}为校准基准）')
        print(f'{"="*80}')
        print(f'计算公式：')
        print(f'  绝对温漂: Drift(T) = Measured(T) - Measured({ref_temp})  [mA]')
        ref_temp_val = int(ref_temp[:-1])
        print(f'  温漂系数: TC(T) = Drift(T) / (T - {ref_temp_val}°C)  [mA/°C]')
        print(f'{"="*80}')
        
        # 计算温漂（相对于25°C）
        df_drift = df.drop(columns=['set_current']).copy()
        drift_ref = df_drift[ref_temp].values
        
        for col in df_drift.columns:
            temp_val = int(col[:-1])
            delta_T = temp_val - ref_temp_val
            if delta_T != 0:
                df_drift[col] = df_drift[col] - drift_ref
            else:
                df_drift[col] = 0.0  # 校准温度点的漂移为0
        
        df_drift['set_current'] = df['set_current']
        df_drift = df_drift[['set_current'] + available_temps]
        
        # 保存温漂数据
        drift_csv = 'temperature_drift_analysis.csv'
        df_drift.to_csv(drift_csv, index=False)
        print(f'\n温漂数据已保存至 {drift_csv}')

        # ==============================================================================
        # 第三部分：温漂系数统计分析
        # ==============================================================================
        
        # 提取温漂列（排除set_current和校准温度）
        drift_temp_cols = [c for c in available_temps if c != ref_temp]
        
        # 为每个温度计算温漂系数
        df_tc = pd.DataFrame()
        df_tc['set_current'] = df['set_current']
        
        print(f'\n温漂系数统计 (单位: mA/°C)')
        print(f'{"="*80}')
        print(f'{"电流(mA)":>10s}', end='')
        for temp in drift_temp_cols:
            temp_val = int(temp[:-1])
            delta_T = temp_val - ref_temp_val
            df_tc[f'TC_{temp}'] = df_drift[temp] / delta_T
            print(f'  {temp:>4s} ', end='')
        print(f'  平均值')
        print('-' * 80)
        
        # 计算平均温漂系数
        df_tc['TC_mean'] = df_tc[[f'TC_{t}' for t in drift_temp_cols]].mean(axis=1)
        df_tc['TC_std'] = df_tc[[f'TC_{t}' for t in drift_temp_cols]].std(axis=1)
        
        # 显示部分数据
        display_indices = list(range(0, len(df), 50)) + [len(df)-1]
        for idx in display_indices:
            if idx < len(df):
                current = df.loc[idx, 'set_current']
                print(f'{current:10.1f}', end='')
                for temp in drift_temp_cols:
                    print(f'  {df_tc.loc[idx, f"TC_{temp}"]:8.5f}', end='')
                print(f'  {df_tc.loc[idx, "TC_mean"]:8.5f}')
        
        # 全局统计
        print(f'\n{"="*80}')
        print(f'全局温漂特性统计')
        print(f'{"="*80}')
        print(f'平均温漂系数: {df_tc["TC_mean"].mean():.6f} mA/°C')
        print(f'温漂系数标准差: {df_tc["TC_std"].mean():.6f} mA/°C')
        print(f'温漂系数变异系数: {(df_tc["TC_std"].mean() / abs(df_tc["TC_mean"].mean()) * 100):.2f}%')
        
        # 温度分段分析
        print(f'\n{"="*80}')
        print(f'温度分段漂移分析')
        print(f'{"="*80}')
        
        # 低温区和高温区分析（相对于校准温度）
        low_temps = [t for t in drift_temp_cols if int(t[:-1]) < ref_temp_val]
        high_temps = [t for t in drift_temp_cols if int(t[:-1]) > ref_temp_val]
        
        if low_temps:
            low_drift_avg = df_drift[low_temps].mean().mean()
            print(f'低温区平均漂移 (<{ref_temp}): {low_drift_avg:.6f} mA')
        if high_temps:
            high_drift_avg = df_drift[high_temps].mean().mean()
            print(f'高温区平均漂移 (>{ref_temp}): {high_drift_avg:.6f} mA')
        
        # 不同电流区间的温漂特性
        print(f'\n不同电流区间的温漂特性:')
        current_ranges = [(0, 100), (100, 200), (200, 300), (300, 400), (400, 500)]
        for r_start, r_end in current_ranges:
            mask = (df_tc['set_current'] >= r_start) & (df_tc['set_current'] < r_end)
            if mask.any():
                mean_tc = df_tc.loc[mask, 'TC_mean'].mean()
                std_tc = df_tc.loc[mask, 'TC_std'].mean()
                print(f'  {r_start:3d}-{r_end:3d} mA: 平均温漂系数 = {mean_tc:.6f} ± {std_tc:.6f} mA/°C')
        
        # 最大温漂点分析
        df_drift['max_abs_drift'] = df_drift[drift_temp_cols].abs().max(axis=1)
        max_drift_idx = df_drift['max_abs_drift'].idxmax()
        print(f'\n最大温漂点:')
        print(f'  电流: {df.loc[max_drift_idx, "set_current"]} mA')
        print(f'  最大绝对温漂: {df_drift.loc[max_drift_idx, "max_abs_drift"]:.6f} mA')
        for temp in drift_temp_cols:
            print(f'  {temp}: {df_drift.loc[max_drift_idx, temp]:+.6f} mA')
        
        # 保存温漂系数数据
        tc_csv = 'temperature_coefficient_analysis.csv'
        df_tc.to_csv(tc_csv, index=False)
        print(f'\n温漂系数数据已保存至 {tc_csv}')

        # ==============================================================================
        # 第四部分：可视化分析
        # ==============================================================================

        fig = plt.figure(figsize=(15, 12))

        # 图1：原始测量值随温度变化
        ax1 = plt.subplot(3, 2, 1)
        for col in available_temps:
            ax1.plot(df['set_current'], df[col], marker='o', markersize=3, label=col)
        ax1.set_xlabel('设定电流 (mA)')
        ax1.set_ylabel('测量电流 (mA)')
        ax1.set_title('不同温度下的电流测量值')
        ax1.grid(True, alpha=0.3)
        ax1.legend()

        # 图2：温漂曲线
        ax2 = plt.subplot(3, 2, 2)
        for temp in drift_temp_cols:
            ax2.plot(df_drift['set_current'], df_drift[temp], marker='s', markersize=3, label=temp)
        ax2.axhline(y=0, color='k', linestyle='--', alpha=0.5, label=f'校准点({ref_temp})')
        ax2.set_xlabel('设定电流 (mA)')
        ax2.set_ylabel('温漂 (mA)')
        ax2.set_title(f'温度漂移曲线 (相对于{ref_temp})')
        ax2.grid(True, alpha=0.3)
        ax2.legend()

        # 图3：温漂系数随电流变化
        ax3 = plt.subplot(3, 2, 3)
        for temp in drift_temp_cols:
            ax3.plot(df_tc['set_current'], df_tc[f'TC_{temp}'], marker='^', markersize=3, label=temp)
        ax3.plot(df_tc['set_current'], df_tc['TC_mean'], 'k-', linewidth=2, label='平均')
        ax3.set_xlabel('设定电流 (mA)')
        ax3.set_ylabel('温漂系数 (mA/°C)')
        ax3.set_title('温漂系数随电流变化')
        ax3.grid(True, alpha=0.3)
        ax3.legend()

        # 图4：温漂系数标准差
        ax4 = plt.subplot(3, 2, 4)
        ax4.plot(df_tc['set_current'], df_tc['TC_std'], 'r-', linewidth=2)
        ax4.fill_between(df_tc['set_current'], 
                         df_tc['TC_mean'] - df_tc['TC_std'],
                         df_tc['TC_mean'] + df_tc['TC_std'],
                         alpha=0.2, color='blue')
        ax4.set_xlabel('设定电流 (mA)')
        ax4.set_ylabel('温漂系数标准差 (mA/°C)')
        ax4.set_title('温漂系数的一致性分析')
        ax4.grid(True, alpha=0.3)

        # 图5：误差随温度变化（以200mA为例）
        ax5 = plt.subplot(3, 2, 5)
        sample_current = 200
        if sample_current in df['set_current'].values:
            sample_idx = df[df['set_current'] == sample_current].index[0]
            temps = [int(t[:-1]) for t in available_temps]
            measured_vals = [df.loc[sample_idx, t] for t in available_temps]
            drift_vals = [df_drift.loc[sample_idx, t] for t in available_temps]
            
            ax5_twin = ax5.twinx()
            ax5.plot(temps, measured_vals, 'bo-', label='测量值', markersize=8)
            ax5_twin.plot(temps, drift_vals, 'ro--', label='温漂', markersize=8)
            ax5_twin.axhline(y=0, color='k', linestyle=':', alpha=0.5)
            ax5.set_xlabel('温度 (°C)')
            ax5.set_ylabel('测量电流 (mA)', color='b')
            ax5_twin.set_ylabel('温漂 (mA)', color='r')
            ax5.set_title(f'设定电流={sample_current}mA的温漂特性')
            ax5.grid(True, alpha=0.3)
            
            # 合并图例
            lines1, labels1 = ax5.get_legend_handles_labels()
            lines2, labels2 = ax5_twin.get_legend_handles_labels()
            ax5.legend(lines1 + lines2, labels1 + labels2, loc='best')

        # 图6：各温度点相对于校准温度的漂移分布
        ax6 = plt.subplot(3, 2, 6)
        drift_matrix = df_drift[drift_temp_cols].values
        ax6.boxplot(drift_matrix, labels=drift_temp_cols)
        ax6.axhline(y=0, color='k', linestyle='--', alpha=0.5)
        ax6.set_xlabel('温度')
        ax6.set_ylabel('温漂 (mA)')
        ax6.set_title(f'温漂分布箱线图 (相对于{ref_temp})')
        ax6.grid(True, alpha=0.3)

        plt.tight_layout()
        plt.savefig('temperature_drift_analysis.png', dpi=300)
        print(f'\n可视化图表已保存至 temperature_drift_analysis.png')
        plt.show()
    else:
        print('未找到校准温度，无法进行温漂分析和可视化')

except Exception as e:
    print(f'\n程序运行出错: {e}')
    print(f'错误详情:')
    traceback.print_exc()

python 复制代码

#!/usr/local/bin/python
# -*- coding: gbk -*-
#============================================================
# TEST6.PY                     -- by Dr. BaiChao 2025-12-30
#
# Note:
#============================================================
from headm import *
from tsmodule.tsvisa        import *
import serial
from _ast import Or
from serial.serialutil import SerialException
import numpy as np

dm3068open(4)
#------------------------------------------------------------
vi = serial.Serial()
vi.baudrate = 115200
vi.timeout = 0.05
try:
    vi.port = 'COM8'
except:
    printf('Set vi port COM8 error. ')
try:
    vi.open()
except serial.serialutil.SerialException:
    printf('Open vi port COM8 error.')
else:
    printf('Open vi port COM8 Ok.')
#------------------------------------------------------------
def set_current(c):
    vi.write(b'setc %f\r'%c)
#------------------------------------------------------------
idim = list(range(0, 501, 5))  # 0~500mA，可配置增量
name = "err-8-2-60"

mdim = []
for i in idim:
    set_current(i)
    time.sleep(1.5)
    c = dm3068cdc()*1000.0
    mdim.append(c)
    tspsave(name, idim=idim, mdim=mdim)
    printf(i, c)
#------------------------------------------------------------

erfit = np.array(idim) - np.array(mdim)
#------------------------------------------------------------
plt.plot(idim, erfit, lw=3)
plt.xlabel("Set(mA)", color="steelblue", fontsize=24)
plt.ylabel(name, color="steelblue", fontsize=24)
plt.grid(True, which='both', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
#------------------------------------------------------------
printf("\a")
#------------------------------------------------------------
#        END OF FILE : TEST6.PY
#============================================================

四、总结

▲ 图14 总结

基于上述分析，以25℃测量值为校准基准，温漂参数定义及计算如下：

绝对温漂：Drift(T) = Measured(T) - Measured(25°C) [mA]
温漂系数：TC(T) = Drift(T) / (T - 25°C) [mA/°C]

整体统计特性：

平均温漂系数：-0.005702 mA/°C
温漂系数标准差：0.006395 mA/°C
温漂系数变异系数：112.16%

分区统计特性：

低温区（<25℃）平均漂移：-0.014905 mA
高温区（>25℃）平均漂移：-0.214639 mA

不同电流区间的温漂系数：

0-100 mA：-0.007045 ± 0.006411 mA/°C
100-200 mA：-0.006405 ± 0.006934 mA/°C
200-300 mA：-0.005568 ± 0.006003 mA/°C
300-400 mA：-0.004951 ± 0.006087 mA/°C
400-500 mA：-0.004598 ± 0.006595 mA/°C

● 相关图表链接:

[图1 平均误差与环境温度线性建模](#图1 平均误差与环境温度线性建模)
[图2 8-2-5](#图2 8-2-5)
[图3 8-2-10](#图3 8-2-10)
[图4 8-2-15](#图4 8-2-15)
[图5 8-2-20](#图5 8-2-20)
[图6 8-2-25](#图6 8-2-25)
[图7 8-2-30](#图7 8-2-30)
[图8 8-2-35](#图8 8-2-35)
[图9 8-2-40](#图9 8-2-40)
[图10 8-2-45](#图10 8-2-45)
[图11 8-2-50](#图11 8-2-50)
[图12 8-2-55](#图12 8-2-55)
[图13 8-2-60](#图13 8-2-60)
[图14 总结](#图14 总结)