动态因素下时序称重模型的建立

Establishment of time series weighing model under dynamic factors

物流秤在动态测量过程中,各类时序干扰信号极易对压力传感器测量精度造成影响。基于正交试验法获取物流秤在不同带速、载重及采样频率下台面的压力及秤体三轴加速度信号,以此作为样本集,基于五折交叉验证原则依次建立岭回归、Xgboost以及改进的LSTM测量补偿模型。结果表明Ridge模型具有最低的算法复杂度,且较传统线性回归模型提升明显,补偿平均损失为0.317 kg;Xgboost模型平均损失为0.219 kg且基于F检验分析误差成分;此外提出一种改进的LSTM神经网络模型,通过在原有结构基础上堆叠全连接层,将采样信号作为时间序列变量输入模型,最终测试结果表明虽模型训练时间与空间复杂度较大,但补偿测量准确度最佳损失低至0.142 kg,且对采样频率不敏感具有最好的鲁棒性。

In the dynamic measurement process of logistics scale,all kinds of time sequence interference signals are easy to affect the measurement accuracy of pressure sensor.Based on the orthogonal test method,the pressure signal of the platform and the three-axis acceleration signal of the scale body under different belt speed,load and sampling frequency are obtained.Based on the 50 fold cross validation principle,Ridge regression,xgboost and LSTM measurement compensation models are established in turn.The results show that ridge model has the lowest algorithm complexity,and the average compensation loss is 0.317 kg compared with the traditional linear regression model;the average loss of xgboost model is 0.219 kg,and the error components are analyzed based on F-test;in addition,an improved LSTM neural network model is proposed,which directly takes the sampled signal as time series by stacking the full connection layer on the basis of the original structure The final test results show that although the training time and space complexity of the model is large,the best compensation measurement accuracy loss is as low as 0.142 kg,and it is insensitive to the sampling frequency and has the best robustness.