基于多源异构传感器数据的拖拉机犁耕阻力预测方法

Prediction Method of Tractor Ploughing Resistance Based on Multi-source Heterogeneous Sensor Data

摘要:

为实现拖拉机犁耕作业中犁耕阻力精准控制并提升牵引效率，本文提出了一种犁耕阻力预测方法。提出了一种基于上拉杆力的犁耕阻力感知模型，并利用试验台进行了试验验证。针对实际田间作业中仅通过上拉杆力感知犁耕阻力方法存在测量结果不稳定问题，搭建了拖拉机犁耕作业参数测试平台，得到了基于耕深、上拉杆力、车速和轮速的多源异构传感器数据并构建了预测样本。将小波阈值去噪（WTD）和麻雀搜索算法（SSA）引入最小二乘支持向量机（LSSVM）中，搭建了基于WTD-SSA-LSSVM的拖拉机犁耕阻力组合预测模型并进行了模型性能验证。结果表明，与仅采用上拉杆力感知方法对比，模型预测方法具有更高精度。进行了不同预测方法对比，采用组合模型方法得到的测试集决定系数（R2）、平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别为0.97、118.1N、151.4N和2.2%。相对于LSSVM和SSA-LSSVM预测模型，R2分别提高8.9%和5.4%；MAE分别降低49.7%和42.2%；RMSE分别降低46.7%和39.1%；MAPE分别降低56.8%和48.8%。由此可知，本文方法具有更好的预测性能，更适用于拖拉机犁耕阻力预测。

Abstract:

In order to realize the precise control of ploughing resistance and improve the traction efficiency of tractor in ploughing operation, a prediction method of ploughing resistance was proposed. A plowing resistance perception model based on the upper pull rod force was proposed, and the experimental verification was carried out. In view of the problem that the measurement results were unstable by only sensing the ploughing resistance through the upper pull rod force in the actual field operation, a ploughing operation parameter test platform was built, and the multi-source heterogeneous sensor data based on tillage depth, upper pull rod force, vehicle speed and wheel speed were obtained and the prediction samples were constructed. The wavelet threshold denoising (WTD) and sparrow search algorithm (SSA) were introduced into the least squares support vector machine (LSSVM), and the combined prediction model of ploughing resistance based on WTD-SSA-LSSVM was established and the model performance was verified. The results showed that the model prediction method had higher accuracy than that of the upper pull rod force. In addition, different prediction methods were compared. The R2, MAE, RMSE and MAPE of the test set obtained by the combined prediction model method were 0.97, 118.1N, 151.4N and 2.2%, respectively. Compared with prediction models of LSSVM and SSA-LSSVM, R2 was increased by 8.9% and 5.4%, respectively. MAE was decreased by 49.7% and 42.2%, respectively. RMSE was decreased by 46.7% and 39.1%, respectively. MAPE was decreased by 56.8% and 48.8%, respectively. Therefore, the method proposed had better prediction performance and was more suitable for the prediction of tractor plough resistance.