基于BP神经网络的整株秸秆还田装置多目标参数优化——以1ZT-210还田机为例  

Multi-objective Parameter Optimization of Whole-straw ReturningDevice Based on BP Neural Network——Taking the 1ZT-210 Field Returning Machine as an Example

作　　者：董志贵 张庆柱[2] 刘理 杨天一 Dong Zhigui;Zhang Qingzhu;Liu Li;Yang Tianyi(School of Electronic and Information Engineering,Liaoning Institute of Science and Technology,Benxi 117004,China;Heilongjiang Academy of Agricultural Machinery Engineering Sciences,Harbin 150081,China)

机构地区：[1]辽宁科技学院电子与信息工程学院,辽宁本溪117004 [2]黑龙江省农业机械工程科学研究院,哈尔滨150081

出　　处：《农机化研究》2025年第7期52-58,共7页Journal of Agricultural Mechanization Research

基　　金：辽宁省自然科学基金项目(2021-MS-078);辽宁省教育厅基本科研(面上)项目(LJKMZ20221691);辽宁科技学院先锋科研创新团队项目(XKT202306);辽宁科技学院博士启动基金项目(2307B06)。

摘　　要：为解决整株秸秆还田装置多目标参数优化时拟合误差精度差和多目标优化准确性低等缺陷,提出了一种高精度和高稳定性的基于BP神经网络的多目标优化方法。以1ZT-210型水稻整株秸秆还田装置为研究对象,选取机具前进速度、刀辊转速为试验因素,以及还田机作业功耗和秸秆还田率为影响指标,以二次正交旋转组合试验数据为训练样本,获得作业功耗和秸秆还田率与影响因素的BP神经网络模型。最佳参数组合:机具前进速度1.20 km/h、刀辊转速225 r/min时,还田装置的作业功耗最小值为12.43 kW,秸秆还田率最大值为93.25%;试验条件下还田机最小作业功耗优于回归分析法所得最小功耗14.32 kW,秸秆还田率优于回归分析法所得最大还田率93.14%。以BP神经网络优化结果进行验证试验,测得作业功耗为12.68 kW,与BP神经网络优化结果绝对误差为0.25 kW,相对误差为2.01%;秸秆还田率为93.13%,与BP神经网络优化结果绝对误差为-0.12%,相对误差为0.13%。试验结果表明:该优化方法实用性强,拟合精度高,优化结果准确稳定,为解决农业工程领域中类似优化问题提供了一种新方法。In order to solve the problems of poor fitting degree of errors in multi-objective parameter optimization and low accuracy for the whole-straw returning device,a multi-objective optimization method based on BP Neural Network with high accuracy and stability was proposed.By taking the 1ZT-210 type whole-straw returning device for rice as the research object,advancing speed,blade roll rotating speed as test factors,power consumption and straw returning rate as test indexes,and taking the data in the quadratic orthogonal regression rotary combination test as training samples,a BP neural network model on power consumption,straw returning rate and the influencing factors was obtained.The optimal parameter combination of test factors was:the advancing speed of the device was 1.20 km/h,blade roll rotating speed was 225 r/min,and under such circumstance,the minimum power consumption of the device was 12.43 kW and the maximum straw returning rate was 93.25%.Under such test condition,the minimum power consumption of the device was 14.32 kW,lower than that by regression analysis method,and the straw returning rate was 93.14%,better than that by regression analysis method.At last,verification test was conducted on the results of BP neural network optimization,and the power consumption of the test was 12.68 kW,having an absolute error of 0.25 kW with the results of BP neural network optimization,and a relative error of 2.01%;the straw returning rate was 93.13%,with an absolute error of-0.12%with the results of BP neural network optimization,and a relative error of 0.13%.Test results indicated that,the optimization method had good practicability with high fitting degree,and achieved accurate and stable optimization results,and could provide a new method for solving similar problems in optimization in the field of agricultural engineering.

关 键 词：整株秸秆 还田装置 BP神经网络 参数优化 

分 类 号：S224.9[农业科学—农业机械化工程]