基于农机运行轨迹点多维特征的作业状态辨识方法与试验

Operation Status Recognition Method and Experiment Based on Multidimensional Features of Agricultural Machinery Spatial Track

摘要:

农业机械（农机）的作业状态是评估农业机械化效率和精准管理的关键指标，为实现数据管理平台对农机作业状态的精准监测，本文提出一种基于农机运行轨迹多维特征的作业状态辨识方法。首先，基于物联网现代信息技术支持的大数据管理平台，研究了农机作业空间轨迹点的潜在特征，分析了速度、平均加速度、转向率和轨迹点密度等特征的分布规律。其次，依据各特征的分布特点和作业状态辨识需求，采用多策略分箱处理方法对特征进行定量划分，并引入证据权重（Weight of evidence，WOE）和信息值（Information value, IV）方法量化不同特征对农机作业状态的影响权重，从而评估关键特征对作业状态的识别能力。最后，基于农机空间轨迹点的多维关键特征，结合BP神经网络与AdaBoost的融合算法对农机作业状态进行辨识。试验结果表明，所提出的算法模型在农机作业状态预测中的准确率高达97.3%，表明基于农机多维特征的辨识方法可准确辨识农机的作业状态。

Abstract:

The operation state of agricultural machinery is a key indicator for assessing the efficiency of agricultural mechanization and precise management. To realize the precise monitoring of the operation state of farm machinery by the data management platform, an operation state identification method was proposed based on the multidimensional features of the spatial track of farm machinery. Firstly, based on the big data management platform supported by the modern information technology of Internet of Things, the potential characteristics of the trajectory points in the operating space of agricultural machinery were studied, and the distribution laws of the characteristics such as speed, acceleration, steering rate and distribution density of trajectory points were analyzed. Secondly, based on the distribution characteristics of each feature and the demand for operation state recognition, a multi-strategy split-box processing method was used to quantitatively divide the features, and weight of evidence (WOE) and information value (IV) methods were introduced to quantify the influence weight of different features on the operation state of the farm machinery, so as to assess the impact on operation state the key features of the recognition ability were evaluated. Finally, based on the multidimensional key features of the spatial trajectory points of the farm machinery, the fusion algorithm of BP neural network and AdaBoost was combined to recognize the operation state of the farm machinery. The experimental results showed that the accuracy of the proposed algorithm model in the prediction of the operation state of agricultural machinery was as high as 97.3%, indicating that the recognition method based on the multidimensional features of agricultural machinery can accurately recognize the operation state of agricultural machinery.