关键词: 农机轨迹行为模式识别, 多维时空交互网络（MSINet）, 多维信息交互（MII）, 多路径特征提取（MFE）, 语义聚焦（SF）

Abstract: Agricultural machinery trajectory operation mode identification is a multivariate time series classification (MTSC) task, which aims to extract the spatiotemporal features embedded in agricultural machine trajectory data to identify the agricultural machine trajectory operation mode and assign the corresponding semantic labels to each trajectory point. Aiming at the problems of insufficient ability to capture spatiotemporal information of trajectories and poor identification accuracy of existing methods, multidimensional spatiotemporal interaction network (MSINet) is proposed to identify the operation mode of agricultural machine trajectories. Firstly, a multidimensional information interaction (MII) module is proposed, which integrates graph convolution and self-attention mechanisms to capture local associations and global dependencies between trajectory points, and also utilizes a bidirectional perception mechanism to achieve complementary information between channel and spatial dimensions. Subsequently, the multipath feature extraction (MFE) module is designed to effectively extract multi-scale temporal features of trajectory data using convolutional paths with different dilation rates. Finally, a semantic focus (SF) module is developed to efficiently capture the key information in the trajectory data. To verify the effectiveness of the proposed method, experiments have conducted on the trajectory dataset provided by the Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs. The experimental results show that the accuracy of MSINet on the rice harvester and wheat harvester trajectory datasets is 91.62% and 91.34%, respectively, and the F1 score is 91.57% and 86.98%, respectively. Compared with the current best-performing model generative adversarial network-bidirectional long short-term memory network(GAN-BiLSTM), the F1 score has increased by 5.57 and 3.72 percentage points, respectively.

Key words: agricultural machinery trajectory operation mode identification, multidimensional spatiotemporal interaction network (MSINet), multidimensional information interaction (MII), multipath feature extraction (MFE), semantic focus (SF)