<p>Lithium batteries are widely used in new energy vehicles and electronic equipment. Aiming at the typical defects that are easy to occur in the production process of lithium batteries, this paper improves the performance and recognition accuracy of the algorithm by integrating void convolution and attention mechanism into the YOLOv5 basic framework. At the same time, whale algorithm is used to automatically optimize the algorithm parameters in the process of optimization. Finally, through simulation experiments. This method realizes the rapid and accurate identification of lithium battery defects in the rapid production process of automatic production line.</p> <p>&nbsp;</p>

<p>This article proposes a multi-objective function that includes AGV running time, production workshop energy consumption, and machine running efficiency, in response to the problems of path conflicts, single planning objectives, and isolation of planning stages in the current flexible production workshop AGV car planning. Then, the flying mouse algorithm is used to solve the problem using multiple functions. In order to avoid falling into local optima during the solving process, a simulated annealing strategy is incorporated into the flying mouse algorithm. Finally, taking the production of new energy vehicle on-board batteries as an example, a collaborative planning analysis was conducted using the method proposed in this paper. The results showed that the algorithm proposed in this paper can save 30% of running time and improve machine operating efficiency by 22.7%. </p> <p>&nbsp;</p>