An optimization method for BP neural networks based on the butterfly optimization algorithm （BOA） is proposed to address the issue of inaccurate path planning for quadcopters in multi-obstacle environments. The points of the quadcopter along the designated path are used as training samples for the neural network， and the BOA-BP algorithm is employed to train the network to determine the optimal flight path. The simulation results show that the proposed BOA-BP model effectively reduces the path error of the quadcopter compared to the traditional BOA algorithm， with the root mean square error decreasing from 1.60% to 0.003%.

With the continuous improvement of war informatization， systematization and intelligence， the demand of military users for missile equipment support has changed from post maintenance and regular maintenance to condition-based maintenance and intelligent support， which puts forward higher requirements for missile equipment health management. Through the demand analysis of missile equipment health management system， this paper puts forward the overall system architecture for missile equipment support， the system physical architecture for complex application scenarios in military plants and the system technical architecture based on micro-service， and carries out the system function modules by using structural design method， describes the key technologies of the system， constructes and applies the first phase of the system for the pilot equipment. The application results show the feasibility of this study， which can provide reference for the health management of other types of equipment.