Research And Implementation Of Key Technologies Of Intelligent Recommendation System For Joint Operations

With the continuous development of information warfare theory and practice,in order to adapt to the complex and changing battlefield environment,it is an important part of joint operations to accurately and quickly and efficiently select and recommend battle plans.The joint battle plan recommendation combines data such as mission descriptions,situation judgments,commander preferences,and battle plans,and makes full use of the information that can be collected to quickly optimize and recommend battle plans.Effective time lays an important foundation for successful operations.This paper analyzes the joint battle plan recommendation problem in the joint battle planning process,studies the structured and formalized method of the battle plan,analyzes the problems and limitations of the traditional battle plan recommendation solution algorithm,and introduces the CTR estimation model solves the shortcomings of the original algorithm's weak ability to analyze and learn features.Based on the overview of the CTR estimation model and the introduction of related algorithms,a wide and deep improved algorithm is designed to integrate the advantages of shallow and deep representations for feature processing in the CTR estimation algorithm.The Wide layer and Deep layer of the improved algorithm solve the joint battle plan recommendation problem from two aspects of the shallow network representation and the deep network representation,and carry out experimental verification in simulation data.The verification results show that the algorithm designed in this paper improves the battle plan,the recommended effects can be applied to actual projects.