关键词: 空中任务分配, 罗素近似, 有向图网络

Abstract: Before the war, it often takes several days, ten days, or even dozens of days to plan the daily air task orders required for joint air operations. How to rationally utilize aircraft and ammunition in the entire process of the battle, adopt appropriate mounting schemes and flight profiles, and implement air interception, air assault, and close-range aerial fire support tasks under various weather conditions, aircraft wear and tear, ammunition consumption, battlefield assessment, and other factors is an extremely complex and important optimization problem that directly affects the efficiency of combat operations. However, until now, there is still a lack of proven optimization idea on this issue, making it more difficult to optimize the air force in the entire process of the battle. It is urgent to conduct in-depth research on air task allocation issues, that is, given the type and quantity of combat aircraft, based on certain battlefield environment, tactical knowledge, and mission requirements, we can allocate one or a group of ordered tasks (target or spatial task points) to each combat aircraft so as to achieve the maximum possible number of tasks while achieving optimal overall efficiency in air force combat operations. Joint air combat task allocation includes pre-war task allocation and dynamic task allocation. Pre-war task allocation is used for detailed planning before task execution, generally with larger problem sizes and more comprehensive considerations, making it more difficult to solve and often using centralized solving methods. Dynamic task allocation is used for task instruction generation activities that occur during the execution of wartime tasks, highlighting real-time characteristics and often using distributed solving methods.
This article focuses on the dynamic task allocation problem in wartime joint air operations, with the research goal of dynamically generating task allocation instructions for fighter aircrafts. Based on the summary of the advantages and disadvantages of existing methods, this paper focuses on the problem that existing joint air combat planning models are difficult to efficiently solve in the face of large-scale air combat, highlighting the requirements of high reliability and timeliness in wartime. A “human-in-the-loop” wartime joint air combat mission planning method is proposed. The model first calculates the available aircraft sorties and takeoff time windows within the air task instruction cycle. By analyzing the task requirement nodes and fighter resource nodes of joint air operations, it calculates the loss function based on the matching degree of ‘flight unit-operational task’, and uses the Russell approximation matrix to construct the solution space. Subsequently, the initial air task allocation solution space is mapped to a directed graph network. By regularizing fighter sorties that flight unit provides and fighter sorties that operational task requires, further optimized solution space can be obtained. With strong human-machine adaptability, the entire planning model incorporates the commander’s intentions, which can better adapt to the impact of the commander’s handling of unexpected situations on the generation of air task instructions during the generation cycle of air task instructions.
Following the basic idea of solving complex problems with simple methods, the method proposed in this paper can effectively compress the solution space in a relatively short period of time, solving the problem of the complexity of wartime planning rapidly increasing with the scale of air combat. At the same time, due to the human involvement in the task planning loop, the reliability of task allocation schemes in the face of changing battlefield environments is higher. It is worth noting that the purpose of the entire allocation of joint air mission instructions is to orderly form air mission instructions for each operational day, assisting commanders in commanding joint air combat operations. Therefore, while calculating and optimizing the solution space of ‘flight unit-operational task’, it is also necessary to comprehensively consider factors such as the pilot’s ability of each flight unit, experience in executing relevant combat tasks, and reflect them in the process of solving the loss function.

Key words: air task allocation, Russell approximation, directed graph network