Cluster algorithm training evolution system and method based on architecture model definition

Through the cluster algorithm training of the evolution system based on the architecture model, the simulation unified description problem in the coordinated combat scenario of multi-weapon systems is solved, and the simulation results of efficient compatibility and credibility are achieved, and the scalability and interpretability of cluster decisions are optimized.

CN120278227BActive Publication Date: 2025-08-22YITONG XINGYUN (BEIJING) TECH DEV CO LTD
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Patent Information

Application Number
CN202510758668.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-09
Publication Date
2025-08-22
Estimated Expiration
2045-06-09

AI Technical Summary

Technical Problem

Traditional pure rules-based methods or single machine learning methods are difficult to achieve unified description and efficient simulation in the coordinated combat scenarios of multi-weapon systems, and lack of systematic system perspective, which makes it difficult for simulation results to reflect the real combat logic and the laws of troops' actions, and the system is insufficient in scalability and explanatory.

Method used

The cluster algorithm is used to train the evolution system based on the architecture model, including data acquisition, architecture, algorithm training, simulation evaluation and result output units, and the task resources and goals are uniformly defined through the architecture model, deep reinforcement learning is used to optimize the cluster decision strategy, and a closed-loop feedback mechanism is formed.

Benefits of technology

It realizes efficient compatibility and interoperability between multi-weapon systems, improves the credibility and transparency of simulation results, reduces the difficulty of system upgrades and scenario switching, and ensures the efficiency and traceability of cluster decisions.

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Abstract

The cluster algorithm training and evolution system and method defined based on the architecture model include: a data acquisition unit that collects task objectives and task resources; an architecture unit that analyzes task resources based on the task objectives and outputs an architecture model; an algorithm training unit that performs algorithm training based on the architecture model and outputs a cluster decision-making strategy and task allocation scheme; a simulation evaluation unit that simulates and deduces the cluster decision-making strategy, evaluates the effectiveness of the task allocation scheme, and outputs a corresponding evaluation report; and a result output unit that outputs the evaluation report. The present invention first uniformly defines the functions and interfaces of multiple task resources using the architecture model to ensure compatibility, scalability, and interoperability among different task resources. Secondly, the architecture model is combined with the cluster decision-making algorithm to optimize decision-making efficiency and improve traceability and transparency.
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