Human-machine intelligent confrontation method and system based on cloud architecture

Through federated learning and game optimization under cloud architecture, the problems of poor model generalization ability and resource islands in the human-machine confrontation system are solved, rapid optimization and strategy migration of intelligent agents are achieved, and the adaptability and decision-making ability of the human-machine confrontation system are improved.

CN120653113APending Publication Date: 2025-09-16ARMOR ACADEMY OF CHINESE PEOPLES LIBERATION ARMY
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Patent Information

Application Number
CN202510811009.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-17
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing human-machine adversarial systems have problems such as poor model generalization ability and serious resource silos, which lead to single strategies and low migration efficiency.

Method used

Adopting cloud-based architecture, through federated learning and cloud-based game optimization, we can achieve distributed collection and training of user behavior data from multiple terminal devices. The cloud server performs model aggregation and strategy optimization, generates a global model and conducts human-machine confrontation simulation, constructs a human-machine strategy benefit matrix and solves the game.

Benefits of technology

It significantly improves the generalization ability and flexibility of the model under diverse human behavior data, improves the adversarial adaptability and intelligent decision-making ability of AI strategies in complex environments, solves the resource island phenomenon, and realizes the rapid optimization and collaborative evolution of intelligent bodies.

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Abstract

The invention belongs to the technical field of computers, and aims to provide a man-machine intelligent confrontation method and system based on a cloud architecture. On one hand, multiple terminal devices are used for collecting user behavior data in a distributed mode and generating local confrontation samples, strategy training is conducted through the multiple terminal devices on the basis of the local confrontation samples of the terminal devices, and a global model is formed through aggregation optimization of a cloud server; the generalization ability and flexibility of the model under diversified human behavior data are remarkably improved, and the problems of single strategy and poor adaptability in the prior art are solved; and on the other hand, after the cloud server completes global model training, a strategy revenue matrix is generated through large-scale man-machine confrontation simulation, and a game optimization method is further introduced to solve an equilibrium strategy, so that the confrontation adaptability and the intelligent decision-making capability of the AI strategy in a complex human behavior mode are effectively improved.
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