An artificial intelligence-based smart campus management method, system, device and medium

The smart campus management system, through federated learning, interpretable decision-making, and fairness constraints, solves the problems of opaque and biased algorithmic decision-making in existing systems, improves the credibility and fairness of the system, and achieves continuous optimization and adaptability of the model.

CN122199215APending Publication Date: 2026-06-12ANHUI TECHN COLLEGE OF IND & ECONOMY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ANHUI TECHN COLLEGE OF IND & ECONOMY
Filing Date
2026-03-09
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

The algorithmic decision-making process of existing smart campus systems is opaque, leading to a lack of trust and model bias, which affects educational equity. Furthermore, the model output is difficult for humans to understand and verify.

Method used

Federated learning training units are used to exchange encrypted model parameters among multiple campus business units. Combined with interpretable decision units and fairness constraint units, adversarial training is used to eliminate model bias, and closed-loop feedback units are introduced to update the model.

Benefits of technology

It enhances the trust of administrators and students in the smart campus system, increases the adoption rate of model outputs, ensures consistent prediction accuracy of the model for different groups, promotes educational equity, and achieves continuous model optimization through a closed-loop feedback mechanism.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses an intelligent campus management method, system and device based on artificial intelligence and a medium, belongs to the technical field of artificial intelligence and intelligent campus, and relates to an intelligent campus management system based on artificial intelligence, which comprises a federal learning training unit, data nodes configured to be connected with multiple campus business departments, and a globally unified campus management prediction model trained through encrypted model parameter exchange on the premise that original data of each department is not exported from the local. The application uses a KernelSHAP algorithm to calculate the contribution value of each feature to the prediction result through an interpretable decision unit, and generates an explanation text in natural language, so that the basis for AI judgment can be clearly understood by management personnel and students, the adoption rate of model output is significantly improved, and the trust of teachers and students in the intelligent campus system is enhanced.
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