Data-driven method for calculating dynamic credibility level of programmable logic controller

Through the data-driven PLC dynamic trust level calculation method, the radial basis function neural network and dynamic threshold adjustment mechanism are used to solve the problem that traditional PLC systems are difficult to accurately evaluate trusted states in real time, and the accurate dynamic trust level evaluation and real-time adjustment of PLC systems are realized, reducing the risk of system failures and safety accidents.

CN119644898AActive Publication Date: 2025-03-18BEIJING UNIV OF TECH
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Patent Information

Application Number
CN202411800653.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-09
Publication Date
2025-03-18
Estimated Expiration
2044-12-09

AI Technical Summary

Technical Problem

The traditional PLC system trusted evaluation method is difficult to accurately reflect the system's trusted state changes in dynamic operation in real time, resulting in the inability to respond to system status changes and security threats in a timely manner, increasing the risk of system failures and safety accidents.

Method used

A data-driven PLC dynamic trust level calculation method is designed, and a radial basis function neural network is used to conduct comprehensive analysis based on the system's state and behavior data, dynamically adjust the model structure and parameters, real-time calculation of the PLC trust level, and ensure the accuracy of the evaluation through a dynamic threshold adjustment mechanism.

Benefits of technology

It realizes the accurate dynamic trustworthiness level assessment of the PLC system, can promptly respond to system status changes and security threats, reduces the risks of system failures and safety accidents, and adapts to the trend of industrial intelligence.

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Abstract

The invention provides a data-driven method for calculating the dynamic credibility level of a programmable logic controller, which solves the problem that accurate evaluation of the credibility level of the programmable logic controller in a complex operation environment is difficult to realize, and realizes real-time and accurate dynamic evaluation of the credibility level of the programmable logic controller. The innovation content mainly comprises the following steps: firstly, constructing a dynamic trusted computing model by using a radial basis function neural network as a carrier of a data driving model, and adjusting the structure and parameters of the model on line according to variable data such as system access time, access failure time and task execution time; dynamic calculation of the credibility level of the programmable logic controller is realized; secondly, designing a dynamic threshold adjustment mechanism, adaptively determining a trust level division threshold according to historical data and real-time data, and ensuring the accuracy of trust level evaluation; and finally, realizing accurate division and evaluation of the dynamic credibility level of the programmable logic controller in combination with a model and a threshold adjustment mechanism.
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