Project risk quantification and strategy adjustment method combining reinforcement learning with AHP (Analytic Hierarchy Process), electronic equipment and storage medium

By combining the AHP hierarchical analysis method with the reinforcement learning algorithm, a project risk quantification and strategy adjustment method was constructed, which solved the problems of inaccurate project risk assessment and inflexible strategy adjustment, achieved comprehensive quantification and dynamic management of project risks, and improved the accuracy and efficiency of risk management.

CN120746288APending Publication Date: 2025-10-03CHINA TELECOM DIGITAL INTELLIGENCE TECH CO LTD
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Patent Information

Application Number
CN202510914577.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-03
Publication Date
2025-10-03

AI Technical Summary

Technical Problem

Existing project risk assessment methods are not comprehensive enough, resulting in inaccurate assessment results, and manual adjustment of risk management strategies cannot effectively respond to changes in project status, resulting in the inability to minimize overall risk.

Method used

Combining the AHP analytic hierarchy process with the reinforcement learning algorithm, a hierarchical model is constructed to determine the risk dimensions and sub-dimension weights, collect project status data, design the state space and action space, and use the reinforcement learning algorithm to train the model to dynamically adjust the risk management strategy.

Benefits of technology

It achieves comprehensive quantification and accurate assessment of project risks, can dynamically adjust risk management strategies according to project status, improves the flexibility and efficiency of risk management, and ensures risk minimization.

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Abstract

The invention provides a project risk quantification and strategy adjustment method combining reinforcement learning with AHP (Analytic Hierarchy Process), electronic equipment and a storage medium, and relates to the field of intelligent project risk management. According to the method, the project information is integrated into the state space, so that the reinforcement learning model can more accurately understand the change of the project state, and an optimal decision is made according to the change. It is ensured that the reinforcement learning model can comprehensively understand the project state, and an optimal decision is made accordingly; through detailed state space design, key information in project progress can be captured, and a rich decision basis is provided for the model. And the risk score is converted into the key parameter in the reward function, so that the model can intuitively understand the effect of risk management, and the strategy is adjusted according to the result to realize the target of risk minimization.
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