An Adaptive Decision Path Planning Method and System Based on Multi-Level Risk

By constructing an adaptive decision-making path planning method based on multi-level risks, combining gray clustering and long short-term memory networks for dynamic risk assessment, and updating the knowledge base with fuzzy logic feedback and empirical group data, the static nature and scenario fragmentation of elderly disability risk assessment and intervention programs are solved, achieving dynamic and accurate early warning and full-cycle closed-loop management.

CN121601255BActive Publication Date: 2026-05-26ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2026-01-29
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies suffer from static risk assessments for disability among the elderly, rigid intervention plans, and fragmented management scenarios. This results in the inability to achieve dynamic and accurate early warning of risks and adaptive adjustment of decision-making paths, and a lack of full-cycle closed-loop management.

Method used

An adaptive decision-making path planning method based on multi-level risk is constructed. Dynamic risk assessment is carried out through a gray clustering and long short-term memory network coupled model, path adaptive adjustment is combined with a fuzzy logic feedback mechanism, and the knowledge base is updated through self-evolution using empirical data from the population.

Benefits of technology

It enables dynamic and accurate early warning of disability risk in the elderly, provides personalized and adaptive intervention decision-making paths, and supports closed-loop management throughout the entire cycle, thereby improving the effectiveness and adaptability of intervention programs.

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Abstract

This invention discloses an adaptive decision-making path planning method and system based on multi-level risk. The method includes: applying a gray clustering algorithm coupled with a long short-term memory network to a warning model for dynamically assessing and classifying the disability risk of a target object; generating an initial decision path based on the risk assessment results and a disability prevention and control knowledge base by solving a multi-objective optimization function that integrates expected effectiveness, feasibility, and burden; collecting effect deviations and execution deviations during path execution, and dynamically adjusting the weights of the multi-objective optimization function using a fuzzy weight controller to reconstruct the decision path; and iteratively updating the knowledge base based on group feedback data. This invention helps solve the problems of static disability risk assessment, rigid intervention plans, and fragmented management scenarios in existing technologies, achieving dynamic and accurate early warning of disability risk in the elderly, deep adaptive adjustment of intervention paths, and full-cycle closed-loop management.
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