Project aid decision-making and risk prediction method and device based on large model

By employing a large-model-based project management approach, which utilizes machine learning and deep learning technologies to identify risks, generate implementation plans, and provide decision-making information, this approach addresses the issues of high resource and time costs and strong subjectivity inherent in traditional project management, thereby achieving efficient and scientific project management.

CN121961441APending Publication Date: 2026-05-01JIANGSU MINGYUE INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202511789183.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-01
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Traditional project management relies on experienced expert teams, resulting in large resource inputs, high time costs, complex and subjective decision-making processes, a lack of consistency and traceability, and difficulty in standardization and systematization.

Method used

By adopting a large model-based approach, project implementation plans are generated through the acquisition, classification, and storage of project information. Risk warnings and decision-making information are provided, and machine learning and deep learning technologies are used to identify potential risks. Intelligent decision-making is carried out by combining multi-objective optimization techniques, simulating expert judgment behavior, and reducing reliance on a single expert.

Benefits of technology

It improved the efficiency and consistency of project management, shortened the project cycle, enhanced the scientific nature of project planning and the ability to optimize processes, and enabled the accumulation and continuous optimization of project management knowledge.

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Abstract

The invention relates to a project aid decision-making and risk prediction method based on a large model. The method comprises the following steps: acquiring project information related to a project; performing classified management and storage on the project information; before project implementation, according to project information, generating a project implementation plan, drawing a resource input trend chart in a project period, listing a risk problem list, and in the project implementation process, according to the project information, dynamically optimizing the project implementation plan, providing risk early warning, providing decision information for project implementation personnel, and generating a project implementation document; distributing project implementation tasks, pushing risk early warning and decision information, synchronizing project progress information, and updating project implementation documents; and evaluating the decision information and risk early warning effect, and carrying out model training and optimization. Dependence on a single expert is reduced, the management efficiency is improved, the consistency and scientificity of project planning are improved, the review decision process is accelerated, the project period is shortened, and precipitation, reuse and continuous optimization of project management knowledge are achieved.
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