Project information data evaluation method and system based on big data

By automatically collecting and analyzing data based on big data, we build financial, schedule, and personnel risk models, solving the problems of low efficiency and poor accuracy in existing technologies and achieving efficient and comprehensive project information data evaluation.

CN120746751AInactive Publication Date: 2025-10-03GUANGZHOU HUIYUAN COMM CONSTR SUPERVISION CO LTD
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Patent Information

Application Number
CN202511213692.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-28
Publication Date
2025-10-03
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing project information data assessment methods rely on manual collection and analysis, which are inefficient and inaccurate. They are unable to assess project risks in multiple dimensions, resulting in a lack of comprehensiveness and objectivity in the assessment results.

Method used

Adopting automated data collection, processing and analysis methods based on big data, by constructing mathematical models of financial, schedule and personnel risk coefficients, combined with data cleaning and preprocessing, key features are extracted for multi-dimensional risk assessment.

Benefits of technology

It improves assessment efficiency and accuracy, can quickly process large-scale data, cover key risk points and capture hidden risks, and provide a more comprehensive basis for project decision-making.

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Abstract

The invention relates to the field of data risk assessment, and discloses a project information data assessment method and system based on big data, and the method comprises the steps: obtaining a calculation budget execution rate, a cost deviation and an unallocated cost, substituting the calculation budget execution rate, the cost deviation and the unallocated cost into a financial risk model to calculate a project financial risk, and then obtaining a milestone achievement rate, a progress deviation and floating time; substituting into a progress risk model, calculating a project progress risk, then obtaining a professional skill matching degree, average working experience, a personnel loss rate and average performance, substituting into a personnel risk model, selecting a project personnel risk, if financial, progress or personnel risk coefficients exceed a threshold value, triggering a high-risk alarm, and if all risk coefficients are lower than the threshold value, judging that the project is abnormal. And calculating a potential risk coefficient, if the potential risk coefficient exceeds a threshold value, prompting a potential risk, evaluating the risk from multiple dimensions of finance, progress and personnel, covering key risk points of a project, introducing the potential risk coefficient, and capturing a hidden risk.
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