A medical institution safety production dynamic evaluation method and system based on multi-source heterogeneous data fusion

By cleaning, normalizing, and extracting features from multi-source heterogeneous data from medical institutions, and combining weighted fusion and deep learning, a safety feature association system is constructed. This solves the problem of data aggregation in the safety production management of medical institutions, and enables real-time risk identification and proactive prevention and control.

CN122599089APending Publication Date: 2026-08-18PEKING UNION MEDICAL COLLEGE HOSPITAL
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Patent Information

Application Number
CN202610763739.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-29
Publication Date
2026-08-18

AI Technical Summary

Technical Problem

The safety management of medical institutions faces the problem of difficulty in unifying and efficiently utilizing multi-source heterogeneous data. Traditional systems cannot achieve effective correlation and fusion analysis, resulting in delayed discovery of hidden dangers, inaccurate risk assessment, one-sided evaluation results, and a lack of real-time updates and proactive prevention and control capabilities.

Method used

By acquiring multi-source heterogeneous data, cleaning, normalizing and feature extraction are performed. Weighted fusion, spatiotemporal correlation analysis and deep learning models are used to achieve deep data coupling, generating a dynamic safety production assessment fusion dataset. Combining the analytic hierarchy process and fuzzy comprehensive evaluation, a safety feature association system is constructed, and edge cloud collaborative computing is used for real-time updates and anomaly warnings.

Benefits of technology

It enables standardized processing and deep integration of multi-source heterogeneous data, improves the traceability of risk assessment and the objectivity and stability of assessment results, supports dynamic perception and proactive prevention and control, reduces transmission pressure, and improves security management efficiency.

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Abstract

The application provides a medical institution safety production dynamic evaluation method and system based on multi-source heterogeneous data fusion, and is applied to the field of data processing. The application first collects multi-source heterogeneous full-dimensional data of equipment operation, environment monitoring, personnel operation, fire safety, logistics energy consumption and business process, realizes standardized conversion through cleaning, normalization and feature extraction, completes data deep coupling through weighted fusion, space-time correlation analysis and deep learning, and forms a fusion data set. Then, according to risk identification, grade determination and trend prediction, three-dimensional correlation nodes are constructed in layers, and a safety feature correlation system is established; combined with the analytic hierarchy process and fuzzy comprehensive evaluation modeling, core features are extracted, and a safety feature vector is generated. Relying on the edge-cloud collaborative engine, real-time updating, early warning and iterative optimization are completed, and through accuracy verification and dynamic correction, the final dynamic evaluation result supporting fine safety management and active risk prevention and control is formed.
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