Industrial Internet of Things production line safety analysis method, system, equipment and medium

By using deep neural networks for intelligent fusion and correlation analysis of multi-source data, the semantic gap between operating environment and fault record data in industrial IoT production lines has been solved. This enables multi-granularity safety status assessment of the entire production line and individual equipment, improving the accuracy of early warning in industrial safety monitoring and the precision maintenance capabilities at the equipment level.

CN121364696APending Publication Date: 2026-01-20CHENGDU QINCHUAN IOT TECH CO LTD
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Patent Information

Application Number
CN202511893627.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-16
Publication Date
2026-01-20

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively integrate time-series data of the operating environment and fault record data in industrial IoT production lines. They lack deep correlation models, resulting in insufficient predictive capabilities for potential faults and a lack of refined safety status assessments for individual production equipment, making it difficult to meet the needs of modern intelligent manufacturing systems.

Method used

Deep neural networks are used for intelligent fusion and correlation analysis of multi-source data. Semantic mining models are used to semantically encode time-series data and fault record data. Semantic analysis models are used for cross-domain correlation semantic encoding. Combined with safety assessment models, safety situation assessment is carried out to achieve multi-granularity safety situation assessment of the entire production line and individual equipment.

Benefits of technology

It solves the semantic gap problem between heterogeneous data, accurately mines the intrinsic correlation between operating environment parameters and equipment failures, and improves the early warning accuracy of industrial safety monitoring and the precise maintenance capability at the equipment level.

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Abstract

The invention discloses an industrial Internet of Things production line safety analysis method, system and device and a medium, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: obtaining the operation environment time sequence data of a target production line and the corresponding fault record data; through a semantic mining model included in a target production line data analysis network, respectively performing semantic coding on the operation environment time sequence data and the fault record data to obtain corresponding operation environment features and fault features; performing cross-domain association semantic coding on the operating environment features and the fault features through the semantic analysis model to obtain corresponding environment fault association features; and performing safety situation assessment on the target production line according to the environment fault correlation features through a safety assessment model to obtain a corresponding operation safety assessment result. The method has the effect of improving the accuracy of safety analysis of the production line.
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