Power business early warning and whole-process management and control method based on multi-source data collaboration

CN122371450APending Publication Date: 2026-07-10FOSHAN POWER SUPPLY BUREAU GUANGDONG POWER GRID
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
FOSHAN POWER SUPPLY BUREAU GUANGDONG POWER GRID
Filing Date
2026-04-02
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

The power industry suffers from problems such as cross-departmental data silos, unclear division of responsibilities, reliance on manual identification for business early warnings, and a lack of closed-loop verification in the entire process control, resulting in low efficiency in business collaboration and delayed response.

Method used

We construct a three-in-one architecture of 'data-task-responsibility' for multi-source data collaboration, realize data fusion and government-enterprise data interoperability through a lightweight API gateway, use organizational tree algorithm and rule engine for task decomposition and responsibility matching, combine streaming computing and dual-channel access mechanism for real-time early warning, and build a full-link digital closed-loop management and control module to verify the effect.

Benefits of technology

It has achieved efficient integration of multi-source data and accurate matching of responsibilities, improved the foresight and reach efficiency of business response, formed an intelligent full-process control closed loop, and improved the collaborative efficiency and management optimization capabilities of power business.

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Abstract

This invention discloses a method for early warning supervision and full-process control of power business based on multi-source data collaboration, belonging to the field of digital operation and control technology of power systems. The method constructs a three-in-one collaborative architecture of "data-task-responsibility," achieving multi-source heterogeneous data fusion and government-enterprise data interoperability through unified data elements and a lightweight API gateway. It employs a dual-drive approach of organizational tree algorithm and rule engine to automatically decompose business tasks and accurately map four-level responsibility nodes (city-district-station-person). It relies on a configurable supervision rule base and streaming computing to achieve real-time identification and hierarchical early warning of business anomalies, and combines a dual-channel multimodal reach mechanism to improve response efficiency. A full-link closed-loop control module is constructed, using a closed-loop verification algorithm to quantitatively evaluate the supervision effect and drive dynamic optimization of control rules. This invention forms a complete full-process closed-loop control technology chain, effectively solving industry pain points such as data silos, ambiguous responsibilities, ineffective supervision, and missing closed loops in power business.
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