Power grid project multi-dimensional digital supervision method based on big data

By integrating multi-dimensional data sources through big data technology and combining computer vision and anomaly detection algorithms, a multi-dimensional supervision system is constructed, which solves the problems of data silos and delayed response in traditional power grid project supervision, realizes multi-objective collaborative optimization and real-time risk warning of power grid projects, and improves supervision efficiency and accuracy.

CN120634245APending Publication Date: 2025-09-12STATE GRID SHANGHAI ELECTRIC POWER DESIGN
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Patent Information

Application Number
CN202510731326.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-03
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

Traditional power grid project supervision relies on manual inspections and decentralized systems, resulting in isolated data, delayed responses, and insufficient risk warnings, making it difficult to achieve multi-objective collaborative optimization. The lack of multi-source heterogeneous data fusion and analysis capabilities leads to a single supervision dimension, making it difficult to achieve multi-objective collaborative optimization of quality, safety, progress, cost, and environment.

Method used

A multi-dimensional digital supervision method for power grid projects based on big data is adopted. Through the multi-dimensional data integration of BIM models, GIS positioning, IoT sensor data and environmental monitoring data, combined with computer vision, time series prediction and anomaly detection algorithms, real-time fusion and risk warning of multi-source heterogeneous data are achieved. A multi-dimensional supervision indicator system is constructed, and a reinforcement learning model is used to generate control strategies to achieve a closed loop of "supervision-decision-making-execution".

Benefits of technology

It realizes the real-time integration of multi-source heterogeneous data, breaks down information silos, captures quality defects, safety hazards and environmental risks in real time, improves supervision effects, realizes multi-objective collaborative optimization, and improves supervision efficiency and early warning accuracy.

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Abstract

The invention relates to a power grid project multi-dimensional digital supervision method based on big data, and belongs to the technical field of power grid project digital supervision, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-dimensional data, and constructing a supervision system through four supervision layers and a digital twinborn body; s2, multi-dimensional supervision indexes are intelligently calculated, and a supervision dimension index system is constructed; s3, risk dynamic early warning and root cause tracing; and S4, decision optimization and closed-loop control. According to the method, multi-source heterogeneous data integration is carried out by fusing multi-dimensional data sources of BIM model, GIS positioning, Internet of Things sensing data, environment monitoring and business system data, so that single data source deviation is avoided, information islands are broken, multi-target collaborative optimization is realized, a'supervision-decision-execution 'closed loop is realized, an optimal regulation and control strategy is convenient to calculate, and the system performance is improved. An optimal regulation and control strategy can be generated based on the reinforcement learning model, and an early warning instruction is directly connected to a field terminal, so that the power grid project monitoring and early warning efficiency can be effectively improved.
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