Intelligent monitoring and rpa-based power consumption curve data acquisition exception processing method and system

By combining intelligent monitoring with RPA to handle anomalies in electricity consumption curve data collection, the problem of lagging monitoring and early warning and low efficiency in traditional electricity consumption curve data collection and management has been solved. It has achieved full terminal detection coverage, accurate early warning and efficient data processing, forming a long-term closed-loop management process, reducing operating costs and improving management efficiency.

CN122434104APending Publication Date: 2026-07-21STATE GRID SHANDONG ELECTRIC POWER CO LAIXI CITY POWER SUPPLY CO
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
STATE GRID SHANDONG ELECTRIC POWER CO LAIXI CITY POWER SUPPLY CO
Filing Date
2026-03-30
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Traditional electricity consumption curve data acquisition and management suffers from lagging monitoring and early warning, passive anomaly detection, incomplete manual inspection coverage, lack of quantitative identification standards, single early warning push leading to untimely response, low efficiency, high cost and low accuracy of manual processing, lack of batch processing capabilities, inability to adapt to data format requirements of multiple terminal types, missing management process closed loop, recurring problems, unclear division of responsibilities, lack of data-driven optimization mechanisms, inability to adapt to dynamic changes in electricity load and insufficient full terminal detection coverage, unclear division of responsibilities for anomaly handling, and low integration of technology and management.

Method used

The method for handling anomalies in electricity consumption curve data collection based on intelligent monitoring and RPA includes building a regional peak electricity load identification model, multi-dimensional early warning, key issue marking and RPA automated processing, establishing a hierarchical responsibility system, realizing full-terminal timed collection and multi-dimensional early warning through intelligent monitoring and early warning, using RPA automation tools for data supplementation, and building a quantitative assessment mechanism and closed-loop management process.

Benefits of technology

It achieves 100% detection coverage across all terminals, improves anomaly detection efficiency by 60%, achieves 100% accuracy in marking key issues, improves data processing efficiency by 60%, forms a long-term closed loop in management processes, significantly improves problem eradication rate, deeply integrates technology and management, adapts to different terminal types and scenarios, and reduces operating costs.

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Abstract

The present application relates to the technical field of power system electricity information collection, and discloses a method and system for electricity curve data collection anomaly processing based on intelligent monitoring and RPA, which comprises intelligent monitoring early warning, multi-dimensional early warning, key problem marking, RPA automatic processing and closed-loop management, and comprises intelligent monitoring early warning module, multi-dimensional early warning pushing module, key problem marking tracing module, RPA automatic processing module, closed-loop management module and data storage module. The present application combines electricity load characteristic analysis, K-means clustering algorithm, RPA robot process automation technology and three-level closed-loop management mechanism, realizes full detection, active early warning, accurate marking, automatic processing and long-term closed-loop management of electricity curve data collection anomaly, solves problems such as lagging of traditional mode monitoring and early warning, low efficiency and accuracy of manual processing, and no closed-loop management process, greatly improves the success rate of electricity information comprehensive collection, and reduces the cost of human management.
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