High-availability missing data recovery method

By using a distributed monitoring and multi-copy storage system, anomalies and data loss in the industrial robot control system can be monitored and recovered in real time. This solves the problem of monitoring interruption and data loss caused by main control station failure, and achieves high disaster recovery and high availability of data recovery.

CN122431955APending Publication Date: 2026-07-21HUANENG POWER INT INC DALIAN POWER PLANT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUANENG POWER INT INC DALIAN POWER PLANT
Filing Date
2026-04-07
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

In existing technologies, industrial robot control systems cannot detect anomalies in a timely manner when the main control station fails, leading to defects in batches of products and data loss that is difficult to recover, especially affecting production cycle time in high-precision operations.

Method used

It employs distributed monitoring nodes and a multi-replica storage system, uses machine learning models to monitor anomalies in real time, automatically switches to backup systems, uses consistent hashing algorithms to quickly recover data, combines interpolation methods to fill in missing data, and optimizes monitoring and recovery strategies through machine learning.

Benefits of technology

It enables continuous monitoring and efficient data recovery in the event of a main control station failure, preventing robot system paralysis, ensuring data integrity and production continuity for high-precision operations, and reducing system integration complexity.

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Abstract

The application discloses a high-disaster-tolerant program exception monitoring and high-availability missing data recovery method, comprising the following steps: step one, data acquisition and preprocessing; step two, distributed exception monitoring; step three, disaster tolerance triggering and processing; step four, missing data identification; step five, high-availability data recovery; and step six, system feedback and optimization. Through the deployment of distributed monitoring nodes, the application can realize redundant coverage of data acquisition at key positions such as robot controllers and servo drivers, and when a certain monitoring node fails, other nodes can still continue to track the running state of the robot, so that the application effectively avoids the paralysis of the whole monitoring system caused by the failure of a single control station. In terms of data recovery, the application stores multiple real-time copies through a real-time multi-copy storage mechanism and utilizes a distributed storage architecture to save multiple real-time copies, and when data abnormity occurs, the application can quickly reconstruct the lost motion trajectory data through a consistent recovery algorithm.
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