A process index correlation calculation method, device, equipment and storage medium

By acquiring process data from a time-series database, processing outliers and missing values, performing data alignment and stability interval calculation, and combining a voter fusion of multiple correlation methods, the problem of relying on human experience to judge the correlation of process indicators is solved. This enables fast and accurate calculation of the correlation of process indicators, supporting process optimization and production efficiency improvement.

CN115511374BActive Publication Date: 2026-07-07CHINA ORDNANCE EQUIP GRP AUTOMATION RES INST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA ORDNANCE EQUIP GRP AUTOMATION RES INST CO LTD
Filing Date
2022-10-27
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

In existing technologies, the determination of correlations between process indicators in industrial manufacturing relies heavily on workers' professional experience, resulting in low accuracy. In particular, it is difficult to quickly and accurately identify correlations in complex situations, which affects process optimization and production efficiency.

Method used

By acquiring process data from a time-series database, detecting and processing outliers and missing values, performing data alignment and stability interval calculation, and employing multiple correlation calculation methods combined with a voter for fusion, the system achieves automated calculation of the correlation of process indicators.

Benefits of technology

It enables rapid and accurate mining of the correlation between process indicators, reduces the limitations of manual judgment, provides effective data support for process production efficiency prediction and parameter optimization, and improves decision support for the production process.

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Abstract

The application discloses a kind of process index correlation calculation method, device, equipment and storage medium, the method solves the problem that the correlation between process indexes needs to be manually calculated and determined in the field of industrial manufacturing.The method is based on a large amount of process index data generated during the operation of the device, and uses a machine learning algorithm to real-time mine TOP-N group indicators with high correlation between industrial process indexes, greatly avoiding the limitations in accuracy and response speed when first-line workers make judgments. Through this method, the correlation between process indexes can be quickly and accurately mined, providing effective data support for subsequent process production efficiency prediction, process parameter optimization, production process early warning and other services.
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