An artificial intelligence-based production data monitoring method and system

By constructing a quality influencing factor assessment model and multimodal data processing, combined with a prediction model and dynamic adjustment strategy, the problem of delayed detection of quality defects in charging pile production was solved, achieving accurate identification and prediction, and improving production efficiency and quality.

CN120765120BActive Publication Date: 2026-06-23ZHONGKE RUANQI (WUHAN) TECH CO LTD
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Patent Information

Application Number
CN202511039384.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-28
Publication Date
2026-06-23
Estimated Expiration
2045-07-28

AI Technical Summary

Technical Problem

In the current charging pile production process, the reliance on manual inspection and simple sensor data collection makes it difficult to fully capture complex quality problems, resulting in delayed detection of quality defects, increased production rework costs and safety risks, and the inability to dynamically adjust monitoring strategies.

Method used

A quality influencing factor assessment model is built based on artificial intelligence. Through multimodal data collection, fusion processing and quality prediction model, a quality feature vector is generated, the monitoring strategy is dynamically adjusted, and suggestions for assembly process optimization or defect repair solutions are provided.

Benefits of technology

It enables accurate identification and prediction of charging pile assembly quality, reduces the probability of quality defects, improves production efficiency and product quality, and reduces costs and risks.

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Abstract

The application provides a production data monitoring method and system based on artificial intelligence, belonging to the technical field of data processing. The method comprises the following steps: generating an initial monitoring strategy according to a quality influencing factor evaluation model; controlling a multi-modal data acquisition device to acquire multi-source data of a charging pile assembly process based on multi-modal data acquisition parameters; performing fusion processing on the multi-source data to generate a quality feature vector representing an assembly state; comparing the quality feature vector with a quality judgment threshold to output a state monitoring result; inputting the quality feature vector into a preset quality prediction model to obtain an assembly quality prediction result; determining a quality defect type and a risk level of the charging pile based on the state monitoring result and the assembly quality prediction result through a pre-defined quality defect mapping rule; and adjusting at least one item in the initial monitoring strategy to generate an updated monitoring strategy according to the quality defect type and the risk level. The production quality and production efficiency of the charging pile are improved.
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Citation Information

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