Multi-technology fused silver powder production quality control method and system, and storage medium

By constructing a quality control system for silver powder production using a distributed sensor network and an adaptive Bayesian algorithm, the problems of Bayesian control performance degradation under high defect rate parameters and lack of collaborative decision-making for multiple quality characteristics were solved. This enabled collaborative control among silver powder production equipment and improved the overall quality control effect.

CN120949713BActive Publication Date: 2026-03-31河南金渠银通金属材料有限公司
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Patent Information

Application Number
CN202510930365.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-07
Publication Date
2026-03-31
Estimated Expiration
2045-07-07

AI Technical Summary

Technical Problem

Existing silver powder production quality control technologies suffer from performance degradation of traditional Bayesian control under high defect rate parameters, lack of collaborative decision-making and integration of multiple quality characteristics, and lack of collaborative linkage control among production equipment, resulting in insufficient control accuracy and poor overall quality control effect.

Method used

By collecting data from the silver powder production line in real time through a distributed sensor network, a quality characteristic correlation matrix is ​​constructed. The adaptive Bayesian algorithm is used to dynamically adjust the gamma prior distribution hyperparameters. Combined with the decision fusion algorithm, the control strategy is optimized to achieve coordinated control of the ball mill, atomizing drying tower, and grading and screening equipment. The effect is evaluated and the control commands are corrected.

Benefits of technology

It improves control precision, achieves synergistic optimization among multiple quality characteristics, enhances the collaborative control effect among production equipment, and forms a closed-loop self-learning intelligent control system that adapts to changes in production conditions.

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Abstract

The application relates to the technical field of production quality control, and discloses a silver powder production quality control method and system fusing multiple technologies and a storage medium. The method comprises the following steps: collecting silver powder production data through a distributed sensor to obtain a quality characteristic correlation matrix, adjusting a gamma distribution hyperparameter to obtain an adaptive parameter by using a self-adaptive Bayesian algorithm based on a defect rate parameter, generating a device cooperative control instruction set by optimizing a control strategy through a decision fusion algorithm, performing linkage control on a ball mill, an atomization drying tower and a grading screening device to obtain an execution result, evaluating the execution result to obtain a performance index, and correcting the control instruction set according to the performance index. The application solves technical problems such as performance degradation of a traditional Bayesian control under a large defect rate parameter condition in a silver powder production process, insufficient control precision caused by a lack of cooperative decision fusion of multiple quality characteristics, and an influence on overall quality control effect caused by a lack of cooperative linkage control among production devices.
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