Material data processing method and system

By calculating the discreteness and attribution possibility of material data and dynamically adjusting the location of cluster centers, the problem of inaccurate clustering results in existing technologies is solved, more accurate material data classification and management is achieved, and inventory and production efficiency is improved.

CN120763641AActive Publication Date: 2025-10-10MAIWEI TECH (GUANGZHOU) CO LTD
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Patent Information

Application Number
CN202510945835.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-09
Publication Date
2025-10-10
Estimated Expiration
2045-07-09

AI Technical Summary

Technical Problem

In the existing technology, when analyzing automobile parts material data through the kmeans clustering algorithm, due to deviations in the data collection process, the clustering results are inaccurate and the center point is offset, affecting the accuracy of data processing.

Method used

By calculating the discrete degree and attribution possibility of material data, dynamically adjusting the position of the cluster center point, and using the influence weight to optimize the clustering results, the clustering results are gradually optimized to ensure that the cluster center point is close to the actual distribution of the data, and the cluster center point is recalculated using the weighted average method.

Benefits of technology

The accuracy and robustness of clustering results are improved, which can more accurately reflect the actual characteristics of material data, optimize inventory management and production scheduling, and reduce resource waste and management errors.

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Abstract

The invention relates to the field of automobile part data processing, in particular to a material data processing method and system. The method comprises the following steps: acquiring material data of a plurality of automobile parts; and clustering the material data to obtain a final cluster, and managing the material data of the automobile parts according to the final cluster. According to the technical scheme, the accuracy of material data clustering of the automobile parts can be improved, and the precision of a data processing result is improved.
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