Electric power material warehouse supplementing method and system

By analyzing and clustering data on power grid projects and power materials, an intelligent inventory replenishment strategy was constructed, which solved the problems of inventory backlog and stockouts in the material management of power grid enterprises and achieved an improvement in the scientificity and intelligence of material management.

CN120688972APending Publication Date: 2025-09-23STATE GRID FUJIAN ELECTRIC POWER CO LTD
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Patent Information

Application Number
CN202510627934.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-15
Publication Date
2025-09-23

AI Technical Summary

Technical Problem

Power grid companies face problems with inventory backlogs, stockouts, and waste in their material management. Existing inventory quotas rely on manual experience, lack intelligent and scientific tool support, and are unable to adapt to demand fluctuations in various business types.

Method used

By obtaining basic data on power grid projects and power materials, conducting feature analysis and clustering, constructing project description word vectors and material feature vectors, using demand forecasting models to implement intelligent inventory replenishment strategies, and combining inventory management strategies to optimize inventory structure.

Benefits of technology

It has achieved improvements in the scientific and intelligent level of material management, optimized inventory structure, improved responsiveness and supply chain flexibility, and reduced inventory backlogs and out-of-stock risks.

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Abstract

The invention discloses an electric power material library supplementing method and system, and the method comprises the steps: carrying out the characteristic analysis of electric power materials based on basic data, obtaining material characteristics, determining the index factors of the electric power materials based on the basic data, carrying out the clustering analysis of the electric power materials according to the index factors, and obtaining material types; determining an inventory management strategy of electric power materials according to the material category, constructing a project description word vector and a project material feature vector of a power grid project based on the basic data, performing clustering analysis on the power grid project according to the project description word vector and the project material feature vector to obtain a project type, and determining a demand prediction model corresponding to the project type according to the material feature; and on the basis of the material characteristics, the project description word vectors and the project material characteristic vectors, the demand prediction model is used for carrying out demand prediction on the electric power materials, and an intelligent library supplementing strategy of the electric power materials is determined on the basis of an inventory management strategy and a demand prediction value, so that the scientificity, responsiveness and intelligent level of material management are improved.
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