Automobile parts inventory management scheduling method and system based on internet of things

By using IoT real-time data collection and dynamic health assessment, combined with intelligent demand matching and multi-objective game optimization, the problems of information opacity and insufficient risk control in auto parts inventory management have been solved, realizing intelligent and efficient scheduling of inventory management and improving the agility and robustness of the supply chain.

CN122390626APending Publication Date: 2026-07-14
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Filing Date
2026-04-18
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing auto parts inventory management systems suffer from problems such as lagging data collection, limited data dimensions, lack of intelligent and accurate demand matching, limited allocation decisions to local optimization, and weak risk control mechanisms. These issues lead to low inventory turnover efficiency, frequent stockout risks, and unbalanced resource allocation, making it difficult to meet the requirements of high timeliness and high reliability in supply.

Method used

By deploying IoT sensing devices to collect spare parts unit data in real time, a dynamic fingerprint matrix of spare parts is constructed, an inventory health index is calculated, and similarity matching is performed in combination with maintenance work order data to identify scheduling demand tags. A transfer network topology is constructed, and a dynamic game algorithm is used to solve the Pareto optimal solution set, select the scheduling scheme with the lowest risk, and optimize inventory management by reverse adjustment of weight parameters.

Benefits of technology

It has enabled the transformation of inventory management from passive response to proactive forecasting, and from experience-based decision-making to data-driven approaches, significantly improving the agility, robustness, and intelligence of inventory management, increasing the efficiency of inventory resource utilization, reducing emergency allocations and resource waste, and optimizing inventory balance and time efficiency.

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Abstract

The application discloses a kind of based on Internet of Things's automobile accessory inventory management scheduling method and system, belong to Internet of Things technical field, including through the real-time collection of accessory unit data by being deployed in each warehouse and line edge library Internet of Things sensing equipment, constructs the accessory dynamic fingerprint matrix of each warehouse, the present application is based on accessory dynamic fingerprint matrix, through adaptive weighting algorithm calculation inventory health index, adaptive weight distribution mechanism can intelligently identify the key attribute that the greatest impact on inventory state, consider the physical state of accessory, storage environment, turnover efficiency and timeliness and other multidimensional factors, comprehensively reflect the actual availability and potential risk of inventory;Through the introduction of variation coefficient, it can automatically adapt to the characteristic difference and business characteristics of different warehouses, avoid the subjectivity and inadaptability of manual weight setting, effectively prevent the maintenance delay caused by inventory quality problems, significantly improve the utilization efficiency and overall management level of inventory resources.
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