Light storage and charging control system and method based on short-distance logistics vehicle

Through the application of real-time data acquisition and deep learning models, combined with the blockchain transaction management module, the problems of unstable photovoltaic power output and inaccurate load demand prediction in short-distance logistics vehicles are solved, efficient energy management and transactions are achieved, and the energy efficiency and economicality of logistics vehicles are improved.

CN119693181AActive Publication Date: 2025-03-25JILIN UNIVERSITY
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Patent Information

Application Number
CN202411529275.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-30
Publication Date
2025-03-25
Estimated Expiration
2044-10-30

AI Technical Summary

Technical Problem

The prior art is difficult to achieve the stability of photovoltaic power output power and accurate prediction of load demand in short-distance logistics vehicles, resulting in insufficient power and waste of resources.

Method used

The data acquisition module is used to collect the operating parameters of the logistics vehicle in real time, and the thermal compensation coefficient and effective lighting intensity are generated through dimensionless processing. Load demand prediction is carried out in combination with the deep learning model, and energy transactions are optimized through the blockchain transaction management module.

Benefits of technology

It improves the efficiency and reliability of photovoltaic power generation, realizes accurate prediction of load demand and dynamic matching of energy supply and demand, optimizes the utilization and transaction of electricity, and improves overall energy efficiency and economy.

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Abstract

The invention provides a light storage and charging control system and method based on a short-distance logistics vehicle, and relates to the technical field of light storage and charging, and the method specifically comprises the steps: collecting the operation parameters of the logistics vehicle in real time, and generating a thermal compensation coefficient and effective illumination intensity based on the operation parameters; the thermal compensation coefficient and the effective illumination intensity are integrated, and the actual power generation power is calculated; the method comprises the following steps: acquiring historical time sequence data and corresponding load demand data in the running process of a plurality of groups of logistics vehicles, constructing a deep learning model, and training the model to obtain a load demand prediction model; inputting real-time operation parameters into the model, outputting a predicted load demand, and performing energy supply and demand judgment; and establishing a block chain network, defining an intelligent contract, and calculating available electric energy. According to the invention, the efficiency and reliability of photovoltaic power generation in the operation process of the logistics vehicle are improved. Through application of data analysis and a deep learning model, the logistics vehicle can adjust an energy supply and demand strategy in time according to environmental changes, and sufficient energy is ensured.
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