Storage battery fault diagnosis system and method based on big data

Abstract

The technical scheme of the invention comprises a storage battery pack fault diagnosis system based on big data. The system comprises an interpreter which is used for human-computer interaction; an inference machine which is used for analyzing a storage battery pack fault and performing maintenance; an expert model component which is used for storing storage battery pack configuration and maintenance recommendations; a database which is used for storing historical and real-time data; and an automatic learning machine which is used for automatically learning fault characteristics. The system also comprises a storage battery pack fault diagnosis method based on the big data. The method comprises the steps that instruction transmission is performed on a human-computer interface, the inference machine performs data access and analysis according to instructions, the fault is stored if the fault is a new fault, and the interpreter receives the result and displays the result to a user. The beneficial effects of the storage battery pack fault diagnosis system and method based on the big data are that judgment and analysis are performed according to a model knowledge base by adopting an intelligent summery data, degree of expertise of maintenance personnel is reduced, efficiency is enhanced and thus the system and the method are convenient and rapid.

Description

technical field [0001] The invention relates to a system and method for fault diagnosis of battery packs based on big data, and belongs to the field of electric power technology. Background technique [0002] In today's era of rapid energy development, it is very important to manage and process energy. In the current fault diagnosis process of battery packs, manual data collection, comparison, and maintenance are usually used, which is not only very cumbersome and complicated, but also very inefficient, and requires maintenance personnel to have very high professional knowledge. And for the fault diagnosis of existing battery packs, it cannot be processed based on a large amount of data. Therefore, there is an urgent need for a deep understanding of the industry and the ability to analyze large amounts of data. This requirement involves the field of experts and can meet the actual situation. Contents of the invention [0003] Aiming at the cumbersome and low efficiency of...

AI Technical Summary

Problems solved by technology

In the current fault diagnosis process of battery packs, manual data collection, comparison, and maintenance are usually used, which is not only very cumbersome and complicated, but also very inefficient, and requires maintenance personnel to have very high professional knowledge

And for the fault diagnosis of existing battery packs, it cannot be processed based on a large amount of data. Therefore, there is an urgent need for a method based on a deep understanding of the industry and the ability to analyze large amounts of data. This requirement involves the field of experts and can meet the actual situation.

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