An artificial intelligence electric power first-aid repair system and method based on big data

Abstract

The invention relates to an artificial intelligence power repair system and method based on big data, and the system is characterized in that the system comprises a data information collection modulewhich collects data of power equipment, fault repair, meteorological information, traffic conditions, repair vehicle tracks, repair processing, and service evaluation in real time; a data platform building module which is used for building a Hadoop big data platform by applying a MapReduce technology, a Hive technology and an HDFS technology; a learning optimization module which is used for searching rules and knowledge from the data, realizing basic functions of fault image recognition, voice recognition, fault type study and judgment, future fault quantity prediction, intelligent order distribution and ETA estimation, and providing accurate basic information for intelligent decision making; And a decision providing module which is used for providing active decision and reminding servicefor rush-repair personnel through a full-process application voice assistant based on electric power rush-repair full-service scene recognition and prediction, and liberating the hands of the rush-repair personnel.

Description

technical field [0001] The invention belongs to the field of big data application technology, and in particular relates to a big data-based artificial intelligence power emergency repair system and method. Background technique [0002] In the prior art, there are mainly two ways of power failure warranty: [0003] The first method: call for repair through 95598 customer service: [0004] Customers call 95598 for fault repairs. The 95598 customer service center accepts customer appeals, forms a repair work order and sends it to the power supply company for processing. Personnel verify and confirm the location of the fault, carry out emergency repair after on-site investigation and judgment of the cause of the fault, and feedback the emergency repair information step by step after the emergency repair task is completed. [0005] The second method is to report online for repairs through APPs such as Pocket Electric Power: [0006] Customers can self-report faults through APP...

AI Technical Summary

Problems solved by technology

[0008] 1. Poor convenience

Customers need to install the APP and register an account before using the APP fault repair function. However, power fault repair is not a function that customers often use. Installing the APP consumes time and mobile phone memory. Customers have resistance, and cumbersome operations restrict the service experience.

[0009] 2. Poor interactivity

The existing online repair method is usually a simple push of time node information. Customers cannot intuitively understand the progress of the whole process of fault repair, on-site repair situation, estimated time for power recovery, etc., and customers still feel anxious about waiting for power recovery.

[0010] 3. Low intelligence

The existing online repair method only provides a simple repair function, lacks the deep mining application of a large amount of repair data and power equipment data, and has insufficient business support for electric power repair personnel, making it difficult to fundamentally improve the repair efficiency

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