A rag-based charging station intelligent control method and system

By establishing a matching mapping relationship and continuous sequence between events and processes in the charging station, and dynamically calculating control weights, the control contradictions in multi-event concurrent scenarios are resolved, and the overall operational efficiency of the charging station is optimized.

CN122043970BActive Publication Date: 2026-07-14国网(山东)电动汽车服务有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
国网(山东)电动汽车服务有限公司
Filing Date
2026-04-16
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing RAG-based charging station control methods cannot effectively coordinate V2G revenue targets and clean energy consumption targets in multi-event concurrent scenarios, leading to conflicting control commands and operational efficiency losses.

Method used

By establishing a systematic matching mapping relationship between event-driven tasks and process-driven tasks, a matching relationship matrix is ​​generated, a continuous sequence of the inherent temporal laws of event occurrence is constructed, and based on this, the control weight coefficients of V2G events and clean energy events are dynamically calculated. Combined with a multi-objective optimization model, dynamic trade-offs and coordination are achieved.

Benefits of technology

It significantly improves the overall operational efficiency and adaptive control capabilities of charging stations in vehicle-grid interaction and fluctuating clean energy environments, and solves the problems of control contradictions and operational efficiency losses in multi-event concurrent scenarios.

✦ Generated by Eureka AI based on patent content.

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Abstract

The present application relates to the technical field of charging station operation optimization decision, and particularly relates to a charging station intelligent control method and system based on RAG. The method comprises the following steps: S1: obtaining process-driven tasks and event-driven tasks of a charging station, matching and mapping each stage in the event-driven tasks and the process-driven tasks, and generating a mapping pair set; S2: obtaining a matching relationship matrix based on the mapping pair set, and constructing a continuity sequence of the event-driven tasks according to the matching relationship matrix; S3: performing compression and reservation operations on RAG data corresponding to the event-driven tasks based on the continuity sequence, obtaining a compression priority and a reservation priority, and obtaining control weight coefficients of V2G event-driven tasks and clean energy event-driven tasks based on the compression priority and the reservation priority. Through systematic event-process mapping and dynamic weight distribution, the present application realizes adaptive collaborative optimization control of the charging station under a multi-event concurrent scenario.
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