基于组织策略与车辆上下文的生成式AI运营决策系统

By constructing a generative AI operation decision-making system based on organizational strategy and vehicle context, the illusion problem and unpredictability of generative AI operation decision-making in existing technologies have been solved, realizing safe, reliable, compliant and controllable intelligent operation decision-making, and the system has been continuously optimized through a closed-loop learning mechanism.

CN121981491BActive Publication Date: 2026-07-17BEIJING ZHONGKEHUIJU SCI & TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING ZHONGKEHUIJU SCI & TECH CO LTD
Filing Date
2026-02-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing vehicle operation decision-making systems are prone to illusion problems, lack of constraints, and unpredictability when applying generative artificial intelligence. They are unable to understand unstructured organizational strategy texts, resulting in insufficient strategy understanding, poor situational adaptability, lack of decision-making creativity, delayed compliance verification, and black box problems in decision-making, and lack a continuous learning mechanism.

Method used

The system employs a multi-source contextual data access module, a strategy knowledge graph construction module, a multimodal contextual fusion module, a generative AI decision-making module, a digital twin simulation verification module, a strategy compliance arbitration module, a decision optimization and interpretation generation module, and a decision execution and monitoring module to construct a closed-loop system. By structuring organizational strategies and deeply integrating them with real-time data, it dynamically generates candidate decisions and performs high-fidelity simulation prediction and automatic compliance verification to ensure the reliability and compliance of decisions.

Benefits of technology

It achieves safe, reliable, compliant, controllable, stable, and explainable intelligent operational decisions, ensuring the credibility, compliance, and traceability of decisions. Through feedback learning and system optimization, a closed-loop learning mechanism is formed, enabling the system to continuously self-optimize.

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Abstract

本申请公开了一种基于组织策略与车辆上下文的生成式AI运营决策系统，集多源情境数据接入模块、策略知识图谱构建模块、多模态情境融合模块、生成式AI决策模块、数字孪生仿真验证模块、策略合规性仲裁模块、决策优化与解释生成模块和决策执行与监控模块于一体的闭环系统，通过将组织策略结构化并与实时数据深度融合来动态生成候选决策，再经高保真仿真预测可行性与风险，并通过自动合规性校验确保决策与规则高度一致，最终输出经过多目标优化且附带自然语言解释的可靠决策，从而解决生成式AI运用于车辆运营的幻觉问题、缺乏约束与输出不可预测的缺陷，实现了安全可靠、合规可控且稳定可解释的智能运营决策。
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Citation Information

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