一种基于机器学习的AI智能决策推理方法及系统
By introducing causal time-series windows and adaptive calibration processes into the machine learning model, the problem of existing technologies being unable to identify the cumulative risks of multiple small events is solved, enabling early identification and reliable warning of complex, slow-burning systemic risks.
CN120952185BActive Publication Date: 2026-07-17SHENZHEN DIGITAL CONVERGENCE TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN DIGITAL CONVERGENCE TECH CO LTD
- Filing Date
- 2025-08-27
- Publication Date
- 2026-07-17
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Figure CN120952185B_ABST
Abstract
本发明涉及机器学习智能决策技术领域，公开一种基于机器学习的AI智能决策推理方法及系统，包括：通过监测不达到报警阈值的微事件以开启时序窗口，并在窗口内将多个传感器之间的时序因果关联编码为一不含原始幅值的指纹向量，同时将数据流的非预期波动编码为一噪声特征向量，再将两向量共同输入机器学习模型，以进行经置信度修正的决策推理，本发明跳出了传统机器学习依赖静态数据快照的局限，通过将隐蔽的动态因果链直接编码为可识别的指纹，使系统能够在由多个微小事件累积而成的系统性风险的萌芽期而非爆发期做出精准预警，实现了从事后诊断到事前预见的转变。
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