轨道交通信号智慧运维数据集

1.故障诊断通过Drools规则引擎并结合Spark监测技术对故障场景进行监测和诊断。Drools规则引擎将场景中与、或、非等逻辑判断与业务字段结合生成专家知识库，Spark技术实时消费kafka数据并与故障场景中的字段进行对比，最终形成故障信息流，实现故障诊断。
2.健康评价采用层次分析法，将影响相关各个设备健康状态的各因素(指标)按照从总到分、自上而下的顺序进行排列，并通过两两元素之间的重要性比较及分析计算，得到各指标对各设备健康的影响权重。建立各指标的评分标准，根据各指标的权重值和评分计算(相乘后求和)对应设备的健康评分值。

1. Fault diagnosis is conducted by monitoring and diagnosing fault scenarios using the Drools rule engine combined with Spark monitoring technology. The Drools rule engine combines logical judgments such as AND, OR, NOT in the scenarios with business fields to generate an expert knowledge base. Spark technology consumes Kafka data in real time and compares it with the fields in the fault scenarios, ultimately forming a fault information flow to realize fault diagnosis.
2. Health evaluation adopts the Analytic Hierarchy Process (AHP). The factors (indicators) affecting the health status of various related equipment are arranged in a top-down, general-to-specific order. Through importance comparison and analytical calculation between every pair of elements, the impact weights of each indicator on the health of each device are obtained. Scoring standards for each indicator are established, and the health score of the corresponding equipment is calculated based on the weight values and scores of each indicator (multiply each indicator's score by its corresponding weight, then sum the resulting products).

搜集汇总

数据集介绍

特点

轨道交通信号智慧运维数据集由天津津铁通信有限公司提供，包含80000条数据，每日更新。数据集主要用于轨道交通信号设备的故障诊断和健康评价，适用于地铁运营、厂商和科研院所，通过Drools规则引擎和层次分析法实现故障诊断和健康评分。