流程行业智能工厂攻击安全、故障安全、失效安全风险特性与演化机理数据集

流程行业智能工厂攻击安全、故障安全、失效安全风险特性与演化机理数据集基于流程行业智能工厂典型生产过程，涵盖攻击安全、故障安全及失效安全风险特性与演化机理。基础工艺特征数据通过爬取行业标准文档、企业技术白皮书及专利数据库获取，结合山东省《重点监管危险化工工艺目录》人工筛选清洗形成结构化数据。攻击安全风险案例数据源自工业事故数据库、企业安全报告及公开文献，提取DCS协议漏洞利用、APT攻击等事件，通过时空关联分析（时序匹配与拓扑映射）构建数据集。故障安全与失效安全数据通过合作调研获取，包括DCS/SCADA系统实时数据（OPC UA协议导出）及EAM系统维护日志等，经结构化验证与跨源对齐形成数据集。基于攻击安全风险与故障安全、失效安全风险数据，研究流程行业智能工厂域外信息攻击与域内工艺层界故障不同组合情形下的系统故障演化机理及路径并构建数据集。采用多源交叉验证、人工筛选清洗及跨源对齐技术实现质量控制，数据存储为结构化Excel表格，具备高兼容性。数据集为流程行业智能工厂域外攻击防御、域内工艺层界故障风险预测以及跨域风险协同防控提供理论支持，助力流程行业安全智能化升级。Dataset on Attack Safety, Fault Safety and Failure Safety Risk Characteristics and Evolution Mechanisms of Intelligent Plants in the Process Industry Based on the typical production processes of intelligent plants in the process industry, this dataset covers the risk characteristics and evolution mechanisms of attack safety, fault safety and failure safety. The basic process feature data is obtained by crawling industry standard documents, enterprise technical white papers and patent databases, then manually screened and cleaned in combination with the Key Regulated Hazardous Chemical Process Catalogue of Shandong Province to form structured data. The attack safety risk case data is sourced from industrial accident databases, enterprise safety reports and open literature, where events such as DCS protocol vulnerability exploitation and APT attacks are extracted, and the dataset is constructed through spatiotemporal correlation analysis including temporal matching and topological mapping. Fault safety and failure safety data is collected via cooperative surveys, including real-time data from DCS/SCADA systems exported via OPC UA protocol and maintenance logs of EAM systems, etc., and the dataset is finalized through structured verification and cross-source alignment. Based on the attack safety risk data and fault/failure safety risk data, this dataset is built by studying the system fault evolution mechanisms and paths under various combinations of cross-domain information attacks and intra-domain process layer faults in intelligent plants of the process industry. Quality control is implemented using multi-source cross-validation, manual screening and cleaning, and cross-source alignment technologies, with all data stored as structured Excel spreadsheets featuring high compatibility. This dataset provides theoretical support for cross-domain attack defense, intra-domain process layer fault risk prediction and cross-domain risk collaborative prevention and control of intelligent plants in the process industry, and assists the intelligent upgrading of safety management in the process industry.

搜集汇总

背景与挑战

背景概述

该数据集聚焦流程行业智能工厂，系统整合了攻击安全、故障安全和失效安全三类风险特性与演化机理数据。数据来源于行业标准、事故报告、实时系统日志等多渠道，经结构化处理与验证后形成高兼容性Excel文件。它旨在为流程行业智能工厂的跨域风险防控与安全智能化升级提供理论支撑。