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完整性管理是基于数据科学决策的方法论,数据管理作为基础直接制约了完整性管理中各项工作的开展。基于中国石油管道完整性管理的现状,明确提出了9个专题61类数据的范围,通过支持精细化管理实现安全与经济的最佳平衡。数据管理的目标从风险专家评估转变为数据分析人员分析风险,再发展成为基于大数据分析的完整性管理,已从宏观到微观各层面为管道管理带来深刻变革。对数据管理的现状和挑战进行分析,指出当前应进一步完善管道线性资产数据管理的数据模型技术、多维度时空数据的管理、数据对齐、数据利用4种关键技术,并结合国家和公众对管道安全与环保的要求、石油行业经营成本与利润压力持续加大的现状,对未来管道完整性管理中数据与业务的深入融合趋势进行预测,为下一阶段数据管理技术发展指明方向。
Integrity management is based on data science decision-making methodology, data management as a direct constraint directly to the integrity management of the work carried out. Based on the status quo of the integrity management of China’s oil pipelines, the scope of the 61 topics for the nine special topics was clearly set out to achieve the best compromise between safety and economy through supporting refined management. The goal of data management has shifted from a risk expert assessment to a data analyst analysis of risk and then into integrity management based on big data analysis, which has brought profound changes to pipeline management from a macro to a micro level. This paper analyzes the present situation and challenge of data management and points out that at present, there are four key technologies that should be further improved, such as the data model of pipeline linear asset data management, the management of multi-dimensional spatio-temporal data, data alignment and data utilization, Environmental protection requirements, operating costs and profit pressures in the oil industry continue to increase the status quo, the future integration of data integrity management and pipeline business trends to predict the next phase of data management technology development direction.