基于数据驱动的管道云边协同泄漏检测方法

Data-driven pipeline leak detection method based on cloud-edge collaboration

随着管道运输规模的不断扩大和泄漏检测流程的日趋复杂,管道泄漏检测系统中数据采集、传输和处理等任务难度呈几何级数上升.鉴于此,针对基于数据驱动的管道云边协同泄漏检测方法展开研究,首先针对系统在数据获取方面中压力数据采集量大、数据之间存在冗余的问题,提出一种自适应数据压缩与采集算法;然后依据云边协同调度策略的需求,对云边协同系统中各个环节进行任务细粒度划分,并根据划分后子任务的计算时延和传输时延提出云边协同下管道泄漏检测系统的任务拓扑模型;最后将系统的优化目标定义为在任务执行时间限制下的边缘控制器利用率,进而通过遗传算法求解时间限制下的最优调度策略.仿真分析验证了管道云边协同泄漏检测方法的有效性,所提出方法可以实现管道泄漏事件快速报警.

To enhance the pipeline leak detection system’s efficiency,this paper presents a data-driven approach based on a collaborative cloud-side system.The increasing complexity of the leak detection process and the growing scale of pipeline transportation create challenges for data acquisition,transmission,and processing.Our proposed approach addresses these challenges by introducing an adaptive data compression and acquisition algorithm,which effectively reduces data redundancy and enables efficient collection of large volumes of pressure data.Fine-grained task division is performed for each link in the cloud-side collaborative system,based on the requirements of the cloud-side collaborative scheduling strategy.We propose a task for the pipeline leak detection system under the cloud-side collaborative system,based on the computation delay and transmission delay of the divided subtasks.The topological model of the pipeline leak detection system under cloud edge collaboration is also presented,and the optimization objective is defined as the edge controller utilization under the task execution time constraint.Furthermore,we use a genetic algorithm to solve the optimal scheduling strategy under the time constraint.And then we verify the effectiveness of the pipeline cloud edge collaborative leak detection method,achieving a rapid pipeline leak time alarm.