滑坡实时监测预警模型调度算法优化研究

Optimization of Model Scheduling Algorithm in Real-Time Monitoring and Early Warning of Landslide

  • 摘要: 监测预警是地质灾害防灾减灾的重要手段。在滑坡实时监测预警中,预警模型的调度算法直接关系到预警的成功与否。传统的做法是根据一定时间间隔启动预警服务,这种方法易于实现,但受到预警时间间隔的影响,其预警结论及处置建议的发布存在一定的延迟,无法达到真正的实时预警。如何通过预警模型和实时监测数据快速得到预警等级, 如何控制好预警信息发送的时间与频率,是需要解决的两个关键问题。为了尽量缩短从数据采集到预警信息发出的时间间隔,为后续预警处置赢得更多时间,从预警流程和模型调度算法的角度研究基于时间驱动和数据驱动的预警模型调度方式,结合实时监测数据类型及其特征,提出预警模型调度策略及预警信息发布策略,以期提高滑坡的预警精度;研发一套预警等级求解器,采用多线程思路,构建基于策略的预警模型通用计算框架,实现对滑坡的实时过程跟踪预警,充分发挥预警平台的作用。

     

    Abstract:
      Objectives  Monitoring and early warning is an important means of geohazard prevention and mitigation. In the real-time monitoring and early warning of landslide, the scheduling algorithm of early warning model is directly related to the success of early warning. The traditional way is to start the early warning service regularly, which is easy to realize and is also a common practice in the industry. However, due to the influence of the time interval, there is a certain delay in the issuance of the early warning conclusion, which cannot achieve real-time early warning. How to get early warning level quickly through early warning model and real-time monitoring data, and how to control the time and frequency of sending early warning information are two key problems to be solved.
      Methods  In order to shorten the time interval from data collection to early warning information sending, and win more time for the follow-up early warning processing, this paper studies the early warning process and model scheduling algorithm. Based on the time-driven and data-driven scheduling mode of early warning model, combined with the types and characteristics of real-time monitoring data, the scheduling strategy of early-warning model and the release strategy of early warning information are proposed to improve the warning accuracy of landslide, and a set of early warning level solver is developed.
      Results  For the early warning model scheduling of displacement monitoring data, the time-driven mode can be used in the initial stage of landslide deformation, and the data-driven mode can be used after the accelerated deformation stage. This can greatly reduce the number of early warning calculation, and ensure that the key deformation information can be captured in time. For the state monitoring data, the data-driven mode can be used for early warning to ensure the timeliness of early warning. For rainfall data, time-driven mode has a good effect. Here, the time interval can be determined by combining the data characteristics.
      Conclusions  The multi thread technology is used to construct the general computing framework of the early warning model based on strategy, to realize the real-time process tracking and early warning of landslide, and give full play to the role of landslide early warning platform. and the general computing framework of early warning model based on strategy is constructed by using multi-threading technology.

     

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