基于监测关键数据的综合智能处理技术研究及应用

王亚飞; 周根郯; 曾长贤; 李富年

doi:10.13238/j.issn.1004-2954.202408140002

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基于监测关键数据的综合智能处理技术研究及应用

线路/路基/轨道 | 更新时间：2026-07-08

- 基于监测关键数据的综合智能处理技术研究及应用
- Research and Application of Integrated Intelligent Processing Technology Based on Key Monitoring Data
- 铁道标准设计 2026年70卷第7期页码：33-40
- 作者机构：
  
  中铁第四勘察设计院集团有限公司，武汉 430063
  武汉科技大学信息科学与工程学院，武汉 433081
- 作者简介：
  
  作者简介：王亚飞（1981—），男，正高级工程师，2002年毕业于中国地质大学（武汉）水文与水资源工程专业，工学学士，主要从事铁路地质与路基研究工作，E-mail：5951229@qq. com。
- 基金信息：
  
  国家重点研发计划课题(2021YFB2600400)
- DOI：10.13238/j.issn.1004-2954.202408140002
  中图分类号： U213.1;P642.22
- 收稿：2024-08-14，
  
  修回：2024-10-25，
  
  网络首发：2025-04-29，
  
  纸质出版：2026-07-20
- 稿件说明：
移动端阅览
王亚飞, 周根郯, 曾长贤, 等. 基于监测关键数据的综合智能处理技术研究及应用[J]. 铁道标准设计, 2026,70(7):33-40.

WANG Yafei, ZHOU Gentan, ZENG Changxian, et al. Research and Application of Integrated Intelligent Processing Technology Based on Key Monitoring Data[J]. Railway Standard Design, 2026, 70(7): 33-40.
王亚飞, 周根郯, 曾长贤, 等. 基于监测关键数据的综合智能处理技术研究及应用[J]. 铁道标准设计, 2026,70(7):33-40. DOI： 10.13238/j.issn.1004-2954.202408140002.

WANG Yafei, ZHOU Gentan, ZENG Changxian, et al. Research and Application of Integrated Intelligent Processing Technology Based on Key Monitoring Data[J]. Railway Standard Design, 2026, 70(7): 33-40. DOI： 10.13238/j.issn.1004-2954.202408140002.

摘要

【

目的

】为解决滑坡监测数据处理与分析中的准确性和效率问题，提出一种综合智能处理技术以监测关键勘察数据，并开发相应的云平台系统。【

方法

】该系统通过联合多个数据库，实现了各类监测数据的高效存储与管理。具体而言，采用改进的切线角算法来识别滑坡滑面深度，并基于此构建滑坡三维模型。与传统切线角识别算法的识别正确率5/12相比，改进后的算法达到12/12，识别准确性显著提高。在滑坡模型构建方面，相较于点云模型数小时的构建时间，使用Three. js与Delaunay生成的方法仅需几秒即可准确构建三维滑坡模型。此外，系统利用Web-GIS技术实现了监测数据的可视化管理和预警功能，使用户能够直观地观察滑坡的动态变化和潜在风险。【

结果

】研究结果表明：该系统能够高效处理和分析多类型监测数据，成功实现滑坡滑带位置、滑体空间尺寸和形态的智能分析与动态监测；实现了滑坡变形数据的连续监测和记录，可全面、持续地追踪滑坡变形的演化过程，从而提高预警的及时性和准确性；提出的综合智能处理技术和云平台为边坡滑坡防治提供了一种通用化、智能化的工具，对滑坡的预防和治理具有重要意义。【

结论

】所提方法不仅实现了滑坡变形数据的连续监测与记录，还能帮助相关部门及时采取防范措施，保障人民生命和财产安全。

Abstract

[

Objective

]

To address the accuracy and efficiency problems in landslide monitoring data processing and analysis，an integrated intelligent processing technology for monitoring key survey data is proposed，and a corresponding cloud platform system is developed. [

Methods

]

By integrating multiple databases，the system realized efficient storage and management of various types of monitoring data. Specifically，an improved tangent-angle algorithm was adopted to identify the depth of the landslide slip surface，and a three-dimensional landslide model was constructed on this basis. Compared with the recognition accuracy of 5/12 of the traditional tangent-angle algorithm，the improved algorithm achieved 12/12，significantly improving the recognition accuracy. In terms of landslide model construction，compared with the several hours required for point cloud model construction，the method using Three. js and Delaunay generation could accurately construct a three-dimensional landslide model within only a few seconds. In addition，the system used Web-GIS technology to realize visualized management and early warning of monitoring data，enabling users to intuitively observe dynamic changes and potential landslide risks. [

Results

]

The results showed that the system efficiently processed and analyzed multiple types of monitoring data and successfully realized intelligent analysis and dynamic monitoring of landslide slip-zone position，landslide spatial dimensions，and morphology. It also enabled continuous monitoring and recording of landslide deformation data and could comprehensively and continuously track the evolution of landslide deformation，thereby improving the timeliness and accuracy of early warning. The proposed integrated intelligent processing technology and cloud platform provided a generalized and intelligent tool for slope landslide prevention and control，which was of great significance for landslide prevention and treatment. [

Conclusion

]

The proposed method not only realizes continuous monitoring and recording of landslide deformation data，but also helps relevant departments take timely preventive measures to safeguard people's lives and property.

关键词

Keywords

references

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