2024年排水管网内窥检测数据集

排水管网检测记录和缺陷记录作为城市地下管网管理的核心数据资产，在多个智能化应用场景中发挥着关键作用。在‌科学决策支持‌方面，这些记录通过量化缺陷类型、等级和空间分布，为管网维护优先级排序、修复方案比选和资金分配优化提供客观依据，例如基于RI指数（修复指数）的差异化修复策略可节省30%以上的维护成本。在‌智慧排水系统建设‌中，检测数据与GIS、BIM系统融合，构建数字孪生管网模型，支撑实时监控预警平台开发，如青岛市的案例显示，通过整合管网检测数据建立的智慧平台可提升应急响应效率。对于‌缺陷智能化分析‌，海量的检测记录和结构化缺陷记录构成AI训练集，通过深度学习技术，可以实现缺陷的自动化智能化解析，解放人力，减少误判。三者形成闭环：智能化分析产出高精度缺陷数据，支撑智慧系统动态更新，最终反馈优化科学决策流程，实现从被动抢修到预测性维护的范式转变。

Drainage pipe network inspection records and defect records, as core data assets for urban underground pipe network management, play a key role in multiple intelligent application scenarios. In terms of scientific decision-making support, these records quantify defect types, severity levels and spatial distributions, providing objective basis for prioritizing pipe network maintenance, comparing repair schemes and optimizing fund allocation. For example, differentiated repair strategies based on the Repair Index (RI) can save more than 30% of maintenance costs. In the construction of smart drainage systems, inspection data is integrated with GIS and BIM systems to build digital twin pipe network models, supporting the development of real-time monitoring and early warning platforms. As demonstrated by the case of Qingdao, the smart platform established by integrating pipe network inspection data can improve emergency response efficiency. For intelligent defect analysis, massive inspection records and structured defect records form an AI training dataset. Through deep learning technology, automated and intelligent analysis of defects can be realized, reducing labor burden and misjudgments. The three parts form a closed loop: intelligent analysis produces high-precision defect data to support the dynamic update of smart systems, which ultimately feeds back to optimize the scientific decision-making process, realizing the paradigm shift from passive emergency repair to predictive maintenance.

搜集汇总

背景与挑战

背景概述

该数据集包含2024年通过管道潜望镜和检测机器人采集的排水管网检测记录与缺陷记录，采用xlsx格式存储，每日更新且每段管道4年检测一次。数据主要用于支撑管网维护科学决策、智慧排水系统建设和AI缺陷识别算法训练，已通过深圳市数据知识产权登记（登记号SZ2025120009231.5）。