管道缺陷检测数据

通过远程遥控智能检测装置进入管道内部智能检测，采集管道内部的图像和视频，自动形成缺陷数据，监测管道异常情况，方便及时评估管道内缺陷数据，为维护管道提供更新数据。有利于市政、企业的管道维护和清理，有利于城市的可持续发展。根据管道的结构性缺陷数量和分值，评估管段的损坏程度，计算出准确的管段损坏状况参数S:
1. 数据收集与整理：利用智能检测装置进入管道内部收集管道内现场图片和视频，自动计算判断得出净距大于1.5m缺陷数量（n1）和净距1.0m至1.5m缺陷数量（n2），自动计算得出缺陷数量n=n1+ n2。同时智能检测装置检测到每个管道缺陷并给出该缺陷分值，得出p1和p2数据，p1为检测管道段内净距大于1.5m缺陷分值之和，p2为净距1.0m至1.5m缺陷分值之和。系统根据净距1.0m至1.5m缺陷情况给出对应的结构性缺陷影响系数α。
2. 根据数据计算公式：管段损坏状况参数S=[净距大于1.5m缺陷分值和（p1）+结构性缺陷影响系数α×净距1.0m至1.5m缺陷分值和（p2）]/ 缺陷数量n。
3. 数据应用：运用所检测的管道缺陷情况，建立管段损坏状况模型，预测和分析管段缺陷情况，为下一步维护和修理做铺垫。
综上所述，通过以上流程，我们能够建立可靠的统计学模型，用于分析管段缺陷情况。这将为市政、企业等各类管道进行积极预防，有利于城市的可持续发展。

Remotely controlled intelligent inspection devices are deployed to perform intra-pipeline intelligent inspection, collecting images and videos inside pipelines, automatically generating defect data, monitoring abnormal pipeline conditions, facilitating timely evaluation of in-pipeline defect data, and providing updated data for pipeline maintenance. This benefits pipeline maintenance and cleaning for municipal authorities and enterprises, and supports sustainable urban development. Based on the quantity and score of pipeline structural defects, the damage degree of pipeline segments is evaluated, and the accurate pipeline segment damage status parameter S is calculated as follows:
1. Data Collection and Sorting:
The intelligent inspection device enters the pipeline to collect on-site images and videos inside the pipeline, automatically calculates and determines the number of defects with clearance greater than 1.5m (n1) and the number of defects with clearance between 1.0m and 1.5m (n2), then computes the total number of defects n = n1 + n2. Meanwhile, the device detects each pipeline defect and assigns a corresponding score, obtaining data p1 and p2, where p1 is the sum of scores of defects with clearance greater than 1.5m in the inspected pipeline segment, and p2 is the sum of scores of defects with clearance between 1.0m and 1.5m. The system assigns the corresponding structural defect influence coefficient α based on the condition of defects with clearance between 1.0m and 1.5m.
2. Data Calculation Formula:
The pipeline segment damage status parameter S is calculated via:
S = [sum of scores of defects with clearance greater than 1.5m (p1) + structural defect influence coefficient α × sum of scores of defects with clearance between 1.0m and 1.5m (p2)] / total number of defects n.
3. Data Application:
Using the detected pipeline defect conditions, a pipeline segment damage status model is established to predict and analyze segment defect conditions, laying a foundation for subsequent maintenance and repair work.
In summary, through the above workflow, a reliable statistical model can be established for analyzing pipeline segment defect conditions. This enables proactive prevention for various pipelines of municipal authorities and enterprises, contributing to sustainable urban development.

搜集汇总

数据集介绍

特点

管道缺陷检测数据集包含164条记录，每周更新，通过智能检测装置采集管道内部数据，用于评估管道损坏状况，支持市政和企业管道维护。