降低CCTV探测成本的管道淤积负荷预测大模型分析数据

污水管道中排入的杂物、水泥砂容易发生沉淀和淤积并造成管道堵塞，如果不进行管道清淤疏通就会造成污水滥流，从而污染环境并影响市民生活。CCTV探测是最常用的一种用来获取管道淤积情况的方式，但由于管道众多且纵横分布，因此CCTV探测是一项费时费工的工作。
通过管道淤积负荷预测大模型分析数据，定位可能出现淤积风险的管段，能有效缩小CCTV探测范围，提高CCTV探测工作效率，定制“精准狠”的管网养护计划，从而降低在管道淤积探测工作中的成本。算法原理：
若某段管道存在淤积，会导致水位壅高、排水不畅。当下游泵站抽水时，上下游检查井的液位差将变大。根据这一水力特征，对上下游设置液位计的多段管道进行淤积趋势分析，根据每天开泵后的液位差特征值绘制月度淤积趋势图，若存在液位差增加的情况，则可判定存在管道淤积趋势风险。液位差特征值取每日开泵后的上下游液位差均值，并且保证上游液位大于下游液位。
数据采集：
液位计能够实时对污水管网的污水液位进行监测。系统对各液位计按地理位置进行编号，并通过物联网采集各个液位计的监测数据，采集频率为1分钟/次。
数据分析：
分析周期为2个月。其中【当天液位差】=【当天上游液位】-【当天下游液位】。为消除由液位计数据质量导致的极端值问题，将后一个月的【当天液位差】逐个与【上月液位差均值】作比较，如果【当天液位差】＞【上月液位差均值】，则【本月淤积系数】+1。
【本月淤积系数】≥30，则【本月淤积程度】判定为严重淤积。
20≤【本月淤积系数】<30，则【本月淤积程度】判定为中度淤积。
5≤【本月淤积系数】<20，则【本月淤积程度】判定为轻微淤积。
【本月淤积系数】<5，，则【本月淤积程度】判定为无淤积。

Debris and cement sand discharged into sewer pipelines are prone to sedimentation and siltation, leading to pipe blockages. If dredging and unblocking operations are not carried out, sewage overflow will occur, causing environmental pollution and disrupting residents' daily lives. CCTV detection is the most commonly used method to obtain pipeline siltation status. However, due to the large number and crisscross distribution of pipelines, CCTV detection is a time-consuming and labor-intensive work.
By analyzing data with the pipeline siltation load prediction large language model (LLM) to identify pipe sections at risk of siltation, the scope of CCTV detection can be effectively narrowed, the efficiency of CCTV detection work improved, and targeted and efficient pipeline network maintenance plans formulated, thereby reducing costs in pipeline siltation detection work.
Algorithm Principle:
If siltation occurs in a certain pipe section, it will lead to water level backing up and poor drainage. When the downstream pumping station pumps water, the liquid level difference between the upstream and downstream manholes will increase. Based on this hydraulic characteristic, siltation trend analysis is conducted on multiple pipe sections equipped with level gauges upstream and downstream. A monthly siltation trend chart is drawn based on the characteristic value of the liquid level difference after pumping each day. If the liquid level difference increases, it can be determined that there is a risk of pipeline siltation trend. The characteristic value of the liquid level difference is taken as the average value of the upstream and downstream liquid level differences after daily pumping, and it is ensured that the upstream liquid level is greater than the downstream liquid level.
Data Collection:
Level gauges can monitor the sewage level of the sewage pipe network in real time. The system numbers each level gauge according to its geographical location, and collects monitoring data from each level gauge through the Internet of Things (IoT), with a sampling frequency of once per minute.
Data Analysis:
The analysis cycle is 2 months. Among them, "Daily Liquid Level Difference" = "Daily Upstream Liquid Level" - "Daily Downstream Liquid Level". To eliminate outliers caused by poor data quality of level gauges, the "Daily Liquid Level Difference" of the latter month is compared with the average value of the liquid level differences of the previous month one by one. If "Daily Liquid Level Difference" > "Average Monthly Liquid Level Difference of the Previous Month", then "Monthly Siltation Coefficient" +1.
- If "Monthly Siltation Coefficient" ≥ 30, the "Monthly Siltation Degree" is determined as severe siltation.
- If 20 ≤ "Monthly Siltation Coefficient" < 30, the "Monthly Siltation Degree" is determined as moderate siltation.
- If 5 ≤ "Monthly Siltation Coefficient" < 20, the "Monthly Siltation Degree" is determined as slight siltation.
- If "Monthly Siltation Coefficient" < 5, the "Monthly Siltation Degree" is determined as no siltation.

搜集汇总

数据集介绍

特点

该数据集由嘉兴市联合污水处理有限责任公司产生，包含546条记录，每日更新。数据用于预测污水管道淤积情况，通过分析液位差等参数，帮助定位淤积风险管段，从而降低CCTV探测成本。