Water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm

Abstract

The invention discloses a water supply pipeline network intelligent monitoring system based on an artificial intelligence algorithm, which relates to the technical field of water supply pipeline monitoring and realizes real-time monitoring and intelligent management of the running state of a water supply pipeline by integrating a plurality of functional modules such as data acquisition, data processing, calculation analysis and intelligent alarm. Compared with a traditional underwater sound leakage detection and manual inspection mode, the system improves the accuracy and efficiency of leakage detection to a certain extent, reduces the workload and cost of manual inspection, and can quickly position a leakage point and give an alarm in time based on DMA partition metering and intelligent analysis technologies, so that the leakage detection efficiency is improved. The operation efficiency and safety of the water supply network are improved, the overall management level of the water supply system is improved to a certain extent, the system can accurately position the position where a problem possibly exists in the pipeline network by calculating the position judgment function E (in), and the accurate positioning can quickly respond and accurately repair the abnormal or fault point of the pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply pipeline monitoring, and in particular to an intelligent monitoring system for a water supply pipeline network based on an artificial intelligence algorithm. Background Art

[0002] The water supply pipeline network intelligent monitoring system is a system that integrates advanced sensor technology, data acquisition, communication, data analysis and processing, and intelligent control. It is used to monitor, analyze and manage the operation status of the water supply pipeline network in real time. Its main purpose is to improve the operating efficiency, safety and reliability of the water supply pipeline, timely detect and deal with problems such as water leakage and pipeline rupture, and ensure the stable operation of the water supply system.

[0003] There is no systematic theory and standardized equipment for water supply network leakage monitoring at home and abroad. Tracer gas method is usually used to locate leakage abroad, but it is not suitable for the complex national conditions of China's pipeline network and is difficult to apply. The level of urbanization development in my country is uneven, and the leakage forms and dominant factors of pipeline networks are different. As a result, leakage monitoring has long been based on passive leakage detection of "hydroacoustic leak detection + manual inspection", with problems such as backward technology and equipment, lack of theoretical guidance, and low work efficiency.

[0004] DMA zone metering, which has emerged in recent years, has achieved certain results by deploying sensors in pipe network zones to measure pipeline hydraulic parameters and realize hidden leak detection. It has become a development trend of leakage detection. However, due to the lack of artificial intelligence algorithm intervention, the existing zone metering still has limitations such as low concurrent transmission efficiency, high data processing pressure, and weak system coordination ability. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides an intelligent monitoring system for water supply pipeline networks based on artificial intelligence algorithms, which solves the limitations of the existing zone metering, such as low concurrent transmission efficiency, high data processing pressure, and weak system coordination capabilities due to the lack of intervention of artificial intelligence algorithms mentioned in the above background technology.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring system for a water supply pipeline network based on an artificial intelligence algorithm, comprising a data acquisition module, a data processing module, a first calculation module, a first analysis module, a second calculation module and an alarm module;

[0009] The data acquisition module is used to collect multi-source data for the pipeline, including: water inlet flow value, water outlet flow value, starting point pressure value, end point pressure value, midpoint pressure value, fluid viscosity, pH value, residual chlorine value, pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, and input the collected multi-source data into the data processing module;

[0010] The data processing module is used to pre-process the collected multi-source data, and reorganize the pre-processed multi-source data into a first data set, a second data set, a third data set, and a fourth data set;

[0011] The first calculation module is used to integrate the first data set, the second data set, the third data set and the fourth data set to obtain the outlier value YCZ in ;

[0012] The first analysis module calculates the outlier value YCZ in Compare with the preset first threshold value Y to generate a comparison result, and if the comparison result is normal, send a signal to the second calculation module;

[0013] After receiving the signal sent by the first analysis module, the second calculation module integrates and calculates the first data set, the second data set, the third data set and the fourth data set again, thereby generating a position determination function E(in);

[0014] The alarm module is used to compare the position determination function E(in) with the preset second threshold R, thereby generating a second comparison result, and judging the position of the abnormal point according to the second comparison result, and sending an alarm signal and coordinates to the master control.

[0015] Preferably, the water supply pipeline is divided into a plurality of regions by a segmentation method, and each region is respectively recorded as i1, i2, i3, ..., in;

[0016] A data acquisition module is provided in each area, and the data acquisition module includes a flow data acquisition unit, a pressure data acquisition unit, a water quality data acquisition unit and a pipeline characteristic data acquisition unit;

[0017] The flow data collection unit is used to collect the inlet flow value and the outlet flow value in the area in;

[0018] The pressure data acquisition unit is used to collect the starting point pressure value, the end point pressure value and the midpoint pressure value in the area;

[0019] The water quality data collection unit is used to collect the fluid viscosity, pH value and residual chlorine value in the area in;

[0020] The pipeline characteristic data acquisition unit is used to collect the pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness in the area in.

[0021] Preferably, the data processing module includes a preprocessing unit and an integration unit;

[0022] The preprocessing unit is used to preprocess the collected multi-source data, and the integration unit is used to re-divide the preprocessed data set according to the area label and the time stamp, so as to form a first data set, a second data set, a third data set and a fourth data set.

[0023] Preferably, the first data set includes an inlet flow value and an outlet flow value, wherein the inlet flow is recorded as JSL according to the area number and the timestamp. inT1 、JSL inT2 、JSL inT3 ,..., JSL inTn ;

[0024] The outflow flow value is recorded as CSL according to the area number and time stamp. inT1 、CSL inT2 、CSL inT3 、...、CSL inTn ;

[0025] The second data set includes a starting point pressure value, an end point pressure value, and a midpoint pressure value, wherein the starting point pressure value is recorded as QY according to the area number and time stamp. inT1 , QY inT2 , QY inT3 、...、QY inTn ;

[0026] The end point pressure value is recorded as ZY according to the area number and time stamp. inT1 , ZY inT2 , ZY inT3 ,...,ZY inTn ;

[0027] The midpoint pressure value is recorded as CY according to the area number and time stamp. inT1 CY inT2 CY inT3 ,...,CY inTn ;

[0028] The third data set includes fluid viscosity, pH value and residual chlorine value, where fluid viscosity is recorded as HD according to the area number and time stamp. inT1 , HD inT2 , HD inT3 、...、HD inTn ;

[0029] The pH value is recorded as PH according to the area number and time stamp. inT1 , PH inT2 , PH inT3 , ..., PH inTn ;

[0030] The residual chlorine value is recorded as LY according to the area number and time stamp. inT1 LY inT2 LY inT3 ,...,LY inTn ;

[0031] The fourth data set includes pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, where the pipeline length is recorded as GDC according to the area number. i1 、GDC i2 、GDC i3 、...、GDC in ;

[0032] The pipe diameter is denoted as GDZ according to the area number. i1 , GDZ i2 , GDZ i3 ,...,GDZ in ;

[0033] The cross-sectional area of ​​the pipeline is recorded as GDJ according to the area number. i1 、GDJ i2 、GDJ i3 、...、GDJ in ;

[0034] The roughness of the pipeline is recorded as GDD according to the regional number. i1 、GDD i2 、GDD i3 ,...,GDD in .

[0035] Preferably, the outlier value YCZ of region in in Obtained by the following formula:

[0036] ;

[0037] Wherein: e is a natural function, S1, S2, S3 and S4 are the first stage value, the second stage value and the third stage value respectively, the first stage value S1, the second stage value S2 and the third stage value S3 are calculated by integrating the first data set, the second data set and the third data set under the area in respectively, k is the corrected intervention value, and the corrected intervention value k is calculated by the fourth data set under the area in.

[0038] Preferably, the first comparison result is as follows:

[0039] when When , it means that there is no abnormality in the current water supply pipeline;

[0040] when , it means that there is an abnormality in the current water supply pipeline.

[0041] Preferably, the specific calculation formula of the position determination function E(in) is as follows:

[0042] ;

[0043] Where: BE1 is the standard value of the first function, E1 Tn is the real-time value of the first function, BE2 is the standard value of the second function, E2 Tn is the real-time value of the second function.

[0044] Preferably, the first function standard value BE1 and the first function real-time value E1 Tn They are calculated by the following formulas respectively;

[0045] ; ;

[0046] Where: JSLB is the standard water inlet flow rate, CSLB is the standard water outlet flow rate, obtained when the pipeline is first installed, and JSL inTn is the real-time water inflow at the Tnth time stamp in the area in, CSL inTn is the real-time outflow flow at the Tnth timestamp in area in.

[0047] Preferably, the second function standard value BE2 and the second function real-time value E2 Tn They are calculated by the following formulas respectively;

[0048] ; ;

[0049] Where: BHD is the standard fluid viscosity, BGDC is in is the standard pipe length in the in area, BGDJ in is the standard pipe cross-sectional area in the area, HD inTn is the real-time fluid viscosity at the Tnth time stamp in the region in, GDC in For the real-time pipeline length in area in, GDJ in is the real-time pipe cross-sectional area in the area in.

[0050] Preferably, the second comparison result is:

[0051] when When it is on, it means there is no water leakage in the current detection coordinate area, and the terminal indicator light is green;

[0052] when , it means that there is a first-level water leakage in the current detection coordinate area, the terminal indicator light switches to yellow and flashes twice per second;

[0053] when , it means that there is a second-level water leakage in the current detection coordinate area. It is a second-level water leakage. The terminal indicator light switches to red and flashes 3 times per second to give a prompt.

[0054] (III) Beneficial effects

[0055] The present invention provides an intelligent monitoring system for water supply pipeline network based on artificial intelligence algorithm, which has the following beneficial effects:

[0056] 1. The intelligent monitoring system for water supply pipeline network based on artificial intelligence algorithm realizes real-time monitoring and intelligent management of the operation status of water supply pipeline by integrating multiple functional modules such as data acquisition, data processing, artificial intelligence calculation and analysis, and intelligent alarm. Compared with the traditional "hydroacoustic leak detection + manual inspection" method, this system has improved the accuracy and efficiency of leakage detection to a certain extent, reduced the workload and cost of manual inspection, and based on DMA partition metering and artificial intelligence algorithm analysis technology, the system can quickly locate the leakage point and alarm in time, improve the operation efficiency and safety of the water supply network, and improve the overall management level of the water supply system to a certain extent.

[0057] 2. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm calculates the abnormal value YCZ of area in by setting the first artificial intelligence algorithm calculation and the second artificial intelligence algorithm to integrate multi-source data respectively. in As well as the location determination function E(in) of area in, the system can more accurately locate the location where problems may exist in the pipeline network, and evaluate and diagnose the pipeline from multiple dimensions. This precise positioning can quickly respond and accurately provide pipeline anomalies or fault points. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the structure of the present invention.

[0059] In the figure: 1. Data acquisition module; 11. Flow data acquisition unit; 12. Pressure data acquisition unit; 13. Water quality data acquisition unit; 14. Pipeline characteristic data acquisition unit; 2. Data processing module; 21. Preprocessing unit; 22. Integration unit; 3. First calculation module; 4. First analysis module; 5. Second calculation module; 6. Alarm module. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] See also Figure 1 , an intelligent monitoring system for a water supply pipeline network based on an artificial intelligence algorithm, comprising a data acquisition module 1, a data processing module 2, a first calculation module 3, a first analysis module 4, a second calculation module 5 and an alarm module 6;

[0063] The data acquisition module 1 is used to collect multi-source data of the pipeline, and the multi-source data includes: water inlet flow value, water outlet flow value, starting point pressure value, end point pressure value, midpoint pressure value, fluid viscosity, pH value, residual chlorine value, pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, and the collected multi-source data is input into the data processing module 2;

[0064] The data processing module 2 is used to pre-process the collected multi-source data, and reorganize the pre-processed multi-source data into a first data set, a second data set, a third data set, and a fourth data set;

[0065] The first calculation module 3 is used to integrate the first data set, the second data set, the third data set and the fourth data set to obtain the abnormal value YCZ in ;

[0066] The first analysis module 4 calculates the abnormal value YCZ in Compare with the preset first threshold value Y to generate a comparison result, and if the comparison result is normal, send a signal to the second calculation module 5;

[0067] After receiving the signal sent by the first analysis module 4, the second calculation module 5 integrates and calculates the first data set, the second data set, the third data set and the fourth data set again, thereby generating a position determination function Ein;

[0068] The alarm module 6 is used to compare the position determination function Ein with a preset second threshold R, thereby generating a second comparison result, and judging the position of the abnormal point according to the second comparison result, and sending an alarm signal and coordinates to the master control.

[0069] In this embodiment: the data acquisition module 1 is used to collect multi-source data of the pipeline, including flow, pressure, water quality and other parameters. Through sensors arranged at various locations in the pipeline, these key data are collected and recorded in real time to ensure the real-time and accuracy of the data. The beneficial effect of this module is that it can more comprehensively monitor the operating status of the water supply pipeline, providing a reliable data basis for subsequent data processing and analysis, thereby improving the management level and safety of the water supply system.

[0070] The data processing module 2 is responsible for processing the collected multi-source data, including data cleaning, format conversion, classification and sorting. By processing the original data, it is rearranged into the first data set, the second data set, the third data set and the fourth data set, making the data more standardized and structured. The beneficial effect of this module is to improve the availability and analysis efficiency of the data, and ensure the accuracy and stability of subsequent calculations and analyses.

[0071] The first calculation module 3 is used to integrate the processed first data set, second data set, third data set and fourth data set, and obtain abnormal values ​​by setting the first artificial intelligence algorithm E (in). This module can identify potential abnormal conditions through comprehensive analysis of multi-source data. Its beneficial effect is that it can detect abnormal conditions in the water supply pipeline in advance, such as water leakage, pipeline rupture and other problems, improve the early warning ability and response speed of the water supply system, thereby reducing potential losses and risks.

[0072] The first analysis module 4 compares the calculated abnormal value with the preset first threshold value Y to generate a comparison result. If the comparison result shows an abnormality, the system will send a signal to the second calculation module 5. The beneficial effect of this module is that by setting a reasonable threshold, it realizes the preliminary screening and judgment of abnormal situations, ensuring that subsequent in-depth analysis will be triggered only when there is an abnormality, thereby improving the efficiency and accuracy of the system.

[0073] After receiving the signal sent by the first analysis module 4, the second calculation module 5 integrates and calculates the first data set, the second data set, the third data set and the fourth data set again, and generates a position determination function through the second artificial intelligence algorithm. The beneficial effect of this module is that through more detailed and in-depth data analysis, the specific location of the abnormal situation can be accurately located, accurate coordinate information can be provided, and a reliable basis for subsequent maintenance and processing can be provided.

[0074] The alarm module 6 compares the position determination function with the preset second threshold R to generate a second comparison result. According to the second comparison result, the position of the abnormal point is determined, and finally an alarm signal and coordinates are sent. The beneficial effect of this module is that after an abnormal situation is found, relevant personnel can be notified in time and accurate location information can be provided, so that measures can be taken quickly to prevent the situation from further deteriorating and ensure the safe and stable operation of the water supply system.

[0075] The above-mentioned intelligent monitoring system for water supply pipeline network realizes real-time monitoring and intelligent management of the operation status of water supply pipelines by integrating multiple functional modules such as data collection, data processing, artificial intelligence calculation and analysis, and intelligent alarm. Compared with the traditional "hydroacoustic leak detection + manual inspection" method, the system has improved the accuracy and efficiency of leakage detection to a certain extent, reduced the workload and cost of manual inspection, and at the same time, based on DMA partition metering and intelligent analysis technology, the system can quickly locate the leakage point and alarm in time, improve the operation efficiency and safety of the water supply network, and improve the overall management level of the water supply system to a certain extent.

[0076] Example 2

[0077] See also Figure 1 , the water supply pipeline is divided into several areas by segmentation method, and each area is recorded as i1, i2, i3, ..., in;

[0078] A data acquisition module 1 is provided in each area, and the data acquisition module 1 includes a flow data acquisition unit 11, a pressure data acquisition unit 12, a water quality data acquisition unit 13 and a pipeline characteristic data acquisition unit 14;

[0079] The flow data collection unit 11 is used to collect the inlet flow value and the outlet flow value in the area in;

[0080] The pressure data collection unit 12 is used to collect the starting point pressure value, the end point pressure value and the midpoint pressure value in the area in;

[0081] The water quality data collection unit 13 is used to collect the fluid viscosity, pH value and residual chlorine value in the area in;

[0082] The pipeline characteristic data acquisition unit 14 is used to collect the pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness in the area in.

[0083] In this embodiment: the flow data collection unit 11 is used to collect the water inlet flow value and the water outlet flow value, and can monitor the flow changes in each area in real time and quickly identify flow anomalies.

[0084] The pressure data acquisition unit 12 is used to collect the starting point pressure value, the end point pressure value and the midpoint pressure value, and can detect the pressure changes in each area and find pressure anomalies in time.

[0085] The water quality data collection unit 13 is used to collect fluid viscosity, pH value and residual chlorine value, monitor water quality changes, and ensure water supply quality.

[0086] The pipeline characteristic data acquisition unit 14 is used to collect pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, provide pipeline characteristic data, and support accurate analysis and prediction.

[0087] By setting up data collection modules in different areas, the monitoring scope can be refined to make the monitoring of each area more accurate, avoid blind spots in the overall monitoring, and improve the efficiency and accuracy of detection. Through real-time data collection and analysis, abnormal situations such as water leaks, pipe ruptures, etc. can be quickly discovered, and timely warnings can be issued to reduce losses caused by leakage and failures. Real-time monitoring and rapid response capabilities enable the water supply network to take immediate measures when abnormal situations occur to ensure the stable operation of the water supply system.

[0088] At the same time, the data collected by each data acquisition module can be processed and integrated by the data processing module to form a systematic data set, which is convenient for comprehensive analysis and decision support. The comprehensive analysis of multi-source data can provide more comprehensive and accurate monitoring results, and provide a scientific basis for the optimization and management of the water supply network. Through precise monitoring and timely troubleshooting, the frequency of water leakage and other problems is reduced, and the operating efficiency of the water supply network is improved. The application of real-time monitoring and early warning systems improves the safety of the water supply system and ensures the continuity and reliability of water supply. In addition, the intelligent monitoring system replaces the traditional "hydroacoustic leak detection + manual inspection" method, reduces the workload and cost of manual inspection, improves the efficiency of monitoring and maintenance, optimizes resource allocation, and reduces operating costs.

[0089] Example 3

[0090] See also Figure 1 , the data processing module 2 includes a preprocessing unit 21 and an integration unit 22;

[0091] The preprocessing unit 21 is used to preprocess the collected multi-source data, and the integration unit 22 is used to re-divide the preprocessed data set according to the area label and the time stamp, so as to form a first data set, a second data set, a third data set and a fourth data set.

[0092] In this embodiment: the preprocessing unit 21 can perform operations such as cleaning, filtering, and denoising on the collected raw data, eliminate noise and outliers in the data, ensure the accuracy and reliability of the data, ensure the integrity of the data by interpolating missing data, improve the accuracy of data analysis, standardize and normalize the data, unify the data format and unit, and make subsequent data analysis and integration more convenient and consistent.

[0093] The integrated unit 22 re-divides the pre-processed data set according to the area label and time stamp to form a systematic first data set, a second data set, a third data set and a fourth data set. The systematic management of data enables different types of data to be stored and accessed in an orderly manner, which is convenient for subsequent comprehensive analysis and application.

[0094] The introduction and application of the preprocessing unit 21 and the integration unit 22 of the data processing module 2 realize high-quality processing and systematic management of the collected data. The preprocessing unit improves the accuracy, reliability and integrity of the data through data cleaning, filtering, denoising and standardization. The integration unit enhances the relevance and availability of the data by integrating and re-dividing the data, and supports multi-dimensional analysis.

[0095] Example 4

[0096] See also Figure 1 The first data set includes the inlet flow value and the outlet flow value, where the inlet flow is recorded as JSL according to the area number and time stamp. inT1 、JSL inT2 、JSL inT3 ,..., JSL inTn ;

[0097] The outflow flow value is recorded as CSL according to the area number and time stamp. inT1 、CSL inT2 、CSL inT3 、...、CSL inTn ;

[0098] The second data set includes a starting point pressure value, an end point pressure value, and a midpoint pressure value, wherein the starting point pressure value is recorded as QY according to the area number and time stamp. inT1 , QY inT2 , QY inT3 、...、QY inTn ;

[0099] The end point pressure value is recorded as ZY according to the area number and time stamp. inT1 , ZY inT2 , ZY inT3 ,...,ZY inTn ;

[0100] The midpoint pressure value is recorded as CY according to the area number and time stamp. inT1 CY inT2 CY inT3 ,...,CY inTn ;

[0101] The third data set includes fluid viscosity, pH value and residual chlorine value, where fluid viscosity is recorded as HD according to the area number and time stamp. inT1 , HD inT2 , HD inT3 、...、HD inTn ;

[0102] The pH value is recorded as PH according to the area number and time stamp. inT1 , PH inT2 , PH inT3 , ..., PH inTn ;

[0103] The residual chlorine value is recorded as LY according to the area number and time stamp. inT1 LY inT2 LY inT3 ,...,LY inTn ;

[0104] The fourth data set includes pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, where the pipeline length is recorded as GDC according to the area number. i1 、GDC i2 、GDC i3 、...、GDC in ;

[0105] The pipe diameter is denoted as GDZ according to the area number. i1 , GDZ i2 , GDZ i3 ,...,GDZ in ;

[0106] The cross-sectional area of ​​the pipeline is recorded as GDJ according to the area number. i1 、GDJ i2 、GDJ i3 、...、GDJ in ;

[0107] The roughness of the pipeline is recorded as GDD according to the regional number. i1 、GDD i2 、GDD i3 ,...,GDD in .

[0108] In this embodiment: This system realizes all-round monitoring and management of the water supply network by integrating advanced sensor technology and data analysis methods. Compared with the traditional "hydroacoustic leak detection + manual inspection" method, this system improves the accuracy and efficiency of monitoring through real-time data collection and intelligent analysis, reduces the workload of manual inspection, and improves the operating efficiency and safety of the water supply system to a certain extent.

[0109] Example 5

[0110] See also Figure 1 , the outlier value YCZ of region in in Obtained by the following formula:

[0111] ;

[0112] Wherein: e is a natural function, S1, S2, S3 and S4 are the first stage value, the second stage value and the third stage value respectively, the first stage value S1, the second stage value S2 and the third stage value S3 are calculated by integrating the first data set, the second data set and the third data set under the area in respectively, k is the corrected intervention value, and the corrected intervention value k is calculated by the fourth data set under the area in.

[0113] In this embodiment: by integrating the first data set, the second data set and the third data set of water quality parameters, the operating status of the area in can be reflected more comprehensively and comprehensively, so that the result of the first artificial intelligence algorithm improves the depth and breadth of data analysis, making the judgment of pipeline abnormalities more accurate.

[0114] The introduction of the modified intervention value k makes the first artificial intelligence algorithm correct for the abnormal value YCZ of region in in The calculation can be adjusted dynamically according to the actual situation, which improves the flexibility and adaptability of the model. The modified intervention value k is calculated by the fourth data set under the area in to ensure the accuracy of the outlier calculation.

[0115] By using the rapid growth characteristics of the natural function e, the first artificial intelligence algorithm can detect the outliers YCZ in the region in. in Controllable adjustments are made to improve the sensitivity of monitoring.

[0116] This system sets up the first artificial intelligence algorithm by comprehensively considering data from multiple dimensions such as flow, pressure, water quality and pipeline characteristics. The first artificial algorithm realizes comprehensive monitoring and management of water supply pipelines. Compared with traditional monitoring methods, the system has a certain degree of improvement in data integration, dynamic adjustment, early warning, monitoring accuracy and data processing efficiency. Especially in complex pipe network structures, the system can quickly and accurately locate leaks and abnormal positions.

[0117] The first stage value S1, the second stage value S2, the third stage value S3 and the value of k are calculated by the following formulas:

[0118]

[0119] Among them, JSL inTn-1为 Real-time water inflow at the Tn-1th time stamp in area in, CSL inTn-1为 The real-time outflow flow at the Tnth time stamp in the area in, QY inTn-1为 The real-time starting pressure at the Tnth timestamp in the area in, ZY inTn-1为 Real-time endpoint pressure at the Tnth timestamp in area in, CY inTn-1为 Real-time midpoint pressure at the Tnth timestamp in area in, HD inTn-1为 Real-time fluid viscosity at the Tnth time stamp in area in, PH inTn-1为 Real-time pH value at the Tnth timestamp in area in, LY inTn-1为 Real-time residual chlorine at the Tnth timestamp in area in;

[0120] JSL inTn为 Real-time water inflow at the Tnth time stamp in area in, CSL inTn为 The real-time outflow flow at the Tnth time stamp in the area in, QY inTn为 The real-time starting pressure at the Tnth timestamp in the area in, ZY inTn为 Real-time endpoint pressure at the Tnth timestamp in area in, CY inTn为 The midpoint pressure at the Tnth time stamp in the region in, HD inTn为 Real-time fluid viscosity at the Tnth time stamp in area in, PH inTn为 Real-time pH value at the Tnth timestamp in area in, LY inTn为 Real-time residual chlorine at the Tnth timestamp in area in;

[0121] GDC in is the real-time pipeline length in area in, GDZ in is the real-time pipe diameter in the area in, GDJ in is the real-time pipe cross-sectional area in the area in, GDD in is the real-time pipeline roughness in area in.

[0122] Example 6

[0123] See also Figure 1 ,The first comparison results are as follows;

[0124] when When , it means that there is no abnormality in the current water supply pipeline;

[0125] when , it means that there is an abnormality in the current water supply pipeline.

[0126] In this embodiment: by setting the threshold value Y as the criterion for determining whether there is an abnormality in the water supply pipeline, the operation status of the pipeline can be quickly determined, and the operation status of the water supply pipeline can be monitored in real time by using the first comparison result. When YCZ exceeds the threshold value Y, the system will immediately identify the abnormality and trigger the corresponding processing mechanism.

[0127] The intelligent monitoring system realizes accurate monitoring and management of water supply pipelines through clear first comparison result judgment criteria. By timely detecting pipeline anomalies, the system can effectively ensure the stable operation of the water supply network and improve the operating efficiency and reliability of the water supply system.

[0128] Example 7

[0129] See also Figure 1 , the specific calculation formula of the position determination function E (in) is as follows;

[0130] ;

[0131] Where: BE1 is the standard value of the first function, E1 Tn is the real-time value of the first function, BE2 is the standard value of the second function, E2 Tn is the real-time value of the second function.

[0132] In this embodiment: By setting the second artificial intelligence algorithm, the entire multi-source data calculates the position determination function E(in), and the system can accurately locate the location where there may be problems in the pipeline network. This precise positioning can quickly respond to and accurately repair pipeline anomalies or fault points, using the real-time values ​​of the first function and the second function E1 Tn and E2 Tn , can quickly calculate the value of E (in) and evaluate and diagnose the pipeline from multiple dimensions. This comprehensive evaluation can comprehensively analyze the pipeline operation status and accurately determine the root cause and impact range of the problem.

[0133] The application of the position determination function E(in) makes the management and maintenance of the water supply pipeline network more efficient and reliable. Through real-time calculation and multi-dimensional evaluation, the system can quickly identify and handle pipeline anomalies, improving the safety, reliability and response speed of pipeline operation.

[0134] Example 8

[0135] See also Figure 1 , the first function standard value BE1 and the first function real-time value E1 are calculated and obtained by the following formulas respectively;

[0136] ; ;

[0137] Where: JSLB is the standard water inlet flow rate, CSLB is the standard water outlet flow rate, obtained when the pipeline is first installed, and JSL inTn is the real-time water inflow at the Tnth time stamp in the area in, CSL inTn is the real-time outflow flow at the Tnth timestamp in area in.

[0138] In this embodiment: by integrating multi-source data through the second artificial intelligence algorithm to calculate the standard value BE1 of the first function, the inlet and outlet flow of the pipeline can be compared with the initially set standard. This standardized comparison can detect potential problems in pipeline operation, such as abnormal flow or leakage. The standard value BE1 provides a reference for the expected inlet and outlet flow of the pipeline under normal operation, which can detect abnormalities in the pipeline operation status early.

[0139] Through the second artificial intelligence algorithm, the real-time value E1Tn of the first function of multi-source data is integrated to reflect the actual operation of the pipeline at the current moment. The real-time value E1Tn can be compared with the standard value BE1 to achieve real-time monitoring and evaluation of the pipeline operation status. This real-time monitoring can quickly identify pipeline problems and reduce potential water supply interruptions or quality problems.

[0140] The calculation and comparison of the first function standard value BE1 and real-time value E1Tn not only optimizes the operation and management of the pipeline, but also improves the response capability and service level of the water supply system, which meets the development needs of the modern water supply pipeline network intelligent monitoring system.

[0141] Example 9

[0142] See also Figure 1 , the second function standard value BE2 and the second function real-time value E2 Tn They are calculated by the following formulas respectively;

[0143] ; ;

[0144] Where: BHD is the standard fluid viscosity, BGDC is in is the standard pipe length in the in area, BGDJ in is the standard pipe cross-sectional area in the area, HD inTn is the real-time fluid viscosity at the Tnth time stamp in the region in, GDC in For the real-time pipeline length in area in, GDJ inis the real-time pipe cross-sectional area in the area in.

[0145] In this embodiment: by integrating multi-source data through the second artificial intelligence algorithm, the second function standard value BE2 and the second function real-time value E2 are obtained Tn By comparing the real-time value E2 Tn And the standard value BE2, can find any abnormalities in the pipeline, such as changes in fluid viscosity, pipeline wear or blockage, etc. The standard value BE2 and real-time value E2 of the second function Tn The calculation and comparison not only improves the transparency of pipeline operation and data-driven management capabilities, but also enhances the stability and sustainability of the water supply system, which meets the development needs of modern water supply pipeline network intelligent monitoring systems.

[0146] Example 10

[0147] See also Figure 1 , the second comparison result is;

[0148] when When it is on, it means there is no water leakage in the current detection coordinate area, and the terminal indicator light is green;

[0149] when , it means that there is a first-level water leakage in the current detection coordinate area, the terminal indicator light switches to yellow and flashes twice per second;

[0150] when , it means that there is a second-level water leakage in the current detection coordinate area. It is a second-level water leakage. The terminal indicator light switches to red and flashes 3 times per second to give a prompt.

[0151] In this embodiment: by setting the position determination function E (in), the operating status of each area of ​​the pipeline, especially the water leakage, can be monitored in real time. Indicator lights of different colors and flashing frequencies clarify the level of water leakage, helping to quickly identify and respond to problems. Real-time monitoring of water leakage and rapid alarms enable pipeline managers to take quick measures to prevent the expansion of water leakage and water supply interruption. This can reduce water outage time and maintenance costs, improve the reliability and stability of the water supply system, and introduce data-driven management into the pipeline monitoring system through the calculation and comparison of the position determination function. The monitoring results and alarm information are based on actual data, enabling managers to make decisions and optimize pipeline operation based on facts. The different colors and flashing frequencies of the terminal indicator lights make pipeline operation and maintenance management more intelligent and visual. Operators can intuitively understand the status of the pipeline, adjust maintenance strategies in a timely manner, and improve management efficiency and response speed.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for water supply pipeline network based on artificial intelligence algorithm, characterized by: It comprises a data acquisition module (1), a data processing module (2), a first calculation module (3), a first analysis module (4), a second calculation module (5) and an alarm module (6); The data acquisition module (1) is used to collect multi-source data on the pipeline, the multi-source data including: water inlet flow value, water outlet flow value, starting point pressure value, end point pressure value, midpoint pressure value, fluid viscosity, pH value, residual chlorine value, pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, and the collected multi-source data is input into the data processing module (2); The data processing module (2) is used to pre-process the collected multi-source data, and reorganize the pre-processed multi-source data into a first data set, a second data set, a third data set, and a fourth data set; The first calculation module (3) is used to integrate the first data set, the second data set, the third data set and the fourth data set to obtain the outlier value YCZ in ; The first analysis module (4) calculates the outlier value YCZ in Comparing with a preset first threshold value Y to generate a comparison result, and if the comparison result is normal, sending a signal to a second calculation module (5); After receiving the signal sent by the first analysis module (4), the second calculation module (5) generates a position determination function E(in) by re-integrating and calculating the first data set, the second data set, the third data set and the fourth data set; The alarm module (6) is used to compare the position determination function E(in) with a preset second threshold value R, thereby generating a second comparison result, and judging the position of the abnormal point according to the second comparison result, and sending an alarm signal and coordinates to the master control.

2. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 1 is characterized in that: The water supply pipeline is divided into several areas by segmentation method, and each area is denoted as i1, i2, i3, ..., in; A data acquisition module (1) is provided in each area, and the data acquisition module (1) comprises a flow data acquisition unit (11), a pressure data acquisition unit (12), a water quality data acquisition unit (13), and a pipeline characteristic data acquisition unit (14); The flow data collection unit (11) is used to collect the inlet flow value and the outlet flow value in the area in; The pressure data acquisition unit (12) is used to collect the starting point pressure value, the end point pressure value and the midpoint pressure value in the area; The water quality data collection unit (13) is used to collect the viscosity, pH value and residual chlorine value of the fluid in the area in; The pipeline characteristic data acquisition unit (14) is used to collect the pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness in the area in.

3. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 2 is characterized in that: The data processing module (2) comprises a preprocessing unit (21) and an integration unit (22); The preprocessing unit (21) is used to preprocess the collected multi-source data, and the integration unit (22) is used to re-divide the preprocessed data set according to the area label and the time stamp, thereby forming a first data set, a second data set, a third data set and a fourth data set.

4. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 3 is characterized by: The first data set includes the inlet flow value and the outlet flow value, where the inlet flow is recorded as JSL according to the area number and time stamp. inT1 、JSL inT2 、JSL inT3 ,..., JSL inTn ; The outflow flow value is recorded as CSL according to the area number and time stamp. inT1 、CSL inT2 、CSL inT3 、...、CSL inTn ; The second data set includes a starting point pressure value, an end point pressure value, and a midpoint pressure value, wherein the starting point pressure value is recorded as QY according to the area number and time stamp. inT1 , QY inT2 , QY inT3 、...、QY inTn ; The end point pressure value is recorded as ZY according to the area number and time stamp. inT1 , ZY inT2 , ZY inT3 ,...,ZY inTn ; The midpoint pressure value is recorded as CY according to the area number and time stamp. inT1 CY inT2 CY inT3 ,...,CY inTn ; The third data set includes fluid viscosity, pH value and residual chlorine value, where fluid viscosity is recorded as HD according to the area number and time stamp. inT1 , HD inT2 , HD inT3 、...、HD inTn ; The pH value is recorded as PH according to the area number and time stamp. inT1 , PH inT2 , PH inT3 , ..., PH inTn ; The residual chlorine value is recorded as LY according to the area number and time stamp. inT1 LY inT2 LY inT3 ,...,LY inTn ; The fourth data set includes pipeline length, pipeline diameter, pipeline cross-sectional area and pipeline roughness, where the pipeline length is recorded as GDC according to the area number. i1 、GDC i2 、GDC i3 、...、GDC in ; The pipe diameter is denoted as GDZ according to the area number. i1 , GDZ i2 , GDZ i3 ,...,GDZ in ; The cross-sectional area of ​​the pipeline is recorded as GDJ according to the area number. i1 、GDJ i2 、GDJ i3 、...、GDJ in ; The roughness of the pipeline is recorded as GDD according to the regional number. i1 、GDD i2 、GDD i3 ,...,GDD in .

5. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 4 is characterized in that: Outlier value YCZ in region in Obtained by the following formula: ； Wherein: e is a natural function, S1, S2, S3 and S4 are the first stage value, the second stage value and the third stage value respectively, the first stage value S1, the second stage value S2 and the third stage value S3 are calculated by integrating the first data set, the second data set and the third data set under the area in respectively, k is the corrected intervention value, and the corrected intervention value k is calculated by the fourth data set under the area in.

6. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 5 is characterized by: The first comparison results are as follows: when When , it means that there is no abnormality in the current water supply pipeline; when , it means that there is an abnormality in the current water supply pipeline.

7. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 6 is characterized by: The specific calculation formula of the position determination function E (in) is as follows; ； Where: BE1 is the standard value of the first function, E1 Tn is the real-time value of the first function, BE2 is the standard value of the second function, E2 Tn is the real-time value of the second function.

8. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 7 is characterized by: The first function standard value BE1 and the first function real-time value E1 Tn They are calculated by the following formulas respectively; ； ； Where: JSLB is the standard water inlet flow rate, CSLB is the standard water outlet flow rate, obtained when the pipeline is first installed, and JSL inTn is the real-time water inflow at the Tnth time stamp in the area in, CSL inTn is the real-time outflow flow at the Tnth timestamp in area in.

9. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 8, characterized in that: The second function standard value BE2 and the second function real-time value E2 Tn They are calculated by the following formulas respectively; ； ； Where: BHD is the standard fluid viscosity, BGDC in is the standard pipe length in the in area, BGDJ in is the standard pipe cross-sectional area in the in area, HD inTn is the real-time fluid viscosity at the Tnth time stamp in the region in, GDC in For the real-time pipeline length in area in, GDJ in is the real-time pipe cross-sectional area in the area in.

10. The water supply pipeline network intelligent monitoring system based on artificial intelligence algorithm according to claim 9, characterized in that: The second comparison result is: when When it is on, it means there is no water leakage in the current detection coordinate area, and the terminal indicator light is green; when , it means that there is a first-level water leakage in the current detection coordinate area, the terminal indicator light switches to yellow and flashes twice per second; when , it means that there is a second-level water leakage in the current detection coordinate area. It is a second-level water leakage. The terminal indicator light switches to red and flashes 3 times per second to give a prompt.

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