Water supply network water quality monitoring method

Abstract

The invention relates to the technical field of urban water supply, in particular to a water supply network water quality monitoring method. Comprising the following steps: constructing a water supply network visual model; acquiring comprehensive water quality monitoring data of the water supply network; processing the comprehensive water quality monitoring data of the water supply pipe network by using a fuzzy analytic hierarchy process, and performing fuzzy quantification on the comprehensive water quality monitoring data of the water supply pipe network; and displaying the comprehensive water quality monitoring data of the water supply pipe network by using the water supply pipe network visual model. The sensors are arranged at different nodes of the water supply network, various indexes of water quality are monitored in real time, and data support is provided for a subsequent water quality early warning model. Based on multi-source monitoring data, a water quality early warning model is constructed, and the model can predict abnormal conditions of water quality and give early warning information.

Description

Technical Field

[0001] The invention relates to the technical field of urban water supply, and in particular to a method for monitoring water quality in a water supply network. Background Art

[0002] Urban water supply is a complex and huge system project. According to its functions, it mainly includes water intake structures, water treatment structures, water pumping stations, water transmission and distribution pipelines, and regulating structures. The weakest and most difficult to control link is the pipeline network. Due to its large scale and wide coverage, it is relatively unsafe and more susceptible to invasion by pollutants. The time, place, nature and concentration of pollution are unpredictable. Once a node in the pipeline network is polluted, the entire water supply network will be affected, and the safety of the urban water supply system cannot be guaranteed. The consequences will be very serious.

[0003] At present, many cities have established real-time monitoring systems for urban water supply networks based on SCADA and other systems. This system mainly monitors conventional water quality, water volume, and water pressure. However, water quality monitoring of water supply networks can only reflect real-time water quality conditions, and cannot indicate whether a certain water quality indicator is at risk of exceeding the standard. In addition, the current urban water supply network system lacks a standardized, normalized, and systematic water quality early warning system for water supply networks. Summary of the invention

[0004] The present invention discloses a method for monitoring water quality in a water supply network, and the specific method is as follows:

[0005] Construct a visual model of the water supply network;

[0006] Obtain comprehensive water quality monitoring data for water supply networks;

[0007] The fuzzy analytic hierarchy process is used to process the comprehensive water quality monitoring data of the water supply network, and the comprehensive water quality monitoring data of the water supply network is fuzzy quantified;

[0008] The comprehensive water quality monitoring data of the water supply network is displayed using a water supply network visualization model.

[0009] Furthermore, the comprehensive water quality monitoring data of the water supply network is collected at water quality online monitoring and early warning points, and sensors are deployed at the online monitoring and early warning points to obtain the comprehensive water quality monitoring data of the water supply network.

[0010] Furthermore, a visualization model of the water supply network is constructed. The specific method is as follows:

[0011] Determine the topological structure of the water supply network, including: water supply network connection relationship, network attribute data and node attribute data;

[0012] Determine pipeline hydraulic coefficients, node water distribution, valve data and inlet known data;

[0013] Export the shp file through the GIS system and convert the shp file into an inp file;

[0014] Simplify and build a visual model of the water supply network;

[0015] Furthermore, after building the water supply network visualization model, the model verification needs to be completed. The specific verification includes:

[0016] Measurement errors, errors caused by incorrect topology of the water supply network, water level errors in high-level water tanks, errors caused by simplification of the network, errors caused by inaccurate pump valve status, errors caused by the pump characteristic curve not conforming to the actual situation, errors caused by the difference between the calculated inner diameter and the actual inner diameter of the pipe section, pipe friction errors and errors in water consumption distribution in the water supply network.

[0017] Furthermore, the method also includes accident warning, and the specific method is as follows:

[0018] Complete water age calculation, residual chlorine calculation, and pollutant diffusion analysis calculation based on comprehensive water quality monitoring data of the water supply network;

[0019] Construct a water supply network early warning model and complete accident early warning based on the calculation results.

[0020] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0021] 1. The present invention deploys sensors at different nodes of the water supply network to monitor various water quality indicators in real time and provide data support for subsequent water quality early warning models.

[0022] 2. The present invention constructs a water quality early warning model based on multi-source monitoring data. The model can predict abnormal water quality and provide early warning information.

[0023] 3. Research and establish a scientific and reasonable early warning response mechanism, including the release of early warning information, to ensure a rapid and effective response to abnormal water quality events.

[0024] 4. The present invention provides a water quality management platform that integrates data collection, analysis, early warning and decision support functions, which is convenient for management departments to grasp the water quality status of the water supply network in real time and provide intelligent water quality management solutions.

[0025] 5. Through research and application, the water quality monitoring and early warning capabilities of the entire water supply network will be improved, thereby effectively ensuring the safety of residents’ drinking water and reducing the frequency of water supply network pollution incidents.

[0026] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings of the present invention are as follows.

[0028] Figure 1 This is a schematic diagram of the overall structure of the water supply network visualization model.

[0029] Figure 2 Schematic diagram of the operation interface for exporting shp files.

[0030] Figure 3 This is a simplified display interface diagram of the pipeline network.

[0031] Figure 4 This is a schematic diagram of the water consumption management operation interface.

[0032] Figure 5 This is a schematic diagram of the model data table.

[0033] Figure 6 Schematic diagram of the SCADA data import operation interface.

[0034] Figure 7 The figure is a schematic diagram of the verification process.

[0035] Figure 8 Schematic diagram of the operation interface for calculating and updating the visualization model of the water supply network.

[0036] Fig. 9 Schematic diagram of the display interface for calculating and updating the water supply network visualization model.

[0037] Fig.10 This is a display diagram of the zoom in, zoom out, and roaming operation interface of the Web interface graphics.

[0038] Fig.11 It is a graph showing the results of pressure, velocity, flow rate, head loss, etc.

[0039] Fig.12 To select a scheduling plan through a window, switch the display diagram of the model.

[0040] Fig.13 This is an information display of the changes in the hydraulic power of the pipeline network at different time periods.

[0041] Fig.14 A display diagram for viewing water quality information of pipelines or nodes.

[0042] Fig.15 This is a diagram showing the layer window operation interface.

[0043] Fig.16 A diagram showing the simulation and monitoring parameter information of the key nodes of the current map. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] A water quality monitoring method for a water supply network, the specific method is as follows:

[0046] S1. Construct a visualization model of the water supply network, such as Figure 1 shown.

[0047] In step S1, basic hydraulic data is obtained by constructing a visualization model of the water supply network. A detailed investigation is conducted on the current status of the water supply network, and information such as the diameter, material, service life, flow rate at each node, and hourly water quality data such as residual chlorine and turbidity at the current water quality online monitoring points are collected. A batch of online monitoring instruments for specific water quality indicators are appropriately installed, and the water supply network is simplified in combination with the characteristics of the water supply network, and a hydraulic water quality model of the water supply network based on the Epanet2.0 software is established.

[0048] Construct an online water quality early warning point based on the comprehensive evaluation of water quality risk at water supply network nodes. In view of the difficulty in quantifying the various factors affecting the water quality in the network, the hydraulic conditions, water quality conditions, pipeline conditions and location conditions of the water supply network nodes are comprehensively considered, and the fuzzy analytic hierarchy process is used to quantify each evaluation index and establish a comprehensive evaluation system for water quality risk at water supply network nodes. Secondly, since the conventional water quality monitoring point optimization site selection model covering water quantity uses water quantity to characterize water quality, there are limitations and shortcomings. Therefore, the risk assessment system is combined as the objective function to construct a multi-objective optimization site selection model for monitoring points.

[0049] Construct a visualization model of the water supply network, including the following contents:

[0050] S11. Determination of topological structure:

[0051] The topological structure of the water supply network visualization model includes the connection relationship of the network, the attribute data of the pipeline, and the attribute data of the node. The attribute data of the pipeline includes: diameter, material, length, burial year, etc. Nodes include: all point type nodes such as water plants and pumping stations. The attribute data of nodes include: type, coordinates, ground elevation and other characteristic attributes.

[0052] These data are exported to shp files through the GIS system. The offline model system can convert the shp files into inp files for offline hydraulic model system modeling. The operation interface is as follows: Figure 2 shown.

[0053] After the topology is generated, it needs to be checked for accuracy and processed. Modeling engineers can use offline model software to check. The inspection work includes two aspects: one is whether there are errors in the model simplification process, such as whether some pipes with diameters below DN100 that affect hydraulic calculations are simplified after simplification; the other is the data attribute editing errors in GIS that lead to missing or invalid data after import, isolated pipe points and pipelines, and disconnected connecting pipelines.

[0054] S12. Simplify the pipeline network.

[0055] Extract the corresponding node and pipeline information from the existing pipe network GIS data SHP file. Since there are many water supply pipelines, especially in large cities, it is actually unnecessary and sometimes even impossible to calculate all pipelines. For this reason, modeling engineers can use offline model software to appropriately generalize / simplify the actual pipe network, omit minor pipelines, and retain major pipelines, but the simplified pipe network should basically reflect the actual water use situation.

[0056] The main generalization / simplification principles are: the macro-equivalence principle, that is, after simplifying some parts of the water supply and drainage network, its function should be maintained and the relationship between the elements should remain unchanged. The micro-error principle, simplification will inevitably bring errors between the model and the actual system, but as long as the error is controlled within a certain range, it is allowed. The allowable error of simplification should also be controlled flexibly and specifically, and generally meet the requirements of the project. According to the above principles, some nodes and pipelines were merged, and the network attribute information, water use area information, online measurement points and pipelines that have not been updated in GIS were added. The simplified display interface of the pipeline network is as follows: Figure 3 shown.

[0057] S13. Determine the friction coefficient.

[0058] The hydraulic friction coefficient of the pipeline depends on the material, age, coating type, water quality and hydraulic conditions of the pipeline. When modeling, the offline model software will assign a default value to each section of the pipeline network based on the material and age of the pipeline. In order to make the hydraulic friction coefficient of the pipeline closer to reality, before the initial calculation, the default value in the software is uniformly corrected after dividing it according to an incremental change every 5 years based on the material and laying date of the pipeline recorded in the GIS data and the empirical value provided in the specification.

[0059] S14, node water allocation.

[0060] The historical metered water consumption data of users in the water company's business system is the data source for node water allocation. The business system is used to export a list containing user numbers, addresses, and historical water consumption data; at the same time, the GIS system matches a coordinate for each user / water meter according to the user address; the offline hydraulic model system can automatically allocate each user and the user's water volume to the nearest node according to the principle of "proximity allocation" to complete water consumption allocation. At the same time, the offline hydraulic model system can allocate the water volume of each node to a more accurate time based on reference factors such as the regional water consumption pattern or the water volume fluctuation of the regional water supply on a time scale.

[0061] When the offline hydraulic model system imports "users and user water consumption data" during modeling, it is necessary to store the list of nodes and corresponding users for the user analysis module to call when it is running. Figure 4 As shown. The difference between production and sales is also data that needs to be entered when allocating water. The offline hydraulic model system software can allocate the difference between production and sales to each node according to the proportion of the length of each pipe section to the total length of the pipe network based on the situation of the preliminary investigation.

[0062] S15. The valve data is determined.

[0063] The working status of each valve in the pipe network is another key information for hydraulic modeling. The offline hydraulic model software can assign different default resistance coefficients to the corresponding nodes according to the switch status of different valves. The offline hydraulic model software can import the converted valve layer file in the GIS system. The file contains the type, caliber, material and switch status of the valve (non-remote control valve) required by the offline model. The status information of the remote control valve is provided by the SCADA system, and the model data table is as follows: Figure 5 shown.

[0064] S16. The known data of the entry is determined.

[0065] The known inlet data refers to the known inlet pressure or flow data. That is, the offline hydraulic model system software can import the historical data of each inlet point saved in the SCADA system, such as water plants, pumping stations, regulating tanks, etc. The historical data can be imported into the model through CSV or Excel format files. After the SCADA data is imported into the model, the model can clean the data through the data cleaning module. The operation interface is as follows: Figure 6 As shown in the figure, the role of the data cleaning module is to help solve potential problems in SCADA data. The data cleaning module integrates common missing value processing, noise data removal and consistency check methods to provide rich trainable data support for model training.

[0066] S17. Determination of verification data.

[0067] Model verification is to compare the standard value of the designated test point of the pipeline network with the simulated value calculated by the model simulation, repeatedly eliminate the topological errors of the model and correct the values ​​of various parameters. The verification data includes pressure test data, flow test data, water use mode test data and water pump test data.

[0068] Model verification refers to comparing the flow and pressure monitoring values ​​of field tests with the calculated values ​​calculated by the water supply network model adjustment, so as to find errors, continuously improve errors, and correct model errors until the difference between the field test values ​​and the model calculated values ​​meets the specified accuracy standards. The specific operation process is as follows Figure 7 shown.

[0069] The sources of errors include: measurement errors, errors caused by incorrect pipe network topology, high-level water tank water level errors, errors caused by pipe network simplification, errors caused by inaccurate pump valve status, errors caused by the pump characteristic curve not conforming to the actual situation, errors caused by the difference between the calculated inner diameter and the actual inner diameter of the pipe section, pipe friction errors, and errors in water consumption distribution in the water supply network.

[0070] According to different error sources and the size of the difference in error comparison, model verification is generally divided into two steps: rough verification and manual verification, namely macro verification, and fine verification and adaptive verification, namely micro verification.

[0071] Rough verification and manual verification are complex and tedious tasks, mainly for the topological relationship of the pipe network, pipe diameter, pipe length, valve opening, pump characteristic curve and other relatively certain factors to check and correct errors, to ensure the accuracy of the basic data of the pipe network. In addition, in actual projects, there are inevitable common problems such as errors in instrument monitoring data itself, incomplete GIS data provided, incomplete drawings and materials, elevation and coordinate data errors, water volume calculation errors, etc. Only by close cooperation between modeling engineers and users, full communication with technical personnel familiar with the pipe network, careful verification of basic original data, field surveys and field tests for doubtful points, and continuous troubleshooting based on empirical analysis, can the large differences in the pipe network model be eliminated and the model error be controlled within a certain range.

[0072] Fine calibration and adaptive calibration refer to the fine adjustment of the friction coefficient of the pipe section in the pipe network, node flow and other parameters on the basis of manual calibration, so that the pipe network model and the actual operation of the pipe network can be matched to the greatest extent. The objective function can be set to the minimum value of the difference between the calculated value and the monitored value of the pipe network model. The constraints for general parameter calibration are: the range of the friction coefficient of the pipe section is limited to 80-130, and the adjustment of the node flow is limited to ±20% of the initial value. Adaptive calibration depends largely on the optimization algorithm used and the computing power of the computer. It is generally completed by automatic calculation by the modeling software. This project uses the self-developed hydraulic simulation software platform for water supply pipe networks.

[0073] The real operating conditions of the actual 24-hour operation of the pipeline network are simulated in real time, so the minimum period for model verification is 24 hours. The selection principles of the model verification date include: the date of model verification should be the date of on-site pressure or flow measurement, preferably the date of simultaneous pressure and flow measurement; the more verification points and valid data, and the wider the coverage of valid data points, the better; the fewer factors that affect the verification, the better, so avoid days with pipeline network accidents; dates where irregular scheduling operations occur, which will ensure the accuracy of the 24-hour flow data, pressure data, and pump station operation data of the on-site test; try to choose dates with fewer pump and valve operations.

[0074] In the implementation of the modeling project, it is required to calibrate all pressure and flow measuring equipment before field testing to ensure data reliability to the greatest extent. After the calibration date is selected, the monitoring data of the calibration date shall be preprocessed, including SCADA pressure measuring point data, temporary pressure measuring point data, online flow meter and online water meter data. Obviously erroneous data shall be directly discarded, and suspicious data shall be judged and replaced with reasonable interpolation. There are many difficulties in the actual use of monitoring data, such as wrong readings, inconsistent time measurement, and missing readings. Common and obvious types of data errors include the loss of some values ​​in continuous data, negative values ​​when the quantity should be non-negative, and very large fluctuations beyond the normal range.

[0075] The functions of the water supply network visualization model include:

[0076] S18, real-time calculation and update.

[0077] The data center can automatically obtain real-time data from the SCADA system at regular intervals to perform real-time online calculations, and realize dynamic and real-time calculations of each node, pipeline, and water plant of the hydraulic model simulation network 24 hours a day. The online hydraulic model service calculates and analyzes the real-time water plant, pipeline network monitoring data, and revenue meter reading data collected from the data center interface at regular intervals, and dynamically displays the analysis results to the front-end hydraulic model system platform. The operation and display interfaces are as follows: Figure 8 and Fig. 9 shown.

[0078] S19. Web interface display.

[0079] The online hydraulic model system is based on the online map to intuitively display the pipe network model in the form of Web, including pipelines, nodes, valves, water meters, pressure monitoring points, flow monitoring points, water plants, municipal pumping stations, etc., and can realize the operation of zooming in, zooming out, roaming, etc. Fig.10 shown.

[0080] The online hydraulic model system can quickly display the pressure, flow rate, flow rate, head loss and other results calculated by the hydraulic model by clicking the nodes and pipelines with the mouse in the model map window, and can dynamically display the direction of water flow in the pipeline in the system, such as Fig.11 shown.

[0081] S20. Water quality operation assessment.

[0082] It can reflect the current status of water quality, water age, water supply demarcation line, etc. in the pipe network in real time with rich color analysis charts. It can compare and analyze at different times to display the information that has changed. It can also display statistics for users in areas with insufficient water supply capacity or affected by accidents. Through different operation scheduling schemes, the hydraulic changes of the pipe network in 24 hours a day can be seen. Select the scheduling scheme through the window to switch the model, as shown in Figure 12.

[0083] Check the changes in the hydraulic conditions of the pipe network at different time periods to see whether they meet the scheduling requirements, such as Fig.13 shown.

[0084] You can zoom in or out the map by scrolling the mouse, and view the water quality information of pipelines or nodes on the map by clicking the mouse. Fig.14 shown.

[0085] The model map window displays different layers such as pipelines, pipe points, flow directions, valves, etc. You can open or close layers by clicking the icon in the upper right corner of the map window to open the layer control window, such as Fig.15 shown.

[0086] The key nodes in the right property window display the simulation and monitoring parameter information of the key nodes of the current map, such as the monitoring value and simulation value of water quality, and the positioning function of the map can be realized by clicking the key nodes. Fig.16 The chart window on the right shows the statistical information of the current hydraulic model and the statistical proportions of different parameter levels.

[0087] S2. Obtain comprehensive water quality monitoring data of the water supply network.

[0088] S3. Use fuzzy analytic hierarchy process to process the comprehensive water quality monitoring data of water supply network, and fuzzy quantify the comprehensive water quality monitoring data of water supply network.

[0089] Based on historical data, with the help of mathematical algorithms, the data quality of real-time monitoring equipment is analyzed regularly every day to evaluate the authenticity and reliability of the data. The monitoring points are divided into four levels: high credibility, medium credibility, low credibility, and equipment failure. The pipe network water quality monitoring data collected regularly from the data center interface is analyzed and calculated by hydraulic model, and statistically analyzed and evaluated with the historical monitoring point database and the historical data of the hydraulic model of the corresponding location node, and an evaluation report and statistical results are generated.

[0090] S4. Display the comprehensive water quality monitoring data of the water supply network using a water supply network visualization model.

[0091] S5. Accident warning.

[0092] Accident warning is based on the comprehensive water quality monitoring data of the water supply network to complete water age calculation, residual chlorine calculation, and pollutant diffusion analysis calculation.

[0093] S51. Water age analysis.

[0094] The water age and water quality in each area are calculated through the basic data of the pipe network and the monitoring data. The model can calculate the longest water age and average water age of any node / pipeline in the water supply network at present or in the selected time period. When the threshold is exceeded, an alarm prompt is provided. The distribution and changes of water age and water quality in the whole network are displayed through the map page.

[0095] In the water age interface, you can view the water age value and distribution of each node in the current state, and view the changes in water age in the past 24 hours. At the same time, you can choose to display the water age at any time in the past 24 hours, and locate it in the pipe network according to the classification, export the pipes and related users in the corresponding range, and operate the same as the pipe flow module.

[0096] S52. Residual chlorine analysis.

[0097] The model can calculate the distribution and change of residual chlorine concentration in the entire network within the current or selected time period based on the residual chlorine monitoring data of the water plant in the network, the operation data of the network, such as flow rate and flow, combined with the attenuation law of chemical agents and water demand forecast data. When the threshold is exceeded, an alarm prompt is provided.

[0098] S53. Emergency disposal of pollutant spread.

[0099] According to the numerical monitoring of the pipeline network or the manual sampling and analysis data, the pollution source path of the pipeline network can be quickly tracked. And the treatment plan consisting of closing the valve and flushing the pipeline is simulated and calculated. The indicators of the pollution before and after treatment are compared, and the comparison results are displayed on the map.

[0100] Pollutant diffusion analysis can help dispatchers deal with water pollution emergencies in a timely and effective manner. When pollution occurs somewhere in the pipe network, the platform can simulate and analyze the spread of pollutants in different time periods. And it can simulate the disposal plan in combination with the valve closing plan. The following is the pollutant diffusion analysis function: You can zoom in or out the map by scrolling the mouse, and you can view the pollution value information of the pipeline or node on the map with the mouse.

[0101] S54. Accident warning.

[0102] By connecting with the SCADA system in real time, it can monitor the status of the pipeline network in real time and perceive the occurrence of abnormal operation of the pipeline network system. When the water quality of the pipeline network is abnormal, it can immediately issue an alarm message in a striking manner on the topographic map. It can also conduct source tracing analysis to help technicians quickly locate the location and cause of the fault and formulate solutions to the problem in a timely manner.

[0103] The system's online hydraulic model service will collect real-time water plant and pipeline monitoring data from the data center interface at regular intervals, monitor the operating status of the pipeline network in real time, issue abnormal alarms, and display them on the GIS map.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring water quality in a water supply network, characterized in that: The specific method is as follows: Construct a visual model of the water supply network; Obtain comprehensive water quality monitoring data for water supply networks; The fuzzy analytic hierarchy process is used to process the comprehensive water quality monitoring data of the water supply network, and the comprehensive water quality monitoring data of the water supply network is fuzzy quantified; The comprehensive water quality monitoring data of the water supply network is displayed using a water supply network visualization model.

2. The water quality monitoring method for a water supply network according to claim 1, characterized in that: The comprehensive water quality monitoring data of the water supply network is collected at water quality online monitoring and early warning points, and sensors are deployed at the online monitoring and early warning points to obtain the comprehensive water quality monitoring data of the water supply network.

3. The water quality monitoring method for a water supply network according to claim 1, characterized in that: Construct a visualization model of the water supply network. The specific methods are as follows: Determine the topological structure of the water supply network, including: water supply network connection relationship, network attribute data and node attribute data; Determine pipeline hydraulic coefficients, node water distribution, valve data and inlet known data; Export the shp file through the GIS system and convert the shp file into an inp file; Simplify and visualize a water supply network model.

4. The water quality monitoring method of the water supply network according to claim 3, characterized in that: After building the water supply network visualization model, the model verification needs to be completed. The specific verification includes: Measurement errors, errors caused by incorrect topology of the water supply network, water level errors in high-level water tanks, errors caused by simplification of the network, errors caused by inaccurate pump valve status, errors caused by the pump characteristic curve not conforming to the actual situation, errors caused by the difference between the calculated inner diameter and the actual inner diameter of the pipe section, pipe friction errors and errors in water consumption distribution in the water supply network.

5. The water quality monitoring method for a water supply network according to claim 1, characterized in that: The method also includes accident early warning, and the specific method is as follows: Complete water age calculation, residual chlorine calculation, and pollutant diffusion analysis calculation based on comprehensive water quality monitoring data of the water supply network; Construct a water supply network early warning model and complete accident early warning based on the calculation results.

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