Water quality online monitoring and early warning platform and method

Abstract

The invention relates to the technical field of water quality monitoring, in particular to a water quality online monitoring and early warning platform and method. The method comprises the following steps: acquiring pipe network topological structure data and water quality monitoring data, and determining a pipe network operation characteristic spectrum according to the pipe network topological structure data and the water quality monitoring data; analyzing the pipe network operation characteristic spectrum and the water quality monitoring data, and establishing a dynamic risk assessment model; according to the pipe network topological structure data and the dynamic risk assessment model, determining spatial distribution of water quality disadvantageous points; and according to the water quality disadvantageous point space distribution and the pipe network topological structure data, generating a multi-stage early warning instruction and sending the multi-stage early warning instruction to management equipment. By analyzing the roughness of the pipe wall, the water flow characteristics can be predicted more accurately. And evaluating the water quality risk levels of different pipe sections by calculating the turbulence coefficient. The water detention time is evaluated, and the water quality is deteriorated due to overlong detention time. The dynamic change of the water flow is analyzed, and a more accurate basis is provided for multi-stage early warning instructions.

Description

Technical Field

[0001] The present application relates to the technical field of water quality monitoring, and in particular to an online water quality monitoring and early warning platform and method. Background Art

[0002] As an important water quality management tool, the online water quality monitoring and early warning system has been widely used in the monitoring of urban water supply networks. It aims to obtain real-time water quality data in the network so as to promptly detect potential water quality problems.

[0003] Traditional water quality monitoring methods typically rely on monitoring equipment installed at various points in the pipe network, collecting and analyzing real-time water quality data. Consequently, they are unable to effectively identify potential structural defects or operational anomalies within the pipe network, preventing timely resolution of water quality issues and ultimately impacting the safety of the water supply system. Summary of the Invention

[0004] This application provides a water quality online monitoring and early warning platform and method to solve the above problems.

[0005] In a first aspect, the present application provides a method for online water quality monitoring and early warning, the method comprising: Obtaining pipe network topology data and water quality monitoring data, and determining the pipe network operation characteristic map based on the pipe network topology data and water quality monitoring data; Analyze the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model; Determining the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; A multi-level warning instruction is generated according to the spatial distribution of the unfavorable water quality points and the pipe network topology data and sent to the management device.

[0006] This solution acquires pipe network topology data, facilitating analysis of water flow characteristics. Real-time monitoring of water quality data within the pipe network reflects water quality conditions and provides a foundation for early warning systems. It identifies points of sudden changes in pipe diameter, angle anomalies, and height anomalies, predicting water flow paths and velocities and assessing the likelihood of water quality changes. Creating a pipe network operational characteristic map helps identify areas at high risk for water quality issues. Analyzing pipe wall roughness allows for more accurate prediction of water flow characteristics. Calculating the turbulence coefficient assesses water quality risk levels across different pipe sections. Water retention time is assessed; excessive retention time can lead to deteriorating water quality. Analyzing dynamic water flow changes provides a more accurate basis for multi-level early warning instructions. By determining the spatial distribution of adverse points, targeted monitoring and intervention can be implemented. Multi-level early warning instructions are transmitted to management devices via the device platform, ensuring timely information delivery. Through the transmission of multi-level early warning instructions, management personnel can promptly identify water quality conditions and take appropriate measures to ensure water supply security.

[0007] Optionally, determining the pipe network operation characteristic map based on the pipe network topology data and water quality monitoring data includes: Analyze the pipe network topology data and determine the pipe diameter data of each pipe network node; Determining the pipe diameter change rate of adjacent pipe sections based on the pipe diameter data; Comparing the pipe diameter change rate with a preset pipe diameter change threshold, and determining a pipe diameter mutation point based on the comparison result; Determine the connection angle of the connecting pipes and the elevation difference per unit pipe length according to the pipe network topology data; Obtaining a pipe network design standard, and determining an angle abnormal point and an elevation abnormal point based on the pipe network design standard and the connection angle and the elevation drop; Determine the abnormal area of the pipe network structure according to the pipe diameter mutation point, the angle abnormal point and the elevation abnormal point; A pipeline network operation characteristic map is determined based on the abnormal pipeline network structure area and the water quality monitoring data.

[0008] This solution analyzes pipe network topology data to obtain pipe diameter data for each network node, facilitating the assessment of water flow velocity and flow distribution. Calculating the diameter change rate between adjacent pipe segments helps identify diameter mutation points that impact water flow and facilitates optimization of pipe network design and operation. By comparing the diameter change rate with a preset diameter change threshold, diameter mutation points with high water quality risks can be accurately identified. Analyzing the connection angles and elevation drops of connecting pipes helps identify structural anomalies that affect water flow direction and velocity. By comparing the connection angles and elevation drops with pipe network design standards, angular and elevation anomalies can be identified, representing high-risk areas for water quality issues. By integrating diameter mutation points, angular anomalies, and elevation anomalies, areas of pipe network structural anomalies with high water quality risks can be accurately identified. By analyzing water quality monitoring data, basic water quality conditions and changing trends can be identified, facilitating water quality risk assessment. Combining structural anomalies with water quality monitoring data allows for a comprehensive assessment of pipe network operational characteristics, including water velocity, residence time, and turbulence coefficient. Construct a pipeline network operation characteristic map to reflect the water quality status and potential risks in each area of the pipeline network, providing a basis for early warning.

[0009] Optionally, analyzing the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model includes: Acquiring structural attribute information; determining the pipe wall roughness based on the structural attribute information; Determining the turbulence coefficient of any pipe section according to the pipe wall roughness and the pipe network operation characteristic map; Determining the pipe network terminal pressure according to the turbulence coefficient; Establishing a daily water consumption fluctuation curve based on the water quality monitoring data; A dynamic risk assessment model is established based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve.

[0010] This solution comprehensively identifies the structural condition of the pipe network by collecting information on its structural properties. Pipe wall roughness influences flow characteristics and the turbulence coefficient, and also helps assess water quality risks. The turbulence coefficient helps assess flow characteristics; a high turbulence coefficient leads to deteriorating water quality. Pipeline tip pressure helps assess network operation and influences the distribution and velocity of water flow. Daily water consumption fluctuations reflect the dynamic changes in water flow. The dynamic risk assessment model provides real-time assessment of the water quality risk level for each pipe segment in the network, providing a scientific basis for early warning.

[0011] Optionally, determining the pipe wall roughness according to the structural attribute information includes: Determine the inner wall material of the pipe section and the construction age of the pipe network based on the structural attribute information; Determining water corrosivity indicators based on the water quality monitoring data; Determining the inner wall loss per unit time based on the water corrosivity index and the inner wall material of the pipe section; The pipe wall roughness at the current moment is determined according to the pipe network construction age and the inner wall loss.

[0012] This solution uses structural attribute information to assess the condition of pipe segment inner walls and calculate wall roughness. The inner wall material and the age of the pipeline network are determined based on the structural attribute information. Different inner wall materials and the age of the pipeline network affect the degree of aging and corrosion of the inner wall. Water quality monitoring data is used to analyze the corrosiveness of the water. The corrosiveness index reflects the degree of water erosion on the inner wall of the pipe segment. The inner wall loss per unit time is calculated by combining the water corrosiveness index and the inner wall material of the pipe segment. Different water corrosiveness indexes and different inner wall materials of the pipe segment will result in different degrees of inner wall loss. The current pipe wall roughness is calculated based on the age of the pipeline network and the inner wall loss of the pipe segment. Over time and with increasing inner wall loss, the pipe wall roughness gradually increases, affecting water flow characteristics and water quality.

[0013] Optionally, establishing a dynamic risk assessment model based on the turbulence coefficient, the pipe network terminal pressure, and the daily water consumption fluctuation curve includes: Constructing a hydraulic residence time model according to the pipe network terminal pressure; Analyze the daily water consumption fluctuation curve according to the hydraulic retention time model to determine the water retention time per unit time in each pipe section; A dynamic risk assessment model is established based on the turbulence coefficient and the water residence time.

[0014] This solution analyzes the pipe network's terminal pressure and constructs a hydraulic retention time model, which helps predict water retention time and thus assess potential water quality risks. Areas with long water retention times are prone to water quality issues, such as bacterial growth and water quality deterioration. By analyzing the daily water consumption fluctuation curve, the dynamic changes in water flow within the pipe network can be identified. For example, when water consumption is low, water flow is slow and retention time is long, leading to water quality deterioration. Based on the hydraulic retention time model and the daily water consumption fluctuation curve, the water retention time per unit time is determined for each pipe section. Excessive water retention time leads to water quality deterioration, so determining water retention time facilitates water quality management. Combining the turbulence coefficient and water retention time, a dynamic risk assessment model is developed to assess the water quality risk level of each pipe network section in real time. This dynamic risk assessment model helps predict water quality issues and enable timely measures to ensure water supply safety.

[0015] Optionally, determining the pipe network operation characteristic map based on the pipe network structural abnormality area and the water quality monitoring data includes: Analyze the water quality monitoring data to determine turbidity, residual chlorine and total organic carbon; Analyze the abnormal area of the pipe network structure to determine the pipe diameter change rate of the pipe diameter mutation point, the angular deviation value of the angle abnormal point, and the elevation difference of the elevation abnormal point; Determining a correlation coefficient between water quality and pipe network structure based on the turbidity, the residual chlorine, the total organic carbon, the pipe diameter change rate, the angular deviation value, and the elevation drop; According to the correlation coefficient, a pipeline network operation characteristic map is determined.

[0016] This solution analyzes water quality monitoring data to accurately identify the water quality status of each monitoring point in the pipe network, including data such as turbidity, residual chlorine, and total organic carbon, which helps assess water quality and identify water quality issues. It also analyzes pipe network topology data to identify pipe diameter mutation points, angle anomalies, and elevation anomalies, locating potential water quality adverse points. Calculating pipe diameter change rates, angular deviation values, and elevation drops helps construct a dynamic risk assessment model. The correlation coefficient reflects the degree of correlation between water quality monitoring data and pipe network structure data. The correlation coefficient helps establish a dynamic risk assessment model. Based on the correlation coefficient, a pipe network operation characteristic map is constructed to display the relationship between water quality monitoring data and pipe network structure data, helping to identify water quality adverse points and potential water quality issues.

[0017] Optionally, establishing a dynamic risk assessment model based on the turbulence coefficient and the water residence time includes: Calculating the Reynolds number of the pipe section according to the turbulence coefficient; Determining pipe section design parameters according to the pipe network design standard, and determining a standard residence time according to the pipe section design parameters; Establishing a water age correction function based on the pipe section Reynolds number, the standard residence time, and the water body residence time; A dynamic risk assessment model is established based on the residual chlorine content, the total organic carbon content and the water age correction function.

[0018] This solution evaluates the turbulence coefficient of the water flow within a pipe section by calculating the Reynolds number. A high Reynolds number generally indicates turbulent flow, which affects the transport of substances in the water and water quality. Determining pipe section design parameters, such as design flow velocity and design pressure, provides a benchmark for pipeline network design and operation, helping to ensure the safe operation of the network under normal conditions. Determining the standard residence time helps evaluate the operating efficiency of the pipeline network under ideal conditions and helps managers identify whether the pipeline network design meets actual needs and whether there is potential for optimization. Establishing a water age correction function adjusts the theoretical residence time to reflect the water age under actual operating conditions, helping to more accurately predict water quality changes and providing a basis for water quality management. Establishing a dynamic risk assessment model allows for real-time assessment of the water quality risk level of each pipe section in the pipeline network, considering the impact of residual chlorine, total organic carbon, water age, and areas of abnormal pipe network structure on water quality risk, helping to improve the targeted and efficient water quality management.

[0019] Optionally, determining the pipe wall roughness at a current moment based on the pipe network construction age and the inner wall loss includes: Obtaining historical maintenance records, analyzing the historical maintenance records, and determining historical corrosion conditions; Determine the cause of historical corrosion based on the historical corrosion conditions; The pipe wall roughness at a current moment is determined based on the historical corrosion causes, the pipe network construction age, and the inner wall loss.

[0020] This solution captures historical maintenance records to analyze corrosion conditions and causes, helping to identify areas and types of corrosion. Analyzing historical maintenance records identifies the frequency and severity of historical corrosion events within the pipeline network, helping to assess the impact of corrosion on the network and inform maintenance planning. Identifying the causes of historical corrosion facilitates targeted corrosion prevention and control measures. If corrosion is caused by water quality issues, water treatment processes can be adjusted; if corrosion is caused by pipe material issues, pipe material replacement can be considered. Analyzing the age of the pipeline network helps assess pipeline aging and predict future corrosion risks. Aged pipelines are more susceptible to corrosion and therefore require more frequent inspections and maintenance. Assessing the extent of internal wall wear helps identify the impact of corrosion on the internal walls of pipe sections and the remaining service life of the pipeline, facilitating the development of appropriate maintenance and replacement plans. Establishing a pipe wall roughness model helps predict the impact of corrosion on pipe wall roughness. Calculating the current pipe wall roughness helps assess the impact of corrosion on pipeline network operations and inform appropriate maintenance and replacement measures.

[0021] Optionally, determining a water corrosivity index based on the water quality monitoring data includes: Analyzing the water quality monitoring data to determine the type of water quality change; The water quality corrosivity index of each water quality type is determined according to the water quality change type and the inner wall material of the pipe section.

[0022] This solution analyzes water quality change patterns to help identify the nature and source of water quality issues. Analyzing pipe section inner wall materials helps identify the potential impact of pipelines on water quality. Different pipe section inner wall materials have different effects on water quality. Determining water quality corrosivity indicators helps assess the potential corrosion risk to pipelines. Corrosivity indicators, including corrosion rate, type, and severity, assist in developing anti-corrosion measures and maintenance plans.

[0023] In a second aspect, the present application provides an online water quality monitoring and early warning platform, which includes: A graph determination module is used to obtain pipe network topology data and water quality monitoring data, and determine the pipe network operation characteristic graph based on the pipe network topology data and water quality monitoring data; A model building module, used to analyze the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model; An unfavorable point determination module, configured to determine the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; The instruction generation module is used to generate multi-level early warning instructions based on the spatial distribution of the unfavorable water quality points and the pipe network topology data and send them to the management device.

[0024] Optionally, when the map determination module determines the pipeline network operation characteristic map based on the pipeline network topology data and the water quality monitoring data, it is used to: Analyze the pipe network topology data and determine the pipe diameter data of each pipe network node; Determining the pipe diameter change rate of adjacent pipe sections based on the pipe diameter data; Comparing the pipe diameter change rate with a preset pipe diameter change threshold, and determining a pipe diameter mutation point based on the comparison result; Determine the connection angle of the connecting pipes and the elevation difference per unit pipe length according to the pipe network topology data; Obtaining a pipe network design standard, and determining an angle abnormal point and an elevation abnormal point based on the pipe network design standard and the connection angle and the elevation drop; Determine the abnormal area of the pipe network structure according to the pipe diameter mutation point, the angle abnormal point and the elevation abnormal point; A pipeline network operation characteristic map is determined based on the abnormal pipeline network structure area and the water quality monitoring data.

[0025] Optionally, the model building module analyzes the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model, which is used to: Acquiring structural attribute information; determining the pipe wall roughness based on the structural attribute information; Determining the turbulence coefficient of any pipe section according to the pipe wall roughness and the pipe network operation characteristic map; Determining the pipe network terminal pressure according to the turbulence coefficient; Establishing a daily water consumption fluctuation curve based on the water quality monitoring data; A dynamic risk assessment model is established based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve.

[0026] Optionally, when determining the pipe wall roughness based on the structural attribute information, the model building module is configured to: Determine the inner wall material of the pipe section and the construction age of the pipe network based on the structural attribute information; Determining water corrosivity indicators based on the water quality monitoring data; Determining the inner wall loss per unit time based on the water corrosivity index and the inner wall material of the pipe section; The pipe wall roughness at the current moment is determined according to the pipe network construction age and the inner wall loss.

[0027] Optionally, when the model building module builds a dynamic risk assessment model based on the turbulence coefficient, the pipe network terminal pressure, and the daily water consumption fluctuation curve, it is used to: Constructing a hydraulic residence time model according to the pipe network terminal pressure; Analyze the daily water consumption fluctuation curve according to the hydraulic retention time model to determine the water retention time per unit time in each pipe section; A dynamic risk assessment model is established based on the turbulence coefficient and the water residence time.

[0028] Optionally, when the map determination module determines the pipe network operation characteristic map based on the pipe network structural abnormal area and the water quality monitoring data, it is used to: Analyze the water quality monitoring data to determine turbidity, residual chlorine and total organic carbon; Analyze the abnormal area of the pipe network structure to determine the pipe diameter change rate of the pipe diameter mutation point, the angular deviation value of the angle abnormal point, and the elevation difference of the elevation abnormal point; Determining a correlation coefficient between water quality and pipe network structure based on the turbidity, the residual chlorine, the total organic carbon, the pipe diameter change rate, the angular deviation value, and the elevation drop; According to the correlation coefficient, a pipeline network operation characteristic map is determined.

[0029] Optionally, when the model building module establishes a dynamic risk assessment model based on the turbulence coefficient and the water body residence time, it is used to: Calculating the Reynolds number of the pipe section according to the turbulence coefficient; Determining pipe section design parameters according to the pipe network design standard, and determining a standard residence time according to the pipe section design parameters; Establishing a water age correction function based on the pipe section Reynolds number, the standard residence time, and the water body residence time; A dynamic risk assessment model is established based on the residual chlorine content, the total organic carbon content and the water age correction function.

[0030] Optionally, when the model building module determines the pipe wall roughness at the current moment based on the pipe network construction age and the inner wall loss, it is used to: Obtaining historical maintenance records, analyzing the historical maintenance records, and determining historical corrosion conditions; Determine the cause of historical corrosion based on the historical corrosion conditions; The pipe wall roughness at a current moment is determined based on the historical corrosion causes, the pipe network construction age, and the inner wall loss.

[0031] Optionally, when the model building module determines the water corrosivity index based on the water quality monitoring data, it is used to: Analyzing the water quality monitoring data to determine the type of water quality change; The water quality corrosivity index of each water quality type is determined according to the water quality change type and the inner wall material of the pipe section. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0033] Figure 1 A schematic diagram of an application scenario provided in one embodiment of the present application; Figure 2 A flow chart of a method for online water quality monitoring and early warning provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of an online water quality monitoring and early warning platform provided in one embodiment of the present application. DETAILED DESCRIPTION

[0034] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0036] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0037] Traditional water quality monitoring methods typically rely on monitoring equipment installed at various points in the pipe network, collecting and analyzing real-time water quality data. Consequently, they are unable to effectively identify potential structural defects or operational anomalies within the pipe network, preventing timely resolution of water quality issues and ultimately impacting the safety of the water supply system.

[0038] Based on this, the present application provides an online water quality monitoring and early warning platform and method. These methods acquire pipe network topology data and water quality monitoring data, determine a pipe network operation characteristic map based on the data, and then establish a dynamic risk assessment model based on the analysis of the pipe network operation characteristic map and water quality monitoring data. Based on the pipe network topology data and the dynamic risk assessment model, the platform determines the spatial distribution of unfavorable water quality points. Based on the spatial distribution of unfavorable water quality points and the pipe network topology data, the platform generates multi-level early warning instructions and sends them to management devices. Acquiring pipe network topology data facilitates analysis of water flow characteristics within the pipe network. Real-time monitoring of water quality monitoring data within the pipe network reflects water quality conditions and provides a foundation for early warning devices. Pipeline diameter abrupt changes, angle anomalies, and height anomalies are identified to predict the flow path and velocity, thereby assessing the likelihood of water quality changes. Establishing a pipe network operation characteristic map helps identify high-risk areas for water quality issues. By analyzing pipe wall roughness, the platform more accurately predicts water flow characteristics. By calculating the turbulence coefficient, the platform assesses the water quality risk level of different pipe sections. Water retention time is assessed; excessive retention time leads to deteriorating water quality. Analyzing the dynamics of water flow provides a more accurate basis for multi-level warning instructions. By determining the spatial distribution of adverse points, targeted monitoring and intervention can be implemented. Multi-level warning instructions are sent to management devices via the device platform, ensuring timely information transmission. Through the issuance of multi-level warning instructions, management personnel can promptly identify water quality conditions and take appropriate measures to ensure water supply safety.

[0039] Figure 1 This is a schematic diagram of an application scenario provided by this application. The method provided by this application is applied during water quality monitoring.

[0040] Specifically, the method provided in this application is applied to any server, and the server interacts with sensors and management equipment to obtain and analyze the pipe network topology data and water quality monitoring data collected by the sensors, identify pipe diameter mutation points, angle abnormal points and height abnormal points, and predict the path and speed of water flow, thereby evaluating the possibility of water quality changes and providing a more accurate basis for multi-level early warning instructions. By determining the spatial distribution of unfavorable points, targeted monitoring and intervention are carried out. Multi-level early warning instructions are sent to management equipment to ensure timely transmission of information. By sending multi-level early warning instructions, management personnel can identify water quality conditions in a timely manner and take corresponding measures to ensure water supply safety. For specific implementation methods, please refer to the following embodiments.

[0041] Figure 2 This is a flow chart of a method for online water quality monitoring and early warning provided by an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201. Obtain pipe network topology data and water quality monitoring data, and determine a pipe network operation characteristic map based on the pipe network topology data and the water quality monitoring data; The pipe network topology data may be data describing the geometric structure and physical characteristics of the water supply pipe network, such as pipe connection relationships, pipe diameters, lengths, slopes, connection angles, and pipe materials.

[0042] Water quality monitoring data can be water quality parameter data such as turbidity, residual chlorine, pH value, total organic carbon, water temperature, and conductivity collected in real time by sensors installed in the pipe network.

[0043] The pipeline network operation characteristic map can be a graphical model that describes the operation status of the pipeline network.

[0044] Specifically, sensors installed throughout the pipe network collect real-time data on the network's topology, including pipe connections, diameters, lengths, slopes, and connection angles, as well as water quality monitoring data such as turbidity, residual chlorine, pH, and total organic carbon. This topology data is analyzed to identify areas of structural anomalies, such as sudden changes in pipe diameter, angles, and heights. Using this water quality monitoring data, water quality trends in these areas of structural anomalies are analyzed to create a characteristic map of the network's operation.

[0045] S202. Analyze the pipe network operation characteristic map and water quality monitoring data to establish a dynamic risk assessment model; The dynamic risk assessment model can be a mathematical model for real-time assessment of water quality risks in the pipe network.

[0046] Specifically, the pipe wall roughness, which affects the turbulence coefficient, is determined. Based on the pipe network's operational characteristic map and the pipe wall roughness, the turbulence coefficient for each pipe section is calculated. Based on the turbulence coefficient calculations, the network's distal pressure is analyzed and the water retention time is estimated. Based on water quality testing data, the daily water consumption fluctuation curve is analyzed to determine the dynamic changes in water flow. Combining the water retention time and daily water consumption fluctuation curve, a dynamic risk assessment model is established.

[0047] S203. Determine the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; The spatial distribution of unfavorable water quality points can be the spatial location distribution of pipe sections with higher water quality risks in the pipe network.

[0048] Specifically, based on the network topology data, each node and each pipe segment in the network is identified. A dynamic risk assessment model is used to assess the risk level of each pipe segment. Based on the risk level assessment results, pipe segments with higher risk levels are identified, thus identifying water quality adverse points. Using the network topology data, these identified water quality adverse points are spatially located to determine their spatial distribution.

[0049] S204: Generate multi-level warning instructions based on the spatial distribution of unfavorable water quality points and pipe network topology data and send them to the management device.

[0050] Multi-level warning instructions can be generated based on the spatial distribution of unfavorable water quality points and the pipe network topology data, and include multi-level warning instructions such as adjusting water pressure, cleaning pipes, and replacing aging pipes based on the physical coordinates of unfavorable water quality points and water quality corrosiveness indicators.

[0051] The management device may be a device for receiving, processing and displaying multi-level water quality warning instructions.

[0052] Specifically, the system uses pipe network topology data to determine the spatial distribution of unfavorable water quality points. A dynamic risk assessment model is then used to assess the risk of these points. Based on the risk assessment results, these points are classified into different risk levels. Based on the spatial distribution of these points and their risk levels, multi-level warning instructions are developed. The management device that receives these warning instructions is identified, and the generated multi-level warning instructions are sent to these management devices via the device platform.

[0053] This solution acquires pipe network topology data, facilitating analysis of water flow characteristics. Real-time monitoring of water quality data within the pipe network reflects water quality conditions and provides a foundation for early warning systems. It identifies points of sudden changes in pipe diameter, angle anomalies, and height anomalies, predicting water flow paths and velocities and assessing the likelihood of water quality changes. Creating a pipe network operational characteristic map helps identify areas at high risk for water quality issues. Analyzing pipe wall roughness allows for more accurate prediction of water flow characteristics. Calculating the turbulence coefficient assesses water quality risk levels across different pipe sections. Water retention time is assessed; excessive retention time can lead to deteriorating water quality. Analyzing dynamic water flow changes provides a more accurate basis for multi-level early warning instructions. By determining the spatial distribution of adverse points, targeted monitoring and intervention can be implemented. Multi-level early warning instructions are transmitted to management devices via the device platform, ensuring timely information delivery. Through the transmission of multi-level early warning instructions, management personnel can promptly identify water quality conditions and take appropriate measures to ensure water supply security.

[0054] In some embodiments, the pipe network topology data is analyzed to determine the pipe diameter data of each pipe network node; based on the pipe diameter data, the pipe diameter change rate of adjacent pipe sections is determined; the pipe diameter change rate is compared with a preset pipe diameter change threshold, and based on the comparison result, the pipe diameter mutation point is determined; based on the pipe network topology data, the connection angle of the connecting pipe and the elevation drop per unit pipe length are determined; the pipe network design standard is obtained, and based on the pipe network design standard, the angle abnormal points and the elevation abnormal points are determined according to the connection angle and the elevation drop; based on the pipe diameter mutation point, the angle abnormal points and the elevation abnormal points, the abnormal area of the pipe network structure is determined; based on the abnormal area of the pipe network structure and the water quality monitoring data, the pipe network operation characteristic map is determined.

[0055] A pipe network node can be a connection point such as the starting point, end point, or branch point of a pipe in a pipe network.

[0056] The pipe diameter data may be the diameter information of the pipe.

[0057] Adjacent pipe segments can be two pipe segments that are directly connected in the pipe network.

[0058] The pipe diameter change rate may be a ratio of diameters of adjacent pipe sections, ie, the diameter of the larger pipe section divided by the diameter of the smaller pipe section.

[0059] The preset pipe diameter change threshold value may be a preset standard value for determining whether the pipe diameter change exceeds a normal range, and is pre-stored in the server and called when used.

[0060] The pipe diameter mutation point may be a connection node where the pipe diameter change rate exceeds a preset pipe diameter change threshold.

[0061] The connecting pipe can be a directly connected pipe in the pipe network.

[0062] The connection angle may be the angle between the connecting pipes.

[0063] Unit pipe length can refer to the length of the pipeline.

[0064] Elevation drop can be the difference in vertical distance between connecting pipes.

[0065] Pipeline network design standards may be the technical specifications and standards followed when designing a pipeline network.

[0066] An outlier angle point can be a connection node where the connection angle exceeds the design standard of the pipe network.

[0067] An elevation anomaly point may be a connection node whose elevation difference exceeds the design standard of the pipeline network.

[0068] The abnormal area of the pipe network structure can be an area composed of pipe diameter mutation points, angle abnormal points and elevation abnormal points.

[0069] Specifically, pipe diameter data for each pipe network node is extracted from the pipe network topology data. Adjacent pipe segments to be analyzed are identified. The diameters of two adjacent segments are compared, and the diameter of the larger segment is divided by the diameter of the smaller segment to calculate the diameter change rate. A diameter change threshold is set based on the pipe network design standards, operational experience, or other relevant factors. The calculated diameter change rate is compared with the preset diameter change threshold. Connection nodes exceeding the preset diameter change threshold are identified as diameter mutation points. Based on the pipe network topology data, information such as the start and end coordinates of the pipes, segment length, and pipe inclination angle is determined. The horizontal and vertical distances between the start and end points of the pipes are used to calculate the connection angle between the connecting pipes. Based on the pipe network topology data, the connection angle and the elevation drop per unit pipe length are calculated between the connecting pipes. Relevant pipe network design standards are obtained from the city water supply management department. The connection angle and elevation drop are compared with the pipe network design standards. If the connection angle or elevation drop per unit pipe length exceeds the pipe network design standards, it is identified as an angle or elevation anomaly point. Analyze the distribution of pipe diameter mutation points, angle anomalies, and elevation anomalies, and classify the areas surrounding these points as areas of pipe network structural anomalies. Analyze the relationship between water quality monitoring data and areas of pipe network structural anomalies. Based on the analysis results, construct a pipeline network operation characteristic map.

[0070] This solution analyzes pipe network topology data to obtain pipe diameter data for each network node, facilitating the assessment of water flow velocity and flow distribution. Calculating the diameter change rate between adjacent pipe segments helps identify diameter mutation points that impact water flow and facilitates optimization of pipe network design and operation. By comparing the diameter change rate with a preset diameter change threshold, diameter mutation points with high water quality risks can be accurately identified. Analyzing the connection angles and elevation drops of connecting pipes helps identify structural anomalies that affect water flow direction and velocity. By comparing the connection angles and elevation drops with pipe network design standards, angular and elevation anomalies can be identified, representing high-risk areas for water quality issues. By integrating diameter mutation points, angular anomalies, and elevation anomalies, areas of pipe network structural anomalies with high water quality risks can be accurately identified. By analyzing water quality monitoring data, basic water quality conditions and changing trends can be identified, facilitating water quality risk assessment. Combining structural anomalies with water quality monitoring data allows for a comprehensive assessment of pipe network operational characteristics, including water velocity, residence time, and turbulence coefficient. Construct a pipeline network operation characteristic map to reflect the water quality status and potential risks in each area of the pipeline network, providing a basis for early warning.

[0071] In some embodiments, structural attribute information is obtained; based on the structural attribute information, the roughness of the pipe wall is determined; based on the pipe wall roughness and the pipe network operation characteristic map, the turbulence coefficient of any pipe section is determined; based on the turbulence coefficient, the pipe network terminal pressure is determined; based on water quality monitoring data, a daily water consumption fluctuation curve is established; based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve, a dynamic risk assessment model is established.

[0072] Structural attribute information may be information describing the physical characteristics of the water supply network, such as the inner wall material of the pipe section, the age of the pipe network, historical maintenance records, pipe diameter, length, slope, etc.

[0073] Pipe wall roughness can be the roughness of the inner wall surface of the pipe section.

[0074] A pipe segment can be a section of pipe in a water supply network, connecting two nodes or a connection point and a node.

[0075] The turbulence coefficient may be a coefficient that describes the degree of turbulence of a water flow.

[0076] The network terminal pressure may be the pressure value at the end of the water supply network.

[0077] The daily water consumption fluctuation curve may be a curve reflecting the change of daily water consumption over time.

[0078] Specifically, information on structural properties of the pipe network, such as the inner wall material, the age of the pipe network, and historical maintenance records, is collected. Based on this structural property information and combined with water quality monitoring data, the degree of aging and corrosion of the pipe wall is analyzed to determine the wall roughness. Using the pipe wall roughness and the network's operational characteristic map, the Darcy-Weisbach formula in fluid mechanics is used to calculate the turbulence coefficient of any pipe segment in the network. Based on the turbulence coefficient, the water flow characteristics of the network are analyzed, and the network's terminal pressure is calculated. Based on water quality monitoring data, daily water consumption fluctuations are analyzed, and a daily water consumption fluctuation curve is established. A dynamic risk assessment model is established by combining the turbulence coefficient, the network's terminal pressure, and the daily water consumption fluctuation curve.

[0079] This solution comprehensively identifies the structural condition of the pipe network by collecting information on its structural properties. Pipe wall roughness influences flow characteristics and the turbulence coefficient, and also helps assess water quality risks. The turbulence coefficient helps assess flow characteristics; a high turbulence coefficient leads to deteriorating water quality. Pipeline tip pressure helps assess network operation and influences the distribution and velocity of water flow. Daily water consumption fluctuations reflect the dynamic changes in water flow. The dynamic risk assessment model provides real-time assessment of the water quality risk level for each pipe segment in the network, providing a scientific basis for early warning.

[0080] In some embodiments, the inner wall material of the pipe section and the age of the pipeline network are determined based on structural attribute information; the water quality corrosivity index is determined based on water quality monitoring data; the inner wall loss per unit time is determined based on the water quality corrosivity index and the inner wall material of the pipe section; and the pipe wall roughness at the current moment is determined based on the age of the pipeline network and the inner wall loss.

[0081] The inner wall material of the pipe section can be steel, cast iron, plastic, stainless steel, gray cast iron and other material types used for the inner wall of the pipe section.

[0082] The pipeline network construction period can be the construction time of the water supply pipeline network, which can be expressed in years.

[0083] Water quality corrosivity indicators can be parameter indicators such as pH value, total dissolved solids, chloride ion concentration, sulfate ion concentration, hardness, dissolved oxygen, etc., which are used to evaluate the corrosiveness of water quality to pipelines.

[0084] The unit time can be used to calculate the influence of water corrosiveness index on the inner wall of the pipe section.

[0085] Inner wall loss can be the degree of loss of the inner wall of a pipe section due to corrosion or other reasons per unit time.

[0086] Specifically, based on the structural attribute information, determine the inner wall material of each pipe section, such as steel, cast iron, plastic, stainless steel, or gray cast iron. Calculate the construction age of each pipe section based on the installation date of the pipeline. Analyze parameters such as pH value, total dissolved solids, chloride ion concentration, sulfate ion concentration, hardness, and dissolved oxygen in the water quality monitoring data to determine the water quality corrosiveness index. Establish a corrosion rate model based on the water quality corrosiveness index and the inner wall material of the pipe section. Use the corrosion rate model to calculate the inner wall loss per unit time. Establish a pipe wall roughness model. Use the pipe wall roughness model to calculate the pipe wall roughness at the current moment based on the construction age of the pipeline network and the inner wall loss rate.

[0087] This solution uses structural attribute information to assess the condition of pipe segment inner walls and calculate wall roughness. The inner wall material and the age of the pipeline network are determined based on the structural attribute information. Different inner wall materials and the age of the pipeline network affect the degree of aging and corrosion of the inner wall. Water quality monitoring data is used to analyze the corrosiveness of the water. The corrosiveness index reflects the degree of water erosion on the inner wall of the pipe segment. The inner wall loss per unit time is calculated by combining the water corrosiveness index and the inner wall material of the pipe segment. Different water corrosiveness indexes and different inner wall materials of the pipe segment will result in different degrees of inner wall loss. The current pipe wall roughness is calculated based on the age of the pipeline network and the inner wall loss of the pipe segment. Over time and with increasing inner wall loss, the pipe wall roughness gradually increases, affecting water flow characteristics and water quality.

[0088] In some embodiments, a hydraulic retention time model is constructed based on the terminal pressure of the pipeline network; based on the hydraulic retention time model, the daily water consumption fluctuation curve is analyzed to determine the water retention time of each pipe section per unit time; and based on the turbulence coefficient and water retention time, a dynamic risk assessment model is established.

[0089] A hydraulic retention time model can be a model used to simulate and predict the residence time of water in a treatment facility.

[0090] Water residence time can be the time it takes for water to travel from the source to the user in the water supply network.

[0091] Specifically, a hydraulic residence time model is constructed based on the network's terminal pressure and combined with network topology information. Using this model, each pipe section in the network is treated as a processing unit, and the hydraulic residence time of each processing unit is calculated. Based on the daily water consumption fluctuation curve, the water consumption for each period is calculated, and then the water flow velocity for each period is calculated. The calculated water flow velocity is substituted into the hydraulic residence time model to determine the water retention time per unit time for each pipe section. The turbulence coefficient of the network is collected. Based on the turbulence coefficient and water retention time, a dynamic risk assessment model is constructed.

[0092] This solution analyzes the pipe network's terminal pressure and constructs a hydraulic retention time model, which helps predict water retention time and thus assess potential water quality risks. Areas with long water retention times are prone to water quality issues, such as bacterial growth and water quality deterioration. By analyzing the daily water consumption fluctuation curve, the dynamic changes in water flow within the pipe network can be identified. For example, when water consumption is low, water flow is slow and retention time is long, leading to water quality deterioration. Based on the hydraulic retention time model and the daily water consumption fluctuation curve, the water retention time per unit time is determined for each pipe section. Excessive water retention time leads to water quality deterioration, so determining water retention time facilitates water quality management. Combining the turbulence coefficient and water retention time, a dynamic risk assessment model is developed to assess the water quality risk level of each pipe network section in real time. This dynamic risk assessment model helps predict water quality issues and enable timely measures to ensure water supply safety.

[0093] In some embodiments, water quality monitoring data is analyzed to determine turbidity, residual chlorine and total organic carbon; abnormal areas of the pipe network structure are analyzed to determine the pipe diameter change rate at the pipe diameter mutation point, the angular deviation value at the angle abnormal point, and the elevation difference at the elevation abnormal point; based on the turbidity, residual chlorine, total organic carbon, the pipe diameter change rate, the angular deviation value and the elevation difference, the correlation coefficient between water quality and the pipe network structure is determined; based on the correlation coefficient, the pipe network operation characteristic map is determined.

[0094] Turbidity can be the amount of suspended solids in water.

[0095] Residual chlorine can be the amount of chlorine remaining in the water after the water has been chlorinated.

[0096] Total organic carbon can be the total amount of carbon contained in dissolved and suspended organic matter in water.

[0097] The angular deviation value can be the deviation between the angle of the pipe connection and the design standard angle.

[0098] Water quality can be the physical, chemical and microbiological properties of water.

[0099] The network structure can be the physical layout and composition of the water supply network.

[0100] The correlation coefficient can be used to measure the strength and direction of the linear relationship between two variables.

[0101] Specifically, based on the water quality monitoring data collected in real time, the turbidity, residual chlorine and total organic carbon content are determined. Based on the abnormal areas of the pipe network structure, the changes in the pipe diameter are analyzed and the pipe diameter mutation points are identified. Based on each pipe diameter mutation point, the pipe diameter change rate is calculated. The changes in the pipe connection angle are analyzed and the angle abnormal points are identified. Based on each angle abnormal point, the angular deviation value is calculated. The changes in the pipeline elevation are analyzed and the elevation abnormal points are identified. Based on each elevation abnormal point, the elevation drop is calculated. Statistical software is used to perform statistical analysis on turbidity, residual chlorine and total organic carbon. The Pearson correlation coefficient calculation method is used to calculate the correlation coefficient between turbidity, residual chlorine and total organic carbon and the pipe diameter change rate, angular deviation value and elevation drop. Based on the calculated correlation coefficient, a pipe network operation characteristic map is constructed.

[0102] This solution analyzes water quality monitoring data to accurately identify the water quality status of each monitoring point in the pipe network, including data such as turbidity, residual chlorine, and total organic carbon, which helps assess water quality and identify water quality issues. It also analyzes pipe network topology data to identify pipe diameter mutation points, angle anomalies, and elevation anomalies, locating potential water quality adverse points. Calculating pipe diameter change rates, angular deviation values, and elevation drops helps construct a dynamic risk assessment model. The correlation coefficient reflects the degree of correlation between water quality monitoring data and pipe network structure data. The correlation coefficient helps establish a dynamic risk assessment model. Based on the correlation coefficient, a pipe network operation characteristic map is constructed to display the relationship between water quality monitoring data and pipe network structure data, helping to identify water quality adverse points and potential water quality issues.

[0103] In some embodiments, the Reynolds number of the pipe section is calculated based on the turbulence coefficient; the design parameters of the pipe section are determined based on the pipeline network design standards, and the standard residence time is determined based on the pipe section design parameters; a water age correction function is established based on the Reynolds number of the pipe section, the standard residence time, and the water body residence time; and a dynamic risk assessment model is established based on the residual chlorine content, the total organic carbon content, and the water age correction function.

[0104] The pipe section Reynolds number can be a dimensionless number used to describe the flow state of a fluid in a pipe.

[0105] The pipe segment design parameters may be parameters determined during the pipe network design phase and used to guide the construction and operation of the pipe network.

[0106] The standard residence time can be the theoretical residence time of water in the pipe network under ideal conditions.

[0107] The water age correction function may be a mathematical model used to adjust the standard residence time to reflect the water age under actual operating conditions.

[0108] Specifically, the Reynolds number of the pipe section is calculated based on the turbulence coefficient, combined with the diameter and flow rate of the pipe section. According to the pipeline network design standards, the design flow rate, design pressure and other pipe section design parameters are determined. According to the pipe section design parameters, the standard residence time is calculated. Based on the standard residence time, a theoretical water age model is established. The actual water age is calculated using the actual water body residence time. The difference between the actual water age and the theoretical water age model is calculated to determine the actual water age correction factor. Based on the Reynolds number, standard residence time and actual water age correction factor, a water age correction function is constructed. The residual chlorine and total organic carbon content are collected at each monitoring point in the pipeline network. Based on the residual chlorine content, total organic carbon content and water age correction function, a dynamic risk assessment model is constructed.

[0109] This solution evaluates the turbulence coefficient of the water flow within a pipe section by calculating the Reynolds number. A high Reynolds number generally indicates turbulent flow, which affects the transport of substances in the water and water quality. Determining pipe section design parameters, such as design flow velocity and design pressure, provides a benchmark for pipeline network design and operation, helping to ensure the safe operation of the network under normal conditions. Determining the standard residence time helps evaluate the operating efficiency of the pipeline network under ideal conditions and helps managers identify whether the pipeline network design meets actual needs and whether there is potential for optimization. Establishing a water age correction function adjusts the theoretical residence time to reflect the water age under actual operating conditions, helping to more accurately predict water quality changes and providing a basis for water quality management. Establishing a dynamic risk assessment model allows for real-time assessment of the water quality risk level of each pipe section in the pipeline network, considering the impact of residual chlorine, total organic carbon, water age, and areas of abnormal pipe network structure on water quality risk, helping to improve the targeted and efficient water quality management.

[0110] In some embodiments, historical maintenance records are obtained and analyzed to determine historical corrosion conditions; based on the historical corrosion conditions, the causes of historical corrosion are determined; and based on the causes of historical corrosion, the age of the pipeline network, and the inner wall loss, the roughness of the pipe wall at the current moment is determined.

[0111] The historical maintenance record may be a record of all maintenance activities that have occurred in the water supply network in the past.

[0112] Historical corrosion conditions can be instances where damage has occurred in the water supply network in the past due to corrosion.

[0113] Historical corrosion causes can be those that caused corrosion in the water network in the past.

[0114] Specifically, historical maintenance records, including maintenance time, location, reason, and measures, are collected from pipeline network management equipment. Historical maintenance records are analyzed to identify historical corrosion patterns. Based on these historical maintenance records, historical corrosion causes, such as water corrosivity, pipeline material, water flow rate, and temperature fluctuations, are analyzed. The age of the pipeline network is analyzed to determine the degree of pipeline aging. Based on water quality monitoring data and historical corrosion patterns, the degree of inner wall wear of the pipes is assessed. A pipe wall roughness model is established and used to calculate the current pipe wall roughness based on the age of the pipeline network, the inner wall wear rate, and historical corrosion patterns.

[0115] This solution captures historical maintenance records to analyze corrosion conditions and causes, helping to identify areas and types of corrosion. Analyzing historical maintenance records identifies the frequency and severity of historical corrosion events within the pipeline network, helping to assess the impact of corrosion on the network and inform maintenance planning. Identifying the causes of historical corrosion facilitates targeted corrosion prevention and control measures. If corrosion is caused by water quality issues, water treatment processes can be adjusted; if corrosion is caused by pipe material issues, pipe material replacement can be considered. Analyzing the age of the pipeline network helps assess pipeline aging and predict future corrosion risks. Aged pipelines are more susceptible to corrosion and therefore require more frequent inspections and maintenance. Assessing the extent of internal wall wear helps identify the impact of corrosion on the internal walls of pipe sections and the remaining service life of the pipeline, facilitating the development of appropriate maintenance and replacement plans. Establishing a pipe wall roughness model helps predict the impact of corrosion on pipe wall roughness. Calculating the current pipe wall roughness helps assess the impact of corrosion on pipeline network operations and inform appropriate maintenance and replacement measures.

[0116] In some embodiments, water quality monitoring data is analyzed to determine the type of water quality change; and based on the type of water quality change and the inner wall material of the pipe section, a water quality corrosivity index for each water quality type is determined.

[0117] The water quality change type may be a change trend of water quality monitoring data over time.

[0118] Water quality types can be classified according to characteristics of water bodies such as drinking water, industrial water, agricultural water, and domestic water.

[0119] Specifically, statistical methods are used to analyze the changing trends of water quality monitoring data over time. Pattern recognition techniques are used to analyze the changing patterns in water quality. Anomaly detection algorithms are used to analyze abnormal changes in water quality monitoring data. Based on the changing patterns and abnormal changes, the type of water quality change is determined. Different pipe wall materials in the pipe network are identified. Based on the type of water quality change and the pipe wall material, appropriate water quality corrosivity indicators (such as chemical corrosivity, physical corrosivity, and microbial corrosivity) are selected.

[0120] This solution analyzes water quality change patterns to help identify the nature and source of water quality issues. Analyzing pipe section inner wall materials helps identify the potential impact of pipelines on water quality. Different pipe section inner wall materials have different effects on water quality. Determining water quality corrosivity indicators helps assess the potential corrosion risk to pipelines. Corrosivity indicators, including corrosion rate, type, and severity, assist in developing anti-corrosion measures and maintenance plans.

[0121] Figure 3 This is a schematic diagram of the structure of a water quality online monitoring and early warning platform provided in one embodiment of the present application, such as Figure 3 As shown, the water quality online monitoring and early warning platform 300 of this embodiment includes: a spectrum determination module 301, a model establishment module 302, an unfavorable point determination module 303, and an instruction generation module 304.

[0122] A graph determination module 301 is used to obtain pipe network topology data and water quality monitoring data, and determine a pipe network operation characteristic graph based on the pipe network topology data and water quality monitoring data; A model building module 302 is used to analyze the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model; Unfavorable point determination module 303, for determining the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; The instruction generation module 304 is used to generate multi-level warning instructions based on the spatial distribution of the water quality unfavorable points and the pipe network topology data and send them to the management device.

[0123] Optionally, when determining the pipe network operation characteristic map based on the pipe network topology data and the water quality monitoring data, the map determination module 301 is used to: Analyze the pipe network topology data and determine the pipe diameter data of each pipe network node; Determining the pipe diameter change rate of adjacent pipe sections based on the pipe diameter data; Comparing the pipe diameter change rate with a preset pipe diameter change threshold, and determining a pipe diameter mutation point based on the comparison result; Determine the connection angle of the connecting pipes and the elevation difference per unit pipe length according to the pipe network topology data; Obtaining a pipe network design standard, and determining an angle abnormal point and an elevation abnormal point based on the pipe network design standard and the connection angle and the elevation drop; Determine the abnormal area of the pipe network structure according to the pipe diameter mutation point, the angle abnormal point and the elevation abnormal point; A pipeline network operation characteristic map is determined based on the abnormal pipeline network structure area and the water quality monitoring data.

[0124] Optionally, the model building module 302 analyzes the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model, which is used to: Acquiring structural attribute information; determining the pipe wall roughness based on the structural attribute information; Determining the turbulence coefficient of any pipe section according to the pipe wall roughness and the pipe network operation characteristic map; Determining the pipe network terminal pressure according to the turbulence coefficient; Establishing a daily water consumption fluctuation curve based on the water quality monitoring data; A dynamic risk assessment model is established based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve.

[0125] Optionally, when determining the pipe wall roughness based on the structural attribute information, the model building module 302 is configured to: Determine the inner wall material of the pipe section and the construction age of the pipe network based on the structural attribute information; Determining water corrosivity indicators based on the water quality monitoring data; Determining the inner wall loss per unit time based on the water corrosivity index and the inner wall material of the pipe section; The pipe wall roughness at the current moment is determined according to the pipe network construction age and the inner wall loss.

[0126] Optionally, when the model building module 302 builds a dynamic risk assessment model based on the turbulence coefficient, the pipe network terminal pressure, and the daily water consumption fluctuation curve, it is used to: Constructing a hydraulic residence time model according to the pipe network terminal pressure; Analyze the daily water consumption fluctuation curve according to the hydraulic retention time model to determine the water retention time per unit time in each pipe section; A dynamic risk assessment model is established based on the turbulence coefficient and the water residence time.

[0127] Optionally, when the graph determination module 301 determines the pipe network operation characteristic graph based on the pipe network structural abnormal area and the water quality monitoring data, it is configured to: Analyze the water quality monitoring data to determine turbidity, residual chlorine and total organic carbon; Analyze the abnormal area of the pipe network structure to determine the pipe diameter change rate of the pipe diameter mutation point, the angular deviation value of the angle abnormal point, and the elevation difference of the elevation abnormal point; Determining a correlation coefficient between water quality and pipe network structure based on the turbidity, the residual chlorine, the total organic carbon, the pipe diameter change rate, the angular deviation value, and the elevation drop; According to the correlation coefficient, a pipeline network operation characteristic map is determined.

[0128] Optionally, when the model building module 302 builds a dynamic risk assessment model based on the turbulence coefficient and the water residence time, it is used to: Calculating the Reynolds number of the pipe section according to the turbulence coefficient; Determining pipe section design parameters according to the pipe network design standard, and determining a standard residence time according to the pipe section design parameters; Establishing a water age correction function based on the pipe section Reynolds number, the standard residence time, and the water body residence time; A dynamic risk assessment model is established based on the residual chlorine content, the total organic carbon content and the water age correction function.

[0129] Optionally, when the model building module 302 determines the pipe wall roughness at the current moment based on the pipe network construction age and the inner wall loss, it is configured to: Obtaining historical maintenance records, analyzing the historical maintenance records, and determining historical corrosion conditions; Determine the cause of historical corrosion based on the historical corrosion conditions; The pipe wall roughness at a current moment is determined based on the historical corrosion causes, the pipe network construction age, and the inner wall loss.

[0130] Optionally, when determining the water corrosivity index based on the water quality monitoring data, the model building module 302 is configured to: Analyzing the water quality monitoring data to determine the type of water quality change; The water quality corrosivity index of each water quality type is determined according to the water quality change type and the inner wall material of the pipe section.

[0131] The platform of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A water quality online monitoring and early warning method, characterized in that: include: Obtaining pipe network topology data and water quality monitoring data, and determining the pipe network operation characteristic map based on the pipe network topology data and water quality monitoring data; Analyze the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model; Determining the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; A multi-level warning instruction is generated according to the spatial distribution of the unfavorable water quality points and the pipe network topology data and sent to the management device.

2. The method according to claim 1, characterized in that Determining the pipe network operation characteristic map based on the pipe network topology data and water quality monitoring data includes: Analyze the pipe network topology data and determine the pipe diameter data of each pipe network node; Determining the pipe diameter change rate of adjacent pipe sections based on the pipe diameter data; Comparing the pipe diameter change rate with a preset pipe diameter change threshold, and determining a pipe diameter mutation point based on the comparison result; Determine the connection angle of the connecting pipes and the elevation difference per unit pipe length according to the pipe network topology data; Obtaining a pipe network design standard, and determining an angle abnormal point and an elevation abnormal point based on the pipe network design standard and the connection angle and the elevation drop; Determine the abnormal area of the pipe network structure according to the pipe diameter mutation point, the angle abnormal point and the elevation abnormal point; A pipeline network operation characteristic map is determined based on the abnormal pipeline network structure area and the water quality monitoring data.

3. The method according to claim 2, characterized in that The analyzing the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model includes: Acquiring structural attribute information; determining the pipe wall roughness based on the structural attribute information; Determining the turbulence coefficient of any pipe section according to the pipe wall roughness and the pipe network operation characteristic map; Determining the pipe network terminal pressure according to the turbulence coefficient; Establishing a daily water consumption fluctuation curve based on the water quality monitoring data; A dynamic risk assessment model is established based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve.

4. The method according to claim 3, characterized in that Determining the pipe wall roughness according to the structural attribute information includes: Determine the inner wall material of the pipe section and the construction age of the pipe network based on the structural attribute information; Determining water corrosivity indicators based on the water quality monitoring data; Determining the inner wall loss per unit time based on the water corrosivity index and the inner wall material of the pipe section; The pipe wall roughness at the current moment is determined according to the pipe network construction age and the inner wall loss.

5. The method according to claim 3, characterized in that The dynamic risk assessment model is established based on the turbulence coefficient, the pipe network terminal pressure and the daily water consumption fluctuation curve, including: Constructing a hydraulic residence time model according to the pipe network terminal pressure; Analyze the daily water consumption fluctuation curve according to the hydraulic retention time model to determine the water retention time per unit time in each pipe section; A dynamic risk assessment model is established based on the turbulence coefficient and the water residence time.

6. The method according to claim 5, characterized in that The determining of the pipe network operation characteristic map based on the pipe network structural abnormal area and the water quality monitoring data includes: Analyze the water quality monitoring data to determine turbidity, residual chlorine and total organic carbon; Analyze the abnormal area of the pipe network structure to determine the pipe diameter change rate of the pipe diameter mutation point, the angular deviation value of the angle abnormal point, and the elevation drop of the elevation abnormal point; Determining a correlation coefficient between water quality and pipe network structure based on the turbidity, the residual chlorine, the total organic carbon, the pipe diameter change rate, the angular deviation value, and the elevation drop; According to the correlation coefficient, a pipeline network operation characteristic map is determined.

7. The method according to claim 6, characterized in that The dynamic risk assessment model is established according to the turbulence coefficient and the water residence time, including: Calculating the Reynolds number of the pipe section according to the turbulence coefficient; Determining pipe section design parameters according to the pipe network design standard, and determining a standard residence time according to the pipe section design parameters; Establishing a water age correction function based on the pipe section Reynolds number, the standard residence time, and the water body residence time; A dynamic risk assessment model is established based on the residual chlorine content, the total organic carbon content and the water age correction function.

8. The method according to claim 4, characterized in that The determining of the pipe wall roughness at the current moment according to the pipe network construction age and the inner wall loss includes: Obtaining historical maintenance records, analyzing the historical maintenance records, and determining historical corrosion conditions; Determine the cause of historical corrosion based on the historical corrosion conditions; The pipe wall roughness at a current moment is determined based on the historical corrosion causes, the pipe network construction age, and the inner wall loss.

9. The method according to claim 4, characterized in that Determining the water corrosivity index based on the water quality monitoring data includes: Analyzing the water quality monitoring data to determine the type of water quality change; The water quality corrosivity index of each water quality type is determined according to the water quality change type and the inner wall material of the pipe section.

10. A water quality online monitoring and early warning platform, characterized in that: The method as claimed in any one of claims 1 to 9 comprises: A graph determination module is used to obtain pipe network topology data and water quality monitoring data, and determine the pipe network operation characteristic graph based on the pipe network topology data and water quality monitoring data; A model building module, used to analyze the pipe network operation characteristic map and the water quality monitoring data to establish a dynamic risk assessment model; An unfavorable point determination module, configured to determine the spatial distribution of unfavorable water quality points based on the pipe network topology data and the dynamic risk assessment model; The instruction generation module is used to generate multi-level early warning instructions based on the spatial distribution of the unfavorable water quality points and the pipe network topology data and send them to the management device.

Images