Water quality monitoring, early warning and tracing system and method for drainage pipe network

Abstract

The invention discloses a drainage pipe network water quality monitoring, early warning and traceability system and method, and the system comprises the steps: arranging pipe network water quality monitoring stations, building pipe network water quality early warning monitoring stations, carrying out water quality early warning, building a traceability business platform, and building a water quality early warning traceability model. Constructing a water quality pollution traceability mechanism flow; the pipe network early-warning traceability supervision mechanism can quickly study and judge the water quality of the inlet of the pipe network, can track pollution sources of secret discharge, leakage discharge and excessive discharge, provides early warning for inlet water of a sewage treatment plant, reserves emergency disposal time, realizes early-warning traceability of the sewage pipe network, quickly and efficiently searches for the pollution sources, improves supervision efficiency, and ensures that the water quality of the inlet of the sewage treatment plant is ensured. Stable operation of a sewage treatment plant is guaranteed, and impact is effectively reduced; the development of the project provided by the invention plays a demonstration role in an environmental protection system, plays a leading role in improving the water quality of a sewage pipe network and surface water, and provides a scientific basis for water environment management.

Description

Technical Field

[0001] The present invention belongs to the technical field of sewage pipe network water quality monitoring, and specifically relates to a drainage pipe network water quality monitoring, early warning and tracing system and method. Background Art

[0002] The sewage concentration at the sewage treatment plant is too high. Once it exceeds the treatment load, it will be impacted and seriously affect the activity of the sludge, causing a large number of sludge bacteria to die, affecting the normal operation of the sewage treatment plant, causing the effluent quality of the sewage treatment plant to exceed the standard, and then affecting the water quality of the downstream river. It is necessary to conduct early warning and traceability analysis of water quality pollution in the sewage pipe network. The drainage pipe network is located underground, and its structure is intricate. Abnormal water inflow in the pipe network is mostly intermittent or one-time discharge. The dumping, secret discharge, and leakage of wastewater in the pipe are random, and traditional "civil defense" traceability is extremely difficult.

[0003] Currently, water environment management relies on water quality monitoring data from online automatic monitoring stations. However, the existing system lacks accurate data analysis, early warning and traceability functions, resulting in poor model simulation accuracy and untraceability of water pollution. It cannot meet the needs of rapid response to water pollution incidents. Water quality monitoring only at the water inlet of the sewage treatment plant cannot provide early warning of water pollution and pollution prevention and control.

[0004] Traditional water quality monitoring methods, such as manual sampling and laboratory analysis, usually have problems such as low monitoring efficiency, difficulty in tracing the source of water pollution, high costs of manpower and material resources, and discontinuous data.

[0005] In order to solve these problems, a drainage network water quality monitoring, early warning and tracing system and method are proposed. Summary of the invention

[0006] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a drainage network water quality monitoring, early warning and tracing system and method, which effectively solves the problems raised by the background technology.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a drainage network water quality monitoring, early warning and tracing system, including the layout of network water quality monitoring stations, the construction of network water quality early warning monitoring stations, the construction of a water quality early warning and tracing business platform, the construction of a water quality early warning and tracing model, and the construction of a water quality pollution tracing mechanism process.

[0008] A method for establishing a drainage network water quality monitoring, early warning and tracing system comprises the following steps:

[0009] Step 1: Set up monitoring points by investigating the current status of the drainage network, sewage outlets and surrounding areas, identifying the pollution source and analyzing the impact, and combining the actual working conditions of the on-site points;

[0010] Step 2: Focusing on the problems of excessive influent concentration in sewage treatment plants, network health, and intermittent illegal discharge of pollution, water quality monitoring equipment and low-power network water quality data collection and remote transmission terminals are deployed and installed at various nodes of the sewage network, at the intersection of enterprises, and in suspicious areas to conduct online real-time monitoring of water quality. The data monitored in real time by hardware equipment at each point is uploaded to the sewage network water quality monitoring and early warning tracing platform for data analysis and collation, spatial information visualization, early warning and forecast of abnormal monitoring data, pollution source tracking, and patrol and dispatch management;

[0011] Step 3: Based on real-time online monitoring of pipe network water quality, use big data analysis, artificial intelligence, GIS and other technologies to extract pipe network operation characteristics, train and deploy drainage pipe network operation AI models, establish a water quality early warning business platform, and realize the prediction and early warning functions of pipe network water quality pollutants;

[0012] Step 4: Establish a water quality traceability business platform. By importing the monitoring source data of the production of the perception layer into the monitoring center database, the traceability mechanism is activated through the water quality traceability platform database to accurately lock the source of pollution and issue a task dispatch order to the supervision department personnel. After accepting the task, the supervisory personnel upload the on-site pictures, text descriptions and videos to the system and generate a problem number. The problem rectification and feedback are also uploaded through the system, forming a rapid response closed loop of "monitoring and early warning-expert consultation-tracking and tracing-follow-up supervision", so as to achieve real-time control of monitoring data and full supervision of the pollution source tracing process;

[0013] Step 5: Based on the large amount of data monitored by the online water quality monitoring equipment, the SWMM urban drainage network water quality model, Bayesian-MCMC Monte Carlo sampling algorithm, and time series warning model are constructed. Through the spatial logic chain of "factory-network-source", the model intelligently traces back to the suspicious network segment, and then investigates and traces the specific enterprises, gradually narrowing and locking the monitoring and investigation scope of the network from the surface to the point in time and space, gradually narrowing the abnormal area, and gradually narrowing and locating the problem points of illegal discharge and leakage by analyzing the water quality characteristics, and locking the sewage discharge area;

[0014] Step 6: Use the basic data of the drainage network to construct a tree-like topological relationship structure diagram of the drainage network, and conduct water flow path analysis and water quality time series data gradient analysis. The system calculates the water quality changes of the relevant nodes in the water flow direction according to the model, forms an early warning case according to the level, and starts the workflow mechanism. Once an abnormal water quality occurs, the platform technicians immediately notify the on-site tracing service personnel and go to the suspected points in time to trace the source and investigate, promote the source control of pollution sources, and form an "early warning-tracing" linkage working mechanism. After a period of operation, optimize the workflow, data model algorithm, and establish a complete set of early warning tracing methods and mechanism processes.

[0015] Preferably, in step one, monitoring points are arranged according to different scenarios, including commercial areas, industrial areas, residential areas, and sewage outlets.

[0016] Preferably, in step 2, the water quality monitoring equipment includes monitoring factors pH, conductivity, COD, and turbidity.

[0017] Preferably, in step three, the water quality early warning business platform sends alarm information to the heads of relevant regulatory units and sewage treatment plants in real time to guide the sewage treatment plants to prepare for response in advance. The main functions of the water quality early warning business platform include real-time display of monitoring data, early warning and forecast of abnormal monitoring data, visualization of spatial information, data statistical analysis, data query, and system management.

[0018] Preferably, in step four, the main functions of the water quality traceability business platform include real-time display of monitoring data, visualization of spatial information, data statistical analysis, data query, system management, pollution source tracing, patrol office management, and intelligent analysis and decision-making.

[0019] Preferably, in the step five, based on a large amount of data monitored by the online water quality monitoring equipment, a SWMM urban drainage network water quality model, a Bayesian-MCMC Monte Carlo sampling algorithm, and a time series warning model are constructed to convert the problem of identifying the source of illegal sewage discharge in the sewage network system into a mathematical problem, and the water quality model is predicted for all possible nodes, discharge volume and discharge time. A process line of pollutant concentration changes over time is established to quickly predict different discharge scenarios and improve the efficiency of physical tracing. The Bayesian method is used for statistical inversion, fully considering the influence of prior information, observation noise and model errors, and the results are distributed in the form of probability density to obtain practical results. The data processing function of the cloud platform technology is used to realize a large number of calculations involved in the MCMC sampling method, quickly and accurately analyze the likelihood value of each parameter combination, and quickly determine the possible nodes, discharge concentrations and discharge volumes of direct sewage discharge.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The pipeline network early warning and tracing supervision mechanism of the present invention can quickly judge the water quality of the pipeline network inlet, trace the pollution sources of illegal discharge, leakage and excessive discharge, provide early warning for the water inlet of the sewage treatment plant, reserve time for emergency disposal, realize early warning and tracing of the sewage pipeline network, quickly and efficiently find the pollution source, improve the supervision efficiency, ensure the stable operation of the sewage treatment plant, and effectively reduce the impact; it has accumulated a lot of experience and first-hand information on tracing monitoring work, assisted in solving the problem of rivers with greater pressure on regional surface water assessment, further optimized project resources, coordinated and allocated them, and provided strong support for relevant decisions on water environment management; it has a strong deterrent effect on illegal discharge, greatly deterred illegal acts such as illegal discharge and leakage, formed an "early warning-tracing" linkage working mechanism, identified the water environment pollution sections and pollution types in the region, and found the focus of governance and management and the responsible subjects;

[0022] When water quality exceeds the standard, the intelligent tracing algorithm is used to track and analyze the water quality, and the over-discharge area or specific suspected enterprise is locked in a short time, so as to quickly complete the pollution emergency response, improve the efficiency of intelligent drainage management, and command and dispatch law enforcement tasks to form a closed loop from formation, issuance, processing, and feedback. By accurately locating the pollution source, unnecessary investigation and governance costs can be reduced, the direct economic losses caused by water pollution incidents can be reduced, the investment of manpower and material resources can be reduced, and the efficiency of the use of financial funds can be improved;

[0023] Strengthen cross-departmental information exchange, joint law enforcement, promote cooperation between the government, enterprises, non-governmental organizations and the public, jointly promote water resource protection and pollution control, and improve the efficiency and effectiveness of environmental protection through multi-party cooperation; the implementation of the project of this invention will play a demonstration role in the environmental protection system, will play a leading role in improving the quality of sewage pipe networks and surface water, and provide a scientific basis for water environment management. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Embodiment 1, the present invention relates to a drainage network water quality monitoring, early warning and tracing system, including the layout of network water quality monitoring stations, the construction of network water quality early warning monitoring stations, the construction of a water quality early warning and tracing business platform, the construction of a water quality early warning and tracing model, and the construction of a water quality pollution tracing mechanism process.

[0026] A method for establishing a drainage network water quality monitoring, early warning and tracing system comprises the following steps:

[0027] Step 1: Set up monitoring points by investigating the current status of the drainage network, sewage outlets and surrounding areas, identifying the pollution source and analyzing the impact, and combining the actual working conditions of the on-site points;

[0028] Step 2: Focusing on the problems of excessive influent concentration in sewage treatment plants, network health, and intermittent illegal discharge of pollution, water quality monitoring equipment and low-power network water quality data collection and remote transmission terminals are deployed and installed at various nodes of the sewage network, at the intersection of enterprises, and in suspicious areas to conduct online real-time monitoring of water quality. The data monitored in real time by hardware equipment at each point is uploaded to the sewage network water quality monitoring and early warning tracing platform for data analysis and collation, spatial information visualization, early warning and forecast of abnormal monitoring data, pollution source tracking, and patrol and dispatch management;

[0029] Step 3: Based on real-time online monitoring of pipe network water quality, use big data analysis, artificial intelligence, GIS and other technologies to extract pipe network operation characteristics, train and deploy drainage pipe network operation AI models, establish a water quality early warning business platform, and realize the prediction and early warning functions of pipe network water quality pollutants;

[0030] Step 4: Establish a water quality traceability business platform. By importing the monitoring source data of the production of the perception layer into the monitoring center database, the traceability mechanism is activated through the water quality traceability platform database to accurately lock the source of pollution and issue a task dispatch order to the supervision department personnel. After accepting the task, the supervisory personnel upload the on-site pictures, text descriptions and videos to the system and generate a problem number. The problem rectification and feedback are also uploaded through the system, forming a rapid response closed loop of "monitoring and early warning-expert consultation-tracking and tracing-follow-up supervision", so as to achieve real-time control of monitoring data and full supervision of the pollution source tracing process;

[0031] Step 5: Based on the large amount of data monitored by the online water quality monitoring equipment, the SWMM urban drainage network water quality model, Bayesian-MCMC Monte Carlo sampling algorithm, and time series warning model are constructed. Through the spatial logic chain of "factory-network-source", the model intelligently traces back to the suspicious network segment, and then investigates and traces the specific enterprises, gradually narrowing and locking the monitoring and investigation scope of the network from the surface to the point in time and space, gradually narrowing the abnormal area, and gradually narrowing and locating the problem points of illegal discharge and leakage by analyzing the water quality characteristics, and locking the sewage discharge area;

[0032] Step 6: Use the basic data of the drainage network to construct a tree-like topological relationship structure diagram of the drainage network, and conduct water flow path analysis and water quality time series data gradient analysis. The system calculates the water quality changes of the relevant nodes in the water flow direction according to the model, forms an early warning case according to the level, and starts the workflow mechanism. Once an abnormal water quality occurs, the platform technicians immediately notify the on-site tracing service personnel and go to the suspected points in time to trace the source and investigate, promote the source control of pollution sources, and form an "early warning-tracing" linkage working mechanism. After a period of operation, optimize the workflow, data model algorithm, and establish a complete set of early warning tracing methods and mechanism processes.

[0033] In step one, monitoring points are deployed according to different scenarios, including commercial areas, industrial areas, residential areas, and sewage outlets.

[0034] In step 2, the water quality monitoring equipment includes monitoring factors pH, conductivity, COD, and turbidity.

[0035] In step three, the water quality early warning business platform sends alarm information to the heads of relevant regulatory units and sewage treatment plants in real time to guide the sewage treatment plants to prepare for response in advance. The main functions of the water quality early warning business platform include real-time display of monitoring data, early warning and forecast of abnormal monitoring data, visualization of spatial information, data statistical analysis, data query, and system management.

[0036] In step four, the main functions of the water quality traceability business platform include real-time display of monitoring data, visualization of spatial information, data statistical analysis, data query, system management, pollution source tracking, patrol office management, and intelligent analysis and decision-making.

[0037] In step five, based on the large amount of data monitored by the online water quality monitoring equipment, the SWMM urban drainage network water quality model, the Bayesian-MCMC Monte Carlo sampling algorithm, and the time series warning model are constructed to transform the problem of identifying the source of illegal sewage discharge in the sewage network system into a mathematical problem, and the water quality model is predicted for all possible nodes, discharge volume and discharge time. The process line of pollutant concentration changes over time is established to quickly predict different emission scenarios and improve the efficiency of physical tracing. The Bayesian method is used for statistical inversion, fully considering the influence of prior information, observation noise and model errors. The results are distributed in the form of probability density to obtain practical results. The data processing function of the cloud platform technology is used to realize the large amount of calculations involved in the MCMC sampling method, quickly and accurately analyze the likelihood value of each parameter combination, and quickly determine the possible nodes, discharge concentrations and discharge volumes of direct sewage discharge.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0039] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A drainage network water quality monitoring, early warning and tracing system, characterized by: It includes the layout of water quality monitoring stations in the pipeline network, the construction of water quality early warning monitoring stations in the pipeline network, the construction of water quality early warning and traceability business platforms, the construction of water quality early warning traceability models, and the construction of water pollution traceability mechanism processes.

2. A method for establishing a drainage network water quality monitoring, early warning and tracing system, characterized by: The steps include: Step 1: Set up monitoring points by investigating the current status of the drainage network, sewage outlets and surrounding areas, identifying the pollution source and analyzing the impact, and combining the actual working conditions of the on-site points; Step 2: Focusing on the problems of excessive influent concentration in sewage treatment plants, network health, and intermittent illegal discharge of pollution, water quality monitoring equipment and low-power network water quality data collection and remote transmission terminals are deployed and installed at various nodes of the sewage network, at the intersection of enterprises, and in suspicious areas to conduct online real-time monitoring of water quality. The data monitored in real time by hardware equipment at each point is uploaded to the sewage network water quality monitoring and early warning tracing platform for data analysis and collation, spatial information visualization, early warning and forecast of abnormal monitoring data, pollution source tracking, and patrol and dispatch management; Step 3: Based on real-time online monitoring of pipe network water quality, use big data analysis, artificial intelligence, GIS and other technologies to extract pipe network operation characteristics, train and deploy drainage pipe network operation AI models, establish a water quality early warning business platform, and realize the prediction and early warning functions of pipe network water quality pollutants; Step 4: Establish a water quality traceability business platform. By importing the monitoring source data of the production of the perception layer into the monitoring center database, the traceability mechanism is activated through the water quality traceability platform database to accurately lock the source of pollution and issue a task dispatch order to the supervision department personnel. After accepting the task, the supervisory personnel upload the on-site pictures, text descriptions and videos to the system and generate a problem number. The problem rectification and feedback are also uploaded through the system, forming a rapid response closed loop of "monitoring and early warning-expert consultation-tracking and tracing-follow-up supervision", so as to achieve real-time control of monitoring data and full supervision of the pollution source tracing process; Step 5: Based on the large amount of data monitored by the online water quality monitoring equipment, the SWMM urban drainage network water quality model, Bayesian-MCMC Monte Carlo sampling algorithm, and time series warning model are constructed. Through the spatial logic chain of "factory-network-source", the model intelligently traces back to the suspicious network segment, and then investigates and traces the specific enterprises, gradually narrowing and locking the network monitoring and investigation scope from the surface to the point in time and space, gradually narrowing the abnormal area, and gradually narrowing and locating the problem points of illegal discharge and leakage by analyzing the water quality characteristics, and locking the sewage discharge area; Step 6: Use the basic data of the drainage network to construct a tree-like topological relationship structure diagram of the drainage network, and conduct water flow path analysis and water quality time series data gradient analysis. The system calculates the water quality changes of the relevant nodes in the water flow direction according to the model, forms an early warning case according to the level, and starts the workflow mechanism. Once the water quality is abnormal, the platform technicians immediately notify the on-site tracing service personnel and go to the suspected points in time to trace the source, promote the source control of pollution sources, and form an "early warning-tracing" linkage working mechanism. After a period of operation, optimize the workflow, data model algorithm, and establish a complete set of early warning tracing methods and mechanism processes.

3. The method for establishing a drainage network water quality monitoring, early warning and tracing system according to claim 2 is characterized by: In step 1, monitoring points are arranged according to different scenarios, including commercial areas, industrial areas, residential areas, and sewage outlets.

4. The method for establishing a drainage network water quality monitoring, early warning and tracing system according to claim 2 is characterized by: In step 2, the water quality monitoring equipment includes monitoring factors such as pH, conductivity, COD, and turbidity.

5. The method for establishing a drainage network water quality monitoring, early warning and tracing system according to claim 2, characterized in that: In the step three, the water quality early warning business platform sends alarm information to the heads of relevant regulatory units and sewage treatment plants in real time to guide the sewage treatment plants to prepare for response in advance. The main functions of the water quality early warning business platform include real-time display of monitoring data, early warning and forecast of abnormal monitoring data, visualization of spatial information, data statistical analysis, data query, and system management.

6. The method for establishing a drainage network water quality monitoring, early warning and tracing system according to claim 2, characterized in that: In step 4, the main functions of the water quality traceability business platform include real-time display of monitoring data, visualization of spatial information, data statistical analysis, data query, system management, pollution source tracking, patrol office management, and intelligent analysis and decision-making.

7. The method for establishing a drainage network water quality monitoring, early warning and tracing system according to claim 2, characterized in that: In the step five, based on a large amount of data monitored by the online water quality monitoring equipment, a SWMM urban drainage network water quality model, a Bayesian-MCMC Monte Carlo sampling algorithm, and a time series warning model are constructed to transform the problem of identifying the source of illegal sewage discharge in the sewage network system into a mathematical problem, and to perform water quality model predictions on all possible nodes, discharge volume, and discharge time parameters; a process line of pollutant concentration changes over time is established to quickly predict different discharge scenarios and improve the efficiency of physical tracing; a Bayesian method is used for statistical inversion, fully considering the influence of prior information, observation noise, and model errors, and the results are in the form of probability density distribution to obtain practical results; the data processing function of the cloud platform technology is used to realize a large number of calculations involved in the MCMC sampling method, quickly and accurately analyze the likelihood values ​​of each parameter combination, and quickly determine the possible nodes, discharge concentrations, and discharge volumes of direct sewage discharge.