Intelligent runoff pollution monitoring and responding method and system

Abstract

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

Description

Technical Field

[0001] The present invention relates to the technical field of runoff pollution monitoring, and in particular to an intelligent runoff pollution monitoring and response method. Background Art

[0002] With the acceleration of urbanization, runoff pollution has become a major issue affecting water quality and ecological environment. Traditional water quality monitoring methods often rely on manual sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and rapid response to pollution events.

[0003] Although the existing intelligent monitoring technology has improved the monitoring efficiency to a certain extent, there are still some problems. For example, the unreasonable layout of monitoring points leads to the inability of monitoring data to fully reflect the pollution status of the runoff area; insufficient data processing and analysis capabilities make it difficult to accurately assess the degree of pollution and mine key water quality parameters; the alarm mechanism is not sensitive and cannot timely detect and warn of abnormal pollution; the pollution source tracing technology is backward and it is difficult to quickly locate the pollution source area. In response to these problems, this application proposes an intelligent runoff pollution monitoring and response method and system, which aims to improve monitoring efficiency, accurately assess the degree of pollution, timely detect and warn of abnormal pollution, and quickly locate the pollution source area. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide an intelligent runoff pollution monitoring and response method and system to solve the problem in the prior art that it is difficult to monitor and respond quickly to runoff pollution in real time.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the present invention provides the following technical solutions:

[0006] The intelligent runoff pollution monitoring and response method includes the following steps:

[0007] S1, multiple monitoring points are arranged in the runoff area, and sensors are used to collect water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and the collected data are transmitted to the data processing center using a wireless sensor network;

[0008] S2, the data processing center analyzes and processes the collected data, evaluates the pollution index of each monitoring point according to the processing results, analyzes the key water quality parameters that are highly correlated with runoff pollution for the monitoring points whose pollution index exceeds the index threshold, and performs parameter-level evaluation on these key water quality parameters;

[0009] S3, setting a parameter threshold according to the parameter level assessment, wherein the parameter threshold and the index threshold are both regarded as pollution thresholds. Once the runoff exceeds the preset pollution threshold, it is regarded as an abnormal situation and triggers an alarm mechanism;

[0010] S4, once the alarm mechanism is triggered, the water flow model of the pipe network and the geographic information system are combined to trace the source of pollution in reverse, and the pollution source area is located by analyzing the flow and water quality changes at each monitoring point.

[0011] To implement the above technical solution, multiple monitoring points are scientifically arranged in the runoff area, and each monitoring point is equipped with advanced sensors for real-time collection of water quality parameters (such as dissolved oxygen, pH value, heavy metal content, etc.), hydrological parameters (such as flow, flow velocity, etc.) and meteorological data (such as rainfall, wind speed, etc.). These data are efficiently and reliably transmitted to the data processing center through the wireless sensor network to ensure the real-time and accuracy of the information; after receiving the data from each monitoring point, the data processing center cleans, integrates and analyzes the data. By calculating the pollution index, the pollution status of each monitoring point is quantitatively evaluated. For monitoring points where the pollution index exceeds the index threshold, the key water quality parameters related to runoff pollution are further analyzed, and parameter-level evaluation is performed to provide a basis for setting reasonable parameter thresholds; according to the evaluation results of the pollution index and key water quality parameters, the pollution threshold (including index threshold and parameter threshold) is set. Once the data from a certain monitoring point exceeds these thresholds, it is considered an abnormal situation and the alarm mechanism is immediately triggered, sending an alarm message to the management personnel to remind them to take countermeasures. After the alarm is triggered, the pipe network water flow model and geographic information system (GIS) technology are used, combined with the flow and water quality change data of each monitoring point, to reversely trace the transmission path of pollutants and accurately locate the pollution source area. This step helps to quickly identify the source of pollution and provide key information for subsequent pollution control and governance.

[0012] In one embodiment of the present invention, the S1 includes:

[0013] S11, determine the location of monitoring points based on the topography, landforms, flow characteristics, land use type and potential pollution sources in the runoff area;

[0014] S12, water quality sensors, hydrological sensors and meteorological data sensors are installed at each monitoring point, and data are automatically collected at preset time intervals to ensure that the data collection timestamps of each monitoring point are consistent;

[0015] S13, the data processing center establishes an efficient data receiving interface for simultaneously processing a large amount of raw runoff data from multiple monitoring points, and after preprocessing the raw runoff data, stores the preprocessed runoff data in a distributed database.

[0016] To implement the above technical solution, the selection of these locations is aimed at comprehensively covering the runoff area, while focusing on potential pollution sources and vulnerable areas to ensure the effectiveness and accuracy of monitoring, based on the topography, landforms, water flow characteristics, land use types and potential pollution sources of the runoff area. Water quality sensors, hydrological sensors and meteorological data sensors are installed at each determined monitoring point. The sensors automatically collect data at preset time intervals and transmit them to the data processing center through the wireless sensor network. In order to ensure the accuracy and consistency of the data, the data collection timestamps of each monitoring point are uniformly set for subsequent data analysis and processing. The data processing center establishes an efficient data receiving interface for simultaneously processing large quantities of raw runoff data from multiple monitoring points. These interfaces have high concurrent processing capabilities and data integrity verification mechanisms to ensure accurate reception and transmission of data. The received raw runoff data undergoes preprocessing operations, such as data cleaning, format conversion, outlier processing, etc., to improve the quality and availability of the data. The preprocessed runoff data is stored in a distributed database for subsequent data analysis and application.

[0017] In one embodiment of the present invention, S2 includes:

[0018] S21, the data processing center obtains the pre-processed runoff data of each monitoring point from the distributed database, and generates a comprehensive pollution index for each monitoring point;

[0019] S22, compare the comprehensive pollution index with the set index threshold. Once the comprehensive pollution index is greater than the index threshold, the key water quality parameters that are highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the concentration change amount and concentration change rate of these key water quality parameters in the set time step are evaluated.

[0020] To implement the above technical solution, the data processing center obtains the pre-processed runoff data of each monitoring point from the distributed database, and generates a comprehensive pollution index for each monitoring point. This comprehensive index is a comprehensive evaluation indicator that can reflect the overall situation of water pollution at the monitoring point; the data processing center compares the generated comprehensive pollution index with the set index threshold. Once the comprehensive pollution index is greater than the index threshold, it indicates that the water quality is polluted. The data processing center will further conduct correlation analysis and principal component analysis. Correlation analysis aims to identify key water quality parameters that are highly correlated with the comprehensive pollution index. These parameters have the greatest impact on water pollution and are the focus of subsequent evaluation and response; principal component analysis is used to further simplify the data and extract the main components or factors that affect water pollution; after identifying the key water quality parameters, the data processing center evaluates the concentration change and concentration change rate of these key water quality parameters at the set time step to predict the development trend of key water quality parameters in the future.

[0021] In one embodiment of the present invention, the S21 includes:

[0022] S211, determine relevant water quality parameters of runoff pollution and set weights for each relevant water quality parameter based on historical data, water quality standards and expert opinions;

[0023] S212, performing standardization processing on various relevant water quality parameters, wherein the standardization processing method is as follows:

[0024]

[0025] in, It is The original values ​​of the relevant water quality parameters, It is The minimum value of the relevant water quality parameters, It is The maximum value of the relevant water quality parameters, It is Standardized values ​​of relevant water quality parameters;

[0026] S213, using a weighted average method to calculate the comprehensive pollution index of each monitoring point, the comprehensive pollution index is obtained as follows:

[0027]

[0028] in, is the comprehensive pollution index, It is The weights of the relevant water quality parameters, It is The standardized values ​​of relevant water quality parameters are is the number of relevant water quality parameters.

[0029] To implement the above technical solution, water quality parameters closely related to pollution are identified according to the characteristics of runoff pollution, and reasonable weights are set for these relevant water quality parameters based on water quality standards, historical data and expert opinions. The weight setting takes into account the contribution of each relevant parameter to water quality pollution, toxicity and impact on the ecosystem; in order to eliminate the impact of different water quality parameter dimensions and value ranges, each parameter needs to be standardized. The standardized processing method uses linear transformation to convert the original value of each parameter into a standardized value between 0 and 1; after completing the standardized processing of water quality parameters, the weighted average method is used to calculate the comprehensive pollution index of each monitoring point, thereby comprehensively reflecting the water quality pollution status of each monitoring point and providing a basis for subsequent analysis and response.

[0030] In one embodiment of the present invention, the S22 includes:

[0031] If the comprehensive pollution index is greater than the index threshold, the key water quality parameters highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the first-order difference of the key water quality parameters is calculated to measure the concentration change of the key water quality parameters. The first-order difference calculation method is as follows:

[0032]

[0033] in, is the first-order difference of the key water quality parameters, It's time point The concentration of key water quality parameters, It's time point concentrations of key water quality parameters;

[0034] According to the first-order difference, the second-order difference of the key water quality parameter is calculated to measure the concentration change rate of the key water quality parameter. The second-order difference calculation method is as follows:

[0035]

[0036] in, is the second-order difference of key water quality parameters, It's time point concentrations of key water quality parameters.

[0037] To implement the above technical solution, firstly, correlation analysis and principal component analysis methods are used to screen out key water quality parameters that are highly correlated with runoff pollution from numerous relevant water quality parameters. These key water quality parameters are usually indicators that have a significant impact on the water pollution status and change significantly. For the screened key water quality parameters, their first-order differences are calculated to measure the concentration change within a set time step, and then on the basis of the calculated first-order differences, the second-order differences are further calculated to measure the concentration change rate, which is helpful to predict the future development trend of water pollution.

[0038] In one embodiment of the present invention, the S22 further includes:

[0039] Using first-order differences and second-order differences as independent variables, a concentration prediction model was constructed to predict key water quality parameters at time points. The concentration prediction model is as follows:

[0040]

[0041] in, Is the key water quality parameter at the time point The predicted concentration of is the intercept term, which represents the baseline concentration level of this key water quality parameter; is the regression coefficient of the first-order difference, is the regression coefficient of the second-order difference, is the error term.

[0042] To implement the above technical solution, by introducing first-order differences and second-order differences as independent variables, the concentration prediction model can more accurately capture the changing trends and laws of key water quality parameters, thereby improving the accuracy of the prediction; the concentration prediction model has important application value in intelligent runoff pollution monitoring and response methods. It can improve prediction accuracy, support early warning, optimize resource allocation and promote technological innovation.

[0043] In one embodiment of the present invention, S3 includes:

[0044] Set the upper threshold of the first-order difference ,if , it is identified as a level 1 alarm;

[0045] Set the upper threshold of the second-order difference ,if , it is identified as a level 2 alarm;

[0046] Setting the concentration of key water quality parameters The upper threshold ,if , it is identified as a level 3 alarm;

[0047] Set comprehensive pollution index The upper threshold ,if , it is identified as a level 4 alarm.

[0048] To implement the above technical solution, the first-level alarm indicates that the concentration of key water quality parameters has changed significantly in a short period of time; the second-level alarm indicates that the concentration change rate of key water quality parameters is abnormal; the third-level alarm indicates that at a future time point The concentration of key water quality parameters may exceed the safety threshold; the fourth level alarm indicates that the overall water pollution is serious; by setting different levels of alarm thresholds, the system can monitor water quality changes in real time and quickly identify potential risks to achieve timely warning.

[0049] In one embodiment of the present invention, the S4 includes:

[0050] Based on the detailed design drawings, pipe diameter, pipe length, and slope information of the drainage pipe network, a pipe network water flow model is constructed using hydraulic principles. In the pipe network water flow model, the pipe network is divided into pipe sections for water flow transportation and nodes for water flow convergence or diversion, and the nodes are used as monitoring points;

[0051] The topography, land use type, and geographic location data of the drainage network in the geographic information system are integrated with the water flow model of the network. In the geographic information system, the network layout is displayed in the form of a map layer, and each node is associated with the surrounding geographical environment information.

[0052] Among all the monitored node data, the set of nodes with excessive pollution are screened out according to the set pollution threshold. , taking these pollution nodes as the starting point for tracing back to the source of pollution.

[0053] To implement the above technical solution, the system integrates the topography, land use type, and geographic location data of the drainage network in the geographic information system with the water flow model of the network. Through the integration, the geographic information system can intuitively display the network layout in the form of map layers, and at the same time associate each node with the surrounding geographic environment information. This integration not only improves the visualization of the network information, but also provides rich geographic environment background information for subsequent pollution tracing. In addition, the system selects a set of nodes with excessive pollution through a preset pollution threshold. ,These pollution nodes serve as the starting point of tracing the pollution source in reverse direction.

[0054] In one embodiment of the present invention, the method for reversely tracing the pollution source is as follows:

[0055] B1, for each polluted node , trace back to its upstream adjacent nodes , compute nodes and The flow difference between and water quality parameter concentration change rate ,if and , it indicates that the key water quality parameters are from the upstream node To Node Transmission, while the node Mark as a suspected pollution source transmission node and add it to the suspected node set ,in Polluted Node of traffic, For the Neighboring nodes of traffic, Indicates polluted node Middle The concentration of key water quality parameters, Representation Node Middle concentrations of key water quality parameters;

[0056] B2, based on the suspected node With polluted nodes The length of the pipe section between , water flow speed , No. Attenuation coefficients of key water quality parameters , calculate key water quality parameters Slave Node Propagate to nodes The path probability is calculated as follows:

[0057]

[0058] in, Represents a slave node To Node The path probability, represents the weighted sum of all possible paths;

[0059] B3, sort the path probabilities from large to small, and sort the suspected node set according to the sorting results. Sort the nodes in ;

[0060] B4, the suspected node set The node with the highest probability is used as the new starting point, and steps B1-B3 are repeated to continuously trace the contaminated path upstream and update the set of suspected nodes. and path probability distribution until the source of the pipe network or Less than the preset value .

[0061] To implement the above technical solution, by scientifically and rationally combining the flow difference and the concentration change rate of water quality parameters to conduct preliminary screening of suspected pollution sources, it is possible to quickly and accurately screen out possible pollution transmission nodes from numerous pipe network nodes, avoiding the waste of time and resources caused by blind investigation; based on the dynamic analysis of real-time monitoring data, it ensures that the locking of suspected pollution sources can timely reflect the latest changes in pollution transmission; at the same time, the path probability calculation formula is introduced to quantify the possibility of pollution transmission paths, providing a scientific decision-making basis for investigation work; as the tracing process progresses, the dynamic update of path probabilities and the real-time adjustment of investigation priorities enable continuous optimization of resource allocation. At different stages, the focus is always placed on the most likely pollution source direction to avoid ineffective investment of resources on low-probability paths, achieving effective control of tracing costs and maximization of investigation effects.

[0062] In another embodiment of the present invention, a smart runoff pollution monitoring and response system is provided, comprising:

[0063] The monitoring network module is used to arrange multiple monitoring points in the runoff area. Each monitoring point is equipped with a water quality sensor, a hydrological sensor and a meteorological data sensor to collect the water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and transmit the collected data to the data processing center through a wireless sensor network;

[0064] A data processing and analysis module is used to receive data from the monitoring network module, pre-process, store, analyze and process the data to evaluate the pollution index of each monitoring point, identify key water quality parameters, and perform parameter-level evaluation on these key water quality parameters;

[0065] The alarm trigger module is used to set the pollution threshold. According to the pollution index and key water quality parameter evaluation results provided by the data processing and analysis module, once the runoff exceeds the preset pollution threshold, the alarm mechanism is triggered;

[0066] Pollution source tracking module: Once the alarm trigger module issues an alarm, the pollution source tracking module combines the pipe network water flow model with the geographic information system to reversely track the source of pollution and locate the pollution source area by analyzing the flow and water quality changes at each monitoring point;

[0067] The user interface module is used to provide users with a system operation interface, display monitoring data, analysis results, alarm information and pollution source tracking results, and allow users to set parameters and configure the system.

[0068] To implement the above technical solution, the monitoring network module includes a monitoring point layout submodule, which is used to determine the location of the monitoring point according to the topography, landform, water flow characteristics, land use type and potential pollution sources of the runoff area; a data acquisition and transmission submodule, which is used to install sensors at each monitoring point, automatically collect data at preset time intervals, and transmit the data to the data processing center through a wireless sensor network. The data processing and analysis module includes a data preprocessing submodule, which is used to perform preprocessing operations on the received raw data, such as data cleaning, format conversion, etc., and store the preprocessed data in a distributed database; a pollution index evaluation submodule, which is used to generate a comprehensive pollution index for each monitoring point, and compare it with the set index threshold to evaluate the degree of pollution; a key water quality parameter identification and evaluation submodule, which is used to select key water quality parameters that are highly correlated with the pollution index through correlation analysis and principal component analysis when the pollution index exceeds the index threshold, and evaluate the concentration change and concentration change rate of these parameters. The alarm trigger module includes an alarm rule setting submodule, which is used to set the thresholds of the first-order difference, second-order difference, concentration of key water quality parameters and comprehensive pollution index; an alarm level determination submodule, which is used to determine the alarm level according to the data provided by the data processing and analysis module, and trigger the corresponding alarm mechanism. The pollution source tracking module also includes a pipe network water flow model construction submodule, which is used to construct a pipe network water flow model based on the design drawings, pipe diameter, pipe length and slope information of the drainage pipe network using hydraulic principles; a geographic information system fusion submodule, which is used to integrate the topography, land use type and geographical location data of the drainage pipe network in the geographic information system with the pipe network water flow model, and display the pipe network layout in the form of a map layer; a pollution path backtracking submodule, which is used to reversely track the source of pollution from the node where the pollution exceeds the standard, and locate the pollution source area by analyzing the flow and water quality changes of each monitoring point. The pollution path backtracking submodule includes a suspected pollution source node screening submodule, which is used to screen nodes with increased flow and increased concentration of key water quality parameters among upstream adjacent nodes as suspected pollution source propagation nodes; a path probability calculation submodule, which is used to calculate the path probability of key water quality parameters propagating from suspected nodes to polluted nodes based on the length of the pipe section between the suspected node and the polluted node, the water flow velocity, and the attenuation coefficient of the key water quality parameters; a sorting and backtracking submodule, which is used to sort the suspected nodes, and give priority to checking the paths and nodes with the highest probability, and continuously trace the pollution path upstream until the source of the pipe network is traced back or the path probability is less than a preset value.

[0069] As described above, the intelligent runoff pollution monitoring and response method and system of the present invention have the following beneficial effects:

[0070] 1. Comprehensive coverage and accurate monitoring: By scientifically and rationally arranging monitoring points and equipping them with advanced sensors, water quality parameters, hydrological parameters and meteorological data can be collected in real time to ensure the comprehensiveness and accuracy of the data. At the same time, wireless sensor networks are used to achieve efficient data transmission to ensure the real-time and reliability of information.

[0071] 2. In-depth analysis and scientific evaluation: The data processing center has strong data analysis capabilities, which can clean, integrate and analyze the collected data, generate a comprehensive pollution index, and conduct parameter-level evaluation of key water quality parameters. This scientific evaluation system from macro to micro provides a comprehensive and high-precision basis for pollution control decisions.

[0072] 3. Intelligent warning and timely response: Set reasonable pollution thresholds and provide a sensitive warning mechanism. Once runoff pollution exceeds the preset threshold, relevant departments and personnel will be notified quickly and the emergency plan will be activated immediately. This helps to take intervention measures at the early stage of pollution and effectively curb the further spread of pollution.

[0073] 4. Accurate tracing and efficient governance: Combining the pipe network water flow model with geographic information system (GIS) technology, it is possible to reversely trace the transmission path of pollutants and accurately locate the pollution source area. This step helps to quickly identify the source of pollution and provide key information for subsequent pollution control and governance, thereby greatly improving governance efficiency and effectiveness.

[0074] 5. Dynamic adjustment and continuous optimization: In the process of tracing the source, by dynamically updating the path probability and investigation priority, we can continuously optimize resource allocation and ensure that the focus is always on the most likely pollution source at different stages. This avoids the ineffective investment of resources on low-probability paths, and achieves effective control of tracing costs and maximization of investigation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 Shown is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0076] The following specific embodiments illustrate the implementation of the present invention, and those familiar with the technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0077] See also Figure 1The present invention provides an intelligent runoff pollution monitoring and response method and system, and the method includes the following steps: S1, arranging multiple monitoring points in the runoff area, using sensors to collect water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and using a wireless sensor network to transmit the collected data to a data processing center; S2, the data processing center analyzes and processes the collected data, evaluates the pollution index of each monitoring point according to the processing results, analyzes the key water quality parameters highly related to the runoff pollution for the monitoring points whose pollution index exceeds the index threshold, and performs parameter-level evaluation on these key water quality parameters; S3, setting a parameter threshold according to the parameter-level evaluation, the parameter threshold and the index threshold are both regarded as pollution thresholds, once the runoff exceeds the preset pollution threshold, it is regarded as an abnormal situation and an alarm mechanism is triggered; S4, once the alarm mechanism is triggered, the water flow model of the pipe network and the geographic information system are combined to reversely trace the source of pollution, and the pollution source area is located by analyzing the flow and water quality changes of each monitoring point.

[0078] The working principle of the above technical solution is: scientifically arrange multiple monitoring points in the runoff area, and each monitoring point is equipped with advanced sensors for real-time collection of water quality parameters (such as dissolved oxygen, pH value, heavy metal content, etc.), hydrological parameters (such as flow, flow velocity, etc.) and meteorological data (such as rainfall, wind speed, etc.). These data are efficiently and reliably transmitted to the data processing center through the wireless sensor network to ensure the real-time and accuracy of the information; after receiving the data from each monitoring point, the data processing center cleans, integrates and analyzes the data. By calculating the pollution index, the pollution status of each monitoring point is quantitatively evaluated. For monitoring points where the pollution index exceeds the index threshold, the key water quality parameters related to runoff pollution are further analyzed, and parameter-level evaluation is performed to provide a basis for setting reasonable parameter thresholds; according to the evaluation results of the pollution index and key water quality parameters, the pollution threshold (including index threshold and parameter threshold) is set. Once the data from a certain monitoring point exceeds these thresholds, it is considered an abnormal situation and the alarm mechanism is immediately triggered, sending an alarm message to the management personnel to remind them to take countermeasures. After the alarm is triggered, the pipe network water flow model and geographic information system (GIS) technology are used, combined with the flow and water quality change data of each monitoring point, to reversely trace the transmission path of pollutants and accurately locate the pollution source area. This step helps to quickly identify the source of pollution and provide key information for subsequent pollution control and governance.

[0079] The beneficial effects of the above technical solution are: the in-depth analysis capability of the data processing center can not only give the comprehensive pollution index of each monitoring point, but also dig out the key water quality parameters and their dynamic change characteristics for the pollution exceeding the standard. This scientific evaluation system from macro to micro provides a comprehensive and high-precision basis for pollution control decision-making. Setting a reasonable pollution threshold and matching a sensitive alarm mechanism can realize the intelligent and timely discovery and early warning of abnormal pollution. Once the runoff pollution exceeds the threshold, the relevant departments and personnel can receive a notification quickly, immediately start the emergency plan, take intervention measures at the early stage of pollution, and effectively curb the further spread of pollution. By collecting water quality, hydrological and meteorological data in real time, the comprehensive impact of natural factors (such as rainfall and temperature) and human activities (such as sewage discharge) on runoff pollution is fully considered, making the grasp of pollution dynamics more accurate. For example, after a heavy rain, it is possible to monitor in time that a large amount of surface pollutants are merged into the runoff due to rainwater erosion, so as to warn in advance of possible pollution peaks. Through detailed analysis of the flow rate and water quality change data at each monitoring point during the pollution source tracing process, we can further understand the transmission path and diffusion pattern of pollution in the pipeline network; the organic combination of the pipeline network water flow model and the geographic information system provides strong technical support for pollution source tracing and can accurately locate the pollution source area.

[0080] The S1 includes S11, determining the location of the monitoring point according to the topography, landform, water flow characteristics, land use type and potential pollution sources of the runoff area; S12, installing water quality sensors, hydrological sensors and meteorological data sensors at each monitoring point, and automatically collecting data at preset time intervals to ensure that the data collection timestamps of each monitoring point are consistent; S13, the data processing center establishes an efficient data receiving interface for simultaneously processing large quantities of raw runoff data from multiple monitoring points, and after preprocessing the raw runoff data, stores the preprocessed runoff data in a distributed database.

[0081] Based on the topography, landforms, flow characteristics, land use types and potential pollution sources of the runoff area, the selection of these locations aims to fully cover the runoff area, while focusing on potential pollution sources and vulnerable areas to ensure the effectiveness and accuracy of monitoring. Water quality sensors, hydrological sensors and meteorological data sensors are installed at each determined monitoring point. The sensors automatically collect data at preset time intervals and transmit them to the data processing center through the wireless sensor network. In order to ensure the accuracy and consistency of the data, the data collection timestamps of each monitoring point are uniformly set for subsequent data analysis and processing. The data processing center establishes efficient data receiving interfaces for simultaneously processing large batches of raw runoff data from multiple monitoring points. These interfaces have high concurrent processing capabilities and data integrity verification mechanisms to ensure accurate data reception and transmission. The received raw runoff data undergoes preprocessing operations such as data cleaning, format conversion, outlier processing, etc. to improve the quality and availability of the data. The preprocessed runoff data is stored in a distributed database for subsequent data analysis and application.

[0082] The S2 includes: S21, the data processing center obtains the pre-processed runoff data of each detection point from the distributed database, and generates a comprehensive pollution index for each monitoring point; S22, compares the comprehensive pollution index with the set index threshold. Once the comprehensive pollution index is greater than the index threshold, the key water quality parameters that are highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the concentration change amount and concentration change rate of these key water quality parameters in the set time step are evaluated.

[0083] The data processing center obtains the pre-processed runoff data of each monitoring point from the distributed database and generates a comprehensive pollution index for each monitoring point. This comprehensive index is a comprehensive evaluation indicator that can reflect the overall situation of water pollution at the monitoring point. The data processing center compares the generated comprehensive pollution index with the set index threshold. Once the comprehensive pollution index is greater than the index threshold, it indicates that the water quality is polluted. The data processing center will further conduct correlation analysis and principal component analysis. Correlation analysis aims to identify key water quality parameters that are highly correlated with the comprehensive pollution index. These parameters have the greatest impact on water pollution and are the focus of subsequent evaluation and response. Principal component analysis is used to further simplify the data and extract the main components or factors that affect water pollution. After identifying the key water quality parameters, the data processing center evaluates the concentration change and concentration change rate of these key water quality parameters at the set time step to predict the development trend of key water quality parameters in the future.

[0084] The S21 includes: S211, determining relevant water quality parameters of runoff pollution, and setting weights of various relevant water quality parameters according to historical data, water quality standards and expert opinions; S212, performing standardization processing on various relevant water quality parameters, and the standardization processing method is as follows:

[0085]

[0086] in, It is The original values ​​of the relevant water quality parameters, It is The minimum value of the relevant water quality parameters, It is The maximum value of the relevant water quality parameters, It is Standardized values ​​of relevant water quality parameters;

[0087] S213, using a weighted average method to calculate the comprehensive pollution index of each monitoring point, the comprehensive pollution index is obtained as follows:

[0088]

[0089] in, is the comprehensive pollution index, It is The weights of the relevant water quality parameters, It is The standardized values ​​of relevant water quality parameters are is the number of relevant water quality parameters.

[0090] According to the characteristics of runoff pollution, water quality parameters closely related to pollution are identified. According to water quality standards, historical data and expert opinions, reasonable weights are set for these related water quality parameters. The weight setting takes into account the contribution of each related parameter to water quality pollution, toxicity and impact on the ecosystem; in order to eliminate the impact of different water quality parameter dimensions and value ranges, each parameter needs to be standardized. The standardization method uses linear transformation to convert the original value of each parameter into a standardized value between 0 and 1; after completing the standardization of water quality parameters, the weighted average method is used to calculate the comprehensive pollution index of each monitoring point, thereby comprehensively reflecting the water quality pollution status of each monitoring point and providing a basis for subsequent analysis and response.

[0091] The S22 includes: if the comprehensive pollution index is greater than the index threshold, then the key water quality parameters highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the first-order difference of the key water quality parameters is calculated to measure the concentration change of the key water quality parameters. The first-order difference calculation method is as follows:

[0092]

[0093] in, is the first-order difference of the key water quality parameters, It's time point The concentration of key water quality parameters, It's time point concentrations of key water quality parameters;

[0094] According to the first-order difference, the second-order difference of the key water quality parameter is calculated to measure the concentration change rate of the key water quality parameter. The second-order difference calculation method is as follows:

[0095]

[0096] in, is the second-order difference of key water quality parameters, It's time point concentrations of key water quality parameters.

[0097] Firstly, the correlation analysis and principal component analysis methods are used to screen out the key water quality parameters that are highly correlated with runoff pollution from many relevant water quality parameters. These key water quality parameters are usually indicators that have an important impact on the water pollution status and change significantly. For the screened key water quality parameters, their first-order differences are calculated to measure the concentration change within the set time step, and then on the basis of the first-order differences, the second-order differences are further calculated to measure the concentration change rate, which is helpful to predict the future development trend of water pollution.

[0098] The S22 also includes: using the first-order difference and the second-order difference as independent variables to construct a concentration prediction model for predicting the key water quality parameters at a time point The concentration prediction model is as follows:

[0099]

[0100] in, Is the key water quality parameter at the time point The predicted concentration of is the intercept term, which represents the baseline concentration level of this key water quality parameter; is the regression coefficient of the first-order difference, is the regression coefficient of the second-order difference, is the error term.

[0101] By introducing first-order differences and second-order differences as independent variables, the concentration prediction model can more accurately capture the changing trends and laws of key water quality parameters, thereby improving the accuracy of the prediction; the concentration prediction model has important application value in intelligent runoff pollution monitoring and response methods. It can improve prediction accuracy, support early warning, optimize resource allocation and promote technological innovation.

[0102] S3 includes: setting the upper threshold of the first-order difference ,if , it is identified as a first-level alarm; set the upper threshold of the second-order difference ,if , it is identified as a second-level alarm; set the concentration of key water quality parameters The upper threshold ,if , it is identified as a third-level alarm; set the comprehensive pollution index The upper threshold ,if , it is identified as a level 4 alarm.

[0103] Level 1 alarm means that the concentration of key water quality parameters has changed significantly in a short period of time; Level 2 alarm means that the concentration change rate of key water quality parameters is abnormal; Level 3 alarm means that at a future time point The concentration of key water quality parameters may exceed the safety threshold; the fourth level alarm indicates that the overall water pollution is serious; by setting different levels of alarm thresholds, the system can monitor water quality changes in real time and quickly identify potential risks to achieve timely warning.

[0104] The S4 includes: based on the detailed design drawings, pipe diameter, pipe length, and slope information of the drainage pipe network, using the hydraulics principle to construct a pipe network water flow model, dividing the pipe network into pipe sections for water flow transmission and nodes for water flow convergence or diversion in the pipe network water flow model, and using the nodes as monitoring points; integrating the topography, land use type, and geographical location data of the drainage pipe network in the geographic information system with the pipe network water flow model, and displaying the pipe network layout in the form of a map layer in the geographic information system, while associating each node with the surrounding geographic environment information; among all the monitored node data, screening out the node set with excessive pollution according to the set pollution threshold , taking these pollution nodes as the starting point for tracing back to the source of pollution.

[0105] The system integrates the topography, land use type, and geographic location data of the drainage network in the geographic information system with the water flow model of the network. Through integration, the geographic information system can intuitively display the network layout in the form of map layers, and at the same time associate each node with the surrounding geographic environment information. This integration not only improves the visualization of the network information, but also provides rich geographic environment background information for subsequent pollution tracing. In addition, the system selects a set of nodes with excessive pollution through a preset pollution threshold. ,These pollution nodes serve as the starting point of tracing the pollution source in reverse direction.

[0106] The method for reversely tracing the pollution source is as follows: B1, for each polluted node , trace back to its upstream adjacent nodes , compute nodes and The flow difference between and water quality parameter concentration change rate ,if and , it indicates that the key water quality parameters are from the upstream node To Node Transmission, while the node Mark as a suspected pollution source transmission node and add it to the suspected node set ,in Polluted Node of traffic, For the Neighboring nodes of traffic, Indicates polluted node Middle The concentration of key water quality parameters, Representation Node Middle concentrations of key water quality parameters;

[0107] B2, based on the suspected node With polluted nodes The length of the pipe section between , water flow speed , No. Attenuation coefficients of key water quality parameters , calculate key water quality parameters Slave Node Propagate to nodes The path probability is calculated as follows:

[0108]

[0109] in, Represents a slave node To Node The path probability, represents the weighted sum of all possible paths;

[0110] B3, sort the path probabilities from large to small, and sort the suspected node set according to the sorting results. Sort the nodes in ;

[0111] B4, the suspected node set The node with the highest probability is used as the new starting point, and steps B1-B3 are repeated to continuously trace the contaminated path upstream and update the set of suspected nodes. and path probability distribution until the source of the pipe network or Less than the preset value .

[0112] By scientifically and rationally combining the flow difference and the concentration change rate of water quality parameters to conduct preliminary screening of suspected pollution sources, it is possible to quickly and accurately screen out possible pollution transmission nodes from numerous pipe network nodes, avoiding the waste of time and resources caused by blind investigation; based on the dynamic analysis of real-time monitoring data, it ensures that the locking of suspected pollution sources can timely reflect the latest changes in pollution transmission; at the same time, the path probability calculation formula is introduced to quantify the possibility of pollution transmission paths, providing a scientific decision-making basis for investigation work; as the tracing process progresses, the dynamic update of path probabilities and the real-time adjustment of investigation priorities enable continuous optimization of resource allocation. At different stages, the focus is always placed on the most likely pollution source direction to avoid ineffective investment of resources on low-probability paths, achieving effective control of tracing costs and maximization of investigation effects.

[0113] In another embodiment of the present invention, a smart runoff pollution monitoring and response system is provided, comprising:

[0114] The monitoring network module is used to arrange multiple monitoring points in the runoff area. Each monitoring point is equipped with a water quality sensor, a hydrological sensor and a meteorological data sensor to collect the water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and transmit the collected data to the data processing center through the wireless sensor network; the data processing and analysis module is used to receive the data from the monitoring network module, pre-process, store, analyze and process the data to evaluate the pollution index of each monitoring point, identify key water quality parameters, and perform parameter-level evaluation on these key water quality parameters; the alarm trigger module is used to set the pollution threshold. According to the pollution index and key water quality parameter evaluation results provided by the data processing and analysis module, once the runoff exceeds the preset pollution threshold, the alarm mechanism is triggered; the pollution source tracking module, once the alarm trigger module issues an alarm, the pollution source tracking module combines the pipe network water flow model with the geographic information system to reversely track the pollution source, and locate the pollution source area by analyzing the flow and water quality changes of each monitoring point; the user interface module is used to provide the user with a system operation interface, display monitoring data, analysis results, alarm information and pollution source tracking results, and allow the user to set parameters and configure the system.

[0115] The monitoring network module includes a monitoring point layout submodule, which is used to determine the location of monitoring points based on the topography, landforms, water flow characteristics, land use types and potential pollution sources of the runoff area; a data acquisition and transmission submodule, which is used to install sensors at each monitoring point, automatically collect data at preset time intervals, and transmit the data to a data processing center through a wireless sensor network.

[0116] The data processing and analysis module includes a data preprocessing submodule, which is used to perform preprocessing operations on the received raw data, such as data cleaning, format conversion, etc., and store the preprocessed data in a distributed database; a pollution index evaluation submodule, which is used to generate a comprehensive pollution index for each monitoring point, and compare it with the set index threshold to evaluate the degree of pollution; a key water quality parameter identification and evaluation submodule, which is used to select key water quality parameters that are highly correlated with the pollution index through correlation analysis and principal component analysis when the pollution index exceeds the index threshold, and evaluate the concentration change and concentration change rate of these parameters.

[0117] The alarm trigger module includes an alarm rule setting submodule, which is used to set the thresholds of the first-order difference, second-order difference, key water quality parameter concentration and comprehensive pollution index; an alarm level determination submodule, which is used to determine the alarm level according to the data provided by the data processing and analysis module, and trigger the corresponding alarm mechanism.

[0118] The pollution source tracking module also includes a pipe network water flow model construction submodule, which is used to construct a pipe network water flow model based on the design drawings, pipe diameter, pipe length, and slope information of the drainage pipe network using hydraulic principles; a geographic information system fusion submodule, which is used to fuse the topography, land use type, and geographical location data of the drainage pipe network in the geographic information system with the pipe network water flow model, and display the pipe network layout in the form of a map layer; a pollution path backtracking submodule, which is used to reversely trace the source of pollution from the node where the pollution exceeds the standard, and locate the pollution source area by analyzing the flow and water quality changes at each monitoring point.

[0119] The pollution path backtracking submodule includes a suspected pollution source node screening submodule, which is used to screen nodes with increased flow and increased concentration of key water quality parameters among upstream adjacent nodes as suspected pollution source propagation nodes; a path probability calculation submodule, which is used to calculate the path probability of key water quality parameters propagating from suspected nodes to polluted nodes based on the length of the pipe section between the suspected node and the polluted node, the water flow velocity, and the attenuation coefficient of the key water quality parameters; a sorting and backtracking submodule, which is used to sort the suspected nodes, and give priority to checking the paths and nodes with the highest probability, and continuously trace the pollution path upstream until the source of the pipe network is traced back or the path probability is less than a preset value.

[0120] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. All equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. Intelligent runoff pollution monitoring and response method, characterized in that: The following steps are involved: S1, multiple monitoring points are arranged in the runoff area, and sensors are used to collect water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and the collected data are transmitted to the data processing center using a wireless sensor network; S2, the data processing center analyzes and processes the collected data, evaluates the pollution index of each monitoring point according to the processing results, analyzes the key water quality parameters that are highly correlated with runoff pollution for the monitoring points whose pollution index exceeds the index threshold, and performs parameter-level evaluation on these key water quality parameters; S3, setting a parameter threshold according to the parameter level assessment, wherein the parameter threshold and the index threshold are both regarded as pollution thresholds. Once the runoff exceeds the preset pollution threshold, it is regarded as an abnormal situation and triggers an alarm mechanism; S4, once the alarm mechanism is triggered, the water flow model of the pipe network and the geographic information system are combined to trace the source of pollution in reverse, and the pollution source area is located by analyzing the flow and water quality changes at each monitoring point; The S1 includes: S11, determine the location of monitoring points based on the topography, landforms, flow characteristics, land use type and potential pollution sources in the runoff area; S12, water quality sensors, hydrological sensors and meteorological data sensors are installed at each monitoring point, and data are automatically collected at preset time intervals to ensure that the data collection timestamps of each monitoring point are consistent; S13, the data processing center establishes an efficient data receiving interface for simultaneously processing a large amount of raw runoff data from multiple monitoring points, and after preprocessing the raw runoff data, stores the preprocessed runoff data in a distributed database.

2. The intelligent runoff pollution monitoring and response method according to claim 1 is characterized in that: The S2 includes: S21, the data processing center obtains the pre-processed runoff data of each monitoring point from the distributed database, and generates a comprehensive pollution index for each monitoring point; S22, compare the comprehensive pollution index with the set index threshold. Once the comprehensive pollution index is greater than the index threshold, the key water quality parameters that are highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the concentration change amount and concentration change rate of these key water quality parameters in the set time step are evaluated.

3. The intelligent runoff pollution monitoring and response method according to claim 2 is characterized in that: The S21 includes: S211, determine relevant water quality parameters of runoff pollution and set weights for each relevant water quality parameter based on historical data, water quality standards and expert opinions; S212, performing standardization processing on various relevant water quality parameters, wherein the standardization processing method is as follows: in, It is The original values ​​of the relevant water quality parameters, It is The minimum value of the relevant water quality parameters, It is The maximum value of the relevant water quality parameters, It is Standardized values ​​of relevant water quality parameters; S213, using a weighted average method to calculate the comprehensive pollution index of each monitoring point, the comprehensive pollution index is obtained as follows: in, is the comprehensive pollution index, It is The weights of the relevant water quality parameters, It is The standardized values ​​of relevant water quality parameters are is the number of relevant water quality parameters.

4. The intelligent runoff pollution monitoring and response method according to claim 3 is characterized in that: The S22 includes: If the comprehensive pollution index is greater than the index threshold, the key water quality parameters highly correlated with runoff pollution are selected through correlation analysis and principal component analysis, and the first-order difference of the key water quality parameters is calculated to measure the concentration change of the key water quality parameters. The first-order difference calculation method is as follows: in, is the first-order difference of the key water quality parameters, It's time point The concentration of key water quality parameters, It's time point concentrations of key water quality parameters; According to the first-order difference, the second-order difference of the key water quality parameter is calculated to measure the concentration change rate of the key water quality parameter. The second-order difference calculation method is as follows: in, is the second-order difference of key water quality parameters, It's time point concentrations of key water quality parameters.

5. The intelligent runoff pollution monitoring and response method according to claim 4 is characterized in that: The S22 further includes: Using first-order differences and second-order differences as independent variables, a concentration prediction model was constructed to predict key water quality parameters at time points. The concentration prediction model is as follows: in, Is the key water quality parameter at the time point The predicted concentration of is the intercept term, which represents the baseline concentration level of this key water quality parameter; is the regression coefficient of the first-order difference, is the regression coefficient of the second-order difference, is the error term.

6. The intelligent runoff pollution monitoring and response method according to claim 5 is characterized in that: The S3 includes: Set the upper threshold of the first-order difference ,if , it is identified as a level 1 alarm; Set the upper threshold of the second-order difference ,if , it is identified as a level 2 alarm; Setting the concentration of key water quality parameters The upper threshold ,if , it is identified as a level 3 alarm; Set comprehensive pollution index The upper threshold ,if , it is identified as a level 4 alarm.

7. The intelligent runoff pollution monitoring and response method according to claim 1 is characterized in that: The S4 includes: Based on the detailed design drawings, pipe diameter, pipe length, and slope information of the drainage pipe network, a pipe network water flow model is constructed using hydraulic principles. In the pipe network water flow model, the pipe network is divided into pipe sections for water flow transportation and nodes for water flow convergence or diversion, and the nodes are used as monitoring points; The topography, land use type, and geographic location data of the drainage network in the geographic information system are integrated with the water flow model of the network. In the geographic information system, the network layout is displayed in the form of a map layer, and each node is associated with the surrounding geographical environment information. Among all the monitored node data, the set of nodes with excessive pollution are screened out according to the set pollution threshold. , taking these pollution nodes as the starting point for tracing back to the source of pollution.

8. The intelligent runoff pollution monitoring and response method according to claim 7 is characterized in that: The method for reversely tracing the pollution source is as follows: B1, for each polluted node , trace back to its upstream adjacent nodes , compute nodes and The flow difference between and water quality parameter concentration change rate ,if and , it indicates that the key water quality parameters are from the upstream node To Node Transmission, while the node Mark as a suspected pollution source transmission node and add it to the suspected node set ,in For polluted nodes of traffic, For the Neighboring nodes of traffic, Indicates a polluted node Middle The concentration of key water quality parameters, Representation Node Middle concentrations of key water quality parameters; B2, based on the suspected node With polluted nodes The length of the pipe section between , water flow speed , No. Attenuation coefficients of key water quality parameters , calculate key water quality parameters Slave Node Propagate to nodes The path probability is calculated as follows: in, Represents a slave node To Node The path probability, represents the weighted sum of all possible paths; B3, sort the path probabilities from large to small, and sort the suspected node set according to the sorting results. Sort the nodes in ; B4, the suspected node set The node with the highest probability is used as the new starting point, and steps B1-B3 are repeated to continuously trace the contaminated path upstream and update the set of suspected nodes. and path probability distribution until the source of the pipe network or Less than the preset value .

9. A system for implementing the method according to any one of claims 1 to 8, characterized in that: include: The monitoring network module is used to arrange multiple monitoring points in the runoff area. Each monitoring point is equipped with a water quality sensor, a hydrological sensor and a meteorological data sensor to collect the water quality parameters, hydrological parameters and meteorological data of the runoff in real time, and transmit the collected data to the data processing center through a wireless sensor network; A data processing and analysis module is used to receive data from the monitoring network module, pre-process, store, analyze and process the data to evaluate the pollution index of each monitoring point, identify key water quality parameters, and perform parameter-level evaluation on these key water quality parameters; The alarm trigger module is used to set the pollution threshold. According to the pollution index and key water quality parameter evaluation results provided by the data processing and analysis module, once the runoff exceeds the preset pollution threshold, the alarm mechanism is triggered; Pollution source tracking module: Once the alarm trigger module issues an alarm, the pollution source tracking module combines the pipe network water flow model with the geographic information system to reversely track the source of pollution and locate the pollution source area by analyzing the flow and water quality changes at each monitoring point; The user interface module is used to provide users with a system operation interface, display monitoring data, analysis results, alarm information and pollution source tracking results, and allow users to set parameters and configure the system.

Images