River pollution source tracking system and method combined with water quality monitoring

Abstract

The invention provides a river pollution source tracking system and method combined with water quality monitoring, and relates to the technical field of water quality monitoring. The distribution gradient analysis module is used for analyzing the collection distribution gradient of each collection target; the uniformity evaluation module is used for carrying out distribution uniformity evaluation according to the collection distribution gradient and configuring a water quality monitoring strategy; the path analysis module is used for collecting water quality monitoring data and analyzing a pollutant propagation path in combination with the collected water flow data to obtain a propagation moving line of each pollutant; and the pollution source tracing module is used for tracing the pollution source through a reverse tracing algorithm and positioning the position and the type of the pollution source. The technical problems that water quality monitoring in the prior art depends on fixed monitoring points, the water quality is evaluated through regular sampling and water quality parameter analysis, the change condition of the water quality in a large range cannot be comprehensively reflected, and the exact position and type of a pollution source are difficult to accurately trace are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a system and method for tracing river pollution sources in combination with water quality monitoring. Background Art

[0002] As water pollution becomes increasingly serious, especially the discharge of industrial wastewater, agricultural runoff, domestic sewage, etc., water pollution has become an important problem in environmental governance. Traditional water quality monitoring relies on fixed monitoring points to assess water quality by regularly sampling and analyzing water quality parameters. These monitoring methods are usually limited to fixed locations and cannot fully reflect the changes in water quality over a large area. In addition, traditional pollution source tracking methods mainly rely on local monitoring data to infer the pollution source through changes in pollutant concentrations. However, these methods cannot fully consider factors such as water flow, spatial distribution and time fluctuations, and often have errors, making it difficult to accurately trace the exact location and type of pollution sources, especially in complex water environments. The spread of pollutants is often dynamic and nonlinear, which is difficult for traditional methods to cope with. Summary of the invention

[0003] The present application provides a river pollution source tracking system and method combined with water quality monitoring, aiming to solve the technical problem that water quality monitoring in the prior art relies on fixed monitoring points, evaluates water quality by regular sampling and analysis of water quality parameters, cannot fully reflect the changes in water quality over a large range, and makes it difficult to accurately trace the exact location and type of the pollution source.

[0004] The first aspect disclosed in the present application provides a river pollution source tracking system combined with water quality monitoring, the system comprising: a monitoring record extraction module, used to connect to a water quality monitoring platform to extract monitoring record data; a distribution gradient analysis module, used to perform spatial and temporal alignment of the monitoring record data, and analyze the collection distribution gradient of each collection target, the collection distribution gradient includes a time distribution gradient and a spatial distribution gradient; a uniformity evaluation module, used to evaluate the distribution uniformity according to the collection distribution gradient, and configure a water quality monitoring strategy; a path analysis module, used to collect water quality monitoring data based on the water quality monitoring strategy, analyze the pollutant propagation path according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data, and obtain the propagation lines of each pollutant; a pollution source tracing module, used to disassemble the diffusion relationship of the propagation line according to the propagation and diffusion characteristics of the pollutants, and use the disassembled multiple diffusion lines and concentration distribution relationships to trace the pollution source through a reverse tracing algorithm to locate the location and type of the pollution source.

[0005] The second aspect disclosed in the present application provides a method for tracing river pollution sources in combination with water quality monitoring, and the method is implemented through the above-mentioned river pollution source tracing system in combination with water quality monitoring, and the method includes: connecting to a water quality monitoring platform to extract monitoring record data; spatially and temporally aligning the monitoring record data, and parsing the collection distribution gradient of each collection target, wherein the collection distribution gradient includes a time distribution gradient and a spatial distribution gradient; performing a distribution uniformity evaluation based on the collection distribution gradient, and configuring a water quality monitoring strategy; collecting water quality monitoring data based on the water quality monitoring strategy, parsing the pollutant propagation path according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data, and obtaining the propagation lines of each pollutant; disassembling the diffusion relationship of the propagation lines according to the propagation and diffusion characteristics of the pollutants, and tracing the pollution source through a reverse tracking algorithm using the disassembled multiple diffusion lines and concentration distribution relationship to locate the location and type of the pollution source.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By connecting to the water quality monitoring platform, water quality monitoring record data can be extracted in real time or regularly to provide basic data for subsequent analysis and decision-making; the monitoring data is spatially and temporally aligned, and the analysis of the time distribution gradient can evaluate the change of pollutant concentration over time, while the spatial distribution gradient helps to evaluate the diffusion range of pollutants in space. This step provides detailed basic data for subsequent pollution source tracking and monitoring strategy formulation, ensuring the consistency and comparability of data in time and space; by evaluating the collection distribution gradient, the spatial and temporal distribution uniformity of the monitoring points can be evaluated. If the monitoring points are unevenly distributed, the monitoring strategy is adjusted to optimize the sampling location and sampling frequency, thereby ensuring the water quality monitoring strategy. Rationality and efficiency can avoid wasting monitoring resources and provide accurate data support for tracking pollution sources; water quality monitoring data is collected based on water quality monitoring strategies, and combined with water flow data to analyze the pollutant propagation path. By combining the fluctuation time and spatial distribution of water quality data, the diffusion law of pollutants is analyzed, thereby constructing the propagation line of pollutants, which helps to reveal the propagation path and spatiotemporal variation characteristics of pollutants in water bodies, and provides strong data support for the positioning of pollution sources; through the reverse tracking algorithm, the pollutant propagation path is combined with the concentration distribution relationship, and multiple diffusion paths are disassembled to realize the tracing of pollution sources, accurately locate the location and type of pollution sources, and provide detailed information on the source of pollutants.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of a river pollution source tracking system combined with water quality monitoring provided in an embodiment of the present application.

[0009] Figure 2 A schematic flow chart of a method for tracing river pollution sources in combination with water quality monitoring provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: monitoring record extraction module 10 , distribution gradient analysis module 20 , uniformity evaluation module 30 , path analysis module 40 , pollution source tracing module 50 . DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a river pollution source tracking system and method combined with water quality monitoring, thereby solving the technical problem that water quality monitoring in the prior art relies on fixed monitoring points, evaluates water quality by regularly sampling and analyzing water quality parameters, and cannot fully reflect the changes in water quality over a large range, making it difficult to accurately trace the exact location and type of the pollution source.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a river pollution source tracking system combined with water quality monitoring, the system comprising: The monitoring record extraction module 10 is used to connect to the water quality monitoring platform to extract monitoring record data.

[0014] The distribution gradient analysis module 20 is used to align the monitoring record data in space and time, and analyze the acquisition distribution gradient of each acquisition target, wherein the acquisition distribution gradient includes a time distribution gradient and a space distribution gradient.

[0015] The uniformity evaluation module 30 is used to evaluate the distribution uniformity according to the collected distribution gradient and configure the water quality monitoring strategy.

[0016] The path analysis module 40 is used to collect water quality monitoring data based on the water quality monitoring strategy, analyze the pollutant propagation path according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data, and obtain the propagation lines of each pollutant.

[0017] The pollution source tracing module 50 is used to decompose the diffusion relationship of the propagation line according to the propagation and diffusion characteristics of the pollutants, and to trace the pollution source through a reverse tracing algorithm using the decomposed multiple diffusion lines and concentration distribution relationship to locate the location and type of the pollution source.

[0018] Furthermore, the monitoring record extraction module includes: The monitoring equipment deployment unit is used to deploy river monitoring equipment, which includes fixed river monitors, ship-borne mobile sensors, and drones.

[0019] The monitoring data acquisition unit is used to connect to the data interface of the river monitoring equipment to obtain the monitoring data of the multi-source monitoring equipment.

[0020] The data integration unit is used to integrate the monitoring data of multi-source monitoring equipment according to the collection time and equipment collection location to construct the monitoring record data, which includes the data source type, water quality monitoring indicators, collection timestamp, and monitoring location location.

[0021] Furthermore, the distributed gradient parsing module includes: The timing alignment unit is used to locate the monitoring position of the monitoring and recording data, unify them into a spatial coordinate system, unify the collection time stamps of different monitoring devices into the same time base, and perform spatial and temporal alignment of the monitoring and recording data.

[0022] The spatial distribution gradient acquisition unit is used to perform spatial analysis on the water quality parameters of each monitoring point based on the spatial alignment relationship, calculate the parameter changes at different spatial positions, and obtain the spatial distribution gradient.

[0023] The time series analysis unit is used to perform time series analysis on the water quality parameters of each monitoring point based on the time series alignment relationship, calculate the change of the water quality parameters over time, and obtain the time distribution gradient.

[0024] Furthermore, the distribution gradient parsing module also includes: The graph network structure construction unit is used to treat each monitoring point as a node in the graph. The node attributes include the geographical location, monitoring time, and water quality parameters of the monitoring point. The edges are defined according to the spatial and temporal relationship between the monitoring points. The weight of the edge represents the change in water quality parameters between the monitoring points, and the graph network structure is constructed.

[0025] Furthermore, the uniformity evaluation module includes: The monitoring data analysis unit is used to perform monitoring data analysis on each node in the longitudinal time dimension based on the graph network structure to determine the peak value of the time gradient change.

[0026] The environmental data fitting unit is used to fit the weather environmental data according to the peak value of the time gradient change to determine the environmental impact relationship.

[0027] A water quality monitoring strategy acquisition unit is used to set adaptive conditions for the water quality monitoring strategy according to the environmental impact relationship, wherein the adaptive conditions correspond to the environmental impact relationship. When the adaptive conditions are met, the collection frequency of the monitoring point is configured according to the change in the peak value of the time gradient change to obtain the water quality monitoring strategy.

[0028] Furthermore, the uniformity evaluation module also includes: The spatial gradient change amount determination unit is used to perform monitoring data analysis of each node in the horizontal spatial dimension based on the graph network structure to determine the spatial gradient change amount.

[0029] The data source type identification unit is used to perform excess gradient screening on the spatial gradient change amount using a gradient screening threshold, identify the data source type based on excess gradient distribution nodes, and mark mobile monitoring points.

[0030] The path optimization search unit is used to perform path optimization search on the mobile monitoring point with the gradient difference as the target to obtain the water quality monitoring strategy.

[0031] Furthermore, the uniformity evaluation module also includes: The strategy merging unit is used to merge the water quality monitoring strategies determined by the horizontal spatial dimension and the vertical time dimension to obtain a complete water quality monitoring strategy.

[0032] The uniformity evaluation unit is used to perform uniformity evaluation on the complete water quality monitoring strategy based on the graph network structure, and determine the water quality monitoring strategy when the uniformity evaluation requirements are met.

[0033] Furthermore, the path resolution module includes: The projection unit is used to project the water quality monitoring data into a continuous spatial grid according to the distribution spatial coordinates of the data, and the resolution is dynamically adjusted according to the width of the river.

[0034] The propagation flow path analysis unit is used to perform propagation flow path analysis based on water flow data to obtain the propagation path line.

[0035] The water quality monitoring data fitting unit is used to fit the water quality monitoring data based on the propagation path line according to the correspondence between the fluctuation time and the spatial network, and construct the propagation lines of the pollutants.

[0036] Furthermore, the pollution source tracing module includes: The equation fitting unit is used to fit the convection-diffusion equation according to the propagation and diffusion characteristics of pollutants combined with water flow data.

[0037] The attenuation characteristic fitting unit is used to couple the pollutant concentration attenuation characteristics according to the convection-diffusion equation, including the concentration attenuation probability and attenuation direction, and to disassemble multiple diffusion routes and concentration distributions.

[0038] The pollution type determination unit is used to track and trace the source of pollution in reverse order from small to large according to the diffusion line and concentration distribution, locate the pollution source, and determine the pollution type by matching the pollutant monitoring concentration with the pollutant fingerprint library.

[0039] Through the subsequent detailed description of the river pollution source tracking method combined with water quality monitoring in this specification, technical personnel in this field can clearly understand the river pollution source tracking system combined with water quality monitoring in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0040] Embodiment 2 is based on the same inventive concept as the river pollution source tracking system combined with water quality monitoring in the previous embodiment. Figure 2 As shown, the embodiment of the present application provides a method for tracing river pollution sources in combination with water quality monitoring, the method comprising: Connect to the water quality monitoring platform to extract monitoring record data.

[0041] Different types of river monitoring equipment are deployed in the river, including fixed river monitors, ship-borne mobile sensors, and drones. These river monitoring equipment are connected through various communication interfaces to obtain monitoring data from multi-source monitoring equipment. The data generated by different types of monitoring equipment are usually heterogeneous. For example, the types of sensors and collection methods are different. Therefore, it is necessary to integrate these data based on the collection timestamp and device positioning. The data from different monitoring devices are integrated into unified monitoring record data through the data timestamp and the collection positioning information of the device.

[0042] The monitoring record data are spatially and temporally aligned, and the acquisition distribution gradient of each acquisition target is analyzed, wherein the acquisition distribution gradient includes a time distribution gradient and a space distribution gradient.

[0043] The data collected from different devices are processed uniformly and aligned in space and time dimensions to further analyze the collection distribution gradient. Specifically, all monitoring data are unified into the same spatial coordinate system, for example, using GPS coordinates or local coordinate systems. This allows data from different devices to be compared and analyzed in a unified spatial framework. For example, a fixed monitor is located at a specific point, while mobile monitoring devices and drones collect data at different times and locations. Spatial alignment ensures that these data points can be accurately mapped to the same river coordinates to calculate the differences in water quality parameters between different monitoring points. This difference constitutes the spatial distribution gradient; the data collection timestamps of different devices may be inconsistent. The purpose of time series alignment is to align the data of all devices to the same time reference, which means unifying the data collection time of different devices to ensure that the data is synchronized in the time dimension. Based on the data after time series alignment, the change in data from different monitoring points over time can be analyzed, and the water quality changes at each monitoring point at different time intervals can be calculated. This can reflect the dynamic changes in pollutant concentrations or other water quality indicators, thereby obtaining the time distribution gradient.

[0044] The distribution uniformity is evaluated based on the collected distribution gradient, and a water quality monitoring strategy is configured.

[0045] The uniformity of the distribution is evaluated based on the collection distribution gradient, and then a more adaptable water quality monitoring strategy is configured. For example, historical data analysis is performed. By analyzing the historical monitoring data, it is found that the water quality parameters of a certain section of the river have changed little. For example, parameters such as pH value and dissolved oxygen remain stable for a long time. The sampling frequency is adjusted. For the monitoring points of this section of the river, the sampling frequency is adjusted from once an hour to once every two hours to reduce the amount of data collection. The sampling points are dynamically adjusted to reduce the density of monitoring points in this section of the river, and some monitoring resources are transferred to river areas with larger changes, such as tributary confluences or near industrial discharge outlets. Path planning and adjustment. The path planning algorithm is used to plan the collection path of drones or ship-borne sensors to reduce visits to areas with smaller changes and improve collection efficiency.

[0046] Based on the water quality monitoring strategy, water quality monitoring data is collected, and pollutant propagation paths are analyzed according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data to obtain the propagation lines of each pollutant.

[0047] According to the configured water quality monitoring strategy, water quality monitoring data is collected according to the adjusted sampling frequency and path planning. The collected data includes water quality parameters such as pollutant concentration, dissolved oxygen, pH value, etc., as well as water flow data.

[0048] The fluctuation time of water quality monitoring data refers to the fluctuation of water quality data at the monitoring point according to time during the data collection process. It records the changes in water quality within these time periods, such as the increase or decrease in pollutant concentration, and analyzes the relationship between these changes and water flow; the distribution space is the spatial coordinates of each sampling point in the monitoring data. Through the spatial distribution data, we can understand the concentration and changes of pollutants in different geographical locations; the collection of water flow data provides information on the flow path and speed of water in the river, which is critical for the diffusion and propagation of pollutants. By combining water flow data, we can analyze how pollutants are propagated in the water flow and predict their future propagation path.

[0049] After parsing the time, spatial distribution and water flow data, the propagation routes of pollutants are calculated through modeling and data analysis techniques. These routes reflect the propagation paths of pollutants from pollution sources to other locations and provide key information for subsequent tracing of pollution sources.

[0050] The diffusion relationship of the propagation line is disassembled according to the propagation and diffusion characteristics of the pollutants, and the pollution source is traced through the reverse tracing algorithm using the disassembled multiple diffusion lines and concentration distribution relationship to locate the location and type of the pollution source.

[0051] When pollutants spread in water bodies, they are affected by many factors, including water flow speed, water mixing, water flow direction, river topography, temperature, etc. The way each pollutant diffuses in the water may also be different. For example, some pollutants may diffuse evenly with the water flow, while some pollutants may gather in certain specific locations in some way.

[0052] By analyzing the dynamic lines of pollutant propagation, they are broken down into multiple diffusion paths. Different pollutants and water flow conditions will lead to different diffusion patterns. Therefore, the dynamic lines of pollutant propagation need to be decomposed into multiple diffusion lines. These diffusion lines represent the propagation paths of pollutants from the source to other areas. As pollutants spread, water quality data will show a trend of concentration attenuation. Pollutant concentrations vary with the distance from the pollution source. These changes are controlled by the speed and direction of the water flow and other environmental factors. By analyzing the monitoring data, the relationship between pollutant concentration changes over time and space can be established.

[0053] The reverse tracking algorithm is used to locate the pollution source. The basic idea of ​​the reverse tracking algorithm is to reversely infer from the end of the pollutant diffusion path (i.e. the area with higher pollutant concentration) to the possible source of the pollutant to find the location and type of the pollution source. Among them, the type of pollution source can be determined by monitoring the type of pollutants and analyzing the environment around the pollution source. For example, if the concentration of pollutants is mainly concentrated near a certain industrial area, it may be an industrial emission source; if the pollutants are widely distributed, it may be pollution from agricultural sources.

[0054] Furthermore, the water quality monitoring platform is connected to extract monitoring record data, including: Deploy river monitoring equipment, which includes fixed river monitors, ship-borne mobile sensors, and drones; connect the data interface of the river monitoring equipment to obtain monitoring data from multi-source monitoring equipment; integrate the monitoring data from multi-source monitoring equipment according to collection time and equipment collection location to construct the monitoring record data, which includes data source type, water quality monitoring indicators, collection timestamp, and monitoring location location.

[0055] River monitoring equipment is deployed, including fixed river monitors, ship-borne mobile sensors, and drones. Fixed river monitors are installed at key locations in the river, such as the inlet, outlet, and confluence of tributaries, to monitor water quality parameters in real time, such as pH, dissolved oxygen, COD, ammonia nitrogen, etc.; ship-borne mobile sensors are used to conduct regular inspections of the river and collect water quality data; drones are equipped with multispectral cameras or other sensors for aerial monitoring of the river and collecting images or spectral data of the water surface and the coast.

[0056] Each monitoring device has its own data interface, which allows the device to exchange data with the water quality monitoring platform. The data interface usually adopts standard communication protocols, such as Wi-Fi, Bluetooth, LTE and other wireless communication technologies. The data sources of these devices are different. For example, fixed monitors obtain continuous data at a specific location in the river, while shipborne sensors or drones provide dynamic data, reflecting the water quality conditions at different locations and time points. The data interface is used to collect monitoring data from multiple devices.

[0057] The sampling time of each monitoring device may be different, so the data of different devices need to be aligned to a unified time base, which is achieved by standardizing the timestamp of the device. Time alignment can ensure that the data from different devices can be effectively compared and combined at the same time point. Each monitoring device collects data at a specific geographical location, especially ship-borne sensors and drones. The collected data contains different geographical coordinate information. In order to integrate data from different devices, the geographical locations of these devices need to be uniformly mapped to ensure that the data collected by different devices can be compared in the same spatial coordinate system.

[0058] By integrating the equipment time and location, a unified monitoring record data set is eventually generated, including data source type, water quality monitoring indicators, collection timestamp, and monitoring location positioning. The data source types include fixed monitors, shipborne sensors, drones, etc.; water quality monitoring indicators include specific monitoring data such as dissolved oxygen, pH value, and pollutant concentration; the collection timestamp is the collection time of each data point; and the monitoring location positioning is the geographical location of each data point.

[0059] Furthermore, the acquisition distribution gradient of each acquisition target is analyzed, and the acquisition distribution gradient includes a time distribution gradient and a space distribution gradient, including: According to the monitoring position positioning of the monitoring record data, it is unified into the spatial coordinate system, the collection time stamps of different monitoring devices are unified to the same time base, and the spatial and temporal alignment of the monitoring record data is performed; based on the spatial alignment relationship, the water quality parameters of each monitoring point are spatially analyzed, and the parameter changes at different spatial positions are calculated to obtain the spatial distribution gradient; based on the temporal alignment relationship, the water quality parameters of each monitoring point are time series analyzed, and the changes in water quality parameters over time are calculated to obtain the time distribution gradient.

[0060] Each monitoring device collects data at a different geographical location. First, all monitoring data need to be unified into a common spatial coordinate system, such as the GPS coordinate system or the local coordinate system, to ensure that the data from different devices can be compared and analyzed in the same spatial framework. For example, longitude and latitude are used to represent the coordinates of each monitoring point, and the monitoring data collected by all devices are spatially aligned according to these unified coordinates.

[0061] The monitoring data of different devices have different timestamps. For example, fixed monitors may collect data at the top of the hour, while shipborne sensors and drones may collect data at different time intervals. In order to ensure that the monitoring data of all devices can be effectively compared in the time dimension, the timestamps of different devices need to be unified to the same time reference. Interpolation, time standardization and other methods can be used to align the data of all devices on the same time axis.

[0062] Spatial alignment is to convert the collection positions of all monitoring devices into a unified spatial coordinate system to ensure that each data point corresponds to an accurate geographical location; temporal alignment is to unify the timestamps collected by different devices into a common time base to ensure the consistency of the time sequence of the data and provide an accurate time reference for subsequent analysis.

[0063] After completing the spatial alignment, the data of each monitoring point are spatially analyzed. The purpose of spatial analysis is to understand the changes in water quality parameters between different locations. For example, spatial cluster analysis is performed. Through cluster analysis, areas with relatively large water quality changes are identified, and hot spots in space are found. By analyzing the water quality parameters at different spatial locations, the spatial distribution gradient is calculated. The spatial distribution gradient reflects the extent of changes in water quality parameters in different geographical locations.

[0064] After completing the time series alignment, a time series analysis is performed on the water quality parameters of each monitoring point. This means analyzing the changing trends of water quality parameters over time. Common time series analysis methods, such as trend analysis, seasonal analysis, smoothing filtering, etc., can be used to identify the changing patterns of water quality parameters over time. According to the time series analysis results, the change of water quality parameters at each monitoring point over time is calculated to obtain the time distribution gradient, which reflects the changing rate and fluctuation intensity of water quality parameters.

[0065] Furthermore, analyzing the collection distribution gradient of each collection target also includes: Each monitoring point is regarded as a node in the graph. The node attributes include the geographical location, monitoring time, and water quality parameters of the monitoring point. The edges are defined according to the spatial and temporal relationship between the monitoring points. The weight of the edge represents the change in water quality parameters between the monitoring points, and the graph network structure is constructed.

[0066] Each monitoring point is regarded as a node in a graph. Each monitoring point has attributes associated with it, including the geographical location, monitoring time, and water quality parameters of the monitoring point. Each monitoring point has a specific geographical location, usually represented by longitude and latitude. This information is spatial attributes and is used for subsequent spatial relationship analysis. The sampling time of each monitoring point marks the position of the data point on the timeline. This information is time attributes and helps with time series analysis. The water quality parameters collected at each monitoring point, such as dissolved oxygen, pH value, pollutant concentration, etc., are the core attributes of the node and represent the water quality status of the point.

[0067] In the graph, edges are used to represent the relationship between monitoring points. Specifically, edges connect adjacent or related monitoring points. The relationship between monitoring points can be defined based on spatial distance and time synchronization. If two monitoring points are geographically close, for example, at adjacent locations in a river, they will be connected by an edge. If the sampling times of two monitoring points are similar, for example, they collect data within the same time window, an edge can also be defined between them, which means that there is temporal similarity between these monitoring points and time series analysis can be performed together.

[0068] The weight of the edge represents the change in water quality parameters between monitoring points. The water quality difference between two nodes is usually calculated. For example, if the water quality parameters of two monitoring points are very different, then the edge weight between them is higher, indicating that the water quality change between them is more significant.

[0069] When constructing the graph, each monitoring point is regarded as a node, and the edges between nodes represent the relationship between these nodes. The entire monitoring network can be regarded as a graph network structure, in which each edge has a weight, which represents the magnitude or correlation of water quality changes. Through such a graph structure, the algorithms and methods in graph theory can be used to analyze water quality monitoring data.

[0070] Furthermore, the distribution uniformity evaluation is performed according to the collected distribution gradient, and a water quality monitoring strategy is configured, including: Based on the graph network structure, the monitoring data of each node is analyzed in the longitudinal time dimension to determine the peak value of the time gradient change; according to the peak value of the time gradient change, the weather environment data is fitted to determine the environmental impact relationship; according to the environmental impact relationship, the adaptive conditions of the water quality monitoring strategy are set, wherein the adaptive conditions correspond to the environmental impact relationship, and when the adaptive conditions are met, the collection frequency of the monitoring point is configured according to the change amount of the peak value of the time gradient change to obtain the water quality monitoring strategy.

[0071] In the graph network structure, nodes represent different monitoring points, and the weights of edges represent changes in water quality. Each node is analyzed in the time dimension, and the water quality data of these nodes that change over time are analyzed. Specifically, the analysis in the time dimension is a time series analysis of the water quality parameters of each monitoring point, with the focus on calculating the rate of change of water quality parameters over time. The peak values ​​of these change rates are signs of changes in the time gradient.

[0072] The time gradient refers to the rate of change of water quality parameters over time. By calculating the rate of change of water quality within a time interval, such as changes in pollutant concentrations and pH values, the peak period of change in water quality parameters, that is, the peak value of the time gradient change, can be identified. For example, if the water quality change at a certain monitoring point fluctuates dramatically within a certain period of time, for example, the pollutant concentration rises or falls rapidly, the gradient change in that time period will be large, which may indicate the impact of some external factors on water quality, such as pollution source emissions, rainfall, etc.

[0073] The peak value of the time gradient change is combined with environmental factors (such as temperature, precipitation, wind speed, etc.) for fitting analysis. By fitting the relationship between environmental data and water quality changes, the impact of certain environmental factors on water quality changes can be found. For example, heavy precipitation may cause drastic changes in water quality parameters, such as increased pollutant concentrations, or increased temperatures may accelerate the dissolution or diffusion of certain pollutants. By fitting environmental data, the degree of influence of different environmental factors on water quality can be quantified and the environmental impact relationship can be determined.

[0074] Based on the environmental impact relationship, adaptive conditions are set for water quality monitoring. Adaptive conditions mean that under certain environmental conditions, such as precipitation exceeding a certain threshold, rising temperature, etc., it is necessary to adjust the sampling strategy and frequency of water quality monitoring. For example, when the temperature exceeds a certain value, it may cause water quality parameters (such as dissolved oxygen, pH value, etc.) to change rapidly, and the sampling frequency can be automatically adjusted according to this environmental change.

[0075] When certain adaptive conditions are met, the sampling frequency is adjusted according to the change in the peak value of the time gradient. Specifically, if the time gradient peak of the monitoring data is large, that is, the water quality changes are more drastic, the sampling frequency needs to be increased in order to more accurately capture the water quality changes. Conversely, if the time gradient changes are small, that is, the water quality changes are stable, the sampling frequency can be reduced to reduce the frequency of data collection and resource consumption.

[0076] Through adaptive conditions and sampling frequency adjustments, water quality monitoring strategies can be dynamically adjusted according to environmental changes and water quality, thereby optimizing monitoring efficiency and accuracy.

[0077] Furthermore, configuring a water quality monitoring strategy also includes: Based on the graph network structure, the monitoring data of each node in the horizontal spatial dimension is analyzed to determine the spatial gradient change; the spatial gradient change is screened for excess gradients using a gradient screening threshold, and the data source type is identified based on the excess gradient distribution nodes, and the mobile monitoring points are marked; with the gradient difference as the target, a path optimization search is performed on the mobile monitoring points to obtain the water quality monitoring strategy.

[0078] The horizontal spatial dimension refers to the position distribution in the water body space. The water quality parameters of each node are analyzed and the differences in water quality changes between adjacent nodes are calculated. The spatial gradient is described by calculating the changes in water quality parameters between adjacent monitoring points. If the water quality differences between adjacent monitoring points are small, it means that their spatial distribution is relatively uniform; conversely, monitoring points with large differences in water quality need to be rearranged.

[0079] Set a gradient screening threshold. When the water quality change gradient of adjacent nodes exceeds the threshold, it is considered that there is a large water quality change in the area, which belongs to the excess gradient area. This threshold can be adjusted based on historical water quality data and environmental characteristics. After determining the gradient screening threshold, each pair of adjacent monitoring points is screened. If the water quality change between them exceeds the set threshold, the two monitoring points are regarded as excess gradient points and need priority attention. Based on the screened excess gradient points, identify the areas that need to be monitored. If these excess gradient points are within the coverage of dynamic monitoring equipment (such as ships or drones), these areas can be marked as mobile monitoring points. The marking of mobile monitoring points means that these points need to be monitored using mobile devices to obtain water quality data more flexibly and in real time.

[0080] Gradient difference refers to the difference in changes in water quality parameters between adjacent monitoring points. When optimizing the path, the goal is to minimize the gradient difference so that the sampling path of the mobile monitoring equipment covers areas with large water quality changes as much as possible, while avoiding repeated coverage of areas with small water quality changes. For mobile monitoring equipment, such as ships or drones, the sampling route is determined by a path optimization algorithm. The goal of optimization is to ensure that the water quality monitoring equipment can effectively cover all excess gradient areas while improving monitoring efficiency. Path optimization needs to consider the equipment's movement range, time, energy and other limitations, and try to ensure the uniformity of the monitoring points to avoid blind spots or repeated monitoring. Based on the results of path optimization, a final water quality monitoring strategy is formulated, which can ensure that the monitoring equipment can effectively collect data within a uniform range and meet the comprehensive requirements of water quality monitoring.

[0081] Furthermore, configuring a water quality monitoring strategy also includes: The water quality monitoring strategies determined by the horizontal spatial dimension and the vertical time dimension are combined to obtain a complete water quality monitoring strategy; the uniformity of the complete water quality monitoring strategy is evaluated based on the graph network structure, and when the uniformity evaluation requirements are met, the water quality monitoring strategy is determined.

[0082] The horizontal spatial dimension strategy involves optimizing the spatial layout of monitoring points. These optimizations are mainly achieved through spatial gradient analysis and path optimization to ensure that the sampling frequency and sampling point density are increased in areas with large changes in water quality or pollution risks, while reducing the monitoring frequency in areas with stable water quality changes. The vertical time dimension strategy mainly involves adjusting the monitoring frequency within a time period. During periods of drastic changes in water quality, the sampling frequency needs to be increased, especially during peak periods under the influence of pollution sources or meteorological factors.

[0083] By combining the spatial and temporal monitoring strategies, a comprehensive water quality monitoring strategy is formed. This complete monitoring strategy takes into account the changes in water quality in different regions (spatial dimension) and the fluctuations in water quality in different time periods (temporal dimension). The combined strategy can ensure intensive monitoring of areas with high pollution risks in space and high-frequency monitoring of periods with drastic changes in time, thereby comprehensively and dynamically covering the entire monitoring area.

[0084] Uniformity evaluation is to ensure that the water quality monitoring strategy has a reasonable distribution in space and time, to avoid too intensive data collection in some areas or time periods, and insufficient data collection in other areas or time periods. Uniformity can be evaluated by information entropy, which is a method to measure system uncertainty or distribution uniformity. In the uniformity evaluation of water quality monitoring strategies, information entropy can be used to measure whether the data distribution in different areas and time periods is balanced. Among them, low entropy values ​​indicate that the water quality monitoring data is more evenly distributed, indicating that the sampling strategy is more uniform, covering the entire monitoring area, and has a reasonable sampling frequency for changes in each time period in time; high entropy values ​​indicate that the water quality monitoring data is unevenly distributed, and the monitoring frequency in some areas or time periods may be too high, while the monitoring frequency in other areas or time periods is too low, or there is excessive monitoring.

[0085] When the uniformity evaluation is passed, that is, the information entropy value is low, and the water quality monitoring strategy meets the uniformity requirements, it means that the existing water quality monitoring strategy can effectively cover the spatial and temporal changes in the monitoring area and meet the comprehensiveness and representativeness of the monitoring. In this case, the water quality monitoring strategy is determined. The strategy has undergone spatial, temporal and uniformity analysis, which can ensure the comprehensiveness and effectiveness of the data and has a higher monitoring efficiency.

[0086] If the uniformity evaluation does not meet the requirements, that is, the information entropy value is too high, it is necessary to adjust the water quality monitoring strategy, optimize the sampling point location and sampling frequency, and ensure the balance of monitoring coverage.

[0087] Furthermore, the pollutant propagation path is analyzed according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data to obtain the propagation lines of each pollutant, including: According to the distribution spatial coordinates of the water quality monitoring data, they are projected into a continuous spatial grid, and the resolution is dynamically adjusted according to the width of the river channel; the propagation flow path analysis is performed based on the water flow data to obtain the propagation path line; based on the propagation path line, the water quality monitoring data is fitted according to the correspondence between the fluctuation time and the spatial network, and the propagation lines of the pollutants are constructed.

[0088] Water quality monitoring data contains the spatial coordinates of each monitoring point and the corresponding water quality parameters. These monitoring data are distributed at different locations in the river according to their geographical locations. In order to facilitate analysis, the spatial coordinates of the water quality monitoring data are projected into a continuous spatial grid. This grid divides the river area into multiple small units, each of which has a fixed spatial position. The width, depth and morphology of the water area vary greatly, so the resolution projected into the grid needs to be dynamically adjusted according to the width of the river. Wider rivers can use lower resolution grids, while narrower rivers or areas with drastic water quality changes should use higher resolution grids to more accurately reflect water quality changes.

[0089] Water flow data includes information such as the speed, direction, and flow rate of water flow. These data can be obtained through hydrological monitoring equipment, flow meters, or mathematical models. The dynamic changes of water flow directly affect the propagation path of water quality pollutants. According to the direction and speed of water flow, the propagation flow path analysis is carried out to determine the propagation route of pollutants in the water flow. The faster the water flow, the faster the propagation speed of pollutants. By analyzing water flow data, the time and path of pollutants from the source to different areas can be inferred. Through simulation and calculation, the propagation path of pollutants in water flow can be obtained. These routes show how pollutants spread downstream or in other directions with the water flow within a specific time.

[0090] In water quality monitoring, the concentration of pollutants changes continuously with time and space. In order to accurately simulate the propagation process of pollutants, it is necessary to combine the propagation path line with the fluctuation time and spatial changes of pollutants. Fluctuation time usually refers to the change of pollutant concentration over time, while the spatial network refers to the spatial distribution of water quality monitoring data.

[0091] By fitting the relationship between water quality monitoring data and propagation path lines, the concentration changes of pollutants at different times and locations can be obtained. This fitting process will combine the water quality monitoring data with the propagation path of the water flow to construct a dynamic model of pollutant propagation. The propagation path lines and water quality data obtained after fitting are used to construct the propagation lines of pollutants. This propagation line shows the dynamic changes of pollutants in space and time.

[0092] Furthermore, the pollution sources are traced back by using the disassembled diffusion lines and concentration distribution relationships through the reverse tracing algorithm to locate the location and type of pollution sources, including: According to the propagation and diffusion characteristics of pollutants, combined with water flow data, the convection-diffusion equation is fitted; according to the convection-diffusion equation, the pollutant concentration attenuation characteristics are coupled, including the concentration attenuation probability and attenuation direction, and multiple diffusion routes and concentration distributions are disassembled; according to the diffusion routes and concentration distributions, the concentration distribution is traced back from small to large to locate the pollution source, and the pollutant monitoring concentration is matched with the pollutant fingerprint library to determine the pollution type.

[0093] The propagation and diffusion characteristics of pollutants can be composed of two parts: convection and diffusion. Convection is the movement of pollutants with water flow, which is manifested as the overall flow of pollutants; while diffusion is the microscopic expansion process of pollutants, which is manifested as the random diffusion of pollutants in water flow. Water flow data includes information such as water flow speed, direction, and flow rate. These data directly affect the convection process of pollutants. Combining these water flow data can more accurately describe the convection and diffusion behavior of pollutants in water.

[0094] By fitting the convection-diffusion equation, we can obtain a mathematical model that describes the spread of pollutants and calculate how the concentration of pollutants in water changes with time and space. This fitting process combines water flow data, pollutant diffusion characteristics and initial concentration distribution, providing theoretical support for subsequent pollution source tracking and concentration prediction.

[0095] During the spread of pollutants, the concentration will decay over time. This decay process includes multiple factors, such as natural degradation, sedimentation, chemical reactions, etc. The concentration decay probability indicates the rate at which the pollutant concentration decays, which is usually a function of time; while the decay direction refers to the direction of concentration change when the pollutant settles or reacts in the water flow.

[0096] According to the convection-diffusion equation, after coupling the concentration attenuation characteristics, the concentration distribution of pollutants will no longer be uniform, but will show an attenuation trend. By simulating these attenuation processes, the changes in pollutant concentrations in different times and spaces can be predicted, thereby decomposing multiple diffusion routes. These routes represent different propagation paths of pollutants in water bodies, involving different water flow velocities and diffusion characteristics. By analyzing the concentration distribution of each diffusion route, the detailed diffusion process of pollutants in water bodies can be obtained, revealing the concentration changes of pollutants from pollution sources to different areas.

[0097] During the process of pollutant diffusion, the concentration of pollutants will change with the distance from the pollution source. By analyzing the concentration distribution of pollutants, starting from the area with lower concentration and gradually tracing back to the area with higher concentration, the source of the pollutants can be traced back. The reverse tracking process is actually tracing back to the location of the pollution source step by step through the concentration gradient and diffusion path. This process relies on the accurate fitting of the convection-diffusion equation and the precise measurement of the concentration distribution.

[0098] Each pollutant has its own specific chemical or physical fingerprint, such as specific chemical composition, isotope composition or characteristic concentration distribution. By tracing back to the source and matching it with the pollutant fingerprint library, the location of the pollution source and the type of pollutant can be accurately determined, such as industrial wastewater, agricultural polluted water, domestic sewage, etc., providing an important basis for pollution source control and governance.

[0099] In summary, the river pollution source tracing method combined with water quality monitoring provided in the embodiment of the present application has the following technical effects: By connecting to the water quality monitoring platform, water quality monitoring record data can be extracted in real time or regularly to provide basic data for subsequent analysis and decision-making; the monitoring data is spatially and temporally aligned, and the analysis of the time distribution gradient can evaluate the change of pollutant concentration over time, while the spatial distribution gradient helps to evaluate the diffusion range of pollutants in space. This step provides detailed basic data for subsequent pollution source tracking and monitoring strategy formulation, ensuring the consistency and comparability of data in time and space; by evaluating the collection distribution gradient, the spatial and temporal distribution uniformity of the monitoring points can be evaluated. If the monitoring points are unevenly distributed, the monitoring strategy is adjusted to optimize the sampling location and sampling frequency, thereby ensuring the water quality monitoring strategy. Rationality and efficiency can avoid wasting monitoring resources and provide accurate data support for tracking pollution sources; water quality monitoring data is collected based on water quality monitoring strategies, and combined with water flow data to analyze the pollutant propagation path. By combining the fluctuation time and spatial distribution of water quality data, the diffusion law of pollutants is analyzed, thereby constructing the propagation line of pollutants, which helps to reveal the propagation path and spatiotemporal variation characteristics of pollutants in water bodies, and provides strong data support for the positioning of pollution sources; through the reverse tracking algorithm, the pollutant propagation path is combined with the concentration distribution relationship, and multiple diffusion paths are disassembled to realize the tracing of pollution sources, accurately locate the location and type of pollution sources, and provide detailed information on the source of pollutants.

[0100] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The river pollution source tracking system combined with water quality monitoring is characterized by: The system comprises: Monitoring record extraction module, used to connect to the water quality monitoring platform to extract monitoring record data; A distribution gradient analysis module is used to align the monitoring record data in space and time, and analyze the collection distribution gradient of each collection target, wherein the collection distribution gradient includes a time distribution gradient and a space distribution gradient; A uniformity evaluation module, used to evaluate the distribution uniformity according to the collected distribution gradient and configure a water quality monitoring strategy; A path analysis module is used to collect water quality monitoring data based on the water quality monitoring strategy, analyze the pollutant propagation path according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data, and obtain the propagation lines of each pollutant; The pollution source tracing module is used to decompose the diffusion relationship of the propagation line according to the propagation and diffusion characteristics of the pollutants, and use the decomposed multiple diffusion lines and concentration distribution relationships to trace the pollution source through a reverse tracing algorithm to locate the location and type of the pollution source.

2. The river pollution source tracking system combined with water quality monitoring according to claim 1 is characterized in that: Monitoring record extraction module, including: Monitoring equipment deployment unit, used to deploy river monitoring equipment, including fixed river monitors, ship-borne mobile sensors, and drones; A monitoring data acquisition unit, used to connect to the data interface of the river monitoring equipment to obtain monitoring data of multi-source monitoring equipment; The data integration unit is used to integrate the monitoring data of multi-source monitoring equipment according to the collection time and equipment collection location to construct the monitoring record data, which includes the data source type, water quality monitoring indicators, collection timestamp, and monitoring location location.

3. The river pollution source tracking system combined with water quality monitoring according to claim 2 is characterized in that: Distributed gradient parsing module, including: A timing alignment unit, used to unify the monitoring and recording data into a spatial coordinate system according to the monitoring position positioning, unify the acquisition time stamps of different monitoring devices into the same time reference, and perform spatial and temporal alignment of the monitoring and recording data; The spatial distribution gradient acquisition unit is used to perform spatial analysis on the water quality parameters of each monitoring point based on the spatial alignment relationship, calculate the parameter changes at different spatial positions, and obtain the spatial distribution gradient; The time series analysis unit is used to perform time series analysis on the water quality parameters of each monitoring point based on the time series alignment relationship, calculate the change of the water quality parameters over time, and obtain the time distribution gradient.

4. The river pollution source tracking system combined with water quality monitoring according to claim 3 is characterized in that: The distributed gradient parsing module also includes: The graph network structure construction unit is used to treat each monitoring point as a node in the graph. The node attributes include the geographical location, monitoring time, and water quality parameters of the monitoring point. The edges are defined according to the spatial and temporal relationship between the monitoring points. The weight of the edge represents the change in water quality parameters between the monitoring points, and the graph network structure is constructed.

5. The river pollution source tracking system combined with water quality monitoring according to claim 4 is characterized in that: Uniformity evaluation module, including: A monitoring data analysis unit, used to perform monitoring data analysis on each node in a longitudinal time dimension based on the graph network structure, and determine a peak value of a time gradient change; An environmental data fitting unit, used to fit weather environmental data according to the time gradient change peak value, and determine the environmental impact relationship; A water quality monitoring strategy acquisition unit is used to set adaptive conditions for the water quality monitoring strategy according to the environmental impact relationship, wherein the adaptive conditions correspond to the environmental impact relationship. When the adaptive conditions are met, the collection frequency of the monitoring point is configured according to the change in the peak value of the time gradient change to obtain the water quality monitoring strategy.

6. The river pollution source tracking system combined with water quality monitoring according to claim 5 is characterized in that: The uniformity evaluation module also includes: A spatial gradient change amount determination unit, used to perform monitoring data analysis of each node in a horizontal spatial dimension based on the graph network structure to determine the spatial gradient change amount; A data source type identification unit, configured to perform excess gradient screening on the spatial gradient variation using a gradient screening threshold, perform data source type identification based on excess gradient distribution nodes, and mark mobile monitoring points; The path optimization search unit is used to perform path optimization search on the mobile monitoring point with the gradient difference as the target to obtain the water quality monitoring strategy.

7. The river pollution source tracking system combined with water quality monitoring according to claim 6 is characterized in that: The uniformity evaluation module also includes: A strategy merging unit is used to merge the water quality monitoring strategies determined by the horizontal spatial dimension and the vertical time dimension to obtain a complete water quality monitoring strategy; The uniformity evaluation unit is used to perform uniformity evaluation on the complete water quality monitoring strategy based on the graph network structure, and determine the water quality monitoring strategy when the uniformity evaluation requirements are met.

8. The river pollution source tracking system combined with water quality monitoring according to claim 1 is characterized in that: Path parsing module, including: The projection unit is used to project the distribution spatial coordinates of water quality monitoring data into a continuous spatial grid, and the resolution is dynamically adjusted according to the width of the river channel; A propagation flow path analysis unit, used to perform propagation flow path analysis based on water flow data to obtain a propagation path line; The water quality monitoring data fitting unit is used to fit the water quality monitoring data based on the propagation path line according to the correspondence between the fluctuation time and the spatial network, and construct the propagation lines of the pollutants.

9. The river pollution source tracking system combined with water quality monitoring according to claim 8 is characterized in that: Pollution source tracing module, including: The equation fitting unit is used to fit the convection-diffusion equation according to the propagation and diffusion characteristics of pollutants combined with water flow data; An attenuation characteristic fitting unit, used to couple the pollutant concentration attenuation characteristics according to the convection-diffusion equation, including the concentration attenuation probability and attenuation direction, and to disassemble multiple diffusion routes and concentration distributions; The pollution type determination unit is used to track and trace the source of pollution in reverse order from small to large according to the diffusion line and concentration distribution, locate the pollution source, and determine the pollution type by matching the pollutant monitoring concentration with the pollutant fingerprint library.

10. A method for tracing river pollution sources combined with water quality monitoring, characterized in that: Based on the implementation of the river pollution source tracking system combined with water quality monitoring according to any one of claims 1 to 9, the method comprises: Connect to the water quality monitoring platform to extract monitoring record data; Performing spatial and temporal alignment on the monitoring and recording data, and analyzing the acquisition distribution gradient of each acquisition target, wherein the acquisition distribution gradient includes a temporal distribution gradient and a spatial distribution gradient; Evaluate the distribution uniformity according to the collected distribution gradient and configure a water quality monitoring strategy; Based on the water quality monitoring strategy, water quality monitoring data is collected, and pollutant propagation paths are analyzed according to the fluctuation time and distribution space of the water quality monitoring data combined with the collected water flow data to obtain the propagation lines of each pollutant; The diffusion relationship of the propagation line is disassembled according to the propagation and diffusion characteristics of the pollutants, and the pollution source is traced through the reverse tracing algorithm using the disassembled multiple diffusion lines and concentration distribution relationship to locate the location and type of the pollution source.

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