Sampling system and method for water environment treatment

Abstract

The invention provides a sampling system and method for water environment treatment, and the method comprises the steps: determining the water quality change characteristics of a polluted water body through the water quality data of the polluted water body, and determining a pollution hot spot region according to the water quality change characteristics and the pollution source distribution of the polluted water body; extracting layering characteristics of water flow velocity in the polluted water body from the flow velocity data of the polluted water body, and determining a migration trend of pollutants in the polluted water body according to the layering characteristics of the water flow velocity and the polluted hot spot area; determining a dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area; determining the correlation offset of each initial sampling point in the polluted water body according to the migration trend of the pollutants and the dynamic sampling area, and determining a plurality of dynamic sampling points when the polluted water body is sampled according to all the correlation offsets. By adopting the scheme of the invention, the sampling points in the polluted water body can be dynamically adjusted based on the real-time hydrological data.

Description

Technical Field

[0001] This application relates to the technical field of sampling, and more specifically, to a sampling system and method for water environment treatment. Background Art

[0002] Sampling refers to the process of selecting a part of individuals from a larger population as the research object according to a certain method. The purpose of sampling is to infer the characteristics and laws of the population through the analysis and research of part of the individuals. Sampling is a commonly used method in fields such as scientific research, statistical analysis, environmental monitoring, and quality control.

[0003] In water environment treatment, the definition of the sampling method refers to the process of extracting water samples from the water body according to the specified method and a certain proportion. By detecting and analyzing the extracted water samples, the degree of water pollution, the types of pollutants, and their distribution laws can be determined. Since most of the existing water sample collection technologies rely on artificial experience or simple rules to select sampling points, they cannot make full use of real-time water quality data (such as water quality, flow rate, etc.) to dynamically adjust the sampling strategy. This static sampling method may lead to unreasonable distribution of sampling points, unable to accurately capture the aggregation areas of pollutants and the flow of pollutants in the polluted water body, and thus affect the subsequent water environment treatment effect. Therefore, how to dynamically adjust the sampling points in the polluted water body based on real-time hydrological data has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a sampling system and method for water environment treatment, which can dynamically adjust the sampling points in the polluted water body based on real-time hydrological data.

[0005] In a first aspect, this application provides a sampling method for water environment treatment, including the following steps: Collect water quality data of the polluted water body by a water quality sensor; Extract the water quality change characteristics of the polluted water body from the water quality data, and determine the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body; Collect flow rate data of the polluted water body by a flow rate sensor, extract the stratified characteristics of the water flow rate in the polluted water body from the flow rate data, perform gradient analysis on the pollution load distribution of the polluted water body according to the stratified characteristics of the water flow rate and the pollution hot spot area, and then obtain the migration trend of pollutants in the polluted water body; Obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area; Correlate and fuse the migration trend of the pollutants and the dynamic sampling area, and then obtain the associated offset of each initial sampling point in the polluted water body. Determine multiple dynamic sampling points for sampling the polluted water body based on all the associated offsets.

[0006] In some embodiments, extracting the water quality change characteristics of the polluted water body from the water quality data specifically includes: Determine the water quality change area in the polluted water body through the water quality data; Perform a fluctuation analysis on the water quality in the polluted water body according to the water quality change area to obtain the water quality change characteristics of the polluted water body.

[0007] In some embodiments, determining the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body specifically includes: Determine the pollution source distribution of the polluted water body; Determine multiple pollution aggregation points in the polluted water body through the pollution source distribution; Determine the pollution hot spot area of the polluted water body according to the water quality change characteristics and all the pollution aggregation points.

[0008] In some embodiments, extracting the layered characteristics of the water flow velocity in the polluted water body from the flow velocity data specifically includes: Determine the flow velocity characteristics of the polluted water body according to the flow velocity data; Determine the layered characteristics of the water flow velocity in the polluted water body through the flow velocity characteristics.

[0009] In some embodiments, performing a gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, and then obtaining the migration trend of the pollutants in the polluted water body specifically includes: Determine the diffusion trend of the pollutants in the polluted water body according to the layered characteristics of the water flow velocity; Determine the gradient distribution of the pollution load in the polluted water body according to the pollution hot spot area; Determine the migration trend of the pollutants in the polluted water body through the diffusion trend of the pollutants and the gradient distribution of the pollution load.

[0010] In some embodiments, determining the dynamic sampling area of the polluted water body according to the concentration gradient of the pollutants in the pollution hot spot area and the initial sampling point area specifically includes: Determine the position adjustment information of the sampling points in the polluted water body according to the pollution hot spot area; Determine the concentration gradient of the pollutants in the initial sampling point area; Determine a dynamic sampling area for sampling the polluted water body according to the position adjustment information and the concentration gradient.

[0011] In some embodiments, associating and integrating the migration trend of the pollutant and the dynamic sampling area, and then obtaining the associated offset of each initial sampling point in the polluted water body specifically includes: Obtain each initial sampling point in the polluted water body; Associate the migration trend of the pollutant with the dynamic sampling area to obtain a plurality of associated points; Determine the dynamic adjustment amount of each initial sampling point when sampling the polluted water body according to the dynamic sampling area; Perform a fusion process on all the dynamic adjustment amounts and all the associated points to obtain a plurality of dynamic fusion amounts; Determine the associated offset of each initial sampling point in the polluted water body according to all the dynamic fusion amounts.

[0012] In a second aspect, the present application provides a sampling system for water environment treatment, including: A collection module for collecting water quality data of the polluted water body by a water quality sensor; A processing module for extracting the water quality change characteristics of the polluted water body from the water quality data, and determining the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body; The processing module is further configured to collect the flow velocity data of the polluted water body by a flow velocity sensor, extract the layered characteristics of the water flow velocity in the polluted water body from the flow velocity data, and perform gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, so as to obtain the migration trend of the pollutant in the polluted water body; The processing module is further configured to obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of the pollutant in the pollution hot spot area and the initial sampling point area; An execution module for associating and integrating the migration trend of the pollutant and the dynamic sampling area, and then obtaining the associated offset of each initial sampling point in the polluted water body, and determining a plurality of dynamic sampling points when sampling the polluted water body according to all the associated offsets.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned sampling method for water environment treatment.

[0014] Fourthly, the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above-described sampling method for water environment treatment.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In the sampling system and method for water environment treatment provided by the present application, first, a water quality sensor is used to collect water quality data of the polluted water body; water quality change characteristics of the polluted water body are extracted from the water quality data, and a pollution hot spot area of the polluted water body is determined according to the water quality change characteristics and the pollution source distribution of the polluted water body; a flow velocity sensor is used to collect flow velocity data of the polluted water body, and a stratification characteristic of the water flow velocity in the polluted water body is extracted from the flow velocity data, and a gradient analysis of the pollution load distribution of the polluted water body is performed according to the stratification characteristic of the water flow velocity and the pollution hot spot area, so as to obtain the migration trend of pollutants in the polluted water body; an initial sampling point area when sampling the polluted water body is obtained, and a dynamic sampling area of the polluted water body is determined according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area; the migration trend of the pollutants and the dynamic sampling area are associated and fused, so as to obtain the associated offset amount of each initial sampling point in the polluted water body, and a plurality of dynamic sampling points when sampling the polluted water body are determined based on all the associated offset amounts.

[0016] It can be seen that in the sampling process for water environment treatment of the present application, firstly, by collecting multi-source data through a water quality sensor and a flow velocity sensor, and combining the pollution source distribution and water quality change characteristics, the pollution hot spot area and the pollutant migration trend can be accurately identified, avoiding the subjectivity and blindness of traditional sampling methods, and improving the scientificity and representativeness of sampling. Secondly, according to the pollution hot spot area and the pollutant concentration gradient, the sampling area is dynamically adjusted to ensure that the sampling points cover the high-concentration areas and migration paths of pollutants, improving the comprehensiveness and accuracy of sampling data and avoiding missing important pollution areas. Thus, through the gradient analysis of the flow velocity stratification characteristics and the pollution load distribution, the migration trend of pollutants is accurately predicted, providing a scientific basis for the traceability, diffusion prediction and treatment plan formulation of pollutants. Finally, the pollutant migration trend is associated and fused with the dynamic sampling area, the associated offset amount of the initial sampling point is calculated, and the layout of the dynamic sampling points is optimized, realizing the intelligent adjustment of the sampling points and improving the flexibility and adaptability of sampling. By adopting the above solution, the sampling points in the polluted water body can be dynamically adjusted based on real-time hydrological data. Description of the Drawings

[0017] Figure 1It is an exemplary flowchart of a sampling method for water environment treatment shown in some embodiments of the present application; Figure 2 It is an exemplary flowchart of determining a pollution hot spot area shown in some embodiments of the present application; Figure 3 It is a distribution diagram of the water flow velocity in the water body shown in some embodiments of the present application; Figure 4 It is a schematic structural diagram of a sampling system for water environment treatment shown in some embodiments of the present application; Figure 5 It is a schematic structural diagram of a computer device for implementing the sampling method for water environment treatment shown in some embodiments of the present application. Detailed implementation manners In order to better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in conjunction with the specification drawings and specific implementation manners.

[0018] Refer to Figure 1 , this figure is an exemplary flowchart of a sampling method for water environment treatment shown in some embodiments of the present application. The sampling method 100 for water environment treatment mainly includes the following steps: In step 101, a water quality sensor collects water quality data of the polluted water body.

[0019] Specifically, when implementing, a plurality of sensing nodes are arranged in the polluted water body, and a water quality sensor collects the physical properties (such as water temperature, color, transparency, etc.) and chemical properties (acidity and alkalinity (pH value), dissolved oxygen (DO), chemical oxygen demand (COD), etc.) of the water quality at each sensing node, and takes the set of all physical properties (such as water temperature, color, transparency, etc.) and chemical properties (acidity and alkalinity (pH value), dissolved oxygen (DO), chemical oxygen demand (COD), etc.) of the collected water quality as the water quality data of the polluted water body; in other embodiments, other methods can also be used for collection, which is not limited here.

[0020] It should be noted that when arranging a plurality of sensing nodes in the polluted water body in the present application, the positions of the respective sensing nodes are determined based on node deployment of an optimization algorithm in combination with historical water quality data of the polluted water body, so as to increase the persuasiveness of the collected water quality.

[0021] In step 102, the water quality change characteristics of the polluted water body are extracted from the water quality data, and the pollution hot spot area of the polluted water body is determined according to the water quality change characteristics and the pollution source distribution of the polluted water body.

[0022] In some embodiments, the extraction of the water quality change characteristics of the polluted water body from the water quality data can be implemented by the following steps: Determine the water quality change area in the polluted water body based on the water quality data; Conduct a fluctuation analysis on the water quality in the polluted water body according to the water quality change area to obtain the water quality change characteristics of the polluted water body.

[0023] Specifically, when implemented, determining the water quality change area in the polluted water body based on the water quality data can be achieved by the following method, that is: use the statistical methods in the prior art to detect and process all abnormal data points in the water quality data, where the abnormal data points represent the points where the water quality data changes significantly, and regard the areas corresponding to all abnormal data points as the water quality change areas in the polluted water body, where the water quality change area represents the area where the water quality in the polluted water body changes abnormally; conducting a fluctuation analysis on the water quality in the polluted water body according to the water quality change area to obtain the water quality change characteristics of the polluted water body can be achieved by the following method, that is: use the statistical methods in the prior art to detect and process all abnormal data points in the water quality change area, and extract the trend of water quality change from all the processed abnormal data points through time series feature extraction methods (such as sliding window, exponential smoothing), where this trend of water quality change represents the change trend of the pollution degree of the water quality in the polluted water body, and regard this trend of water quality change as the water quality change characteristics of the polluted water body; in other embodiments, other methods can also be used for determination, which is not limited here.

[0024] It should be noted that the water quality change characteristics in this application represent the characteristics of the change trend of the water quality pollution situation of the target water body, which can be used to predict the pollution situation of the target water body and facilitate the formulation of corresponding countermeasures.

[0025] In some embodiments, refer to Figure 2 As shown, this figure is an exemplary flowchart for determining the pollution hot spot area in some embodiments of this application. In this embodiment, determining the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body can be achieved by the following steps: First, in step 1021, determine the pollution source distribution of the polluted water body; Secondly, in step 1022, determine multiple pollution aggregation points of the polluted water body through the pollution source distribution; Finally, in step 1023, determine the pollution hot spot area of the polluted water body according to the water quality change characteristics and all the pollution aggregation points.

[0026] In specific implementation, the distribution of pollution sources of the polluted water body can be determined in the following manner: obtaining each pollution source around the polluted water body through UAV monitoring, and taking the distribution of each pollution source around the polluted water body as the distribution of pollution sources of the polluted water body, where the pollution source represents the source emitting pollutants; determining multiple pollution aggregation points of the polluted water body based on the distribution of pollution sources can be achieved in the following manner: selecting a group of adjacent pollution sources in the distribution of pollution sources as the selected adjacent pollution sources, measuring the distance between the selected adjacent pollution sources through an industrial-level ranging sensor in the prior art, and taking the point corresponding to the middle distance of this distance as the pollution aggregation point of the polluted water body, and continuing to determine multiple pollution aggregation points of the polluted water body, where the pollution aggregation point represents the point where pollutants in the polluted water body aggregate. Since the pollution source diffuses around after discharging pollutants and meets the pollutants discharged by adjacent pollution sources, the approximate meeting position is at the middle position between adjacent pollution sources; determining the pollution hot spot area of the polluted water body based on the water quality change characteristics and all pollution aggregation points can be achieved in the following manner: based on the machine learning method in the prior art, combining the water quality change characteristics and all pollution aggregation points, inferring the propagation path of pollutants and the changed distance of the aggregation points, and then using a visualization tool (such as ArcGIS, QGIS) to mark the area where pollutants aggregate in the polluted water body in combination with the propagation path and the changed distance of the aggregation points, and taking the marked area where pollutants aggregate as the pollution hot spot area of the polluted water body. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0027] It should be noted that the pollution hot spot area in this application represents the area with a serious pollutant concentration in the polluted water body, which can be used to judge the pollution state in the polluted water body and facilitate relevant departments to make corresponding countermeasures.

[0028] In step 103, the flow velocity sensor collects the flow velocity data of the polluted water body, extracts the stratified characteristics of the water flow velocity in the polluted water body from the flow velocity data, and performs gradient analysis on the pollution load distribution of the polluted water body based on the stratified characteristics of the water flow velocity and the pollution hot spot area, so as to obtain the migration trend of pollutants in the polluted water body.

[0029] In specific implementation, the flow velocity sensor collecting the flow velocity data of the polluted water body can be achieved in the following manner: collecting the flow velocity distribution at each sensor node position (such as the flow velocities of the upper, middle, and lower layers) through the flow velocity sensor, and taking all the flow velocity distributions (such as the flow velocities of the upper, middle, and lower layers) as the flow velocity data of the polluted water body. Among them, in natural water bodies, the flow velocities at the water surface, in the water, and at the bottom are different; in other embodiments, other methods can also be used for collection, which is not limited here.

[0030] In some embodiments, with reference to Figure 3 As shown, this figure is a distribution diagram of the water flow velocity in the water body in some embodiments of the present application. As Figure 3 described, the closer to the middle part of the water body, the greater the water flow velocity, and the closer to the water surface and the bottom, the smaller the water flow velocity.

[0031] In some embodiments, extracting the layered characteristics of the water flow velocity in the polluted water body from the velocity data can be achieved by the following steps: Determine the velocity characteristics of the polluted water body according to the velocity data; Determine the layered characteristics of the water flow velocity in the polluted water body through the velocity characteristics.

[0032] Specifically, when implementing, determining the velocity characteristics of the polluted water body according to the velocity data can be achieved in the following manner: Select a velocity distribution in the velocity data as the selected velocity distribution, subtract the velocity in the upper layer from the velocity in the middle layer in the selected velocity distribution, and use the obtained subtracted value as the upper-middle difference value. Subtract the velocity in the lower layer from the velocity in the middle layer in the selected velocity distribution, and use the obtained subtracted value as the middle-lower difference value. Use the upper-middle difference value and the middle-lower difference value as the difference characteristics of the selected velocity distribution, and continue to determine the difference characteristics of the remaining velocity distributions. Among them, the difference characteristics represent the characteristics of the velocity distribution difference of the sensing nodes, and use all the difference characteristics as the velocity characteristics of the polluted water body. Among them, the velocity characteristics represent the difference characteristics of the water flow velocity in the polluted water body; Determining the layered characteristics of the water flow velocity in the polluted water body through the velocity characteristics can be achieved in the following manner: Extract all the upper-middle difference values in the velocity characteristics, use the average value of all the extracted upper-middle difference values as the first-layer difference value, extract all the middle-lower difference values in the velocity characteristics, use the average value of all the extracted middle-lower difference values as the second-layer difference value, and use the set of the first-layer difference value and the second-layer difference value as the layered characteristics of the water flow velocity in the polluted water body; In other embodiments, other methods can also be used for determination, which are not limited here.

[0033] It should be noted that the layered characteristics of the water flow velocity in the present application represent the difference characteristics of the water flow velocity distribution in the vertical direction in the polluted water body, which can be used to predict the flow direction of pollutants in the polluted water body and facilitate the treatment of the polluted water body.

[0034] In some embodiments, performing a gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, and then obtaining the migration trend of pollutants in the polluted water body can be achieved by the following steps: Determine the diffusion trend of pollutants in the polluted water body according to the layered characteristics of the water flow velocity; Determine the gradient distribution of the pollution load in the polluted water body according to the pollution hot spot area; Determine the migration trend of pollutants in the polluted water body based on the diffusion trend of the pollutants and the gradient distribution of the pollution load.

[0035] When specifically implemented, determining the diffusion trend of pollutants in the polluted water body according to the stratified characteristics of the water flow velocity can be achieved by the following method: Select a group of adjacent sensing nodes as the selected adjacent sensing nodes. Subtract the average value of all flow velocities corresponding to the second sensing node from the average value of all flow velocities corresponding to the first sensing node among the selected adjacent sensing nodes. If the subtracted value is positive, it indicates that the first sensing node is the diffusion direction of pollutants between the adjacent sensing nodes. If the subtracted value is negative, it indicates that the second sensing node is the diffusion direction of pollutants between the adjacent sensing nodes. Continue to determine the diffusion direction of pollutants between the remaining adjacent sensing nodes. Subtract the second difference value from the first difference value in the stratified characteristics of the water flow velocity. If the subtracted value is positive, it indicates that the upper layer of the polluted water body is the diffusion direction of pollutants. If the subtracted value is negative, it indicates that the lower layer of the polluted water body is the diffusion direction of pollutants. Take the set of all diffusion directions as the diffusion trend of pollutants in the polluted water body, where the diffusion trend represents the trend of the diffusion direction of pollutants in the polluted water body and can be used to predict the flow trend of pollutants; in other embodiments, it can also be determined by other methods, which are not limited here.

[0036] When specifically implemented, determining the gradient distribution of the pollution load in the polluted water body according to the pollution hot spot area can be achieved by the following method: Collect the pollutant concentrations in each pollution-aggregated area in the pollution hot spot area. Select a group of adjacent pollution-aggregated areas as the selected adjacent pollution-aggregated areas. Subtract the pollutant concentration corresponding to the second pollution-aggregated area from the pollutant concentration corresponding to the first pollution-aggregated area among the adjacent pollution-aggregated areas. Take the subtracted value as the pollutant concentration difference value of the selected adjacent pollution-aggregated areas. Continue to determine the pollutant concentration difference values of the remaining adjacent pollution-aggregated areas, where the pollutant concentration difference value represents the parameter value of the difference degree of pollutant concentrations between adjacent pollution-aggregated areas. Distribute each pollutant concentration difference value according to the positions of the corresponding adjacent pollution-aggregated areas in the pollution hot spot area. Take the obtained distribution result as the gradient distribution of the pollution load in the polluted water body, where the gradient distribution represents the gradient change characteristics of pollutant concentrations in the polluted water body and can be used to judge the pollutants in the polluted water body; in other embodiments, it can also be determined by other methods, which are not limited here.

[0037] In specific implementation, determining the migration trend of pollutants in the polluted water body based on the diffusion trend of the pollutants and the gradient distribution of the pollution load can be achieved in the following manner: Initialize a migration trend model, use the diffusion trend of the pollutants as the constraint parameter of this migration trend model, use the gradient distribution of the pollution load as the initialization parameter of this migration trend model, and output the migration trend of pollutants in the polluted water body through this migration trend model. The migration trend model is a trend model for establishing the migration trend using machine learning algorithms (such as regression algorithms, neural networks, etc.). For example, the trend model is: Migration trend = Diffusion trend of pollutants * A + Gradient distribution of pollution load * B, where A and B are weight coefficients, and A and B can be determined based on a large number of migration trends. In other embodiments, other methods can also be used for determination, which are not limited here.

[0038] It should be noted that the migration trend of pollutants in this application represents the directional characteristics of the transmission and diffusion of pollutants from the source area to other areas in the polluted water body, and can be used to determine the sampling points when sampling the polluted water body, thereby improving the representativeness of the sampling points when sampling the polluted water body.

[0039] In step 104, obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area.

[0040] In specific implementation, obtaining the initial sampling point area when sampling the polluted water body can be achieved in the following manner: Obtain the initial sampling point area when sampling the polluted water body from the database corresponding to the polluted water body. The initial sampling point area represents the area where the sampling points are initially set when sampling the polluted water body, which is convenient for judging the sampling points when sampling the polluted water body. In other embodiments, other methods can also be used for obtaining, which are not limited here.

[0041] In some embodiments, determining the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area can be achieved through the following steps: Determine the position adjustment information of the sampling points in the polluted water body according to the pollution hot spot area; Determine the concentration gradient of pollutants in the initial sampling point area; Determine the dynamic sampling area when sampling the polluted water body according to the position adjustment information and the concentration gradient.

[0042] In specific implementation, the position adjustment information of the sampling points in the polluted water body can be determined according to the polluted hot spot area in the following way, that is: calculate the average area of all the polluted aggregation areas in the polluted hot spot area, extract all the polluted aggregation areas in the polluted hot spot area whose area is greater than the average area, set two sampling points in each of the extracted polluted aggregation areas, and determine the pollution concentration of each of the extracted polluted aggregation areas through the three-dimensional difference model in the prior art. The positions corresponding to the two points with the highest pollutant concentration are used as the positions of the two set sampling points, and the positions of the centers of the unextracted polluted aggregation areas are used as the positions of the sampling points in the polluted aggregation areas. All the positions are used as the position adjustment information of the sampling points in the polluted water body, where the position prediction information represents the information for adjusting the positions of the sampling points in the polluted water body and can be used to adjust the positions of the sampling points in the polluted water body; in other embodiments, other methods can also be used for determination, which are not limited here.

[0043] In specific implementation, the concentration gradient of the pollutants in the initial sampling point area can be determined in the following way, that is: extract the regional water quality data of the initial sampling point area from the water quality data, and calculate the concentration of the pollutants at each initial sampling point by combining the concentration calculation of chemical analysis (such as COD) with the regional water quality data, and calculate the difference value of the concentration of the pollutants between each adjacent initial sampling point. All the difference values are used as the concentration gradient of the pollutants in the initial sampling point area, where the concentration gradient represents the gradient of the change in the concentration difference of the pollutants in the initial sampling point area; the dynamic sampling area for sampling the polluted water body can be determined according to the position adjustment information and the concentration gradient in the following way, that is: obtain the concentration difference threshold of the pollutants in the polluted water body from the database corresponding to the polluted water body, where the concentration difference threshold represents the parameter value of the degree of difference in the pollutant concentration between adjacent sampling points in the polluted water body and can be used to judge the positions of the sampling points. Extract all the difference values greater than the concentration difference threshold from the concentration gradient, and the areas including the sampling points corresponding to each of the extracted difference values and the areas including all the positions in the position adjustment information are used as the dynamic sampling area for sampling the polluted water body; in other embodiments, other methods can also be used for determination, which are not limited here.

[0044] It should be noted that the dynamic sampling area in this application represents the area where the sampling points are dynamically adjusted when sampling the polluted water body, and can be used to adjust the sampling points in the polluted area, so as to improve the representativeness of the sampling points in combination with the actual situation of the polluted water body.

[0045] In step 105, the migration trend of the pollutant and the dynamic sampling area are associated and fused, so as to obtain the associated offset of each initial sampling point in the polluted water body, and a plurality of dynamic sampling points for sampling the polluted water body are determined based on all the associated offsets.

[0046] In some embodiments, the associated offset of each initial sampling point in the polluted water body can be obtained by associating and fusing the migration trend of the pollutant and the dynamic sampling area, which can be implemented by the following steps: Obtain each initial sampling point in the polluted water body; Associate the migration trend of the pollutant with the dynamic sampling area to obtain a plurality of associated points; Determine the dynamic adjustment amount of each initial sampling point when sampling the polluted water body according to the dynamic sampling area; Perform a fusion process on all the dynamic adjustment amounts and all the associated points to obtain a plurality of dynamic fusion amounts; Determine the associated offset of each initial sampling point in the polluted water body according to all the dynamic fusion amounts.

[0047] When specifically implemented, the association of the migration trend of the pollutant with the dynamic sampling area to obtain a plurality of associated points can be implemented in the following manner: select a position in the dynamic sampling area as the selected position, and determine the nearest pollution source to the selected position. If the diffusion direction of the pollution source in the migration trend of the pollutant has a direction towards the selected position, then take the center point between the selected position and the pollution source as the associated point of the selected position. If the diffusion direction of the pollution source in the migration trend of the pollutant does not have a direction towards the selected position, then take the point of the selected position as the associated point of the selected position, and continue to determine the associated points of the remaining positions in the dynamic sampling area, where the associated point represents a point where there is an association between the pollution source and the sampling area; the dynamic adjustment amount of each initial sampling point when sampling the polluted water body can be determined according to the dynamic sampling area in the following manner: input the dynamic sampling area and each initial sampling point into the adjustment model of the Geographic Information System (GIS), and calculate the adjustment amount in terms of distance of each initial sampling point when sampling the polluted water body by using the spatial difference method in the adjustment model in combination with the dynamic sampling area, and take each adjustment amount as the dynamic adjustment amount, where the dynamic adjustment amount represents a parameter value of the dynamic adjustment degree of the sampling point in the polluted water body, that is, the maximum movable dynamic adjustment amount of the sampling point, which can be used to adjust the sampling point; in other embodiments, it can also be determined by other methods, which are not limited here.

[0048] In specific implementation, all dynamic adjustment amounts and all associated points are fused to obtain multiple dynamic fusion amounts, which can be implemented in the following manner: All dynamic adjustment amounts and all associated points are fused through fusion methods in the prior art (such as linear fusion, machine learning-based fusion). For example, one dynamic adjustment amount is selected as the selected dynamic adjustment amount, and the selected dynamic adjustment amount is analyzed for association based on the associated points. Then, the selected dynamic adjustment amount is optimized and calculated through an optimization algorithm (such as the gradient descent method), and the calculated value is used as the dynamic fusion amount of the selected dynamic adjustment amount. Finally, the dynamic fusion amounts of each dynamic adjustment amount are determined to obtain multiple dynamic fusion amounts. Among them, the dynamic fusion amount represents a parameter value that adjusts the degree of the initial sampling point and fuses the association relationship between the pollution source and the sampling area; Determining the associated offset amount of each initial sampling point in the polluted water body according to all the dynamic fusion amounts can be implemented in the following manner: Calculate the average value of the pollutant concentration in the polluted water body, perform a natural exponential operation on this average value, and take the reciprocal of the value obtained from the natural exponential operation as the first value. Select an initial sampling point as the selected initial sampling point, multiply the dynamic fusion amount corresponding to the selected initial sampling point by the first value, and use the obtained value as the associated offset amount of the selected initial sampling point in the polluted water body. Then continue to determine the associated offset amounts of the remaining initial sampling points in the polluted water body. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0049] It should be noted that the associated offset amount in this application represents a parameter value of the offset degree of the sampling point in the polluted water body after considering the actual pollution situation, and can be used to adjust the initial sampling point, thereby improving the actual sampling effect of the sampling point.

[0050] In some embodiments, determining multiple dynamic sampling points when sampling the polluted water body based on all the associated offset amounts can be implemented through the following steps: Obtain the sampling plan of the polluted water body; Determine multiple dynamic sampling points when sampling the polluted water body according to the sampling plan of the polluted water body and all the associated offset amounts.

[0051] In specific implementation, the sampling scheme of the contaminated water body can be obtained in the following way, that is: obtain the sampling scheme of the contaminated water body from the sampler corresponding to the contaminated water body, and the sampling scheme includes sampling methods, sampling steps, sampling quantities, sampling points, etc.; determining multiple dynamic sampling points for sampling the contaminated water body according to the sampling scheme of the contaminated water body and all associated offsets can be achieved in the following way, that is: initialize a dynamic sampling point model, use the sampling scheme of the contaminated water body as the constraint parameter of this dynamic sampling point model, use all associated offsets as the initialization parameters of this dynamic sampling point model, and output multiple dynamic sampling points for sampling the contaminated water body through this dynamic sampling point model. The dynamic sampling point model is a dynamic sampling point model that uses machine learning algorithms (such as regression algorithms, neural networks, etc.) to establish dynamic sampling points. For example, the dynamic sampling point model is: multiple dynamic sampling points = sampling scheme of the contaminated water body * C + all associated offsets * D, where C and D are weight coefficients, and C and D can be determined based on a large number of dynamic sampling points, and can also be determined in other ways in other embodiments, which are not limited here.

[0052] It should be noted that the dynamic sampling points in this application represent the sampling points after dynamically adjusting the sampling points in the contaminated area, and can be used to determine the sampling points in the contaminated area, thereby improving the reliability of the sampling points.

[0053] In addition, on the other hand of this application, in some embodiments, this application provides a sampling system for water environment treatment. Refer to Figure 4 , this figure is a schematic structural diagram of a sampling system for water environment treatment shown in some embodiments of this application. The sampling system 400 for water environment treatment includes: a collection module 401, a processing module 402, and an execution module 403, which are described as follows: Collection module 401, in this application, the collection module 401 is mainly used to collect the water quality data of the contaminated water body by a water quality sensor; Processing module 402, in this application, the processing module 402 is used to extract the water quality change characteristics of the contaminated water body from the water quality data, and determine the pollution hot spot area of the contaminated water body according to the water quality change characteristics and the pollution source distribution of the contaminated water body; It should be noted that in this application, the processing module 402 is also used to collect the flow velocity data of the contaminated water body by a flow velocity sensor, extract the stratified characteristics of the water flow velocity in the contaminated water body from the flow velocity data, perform gradient analysis on the pollution load distribution of the contaminated water body according to the stratified characteristics of the water flow velocity and the pollution hot spot area, and further obtain the migration trend of pollutants in the contaminated water body; In addition, it should be noted that the processing module 402 in this application is further configured to obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hotspot area and the initial sampling point area; An execution module 403. In this application, the execution module 403 is mainly configured to associate and fuse the migration trend of the pollutants and the dynamic sampling area, so as to obtain the associated offset of each initial sampling point in the polluted water body, and determine multiple dynamic sampling points when sampling the polluted water body based on all the associated offsets.

[0054] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned sampling method for water environment treatment.

[0055] In some embodiments, refer to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the sampling method for water environment treatment according to some embodiments of this application. The sampling method for water environment treatment in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0056] The processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0057] The communication bus 502 can be used to transmit information between the above components.

[0058] The memory 503 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0059] Among them, the memory 503 is used to store the program code for executing the solution of this application and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The methods used in the above embodiments can be implemented by one or more software modules in the program code in the processor 501 and the memory 503.

[0060] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0061] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0062] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0063] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-described sampling method for water environment treatment is implemented.

[0064] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A sampling method for water environment treatment, characterized in that The method includes the following steps: Collect water quality data of the polluted water body by a water quality sensor; Extract the water quality change characteristics of the polluted water body from the water quality data, and determine the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body; Collect the flow velocity data of the polluted water body by a flow velocity sensor, extract the layered characteristics of the water flow velocity in the polluted water body from the flow velocity data, conduct a gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, and further obtain the migration trend of pollutants in the polluted water body; Obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area; Associate and fuse the migration trend of the pollutants and the dynamic sampling area, and further obtain the associated offset amount of each initial sampling point in the polluted water body, and determine multiple dynamic sampling points when sampling the polluted water body based on all the associated offset amounts.

2. The method according to claim 1, characterized in that, Specifically, extracting the water quality change characteristics of the polluted water body from the water quality data includes: Determine the water quality change area in the polluted water body through the water quality data; Conduct a fluctuation analysis on the water quality in the polluted water body according to the water quality change area to obtain the water quality change characteristics of the polluted water body.

3. The method according to claim 1, characterized in that Specifically, determining the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body includes: Determine the pollution source distribution of the polluted water body; Determine multiple pollution aggregation points of the polluted water body through the pollution source distribution; Determine the pollution hot spot area of the polluted water body according to the water quality change characteristics and all the pollution aggregation points.

4. The method according to claim 1, wherein Specifically, extracting the layered characteristics of the water flow velocity in the polluted water body from the flow velocity data includes: Determine the flow velocity characteristics of the polluted water body according to the flow velocity data; Determine the layered characteristics of the water flow velocity in the polluted water body through the flow velocity characteristics.

5. The method according to claim 1, wherein Specifically, conducting a gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, and further obtaining the migration trend of pollutants in the polluted water body includes: Determine the diffusion trend of pollutants in the polluted water body according to the layered characteristics of the water flow velocity; Determine the gradient distribution of the pollution load in the polluted water body according to the pollution hot spot area; Determine the migration trend of pollutants in the polluted water body through the diffusion trend of the pollutants and the gradient distribution of the pollution load.

6. The method according to claim 1, wherein Specifically, determining the dynamic sampling area of the polluted water body according to the concentration gradient of pollutants in the pollution hot spot area and the initial sampling point area includes: Determine the position adjustment information of the sampling points in the polluted water body according to the pollution hot spot area; Determine the concentration gradient of pollutants in the initial sampling point area; Determine the dynamic sampling area when sampling the polluted water body according to the position adjustment information and the concentration gradient.

7. The method according to claim 1, wherein Associating and integrating the migration trend of the pollutants with the dynamic sampling area, and then obtaining the associated offset of each initial sampling point in the polluted water body specifically includes: Obtaining each initial sampling point in the polluted water body; Associating the migration trend of the pollutants with the dynamic sampling area to obtain a plurality of associated points; Determining the dynamic adjustment amount of each initial sampling point when sampling the polluted water body according to the dynamic sampling area; Performing a fusion process on all the dynamic adjustment amounts and all the associated points to obtain a plurality of dynamic fusion amounts; Determining the associated offset of each initial sampling point in the polluted water body according to all the dynamic fusion amounts.

8. A sampling system for water environment treatment, characterized in that, Including: A collection module for collecting water quality data of the polluted water body by a water quality sensor; A processing module for extracting the water quality change characteristics of the polluted water body from the water quality data, and determining the pollution hot spot area of the polluted water body according to the water quality change characteristics and the pollution source distribution of the polluted water body; The processing module is further configured to collect the flow velocity data of the polluted water body by a flow velocity sensor, extract the layered characteristics of the water flow velocity in the polluted water body from the flow velocity data, and perform a gradient analysis on the pollution load distribution of the polluted water body according to the layered characteristics of the water flow velocity and the pollution hot spot area, so as to obtain the migration trend of the pollutants in the polluted water body; The processing module is further configured to obtain the initial sampling point area when sampling the polluted water body, and determine the dynamic sampling area of the polluted water body according to the concentration gradient of the pollutants in the pollution hot spot area and the initial sampling point area; An execution module for associating and integrating the migration trend of the pollutants with the dynamic sampling area, and then obtaining the associated offset of each initial sampling point in the polluted water body, and determining a plurality of dynamic sampling points when sampling the polluted water body according to all the associated offsets.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the sampling method for water environment treatment according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the sampling method for water environment treatment according to any one of claims 1 to 7.

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