Groundwater pollution monitoring well site selection method, device, electronic equipment and storage medium

Abstract

The present invention relates to the field of groundwater pollution monitoring technology, and in particular to a method, device, electronic device and storage medium for selecting a groundwater pollution monitoring well site. The method of the present invention first obtains multiple groundwater monitoring vectors, then for each groundwater monitoring vector, selects multiple target nodes from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and adjusts the parameters of each target node according to the groundwater monitoring data set. Then, based on the density clustering method, the multiple first parameter vectors are clustered into multiple node classes, and the centers of the multiple node classes are used as multiple first center vectors. Finally, for each first center vector, the vector closest to the first center vector is selected from multiple groundwater monitoring vectors as the target monitoring vector, and the groundwater pollution monitoring well is arranged according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector. The monitoring well of the present invention can more accurately and timely reflect the status of groundwater pollution.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater pollution monitoring, and in particular to a groundwater pollution monitoring well site selection method, device, electronic equipment and storage medium. Background Art

[0002] Lead and zinc mining can pollute groundwater through the following pathways:

[0003] Direct infiltration: If the wastewater treatment facilities in the mining area are not perfect, the wastewater is discharged directly to the ground without effective treatment, or the waste residue is piled up at random, the leachate will directly infiltrate into the groundwater through the soil pores, causing groundwater pollution.

[0004] Surface runoff: Rainwater runoff in mining areas will carry slag, mine dust and other harmful substances and flow into surface water bodies such as rivers and lakes. Through the hydraulic connection between surface water and groundwater, it will seep into the groundwater and cause groundwater pollution.

[0005] Aquifer destruction: Mining activities may destroy the structure of underground aquifers and the integrity of impermeable layers, causing hydraulic connections between previously relatively isolated aquifers, leading to the mixing of contaminated groundwater with uncontaminated groundwater and expanding the scope of pollution.

[0006] Long-term consumption of contaminated groundwater accumulates in the human body, leading to chronic poisoning and various diseases. For example, lead can affect the nervous system, blood system, and kidney function; cadmium can damage the kidneys, bones, and respiratory system; and arsenic, a carcinogen, increases the risk of cancer. Contaminated groundwater can also affect the quality of surrounding soil, causing soil contamination and, in turn, affecting vegetation growth. Furthermore, when pollutants in groundwater reach surface water bodies, they can damage aquatic ecosystems, affecting the survival and reproduction of aquatic life and disrupting the ecological balance.

[0007] Mainstream lead-zinc mines' prevention and control measures for groundwater pollution include: establishing a groundwater monitoring network to regularly monitor the water level and quality in and around the mining area; and using wastewater treatment facilities to remove heavy metal ions and other pollutants from wastewater, thereby reducing wastewater discharge.

[0008] Among them, the construction of the groundwater monitoring network needs to be based on multiple monitoring wells to regularly obtain various groundwater data. The site selection of water pollution monitoring wells is crucial to the monitoring effect. However, the current site selection of water pollution monitoring wells mainly relies on selecting the location of monitoring wells near the pollution source based on the direction of groundwater flow, burial depth, and geological conditions. There is often a certain degree of blindness in the specific location selection, resulting in the monitoring results being unable to accurately and timely reflect the status of groundwater pollution.

[0009] Based on this, it is necessary to develop and design a method for selecting the site for groundwater pollution monitoring wells. Summary of the Invention

[0010] The embodiments of the present invention provide a method, device, electronic equipment and storage medium for selecting a groundwater pollution monitoring well, which are used to solve the problem of inaccurate results in selecting a groundwater pollution monitoring well in the prior art.

[0011] In a first aspect, an embodiment of the present invention provides a method for selecting a site for a groundwater pollution monitoring well, comprising:

[0012] Acquire multiple groundwater monitoring vectors, where each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data representing groundwater quality;

[0013] For each groundwater monitoring vector, select multiple target nodes from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and adjust the parameters of each target node based on the groundwater monitoring data set, wherein the multiple first nodes are arranged into a dot matrix in a preset order, and the first nodes are provided with multiple parameters having the same number of dimensions as the groundwater monitoring vector;

[0014] Clustering the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and using the centers of the plurality of node classes as a plurality of first center vectors, wherein the first parameter vectors are vectors constructed according to the plurality of parameters of the first nodes, and each first parameter vector corresponds to a first node;

[0015] For each first central vector, a vector closest to the first central vector is selected from the multiple groundwater monitoring vectors as a target monitoring vector, and groundwater pollution monitoring wells are arranged according to the coordinates of the pollution source and the coordinates of the observation wells corresponding to the target monitoring vector.

[0016] In one possible implementation, for each groundwater monitoring vector, selecting multiple target nodes from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and adjusting parameters of each target node based on the groundwater monitoring dataset include:

[0017] ergodicly extracting a vector from the plurality of groundwater monitoring vectors as a parameter adjustment vector;

[0018] Selecting a first node whose parameter is closest to the tuning parameter vector from the multiple first nodes as a reference node;

[0019] determining a first quantity based on the call count, wherein the first quantity is negatively correlated with the call count;

[0020] selecting a first number of first nodes closest to the reference node from the dot matrix as a plurality of target nodes;

[0021] Adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector;

[0022] If the call count does not reach the count threshold, jump to the step of selecting the first node whose parameter is closest to the tuning parameter vector from the multiple first nodes as a reference node.

[0023] In one possible implementation, adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector includes:

[0024] Adjust the parameters of each target node according to a first formula and the distance between the reference node and the parameter adjustment vector, wherein the first formula is:

[0025]

[0026] Where, For the After the call, the position of the dot matrix is The parameter set of the target node, For the After the call, the position of the dot matrix is The parameter set of the target node, is a natural constant, is the call count, is the proportionality coefficient, The dot position is The lattice distance from the reference node, For the The difference vector between the parameter vector and the reference node parameter after the call, is the number of rows of the reference node in the lattice, is the number of columns of the reference node in the lattice, is the tuning parameter vector, is the first quantity, For the The parameter set of the reference node after the first call.

[0027] In one possible implementation, clustering the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and using the centers of the plurality of node classes as a plurality of first center vectors, includes:

[0028] Get the dot frame, density radius and third quantity;

[0029] For each first node, using a dot matrix frame positioned with the first node as the center, extracting multiple nodes from the dot matrix as multiple second nodes, and counting the number of the multiple second nodes whose parameter vector distances to the first node are less than the density radius to obtain the number of neighbors of the first node;

[0030] Taking a node among the plurality of first nodes whose number of neighbors is greater than the third number as a clustering starting point;

[0031] Clustering the plurality of first nodes using a clustering starting point as a clustering reference point to obtain the plurality of node classes;

[0032] The centers of the plurality of node classes are used as a plurality of first center vectors.

[0033] In one possible implementation, clustering the plurality of first nodes using a clustering starting point as a clustering reference point to obtain the plurality of node classes includes:

[0034] Class creation step: create a new node class, and randomly select a node from multiple unclustered cluster starting points as a cluster reference point and add it to the new node class;

[0035] Using a dot matrix frame positioned with the cluster reference point as the center, extracting a plurality of non-clustered nodes from the dot matrix as a plurality of third nodes;

[0036] Class node search step: if there is a third node among the third nodes whose parameter vector distance to the cluster reference point is less than the density radius, then the third node whose parameter vector distance to the cluster reference point is less than the density radius is used as a class node, and the class node is added to the newly created node class;

[0037] If a clustering starting point exists in the class node in the newly created node class, the clustering starting point in the class node is used as a clustering reference point, and the process jumps to the class node search step;

[0038] Otherwise, if there are unclustered clustering starting points, jump to the cluster creation step.

[0039] In one possible implementation, the arrangement of groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation wells corresponding to the target monitoring vector includes:

[0040] Obtaining the coordinates of the pollution source and the coordinates of multiple typical observation wells, wherein the coordinates of the typical observation wells are determined according to the target monitoring vector;

[0041] Taking the coordinates of the pollution source as a starting point, constructing a first ray passing through the coordinates of each typical observation well;

[0042] selecting a ray from the plurality of first rays as a background ray according to the angle between the first rays;

[0043] selecting a plurality of rays from a plurality of first rays other than the background rays as a plurality of contamination diffusion rays;

[0044] Arrange background monitoring wells on the background rays;

[0045] Water pollution monitoring wells are laid out along the pollution diffusion rays.

[0046] In one possible implementation, selecting a ray from a plurality of first rays as a background ray according to the angle between the first rays includes:

[0047] For each first ray, calculate the angle between two adjacent rays as the first angle;

[0048] The first ray corresponding to the first angle with the largest value is taken as the background ray.

[0049] In a second aspect, an embodiment of the present invention provides a groundwater pollution monitoring well site selection device for implementing the groundwater pollution monitoring well site selection method described in the first aspect or any possible implementation of the first aspect, the groundwater pollution monitoring well site selection device comprising:

[0050] A monitoring data acquisition module is used to obtain multiple groundwater monitoring vectors, wherein each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data representing groundwater quality;

[0051] a node construction module, configured to, for each groundwater monitoring vector, select a plurality of target nodes from a plurality of first nodes based on the distance between the groundwater monitoring vector and the node, and adjust parameters of each target node based on the groundwater monitoring dataset, wherein the plurality of first nodes are arranged into a lattice in a preset order, and the first nodes are provided with a plurality of parameters having the same number of dimensions as the groundwater monitoring vector;

[0052] a clustering module, configured to cluster the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and use the centers of the plurality of node classes as a plurality of first center vectors, wherein the first parameter vectors are vectors constructed according to a plurality of parameters of the first nodes, and each first parameter vector corresponds to a first node;

[0053] as well as,

[0054] The monitoring well site selection module is used to select, for each first central vector, the vector closest to the first central vector from the multiple groundwater monitoring vectors as the target monitoring vector, and to arrange groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector.

[0055] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] An embodiment of the present invention discloses a method for selecting a site for groundwater pollution monitoring wells, which first obtains multiple groundwater monitoring vectors, wherein each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data characterizing groundwater quality; then, for each groundwater monitoring vector, multiple target nodes are selected from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and parameters of each target node are adjusted based on the groundwater monitoring data set, wherein the multiple first nodes are arranged into a lattice according to a preset order, and the first node is provided with multiple parameters with the same number of dimensions as the groundwater monitoring vector; then, based on a density clustering method, the multiple first parameter vectors are clustered into multiple node classes, and the centers of the multiple node classes are used as multiple first center vectors, wherein the first parameter vector is a vector constructed based on multiple parameters of the first node, and each first parameter vector corresponds to a first node; finally, for each first center vector, the vector closest to the first center vector is selected from the multiple groundwater monitoring vectors as the target monitoring vector, and the groundwater pollution monitoring wells are arranged according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector. The embodiment of the present invention flattens the groundwater monitoring vectors obtained from the observation wells and then clusters them using the density clustering method, thereby extracting groundwater monitoring vectors with data typicality. Based on the observation wells and pollution sources corresponding to the typical groundwater monitoring vectors, typical pollutant diffusion lines are determined, and the monitoring wells are sited based on the diffusion lines, so that the monitoring wells have typical pollution source diffusion characteristics. The water quality monitoring data obtained from the monitoring wells is more representative, which can ensure that the status of groundwater pollution is reflected more accurately and timely, avoids the repeated construction of monitoring wells, and reduces the resource investment in constructing monitoring wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0060] Figure 1 This is a flow chart of a method for selecting a groundwater pollution monitoring well site provided by an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of a process for clustering parameters of a dot matrix using a density clustering method according to an embodiment of the present invention;

[0062] Figure 3 This is a functional block diagram of a groundwater pollution monitoring well site selection device provided by an embodiment of the present invention;

[0063] Figure 4 This is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in alternative embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0065] In order to make the objectives, technical solutions and advantages of the present invention more clear, the following will be described through specific implementation methods in conjunction with the accompanying drawings.

[0066] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiment.

[0067] Figure 1 Flowchart of a method for selecting a groundwater pollution monitoring well site provided in an embodiment of the present invention.

[0068] like Figure 1 As shown, it shows a flowchart of the implementation method of the groundwater pollution monitoring well site selection method provided by the embodiment of the present invention, which is detailed as follows:

[0069] In step 101, a plurality of groundwater monitoring vectors are obtained, wherein each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on a plurality of detection data representing groundwater quality.

[0070] In step 102, for each groundwater monitoring vector, multiple target nodes are selected from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and parameters of each target node are adjusted according to the groundwater monitoring data set, wherein the multiple first nodes are arranged into a dot matrix in a preset order, and the first nodes are provided with multiple parameters having the same number of dimensions as the groundwater monitoring vector.

[0071] In some embodiments, for each groundwater monitoring vector, selecting multiple target nodes from multiple first nodes based on the distance between the groundwater monitoring vector and the node, and adjusting parameters of each target node based on the groundwater monitoring dataset include:

[0072] ergodicly extracting a vector from the plurality of groundwater monitoring vectors as a parameter adjustment vector;

[0073] Selecting a first node whose parameter is closest to the tuning parameter vector from the multiple first nodes as a reference node;

[0074] determining a first quantity based on the call count, wherein the first quantity is negatively correlated with the call count;

[0075] selecting a first number of first nodes closest to the reference node from the dot matrix as a plurality of target nodes;

[0076] Adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector;

[0077] If the call count does not reach the count threshold, jump to the step of selecting the first node whose parameter is closest to the tuning parameter vector from the multiple first nodes as a reference node.

[0078] In some embodiments, adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector includes:

[0079] Adjust the parameters of each target node according to a first formula and the distance between the reference node and the parameter adjustment vector, wherein the first formula is:

[0080]

[0081] Where, For the After the call, the position of the dot matrix is The parameter set of the target node, For the After the call, the position of the dot matrix is The parameter set of the target node, is a natural constant, is the call count, is the proportionality coefficient, The dot position is The lattice distance from the reference node, For the The difference vector between the parameter vector and the reference node parameter after the call, is the number of rows of the reference node in the lattice, is the number of columns of the reference node in the lattice, is the tuning parameter vector, is the first quantity, For the The parameter set of the reference node after the first call.

[0082] Exemplarily, the present invention is intended to provide a method for reflecting the typical diffusion path of pollutants underground by monitoring the number of existing observation wells, and setting background monitoring wells and pollutant monitoring wells based on the typical diffusion path.

[0083] Observation wells are usually existing wells, such as domestic water wells and other water intake wells. By finding several observation wells around the pollution source, water samples are extracted from each observation well to test various water quality data, and a groundwater monitoring vector is constructed. For lead and zinc mine pollution, the water quality data of the observation wells usually include: hexavalent chromium, mercury, cadmium, lead, copper, zinc, arsenic, iron, manganese, fluoride, sulfate, chloride, and may also include: nitrite, nitrate, total dissolved solids, total hardness, pH value, permanganate index, etc.

[0084] We can see that the constructed monitoring vector has a high dimension and faces great difficulty in clustering. Therefore, the present invention proposes to construct a lattice, and each node in the lattice corresponds to multiple parameters. The groundwater monitoring vector obtained by the above steps adjusts the parameters of the node to complete the flattening of the data, and then clusters the flattened data. The clustering results reflect the diffusion of pollutants.

[0085] In terms of parameter adjustment, the present invention uses the groundwater monitoring vector to find the first node closest to the lattice and adjusts the parameters of this node and the nodes surrounding it. This achieves the goal of minimizing the differences between adjacent nodes. To ensure that the adjusted parameters gradually converge, the number of surrounding nodes is gradually reduced. By erroneously extracting vectors from the groundwater monitoring vector and repeating the above steps, the nodes in the lattice will form a characteristic of having similar parameters between adjacent nodes. The present invention sets a number of calls to the above steps, and when the number of calls is reached, the parameter adjustment process ends.

[0086] In terms of parameter adjustment, the present invention applies the first formula to adjust the parameters of the node. The first formula is:

[0087]

[0088] Where, For the After the call, the position of the dot matrix is The parameter set of the target node, For the After the call, the position of the dot matrix is The parameter set of the target node, is a natural constant, is the call count, is the proportionality coefficient, The dot position is The lattice distance from the reference node, For the The difference vector between the parameter vector and the reference node parameter after the call, is the number of rows of the reference node in the lattice, is the number of columns of the reference node in the lattice, is the tuning parameter vector, is the first quantity, For the The parameter set of the reference node after the first call.

[0089] In step 103, multiple first parameter vectors are clustered into multiple node classes based on the density clustering method, and the centers of the multiple node classes are used as multiple first center vectors, wherein the first parameter vector is a vector constructed according to multiple parameters of the first node, and each first parameter vector corresponds to a first node.

[0090] In some embodiments, clustering the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and using the centers of the plurality of node classes as a plurality of first center vectors, includes:

[0091] Get the dot frame, density radius and third quantity;

[0092] For each first node, using a dot matrix frame positioned with the first node as the center, extracting multiple nodes from the dot matrix as multiple second nodes, and counting the number of the multiple second nodes whose parameter vector distances to the first node are less than the density radius to obtain the number of neighbors of the first node;

[0093] Taking a node among the plurality of first nodes whose number of neighbors is greater than the third number as a clustering starting point;

[0094] Clustering the plurality of first nodes using a clustering starting point as a clustering reference point to obtain the plurality of node classes;

[0095] The centers of the plurality of node classes are used as a plurality of first center vectors.

[0096] In some embodiments, clustering the plurality of first nodes using the clustering starting point as a clustering reference point to obtain the plurality of node classes includes:

[0097] Class creation step: create a new node class, and randomly select a node from multiple unclustered cluster starting points as a cluster reference point and add it to the new node class;

[0098] Using a dot matrix frame positioned with the cluster reference point as the center, extracting a plurality of non-clustered nodes from the dot matrix as a plurality of third nodes;

[0099] Class node search step: if there is a third node among the third nodes whose parameter vector distance to the cluster reference point is less than the density radius, then the third node whose parameter vector distance to the cluster reference point is less than the density radius is used as a class node, and the class node is added to the newly created node class;

[0100] If a clustering starting point exists in the class node in the newly created node class, the clustering starting point in the class node is used as a clustering reference point, and the process jumps to the class node search step;

[0101] Otherwise, if there are unclustered clustering starting points, jump to the cluster creation step.

[0102] For example, the lattice obtained through the above steps has similar parameters between adjacent nodes, and the overall distribution of groundwater quality parameters can be more clearly seen. However, due to the transition between the parameters in the lattice, there is no clear boundary (low density). Therefore, density clustering is required.

[0103] like Figure 2 As shown, the density clustering method adopted by the present invention is to cluster the parameter density based on the position of the dot matrix. Specifically, a dot matrix frame 202 is used to slide and extract multiple first nodes 201 from the dot matrix. The dot matrix frame 202 is positioned based on its center. The node corresponding to the positioning center is calculated with the other nodes 203 in the dot matrix frame 202 for parameter vector distance. After the calculation is completed, the number of nodes smaller than the density radius is counted. If the statistical value is greater than the threshold, it will be used as the clustering starting point 204.

[0104] When the slip is completed, the cluster starting points 204 in the dot matrix are obtained. By clustering these cluster starting points 204, multiple node classes 205 can be obtained. The class center of the node class 205 is data with typical data, which is used to indicate the location of the water pollution monitoring well.

[0105] When clustering by cluster starting point, first select a node from the unclustered cluster starting points as a cluster reference point and add this node to the newly created node class. Then, position the dot matrix frame with the cluster reference point as the center of the dot matrix frame, extract multiple nodes from the dot matrix frame, and determine the parameter vector distance between these multiple nodes and the cluster reference point. If the parameter vector distance is less than the density radius, then add them to the newly created node class. After all nodes have been added, the newly added nodes are checked for cluster starting points. If so, these nodes are used as cluster reference points. Repeat the above steps until the newly added nodes have no cluster starting points. At this point, if there are unclustered cluster starting points, repeat the above steps of using them as cluster reference points and creating a new node class.

[0106] This completes the density clustering process. The node clusters obtained through the above process have similar lattice distances and parameter vector distances. The cluster centers of the node clusters are very typical. In addition, some noise nodes cannot be clustered into the node cluster because they cannot be clustered, thus completing the data noise removal.

[0107] In step 104, for each first central vector, a vector closest to the first central vector is selected from the multiple groundwater monitoring vectors as a target monitoring vector, and groundwater pollution monitoring wells are arranged according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector.

[0108] In some embodiments, the arrangement of groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation wells corresponding to the target monitoring vector includes:

[0109] Obtaining the coordinates of the pollution source and the coordinates of multiple typical observation wells, wherein the coordinates of the typical observation wells are determined according to the target monitoring vector;

[0110] Taking the coordinates of the pollution source as a starting point, constructing a first ray passing through the coordinates of each typical observation well;

[0111] selecting a ray from the plurality of first rays as a background ray according to the angle between the first rays;

[0112] selecting a plurality of rays from a plurality of first rays other than the background rays as a plurality of contamination diffusion rays;

[0113] Arrange background monitoring wells on the background rays;

[0114] Water pollution monitoring wells are laid out along the pollution diffusion rays.

[0115] In some embodiments, selecting a ray from a plurality of first rays as a background ray based on an angle between the first rays includes:

[0116] For each first ray, calculate the angle between two adjacent rays as the first angle;

[0117] The first ray corresponding to the first angle with the largest value is taken as the background ray.

[0118] For example, after obtaining the class center of the node class, the groundwater monitoring vector with the closest vector distance is found from the groundwater monitoring vector according to the distance to the class center of the node class, that is, the target monitoring vector is found. The coordinates of the observation wells corresponding to these vectors and the coordinates of the pollution source are connected by a more typical pollutant diffusion line. The water quality data obtained by the monitoring wells set on the connection line is more typical, and avoids repeatedly setting up monitoring wells in areas with the same or similar water quality change characteristics, thereby reducing the resource investment in building monitoring wells.

[0119] In addition, in some scenarios, the background monitoring well can be determined based on the line connecting the coordinates of the observation well corresponding to the target monitoring vector and the coordinates of the pollution source. Specifically, for each line, take the closest line from each side and calculate the angle between the two lines. The line with the largest angle value is the line where the background monitoring well is located.

[0120] The present invention provides an implementation method for selecting a site for a groundwater pollution monitoring well. The method first obtains a plurality of groundwater monitoring vectors, wherein each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on a plurality of detection data characterizing groundwater quality; then, for each groundwater monitoring vector, a plurality of target nodes are selected from a plurality of first nodes based on the distance between the groundwater monitoring vector and the node, and parameters of each target node are adjusted based on the groundwater monitoring data set, wherein the plurality of first nodes are arranged into a lattice according to a preset order, and the first node is provided with a plurality of parameters having the same number of dimensions as the groundwater monitoring vector; then, based on a density clustering method, the plurality of first parameter vectors are clustered into a plurality of node classes, and the centers of the plurality of node classes are used as a plurality of first center vectors, wherein the first parameter vector is a vector constructed based on the plurality of parameters of the first node, and each first parameter vector corresponds to a first node; finally, for each first center vector, a vector closest to the first center vector is selected from the plurality of groundwater monitoring vectors as a target monitoring vector, and the groundwater pollution monitoring well is arranged based on the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector. The embodiment of the present invention flattens the groundwater monitoring vectors obtained from the observation wells and then clusters them using the density clustering method, thereby extracting groundwater monitoring vectors with data typicality. Based on the observation wells and pollution sources corresponding to the typical groundwater monitoring vectors, typical pollutant diffusion lines are determined, and the monitoring wells are sited based on the diffusion lines, so that the monitoring wells have typical pollution source diffusion characteristics. The water quality monitoring data obtained from the monitoring wells is more representative, which can ensure that the status of groundwater pollution is reflected more accurately and timely, avoids the repeated construction of monitoring wells, and reduces the resource investment in constructing monitoring wells.

[0121] It should be understood that the size of the serial numbers of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0122] The following is an embodiment of the device of the present invention. For details not described in detail, please refer to the corresponding method embodiment described above.

[0123] Figure 3 This is a functional block diagram of the groundwater pollution monitoring well site selection device provided by the embodiment of the present invention, referring to Figure 3 The groundwater pollution monitoring well site selection device includes: a monitoring data acquisition module 301, a node construction module 302, a clustering module 303 and a monitoring well site selection module 304, wherein:

[0124] A monitoring data acquisition module 301 is used to acquire multiple groundwater monitoring vectors, where each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data representing groundwater quality;

[0125] A node construction module 302 is configured to select, for each groundwater monitoring vector, a plurality of target nodes from a plurality of first nodes based on the distance between the groundwater monitoring vector and the node, and adjust parameters of each target node based on the groundwater monitoring dataset, wherein the plurality of first nodes are arranged into a dot matrix in a preset order, and the first nodes are provided with a plurality of parameters equal to the number of dimensions of the groundwater monitoring vector;

[0126] A clustering module 303 is configured to cluster the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and use the centers of the plurality of node classes as a plurality of first center vectors, wherein the first parameter vectors are vectors constructed based on a plurality of parameters of the first nodes, and each first parameter vector corresponds to a first node;

[0127] The monitoring well site selection module 304 is used to select, for each first central vector, the vector closest to the first central vector from the multiple groundwater monitoring vectors as the target monitoring vector, and to arrange groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector.

[0128] Figure 4 : is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the steps in the above-mentioned groundwater pollution monitoring well site selection method and embodiment are implemented, such as Figure 1 Steps 101 to 104 are shown.

[0129] Illustratively, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0130] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will understand that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0131] The processor 400 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0132] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard drive or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 4. Furthermore, the memory 401 may include both an internal storage unit of the electronic device 4 and an external storage device. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is about to be output.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, and will not be repeated here.

[0134] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0135] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0136] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0138] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0139] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method and device embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium.

[0140] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for selecting a site for a groundwater pollution monitoring well, characterized in that: include: Acquire multiple groundwater monitoring vectors, where each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data representing groundwater quality; For each groundwater monitoring vector, multiple target nodes are selected from multiple first nodes according to the distance between the groundwater monitoring vector and the node, and parameters of each target node are adjusted according to the groundwater monitoring dataset, including: ergodicly extracting a vector from the plurality of groundwater monitoring vectors as a parameter adjustment vector; Selecting a first node whose parameter is closest to the parameter adjustment vector from the plurality of first nodes as a reference node, wherein the plurality of first nodes are arranged into a lattice according to a preset order, and the first node is provided with a plurality of parameters having the same number of dimensions as the groundwater monitoring vector; determining a first quantity based on the call count, wherein the first quantity is negatively correlated with the call count; selecting a first number of first nodes closest to the reference node from the dot matrix as a plurality of target nodes; Adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector; If the call count does not reach the count threshold, jump to the step of selecting a first node with a parameter closest to the tuning parameter vector from the multiple first nodes as a reference node; Clustering the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and using the centers of the plurality of node classes as a plurality of first center vectors, wherein the first parameter vectors are vectors constructed according to the plurality of parameters of the first nodes, and each first parameter vector corresponds to a first node; For each first central vector, a vector closest to the first central vector is selected from the multiple groundwater monitoring vectors as a target monitoring vector, and groundwater pollution monitoring wells are arranged according to the coordinates of the pollution source and the coordinates of the observation wells corresponding to the target monitoring vector.

2. The method for selecting a site for a groundwater pollution monitoring well according to claim 1, wherein: The adjusting the parameters of each target node according to the distance between the reference node and the parameter adjustment vector includes: Adjust the parameters of each target node according to a first formula and the distance between the reference node and the parameter adjustment vector, wherein the first formula is: Where, For the After the call, the position of the dot matrix is The parameter set of the target node, For the After the call, the position of the dot matrix is The parameter set of the target node, is a natural constant, is the call count, is the proportionality coefficient, The dot position is The lattice distance from the reference node, For the The difference vector between the parameter vector and the reference node parameter after the call, is the number of rows of the reference node in the lattice, is the number of columns of the reference node in the lattice, is the tuning parameter vector, is the first quantity, For the The parameter set of the reference node after the first call.

3. The method for selecting a site for a groundwater pollution monitoring well according to claim 1, wherein: The method of clustering the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and taking the centers of the plurality of node classes as a plurality of first center vectors, includes: Get the dot frame, density radius and third quantity; For each first node, using a dot matrix frame positioned with the first node as the center, extracting multiple nodes from the dot matrix as multiple second nodes, and counting the number of the multiple second nodes whose parameter vector distances to the first node are less than the density radius to obtain the number of neighbors of the first node; Taking a node among the plurality of first nodes whose number of neighbors is greater than the third number as a clustering starting point; Clustering the plurality of first nodes using a clustering starting point as a clustering reference point to obtain the plurality of node classes; The centers of the plurality of node classes are used as a plurality of first center vectors.

4. The method for selecting a site for a groundwater pollution monitoring well according to claim 3, wherein: The clustering of the plurality of first nodes using the clustering starting point as a clustering reference point to obtain the plurality of node classes includes: Class creation step: create a new node class, and randomly select a node from multiple unclustered cluster starting points as a cluster reference point and add it to the new node class; Using a dot matrix frame positioned with the cluster reference point as the center, extracting a plurality of non-clustered nodes from the dot matrix as a plurality of third nodes; Class node search step: if there is a third node among the third nodes whose parameter vector distance to the cluster reference point is less than the density radius, then the third node whose parameter vector distance to the cluster reference point is less than the density radius is used as a class node, and the class node is added to the newly created node class; If a clustering starting point exists in the class node in the newly created node class, the clustering starting point in the class node is used as a clustering reference point, and the process jumps to the class node search step; Otherwise, if there are unclustered clustering starting points, jump to the cluster creation step.

5. The method for selecting a site for a groundwater pollution monitoring well according to any one of claims 1 to 4, characterized in that: The method of arranging groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation wells corresponding to the target monitoring vector includes: Obtaining the coordinates of the pollution source and the coordinates of multiple typical observation wells, wherein the coordinates of the typical observation wells are determined according to the target monitoring vector; Taking the coordinates of the pollution source as a starting point, constructing a first ray passing through the coordinates of each typical observation well; selecting a ray from the plurality of first rays as a background ray according to the angle between the first rays; selecting a plurality of rays from a plurality of first rays other than the background rays as a plurality of contamination diffusion rays; Arrange background monitoring wells on the background rays; Water pollution monitoring wells are laid out along the pollution diffusion rays.

6. The method for selecting a site for a groundwater pollution monitoring well according to claim 5, wherein: The step of selecting a ray from a plurality of first rays as a background ray according to the angle between the first rays includes: For each first ray, calculate the angle between two adjacent rays as the first angle; The first ray corresponding to the first angle with the largest value is taken as the background ray.

7. A groundwater pollution monitoring well site selection device, characterized in that: Used to implement the groundwater pollution monitoring well site selection method according to any one of claims 1 to 6, the groundwater pollution monitoring well site selection device comprises: A monitoring data acquisition module is used to obtain multiple groundwater monitoring vectors, wherein each groundwater monitoring vector corresponds to an observation well, and the groundwater monitoring vector is constructed based on multiple detection data representing groundwater quality; a node construction module, configured to, for each groundwater monitoring vector, select a plurality of target nodes from a plurality of first nodes based on the distance between the groundwater monitoring vector and the node, and adjust parameters of each target node based on the groundwater monitoring dataset, wherein the plurality of first nodes are arranged into a lattice in a preset order, and the first nodes are provided with a plurality of parameters having the same number of dimensions as the groundwater monitoring vector; a clustering module, configured to cluster the plurality of first parameter vectors into a plurality of node classes based on a density clustering method, and use the centers of the plurality of node classes as a plurality of first center vectors, wherein the first parameter vectors are vectors constructed according to a plurality of parameters of the first nodes, and each first parameter vector corresponds to a first node; as well as, The monitoring well site selection module is used to select, for each first central vector, the vector closest to the first central vector from the multiple groundwater monitoring vectors as the target monitoring vector, and to arrange groundwater pollution monitoring wells according to the coordinates of the pollution source and the coordinates of the observation well corresponding to the target monitoring vector.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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