A method and apparatus for determining a layout of detection points, and an electronic device

Abstract

This application provides a method for determining the layout of detection points. The method includes: acquiring the location information of N candidate detection points planned for a specified area; determining a first distance between every two candidate detection points based on the location information of each candidate detection point; generating a detection point network graph by creating edges between two nodes whose first distance satisfies the constraint condition, using the candidate detection points as nodes; acquiring the attribute value, centrality index, and utility index of each candidate detection point based on the network graph and the personnel information of each candidate detection point; and determining the target detection points for the final layout within the specified area from the N candidate detection points based on the attribute value, centrality index, and utility index of each candidate detection point. This application analyzes the detection point network graph and the personnel information of each candidate detection point, and optimizes the layout of the detection points based on the analysis results, making the layout of the detection points more scientific, systematic, and rational.

Description

Technical Field

[0001] This application relates to the field of monitoring layout technology, and in particular to a method, apparatus and electronic device for determining the layout of detection points. Background Technology

[0002] For testing points, it is crucial to lay them out in a reasonable and effective manner. However, in related technologies, the layout of testing points mainly considers personnel density factors, while giving less consideration to personnel structure factors, resulting in an unreasonable layout of testing points. Therefore, how to accurately and reasonably optimize the layout of testing points to improve its rationality has become an urgent problem to be solved. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first objective of this application is to propose a method for determining the layout of detection points, in order to solve the problems of unreasonable and inaccurate layout of detection points in the prior art.

[0005] To achieve the above objectives, a first aspect of this application provides a method for determining the layout of detection points. The method includes: acquiring position information of N candidate detection points planned for a specified layout within a set range, wherein N is an integer greater than or equal to 1; determining a first distance between every two candidate detection points based on the position information of each candidate detection point; generating a detection point network graph by using the candidate detection points as nodes and creating edges between two nodes whose first distance satisfies a constraint condition; acquiring attribute values, centrality indices, and utility indices of each candidate detection point based on the detection point network graph and personnel information of each candidate detection point; and determining target detection points for the final layout within the set range from the N candidate detection points based on the attribute values, centrality indices, and utility indices of each candidate detection point.

[0006] In addition, the method for determining the layout of detection points according to the above embodiments of this application may also have the following additional technical features:

[0007] According to one embodiment of this application, determining the target detection point to be finally deployed within the set range from the N candidate detection points based on the attribute value, centrality index, and utility index of each candidate detection point includes: under the constraints of optimal cost and detection coverage, obtaining the value of the decision factor of each candidate detection point based on the attribute value, centrality index, and utility index of each candidate detection point, wherein the value of the decision factor of the candidate detection point is used to indicate whether the candidate detection point is finally deployed; and determining the target detection point to be finally deployed within the set range from the N candidate detection points based on the value of the decision factor of each candidate detection point.

[0008] According to one embodiment of this application, the step of obtaining the value of the decision factor for each candidate detection point based on the attribute value, centrality index, and utility index of each candidate detection point under the constraints of optimal cost and detection coverage includes: constructing the decision factor for each candidate detection point; for any candidate detection point among the N candidate detection points, determining the cost factor and coverage factor of the candidate detection point based on the attribute value, centrality index, utility index, and decision factor of the candidate detection point; summing the cost factors and detection coverage factors of all candidate detection points to generate a layout satisfaction function; and optimizing the layout satisfaction function to obtain the value of the decision factor for each candidate detection point.

[0009] According to one embodiment of this application, the process of determining the cost factor of any candidate detection point includes: determining the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of the candidate detection point, wherein the layout evaluation value is used to indicate the importance of the candidate detection point being laid out; determining the total layout cost of the detection points and the cost of any candidate detection point; and determining the cost factor of any candidate detection point based on the cost of the candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

[0010] According to one embodiment of this application, the process of determining the coverage factor of any candidate detection point includes: determining the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of the candidate detection point; determining the total layout coverage area of ​​the detection points and the coverage area of ​​the candidate detection point, and determining a first detection coverage factor of the candidate detection point based on the coverage area, decision factor, layout evaluation value, and total coverage area of ​​the candidate detection point; obtaining one or more adjacent detection points of the candidate detection point, and determining the overlap area, layout evaluation value, and decision factor between the adjacent detection points and the candidate detection point; determining a second detection coverage factor of the candidate detection point based on the overlap area, layout evaluation value, and decision factor of the adjacent detection points, the decision factor and layout evaluation value of the candidate detection point, and the total layout coverage area; and determining the detection coverage factor of the candidate detection point based on the first and second detection coverage factors.

[0011] According to one embodiment of this application, the optimization solution of the layout satisfaction function to obtain the value of the determination factor of each candidate detection point includes: sorting the N candidate detection points in ascending order according to the layout evaluation value of the candidate detection points to obtain a detection point sequence; starting from the fact that the determination factor values ​​of the N candidate detection points are all first values, optimizing and iterating the layout satisfaction function; determining that the final number of un-layout detection points is Z in the z-th iteration, selecting the first Z candidate detection points from the detection point sequence, and determining that the determination factor values ​​of the first Z candidate detection points are second values ​​to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; wherein, Z≤N, and z is greater than or equal to 1; if the satisfaction value z is less than the satisfaction value z-1 in the (z-1)-th iteration, and is greater than or equal to a preset satisfaction threshold, the optimization iteration of the layout satisfaction function ends; wherein the determination factor values ​​of the first Z candidate detection points in the detection point sequence are second values, and the determination factor values ​​of the remaining candidate detection points are first values.

[0012] According to one embodiment of this application, the method further includes: if the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, determining that the layout satisfaction function needs to continue iterative optimization; determining the final number Z+1 of unlaid detection points in the (z+1)th iteration and executing subsequent iteration steps.

[0013] According to one embodiment of this application, the process of determining the centrality index of the candidate detection point includes: for any candidate detection point among the N candidate detection points, determining the node i corresponding to the candidate detection point and the number of edges of the edge of the node i according to the detection point network graph, and determining the degree centrality parameter of the candidate detection point i according to the number of edges of the node i, wherein 1≤i≤N; determining the adjacent nodes of the node i according to the detection point network graph, and determining the proximity centrality parameter of the candidate detection point according to the first distance between the adjacent nodes and the node i; determining any two nodes other than the node i that form a node pair, obtaining the candidate path between each node pair, determining the first path that passes through the node i and is the shortest path in the candidate path, and the number of the shortest paths among the candidate paths. The number of second paths in the shortest path; the betweenness centrality parameter of any candidate detection point is obtained based on the number of first paths and the number of second paths for each node pair; the feature vector of each node is obtained, and the center vector is obtained based on the feature vector of each node, and the adjacency matrix of node i is obtained, and the feature vector centrality parameter of any candidate detection point is obtained based on the adjacency matrix and the center vector; all neighboring nodes of node i and the degree of the neighboring nodes are determined, and the number of neighboring nodes with a degree greater than or equal to a set value is determined, and the H exponent is determined based on the number; the centrality index of each candidate detection point is determined based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the feature vector centrality parameter and the H exponent.

[0014] According to one embodiment of this application, determining the centrality index of each candidate detection point based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point includes: constructing a first matrix using the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point, wherein each row in the first matrix represents a candidate detection point and each column represents a parameter dimension; standardizing the first matrix to obtain a standardized second matrix; for each parameter dimension, determining the information entropy of that parameter dimension based on the matrix elements of that parameter dimension in each row of the second matrix, and determining the entropy weight of that parameter dimension based on the information entropy of that parameter dimension; for each candidate detection point, determining the row in the second matrix where the candidate detection point is located, and weighting the matrix elements of each parameter dimension in the row where the candidate detection point is located and the entropy weight of each parameter dimension to obtain the centrality index of the candidate detection point.

[0015] According to one embodiment of this application, the process of determining the utility index of the candidate detection point includes: for any candidate detection point among the N candidate detection points, determining K neighboring nodes of node i corresponding to any candidate detection point according to the detection point network graph, wherein 1≤K≤N-1; obtaining the centroid of node i and the K neighboring nodes; obtaining the second distance between the centroid of node i and node i; and obtaining the utility index of any candidate detection point according to the second distance and the detection coverage radius.

[0016] According to one embodiment of this application, the process of determining the attribute value of the candidate detection point includes: for any candidate detection point among the N candidate detection points, obtaining the personnel density and personnel structure information within the coverage radius of the candidate detection point; and weighting the personnel density and the personnel structure information to obtain the attribute value of the candidate detection point.

[0017] According to one embodiment of this application, after determining the target detection points to be finally laid out within the set range, the method further includes: constructing Thiessen polygons based on the Thiessen polygon theory, with different target detection points as the core, wherein the Thiessen polygons constructed from the target detection points represent the detection coverage area of ​​the target detection points.

[0018] To achieve the above objectives, a second aspect of this application provides a device for determining the layout of detection points. The device includes: a first acquisition module, configured to acquire position information of N candidate detection points planned for a layout within a set range, wherein N is an integer greater than or equal to 1; a first determination module, configured to determine a first distance between every two candidate detection points based on the position information of each candidate detection point; a generation module, configured to generate the detection point network graph by using the candidate detection points as nodes and creating edges between two nodes whose first distance satisfies a constraint condition; a second acquisition module, configured to acquire attribute values, centrality indices, and utility indices of each candidate detection point based on the detection point network graph and personnel information of each candidate detection point; and a second determination module, configured to determine the target detection point to be finally laid out within the set range from the N candidate detection points based on the attribute values, centrality indices, and utility indices of each candidate detection point.

[0019] In addition, the detection point layout determination device according to the above embodiments of this application may also have the following additional technical features:

[0020] According to one embodiment of this application, the second determining module is further configured to: under the constraints of optimal cost and detection coverage, obtain the value of the determination factor of each candidate detection point based on the attribute value, centrality index, and utility index of each candidate detection point, wherein the value of the determination factor of the candidate detection point is used to indicate whether the candidate detection point is ultimately deployed; and determine the target detection point to be deployed within the set range from the N candidate detection points based on the value of the determination factor of each candidate detection point.

[0021] According to one embodiment of this application, the second determining module is further configured to: construct a determination factor for each candidate detection point; for any candidate detection point among the N candidate detection points, determine the cost factor and coverage factor of the candidate detection point according to the attribute value, centrality index, utility index and determination factor of the candidate detection point; sum the cost factors and detection coverage factors of all candidate detection points to generate a layout satisfaction function; and perform optimization on the layout satisfaction function to obtain the value of the determination factor for each candidate detection point.

[0022] According to one embodiment of this application, the second determining module is further configured to: determine the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of any candidate detection point, wherein the layout evaluation value is used to indicate the importance of the layout of any candidate detection point; determine the total layout cost of the detection points and the cost of any candidate detection point, and determine the cost factor of any candidate detection point based on the cost of any candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

[0023] According to one embodiment of this application, the second determining module is further configured to: determine the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of any candidate detection point; determine the total layout coverage area of ​​the detection points and the coverage area of ​​any candidate detection point, and determine a first detection coverage factor of any candidate detection point based on the coverage area, decision factor, layout evaluation value, and total coverage area of ​​any candidate detection point; obtain one or more adjacent detection points of any candidate detection point, and determine the overlap area, layout evaluation value, and decision factor between the adjacent detection points and any candidate detection point; determine a second detection coverage factor of any candidate detection point based on the overlap area, layout evaluation value, and decision factor of the adjacent detection points, the decision factor and layout evaluation value of any candidate detection point, and the total layout coverage area; and determine the detection coverage factor of any candidate detection point based on the first and second detection coverage factors of the candidate detection points.

[0024] According to one embodiment of this application, the second determining module is further configured to: sort the N candidate detection points in ascending order according to the layout evaluation values ​​of the candidate detection points to obtain a detection point sequence; optimize and iterate the layout satisfaction function starting from the fact that the values ​​of the determination factors of the N candidate detection points are all first values; determine that the number of detection points that are not ultimately laid out is Z in the z-th iteration, select the first Z candidate detection points from the detection point sequence, and determine that the values ​​of the determination factors of the first Z candidate detection points are second values ​​to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; wherein, Z≤N, and z is greater than or equal to 1; if the satisfaction value z is less than the satisfaction value z-1 in the (z-1)-th iteration, and is greater than or equal to a preset satisfaction threshold, end the optimization and iteration of the layout satisfaction function; wherein the values ​​of the determination factors of the first Z candidate detection points in the detection point sequence are second values, and the values ​​of the determination factors of the remaining candidate detection points are first values.

[0025] According to one embodiment of this application, the apparatus is further configured to: if the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, determine that the layout satisfaction function needs to be iteratively optimized; and at the (z+1)th iteration, determine the final number of detection points that are not laid out, Z+1, and execute subsequent iteration steps.

[0026] According to one embodiment of this application, the second determining module is further configured to: for any candidate detection point among the N candidate detection points, determine the number of edges of node i corresponding to the candidate detection point and the edge of node i according to the detection point network graph, and determine the degree centrality parameter of the candidate detection point i according to the number of edges of node i, wherein 1≤i≤N; determine the adjacent nodes of node i according to the detection point network graph, and determine the proximity centrality parameter of the candidate detection point according to the first distance between the adjacent nodes and node i; determine any two nodes other than node i that form a node pair, and obtain the candidate path between each node pair, determine the number of first paths that pass through node i and are the shortest paths in the candidate paths, and the number of shortest paths in the candidate paths. The second path count; based on the first path count and the second path count for each node pair, the betweenness centrality parameter of any candidate detection point is obtained; the feature vector of each node is obtained, and the center vector is obtained based on the feature vector of each node, and the adjacency matrix of node i is obtained, and the feature vector centrality parameter of any candidate detection point is obtained based on the adjacency matrix and the center vector; all adjacent nodes of node i and the degree of the adjacent nodes are determined, and the number of adjacent nodes with a degree greater than or equal to a set value is determined, and the H exponent is determined based on the number; the centrality index of each candidate detection point is determined based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the feature vector centrality parameter and the H exponent.

[0027] According to one embodiment of this application, the second determining module is further configured to: construct a first matrix using the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point, wherein a row in the first matrix represents a candidate detection point and a column represents a parameter dimension; perform standardization processing on the first matrix to obtain a standardized second matrix; for each parameter dimension, determine the information entropy of the parameter dimension based on the matrix elements of the parameter dimension in each row of the second matrix, and determine the entropy weight of the parameter dimension based on the information entropy of the parameter dimension; for each candidate detection point, determine the row in the second matrix where the candidate detection point is located, and weight the matrix elements of each parameter dimension in the row where the candidate detection point is located and the entropy weight of each parameter dimension to obtain the centrality index of the candidate detection point.

[0028] According to one embodiment of this application, the second determining module is further configured to: for any candidate detection point among the N candidate detection points, determine K neighboring nodes of node i corresponding to any candidate detection point according to the detection point network graph, wherein 1≤K≤N-1; obtain the centroid of node i and the K neighboring nodes; obtain the second distance between the centroid of node i and node i; and obtain the utility index of any candidate detection point according to the second distance and the detection coverage radius.

[0029] According to one embodiment of this application, the second determining module is further configured to: for any candidate detection point among the N candidate detection points, obtain personnel density and personnel structure information within the coverage radius of the candidate detection point; and weight the personnel density and the personnel structure information to obtain the attribute value of the candidate detection point.

[0030] According to one embodiment of this application, the apparatus is further configured to: construct Thiessen polygons based on the Thiessen polygon theory, using different target detection points as the core, wherein the Thiessen polygons constructed from the target detection points constitute the detection coverage area of ​​the target detection points.

[0031] To achieve the above objectives, a third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned method for determining the layout of detection points.

[0032] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for determining the layout of detection points.

[0033] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the detection point layout determination method as described above.

[0034] The technical solutions provided in this application have at least the following beneficial effects:

[0035] This application provides a method for determining the layout of detection points. The method involves obtaining the location information of N candidate detection points within a defined range, where N is an integer greater than or equal to 1. Based on the location information of each candidate detection point, a first distance is determined between every two candidate detection points. Using the candidate detection points as nodes, edges are created between nodes whose first distance satisfies the constraint condition, generating a detection point network graph. Based on the detection point network graph and the personnel information of each candidate detection point, the attribute value, centrality index, and utility index of each candidate detection point are obtained. Based on the attribute value, centrality index, and utility index of each candidate detection point, the target detection points for the final layout within the defined range are determined from the N candidate detection points. Therefore, by analyzing the detection point network graph and the personnel information of each candidate detection point, and optimizing the layout of the detection points based on the analysis results, this application can make the layout of the detection points more scientific, systematic, and rational.

[0036] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0037] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein:

[0038] Figure 1 A flowchart illustrating a method for determining the layout of detection points provided in an embodiment of this application;

[0039] Figure 2 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0040] Figure 3 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0041] Figure 4 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0042] Figure 5 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0043] Figure 6 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0044] Figure 7 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0045] Figure 8 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0046] Figure 9 A flowchart illustrating another method for determining the layout of detection points provided in an embodiment of this application;

[0047] Figure 10 A schematic diagram illustrating the layout of detection points provided in an embodiment of this application;

[0048] Figure 11 A schematic diagram illustrating the detection coverage of the target detection points provided in the embodiments of this application;

[0049] Figure 12 This is a schematic diagram of the structure of a detection point layout determination device provided in an embodiment of this application;

[0050] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0052] The following embodiments provide a detailed description of the detection point layout determination method, apparatus, and electronic equipment of this application.

[0053] Figure 1 This is a flowchart illustrating a method for determining the layout of detection points provided in an embodiment of this application.

[0054] It should be noted that the execution entity of the detection point layout determination method in this embodiment is the detection point layout determination device, which can be a hardware device or software within a hardware device. The hardware device can be, for example, a terminal device or a server.

[0055] like Figure 1 As shown, the method for determining the layout of detection points proposed in this embodiment includes the following steps:

[0056] S101. Obtain the location information of N candidate detection points within the set range, where N is an integer greater than or equal to 1.

[0057] It should be noted that this application does not limit the specific method for obtaining the location information of N candidate detection points within the set range.

[0058] Optionally, the latitude and longitude information of N candidate detection points can be obtained, and the location information of the N candidate detection points can be determined based on the latitude and longitude information.

[0059] For example, if there are 20 candidate detection points in the planned layout within a set range, the latitude and longitude information of each of the 20 candidate detection points can be obtained to obtain the location information of the 20 candidate detection points.

[0060] S102. Based on the location information of each candidate detection point, determine the first distance between every two candidate detection points.

[0061] In this embodiment of the application, after obtaining the location information of the candidate detection points, the first distance between every two candidate detection points can be determined based on the location information of each candidate detection point.

[0062] It should be noted that the distance between any two candidate detection points may be the same or different.

[0063] S103. Using candidate detection points as nodes, and creating edges between two nodes whose first distance satisfies the constraint condition, generate a detection point network graph.

[0064] It should be noted that this application does not impose any restrictions on the setting of constraints, which can be set according to the actual situation.

[0065] Optionally, the constraint can be the walking distance of an adult in 15 minutes, where the average walking speed of an adult is about 1.5 m / s and the walking distance of an adult in 15 minutes is about 1350 m. 1350 m is denoted as the coverage radius R of the detection point.

[0066] For example, if the distance between two nodes is less than 2R, an edge is created between the two nodes to generate a detection point network graph; if the distance between two nodes is not less than 2R, no edge is created between the two nodes.

[0067] S104. Based on the network diagram of the detection points and the personnel information of each candidate detection point, obtain the attribute value, centrality index and utility index of each candidate detection point.

[0068] Optionally, for any candidate detection point among the N candidate detection points, the population density and population structure information within the coverage radius of any candidate detection point can be obtained, and the population density and population structure information can be weighted to obtain the attribute value of any candidate detection point.

[0069] It should be noted that the population density f within the coverage radius... aThe larger the area, the more important the detection point, especially considering personnel structure information. b The age of people within the coverage radius can be divided into three groups: 0-14 years old, 15-64 years old, and 65 years old and above. The total proportion of people aged 0-14 and 65 years old and above is denoted as f. b f b The larger the number, the more children and elderly people there are, and the more important the testing site is.

[0070] It should be noted that weights q can be set for personnel density and personnel structure information. a q b This allows us to obtain the attribute value f of any candidate detection point. i = a f a + b f b Among them, f a For personnel density, f b For personnel structure information, q a q b The weights corresponding to personnel density and personnel structure information, f i Let i be the attribute value of the candidate detection point i.

[0071] It should be noted that this disclosure does not limit the specific method for obtaining the centrality index, and the appropriate method can be selected according to the actual situation.

[0072] Optionally, the centrality index can be obtained based on the entropy weight method.

[0073] It should be noted that the lower the utility index of the candidate detection points, the higher the overlap rate of the candidate detection points within the set range.

[0074] S105. Based on the attribute value, centrality index, and utility index of each candidate detection point, determine the target detection points to be finally laid out within the set range from the N candidate detection points.

[0075] In this embodiment of the application, after obtaining the attribute value, centrality index and utility index of each candidate detection point, the target detection point to be finally laid out within a set range can be determined from N candidate detection points based on the attribute value, centrality index and utility index of each candidate detection point.

[0076] This application provides a method for determining the layout of detection points. It involves obtaining the location information of N candidate detection points within a defined range, where N is an integer greater than or equal to 1. Based on the location information of each candidate detection point, a first distance is determined between every two candidate detection points. Using the candidate detection points as nodes, edges are created between nodes whose first distance satisfies the constraint condition, generating a detection point network graph. Based on the detection point network graph and the personnel information of each candidate detection point, the attribute values, centrality index, and utility index of each candidate detection point are obtained. Based on these attributes, centrality index, and utility index, the target detection points for the final layout within the defined range are determined from the N candidate detection points. Therefore, this application, by analyzing the detection point network graph and the personnel information of each candidate detection point, and optimizing the layout of the detection points based on the analysis results, can make the layout of the detection points more scientific, systematic, and rational.

[0077] As one possible implementation, such as Figure 2 As shown, based on the above embodiments, the specific process of determining the target detection points to be finally laid out within a set range from N candidate detection points according to the attribute values, centrality index, and utility index of each candidate detection point includes the following steps:

[0078] S201. Under the constraints of optimal cost and detection coverage, obtain the value of the decision factor for each candidate detection point based on the attribute value, centrality index, and utility index of each candidate detection point. The value of the decision factor for each candidate detection point is used to indicate whether to deploy the candidate detection point in the end.

[0079] As one possible implementation, such as Figure 3 As shown, based on the above embodiments, the specific process of obtaining the value of the decision factor for each candidate detection point according to the attribute value, centrality index, and utility index of each candidate detection point under the constraints of optimal cost and detection coverage includes the following steps:

[0080] S301. Construct the decision factor for each candidate detection point.

[0081] It should be noted that by constructing a discriminant factor 'a' for each candidate detection point, it can be used to determine whether each candidate detection point should be established. The discriminant factor 'a' takes the value of 0 or 1. When the discriminant factor is 0 (a = 0), it indicates that the candidate detection point should not be established; when the discriminant factor is 1 (a = 1), it indicates that the candidate detection point should not be established. The discriminant factors for different candidate detection points can form a discriminant factor list L. a =[a1,a2,…, i ,…, N ].

[0082] S302. For any candidate detection point among N candidate detection points, determine the cost factor and coverage factor of any candidate detection point based on the attribute value, centrality index, utility index and decision factor of the candidate detection point.

[0083] As one possible implementation, such as Figure 4 As shown, based on the above embodiments, the specific process of determining the cost factor of any candidate detection point in the above steps includes the following steps:

[0084] S401. Based on the attribute value, centrality index, and utility index of any candidate detection point, determine the layout evaluation value of any candidate detection point. The layout evaluation value is used to indicate the importance of the layout of any candidate detection point.

[0085] Alternatively, the layout evaluation value of any candidate detection point can be determined according to the following formula. Among them, f i For any candidate detection point, u i For utility index, c i It is a centrality index.

[0086] S402. Determine the total layout cost of the detection points and the cost of any candidate detection point, and determine the cost factor of any candidate detection point based on the cost of any candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

[0087] Alternatively, the cost factor for any candidate detection point can be determined according to the following formula. Where P is the total cost of the detection point layout, P i Cost (fixed value) for any candidate detection point.

[0088] As one possible implementation, such as Figure 5 As shown, based on the above embodiments, the specific process of determining the coverage factor of any candidate detection point in the above steps includes the following steps:

[0089] S501. Determine the layout evaluation value of any candidate detection point based on its attribute value, centrality index, and utility index.

[0090] Alternatively, the layout evaluation value of any candidate detection point can be determined according to the following formula. Among them, h i For any candidate detection point, the layout evaluation value, f i For any candidate detection point, u i For utility index, c i It is a centrality index.

[0091] S502. Determine the total coverage area of ​​the detection points and the coverage area of ​​any candidate detection point, and determine the first detection coverage factor of any candidate detection point based on the coverage area of ​​any candidate detection point, the judgment factor, the layout evaluation value, and the total coverage area.

[0092] Alternatively, the first detection coverage factor for any candidate detection point can be determined according to the following formula. Among them, f i For any candidate detection point, u i For utility index, c i Centrality index, S i Let S be the coverage area of ​​any candidate detection point, and S be the total coverage area of ​​the detection points.

[0093] S503. Obtain one or more adjacent detection points of any candidate detection point, and determine the overlap area, layout evaluation value and decision factor of the adjacent detection point and any candidate detection point.

[0094] For example, the number k of adjacent detection points can be obtained. i The overlap area S between candidate detection point i and adjacent detection point j ij

[0095] S504. Based on the overlapping area, layout evaluation value, and decision factor of adjacent detection points, the decision factor and layout evaluation value of any candidate detection point, and the total layout coverage area, determine the second detection coverage factor of any candidate detection point.

[0096] Alternatively, the second detection coverage factor for any candidate detection point can be determined according to the following formula. Among them, f i For any candidate detection point, u i For utility index, c i Centrality index, S i Let S be the coverage area of ​​any candidate detection point, S be the total coverage area of ​​the detection points, and k be the total coverage area of ​​the detection points. i S represents the number of adjacent detection points. ij Let be the overlap area between candidate detection point i and adjacent detection point j.

[0097] S505. Determine the detection coverage factor of any candidate detection point based on the first and second detection coverage factors of the candidate detection points.

[0098] In this embodiment of the application, after obtaining the first detection coverage factor and the second detection coverage factor of the candidate detection point, the difference between the first detection coverage factor and the second detection coverage factor can be calculated to determine the detection coverage factor of any candidate detection point.

[0099] Alternatively, the detection coverage factor for any candidate detection point can be determined using the following formula:

[0100]

[0101] Among them, f i For any candidate detection point, u i For utility index, c i Centrality index, S i Let S be the coverage area of ​​any candidate detection point, S be the total coverage area of ​​the detection points, and k be the total coverage area of ​​the detection points. i S represents the number of adjacent detection points. ij Let be the overlap area between candidate detection point i and adjacent detection point j.

[0102] S303. Sum the cost factors and detection coverage factors of all candidate detection points to generate a layout satisfaction function.

[0103] For example, the layout satisfaction function M(a) can be obtained using the following formula:

[0104]

[0105] Where P is the total cost of the detection point layout, P i For the cost of any candidate detection point, f i For any candidate detection point, u i For utility index, c i Centrality index, S i Let S be the coverage area of ​​any candidate detection point, S be the total coverage area of ​​the detection points, and k be the total coverage area of ​​the detection points. i S represents the number of adjacent detection points. ij Let N be the overlap area between candidate detection point i and its adjacent detection point j, and N be the number of candidate detection points.

[0106] S304. Optimize the layout satisfaction function to obtain the value of the decision factor for each candidate detection point.

[0107] As one possible implementation, such as Figure 6 As shown, based on the above embodiments, the specific process of optimizing the layout satisfaction function in the above steps to obtain the value of the decision factor for each candidate detection point includes the following steps:

[0108] S601. Sort the N candidate detection points in ascending order according to their layout evaluation values ​​to obtain the detection point sequence.

[0109] Optionally, based on the layout evaluation values ​​of the candidate detection points, the N candidate detection points can be sorted in ascending order according to their layout evaluation values ​​to obtain the detection point sequence [h1, h2, ..., h]. i ,…,h N ].

[0110] S602. Starting from the first value of the judgment factor of N candidate detection points, optimize and iterate the layout satisfaction function.

[0111] For example, the discriminant sequence L can be initialized. a =[a1,a2,…,a i ,…,a N Starting with the decision factor for all N candidate detection points being the first value 1, all candidate detection points are constructed, and the current satisfaction level is obtained as M0. Simultaneously, a satisfaction threshold is set.

[0112] S603. In the z-th iteration, determine the final number of unplaced detection points as Z, select the first Z candidate detection points from the detection point sequence, and determine the value of the judgment factor of the first Z candidate detection points as the second value, so as to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; where Z≤N, z is greater than or equal to 1.

[0113] It should be noted that when the second value is 0, no candidate detection points will be constructed. When Z equals 1, one candidate detection point will not be constructed.

[0114] S604. If the satisfaction value z is less than the satisfaction value z-1 of the (z-1)th iteration, and is greater than or equal to the preset satisfaction threshold, the optimization iteration of the layout satisfaction function ends.

[0115] Among them, the determination factor of the first Z candidate detection points in the detection point sequence is the second value, and the determination factor of the remaining candidate detection points is the first value.

[0116] Optionally, if the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, it is determined that the layout satisfaction function needs to be iteratively optimized. In the z+1th iteration, the final number of unlaid detection points Z+1 is determined and subsequent iteration steps are executed.

[0117] As one possible implementation, such as Figure 7 As shown, based on the above embodiments, the specific process of determining the centrality index of candidate detection points in the above steps includes the following steps:

[0118] S701. For any candidate detection point among N candidate detection points, determine the node i and the number of edges of node i corresponding to any candidate detection point according to the detection point network graph, and determine the degree centrality parameter of candidate detection point i according to the number of edges of node i, 1≤i≤N.

[0119] Alternatively, the degree centrality parameter of candidate detection point i can be determined according to the following formula. Among them, DC i Let k be the degree centrality parameter of candidate detection point i. i Let i be the node i corresponding to the candidate detection point and the number of edges of the edge corresponding to node i.

[0120] S702. Based on the detection point network diagram, determine the adjacent nodes of node i, and based on the first distance between the adjacent nodes and node i, determine the proximity centrality parameter of any candidate detection point.

[0121] Alternatively, the proximity centrality parameter of any candidate detection point can be determined according to the following formula. Among them, CC i Let d be the proximity centrality parameter of any candidate detection point. i Let d be the average distance between node i and the other nodes. ij Let be the first distance between node i and node j.

[0122] S703. Determine any two nodes other than node i to form a node pair, and obtain the candidate path between each node pair. Determine the number of first paths that pass through node i and are the shortest paths in the candidate paths, and the number of second paths that are the shortest paths in the candidate paths.

[0123] S704. Based on the number of first paths and the number of second paths for each node pair, obtain the betweenness centrality parameter of any candidate detection point.

[0124] Alternatively, the betweenness centrality parameter of any candidate detection point can be determined according to the following formula. Among them BC i For any candidate detection point, the betweenness centrality parameter, For the number of first paths, g st This represents the number of second paths.

[0125] S705. Obtain the feature vector of each node, and obtain the center vector based on the feature vector of each node, as well as the adjacency matrix of node i. Based on the adjacency matrix and the center vector, obtain the feature vector centrality parameter of any candidate detection point.

[0126] Alternatively, the eigenvector centrality parameter of any candidate detection point can be determined according to the following formula. Among them, c eλ is the center vector, λ is the eigenvalue corresponding to the center vector, and A is the adjacency matrix of node i.

[0127] S706. Determine all adjacent nodes of node i and their degrees, and determine the number of adjacent nodes with a degree greater than or equal to a set value. Determine the H index based on the number of adjacent nodes.

[0128] Optionally, determine all adjacent nodes of node i and their degrees, and determine the number of adjacent nodes with a degree greater than or equal to a set value. The set value can be set to H, and the H exponent can be determined based on the number.

[0129] S707. Determine the centrality index of each candidate detection point based on its degree centrality parameter, proximity centrality parameter, betweenness centrality parameter, eigenvector centrality parameter, and H-index.

[0130] As one possible implementation, such as Figure 8 As shown, based on the above embodiments, the specific process of determining the centrality index of each candidate detection point according to the degree centrality parameter, proximity centrality parameter, betweenness centrality parameter, eigenvector centrality parameter, and H-index in the above steps includes the following steps:

[0131] S801. Construct a first matrix using the degree centrality parameter, proximity centrality parameter, betweenness centrality parameter, eigenvector centrality parameter, and H-index of each candidate detection point. In the first matrix, one row represents a candidate detection point, and one column represents a parameter dimension.

[0132] In this embodiment of the application, after obtaining the degree centrality parameter, proximity centrality parameter, betweenness centrality parameter, eigenvector centrality parameter and H exponent of each candidate detection point, a first matrix can be constructed.

[0133] For example, the first matrix In the first matrix, each row represents a candidate detection point, each column represents a parameter dimension, and m is the number of centrality indices.

[0134] S802. Standardize the first matrix to obtain a standardized second matrix.

[0135] Alternatively, matrix P can be standardized according to the following formula to obtain the matrix elements. Then, a standardized matrix B is obtained.

[0136] S803. For each parameter dimension, determine the information entropy of that parameter dimension based on the matrix elements of that parameter dimension in each row of the second matrix, and determine the entropy weight of that parameter dimension based on the information entropy of that parameter dimension.

[0137] For example, the information entropy S of this parameter dimension can be determined using the following formula. j :

[0138] K = 1 / ln N Among them, b ij S is a matrix element. j The information entropy of this parameter dimension.

[0139] Optionally, after obtaining the information entropy, the entropy weight of this parameter dimension can be determined according to the following formula. Where, ω j For the entropy weight of this parameter dimension, S j The information entropy of this parameter dimension.

[0140] S804. For each candidate detection point, determine the row in which the candidate detection point is located in the second matrix, and weight the matrix elements of each parameter dimension and the entropy weight of each parameter dimension in the row in which the candidate detection point is located to obtain the centrality index of the candidate detection point.

[0141] For example, the centrality index c of the candidate detection point can be obtained by weighting the matrix elements of each parameter dimension and the entropy weight of each parameter dimension in the row where the candidate detection point is located. i :

[0142] c i =S1*b i1 +S2*b i2 +…+S m *b im

[0143] Among them, c i Centrality index of candidate detection points, S1… m Entropy weights for each parameter dimension, b i1 … im Matrix elements for each parameter dimension.

[0144] Furthermore, a list L of centrality indices can be constructed based on the centrality index of each candidate detection point. C =[c1,c2,…,c i ,…,c N ].

[0145] As one possible implementation, such as Figure 9 As shown, based on the above embodiments, the specific process of determining the utility index of candidate detection points in the above steps includes the following steps:

[0146] S901. For any candidate detection point among N candidate detection points, determine the K adjacent nodes of node i corresponding to any candidate detection point according to the detection point network graph, where 1≤K≤N-1.

[0147] It should be noted that, based on the detection point network graph, the K neighboring nodes of node i corresponding to any candidate detection point can be determined. The list of neighboring nodes of node i can be set as [N1, N2, ..., N]. k ], 1≤K≤N-1.

[0148] S902. Obtain the centroid of node i and its K neighboring nodes.

[0149] Optionally, node i and its K neighboring nodes [N1, N2, ..., N] can be obtained. k The centroid of the k+1 nodes formed by [G] is denoted as G. i .

[0150] S903. Obtain the centroid of node i and the second distance between node i and node i.

[0151] It should be noted that the centroid G corresponding to node i can be calculated. i The second distance from node i is denoted as Among them, the second distance The smaller the value, the higher the overlap rate within the detection coverage radius of node i, and the smaller the utility index.

[0152] S904. Based on the second distance and the detection coverage radius, obtain the utility index of any candidate detection point.

[0153] In this embodiment of the application, after obtaining the second distance and the detection coverage radius, the utility index of any candidate detection point can be obtained based on the second distance and the detection coverage radius.

[0154] For example, the utility index of any candidate detection point can be obtained using the following formula. Among them, u i R is the utility index of any candidate detection point, and R is the detection coverage radius. This is the second distance.

[0155] Furthermore, a utility index list L can be constructed based on the utility index of each candidate detection point. u =[u1,u2,…,u i ,…,u N ].

[0156] S202. Based on the value of the determination factor for each candidate detection point, determine the target detection points to be finally laid out within the set range from the N candidate detection points.

[0157] In this embodiment of the application, after obtaining the value of the determination factor of each candidate detection point, the target detection point to be finally laid out within the set range can be determined from N candidate detection points based on the value of the determination factor of each candidate detection point.

[0158] For example, such as Figure 10 As shown, when there are 10 detection points within the set range, the attribute values ​​of the 10 detection points are obtained, L f =[f1,f2,…,f i ,…,f 10 ], Centrality index of 10 detection points, L C =[c1,c2,…,c i ,…,c 10 The utility index of 10 testing points, L u =[u1,u2,…,u i ,…,u 10 Then, the layout satisfaction function is optimized to obtain the value of the decision factor for each candidate detection point, thus obtaining the decision factor sequence L. a =[a1,a2,…,a i ,…,a 10 The values ​​of a4=0 and a5=0 indicate that the overlap rate between detection points 4 and 5 and other detection points is the highest. Therefore, the necessity of detection points 4 and 5 is low, and it is not recommended to construct detection points 4 and 5.

[0159] Optionally, based on the Thiessen polygon theory, Thiessen polygons can be constructed with different target detection points as the core, wherein the Thiessen polygons constructed by the target detection points represent the detection coverage area of ​​the target detection points.

[0160] It should be noted that, based on the Thiessen polygon theory, Thiessen polygons are constructed with different target detection points as the core. Each Thiessen polygon represents the service range of the detection point, so as to reasonably allocate the detection point tasks and optimize the problem of the service blind spot of the detection point. The service blind spot is the area (people) that is not covered by the detection coverage radius.

[0161] For example, after optimizing the layout satisfaction function, i.e., when the cost factor and coverage factor are optimally balanced, the service blind spot problem can be addressed by using Thiessen polygon theory to allocate it to nearby detection points, thus optimizing the service blind spot problem. Figure 11 The diagram shows the detection coverage of the target detection points after layout optimization (detection points 4 and 5 are not constructed).

[0162] Therefore, this application analyzes the network diagram of testing points and the personnel information of each candidate testing point, quantitatively characterizes the problem of overlapping service areas and service blind spots of testing points through the utility index, and then solves the optimal testing point layout scheme under multiple factors by comprehensively considering testing point attributes, costs, and service satisfaction factors, making the layout of testing points more scientific, systematic and reasonable.

[0163] To implement the above embodiments, this embodiment provides a device for determining the layout of detection points. Figure 12 This is a schematic diagram of a detection point layout determination device provided in an embodiment of this application.

[0164] like Figure 12 As shown, the layout determination device 1000 for the detection points includes: a first acquisition module 110, a first determination module 120, a generation module 130, a second acquisition module 140, and a second determination module 140. Wherein,

[0165] The first acquisition module 110 is used to acquire the position information of N candidate detection points planned and laid out within a set range, wherein N is an integer greater than or equal to 1;

[0166] The first determining module 120 is used to determine the first distance between every two candidate detection points based on the position information of each candidate detection point;

[0167] The generation module 130 is used to generate the detection point network graph by taking the candidate detection points as nodes and creating edges between two nodes whose first distance satisfies the constraint condition.

[0168] The second acquisition module 140 is used to acquire the attribute value, centrality index and utility index of each candidate detection point based on the detection point network diagram and the personnel information of each candidate detection point;

[0169] The second determining module 150 is used to determine the target detection points to be finally laid out within the set range from the N candidate detection points based on the attribute values, centrality index and utility index of each candidate detection point.

[0170] According to one embodiment of this application, the second determining module 150 is further configured to: under the constraints of optimal cost and detection coverage, obtain the value of the determination factor of each candidate detection point based on the attribute value, centrality index, and utility index of each candidate detection point, wherein the value of the determination factor of the candidate detection point is used to indicate whether the candidate detection point is ultimately deployed; and determine the target detection point to be deployed within the set range from the N candidate detection points based on the value of the determination factor of each candidate detection point.

[0171] According to one embodiment of this application, the second determining module 150 is further configured to: construct a determination factor for each candidate detection point; for any candidate detection point among the N candidate detection points, determine the cost factor and coverage factor of the candidate detection point according to the attribute value, centrality index, utility index and determination factor of the candidate detection point; sum the cost factors and detection coverage factors of all candidate detection points to generate a layout satisfaction function; and perform optimization on the layout satisfaction function to obtain the value of the determination factor for each candidate detection point.

[0172] According to one embodiment of this application, the second determining module 150 is further configured to: determine the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of any candidate detection point, wherein the layout evaluation value is used to indicate the importance of the layout of any candidate detection point; determine the total layout cost of the detection points and the cost of any candidate detection point, and determine the cost factor of any candidate detection point based on the cost of any candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

[0173] According to one embodiment of this application, the second determining module 150 is further configured to: determine the layout evaluation value of any candidate detection point based on the attribute value, centrality index, and utility index of any candidate detection point; determine the total layout coverage area of ​​the detection points and the coverage area of ​​any candidate detection point, and determine a first detection coverage factor of any candidate detection point based on the coverage area, decision factor, layout evaluation value, and total coverage area of ​​any candidate detection point; obtain one or more adjacent detection points of any candidate detection point, and determine the overlap area, layout evaluation value, and decision factor between the adjacent detection points and any candidate detection point; determine a second detection coverage factor of any candidate detection point based on the overlap area, layout evaluation value, and decision factor of the adjacent detection points, the decision factor and layout evaluation value of any candidate detection point, and the total layout coverage area; and determine the detection coverage factor of any candidate detection point based on the first and second detection coverage factors of the candidate detection points.

[0174] According to one embodiment of this application, the second determining module 150 is further configured to: sort the N candidate detection points in ascending order according to the layout evaluation values ​​of the candidate detection points to obtain a detection point sequence; optimize and iterate the layout satisfaction function starting from the fact that the values ​​of the determination factors of the N candidate detection points are all first values; determine that the number of detection points that are not ultimately laid out is Z in the z-th iteration, select the first Z candidate detection points from the detection point sequence, and determine that the values ​​of the determination factors of the first Z candidate detection points are second values ​​to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; wherein, Z≤N, and z is greater than or equal to 1; if the satisfaction value z is less than the satisfaction value z-1 in the (z-1)-th iteration, and is greater than or equal to a preset satisfaction threshold, end the optimization and iteration of the layout satisfaction function; wherein the values ​​of the determination factors of the first Z candidate detection points in the detection point sequence are second values, and the values ​​of the determination factors of the remaining candidate detection points are first values.

[0175] According to one embodiment of this application, the apparatus 1000 is further configured to: if the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, determine that the layout satisfaction function needs to be iteratively optimized; and at the (z+1)th iteration, determine the final number of detection points that are not laid out, Z+1, and execute subsequent iteration steps.

[0176] According to one embodiment of this application, the second determining module 150 is further configured to: for any candidate detection point among the N candidate detection points, determine the node i corresponding to the candidate detection point and the number of edges of the edge of the node i according to the detection point network graph, and determine the degree centrality parameter of the candidate detection point i according to the number of edges of the node i, wherein 1≤i≤N; determine the adjacent nodes of the node i according to the detection point network graph, and determine the proximity centrality parameter of the candidate detection point according to the first distance between the adjacent nodes and the node i; determine any two nodes other than the node i that form a node pair, and obtain the candidate path between each node pair, determine the number of first paths in the candidate paths that pass through the node i and are the shortest paths, and the number of shortest paths in the candidate paths. The second path count; based on the first path count and the second path count for each node pair, the betweenness centrality parameter of any candidate detection point is obtained; the feature vector of each node is obtained, and the center vector is obtained based on the feature vector of each node, and the adjacency matrix of node i is obtained, and the feature vector centrality parameter of any candidate detection point is obtained based on the adjacency matrix and the center vector; all adjacent nodes of node i and the degree of the adjacent nodes are determined, and the number of adjacent nodes with a degree greater than or equal to a set value is determined, and the H exponent is determined based on the number; the centrality index of each candidate detection point is determined based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the feature vector centrality parameter and the H exponent.

[0177] According to one embodiment of this application, the second determining module 150 is further configured to: construct a first matrix using the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point, wherein a row in the first matrix represents a candidate detection point and a column represents a parameter dimension; perform standardization processing on the first matrix to obtain a standardized second matrix; for each parameter dimension, determine the information entropy of the parameter dimension based on the matrix elements of the parameter dimension in each row of the second matrix, and determine the entropy weight of the parameter dimension based on the information entropy of the parameter dimension; for each candidate detection point, determine the row in the second matrix where the candidate detection point is located, and weight the matrix elements of each parameter dimension in the row where the candidate detection point is located and the entropy weight of each parameter dimension to obtain the centrality index of the candidate detection point.

[0178] According to one embodiment of this application, the second determining module 150 is further configured to: for any candidate detection point among the N candidate detection points, determine K neighboring nodes of node i corresponding to any candidate detection point according to the detection point network graph, wherein 1≤K≤N-1; obtain the centroid of node i and the K neighboring nodes; obtain the second distance between the centroid of node i and node i; and obtain the utility index of any candidate detection point according to the second distance and the detection coverage radius.

[0179] According to one embodiment of this application, the second determining module 150 is further configured to: for any candidate detection point among the N candidate detection points, obtain personnel density and personnel structure information within the coverage radius of the candidate detection point; and weight the personnel density and the personnel structure information to obtain the attribute value of the candidate detection point.

[0180] According to one embodiment of this application, the apparatus 1000 is further configured to: construct a Thiessen polygon based on the Thiessen polygon theory, using different target detection points as the core, wherein the Thiessen polygon constructed from the target detection points is the detection coverage area of ​​the target detection points.

[0181] According to the detection point layout determination device provided in this application, the location information of N candidate detection points planned for a set range is obtained, wherein N is an integer greater than or equal to 1; based on the location information of each candidate detection point, a first distance between every two candidate detection points is determined; using the candidate detection points as nodes, edges are created between two nodes whose first distance satisfies the constraint condition to generate the detection point network graph; based on the detection point network graph and the personnel information of each candidate detection point, the attribute value, centrality index, and utility index of each candidate detection point are obtained; based on the attribute value, centrality index, and utility index of each candidate detection point, the target detection point to be finally laid out within the set range is determined from the N candidate detection points. Therefore, by analyzing the detection point network graph and the personnel information of each candidate detection point, and optimizing and improving the layout of the detection points based on the analysis results, this application can make the layout of the detection points more scientific, systematic, and rational.

[0182] To implement the above embodiments, this application also proposes an electronic device 3000, such as... Figure 13 As shown, it includes a memory 310, a processor 320, and a computer program stored in the memory 310 and capable of running on the processor 320. When the processor executes the program, it implements the aforementioned method for determining the layout of detection points.

[0183] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned method for determining the layout of detection points.

[0184] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the detection point layout determination method as described above.

[0185] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0186] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining the layout of detection points, characterized in that, The method includes: Obtain the location information of N candidate detection points within a set range, where N is an integer greater than or equal to 1; Based on the location information of each candidate detection point, determine the first distance between every two candidate detection points; Using the candidate detection points as nodes, and creating edges between two nodes whose first distance satisfies the constraint condition, the detection point network graph is generated. Based on the network diagram of the detection points and the personnel information of each candidate detection point, the attribute values, centrality index, and utility index of each candidate detection point are obtained. The process of determining the attribute values ​​of the candidate detection points includes: For any candidate detection point among the N candidate detection points, obtain the personnel density and personnel structure information within the coverage radius of any candidate detection point; The attribute value of any candidate detection point is obtained by weighting the personnel density and the personnel structure information. The process of determining the centrality index of the candidate detection points includes: For any candidate detection point among the N candidate detection points, based on the detection point network graph, determine the node i corresponding to the candidate detection point and the number of edges of the node i, and determine the degree centrality parameter of the candidate detection point i based on the number of edges of the node i, where 1≤i≤N; Based on the detection point network diagram, the neighboring nodes of node i are determined, and based on the first distance between the neighboring nodes and node i, the proximity centrality parameter of any candidate detection point is determined. Determine any two nodes other than node i to form a node pair, and obtain candidate paths between each node pair. Determine the number of first paths that pass through node i and are the shortest paths in the candidate paths, and the number of second paths that are the shortest paths in the candidate paths. Based on the number of the first path and the number of the second path for each node pair, the betweenness centrality parameter of any candidate detection point is obtained; Obtain the feature vector of each node, and obtain the center vector based on the feature vector of each node, and obtain the adjacency matrix of node i. Based on the adjacency matrix and the center vector, obtain the feature vector centrality parameter of any candidate detection point. Determine all adjacent nodes of node i and the degree of each adjacent node, and determine the number of adjacent nodes whose degree is greater than or equal to a set value, and determine the H index based on the number of adjacent nodes; The centrality index of each candidate detection point is determined based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index. The process of determining the utility index of the candidate detection points includes: For any candidate detection point among the N candidate detection points, K adjacent nodes of node i corresponding to any candidate detection point are determined according to the detection point network graph, wherein 1≤K≤N-1; Obtain the centroid of node i and its K neighboring nodes; Obtain the centroid of node i and its second distance from node i; The utility index of any candidate detection point is obtained based on the second distance and the detection coverage radius; Based on the attribute values, centrality index, and utility index of each candidate detection point, the target detection points to be finally deployed within the set range are determined from the N candidate detection points, including: Under the constraints of optimal cost and detection coverage, the value of the decision factor for each candidate detection point is obtained based on the attribute value, centrality index, and utility index of each candidate detection point. The value of the decision factor for each candidate detection point is used to indicate whether the candidate detection point is ultimately deployed. Based on the value of the determination factor for each candidate detection point, the target detection points to be finally laid out within the set range are determined from the N candidate detection points.

2. The method according to claim 1, characterized in that, Under the constraints of optimal cost and detection coverage, the determination factor for each candidate detection point is obtained based on its attribute value, centrality index, and utility index, including: Construct the decision factors for each candidate detection point; For any candidate detection point among the N candidate detection points, the cost factor and coverage factor of the candidate detection point are determined according to the attribute value, centrality index, utility index and decision factor of the candidate detection point, respectively. Sum the cost factor and detection coverage factor of all candidate detection points to generate a layout satisfaction function; The layout satisfaction function is optimized to obtain the value of the decision factor for each candidate detection point.

3. The method according to claim 2, characterized in that, The process of determining the cost factor for any candidate detection point includes: Based on the attribute value, centrality index, and utility index of any candidate detection point, a layout evaluation value for any candidate detection point is determined, and the layout evaluation value is used to indicate the importance of the layout of any candidate detection point. Determine the total layout cost of the detection points and the cost of any candidate detection point, and determine the cost factor of any candidate detection point based on the cost of the candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

4. The method according to claim 2, characterized in that, The process of determining the coverage factor of any candidate detection point includes: The layout evaluation value of any candidate detection point is determined based on its attribute value, centrality index, and utility index. Determine the total coverage area of ​​the detection points and the coverage area of ​​any candidate detection point, and determine the first detection coverage factor of any candidate detection point based on the coverage area, decision factor, layout evaluation value and the total coverage area; Obtain one or more adjacent detection points of any candidate detection point, and determine the overlap area, layout evaluation value, and decision factor of the adjacent detection point and any candidate detection point; Based on the overlapping area, layout evaluation value, and decision factor of the adjacent detection points, the decision factor and layout evaluation value of any candidate detection point, and the total layout coverage area, the second detection coverage factor of any candidate detection point is determined. The detection coverage factor of any candidate detection point is determined based on the first detection coverage factor and the second detection coverage factor of the candidate detection points.

5. The method according to any one of claims 2-4, characterized in that, The optimization of the layout satisfaction function to obtain the value of the decision factor for each candidate detection point includes: Based on the layout evaluation values ​​of the candidate detection points, the N candidate detection points are sorted in ascending order to obtain a detection point sequence; Starting from the fact that the values ​​of the determination factors for the N candidate detection points are all the first value, the layout satisfaction function is optimized and iterated. In the z-th iteration, the number of detection points that are not ultimately placed is determined to be Z. The first Z candidate detection points are selected from the detection point sequence, and the determination factor of the first Z candidate detection points is determined to be a second value, so as to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; wherein, Z≤N, and z is greater than or equal to 1. If the satisfaction value z is less than the satisfaction value z-1 of the (z-1)th iteration, and is greater than or equal to the preset satisfaction threshold, the optimization iteration of the layout satisfaction function ends. The determination factor of the first Z candidate detection points in the detection point sequence is a second value, and the determination factor of the remaining candidate detection points is a first value.

6. The method according to claim 5, characterized in that, The method further includes: If the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, it is determined that the layout satisfaction function needs to be iteratively optimized. In the (z+1)th iteration, determine the final number of unplaced detection points Z+1 and execute subsequent iteration steps.

7. The method according to claim 1, characterized in that, The step of determining the centrality index of each candidate detection point based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point includes: A first matrix is ​​constructed using the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the feature vector centrality parameter, and the H-index of each candidate detection point, wherein each row in the first matrix represents a candidate detection point and each column represents a parameter dimension. The first matrix is ​​standardized to obtain a standardized second matrix; For each parameter dimension, the information entropy of that parameter dimension is determined based on the matrix elements of that parameter dimension in each row of the second matrix, and the entropy weight of that parameter dimension is determined based on the information entropy of that parameter dimension. For each candidate detection point, the row in which the candidate detection point is located in the second matrix is ​​determined, and the matrix elements of each parameter dimension and the entropy weight of each parameter dimension in the row in which the candidate detection point is located are weighted to obtain the centrality index of the candidate detection point.

8. The method according to any one of claims 1-4, characterized in that, After determining the target detection points for the final layout within the defined range, the process also includes: Based on the Thiessen polygon theory, Thiessen polygons are constructed with different target detection points as the core, wherein the Thiessen polygons constructed by the target detection points represent the detection coverage area of ​​the target detection points.

9. A device for determining the layout of detection points, characterized in that, include: The first acquisition module is used to acquire the location information of N candidate detection points planned and laid out within a set range, wherein N is an integer greater than or equal to 1; The first determining module is used to determine the first distance between every two candidate detection points based on the location information of each candidate detection point; The generation module is used to generate the detection point network graph by taking the candidate detection points as nodes and creating edges between two nodes whose first distance satisfies the constraint condition. The second acquisition module is used to acquire the attribute value, centrality index and utility index of each candidate detection point based on the detection point network diagram and the personnel information of each candidate detection point; The second determining module is used to determine the target detection points to be finally laid out within the set range from the N candidate detection points based on the attribute values, centrality index and utility index of each candidate detection point. The second acquisition module is further configured to: For any candidate detection point among the N candidate detection points, obtain the personnel density and personnel structure information within the coverage radius of any candidate detection point; The attribute value of any candidate detection point is obtained by weighting the personnel density and the personnel structure information. For any candidate detection point among the N candidate detection points, K adjacent nodes of node i corresponding to any candidate detection point are determined according to the detection point network graph, wherein 1≤K≤N-1; Obtain the centroid of node i and its K neighboring nodes; Obtain the centroid of node i and its second distance from node i; The utility index of any candidate detection point is obtained based on the second distance and the detection coverage radius; For any candidate detection point among the N candidate detection points, based on the detection point network graph, determine the node i corresponding to the candidate detection point and the number of edges of the node i, and determine the degree centrality parameter of the candidate detection point i based on the number of edges of the node i, where 1≤i≤N; Based on the detection point network diagram, the neighboring nodes of node i are determined, and based on the first distance between the neighboring nodes and node i, the proximity centrality parameter of any candidate detection point is determined. Determine any two nodes other than node i to form a node pair, and obtain candidate paths between each node pair. Determine the number of first paths that pass through node i and are the shortest paths in the candidate paths, and the number of second paths that are the shortest paths in the candidate paths. Based on the number of the first path and the number of the second path for each node pair, the betweenness centrality parameter of any candidate detection point is obtained; Obtain the feature vector of each node, and obtain the center vector based on the feature vector of each node, and obtain the adjacency matrix of node i. Based on the adjacency matrix and the center vector, obtain the feature vector centrality parameter of any candidate detection point. Determine all adjacent nodes of node i and the degree of each adjacent node, and determine the number of adjacent nodes whose degree is greater than or equal to a set value, and determine the H index based on the number of adjacent nodes; Based on the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the eigenvector centrality parameter, and the H-index of each candidate detection point, the centrality index of each candidate detection point is determined. The second determining module is further configured to: Under the constraints of optimal cost and detection coverage, the value of the decision factor for each candidate detection point is obtained based on the attribute value, centrality index, and utility index of each candidate detection point. The value of the decision factor for each candidate detection point is used to indicate whether the candidate detection point is ultimately deployed. Based on the value of the determination factor for each candidate detection point, the target detection points to be finally laid out within the set range are determined from the N candidate detection points.

10. The detection point layout determination device according to claim 9, characterized in that, The second determining module is further configured to: Construct the decision factors for each candidate detection point; For any candidate detection point among the N candidate detection points, the cost factor and coverage factor of the candidate detection point are determined according to the attribute value, centrality index, utility index and decision factor of the candidate detection point, respectively. Sum the cost factor and detection coverage factor of all candidate detection points to generate a layout satisfaction function; The layout satisfaction function is optimized to obtain the value of the decision factor for each candidate detection point.

11. The detection point layout determination device according to claim 10, characterized in that, The second determining module is further configured to: Based on the attribute value, centrality index, and utility index of any candidate detection point, a layout evaluation value for any candidate detection point is determined, and the layout evaluation value is used to indicate the importance of the layout of any candidate detection point. Determine the total layout cost of the detection points and the cost of any candidate detection point, and determine the cost factor of any candidate detection point based on the cost of the candidate detection point, the decision factor, the layout evaluation value, and the total layout cost.

12. The detection point layout determination device according to claim 10, characterized in that, The second determining module is further configured to: The layout evaluation value of any candidate detection point is determined based on its attribute value, centrality index, and utility index. Determine the total coverage area of ​​the detection points and the coverage area of ​​any candidate detection point, and determine the first detection coverage factor of any candidate detection point based on the coverage area, decision factor, layout evaluation value and the total coverage area; Obtain one or more adjacent detection points of any candidate detection point, and determine the overlap area, layout evaluation value, and decision factor of the adjacent detection point and any candidate detection point; Based on the overlapping area, layout evaluation value, and decision factor of the adjacent detection points, the decision factor and layout evaluation value of any candidate detection point, and the total layout coverage area, the second detection coverage factor of any candidate detection point is determined. The detection coverage factor of any candidate detection point is determined based on the first detection coverage factor and the second detection coverage factor of the candidate detection points.

13. The apparatus for determining the layout of detection points according to any one of claims 10-12, characterized in that, The second determining module is further configured to: Based on the layout evaluation values ​​of the candidate detection points, the N candidate detection points are sorted in ascending order to obtain a detection point sequence; Starting from the fact that the values ​​of the determination factors for the N candidate detection points are all the first value, the layout satisfaction function is optimized and iterated. In the z-th iteration, the number of detection points that are not ultimately placed is determined to be Z. The first Z candidate detection points are selected from the detection point sequence, and the determination factor of the first Z candidate detection points is determined to be a second value, so as to obtain the satisfaction value z of the layout satisfaction function in the z-th iteration; wherein, Z≤N, and z is greater than or equal to 1. If the satisfaction value z is less than the satisfaction value z-1 of the (z-1)th iteration, and is greater than or equal to the preset satisfaction threshold, the optimization iteration of the layout satisfaction function ends. The determination factor of the first Z candidate detection points in the detection point sequence is a second value, and the determination factor of the remaining candidate detection points is a first value.

14. The detection point layout determination device according to claim 13, characterized in that, The device is also used for: If the satisfaction value z is greater than the satisfaction value z-1, and / or the satisfaction value z is less than the satisfaction value z-1 and less than the satisfaction threshold, it is determined that the layout satisfaction function needs to be iteratively optimized. In the (z+1)th iteration, determine the final number of unplaced detection points Z+1 and execute subsequent iteration steps.

15. The detection point layout determination device according to claim 9, characterized in that, The second determining module is further configured to: A first matrix is ​​constructed using the degree centrality parameter, the proximity centrality parameter, the betweenness centrality parameter, the feature vector centrality parameter, and the H-index of each candidate detection point, wherein each row in the first matrix represents a candidate detection point and each column represents a parameter dimension. The first matrix is ​​standardized to obtain a standardized second matrix; For each parameter dimension, the information entropy of that parameter dimension is determined based on the matrix elements of that parameter dimension in each row of the second matrix, and the entropy weight of that parameter dimension is determined based on the information entropy of that parameter dimension. For each candidate detection point, the row in which the candidate detection point is located in the second matrix is ​​determined, and the matrix elements of each parameter dimension and the entropy weight of each parameter dimension in the row in which the candidate detection point is located are weighted to obtain the centrality index of the candidate detection point.

16. The apparatus for determining the layout of detection points according to any one of claims 9-12, characterized in that, The device is also used for: Based on the Thiessen polygon theory, Thiessen polygons are constructed with different target detection points as the core, wherein the Thiessen polygons constructed by the target detection points represent the detection coverage area of ​​the target detection points.

17. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

19. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-8.

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