Mesh point distribution processing method and device, computer device and storage medium

Abstract

The application relates to an artificial intelligence technology and provides a network point distribution processing method and device, computer equipment and a storage medium, which comprises the following steps: acquiring an initial network point set of a target area, constructing a target grid with each initial network point in the initial network point set as a center point, and obtaining a target grid set; acquiring user coordinates in the target area and aggregating the user coordinates to the target grid closest to the user coordinates; detecting whether there is a to-be-aggregated user coordinate not aggregated to the target grid; when the detection result is that there is the to-be-aggregated user coordinate not aggregated to the target grid, acquiring the to-be-aggregated user coordinate, determining a new aggregation point with the to-be-aggregated user coordinate as a center point; acquiring target aggregation information corresponding to the new aggregation point; and determining whether a new network point is added according to the target aggregation information. The application can improve the accuracy of network point distribution and promote the rapid development of smart cities.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, computer equipment and storage medium for processing network distribution. Background Technology

[0002] Displaying information about service outlets within a region via maps is currently a relatively intuitive method. In real-world production and business activities, many operations rely on service outlets, and the rationality of their distribution can directly impact a company's ability to gain a larger market share and achieve profitability.

[0003] In the process of developing this application, the applicant discovered the following problems with the existing technology: Common existing methods for grid point distribution divide the map into several grids, with each grid displaying only a center point. User coordinates falling within a grid are aggregated at the center point, and the number of aggregated points is used as the basis for determining whether to establish a grid point. However, for adjacent user coordinates, because the map is divided into different grids, the user coordinates are forced to be calculated separately in different grids, which cannot accurately reflect the aggregated user data and the accuracy of the grid point distribution.

[0004] Therefore, it is necessary to provide a method for processing network point distribution that can improve the accuracy of network point distribution. Summary of the Invention

[0005] In view of the above, it is necessary to propose a network point distribution processing method, network point distribution processing device, computer equipment and storage medium, which can improve the accuracy of network point distribution.

[0006] The first aspect of this application also provides a method for processing network point distribution, the method comprising:

[0007] Obtain the initial set of dots for the target area, and construct the target grid with each initial dot in the initial set of dots as the center point to obtain the target grid set;

[0008] Obtain the user coordinates within the target area, and aggregate the user coordinates into the target grid that is closest to the user coordinates;

[0009] Detect whether there are any user coordinates to be aggregated that have not been aggregated to the target grid;

[0010] When the detection result indicates that there are user coordinates to be aggregated that have not been aggregated into the target grid, the user coordinates to be aggregated are obtained, and a new aggregation point is determined with the user coordinates to be aggregated as the center point;

[0011] Obtain the target aggregation information corresponding to the new aggregation point;

[0012] Whether to add new outlets is determined based on the target aggregation information.

[0013] Furthermore, in the above-described network distribution processing method provided in the embodiments of this application, obtaining the initial network set of the target area includes:

[0014] Detect whether there are network outlet signs in the target area;

[0015] When the detection result indicates that the network identifier exists in the target area, the network identifier is obtained;

[0016] The initial set of nodes is obtained by traversing the pre-set mapping relationship between the node identifiers and the nodes.

[0017] Furthermore, in the above-described dot distribution processing method provided in the embodiments of this application, the step of constructing a target grid with each initial dot in the initial dot set as the center point to obtain a target grid set includes:

[0018] Construct an outer square as the grid, centered on the initial dots;

[0019] Obtain the first distance from the side length of the outer square to the center point, where the maximum value corresponding to the first distance is parameter Y;

[0020] Obtain the second distance between the initial network points in the initial network point set, where the value of the second distance is parameter Z;

[0021] Detect whether the maximum value corresponding to the first distance is greater than the second distance;

[0022] When the detection result shows that the maximum value corresponding to the first distance is greater than the second distance, the value of the first distance is set to parameter Z;

[0023] When the detection result is that the maximum value corresponding to the first distance is not greater than the second distance, the value of the first distance is set to parameter Y;

[0024] The grid with the determined first distance value is used as the target grid of the initial grid point, and the target grids are combined to obtain the target grid set.

[0025] Furthermore, in the above-described grid distribution processing method provided in this application embodiment, the step of aggregating the user coordinates into the target grid that is closest to the user coordinates includes:

[0026] Detect whether the user coordinates are within one of the target grids;

[0027] When the detection result indicates that the user coordinates are within one of the target grids, the user coordinates are aggregated into the target grid where they are located;

[0028] When the detection result indicates that the user coordinates are located in multiple target grids, the distance between the user coordinates and each target grid is obtained, and the user coordinates are aggregated into the target grid that is closest to the user coordinates.

[0029] Furthermore, in the above-described network distribution processing method provided in the embodiments of this application, the step of determining a new aggregation point with the coordinates of the user to be aggregated as the center point includes:

[0030] Obtain the coordinates of the users to be aggregated, and construct an outer square with the coordinates of the users to be aggregated as the center point to obtain the first grid of users to be aggregated;

[0031] Obtain the coordinates of the users to be aggregated, and construct an outer square with the coordinates of the users to be aggregated as the center point to obtain the grid of users to be aggregated;

[0032] Obtain the number of coordinates of the users to be aggregated within the user grid to be aggregated, and the intersection information between the user grids to be aggregated;

[0033] A new aggregation point is determined based on the number of user coordinates to be aggregated and the intersection information.

[0034] Furthermore, in the above-described network distribution processing method provided in this application embodiment, obtaining the target aggregation information corresponding to the new aggregation point includes:

[0035] Obtain the preset coordinate point within the grid corresponding to the new aggregation point, and determine the preset weight corresponding to the preset coordinate point;

[0036] The coordinates of the users to be aggregated are used as data points, and the number of data points in the grid corresponding to the new aggregation point is determined according to the preset weight.

[0037] Determine the quantity level corresponding to the number of data points, and use the quantity level as the target aggregation information.

[0038] Furthermore, in the above-described network distribution processing method provided in the embodiments of this application, the step of determining whether to add a new network based on the target aggregation information includes:

[0039] Detect whether the quantity level has reached a preset level;

[0040] When the detection result indicates that the quantity level has reached the preset level, the target network size is obtained by traversing the preset query relationship between the quantity level and the network size according to the quantity level, and a new network is added according to the target network size.

[0041] If the test result indicates that the quantity level has not reached the preset level, it is determined that no new outlets will be added.

[0042] A second aspect of this application also provides a network distribution processing device, the network distribution processing device comprising:

[0043] The node acquisition module is used to acquire an initial node set of the target area, and construct a target grid with each initial node in the initial node set as the center point to obtain a target grid set;

[0044] A coordinate aggregation module is used to obtain user coordinates within the target area and aggregate the user coordinates into the target grid that is closest to the user coordinates;

[0045] The coordinate detection module is used to detect whether there are any user coordinates to be aggregated that have not been aggregated into the target grid;

[0046] The aggregation processing module is used to obtain the coordinates of the user to be aggregated when the detection result indicates that there are user coordinates that have not been aggregated into the target grid, and to determine a new aggregation point with the user coordinates to be aggregated as the center point;

[0047] The information acquisition module is used to acquire the target aggregation information corresponding to the new aggregation point;

[0048] The site determination module is used to determine whether to add a new site based on the target aggregation information.

[0049] A third aspect of this application also provides a computer device, the computer device including a processor, the processor being configured to execute a computer program stored in a memory to implement the network distribution processing method as described in any of the above embodiments.

[0050] A fourth aspect of this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the network distribution processing method described in any one of the above embodiments.

[0051] The network point distribution processing method, device, computer equipment, and computer-readable storage medium provided in this application embodiment construct a target grid with initial network points within the target area as the center point. User coordinates within the target area are aggregated to the target grid closest to the user coordinates. Unaggregated user coordinates are re-aggregated to obtain new aggregation points and corresponding target aggregation information. Based on the target aggregation information, it is determined whether to add new network points. This application uses known aggregation points and a dynamic grid division method, which can compensate for the inaccurate aggregation of adjacent data caused by the map within the area being divided into different grids. Combined with the aggregation and display of existing network points, it provides network point distribution planning for business operations, provides a reference for judging whether the service network point distribution is reasonable, and improves the accuracy of network point distribution. This application can be applied to various functional modules of smart cities such as smart government affairs and smart transportation, such as the network point distribution processing module of smart government affairs, and can promote the rapid development of smart cities. Attached Figure Description

[0052] Figure 1 This is a flowchart of the network distribution processing method provided in Embodiment 1 of this application.

[0053] Figure 2 This is a structural diagram of the network distribution processing device provided in Embodiment 2 of this application.

[0054] Figure 3 This is a schematic diagram of the structure of the computer device provided in Embodiment 3 of this application.

[0055] The following detailed description, in conjunction with the accompanying drawings, will further illustrate this application. Detailed Implementation

[0056] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0057] Numerous specific details are set forth in the following description in order to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0059] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0060] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0061] The network distribution processing method provided in this embodiment of the invention is executed by a computer device, and correspondingly, the network distribution processing device runs in the computer device. Figure 1 This is a flowchart of the network distribution processing method provided in Embodiment 1 of this application. Figure 1 As shown, the network distribution processing method may include the following steps. The order of these steps in the flowchart may be changed, and some may be omitted, depending on different requirements:

[0062] S11, obtain the initial set of dots for the target area, and construct the target grid with each initial dot in the initial set of dots as the center point to obtain the target grid set.

[0063] In at least one embodiment of this application, the target area refers to the area where service point distribution processing is to be performed. The initial service point set refers to a collection of several initial service points; the initial service point set may contain one or more initial service points. This application embodiment uses the example of multiple initial service points in the initial service point set. The initial service point refers to an existing service point in the target area, used to provide related services to users in the target area. The target grid set refers to a collection of several target grids; for each initial service point, there exists a corresponding target grid.

[0064] Optionally, obtaining the initial set of dots for the target area includes:

[0065] Detect whether there are network outlet signs in the target area;

[0066] When the detection result indicates that the network identifier exists in the target area, the network identifier is obtained;

[0067] The initial set of nodes is obtained by traversing the pre-set mapping relationship between the node identifiers and the nodes.

[0068] The service point identifier is used to identify service point information. The service point identifier can be a number, color, or letter, and there is no limitation thereto. There is a mapping relationship between the service point identifier and the service points. By traversing this mapping relationship, the initial service points corresponding to the service point identifier can be obtained. Combining several of these initial service points yields an initial service point set.

[0069] Optionally, the step of constructing a target grid with each initial point in the initial grid set as the center point to obtain a target grid set includes:

[0070] Construct an outer square as the grid, centered on the initial dots;

[0071] Obtain the first distance from the side length of the outer square to the center point, where the maximum value corresponding to the first distance is parameter Y;

[0072] Obtain the second distance between the initial network points in the initial network point set, where the value of the second distance is parameter Z;

[0073] Detect whether the maximum value corresponding to the first distance is greater than the second distance;

[0074] When the detection result shows that the maximum value corresponding to the first distance is greater than the second distance, the value of the first distance is set to parameter Z;

[0075] When the detection result is that the maximum value corresponding to the first distance is not greater than the second distance, the value of the first distance is set to parameter Y;

[0076] The grid with the determined first distance value is used as the target grid of the initial grid point, and the target grids are combined to obtain the target grid set.

[0077] In this process, an outer square is constructed with the initial dot as the center point to form a grid. The distance from the side length of the outer square to the center point is set accordingly to obtain the target grid corresponding to the initial dot. Combining these target grids yields a target grid set. The first distance from the side length of the outer square to the center point is a floating parameter, and its maximum value is parameter Y. A second distance between the initial dots in the initial dot set is obtained, with the value of the second distance being parameter Z. By comparing the value of the second distance Z with the maximum value parameter Y corresponding to the first distance, the value of the first distance is determined. The grid with the determined first distance value is then used as the target grid for the initial dot. Combining these target grids yields the target grid set.

[0078] S12, obtain the user coordinates within the target area, and aggregate the user coordinates into the target grid that is closest to the user coordinates.

[0079] In at least one embodiment of this application, the target area includes several users. For each user, there is a corresponding user coordinate. The user coordinates are aggregated into the target grid that is closest to the user coordinates. The user can refer to the entity enjoying the service provided by the service point. The user coordinates can be determined by locating the mobile terminal corresponding to the user, and are not limited here. The embodiments of this application adopt a dynamic grid division method, which can make up for the problem of inaccurate aggregation caused by adjacent data being divided into different grids.

[0080] Optionally, aggregating the user coordinates into the target grid that is closest to the user coordinates includes:

[0081] Detect whether the user coordinates are within one of the target grids;

[0082] When the detection result indicates that the user coordinates are within one of the target grids, the user coordinates are aggregated into the target grid where they are located;

[0083] When the detection result indicates that the user coordinates are located in multiple target grids, the distance between the user coordinates and each target grid is obtained, and the user coordinates are aggregated into the target grid that is closest to the user coordinates.

[0084] Specifically, obtaining the distance between the user's coordinates and each of the target grids is equivalent to obtaining the distance between the user's coordinates and the center point of each of the target grids. This application employs a known aggregation point and a dynamic grid division method, which can compensate for the problem of inaccurate aggregation of adjacent data caused by the map within the region being divided into different grids.

[0085] S13, detect whether there are any user coordinates to be aggregated that have not been aggregated into the target grid.

[0086] When the detection result indicates that there are user coordinates to be aggregated that have not been aggregated into the target grid, step S14 is executed.

[0087] In at least one embodiment of this application, the user coordinates to be aggregated refer to user coordinates that have not been aggregated into the corresponding target grid. When the detection result indicates that there are no user coordinates to be aggregated that have not been aggregated into the target grid, then all user coordinates are aggregated into the corresponding target grid. By determining the number of user coordinates in the target grid, a network distribution reference is provided for the service.

[0088] S14, obtain the coordinates of the user to be aggregated, and determine a new aggregation point with the coordinates of the user to be aggregated as the center point.

[0089] In at least one embodiment of this application, the coordinates of users who fail to be aggregated into the corresponding target grid are obtained, that is, the coordinates of the users to be aggregated are obtained, and a new aggregation point is determined with the coordinates of the users to be aggregated as the center point.

[0090] Optionally, determining the new aggregation point using the coordinates of the user to be aggregated as the center point includes:

[0091] Obtain the coordinates of the users to be aggregated, and construct an outer square with the coordinates of the users to be aggregated as the center point to obtain the grid of users to be aggregated;

[0092] Obtain the number of coordinates of the users to be aggregated within the user grid to be aggregated, and the intersection information between the user grids to be aggregated;

[0093] A new aggregation point is determined based on the number of user coordinates to be aggregated and the intersection information.

[0094] In this process, user coordinates already aggregated into the target grid are removed, leaving user coordinates to be aggregated that are not yet aggregated into the target grid. These user coordinates are then obtained, and an outer square is constructed with each user coordinate as its center point to create a user grid to be aggregated. The number of user coordinates to be aggregated is the same as the number of user grids to be aggregated. In one embodiment, the user grids to be aggregated may intersect. For example, user grid A may intersect only one user grid B, or it may intersect with user grids B, C, and D simultaneously. Correspondingly, a user grid to be aggregated may contain only one user coordinate or multiple user coordinates. The distance from the center point to the side of the outer square constructed with the user coordinates to be aggregated as its center point is a floating parameter. The number of user coordinates to be aggregated within each user grid and the intersection information between user grids to be aggregated are used as the basis for selecting the initial aggregation point. The selection conditions are: Condition 1: the grid with the most intersections between user grids to be aggregated; Condition 2: the total number of user coordinates to be aggregated within each user grid to be aggregated. The weight of condition one is greater than the weight of condition two. In one embodiment, the center point of the grid with the most intersecting points is taken as the new aggregation point, or the center point of the grid with the largest total number of user coordinates to be aggregated is taken as the new aggregation point. The number of new aggregation points can be one or more, and there is no limitation here.

[0095] S15, obtain the target aggregation information corresponding to the new aggregation point.

[0096] In at least one embodiment of this application, the target aggregation information refers to the quantity level information corresponding to the number of data points within the grid corresponding to the new aggregation point. After determining the new aggregation point, an outer square is constructed with the new aggregation point as the center point as the grid, and a new grid is constructed using the distance from the side length of the grid to the center point. The number of new grids can be one or more, and the coordinates of the users to be aggregated are aggregated into the new grids. The grid construction method and the method of aggregating coordinates into the grids have been described above and will not be repeated here.

[0097] Optionally, obtaining the target aggregation information corresponding to the new aggregation point includes:

[0098] Obtain the preset coordinate point within the grid corresponding to the new aggregation point, and determine the preset weight corresponding to the preset coordinate point;

[0099] The coordinates of the users to be aggregated are used as data points, and the number of data points in the grid corresponding to the new aggregation point is determined according to the preset weight.

[0100] Determine the quantity level corresponding to the number of data points, and use the quantity level as the target aggregation information.

[0101] The quantity level can be a pre-set level used to evaluate the size of the number of data points. For example, the quantity levels, from high to low, are quantity level A, quantity level B, and quantity level C, where quantity level A has the most data points and quantity level C has the fewest. There is a correspondence between the number of data points and the quantity level; by querying this correspondence, the quantity level corresponding to the number of data points can be obtained. This application determines the quantity level corresponding to the new aggregation point as the target aggregation information, and determines whether to add a new network point and what size network point to add based on the target aggregation information, thereby providing network point distribution planning for related businesses and improving the accuracy of network point distribution.

[0102] The preset coordinate points refer to the coordinate points that are pre-set and related to the services provided by the network points. The preset coordinate points can be residential areas, office buildings, etc. By setting preset weights for places with high user concentration such as residential areas and office buildings, and using the preset weights as a consideration indicator for the number of data points of the grid points corresponding to the new aggregation point, the accuracy of determining the number of data points can be improved, thereby improving the accuracy of network point distribution processing.

[0103] In one embodiment, before obtaining the preset coordinate point within the grid corresponding to the new aggregation point, the method further includes: detecting whether a preset coordinate point identifier exists within the grid corresponding to the new aggregation point; when the detection result indicates that the preset coordinate point identifier exists within the grid corresponding to the new aggregation point, determining that a preset coordinate point exists within the grid corresponding to the new aggregation point, and determining the corresponding preset coordinate point based on the preset coordinate point identifier. The preset coordinate point identifier refers to information used to identify the preset coordinate point, and the identification method can be text, color, or letter identifiers, etc., without limitation. It is understood that there may be one, multiple, or no preset coordinate point within the grid corresponding to the new aggregation point. The weight of the preset coordinate point is set based on its distance from the center point of the corresponding new aggregation point; the closer the preset coordinate point is to the center point of the new aggregation point, the greater its weight; the farther the preset coordinate point is from the center point of the new aggregation point, the smaller its weight.

[0104] In one embodiment, a pre-defined correspondence between preset weights and the number of data points is established. The number of data points corresponding to the preset weights can be obtained by querying this correspondence. For example, the preset weights, from largest to smallest, are 3, 2, and 1, where preset weight 3 corresponds to 30 data points, preset weight 2 corresponds to 20 data points, and preset weight 1 corresponds to 10 data points. Determining the number of data points within the grid corresponding to the new aggregation point based on the preset weights includes: obtaining a first number of user coordinates to be aggregated within the grid corresponding to the new aggregation point; obtaining a second number corresponding to the preset weights; and summing the first number and the second number to obtain the number of data points within the grid corresponding to the new aggregation point. For example, when the new aggregation point contains user coordinates to be aggregated and two preset coordinate points, the number of user coordinates to be aggregated is 5, and the preset weights of the preset coordinate points are 1 and 2, respectively. Preset weight 1 corresponds to 10 data points, and preset weight 2 corresponds to 20 data points. In this case, the number of data points within the grid corresponding to the new aggregation point is 35.

[0105] S16, determine whether to add new outlets based on the target aggregation information.

[0106] In at least one embodiment of this application, determining whether to add a new branch based on the target aggregation information includes:

[0107] Detect whether the quantity level has reached a preset level;

[0108] When the detection result indicates that the quantity level has reached the preset level, the target network size is obtained by traversing the preset query relationship between the quantity level and the network size according to the quantity level, and a new network is added according to the target network size.

[0109] If the test result indicates that the quantity level has not reached the preset level, it is determined that no new outlets will be added.

[0110] The preset level is a pre-set level that meets the requirements for adding new service points. For example, the preset level can be quantity level A. The service point size refers to the service coverage area that the service point can provide. It can be understood that the larger the service point size, the larger the service coverage area that the service point can provide.

[0111] The network point distribution processing method provided in this application embodiment constructs a target grid with initial network points within the target area as the center point, aggregates user coordinates within the target area to the target grid closest to the user coordinates, and re-aggregates user coordinates that are not aggregated to the target grid to obtain new aggregation points and corresponding target aggregation information. Based on the target aggregation information, it is determined whether to add new network points. This application uses known aggregation points and a dynamic grid division method, which can compensate for the problem of inaccurate aggregation of adjacent data caused by the map within the area being divided into different grids. Combined with the aggregation and display of existing network points, it provides network point distribution planning for business operations, provides a reference for judging whether the distribution of service network points is reasonable, and improves the accuracy of network point distribution. This application can be applied to various functional modules of smart cities such as smart government affairs and smart transportation, such as the network point distribution processing module of smart government affairs, and can promote the rapid development of smart cities.

[0112] Figure 2 This is a structural diagram of the network distribution processing device provided in Embodiment 2 of this application.

[0113] In some embodiments, the network distribution processing device 20 may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the network distribution processing device 20 may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality of network point distribution processing.

[0114] In this embodiment, the dot distribution processing device 20 can be divided into multiple functional modules according to its functions. These functional modules may include: a dot acquisition module 201, a coordinate aggregation module 202, a coordinate detection module 203, an aggregation processing module 204, an information acquisition module 205, and a dot determination module 206. The term "module" in this application refers to a series of computer program segments that can be executed by at least one processor and perform a fixed function, and which are stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0115] The point acquisition module 201 can be used to acquire an initial point set of the target area, and construct a target grid with each initial point in the initial point set as the center point to obtain a target grid set.

[0116] In at least one embodiment of this application, the target area refers to the area where service point distribution processing is to be performed. The initial service point set refers to a collection of several initial service points; the initial service point set may contain one or more initial service points. This application embodiment uses the example of multiple initial service points in the initial service point set. The initial service point refers to an existing service point in the target area, used to provide related services to users in the target area. The target grid set refers to a collection of several target grids; for each initial service point, there exists a corresponding target grid.

[0117] Optionally, obtaining the initial set of dots for the target area includes:

[0118] Detect whether there are network outlet signs in the target area;

[0119] When the detection result indicates that the network identifier exists in the target area, the network identifier is obtained;

[0120] The initial set of nodes is obtained by traversing the pre-set mapping relationship between the node identifiers and the nodes.

[0121] The service point identifier is used to identify service point information. The service point identifier can be a number, color, or letter, and there is no limitation thereto. There is a mapping relationship between the service point identifier and the service points. By traversing this mapping relationship, the initial service points corresponding to the service point identifier can be obtained. Combining several of these initial service points yields an initial service point set.

[0122] Optionally, the step of constructing a target grid with each initial point in the initial grid set as the center point to obtain a target grid set includes:

[0123] Construct an outer square as the grid, centered on the initial dots;

[0124] Obtain the first distance from the side length of the outer square to the center point, where the maximum value corresponding to the first distance is parameter Y;

[0125] Obtain the second distance between the initial network points in the initial network point set, where the value of the second distance is parameter Z;

[0126] Detect whether the maximum value corresponding to the first distance is greater than the second distance;

[0127] When the detection result shows that the maximum value corresponding to the first distance is greater than the second distance, the value of the first distance is set to parameter Z;

[0128] When the detection result is that the maximum value corresponding to the first distance is not greater than the second distance, the value of the first distance is set to parameter Y;

[0129] The grid with the determined first distance value is used as the target grid of the initial grid point. The target grids are combined to obtain a target grid set. Specifically, an outer square is constructed with the initial grid point as the center point, and the distance from the side length of the outer square to the center point is set accordingly to obtain the target grid corresponding to the initial grid point. The target grid set is obtained by combining these target grids. The first distance from the side length of the outer square to the center point is a floating parameter, and the maximum value corresponding to the first distance is parameter Y. A second distance between the initial grid points in the initial grid point set is obtained, and the value of the second distance is parameter Z. By comparing the value of the second distance Z with the maximum value parameter Y corresponding to the first distance, the value of the first distance is determined. The grid with the determined first distance value is then used as the target grid of the initial grid point. The target grid set is obtained by combining these target grids.

[0130] The coordinate aggregation module 202 can be used to obtain user coordinates within the target area and aggregate the user coordinates into the target grid that is closest to the user coordinates.

[0131] In at least one embodiment of this application, the target area includes several users. For each user, there is a corresponding user coordinate. The user coordinates are aggregated into the target grid that is closest to the user coordinates. The user can refer to the entity enjoying the service provided by the service point. The user coordinates can be determined by locating the mobile terminal corresponding to the user, and are not limited here. The embodiments of this application adopt a dynamic grid division method, which can make up for the problem of inaccurate aggregation caused by adjacent data being divided into different grids.

[0132] Optionally, aggregating the user coordinates into the target grid that is closest to the user coordinates includes:

[0133] Detect whether the user coordinates are within one of the target grids;

[0134] When the detection result indicates that the user coordinates are within one of the target grids, the user coordinates are aggregated into the target grid where they are located;

[0135] When the detection result indicates that the user coordinates are located in multiple target grids, the distance between the user coordinates and each target grid is obtained, and the user coordinates are aggregated into the target grid that is closest to the user coordinates.

[0136] Specifically, obtaining the distance between the user's coordinates and each of the target grids is equivalent to obtaining the distance between the user's coordinates and the center point of each of the target grids. This application employs a known aggregation point and a dynamic grid division method, which can compensate for the problem of inaccurate aggregation of adjacent data caused by the map within the region being divided into different grids.

[0137] The coordinate detection module 203 can be used to detect whether there are any user coordinates to be aggregated that have not been aggregated into the target grid.

[0138] In at least one embodiment of this application, the user coordinates to be aggregated refer to user coordinates that have not been aggregated into the corresponding target grid. When the detection result indicates that there are no user coordinates to be aggregated that have not been aggregated into the target grid, then all user coordinates are aggregated into the corresponding target grid. By determining the number of user coordinates in the target grid, a network distribution reference is provided for the service.

[0139] The aggregation processing module 204 can be used to obtain the coordinates of the user to be aggregated when the detection result shows that there are user coordinates to be aggregated that have not been aggregated into the target grid, and determine a new aggregation point with the user coordinates to be aggregated as the center point.

[0140] In at least one embodiment of this application, the coordinates of users who fail to be aggregated into the corresponding target grid are obtained, that is, the coordinates of the users to be aggregated are obtained, and a new aggregation point is determined with the coordinates of the users to be aggregated as the center point.

[0141] Optionally, determining the new aggregation point using the coordinates of the user to be aggregated as the center point includes:

[0142] Obtain the coordinates of the users to be aggregated, and construct an outer square with the coordinates of the users to be aggregated as the center point to obtain the grid of users to be aggregated;

[0143] Obtain the number of coordinates of the users to be aggregated within the user grid to be aggregated, and the intersection information between the user grids to be aggregated;

[0144] A new aggregation point is determined based on the number of user coordinates to be aggregated and the intersection information.

[0145] In this process, user coordinates already aggregated into the target grid are removed, leaving user coordinates to be aggregated that are not yet aggregated into the target grid. These user coordinates are then obtained, and an outer square is constructed with each user coordinate as its center point to create a user grid to be aggregated. The number of user coordinates to be aggregated is the same as the number of user grids to be aggregated. In one embodiment, the user grids to be aggregated may intersect. For example, user grid A may intersect only one user grid B, or it may intersect with user grids B, C, and D simultaneously. Correspondingly, a user grid to be aggregated may contain only one user coordinate or multiple user coordinates. The distance from the center point to the side of the outer square constructed with the user coordinates to be aggregated as its center point is a floating parameter. The number of user coordinates to be aggregated within each user grid and the intersection information between user grids to be aggregated are used as the basis for selecting the initial aggregation point. The selection conditions are: Condition 1: the grid with the most intersections between user grids to be aggregated; Condition 2: the total number of user coordinates to be aggregated within each user grid to be aggregated. The weight of condition one is greater than the weight of condition two. In one embodiment, the center point of the grid with the most intersecting points is taken as the new aggregation point, or the center point of the grid with the largest total number of user coordinates to be aggregated is taken as the new aggregation point. The number of new aggregation points can be one or more, and there is no limitation here.

[0146] The information acquisition module 205 can be used to acquire the target aggregation information corresponding to the new aggregation point.

[0147] In at least one embodiment of this application, the target aggregation information refers to the quantity level information corresponding to the number of data points within the grid corresponding to the new aggregation point. After determining the new aggregation point, an outer square is constructed with the new aggregation point as the center point as the grid, and a new grid is constructed using the distance from the side length of the grid to the center point. The number of new grids can be one or more, and the coordinates of the users to be aggregated are aggregated into the new grids. The grid construction method and the method of aggregating coordinates into the grids have been described above and will not be repeated here.

[0148] Optionally, obtaining the target aggregation information corresponding to the new aggregation point includes:

[0149] Obtain the preset coordinate point within the grid corresponding to the new aggregation point, and determine the preset weight corresponding to the preset coordinate point;

[0150] The coordinates of the users to be aggregated are used as data points, and the number of data points in the grid corresponding to the new aggregation point is determined according to the preset weight.

[0151] Determine the quantity level corresponding to the number of data points, and use the quantity level as the target aggregation information.

[0152] The quantity level can be a pre-set level used to evaluate the size of the number of data points. For example, the quantity levels, from high to low, are quantity level A, quantity level B, and quantity level C, where quantity level A has the most data points and quantity level C has the fewest. There is a correspondence between the number of data points and the quantity level; by querying this correspondence, the quantity level corresponding to the number of data points can be obtained. This application determines the quantity level corresponding to the new aggregation point as the target aggregation information, and determines whether to add a new network point and what size network point to add based on the target aggregation information, thereby providing network point distribution planning for related businesses and improving the accuracy of network point distribution.

[0153] The preset coordinate points refer to the coordinate points that are pre-set and related to the services provided by the network points. The preset coordinate points can be residential areas, office buildings, etc. By setting preset weights for places with high user concentration such as residential areas and office buildings, and using the preset weights as a consideration indicator for the number of data points of the grid points corresponding to the new aggregation point, the accuracy of determining the number of data points can be improved, thereby improving the accuracy of network point distribution processing.

[0154] In one embodiment, before obtaining the preset coordinate point within the grid corresponding to the new aggregation point, the method further includes: detecting whether a preset coordinate point identifier exists within the grid corresponding to the new aggregation point; when the detection result indicates that the preset coordinate point identifier exists within the grid corresponding to the new aggregation point, determining that a preset coordinate point exists within the grid corresponding to the new aggregation point, and determining the corresponding preset coordinate point based on the preset coordinate point identifier. The preset coordinate point identifier refers to information used to identify the preset coordinate point, and the identification method can be text, color, or letter identifiers, etc., without limitation. It is understood that there may be one, multiple, or no preset coordinate point within the grid corresponding to the new aggregation point. The weight of the preset coordinate point is set based on its distance from the center point of the corresponding new aggregation point; the closer the preset coordinate point is to the center point of the new aggregation point, the greater its weight; the farther the preset coordinate point is from the center point of the new aggregation point, the smaller its weight.

[0155] In one embodiment, a pre-defined correspondence between preset weights and the number of data points is established. The number of data points corresponding to the preset weights can be obtained by querying this correspondence. For example, the preset weights, from largest to smallest, are 3, 2, and 1, where preset weight 3 corresponds to 30 data points, preset weight 2 corresponds to 20 data points, and preset weight 1 corresponds to 10 data points. Determining the number of data points within the grid corresponding to the new aggregation point based on the preset weights includes: obtaining a first number of user coordinates to be aggregated within the grid corresponding to the new aggregation point; obtaining a second number corresponding to the preset weights; and summing the first number and the second number to obtain the number of data points within the grid corresponding to the new aggregation point. For example, when the new aggregation point contains user coordinates to be aggregated and two preset coordinate points, the number of user coordinates to be aggregated is 5, and the preset weights of the preset coordinate points are 1 and 2, respectively. Preset weight 1 corresponds to 10 data points, and preset weight 2 corresponds to 20 data points. In this case, the number of data points within the grid corresponding to the new aggregation point is 35.

[0156] The site determination module 206 is used to determine whether to add a new site based on the target aggregation information.

[0157] In at least one embodiment of this application, determining whether to add a new branch based on the target aggregation information includes:

[0158] Detect whether the quantity level has reached a preset level;

[0159] When the detection result indicates that the quantity level has reached the preset level, the target network size is obtained by traversing the preset query relationship between the quantity level and the network size according to the quantity level, and a new network is added according to the target network size.

[0160] If the test result indicates that the quantity level has not reached the preset level, it is determined that no new outlets will be added.

[0161] The preset level is a pre-set level that meets the requirements for adding new service points. For example, the preset level can be quantity level A. The service point size refers to the service coverage area that the service point can provide. It can be understood that the larger the service point size, the larger the service coverage area that the service point can provide.

[0162] See Figure 3 The diagram shown is a structural schematic of a computer device provided in Embodiment 3 of this application. In a preferred embodiment of this application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

[0163] Those skilled in the art should understand that Figure 3The structure of the computer device shown does not constitute a limitation of the embodiments of this application. It can be a bus structure or a star structure. The computer device 3 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0164] In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The computer device 3 may also include client devices, which include, but are not limited to, any electronic product that can interact with a client via a keyboard, mouse, remote control, touchpad, or voice control device, such as personal computers, tablet computers, smartphones, and digital cameras.

[0165] It should be noted that the computer device 3 described is merely an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0166] In some embodiments, the memory 31 stores a computer program that, when executed by the at least one processor 32, implements all or part of the steps in the dot distribution processing method described above. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0167] Furthermore, the computer-readable storage medium may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system, at least one application required for a function, etc.; and the data storage area may store data created based on the use of blockchain nodes, etc.

[0168] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0169] In some embodiments, the at least one processor 32 is the control unit of the computer device 3, connecting various components of the computer device 3 via various interfaces and lines. It executes programs or modules stored in the memory 31 and calls data stored in the memory 31 to perform various functions and process data of the computer device 3. For example, when the at least one processor 32 executes the computer program stored in the memory, it implements all or part of the steps of the dot-matrix distribution processing method described in this application embodiment; or it implements all or part of the functions of the dot-matrix distribution processing device. The at least one processor 32 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0170] In some embodiments, the at least one communication bus 33 is configured to enable communication between the memory 31 and the at least one processor 32, etc.

[0171] Although not shown, the computer device 3 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 32 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0172] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute portions of the methods described in the various embodiments of this application.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0174] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0176] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other elements or, and the singular does not exclude the plural. Multiple elements or devices recited in the specification may also be implemented by a single element or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A halftone dot distribution processing method characterized by comprising: The method comprises the following steps: obtaining an initial node set of a target region, and constructing a target grid set with each initial node in the initial node set as a center node; obtaining user coordinates in the target region, and aggregating the user coordinates into the target grid closest to the user coordinates; detecting whether there are to-be-aggregated user coordinates not aggregated into the target grid; when the detection result is that there are to-be-aggregated user coordinates not aggregated into the target grid, obtaining the to-be-aggregated user coordinates; determining a new aggregation point with the to-be-aggregated user coordinates as the center node, which comprises: obtaining the to-be-aggregated user coordinates, and constructing an external square with the to-be-aggregated user coordinates as the center node to obtain a to-be-aggregated user grid; obtaining the number of to-be-aggregated user coordinates in the to-be-aggregated user grid and intersection information between the to-be-aggregated user grids; and determining the new aggregation point according to the number of to-be-aggregated user coordinates and the intersection information; obtaining target aggregation information corresponding to the new aggregation point; determining whether to add a new node according to the target aggregation information.

2. The screen dot distribution processing method according to claim 1, characterized by, The method comprises the following steps: detecting whether there are node identifiers in the target region; when the detection result is that there are node identifiers in the target region, obtaining the node identifiers; traversing a pre-set mapping relationship between node identifiers and nodes according to the node identifiers to obtain an initial node set.

3. The screen dot distribution processing method according to claim 1, characterized by, The method comprises the following steps: constructing an external square as a grid with the initial node as the center node; obtaining a first distance from the side length of the external square to the center node, and the maximum value corresponding to the first distance is parameter Y; obtaining a second distance between the initial nodes in the initial node set, and selecting the value of one of the second distances as parameter Z; detecting whether the maximum value corresponding to the first distance is greater than the second distance; when the detection result is that the maximum value corresponding to the first distance is greater than the second distance, setting the value of the first distance as parameter Z; when the detection result is that the maximum value corresponding to the first distance is not greater than the second distance, setting the value of the first distance as parameter Y; combining the target grid set by taking the grid with the determined first distance value as the target grid of the initial node.

4. The screen dot distribution processing method according to claim 1, characterized by, The method comprises the following steps: detecting whether the user coordinates are in one target grid; when the detection result is that the user coordinates are in one target grid, aggregating the user coordinates into the target grid; when the detection result is that the user coordinates are in multiple target grids, obtaining the distance between the user coordinates and each target grid, and aggregating the user coordinates into the target grid closest to the user coordinates.

5. The screen dot distribution processing method according to claim 1, characterized by, The method comprises the following steps: obtaining a preset coordinate point in the grid corresponding to the new aggregation point, and determining a preset weight corresponding to the preset coordinate point; The to-be-polymerized user coordinate is taken as a data point, and the number of data points in a grid corresponding to the new polymerization point is determined according to the preset weight; A quantity level corresponding to the number of data points is determined, and the quantity level is taken as the target polymerization information.

6. The screen dot distribution processing method according to claim 5, characterized by, The determining whether to add a new grid point according to the target polymerization information comprises: detecting whether the quantity level reaches a preset level; when the detection result is that the quantity level reaches the preset level, traversing a preset query relationship between a quantity level and a grid point scale according to the quantity level to obtain a target grid point scale, and adding a grid point according to the target grid point scale; when the detection result is that the quantity level does not reach the preset level, determining not to add a grid point.

7. A dot distribution processing apparatus for executing the dot distribution processing method according to any one of claims 1 to 6, characterized by The grid point distribution processing apparatus comprises: a grid point acquisition module, configured to acquire an initial grid point set of a target region, and construct a target grid set by taking each initial grid point in the initial grid point set as a center point; a coordinate polymerization module, configured to acquire user coordinates in the target region, and polymerize the user coordinates into the target grid closest to the user coordinates; a coordinate detection module, configured to detect whether there is a to-be-polymerized user coordinate that is not polymerized into the target grid; a polymerization processing module, configured to, when the detection result is that there is the to-be-polymerized user coordinate that is not polymerized into the target grid, acquire the to-be-polymerized user coordinate, and determine a new polymerization point by taking the to-be-polymerized user coordinate as a center point; an information acquisition module, configured to acquire target polymerization information corresponding to the new polymerization point; a grid point determination module, configured to determine whether to add a grid point according to the target polymerization information.

8. A computer device, comprising: The computer device comprises a processor, and the processor is configured to implement the grid point distribution processing method according to any one of claims 1 to 6 when executing a computer program stored in a memory.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is configured to implement the grid point distribution processing method according to any one of claims 1 to 6 when executed by a processor.

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