CN118069764A

CN118069764A - Method, device, equipment and storage medium for generating interest surface data

Info

Publication number: CN118069764A
Application number: CN202311801544.3A
Authority: CN
Inventors: 陈沛林
Original assignee: Tiktok Zhitu Technology Co ltd
Current assignee: Tiktok Zhitu Technology Co ltd
Priority date: 2023-12-25
Filing date: 2023-12-25
Publication date: 2024-05-24

Abstract

The embodiment of the disclosure provides a method, a device, equipment and a storage medium for generating interest surface data, wherein service data in a target period are obtained, and the service data represent a service request aiming at a target merchant; obtaining a position cluster formed by the initiating positions of at least two service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions; and generating interest surface data according to the position clusters, wherein the interest surface data represents the regional outline of the target merchant in a preset map. By taking service data representing the initiating position of the service request as a data source, constructing a position cluster based on the region density, further generating interest surface data representing the region outline of the target merchant, realizing the automatic generation of the interest surface data, and improving the generation efficiency and accuracy of the interest surface data.

Description

Method, device, equipment and storage medium for generating interest surface data

Technical Field

The embodiment of the disclosure relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for generating interest surface data.

Background

An interest plane (area of interest), also known as an information plane, is used to mark and define a designated area, such as a merchant, a building, etc., in an electronic map, so as to facilitate various applications to provide more targeted services to users based on interest plane data.

In the prior art, the geographical information of a specific target is usually acquired by manual mapping, so that the interest surface data is generated, however, the problems of low interest surface data generation efficiency and poor accuracy exist in the scheme in the prior art.

Disclosure of Invention

The embodiment of the disclosure provides a method, a device, equipment and a storage medium for generating interest surface data, which are used for solving the problems of low efficiency and poor accuracy of generating the interest surface data in the prior art.

In a first aspect, an embodiment of the present disclosure provides a method for generating interest surface data, including:

Acquiring service data in a target period, wherein the service data represents a service request aiming at a target merchant; obtaining a position cluster formed by at least two initiating positions of the service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions; and generating interest surface data according to the position cluster, wherein the interest surface data represents the area outline of the target merchant in a preset map.

In a second aspect, an embodiment of the present disclosure provides an apparatus for generating interest surface data, including:

the acquisition module is used for acquiring service data in a target period, wherein the service data represents a service request aiming at a target merchant;

The processing module is used for obtaining a position cluster formed by at least two initiating positions of the service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions;

the generation module is used for generating interest surface data according to the position cluster, wherein the interest surface data represents the regional outline of the target merchant in a preset map.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: a processor and a memory;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory such that the at least one processor performs the method of generating face of interest data as described above in the first aspect and the various possible designs of the first aspect.

In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement the method for generating interest surface data according to the first aspect and the various possible designs of the first aspect.

In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising a computer program which, when executed by a processor, implements the method of generating surface of interest data as described above for the first aspect and the various possible designs of the first aspect.

The method, the device, the equipment and the storage medium for generating the interest surface data are characterized in that service data in a target period are obtained, and the service data represent a service request aiming at a target merchant; obtaining a position cluster formed by at least two initiating positions of the service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions; and generating interest surface data according to the position cluster, wherein the interest surface data represents the area outline of the target merchant in a preset map. By taking service data representing the initiating position of the service request as a data source, constructing a position cluster based on the region density, further generating interest surface data representing the region outline of the target merchant, realizing the automatic generation of the interest surface data, and improving the generation efficiency and accuracy of the interest surface data.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the description of the prior art, it being obvious that the drawings in the following description are some embodiments of the present disclosure, and that other drawings may be obtained from these drawings without inventive effort to a person of ordinary skill in the art.

Fig. 1 is an application scenario diagram of a method for generating interest surface data according to an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating a method for generating interest surface data according to an embodiment of the present disclosure;

FIG. 3 is a flowchart of a specific implementation of step S102 in the embodiment shown in FIG. 2;

FIG. 4 is a schematic diagram of deduplication of overlay grid coordinates according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of a specific implementation of step S103 in the embodiment shown in FIG. 2;

FIG. 6 is a schematic diagram of a generation process of interest surface data according to an embodiment of the disclosure;

FIG. 7 is a second flowchart of a method for generating interest surface data according to an embodiment of the present disclosure;

FIG. 8 is a flowchart of a specific implementation of step S204 in the embodiment shown in FIG. 2;

FIG. 9 is a flowchart of a specific implementation of step S2042 in the embodiment of FIG. 8;

FIG. 10 is a flowchart of a specific implementation of step S2043 in the embodiment of FIG. 8;

FIG. 11 is a block diagram of an apparatus for generating interest surface data according to an embodiment of the present disclosure;

Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure;

Fig. 13 is a schematic hardware structure of an electronic device according to an embodiment of the disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and be provided with corresponding operation entries for the user to select authorization or rejection.

The application scenario of the embodiments of the present disclosure is explained below:

Fig. 1 is an application scenario diagram of an interest surface data generating method provided by an embodiment of the present disclosure, where the interest surface data generating method provided by the embodiment of the present disclosure may be applied to a scenario in which interest surface data is generated, and more specifically, may be applied to a scenario in which map data of an application program is generated, updated, and maintained, where the map data includes interest surface data pointed by the embodiment. The execution body of the embodiment may be a terminal device, a data server, or other electronic devices that perform the above-mentioned functions of generating the interest surface data. The carrier of the method for generating the interest surface data provided in this embodiment may be a program (product), and the execution body executes the program to implement execution of the method for generating the interest surface data provided in this embodiment. Specifically, referring to fig. 1, the execution body of the present embodiment is, for example, a data server for storing merchant data, an application program running in a terminal device communicates with a service end running in a service server, and at the same time, the service server may obtain the interest plane data from the data server and respond to a service request sent to the terminal device (return a service response) based on the interest plane data, so as to provide services, such as ticket service, consumption service, etc., of a designated merchant to a user. In this process, service data generated in the process of providing services to users by the application program is stored in the service server. Then, in one possible implementation manner, the data server obtains part of service data stored in the service server by periodically executing the program corresponding to the method for generating the interest surface data provided by the embodiment, and uses the part of service data to generate, update or maintain the interest surface data, so that the geographic information of the merchant in the application program is ensured to be accurate and real-time.

In the prior art, the geographical information of a specific target is usually collected by manual mapping, and further the interest surface data is generated, and because a large amount of interest surface data (such as positions of merchants) can change along with the specific operation condition of a business object (such as a merchant), the interest surface data in a server has a real-time update requirement, however, in the prior art, due to the problems of low interest surface data generation efficiency and poor accuracy in the manual mapping scheme, the real-time and accurate update of the interest surface data cannot be met, and further the use experience of a user is influenced.

The embodiment of the disclosure provides a method for generating interest surface data to solve the problems.

Referring to fig. 2, fig. 2 is a flowchart illustrating a method for generating interest surface data according to an embodiment of the disclosure. The method of the present embodiment may be applied to a server, for example, a data server shown in fig. 1, where the method for generating the interest surface data includes:

step S101: service data characterizing a service request for a target merchant during a target period is obtained.

For example, referring to the application scenario diagram shown in fig. 1, service data is data generated in a process in which a user transmits a service request (through a terminal device) to (a server of) a target merchant. The service herein specifically includes an offline service provided by a merchant, and the service request includes a request for the offline service, for example, a service verification request, and the like, which is sent by a user through a terminal device. Taking a service verification request as an example, the service data generation process will be described first: illustratively, after the user purchases the "ticket service" of the target merchant, the user checks out the "ticket service" by operating an application on the terminal device after reaching the actual location of the target merchant, e.g., the "zoo ticket office". In one possible implementation manner, the verification process may be independently completed by the terminal device used by the user, for example, after the terminal device responds to the user operation and triggers a service verification instruction, the terminal device sends a service verification request to a server (service end) of the application program, receives a service credential returned by the server, and after the terminal device receives the service credential, the terminal device is regarded as a service verification trigger. The user may then proceed with the consumption of the service directly at the target merchant through information provided by the service credential (e.g., a meal code).

In another possible implementation manner, the verification process may be completed after information interaction is performed between the terminal device used by the user and the merchant device, for example, the terminal device displays a verification password (such as a verification code, a bar code, etc.) in response to user operation, then the merchant device obtains the verification password by scanning, etc., and uploads the verification password to the server for verification, after the verification is successful, the server returns and pushes verification success information to the terminal device and the merchant device of the user, respectively, and then the user may perform service consumption at the target merchant. In the process, according to specific settings, when the terminal equipment displays the verification password, the verification password can be regarded as service verification trigger, and then the terminal equipment sends the current positioning to the server, so that the server obtains the coordinates of the initiating position for initiating the service verification request aiming at the target merchant. Or the terminal device or the merchant device can be regarded as service verification trigger after receiving the verification success information, and then the terminal device sends the current positioning to the server to enable the server to obtain the coordinates of the initiating position for initiating the service verification request aiming at the target merchant. In the two possible ways, the server can obtain the corresponding initiating position, namely the positioning coordinate of the terminal equipment, when the service verification request is initiated, so as to generate service verification data (service data) required subsequently.

Based on the above description, after the coordinates of the initiation position of the service request of the target merchant are sent to the server, the server generates corresponding service data in units of the target merchant. Then, the server may update the service data, for example, save the service data of the last month, based on a preset update interval, for example, one week or one month, and then the server obtains the service data to execute the subsequent steps to obtain the corresponding interest surface data. Wherein the server may filter the service data based on a target period, such as the last month. The specific implementation manner can be set according to the needs, and will not be described herein.

Step S102: and obtaining a position cluster formed by the initiating positions of at least two service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions.

The server performs region division according to the initiation position indicated by the service data to generate corresponding map regions, and then clusters according to the region density of each map region to obtain a position cluster, wherein the position cluster can be regarded as a set of a plurality of coordinate points. In one possible implementation manner, the service data includes at least two service coordinates, where the service coordinates represent longitude and latitude of the terminal device when the service request is initiated, after obtaining the service data, the service data is first processed and mapped into a preset map to form coordinates based on a map coordinate system, and further form a location cluster, and as shown in fig. 3, the specific steps in step S102 include:

step S1021: and obtaining grid coordinates for indicating map grids in the preset map by inputting at least two service coordinates into the preset map.

Step S1022: and obtaining a position cluster formed by at least two map grids according to the grid coordinates.

Wherein, the map grids are grids divided based on equidistant intervals in a preset map (data), and each map grid corresponds to the abscissa and the ordinate of a map coordinate system. And then, mapping the service coordinates (longitude and latitude) corresponding to the terminal equipment into a map network according to the calibration data of the preset map to form corresponding grid coordinates, and realizing the conversion from a world coordinate system to a map coordinate system. Specifically, for example, the service data includes a service coordinate w_ Coor = [115.58322,31.84336]. According to the calibration data of the preset map, the calibration data is mapped to the grid coordinates m_ Coor = [1025,320]. The mapping relation is only exemplary, and the implementation manner can be set according to the size data and the calibration data of the preset map.

And after mapping the service coordinates to the corresponding grid coordinates, carrying out clustering processing based on the map grid indicated by the grid coordinates to form a series of grid coordinate sets, namely position clusters. In the step of this embodiment, the conversion from the world coordinate system to the map coordinate system is achieved by mapping the service coordinate to the grid coordinate corresponding to the preset map, so as to achieve the purpose of generating the interest plane data based on the map coordinate system in the subsequent step.

Further, in order to further improve the efficiency of generating the interest surface data subsequently, after the grid coordinates are obtained, downsampling is further performed on a plurality of grid coordinates, and overlapping points are removed, so that the number of grid coordinate points is reduced. Specifically, the grid coordinates include coincident grid coordinates and non-coincident grid coordinates, and after step S1021, further:

Step S1021A: and de-duplicating at least two coincident grid coordinates positioned in the same map grid to obtain corresponding non-coincident grid coordinates.

Accordingly, the implementation manner of step S1022 includes: and obtaining a position cluster formed by at least two map grids according to the non-coincident grid coordinates.

For example, after mapping the service coordinates to the corresponding map grids, the service coordinates with longer latitude and longitude distances are mapped to different map grids, so as to obtain corresponding different grid coordinates, namely non-coincident grid coordinates, and more than two service coordinates with shorter latitude and longitude distances are mapped to the same map grid, so as to form coincident grid coordinates. In this case, the server performs deduplication on the coincident grid coordinates mapped into the same map network, i.e., each map grid only retains at most one grid coordinate (generated by the service coordinates), thereby realizing reduction of the number of coordinate points and improvement of data processing efficiency and speed in subsequent steps.

Specifically, fig. 4 is a schematic diagram of performing deduplication on the overlay grid coordinate according to the embodiment of the present disclosure, and as shown in fig. 4, the server obtains, illustratively, service coordinate a, service coordinate B, service coordinate C, and service coordinate D based on the previous steps. Then map grid mapping is carried out on the four service coordinates, and according to the specific longitude and latitude of each service coordinate, the grid coordinates corresponding to the map grid mapped by the service coordinate A are M1 (M1, M2); the grid coordinates corresponding to the mapping of the service coordinates B and the service coordinates C to the map grid are M2 (M3, M4) and M3 (M3, M4) respectively; the grid coordinates corresponding to the map grid mapped to the service coordinates D are M4 (M5, M6). Based on the mapping result, the grid coordinate M1 corresponding to the service coordinate a and the grid coordinate M4 corresponding to the service coordinate C are non-coincident grid coordinates, and the grid coordinates M2 (M3, M4) and M3 (M3, M4) corresponding to the service coordinate B and the service coordinate C are coincident grid coordinates. Then, the overlapping grid coordinates M2 (M3, M4) and M3 (M3, M4) are de-duplicated, for example, the overlapping grid coordinates M2 (M3, M4) are removed and M3 (M3, M4) is reserved, and finally 3 non-overlapping grid coordinates M1, M3 and M4 are obtained.

In the implementation step, the duplicate-removal processing is performed on the overlapped grid coordinates, so that low-efficiency coordinate points with too close distances can be removed, the number of coordinate points for subsequent aggregation is reduced, and the data processing efficiency is improved.

Optionally, after performing de-duplication on at least two coincident grid coordinates located in the same map grid to obtain corresponding non-coincident grid coordinates, the method further includes the steps of: and eliminating the isolated point coordinates in the non-coincident grid coordinates. Specifically, for example, the non-coincident grid coordinates after downsampling are subjected to statistical filtering, coordinates with average distance larger than 2 times of standard deviation are determined to be isolated point coordinates, and then the isolated point coordinates are removed, so that the purposes of reducing data noise and improving data quality are achieved.

Step S103: and generating interest surface data according to the position clusters, wherein the interest surface data represents the regional outline of the target merchant in a preset map.

After the location cluster is generated, the location cluster is a set of coordinate points described based on a map coordinate system, and the contour of the location cluster is estimated, so that corresponding information describing the region range, namely, the interest surface data representing the region contour of the target merchant in the preset map can be generated. Optionally, before the step of generating the interest surface data based on the location clusters is performed, the plurality of location clusters may be further screened to remove abnormal or inaccurate location clusters, thereby improving the accuracy of the interest surface data. Specifically, in this embodiment, before step S103, the method further includes:

Step S103A: and acquiring the center point coordinates of each position cluster.

Step S103B: and screening out target position clusters from the position clusters based on the central point coordinates, wherein the distance between the central point coordinates of the target position clusters and the positioning coordinates corresponding to the target merchants is smaller than a first distance threshold.

First, an average calculation is performed according to coordinate points in each position cluster, so that coordinates of a center point of the position cluster can be obtained, and detailed implementation steps are not repeated. And then, acquiring positioning coordinates corresponding to the target merchant, wherein the positioning coordinates are coordinates for describing positioning points of the target merchant in a preset map, for example, the positioning coordinates can be coordinates of points of interest (Point of Interest, POI), and the positioning coordinates can be pre-generated in the embodiment, and the specific generation mode is not limited herein. And then, sequentially calculating the distance between each center point coordinate and the positioning coordinate, and comparing the distance with a preset first distance threshold value, wherein in one possible implementation manner, the position clusters with the distance between the center point coordinate and the positioning coordinate larger than the first distance threshold value can be removed, and the rest position clusters, namely the position clusters with the distance between the center point coordinate and the positioning coordinate smaller than or equal to the first distance threshold value, are determined as target position clusters. Thereafter, the target location cluster is used to generate the interest surface data, that is, the specific implementation of step S103 includes: and generating the interest surface data according to the target position cluster.

Further, the following describes a specific process of generating the interest surface data, and in one possible implementation, as shown in fig. 5, the specific implementation of step S103 includes:

step S1031: and performing convex hull calculation on the position clusters to obtain convex hull line segments corresponding to the position clusters.

Step S1032: and generating the interest surface data according to the convex hull line segments.

Illustratively, where convex hull (Convex Hull) is a concept in computational graphics, in a real vector space, for a given set X, intersection S of all convex sets containing X is referred to as the convex hull of X. In the step of this embodiment, a closed curve segment, that is, a convex hull segment, surrounding all coordinate points of the position cluster and having the smallest surrounding area can be obtained through convex hull calculation, and a map area, that is, an area contour of the target merchant in the preset map, is surrounded by the convex hull segment. The specific process of performing convex hull calculation on the set of multiple points to obtain the corresponding convex hull (contour point set) is known to those skilled in the art, and is not described herein.

Fig. 6 is a schematic diagram of a generation process of interest surface data according to an embodiment of the present disclosure, as shown in fig. 6, after a position cluster #1 of a preset map arrangement is obtained, convex hull calculation is performed on the position cluster #1 to obtain a closed curve surrounding coordinate points in all the position clusters #1, where, as shown in the figure, the closed curve is formed by four line segments L1, L2, L3, and L4, and the data of the closed curve L may be described, that is, the interest surface data, for example, a set of start point coordinates corresponding to the four line segments including the L1, L2, L3, and L4.

In this embodiment, service data representing a service request for a target merchant by acquiring service data in a target period; obtaining a position cluster formed by the initiating positions of at least two service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions; and generating interest surface data according to the position clusters, wherein the interest surface data represents the regional outline of the target merchant in a preset map. By taking service data representing the initiating position of the service request as a data source, constructing a position cluster based on the region density, further generating interest surface data representing the region outline of the target merchant, realizing the automatic generation of the interest surface data, and improving the generation efficiency and accuracy of the interest surface data.

Referring to fig. 7, fig. 7 is a second flowchart of a method for generating interest surface data according to an embodiment of the disclosure. The embodiment further refines step S102 on the basis of the embodiment shown in fig. 2, and the method for generating the interest surface data includes:

step S201: service data is acquired over a target period, the service data characterizing an originating location of a service request for a target merchant.

Step S202: and obtaining the category identification of the service category corresponding to the service request.

Step S203: and determining first algorithm configuration information according to the category identification, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position.

Based on the description of the embodiment shown in fig. 2, in a possible implementation manner, after obtaining the service data, the server may process according to the initiation position of the service request for the target merchant, which is characterized by the service data, for example, using an aggregation algorithm with fixed parameters, so as to obtain a corresponding position cluster, and further generate the interest surface data. In another possible implementation manner, a dynamic parameter mode can be adopted to generate a corresponding position cluster, so that accuracy of the data of the interest surface and service matching performance are improved.

Illustratively, first, the server obtains a class identification of the service class to which the service request corresponds, which in one possible implementation may be sub-data or attribute information contained in the service data. For example, the service class corresponding to the service request is "ticket service", and the service data generated at the server side includes a class identifier representing the "ticket service"; after the server obtains the service data in the target period, the category identification can be obtained by analyzing the service data. In another possible implementation, since the service request is for the target merchant, the service class corresponding to the service request, that is, the service class corresponding to the target merchant, specifically, in this implementation, the class identifier corresponds to the service class of the merchant and is stored as merchant information in the server. When the server generates the interest surface data of the target merchant, the server obtains the service data corresponding to the target merchant and obtains the category identification representing the service category through the prestored merchant information, for example, the category identification comprises [ #food ], and the service category corresponding to the target merchant is represented as 'catering category'.

And then, the server maps the obtained category identification to corresponding first algorithm configuration information, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position, and the position cluster is determined based on the area density, so that the first algorithm configuration information can be used as a dynamic parameter to control the determination of the position cluster, and the generated position cluster is more accurate. Further, there are various implementations of the first algorithm configuration information, and a specific calculation process based on the location cluster may be used to determine one or more parameters in the calculation process, and the specific implementation process is described in detail in the following embodiments.

Step S204: and obtaining a position cluster formed by the initiating positions of at least two service requests according to the determined first algorithm configuration information and the service data.

After the server obtains the first algorithm configuration information and the service data, the service data is processed by using the first algorithm configuration information as a dynamic parameter, so that the generation process of the location cluster considers specific service characteristics, specifically, as shown in fig. 8, the specific implementation steps of step S204 include:

Step S2041: and acquiring the coordinates to be processed in the service data.

Step S2042: and acquiring the area density of the current area where the coordinates to be processed are located according to the first algorithm configuration information.

Step S2043: and marking the position cluster corresponding to the coordinates to be processed according to the area density.

Step S2044: the remaining coordinates in the service data that are not marked are detected.

Step S2045: and if the service data contains the remaining coordinates which are not marked, updating the coordinates to be processed into the remaining coordinates.

Step S2046: and if the service data does not contain the remaining coordinates which are not marked, triggering a preset condition.

Step S2047: if the preset condition is reached, the cycle is ended, otherwise, the step S2041 is returned.

For example, first, in this embodiment, the service data includes a plurality of coordinates that characterize the initiating position of the service request, and specifically, the coordinates may be any one of the service coordinates, the grid coordinates, and the non-coincident grid coordinates in the foregoing embodiment, and the coordinates to be processed are coordinates that are not marked as a position cluster in the foregoing coordinates. Then, according to the first algorithm configuration information, obtaining the area density of the current area where the coordinates to be processed are located, specifically, obtaining the current area corresponding to the coordinates to be processed, wherein the range of the current area is determined by the first algorithm configuration information; and then obtaining the region density through the coordinate number in the current region.

Specifically, in one possible implementation manner, as shown in fig. 9, the specific implementation manner of step S2042 includes:

step S2042A: calculating the number of coordinates in the radius of the target by taking the coordinates to be processed as the center;

Step S2042B: and if the number of the coordinates is larger than the threshold value of the number of the coordinates, obtaining the area density of the current area where the coordinates to be processed are located according to the number of the coordinates and the radius of the target.

Step S2042C: if the number of coordinates is less than or equal to the threshold number of coordinates, the process goes to step S2044.

For example, first, a coordinate that is not marked as (one coordinate in) a location cluster is randomly selected from service data and is used as a coordinate to be processed, and then, under a map coordinate system, the number of coordinates within a target radius is calculated with the coordinate to be processed as a center, where the target radius may be a preset value or a dynamic value determined based on first algorithm configuration information. For example, when the service class represented by the class identifier corresponding to the first algorithm configuration information is "outdoor location service", the target radius is set to be a larger target radius, so that the coverage of the regional outline of the target merchant in the preset map is as large as possible, and the generated interest surface data can better represent the regional outline corresponding to the "outdoor location service". And when the service class represented by the class identifier corresponding to the first algorithm configuration information is 'indoor location service', setting the target radius as a smaller target radius, thereby improving the concentration of coordinate points in the position cluster and improving the description accuracy of the interest surface data on the regional outline corresponding to the 'indoor location service'.

Then, judging based on the area density, if the area density is larger than a preset density threshold, clustering the area density, and executing a step S2043 to realize marking of a position cluster corresponding to the coordinates to be processed; if the area density is less than or equal to the preset density threshold, the coordinates to be processed are not satisfied with the area density requirement, and the step is skipped to step S2044.

In one possible implementation manner, as shown in fig. 10, the specific implementation manner of step S2043 includes:

step S2043A: and carrying out width priority search on the coordinates to be processed to obtain density reachable coordinates corresponding to the coordinates to be processed.

Step S2043B: and marking the set of the coordinates to be processed and the density reachable coordinates as a position cluster.

Illustratively, the clustering algorithm used in the steps of the present embodiment is, for example, a density-based clustering algorithm (DBSCAN) algorithm. By defining a location cluster as the largest set of densely connected points, it is possible to divide a region with a sufficiently high density into location clusters, and to find clusters of arbitrary shape in a noisy spatial database. Among them, density reachability-reachable is a concept in clustering algorithms. Density reachable means that for a certain object p in a given cluster structure, if there is an object q, we can say that the object p is reachable by the object q density if the following two conditions are satisfied:

1. Object p and object q belong to the same cluster.

2. A dense connection may be made from object q to object p along the objects in the cluster.

The coordinates in this embodiment, that is, the objects in the definition, perform a width-first search on the coordinates to be processed, and calculate the density corresponding to each coordinate, so as to obtain the density reachable coordinates in which the coordinates to be processed are connected in series. The breadth-first search is one of the commonly used graph search algorithms, and the specific implementation process of the breadth-first search for the density reachable coordinates is not described here again.

And then, carrying out width priority search by taking the coordinates to be processed as a core, finding out all the coordinates (points) with reachable densities, and marking the coordinates with the coordinates to be processed as clustered coordinates, namely the marked coordinates in the subsequent step. In this process, the remaining coordinates that are not marked as clustered coordinates, i.e., the remaining coordinates that are not marked in the subsequent step, are not marked. And repeating the steps for the residual coordinates until all the residual coordinates are marks or the preset cycle times or execution time length are reached, triggering preset conditions and jumping out of the cycle.

Step S205: and generating interest surface data according to the position clusters, wherein the interest surface data represents the regional outline of the target merchant in a preset map.

In this embodiment, the implementation manner of step S201 and step S205 is the same as the implementation manner of step 101 and step S103 in the embodiment shown in fig. 2 of the present disclosure, and will not be described in detail here.

Corresponding to the method for generating the interest surface data in the above embodiment, fig. 11 is a block diagram of the structure of the apparatus for generating the interest surface data according to the embodiment of the present disclosure. For ease of illustration, only portions relevant to embodiments of the present disclosure are shown. Referring to fig. 11, the interest surface data generating apparatus 3 includes:

an acquisition module 31, configured to acquire service data in a target period, where the service data represents a service request for a target merchant;

The processing module 32 is configured to obtain, according to the service data, a location cluster formed by the originating locations of at least two service requests, where the location cluster is determined based on the area density corresponding to the originating locations;

The generating module 33 generates, according to the location cluster, interest surface data, where the interest surface data characterizes an area profile of the target merchant in a preset map.

According to one or more embodiments of the present disclosure, the service data includes at least two service coordinates, the service coordinates characterizing the longitude and latitude of the terminal device at the time of initiation of the service request, and the processing module 32 is further configured to: at least two service coordinates are added into a preset map to obtain grid coordinates for indicating map grids in the preset map; the processing module 32 is specifically configured to, when obtaining a location cluster formed by at least two service request initiation locations according to the service data: and obtaining a position cluster formed by at least two map grids according to the grid coordinates.

In accordance with one or more embodiments of the present disclosure, the grid coordinates include coincident grid coordinates and non-coincident grid coordinates; the processing module 32 is further configured to, after obtaining the grid coordinates indicating the map grid in the preset map, send the service coordinates to the preset map: performing de-duplication on at least two coincident grid coordinates positioned in the same map grid to obtain corresponding non-coincident grid coordinates; eliminating isolated point coordinates in non-coincident grid coordinates; the processing module 32 is specifically configured to, when obtaining a location cluster formed by at least two map grids according to the grid coordinates: and obtaining a position cluster formed by at least two map grids according to the non-coincident grid coordinates.

In accordance with one or more embodiments of the present disclosure, the processing module 32 is further configured to: acquiring a class identifier of a service class corresponding to the service request; determining first algorithm configuration information according to the category identification, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position; the processing module 32 is specifically configured to, when obtaining a location cluster formed by at least two service request initiation locations according to the service data: and obtaining a position cluster formed by the initiating positions of at least two service requests according to the determined first algorithm configuration information and the service data.

According to one or more embodiments of the present disclosure, the processing module 32 is specifically configured to, when obtaining, according to service data, a location cluster composed of at least two service request initiation locations: the following steps are circularly executed until the preset condition is reached: acquiring coordinates to be processed in service data; acquiring the area density of a current area where the coordinates to be processed are located; marking a position cluster corresponding to the coordinates to be processed according to the area density; if the service data contains the remaining coordinates which are not marked, updating the coordinates to be processed into the remaining coordinates; and if the service data does not contain the remaining coordinates which are not marked, triggering a preset condition.

In accordance with one or more embodiments of the present disclosure, the processing module 32 is specifically configured to, when acquiring the area density of the current area where the coordinates to be processed are located: calculating the number of coordinates in the radius of the target by taking the coordinates to be processed as the center; if the number of the coordinates is larger than the threshold value of the number of the coordinates, obtaining the area density of the current area where the coordinates to be processed are located according to the number of the coordinates and the radius of the target; wherein the target radius is determined based on the service class to which the service request corresponds.

According to one or more embodiments of the present disclosure, the processing module 32 is specifically configured to, when marking a location cluster corresponding to a coordinate to be processed according to the area density: performing width priority search on the coordinates to be processed to obtain density reachable coordinates corresponding to the coordinates to be processed; and marking the set of the coordinates to be processed and the density reachable coordinates as a position cluster.

In accordance with one or more embodiments of the present disclosure, when the location cluster includes at least two, the processing module 32 is further configured to: acquiring the center point coordinates of each position cluster; screening out target position clusters from the position clusters based on the center point coordinates, wherein the distance between the center point coordinates of the target position clusters and the positioning coordinates corresponding to target merchants is smaller than a first distance threshold; the generating module 33 is specifically configured to: and generating the interest surface data according to the target position cluster.

In accordance with one or more embodiments of the present disclosure, the generating module 33 is specifically configured to: performing convex hull calculation on the position clusters to obtain convex hull line segments corresponding to the position clusters; and generating the interest surface data according to the convex hull line segments.

The acquisition module 31, the processing module 32 and the generation module 33 are sequentially connected. The technical scheme of the method embodiment can be executed by the interest surface data generating device 3 provided in this embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, as shown in fig. 12, the electronic device 4 includes:

A processor 41 and a memory 42 communicatively connected to the processor 41;

memory 42 stores computer-executable instructions;

The processor 41 executes computer-executable instructions stored in the memory 42 to implement the method of generating the surface of interest data in the embodiment shown in fig. 2-10.

Wherein optionally the processor 41 and the memory 42 are connected by a bus 43.

The relevant descriptions and effects corresponding to the steps in the embodiments corresponding to fig. 2 to fig. 10 may be understood correspondingly, and are not described in detail herein.

The embodiments of the present disclosure provide a computer readable storage medium, in which computer executable instructions are stored, where the computer executable instructions are used to implement the method for generating the interest surface data provided in any one of the embodiments corresponding to fig. 2 to 10 of the present disclosure when executed by a processor.

The embodiments of the present disclosure provide a computer program product, including a computer program, which when executed by a processor implements the method for generating interest surface data provided in any of the embodiments corresponding to fig. 2 to 10 of the present disclosure.

In order to achieve the above embodiments, the embodiments of the present disclosure further provide an electronic device.

Referring to fig. 13, there is shown a schematic structural diagram of an electronic device 900 suitable for use in implementing embodiments of the present disclosure, where the electronic device 900 may be a terminal device or a server. The terminal device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a Personal Digital Assistant (PDA) or the like, a tablet computer (Portable Android Device) or the like, a Portable Multimedia Player (PMP) or the like, a car-mounted terminal (e.g., car navigation terminal) or the like, and a fixed terminal such as a digital TV or a desktop computer or the like. The electronic device shown in fig. 13 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 13, the electronic device 900 may include a processing means (e.g., a central processor, a graphics processor, etc.) 901, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage means 908 into a random access Memory (Random Access Memory RAM) 903. In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.

In general, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 907 including, for example, a Liquid Crystal Display (LCD) CRYSTAL DISPLAY, a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device 900 to communicate wirelessly or by wire with other devices to exchange data. While fig. 13 shows an electronic device 900 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 909, or installed from the storage device 908, or installed from the ROM 902. When executed by the processing device 901, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above-described embodiments.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (Local Area Network, LAN for short) or a wide area network (Wide Area Network, WAN for short), or may be connected to an external computer (e.g., through the internet using an internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

In a first aspect, according to one or more embodiments of the present disclosure, there is provided a method for generating interest surface data, including:

According to one or more embodiments of the present disclosure, the service data includes at least two service coordinates, the service coordinates characterizing a longitude and latitude of a terminal device at the time of initiation of the service request, the method further includes: the at least two service coordinates are added into the preset map to obtain grid coordinates for indicating map grids in the preset map; the obtaining, according to the service data, a location cluster formed by at least two originating locations of the service request includes: and obtaining a position cluster formed by at least two map grids according to the grid coordinates.

According to one or more embodiments of the present disclosure, the grid coordinates include coincident grid coordinates and non-coincident grid coordinates; after the service coordinates are obtained from the preset map, the method further comprises the steps of: performing de-duplication on at least two coincident grid coordinates positioned in the same map grid to obtain corresponding non-coincident grid coordinates; removing isolated point coordinates in the non-coincident grid coordinates; obtaining a position cluster formed by at least two map grids according to the grid coordinates, wherein the position cluster comprises: and obtaining a position cluster formed by at least two map grids according to the non-coincident grid coordinates.

According to one or more embodiments of the present disclosure, the method further comprises: acquiring a class identifier of a service class corresponding to the service request; determining first algorithm configuration information according to the category identification, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position; obtaining a location cluster formed by at least two initiation locations of the service request according to the service data, wherein the location cluster comprises: and obtaining a position cluster formed by at least two initiating positions of the service requests according to the determined first algorithm configuration information and the service data.

According to one or more embodiments of the present disclosure, the obtaining, according to the service data, a location cluster including at least two originating locations of the service requests includes: the following steps are circularly executed until the preset condition is reached: acquiring coordinates to be processed in the service data; acquiring the area density of the current area where the coordinates to be processed are located; marking a position cluster corresponding to the coordinates to be processed according to the area density; if the service data contains the remaining coordinates which are not marked, updating the coordinates to be processed into the remaining coordinates; and if the service data does not contain the remaining coordinates which are not marked, triggering the preset condition.

According to one or more embodiments of the present disclosure, the obtaining the area density of the current area where the coordinates to be processed are located includes: calculating the number of coordinates in the radius of the target by taking the coordinates to be processed as a center; if the coordinate number is larger than a coordinate number threshold, obtaining the area density of the current area where the coordinate to be processed is located according to the coordinate number and the target radius; wherein the target radius is determined based on a service class to which the service request corresponds.

According to one or more embodiments of the present disclosure, marking, according to the area density, a location cluster corresponding to the coordinates to be processed includes: performing width priority search on the coordinates to be processed to obtain density reachable coordinates corresponding to the coordinates to be processed; and marking the set of the coordinates to be processed and the density reachable coordinates as a position cluster.

According to one or more embodiments of the present disclosure, when the location cluster includes at least two, the method further includes: acquiring the center point coordinates of each position cluster; screening out a target position cluster from the position clusters based on the center point coordinates, wherein the distance between the center point coordinates of the target position cluster and the positioning coordinates corresponding to the target merchant is smaller than a first distance threshold; generating the interest surface data according to the position cluster comprises the following steps: and generating the interest surface data according to the target position cluster.

According to one or more embodiments of the present disclosure, the generating the interest surface data according to the location cluster includes: performing convex hull calculation on the position clusters to obtain convex hull line segments corresponding to the position clusters; and generating the interest surface data according to the convex hull line segments.

In a second aspect, according to one or more embodiments of the present disclosure, there is provided a surface of interest data generating apparatus, including:

According to one or more embodiments of the present disclosure, the service data includes at least two service coordinates, the service coordinates characterize a longitude and latitude of the terminal device when the service request is initiated, and the processing module is further configured to: the at least two service coordinates are added into the preset map to obtain grid coordinates for indicating map grids in the preset map; the processing module is specifically configured to, when obtaining a location cluster formed by at least two initiation locations of the service request according to the service data: and obtaining a position cluster formed by at least two map grids according to the grid coordinates.

According to one or more embodiments of the present disclosure, the grid coordinates include coincident grid coordinates and non-coincident grid coordinates; the processing module is further configured to, after the service coordinates are obtained from the preset map, obtain grid coordinates for indicating a map grid in the preset map, and then: performing de-duplication on at least two coincident grid coordinates positioned in the same map grid to obtain corresponding non-coincident grid coordinates; removing isolated point coordinates in the non-coincident grid coordinates; the processing module is specifically configured to, when obtaining a position cluster formed by at least two map grids according to the grid coordinates: and obtaining a position cluster formed by at least two map grids according to the non-coincident grid coordinates.

According to one or more embodiments of the present disclosure, the processing module is further configured to: acquiring a class identifier of a service class corresponding to the service request; determining first algorithm configuration information according to the category identification, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position; the processing module is specifically configured to, when obtaining a location cluster formed by at least two initiation locations of the service request according to the service data: and obtaining a position cluster formed by at least two initiating positions of the service requests according to the determined first algorithm configuration information and the service data.

According to one or more embodiments of the present disclosure, when obtaining a location cluster formed by at least two originating locations of the service requests according to the service data, the processing module is specifically configured to: the following steps are circularly executed until the preset condition is reached: acquiring coordinates to be processed in the service data; acquiring the area density of the current area where the coordinates to be processed are located; marking a position cluster corresponding to the coordinates to be processed according to the area density; if the service data contains the remaining coordinates which are not marked, updating the coordinates to be processed into the remaining coordinates; and if the service data does not contain the remaining coordinates which are not marked, triggering the preset condition.

According to one or more embodiments of the present disclosure, when obtaining the area density of the current area where the coordinates to be processed are located, the processing module is specifically configured to: calculating the number of coordinates in the radius of the target by taking the coordinates to be processed as a center; if the coordinate number is larger than a coordinate number threshold, obtaining the area density of the current area where the coordinate to be processed is located according to the coordinate number and the target radius; wherein the target radius is determined based on a service class to which the service request corresponds.

According to one or more embodiments of the present disclosure, the processing module is specifically configured to, when marking, according to the area density, a location cluster corresponding to the coordinates to be processed: performing width priority search on the coordinates to be processed to obtain density reachable coordinates corresponding to the coordinates to be processed; and marking the set of the coordinates to be processed and the density reachable coordinates as a position cluster.

According to one or more embodiments of the present disclosure, when the location cluster includes at least two, the processing module is further configured to: acquiring the center point coordinates of each position cluster; screening out a target position cluster from the position clusters based on the center point coordinates, wherein the distance between the center point coordinates of the target position cluster and the positioning coordinates corresponding to the target merchant is smaller than a first distance threshold; the generating module is specifically configured to: and generating the interest surface data according to the target position cluster.

According to one or more embodiments of the present disclosure, the generating module is specifically configured to: performing convex hull calculation on the position clusters to obtain convex hull line segments corresponding to the position clusters; and generating the interest surface data according to the convex hull line segments.

In a third aspect, according to one or more embodiments of the present disclosure, there is provided an electronic device comprising: at least one processor and memory;

the memory stores computer-executable instructions;

The at least one processor executes the computer-executable instructions stored by the memory such that the at least one processor performs the method of generating face of interest data as described above in the first aspect and the various possible designs of the first aspect.

In a fourth aspect, according to one or more embodiments of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the method for generating interest surface data as described above in the first aspect and the various possible designs of the first aspect.

In a fifth aspect, according to one or more embodiments of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of generating surface of interest data as described above in the first aspect and the various possible designs of the first aspect.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. A method for generating interest surface data, comprising:

acquiring service data, wherein the service data represents a service request aiming at a target merchant;

obtaining a position cluster formed by at least two initiating positions of the service requests according to the service data, wherein the position cluster is determined based on the area density corresponding to the initiating positions;

And generating interest surface data according to the position cluster, wherein the interest surface data represents the area outline of the target merchant in a preset map.

2. The method of claim 1, wherein the service data includes at least two service coordinates characterizing a latitude and longitude of a terminal device at the time the service request was initiated, the method further comprising:

The at least two service coordinates are added into the preset map to obtain grid coordinates for indicating map grids in the preset map;

The obtaining, according to the service data, a location cluster formed by at least two originating locations of the service request includes:

And obtaining a position cluster formed by at least two map grids according to the grid coordinates.

3. The method of claim 2, wherein the grid coordinates comprise coincident grid coordinates and non-coincident grid coordinates; after the service coordinates are obtained from the preset map, the method further comprises the steps of:

performing de-duplication on at least two coincident grid coordinates positioned in the same map grid to obtain corresponding non-coincident grid coordinates;

Removing isolated point coordinates in the non-coincident grid coordinates;

obtaining a position cluster formed by at least two map grids according to the grid coordinates, wherein the position cluster comprises:

And obtaining a position cluster formed by at least two map grids according to the non-coincident grid coordinates.

4. The method according to claim 1, wherein the method further comprises:

Acquiring a class identifier of a service class corresponding to the service request;

determining first algorithm configuration information according to the category identification, wherein the first algorithm configuration information is used for determining the area density corresponding to the initiating position;

Obtaining a location cluster formed by at least two initiation locations of the service request according to the service data, wherein the location cluster comprises:

and obtaining a position cluster formed by at least two initiating positions of the service requests according to the determined first algorithm configuration information and the service data.

5. The method according to claim 1, wherein said obtaining a location cluster consisting of at least two originating locations of said service requests from said service data comprises:

the following steps are circularly executed until the preset condition is reached:

Acquiring coordinates to be processed in the service data; acquiring the area density of the current area where the coordinates to be processed are located; marking a position cluster corresponding to the coordinates to be processed according to the area density; if the service data contains the remaining coordinates which are not marked, updating the coordinates to be processed into the remaining coordinates; and if the service data does not contain the remaining coordinates which are not marked, triggering the preset condition.

6. The method of claim 5, wherein the obtaining the area density of the current area in which the coordinates to be processed are located comprises:

calculating the number of coordinates in the radius of the target by taking the coordinates to be processed as a center;

If the coordinate number is larger than a coordinate number threshold, obtaining the area density of the current area where the coordinate to be processed is located according to the coordinate number and the target radius;

Wherein the target radius is determined based on a service class to which the service request corresponds.

7. The method according to claim 5, wherein marking the location cluster corresponding to the coordinates to be processed according to the area density comprises:

Performing width priority search on the coordinates to be processed to obtain density reachable coordinates corresponding to the coordinates to be processed;

and marking the set of the coordinates to be processed and the density reachable coordinates as a position cluster.

8. The method of claim 1, wherein when the location cluster comprises at least two, the method further comprises:

acquiring the center point coordinates of each position cluster;

Screening out a target position cluster from the position clusters based on the center point coordinates, wherein the distance between the center point coordinates of the target position cluster and the positioning coordinates corresponding to the target merchant is smaller than a first distance threshold;

generating the interest surface data according to the position cluster comprises the following steps:

And generating the interest surface data according to the target position cluster.

9. The method of claim 1, wherein generating the interest surface data from the location clusters comprises:

Performing convex hull calculation on the position clusters to obtain convex hull line segments corresponding to the position clusters;

and generating the interest surface data according to the convex hull line segments.

10. An apparatus for generating interest surface data, comprising:

The acquisition module is used for acquiring service data, wherein the service data represents a service request aiming at a target merchant;

11. An electronic device, comprising: a processor and a memory;

the memory stores computer-executable instructions;

the processor executing computer-executable instructions stored in the memory causes the processor to perform the method of generating surface of interest data as claimed in any one of claims 1 to 9.

12. A computer readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the method of generating surface of interest data as claimed in any one of claims 1 to 9.

13. A computer program product comprising a computer program which, when executed by a processor, implements the method of generating surface of interest data as claimed in any one of claims 1 to 9.