Data processing method, device and equipment and readable storage medium

Abstract

The embodiment of the invention discloses a data processing method and device, equipment and a computer readable storage medium. The method comprises the steps that point location information corresponding to a transaction entity of a specified type is acquired, and the point location information is used for representing the geographic position of the transaction entity in a map; selecting a target road meeting a preset distance condition from a plurality of roads adjacent to the point location information; determining point location information of a plurality of transaction entities contained in a specified area in the map; and determining target point location information in the specified area from the point location information of the plurality of transaction entities based on the road information of the target road corresponding to the transaction entity of the specified type in the plurality of transaction entities. Through the method, the accuracy of site selection recommendation can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, apparatus, device, and readable storage medium. Background Art

[0002] Business site selection refers to the process of choosing a suitable location for opening a business. This process involves not only choosing a location but also considering factors such as the market and customer flow.

[0003] Recommendations for business locations are typically made by industry experts through market research and analysis. However, these experts' experience can be influenced by subjective preferences and limited experience, resulting in less than objective and comprehensive recommendations. Therefore, related technologies often rely on big data and machine learning to mine and analyze large amounts of demographic, geographic, and business environment data to make recommendations. However, these methods typically only provide recommendations within a 100-meter radius and lack the ability to provide more precise site recommendations.

[0004] Therefore, how to improve the accuracy of site recommendation is an urgent problem to be solved. Summary of the Invention

[0005] To solve the above technical problems, embodiments of the present application provide a data processing method, apparatus, device, and computer-readable storage medium.

[0006] Among them, the technical solutions adopted in this application are:

[0007] A data processing method, comprising:

[0008] Obtaining location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographic location of the transaction entity on a map;

[0009] Selecting a target road that meets a preset distance condition from a plurality of roads adjacent to the point information;

[0010] Determining location information of multiple trading entities within a specified area in the map;

[0011] Based on the road information of the target road corresponding to the transaction entity of the specified type among the multiple transaction entities, the target point information in the specified area is determined from the point information of the multiple transaction entities.

[0012] A data processing device, comprising:

[0013] an acquiring unit, configured to acquire location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographical location of the transaction entity on a map;

[0014] A selection unit, configured to select a target road that meets a preset distance condition from a plurality of roads adjacent to the point information;

[0015] A processing unit, configured to determine location information of multiple trading entities within a specified area in the map

[0016] The processing unit is further configured to determine target point information in the designated area from point information of the plurality of transaction entities based on road information of target roads corresponding to the transaction entities of the designated type among the plurality of transaction entities.

[0017] In one embodiment of the present application, based on the aforementioned scheme, the road information includes road grade information; the processing unit is further used to take the transaction entity with the lowest road grade information among the target roads corresponding to the transaction entities of the specified type as the target transaction entity; and take the point information corresponding to the target transaction entity as the target point information.

[0018] In one embodiment of the present application, based on the aforementioned scheme, after taking the transaction entity with the lowest road grade information among the target roads corresponding to the transaction entities of the specified type as the target transaction entity, the processing unit is also used to take the point information corresponding to the target transaction entity with the highest number of transactions within a preset time period as the target point information if there are multiple target transaction entities.

[0019] In one embodiment of the present application, based on the aforementioned scheme, the processing unit is further used to determine the location information of the multiple adjacent roads based on the point information; calculate the spherical distances between the point information and the location information of the multiple adjacent roads; and take the road with the shortest spherical distance to the point information as the target road that meets the preset distance condition.

[0020] In one embodiment of the present application, based on the aforementioned scheme, the processing unit is further used to determine the search area corresponding to the point information based on the point information; and determine the location information of the multiple adjacent roads based on the straight-line distance between the point information and each road in the search area.

[0021] In one embodiment of the present application, based on the aforementioned scheme, the acquisition unit is further used to obtain the circumscribed boundary corresponding to each road in the search area; the selection unit is further used to select the target circumscribed boundary based on the positional relationship between the point information and the area enclosed by the circumscribed boundary; calculate the straight-line distance between the point information and the road corresponding to the target circumscribed boundary; the processing unit is further used to take a preset number of roads that are closest to the point information in a straight-line distance as the multiple adjacent roads, and determine the position information of the multiple adjacent roads.

[0022] In one embodiment of the present application, based on the aforementioned scheme, the processing unit is further used to determine that the pending circumscribed boundary is the target circumscribed boundary if the point information is within the area enclosed by the pending circumscribed boundary; if the point information is outside the area enclosed by the pending circumscribed boundary, determine that the pending circumscribed boundary is not the target circumscribed boundary.

[0023] In one embodiment of the present application, based on the aforementioned scheme, the acquisition unit is further used to obtain multiple fitting points corresponding to the roads of the target circumscribed boundary; the processing unit is further used to respectively calculate the straight-line distances between the point information and the multiple fitting points, and the nearest straight-line distance is used as the straight-line distance between the point information and the road of the target circumscribed boundary.

[0024] In one embodiment of the present application, based on the aforementioned scheme, the transceiver unit is used to receive recommended indication information, wherein the recommended indication information is used to indicate the specified type of transaction entity; the processing unit is also used to detect the transaction entity of the specified type from the map based on the recommended indication information; the acquisition unit is also used to obtain the point information corresponding to the detected transaction entity.

[0025] A data processing device includes a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above data processing method is implemented.

[0026] A computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the above data processing method.

[0027] A computer program product includes computer-readable instructions, which implement the above data processing method when executed by a processor.

[0028] In the above technical solution:

[0029] After obtaining the location information corresponding to a specific type of transaction entity, a target road that meets preset conditions can be selected from multiple roads adjacent to that location information. For a specific area on a map containing location information for multiple transaction entities, the road grades of the target roads corresponding to the specific type of transaction entity within those transaction entities can be used to select target location information within that area and recommend it to the user.

[0030] On the one hand, in the process of site recommendation, the roads adjacent to each trading entity are taken into consideration to ensure that the site is closer to the road. In addition, road information is also taken into consideration to ensure that the site is close to a more suitable road, ensure passenger flow, and improve the quality of site recommendation.

[0031] On the other hand, the target point information of the site recommendation has a clear geographical location, which improves the accuracy of the site recommendation.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0034] Figure 1 It is a schematic diagram of an implementation environment involved in this application;

[0035] Figure 2 This is a schematic diagram of a process of using an R-tree index to accelerate the search for adjacent roads of a point information involved in this application;

[0036] Figure 3 is a flow chart showing a data processing method according to an exemplary embodiment;

[0037] Figure 4 This is a schematic diagram of a recommendation index evaluation for each region involved in this application;

[0038] Figure 5 is a flow chart showing a data processing method according to another exemplary embodiment;

[0039] Figure 6 is a flow chart showing a data processing method according to another exemplary embodiment;

[0040] Figure 7 is a flow chart showing a data processing method according to another exemplary embodiment;

[0041] Figure 8 is a flow chart showing a data processing method according to another exemplary embodiment;

[0042] Figure 9 is a flow chart showing a data processing method according to another exemplary embodiment;

[0043] Figure 10 is a block diagram of a data processing device according to an exemplary embodiment;

[0044] Figure 11 The diagram is a structural diagram of a computer system of a data processing device according to an exemplary embodiment. DETAILED DESCRIPTION

[0045] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments applicable to the present application. Rather, they are merely examples of apparatus and methods applicable to certain aspects of the present application, as detailed in the appended claims.

[0046] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0047] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be integrated or partially integrated. Therefore, the actual execution order may vary depending on the actual situation.

[0048] It should be noted that the term "plurality" used in this application refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0049] It should be noted that in the specific implementation of this application, when user-related data is involved, when the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. At the same time, the formulas involved in the embodiments of this application can be flexibly adjusted, such as adding or reducing corresponding parameters.

[0050] Before introducing the technical solutions of the embodiments of the present application, the technical terms involved in the embodiments of the present application are first introduced here.

[0051] A Geographic Information System (GIS) is an integrated computer system used to capture, store, analyze, manage, and present various geographic data. GIS combines geographic features in the real world with information in a database, allowing users to view, understand, interpret, and visualize data in a variety of ways, usually presented as maps, reports, and charts. GIS systems can be used for various purposes, including but not limited to urban planning, environmental management, resource management, geological exploration, disaster management and emergency response, transportation and logistics planning, etc. GIS is capable of multi-level and multi-dimensional analysis, such as calculating changes in ground cover, analyzing changes in geographic events over time, and planning transportation networks. Points of interest are an element of geospatial data and an important component of a GIS database.

[0052] Points of Interest (POI) are specific locations marked on a map or Geographic Information System (GIS) that are of particular interest or value to the user. POIs can be commercial locations (such as restaurants, stores, and cinemas), public facilities (such as schools, hospitals, and parks), or any other important geographical locations. In a GIS, a POI not only represents the geographic coordinates of a physical location, but also typically contains additional information about the location, such as its name, address, type, and contact information. The address can be represented using longitude and latitude coordinates.

[0053] Line is a basic geographic data type in a geographic information system, used to represent geographic features with length and direction but no width. A line is composed of two or more coordinate points (i.e., vertices) connected in sequence, and is used to represent various linear geographic features, such as rivers, roads, administrative boundaries, etc. In an embodiment of the present application, a line can represent a road, and each road has its own unique identifier (ID), which is used to distinguish different roads. Each road is composed of multiple fitting point coordinates, which is equivalent to sampling on the road to obtain multiple discrete points. These discrete points are all on the road and can characterize the shape and direction of the road. These discrete points can be represented by character strings, such as well-known text.

[0054] Well-Known Text (WKT) is a standardized text symbol specification used to represent the shape of geometric objects. It is a standard developed by the Open Geospatial Consortium (OGC) and is widely used to exchange geometric data between software and spatial databases. WKT can be used to represent a variety of collection objects, such as points, lines, polygons, and polylines. In an embodiment of the present application, WKT can be used to represent multiple discrete points of each road, and each road can be represented by recording the coordinate points that the road passes through. For example, LINESTRING((10,10),(20,20),(30,40)) can indicate that the road starts from (10,10), passes through (20,20), and arrives at (30,40).

[0055] R-tree is a balanced tree data structure used to store spatial objects such as points, line segments, polygons, etc., so as to quickly perform range queries, proximity queries and spatial indexes. It is mainly used to manage large amounts of spatial data in database systems. Due to its efficient query performance, R-tree is widely used in geographic information systems (GIS), spatial databases, multidimensional data indexes and other fields that require spatial indexes. In an embodiment of the present application, R-tree can be used to quickly search for roads near each POI. At this time, each road can be regarded as an area in a two-dimensional space, and each road can be framed with a minimum bounding rectangle (MBR), thereby simplifying the complexity of each road and replacing it with a unified rectangular frame, thereby accelerating the search and positioning of the road.

[0056] Geohash is a geographic coding system that represents any location on Earth as a string. Geohash is achieved by dividing the Earth's surface into a grid and using a specific code to represent each grid cell. This coding method makes it possible for points with similar geographical locations to have similar codes after coding, so that location queries can be easily performed in the database. In an embodiment of the present application, the map can be divided into a grid, and the grid can be a grid or a hexagonal honeycomb. The specific shape can be set by a person skilled in the art, and the embodiment of the present application is not limited thereto.

[0057] In related technologies, business site recommendations are typically made by industry experts through market research and analysis. However, expert experience can be influenced by personal preferences and limited experience, resulting in less than objective and comprehensive site recommendations. Consequently, related technologies often rely on big data and machine learning to mine and analyze large amounts of demographic, geographic, and business environment data to make site recommendations. However, these site recommendation methods typically only provide recommended areas within a 100-meter radius and lack the ability to provide more precise site recommendations.

[0058] Based on this, the embodiments of the present application respectively propose a data processing method, a data processing device, a data processing equipment, a computer-readable storage medium and a computer program product. In these embodiments, after obtaining the point information corresponding to the specified type of transaction entity, the target road that meets the preset conditions can be selected from the multiple roads adjacent to the point information. For a specified area in a map, which contains the point information of multiple transaction entities, based on the road grade of the target road corresponding to the specified type of transaction entity among these transaction entities, the target point information of the specified area can be selected, so that the target point information can be recommended to the user. On the one hand, in the process of site recommendation, the roads adjacent to each transaction entity are taken into account to ensure that the site is closer to the road. In addition, road information is also taken into account to ensure that the site is close to a more suitable road, ensure passenger flow, and improve the quality of site recommendation. On the other hand, the target point information of the site recommendation has a clear geographical location, which improves the accuracy of the site recommendation.

[0059] See also Figure 1 , Figure 1 It is a schematic diagram of an implementation environment involved in this application.

[0060] Figure 1 The illustrated implementation environment can be applied to a computer, which includes a database 120 of operating trading entities, a recommendation unit 140 and a location display unit 150 .

[0061] In addition, the computer also includes a city-wide unit division module 113 , a unit feature extraction module 114 , a candidate set 131 and a training set 132 .

[0062] Among them, the city-wide unit division module 113 is used to perform geometric hash division on the map corresponding to the selected city 111, and can use the shape of a grid or hexagonal honeycomb to divide it, so that the map of the city appears with multiple units, each unit covering an area in the city.

[0063] The unit feature extraction module 114 can perform feature extraction on the multiple units divided by the city-wide unit division module 113, for example, it can obtain the point information of each transaction entity in each unit and the road information of each road, etc. Among them, the transaction entity can be a shop in the embodiment of the present application, such as a shopping mall, a restaurant, a convenience store, a barber shop, and the like. Each shop has its corresponding point information, which can be represented by longitude and latitude. When performing feature extraction, the unit feature extraction module 114 can also filter some features according to the exclusion condition 112, for example, skipping closed transaction entities, skipping transaction entities that are too far from the nearest road, etc. Among them, the exclusion condition 112 can be provided by the user through the client, or it can be set by a person skilled in the art, and the embodiment of the present application does not limit it.

[0064] The unit feature extraction module 114 , the candidate set 131 and the training set 132 may all be processed based on the exclusion condition 112 , thereby obtaining a set of points that meet the exclusion condition 112 .

[0065] The computer's database of established trading entities 120 stores a large amount of location information for established trading entities. This location information can be input into the unit feature extraction module 114, the candidate set 131, and the training set 132. Based on the training set 132, the computer can train a site selection model; based on the candidate set 131, the computer can predict the site selection model. The site selection model can output a site selection strategy during prediction. The recommendation unit 140 can receive the site selection strategy and the recommendation indication information 110 to output target location information.

[0066] The recommendation unit 140 is used to recommend target location information based on the site selection strategy and the recommendation indication information 110, that is, it can output the target location information and display it through the location information display unit 150. The recommendation indication information 110 can be input by the user through the client and is used to indicate a specified type of transaction entity.

[0067] If the computer detects a newly opened trading entity, it can mark it and add it to the database of trading entities that have been in operation 120 to save real-time information of trading entities that have been in operation. Similarly, if a trading entity is closed, it can also be deleted from the database of trading entities that have been in operation 120.

[0068] It should be noted that the client in the embodiment of the present application may be a terminal device, which may be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile Internet device (MID), a vehicle-mounted device, an aircraft, a wearable device (such as a smart watch, a smart bracelet, a pedometer, etc.), a virtual reality device (such as a VR (Virtual Reality) device, an AR (Augmented Reality) device), and the like.

[0069] The computer in the embodiments of the present application can be deployed in a server, which can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0070] See also Figure 2 , Figure 2 This is a schematic diagram of a process for using an R-tree index to accelerate the search for adjacent roads related to a point, as described in an embodiment of the present application. The R-tree is a hierarchical tree-like data structure used for spatial data indexing, making related search operations more efficient. The figure shows the three main parts of an R-tree: the root page, branch pages, and leaf pages.

[0071] The root node at the top level contains pointers to child nodes, which represent larger areas covering the entire index range. The nodes in the middle level represent smaller areas, and each branch node points to child nodes (which can be other branch nodes or leaf nodes) that further subdivide the space of their parent node. The leaf nodes are the nodes at the bottom level and contain pointers to actual spatial data objects (such as roads, buildings, etc.). These leaf nodes represent the smallest spatial units in the R-tree.

[0072] The following describes the process of searching for adjacent roads for point information:

[0073] (1) The computer needs to start from the root node and select the branch node that intersects or is closest to the point information based on the current point information, such as Figure 1 In the figure, the point information is located in the area surrounded by R1 (root node).

[0074] (2) The computer needs to continue searching for the adjacent roads of the point information from the branch node of R1. The computer will detect that the point information is located in the area surrounded by R3 (branch node).

[0075] (3) The computer will search for the adjacent roads of the point information in the leaf node corresponding to R3. R3 contains three regions: R8, R9, and R10. The roads D1, D2, and D3 contained in these three regions can be used as the adjacent roads of the current point information.

[0076] This search process demonstrates the advantage of R-tree indexes when processing spatial data queries: the computer can quickly locate the relevant area without having to traverse all regions (also called spatial units). For example, when the point information is confirmed to be within region R1, R2 and its corresponding space are no longer considered, thus avoiding unnecessary searches and calculations, greatly improving query efficiency and reducing processing time. In this way, regional queries for large-scale point information become faster, improving data query efficiency.

[0077] See also Figure 3 , Figure 3 This is a flow chart of a data processing method according to an exemplary embodiment. Figure 1 The implementation environment shown in FIG. 1 is specifically executed by a computer. Of course, the method can also be applied to other implementation environments, and the execution subject of the method is not limited here.

[0078] The data processing method will be described in detail below using a computer as an exemplary execution subject. Figure 3 As shown, in an exemplary embodiment, the method includes at least the following steps:

[0079] S310: Acquire location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographical location of the transaction entity on a map.

[0080] The map is stored in a computer and contains location information of multiple trading entities. Different cities have different location information of trading entities.

[0081] In one embodiment of the present application, before obtaining location information corresponding to a specified type of trading entity, the computer needs to receive recommendation information entered by the user through the client. This recommendation information can indicate the types of trading entities that the user is focusing on. For example, if the user wants to open a beer shop, they may prefer to open the beer shop near hot pot restaurants, lobster restaurants, and barbecue restaurants to increase beer sales. The user can then enter the trading entity types of hot pot restaurants, lobster restaurants, and barbecue restaurants in the client, so that the computer can focus on analyzing these types of trading entities in the city.

[0082] It should be noted that when a user enters a recommendation instruction, the computer can obtain the user's designated city. Thus, upon receiving the recommendation instruction, the computer only retrieves location information for trading entities of the specified type in that city, thereby conserving computing resources. Furthermore, each trading entity's location information also includes the city ID to which it belongs. Based on this city ID, the computer can retrieve location information for trading entities of the specified type.

[0083] The location information of each transaction entity can reflect the geographical location of the transaction entity on the map. The geographical location can be expressed using longitude and latitude. For example, the location information of a transaction entity is (118.88181740000005°E, 32.017232300000046°N), which means that the transaction entity is located at 118.88181740000005 degrees east longitude and 32.017232300000046 degrees north latitude.

[0084] The computer needs to detect transaction entities of a specified type from the map based on the recommended indication information, and then obtain the point information corresponding to the detected transaction entities.

[0085] S320: Select a target road that meets a preset distance condition from a plurality of roads adjacent to the point information.

[0086] The location information is the positioning information of the transaction entity of the specified type. There may be multiple transaction entities of the specified type. Therefore, the location information is the location information corresponding to any one of the multiple transaction entities of the specified type.

[0087] The computer can first use the R-tree to search for the search area where the point information is located. The search area is an area based on the point information, and its range is smaller than that of the entire city. For example, a search area covers an area of ​​150m*150m.

[0088] Within the search area for the point information, there are multiple roads adjacent to the point information. The computer needs to select a target road that meets a preset distance condition. Specifically, the computer first needs to determine which roads within the search area are adjacent to the point information, that is, determine the location information of the multiple adjacent roads. Then, the computer can calculate the spherical distance between the point information and each adjacent road, and select the road with the shortest spherical distance as the target road that meets the preset distance condition.

[0089] Since there are multiple transaction entities of a specified type, the computer needs to calculate the corresponding target road for each transaction entity of the specified type. This results in a data set that can be represented using df_lbs (DataFrame_Location-Based Services).

[0090] In one embodiment of the present application, a distributed environment can be used to process the location information of multiple transaction entities of a specified type to identify the target road corresponding to each of the specified transaction entities. This distributed environment can be Spark. A person skilled in the art can call upon multiple computers, each assigned a batch of location information, to identify the target roads corresponding to that batch of location information. Finally, the target road sets processed by these multiple computers can be integrated to form the data set df_lbs.

[0091] Optionally, before searching for the target road of a certain point information, the computer can also directly search for the point information in the data set df_lbs. If the corresponding target road is found, there is no need to search for the corresponding target road again, which can save the computer's computing resources.

[0092] S330: Determine location information of multiple transaction entities within a designated area in the map.

[0093] The map includes multiple regions, each of which is divided into a grid or hexagonal honeycomb grid. Each region contains location information for multiple trading entities. The designated region is any one of the multiple regions, or the designated region is user-specified, indicating that the user wishes to view recommended locations for trading entities within the designated region.

[0094] After determining the designated area, the computer needs to obtain the point information corresponding to multiple trading entities in the area in order to make site recommendations.

[0095] S340 , based on the road information of the target road corresponding to the transaction entity of the specified type among the multiple transaction entities, determine the target point information in the specified area from the point information of the multiple transaction entities.

[0096] Among the multiple transaction entities in the specified area, if there is a transaction entity of a specified type, the target road corresponding to the transaction entity of the specified type can be obtained from the data set df_lbs.

[0097] In one embodiment of the present application, if there are multiple transaction entities of a specified type in the designated area, the road information of the target roads of these multiple transaction entities of the specified type can be compared. The road information can refer to the road grade. The lower the road grade, the better the road quality. Therefore, the transaction entity corresponding to the target road with the lowest road grade can be used as the target transaction entity, and the point information of the target transaction entity can be used as the target point information of the designated area.

[0098] In one embodiment of the present application, if the number of transaction entities of a specified type in the specified area is 1, then the point information of the transaction entity is directly used as the target point information of the specified area.

[0099] In one embodiment of the present application, if the number of transaction entities of a specified type in the specified area is 0, a prompt message may be output to the user to inform him that it is not recommended to open a transaction entity in the area.

[0100] In one embodiment of the present application, the computer can also score the location recommendation index of each area. The scoring can be based on the number of target point information, the number of transactions of all target point information in the area (i.e., store sales amount or store sales volume), and other dimensions. The specific settings can be made by those skilled in the art. Figure 4 As shown in the figure, we can get the recommended site selection index of each area. For example, some areas have a score of 4.8, which means that the site selection in this area is more recommended, and some areas have a score of 4.0, which means that the site selection in this area is relatively general, and so on.

[0101] Optionally, the computer can set a recommendation index threshold and not display the recommendation index corresponding to areas with a recommendation index below the threshold, thereby making the user's client display more concise. The user can also click a specific button on the client or use a specific gesture to display or hide the recommendation index of certain areas, improving the user experience.

[0102] In one embodiment of this application, in addition to processing the number of transactions, the computer can also consider factors such as the surrounding consumption level, housing prices, population density, transportation convenience, and the number of competitors within each area. By comprehensively ranking these factors, the potential of the unit can be more accurately assessed and more targeted site recommendations can be provided.

[0103] In one embodiment of the present application, the user can specify the type of trading entity he wants to open in the process of requesting a recommendation, and provide the transaction data (at least including sales volume or sales amount) and location information of the same type of trading entity he has already operated. The computer can analyze the location information and transaction data of the user's existing trading entity, and then predict the transaction data (which can be called predicted transaction data) that may be generated by opening the same type of trading entity in other different geographical locations (this geographical location can be a grid area or a specific location). The prediction process can use a specific machine learning model. Among them, if the geographical location is a grid area, the numerical range of the predicted transaction data can be calculated. In this way, the predicted transaction data of each geographical location can be displayed to the user, providing richer recommendation information.

[0104] For example, if a user has opened 100 beer shops, they can provide the transaction data and geographic location information of these 100 beer shops to the server computer. The computer can then analyze the transaction data and geographic location information of these 100 beer shops and calculate the predicted transaction data for opening beer shops in other locations or grid areas. In this way, the user can obtain the approximate profit of opening beer shops in various locations.

[0105] Through this method, after obtaining the location information corresponding to a specified type of transaction entity, the computer can select a target road that meets preset conditions from multiple roads adjacent to that location information. For a specified area on a map containing location information for multiple transaction entities, the target location information for that area can be selected based on the road grades of the target roads corresponding to the specified type of transaction entity within those transaction entities, and this target location information can then be recommended to the user.

[0106] First, in the process of site recommendation, the roads adjacent to each trading entity are taken into consideration to ensure that the site is closer to the road. In addition, road information is also taken into consideration to ensure that the site is close to a more suitable road, ensure passenger flow, and improve the quality of site recommendation.

[0107] Second, during the site selection process, recommendations can be made based on the specified types of transaction entities provided by the user, so as to know which types of transaction entities the user wants to open stores near, satisfying the personalized transaction entity site selection recommendations.

[0108] Third, the target point information of the site recommendation has a clear geographical location, which improves the accuracy of the site recommendation.

[0109] In one embodiment of the present application, another data processing method is provided, which can be executed by a computer. Figure 5As shown, the data processing method may include S310, S510 to S530, and S330 to S340. That is, S510 to S530 are Figure 3 The specific implementation method of S320 is shown.

[0110] S510 to S530 are described below:

[0111] S510: Determine location information of a plurality of adjacent roads based on the point information.

[0112] The point information is any one of the point information of multiple specified types of transaction entities.

[0113] Specifically, S510 may include S511 to S512.

[0114] S511 to S512 are described below:

[0115] S511: Determine a search area corresponding to the point information based on the point information.

[0116] The computer can first search the search area where the point information is located through the R-tree. The search area is an area based on the point information. Its range is smaller than the range of the entire city. For example, a search area covers an area of ​​150m*150m. For example, Figure 2 As shown, the R3 area can be used as the search area for its corresponding point information. The search area includes three areas: R8, R9 and R10, and these areas also include multiple roads.

[0117] Optionally, the size of each search area can be determined based on the number of roads surrounding the point information. If the number of roads is small, the search area can be appropriately enlarged; if the number of roads is large, the search area can be appropriately reduced. The specific delineation of the search area can be set by those skilled in the art and is not limited in the present embodiment.

[0118] S512: Determine location information of a plurality of adjacent roads based on the straight-line distance between the point information and each road in the search area.

[0119] A search area may contain multiple roads. The computer can calculate the straight-line distance between each road and the point information, use the first n closest roads as the adjacent roads, and obtain the location information of these n roads. The number n can be set by those skilled in the art, and the straight-line distance can refer to the Euclidean distance.

[0120] S520: Calculate the spherical distances between the point information and the position information of a plurality of adjacent roads.

[0121] When calculating the spherical distance, the Earth's characteristic of being an approximate ellipsoid is taken into account, and the Earth's curvature is incorporated into the calculation to make the distance between the point information and the road more accurate. In the embodiment of the present application, a spherical distance calculation formula, such as the Haversine formula, can be used to calculate the spherical distance between the point information and the road.

[0122] It should be noted that in this embodiment of the application, each road is composed of multiple fitting points, that is, each road corresponds to multiple fitting point coordinates, which can be represented by wkt_all. For example, the wkt_all of a road is MULTILINESTRING((118.881°E, 32.02°N), ((118.882°E, 32.02°N), (118.883°E, 32.02°N), ...).

[0123] When the computer calculates the spherical distance between the current point information and a road, it can calculate the spherical distances of all the fitted point coordinates of the road and the current point information respectively, and obtain multiple spherical distances. The smallest spherical distance can be used as the spherical distance between the point information and the road.

[0124] Optionally, the computer can also find the projection point of the point information on the road, that is, the point with the shortest vertical distance from the point information to the road. Then, the haversine formula is used to calculate the spherical distance between the point information and the point with the shortest vertical distance.

[0125] S530: The road with the shortest spherical distance to the point information is used as a target road that meets a preset distance condition.

[0126] In other words, the preset distance condition is the minimum spherical distance value among multiple spherical distances. A point can have spherical distances with multiple roads. After the computer calculates each spherical distance, it selects the minimum spherical distance value and then determines which road corresponds to this minimum spherical distance value. This road is then used as the target road for the point, and therefore the target road for the specified type of transaction entity.

[0127] In the present application, the process of finding the nearest road to a point information can be executed using a custom function, which can be named point_nearest_line_with_rtree, where "rtree" indicates that this process is implemented by using an R-tree. Specifically, the computer will first insert the location of each road into the R-tree in the form of an external boundary, so that each road is converted into the form of an external boundary. Next, the computer will receive a point information as input, and the computer can use the R-tree to perform a spatial query, and quickly and conveniently find multiple roads within the search area of ​​the point information to find multiple roads adjacent to the point information. Finally, the spherical distance between the multiple adjacent roads and the point information is calculated to determine the target road corresponding to the point information. The custom function point_nearest_line_with_rtree can be embedded in a geographic management system to promote the efficiency of finding target roads and increase the speed of site recommendation.

[0128] In one embodiment of the present application, after calculating the spherical distances between the current point information and n adjacent roads and selecting a target road, the computer may also retain the remaining n-1 roads, including their corresponding spherical distances, road information, and the coordinates of the multiple fitted points contained in the roads. This allows the computer to further verify the spherical distances between the target road and the point information to ensure that the selected target road is the road with the closest spherical distance to the point information among the n adjacent roads.

[0129] Through this method, the computer can calculate the spherical distances between each point information and multiple adjacent roads, thereby finding the target road with the closest spherical distance, improving the accuracy of target road determination, and thus providing higher calculation accuracy for the subsequent site recommendation process, which helps to improve the accuracy of site recommendation.

[0130] In one embodiment of the present application, another data processing method is provided, which can be executed by a computer. Figure 6 As shown, the data processing method may include S310, S511, S610 to S640, S520 to S530 and S330 to S340. That is, S610 to S640 are Figure 5 The specific implementation method of S512 is shown.

[0131] S610 to S640 are described below:

[0132] S610: Obtain the circumscribed boundary corresponding to each road in the search area.

[0133] The bounding box of each road is preset, and the computer can use a bounding rectangle to frame each road to obtain the bounding box corresponding to each road. The bounding box is the boundary of the minimum bounding rectangle.

[0134] S620: Select a target circumscribed boundary based on the positional relationship between the point information and the area enclosed by the circumscribed boundary.

[0135] Specifically, if the point information is within the area enclosed by the pending circumscribed boundary, the pending circumscribed boundary is determined to be the target circumscribed boundary. If the point information is outside the area enclosed by the pending circumscribed boundary, the pending circumscribed boundary is determined not to be the target circumscribed boundary.

[0136] That is, the computer needs to determine the outer boundary within the area as the target outer boundary, so that roads that are far away can be filtered out, thereby improving the efficiency of determining the target road.

[0137] S630: Calculate the straight-line distance between the point information and the target road corresponding to the target circumscribed boundary.

[0138] The straight-line distance may also be the Euclidean distance.

[0139] S640: A preset number of roads having the shortest straight-line distance to the point information are regarded as a plurality of adjacent roads, and position information of the plurality of adjacent roads is determined.

[0140] The preset number may be n, and its value may be set by those skilled in the art, for example, 10. Determining the n target roads closest to the point information in straight line distance helps to find the target road closest to the spherical distance more quickly.

[0141] This method allows the computer to filter the circumscribed boundaries based on the positional relationship between the area enclosed by each circumscribed boundary and the point information, thereby more quickly obtaining multiple adjacent roads. Furthermore, these multiple adjacent roads are also obtained after calculating the straight-line distance, which improves the accuracy of determining multiple adjacent roads.

[0142] In one embodiment of the present application, another data processing method is provided, which can be executed by a computer. Figure 7 As shown, the data processing method may include S310, S511, S610 to S620, S710 to S720, S640, S520 to S530 and S330 to S340. That is, S710 to S720 are Figure 6 The specific implementation method of S630 is shown.

[0143] S710 to S720 are described below:

[0144] S710: Acquire multiple fitting points corresponding to the roads of the target circumscribed boundary.

[0145] The computer can obtain the coordinates of multiple fitting points corresponding to the roads of the target circumscribed boundary, namely wkt_all.

[0146] S720: Calculate straight-line distances between the point information and multiple fitting points respectively, and use the closest straight-line distance as the straight-line distance between the point information and the road of the target circumscribed boundary.

[0147] The computer needs to calculate the straight-line distance between the point information and multiple fitting points respectively, where the minimum straight-line distance is the straight-line distance between the point information and the road of the target circumscribed boundary.

[0148] Through this method, the computer can more accurately calculate the straight-line distance between the point information and the road of the target's external boundary, which helps to improve the accuracy of subsequent site selection recommendations.

[0149] In one embodiment of the present application, another data processing method is provided, which can be executed by a computer. Figure 8 As shown, the data processing method may include S310 to S330 and S810 to S820. That is, S810 to S820 are Figure 3 The specific implementation method of S340 is shown.

[0150] S810 to S820 are described below:

[0151] S810: Among target roads corresponding to transaction entities of a specified type, the transaction entity with the lowest road grade information is selected as the target transaction entity.

[0152] In the embodiment of the present application, the road grade information can refer to Table 1, which shows the road grades corresponding to different road names.

[0153]

[0154]

[0155] Table 1

[0156] Road grades increase in order from 1 to 10. That is, in Table 1, expressways have the lowest grade and pedestrian roads have the highest grade. It should be noted that the road grades shown in Table 1 are merely examples, and those skilled in the art may reasonably adjust them based on actual circumstances. This embodiment of the present application does not limit these grades.

[0157] When the target road is a pedestrian road, it may mean that the location information of the corresponding transaction entity is located inside a certain community. However, due to the low flow of people inside the community, it cannot be a high-quality location. Therefore, it is necessary to avoid choosing such a location as much as possible.

[0158] S820: Use the point information corresponding to the target transaction entity as the target point information.

[0159] That is, the computer can use the location information corresponding to the target trading entity as the target location information corresponding to the designated area. There can be multiple target locations. In other words, within the designated area, if the target roads corresponding to multiple trading entities have the same road grade, they can all be recommended as high-quality locations.

[0160] In the embodiment of the present application, roads with grades 5-7 are generally of higher quality, which often means that trading entities located near these roads are located in more prosperous areas, such as shopping malls, street streets, etc. Roads with grades 1-4 are generally not located in the same area as roads with grades 5-10. Therefore, trading entities with target roads with the lowest possible road grade can be selected as target trading entities for recommendation.

[0161] Alternatively, if there are multiple target transaction entities, the location information corresponding to the target transaction entity with the highest number of transactions within a preset time period may be used as the target location information. In other words, the location information with the highest sales volume and sales amount may be prioritized as the target location information, indicating that the location generally has a high flow of customers and people are willing to shop there.

[0162] The computer can eventually recommend the target point information to the user, and the user can choose to open a store near the target point information, thereby obtaining higher customer flow and popularity.

[0163] Through this method, the computer can select target location information with higher customer flow and popularity based on the road grade. In this way, the computer recommends a specific store address to the user. Compared with the traditional method of only recommending a store location in a certain area, the accuracy of the location recommendation is improved.

[0164] In one embodiment of the present application, another data processing method is provided, which can be executed by a computer. Figure 9 As shown, the data processing method may include S901 to S915.

[0165] S901 to S915 are described below:

[0166] S901: Receive recommendation indication information input by a user.

[0167] S902: Detect transaction entities of a specified type from the map based on the recommendation indication information.

[0168] S903, obtaining the point information corresponding to the detected transaction entity.

[0169] S904: Select a target road that meets a preset distance condition from a plurality of roads adjacent to the point information.

[0170] S905: Collect the target roads of each transaction entity to obtain the data set df_lbs.

[0171] S906, let i=1.

[0172] S907: Determine the location information of multiple transaction entities included in area i in the map.

[0173] S908: Whether the multiple transaction entities include a transaction entity of a specified type.

[0174] If yes, execute S910; if no, execute S909.

[0175] S909: Skip the region i.

[0176] Skipping area i means there is no recommended target point information for that area. Therefore, when the computer finally recommends a site, it can display target point information for areas that were not skipped instead of for that area. This makes the display more concise and enhances the user experience.

[0177] After executing S909, S913 may be executed.

[0178] S910 , among the target roads corresponding to the transaction entities of the specified type in area i, the transaction entity with the lowest road grade information is taken as the target transaction entity.

[0179] S911: Use the point information corresponding to the target transaction entity as the target point information.

[0180] S912: Use the target point information as the recommended point for area i.

[0181] S913, whether i is equal to m.

[0182] Where m is the total number of regions in the current city.

[0183] If yes, execute S915; if no, execute S914.

[0184] S914, let i=i+1.

[0185] After executing S913, S907 may be executed.

[0186] S915: Send the target point information corresponding to each area to the user.

[0187] Through this method, after obtaining the location information corresponding to a specified type of transaction entity, the computer can select a target road that meets preset conditions from multiple roads adjacent to that location information. For a specified area on a map containing location information for multiple transaction entities, the target location information for that area can be selected based on the road grades of the target roads corresponding to the specified type of transaction entity within those transaction entities, and this target location information can then be recommended to the user.

[0188] First, in the process of site recommendation, the roads adjacent to each trading entity are taken into consideration to ensure that the site is closer to the road. In addition, road information is also taken into consideration to ensure that the site is close to a more suitable road, ensure passenger flow, and improve the quality of site recommendation.

[0189] Second, during the site selection process, recommendations can be made based on the specified types of transaction entities provided by the user, so as to know which types of transaction entities the user wants to open stores near, satisfying the personalized transaction entity site selection recommendations.

[0190] Third, the target point information of the site recommendation has a clear geographical location, which improves the accuracy of the site recommendation.

[0191] Figure 10 FIG. 1 is a block diagram of a data processing device according to an embodiment of the present application. Figure 10 As shown, the data processing device can be applied to a computer, and the device includes:

[0192] A data processing device, comprising:

[0193] An acquiring unit 1010 is configured to acquire location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographic location of the transaction entity on a map;

[0194] The selection unit 1020 is configured to select a target road that meets a preset distance condition from a plurality of roads adjacent to the point information;

[0195] The processing unit 1030 is configured to determine location information of multiple transaction entities within a designated area on the map;

[0196] The processing unit 1030 is further configured to determine target point information in a designated area from the point information of the plurality of transaction entities based on the road information of the target road corresponding to the transaction entity of a designated type among the plurality of transaction entities.

[0197] In one embodiment of the present application, based on the aforementioned scheme, the road information includes road grade information; the processing unit 1030 is further used to take the transaction entity with the lowest road grade information in the target road corresponding to the specified type of transaction entity as the target transaction entity; and take the point information corresponding to the target transaction entity as the target point information.

[0198] In one embodiment of the present application, based on the aforementioned scheme, after taking the transaction entity with the lowest road grade information among the target roads corresponding to the transaction entities of the specified type as the target transaction entity, the processing unit 1030 is also used to take the point information corresponding to the target transaction entity with the highest number of transactions within a preset time period as the target point information if there are multiple target transaction entities.

[0199] In one embodiment of the present application, based on the aforementioned scheme, the processing unit 1030 is also used to determine the location information of multiple adjacent roads based on the point information; calculate the spherical distances between the point information and the location information of multiple adjacent roads; and take the road with the shortest spherical distance to the point information as the target road that meets the preset distance conditions.

[0200] In one embodiment of the present application, based on the aforementioned scheme, the processing unit 1030 is further used to determine the search area corresponding to the point information based on the point information; and determine the location information of multiple adjacent roads based on the straight-line distance between the point information and each road in the search area.

[0201] In one embodiment of the present application, based on the aforementioned scheme, the acquisition unit 1010 is further used to obtain the circumscribed boundary corresponding to each road in the search area; the selection unit 1020 is further used to select the target circumscribed boundary based on the positional relationship between the point information and the area enclosed by the circumscribed boundary; calculate the straight-line distance between the point information and the road corresponding to the target circumscribed boundary; the processing unit 1030 is further used to take a preset number of roads that are closest to the point information in a straight-line distance as multiple adjacent roads, and determine the position information of the multiple adjacent roads.

[0202] In one embodiment of the present application, based on the aforementioned scheme, the processing unit 1030 is further used to determine that the pending circumscribed boundary is the target circumscribed boundary if the point information is within the area enclosed by the pending circumscribed boundary; if the point information is outside the area enclosed by the pending circumscribed boundary, determine that the pending circumscribed boundary is not the target circumscribed boundary.

[0203] In one embodiment of the present application, based on the aforementioned scheme, the acquisition unit 1010 is also used to obtain multiple fitting points corresponding to the roads of the target circumscribed boundary; the processing unit 1030 is also used to respectively calculate the straight-line distances between the point information and the multiple fitting points, and the nearest straight-line distance is used as the straight-line distance between the point information and the road of the target circumscribed boundary.

[0204] In one embodiment of the present application, based on the aforementioned scheme, the transceiver unit 1040 is used to receive recommended indication information, which is used to indicate a specified type of transaction entity; the processing unit 1030 is also used to detect transaction entities of the specified type from the map based on the recommended indication information; the acquisition unit 1010 is also used to obtain point information corresponding to the detected transaction entity.

[0205] It should be noted that the apparatus provided in the aforementioned embodiment and the method provided in the aforementioned embodiment belong to the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiment.

[0206] An embodiment of the present application also provides a data processing device, comprising: one or more processors; a memory for storing one or more programs, which enables the electronic device to implement the above data processing method when the one or more programs are executed by the one or more processors.

[0207] Figure 11 It is a structural diagram of a computer system of a data processing device suitable for implementing an embodiment of the present application.

[0208] It should be noted that Figure 11 The computer system 1100 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0209] like Figure 11 As shown, the computer system 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1102 or the program loaded from the storage part 1108 into the random access memory (RAM) 1103, such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the RAM 1103. The CPU 1101, ROM 1102 and RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0210] The following components are connected to the I / O interface 1105: an input section 1106 including a keyboard, a mouse, and the like; an output section 1107 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1108 including a hard disk; and a communication section 1109 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. Removable media 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1110 as needed, so that computer programs read from the removable media can be installed in the storage section 1108 as needed.

[0211] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1109, and / or installed from a removable medium 1111. When the computer program is executed by the central processing unit (CPU) 1101, the various functions defined in the system of the present application are executed.

[0212] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a 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 above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0213] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0214] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0215] Another aspect of the present application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned data processing method. The computer-readable medium may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device.

[0216] Another aspect of the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable medium. A processor of a computer device reads the computer instructions from the computer-readable medium and executes the computer instructions, causing the computer device to perform the data processing method provided in each of the above embodiments.

[0217] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. Ordinary technicians in this field can easily make corresponding changes or modifications based on the main ideas and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A data processing method, characterized in that: include: Obtaining location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographic location of the transaction entity on a map; Selecting a target road that meets a preset distance condition from a plurality of roads adjacent to the point information; Determining location information of multiple trading entities within a specified area in the map; Based on the road information of the target road corresponding to the transaction entity of the specified type among the multiple transaction entities, the target point information in the specified area is determined from the point information of the multiple transaction entities.

2. The method according to claim 1, characterized in that The road information includes road grade information; The determining, based on the road information of the target road corresponding to the transaction entity of the specified type among the multiple transaction entities, the target point information in the specified area from the point information of the multiple transaction entities includes: Among the target roads corresponding to the transaction entities of the specified type, the transaction entity with the lowest road grade information is used as the target transaction entity; The point information corresponding to the target transaction entity is used as the target point information.

3. The method according to claim 2, characterized in that After selecting the transaction entity with the lowest road grade information among the target roads corresponding to the transaction entities of the specified type as the target transaction entity, the method further includes: If there are multiple target transaction entities, the point information corresponding to the target transaction entity with the highest transaction quantity within the preset time period is used as the target point information.

4. The method according to claim 1, wherein Determining a target road that meets a preset distance condition from a plurality of roads adjacent to the point information includes: Determining location information of the plurality of adjacent roads based on the point information; Calculating the spherical distances between the point information and the position information of the adjacent multiple roads; The road with the shortest spherical distance to the point information is used as the target road that meets the preset distance condition.

5. The method according to claim 4, characterized in that The determining the location information of the plurality of adjacent roads based on the point information includes: Determining a search area corresponding to the point information based on the point information; The position information of the plurality of adjacent roads is determined based on the straight-line distance between the point information and each road in the search area.

6. The method according to claim 5, characterized in that The determining the location information of the plurality of adjacent roads based on the straight-line distance between the point information and each road in the search area includes: Obtaining the circumscribed boundary corresponding to each road in the search area; Selecting a target circumscribed boundary based on a positional relationship between the point information and the area enclosed by the circumscribed boundary; Calculating the straight-line distance between the point information and the road corresponding to the target circumscribed boundary; A preset number of roads having the shortest straight-line distance to the point information are used as the adjacent multiple roads, and position information of the adjacent multiple roads is determined.

7. The method according to claim 6, characterized in that The selecting of the target circumscribed boundary based on the positional relationship between the point information and the area enclosed by the circumscribed boundary includes: If the point information is within the area enclosed by the pending circumscribed boundary, determining the pending circumscribed boundary as the target circumscribed boundary; If the point information is outside the area enclosed by the pending circumscribed boundary, it is determined that the pending circumscribed boundary is not the target circumscribed boundary.

8. The method according to claim 6, characterized in that The calculating the straight-line distance between the point information and the target road corresponding to the target circumscribed boundary includes: Acquire multiple fitting points corresponding to the roads of the target circumscribed boundary; The straight-line distances between the point information and the plurality of fitting points are calculated respectively, and the closest straight-line distance is used as the straight-line distance between the point information and the road of the target circumscribed boundary.

9. The method according to claim 1, characterized in that The step of obtaining the point information corresponding to a transaction entity of a specified type includes: receiving recommendation indication information, where the recommendation indication information is used to indicate the transaction entity of the specified type; detecting transaction entities belonging to the specified type from the map based on the recommendation indication information; Get the point information corresponding to the detected transaction entity.

10. A data processing device, characterized in that: include: an acquiring unit, configured to acquire location information corresponding to a transaction entity of a specified type, wherein the location information is used to represent the geographical location of the transaction entity on a map; A selection unit, configured to select a target road that meets a preset distance condition from a plurality of roads adjacent to the point information; a processing unit, configured to determine location information of a plurality of trading entities within a designated area in the map; The processing unit is further configured to determine target point information in the designated area from point information of the plurality of transaction entities based on road information of target roads corresponding to the transaction entities of the designated type among the plurality of transaction entities.

11. A data processing device, characterized in that: include: a memory storing computer-readable instructions; A processor reads the computer-readable instructions stored in the memory to execute the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method according to any one of claims 1 to 9.

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