Data processing method and device, electronic equipment and storage medium

Abstract

The application discloses a data processing method and device, electronic equipment and storage medium. The method comprises the following steps: in response to a data processing instruction, corresponding position information and representation information indicating a first type of object are determined; a corresponding geographical grid is determined according to the position information; a second type of object matching the geographical grid is determined from the candidate objects; a target first type of object matching the representation information is determined from the second type of objects; or, after a preset time interval, a target first type of object matching the representation information is determined from the second type of objects; and a data processing result is obtained based on the plurality of second type of objects and the target first type of object to generate service guide information. The embodiments of the application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The application improves the information dimension of the data processing result used to generate the service guide information, and ensures the accuracy of the service guide information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet communication, and in particular to a data processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] With the development of Internet communication technology, various Internet products emerge in an endless stream. Internet products can provide service experience for users, such as providing a use service of showing the distribution situation of a target population on a map. In the related art, the display of the distribution situation of the target population can be realized by a heat map, so that the user can determine the guide information for the related business according to the heat map information, such as whether to open a store at a specified location. However, the distribution situation of the target population displayed by the heat map is derived from dotting on the map according to the location of the target population. Thus, the reference data derived from the heat map is coarse-grained, which cannot provide more fine-grained reference data for generating business guide information for the specified location, and thus cannot guarantee the accuracy and effectiveness of the business guide information for the specified location. SUMMARY

[0003] In order to solve the problems in the prior art that the reference data derived from the heat map is coarse-grained when generating business guide information for a specified location, and cannot guarantee the accuracy of subsequent business guide information determination using the reference data, the present application provides a data processing method, device, electronic equipment and storage medium:

[0004] According to a first aspect of the present application, a data processing method is provided, the method comprising:

[0005] In response to a data processing instruction, determining corresponding position information and representation information indicating a first type of object;

[0006] Determining a corresponding geographical grid according to the position information;

[0007] Determining a plurality of second type of objects matching the geographical grid from the candidate objects;

[0008] Determining at least one target first type of object matching the representation information from the plurality of second type of objects; or, after a preset time interval, determining at least one target first type of object matching the representation information from the plurality of second type of objects;

[0009] Obtaining a data processing result based on the plurality of second type of objects and the at least one target first type of object to generate business guide information.

[0010] According to a second aspect of the present application, a data processing device is provided, the device comprising:

[0011] The response module is configured to determine corresponding position information and representation information indicating the first type of object in response to the data processing instruction.

[0012] The grid determination module is configured to determine a corresponding geographic grid according to the position information.

[0013] The second type of object determination module is configured to determine a plurality of second type of objects matching the geographic grid from the candidate objects.

[0014] The first type of object determination module is configured to determine at least one target first type of object matching the representation information from the plurality of second type of objects; or, after a preset time interval, determine at least one target first type of object matching the representation information from the plurality of second type of objects.

[0015] The data processing result obtaining module is configured to obtain a data processing result based on the plurality of second type of objects and the at least one target first type of object, to generate service guidance information.

[0016] According to a third aspect of the present application, an electronic device is provided, which includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the data processing method according to the first aspect.

[0017] According to a fourth aspect of the present application, a computer readable storage medium is provided, the storage medium storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the data processing method according to the first aspect.

[0018] According to a fifth aspect of the present application, a computer program product or a computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the data processing method according to the first aspect.

[0019] The data processing method, device, electronic device and storage medium provided by the present application have the following technical effects:

[0020] The application determines corresponding position information and representation information indicating the first type of object in response to a data processing instruction; then determines a corresponding geographic grid according to the position information; further determines a plurality of second type of objects matching the geographic grid from the to-be-selected objects; then determines at least one target first type of object matching the representation information from the plurality of second type of objects; or, after a preset time interval, determines at least one target first type of object matching the representation information from the plurality of second type of objects; finally, obtains a data processing result based on the plurality of second type of objects and the at least one target first type of object to generate service guide information. The application determines objects based on the geographic grid and the representation information indicating the first type of object, and then generates service guide information through the object determination result, thereby improving the information dimension of the reference data used to generate the service guide information and ensuring the accuracy and effectiveness of the service guide information. The data processing result used as the reference data comes from the common result of the position information corresponding to the geographic grid and the representation information indicating the first type of object, the position attribute of the second type of object is limited through the geographic grid, the position accuracy of the determined second type of object is improved, and the data processing result can be used as a reliable basis for data processing of the related grid penetration rate and the related grid conversion rate, thereby providing effective guarantee for the rich information dimension of the data processing result. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0022] Figure 1 is a schematic diagram of an application environment provided by an embodiment of the present application;

[0023] Figure 2 is a flowchart of a data processing method provided by an embodiment of the present application;

[0024] Figure 3 is a flowchart of determining a corresponding geographic grid according to position information provided by an embodiment of the present application;

[0025] Figure 4 is a flowchart of obtaining a data processing result provided by an embodiment of the present application;

[0026] Figure 5 is an interface diagram of obtaining a data processing result provided by an embodiment of the present application;

[0027] Figure 6 is an interface diagram of the number of potential objects in a grid provided by an embodiment of the present application;

[0028] Figure 7 is an interface diagram provided by an embodiment of the present application, which is related to object permeability in a grid;

[0029] Figure 8 is a component block diagram of a data processing device provided by an embodiment of the present application;

[0030] Figure 9 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0032] It should be noted that the terms “include” and “have” and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0033] Please refer to Figure 1 , Figure 1 is a schematic diagram of an application environment provided by an embodiment of the present application, which can include a client 10 and a server 20. The client 10 and the server 20 can be directly or indirectly connected through wired or wireless communication. Related objects (such as users, simulators) can send data processing instructions to the server 20 through the client 10. The server 20 determines the location information and the characteristic information indicating the first type of object according to the received data processing instructions; then determines the corresponding geographical grid according to the location information; then determines a plurality of second type of objects matched with the geographical grid from the candidate objects; then determines at least one target first type of object in the plurality of second type of objects whose current information matches the characteristic information within a preset time interval; or, determines at least one target first type of object matched with the geographical grid and whose current information matches the characteristic information from the candidate objects; then obtains a data processing result based on the plurality of second type of objects and the at least one target first type of object, to generate service guide information. It should be noted that, Figure 1 is only an example.

[0034] The client 10 can be an entity device such as a smart phone, a computer (e.g., a desktop computer, a tablet computer, a notebook computer), an augmented reality (AR) / virtual reality (VR) device, a digital assistant, a smart voice interaction device (e.g., a smart speaker), a smart wearable device, a smart home appliance, a vehicle terminal, etc., or a software such as a computer program running on the entity device. The operating system corresponding to the client 10 can be an Android system, an iOS system (a mobile operating system developed by Apple Inc.), a Linux system, a Microsoft Windows system, etc.

[0035] The server 20 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. The server can include a network communication unit, a processor, a memory, etc. The server can provide background services for the corresponding client.

[0036] In practical applications, 1) the client 10 and the server 20 can be used to build a data processing system. 2) The data processing scheme provided by the embodiments of the present application can be used in the technology related to cloud computing. Cloud computing is a computing mode that distributes computing tasks on a resource pool composed of a large number of computing devices, so that various application systems can obtain computing power, storage space and information services according to needs. The network that provides resources is called "cloud". The resources in the "cloud" can be infinitely expanded in the eyes of the user, and can be obtained at any time, used on demand, expanded at any time, and paid according to use. As a basic capability provider of cloud computing, a cloud computing resource pool (referred to as a cloud platform, generally referred to as an IaaS (Infrastructure as a Service, Infrastructure as a Service) platform) is established, and a plurality of types of virtual resources are deployed in the resource pool for external customers to select and use. The cloud computing resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, network devices. According to logical function division, the PaaS (Platform as a Service, Platform as a Service) layer can be deployed on the IaaS (Infrastructure as a Service, Infrastructure as a Service) layer, and the SaaS (Software as a Service, Software as a Service) layer is deployed on the PaaS layer. SaaS is a variety of business software, such as web portal websites, SMS mass senders, etc. Generally speaking, SaaS and PaaS are upper layers relative to IaaS.

[0037] It should be noted that for the location information, the characteristic information, the second type of object, the target first type of object, the historical first type of object, the attribute information and the like associated with the user information, when the embodiments of the present application are applied to specific products or technologies, the user's permission or consent needs to be obtained, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0038] The following introduces a specific embodiment of a data processing method of the present application, Figure 2is a flowchart of a data processing method provided by an embodiment of the present application. The present application provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps can be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one of the many execution orders of the steps, and does not represent the only execution order. In actual system or product execution, the method order shown in the embodiments or the drawings can be executed in sequence or in parallel (for example, in a parallel processor or multi-thread processing environment). The data processing method provided by the embodiments of the present application can be executed independently by the client, independently by the server, or interactively by the client and the server. Specifically, as shown in Figure 2 The method can include:

[0039] S201: In response to a data processing instruction, determining corresponding position information and representation information indicating a first type of object.

[0040] In the embodiments of the present application, the client or the server determines the corresponding position information and the representation information indicating the first type of object in response to the data processing instruction. The data processing instruction can be generated by the client (or the server), and then the client (or the server) determines the position information and the representation information indicating the first type of object according to the data processing instruction. The data processing instruction can also be generated by the client and sent to the server, and accordingly, the server determines the position information and the representation information indicating the first type of object according to the received data processing instruction. Illustratively, the client (or the server) triggers the generation of the data processing instruction, or the client generates the data processing instruction, which can be triggered by the relevant object (such as a user, a simulator) through the interactive interface provided by the client (or the server).

[0041] The position information and the representation information indicating the first type of object can be used as limiting information in the data processing process, with the former being used for position dimension limitation and the latter being used for object dimension limitation. The position information can indicate a specified position in a preset coordinate system, which can be a world coordinate system, a center coordinate system, etc. The preset coordinate system can be a one-dimensional coordinate system, a two-dimensional coordinate system, a three-dimensional coordinate system, etc. The position information can also be description information for a specified position, which can be a specified province, a specified city, a specified district, a specified street, a specified intersection, etc. For the representation information indicating the first type of object, it can be understood that the first type of object is a reference object or an ideal object, and the representation information can reflect or present the characteristics of the first type of object. An object needs to meet certain conditions (the information of the object matches the representation information) to be converted into the first type of object.

[0042] In actual application, the data processing scheme provided by the embodiment can be applied to a retail scene. A specified object (such as a merchant object) triggers a related control to generate a data processing instruction through an interactive interface provided by a client, and then the client and / or the server end executes steps S201-S205 to generate business guide information. When the specified object indicates a merchant who has opened a store, the business guide information can be used to guide the content delivery of the existing store to increase the number of first-type objects. At this time, the location information can indicate the location of the existing store, the first-type object can indicate a member object of the existing store, and the representation information indicating the first-type object can be a member object identifier; the first-type object can indicate a consumption object of the existing store (i.e., an object that has made a resource value transfer in the existing store), and the representation information indicating the first-type object can be a consumption object identifier; the first-type object can indicate a login object of the existing store (i.e., an object that has logged in an application or a mini program corresponding to the existing store), and the representation information indicating the first-type object can be a login object identifier. When the specified object indicates a merchant who is about to open a store, the business guide information can be used to guide the site selection of the new store. At this time, the location information can indicate the location of a reference store, the first-type object can indicate a member object of the reference store, and the representation information indicating the first-type object can be a member object identifier; the first-type object can indicate a consumption object of the reference store, and the representation information indicating the first-type object can be a consumption object identifier; the first-type object can indicate a login object of the reference store, and the representation information indicating the first-type object can be a login object identifier. The existing store, the new store, and the reference store are all physical stores, and the reference store can be an existing store of the merchant who is about to open a store.

[0043] S202: determining a corresponding geographical grid according to the location information;

[0044] In the embodiment of the present application, the client or the server end determines a corresponding geographical grid according to the location information. In combination with the foregoing description of the location information in step S201, the location information can indicate a specified location in a preset coordinate system, or can be description information of the specified location, and then the determined geographical grid can be a geographical grid to which the specified location belongs. For example, when the specified location is a specified coordinate, the geographical grid can be a region including the specified coordinate in the preset coordinate system. When the specified location is a specified intersection, the geographical grid can be a region including the specified intersection.

[0045] In an exemplary embodiment, as shown in Figure 3 The method further includes:

[0046] S301: when the location information indicates a longitude coordinate and a latitude coordinate, determining a target longitude interval corresponding to the longitude coordinate and a target latitude interval corresponding to the latitude coordinate based on a preset recursive division rule.

[0047] S302: Obtain a geographical grid corresponding to the position information based on the target longitude interval and the target latitude interval.

[0048] Generally, the longitude range is from 180° east to 180° west, and the latitude range is from 90° south to 90° north. If the west longitude is set as negative and the south latitude is set as negative, the longitude range is [-180°, 180°], and the latitude range is [-90°, 90°]. Further, the longitude range [-180°, 0°) can be represented by binary 0, and the longitude range (0°, 180°] can be represented by binary 1; the latitude range [-90°, 0°) can be represented by binary 0, and the latitude range (0°, 90°] can be represented by binary 1. It can be understood that the longitude coordinate indicated by the position information hits one of [-180°, 0°) and (0°, 180°], and the latitude coordinate indicated by the position information hits one of [-90°, 0°) and (0°, 90°]. If the longitude coordinate hits [-180°, 0°) and the latitude coordinate hits [-90°, 0°), then the geographical grid is a region constructed by the target longitude interval [-180°, 0°) and the target latitude interval [-90°, 0°), the longitude binary information corresponding to the target longitude interval [-180°, 0°) indicates 0, and the latitude binary information corresponding to the target latitude interval [-90°, 0°) indicates 0.

[0049] The aforementioned hit longitude range and latitude range can be further divided by half, and the hit longitude range based on the longitude coordinate and the hit latitude range based on the latitude coordinate can be continued, so as to realize the recursive division of the longitude range and the latitude range. Then, the last hit longitude range is taken as the target longitude interval, the last hit latitude range is taken as the target latitude interval, and the geographical grid corresponding to the position information is obtained according to the target longitude interval and the target latitude interval. Generally, the more the number of divisions, the more the longitude range and the latitude range obtained by division, and the smaller the range corresponding to the geographical grid. The embodiments of the present application utilize the preset recursive division rule to determine the target longitude interval and the target latitude interval, and then obtain the geographical grid corresponding to the position information, thereby improving the flexibility and adaptability of determining the geographical grid from the longitude range [-180°, 180°] and the latitude range [-90°, 90°]. In actual application, the preset recursive division rule can include first division information indicating the division by half, and second division information indicating the number of divisions.

[0050] For example, if the longitude coordinate is 115.025°, the longitude coordinate hits (0°, 180°], and a value of 1 is obtained. The longitude coordinate continues to hit (90°, 180°] (a result of halving the previous hit range (0°, 180°]), and a value of 1 is obtained. The longitude coordinate continues to hit (90°, 135°] (a result of halving the previous hit range (90°, 180°]), and a value of 0 is obtained. The longitude coordinate continues to hit (112.5°, 135°] (a result of halving the previous hit range (90°, 135°]), and a value of 1 is obtained. The longitude coordinate continues to hit (112.5°, 123.75°] (a result of halving the previous hit range (112.5°, 135°]), and a value of 0 is obtained. Thus, longitude binary information indicating 11010 is obtained.

[0051] For example, if the latitude coordinate is 40.525°, the latitude coordinate hits (0°, 90°], and a value of 1 is obtained. The latitude coordinate continues to hit (0°, 45°] (a result of halving the previous hit range (0°, 90°]), and a value of 0 is obtained. The latitude coordinate continues to hit (22.5°, 45°] (a result of halving the previous hit range (22.5°, 45°]), and a value of 1 is obtained. The latitude coordinate continues to hit (33.75°, 45°] (a result of halving the previous hit range (22.5°, 45°]), and a value of 1 is obtained. The latitude coordinate continues to hit (39.375°, 45°] (a result of halving the previous hit range (33.75°, 45°]), and a value of 1 is obtained. Thus, latitude binary information indicating 10111 is obtained, and the geographical grid corresponding to (115.025°, 40.525°) is the area formed by the last hit longitude range (112.5°, 123.75°] and the last hit latitude range (33.75°, 45°].

[0052] S203: determining a plurality of second type objects from the candidate objects, which match the geographical grid;

[0053] In the embodiments of the present application, the client or the server determines a plurality of second type objects from the candidate objects, which match the geographical grid. The candidate objects can be objects whose related information can be obtained, and the second type objects are objects in the candidate objects, whose current positions belong to the geographical grid. Three types of ways of determining the second type objects will be introduced below.

[0054] 1) determining a plurality of second type objects from the candidate objects, whose current positions belong to the geographical grid;

[0055] For example, the candidate objects include objects 1-1000. The current positions of objects 1-1000 are determined respectively, and it is determined respectively whether the current positions of objects 1-1000 belong to the geographic grid, and objects 1-500 whose current positions belong to the geographic grid are taken as the second class objects. In combination with the above "(115.025°, 40.525°) corresponds to the geographic grid is the region constructed by the last hit longitude range (112.5°, 123.75°] and the last hit latitude range (33.75°, 45°]", the longitude coordinate of the second class object belongs to (112.5°, 123.75°], and the latitude coordinate of the second class object belongs to (33.75°, 45°], for example (113°, 42°).

[0056] 2) determining a plurality of candidate objects whose current positions belong to the geographic grid from the candidate objects, and determining a plurality of second class objects whose current information has a correlation with the representation information from the plurality of candidate objects;

[0057] In combination with the example in 1), objects 1-500 whose current positions belong to the geographic grid can be taken as the candidate objects. Then, it is determined respectively whether the current information of objects 1-500 has a correlation with the representation information, and objects 1-300 whose current information has a correlation with the representation information are taken as the second class objects. It can be understood that a) the current information has a direct correlation with the representation information, and the current information matches the representation information; b) the current information has an indirect correlation with the representation information, on the one hand, the current information does not match the representation information, and on the other hand, the historical time "current information" can be a contribution parameter of the future time "current information" matching the representation information. The current information of a candidate object includes multiple items, and here we focus on finding at least one item that has a correlation with the representation information in the multiple items. It should be noted that for the target first class object referred to as "conversion object" hereinafter, the current information of the second class object corresponding to the target first class object does not have a direct correlation with the representation information before the preset time interval.

[0058] Taking "the position information can indicate the position of the existing store, the first class object can indicate the member object of the existing store, and the representation information indicating the first class object can be the member object identifier" as an example, if the current information indicates the page sliding, clicking and other operations of the small program corresponding to the existing store, it is determined that the current information has an indirect correlation with the representation information.

[0059] 3) determining a plurality of candidate objects whose current positions belong to the geographical grid from the candidate objects, and determining a plurality of second-type objects whose similarity to the historical first-type objects is greater than a similarity threshold from the plurality of candidate objects; wherein the historical information corresponding to the historical first-type objects matches the representation information.

[0060] In combination with the example in 1), objects 1-500 whose current positions belong to the geographical grid can be taken as candidate objects. Then, it is respectively determined whether the similarity of objects 1-500 to the historical first-type objects is greater than a similarity threshold, and objects 1-200 whose similarity is greater than the similarity threshold are taken as second-type objects. The historical first-type objects are objects whose “current information” at a historical time (compared to this time, the current time) matches the representation information. Considering that there can be multiple historical first-type objects, attribute information corresponding to the multiple historical first-type objects can be calculated, and then the two parties for similarity calculation can be the current information of the candidate objects and the attribute information corresponding to the multiple historical first-type objects. In actual application, a lookalike algorithm can be used to determine the second-type objects as potential objects. The seed objects involved in the algorithm can correspond to the historical first-type objects. The second-type objects can be friend objects of the historical first-type objects.

[0061] The above provides three types of ways to determine the second-type objects. The appropriate way can be selected to determine the potential objects according to the scene requirements, which has good scene adaptability. 1) can ensure the rate of determining potential objects. 2) and 3) can take into account the efficiency and quality of determining potential objects, and the way can be selected according to specific factors such as historical feedback.

[0062] In an exemplary embodiment, in combination with the description of the foregoing steps S301-S302, the target longitude interval indicates longitude binary information, and the target latitude interval indicates latitude binary information. Then, the method can further include the following steps: first, obtaining target binary information based on the longitude binary information and the latitude binary information; and then, performing encoding processing based on the target binary information to obtain a target string. Correspondingly, the step of determining a plurality of second-type objects that match the geographical grid from the candidate objects can include the following steps: determining a plurality of second-type objects that match the geographical grid from the candidate objects based on the target string.

[0063] Since the second type of object is an object in the candidate object whose current position belongs to the geographical grid, here the judgment of whether the current position belongs to the geographical grid is optimized by introducing the target string. In combination with the above example of the longitude coordinate being 115.025° and the latitude coordinate being 40.525°, the longitude binary information indicates 11010, and the latitude binary information indicates 10111. The target binary information indicates 1110011101, in which the “11010” related to the longitude occupies the even bits, and the “10111” related to the latitude occupies the odd bits. The ordering of the target binary information indication is the same as the ordering of the longitude binary information indication, and the ordering of the target binary information indication is the same as the ordering of the latitude binary information indication. Then, the 1110011101 of the target binary information indication is encoded to obtain the target string. The encoding processing can be performed by using a preset Base32 encoding algorithm (generally involving 32 characters). For example, the 32 characters of 0-9, b-z (excluding the letters a, i, l, and o) can be used for encoding. Specifically, “1110011101” can be converted into the decimal expression “28” and “29” first, and then the corresponding Base32 code is generated: 56 (in which “28” corresponds to “5”, and “29” corresponds to “6”; the first 22 of the 32 characters correspond to the first 22 letters in order, and the last 10 correspond to the first 10 numbers in order). Generally, 5 binary bits are converted into one Base32 code. In this way, the “one-dimensional” string is realized for the expression of the “two-dimensional” geographical grid.

[0064] The current position of each object in the candidate object can be stored in the form of a string. The manner of calculating the position string of the object can refer to the determination of the content of the geographical grid in the foregoing steps S301-S302, and the same preset recursive division rule is followed. At the same time, the manner of obtaining the target string is referred to, and the same encoding algorithm is followed. Therefore, the position string of the object essentially represents a “two-dimensional” geographical grid. If the position string is the same as the target string, it means that the two geographical grids overlap, and the current position of the object belongs to the geographical grid corresponding to the position information. The object can be used as a second type of object or a candidate object. The application of the position string and the target string can avoid the direct exposure of the current position of the object, and can improve the security of the candidate object information.

[0065] Further, the object data processing instruction carries range requirement information indicating the size of the geographical grid. The encoding processing based on the target binary information to obtain the target string can include the following steps: first, determining the string length according to the range requirement information; then, encoding processing based on the target binary information to obtain a candidate string; and further, obtaining the target string by truncating the candidate string based on the string length.

[0066] The more the number of divisions is, the more the longitude range and latitude range obtained by the division are, and the smaller the range corresponding to the geographic grid is. Correspondingly, the smaller the range corresponding to the geographic grid is, the longer the string corresponding to the target binary information is. The range corresponding to the geographic grid can be adjusted by adjusting the number of divisions and the length of the string. Under the same preset recursive division rule, there are multiple candidate geographic grids, and the target binary information or the encoding result corresponding to each of them can be calculated and stored. Meanwhile, under the same encoding algorithm, there is a certain corresponding relationship between the string length and the grid range. The encoding result is intercepted according to the string length of the range requirement information to obtain the target string, which can improve the scene adaptability of the target string to better meet the range requirements of the geographic grid.

[0067] In actual application, the content of determining the geographic grid in the foregoing steps S301-S302, the content of obtaining the target string, and the content of applying the range requirement information indicating the size of the geographic grid can use the Geohash algorithm. Taking the position information indicating the position of an existing store or a reference store as an example, the Geohash algorithm can be used to evaluate the geographic grid around the store to improve efficiency. The Geohash algorithm is an address encoding method that can encode two-dimensional spatial longitude and latitude data into a string. The Geohash algorithm understands the earth as a two-dimensional plane, recursively divides the plane into smaller sub-blocks, and each sub-block has the same encoding within a certain longitude and latitude range, which can meet the longitude and latitude retrieval of small-scale data. The prefix of the Geohash encoding can represent a larger area. For example, wx4g0ec1, its prefix wx4g0e represents a larger range including the encoding wx4g0ec1. This feature can be used for nearby place search. According to the current coordinates of the user (which can correspond to the longitude coordinates and latitude coordinates indicated by the position information), the Geohash encoding (such as wx4g0ec1) is calculated, and its prefix is queried (SELECT * FROM place WHERE geohash LIKE 'XXX%'), that is, all nearby places can be queried. The application of the Geohash algorithm can involve the following steps: converting the longitude data and latitude data into binary expressions respectively, then merging the two binary expressions, and then encoding the merged result according to the preset Base32 encoding algorithm. The application of the Geohash algorithm can be realized by the following code: def lnglatEncodeGeohash(lat, lng, precesion):

[0068]

[0069]

[0070]

[0071] In addition, for the precision of Geohash, the length of the Geohash string can be used to represent. The length of the Geohash string commonly used in grid analysis is 6, 7 or 8, and the grid of this precision can lock the distance precision within 100 meters. The longer the Geohash code represents the smaller the range and the more accurate the position. For reference, see Table 1 below:

[0072]

[0073] Table 1

[0074] In an exemplary embodiment, after determining the plurality of second-class objects matching the geographic grid from the candidate objects, the method can further include the following steps: first, determining the attribute information corresponding to the plurality of second-class objects; then, generating second content delivery guide information for a specified store according to the attribute information; wherein the location of the specified store matches the location information.

[0075] The attribute information corresponding to the potential customer object can be determined by clustering the potential customer objects in the grid dimension, and then the attribute information can be used to determine the delivery content, so that the delivery content has better new customer acquisition, customer retention and repurchase effect. The attribute information can be divided into gender, age and other attributes. The delivery content can include but is not limited to short video content, long video content, live video content, animation content, audio content, graphic content, image content, text content, etc. The delivery content can be used for online advertising, offline advertising. Of course, the delivery content can also be related to the promotion of materials, offline activity selection.

[0076] In addition, the association relationship determination basis and similarity calculation requirements related to the above-mentioned ways 2) and 3) for determining the second-class objects can be integrated with the object clustering here to obtain the attribute information corresponding to the potential customer object.

[0077] S204: determining at least one target first-class object matching the representation information from the plurality of second-class objects; or, after a preset time interval, determining at least one target first-class object matching the representation information from the plurality of second-class objects;

[0078] In the embodiments of the present application, the client or the server determines at least one target first-class object matching the representation information from the plurality of second-class objects. The target first-class object is an object whose current location belongs to the geographical grid and whose current information matches the representation information among the candidate objects. Taking the example of "the location information can indicate the location of the existing store, the first-class object can indicate the member object of the existing store, and the representation information indicating the first-class object can be the member object identifier", the foregoing step S203 and step S204 can correspond to the same time, and at this time, the current information of the target first-class object includes the member object identifier, indicating that the current information of the target first-class object matches the representation information. For the convenience of subsequent description, the target first-class object thus determined is referred to as "penetration object".

[0079] Alternatively, after a preset time interval, the client or the server determines at least one target first-class object matching the representation information from the plurality of second-class objects. At this time, the current information of the second-class object matches the representation information. Taking the example of "the location information can indicate the location of the existing store, the first-class object can indicate the member object of the existing store, and the representation information indicating the first-class object can be the member object identifier", the foregoing step S203 corresponds to the historical time, at which time the current information of the second-class object does not include the member object identifier, indicating that the current information of the second-class object does not match the representation information; step S204 corresponds to the current time, at which time the current information of the second-class object includes the member object identifier, indicating that the current information of the second-class object matches the representation information. The preset time interval indicating the interval between the current time and the historical time can be flexibly set according to requirements. The dynamic determination of the target first-class object is realized through the combination of the representation information and the preset time interval. For the convenience of subsequent description, the target first-class object thus determined is referred to as "conversion object".

[0080] S205: obtaining a data processing result based on the plurality of second-class objects and the at least one target first-class object to generate business guidance information.

[0081] In the embodiments of the present application, the client or the server obtains a data processing result based on the plurality of second-class objects and the at least one target first-class object to generate business guidance information. The second-class objects and the target first-class objects can be subjected to data processing in different dimensions to obtain the data processing result. For example, 1) data processing only for the second-class objects; 2) data processing only for the target first-class objects; 3) data processing for both the second-class objects and the target first-class objects; 4) two-by-two combination or three-way combination of the foregoing 1)-3). The business guidance information can be regarded as suggestion information for the related business, such as suggestion information for the store new customer acquisition business or suggestion information for the new store site selection business.

[0082] In an exemplary embodiment, asFigure 4 The data processing result based on the plurality of second-class objects and the at least one target first-class object comprises:

[0083] S401: respectively determine a first quantity corresponding to the plurality of second-class objects and a second quantity corresponding to the at least one target first-class object;

[0084] S402: obtain a first-class quantity determination result for potential objects according to a comparison result of the first quantity and a quantity threshold value;

[0085] S403: obtain a second-class quantity determination result indicating object distribution according to the first quantity and the second quantity.

[0086] The first quantity can represent the quantity of potential objects, and the second quantity can represent the quantity of target objects. When the quantity of target objects is derived from "penetrating objects", the ratio of the second quantity to the first quantity (second quantity / first quantity) can represent the penetration ratio of target objects to potential objects. When the quantity of target objects is derived from "converted objects", the ratio of the second quantity to the first quantity (second quantity / first quantity) can represent the proportion of potential objects converted into target objects. In combination with the example in the foregoing step S203, if "objects 1-500 belonging to the geographic grid at the current location are taken as second-class objects" and the target first-class object is object 1-100, and the quantity threshold value is 400. Then, the first quantity is 500, the second quantity is 100, the ratio of the second quantity to the first quantity is 20%, and the first quantity is greater than the quantity threshold value. Thus, the first-class quantity determination result can be obtained from the comparison result that the first quantity is greater than the quantity threshold value, which can be used to indicate that the potential objects meet a certain quantity requirement. If the first-class quantity determination result is obtained from the comparison result that the first quantity is less than or equal to the quantity threshold value, it can be used to indicate that the potential objects do not meet a certain quantity requirement. The second-class determination result can be obtained from the ratio of the second quantity to the first quantity, which can be used to indicate the penetration of target objects in potential objects, i.e., object penetration rate; or, the conversion of potential objects into target objects, i.e., object conversion rate. Through data processing in two quantity dimensions, the data processing result accurately and effectively reflects the quantity of potential objects and the object penetration rate (or object conversion rate), which can provide reliable data support for generating business guidance information.

[0087] The content of generating business guidance information will be introduced below in combination with "opening a store scenario" and "opening a warehouse scenario":

[0088] A) for "store", "warehouse":

[0089] Store is a store, a shop. The concept of warehouse is greater than that of store, such as fresh food warehouse, front warehouse. In practical application, the business guidance information in the following B) for the "store opening scenario" can also be applicable to the warehouse new customer acquisition business and the new warehouse site selection business.

[0090] B) For "store opening scenario":

[0091] When the first quantity determination result indicates that the first quantity is greater than the quantity threshold, and the second quantity determination result indicates that the distribution of the target first-class objects does not meet the preset object distribution requirement, store site guidance information indicating the position information is generated, or first content delivery guidance information for a specified store is generated; wherein the position of the specified store matches the position information.

[0092] The first quantity determination result and the second quantity determination result are used to define the store opening index in the grid dimension, and on this basis, suggestion information for store new customer acquisition business and suggestion information for new store site selection business are generated, which can improve the accuracy and effectiveness of the suggestion information.

[0093] The first quantity determination result indicates that the potential objects meet a certain quantity requirement. When the second quantity is derived from "penetration objects", the comparison result between the object penetration rate (corresponding to the distribution of target first-class objects) indicated by the second quantity determination result and the penetration rate threshold (corresponding to the preset object distribution requirement) indicates that the object penetration rate is low. When the second quantity is derived from "conversion objects", the comparison result between the object conversion rate (corresponding to the distribution of target first-class objects) indicated by the second quantity determination result and the conversion rate threshold (corresponding to the preset object distribution requirement) indicates that the object conversion rate is low. Whether the object penetration rate is low or the object conversion rate is low, the quantity of potential objects is relatively sufficient, indicating that the geographic grid indicates a region with store opening potential. Then, for the store opening scenario, the position information indicates the position of the reference store, and store site guidance information indicating the position information can be generated, i.e. information suggesting to open a store in the geographic grid. That is, the geographic grid can be used as a candidate area for store opening to guide the determination of the specific store opening position of the new store. For the store opening scenario, the position information indicates the position of the existing store (corresponding to the specified store), and content delivery guidance information for the existing store can be generated, i.e. information suggesting to increase or preferentially deliver content in the existing store. That is, the geographic grid is used as an area for increasing, preferentially marketing, and promoting to guide the existing store to deliver content to attract new customers, thereby improving the efficiency of new customer acquisition.

[0094] C) For "warehouse opening scenario"

[0095] generate first guide information for determining a potential object contribution area in a designated bin when the second quantity determination result indicates that the distribution of the target first-class objects does not meet the preset object distribution requirement, wherein the position of the designated bin matches the position information;

[0096] generate second guide information for improving object resource value conversion when the first quantity determination result indicates that the first quantity is less than or equal to a quantity threshold value, and the second quantity determination result indicates that the distribution of the target first-class objects meets the preset object distribution requirement;

[0097] generate third guide information for binning in the geographic grid when the first quantity determination result indicates that the first quantity is greater than a quantity threshold value, and the second quantity determination result indicates that the distribution of the target first-class objects meets the preset object distribution requirement.

[0098] The first quantity determination result and the second quantity determination result are used to define binning indicators in the grid dimension, and on this basis, suggestion information for bin new customer acquisition business, suggestion information for bin customer retention and repurchase business, and suggestion information for new bin site selection business are generated, which can improve the accuracy and effectiveness of the suggestion information.

[0099] The first quantity determination result indicates that the first quantity is less than or equal to a quantity threshold value, which means that the potential objects do not meet a certain quantity requirement, i.e., "low potential customer number"; otherwise, it is "high potential customer number". When the second quantity is derived from "permeable objects", the second quantity determination result indicates that the object penetration rate (corresponding to the distribution of the target first-class objects) is less than a penetration rate threshold value (corresponding to not meeting the preset object distribution requirement), which means that the object penetration rate is low (corresponding to "low penetration rate" in Table 2 below); otherwise, it is "high penetration rate". Table 2 below shows four classifications of bins:

[0100] Potential customer count - high Potential customer count - low Penetration rate - high Star bin High penetration bin Penetration rate - low High potential bin Low potential low penetration bin

[0101] Table 2

[0102] Different strategies are adopted for different types of warehouses to provide warehouse opening suggestions: a) Star Warehouses: Consider encryption within the fenced area, corresponding to the third guidance information mentioned above regarding adding warehouses within the geographic grid. This can be understood as using the geographic grid as a candidate area for warehouse opening to guide the determination of the specific location for a new warehouse. b) High-Potential Warehouses and Low-Potential Low-Penetration Warehouses: Find potential customer areas and increase warehouse penetration, corresponding to the first guidance information mentioned above regarding identifying potential customer contribution areas within a designated warehouse, i.e., optimizing the area within the designated warehouse. c) High-Penetration Warehouses: Increase repeat purchases from already penetrated customers, corresponding to the second guidance information mentioned above indicating the transfer of resource value from target customers. It should be noted that for scenarios awaiting warehouse opening, the above four warehouse types indicate reference warehouses, so it is not recommended to use the area surrounding low-potential low-penetration warehouses and high-penetration warehouses as new warehouse opening locations. For scenarios where warehouses have already been opened, the above four warehouse types indicate existing warehouses, so it is recommended to increase and prioritize content placement in high-potential warehouses to attract new customers. When the second quantity originates from "conversion target," the classification and suggestions for "penetration target" can be applied similarly, and will not be elaborated further.

[0103] In an exemplary embodiment, after obtaining the data processing result based on the plurality of second-class objects and the at least one target first-class object, the method may further include the following steps: first, determining a map area indicating the geographic raster on a preset map; then, determining the color information corresponding to the data processing result; and further, processing the pixels corresponding to the map area according to the color information.

[0104] By using map areas marked with geographic graticles on a preset map as the labeling objects, and determining the labeling color based on the data processing results, the data processing results corresponding to the geographic graticles are marked on the preset map, which can improve the intuitiveness and efficiency of obtaining data processing results. For example... Figure 6 , 7 As shown, Figure 6 The labeling results based on the number of second-class objects are shown, where the more potential objects a geographic raster corresponds to, the darker the label color indicating the map area of ​​the geographic raster; the fewer potential objects a geographic raster corresponds to, the lighter the label color indicating the map area of ​​the geographic raster. Figure 7 The labeling results are shown based on the number of second-class objects and the number of target first-class objects. The higher the object penetration rate (= number of target first-class objects / number of second-class objects) of a geographic raster, the darker the color of the map area indicating the raster; the lower the object penetration rate, the lighter the color of the map area indicating the raster. Alternatively, the labeling results can also be shown based on the number of target first-class objects, where the more target objects a raster corresponds to, the darker the color of the map area indicating the raster; the fewer target objects a raster corresponds to, the lighter the color of the map area indicating the raster.

[0105] In practical applications, the data processing scheme provided by the embodiments of the present application can be applied to related Internet products, which can provide an interface as shown in the following table 2. Figure 5 The "crowd density" corresponds to the "object penetration rate" described above. The higher the object penetration rate, the higher the crowd density. The "crowd information" can guide the specified object to input the "characteristic information indicating the first type of object". Of course, it can also guide the specified object to upload the crowd package A (corresponding to multiple second type of objects) and the crowd package B (corresponding to at least one target first type of object). The "area information" can guide the specified object to input the "location information". The "grid size" can guide the specified object to input the "range requirement information indicating the size of the geographic grid", such as the "length of the Geohash string" in table 1 above, which is usually 6 or 7.

[0106] The data processing scheme provided by the embodiments of the present application can be used in retail scenarios to accurately calculate and provide the number of potential objects, the number of target objects, and the object penetration rate within the relevant geographic grid (such as within a 100-meter range around a certain store). With the help of a preset map and a designated color, the penetration, loss, and competition of the relevant stores and warehouses in the map area can be intuitively reflected. At the same time, the specified object can be provided with relevant store and warehouse opening suggestions.

[0107] As can be seen from the technical scheme provided by the embodiments of the present application, in response to the data processing instruction, the corresponding location information and the characteristic information indicating the first type of object are determined. Then, the corresponding geographic grid is determined according to the location information. Further, the multiple second type of objects matching the geographic grid are determined from the to-be-selected objects. Next, the at least one target first type of object matching the characteristic information is determined from the multiple second type of objects. Alternatively, after a preset time interval, the at least one target first type of object matching the characteristic information is determined from the multiple second type of objects. Finally, the data processing result is obtained based on the multiple second type of objects and the at least one target first type of object to generate the business guide information. The present application determines the objects based on the geographic grid and the characteristic information indicating the first type of object, and then generates the business guide information through the object determination result, which improves the information dimension of the reference data used to generate the business guide information and ensures the accuracy and effectiveness of the business guide information. The data processing result used as the reference data comes from the common result of the location information corresponding to the geographic grid and the characteristic information indicating the first type of object. The location attribute of the second type of object is limited by the geographic grid, which improves the location accuracy of the determined second type of object and provides an effective guarantee for the rich information dimension of the data processing result.

[0108] The embodiments of the present application also provide a data processing device, as shown in the following table 3. Figure 8As shown, the data processing apparatus 80 comprises:

[0109] The response module 801 is configured to determine corresponding position information and representation information indicating the first type of object in response to the data processing instruction.

[0110] The grid determination module 802 is configured to determine a corresponding geographic grid according to the position information.

[0111] The second type of object determination module 803 is configured to determine a plurality of second type of objects matching the geographic grid from the candidate objects.

[0112] The first type of object determination module 804 is configured to determine at least one target first type of object matching the representation information from the plurality of second type of objects, or determine at least one target first type of object matching the representation information from the plurality of second type of objects after a preset time interval.

[0113] The data processing result obtaining module 805 is configured to obtain a data processing result based on the plurality of second type of objects and the at least one target first type of object, to generate service guidance information.

[0114] It should be noted that the apparatuses in the apparatus embodiments and the method embodiments are based on the same inventive concept.

[0115] The electronic device provided by the embodiments of the present application comprises a processor and a memory, and the memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the data processing method provided by the above method embodiments.

[0116] Further, Figure 9 A hardware structure schematic diagram of an electronic device for implementing the data processing method provided by the embodiments of the present application is shown, and the electronic device can participate in constituting or containing the data processing apparatus provided by the embodiments of the present application. As shown in the figure, Figure 9 As shown, the electronic device 100 can comprise one or more (in the figure, 1002a, 1002b, …, 1002n are used to show) processors 1002 (the processor 1002 can comprise but not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication function. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 9The illustrated structure is merely schematic and does not impose any limitation on the structure of the electronic device described above. For example, the electronic device 100 can further include more or fewer components than those shown in FIG. 1, or have a different configuration from that shown in FIG. 1. Figure 9 Figure 9

[0117] It should be noted that the one or more processors 1002 and / or other data processing circuitry described above can be referred to herein generally as "data processing circuitry". The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single standalone processing module, or incorporated in whole or in part within any one of other elements of the electronic device 100 (or mobile device). As referred to in embodiments of the present application, the data processing circuitry serves as a processor to control, for example, selection of a variable resistance terminal path connected to an interface.

[0118] The memory 1004 can be used to store software programs and modules of application software, and program instructions / data storage means corresponding to the data processing method described in embodiments of the present application. The processor 1002 can execute various functional applications and data processing by running the software programs and modules stored in the memory 1004, i.e. implement the data processing method described above. The memory 1004 can include a high-speed random access memory, and can further include a non-volatile memory such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 1004 can further include a memory disposed remotely with respect to the processor 1002, which can be connected to the electronic device 100 through a network. Examples of the network can include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0119] The transmission device 1006 is configured to receive or send data via a network. Examples of the network can include a wireless network provided by a communication provider of the electronic device 100. In one example, the transmission device 1006 includes a network interface controller (NIC) which can be connected to other network devices through a base station so as to communicate with the Internet. In one embodiment, the transmission device 1006 can be a radio frequency (RF) module configured to communicate with the Internet in a wireless manner.

[0120] The display can be, for example, a touch screen type liquid crystal display (LCD) which can enable a user to interact with a user interface of the electronic device 100 (or mobile device).

[0121] ​​The embodiment of the present application further provides a computer readable storage medium, which can be arranged in an electronic device to store at least one instruction or at least one program related to a data processing method in the method embodiment, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the data processing method provided by the above method embodiment.

[0122] Optionally, in the embodiment, the storage medium can be located in at least one of a plurality of network servers of a computer network. Optionally, in the embodiment, the storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing program codes.

[0123] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0124] Each of the embodiments of the present application is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device and electronic device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0125] Those skilled in the art can understand that all or part of the above-mentioned steps of the embodiments can be completed by hardware, or by program to instruct relevant hardware, and the program can be stored in a computer readable storage medium, and the storage medium can be a read-only memory, a magnetic disk or an optical disk.

[0126] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that, The method includes: In response to data processing instructions, determine the corresponding location information and the representation information indicating the first type of object; The corresponding geographic grid is determined based on the location information; From the candidate objects, identify a plurality of second-class objects that match the geographic raster; Determine at least one target first-class object from the plurality of second-class objects that matches the representation information; or, after a preset time interval, determine at least one target first-class object from the plurality of second-class objects that matches the representation information. Based on the plurality of second-type objects and the at least one target first-type object, the data processing results are obtained as follows: a first quantity corresponding to the plurality of second-type objects and a second quantity corresponding to the at least one target first-type object are determined respectively; a first-type quantity determination result for potential objects is obtained based on the comparison result of the first quantity and the quantity threshold; a second-type quantity determination result indicating the distribution of objects is obtained based on the first quantity and the second quantity. When the distribution of the target first type of object indicated by the second quantity determination result does not meet the preset object distribution requirements, first guidance information is generated to determine the potential object contribution area in the designated warehouse, and the location of the designated warehouse matches the location information. When the first quantity determination result indicates that the first quantity is less than or equal to the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result meets the preset object distribution requirements, second guidance information is generated to instruct the object to transfer resource value. When the first quantity determination result indicates that the first quantity is greater than the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result meets the preset object distribution requirements, third guidance information for adding storage within the geographic grid is generated.

2. The method according to claim 1, characterized in that, Determining the corresponding geographic grid based on the location information includes: When the location information indicates longitude and latitude coordinates, the target longitude interval corresponding to the longitude coordinates and the target latitude interval corresponding to the latitude coordinates are determined based on a preset recursive partitioning rule. The geographic raster corresponding to the location information is obtained based on the target longitude range and the target latitude range.

3. The method according to claim 2, characterized in that: The target longitude interval indicates longitude binary information, the target latitude interval indicates latitude binary information, and the method further includes: Target binary information is obtained based on the longitude binary information and the latitude binary information; The target string is obtained by encoding the target binary information. The step of identifying multiple second-class objects from the candidate objects that match the geographic raster includes: Based on the target string, a number of matching second-type objects are determined from the candidate objects.

4. The method according to claim 3, characterized in that, The object data processing instruction carries range requirement information indicating the size of the geographic raster. The encoding process based on the target binary information to obtain the target string includes: The string length is determined based on the range requirement information; Candidate strings are obtained by encoding the target binary information; The target string is obtained by truncating the candidate string based on the string length.

5. The method according to claim 1, characterized in that, The step of identifying multiple second-class objects from the candidate objects that match the geographic raster includes: From the candidate objects, identify multiple second-class objects whose current location belongs to the geographic raster; Alternatively, multiple candidate objects whose current location belongs to the geographic raster can be determined from the candidate objects, and multiple second-class objects whose current information is associated with the representation information can be determined from the multiple candidate objects; Alternatively, multiple candidate objects whose current location belongs to the geographic raster can be determined from the candidate objects, and multiple second-class objects whose similarity to the historical first-class objects is greater than a similarity threshold can be determined from the multiple candidate objects; wherein the historical information corresponding to the historical first-class objects matches the representation information.

6. The method according to claim 1, characterized in that, After obtaining the data processing result based on the plurality of second-type objects and the at least one target first-type object, the method further includes: Define the map area on the preset map that indicates the geographic grid; Determine the color information corresponding to the data processing result; The pixels corresponding to the map area are processed according to the color information.

7. The method according to claim 1, characterized in that, After obtaining the data processing result based on the plurality of second-type objects and the at least one target first-type object, the method further includes: When the first quantity determination result indicates that the first quantity is greater than the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result does not meet the preset object distribution requirements, store location guidance information indicating the location information is generated, or first content delivery guidance information for a specified store is generated; wherein, the location of the specified store matches the location information.

8. The method according to any one of claims 1-5, characterized in that, After identifying a plurality of second-class objects from the candidate objects that match the geographic raster, the method further includes: Determine the attribute information corresponding to the plurality of second-type objects; Based on the attribute information, a second content delivery guidance message is generated for a specified store; wherein the location of the specified store is matched with the location information.

9. A data processing apparatus, characterized in that, The device includes: Response module: Used to respond to data processing instructions, determine the corresponding location information and indicate the representation information of the first type of object; Raster determination module: used to determine the corresponding geographic raster based on the location information; Second-class object determination module: used to determine multiple second-class objects that match the geographic raster from the candidate objects; First-class object determination module: used to determine at least one target first-class object that matches the representation information from the plurality of second-class objects; or, after a preset time interval, to determine at least one target first-class object that matches the representation information from the plurality of second-class objects; The data processing result obtaining module is used to obtain data processing results based on the plurality of second-type objects and the at least one target first-type object: respectively determining the first quantity corresponding to the plurality of second-type objects and the second quantity corresponding to the at least one target first-type object; obtaining a first-type quantity determination result for potential objects based on the comparison result of the first quantity and the quantity threshold; and obtaining a second-type quantity determination result indicating the distribution of objects based on the first quantity and the second quantity. The business guidance information generation module is used to generate first guidance information for determining the potential object contribution area in a designated warehouse when the distribution of the target first type of object indicated by the second quantity determination result does not meet the preset object distribution requirements; when the first quantity determination result indicates that the first quantity is less than or equal to the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result meets the preset object distribution requirements, it generates second guidance information indicating that the object should transfer resource value; when the first quantity determination result indicates that the first quantity is greater than the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result meets the preset object distribution requirements, it generates third guidance information for adding warehouses within the geographic raster.

10. The apparatus according to claim 9, characterized in that, The grid determination module is also used to: when the location information indicates longitude coordinates and latitude coordinates, determine the target longitude interval corresponding to the longitude coordinates and the target latitude interval corresponding to the latitude coordinates respectively based on a preset recursive division rule; The geographic raster corresponding to the location information is obtained based on the target longitude range and the target latitude range.

11. The apparatus according to claim 10, characterized in that, The target longitude interval indicates longitude binary information, and the target latitude interval indicates latitude binary information. The device is further configured to: obtain target binary information based on the longitude binary information and the latitude binary information; and perform encoding processing based on the target binary information to obtain a target string. The second type of object determination module is further configured to: determine multiple matching second type objects from the candidate objects based on the target string.

12. The apparatus according to claim 11, characterized in that, The object data processing instruction carries range requirement information indicating the size of the geographic raster. The encoding process based on the target binary information to obtain the target string includes: The string length is determined based on the range requirement information; Candidate strings are obtained by encoding the target binary information; The target string is obtained by truncating the candidate string based on the string length.

13. The apparatus according to claim 9, characterized in that, The second type of object determination module is also used for: From the candidate objects, identify multiple second-class objects whose current location belongs to the geographic raster; Alternatively, multiple candidate objects whose current location belongs to the geographic raster can be determined from the candidate objects, and multiple second-class objects whose current information is associated with the representation information can be determined from the multiple candidate objects; Alternatively, multiple candidate objects whose current location belongs to the geographic raster can be determined from the candidate objects, and multiple second-class objects whose similarity to the historical first-class objects is greater than a similarity threshold can be determined from the multiple candidate objects; wherein the historical information corresponding to the historical first-class objects matches the representation information.

14. The apparatus according to claim 9, characterized in that, The device is also used for: Define the map area on the preset map that indicates the geographic grid; Determine the color information corresponding to the data processing result; The pixels corresponding to the map area are processed according to the color information.

15. The apparatus according to claim 9, characterized in that, The device is also used for: When the first quantity determination result indicates that the first quantity is greater than the quantity threshold, and the distribution of the target first type of object indicated by the second quantity determination result does not meet the preset object distribution requirements, store location guidance information indicating the location information is generated, or first content delivery guidance information for a specified store is generated; wherein, the location of the specified store matches the location information.

16. The apparatus according to any one of claims 9-13, characterized in that, The device is also used for: Determine the attribute information corresponding to the plurality of second-type objects; Based on the attribute information, a second content delivery guidance message is generated for a specified store; wherein the location of the specified store is matched with the location information.

17. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the data processing method as described in any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the data processing method as described in any one of claims 1-8.

19. A computer program product, characterized in that, The computer program product includes at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the data processing method as described in any one of claims 1-8.

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