Business district analysis method and device, equipment and medium

Abstract

The invention provides a business district analysis method and device, equipment and a medium, and relates to the technical field of computers, in particular to the field of data processing. The method comprises the steps of determining candidate users of a target entity based on a geographic range corresponding to the target entity; performing spatial aggregation based on the historical positions of the candidate users to determine resident point information of the candidate users; and determining the range of the business district of the target entity based on the resident point information of the candidate users.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the field of data processing, and specifically to a business district analysis method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] For target entities in business (e.g., merchants), there is usually a need to analyze the location sources of their users, so as to analyze the information of the corresponding business district (e.g., the scope of the business district). At present, questionnaires are usually distributed to users who shop in stores to investigate their location source information, and the business district information of the target entity is analyzed based on the collected questionnaire information. However, this questionnaire-based business district analysis method consumes a lot of manpower and material resources, and has certain requirements on the user's cooperation, so it not only takes a lot of time and resources, but also makes it difficult to guarantee the accuracy of the analysis results.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present disclosure provides a business district analysis method, device, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of the present disclosure, a business district analysis method is provided, including: determining candidate users of a target entity based on a geographic scope corresponding to the target entity; performing spatial aggregation based on historical locations of the candidate users to determine permanent point information of the candidate users; and determining the scope of the business district of the target entity based on the permanent point information of the candidate users.

[0006] According to another aspect of the present disclosure, a business district analysis device is provided, including: a user determination module, configured to: determine candidate users of a target entity based on a geographical scope corresponding to the target entity; a permanent point determination module, configured to: perform spatial aggregation based on historical locations of the candidate users to determine permanent point information of the candidate users; and a business district determination module, configured to: determine the scope of the business district of the target entity based on the permanent point information of the candidate users.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above method.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the above method when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, by determining the permanent location information of candidate users of the target entity through their historical locations, the sources of candidate users of the target entity can be accurately and automatically obtained, saving manpower and time costs and improving the accuracy of business district analysis.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A flow chart of a business circle analysis method according to an exemplary embodiment of the present disclosure is shown;

[0014] Figure 2 The embodiment according to the present disclosure is shown in Figure 1 A flowchart of a process of performing spatial aggregation based on the historical locations of candidate users to determine the permanent location information of candidate users in the method;

[0015] Figures 3A-3F A schematic diagram of an application of business district planning according to an exemplary embodiment of the present disclosure is shown;

[0016] Figure 4 A structural block diagram of a commercial district analysis device according to an exemplary embodiment of the present disclosure is shown; and

[0017] Figure 5A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0019] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0020] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0021] As described above, the method of performing business district analysis based on sending questionnaires to users in the related art consumes a lot of human resources and time, and it is difficult to ensure the accuracy of the analysis results. In order to solve the above problems, the present disclosure provides a business district analysis method, including: determining candidate users of a target entity based on the geographical scope corresponding to the target entity; performing spatial aggregation based on the historical locations of the candidate users to determine the permanent point information of the candidate users; and determining the scope of the business district of the target entity based on the permanent point information of the candidate users.

[0022] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0023] Figure 1 FIG. 1 is a flowchart of a business district analysis method 100 according to an exemplary embodiment of the present disclosure. The method 100 is executed by an electronic device (eg, the electronic device 500 described below). Figure 1 As shown, the method 100 includes:

[0024] Step S101: determining candidate users of the target entity based on the geographical scope corresponding to the target entity;

[0025] Step S103: performing spatial aggregation based on the historical locations of the candidate users to determine the permanent location information of the candidate users; and

[0026] Step S105: Determine the scope of the target entity's business district based on the candidate user's permanent location information.

[0027] In the business district analysis method as disclosed in the present invention, the source of the candidate users of the target entity can be accurately and automatically obtained by determining the permanent location information of the candidate users through their historical locations, thereby efficiently and accurately performing business district analysis of the target entity.

[0028] Each step of the method 100 is described in detail below.

[0029] According to some embodiments, the target entity may be various businesses with physical business locations, such as restaurants, shopping stores, supermarkets, ticket agencies, medical service agencies, educational training institutions, fitness venues, entertainment venues, etc.

[0030] According to some embodiments, the candidate users of the target entity include users who have arrived at the business premises of the target entity or are about to arrive at the business premises of the target entity. According to some embodiments, the users who have arrived at the business premises of the target entity include users whose locations are within the physical location range of the target entity, and the users who are about to arrive at the business premises of the target entity include users whose locations are within the range of nearby roads of the target entity.

[0031] According to some embodiments, the geographic scope corresponding to the target entity includes the physical location scope of the target entity and the road scope around the target entity, and based on the geographic scope corresponding to the target entity, determining the candidate users of the target entity includes: determining users whose ordering locations are within the physical location scope and road scope of the target entity as candidate users of the target entity. In the embodiments as described in the present disclosure, by screening users based on whether the ordering location is within the physical location scope and road scope of the target entity, users who are in or near the target entity, or are about to arrive at the target entity, can be screened out, thereby accurately locating candidate users for subsequent business district analysis without the need for active feedback from users.

[0032] According to some embodiments, information of ordering users within a period of time or at a certain moment is obtained, and candidate users of the target entity among the ordering users are screened out based on location information of the ordering users.

[0033] According to some embodiments, in the above-mentioned acquisition of the information of the ordering user, only the information of the users who need to go to the store (for example, users who need to pick up the food, goods, or services in the store) is acquired, and the users who can receive the corresponding goods or services without going to the store through other means such as take-out are not acquired. For users who can receive the corresponding goods or services without going to the store, their location may be limited by the take-out delivery range specified by the time limit or not restricted by geographical distance (for example, online shopping or only enjoying online services), and cannot reflect the geographical range of the target entity.

[0034] According to some embodiments, the business district analysis method as described in the present disclosure further includes: determining the physical location range of the target entity based on the range of the area of ​​interest corresponding to the target entity. In the embodiments as described in the present disclosure, the physical location range of the target entity can be accurately determined to improve the accuracy of the business district analysis. According to some embodiments, the area of ​​interest of the target entity is the geographical range occupied by the target entity on the map.

[0035] According to some embodiments, the area of ​​interest (AOI) in the present disclosure refers to a regional geographic entity in map data, also known as an information surface or an interest surface.

[0036] According to some embodiments, the range of the target entity's interest area is obtained as the target entity's entity position range. According to other embodiments, the range of the target entity's interest area is expanded outward by a certain distance as the target entity's entity position range.

[0037] It should be understood that the entity location range of the target entity may also be determined based on other location information of the target entity (eg, geographic coordinates of the target entity).

[0038] According to other embodiments, the physical location range of the target entity may also be determined based on the geographic coordinates corresponding to the target entity. According to some embodiments, the physical location range of the target entity is formed by expanding outward with the geographic coordinates corresponding to the target entity as the center. According to some embodiments, the outward expansion radius of the target entity is determined according to the commercial level of the target entity (e.g., a first-level store, a second-level store, a third-level store, a fourth-level store, etc.).

[0039] According to some embodiments, the business district analysis method as described in the present disclosure further includes: obtaining a road vector whose navigation distance from the target entity is less than a preset road distance and / or whose navigation time from the target entity is less than a preset road time; and determining the road range of the target entity based on the road vector and the road grade corresponding to the road vector. In the embodiment as described in the present disclosure, the road range of the target entity can be determined based on the road vector corresponding to the road portion relatively close to the target entity, so as to accurately determine the range of the road close to the target entity, so as to accurately screen the users who are about to reach the target entity. According to some embodiments, the "road grade" in the present disclosure is a grade indicating the width of the road.

[0040] According to some embodiments, data of roads that are closer to a target entity are obtained (for example, the navigation distance to the target entity is less than a preset road distance and / or the navigation time to the target entity is less than a preset road time) to determine users on this part of the road as candidate users who are about to reach the target entity.

[0041] Generally, the road geographic information obtained from the geographic database is usually a road vector, rather than a geographical range corresponding to the road. Therefore, it is necessary to determine the geographical range corresponding to the corresponding road based on the vector of the road closer to the target entity determined above. According to some embodiments, for each road vector, based on the road grade corresponding to the road vector (e.g., expressway, first-class highway, second-class highway, third-class highway, fourth-class highway), the road width corresponding to the road vector is determined; and, based on the road vector and the corresponding road width, the road range corresponding to the road vector is determined. According to some embodiments, the road width corresponding to the road vector with a higher grade is wider.

[0042] Figure 2 The embodiment according to the present disclosure is shown in Figure 1 Flow chart of performing spatial aggregation based on the historical locations of candidate users in the method to determine the permanent point information of candidate users (e.g., step S103).

[0043] Step S201: Acquire the historical locations of the candidate user as the candidate locations of the candidate user.

[0044] According to some embodiments, the historical location of the candidate user is a location of the candidate user in history that is within a predetermined range, wherein the predetermined range is a range determined based on at least one of the following: a range of one or more pre-set areas of interest; a range of the administrative region where the target entity is located; and a range where the distance from the target entity is less than a predetermined distance. In the embodiments as described in the present disclosure, by setting the historical location of the candidate user to a location of the candidate user in history that is within a predetermined range, invalid data (for example, location data left when the user mistakenly places an order at the target entity at a distance from the target entity) can be deleted, thereby improving the accuracy of subsequent business district analysis.

[0045] For example, the predetermined range is determined based on the range of the administrative area where the target entity is located and / or the range where the distance from the target entity is less than the predetermined distance, thereby filtering out the user's location that is far away from the target entity (for example, the user is actually in a distant location that the target entity cannot reach and mistakenly selects the target entity), thereby improving the accuracy of subsequent business district analysis.

[0046] For another example, determining a predetermined range based on the range of one or more areas of interest ensures that the acquired historical locations of the candidate users carry information about the areas of interest, thereby facilitating subsequent business district analysis (for example, as will be further described, the business district attributes of the target entity can be analyzed based on the composition of the types of areas of interest of the candidate users corresponding to the target entity).

[0047] According to some other embodiments, all historical locations of the candidate user may be directly obtained as the candidate location of the candidate user, and the area of ​​interest information of the candidate user may be obtained in a subsequent step.

[0048] Step S203: spatially aggregate the candidate positions of the candidate users to obtain one or more aggregated clusters.

[0049] According to some embodiments, for each candidate user, all candidate locations of the user are spatially aggregated to obtain one or more aggregated clusters. It should be understood that each aggregated cluster corresponds to a common activity area of ​​the user (eg, home, company, school, etc.).

[0050] Step S205: Determine the permanent location information of the candidate user based on one or more aggregated clusters.

[0051] According to some embodiments, based on one or more aggregated clusters, determining the permanent location information of the candidate user includes: determining a core cluster in the one or more aggregated clusters; and determining the permanent location information of the candidate user based on the core cluster. In the embodiments described in the present disclosure, by selecting the core cluster, the main activity range can be screened out from the multiple activity ranges of the candidate user, thereby improving the accuracy of the determined permanent location information of the user.

[0052] According to some embodiments, determining a core cluster in one or more aggregated clusters includes: determining a core cluster in one or more aggregated clusters based on the number of candidate locations corresponding to the aggregated clusters and / or the type of interest area corresponding to the aggregated clusters. In the embodiments described in the present disclosure, the main activity range can be accurately screened out from multiple activity ranges of candidate users, thereby improving the accuracy of the determined user's permanent point information.

[0053] According to some embodiments, one or more clusters are scored, and the cluster with the highest score is selected as the core cluster. According to some embodiments, determining the core cluster in one or more clusters includes: determining the scores of one or more clusters based on the number of candidate locations corresponding to the clusters and / or the type of interest area corresponding to the clusters, wherein the score of the cluster can indicate the probability that the user stays in the range corresponding to the cluster; and determining the core cluster in one or more clusters based on the scores of the one or more clusters. For example, the more candidate locations the cluster corresponds to, the higher the score of the cluster; for another example, when determining the core cluster, the priority of the cluster corresponding to the office building is higher than the priority of the cluster corresponding to the residential area, and the priority of the cluster corresponding to the residential area is higher than the priority of the cluster corresponding to other types of clusters (for example, corresponding to sports venues).

[0054] According to some embodiments, the type of the interest area may indicate the land use of the interest area, for example, an office building, a residential area, a sports venue, etc.

[0055] According to some embodiments, when the score of an aggregated cluster is determined based on multiple factors, the score corresponding to each factor may be calculated respectively, and the scores corresponding to the various factors may be weighted to obtain the score of the aggregated cluster.

[0056] According to some embodiments, the type of area of ​​interest of the aggregated cluster is determined according to the type of area of ​​interest of each candidate position corresponding to the aggregated cluster. According to other embodiments, without obtaining the type of area of ​​interest of each candidate position in advance, the type of area of ​​interest of the aggregated cluster can also be determined according to the geographical location corresponding to the aggregated cluster, for example, the position of the centroid of the aggregated cluster is determined, and the type of area of ​​interest where the centroid of the aggregated cluster falls is obtained as the type of area of ​​interest of the aggregated cluster.

[0057] According to some embodiments, when one or more clusters include an aggregate cluster corresponding to an area of ​​interest of a target entity (for example, the overlap rate between the range of the aggregate cluster and the physical location range of the target entity exceeds an area threshold and / or the distance between the centroid of the aggregate cluster and the geographic coordinates of the target entity is less than a distance threshold), the aggregate cluster corresponding to the area of ​​interest of the target entity is deleted from the one or more clusters, because the aggregate cluster may be formed because the user often consumes in the target entity or is about to reach the target entity, and cannot reflect the user's actual permanent point information.

[0058] According to some embodiments, the permanent point information of the candidate user includes the permanent point location of the candidate user, and, based on the core cluster, determining the permanent point information of the candidate user includes: determining the permanent point location of the candidate user based on at least one of the centroid of the core cluster, the geometric center of the core cluster, and the location of the interest area corresponding to the core cluster (e.g., the center location of the interest area). According to the embodiments as described in the present disclosure, the permanent point location of the candidate user can be accurately determined based on the core cluster, thereby efficiently and accurately obtaining the permanent point location of the candidate user without the need for active feedback from the candidate user.

[0059] It should be understood that an aggregated cluster of users corresponds to an area where the user frequently moves, and thus the permanent location of the candidate user can be determined based on the location information of the core cluster (for example, the center of mass of the core cluster, the geometric center of the core cluster and / or the location of the area of ​​interest corresponding to the core cluster).

[0060] According to some embodiments, the permanent point information of the candidate user also includes the permanent point type of the candidate user, and, based on the core cluster, determining the permanent point information of the candidate user further includes: determining the permanent point type of the candidate user based on the type of the interest area corresponding to the core cluster. According to the embodiments as described in the present disclosure, the permanent point type of the candidate user can be accurately determined based on the core cluster, thereby efficiently and accurately obtaining the permanent point type of the candidate user without the need for active feedback from the candidate user.

[0061] According to some embodiments, the permanent location type of the candidate user may be set to the type of the interest area corresponding to the core cluster. For example, the type of the interest area corresponding to the core cluster is a residential area, and the permanent location type of the candidate user is also a residential area.

[0062] According to some embodiments, the permanent point information of the candidate users includes the permanent point locations of the candidate users, and based on the permanent point information of the candidate users, determining the scope of the target entity's business district includes: setting the initial scope of the target entity's business district to the entity location range of the target entity; and expanding the initial scope of the target entity's business district until the number of candidate users whose permanent point locations are within the scope of the target entity's business district is greater than or equal to a predetermined threshold. According to the embodiments as described in the present disclosure, the scope of the target entity's business district can be accurately determined based on the permanent point locations of the candidate users of the target entity, thereby improving the efficiency of determining the scope of the target entity's business district.

[0063] According to some embodiments, each time the business district of the target entity is expanded, the current business district of the target entity is expanded outward by a predetermined distance, and it is determined whether the number of candidate users whose permanent point locations are within the expanded business district of the target entity is greater than or equal to a predetermined threshold.

[0064] According to some embodiments, if the number of candidate users whose permanent point locations are within the expanded business district scope of the target entity is greater than or equal to a predetermined threshold, the expansion of the business district scope of the target entity is stopped, and the current business district scope of the target entity is determined as the business district scope of the target entity, or the current business district scope of the target entity is adjusted to finally determine the business district scope of the target entity.

[0065] According to some embodiments, the scope of the business district of the target entity is adjusted based on relevant data of the target entity, wherein the relevant data includes navigation data for indicating the time taken to reach the target entity, and the navigation data includes at least one of walking time, cycling time, driving time and public transportation time. According to an embodiment as described in the present disclosure, adjusting the scope of the business district based on navigation data can make the scope of the business district more accurate. For example, a range whose navigation time exceeds a predetermined time threshold can be deleted from the business district scope of the target entity to avoid classifying areas that are close in straight-line distance but geographically difficult to reach into the business district scope of the target entity (for example, areas separated by geographical barriers such as mountains and rivers).

[0066] According to some other embodiments, the permanent location of the candidate users in the outermost circle of the current target entity's business circle is selected, and these permanent location are connected to generate the target entity's business circle.

[0067] According to other embodiments, if the number of candidate users whose permanent point locations are within the expanded business district of the target entity is less than a predetermined threshold, the business district of the target entity continues to be expanded. According to some embodiments, each time the business district of the current target entity is expanded outward by a predetermined expansion distance to generate an expanded business district of the target entity.

[0068] According to some embodiments, the predetermined threshold is determined based on at least one of the number of candidate users of the target entity and the business grade of the target entity. According to the embodiments as described in the present disclosure, by determining the predetermined threshold based on the number of candidate users of the target entity and the business grade of the target entity, different predetermined thresholds can be set for different types of target entities to achieve individualized analysis of the business district analysis. For example, based on the business grade of the target entity, the proportion of candidate users of the target entity is determined, and the predetermined threshold is set to the product of the number of candidate users of the target entity multiplied by the proportion of candidate users of the target entity.

[0069] According to some embodiments, the “business grade” in the present disclosure refers to the grade of the business scale of the target entity, wherein the business grade of the target entity may be determined based on the geographic location, turnover and / or franchise store grade of the target entity.

[0070] According to some embodiments, the permanent point information of the candidate user includes the permanent point type of the candidate user, and the business district analysis method further includes: determining the proportion of each permanent point type in the business district of the target entity based on the permanent point type of the candidate user. According to the embodiments as described in the present disclosure, the proportion of candidate users from each permanent point type in the target entity can be accurately determined, thereby providing business district analysis information with more dimensions.

[0071] According to some embodiments, the permanent point type of the candidate user may be a type of interest area corresponding to the permanent point location of the candidate user, for example, the permanent point type of the candidate user determined based on the type of interest area corresponding to the core cluster as described above. For example, the proportion of users from office buildings, users from residential areas, and users from other types of interest areas in the business circle of the target entity among the users of the target entity may be determined.

[0072] Figures 3A-3F A business circle analysis method according to an exemplary embodiment of the present disclosure is shown (for example, Figure 1 Schematic diagram of application of method 100).

[0073] First, if Figure 3A As shown, the road vectors 321a and 321b of the nearby roads of the target entity 301 are determined (for example, the road vectors whose navigation distance from the target entity is less than the preset road distance and / or whose navigation time from the target entity is less than the preset road time as described above). It should be understood that although Figure 3AThe road vectors 321a and 321b shown are directly connected to the range 302 of the building where the target entity 301 is located (for example, the range of the commercial entity where the target entity is located), however, the nearby roads of the target entity 301 may also include roads that are not directly connected to the range 302 of the building where the target entity 301 is located but have a shorter distance to the target entity 301 and / or a shorter navigation time.

[0074] Then, if Figure 3B As shown, the entity position range 311 and the road ranges 312a and 312b of the target entity 301 are determined, wherein, corresponding to the road vector 312a, with the road vector 312a as the center, the road vector 312a is expanded to both sides by the corresponding width w1 of the road vector 312a to obtain the road range 312a, and, corresponding to the road vector 312b, with the road vector 312b as the center, the road vector 312b is expanded to both sides by the corresponding width w2 of the road vector 312b to obtain the road range 312b.

[0075] Then, if Figure 3C As shown, users 331a~331c whose order locations are within the entity range 311 of the target entity 301, users 331d~331e whose order locations are within the road range 312a of the target entity 301, and user 331f whose order location is within the road range 312b of the target entity 301 are determined as candidate users of the target entity 301, while users 331g and 331h located outside the entity range 311 and road ranges 312a, 312b of the target entity 301 are excluded.

[0076] Next, the permanent location information of the candidate user of the target entity 301 is determined. For ease of explanation, the candidate user 331a is taken as an example. Figures 3D to 3E For example, Figure 3D As shown in FIG. 3 , the candidate positions of the obtained candidate user 331a are aggregated to obtain aggregate clusters 341 to 343. Figure 3E As shown, the aggregated cluster 342 is selected as the core cluster, and based on the core cluster 342, the location of the permanent point 351a of the candidate user 331a is determined (eg, the centroid of the core cluster 342).

[0077] Then, if Figure 3F As shown, based on the permanent point locations 351a~351f of the candidate users 331a~331f of the target entity, the business district range 350 of the target entity 301 is determined, wherein the locations of the permanent points 351b~351f of the candidate users 331b~331f are within the business district range 350, while the location of the permanent point 351a of the candidate user 331a is outside the business district range 350.

[0078] Figure 4 FIG. 4 shows a structural block diagram of a trade circle analysis device 400 according to an exemplary embodiment of the present disclosure.

[0079] like Figure 4 As shown, the business district analysis device 400 includes: a user determination module 401, which is configured to: determine the candidate users of the target entity based on the geographical scope corresponding to the target entity; a permanent point determination module 402, which is configured to: perform spatial aggregation based on the historical locations of the candidate users to determine the permanent point information of the candidate users; and a business district determination module 403, which is configured to: determine the scope of the business district of the target entity based on the permanent point information of the candidate users.

[0080] According to some embodiments, the geographical scope corresponding to the target entity includes the physical location scope of the target entity and the road scope around the target entity, and the user determination module 401 includes: a candidate user determination submodule, configured to: determine users whose order locations are within the physical location scope and road scope of the target entity as candidate users of the target entity.

[0081] According to some embodiments, the permanent point determination module 402 includes: a candidate location acquisition submodule, configured to: obtain the historical location of the candidate user as the candidate location of the candidate user; a spatial aggregation submodule, configured to: spatially aggregate the candidate location of the candidate user to obtain one or more aggregation clusters; and a permanent point determination submodule, configured to: determine the permanent point information of the candidate user based on one or more aggregation clusters.

[0082] According to some embodiments, the historical location of the candidate user is a location of the candidate user within a predetermined range, wherein the predetermined range is determined based on at least one of: a range of one or more pre-set areas of interest; a range of the administrative region where the target entity is located; and a range whose distance from the target entity is less than a predetermined range.

[0083] According to some embodiments, the permanent point determination submodule includes: a core cluster determination submodule configured to: determine a core cluster in one or more aggregated clusters; and a permanent point determination submodule configured to: determine the permanent point information of the candidate user based on the core cluster.

[0084] According to some embodiments, the core cluster determination submodule may be further configured to determine a core cluster in one or more clusters based on the number of candidate positions corresponding to the clusters and / or the type of interest area corresponding to the clusters.

[0085] According to some embodiments, the permanent point information of the candidate user includes the permanent point location of the candidate user, and the permanent point determination submodule can also be configured to determine the permanent point location of the candidate user based on at least one of the centroid of the core cluster, the geometric center of the core cluster, and the location of the interest area corresponding to the core cluster.

[0086] According to some embodiments, the permanent point information of the candidate user also includes the permanent point type of the candidate user, and the permanent point determination submodule may be further configured to: determine the permanent point type of the candidate user based on the type of the interest area corresponding to the core cluster.

[0087] According to some embodiments, the permanent point information of the candidate user includes the permanent point location of the candidate user, and the business district determination module 403 includes: a range setting submodule, configured to: set the initial range of the business district of the target entity to the entity location range of the target entity; and a range expansion submodule, configured to: expand the initial range of the business district of the target entity until the number of candidate users whose permanent point locations are within the range of the business district of the target entity is greater than or equal to a predetermined threshold.

[0088] According to some embodiments, the predetermined threshold is determined according to at least one of the number of candidate users of the target entity and the business level of the target entity.

[0089] According to some embodiments, the business circle analysis device 400 further includes: an entity range determination module configured to: determine the entity location range of the target entity based on the range of the interest area corresponding to the target entity.

[0090] According to some embodiments, the business district analysis device 400 also includes: a road range determination module, which is configured to: obtain a road vector whose navigation distance to the target entity is less than a preset road distance and / or whose navigation time to the target entity is less than a preset road time; and determine the road range of the target entity based on the road vector and the road grade corresponding to the road vector.

[0091] According to some embodiments, the business district analysis device 400 also includes: a business district range adjustment module, which is configured to: adjust the range of the business district of the target entity based on relevant data of the target entity, wherein the relevant data includes navigation data for indicating the time taken to reach the target entity, and the navigation data includes: at least one of walking time, cycling time, driving time and public transportation time.

[0092] According to some embodiments, the permanent point information of the candidate user includes the permanent point type of the candidate user, and the business district analysis device 400 also includes: a proportion determination module configured to: determine the proportion of each permanent point type in the business district of the target entity based on the permanent point type of the candidate user.

[0093] According to another aspect of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method.

[0094] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to enable the computer to execute the above method.

[0095] According to another aspect of the present disclosure, a computer program product is also provided, including a computer program, wherein the computer program implements the above method when executed by a processor.

[0096] It should be understood that the technical effects described above with respect to the method are also applicable to the corresponding devices, media and computer program products, and therefore will not be described in detail here.

[0097] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0098] See also Figure 5 , a block diagram of an electronic device 500 that can be used as the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device can be a computer device of different types, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0099] Figure 5 1 shows a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 5 As shown, the electronic device 500 may include at least one processor 501 , a working memory 502 , an I / O device 504 , a display device 505 , a storage device 506 , and a communication interface 507 that can communicate with each other via a system bus 503 .

[0100] The processor 501 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 501 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. The processor 501 may be configured to obtain and execute computer-readable instructions stored in a working memory 502, a storage device 506, or other computer-readable media, such as program codes of an operating system 502a, program codes of an application 502b, and the like.

[0101] Working memory 502 and storage device 506 are examples of computer-readable storage media for storing instructions, which are executed by processor 501 to implement the various functions described above. Working memory 502 may include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, storage device 506 may include a hard disk drive, a solid-state drive, a removable medium, including external and removable drives, memory cards, flash memory, a floppy disk, an optical disk (e.g., CD, DVD), a storage array, a network attached storage, a storage area network, etc. Working memory 502 and storage device 506 may all be collectively referred to herein as memory or computer-readable storage media, and may be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by processor 501 as a specific machine configured to implement the operations and functions described in the examples herein.

[0102] The I / O device 504 may include an input device and / or an output device. The input device may be any type of device capable of inputting information to the electronic device 500, and may include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output device may be any type of device capable of presenting information, and may include but is not limited to a video / audio output terminal, a vibrator, and / or a printer.

[0103] The communication interface 507 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0104] The application 502b in the working register 502 can be loaded to execute the various methods and processes described above, such as Figure 1In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the storage device 706 and / or the communication interface 507. When the computer program is loaded and executed by the processor 501, one or more steps of the method described above may be performed.

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computing system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0112] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A business district analysis method, comprising: Determining candidate users of the target entity based on the geographical scope corresponding to the target entity; Based on the historical locations of the candidate users, spatial aggregation is performed to determine the permanent location information of the candidate users; as well as Based on the permanent location information of the candidate user, the scope of the business circle of the target entity is determined.

2. The method according to claim 1, wherein: The geographical scope corresponding to the target entity includes a physical location scope of the target entity and a road scope around the target entity, and determining the candidate users of the target entity based on the geographical scope corresponding to the target entity includes: Users whose ordering locations are within the entity location range and road range of the target entity are determined as candidate users of the target entity.

3. The method according to claim 1 or 2, wherein: The performing spatial aggregation based on the historical locations of the candidate users to determine the permanent location information of the candidate users includes: Acquire the historical location of the candidate user as the candidate location of the candidate user; spatially aggregating the candidate positions of the candidate users to obtain one or more aggregated clusters; and Based on the one or more aggregated clusters, permanent point information of the candidate user is determined.

4. The method according to claim 3, wherein: The historical location of the candidate user is a location of the candidate user within a predetermined range, wherein the predetermined range is determined based on at least one of the following: the extent of one or more predefined areas of interest; The scope of the administrative region where the target entity is located; and The distance from the target entity is less than a predetermined distance range.

5. The method according to claim 3, wherein: The determining, based on the one or more aggregated clusters, the permanent location information of the candidate user includes: determining a core cluster among the one or more aggregated clusters; and Based on the core cluster, the permanent location information of the candidate user is determined.

6. The method according to claim 5, wherein: Determining a core cluster in the one or more aggregated clusters includes: A core cluster among the one or more clusters is determined based on the number of the candidate positions corresponding to the clusters and / or the type of the area of ​​interest corresponding to the clusters.

7. The method according to claim 5, wherein: The permanent location information of the candidate user includes the permanent location of the candidate user, and the determining the permanent location information of the candidate user based on the core cluster includes: The permanent point location of the candidate user is determined based on at least one of the centroid of the core cluster, the geometric center of the core cluster, and the location of the interest area corresponding to the core cluster.

8. The method according to claim 7, wherein: The permanent location information of the candidate user also includes the permanent location type of the candidate user, and the determining the permanent location information of the candidate user based on the core cluster further includes: Based on the type of the area of ​​interest corresponding to the core cluster, the type of the permanent location of the candidate user is determined.

9. The method according to any one of claims 1 to 8, wherein: The permanent location information of the candidate user includes the permanent location of the candidate user, and determining the scope of the business district of the target entity based on the permanent location information of the candidate user includes: Setting the initial range of the target entity's business district to the physical location range of the target entity; and The initial range of the target entity's business circle is expanded until the number of the candidate users whose permanent point locations are within the range of the target entity's business circle is greater than or equal to a predetermined threshold.

10. The method according to claim 9, wherein: The predetermined threshold is determined according to at least one of the number of candidate users of the target entity and the business level of the target entity.

11. The method according to any one of claims 2 to 10, further comprising: Based on the range of the area of ​​interest corresponding to the target entity, the entity position range of the target entity is determined.

12. The method according to any one of claims 2 to 10, further comprising: Acquire a road vector whose navigation distance to the target entity is less than a preset road distance and / or whose navigation time to the target entity is less than a preset road time; as well as A road range of the target entity is determined based on the road vector and the road grade corresponding to the road vector.

13. The method according to any one of claims 1 to 12, further comprising: Based on relevant data of the target entity, the scope of the business district of the target entity is adjusted, wherein the relevant data includes navigation data for indicating the time consumed to reach the target entity, and the navigation data includes: at least one of walking time, cycling time, driving time and public transportation time.

14. The method according to any one of claims 1 to 12, wherein: The permanent location information of the candidate user includes the permanent location type of the candidate user, and the business district analysis method further includes: Based on the permanent point types of the candidate users, the proportion of each permanent point type in the business circle of the target entity is determined.

15. A business district analysis device, comprising: A user determination module is configured to: determine candidate users of the target entity based on the geographical range corresponding to the target entity; A permanent point determination module is configured to: perform spatial aggregation based on the historical locations of the candidate users to determine the permanent point information of the candidate users; as well as The business district determination module is configured to determine the scope of the business district of the target entity based on the permanent location information of the candidate user.

16. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 14.

17. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 14.

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