Store search method and apparatus, electronic device, and storage medium

Abstract

This application relates to the field of business support technology, and provides a store search method, device, electronic device, and storage medium, including dividing a preset area into at least one sub-area; acquiring first location data of all stores in each sub-area, and determining the average distance between stores in the sub-area; acquiring second location data of a user, and determining a target sub-area corresponding to the second location data; and determining a target store based on the average distance between stores in the target sub-area and a preset adjustment value. This application, by regionalizing store data, enables rapid location based on the user's location and improves search accuracy by using the average distance between stores and a preset adjustment value to determine the target store. It eliminates the need for uniform index accuracy, improves search precision, reduces invalid data in search results, and lowers system consumption.

Description

Technical Field

[0001] This application relates to the field of business support technology, and in particular to a store search method, apparatus, electronic device and storage medium. Background Technology

[0002] With the development of new retail business in the communications industry, the service scope of channel stores for customers has also expanded from offline to online. Recommending the nearest physical store to customers online has become an important means of connecting with customers and improving services.

[0003] Currently, store search uses a uniform indexing precision method. However, the distribution of channel stores within the province is uneven across cities and counties: they are densely distributed in city centers and sparsely distributed in suburbs; in county towns and sparsely distributed in townships; population density is also unevenly distributed. Over 90% of the users developed by channel stores are concentrated in the local city, about 9% are in other cities, and a very small number are users from other provinces. To meet the needs of users accessing nearby store recommendation applications and ensure a high hit rate, nearby store search uniformly uses 500KM as the indexing precision.

[0004] However, when searching for stores across the entire province, using uniform index precision can affect search efficiency, result in a poor user experience, and lead to most of the search results being invalid, increasing system consumption, consuming system resources, and affecting system stability. Summary of the Invention

[0005] This application provides a store search method, apparatus, electronic device, and storage medium to address the deficiencies in the prior art.

[0006] In a first aspect, embodiments of this application provide a store search method, including:

[0007] Divide the preset area into at least one sub-area;

[0008] Obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region;

[0009] Obtain the user's second location data and determine the target sub-region corresponding to the second location data;

[0010] The target store is determined based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value.

[0011] In one embodiment, determining the target store based on the average store distance corresponding to the target sub-region and a preset adjustment value includes:

[0012] The sum of the average distance of the stores corresponding to the target sub-region and the preset adjustment value is used as the index precision, and the target store is determined based on the index precision and the preset store code.

[0013] In one embodiment, determining the target store based on the index precision and a preset store code includes:

[0014] Search for stores based on the index precision and preset store codes;

[0015] If the searched stores do not meet the preset search criteria, the index precision is increased until a store that meets the preset search criteria is found, and the found store is taken as the target store.

[0016] In one embodiment, the adjustment value is determined based on the average number of searches in each sub-region and the average distance to the store.

[0017] In one embodiment, it also includes:

[0018] If the initial retrieval success rate of any of the sub-regions is lower than a preset value, then the adjustment value is increased.

[0019] In one embodiment, obtaining the first location data of all stores within each sub-region and determining the average distance between stores within the sub-region includes:

[0020] Obtain the first location data of all stores in each sub-region, and determine the distance between each store and its adjacent stores based on the first location data;

[0021] The average distance between stores within the sub-region is determined based on the distance between each store and its adjacent stores.

[0022] In one embodiment, determining the target sub-region corresponding to the second location data includes:

[0023] The target sub-region is determined by comparing the second location data with the boundary location data of the sub-region.

[0024] Secondly, embodiments of this application also provide a store search device, including:

[0025] The region division module is used to divide a preset region into at least one sub-region.

[0026] The distance determination module is used to obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region.

[0027] The region determination module is used to acquire the user's second location data and determine the target sub-region corresponding to the second location data;

[0028] The store determination module is used to determine the target store based on the average distance of the stores corresponding to the target sub-region and a preset adjustment value.

[0029] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any of the store search methods described above.

[0030] Fourthly, embodiments of this application also provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the store search method as described above.

[0031] The store search method, apparatus, electronic device, and storage medium provided in this application divide a preset area into at least one sub-area; acquires first location data of all stores within each sub-area, and determines the average distance between stores within the sub-area; acquires second location data of the user, and determines a target sub-area corresponding to the second location data; and determines the target store based on the average distance between stores corresponding to the target sub-area and a preset adjustment value. This application embodiment, by regionalizing store data, performs rapid location based on the user's location and improves search accuracy by using the average distance between stores and a preset adjustment value to determine the target store. It eliminates the need for a uniform index precision, thereby improving search accuracy, reducing invalid data in search results, and lowering system consumption. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the store search method provided in an embodiment of this application;

[0034] Figure 2 This is a schematic diagram of the process for determining the target store provided in an embodiment of this application;

[0035] Figure 3 This is provided by the embodiments of this application. Figure 1 A flowchart illustrating step S120;

[0036] Figure 4 This is a schematic block diagram of the store search device provided in the embodiments of this application;

[0037] Figure 5 This is a schematic diagram of the store search device provided in the embodiments of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] The current store search uses a uniform precision search method, which results in a long response time for search results due to the large amount of computation, leading to a poor user experience. Furthermore, most of the search results are invalid, increasing system consumption, consuming system resources, and affecting system stability.

[0040] Taking a user's location in a certain district of a certain city as an example to search for nearby stores, the system backend performs a store search with a radius of 500KM: a single search retrieves nearly 2000 nearby stores, taking almost 2 seconds. The search results are then sorted by distance, with only the 10 closest stores being retrieved, taking 0.5 seconds. The entire search process is largely wasteful; 99.5% of the 2000 stores retrieved in the first search are invalid data, consuming significant system resources. Furthermore, the sorting by store distance adds to the sorting time, resulting in low efficiency.

[0041] To address this, this application employs a grid-based search approach, dividing store data into grids for rapid location tracking and progressively improving search accuracy. This identifies the best-matching stores, comprehensively and effectively resolving the store search problem. While reducing system overhead and improving search accuracy, it also supports multi-dimensional store searches based on customer preferences.

[0042] The technical solution provided in this application will be described in detail below.

[0043] Figure 1 This is a flowchart illustrating the store search method provided in an embodiment of this application, such as... Figure 1 As shown in the figure, this application embodiment provides a store search method, including:

[0044] S110, divide the preset area into at least one sub-area; the preset area is the whole country or a certain province.

[0045] It can divide the country's latitude and longitude into grids and mark them according to province / city, prefecture-level city, urban area, and county. Specifically, it can mark the boundaries of provinces, prefecture-level cities, and counties nationwide based on GIS (Geographic Information System) maps: for provincial boundaries, it is only necessary to distinguish between in-province and out-of-province boundaries (99% of business users are in-province users); for city boundaries, urban areas, and county boundaries within a province, the boundaries are marked according to latitude and longitude.

[0046] If the preset area is the whole country, then the first-level sub-regions are provinces, the second-level sub-regions are cities, and the third-level sub-regions are urban areas and districts / counties.

[0047] If the preset area is within the province, then the first-level sub-region is the city, and the second-level sub-region is the urban area and the district / county.

[0048] S120, Obtain the first location data of all stores in each sub-region, and determine the average distance between stores in the sub-region;

[0049] The first location data is latitude and longitude data. Based on the latitude and longitude data of all stores, the average distance between stores in a certain sub-region can be determined.

[0050] S130, acquire the user's second location data, and determine the target sub-region corresponding to the second location data;

[0051] The second location data is latitude and longitude data. Based on the second location data, by comparing the latitude and longitude of the boundaries of provinces / outside cities, prefecture-level cities, urban areas, and districts / counties across the country, the attribute tags of the user's location (province / outside, prefecture-level cities, urban areas, districts / counties) can be quickly obtained.

[0052] S140, determine the target store based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value.

[0053] In the initial state, the adjustment values ​​of all sub-regions can be set to zero. After searching for stores for users, the adjustment values ​​of each sub-region can be different.

[0054] It is understood that the embodiments of this application divide stores into regional data, quickly locate users in their respective regions, and improve search accuracy based on the average distance between stores and preset adjustment values ​​to determine the target store. This eliminates the need for uniform index accuracy, thereby improving search accuracy, reducing invalid data in search results, and lowering system consumption.

[0055] Based on the above embodiments, as an optional embodiment, determining the target store according to the average distance of the stores corresponding to the target sub-region and a preset adjustment value includes:

[0056] The sum of the average distance of the stores corresponding to the target sub-region and the preset adjustment value is used as the index precision, and the target store is determined based on the index precision and the preset store code.

[0057] Specifically, index precision = average store distance corresponding to the sub-region + adjustment value;

[0058] Store coding refers to encoding the latitude and longitude of all stores within a preset area using Geohash encoding. Geohash uses a string to represent both latitude and longitude coordinates. One Geohash string value represents a rectangular area; for example, the code `gcpuuz94kkp5` represents a rectangular area. During database searches, a prefix search is performed on the Geohash string value, allowing for more precise range searches. For instance, 'gcpuuz9%' searches a larger range than 'gcpuuz94%'.

[0059] Geohash encoding can be seen in the table below.

[0060]

[0061]

[0062] The figure above shows the definition of 12 levels of Geohash encoding values. The distance accuracy of the service involved in this application embodiment can be within a range of hundreds of meters.

[0063] It is understood that the embodiments of this application improve search accuracy by using the average distance between stores and the adjustment value as the index precision, instead of using a uniform index precision, thereby improving search accuracy, reducing invalid data in search results, and reducing system consumption.

[0064] Figure 2 This is a schematic diagram of the process for determining the target store provided in an embodiment of this application, such as... Figure 2 As shown, based on the above embodiments, as an optional embodiment, determining the target store based on the index precision and the preset store code includes:

[0065] S210, Search for stores based on the index precision and the preset store code;

[0066] Specifically, determining the target store based on the index precision and the preset store code includes:

[0067] The user's second location data is converted into a Geohash string. Based on the index precision, the prefix length of the Geohash string value is determined. Based on the determined prefix length, the characters corresponding to the prefix length are extracted from the Geohash string converted from the second location data as search terms. Then, the target store is determined from the Geohash string database of all stores nationwide based on the search terms.

[0068] S220, if the searched stores do not meet the preset search conditions, the index precision is increased until a store that meets the preset search conditions is found, and the retrieved store is taken as the target store.

[0069] The preset search criteria can be set to find the ten closest stores to the user, or to find the M closest stores to the user, where M is a user-defined integer value.

[0070] If the initial search for stores does not yield any matching results (e.g., the product requires finding the 10 most recent stores, but the number of matching stores is less than 10), a second search will be performed, with the index precision adjusted to twice the precision of the first search. If the results still do not meet the criteria, the index precision will be increased for a third search until a matching result is found.

[0071] Index precision [N] =Index precision [N-1] *2;

[0072] Where N represents the Nth retrieval.

[0073] It is understandable that the embodiments of this application cannot meet the requirements in the first search, so a second search is performed by increasing the index precision to improve user satisfaction.

[0074] Based on the above embodiments, as an optional embodiment, the adjustment value is determined based on the average number of searches in each sub-region and the average distance to the store.

[0075] Specifically, the adjustment value = 2 [平均搜索次数-1] Average store distance;

[0076] The average number of searches refers to the average number of searches required to perform store searches for multiple users within a preset time period.

[0077] It is understood that the embodiments of this application, by setting corresponding adjustment values ​​for each sub-region, better adapt to the differences in each region, reduce system consumption, and ensure user experience.

[0078] Based on the above embodiments, as an optional embodiment, the store search method provided in this application further includes:

[0079] If the initial retrieval success rate of any of the sub-regions is lower than a preset value, then the adjustment value is increased.

[0080] This application continuously tracks the first-hit rate and updates the index precision of the first search and the adjustment values ​​of the average distance between stores in the interval, thereby increasing the first-hit rate. The first-hit rate refers to the probability of finding a target store that meets the preset search conditions on the first search.

[0081] This application embodiment continuously tracks the hit rate of nearby store searches in each region. When the initial hit rate of a region is lower than 90%, the index precision adjustment value of that region is increased in a timely manner, the initial hit rate of that region is reset to zero, and the initial hit rate tracking continues.

[0082] Through continuous tracking of first-hit rate and optimization of adjustment values, the first-hit rate reached 100% after two adjustments, with an average of 10-30 nearby stores per search. Compared to the previous average of 2000 search results, search efficiency improved by 98.5%, and the overall search time was reduced to less than 1 second, greatly improving the user experience.

[0083] Figure 3 This is provided by the embodiments of this application. Figure 1 A flowchart of step S120 is shown below. Figure 3 As shown, based on the above embodiments, as an optional embodiment, obtaining the first location data of all stores in each sub-region and determining the average distance between stores in the sub-region includes:

[0084] S310, Obtain the first location data of all stores in each sub-region, and determine the distance between each store and its adjacent stores based on the first location data;

[0085] S320, determine the average distance between stores in the sub-region based on the distance between each store and its adjacent stores.

[0086] Specifically, this application calculates the average distribution distance of stores in urban areas and counties of each city within the province and marks the average distance of stores in intervals.

[0087] This application uses big data calculations to obtain the distance between stores and adjacent stores in the urban areas and districts of each city within the province, and obtains the average distance between stores in each urban area and district of each city (with a minimum distance of 100 meters) as a basic data cache.

[0088] Based on the above embodiments, as an optional embodiment, determining the target sub-region corresponding to the second location data includes:

[0089] The target sub-region is determined by comparing the second location data with the boundary location data of the sub-region.

[0090] Specifically, based on the latitude and longitude used for the current location, the system compares the boundary maps of provinces / outside the city, prefecture-level cities, urban areas, and districts / counties across the country to quickly obtain the attribute tags of the user's location (province / outside the city, prefecture-level city, urban area, district / county).

[0091] The following is an example illustrating the store search method provided in the embodiments of this application.

[0092] S1 uses Geohash encoding to determine the latitude and longitude of all stores within the province.

[0093] Geohash uses a string to represent both latitude and longitude coordinates. A Geohash string value represents a rectangular area; for example, the code `gcpuuz94kkp5` represents a rectangular area. When searching a database, a prefix search is performed on the Geohash string value, allowing for more precise range searches. For instance, `gcpuuz9%` searches a larger range than `gcpuuz94%`.

[0094] S2 divides the latitude and longitude of the whole country into a grid and marks them according to province / outside, city, urban area and county.

[0095] Based on GIS map capabilities, the boundaries of provinces, cities, districts and counties across the country are marked: for provincial boundaries, it is only necessary to distinguish between those within and outside the province (99% of business targets are users within the province); for city boundaries, urban areas, and district and county boundaries within the province, the boundaries are marked according to latitude and longitude.

[0096] S3 calculates the average distribution distance of stores in urban areas and districts of each city within the province and marks the average distance of stores in the interval.

[0097] Based on big data, the distance between stores and adjacent stores in urban areas and districts of each city in the province is calculated and obtained, and the average distance between stores in each urban area and district of each city is obtained (with a minimum distance of 100 meters), which serves as a basic data cache.

[0098] S4 identifies the assigned interval based on the user's current location latitude and longitude.

[0099] Based on the latitude and longitude used for the current location, compare the boundary maps of provinces / outside the city, cities, urban areas, and counties across the country to quickly obtain the attribute tags of the user's location (province / outside the city, city, urban area, county).

[0100] S5 uses the regional index precision (average distance between stores in the region + adjustment value) as the index precision, performs retrieval by Geohash, and sorts by distance.

[0101] When searching for stores near a user, the index precision is obtained based on the latitude and longitude attribute tags of the user's location (within / outside the province, city, urban area, district / county) for retrieval.

[0102] Index precision = Average store distance in the region + Adjustment value;

[0103] The adjustment value for each region (city / district) is set to 0. Store searches are indexed using the average store distance in the region as the standard index precision, and the search efficiency for each index is recorded.

[0104] S6: If no stores meet the criteria in the first search, increase the index precision for a second search.

[0105] If the initial search for stores yields no matching results (e.g., the product requires finding the 10 most recent stores, but the number of matching stores is less than 10), a second search is performed, with the index precision adjusted to twice the precision of the first search. If the results still do not meet the criteria, the index precision is increased for a third search, until a matching result is found. The system synchronously records the hit rate of each search.

[0106] Index precision [N] =Index precision [N-1] *2.

[0107] S7: The system continuously tracks the first-hit rate and constantly updates the index precision of the first retrieval and the adjustment value of the average distance between stores in the interval, thereby increasing the first-hit rate.

[0108] Continuously track the hit rate of nearby store searches in each region. When the initial hit rate in a region falls below 90%, increase the index precision adjustment value for that region, reset the initial hit rate to zero, and continue tracking the initial hit rate.

[0109] Adjustment value = 2 [平均命中次数-1] *Average store distance in the region.

[0110] This application's embodiments employ a grid-based search approach, dividing store data into grids for rapid regional location and progressively improving search accuracy to pinpoint the best-matching stores, thus comprehensively and effectively solving the store search problem. While reducing system overhead and improving store search accuracy, it also supports multi-dimensional store searches based on customer preferences.

[0111] The store search device provided in this application is described below. The store search device described below can be referred to in correspondence with the store search method described above.

[0112] Figure 4This is a schematic diagram of the store search device provided in this application, such as... Figure 4 As shown in the illustration, this application also provides a store search device, including:

[0113] The region division module 410 is used to divide a preset region into at least one sub-region;

[0114] The distance determination module 420 is used to acquire the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region.

[0115] The region determination module 430 is used to acquire the user's second location data and determine the target sub-region corresponding to the second location data;

[0116] The store determination module 440 is used to determine the target store based on the average distance of the stores corresponding to the target sub-region and a preset adjustment value.

[0117] In one embodiment, the store determination module 440 is further configured to:

[0118] The sum of the average distance of the stores corresponding to the target sub-region and the preset adjustment value is used as the index precision, and the target store is determined based on the index precision and the preset store code.

[0119] In one embodiment, the store determination module 440 is further configured to:

[0120] Search for stores based on the index precision and preset store codes;

[0121] If the searched stores do not meet the preset search criteria, the index precision is increased until a store that meets the preset search criteria is found, and the found store is taken as the target store.

[0122] In one embodiment, the adjustment value is determined based on the average number of searches in each sub-region and the average distance to the store.

[0123] In one embodiment, it also includes:

[0124] An adjustment module is used to increase the adjustment value if the initial retrieval success rate of any of the sub-regions is lower than a preset value.

[0125] In one embodiment, the distance determination module 420 is further configured to:

[0126] Obtain the first location data of all stores in each sub-region, and determine the distance between each store and its adjacent stores based on the first location data;

[0127] The average distance between stores within the sub-region is determined based on the distance between each store and its adjacent stores.

[0128] In one embodiment, the region determination module 430 is further configured to:

[0129] The target sub-region is determined by comparing the second location data with the boundary location data of the sub-region.

[0130] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a store search method, which includes:

[0131] Divide the preset area into at least one sub-area;

[0132] Obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region;

[0133] Obtain the user's second location data and determine the target sub-region corresponding to the second location data;

[0134] The target store is determined based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value.

[0135] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the store search method provided by the above methods, the method including:

[0137] Divide the preset area into at least one sub-area;

[0138] Obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region;

[0139] Obtain the user's second location data and determine the target sub-region corresponding to the second location data;

[0140] The target store is determined based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value.

[0141] Furthermore, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the store search methods provided by the methods described above, the method comprising:

[0142] Divide the preset area into at least one sub-area;

[0143] Obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region;

[0144] Obtain the user's second location data and determine the target sub-region corresponding to the second location data;

[0145] The target store is determined based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A store search method, characterized in that, include: Divide the preset area into at least one sub-area; Obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region; Obtain the user's second location data and determine the target sub-region corresponding to the second location data; The target stores are determined based on the average distance of the stores corresponding to the target sub-region and the preset adjustment value; The adjustment value is determined based on the average number of searches in each sub-region and the average distance between stores. If the first search success rate of any sub-region is lower than the preset value, the adjustment value is increased. The first hit rate is continuously tracked, and the adjustment values ​​of the index precision of the first search and the average distance between stores in the interval are updated to increase the first hit rate. The first hit rate refers to the probability that the target store that meets the preset search conditions can be retrieved on the first search.

2. The store search method according to claim 1, characterized in that, The step of determining the target store based on the average distance of the stores corresponding to the target sub-region and a preset adjustment value includes: The sum of the average distance of the stores corresponding to the target sub-region and the preset adjustment value is used as the index precision, and the target store is determined based on the index precision and the preset store code.

3. The store search method according to claim 2, characterized in that, The process of determining the target store based on the index precision and the preset store code includes: Search for stores based on the index precision and preset store codes; If the searched stores do not meet the preset search criteria, the index precision is increased until a store that meets the preset search criteria is found, and the found store is taken as the target store.

4. The store search method according to claim 1, characterized in that, The step of obtaining the first location data of all stores in each sub-region and determining the average distance between stores in the sub-region includes: Obtain the first location data of all stores in each sub-region, and determine the distance between each store and its adjacent stores based on the first location data; The average distance between stores in the sub-region is determined based on the distance between each store and its adjacent stores.

5. The store search method according to claim 1, characterized in that, Determining the target sub-region corresponding to the second location data includes: The target sub-region is determined by comparing the second location data with the boundary location data of the sub-region.

6. A store search device, characterized in that, include: The region division module is used to divide a preset region into at least one sub-region. The distance determination module is used to obtain the first location data of all stores in each sub-region and determine the average distance between stores in the sub-region. The region determination module is used to acquire the user's second location data and determine the target sub-region corresponding to the second location data; The store determination module is used to determine the target store based on the average distance of the stores corresponding to the target sub-region and a preset adjustment value; The adjustment value is determined based on the average number of searches in each sub-region and the average distance between stores. If the first search success rate of any sub-region is lower than the preset value, the adjustment value is increased. The first hit rate is continuously tracked, and the adjustment values ​​of the index precision of the first search and the average distance between stores in the interval are updated to increase the first hit rate. The first hit rate refers to the probability that the target store that meets the preset search conditions can be retrieved on the first search.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the store search method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the store search method as described in any one of claims 1 to 5.

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