Information push method, device, storage medium and electronic device

Abstract

This specification discloses an information push method, device, storage medium and electronic device. The embodiment of this specification obtains the target feature information collected when the user terminal scans the identification of the beacon device, and then, according to the positioning feature information and target feature information corresponding to each pre-divided area, determines the area matching the target feature information from multiple areas as the target area, and finally pushes the recommended information associated with the target area to the user terminal. In this method, it is not necessary to determine the location information of the beacon device according to the identification of the beacon device scanned by the user terminal, but to determine the target area of ​​the beacon device, that is, the location information, according to the WiFi information and / or longitude and latitude information collected in the positioning feature information. In this way, the problem of being unable to effectively push information to the user due to the inability to determine the correct location of the beacon device can be avoided.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an information push method, device, storage medium and electronic device. Background Art

[0002] In the information push scenario, the information related to the merchant can be pushed to the user based on the identification of the Bluetooth beacon device installed in each merchant store. The merchant-related information may include: merchant page information, merchant coupons, etc.

[0003] In the prior art, merchants can broadcast their own identification to the user end in the signal coverage area of ​​the beacon device through the Bluetooth beacon device installed in the merchant store. The user end parses the received identification and pushes information related to the merchant to the user based on the parsing result. Among them, the identification of the beacon device can be parsed to obtain the location information of the beacon device, and the location information of the beacon device is associated with the merchant.

[0004] However, in the prior art, the identifier broadcast by the beacon device is changeable after encryption. If the decryption method cannot be obtained, the correct location of the beacon device owner cannot be determined, resulting in the inability to recommend effective information to users. Summary of the invention

[0005] The embodiments of this specification provide an information push method, device, storage medium and electronic device to partially solve the problems existing in the above-mentioned prior art.

[0006] The embodiments of this specification adopt the following technical solutions:

[0007] This manual provides an information push method, including:

[0008] In response to the user terminal scanning the identifier of the beacon device, obtaining positioning feature information collected when the user terminal scans the identifier as target feature information, the target feature information including: at least one of WiFi information and longitude and latitude information collected when the user terminal scans the identifier;

[0009] According to the positioning feature information corresponding to each pre-divided area and the target feature information, determine an area matching the target feature information as the target area corresponding to the beacon device;

[0010] Determine recommendation information associated with the target area, and push the recommendation information to the user terminal.

[0011] Optionally, each area is pre-divided into:

[0012] Obtain the historical positioning feature information collected each time each user terminal scanned the beacon device's identification;

[0013] Clustering the historical positioning feature information to obtain at least one cluster;

[0014] Based on the at least one cluster, each region is divided, and according to the cluster center of each cluster, the positioning feature information corresponding to each region is determined.

[0015] Optionally, clustering the historical positioning feature information to obtain at least one cluster includes:

[0016] Get the historical identifiers of beacon devices scanned by each user terminal in the past;

[0017] For each historical identifier, the historical positioning feature information collected when the historical identifier is scanned is clustered to obtain a cluster for the historical identifier.

[0018] Optionally, clustering the historical positioning feature information to obtain at least one cluster includes:

[0019] For any two pieces of historical positioning feature information, determine a first similarity between the two pieces of historical positioning feature information according to the distance between the longitude and latitude information in the two pieces of historical positioning feature information, and determine a second similarity between the two pieces of historical positioning feature information according to the WiFi signal strength contained in the WiFi information in the two pieces of historical positioning feature information;

[0020] Determining a comprehensive similarity between the two pieces of historical positioning feature information according to the first similarity and the second similarity;

[0021] According to the comprehensive similarity between any two pieces of historical positioning feature information, the historical positioning feature information is clustered to obtain at least one cluster.

[0022] Optionally, the method further comprises:

[0023] Obtain the positioning feature information collected when each user terminal scans the beacon device's identification in the current update cycle as the current positioning feature information;

[0024] Clustering the current positioning feature information to obtain at least one current clustering cluster;

[0025] For each current cluster, determine the matching degree between each pre-divided area and the current cluster;

[0026] According to the matching degree between the current cluster and each pre-divided area, a region matching the current cluster is determined from each area as the area to be updated, and the positioning feature information corresponding to the area to be updated is updated through the current cluster.

[0027] Optionally, the method further comprises:

[0028] If, based on the degree of match between the current cluster and each pre-divided area, it is determined that there is no area matching the current cluster in each pre-divided area, based on the current cluster, a region corresponding to the current cluster is divided as a new area, and based on the current cluster, the positioning feature information corresponding to the new area is determined.

[0029] Optionally, for each current cluster, determining the matching degree between each pre-divided region and the current cluster specifically includes:

[0030] Determine, according to each current positioning feature information contained in the current cluster, a current matching feature corresponding to the current cluster, wherein the current matching feature includes: at least one of WiFi average signal strength, WiFi signal variance, WiFi occurrence frequency, and an estimated location of the current cluster;

[0031] The matching degree between each pre-divided area and the current cluster is determined according to the current matching feature and the historical matching feature corresponding to each pre-divided area.

[0032] This manual provides an information push device, including:

[0033] An acquisition module, in response to a user terminal scanning an identifier of a beacon device, acquires positioning feature information collected when the user terminal scans the identifier as target feature information, wherein the target feature information includes at least one of WiFi information and longitude and latitude information collected when the user terminal scans the identifier;

[0034] A determination module, configured to determine, based on the positioning feature information corresponding to each pre-divided area and the target feature information, an area matching the target feature information as the target area corresponding to the beacon device;

[0035] The information push module is used to determine the recommended information associated with the target area and push the recommended information to the user terminal.

[0036] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned information push method is implemented.

[0037] The present specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned information push method when executing the program.

[0038] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0039] In the embodiment of this specification, the positioning feature information collected when the user terminal scans the identification of the beacon device is obtained as the target feature information, and then, based on the positioning feature information corresponding to each pre-divided area and the target feature information, the area matching the target feature information is determined from multiple areas as the target area, and finally, the recommended information associated with the target area is pushed to the user terminal. In this method, it is not necessary to determine the location information of the beacon device based on the identification of the beacon device scanned by the user terminal, but the target area of ​​the beacon device, that is, the location information, is determined based on the WiFi information and / or longitude and latitude information collected in the positioning feature information. In this way, the problem of being unable to effectively push information to the user due to the inability to determine the correct location of the beacon device can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0041] Figure 1 A schematic diagram of the information push process provided in the embodiments of this specification;

[0042] Figure 2 A schematic diagram of the regional division provided in the embodiments of this specification;

[0043] Figure 3 A schematic diagram of updating regional positioning feature information provided in an embodiment of this specification;

[0044] Figure 4 A schematic diagram of the structure of an information push device provided in an embodiment of this specification;

[0045] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0046] In the prior art, in the scenario of pushing information to the user end, beacon devices belonging to the designated shopping platform can be installed in each merchant store that is stationed on the designated shopping platform, or the location information of beacon devices installed in other merchant stores can be obtained through authorization. In this way, when the user end is in the signal coverage area of ​​the beacon device, the location information of the beacon device can be determined by parsing the identifier broadcast by the beacon device, and information related to the merchant can be pushed to the user based on the merchant related to the location information. Among them, other merchant stores refer to merchants that are not stationed on the designated shopping platform.

[0047] However, in the prior art, whether it is installing beacon devices in each merchant's store or obtaining the location information of beacon devices installed in other merchant's stores through authorization, it requires a lot of costs. If the designated shopping platform does not install beacon devices or cannot obtain authorization for beacon devices installed in other merchant's stores, since the identifiers broadcast by beacon devices installed in other merchant's stores are encrypted, the decryption method cannot be obtained if authorization cannot be obtained. Therefore, the designated shopping platform cannot correctly parse the location information of beacon devices installed in other merchant's stores, resulting in the designated shopping platform being unable to push information to users.

[0048] The information push method provided in this specification is intended to determine the location information of the beacon device without relying on the identifier of the beacon device, but to determine the target area of ​​the scanned beacon device, that is, the location information, based on the WiFi information scanned by the user end and the user's latitude and longitude information, and then push recommended information to the user based on the recommended information associated with the target area of ​​the beacon device. The recommended information may be merchant page information, merchant coupons, etc.

[0049] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0050] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0051] Figure 1 The schematic diagram of the information push process provided in the embodiment of this specification includes:

[0052] S100: In response to a user terminal scanning an identifier of a beacon device, obtaining positioning feature information collected when the user terminal scans the identifier as target feature information, wherein the positioning feature information includes at least one of WiFi information and longitude and latitude information collected when the user terminal scans the identifier.

[0053] In the embodiments of the present specification, when the user terminal turns on the WiFi permission, Bluetooth permission and positioning permission, the user terminal can scan the WiFi information, the identification of the beacon device and determine its own longitude and latitude information in real time. At the same time, the shopping platform can also obtain the identification of the beacon device scanned by the user terminal and the positioning feature information, wherein the positioning feature information can be at least one of the WiFi information collected when the user terminal scans the identification of the beacon device and the longitude and latitude information of the user terminal. Among them, the longitude and latitude information can be obtained through the GPS of the user terminal, and can also be obtained through network positioning. WiFi information may include: a WiFi list, the WiFi signal strength of each WiFi in the WiFi list, and the WiFi identification, and the WiFi identification is used to identify a unique WiFi, etc. The shopping platform can be a food delivery platform, an e-commerce platform, etc.

[0054] It should be noted that the ID of the beacon device scanned by the user end is obtained after being processed by the encryption algorithm, so the shopping platform cannot parse the location information corresponding to the ID of the beacon device that is not on the shopping platform scanned by the user end. In addition, for the same beacon device, due to the encryption algorithm processing, the ID broadcast by the beacon device is different every day.

[0055] However, this manual uses Figure 1 The information push method shown does not need to parse the identifier of the beacon device, but determines the location of the beacon device according to the WiFi information and / or longitude and latitude information of the user end.

[0056] In the embodiments of the present specification, for the identifier of each beacon device scanned by each user terminal, when the user terminal scans the identifier of the beacon device, that is, the user terminal is within the signal coverage range broadcast by the beacon device, the shopping platform obtains the WiFi information and / or longitude and latitude information collected when the user terminal scans the identifier of the beacon device.

[0057] S102: According to the positioning feature information corresponding to each pre-divided area and the target feature information, determine an area matching the target feature information as the target area corresponding to the beacon device.

[0058] In an embodiment of the present specification, each historical positioning feature information collected each time each user terminal scanned the identification of a beacon device in history can be pre-acquired, and then, each collected historical positioning feature information can be clustered to obtain at least one cluster cluster. Among them, a cluster cluster can represent a collection of historical positioning feature information collected when the identification of the same beacon device is scanned. According to at least one cluster cluster, at least one area is divided, and according to the cluster center of each cluster cluster, the positioning feature information corresponding to each area is determined. Among them, the longitude and latitude information, WiFi information, etc. of the cluster cluster can be determined according to the cluster center. In other words, each pre-divided area can represent the longitude and latitude information (i.e., location information) of a beacon device, and can also represent the area where the user terminal is located when the user terminal scans a beacon device, that is, the signal coverage area broadcast by this beacon device.

[0059] In addition, the ground may be divided into several regions in advance, and then the collected historical positioning feature information may be clustered to obtain at least one cluster. According to the at least one cluster, a region corresponding to the at least one cluster is selected from the several regions. Finally, according to the cluster center of each cluster, the positioning feature information corresponding to each selected region is determined.

[0060] Among them, the divided area can be obtained by the latitude and longitude area of ​​geohash, such as Figure 2 As shown. Figure 2 In the example, 9 regions are identified as 1 to 9. Each region may correspond to a positioning feature information.

[0061] In the embodiment of the present specification, after determining the positioning feature information corresponding to each pre-divided area, the area matching the target feature information can be determined as the target area corresponding to the beacon device according to the positioning feature information corresponding to each area and the target feature information collected when the user terminal scans the identification of the beacon device. That is, the location where the user terminal scans the beacon device is determined.

[0062] When determining the target area that matches the target feature information, the WiFi information and / or longitude and latitude information in the target feature information can be matched with the WiFi information and / or longitude and latitude information in the positioning feature information corresponding to each area to determine the area that matches the target feature information as the target area.

[0063] Specifically, for each area, the first correlation between the target feature information and the positioning feature information corresponding to the area can be determined based on the WiFi signal strength contained in the WiFi information in the target feature information and the average WiFi signal strength corresponding to the area. The second correlation between the target feature information and the positioning feature information corresponding to the area is determined based on the longitude and latitude information in the target feature information and the longitude and latitude information corresponding to the area. Then, the comprehensive correlation between the target feature information and the positioning feature information corresponding to the area can be obtained based on the first correlation and the second correlation. Based on the comprehensive correlation between the positioning feature information corresponding to each area and the target feature information, the area matching the target feature information is determined as the target area. The area with the largest comprehensive correlation can be selected as the target area.

[0064] In addition, the region with the largest first degree of association or the largest second degree of association may also be determined based only on the first degree of association or the second degree of association as the region matching the target feature information, that is, the target region.

[0065] S104: Determine recommendation information associated with the target area, and push the recommendation information to the user terminal.

[0066] In the embodiment of the present specification, after determining the target area matching the target feature information, the recommended information can be pushed to the user terminal according to the recommended information associated with the target area. The recommended information can include: business information.

[0067] Before pushing the recommendation information to the user terminal, for each pre-divided area, an association relationship can be established between the area and at least one merchant, and the merchant information corresponding to each merchant can be determined. The merchant information may include: merchant identification, merchant page information, merchant coupons, etc. The merchant identification is used to identify a unique merchant.

[0068] When the recommendation information is pushed to the user terminal, at least one merchant associated with the target area may be obtained, and then the merchant information corresponding to the at least one merchant is pushed to the user terminal.

[0069] Specifically, a merchant that meets at least one preset condition can be selected from multiple merchants as the target merchant. And according to the merchant information corresponding to the target merchant, the recommendation information is pushed to the user terminal. Among them, the preset conditions may include: the user terminal is closest to the target merchant, the WiFi information collected when the user terminal scans the identifier of the beacon device matches the WiFi information collected in the target merchant, the number of times the user terminal has consumed in the target merchant in history is greater than the number threshold, the signal strength of the beacon device collected when the user terminal scans the identifier of the beacon device is the same as the signal strength of the beacon device collected in the target merchant, the position of the identifier of the beacon device in the identifier list of all scanned beacon devices when the user terminal scans the identifier of the beacon device is the same as the position of the identifier of the beacon device collected in the target merchant in the identifier list of all scanned beacon devices, etc. The WiFi information collected when the user terminal scans the identifier of the beacon device matches the WiFi information collected in the target merchant, which may mean that: the WiFi identifier in the WiFi information collected when the user terminal scans the identifier of the beacon device is the same as the WiFi identifier collected in the target merchant, the WiFi signal strength in the WiFi information collected when the user terminal scans the identifier of the beacon device is closest to the signal strength of the same WiFi collected in the target merchant, and the position of the WiFi in the WiFi information collected when the user terminal scans the identifier of the beacon device among all WiFi is the same as the position of the WiFi collected in the target merchant among all WiFi.

[0070] Furthermore, when selecting a target merchant from a plurality of merchants associated with a target area, the target merchant may be selected from the plurality of merchants based on a machine learning model.

[0071] Specifically, the fingerprint features of the beacon devices corresponding to the target area scanned by each user terminal at each merchant associated with the target area in history can be obtained as training samples. The fingerprint features collected when each merchant scans the beacon device corresponding to the target area can include: the WiFi information collected when the merchant scans the beacon device corresponding to the target area, the longitude and latitude information, the signal strength of the beacon device corresponding to the target area scanned by the merchant, the position of the identification of the beacon device corresponding to the target area scanned by the merchant in the identification list of all scanned beacon devices, and the number of times the merchant has scanned the beacon device corresponding to the target area in history. The WiFi information collected when the merchant scans the beacon device corresponding to the target area can include: the average WiFi signal strength of each WiFi scanned by each user terminal at the merchant in history, the WiFi signal variance, and the frequency of each WiFi appearing in the list of all WiFi scanned by each user terminal at the merchant.

[0072] Then, the training samples are input into the merchant classification model, and the fingerprint features of the beacon device corresponding to the target area scanned by each user terminal when it is in each merchant and the real fingerprint features marked by each merchant are classified to obtain the classification results. The merchant classification model is trained with the goal of minimizing the difference between the classification results and the marked real classification results.

[0073] After the merchant classification model is trained, the target fingerprint feature collected by the user terminal in the target area is input into the trained merchant classification model, and the merchant corresponding to the target fingerprint feature is determined as the target merchant through the merchant classification model. The target fingerprint feature may include at least one of: target feature information, the signal strength of the beacon device, and the position of the beacon device's identifier in the identifier list of all scanned beacon devices.

[0074] It should be noted that when selecting a target merchant according to the trained merchant classification model, the target feature information does not involve the WiFi signal variance in the WiFi information and the frequency of WiFi appearing in all WiFi lists.

[0075] In addition, the location information of the user terminal may be determined based on the merchant information associated with the target area.

[0076] Specifically, a merchant that meets at least one preset condition can be selected from multiple merchants associated with the target area as the target merchant. And according to the merchant identifier corresponding to the target merchant, the positioning information of the user terminal is determined. The positioning information can be the information of the merchant identifier where the user terminal is located, and the merchant identifier can be the merchant name. It should be noted that the positioning information in this specification refers to semantic positioning information, not longitude and latitude positioning information. The semantic positioning information can indicate the specific name location where the user is located.

[0077] Through the above Figure 1 As can be seen from the method shown, this manual first obtains the positioning feature information collected when the user terminal scans the identification of the beacon device as the target feature information, and then, based on the positioning feature information and target feature information corresponding to each pre-divided area, determines the area that matches the target feature information from multiple areas as the target area, and finally pushes the recommended information associated with the target area to the user terminal. In this method, it is not necessary to determine the location information of the beacon device based on the identification of the beacon device scanned by the user terminal, but to determine the target area of ​​the beacon device, that is, the location information, based on the WiFi information and / or longitude and latitude information collected in the positioning feature information. In this way, the problem of being unable to effectively push information to the user due to the inability to determine the correct location of the beacon device can be avoided.

[0078] Further, in Figure 1 In step S102, the method of clustering the collected historical positioning feature information to obtain at least one cluster may include: clustering the historical positioning feature information directly according to the similarity between the historical positioning feature information to obtain at least one cluster. The clustering method may include: K-means clustering, density-based clustering DBSCAN, hierarchical clustering, mean shift clustering, maximum expectation clustering of Gaussian mixture model, etc.

[0079] In order to improve clustering accuracy, the historical identifiers of beacon devices scanned by various user terminals in history can also be obtained. Then, for each historical identifier, the historical positioning feature information collected when the historical identifier is scanned can be clustered to obtain a cluster cluster for the historical identifier. Among them, clustering methods can include: K-means clustering, density-based clustering DBSCAN, hierarchical clustering, mean shift clustering, maximum expectation clustering of Gaussian mixture model, etc.

[0080] Specifically, after obtaining each historical identification of the beacon device scanned by each user terminal in the past, the historical positioning feature information with the same historical identification can be classified according to each historical identification to obtain at least one historical positioning feature information set. Then, for each historical positioning feature information set, each historical positioning feature information in the historical positioning feature information set is clustered to obtain a cluster cluster for the historical positioning feature information set, that is, to obtain a cluster cluster for the historical identification.

[0081] Regardless of whether each historical positioning feature information is first classified according to the historical identifier and then clustered, or each historical positioning feature information is directly clustered, clustering can be performed based on the similarity between each historical positioning feature information.

[0082] Specifically, when clustering each historical feature information, for any two historical positioning feature information, the first similarity between the two historical positioning feature information can be determined based on the distance between the longitude and latitude information in the two historical positioning feature information. At the same time, the second similarity between the two historical positioning feature information can be determined based on the signal strength contained in the WiFi information in the two historical positioning feature information. Then, based on the first similarity and the second similarity, the comprehensive similarity between the two historical positioning feature information is determined. Based on the comprehensive similarity between any two historical positioning feature information, each historical positioning feature information is clustered to obtain at least one cluster cluster.

[0083] The first similarity may be the distance between the longitude and latitude information in the two historical positioning feature information. The second similarity may be the cosine similarity of the WiFi signal strength in the two historical positioning feature information. The first similarity and the second similarity are weighted averaged to obtain a comprehensive similarity.

[0084] When determining the second similarity between the two historical positioning feature information according to the WiFi signal strengths in the two historical positioning feature information, the WiFi lists in the WiFi information in the two historical positioning feature information can be determined to be the first WiFi list and the second WiFi list. Then, the number of WiFis in the first WiFi list and the second WiFi list are aligned. Finally, the second similarity between the WiFi signal strengths in the first WiFi list and the second WiFi list is calculated.

[0085] For example, the first WiFi list of historical positioning feature information A includes: The second WiFi list of historical positioning feature information B includes: Align the WiFi in the first WiFi list and the second WiFi list to obtain the alignment of the first WiFi list: (WiFi3, -127); the second WiFi list is aligned to (WiFi1, -127), Among them, -127 indicates the weakest signal strength, and Rssi is the signal strength. In addition, the formula for the second similarity is: The calculation formula for comprehensive similarity is: d represents the first similarity.

[0086] In addition, each historical positioning feature information may be clustered only according to the first similarity or the second similarity to obtain at least one cluster.

[0087] After each cluster is obtained, the positioning feature information corresponding to each region can be determined according to the cluster center of each cluster.

[0088] Specifically, for each cluster, each historical positioning feature information in the cluster and the cluster center of the cluster can be determined. The cluster center can be the historical positioning feature information actually collected, or it can be the geometric center of the cluster. According to the longitude and latitude information of the cluster center of the cluster and the WiFi signal strength in each historical positioning feature information in the cluster, the positioning feature information of the area corresponding to the cluster is determined. The positioning feature information of the area corresponding to the cluster can include: at least one of WiFi information and longitude and latitude information.

[0089] For each cluster, when the cluster center of the cluster is the historical positioning feature information actually collected, the longitude and latitude information in the historical positioning feature information corresponding to the cluster center can be used as the longitude and latitude information in the positioning feature information of the area corresponding to the cluster. At the same time, the WiFi average signal strength of the WiFi signal strength in each historical positioning feature information in the cluster is used as the WiFi information in the positioning feature information of the area corresponding to the cluster.

[0090] In addition, for each cluster, when the cluster center of the cluster is the geometric center of the cluster, the longitude and latitude information of the geometric center of the cluster can be used as the longitude and latitude information in the positioning feature information of the area corresponding to the cluster. At the same time, the average WiFi signal strength of the WiFi signal strength in each historical positioning feature information in the cluster is used as the WiFi information in the positioning feature information of the area corresponding to the cluster.

[0091] In order to improve the accuracy of the positioning feature information corresponding to each area, in the process of determining the positioning feature information corresponding to the area, it is necessary to update the positioning feature information of each area in each update cycle according to a preset update cycle. The specific update process is as follows:

[0092] The positioning feature information collected when each user terminal scans the identification of the beacon device in the current update cycle is obtained as the current positioning feature information. Then, the current positioning feature information is clustered to obtain at least one current cluster.

[0093] For each current cluster, determine the matching degree between each pre-divided region and the current cluster. According to the matching degree between each region and the current cluster, determine whether there is a region matching the current cluster in each pre-divided region.

[0094] For each region, if the matching degree between the region and the current cluster is greater than the matching threshold, it is determined that the region matches the current cluster; if the matching degree between the region and the current cluster is not greater than the matching threshold, it is determined that the region does not match the current cluster.

[0095] If, based on the matching degree between the current cluster and each pre-divided area, an area matching the current cluster is determined from each area, the area matching the current cluster is used as the area to be updated, and the positioning feature information corresponding to the area to be updated is updated through the current cluster.

[0096] If, based on the degree of match between the current cluster and each pre-divided area, it is determined that there is no area matching the current cluster in each pre-divided area, based on the current cluster, the area corresponding to the current cluster is divided as a new area, and based on the current cluster, the positioning feature information corresponding to the new area is determined.

[0097] When there is no area matching the current cluster, the new area can be directly divided according to the longitude and latitude information of the cluster center of the current cluster, and the longitude and latitude information and WiFi information corresponding to the current cluster are used as the positioning feature information of the new area. In addition, when the ground is divided into several areas, the area matching the longitude and latitude information of the current cluster can be selected from the areas that originally had no positioning feature information according to the longitude and latitude information of the cluster center of the current cluster as the new area, and the longitude and latitude information and WiFi information corresponding to the current cluster are used as the positioning feature information of the new area.

[0098] Among them, when determining the matching degree between each pre-divided area and the current cluster cluster, the current matching feature corresponding to the current cluster cluster can be determined based on the current positioning feature information contained in the current cluster cluster, and the matching degree between each pre-divided area and the current cluster cluster can be determined based on the current matching feature and the historical matching feature corresponding to each pre-divided area.

[0099] The current matching feature includes at least one of the following: average WiFi signal strength, WiFi signal variance, the frequency of WiFi appearing in all WiFi lists of the current cluster, and the estimated location of the current cluster. The estimated location of the current cluster may be a location estimated by the cluster center of the current cluster, that is, the longitude and latitude information determined according to the cluster center.

[0100] It should be noted that the frequency of each WiFi appearing in all WiFi lists and the WiFi strength variance are only used in the process of updating the positioning feature information of each area. After the positioning feature information of each area is updated, the frequency of each WiFi appearing in all WiFi lists and the WiFi strength variance are not involved when the positioning feature information of each area is used.

[0101] Based on the above description of updating the positioning feature information of each area in each current update cycle, the embodiment of this specification provides a schematic diagram of updating the positioning feature information of the area, such as Figure 3 shown.

[0102] The above is the information push method provided in the embodiments of this specification. Based on the same idea, this specification also provides corresponding devices, storage media and electronic devices.

[0103] Figure 4 A schematic diagram of the structure of an information push device provided in an embodiment of this specification, the device comprising:

[0104] The acquisition module 401 is used for acquiring, in response to the user terminal scanning the identifier of the beacon device, the positioning feature information collected when the user terminal scans the identifier as the target feature information, wherein the target feature information includes: at least one of the WiFi information and the longitude and latitude information collected when the user terminal scans the identifier;

[0105] A determination module 402 is used to determine, based on the positioning feature information corresponding to each pre-divided area and the target feature information, an area matching the target feature information as a target area corresponding to the beacon device;

[0106] The information push module 403 is used to determine the recommended information associated with the target area and push the recommended information to the user terminal.

[0107] Optionally, the determination module 402 is specifically used to obtain historical positioning feature information collected each time each user terminal scanned the identification of the beacon device in history; cluster the historical positioning feature information to obtain at least one cluster; based on the at least one cluster, divide each area, and determine the positioning feature information corresponding to each area according to the cluster center of each cluster.

[0108] Optionally, the determination module 402 is specifically used to obtain each historical identification of the beacon device scanned by each user terminal in history; for each historical identification, cluster the historical positioning feature information collected when the historical identification is scanned to obtain a cluster for the historical identification.

[0109] Optionally, the determination module 402 is specifically used to determine, for any two pieces of historical positioning feature information, a first similarity between the two pieces of historical positioning feature information based on the distance between the longitude and latitude information in the two pieces of historical positioning feature information; determine a second similarity between the two pieces of historical positioning feature information based on the WiFi signal strength contained in the WiFi information in the two pieces of historical positioning feature information; determine a comprehensive similarity between the two pieces of historical positioning feature information based on the first similarity and the second similarity; and cluster the historical positioning feature information based on the comprehensive similarity between any two pieces of historical positioning feature information to obtain at least one cluster cluster.

[0110] Optionally, the determination module 402 is also used to obtain, during the current update cycle, the positioning feature information collected when each user terminal scans the identification of the beacon device, as the current positioning feature information; cluster the current positioning feature information to obtain at least one current clustering cluster; for each current clustering cluster, determine the degree of match between each pre-divided area and the current clustering cluster; based on the degree of match between the current clustering cluster and each pre-divided area, determine from each area an area that matches the current clustering cluster as an area to be updated, and update the positioning feature information corresponding to the area to be updated through the current clustering cluster.

[0111] Optionally, the determination module 402 is also used to, if it is determined based on the degree of match between the current clustering cluster and each pre-divided area that there is no area in each pre-divided area that matches the current clustering cluster, divide the area corresponding to the current clustering cluster according to the current clustering cluster as a new area, and determine the positioning feature information corresponding to the new area according to the current clustering cluster.

[0112] Optionally, the determination module 402 is specifically used to determine the current matching features corresponding to the current clustering cluster according to the current positioning feature information contained in the current clustering cluster, and the current matching features include: at least one of the average WiFi signal strength, the WiFi signal variance, the frequency of WiFi occurrence, and the estimated location of the current clustering cluster; based on the current matching features and the historical matching features corresponding to each pre-divided area, determine the matching degree between each pre-divided area and the current clustering cluster.

[0113] This specification also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, it can be used to perform the above Figure 1 The information push method provided.

[0114] based on Figure 1 The information push method shown in the specification also provides Figure 5 The structural diagram of the electronic device shown in FIG. Figure 5 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The information push method.

[0115] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0116] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0117] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0118] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0119] For the convenience of description, the above device is described by dividing it into various units according to its functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0120] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0121] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0125] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0126] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0128] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0130] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0131] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. An information push method, characterized in that: include: In response to the user terminal scanning the identifier of the Bluetooth beacon device, obtaining positioning feature information collected when the user terminal scans the identifier as target feature information, the target feature information including: at least one of WiFi information and longitude and latitude information collected when the user terminal scans the identifier; According to the positioning feature information corresponding to each pre-divided area and the target feature information, determine an area matching the target feature information as the target area corresponding to the Bluetooth beacon device; Determining recommendation information associated with the target area, and pushing the recommendation information to the user terminal; the pre-dividing each area specifically includes: Obtain the historical positioning feature information collected each time each user terminal scanned the Bluetooth beacon device identifier; Clustering the historical positioning feature information to obtain at least one cluster; Based on the at least one cluster, each region is divided, and according to the cluster center of each cluster, the positioning feature information corresponding to each region is determined; The clustering of the historical positioning feature information to obtain at least one cluster specifically includes: Get the historical identifiers of the Bluetooth beacon devices scanned by each user terminal in the past; For each historical identifier, the historical positioning feature information collected when the historical identifier is scanned is clustered to obtain a cluster for the historical identifier.

2. The method according to claim 1, characterized in that Clustering the historical positioning feature information to obtain at least one cluster includes: For any two pieces of historical positioning feature information, determine a first similarity between the two pieces of historical positioning feature information according to the distance between the longitude and latitude information in the two pieces of historical positioning feature information, and determine a second similarity between the two pieces of historical positioning feature information according to the WiFi signal strength contained in the WiFi information in the two pieces of historical positioning feature information; Determining a comprehensive similarity between the two pieces of historical positioning feature information according to the first similarity and the second similarity; According to the comprehensive similarity between any two pieces of historical positioning feature information, the historical positioning feature information is clustered to obtain at least one cluster.

3. The method according to claim 1, characterized in that The method further comprises: Obtain the positioning feature information collected when each user terminal scans the identification of the Bluetooth beacon device in the current update cycle as the current positioning feature information; Clustering the current positioning feature information to obtain at least one current clustering cluster; For each current cluster, determine the matching degree between each pre-divided area and the current cluster; According to the matching degree between the current cluster and each pre-divided area, a region matching the current cluster is determined from each area as the area to be updated, and the positioning feature information corresponding to the area to be updated is updated through the current cluster.

4. The method according to claim 3, characterized in that The method further comprises: According to the matching degree between the current cluster and each pre-divided area, if there is no area matching the current cluster in each pre-divided area, then according to the current cluster, divide the area corresponding to the current cluster as a new area, and determine the positioning feature information corresponding to the new area according to the current cluster.

5. The method according to claim 4, characterized in that For each current cluster, the matching degree between each pre-divided region and the current cluster is determined, specifically including: Determine, according to each current positioning feature information contained in the current cluster, a current matching feature corresponding to the current cluster, wherein the current matching feature includes: at least one of WiFi average signal strength, WiFi signal variance, WiFi occurrence frequency, and an estimated location of the current cluster; The matching degree between each pre-divided area and the current cluster is determined according to the current matching feature and the historical matching feature corresponding to each pre-divided area.

6. An information push device, characterized in that: include: An acquisition module, configured to acquire, in response to a user terminal scanning an identifier of a Bluetooth beacon device, positioning feature information collected when the user terminal scans the identifier as target feature information, wherein the target feature information includes at least one of WiFi information and longitude and latitude information collected when the user terminal scans the identifier; A determination module is used to determine, according to the positioning feature information corresponding to each pre-divided area and the target feature information, an area matching the target feature information as the target area corresponding to the Bluetooth beacon device, wherein the pre-dividing of each area specifically includes: Obtain the historical positioning feature information collected each time each user terminal scanned the Bluetooth beacon device identifier; Clustering the historical positioning feature information to obtain at least one cluster; Based on the at least one cluster, each region is divided, and according to the cluster center of each cluster, the positioning feature information corresponding to each region is determined, The clustering of the historical positioning feature information to obtain at least one cluster specifically includes: obtaining historical identifiers of Bluetooth beacon devices scanned by various user terminals in history; For each historical identifier, clustering the historical positioning feature information collected when the historical identifier is scanned to obtain a cluster for the historical identifier; The information push module is used to determine the recommended information associated with the target area and push the recommended information to the user terminal.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. 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, the method described in any one of claims 1 to 5 is implemented.

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