A store recommendation method, server and client

Abstract

The disclosure provides a store recommendation method, a server and a client, the store recommendation method comprising: obtaining a plurality of target position data of a client; wherein the target position data is data representing a position of the target client, and the plurality of target position data comprises target latitude and longitude data, scanned target Beacon data and target sensor data; comparing the target position data with position fingerprints of a plurality of points of interest in a preset position fingerprint database to obtain a point of interest matched with any target position data as a candidate point of interest; determining, based on a preset recommendation model, a point of interest visited by the client as a target point of interest according to the target position data and the position fingerprint of the candidate point of interest; and pushing recommendation information of the target point of interest to the client.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of communication technology, and more particularly, to a store recommendation method, a server and a client. BACKGROUND

[0002] With the development of Internet positioning technology, positioning technology can be applied to various scenarios. In recent years, with the rise of takeout business, review business and search business, positioning users to recommend information of nearby merchants to users has become an indispensable part of these businesses.

[0003] Among them, positioning users to recommend information of nearby merchants to users is called store recommendation business, also known as POI awareness. For example, in the search business, users need to view information of nearby merchants. In this case, accurate positioning of merchants near the location of the user is needed.

[0004] In related technologies, accurate positioning of merchants near the location of the user generally uses GPS positioning technology, ultra-wideband positioning technology and inertial positioning technology to position merchants near the location of the user. However, the signal of GPS positioning is weak and can be blocked and reflected by walls, making it difficult to position indoors. Ultra-wideband positioning requires anchor nodes and bridge nodes to be arranged in known positions in advance, which is costly. Inertial positioning relies on gyroscopes and accelerometers and cannot be used alone, making it unsuitable for use on mobile Internet.

[0005] Therefore, the existing store recommendation business has the problems of high cost and low accuracy of merchant identification. SUMMARY

[0006] An object of the present disclosure is to provide a new technical solution for improving the accuracy of merchant positioning.

[0007] According to a first aspect of the present disclosure, a store recommendation method is provided, applied to a server, the method comprising:

[0008] obtaining a plurality of target position data of a client; wherein the target position data is data representing the position of the target client, and the plurality of target position data includes target latitude and longitude data, scanned target Beacon data and target sensor data;

[0009] comparing the target position data with a plurality of location fingerprints of interest points in a preset location fingerprint database to obtain an interest point matched with any target position data as a candidate interest point;

[0010] determine, based on the target location data and the location fingerprints of the candidate points of interest, a point of interest visited by the client as a target point of interest according to a preset recommendation model;

[0011] push recommendation information of the target point of interest to the client.

[0012] Optionally, the determining, based on the target location data and the location fingerprints of the candidate points of interest, a point of interest visited by the client as a target point of interest according to a preset recommendation model includes:

[0013] obtaining a vector value of a selected feature vector according to the target location data and the location fingerprints of the candidate points of interest;

[0014] determining a matching score between the target location data and the location fingerprint of each candidate point of interest according to the vector value of the selected feature vector based on the recommendation model;

[0015] determining a candidate point of interest corresponding to a location fingerprint whose matching score meets a preset recommendation condition as the target point of interest.

[0016] Optionally, the preset recommendation condition is that a candidate point of interest corresponding to a location fingerprint whose descending order of the matching score meets a preset sorting range is the target point of interest.

[0017] Optionally, the location fingerprint includes a polygon latitude and longitude area and a Beacon identifier list corresponding to a point of interest.

[0018] The comparing the target location data with location fingerprints of a plurality of points of interest in a preset location fingerprint database to obtain a point of interest matching any target location data as a candidate point of interest includes:

[0019] determining a point of interest whose polygon latitude and longitude area contains the target latitude and longitude data as the candidate point of interest;

[0020] determining a point of interest whose Beacon identifier list contains any Beacon identifier in the target Beacon data as the candidate point of interest.

[0021] Optionally, the method further includes:

[0022] obtaining to-store business data generated in a preset historical statistical period;

[0023] determining a corresponding point of interest and to-store true value data according to the to-store business data; the to-store true value data includes latitude and longitude data, scanned Beacon data and sensor data;

[0024] According to the to-visit ground truth data corresponding to the point of interest, a location fingerprint of the point of interest is obtained.

[0025] Optionally, the to-visit ground truth data comprises latitude and longitude data, and the location fingerprint comprises at least one of:

[0026] a cluster center of latitude and longitude data corresponding to the point of interest;

[0027] a minimum polygon latitude and longitude area formed by latitude and longitude data corresponding to the point of interest;

[0028] an area center of the minimum polygon latitude and longitude area corresponding to the point of interest;

[0029] an area of the minimum polygon latitude and longitude area corresponding to the point of interest;

[0030] a mean value of distances from latitude and longitude data corresponding to the point of interest to the cluster center;

[0031] a variance of distances from latitude and longitude data corresponding to the point of interest to the cluster center;

[0032] an area of an overlapping area of the minimum polygon latitude and longitude area corresponding to the point of interest and a minimum polygon latitude and longitude area corresponding to another point of interest;

[0033] an area of an independent area of the minimum polygon latitude and longitude area corresponding to the point of interest which does not overlap with a minimum polygon latitude and longitude area corresponding to another point of interest;

[0034] a number of other points of interest corresponding to a minimum polygon latitude and longitude area which at least partially overlaps with the minimum polygon latitude and longitude area corresponding to the point of interest;

[0035] a mean value of areas of the overlapping areas corresponding to the point of interest;

[0036] a variance of areas of the overlapping areas corresponding to the point of interest.

[0037] Optionally, the to-visit ground truth data comprises scanned Beacon data,

[0038] the location fingerprint comprises at least one of:

[0039] a Beacon list corresponding to the point of interest; the Beacon list comprises corresponding Beacon identifiers and signal strengths;

[0040] a mean value of signal strengths of each Beacon identifier corresponding to the point of interest;

[0041] a variance of signal strengths of each Beacon identifier corresponding to the point of interest.

[0042] Optionally, the store-true data comprises sensor data.

[0043] The location fingerprint comprises at least one of:

[0044] a mean value of air pressure of the point of interest collected by an air pressure sensor;

[0045] a variance of air pressure of the point of interest collected by an air pressure sensor;

[0046] a mean value of magnetic force data of the point of interest collected by a magnetometer;

[0047] a variance of magnetic force data of the point of interest collected by a magnetometer;

[0048] a mean value of floor data of the point of interest obtained according to inertia data collected by an inertia sensor;

[0049] a variance of floor data of the point of interest obtained according to inertia data collected by an inertia sensor.

[0050] According to a second aspect of the present disclosure, a store recommendation method applied to a client is provided, and the method comprises:

[0051] acquiring a plurality of target location data of the client, wherein the plurality of target location data comprises target latitude and longitude data, scanned target Beacon data and target sensor data;

[0052] sending the plurality of target location data to a server, so that the server pushes recommendation information of a target point of interest to the client according to the plurality of target location data and a preset location fingerprint database; wherein the target point of interest is a point of interest visited by the client;

[0053] displaying the recommendation information of the target point of interest.

[0054] According to a third aspect of the present disclosure, a store recommendation device is provided, and the device comprises:

[0055] a first data acquisition module configured to acquire a plurality of target location data of a client; wherein the target location data is data representing a location of the target client, and the plurality of target location data comprises target latitude and longitude data, scanned target Beacon data and target sensor data;

[0056] a data comparison module configured to compare the target location data with location fingerprints of a plurality of points of interest in a preset location fingerprint database, to obtain a point of interest matched with any target location data as a candidate point of interest;

[0057] The interest point determination module is configured to determine, based on a preset recommendation model, an interest point visited by the client as a target interest point according to the target location data and the location fingerprint of the candidate interest point.

[0058] The information pushing module is configured to push recommendation information of the target interest point to the client.

[0059] According to a fourth aspect of the present disclosure, a store recommendation device is provided, comprising:

[0060] The second data acquisition module is configured to acquire a plurality of target location data of itself, wherein the plurality of target location data comprises target latitude and longitude data, scanned target Beacon data and target sensor data.

[0061] The data sending module is configured to send the plurality of target location data to a server, so that the server pushes recommendation information of a target interest point to the client according to the plurality of target location data and a preset location fingerprint database; wherein the target interest point is an interest point visited by the client.

[0062] The information display module is configured to display the recommendation information of the target interest point.

[0063] According to a fifth aspect of the present disclosure, a server is provided, comprising a first processor and a first memory, wherein the first memory is configured to store an executable first computer program; and the first computer program is configured to control the first processor to execute the method according to the first aspect of the present disclosure.

[0064] According to a sixth aspect of the present disclosure, a client is provided, comprising a second processor and a second memory, wherein the second memory is configured to store an executable second computer program; and the second computer program is configured to control the second processor to execute the method according to the second aspect of the present disclosure.

[0065] According to a seventh aspect of the present disclosure, a store recommendation system is provided, comprising the server according to the fifth aspect of the present disclosure and the client according to the sixth aspect of the present disclosure.

[0066] By the present embodiment, the accuracy of positioning the store interest point and the accuracy of pushing the store information to the client can be improved. Moreover, the store interest point can be recognized for the client of the IOS system which cannot acquire WiFi data.

[0067] In one example, when a user enters a certain store, the current store can be pushed to the client, the advertisement information, the commodity information or the push information of the current store can be pushed to the client, so that the user can make a consumption decision, and the user experience can be improved.

[0068] Other features of the present disclosure, which are obvious from the above description of the exemplary embodiments of the present disclosure, and advantages thereof will become apparent to those skilled in the art from the following detailed description of the exemplary embodiments of the present disclosure with reference made to the accompanying drawings. The exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, in which: BRIEF DESCRIPTION OF DRAWINGS

[0069] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0070] Figure 1 is a block diagram showing an example of a hardware configuration of a store recommendation system that can be used to implement embodiments of the present disclosure.

[0071] Figure 2 A flowchart illustrating a store recommendation method of a first embodiment of the present disclosure is shown.

[0072] Figure 3 A schematic diagram of a polygonal latitude-longitude region of a point of interest of one embodiment of the present disclosure is shown.

[0073] Figure 4 A block schematic diagram of a store recommendation apparatus of a first embodiment of the present disclosure is shown.

[0074] Figure 5 A schematic block diagram of a server of a first embodiment of the present disclosure is shown.

[0075] Figure 6 A flowchart illustrating a store recommendation method of a second embodiment of the present disclosure is shown.

[0076] Figure 7 A block schematic diagram of a store recommendation apparatus of a second embodiment of the present disclosure is shown.

[0077] Figure 8 A schematic block diagram of a client of a second embodiment of the present disclosure is shown.

[0078] Figure 9 A schematic block diagram of a store recommendation system of a third embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0079] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in these embodiments, the numerical expressions, and the numerical values are not limiting of the scope of the present disclosure unless otherwise specifically stated.

[0080] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the disclosure and its applications or uses.

[0081] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices can be considered part of the present disclosure.

[0082] In all of the compositions and methods shown and discussed herein, any specific numerical value should be interpreted as merely an example, and not as a limitation. Other examples of the exemplary embodiments can have different values.

[0083] It should be noted that like reference numerals and letters refer to like items throughout the attached drawings, and thus once an item is defined in one drawing, it is not necessary that it be further discussed in further detail in other drawings.

[0084] <Hardware Configuration>

[0085] As shown in Figure 1 , the store recommendation system 100 includes a server 1000, a client 2000, and a network 3000.

[0086] The server 1000 provides a service point of processing, database, communication facilities. The server 1000 can be an integrated server or a distributed server across multiple computers or computer data centers. The server can be various types, such as but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interactive server, a database server, or a proxy server. In some embodiments, each server can include hardware, software, or an embedded logic component for performing appropriate functions supported or implemented by the server, or a combination of two or more such components. For example, the server may, for example, be a blade server, a cloud server, etc., or can be a server group composed of multiple servers, and can also include one or more of the above types of servers, etc.

[0087] In one example, the server 1000 can include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, as shown in Figure 1 . Although the server can also include a speaker, a microphone, etc., these components are reasonably irrelevant to the present disclosure, and thus are omitted here.

[0088] The processor 1100 may be, for example, a central processing unit (CPU) or a microprocessor (MCU). The memory 1200 may include, for example, ROM (Read-Only Memory), RAM (Random Access Memory), or non-volatile memory such as a hard disk. The interface device 1300 may include, for example, a USB interface, a serial interface, or an infrared interface. The communication device 1400 may be capable of wired or wireless communication. The display device 1150 may be, for example, a liquid crystal display (LCD), an LED display, or a touchscreen display. The input device 1160 may include, for example, a touchscreen or a keyboard.

[0089] In this embodiment, the client 2000 is an electronic device with communication and service processing functions. The client 2000 can be a mobile terminal, such as a mobile phone, laptop, tablet computer, PDA, etc.

[0090] like Figure 1 As shown, the client 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, etc. The processor 2100 may be a central processing unit (CPU), a microprocessor (MCU), etc. The memory 2200 may include, for example, ROM (Read-Only Memory), RAM (Random Access Memory), or non-volatile memory such as a hard disk. The interface device 2300 may include, for example, a USB interface, a headphone jack, etc. The communication device 2400 may be capable of wired or wireless communication. The display device 2500 may be, for example, an LCD screen, a touch screen, etc. The input device 2600 may include, for example, a touch screen, a keyboard, etc. The user can input / output voice information through the speaker 2700 and the microphone 2800.

[0091] Network 3000 can be a wireless communication network or a wired communication network, and can be a local area network (LAN) or a wide area network (WAN). Figure 1 In the in-store recommendation system shown, client 2000 and server 1000 can communicate via network 3000.

[0092] It should be understood that, despite Figure 1 Only one server 1000 and client 2000 are shown, but this does not mean that the corresponding number is limited. The in-store recommendation system 100 can contain multiple servers 1000 and multiple clients 2000.

[0093] Figure 1 The in-store recommendation system 100 shown is merely illustrative and is by no means intended to limit this disclosure, its application, or its use.

[0094] The memory 1200 of the server 1000 is configured to store instructions for controlling the processor 1100 to perform the method implemented by the server according to the embodiments of the present disclosure.

[0095] Although the server 1000 is shown to have a plurality of devices in Figure 1 , the present disclosure can only involve part of the devices, for example, the server 1000 only involves the memory 1200 and the processor 1100.

[0096] The memory 2200 of the client 2000 is configured to store instructions for controlling the processor 2100 to perform the method implemented by the client according to the embodiments of the present disclosure.

[0097] Although the client 2000 is shown to have a plurality of devices in Figure 1 , the present disclosure can only involve part of the devices, for example, the client 2000 only involves the memory 2200 and the processor 2100.

[0098] In the above description, the skilled person can design instructions according to the solutions disclosed in the present disclosure. How the instructions control the processor to operate is known in the art, and thus will not be described in detail here.

[0099] <First Embodiment>

[0100] <Method>

[0101] The method for recommending a store provided in the present embodiment is implemented by a server, for example, the server can be specifically the server 1000 as shown in Figure 1 .

[0102] As shown in Figure 2 , the method includes steps S2100-S2400.

[0103] In step S2100, a plurality of target location data of a client is acquired.

[0104] The target location data is data representing a location of a target client, and the plurality of target location data includes target latitude and longitude data, target scanned Beacon data and target sensor data.

[0105] The plurality of target location data in the present embodiment can be all collected by the client and uploaded to the server, or only the target Beacon data and the target sensor data can be collected by the client and uploaded to the server.

[0106] In one example, the target longitude and latitude data can be collected by a positioning device such as a GPS set in the client.

[0107] In another example, the target longitude and latitude data is obtained by another server according to GPS positioning data and / or WiFi data collected by the client and sent to the server performing the method of the embodiment. The WiFi data can include the MAC address of the transmitting device sending the WiFi signal and the signal strength of the received WiFi signal. The other server can pre-store the coordinate position of the transmitting device sending each WiFi signal, which corresponds to the MAC address of the corresponding transmitting device. In this way, the other server can calculate the longitude and latitude data of the client according to the coordinate position corresponding to the MAC address in the WiFi data and the signal strength corresponding to the MAC address in the WiFi data.

[0108] The target Beacon data in the embodiment can include all Beacon identifiers scanned by the client and the signal strength corresponding to each Beacon identifier.

[0109] The sensor data in the embodiment can include at least one of the following: air pressure data collected by an air pressure sensor, magnetic force data collected by a magnetometer, and inertial data collected by an inertial sensor. The inertial sensor can include an acceleration sensor and a gyroscope.

[0110] In one embodiment of the disclosure, the server can obtain multiple target position data of the client at a first set frequency. Specifically, the server can send a first position data acquisition instruction to the client, and the client can upload the latest collected target position data to the server in response to the first position data acquisition instruction. Alternatively, the server can send a second position data acquisition instruction of the client to another server, and the other server can send the latest obtained target position data of the client to the server of the embodiment in response to the second position data acquisition instruction.

[0111] Further, the client can collect its own target Beacon data at a second set frequency, collect its own target sensor data at a third set frequency, and collect its own target longitude and latitude data at a fourth set frequency. The other server can obtain the target longitude and latitude data of the client at a fifth set frequency.

[0112] The first set frequency, the second set frequency, the third set frequency, the fourth set frequency, and the fifth set frequency can be pre-set according to application scenarios or specific requirements, respectively. Any two of the first set frequency, the second set frequency, the third set frequency, the fourth set frequency, and the fifth set frequency can be the same or different, which is not limited herein.

[0113] In another embodiment of the present disclosure, the client can upload the newly collected target location data to the server each time new target location data is collected.

[0114] In still another embodiment of the present disclosure, the client can collect a plurality of target location data of itself when detecting that the user changes from a motion state to a stationary state, and upload the newly collected plurality of target location data to the server.

[0115] Specifically, the client can be provided with an inertial sensor, and the inertial sensor can be used to collect inertial data to detect whether the user is in a motion state or a stationary state.

[0116] In step S2200, the target location data is compared with the location fingerprints of the plurality of interest points in the preset location fingerprint database to obtain an interest point matching any of the target location data as a candidate interest point.

[0117] In this embodiment, when comparing the target location data with the location fingerprints of any of the interest points, if any of the target location data matches any of the location fingerprint features of the interest point, it is determined that the interest point matches the target location data, and the interest point is taken as a candidate interest point.

[0118] In one embodiment of the present disclosure, the location fingerprint can include a polygon latitude and longitude area corresponding to the interest point and a Beacon identification list.

[0119] In this embodiment, the polygon latitude and longitude area represents an area in which the latitude and longitude data of the client located at the corresponding interest point is located. The Beacon identification list represents a list composed of Beacon identifications that the client located at the corresponding interest point can scan.

[0120] On this basis, comparing the target location data with the location fingerprints of the plurality of interest points in the preset location fingerprint database to obtain an interest point matching any of the target location data as a candidate interest point can include:

[0121] Determining an interest point whose polygon latitude and longitude area contains the target latitude and longitude data as a candidate interest point; and determining an interest point whose Beacon identification list contains any of the target Beacon data as a candidate interest point.

[0122] In one embodiment of the present disclosure, the location fingerprints of the points of interest in the location fingerprint database can be pre-constructed. Specifically, the method can further include: obtaining to-store business data generated in a preset historical statistical period; determining corresponding points of interest and to-store ground truth data according to the to-store business data; wherein the to-store ground truth data includes longitude and latitude data, scanned Beacon data, and sensor data; and obtaining the location fingerprint of the point of interest according to the to-store ground truth data corresponding to the point of interest.

[0123] The preset historical statistical period can be set in advance according to application scenarios or specific requirements. For example, the preset historical statistical period can be one month in the past.

[0124] The to-store business data in the present embodiment can be data generated by the client when performing a related business at a visited point of interest, for example, data generated by the client when performing a business such as scanning a code to check a coupon, ordering food, queuing, etc. at a visited point of interest.

[0125] When the client performs a related business once, the client will perform positioning once to obtain a to-store ground truth data.

[0126] If the positioning data of the client within a set period of time before and after the client performs a related business is substantially consistent with the to-store ground truth data, the positioning data of the client within the set period of time before and after the client performs a related business is also taken as to-store ground truth data. The set period of time can be set in advance according to application scenarios or specific requirements, for example, the set period of time can be 3 minutes.

[0127] In the present embodiment, the location fingerprint of a point of interest can be constructed according to a plurality of to-store ground truth data corresponding to the point of interest.

[0128] In an embodiment in which the to-store ground truth data includes longitude and latitude data, the location fingerprint of a point of interest can include at least one of the following:

[0129] a cluster center of the longitude and latitude data corresponding to the point of interest;

[0130] a minimum polygon longitude and latitude area formed by the longitude and latitude data corresponding to the point of interest;

[0131] a region center of the minimum polygon longitude and latitude area corresponding to the point of interest;

[0132] an area of the minimum polygon longitude and latitude area corresponding to the point of interest;

[0133] a mean value of distances from the longitude and latitude data corresponding to the point of interest to the cluster center;

[0134] a variance of distances from the longitude and latitude data corresponding to the point of interest to the cluster center;

[0135] an area of an independent region of the minimum polygonal latitude-longitude region corresponding to the point of interest which does not overlap with the minimum polygonal latitude-longitude region corresponding to other points of interest;

[0136] an area of an independent region of the minimum polygonal latitude-longitude region corresponding to the point of interest which does not overlap with the minimum polygonal latitude-longitude region corresponding to other points of interest;

[0137] a number of other points of interest corresponding to the minimum polygonal latitude-longitude region at least partially overlapping with the minimum polygonal latitude-longitude region corresponding to the point of interest;

[0138] a mean value of the area of the overlapping region corresponding to the point of interest;

[0139] a variance of the area of the overlapping region corresponding to the point of interest.

[0140] In an embodiment where the ground truth data comprises scanned Beacon data, the location fingerprint of a point of interest can comprise at least one of:

[0141] a Beacon list corresponding to the point of interest; wherein the Beacon list comprises corresponding Beacon identifiers and signal strengths;

[0142] a mean value of the signal strength of each Beacon identifier corresponding to the point of interest;

[0143] a variance of the signal strength of each Beacon identifier corresponding to the point of interest.

[0144] In an embodiment where the ground truth data comprises sensor data, the location fingerprint of a point of interest can comprise at least one of:

[0145] a mean value of air pressure data of the point of interest collected by an air pressure sensor;

[0146] a variance of air pressure data of the point of interest collected by an air pressure sensor;

[0147] a mean value of magnetic force data of the point of interest collected by a magnetometer;

[0148] a variance of magnetic force data of the point of interest collected by a magnetometer;

[0149] a mean value of floor data of the point of interest derived from inertial data collected by an inertial sensor;

[0150] a variance of floor data of the point of interest derived from inertial data collected by an inertial sensor.

[0151] Step S2300, determining, based on a preset recommendation model, a point of interest visited by the client as the target point of interest according to the target location data and the location fingerprint of the candidate point of interest.

[0152] The recommendation model in this embodiment can be a pre-trained machine learning model, which can determine the target interest point visited by the client from the candidate interest points according to the target location data and the location fingerprints of the candidate interest points.

[0153] In one example, the recommendation model can be an XGB model. Training samples can be constructed based on the store visit data, each of which includes a vector value of a selected feature vector and a corresponding interest point; and the initial recommendation model is trained according to the training samples to obtain the trained recommendation model.

[0154] In one embodiment of the present disclosure, based on the preset recommendation model, the interest point visited by the client is determined as the target interest point according to the target location data and the location fingerprints of the candidate interest points, which can include the following steps S2310-S2330:

[0155] Step S2310, obtaining a vector value of a selected feature vector according to the target location data and the location fingerprints of the candidate interest points.

[0156] The selected feature vector in this embodiment can include a plurality of selected features affecting the interest point perception result of the target location data.

[0157] For example, the selected feature can include a distance between the target latitude and longitude data and a cluster center of the latitude and longitude data corresponding to the interest point, a distance between the interest point and a region center of a minimum polygon latitude and longitude region corresponding to the interest point, and at least one of the following: a cluster center of the latitude and longitude data corresponding to the interest point; a minimum polygon latitude and longitude region formed by the latitude and longitude data corresponding to the interest point; a region center of the minimum polygon latitude and longitude region corresponding to the interest point; an area of the minimum polygon latitude and longitude region corresponding to the interest point; a mean of distances from the latitude and longitude data corresponding to the interest point to the cluster center; a variance of distances from the latitude and longitude data corresponding to the interest point to the cluster center; an area of an overlapping region of the minimum polygon latitude and longitude region corresponding to the interest point and minimum polygon latitude and longitude regions corresponding to other interest points; an area of an independent region of the minimum polygon latitude and longitude region corresponding to the interest point which does not overlap with the minimum polygon latitude and longitude regions corresponding to other interest points; a number of other interest points corresponding to minimum polygon latitude and longitude regions at least partially overlapping with the minimum polygon latitude and longitude region corresponding to the interest point; a mean of areas of the overlapping regions corresponding to the interest point; a variance of areas of the overlapping regions corresponding to the interest point.

[0158] For another example, the selected features can include the signal strength of each Beacon identifier in the target Beacon data, and can also include the Beacon list corresponding to the point of interest, the mean of the signal strength of each Beacon identifier corresponding to the point of interest, and the variance of the signal strength of each Beacon identifier corresponding to the point of interest.

[0159] For another example, the selected features can include the air pressure value, the magnetic force data, and the floor data in the target sensor data, and can also include the mean of the air pressure data corresponding to the point of interest, the variance of the air pressure data corresponding to the point of interest, the mean of the magnetic force data corresponding to the point of interest, the variance of the magnetic force data corresponding to the point of interest, the mean of the floor data corresponding to the point of interest, and the variance of the floor data corresponding to the point of interest.

[0160] In this embodiment, the vector values of the selected feature vectors are different for different candidate points of interest.

[0161] In step S2320, based on the recommendation model, the matching scores between the target location data and the location fingerprints of each candidate point of interest are determined according to the vector values of the selected feature vectors.

[0162] The matching scores determined according to the vector values of the location fingerprints of each candidate point of interest are the matching scores between the corresponding points of interest and the target location data. The matching scores can represent the probability of the client visiting the corresponding candidate points of interest.

[0163] In step S2330, the candidate point of interest corresponding to the location fingerprint whose matching score meets the preset recommendation condition is determined as the target point of interest.

[0164] In one embodiment of the present disclosure, the preset recommendation condition can be that the candidate point of interest corresponding to the location fingerprint whose descending order of the matching scores meets the preset sorting range is determined as the target point of interest.

[0165] Specifically, the candidate points of interest can be sorted in descending order based on the matching scores, the sorting values of each candidate point of interest are obtained, and the candidate point of interest whose sorting value is in the preset sorting range is determined as the target point of interest.

[0166] The preset sorting range can be set in advance according to application scenarios or specific requirements. For example, the preset sorting range can be 1-3.

[0167] In step S2400, the recommendation information of the target point of interest is pushed to the client.

[0168] The recommendation information of the target point of interest can be advertisement information of the target point of interest, can be commodity information of the target point of interest, can be push information of the target point of interest, can be preferential information of the target point of interest, and can be service content of the target point of interest.

[0169] By the embodiment, the accuracy of positioning the in-store point of interest and the accuracy of pushing the in-store information to the client can be improved. Moreover, the in-store point of interest can be recognized for the client of the IOS system which cannot acquire the WiFi data.

[0170] In an example, when the user enters a certain store, the current store can be pushed to the client, advertisement information, commodity information or push information of the current store can be pushed to the client, so that the user can make a consumption decision, and the use experience of the user can be improved.

[0171] In an example, as shown in Figure 3 The candidate points of interest matched with the target position data can include a first point of interest POI1, a second point of interest POI2, a third point of interest POI3 and a fourth point of interest POI4.

[0172] The target latitude and longitude data can be an overlapping region of a minimum polygon latitude and longitude region corresponding to the first point of interest POI1 and a minimum polygon latitude and longitude region corresponding to the second point of interest POI2.

[0173] If the distance between the target latitude and longitude data and the cluster center of the latitude and longitude data corresponding to the first point of interest POI1 is greater than the distance between the target latitude and longitude data and the cluster center of the latitude and longitude data corresponding to the second point of interest POI2, the matching score of the target latitude and longitude data and the first point of interest POI1 is greater than the matching score of the target latitude and longitude data and the second point of interest POI2.

[0174] <Device>

[0175] Corresponding to the above method, the specification also provides an in-store recommendation device 4000. As shown in Figure 4 The in-store recommendation device 4000 can include a first data acquisition module 4100, a data comparison module 4200, a point of interest determination module 4300 and an information push module 4400.

[0176] The first data acquisition module 4100 is configured to acquire a plurality of target position data of a client; wherein the target position data is data representing a position of the target client, and the plurality of target position data includes target latitude and longitude data, scanned target Beacon data and target sensor data.

[0177] The data comparison module 4200 is configured to compare the target location data with location fingerprints of a plurality of points of interest in a preset location fingerprint database, to obtain a point of interest matching any of the target location data as a candidate point of interest.

[0178] The point of interest determination module 4300 is configured to determine, based on a preset recommendation model, a point of interest visited by the client as a target point of interest according to the target location data and the location fingerprint of the candidate point of interest.

[0179] The information pushing module 4400 is configured to push recommendation information of the target point of interest to the client.

[0180] In an embodiment of the present disclosure, the point of interest determination module 4300 can be further configured to:

[0181] obtain a vector value of a selected feature vector according to the target location data and the location fingerprint of the candidate point of interest;

[0182] determine, based on the recommendation model, a matching score between the target location data and the location fingerprint of each of the candidate points of interest according to the vector value of the selected feature vector;

[0183] determine the candidate point of interest corresponding to the location fingerprint with the matching score meeting a preset recommendation condition as the target point of interest.

[0184] In an embodiment of the present disclosure, the preset recommendation condition is that the candidate point of interest corresponding to the location fingerprint with an order of descending matching scores meeting a preset sorting range is the target point of interest.

[0185] In an embodiment of the present disclosure, the location fingerprint includes a polygon latitude and longitude area and a Beacon identifier list of the corresponding point of interest.

[0186] The data comparison module 4200 can be further configured to:

[0187] determine the point of interest whose polygon latitude and longitude area contains the target latitude and longitude data as the candidate point of interest;

[0188] determine the point of interest whose Beacon identifier list contains any of the target Beacon data as the candidate point of interest.

[0189] In an embodiment of the present disclosure, the store recommendation apparatus 4000 can further include:

[0190] a module configured to acquire store business data generated in a preset historical statistical period;

[0191] a module configured to determine a corresponding point of interest and a store visit ground truth data according to the store visit business data; the store visit ground truth data comprises longitude and latitude data, scanned beacon data and sensor data;

[0192] a module configured to obtain a location fingerprint of the point of interest according to the store visit ground truth data corresponding to the point of interest.

[0193] In an embodiment of the present disclosure, the store visit ground truth data comprises longitude and latitude data, and the location fingerprint comprises at least one of:

[0194] a cluster center of longitude and latitude data corresponding to the point of interest;

[0195] a minimum polygon longitude and latitude area formed by longitude and latitude data corresponding to the point of interest;

[0196] an area center of the minimum polygon longitude and latitude area corresponding to the point of interest;

[0197] an area of the minimum polygon longitude and latitude area corresponding to the point of interest;

[0198] a mean value of distances from longitude and latitude data corresponding to the point of interest to the cluster center;

[0199] a variance of distances from longitude and latitude data corresponding to the point of interest to the cluster center;

[0200] an area of an overlapping area of the minimum polygon longitude and latitude area corresponding to the point of interest and a minimum polygon longitude and latitude area corresponding to another point of interest;

[0201] an area of an independent area of the minimum polygon longitude and latitude area corresponding to the point of interest which does not overlap with a minimum polygon longitude and latitude area corresponding to another point of interest;

[0202] a number of other points of interest corresponding to a minimum polygon longitude and latitude area which at least partially overlaps with the minimum polygon longitude and latitude area corresponding to the point of interest;

[0203] a mean value of areas of the overlapping areas corresponding to the point of interest;

[0204] a variance of areas of the overlapping areas corresponding to the point of interest.

[0205] In an embodiment of the present disclosure, the store visit ground truth data comprises scanned beacon data,

[0206] the location fingerprint comprises at least one of:

[0207] a beacon list corresponding to the point of interest; the beacon list comprises corresponding beacon identifiers and signal strengths;

[0208] a mean value of signal strength of each Beacon identifier corresponding to the point of interest;

[0209] a variance of signal strength of each Beacon identifier corresponding to the point of interest.

[0210] In an embodiment of the present disclosure, the store visit ground truth data comprises sensor data;

[0211] The location fingerprint comprises at least one of:

[0212] a mean value of air pressure of the point of interest collected by the air pressure sensor;

[0213] a variance of air pressure of the point of interest collected by the air pressure sensor;

[0214] a mean value of magnetic force data of the point of interest collected by the magnetometer;

[0215] a variance of magnetic force data of the point of interest collected by the magnetometer;

[0216] a mean value of floor data of the point of interest obtained from inertial data collected by the inertial sensor;

[0217] a variance of floor data of the point of interest obtained from inertial data collected by the inertial sensor.

[0218] It is understood by those skilled in the art that the store visit recommendation apparatus 4000 can be implemented in various ways. For example, the store visit recommendation apparatus 4000 can be implemented by configuring a processor with instructions. For example, the instructions can be stored in a ROM, and when the device is started, the instructions are read from the ROM to a programmable device to implement the store visit recommendation apparatus 4000. For example, the store visit recommendation apparatus 4000 can be fixed in a special device (for example, an ASIC). The store visit recommendation apparatus 4000 can be divided into independent units, or they can be combined together to implement. The store visit recommendation apparatus 4000 can be implemented by one of the above-mentioned various implementation ways, or can be implemented by a combination of two or more of the above-mentioned various implementation ways.

[0219] In the present embodiment, the store visit recommendation apparatus 4000 can have various implementation forms, for example, the store visit recommendation apparatus 4000 can be a functional module running in any software product or application program providing store visit recommendation services, or a peripheral embedded part, plug-in, patch of these software products or application programs, etc., and can also be the software products or application programs themselves.

[0220] <Server>

[0221] In the present embodiment, a server 5000 is also provided, which is configured to implement the store visit recommendation apparatus 4000.Figure 5 As shown in FIG. 5, the device 5000 comprises a first memory 5100 and a first processor 5200.

[0222] The first memory 5100 is configured to store a first computer program executable; the computer program is configured to control the first processor 5200 to execute the method for recommending a store provided in any one of the embodiments.

[0223] By the embodiment, the accuracy of positioning a store interest point and the accuracy of pushing store information to a client can be improved. Moreover, the store interest point can be recognized for a client of an IOS system which cannot acquire WiFi data.

[0224] <Second Embodiment>

[0225] <Method>

[0226] The method for recommending a store provided in the embodiment is implemented by a client. For example, the client can be a client 2000 as shown in FIG. 6. Figure 1

[0227] As shown in FIG. 6, the method comprises steps S6100-S6300. Figure 6

[0228] In step S6100, a plurality of target position data of the client is acquired, wherein the plurality of target position data comprises target latitude and longitude data, scanned target Beacon data and target sensor data.

[0229] In step S6200, the plurality of target position data is sent to a server, so that the server pushes recommendation information of a target interest point to the client according to the plurality of target position data and a preset position fingerprint database; wherein the target interest point is an interest point visited by the client.

[0230] In step S6300, the recommendation information of the target interest point is received and displayed.

[0231] By the embodiment, the accuracy of positioning a store interest point and the accuracy of pushing store information to a client can be improved. Moreover, the store interest point can be recognized for a client of an IOS system which cannot acquire WiFi data.

[0232] In one example, when a user enters a store, the current store can be pushed to the client, and advertisement information, commodity information or push information of the current store can be pushed to the client, so that the user can make a consumption decision and the use experience of the user can be improved.

[0233] <Device>

[0234] ​​Corresponding to the above method, the specification also provides a store recommendation device 7000. As shown in Figure 7 The store recommendation device 7000 can include a second data acquisition module 7100, a data sending module 7200, and an information display module 7300.

[0235] The second data acquisition module 7100 is configured to acquire a plurality of target position data of itself, wherein the plurality of target position data includes target latitude and longitude data, scanned target Beacon data, and target sensor data.

[0236] The data sending module 7200 is configured to send the plurality of target position data to a server, so that the server pushes recommendation information of a target point of interest to the client according to the plurality of target position data and a preset position fingerprint database, wherein the target point of interest is a point of interest visited by the client.

[0237] The information display module 7300 is configured to display the recommendation information of the target point of interest.

[0238] Those skilled in the art should understand that the store recommendation device 7000 can be implemented in various ways. For example, the store recommendation device 7000 can be implemented by configuring a processor with instructions. For example, the instructions can be stored in a ROM, and when the device is started, the instructions are read from the ROM to a programmable device to implement the store recommendation device 7000. For example, the store recommendation device 7000 can be fixed in a special device (such as an ASIC). The store recommendation device 7000 can be divided into independent units, or they can be combined together. The store recommendation device 7000 can be implemented by one of the above-mentioned various implementation ways, or can be implemented by a combination of two or more of the above-mentioned various implementation ways.

[0239] In this embodiment, the store recommendation device 7000 can have various implementation forms, for example, the store recommendation device 7000 can be a functional module running in any software product or application program providing store recommendation services, or a peripheral embedded part, plug-in, patch of these software products or application programs, etc., and can also be the software products or application programs themselves.

[0240] <client>

[0241] In this embodiment, a client 8000 is also provided, as shown in Figure 8 The client 8000 includes a second memory 8100 and a second processor 8200.

[0242] The second memory 8100 is configured to store an executable second computer program; and the computer program is configured to control the second processor 8200 to execute the method for recommending a store provided in any one of the embodiments.

[0243] By the embodiment, the accuracy of positioning a store interest point and the accuracy of pushing store information to a client can be improved. Moreover, the store interest point can be recognized for a client of an IOS system which cannot acquire WiFi data.

[0244] <Third Embodiment>

[0245] <system>

[0246] The embodiment provides a store recommendation system 9000, as shown in the figure, which comprises the server 5000 of the first embodiment and the client 8000 of the second embodiment. Figure 9

[0247] By the embodiment, the accuracy of positioning a store interest point and the accuracy of pushing store information to a client can be improved. Moreover, the store interest point can be recognized for a client of an IOS system which cannot acquire WiFi data.

[0248] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0249] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a magneto-optical storage device, a ROM, a flash memory, a portable computer diskette, a hard disk, a RAM, a memory stick, a floppy disk, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0250] ​The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0251] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0252] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0253] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data, programs, program modules, e.g., instructions for operation, or digital content stored thereon or therein for a short time or not at all. The computer readable storage medium can also have instructions stored thereon or therein which may

[0254] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0255] The flow diagrams and block diagrams in the accompanying drawings show archi tectures, functional and operational architectures of possible implementations of systems, methods, and computer program products according to the present disclosure. In this regard, each block in the flow diagrams and block diagrams can represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in some cases, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0256] Having described above several embodiments of the disclosure, any modifications and variations that fall within the scope of the described embodiments are also contemplated by the inventor(s). As such, the foregoing description is not intended to limit the scope of the disclosure, and it is recognized that modifications can be made by one of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The word "comprising" is used herein to mean "including" but not necessarily "consisting of" or "composed of." The word "comprising" therefore should not be interpreted as being limited to the recited items information.

Claims

1. A method for store recommendation, applied to a server, the method comprising: obtaining a plurality of target location data of a client; wherein the target location data is data representing a location of the client, and the plurality of target location data comprises target latitude and longitude data, scanned target Beacon data, and target sensor data; comparing the target location data with location fingerprints of a plurality of points of interest in a preset location fingerprint database to obtain a point of interest matching any of the target location data as a candidate point of interest; determining, based on a preset recommendation model, a point of interest visited by the client as a target point of interest according to the target location data and the location fingerprint of the candidate point of interest; pushing recommendation information of the target point of interest to the client; determining, according to the target location data and the location fingerprint of the candidate point of interest, a point of interest visited by the client as a target point of interest, comprising: obtaining a vector value of a selected feature vector according to the target location data and the location fingerprint of the candidate point of interest; determining, based on the recommendation model, a matching score between the target location data and the location fingerprint of each of the candidate points of interest according to the vector value of the selected feature vector; determining a candidate point of interest corresponding to the location fingerprint with a matching score meeting a preset recommendation condition as the target point of interest; the location fingerprint comprises a polygon latitude and longitude area and a Beacon identifier list corresponding to a point of interest; the comparing the target location data with location fingerprints of a plurality of points of interest in a preset location fingerprint database to obtain a point of interest matching any of the target location data as a candidate point of interest, comprises: determining a point of interest whose polygon latitude and longitude area contains the target latitude and longitude data as the candidate point of interest; determining a point of interest whose Beacon identifier list contains any of the target Beacon data as the candidate point of interest. 2.The method of claim 1, wherein the preset recommendation condition is that a candidate point of interest corresponding to a location fingerprint with an order of descending matching score meeting a preset sorting range is determined as the target point of interest. 3.The method of claim 1, further comprising: obtaining store business data generated in a preset historical statistical period; determining a corresponding point of interest and store truth data according to the store business data; the store truth data comprises latitude and longitude data, scanned Beacon data, and sensor data; obtaining a location fingerprint of the point of interest according to the store truth data corresponding to the point of interest. 4.The method of claim 3, wherein the store truth data comprises latitude and longitude data, and the location fingerprint comprises at least one of the following: a cluster center of latitude and longitude data corresponding to the point of interest; a minimum polygon latitude and longitude area formed by latitude and longitude data corresponding to the point of interest; an area center of the minimum polygon latitude and longitude area corresponding to the point of interest; an area of the minimum polygon latitude and longitude area corresponding to the point of interest. a mean value of distances from the latitude and longitude data corresponding to the interest point to the cluster centers; a variance of distances from the latitude and longitude data corresponding to the interest point to the cluster centers; an area of an overlapping region of the minimum polygon latitude and longitude region corresponding to the interest point and minimum polygon latitude and longitude regions corresponding to other interest points; an area of an independent region in which the minimum polygon latitude and longitude region corresponding to the interest point does not overlap with minimum polygon latitude and longitude regions corresponding to other interest points; a number of other interest points corresponding to minimum polygon latitude and longitude regions at least partially overlapping with the minimum polygon latitude and longitude region corresponding to the interest point; a mean value of areas of the overlapping regions corresponding to the interest point; a variance of areas of the overlapping regions corresponding to the interest point.

5. The method of claim 3, wherein the store visit ground truth data comprises scanned beacon data, the location fingerprint comprises at least one of: a beacon list corresponding to the interest point, the beacon list comprising corresponding beacon identifiers and signal strengths; a mean value of signal strengths of each beacon identifier corresponding to the interest point; a variance of signal strengths of each beacon identifier corresponding to the interest point. and / or, the store visit ground truth data comprises sensor data; the location fingerprint comprises at least one of: a mean value of air pressure of the interest point collected by an air pressure sensor; a variance of air pressure of the interest point collected by an air pressure sensor; a mean value of magnetic force data of the interest point collected by a magnetometer; a variance of magnetic force data of the interest point collected by a magnetometer; a mean value of floor data of the interest point obtained from inertial data collected by an inertial sensor; a variance of floor data of the interest point obtained from inertial data collected by an inertial sensor.

6. A store recommendation method applied to a client, the method comprising: obtaining a plurality of target location data of the client, wherein the plurality of target location data comprises target latitude and longitude data, scanned target beacon data, and target sensor data; sending the plurality of target location data to a server to determine, as candidate interest points, interest points whose polygon latitude and longitude regions in a preset location fingerprint database contain the target latitude and longitude data, and to determine, as candidate interest points matching any target location data, interest points whose beacon identifier lists in the location fingerprint database contain any beacon identifier in the target beacon data; obtaining vector values of selected feature vectors according to the target location data and location fingerprints of the candidate interest points; determining, based on the recommendation model, matching scores between the target location data and location fingerprints of each of the candidate interest points according to the vector values of the selected feature vectors; determining, as target interest points, candidate interest points corresponding to location fingerprints whose matching scores meet a preset recommendation condition, and pushing recommendation information of the target interest points to the client; wherein the target interest points are interest points visited by the client. recommendation information of the target point of interest is displayed.

7. A server comprising a first processor and a first memory for storing a first computer program which is executable; the first computer program is configured to control the first processor to perform the method according to any one of claims 1 to 5.

8. A client comprising a second processor and a second memory for storing a second computer program which is executable; the second computer program is configured to control the second processor to perform the method according to claim 6.

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