CN116095601A

CN116095601A - Base station cell feature library updating method and related device

Info

Publication number: CN116095601A
Application number: CN202210603486.2A
Authority: CN
Inventors: 李辉; 刘思桐
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2022-05-30
Filing date: 2022-05-30
Publication date: 2023-05-09
Anticipated expiration: 2042-05-30
Also published as: CN116095601B

Abstract

The embodiment of the application provides a method and a related device for updating a cell feature library of a base station cell, which relate to the fields of terminal technology, artificial intelligence and intelligent perception and comprise the following steps: acquiring first data from a plurality of devices in a first time period to obtain a first data set, wherein any first data comprises a cell identifier and longitude and latitude; calculating longitude and latitude corresponding to each cell identifier, and clustering the cell identifiers according to the longitude and latitude to obtain M cell families; acquiring second data from the plurality of devices in a second time period to obtain a second data set; and aiming at any cell group, calculating the confidence coefficient of each cell identifier in any cell group according to the effective data of the second data set, and updating the cell identifier with the confidence coefficient meeting the preset condition in an online feature library, wherein the content of the online feature library is used for downloading and updating of the terminal equipment. Therefore, the cell information in the online feature library can be accurate through the terminal server, automatic updating of the cell of the base station cell is realized, and time consumption and labor cost are reduced.

Description

Base station cell feature library updating method and related device

Technical Field

The application relates to the technical field of terminals, the field of artificial intelligence and the field of intelligent perception, in particular to a method and a related device for updating a cell characteristic library of a base station cell.

Background

Geofencing is a new application of location-based services (location based services, LBS) that uses virtual fencing to enclose a virtual geographic boundary, and when a device enters or leaves this virtual boundary, triggers the device to act. For example, when a user enters a subway station, the terminal equipment can automatically pop up a subway card based on a geofence technology, so that one-step code scanning riding is realized.

When the terminal device generates a subway station geofence, the terminal device may generate the geofence based on the location information where the subway station is located. The position information of the subway station may be inaccurate or a condition of newly added subway stations exists, update and correction are needed, and in some implementations, the position information of the subway station is usually corrected by adopting a manual field investigation mode.

Therefore, in the prior art, the update mode of the subway station position information is long in time consumption and high in labor cost.

Disclosure of Invention

The embodiment of the application provides a method and a related device for updating a cell feature library of a base station cell, wherein a terminal server can acquire a first data set in a period of time and a second data set in another period of time, verify cells in each cell group obtained based on the first data set by using effective data in the second data set, and update the verified cells in an online feature library, so that automatic updating of the cells of the base station cell is realized. Thus, when the cell group corresponds to the subway station position information, automatic update of the subway station position information can be realized.

In a first aspect, an embodiment of the present application provides a method for updating a cell feature library of a base station cell, including: acquiring a plurality of first data from a plurality of devices in a first time period to obtain a first data set; any one of the first data in the first data set comprises a cell identifier and a cell longitude and latitude; calculating the longitude and latitude of the cell corresponding to each cell identifier in the first data set; clustering cell identifiers in a first data set according to cell longitude and latitude corresponding to each cell identifier to obtain M cell groups, wherein any cell group comprises one or more cell identifiers, and M is an integer greater than 0; acquiring a plurality of second data from a plurality of devices in a second time period to obtain a second data set; any one of the second data in the second data set comprises a cell identifier, a cell longitude and latitude and verification data for verifying the validity of any one of the second data; the start time of the second time period is later than the start time of the first time period; aiming at a first cell group in the M cell groups, calculating the confidence coefficient of each cell identifier in the first cell group according to the effective data in the second data set; updating cell identifiers of which the confidence in the first cell group meets preset conditions in an online feature library corresponding to the first cell group, wherein the content of the online feature library is used for downloading and updating of terminal equipment. In this way, the terminal server can continuously acquire a plurality of data from the terminal equipment to obtain the first data set and the second data set, the confidence coefficient of each cell identifier in any cell group is obtained by adopting the method, and the information in the online feature library is continuously updated based on the confidence coefficient of each cell identifier in any cell group, so that the cell information in the online feature library is more accurate, the better automatic updating of the cell of the base station cell is realized through the terminal server, and the time consumption and the labor cost are reduced.

In one possible implementation, for the target second data in the second data set, the verification data of the target second data includes: time information reported by the target second data and information related to entering or exiting the subway within a preset time period after the time information; the information related to entering or exiting a subway includes one or more of the following: and whether the intra-application inbound or outbound code scanning page jumps or not, motion state information and via cell information are applied. Therefore, based on the verification data of the target data, the server can verify the validity of the second data in the second data set to obtain more accurate valid data in the second data set so as to evaluate the accuracy of the cell identification in any cell group.

In one possible implementation, the calculating the confidence of each cell identifier in the first cell group according to the valid data in the second data set further includes: acquiring data meeting effective conditions in the second data set to obtain effective data; meeting the effective conditions includes one or more of the following: the method comprises the steps that an inbound or outbound code scanning page in an application in a preset time length jumps, the movement speed in the preset time length is larger than a speed threshold value, and the information of the cells passing through the preset time length comprises cells covered by subway lines. Thus, the confidence coefficient of each cell identifier in the first cell group is calculated according to the effective data obtained after the verification of the second data set, and the cell identifier with higher accuracy can be obtained.

In one possible implementation, calculating the confidence of each cell identifier in the first cell group according to the valid data in the second data set includes: for a first cell identifier in a first cell group, calculating a first number of days that the first cell identifier appears in the valid data, and/or a first number of times that the first cell identifier appears in the valid data; and calculating the confidence coefficient of the first cell identifier according to the first days, the first times, the total days of the second time period and/or the total times of the cell reporting in the second time period.

In one possible implementation manner, calculating the confidence level of the first cell identifier according to the first number of days, the first number of times, the total number of days of the second time period, and/or the total number of times that the cell is reported in the second time period includes: calculating the ratio of the first days to the total days to obtain the confidence coefficient of the first cell identifier; or calculating the ratio of the first days to the total days to obtain a first confidence coefficient of the first cell identifier; calculating the ratio of the first times to the total times to obtain a second confidence coefficient of the first cell identifier; and obtaining the confidence coefficient of the first cell identifier according to the first confidence coefficient and the second confidence coefficient. Thus, the confidence coefficient of each cell identifier in the first cell group is calculated according to the effective data in the second data set, and the accuracy of each cell identifier in the first cell group can be evaluated, so that the cell identifier with higher accuracy is obtained.

In one possible implementation, any one of the first data sets further includes wireless fidelity WiFi information; the method further comprises the steps of: determining WiFi characteristics corresponding to each cell identifier in the first data set; according to the longitude and latitude of the cell corresponding to each cell identifier, clustering the cells in the first data set, including: clustering the cells in the first data set according to the longitude and latitude of the cells corresponding to the cell identifiers and the WiFi characteristics corresponding to the cell identifiers; any cell group further comprises WiFi features corresponding to the cell identifiers in any cell group. Therefore, according to the WiFi information, when the equipment is located on the ground and reports data to the server, the cell identification obtained by the server is more accurate.

In one possible implementation manner, clustering cells in the first data set according to cell longitude and latitude corresponding to each cell identifier and WiFi features corresponding to each cell identifier includes: and clustering cell identifiers with the same WiFi characteristics and/or cell identifiers with the longitude and latitude distance smaller than the distance threshold value in a cell group.

Therefore, according to the WiFi information, when the equipment is located on the ground and reports data to the server, the cell identification obtained by the server is more accurate, and the cell groups obtained based on the same WiFi characteristics can also better correspond to the subway station positions.

In one possible implementation, any one of the first data sets further includes a satellite number, and determining the WiFi feature corresponding to each cell identifier in the first data set includes: dividing the first data set according to cell identifications to obtain source WiFi characteristics corresponding to the cell identifications; and removing WiFi information contained in the first data with the satellite number smaller than a preset value for the source WiFi characteristics corresponding to the cell identifications in the first data set to obtain the WiFi characteristics corresponding to the cell identifications in the first data set. Therefore, the WiFi characteristics corresponding to each cell identifier under the ground can be obtained by removing the WiFi information contained in the first data with the satellite number smaller than the preset value through the satellite number, the accuracy is improved, and the resources are saved.

In one possible implementation manner, calculating the cell longitude and latitude corresponding to each cell identifier in the first data set includes: dividing the first data set according to the cell identifications, and calculating the average longitude and latitude corresponding to each cell identification in the first data set to obtain the cell longitude and latitude corresponding to each cell identification in the first data set. Thus, the longitude and latitude of the cell which are well matched with the position of the cell identifier can be obtained.

In one possible implementation, any cell family further includes one or more cell identities, and the method further includes: acquiring subway station identifiers corresponding to each of M cell groups from a map application according to the longitude and latitude of each cell identifier in the M cell groups; updating the cell identification of which the reliability meets the preset condition in the first cell group in the online feature library corresponding to the first cell group comprises the following steps: and updating the cell identification of which the reliability meets the preset condition in the first cell group in an online feature library where the subway station identification corresponding to the first cell group is located. Therefore, the automatic updating of the cell identification of the subway station corresponding to the first cell group can be realized, the cell identification corresponding to the subway station is accurate, and the time consumption and the labor cost are reduced.

In one possible implementation, the method further includes: when receiving an update request of the terminal equipment, sending content in the online feature library to the terminal equipment, wherein the content is used for registering the subway station geofence by the terminal equipment according to a cell identifier corresponding to the subway station identifier updated in the online feature library, so that the subway card is displayed when the terminal equipment enters the subway station geofence. Therefore, any terminal equipment can download and update the cell identifier corresponding to each subway station from the online feature library and register the geofence based on the cell identifier, so that more accurate and automatic update of the position information of the subway station is realized, and time consumption and labor cost are reduced.

In a second aspect, an embodiment of the present application provides a related device for updating a cell feature library of a base station cell, where the device for updating the cell feature library of the base station cell may be a terminal server, or may be a chip or a chip system in the terminal server. The related apparatus for updating the cell feature library of the base station cell may include a processing unit. The processing unit is configured to implement the first aspect or any method related to processing in any possible implementation manner of the first aspect. The processing unit may be a processor when the relevant device for the cell feature library update of the base station cell is a terminal server. The related apparatus for updating the cell feature library of the base station cell may further include a storage unit, and the storage unit may be a memory. The storage unit is configured to store instructions, and the processing unit executes the instructions stored in the storage unit, so that the terminal server implements a method described in the first aspect or any one of possible implementation manners of the first aspect. The processing unit may be a processor when the relevant means for updating the cell feature library of the base station cell is a chip or a system of chips in the terminal server. The processing unit executes instructions stored by the storage unit to cause the terminal server to implement a method as described in the first aspect or any one of the possible implementations of the first aspect. The storage unit may be a storage unit (e.g., a register, a cache, etc.) in the chip, or may be a storage unit (e.g., a read-only memory, a random access memory, etc.) located outside the chip in the terminal server. The related device for updating the cell characteristic library of the base station cell may further comprise a communication unit, which may also be referred to as an interface circuit, for communicating with other devices.

In one possible implementation, the processing unit is configured to obtain a first data set by acquiring a plurality of first data from a plurality of devices in a first period of time; any one of the first data in the first data set comprises a cell identifier and a cell longitude and latitude; the processing unit is used for calculating the longitude and latitude of the cell corresponding to each cell identifier in the first data set; the processing unit is further used for clustering the cell identifications in the first data set according to the cell longitude and latitude corresponding to each cell identification to obtain M cell families, wherein any cell family comprises one or more cell identifications, and M is an integer greater than 0; the processing unit is further used for acquiring a plurality of second data from the plurality of devices in a second time period to obtain a second data set; any one of the second data in the second data set comprises a cell identifier, a cell longitude and latitude and verification data for verifying the validity of any one of the second data; the start time of the second time period is later than the start time of the first time period; the processing unit is further used for calculating the confidence coefficient of each cell identifier in the first cell group according to the effective data in the second data set for the first cell group in the M cell groups; the processing unit is used for updating the cell identification of which the reliability in the first cell group meets the preset condition in the online feature library corresponding to the first cell group, and the content of the online feature library is used for downloading and updating of the terminal equipment.

In a possible implementation manner, for the target second data in the second data set, verification data of the target second data includes: time information reported by the target second data and information related to entering or exiting the subway within a preset time period after the time information; the information related to entering or exiting a subway includes one or more of the following: and whether the intra-application inbound or outbound code scanning page jumps or not, motion state information and via cell information are applied.

In a possible implementation manner, the processing unit is further configured to obtain, in the second data set, data that meets the validity condition, and obtain valid data; meeting the effective conditions includes one or more of the following: the method comprises the steps that an inbound or outbound code scanning page in an application in a preset time length jumps, the movement speed in the preset time length is larger than a speed threshold value, and the information of the cells passing through the preset time length comprises cells covered by subway lines.

In a possible implementation manner, the processing unit is specifically configured to calculate, for a first cell identifier in the first cell group, a first number of days that the first cell identifier appears in the valid data, and/or a first number of times that the first cell identifier appears in the valid data; and calculating the confidence coefficient of the first cell identifier according to the first days, the first times, the total days of the second time period and/or the total times of reporting the cell identifier in the second time period.

In a possible implementation manner, the processing unit is specifically configured to calculate a ratio of the first number of days to the total number of days, so as to obtain a confidence coefficient of the first cell identifier; or calculating the ratio of the first days to the total days to obtain a first confidence coefficient of the first cell identifier; calculating the ratio of the first times to the total times to obtain a second confidence coefficient of the first cell identifier; and obtaining the confidence coefficient of the first cell identifier according to the first confidence coefficient and the second confidence coefficient.

In a possible implementation manner, any one of the first data sets further includes wireless fidelity WiFi information; the processing unit is further used for determining WiFi features corresponding to the cell identifiers in the first data set; the processing unit is further used for clustering the cell identifications in the first data set according to the cell longitude and latitude corresponding to each cell identification and the WiFi characteristics corresponding to each cell identification; any cell group further comprises WiFi features corresponding to the cell identifiers in any cell group.

In a possible implementation manner, the processing unit is specifically configured to cluster cell identifiers with the same WiFi characteristics and/or cell identifiers with a longitude and latitude distance smaller than a distance threshold value into one cell group.

In a possible implementation manner, any one of the first data sets further includes a satellite number, and the processing unit is further configured to divide the first data set according to cell identifiers to obtain source WiFi features corresponding to the cell identifiers; and removing WiFi information contained in the first data with the satellite number smaller than a preset value for the source WiFi characteristics corresponding to the cell identifications in the first data set to obtain the WiFi characteristics corresponding to the cell identifications in the first data set.

In a possible implementation manner, the processing unit is specifically configured to divide the first data set according to cell identifiers, calculate an average longitude and latitude corresponding to each cell identifier in the first data set, and obtain a cell longitude and latitude corresponding to each cell identifier in the first data set.

In a possible implementation manner, any cell group further includes a cell longitude and latitude of each of one or more cell identifiers, and the processing unit is specifically configured to obtain, from the map application, subway station identifiers corresponding to each of the M cell groups according to the longitude and latitude of each of the M cell identifiers; updating the cell identification of which the reliability in the first cell group meets the preset condition in an online feature library corresponding to the first cell group, wherein the processing unit is also used for updating the cell identification of which the reliability in the first cell group meets the preset condition in the online feature library corresponding to the first cell group, wherein the subway station identification corresponding to the first cell group is located.

In a possible implementation manner, the communication unit is further configured to send, when receiving an update request of the terminal device, content in the online feature library to the terminal device, where the terminal device registers a subway station geofence according to a cell identifier corresponding to the updated subway station identifier in the online feature library, so that the terminal device displays a subway card when entering the subway station geofence.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes memory-stored computer-executable instructions to implement the method described in the first aspect or any one of the possible implementations of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement the method described in the first aspect or any one of the possible implementations of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, implements the method described in the first aspect or any one of the possible implementations of the first aspect.

It should be understood that, the second aspect to the fifth aspect of the present application correspond to the technical solutions of the first aspect of the present application, and the beneficial effects obtained by each aspect and the corresponding possible implementation manner are similar, and are not repeated.

Drawings

Fig. 1 is a schematic view of a scenario provided in an embodiment of the present application;

fig. 2 is a schematic diagram of an equipment interaction scenario provided in an embodiment of the present application;

fig. 3 is a flow chart of a method for updating a cell feature library of a base station cell according to an embodiment of the present application;

fig. 4 is a schematic diagram of a cell group provided in an embodiment of the present application;

fig. 5 is a schematic diagram of updating a cell feature library of a specific base station cell according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a device according to an embodiment of the present application.

Detailed Description

In order to facilitate the clear description of the technical solutions of the embodiments of the present application, the following simply describes some terms and techniques related to the embodiments of the present application:

in the embodiments of the present application, the words "first," "second," and the like are used to distinguish between identical or similar items that have substantially the same function and effect. For example, the first chip and the second chip are merely for distinguishing different chips, and the order of the different chips is not limited. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to denote examples, illustrations, or descriptions. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.

The method for updating the cell characteristic library of the base station cell provided by the embodiment of the application is described in detail below with reference to the accompanying drawings. The term "at … …" in the embodiment of the present application may be instantaneous when a certain situation occurs, or may be a period of time after a certain situation occurs, which is not particularly limited in the embodiment of the present application.

By way of example, fig. 1 shows a schematic view of a scenario in which an embodiment of the present application is applicable.

When the terminal device recognizes that the user may ride a subway, a subway card is displayed at a card of the main screen of the terminal device as shown in a of fig. 1, the information displayed on the subway card may include a current subway station name, and the user clicks the subway card so that the main screen of the terminal device jumps to a subway riding code interface as shown in b of fig. 1.

The subway card can be a resident card or a movable card in a main interface or a negative screen. The resident card is a card which is fixedly arranged, and the position of the resident card in the interface is reserved no matter whether the information needing to be prompted exists in the resident card or not; the movable card is a temporarily generated card, when the movable card has the content to be prompted, the movable card is displayed in the interface, when the movable card has no content to be prompted or the life cycle of the movable card is finished, the movable card is not displayed in the interface, and other content in the interface can fill the position of the movable card.

The subway riding code can be a two-dimensional code, a bar code and the like, and the terminal equipment can be a mobile phone, an intelligent watch and the like, so that the embodiment of the application is not particularly limited.

In the subway card generation process, the terminal equipment can generate the geofence based on the position information of the subway station. The position information of the subway station may be inaccurate or a condition of newly added subway stations exists, update and correction are needed, and in some implementations, the position information of the subway station is usually corrected by adopting a manual field investigation mode. This approach is time consuming and labor costly.

Therefore, in the method for updating the cell feature library of the base station cell provided by the embodiment of the invention, the terminal server can acquire the first data set within a period of time, obtain the cell group with a larger coverage area based on the first data set, verify the cells in each cell group by using the effective data in the second data set within another period of time, so as to improve the accuracy of the cells in each cell group, and update the verified cells in the online feature library, so that the cell group in the online feature library can have a better coverage area and a higher accuracy, thereby realizing better automatic updating of the cell based on the base station cell. It can be appreciated that if the cell family in the embodiment of the present application corresponds to a subway station, more accurate automatic updating of the subway station cell can be achieved, thereby reducing time consumption and labor cost.

It should be noted that, the cell family in the embodiment of the present application may also correspond to a place where any terminal device such as an express cabinet, a mall, etc. may establish a geofence.

Fig. 2 is a schematic diagram of a device interaction scenario applicable to an embodiment of the present application. As shown in fig. 2, taking a cell group corresponding to a subway station as an example, a scenario may include a plurality of terminal devices and a terminal server.

The terminal equipment can report data to the terminal server, the terminal server can update the cell identification with higher confidence corresponding to each subway station in the online feature library based on the data reported by the terminal equipment, further, any terminal equipment can download and update the cell identification corresponding to each subway station from the online feature library, and the terminal equipment can further establish the geofence of the cell identification corresponding to each subway station, so that when the terminal equipment enters the region of the geofence of the cell identification corresponding to each subway station, the subway card can be displayed in the terminal equipment, and a user can conveniently and rapidly open a subway related interface.

The specific steps may be as shown in fig. 2.

The terminal equipment detects that the user possibly takes the subway, and then the terminal equipment reports data to the terminal server.

Wherein the terminal device may detect that the user may take a subway by: 1. the terminal equipment detects that a user clicks a subway card; 2. the terminal equipment detects that a user clicks the subway card and the wrist turning action occurs; 3. the terminal equipment detects that a user clicks the subway card, and after the user clicks the subway card, the screen of the terminal equipment pulls up the code scanning page in the application and the code scanning page jumps.

The terminal server obtains a first data set based on data reported by the terminal equipment, wherein the first data in the first data set comprises a cell identifier and a cell longitude and latitude. And clustering the cell identifications in the first data set according to the cell longitude and latitude corresponding to each cell identification by the terminal server to obtain one or more cell families.

The terminal server obtains a second data set based on the data reported by the terminal equipment, wherein the second data in the second data set comprises a cell identifier, a cell longitude and latitude and verification data for verifying the validity of any second data. And obtaining effective data in the second data set based on the verification data for verifying the validity of any second data, and calculating the confidence coefficient of the cell identification in any cell group according to the effective data in the second data set.

And updating the cell identification in any cell group meeting the confidence coefficient preset condition to an online feature library in the terminal server by the terminal server.

Any terminal equipment can download and update the cell identifiers corresponding to the subway stations from the online feature library, and the terminal equipment can further establish a geofence based on the cell identifiers corresponding to the subway stations.

In summary, the terminal server can update the cell identifier with higher confidence corresponding to each subway station to the online feature library based on the data reported by the plurality of terminal devices, so that the automatic update of the subway station cell is realized, and the accuracy of the subway station cell is improved. Furthermore, any terminal equipment can download and update the cell identifier corresponding to each subway station from the online feature library and register the geofence based on the cell identifier, so that the automatic update of the position information of the subway station is realized, the accuracy of the position information of the subway station is improved, and the time consumption and the labor cost are reduced.

Fig. 3 is a schematic flow chart of a method for updating a cell feature library of a base station according to an embodiment of the present application.

As shown in fig. 3, the method includes:

s301, acquiring a plurality of first data from a plurality of devices in a first time period to obtain a first data set; any one of the first data sets includes a cell identification and a cell longitude and latitude.

In this embodiment of the present application, the first period may be in units of days, weeks, months, and the like, and it may be understood that the longer the duration of the first period, the larger the data range covered by the first data set, and the shorter the duration of the first period, the smaller the data range covered by the first data set.

In this embodiment of the present application, the device may be a smart phone, an intelligent watch, or the like, and the terminal server may obtain a plurality of first data from a certain device, and the terminal server may also obtain a plurality of first data from a plurality of terminal devices.

The first data set is derived from first data reported by the plurality of devices over a first period of time, and the first data set may also be referred to as crowd-sourced data.

Any one of the first data sets includes a cell identification and a cell longitude and latitude. The cell identifier may be a cell identifier to which the device belongs when the device transmits the first data to the server, and the cell identifier may be represented by a cell ID (identifier). The longitude and latitude of the cell are used for indicating the position of the cell identifier where the device belongs when the device sends the first data to the server, and the position is a cell coordinate composed of longitude and latitude.

In this embodiment of the present application, the server may set a storage area, where the storage area is used to store data reported by multiple devices. The server may periodically or in some manner retrieve a plurality of first data from the plurality of devices over a first period of time from the storage area to obtain a first data set.

S302, calculating the longitude and latitude of the cell corresponding to each cell identifier in the first data set.

It may be appreciated that there may be a situation where the cell identifier is repeated in the plurality of first data, and in one possible implementation, the server divides the first data set according to the cell identifier. One cell identity may correspond to one or more latitudes and longitudes.

In one possible implementation, the first data set is divided according to the cell identifiers, and the average longitude and latitude corresponding to each cell identifier in the first data set is calculated to obtain the cell longitude and latitude corresponding to each cell identifier in the first data set. Taking the average longitude and latitude of the cells corresponding to the cell identifiers as the longitude and latitude of the cells corresponding to the cell identifiers. The server may filter the discrete invalid cell longitude and latitude corresponding to each cell identifier by using a density clustering algorithm (density-based spatial clustering of applications with noise, DBSCAN), and then calculate the longitude and latitude corresponding to each cell identifier.

It can be understood that the average longitude and latitude of the cell can be replaced by the longitude and latitude calculated by using variance or the longitude and latitude calculated by any possible algorithm, and after calculation, one cell identifier can also correspond to one or more cell longitudes and latitudes, so that the situation that the cell longitude and latitude can be well matched with the position corresponding to the cell identifier can be satisfied.

S303, clustering the cells in the first data set according to the longitude and latitude of the cells corresponding to each cell identifier to obtain M cell groups, wherein any cell group comprises one or more cell identifiers, and M is an integer greater than 0.

In the embodiment of the application, a cell group may be a set of cell identifiers whose locations are within a certain range, and one or more cell identifiers may be in a cell group. For example, cell1 is included in one cell group, and cell2, cell3, and cell4 are included in the other cell group.

In a possible implementation, after the server calculates the cell longitude and latitude corresponding to each cell identifier according to step S302, the server clusters the cell identifiers with the cell longitude and latitude within a certain range to obtain M cell families. For example, if the server calculates that cell1, cell2, cell3, cell4, and cell5 are in a preset range, then the server clusters cell1, cell2, cell3, cell4, and cell5 to obtain 1 cell group, and a schematic diagram of the cell group is shown in fig. 4.

The cell family shown in fig. 4 includes 5 cell identifiers, cell1, cell2, cell3, cell4, and cell5, respectively. Each cell identifier may correspond to one or more cell longitudes and latitudes, where the cell longitudes and latitudes corresponding to cell1, cell2, cell3, cell4, and cell5 are within a certain range, and may be clustered to obtain a cell group as shown in fig. 4.

S304, acquiring a plurality of second data from a plurality of devices in a second time period to obtain a second data set; any one of the second data in the second data set comprises a cell identifier, a cell longitude and latitude and verification data for verifying the validity of any one of the second data; the start time of the second period is later than the start time of the first period.

In this embodiment of the present application, the second period may be in units of days, weeks, months, and the like, and it may be understood that the longer the duration of the second period, the larger the data range covered by the second data set, and the shorter the duration of the second period, the smaller the data range covered by the second data set. The duration of the second period of time may be the same as or different from the duration of the first period of time, which is not specifically limited in the embodiments of the present application. Since the second data set needs to acquire verification data verifying the validity of any one of the second data, the start time of the second period is later than the start time of the first period.

The second data set is obtained from second data reported by the plurality of devices in a second time period, and is used for calculating the confidence coefficient of each cell identifier in the first cell group and verifying the accuracy of each cell identifier in the first cell group.

Any one of the second data sets includes cell identification, cell longitude and latitude, and verification data for verifying validity of any one of the second data sets. The description of the cell identifier and the cell longitude and latitude in step S301 may refer to the description of the cell identifier and the cell longitude and latitude, which are not described herein.

Wherein the verification data verifying the validity of any second data is used to obtain valid second data in the second data set.

For example, in a subway station scenario, the verification data may include: and the time information reported by the target second data and the information related to entering the subway or exiting the subway within a preset time period after the time information. In this embodiment of the present application, the target second data may be any second data in the second data set.

The time information reported by the target second data may be time information obtained by the server from reported data of a plurality of devices, where the data is reported to the server after the device detects that the user may take a subway, and a method for the device to detect that the user may take a subway may refer to a description of specific steps in fig. 2, which is not repeated herein.

The information related to the entering subway or the exiting subway within the preset time period after the time information may be information related to the entering subway or the exiting subway, which is reported by a plurality of devices and is acquired within the preset time period after the server acquires the time information, and the information related to the entering subway or the exiting subway may be understood as information related to the riding subway of the user.

The information related to entering or exiting a subway includes one or more of the following: and whether the intra-application inbound or outbound code scanning page jumps or not, motion state information and via cell information are applied.

The information about whether the in-application station entering or out-station code scanning page jumps or not can be information obtained by the server after the terminal equipment detects that the user possibly takes a subway and the terminal equipment recognizes that the in-application station entering or out-station code scanning page jumps within a preset time length, and the terminal equipment reports data to the server. The device application in-inbound or outbound code-scanning page jumps may be: 1. refreshing a two-dimensional code of an inbound or outbound code scanning page; 2. the inbound or outbound sweep code page jumps to a sweep code success or pay success page.

The motion state information can be information obtained by the user after the terminal equipment detects that the user possibly rides on the subway and the sensor in the terminal equipment senses the speed of the user within a preset time period and the terminal equipment reports data to the server.

The information of the passing cell can be information obtained by the server after the terminal equipment detects that the user possibly takes the subway and the terminal equipment identifies the passing cell within a preset time length and reports data to the server.

Based on the verification data of the target data, the server can verify the validity of the second data in the second data set to obtain more accurate valid data in the second data set so as to evaluate the accuracy of cell identification in any cell group.

In the embodiment of the application, the server may periodically or in a certain manner fetch a plurality of second data from the plurality of devices in the second period of time from the storage area to obtain a second data set.

S305, calculating the confidence coefficient of each cell identification in the first cell group according to the effective data in the second data set aiming at the first cell group in the M cell groups.

In the embodiment of the present application, the first cell group may also be referred to as any cell group.

The effective data in the second data set is data meeting certain conditions and is used for calculating the confidence coefficient of each cell identifier in the first cell group.

Taking a subway station scene as an example, the effective data in the second data set may be second data reported to the server after the terminal device detects that the user takes a subway, and the method for judging the validity of the second data includes: the method comprises the steps that a server obtains an in-application inbound or outbound code scanning page reported by equipment in a preset time period to jump, the server obtains that the movement speed reported by the equipment is greater than a speed threshold value in the preset time period, and the server obtains that the information of the through cell reported by the equipment comprises cells covered by subway lines in the preset time period.

The method comprises the steps that a server obtains an in-application inbound or outbound code scanning page reported by equipment within a preset time length, for example, the preset time length is 10 minutes, after a terminal equipment detects that a user possibly takes a subway, the terminal equipment recognizes that the in-application inbound code scanning page jumps to a code scanning successful page after 5 minutes, the terminal equipment reports data to the server, the server obtains in-application inbound or outbound code scanning page jumping information, and the server judges second data in a corresponding second data set to be effective data based on the verification information; or after the terminal equipment detects that the user possibly takes the subway, the terminal equipment still does not recognize that the inbound code scanning page in the equipment application jumps after 10 minutes, the terminal equipment reports data to the server, the server acquires the inbound or outbound code scanning page jumping information in the application, and the server judges that the second data in the corresponding second data set is not valid data based on the verification information.

The method comprises the steps that a server acquires that the movement speed reported by equipment is greater than a speed threshold value within a preset duration, for example, the preset duration is 10 minutes, after a terminal device detects that a user possibly takes a subway, a sensing module in the terminal device recognizes that the movement speed of the user at the moment is 80 km/h and is greater than a set speed threshold value by 15 km/h, the terminal device reports data to the server, the server acquires movement state information, and the server judges that second data in a corresponding second data set are effective data based on the verification information; or after the terminal equipment detects that the user possibly takes the subway, the sensing module in the terminal equipment recognizes that the motion speed of the user at the moment is 5 km/h and is smaller than the set speed threshold value for 15 km/h after the terminal equipment detects that the user possibly takes the subway, the terminal equipment reports data to the server, the server acquires motion state information, and the server judges that the second data in the corresponding second data set is not valid data based on the verification information.

The server acquires the information of the through cells reported by the equipment in the preset time length, wherein the information of the through cells comprises cells covered by subway lines, for example, the preset time length is 10 minutes, after the terminal equipment detects that a user possibly takes a subway, the terminal equipment recognizes that the through cells are the cells corresponding to subway stations after 3 minutes, the terminal equipment reports data to the server, the server acquires the information of the through cells, and the server judges that the second data in the corresponding second data set are effective data based on the verification information; or after the terminal equipment detects that the user possibly takes the subway, the terminal equipment recognizes that the passed cell is not the cell corresponding to the subway station after 3 minutes, the terminal equipment reports data to the server, the server acquires the information of the passed cell, and the server judges that the second data in the corresponding second data set is not valid data based on the verification information.

The confidence is used to evaluate the accuracy of the cell identification in the first cell family. The specific description of the confidence coefficient refers to the description of the confidence coefficient hereinafter, and will not be repeated here.

In a possible implementation manner, the server calculates the confidence coefficient of each cell identifier of the first cell group in the M cell groups according to the effective data in the second data set, and can evaluate the accuracy of each cell identifier of the first cell group according to the confidence coefficient to obtain the cell identifier with higher accuracy.

S306, updating the cell identification of which the reliability in the first cell group meets the preset condition in an online feature library corresponding to the first cell group, wherein the content of the online feature library is used for downloading and updating of the terminal equipment.

The online feature library in the server comprises cell identifiers with confidence degrees meeting preset conditions, and the online feature library is used for downloading updated cell identifiers by the terminal equipment.

In one possible implementation manner, the server calculates a confidence level of each cell identifier in the first cell group according to the valid data in the second data set. When the confidence coefficient of the cell identification in the first cell group meets the preset condition, updating the cell identification meeting the preset condition in the online feature library corresponding to the first cell group. For example, the preset condition is that the confidence coefficient is greater than a certain value, and the range of the value may be a rational number greater than or equal to 0 and less than 1, such as 0, 0.5, 0.8, and the like. The smaller the value, the larger the coverage range of cell identification in the online feature library; the greater the value, the higher the accuracy of the cell identification in the online feature library. If the preset condition is that the confidence coefficient is required to be larger than 0.5, the server calculates that the confidence coefficient of the cell1 in one cell group is 0.6, and the cell1 is updated to the online feature library according to the preset condition; the confidence of the cell2 in the cell group is 0.3, the preset condition is not satisfied, the cell2 cannot be updated into the online feature library, and if the cell is already in the online feature library, the cell can be processed offline.

In summary, the terminal server can continuously acquire a plurality of data from the terminal device to obtain the first data set and the second data set, the confidence coefficient of each cell identifier in any cell group is obtained by adopting the method, and the information in the online feature library is continuously updated based on the confidence coefficient of each cell identifier in any cell group, so that the cell information in the online feature library is more accurate, the better automatic updating of the cell of the base station cell is realized through the terminal server, and the time consumption and the labor cost are reduced.

Based on the corresponding embodiment of fig. 3, some possible implementations of the embodiments of the present application are described below.

In a possible implementation manner, calculating the confidence of each cell identifier in the first cell group according to the valid data in the second data set includes:

for a first cell identifier in a first cell group, calculating a first number of days that the first cell identifier appears in the valid data, and/or a first number of times that the first cell identifier appears in the valid data; and calculating the confidence coefficient of the first cell identifier according to the first days, the first times, the total days of the second time period and/or the total times of reporting the cell identifier in the second time period.

In this embodiment of the present application, the first cell identifier may be any cell identifier in the first cell group, the first number of days is a total number of days that the first cell identifier appears in the valid data, and the first number of times is a total number of times that the first cell identifier appears in the valid data.

A specific calculation mode is as follows: and calculating the ratio of the first days to the total days of the second time period to obtain the confidence of the first cell identifier. Expressed by a formula, the following formula can be satisfied:

confidence of first cell identification = first day/total day of second time period.

Another specific calculation mode is as follows: calculating the ratio of the first days to the total days of the second time period to obtain a first confidence coefficient of the first cell identifier; calculating the ratio of the first times to the total times of reporting the cell identifier in the second time period to obtain a second confidence coefficient of the first cell identifier; and obtaining the confidence coefficient of the first cell identifier according to the first confidence coefficient and the second confidence coefficient.

Expressed by a formula, the following formula can be satisfied:

first confidence = first number of days/total number of days of the second time period for the first cell identification;

the second confidence level of the second cell identifier=the first times/the total times of reporting the cell identifier in the second time period;

confidence of first cell identity = first confidence first weight + second confidence second weight.

The first weight is a weight corresponding to the first confidence coefficient, the second weight is a weight corresponding to the second confidence coefficient, for example, the sum of the first weight and the second weight may be 1, and the values of the first weight and the second weight are not specifically limited in the embodiment of the present application.

Therefore, the confidence coefficient of each cell identifier in the first cell group is calculated according to the effective data in the second data set, and the accuracy of each cell identifier in the first cell group can be evaluated, so that the cell identifier with higher accuracy is obtained.

In a possible implementation, any of the first data in the first data set further includes wireless-fidelity (WiFi) information; the method further comprises the steps of: determining WiFi characteristics corresponding to each cell identifier in the first data set; according to the longitude and latitude of the cell corresponding to each cell identifier, clustering the cells in the first data set, including: clustering the cells in the first data set according to the longitude and latitude of the cells corresponding to the cell identifiers and the WiFi characteristics corresponding to the cell identifiers; any cell group further comprises WiFi features corresponding to the cell identifiers in any cell group.

In this embodiment, when the WiFi is used in a subway station scene and the device is located on the ground and reports data to the server, the purpose is to make the cell identifier obtained by the server more accurate, when the WiFi feature indicates that the device sends first data to the server, the geographic range corresponding to the cell identifier to which the device belongs, and the WiFi feature may be indicated by a basic service set identifier (basic service set identifier, BSSID).

According to the longitude and latitude of the cell corresponding to each cell identifier and the WiFi feature corresponding to each cell identifier, clustering the cell identifiers in the first data set, including: and clustering cell identifiers with the same WiFi characteristics and/or cell identifiers with the longitude and latitude distance smaller than the distance threshold value in a cell group.

In the embodiment of the application, the cell group may be a set of cell identifiers with cell longitude and latitude within a certain range, or a set of cell identifiers with same WiFi characteristics, or a set of cell identifiers with cell longitude and latitude within a certain range and same WiFi characteristics. One or more cell identities may be in a cell family. For example, cell1 is included in one cell group, and cell2, cell3, and cell4 are included in the other cell group.

Optionally, after the server calculates the cell longitude and latitude corresponding to each cell identifier according to step S302, clustering the cell identifiers with the cell longitude and latitude smaller than the distance threshold to obtain M cell families. For example, if the server calculates that cell1, cell2, cell3, cell4, and cell5 are in a preset range, then the server clusters cell1, cell2, cell3, cell4, and cell5 to obtain 1 cell group.

Optionally, after obtaining the WiFi features corresponding to the cell identifiers, the server clusters the cell identifiers with the same WiFi features to obtain M cell families. For example, if the server determines that the WiFi characteristics of cell1, cell2, cell3, and cell4 are the same, then clustering cell1, cell2, cell3, and cell4 to obtain 1 cell group.

Optionally, after the server calculates the cell longitude and latitude corresponding to each cell identifier according to step S302, clustering the cell identifiers with the cell longitude and latitude smaller than the distance threshold and the same WiFi characteristic to obtain M cell families. For example, if the longitude and latitude of the cell1, the cell2 and the cell3 are smaller than the distance threshold and the WiFi characteristics are the same, the server clusters the cell1, the cell2, the cell3, the cell4 and the cell5 to obtain 1 cell group.

Therefore, when the equipment is located on the ground and reports data to the server, the cell identification obtained by the server is more accurate, and the cell group obtained by the same clustering based on the WiFi features can also better correspond to the subway station position.

In a possible implementation manner, any first data in the first data set further includes a satellite number, and determining a WiFi feature corresponding to each cell identifier in the first data set includes: dividing the first data set according to cell identifications to obtain source WiFi characteristics corresponding to the cell identifications; and removing WiFi information contained in the first data with the satellite number smaller than a preset value for the source WiFi characteristics corresponding to the cell identifications in the first data set to obtain the WiFi characteristics corresponding to the cell identifications in the first data set.

It will be appreciated that there may be a situation in which the cell identity is repeated in the plurality of first data. In one possible implementation manner, the server divides the first data set according to cell identifiers to obtain source WiFi features corresponding to the cell identifiers, where one cell identifier may correspond to one or more source WiFi features.

In this embodiment of the present application, the number of satellites is used for determining whether the cell in the first data is on the ground or underground, and a number of satellites smaller than a preset value indicates that the cell identifier is on the ground, and a number of satellites larger than the preset value indicates that the cell identifier is on the ground. For underground cell identifications, the reference effect of the WiFi features is not large, so that the server removes WiFi information contained in first data with the satellite number smaller than a preset value to obtain the WiFi features corresponding to the cell identifications in the first data set. For example, if the preset satellite number is 8 and the satellite number in the first data is 7, the server removes the WiFi information included in the first data to obtain WiFi features corresponding to the cell identifiers in the first data set; if the satellite number in the first data is 10, the server reserves WiFi information included in the first data to obtain WiFi features corresponding to the cell identifiers in the first data set.

The WiFi features corresponding to each cell identifier in the first data set may also be obtained by: and matching the subway station original WiFi information corresponding to the cell identifier with the WiFi information in the cell identifier, performing ascending order sequencing on the number value of the matched WiFi, and taking the WiFi with the number value reaching the preset condition as the WiFi characteristic corresponding to the cell identifier. For example, if the preset condition is that the number of matched WiFi is 10%, the WiFi corresponding to the number of the first 10% is taken as the WiFi feature corresponding to the cell identifier.

Therefore, the WiFi characteristics corresponding to the cell identifiers on the ground can be obtained by removing the WiFi information contained in the first data with the satellite number smaller than the preset value through the satellite number, accuracy is improved, and resources are saved.

In a possible implementation manner, any cell group further includes the longitude and latitude of each cell of one or more cell identifiers, and the subway station identifiers corresponding to each of the M cell groups are obtained from the map application according to the longitude and latitude of each cell identifier in the M cell groups. Updating the cell identification of which the reliability meets the preset condition in the first cell group in the online feature library corresponding to the first cell group comprises the following steps: and updating the cell identification of which the reliability meets the preset condition in the first cell group in an online feature library where the subway station identification corresponding to the first cell group is located.

In this embodiment of the present application, the subway station identifier may be a subway station or a subway station port. According to the longitude and latitude of each cell in the cell groups, the subway station identification corresponding to each cell group can be obtained by map application. For example, based on the longitude and latitude of the cell of each cell identifier in one cell group, if the longitude and latitude of the cell are obtained from the map application and are within the range of the subway station a, the subway station identifier corresponding to the cell group is the subway station a.

Therefore, the automatic updating of the cell identification of the subway station corresponding to the first cell group can be realized, the cell identification corresponding to the subway station is accurate, and the time consumption and the labor cost are reduced.

In a possible implementation manner, when receiving an update request of the terminal device, the terminal device sends content in the online feature library to the terminal device, and the content is used for registering the subway station geofence according to the cell identifier corresponding to the subway station identifier updated in the online feature library, so that the terminal device displays the subway card when entering the subway station geofence.

In this embodiment of the present application, the update request may be a request sent by the terminal device to the server to obtain updated content of the online feature library, where the request is used for the terminal device to obtain updated content in the online feature library of the server.

After the terminal equipment acquires the cell identifier corresponding to the updated subway station identifier in the online feature library, registering the subway station geofence based on the cell identifier corresponding to the subway station identifier. The sensing module can be arranged in the terminal equipment, the sensing module can detect whether the terminal equipment enters the geofence, and when the sensing module detects that the terminal equipment enters the subway station fence, the terminal equipment can automatically display the subway card.

Therefore, any terminal equipment can download and update the cell identifier corresponding to each subway station from the online feature library and register the geofence based on the cell identifier, so that more accurate and automatic update of the position information of the subway station is realized, and time consumption and labor cost are reduced.

Fig. 5 is a schematic diagram of updating a cell feature library of a specific base station cell according to an embodiment of the present application. The schematic diagram comprises a first data set, a second data set, an online feature library and terminal equipment.

When the terminal equipment recognizes that a user clicks a subway card and the wrist turning action occurs, the terminal equipment reports data to a server, and the server acquires a plurality of first data from a plurality of devices in a first time period to obtain a first data set, wherein the first time period is 14 days. And clustering the cell identifiers in the first data set according to the steps S302 and S303 to obtain a plurality of cell families to be evaluated.

And the server acquires a plurality of second data from the plurality of terminal devices in a second time period to obtain a second data set. Wherein the second period of time is 14 days. The second data set may include cell identification, cell longitude and latitude, and verification data for verifying the validity of any of the second data. The verification data may include: and the time information reported by the target second data and the information related to entering the subway or exiting the subway within a preset time period after the time information. As shown in fig. 5, the preset duration may be 10 minutes, and the information related to entering or exiting the subway may include information about whether the in-application or out-application scan page jumps, motion state information, via cell information, or other information for determining that the user takes the subway.

As shown in table 1, one of the obtained cell groups to be evaluated consists of cell1, cell2, cell3, cell4 and cell5, and WiFi features corresponding to the cell identifiers are WiFi1, wiFi2, wiFi3, wiFi4 and WiFi5, respectively. The cell family can be expressed as: { "cell" [ "cell1", "cell2", "cell3", "cell4", "cell5" ], "WiFi" [ "WiFi1", "WiFi2", "WiFi3", "WiFi4", "WiFi5" ]. And verifying to obtain the cell1-cell5 as effective data according to the verification information in the second data set.

According to step S305, the server calculates the confidence of cell1-cell5 in the cell group, and the calculated confidence results are shown in table 1.

TABLE 1

cellID	WiFiBSSID	Time of generation	At the time of updatingInterval (C)	Confidence level	Status of
						cell1	WiFi1	20220301	20220301	0.9	On-line
cell2	WiFi2	20220310	20220320	0.8	On-line
						cell3	WiFi3	20220315	20220330	0.8	On-line
cell4	WiFi4	20220312	20220318	0.07	Not on line
						cell5	WiFi5	20220301	20220327	0	Has been taken off line

The preset condition of the confidence level may be set such that the confidence level is greater than 0.5. The confidence coefficient of the cell1, the cell2 and the cell3 in the cell family is larger than 0.5, and the cell1, the cell2 and the cell3 are uploaded to an online feature library of a server, and the cell1, the cell2 and the cell3 are in an online state at the moment; the confidence coefficient of the cell4 is smaller than 0.5, and the cell4 is not online to the online feature library of the server before being online, and the cell4 is not online to the online feature library of the server, and is in an online state at the moment; and the confidence coefficient of the cell5 is smaller than 0.5, and the cell5 is previously online to the online feature library of the server, and then the cell5 is offline from the online feature library of the server, and the cell5 is in an offline state. It is understood that the evaluation period may be different for each cell identity and that the evaluation of each cell identity may be continued.

The terminal equipment can download updated cell identifiers corresponding to the subway stations from the online feature library every day and register the geofence based on the cell identifiers, so that more accurate automatic update of the position information of the subway stations is realized, and time consumption and labor cost are reduced.

As shown in fig. 6, the base station cell feature library updating apparatus may be used in a communication device, a circuit, a hardware component, or a chip, and the base station cell feature library updating apparatus may include: processor 601, interface circuit 602. The processor 601 is configured to support a step of acquiring information processing reported by the terminal device by the server, and the interface circuit 602 is configured to support a step of receiving or transmitting by the cell feature library updating device of the base station cell. The processor 601 may also be referred to as a processing unit and the interface circuit 602 may also be referred to as a communication unit.

The base station cell feature library updating device may further include: a memory 603. The memory 603, the processor 601, and the interface circuit 602 are connected by lines. The memory may also be referred to as a storage unit.

The memory unit 603 may include one or more memories, which may be one or more devices, devices in a circuit for storing programs or data.

The storage unit 603 may exist independently and be connected to the processor 601 of the express delivery prompt device through a communication line. The memory unit 603 may be integrated with the processor 601.

The storage unit 603 may store computer-executable instructions of the method in the terminal device to cause the processor 601 to perform the method in the above-described embodiment.

The storage unit 603 may be a register, a cache, a RAM, or the like, and the storage unit 603 may be integrated with the processor 601. The memory unit 603 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, and the memory unit 603 may be independent of the processor 601.

In a possible implementation manner, the computer-executed instructions in the embodiments of the present application may also be referred to as application program code, which is not specifically limited in this embodiment of the present application.

Optionally, the interface circuit 602 may also include a transmitter and/or a receiver. Alternatively, the processor 601 may include one or more CPUs, and may be other general purpose processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor or in a combination of hardware and software modules within a processor.

Embodiments of the present application also provide a computer program product comprising one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL), or wireless (e.g., infrared, wireless, microwave, etc.), or semiconductor medium (e.g., solid state disk, SSD)) or the like.

Embodiments of the present application also provide a computer-readable storage medium. The methods described in the above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. Computer readable media can include computer storage media and communication media and can include any medium that can transfer a computer program from one place to another. The storage media may be any target media that is accessible by a computer.

As one possible design, the computer-readable medium may include compact disk read only memory (CD-ROM), RAM, ROM, EEPROM, or other optical disk memory; the computer readable medium may include disk storage or other disk storage devices. Moreover, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital versatile disc (digital versatile disc, DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims

1. The method for updating the cell characteristic library of the base station cell is characterized by comprising the following steps of:

acquiring a plurality of first data from a plurality of devices in a first time period to obtain a first data set; any one of the first data in the first data set comprises a cell identifier and a cell longitude and latitude;

calculating cell longitude and latitude corresponding to each cell identifier in the first data set;

Clustering the cell identifications in the first data set according to the cell longitude and latitude corresponding to each cell identification to obtain M cell families, wherein any cell family comprises one or more cell identifications, and M is an integer greater than 0;

acquiring a plurality of second data from a plurality of devices in a second time period to obtain a second data set; any one of the second data in the second data set comprises a cell identifier, a cell longitude and latitude, and verification data for verifying the validity of any one of the second data; the start time of the second time period is later than the start time of the first time period;

for a first cell group in the M cell groups, calculating the confidence coefficient of each cell identifier in the first cell group according to the effective data in the second data set;

updating the cell identification of which the reliability meets the preset condition in the first cell group in an online feature library corresponding to the first cell group, wherein the content of the online feature library is used for downloading and updating of terminal equipment.

2. The method of claim 1, wherein for the target second data in the second dataset, the validation data for the target second data comprises: the time information reported by the target second data and the information related to entering a subway or exiting the subway within a preset duration after the time information; the information related to entering or exiting subways includes one or more of the following: and whether the intra-application inbound or outbound code scanning page jumps or not, motion state information and via cell information are applied.

3. The method of claim 2, wherein the calculating the confidence of each cell identification in the first cell group from the valid data in the second data set further comprises:

acquiring data meeting effective conditions from the second data set to obtain the effective data; the meeting the effective conditions includes one or more of the following: the method comprises the steps that an inbound or outbound code scanning page in an application within a preset time length jumps, the movement speed within the preset time length is larger than a speed threshold value, and the information of the cells passing through the preset time length comprises cells covered by subway lines.

4. A method according to any one of claims 1-3, wherein said calculating a confidence level for each cell identity in the first cell group from the valid data in the second data set comprises:

for a first cell identifier in the first cell family, calculating a first number of days that the first cell identifier appears in the valid data, and/or a first number of times that the first cell identifier appears in the valid data;

and calculating the confidence coefficient of the first cell identifier according to the first days, the first times, the total days of the second time period and/or the total times of reporting the cell identifier in the second time period.

5. The method of claim 4, wherein calculating the confidence of the first cell identity based on the first number of days, the first number of times, the total number of days of the second time period, and/or the total number of times that cells report in the second time period, comprises:

calculating the ratio of the first days to the total days to obtain the confidence coefficient of the first cell identifier;

or alternatively, the process may be performed,

calculating the ratio of the first days to the total days to obtain a first confidence coefficient of the first cell identifier; calculating the ratio of the first times to the total times to obtain a second confidence coefficient of the first cell identifier; and obtaining the confidence coefficient of the first cell identifier according to the first confidence coefficient and the second confidence coefficient.

6. The method of any of claims 1-5, wherein any of the first data in the first data set further comprises wireless fidelity WiFi information; the method further comprises the steps of:

determining WiFi characteristics corresponding to each cell identifier in the first data set;

the clustering of the cells in the first dataset according to the cell longitude and latitude corresponding to each cell identifier includes: clustering the cell identifications in the first data set according to the cell longitude and latitude corresponding to each cell identification and the WiFi characteristics corresponding to each cell identification;

Any cell group further comprises WiFi features corresponding to cell identifiers in any cell group.

7. The method of claim 6, wherein clustering cells in the first dataset according to the cell longitude and latitude corresponding to each cell identifier and the WiFi feature corresponding to each cell identifier comprises:

and clustering cell identifiers with the same WiFi characteristics and/or cell identifiers with the longitude and latitude distance smaller than the distance threshold value in a cell group.

8. The method of claim 6 or 7, wherein any of the first data in the first data set further comprises a satellite count, and wherein the determining the WiFi feature corresponding to each cell identification in the first data set comprises:

dividing the first data set according to cell identifiers to obtain source WiFi characteristics corresponding to the cell identifiers;

and removing WiFi information included in the first data with the satellite number smaller than a preset value for the source WiFi characteristics corresponding to the cell identifiers in the first data set to obtain WiFi characteristics corresponding to the cell identifiers in the first data set.

9. The method of any one of claims 1-8, wherein the calculating a cell longitude and latitude corresponding to each cell identifier in the first dataset comprises:

Dividing the first data set according to cell identifications, and calculating average longitude and latitude corresponding to each cell identification in the first data set to obtain the cell longitude and latitude corresponding to each cell identification in the first data set.

10. The method of any one of claims 1-9, wherein any one of the cell families further comprises cell longitudes and latitudes for each of the one or more cell identities, the method further comprising:

acquiring subway station identifiers corresponding to each of the M cell groups from a map application according to the longitude and latitude of each cell identifier in the M cell groups;

updating the cell identifier of which the reliability in the first cell group meets the preset condition in the online feature library corresponding to the first cell group comprises the following steps:

and updating the cell identification of which the reliability in the first cell group meets the preset condition in an online feature library where the subway station identification corresponding to the first cell group is located.

11. The method as recited in claim 10, further comprising:

and when receiving the update request of the terminal equipment, sending the content in the online feature library to the terminal equipment, wherein the content is used for registering the subway station geofence by the terminal equipment according to the cell identifier corresponding to the subway station identifier updated in the online feature library, so that the subway card is displayed when the terminal equipment enters the subway station geofence.

12. An electronic device, comprising:

a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-11.

13. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-11.

14. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-11.