Identity recognition method, device, computer equipment and readable storage medium

Abstract

The present invention provides an identity recognition method, device, computer equipment and readable storage medium, the method comprising: obtaining signaling data of a user to be identified within a time period to be analyzed; determining a college-related application usage index value of the user to be identified based on application access data in the signaling data; determining a base station connection index value between the user to be identified and a base station inside the college and a residence index value of the user to be identified in the college area based on base station data and signaling interaction time data in the signaling data; wherein the residence index value represents the total residence time and number of residence times of the user to be identified in the college area; if the college-related application usage index value, the base station connection index value and the residence index are respectively greater than or equal to their respective index thresholds, then it is determined that the user to be identified is a student. The present invention can quickly and accurately determine whether the identity of the user to be identified is a college student, and the recognition result is accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an identity recognition method, device, computer equipment and readable storage medium. Background Art

[0002] At present, there are more and more personalized services for college students. Therefore, identifying the user group of college students and understanding their needs can help service providers in various industries accurately grasp the needs of college students and provide more targeted services for college students.

[0003] If we want to understand the needs of college students, we first need to accurately identify college students. At present, the existing methods for identifying college students are mainly based on whether the user has a stable and regular residence situation within the boundaries of the university. However, this identification method may make misjudgments due to the complex environment around the university and the frequent outings of college students, resulting in low accuracy in identifying college students.

[0004] Therefore, there is an urgent need for an identity recognition method that can accurately identify college student users and then accurately analyze the needs of college student users. Summary of the invention

[0005] One of the purposes of the present invention is to provide an identity recognition method, apparatus, computer equipment and readable storage medium for accurately identifying college students. The present application can be implemented as follows:

[0006] In a first aspect, the present invention provides an identity recognition method, the method comprising: obtaining signaling data of a user to be identified within a time period to be analyzed; determining a university-related application usage index value of the user to be identified based on application access data in the signaling data; determining a base station connection index value between the user to be identified and a base station within the university and a residence index value of the user to be identified in the university area based on base station data and signaling interaction time data in the signaling data; wherein the residence index value represents the total residence time and number of residence times of the user to be identified in the university area; if the university-related application usage index value, the base station connection index value and the residence index are respectively greater than or equal to their respective index thresholds, then the user to be identified is determined to be the student.

[0007] In a second aspect, the present invention provides an identity recognition device, an acquisition module, a determination module and an identification module; the acquisition module is used to obtain signaling data of a user to be identified within a time period to be analyzed; the determination module is used to determine the university-related application usage index value of the user to be identified based on the application access data in the signaling data; the determination module is also used to determine the base station connection index value between the user to be identified and the internal base station of the university and the residence index value of the user to be identified in the university area based on the base station data and signaling interaction time data in the signaling data; wherein the residence index value represents the total residence time and number of residence times of the user to be identified in the university area; the identification module is used to determine that the user to be identified is the student if the university-related application usage index value, the base station connection index value and the residence index are respectively greater than or equal to their respective index thresholds.

[0008] In a third aspect, the present invention provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the identity recognition method described in the first aspect.

[0009] In a fourth aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the identity recognition method described in the first aspect.

[0010] The identity recognition method, device, computer equipment and readable storage medium provided by the present invention include: first obtaining signaling data of the user to be identified, and then using the application access data, base station data and time data in the signaling data to determine the university-related application usage index value, the university internal base station connection index value and the residence index value in the university area of ​​the user to be identified during the time period to be analyzed. It can be understood that if a user frequently connects to the internal base station of the university during the time period to be analyzed, accesses university-related applications, and often resides in the university area, then the user is most likely a user within the university. Therefore, after determining the above-mentioned index values, the present application can make a judgment on the user to be identified, that is, if the university-related application usage index value, the base station connection index value and the residence index are respectively greater than or equal to their respective index thresholds, then the user to be identified is determined to be the student, thereby accurately determining whether the identity of the user to be identified is a university student, and the identification result is accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 An application scenario diagram provided for an embodiment of the present application;

[0013] Figure 2 A schematic flow chart of an identity recognition method provided in an embodiment of the present application;

[0014] Figure 3 Another schematic flow chart of the identity recognition method provided in the embodiment of the present application;

[0015] Figure 4 A functional module diagram of an identity recognition device provided in an embodiment of the present application;

[0016] Figure 5 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0020] In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. appear to indicate an orientation or position relationship, they are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the product of the invention is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0021] In addition, the terms “first”, “second”, etc., if used, are merely used to distinguish between the descriptions and should not be understood as indicating or implying relative importance.

[0022] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0023] Existing methods for identifying college students are mainly based on whether the user has a stable and regular residence within the boundaries of the college. Statistics are collected on the residence of users of the right age within the boundaries of the college within a period of time, and analysis is made to determine whether the user has generated a long residence time, while satisfying the behavioral patterns of college students leaving school and returning to school before and after the winter and summer vacations. The degree of difference in the college-related behaviors of all users within the boundaries of the college is used to determine whether the user is a college student.

[0024] The determination method based on the regular residence generated within the boundaries of universities has the following problems:

[0025] 1) There are generally a large number of residential communities or apartments around colleges and universities. The boundary data of colleges and universities and the approximate location data of users given by base stations in mobile phone signaling are inaccurate. This may cause users such as permanent residents in the surrounding areas to be misjudged as college students.

[0026] 2) Among the recent graduates in the user group, some users will do off-campus internships or stay in school to prepare for exams. These users may stay in colleges and universities for a shorter time than the overall average or may not meet the regular schedule of leaving and returning to school during winter and summer vacations, resulting in these users being judged as non-college students.

[0027] The present invention provides a solution for optimizing the identification of college students through the connection status of core base stations of colleges and universities. Based on the users who are stably resident and of appropriate age, the solution obtains and generates users within the boundary of the college and universities, and determines the usage index value of the college-related application APP corresponding to each user through the application access data and traffic usage data in the operator data (voice call data, application access data and traffic usage data), and combines the college-related application APP score table. The core base stations of the college and universities (base stations within the campus: library, teaching building, etc.) are obtained through seed users within the college and universities. Finally, the user's residence index within the boundary of the college, the connection index of the core base station of the college and universities, and the usage score of the college-related application APP are comprehensively considered to determine whether the user is a college student.

[0028] Before introducing the specific implementation of the embodiment of the present application, a possible application scenario is first introduced. Figure 1 , Figure 1 A schematic diagram of an application scenario is shown, including at least one terminal and a computer device, and each terminal is communicatively connected to the computer device.

[0029] The terminal can be, but is not limited to, a smart phone, a smart wearable device, a tablet computer, a portable computer, etc. The terminal is used to surf the Internet, make voice calls, send and receive text messages, video chat, etc. During the above communication activities, it connects with the base station to generate signaling data, which is then transmitted to the computer device.

[0030] The computer device has data processing and analysis functions, and can be a server, a personal computer, an edge gateway, etc. The computer is used to obtain the user's signaling data, and according to the identity recognition method provided in the embodiment of the present application, quickly and accurately identify the college student user based on the signaling data.

[0031] The following takes a user corresponding to a terminal as an example to introduce in detail the identity recognition method provided in the embodiment of the present application.

[0032] See also Figure 2 , Figure 2 A schematic flow chart of an identity recognition method provided in an embodiment of the present application, wherein the identity recognition method is applied to Figure 1 The computer device in the embodiment may include the following steps:

[0033] S201: Acquire signaling data of a user to be identified within a time period to be analyzed.

[0034] In the embodiment of the present application, the time period to be analyzed may be a school year, such as from September of the current year to June of the following year. Considering that college students should usually be in colleges during this period, signaling data of this period is obtained for identity recognition, and the recognition result may be consistent with the actual result.

[0035] Signaling data is the communication data between the terminal and the base station. When the user uses the terminal to make or answer calls, receive or send text messages, browse the web, and other communication behaviors, a communication relationship will be established with the base station near the terminal. Since the location of the communication base station is fixed and known, the location of the base station can reflect the location of the user. Therefore, the signaling data field contains information such as time and location.

[0036] According to the source of signaling data, signaling big data can be divided into three categories, namely, call data, PS domain signaling data, and CS domain signaling data. Among them, call data signaling is the least of the three categories, and signaling can only be triggered when the user makes or receives a call, sends or receives a text message; CS domain signaling refers to signaling such as BSC (base station) switching, location update, power on / off, and location area switching; and PS domain signaling adds Internet access signaling. Based on these types of signaling data, the application usage indicators of the user to be identified, the connection indicators with the internal base station of the university, and the resident indicators in the university area can be determined respectively. The embodiment of this application will combine these three indicators to determine whether the user to be identified is a college student.

[0037] S202: Determine the university-related application usage index value of the user to be identified based on the application access data in the signaling data.

[0038] In the embodiment of the present application, a weight table of university-related applications is pre-established in combination with the application usage habits of college students. University-related applications are applications frequently used by college students. The weight table of university-related applications records the weights of application APPs related to the university. By analyzing the application access data of the user to be identified, the visited application APPs related to the university can be determined, and then the university-related application usage index value of the user to be identified can be determined. If a user frequently visits application APPs related to the university during the time period to be analyzed, then the user is most likely a user within the university. Therefore, by analyzing the university-related application usage index value, the accuracy and credibility of the identification results of college students can be improved.

[0039] S203: Determine the base station connection index value between the user to be identified and the base station inside the university and the residence index value of the user to be identified in the university area according to the base station data and the signaling interaction time data in the signaling data; wherein the residence index value represents the total residence time and the number of residence times of the user to be identified in the university area.

[0040] In an embodiment of the present application, the base station data in the signaling data can reflect the location information of the user to be identified. Combined with the base station data, the connection index between the user to be identified and the core base station in the university (i.e., the base station inside the university) can be determined. It can be understood that when a user frequently connects to the base station inside the university, then the user is likely to be frequently located in the university. Combined with the connection index with the base station inside the university and the length of stay and the number of stays in the university, it can be determined whether the user is a student.

[0041] S204: If the university-related application usage index value, base station connection index value, and residence index are respectively greater than or equal to their respective index thresholds, it is determined that the user to be identified is a student.

[0042] In the above-mentioned identity identification method, the embodiment of the present application first obtains the signaling data of the user to be identified, and then uses the application access data, base station data and time data in the signaling data to determine the university-related application usage index value, the university internal base station connection index value and the residence index value in the university area of ​​the user to be identified during the time period to be analyzed. It can be understood that if a user frequently connects to the internal base station of the university during the time period to be analyzed, accesses the application APP related to the university, and often resides in the university area, then the user is likely to be a user within the university. Therefore, after determining the above-mentioned index values, the present application can make a judgment on the user to be identified, that is, if the university-related application usage index value, the base station connection index value and the residence index are respectively greater than or equal to their respective index thresholds, then the user to be identified is determined to be a student, thereby accurately determining whether the identity of the user to be identified is a university student, and the identification result is accurate and reliable.

[0043] The following is a detailed introduction to each of the above steps.

[0044] In a possible implementation, in order to determine the usage index value of the university-related application of the user to be identified within the time period to be analyzed, a university-related application weight table can be pre-established to maintain the weight of each university-related application, and then the index value is determined in combination with the actual application access data of the user to be identified. Therefore, the above step S202 may include the following steps:

[0045] a1: Based on the application access data, count all the university-related applications visited by the user to be identified and the corresponding usage time of each university-related application.

[0046] a2: Determine the weight of each university-related application from the preset university-related application weight table.

[0047] In the embodiment of the present application, the university-related application weight table may be as shown in Table 1, which records the serial number and weight of each university-related application.

[0048] Table 1

[0049]

[0050] a3: Determine the usage index value of university-related applications based on the weight and usage duration of each university-related application.

[0051] According to the university-related application weight table, combined with the user's usage time of all university-related applications during the time period to be analyzed (combining the application usage time can reduce the impact of erroneous data) and weight, determine the university-related application usage index value corresponding to the user to be identified. The formula is as follows:

[0052]

[0053] Among them, Time i and Weight i They respectively represent the usage duration and weight corresponding to the i-th university-related application; N represents the number of university-related applications.

[0054] In an optional implementation, the seed users of the university can also be determined by the above-mentioned university-related application usage index values. The seed users are users whose identities as students have been confirmed. By analyzing the signaling data corresponding to the seed users, the internal base stations of the university and their weights can be predetermined. The implementation method for determining the internal base stations of the university and their weights is given below, namely:

[0055] b1: Based on the interest data of each university, obtain the permanent users located in the boundary area of ​​each university.

[0056] b2: Based on the preset age threshold of students in school, resident users are screened to obtain users to be confirmed in each university;

[0057] b3: Sort the university-related application usage index values ​​corresponding to all the users to be confirmed in each university, and determine a preset proportion of the users to be confirmed as seed users in descending order.

[0058] In the embodiment of the present application, the corresponding Area of ​​Interest (AOI) data can be obtained according to the geographical location of each university, which is mainly used to express regional geographical entities in the map, such as residential areas, schools, scenic spots, etc. Through the interest data of each university, the permanent population in the time period to be analyzed within the acquisition boundary can be determined, and then these users are preliminarily screened by age, and users who meet the age stage of university students are retained as users to be confirmed.

[0059] From these unconfirmed data, we can combine the university-related application usage index values ​​to screen and obtain university seed users, that is, sort the university-related application usage index values, and determine a preset proportion of unconfirmed users as seed users in order from high to low.

[0060] In the actual implementation process, the preset ratio can be determined according to the number of all users to be confirmed, such as setting it to 15% to ensure that most of the internal base stations of universities can be obtained. The preset ratio will be adjusted according to the number of people in each university and is not limited here.

[0061] In the embodiment of the present application, the determined seed users can be used to determine the internal base stations of the colleges and universities. Therefore, the base station connections of the seed users in each college during the school time period can be counted, the number of connections of these base stations can be counted and the number of connections can be normalized, and the normalized result can be used as the weight of the core base station. Therefore, the present application also provides an implementation method for determining the internal base stations of colleges and universities and their weights, which can include the following steps:

[0062] c1: determine the seed users in each university area;

[0063] c2: From the signaling data of the seed user during the time period when the seed user is in school, determine that the base station to which the seed user has connected is an internal base station of the university.

[0064] c3: Count the number of times each university’s internal base station is connected, and normalize the number to get the weight of each university’s internal base station.

[0065] In the embodiment of the present application, the seed user's school time period is not during winter and summer vacations or other holidays, so the internal base station of the university determined in this way is accurate and reliable.

[0066] In an optional implementation manner, after the internal base stations of the university and their weights are determined through the above implementation manner, the base station data and the signaling interaction time data can be combined to obtain the internal base station connection index value and the residence index value of the university for the user to be identified in the time period to be analyzed, that is, for the above step S203, the embodiment of the present application provides an implementation manner, and step S203 may include the following steps:

[0067] d1: Determine the internal base stations of the university to which the user to be identified has connected and the number of connections from the base station data;

[0068] d2: Determine the base station connection index based on the weight and connection times of each university's internal base station.

[0069] d3: Determine the residence index value based on the signaling interaction time corresponding to the target signaling data of the internal base station of the university and the target signaling data.

[0070] In the embodiment of the present application, the base station connection index value can be calculated according to the following formula:

[0071]

[0072] Among them, Num i and Weight i They respectively represent the internal base station of the i-th university that the user to be identified has connected to and the weight of this base station.

[0073] The residence index value of the user to be identified in the university area can be obtained by analyzing the base station data and time data in the user signaling data using any existing residence rule analysis method, which will not be repeated here.

[0074] In an optional implementation manner, in order to further improve the efficiency of identity recognition, the embodiment of the present application also uses the university-related application index values ​​of users whose identities have been determined as students and non-students, the base station connection index values ​​and the resident index values ​​of the base stations within the university as training data to train the identity recognition model, and then uses this identity recognition model to identify other users and quickly determine whether they are students in the university. Therefore, the present application also provides an implementation method for training the identity recognition model, which may include the following steps:

[0075] e1: In each university, users whose university-related application usage index value, base station connection index value, and residence index are respectively greater than or equal to their respective index thresholds are determined as positive samples, and users whose university-related application usage index value, base station connection index value, and residence index are respectively less than their respective index thresholds are determined as negative samples;

[0076] e2: Train the identity recognition model based on positive and negative samples;

[0077] e3: Use the identity recognition model to determine whether the user to be identified is a student.

[0078] In an embodiment of the present application, the above three indicators corresponding to the positive samples and the negative samples, and even the age indicator can be added, are used as training data for model training. The training process is any existing training method, which will not be repeated here. Finally, an identity recognition model capable of identifying school students is obtained. The identity recognition model is used to identify the user to be identified, which not only improves the recognition efficiency but also ensures the accuracy of identity recognition.

[0079] In an optional embodiment, Figure 2 Based on Figure 3 , Figure 3 The schematic flow chart of another identity recognition method provided in the embodiment of the present application may also include the following steps:

[0080] S205: If it is determined that the user to be identified is a non-school student user, obtain historical signaling data of the user to be identified in the time period to be analyzed in the previous year;

[0081] S206: Determine the historical university-related application usage index value, the historical base station connection index value, and the historical residence index value corresponding to the user to be identified according to the historical signaling data;

[0082] S207: If it is determined that the historical university-related application usage index value, the historical base station connection index value, and the historical residence index value are respectively less than their respective index thresholds, it is determined that the user to be identified is a non-school student.

[0083] In the embodiment of the present application, taking into account that some students who are about to graduate during the time period to be analyzed are doing off-campus internships, there is a possibility that their on-campus residence time is short and the number of core base station connections is small, the final identification result is that they are not students, resulting in a misjudgment phenomenon. Therefore, the embodiment of the present application will make further judgments on the above identification results, that is, use the data of the previous year's user to be identified to make another judgment. If it is still judged that the user is not a college student, the result can be determined.

[0084] Based on the same inventive concept, the present application also provides an identity recognition device. Figure 4 As shown, Figure 4 This is a functional module diagram of an identity recognition device provided in an embodiment of the present application. The identity recognition device 400 may include: an acquisition module 410, a determination module 420 and an identification module 430.

[0085] An acquisition module 410 is used to acquire signaling data of a user to be identified within a time period to be analyzed;

[0086] A determination module 420, configured to determine the university-related application usage index value of the user to be identified based on the application access data in the signaling data;

[0087] The determination module 420 is used to determine the base station connection index value between the user to be identified and the internal base station of the university and the residence index value of the user to be identified in the university area according to the base station data and the signaling interaction time data in the signaling data; wherein the residence index value represents the total residence time and the number of residence times of the user to be identified in the university area;

[0088] The identification module 430 is used to determine that the user to be identified is a student if the university-related application usage index value, base station connection index value and residence index are respectively greater than or equal to their respective index thresholds.

[0089] It is understandable that the acquisition module 410, the determination module 420 and the identification module 430 can be executed in coordination. Figure 2 Each step in the process is performed to achieve the corresponding technical effect.

[0090] In an optional implementation, the determination module 420 is specifically used to count all university-related applications visited by the user to be identified and the corresponding usage time of each university-related application based on the application access data; determine the weight of each university-related application from a preset university-related application weight table; and determine the university-related application usage index value based on the weight and usage time of each university-related application.

[0091] In an optional implementation, the determination module 420 is further specifically used to determine from the base station data the internal base stations of the university to which the user to be identified has been connected and the number of connections; determine the base station connection index based on the weight and number of connections of each internal base station of the university; and determine the residence index value based on the signaling interaction time corresponding to the target signaling data containing the internal base stations of the university and the target signaling data.

[0092] In an optional implementation, the determination module 420 is also specifically used to determine the seed users within each university area; the seed users are students whose identities have been determined; from the signaling data of the seed users during the time period at school, it is determined that the base stations connected by the seed users are internal base stations of the university; the number of times each internal base station of the university is connected is counted, and the weight of each internal base station of the university is obtained after the number is normalized.

[0093] In an optional implementation, the determination module 420 is further specifically used to obtain permanent users located in the boundary area of ​​each university based on the interest data of each university; based on a preset age threshold of students on campus, the permanent users are screened to obtain the users to be confirmed in each university; the university-related application usage index values ​​corresponding to all the users to be confirmed in each university are sorted, and a preset proportion of the users to be confirmed are determined as seed users in descending order.

[0094] In an optional embodiment, the identity recognition device 400 may include a training module for determining, within each university, users whose university-related application usage index values, base station connection index values, and residence index values ​​are respectively greater than or equal to their respective index thresholds as positive samples, and users whose university-related application usage index values, base station connection index values, and residence index values ​​are respectively less than their respective index thresholds as negative samples; training an identity recognition model based on positive samples and negative samples; and using the identity recognition model to identify whether the user to be identified is a student.

[0095] In an optional implementation, the acquisition module 410 is also used to obtain historical signaling data of the user to be identified in the time period to be analyzed in the previous year if it is determined that the user to be identified is a non-school student; the determination module 420 is also used to: determine the historical college-related application usage index value, historical base station connection index value and historical residence index value corresponding to the user to be identified based on the historical signaling data; the identification module 430 is also used to determine that the user to be identified is a student if it is determined that the historical college-related application usage index value, historical base station connection index value and historical residence index value are respectively greater than or equal to their respective index thresholds; if it is determined that the historical college-related application usage index value, historical base station connection index value and historical residence index value are respectively less than their respective index thresholds, the user to be identified is determined to be a non-school student.

[0096] It should be noted that the division of modules in the above embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0098] Based on the above embodiments, the present application also provides a schematic diagram of a computer device, which is used to implement the identity recognition method in the above embodiments. Figure 5 As shown, Figure 5The computer device 500 is a block diagram of a computer device provided in an embodiment of the present application, and includes a memory 501, a processor 502, a communication interface 503, and a bus 504. The memory 501, the processor 502, and the communication interface 503 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0099] Optionally, bus 504 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0100] In the embodiment of the present application, the processor 502 may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiment of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor. The software module may be located in the memory 501, and the processor 502 reads the program instructions in the memory 501, and completes the steps of the above method in combination with its hardware.

[0101] In the embodiment of the present application, the memory 501 may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or a volatile memory (volatile memory), such as RAM. The memory may also be any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present application may also be a circuit or any other device that can implement a storage function, for storing instructions and / or data.

[0102] The memory 501 can be used to store software programs and modules, such as the instructions / modules of the identity recognition device 400 provided in the embodiment of the present application, which can be stored in the memory 501 in the form of software or firmware or fixed in the operating system (OS) of the computer device 500. The processor 502 executes various functional applications and data processing by executing the software programs and modules stored in the memory 501. The communication interface 503 can be used to communicate signaling or data with other node devices.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] Understandably, Figure 5 The structure shown is for illustration only. The computer device 500 may also include Figure 5 More or fewer components as shown, or with Figure 5 Different configurations are shown. Figure 5 The components shown may be implemented in hardware, software or a combination thereof.

[0105] Based on the above embodiments, the present application further provides a storage medium, in which a computer program is stored. When the computer program is executed by a computer, the computer executes the identity recognition method provided in the above embodiments.

[0106] Based on the above embodiments, the embodiments of the present application further provide a computer program, which, when executed on a computer, enables the computer to execute the identity recognition method provided in the above embodiments.

[0107] Based on the above embodiments, the embodiments of the present application further provide a chip, which is used to read a computer program stored in a memory and to execute the identity recognition method provided in the above embodiments.

[0108] A computer program product is also provided in an embodiment of the present application, including instructions, which, when executed on a computer, enable the computer to execute the identity recognition method provided in the above embodiment.

[0109] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by instructions. These instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An identity recognition method, characterized in that: The method comprises: Obtain signaling data of the user to be identified within the time period to be analyzed; Determining the university-related application usage index value of the to-be-identified user according to the application access data in the signaling data, including: determining the university-related application usage index value according to the weight and usage duration of each university-related application; According to the base station data and the signaling interaction time data in the signaling data, the base station connection index value between the user to be identified and the base station inside the university, and the residence index value of the user to be identified in the university area are determined, including: determining the base station connection index value according to the weight and the number of connections of each base station inside the university; wherein the residence index value represents the total residence time and the number of residence times of the user to be identified in the university area; If the university-related application usage index value, the base station connection index value, and the resident index value are respectively greater than or equal to their respective index thresholds, it is determined that the user to be identified is a student; Before obtaining the application access data and signaling data of the user to be identified in the time period to be analyzed, the method further includes: determining the seed users in each of the university areas; the seed users are students whose identities have been determined; from the signaling data of the seed users in the school time period, determining that the base station connected by the seed users is the internal base station of the university; counting the number of times each of the internal base stations of the university is connected, and obtaining the weight of each internal base station of the university after normalizing the number; If it is determined that the user to be identified is a non-school student user, obtaining historical signaling data of the user to be identified in the time period to be analyzed in the previous year; Determine the historical university-related application usage index value, the historical base station connection index value, and the historical residence index value corresponding to the user to be identified according to the historical signaling data; If it is determined that the historical university-related application usage index value, the historical base station connection index value, and the historical residence index value are respectively less than their respective index thresholds, it is determined that the user to be identified is a non-school student.

2. The identity recognition method according to claim 1, characterized in that: Determining the university-related application usage index value of the user to be identified according to the application access data in the signaling data, further comprising: According to the application access data, statistics are collected on all university-related applications visited by the to-be-identified user and the corresponding usage time of each university-related application; The weight of each of the university-related applications is determined from a preset university-related application weight table.

3. The identity recognition method according to claim 1, characterized in that: According to the base station data and the signaling interaction time data in the signaling data, determining the base station connection index value between the user to be identified and the internal base station of the university and the residence index value of the user to be identified in the university area, further comprising: Determine the internal base stations of the university to which the user to be identified has been connected and the number of connections from the base station data; The residence index value is determined based on the signaling interaction time and target signaling data corresponding to the target signaling data of the internal base station of the university.

4. The identity recognition method according to claim 1, characterized in that: Determine the seed users in each of the university areas, including: According to the interest data of each of the colleges and universities, obtaining permanent users located in the boundary area of ​​each of the colleges and universities; Based on a preset age threshold of students in school, the resident users are screened to obtain the users to be confirmed in each of the colleges and universities; The university-related application usage index values ​​corresponding to all the users to be confirmed in each university are sorted, and a preset proportion of the users to be confirmed are determined as the seed users in order from high to low.

5. The identity recognition method according to claim 1, characterized in that: The method further comprises: In each university, users whose university-related application usage index value, base station connection index value, and residence index are respectively greater than or equal to their respective index thresholds are determined as positive samples, and users whose university-related application usage index value, base station connection index value, and residence index are respectively less than their respective index thresholds are determined as negative samples; Training an identity recognition model according to the positive samples and the negative samples; The identity recognition model is used to identify whether the user to be identified is a student.

6. An identity recognition device, characterized in that: include: Acquisition module, determination module and identification module; The acquisition module is used to acquire signaling data of the user to be identified within the time period to be analyzed; The determination module is used to determine the university-related application usage index value of the to-be-identified user according to the application access data in the signaling data, including: determining the university-related application usage index value according to the weight and usage duration of each university-related application; The determination module is further used to determine the base station connection index value between the user to be identified and the internal base station of the university and the residence index value of the user to be identified in the university area according to the base station data and the signaling interaction time data in the signaling data, including: determining the base station connection index value according to the weight and connection number of each internal base station of the university; wherein the residence index value represents the total residence time and the residence number of the user to be identified in the university area; The identification module is used to determine that the user to be identified is a student if the university-related application usage index value, the base station connection index value, and the resident index value are respectively greater than or equal to their respective index thresholds; The determination module is further used to determine seed users in each of the university areas; the seed users are students whose identities have been determined; from the signaling data of the seed users during the school time period, it is determined that the base station connected by the seed users is the internal base station of the university; the number of times each of the internal base stations of the university is connected is counted, and the weight of each internal base station of the university is obtained after the number is normalized; The acquisition module is further configured to acquire historical signaling data of the user to be identified in the time period to be analyzed in the previous year if it is determined that the user to be identified is a non-school student user; The determination module is further used to determine the historical university-related application usage index value, the historical base station connection index value and the historical residence index value corresponding to the user to be identified according to the historical signaling data; The identification module is further used to determine that the user to be identified is a non-school student if it is determined that the historical university-related application usage index value, the historical base station connection index value and the historical residence index value are respectively less than their respective index thresholds.

7. A computer device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the identity recognition method according to any one of claims 1 to 5.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the identity recognition method according to any one of claims 1 to 5 is implemented.

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