CN112307075B - User relationship identification method and device - Google Patents
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- CN112307075B CN112307075B CN201910707564.1A CN201910707564A CN112307075B CN 112307075 B CN112307075 B CN 112307075B CN 201910707564 A CN201910707564 A CN 201910707564A CN 112307075 B CN112307075 B CN 112307075B
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The embodiment of the invention discloses a user relationship identification method and a user relationship identification device, which are used for solving the problems of low accuracy, low application range, high cost and the like of identifying a juxtaposition relationship in the prior art. The method comprises the following steps: collecting communication data of a user pair to be identified in a first appointed time period; determining communication relation data between the user pairs to be identified according to the communication data; and determining whether a user pair to be identified has a juxtaposition relationship or not by utilizing a pre-established relationship identification model according to the communication relationship data. When the edge relationship is identified, the communication data required by the edge relationship identification can be acquired based on the mobile communication system, and additional hardware data is not needed, so that the data acquisition cost is lower; in addition, the technical scheme utilizes the pre-established relationship recognition model to determine whether the users to be recognized have the juxtaposition relationship or not without manually setting related judgment conditions and thresholds, so that the recognition efficiency is improved, and the recognition accuracy is improved.
Description
Technical Field
The present invention relates to the field of mobile communications technologies, and in particular, to a method and apparatus for identifying a user relationship.
Background
The rapid development of mobile communication technology makes the popularity of mobile phones increasingly higher, and mobile communication has become one of the important ways of our social interaction. For the mobile communication industry, identifying the user's relationship is of great importance, such as promoting the accurate marketing of family business packages. The abundant call data and MR (measurement report) data in the mobile communication system objectively reflect the curiosity relation characteristics of the vast users. The relationship between living and living is presented in comparison with blood relationship, earth relationship, industry relationship, funny relationship and the like, and is an interpersonal relationship taking living together as a connecting tie.
The existing user relationship identification mainly focuses on the identification of family relationships, and the identification of family relationships is basically blank. The juggling relationship is not equivalent to the family relationship, for example, there is generally no family relationship between rented users. However, most families can choose to live together, so that the living relationship and the family relationship still have great overlap, and therefore, a method for identifying the living relationship needs to be referred to when the living relationship is identified. Most of the existing family relationship identification methods have the defects of low accuracy, low application range, high cost and the like, and the same defects can occur if the methods are applied to the identification of the juxtaposition relationship.
Disclosure of Invention
The embodiment of the invention provides a user relationship identification method and device, which are used for solving the problems of low accuracy, low application range, high cost and the like in the identification of a juxtaposition relationship in the prior art.
In order to solve the technical problems, the embodiment of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a method for identifying a user relationship, including:
collecting communication data of a user pair to be identified in a first appointed time period; the communication data comprise ticket data and/or measurement report data of each user in the user pair to be identified;
determining communication relation data between the user pairs to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between the user pairs to be identified;
determining whether a juxtaposition relationship exists between the user pairs to be identified or not by utilizing a pre-established relationship identification model according to the communication relationship data; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether a juxtaposition relation exists among the sample user pairs or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
In a second aspect, an embodiment of the present invention further provides a user relationship identifying apparatus, including:
the acquisition module acquires communication data of a user to be identified in a first designated time period; the communication data comprise ticket data and/or measurement report data of each user in the user pair to be identified;
the first determining module is used for determining communication relation data between the user pairs to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between the user pairs to be identified;
the second determining module is used for determining whether the user pair to be identified has a juxtaposition relationship or not by utilizing a pre-established relationship identification model according to the communication relationship data; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether a juxtaposition relation exists among the sample user pairs or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
In a third aspect, an embodiment of the present invention further provides a user relationship identifying apparatus, including:
a memory storing computer program instructions;
a processor, which when executed by the processor, implements a user relationship identification method as claimed in any one of the preceding claims.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium comprising instructions that, when run on a computer, cause the computer to perform a user relationship identification method as claimed in any one of the preceding claims.
In the embodiment of the invention, when the juxtaposition is identified, communication data (including ticket data and/or measurement report data of each user in the user pair to be identified) required for identifying the juxtaposition can be acquired based on the mobile communication system, so that the juxtaposition is rich in data dimension and reliable in data quality, and additional hardware data is not required, and therefore, the data acquisition cost is lower; in addition, according to the collected communication data, the conversation behavior characteristics and/or the communication environment similarity between the user pairs to be identified can be determined, and as the conversation behavior characteristics and/or the communication environment similarity can reflect whether the users reside in the same geographic space to a certain extent, the accuracy of the identification of the juxtaposition relationship is realized; moreover, according to the technical scheme, whether the users to be identified have the edge relationship or not is determined by utilizing a pre-established relationship identification model according to the communication relationship data between the user pairs to be identified, and the edge relationship of the users can be automatically identified without manually setting related judgment conditions and thresholds, so that the identification efficiency and the identification accuracy are greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a user relationship identification method in one embodiment of the invention.
Fig. 2 is a diagram of an acquisition device architecture for ticket data and measurement report data in one embodiment of the invention.
FIG. 3 is a diagram of a device architecture for determining communication relationship data in one embodiment of the invention.
FIG. 4 is a diagram of a determining device architecture of a relationship identification model in one embodiment of the invention.
Fig. 5 is a schematic flow chart diagram of a user relationship identification method in another embodiment of the invention.
Fig. 6 is a schematic structural diagram of a user relationship recognition apparatus in an embodiment of the present invention.
Fig. 7 is a schematic diagram of a network device applied in one embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
FIG. 1 is a schematic flow chart of a user relationship identification method in one embodiment of the invention. The method of fig. 1 may include:
s102, collecting communication data of a user to be identified in a first designated time period.
The user pair to be identified comprises two users, and the representation forms of the user pair to be identified can comprise various types, such as a user name splicing composition of the two users, a communication number splicing composition respectively corresponding to the two users and the like. The communication data includes ticket data and/or measurement report data for each user in the user pair to be identified. The ticket data may be collected by servers of the operators, and the measurement report data may be collected by software by network devices or base stations of the operators.
To ensure accuracy of identification of the juxtaposition, the first specified time period is preferably the last time period, which may be, for example, the last week, the last month, the last year, or the like.
S104, determining communication relation data between the user pairs to be identified according to the communication data.
The communication relation data comprise conversation behavior characteristics and/or communication environment similarity between the user pairs to be identified.
S106, determining whether the user pairs to be identified have a juxtaposition relationship or not by utilizing a pre-established relationship identification model according to the communication relationship data.
The relationship recognition model is obtained by training according to sample communication relationship data among a plurality of sample user pairs and information whether each sample user pair has a juxtaposition relationship or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
In the embodiment of the invention, when the juxtaposition is identified, communication data (including ticket data and/or measurement report data of each user in the user pair to be identified) required for identifying the juxtaposition can be acquired based on the mobile communication system, so that the juxtaposition is rich in data dimension and reliable in data quality, and additional hardware data is not required, and therefore, the data acquisition cost is lower; in addition, according to the collected communication data, the conversation behavior characteristics and/or the communication environment similarity between the user pairs to be identified can be determined, and as the conversation behavior characteristics and/or the communication environment similarity can reflect whether the users reside in the same geographic space to a certain extent, the accuracy of the identification of the juxtaposition relationship is realized; moreover, according to the technical scheme, whether the users to be identified have the edge relationship or not is determined by utilizing a pre-established relationship identification model according to the communication relationship data between the user pairs to be identified, and the edge relationship of the users can be automatically identified without manually setting related judgment conditions and thresholds, so that the identification efficiency and the identification accuracy are greatly improved.
The method of the embodiment of the present invention will be further described with reference to specific examples.
In the above embodiment, before S102 is executed, two users meeting the preset screening condition may be selected from a large number of users to form the user pair to be identified.
In one embodiment, in order to improve the recognition efficiency and save the computing resources, the preset screening conditions may be preferably: at least N times of calls are carried out between the users corresponding to the screened communication number pair identifiers, and the call contact intersection of the two users forming the communication number pair identifiers is not null (namely, the number of call contact intersection is not zero); in order to avoid the chance, N recommended values in the above "N calls" are integers greater than 2.
In the above embodiment, the ticket data may be used to determine the call behavior characteristics between the user pair to be identified; the measurement report data may be used to determine the communication environment similarity between the pair of users to be identified. According to the difference of the communication data and the determined communication relation data, the method for identifying the juxtaposition between the user pairs to be identified is also different. The following describes in detail the method for identifying the juxtaposition corresponding to the different communication data and the different communication relationship data.
In one embodiment, the communication data includes ticket data. And determining the conversation behavior characteristics between the user pairs to be identified according to the collected call ticket data, further determining whether the user pairs to be identified have a juxtaposition relationship or not according to the conversation behavior characteristics between the user pairs to be identified by utilizing a pre-established relationship identification model. The ticket data can comprise communication numbers, two-party call time, calling and called parties and the like of each user in the user pair to be identified; the call behavior feature may include a communication number pair identification of the user pair to be identified, a total number of calls, a call time average duration, a same number of contacts, and the like.
The total number of calls comprises the total number of calls which are not distinguished from the calling party and the called party; the communication number pair identification consists of two communication numbers respectively corresponding to two users in the user pair to be identified in a spliced manner; the average duration of the call times is equal to the total duration of the calls of the two parties in the first appointed time period divided by the total number of calls; the same contact number is the same contact number in the call contacts of each user in the user pair to be identified.
In one embodiment, before determining the call behavior characteristics between the user pair to be identified according to the collected call ticket data, the method further comprises: deleting abnormal data in the ticket data; and/or deleting the appointed character before the communication number; wherein the anomaly data may include non-user numbers, illegal numbers, etc.
Wherein, the non-user number can be an alarm telephone, an enterprise service number, etc.; the illegal number may be a number displayed as an "unknown number"; the specified class characters before the communication number can be country codes (such as China is 86 and the Netherlands is 31), area codes, other prefixes (such as 12593/17951 which can enjoy long-distance preference) and the like for distinguishing different countries.
In this embodiment, by deleting the abnormal data in the ticket data and/or deleting the designated characters before the communication number, the irregular communication number and the abnormal data in the ticket data can be reduced, so that only the effective data which is helpful for identifying the juxtaposition is reserved, the calculation of the ineffective data is effectively reduced, and the quality of the communication data is improved.
In one embodiment, when determining the call behavior characteristics between the user pair to be identified according to the collected call ticket data, the following manner may be adopted:
(1) and splicing the communication numbers of the users together according to a preset splicing mode to obtain the communication number pair identification of the user pair to be identified.
The preset splicing mode can be any one of the following modes: the communication numbers of all users are spliced in sequence according to the sequence of the names of all users; the communication numbers of all users are spliced according to the numerical order of the communication numbers; etc.
(2) According to the call records of the ticket data, counting to obtain the total number of calls; the statistics is that the total number of calls of the calling party and the called party is not distinguished in the call records.
(3) Dividing the total call time length of the user pairs to be identified in the first appointed time period by the total call time number to obtain the call time average time length between the user pairs to be identified.
(4) Respectively determining call contacts of all users; and carrying out intersection operation on call contacts of each user to obtain the same number of contacts between the user pairs to be identified.
In the embodiment, the communication number pair identifiers are spliced in a preset mode, so that the communication number pair identifiers with calculation resources consumed among strangers can be effectively avoided, and the recognition efficiency is improved; the total number of calls is counted through call records, so that the call time average duration is determined, the call condition between the user pairs to be identified can be objectively reflected, and the communication intersection between the user pairs to be identified can be intuitively seen through the same number of contacts, so that whether the user pairs to be identified have a juggling relation or not is reflected.
In one embodiment, the communication data includes measurement report data. And determining the similarity of the communication environment between the user pairs to be identified according to the collected measurement report data, further determining whether the user pairs to be identified have a juxtaposition relationship or not according to the similarity of the communication environment between the user pairs to be identified by utilizing a pre-established relationship identification model.
The measurement report data may include a communication number of each user in the user pair to be identified, a connection time with the serving cell, a cell identification code of the connected serving cell, a signal receiving power of the serving cell, a signal receiving quality of the serving cell, a signal receiving power of a neighboring cell of the serving cell, a signal receiving quality of the neighboring cell, and the like.
The communication environment similarity may include a same rate of the user to be identified to the connected cell identifier, a same total duration of the user to be identified to the connected cell, a signal receiving power similarity of the user to be identified to the connected serving cell, a signal receiving quality similarity of the user to be identified to the connected serving cell, a corresponding neighbor cell similarity of the user to be identified, a signal receiving power similarity of the user to be identified to the corresponding neighbor cell, a signal receiving quality similarity of the user to be identified to the corresponding neighbor cell, and the like.
The second designated time period may be a time point set, a time period set, or the like; the second specified period is a period of a smaller period of bits within the first specified period. For example, the second specified time period is the following set of time points: weekly 0 hours, monthly 0 hours, yearly 0 hours, etc.; the second specified time period is a set of time periods of: 0-5 hours per week, 0-5 hours per month, 0-5 hours per year, etc.; if the first specified time period is the last month, the second specified time period may be the month 0 time, the month 0 time-5 time, or the like.
In the embodiment, the measurement report data embody the signal receiving power and the signal receiving quality of the service cell and the adjacent cell connected by the user, can objectively reflect the communication environment of the user, ensures that the data has rich dimensionality and reliable data quality, and further provides a rich and accurate data basis for identifying the juxtaposition.
In one embodiment, when determining the similarity of the communication environments between the user pairs to be identified according to the collected measurement report data, the following manner may be adopted:
(1) determining the same times of the cell identification codes of the users to be identified to the connection within a second designated time period; dividing the same times of the cell identification codes by the maximum total connection duration of the users to be identified in the first appointed time period to obtain the same rate of the users to be identified to the connected cell identification codes.
The maximum total connection duration is as follows: connecting two users in the user pair to be identified with the maximum value in the number of days of the cell in a first appointed time period; for example, the user pair to be identified includes user a and user B, the first specified time period being the last month and the second specified time period being the early morning 0. Assuming that the number of cell identities connected by the user a and the user B is 16 in the early morning 0, the user a connects the cells for 25 days in one month, and the user B connects the cells for 20 days in one month, then the cell identity ratio of the connection of the user a and the user B is 16 divided by 25 and equal to 0.64.
If the second designated time period is a time point set, dividing the same times of the cell identification codes by the maximum total connection duration of the users to be identified in the first designated time period to obtain the same rate of the users to be identified to the connected cell identification codes; and if the second designated time period is a time period set, dividing the same times of the cell identification codes by the number of integral points in the time period, and dividing the times by the maximum total connection time length of the user to be identified in the first designated time period to obtain the same rate of the user to be identified to the connected cell identification codes.
(2) And determining the same total times of the cell identification codes of the users to be identified to the connection within a second designated time period, and obtaining the same total cell duration of the users to be identified to the connection.
The second designated time period is a time period set, and the same total times of the cell identification codes connected by the users to be identified are equal to the same total time of the cells connected by the users to be identified.
(3) Judging whether the service cells connected by the users to be identified are the same in a second designated time period; if not, determining that the similarity of the signal receiving power of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving power of the user to be identified to the connected serving cell according to a first difference value between the signal receiving power of the user to be identified to the connected serving cell; wherein the signal received power similarity is inversely related to the first difference.
For example, the user pair to be identified includes user A and user B, and the second specified time period is 0 hours in the first morning. Suppose that the signal receiving power of the serving cell connected by the user a on the first morning 0 is P a The signal receiving power of the service cell connected by the user B on the first day 0 in the early morning is P b The signal received power similarity of the serving cells connected by the first morning 0 of the user a and the user B can be expressed as:
along the above example, assuming that the first specified time period is 0-5 hours in the early morning, then according to the above steps, the signal receiving power similarity of the connected serving cell between the user a and the user B in each 0-5 hours in the early morning in one month can be calculated, and the obtained results are summed to calculate the signal receiving power similarity of the user to be identified to the connected serving cell in 0-5 hours in the early morning.
In addition, the similarity of the received power of the user to be identified to the corresponding neighbor signal in the first specified time period can be determined in the following manner:
firstly, judging whether a user pair to be identified has the same neighbor cell in a second designated time period; the signal received power similarity of the neighboring cells is calculated only for the signal received powers of the same neighboring cells.
Secondly, determining the similarity of the signal receiving power of the user to be identified to the corresponding one or more adjacent cells according to a first difference value between the signal receiving power of the user to be identified to the corresponding one or more adjacent cells; wherein the signal received power similarity is inversely related to the first difference.
And thirdly, summing the signal receiving power similarity of the user to be identified to the corresponding one or more adjacent cells to obtain the signal receiving power similarity of the user to be identified to the corresponding adjacent cells in the second designated time period.
And finally, calculating the similarity of the adjacent signal receiving power of each time point in the first appointed time period according to the steps, and summing to obtain the similarity of the adjacent signal receiving power of the user to be identified in the first appointed time period.
(4) Judging whether the service cells connected by the users to be identified are the same in a second designated time period; if not, determining that the signal receiving quality similarity of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving quality of the user to be identified to the connected serving cell according to a second difference value between the signal receiving quality of the user to be identified to the connected serving cell; wherein the signal reception quality similarity is inversely related to the second difference.
For example, the user pair to be identified includes user A and user B, and the second specified time period is 0 hours in the first morning. Suppose that the signal reception quality of the serving cell connected by user a on the first morning 0 is Q a The signal receiving quality of the service cell connected by the user B on the first day 0 in the early morning is Q b The signal reception quality similarity of the serving cells connected by the first morning 0 of the user a and the user B can be expressed as:
along the above example, assuming that the first specified time period is 0-5 hours in the early morning, then according to the above steps, the signal receiving quality similarity of the connected serving cell between the user a and the user B in each 0-5 hours in the early morning in one month can be calculated, and the obtained results are summed to calculate the signal receiving quality similarity of the user to be identified to the connected serving cell in 0-5 hours in the early morning.
In addition, the similarity of the reception quality of the user to be identified to the corresponding neighbor cell signal in the first specified time period can be determined in the following manner:
firstly, judging whether a user pair to be identified has the same neighbor cell in a second designated time period; the neighbor signal reception quality similarity is calculated only for the signal reception quality of the same neighbor.
Secondly, determining the similarity of the signal receiving quality of the user to be identified to the corresponding one or more adjacent cells according to a second difference value between the signal receiving quality of the user to be identified to the corresponding one or more adjacent cells; wherein the signal reception quality similarity is inversely related to the second difference.
And thirdly, summing the signal receiving quality similarity of the user to be identified to the corresponding one or more adjacent cells to obtain the signal receiving quality similarity of the user to be identified to the corresponding adjacent cells in the second designated time period.
And finally, calculating the similarity of the adjacent signal receiving quality of each time point in the first appointed time period according to the steps, and summing to obtain the similarity of the adjacent signal receiving quality of the user to be identified in the first appointed time period.
(5) Executing a union set taking operation on adjacent cells corresponding to each user in the user pair to be identified to obtain an adjacent cell set of the user pair to be identified; determining the same number of adjacent cells of the user pair to be identified; determining the similarity of the users to be identified to the corresponding neighbor cells according to the ratio of the same number of neighbor cells to the neighbor cell set; wherein the neighbor cell similarity is positively correlated with the same number of neighbor cells.
Wherein, the neighbor cells corresponding to the users respectively refer to the neighbor cells of the service cell connected by the users. For example, the number of identical neighbors corresponding to the user pair at n can be M n Representation, to be identifiedThe user can use L for the neighbor set corresponding to n time n A representation; where n is the time point. Then the similarity S of the user pair to be identified corresponding to the neighbor cell when n n The calculation method of (2) can be expressed as:
for another example, the user pair to be identified includes user A and user B, and the second specified time period is 0 hours in the first morning. According to the above steps, the number of the same neighbor cells corresponding to the user A and the user B on the first morning 0 is M 0 The neighbor set corresponding to the user A and the user B on the first morning 0 is L 0 Neighbor cell similarity S corresponding to user A and user B 0 The method comprises the following steps:
along the above example, assuming that the first specified time period is 0-5 hours in the early morning, then according to the above steps, the adjacent cell similarity corresponding to 0-5 hours in each day of early morning of the user a and the user B in one month can be calculated, and the obtained results are summed, and the adjacent cell similarity corresponding to the user a and the user B in 0-5 hours in the early morning can be calculated.
In the above embodiment, fig. 2 is a diagram of an acquisition device architecture for ticket data and measurement report data. The core network device is used for collecting call ticket data, the base station controller/wireless network controller and the evolution node B (such as a base station) are used for collecting measurement report data, and the collected communication data are stored in the data storage server. Fig. 3 is a diagram of a configuration of a determination device for communication relationship data. The data is communication data stored in the data storage server in fig. 2, and the calculated communication relationship data is stored in the communication relationship data storage server.
In one embodiment, the relationship identification model may be trained as follows:
firstly, collecting sample communication relation data among a plurality of sample user pairs; wherein, each sample user pair is marked with information whether to have a juxtaposition or not in advance.
And secondly, learning based on sample communication relation data among a plurality of sample user pairs and information of whether each sample user pair has a juxtaposition relation or not to obtain a relation recognition model.
The sample data, such as a CART decision tree, can be learned by using a supervised machine learning algorithm, which avoids the requirement for data distribution and has no requirement for feature independence. The algorithm uses the radix index as a criterion of attribute decision splitting to find out a logic corresponding relation or rule between the input variable and the output variable value in the data, thereby realizing the prediction of the new data output variable.
In the present embodiment, fig. 4 is a diagram of a configuration of a determination apparatus of a relationship recognition model. The sample communication relation data among a plurality of sample user pairs can form a training data set, the training data set is imported into a machine learning model, and the relation recognition model can be output through learning.
Fig. 5 is a schematic flow chart diagram of a user relationship identification method in another embodiment of the invention. In this embodiment, it is assumed that the first specified period is the last month, and the second specified period is the rest period in the last month, that is, the early morning 0 time-5 time (hereinafter, abbreviated as the early evening 0 time-5 time or the month rest period) in the last month, and the user pair to be identified includes user a and user B. In this embodiment, the communication data and the communication relationship data may be represented in the form of a data wide table. The method of fig. 5 may include:
S501, collecting ticket data and measurement report data of the last month of the user A and the user B.
The data width table of the measurement report data is shown in table one.
List one
| Field name | Field description |
| msisdn | Mobile phone number |
| cellid | Cell identification code |
| starttime | Time |
| servingrsrp | Signal receiving power of serving cell |
| servingrsrq | Signal receiving quality of service cell |
| neighbor1pci | PCI of first neighbor cell |
| neighbor1rsrp | First neighbor cell signal received power |
| neighbor1rsrq | First neighbor cell signal reception quality |
| neighbor2pci | PCI of second neighbor cell |
| neighbor2rsrp | Second neighbor cell signal received power |
| neighbor2rsrq | Second neighbor cell signal reception quality |
| neighborNpci | PCI of N-th neighbor cell |
| neighborNrsrp | N-th neighbor cell signal receiving power |
| neighborNrsrq | N-th neighbor cell signal reception quality |
S502, deleting the abnormal data in the ticket data and the appointed character before the communication number.
Wherein the anomaly data may include non-user numbers, illegal numbers, etc. The non-user number may be an alarm phone, an enterprise service number, etc.; the illegal number may be a number displayed as an "unknown number"; the specified class characters before the communication number can be country codes (such as China is 86 and the Netherlands is 31), area codes, other prefixes (such as 12593/17951 which can enjoy long-distance preference) and the like for distinguishing different countries.
For example, the abnormal data may be numbers 110, 120, etc., and the specified characters of the class before the communication number may be characters 86, 010, 17951, etc.
S503, according to the ticket data and the measurement report data, determining the conversation behavior characteristics and the communication environment similarity between the user A and the user B.
Specifically, the calculation manner for determining the call behavior characteristics and the similarity of the communication environments between the user a and the user B is described in the above embodiments, which is not described herein.
It is assumed that the cell identification codes of the serving cells to which the user a and the user B are connected during the rest period are shown in table two.
Watch II
| Rest period | User' s | Cell identification code | Signal receiving power (dB) |
| 0 | A | Cell1 | -88 |
| 0 | B | Cell2 | -85 |
| 1 | A | Cell3 | -89 |
| 1 | B | Cell3 | -67 |
| 2 | A | Cell3 | -87 |
| 2 | B | Cell3 | -86 |
| 3 | A | Cell3 | -85 |
| 3 | B | Cell3 | -87 |
| 4 | A | Cell3 | -90 |
| 4 | B | Cell3 | -85 |
| 5 | A | Cell3 | -88 |
| 5 | B | No service | - |
Then, according to the calculation method in the above embodiment, the signal reception power similarity of the serving cell connected by the user a and the user B in the month rest period=the signal reception power similarity of the serving cell connected by the month early 0+the signal reception power similarity of the serving cell connected by the month early 1+the signal reception power similarity of the serving cell connected by the month early 2+the signal reception power similarity of the serving cell connected by the month early 3+the signal reception power similarity of the serving cell connected by the month early 4+the signal reception power similarity of the serving cell connected by the month early 5=0+1/(1+| -89- (-67) |/max (| -89|, -67|) +1/(1+| -87- (-86) |/max (| -87|, -86|) +1/(1+) -85+) -87)/max (| -85| -87|) +1+) (1+) -90+) -80|1+) -90+80| -90|80|75|80|0+80| -90|75|+80|75|0|80|75|0+80|75|0.
For another example, assume that the neighbor signal received powers of the user a and the user B corresponding to the rest period of the month are as shown in table three.
Watch III
According to the calculation method in the above embodiment, the adjacent signal received power similarity= (0.9574+0.9314+0.95+0.9505) + (0.9882+0.9884+1+1+1+0.9895+0.9896) + (0.9684+1+0.9775+0.9778+0.9888+0.9406) + (0.956+0.967+0.967+0.9574+0.9681+0.949+0.9592+0.9417) + (0.9667+0.9468+0.9216+0.9314+0.9314+0.9314+ 30.8712) ++0= 30.8712) between the user a and the user B in the rest period can be calculated.
S504, determining whether the user A and the user B have a juxtaposition or not by utilizing a pre-established relation recognition model according to the conversation behavior characteristics and the communication environment similarity.
In this embodiment, the call behavior features include the following: the communication number pair identification, the total number of calls, the call time average duration and the same number of contacts; the communication environment similarity includes the following: the method comprises the steps of 1, 2, 3, 4, 5 and 5, wherein the cell identity code identical rate is connected in the early morning 0, the cell identity code identical rate is connected in the early morning 1, the cell identity code identical rate is connected in the early morning 2, the cell identity code identical rate is connected in the early morning 4, the total time length of the same cells connected in the early morning, the cell identity code identical rate is connected in the early rest period, the signal receiving power similarity of the serving cells connected in the early rest period, the signal receiving quality similarity of the serving cells connected in the early rest period, the adjacent cell similarity corresponding to the early morning 0, the adjacent cell similarity corresponding to the early morning 1, the adjacent cell similarity corresponding to the early morning 2, the adjacent cell similarity corresponding to the early morning 3, the adjacent cell similarity corresponding to the early morning 4, the adjacent cell similarity corresponding to the early morning 5, the adjacent cell similarity corresponding to the early rest period, the signal receiving power similarity of the adjacent cell corresponding to the early rest period, and the signal receiving quality similarity of the adjacent cell signal receiving period corresponding to the early rest period.
In the embodiment, the measurement report data can objectively reflect the communication environment where the users are located in real time, and the data can calculate whether the communication environments where the two users are located in real time are similar or not, so that whether the two users are located in the same environment scene or not can be judged, particularly for a month rest period, the period is critical to judging whether the two users have a juggling relationship or not, and the recognition accuracy can be improved; and by utilizing the relationship identification model, the user edge relationship can be automatically identified without manually setting related judgment conditions and thresholds, so that the identification efficiency and accuracy are improved.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Fig. 6 is a schematic structural diagram of a user relationship recognition apparatus in an embodiment of the present invention. Referring to fig. 6, a user relationship identifying apparatus 600 may include:
The acquisition module 610 acquires communication data of a user pair to be identified in a first designated time period; the communication data comprises ticket data and/or measurement report data of each user in the user pair to be identified;
a first determining module 620, configured to determine communication relationship data between the user pair to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between user pairs to be identified;
a second determining module 630, configured to determine, according to the communication relationship data, whether there is a juxtaposition relationship between the user pairs to be identified using a pre-established relationship identification model; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether each sample user pair has a juxtaposition relation or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
In one embodiment, the call ticket data comprises at least one of a communication number, a double-party call time, a double-party call duration and a call calling party and a call called party; the communication behavior characteristics comprise at least one of communication number pair identification, total communication times, communication time average duration and the same contact number.
In one embodiment, the first determination module 620 includes at least one of:
The splicing unit splices the communication numbers of all users together according to a preset splicing mode to obtain the communication number pair identification of the user pair to be identified;
the computing unit divides the total call duration of the user pair to be identified in the first appointed time period by the total call times to obtain the call time average duration between the user pair to be identified;
the determining unit is used for determining the call contact persons of all the users respectively; and carrying out intersection operation on call contacts of each user to obtain the same number of contacts between the user pairs to be identified.
In one embodiment, the first determination module 620 further includes:
a deleting unit configured to delete abnormal data in the ticket data; and/or deleting the appointed character before the communication number; wherein the abnormal data comprises at least one of a non-user number and an illegal number.
In one embodiment, the measurement report data includes at least one of a communication number, a connection time with a serving cell, a cell identification code of the connected serving cell, a signal reception power of the serving cell, a signal reception quality of the serving cell, a signal reception power of a neighbor cell of the serving cell, and a signal reception quality of the neighbor cell;
the communication environment similarity comprises at least one of the same rate of the user to be identified to the connected cell identification code, the same total time length of the user to be identified to the connected cell, the signal receiving power similarity of the user to be identified to the connected service cell, the signal receiving quality similarity of the user to be identified to the connected service cell, the corresponding adjacent cell similarity of the user to be identified, the signal receiving power similarity of the user to be identified to the corresponding adjacent cell and the signal receiving quality similarity of the user to be identified to the corresponding adjacent cell in the second designated time period.
In one embodiment, the second determination module 630 includes at least one of:
a first determining and calculating unit for determining the same number of times of the user to be identified to the connected cell identification code in a second designated time period; dividing the same times of the cell identification codes by the maximum total connection duration of the users to be identified in a first designated time period to obtain the same rate of the users to be identified to the connected cell identification codes;
the first judging and calculating unit is used for judging whether the user to be identified is the same with the connected service cell in the second designated time period; if not, determining that the similarity of the signal receiving power of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving power of the user to be identified to the connected serving cell according to a first difference value between the signal receiving power of the user to be identified to the connected serving cell; wherein the signal received power similarity is inversely related to the first difference;
the second judging and calculating unit is used for judging whether the user to be identified is the same with the connected service cell in a second designated time period; if not, determining that the signal receiving quality similarity of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving quality of the user to be identified to the connected serving cell according to a second difference value between the signal receiving quality of the user to be identified to the connected serving cell; wherein the signal reception quality similarity is inversely related to the second difference;
The second determining and calculating unit is used for executing a union set taking operation on adjacent cells corresponding to each user in the user pair to be identified to obtain an adjacent cell set of the user pair to be identified; determining the same number of adjacent cells of the user pair to be identified; determining the similarity of the users to be identified to the corresponding neighbor cells according to the ratio of the same number of neighbor cells to the neighbor cell set; wherein the neighbor cell similarity is positively correlated with the same number of neighbor cells.
In one embodiment, the second determination module 630 further includes,
the acquisition unit acquires sample communication relation data among a plurality of sample user pairs; wherein, each sample user pair is marked with information whether to have a juxtaposition or not in advance;
and the learning unit is used for learning based on the sample communication relation data among the plurality of sample user pairs and the information whether each sample user pair has a juxtaposition relation or not, so as to obtain a relation recognition model.
The network device provided in the embodiment of the present invention can implement each process of the user relationship identification method in the above embodiment of the method, and in order to avoid repetition, details are not repeated here.
In the embodiment of the invention, when the juxtaposition is identified, the device can acquire communication data (including the ticket data and/or the measurement report data of each user in the user pair to be identified) required by the juxtaposition identification based on the mobile communication system, so that the juxtaposition identification is rich in data dimension and reliable in data quality, and additional hardware data is not required, thereby the data acquisition cost is lower; in addition, according to the collected communication data, the conversation behavior characteristics and/or the communication environment similarity between the user pairs to be identified can be determined, and as the conversation behavior characteristics and/or the communication environment similarity can reflect whether the users reside in the same geographic space to a certain extent, the accuracy of the identification of the juxtaposition relationship is realized; moreover, according to the technical scheme, whether the users to be identified have the edge relationship or not is determined by utilizing a pre-established relationship identification model according to the communication relationship data between the user pairs to be identified, and the edge relationship of the users can be automatically identified without manually setting related judgment conditions and thresholds, so that the identification efficiency and the identification accuracy are greatly improved.
Referring to fig. 7, fig. 7 is a block diagram of a network device according to an embodiment of the present invention, which can implement details of the user relationship identifying method executed by the network device in the above embodiment, and achieve the same effects. As shown in fig. 7, the network device 700 includes: a processor 701, a transceiver 702, a memory 703, a user interface 704 and a bus interface, wherein:
in an embodiment of the present invention, the network device 700 further includes: a computer program stored on the memory 703 and executable on the processor 701, which when executed by the processor 701 performs the steps of:
collecting communication data of a user pair to be identified in a first appointed time period; the communication data comprises ticket data and/or measurement report data of each user in the user pair to be identified;
determining communication relation data between the user pairs to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between user pairs to be identified;
determining whether a user pair to be identified has a juxtaposition relationship or not by utilizing a pre-established relationship identification model according to the communication relationship data; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether each sample user pair has a juxtaposition relation or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
In fig. 7, a bus architecture may be comprised of any number of interconnected buses and bridges, and in particular, one or more processors represented by the processor 701 and various circuits of memory represented by the memory 703. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 702 may be a number of elements, i.e., including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium. The user interface 704 may also be an interface capable of interfacing with an inscribed desired device for a different user device, including but not limited to a keypad, display, speaker, microphone, joystick, etc.
The processor 701 is responsible for managing the bus architecture and general processing, and the memory 703 may store data used by the processor 701 in performing operations.
Optionally, the call ticket data comprises at least one of a communication number, a communication time of both parties and a calling party and a called party; the communication behavior characteristics comprise at least one of communication number pair identification, total communication times, communication time average duration and the same contact number.
Optionally, the computer program may further implement at least one of the following steps when executed by the processor 701:
splicing the communication numbers of all users together according to a preset splicing mode to obtain the communication number pair identification of the user pair to be identified;
dividing the total call time length of the user pairs to be identified in the first appointed time period by the total call times to obtain the call time average time length between the user pairs to be identified;
respectively determining call contacts of all users; and carrying out intersection operation on call contacts of each user to obtain the same number of contacts between the user pairs to be identified.
Optionally, the computer program may further implement the following steps when executed by the processor 701:
before determining communication relation data between user pairs to be identified, deleting abnormal data in ticket data; and/or deleting the appointed character before the communication number; wherein the abnormal data comprises at least one of a non-user number and an illegal number.
Optionally, the measurement report data includes at least one of a communication number, a connection time with a serving cell, a cell identification code of the connected serving cell, a signal receiving power of the serving cell, a signal receiving quality of the serving cell, a signal receiving power of a neighboring cell of the serving cell, and a signal receiving quality of the neighboring cell;
The communication environment similarity comprises at least one of the same rate of the user to be identified to the connected cell identification code, the same total time length of the user to be identified to the connected cell, the signal receiving power similarity of the user to be identified to the connected service cell, the signal receiving quality similarity of the user to be identified to the connected service cell, the corresponding adjacent cell similarity of the user to be identified, the signal receiving power similarity of the user to be identified to the corresponding adjacent cell and the signal receiving quality similarity of the user to be identified to the corresponding adjacent cell in the second designated time period.
Optionally, the computer program may further implement at least one of the following steps when executed by the processor 701: :
determining the same times of the cell identification codes of the users to be identified to the connection within a second designated time period; dividing the same times of the cell identification codes by the maximum total connection duration of the users to be identified in a first designated time period to obtain the same rate of the users to be identified to the connected cell identification codes;
judging whether the service cells connected by the users to be identified are the same in a second designated time period; if not, determining that the similarity of the signal receiving power of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving power of the user to be identified to the connected serving cell according to a first difference value between the signal receiving power of the user to be identified to the connected serving cell; wherein the signal received power similarity is inversely related to the first difference;
Judging whether the service cells connected by the users to be identified are the same in a second designated time period; if not, determining that the signal receiving quality similarity of the user to be identified to the connected service cell is zero; if so, determining the similarity of the signal receiving quality of the user to be identified to the connected serving cell according to a second difference value between the signal receiving quality of the user to be identified to the connected serving cell; wherein the signal reception quality similarity is inversely related to the second difference;
executing a union set taking operation on adjacent cells corresponding to each user in the user pair to be identified to obtain an adjacent cell set of the user pair to be identified; determining the same number of adjacent cells of the user pair to be identified; determining the similarity of the users to be identified to the corresponding neighbor cells according to the ratio of the same number of neighbor cells to the neighbor cell set; wherein the neighbor cell similarity is positively correlated with the same number of neighbor cells.
Optionally, the computer program may further implement the following steps when executed by the processor 701:
training a relationship recognition model according to the following steps:
collecting sample communication relation data among a plurality of sample user pairs; wherein, each sample user pair is marked with information whether to have a juxtaposition or not in advance;
and learning based on the sample communication relation data among the plurality of sample user pairs and the information whether the sample user pairs have a juxtaposition relation or not, so as to obtain a relation recognition model.
In the embodiment of the invention, when the juxtaposition is identified, communication data (including ticket data and/or measurement report data of each user in the user pair to be identified) required for identifying the juxtaposition can be acquired based on the mobile communication system, so that the juxtaposition is rich in data dimension and reliable in data quality, and additional hardware data is not required, and therefore, the data acquisition cost is lower; in addition, according to the collected communication data, the conversation behavior characteristics and/or the communication environment similarity between the user pairs to be identified can be determined, and as the conversation behavior characteristics and/or the communication environment similarity can reflect whether the users reside in the same geographic space to a certain extent, the accuracy of the identification of the juxtaposition relationship is realized; moreover, according to the technical scheme, whether the users to be identified have the edge relationship or not is determined by utilizing a pre-established relationship identification model according to the communication relationship data between the user pairs to be identified, and the edge relationship of the users can be automatically identified without manually setting related judgment conditions and thresholds, so that the identification efficiency and the identification accuracy are greatly improved.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the above-mentioned user relationship identification method embodiment, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (10)
1. A method for identifying a user relationship, comprising:
collecting communication data of a user pair to be identified in a first appointed time period; the communication data comprise ticket data and/or measurement report data of each user in the user pair to be identified; the measurement report data comprises at least one of a communication number, connection time with a service cell, a cell identification code of the connected service cell, signal receiving power of the service cell, signal receiving quality of the service cell, signal receiving power of a neighbor cell of the service cell and signal receiving quality of the neighbor cell;
determining communication relation data between the user pairs to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between the user pairs to be identified;
Determining whether a juxtaposition relationship exists between the user pairs to be identified or not by utilizing a pre-established relationship identification model according to the communication relationship data; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether a juxtaposition relation exists among the sample user pairs or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
2. The method of claim 1, wherein the call ticket data includes at least one of a communication number, a double-party call time, a double-party call duration, and a call caller and a call recipient; the communication behavior characteristics comprise at least one of communication number pair identification, total communication times, communication time average duration and the same contact number.
3. The method of claim 2, wherein determining communication relationship data between the pair of users to be identified based on the communication data comprises at least one of:
splicing the communication numbers of the users together according to a preset splicing mode to obtain the communication number pair identification of the user pair to be identified;
dividing the total call duration of the user pair to be identified in the first appointed time period by the total call times to obtain the call time average duration between the user pair to be identified;
Respectively determining call contacts of the users; and carrying out intersection operation on the call contacts of each user to obtain the same number of contacts between the user pairs to be identified.
4. The method of claim 2, wherein prior to determining communication relationship data between the pair of users to be identified based on the communication data, further comprising:
deleting abnormal data in the ticket data; and/or deleting the appointed character before the communication number; wherein the abnormal data comprises at least one of a non-user number and an illegal number.
5. The method of claim 1, wherein the communication environment similarity comprises at least one of a same rate of the to-be-identified user to the cell identity of the connection, a same total cell duration of the to-be-identified user to the connection, a signal reception power similarity of the to-be-identified user to a connected serving cell, a signal reception quality similarity of the to-be-identified user to the connected serving cell, a corresponding neighbor cell similarity of the to-be-identified user to the to-be-identified user, a signal reception power similarity of the to-be-identified user to the corresponding neighbor cell, and a signal reception quality similarity of the to-be-identified user to the corresponding neighbor cell for a second specified period of time.
6. The method of claim 5, wherein determining communication relationship data between the pair of users to be identified based on the communication data comprises at least one of:
determining the same number of times of the cell identification code connected by the user to be identified in the second designated time period; dividing the same times of the cell identification codes by the maximum total connection duration of the users to be identified in the first appointed time period to obtain the same rate of the users to be identified to the cell identification codes connected;
judging whether the service cells connected by the users to be identified are the same in the second designated time period; if not, determining that the similarity of the signal receiving power of the user to be identified to the connected service cell is zero; if yes, determining the similarity of the signal receiving power of the user to be identified to the connected service cell according to a first difference value between the signal receiving power of the user to be identified to the connected service cell; wherein the signal received power similarity is inversely related to the first difference value;
judging whether the service cells connected by the users to be identified are the same in the second designated time period; if not, determining that the similarity of the signal receiving quality of the user to be identified to the connected service cell is zero; if yes, determining the similarity of the signal receiving quality of the user to be identified to the connected service cell according to a second difference value between the signal receiving quality of the user to be identified to the connected service cell; wherein the signal reception quality similarity is inversely related to the second difference value;
Executing union set taking operation on the adjacent cells corresponding to each user in the user pair to be identified to obtain an adjacent cell set of the user pair to be identified; determining the same number of adjacent cells of the user pair to be identified; determining the similarity of the users to be identified to the corresponding adjacent cells according to the ratio of the same adjacent cell number to the adjacent cell set; wherein the neighbor cell similarity is positively correlated with the same number of neighbor cells.
7. The method of claim 1, wherein the relationship-identifying model is trained in accordance with the steps of:
collecting sample communication relation data among a plurality of sample user pairs; wherein each sample user pair is pre-marked with information whether there is a juxtaposition or not;
and learning based on the sample communication relation data among the plurality of sample user pairs and the information of whether the sample user pairs have a juxtaposition relation or not, so as to obtain the relation recognition model.
8. A user relationship identification apparatus, comprising:
the acquisition module acquires communication data of a user to be identified in a first designated time period; the communication data comprise ticket data and/or measurement report data of each user in the user pair to be identified; the measurement report data comprises at least one of a communication number, connection time with a service cell, a cell identification code of the connected service cell, signal receiving power of the service cell, signal receiving quality of the service cell, signal receiving power of a neighbor cell of the service cell and signal receiving quality of the neighbor cell;
The first determining module is used for determining communication relation data between the user pairs to be identified according to the communication data; the communication relation data comprise call behavior characteristics and/or communication environment similarity between the user pairs to be identified;
the second determining module is used for determining whether the user pair to be identified has a juxtaposition relationship or not by utilizing a pre-established relationship identification model according to the communication relationship data; the relation recognition model is obtained through training according to sample communication relation data among a plurality of sample user pairs and information whether a juxtaposition relation exists among the sample user pairs or not; the juggling relationship refers to a relationship of users residing in the same geographic space.
9. A user relationship identification apparatus, comprising:
a memory storing computer program instructions;
a processor, which when executed by the processor, implements the user relationship identification method of any one of claims 1 to 7.
10. A computer-readable storage medium comprising instructions that, when run on a computer, cause the computer to perform the user relationship identification method of any one of claims 1 to 7.
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