CN113543115A

CN113543115A - Data processing method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN113543115A
Application number: CN202010289384.9A
Authority: CN
Inventors: 赖柯明; 吴修权; 王建宏; 黄志豪; 刘忱; 涂锋; 戚玉雷; 梁彩燕; 刘伟平; 汤嘉铭
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guangdong Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guangdong Co Ltd
Priority date: 2020-04-14
Filing date: 2020-04-14
Publication date: 2021-10-22
Anticipated expiration: 2040-04-14
Also published as: CN113543115B

Abstract

The invention discloses a data processing method and a device, wherein the method comprises the following steps: acquiring the use data of the mobile communication service use information service of a target operator on a terminal of a user; the mobile communication service usage information service usage data comprises service behavior data and/or service flow data, and the terminal supports a mobile communication service which simultaneously provides at least two SIM cards; extracting the characteristics of the service behavior data and/or the service flow data to obtain service behavior characteristics and/or service flow characteristics for describing the user type; and determining the user type of the user according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

Description

Data processing method and device, electronic equipment and computer readable storage medium

Technical Field

The present invention relates to the field of mobile communications technologies, and in particular, to a data processing method and apparatus, an electronic device, and a computer-readable storage medium.

Background

Currently, there are multiple operators that can provide mobile communication services globally, different operators may have different service features, and a user may use the mobile communication services of multiple operators simultaneously due to different service requirements, so in order to meet such user requirements, a terminal may generally support the provision of the mobile communication services of at least two operators for the user simultaneously.

However, in practical applications, users often pay more attention to the simultaneous use of mobile communication services of at least two operators. For example, the user mainly uses the mobile communication service of the first operator and secondarily uses the mobile communication service of the second operator, or the user mainly uses a certain type of mobile communication service of the first operator and mainly uses another type of mobile communication service of the second operator, and so on.

For operators to know the user dynamics in time and perform service improvement so as to improve the service quality and the user experience, it is very necessary for the technical field to advance. Therefore, it is necessary to provide a scheme for a user to know the type of the user from the use point of view during the use of the mobile communication service of the carrier.

Disclosure of Invention

The data processing method, the data processing device, the electronic equipment and the computer-readable storage medium provided by the embodiment of the invention are used for acquiring the type of the user from the use angle in the process that the user uses the mobile communication service of the operator.

To solve the above technical problem, the embodiment of the present invention is implemented as follows:

the embodiment of the invention adopts the following technical scheme:

a method of data processing, comprising:

acquiring mobile communication service use data of a target operator on a terminal of a user; the mobile communication service use data comprises service behavior data and/or service flow data, and the terminal supports the mobile communication service of simultaneously providing at least two SIM cards;

extracting the characteristics of the service behavior data and/or the service flow data to obtain service behavior characteristics and/or service flow characteristics for describing the user type;

and determining the user type of the user according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

A data processing apparatus comprising: a data acquisition unit, a feature extraction unit, and a type determination unit, wherein,

the data acquisition unit is used for acquiring the mobile communication service use data of a target operator on the terminal of a user; the mobile communication service use data comprises service behavior data and/or service flow data, and the terminal supports the mobile communication service of simultaneously providing at least two SIM cards;

the feature extraction unit is used for extracting features of the service behavior data and/or the service flow data to obtain service behavior features and/or service flow features used for describing user types;

and the type determining unit is used for determining the user type of the user according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

An electronic device, comprising:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to:

The technical scheme provided by the embodiment can be seen that the mobile communication service use data of the target operator on the terminal of the user can be obtained firstly, and then the service behavior data and/or the service flow data in the use data can be subjected to feature extraction respectively to obtain the service behavior features and/or the service flow features, so that the user type of the target operator on the terminal of the user can be determined according to the corresponding relationship between the service behavior features and/or the flow behavior features and the user type, which is mined in advance.

That is, when the user uses the terminal capable of supporting the mobile communication service of at least two SIM cards, by collecting the usage data of the user for the mobile communication service of a certain operator, the user type of the operator on the terminal can be determined according to the usage data in combination with the correspondence between the features mined in advance and the user types.

The method and the device utilize the use data of the user on the terminal to a certain operator and use the behavior data and the flow data in the use data as the basis, so that the type of the user can be accurately obtained from the use aspect in the process that the user uses the mobile communication service through the terminal.

Drawings

In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.

Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a method for identifying a "different network host card" user according to an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

Example 1

The embodiment provides a data processing method, which can acquire the type of a user from a use angle in the process of using the mobile communication service of an operator. Assuming that the execution subject of the method may be a server, a specific flowchart of the method is shown in fig. 1, and includes:

step 102: and acquiring the mobile communication service use data of the user on the terminal aiming at the target operator.

A terminal may also refer to a User Equipment (UE), a mobile device, a User terminal, a terminal device, a wireless communication device, or a User Equipment. The terminal may be a cellular telephone, a cordless telephone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device having Wireless communication capabilities, a computing device or other processing device connected to a Wireless modem, a wearable device, or the like.

The terminal herein may provide a mobile communication service (mobile communication services) for the user, and currently, the mobile communication service corresponding to the SIM card operator may be provided by inserting a physical SIM (Subscriber Identity Module) card into the terminal. The operator herein is an operator that can provide a mobile communication service, and the mobile communication service may include multiple types, for example, may include a basic mobile communication service, a short message service, a data transmission service, and the like, and may further include a video call service, and the like, where the type of the mobile communication service is not limited, and of course, the terminal may also implement the mobile communication service by using a virtual SIM card.

As described above, different operators may have different service characteristics for different purposes, such as stronger coverage of a call service signal of one operator, faster data transmission service rate of one operator, and the like. Therefore, in order to meet different requirements, a user may use mobile communication services of different operators at the same time, and in order to meet the requirement, a terminal at present may support at least two operators. For example, it is common that two SIM cards can be inserted into a terminal, so that a user can use mobile communication services of two same or different operators at the same time, and such a terminal is commonly called a "dual-card dual-standby" terminal.

When a user uses mobile communication services of different operators at the same time, there is a bias for a certain operation, for example, in the case of "dual card dual standby", there is a difference between the primary card and the secondary card, the primary card is used as the primary card, and the secondary card is used as the backup card. Therefore, in order to know the usage of the mobile communication service of the operator by the user and determine the type of the user from the usage perspective, the step can acquire the mobile communication service usage data of the user on the terminal and aiming at a certain target operator.

Specifically, since the user type in the use angle is known when the user uses the mobile communication services of more than one operator at the same time, the terminal herein can support the mobile communication services of at least two operators at the same time, or can support the mobile communication services of at least two SIM cards at the same time.

The hardware information of various terminals can be obtained in advance, for example, by obtaining from the manufacturer of the terminal, or from the official website introduction of the terminal, or obtaining from the aggregation website recording the hardware information of the terminal, so as to screen out a mobile communication service that can support providing at least two operators at the same time, such as the aforementioned "dual-card dual-standby" terminal. In a specific implementation manner, hardware information of the terminal can be crawled through Scaray of Python (general programming language) (an open source network crawler frame written based on Python), so that the terminal of 'dual card dual standby' is identified.

Therefore, before this step, the method may further comprise: and crawling the appointed website through a Scarpay framework, acquiring terminal data, and storing the terminal data supporting the mobile communication service of simultaneously providing at least two SIM cards into a local terminal library.

Specifically, data crawling can be performed on the specified website through a Scrpay framework of Python to obtain terminal data, a terminal supporting mobile communication services for providing at least two SIM cards simultaneously is identified, data such as the model of the terminal can be stored, a local terminal library is generated, when the user uses the mobile communication services, the model of the user can be obtained first, and therefore whether the terminal is a 'dual-card dual-standby' terminal is judged, and if the terminal is the 'dual-card dual-standby' terminal, the user can continue to obtain the mobile communication service use data of the user for a certain operator.

That is, the present step may include: and acquiring the mobile communication service use data of the target operator on the terminal of the user in the local terminal library.

For example, for a certain operator 1, when a user uses a mobile communication service, the terminal model of the user may be obtained and matched with a pre-stored local terminal library, and if the matching result is a "dual-card dual-standby" terminal, the usage information of the mobile communication service that the user is using at home may be obtained.

The service usage data of the mobile communication service may refer to information representing a usage situation generated according to a service operation when the user uses the mobile communication service. The usage information may include service behavior data and service traffic data, and when acquiring, one or both of the service behavior data and the service traffic data may be acquired.

The service behavior data may refer to a service behavior of a user when using a mobile communication service, and may include, for example, a calling party, a called party, a call start-stop time, location information, base station information, a data uplink and downlink time, a short message time, and the like for a call service. The service traffic data may refer to service statistics generated when the user uses the mobile communication service, and may include, for example, call duration, data uplink and downlink traffic, the number of base stations, the number of short messages, and the like.

In practical applications, in order to determine the type of the user more accurately, in general, the usage information of the user in a plurality of periods may be obtained. For example, the usage information of the user in two to three continuous or discontinuous periods is obtained by taking one month as a period, and the like. Therefore, in an embodiment, the step of acquiring the mobile communication service usage data of the user on the terminal and for the target operator may include: and acquiring mobile communication service use data of a user on a terminal aiming at a target operator in a plurality of service periods. The service period may be a period of charging the customer by the operator, or a period of service settlement, for example, one month, the operator may perform settlement statistics on the behavior and traffic of the user every month, and accordingly, the step may obtain the mobile communication service usage data in a plurality of service periods.

In practical applications, it is very likely that a user uses a specific service of a certain operator for quality of service, tariff, or other reasons, for example, in a "dual card dual standby" terminal, the operator 1 and the call service are used simultaneously, and the data transmission service of the operator 2 is used simultaneously, etc. Therefore, in order to accurately know the degree of dependence of the user on a specific mobile communication service of the operator, in an embodiment, the step may include: and acquiring the specific mobile communication service use data of the user on the terminal aiming at the target operator. So that the user type for the specific mobile communication service can be determined through the subsequent steps.

Step 104: and extracting the characteristics of the service behavior data and/or the service flow data to obtain service behavior characteristics and/or service flow characteristics for describing the user type.

The service behavior data and the service flow data of the user contain some characteristics which can directly reflect the use condition of the mobile communication service of the user, so the step can extract the characteristics of the service behavior data and/or the service flow data, thereby obtaining the service behavior characteristics and/or the service flow characteristics for describing the type of the user.

The feature extraction mode can be obtained by continuously summarizing according to business experience. For example, a large number of user samples may be collected, where the user samples include user traffic behavior data and traffic data. Specifically, the service behavior data may include the aforementioned calling party, called party, start-stop time of the call, location information, base station information, uplink and downlink data time, and short message time, and the service traffic data may include call duration, uplink and downlink data traffic, number of base stations, and number of short messages. The user samples here may be collected from a history.

The service behavior data and the service traffic data may be collected original information or information obtained through data statistics, for example, the service behavior data may be the number of calling times, the number of called times, the number and number of position information, the number and number of base station identifiers, the number of data transmission days, and the like, and the service traffic data may be the daily call duration, the daily data uplink and downlink traffic, the number of base stations, the number of short messages per day, and the like.

In a continuous summary discovery, some extraction ways that can be used to describe the service behavior characteristics and/or the service traffic characteristics of the user type can be determined. For example, for the service behavior data of the user, the features for describing the service behavior of the user type may include "turn-on days of the month", "traffic usage days ratio", "average number of base stations of the day", etc., and the features for describing the service traffic of the user may include "usage days of the month", "total traffic of the month", etc.

Therefore, in this embodiment, some feature extraction manners capable of describing the user type may be preset, and based on these feature extraction manners, the service behavior feature and/or the service traffic feature for describing the user type may be extracted.

Step 106: and determining the user type of the user according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

As described in the foregoing step, the service usage data of the user may be extracted by using a feature extraction method that can be used to describe the user type, and then the user type may be determined in this step.

Specifically, the service usage history information may be obtained in advance from the mobile communication service of the target operator used by the user, and the feature extraction result may be obtained according to the feature extraction manner in the foregoing step, so as to generate the user sample. Different user samples may also correspond to different user types, for example, different types may be estimated through empirical analysis of the samples, or results of actual survey through a telephone call, etc.

For example, the average service behavior and the service flow of the user are used as references, and different types of different users are determined according to the actually acquired use information and by combining the actual telephone survey result. That is, the user sample may include service behavior characteristics and/or service traffic characteristics of the user for the carrier mobile communication service, and correspond to different user types.

The types of the users in the user sample may include multiple types, for example, for the terminal supporting the mobile communication service providing at least two SIM cards simultaneously in this embodiment, the terminal may include two types, that is, a user using the target operator as a non-primary operator and a user using the target operator as a primary operator, and certainly there may be more types, for example, a user using the target operator as a non-primary operator and having a lower usage rate, a user using the target operator as a primary operator and having a higher usage rate, and the like.

In practical application, different user types can be named in a self-defined manner, for example, when the terminal is a "dual-card dual-standby" terminal, the user who uses the target operator as a non-main operator can be called a "different network main card" user, that is, SIM cards of other operators different from the main operator are used as main cards; similarly, the user who uses the target operator as the main operator may be referred to as a "home network main card" user, that is, the SIM card of the own operator is used as the main card; the two types of users, i.e., the target operator is a user of a non-primary operator and the usage rate is low, and the target operator is a user of a primary operator and the usage rate is high, may respectively represent different degrees of the "different network master card" and the "home network master card".

In practical application, different service behavior characteristics corresponding to different user types can be mined in a data mining mode. For example, the corresponding relationship between the service behavior characteristics and the user types and the corresponding relationship between the service traffic characteristics and the user types may be mined in a clustering manner or by using a specific regression model.

In the first mode, the service behavior characteristics corresponding to different user types are mined in advance from a user sample in a K-means clustering mode.

For example, a large number of user samples of a certain user type may be collected by using a history, and a service behavior feature capable of embodying the user type may be obtained according to the feature extraction manner for describing the user type in the foregoing steps, and then the service behavior features of different user types may be clustered based on a K-means clustering algorithm, so as to obtain a corresponding relationship between the service behavior feature and the user type.

Specifically, for example, the service behavior characteristics may include multiple dimensions such as "number of startup days in the month", "ratio of number of traffic usage days", "number of base stations per day", and a large number of user samples are placed in a multidimensional space, and a plurality of stable clustering centers are determined in the process of continuous iteration by presetting a limited number of initial clustering centers and by the distance between a user sample and an initial center, so as to obtain the corresponding relationship between the service behavior characteristics and the user type. And then, based on the corresponding relation, the user type can be determined by utilizing the extracted service behavior characteristics.

For example, if the target operator is a user of a non-primary operator, i.e., a "subscriber of a different network host card", this user type may correspond to a large number of user samples, and in each user sample, all can comprise characteristics of multiple dimensions such as startup days in the month, traffic utilization days proportion, daily average base station number and the like, each user sample can be placed in a multidimensional space, and n clustering centers can be set according to the distribution, determining m clustering centers in the continuous iteration process according to the distance between the user sample and the clustering centers, the characteristics that a certain clustering center is a group of characteristics that the number of used terminal days in one month is more than or equal to 20 days, the ratio of used data transmission service days is less than 50 percent, and the number of passed base stations in each day is more than 6 days can be found, and the characteristics can establish a corresponding relation with a user type that a target operator is a user of a non-primary operator (an 'different network master card'). Therefore, when the user type is determined, if the extracted service behavior characteristics meet the set of characteristics, the user type can be determined as the different network main card.

Since mobile communication services have been very popular, the embodiment of different users on actual service behaviors is also obvious, that is, on the service behavior characteristics of mobile communication services, users of different types usually have obvious differences, and the K-means clustering has the advantage that the effect is better when the result clusters are dense and the differences between the clusters are obvious. In the aspect of mobile communication service, the number of samples is huge, and the difference between different clustering clusters (service behavior characteristics) is obvious, so that the K-means clustering algorithm is very suitable for the application scene, and can better mine the service behavior characteristics corresponding to different user types.

In the second mode, service flow characteristics corresponding to different user types are mined in advance from a user sample through a FARIMA regression model.

For example, a large number of user samples of a certain user type may be collected by using a history record, and a service behavior feature that can reflect the user type is obtained according to the feature extraction manner used for describing the user type in the foregoing steps, and then a service traffic feature corresponding to the user type is more accurately described based on a FARIMA model (Autoregressive score Integration Moving Average model).

Specifically, for example, the service traffic characteristics may include multiple dimensions such as "number of days of using traffic in the same month", "total traffic in previous month", "total traffic in the same month/total traffic in previous month", which may reflect the self-similarity of service traffic, may also reflect the periodic characteristics, and are mainly reflected in the superposition: the different service flow characteristics can be formed by overlapping different types of service flows; the periodicity is as follows: traffic characteristics may be caused by periodic activity of the user; segment homogeneity: the continuous, complex and random process can be reasonably segmented according to a certain characteristic, so that each segment can be approximately represented by a simpler and homogeneous random process.

In practice, it is found that the network service flow has self-similarity, and the FARIMA model has the characteristic of describing short correlation and long correlation characteristics at the same time, so that the method is very suitable to be used as a modeling and prediction tool of the service flow. Therefore, the service traffic characteristics in a large number of user samples corresponding to a certain user type can be used as input, and the gamma function and the Gaussian process in the FARIMA model are utilized to perform modeling prediction on the service traffic characteristics, so as to obtain the corresponding relation between various service traffic characteristics and the user type.

For example, the "heterogeneous network master card" may be corresponding to a large number of user samples, each user sample may include service traffic characteristics of multiple dimensions, such as "number of days of using traffic in the month", "total traffic in the month/total traffic in the month", and the like, and the large number of user samples may be input into the FARIMA model, so that, by using a gamma function, a gaussian process, and a specific algorithm, a plurality of sets of service traffic characteristics corresponding to the user type may be described, and in addition, a user-defined name may be performed based on different service traffic characteristics, for example:

flow alternation: current month usage flow days >0, and current month usage flow days < 5;

and (3) sudden flow reduction: (total flow of current month/total flow of previous month-1) < -0.5, and total flow of previous month >0, and total flow of current month < 6.15G;

long-term low flow: total flow in the previous month is <6.15G, and total flow in the current month is < 6.15G;

main flow: total flow in month > 6.15G;

zero flow user: the total flow rate at this month is 0.

Then, when determining the user type, if the extracted service traffic characteristics are matched with a certain set of characteristics, the user type may be determined as the user type corresponding to the set of characteristics. For example, according to the above example, the corresponding service behavior features and service traffic features may be determined for the "different-network master card" user type based on the K-means cluster and the FARIMA model, and then, if the feature result obtained after feature extraction is performed on each user is matched with the corresponding service behavior features and service traffic features, the user type may be determined as the "different-network master card" user.

Therefore, in practical application, it can be determined whether the user type of the target operator is a user with the target operator as a non-primary operator or not on the terminal by the user according to the corresponding relationship between the service behavior characteristics and/or the service traffic characteristics, which are mined in advance, and the user type. Or determining whether the user is a user of the characteristic user type on the terminal aiming at the user type of the target operator according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics mined in advance and the user type.

As introduced in the foregoing step, the obtaining of the specific mobile communication service usage data of the user on the terminal for the target operator may be performed, and then, in this step, in order to obtain the user type of the user for a certain specific mobile communication service of the operator more accurately, this step determines the user type of the user according to the corresponding relationship between the service behavior feature and/or the service traffic feature and the user type, and may include: and determining the user type of the user aiming at the specific mobile communication service according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

For example, for a data transmission service, service usage data of a data transmission service of a target operator in a terminal of a user may be obtained first, feature extraction may be performed on service behavior data and service traffic data of the data transmission service, respectively, and a user type of the data transmission service for the operator may be determined according to a pre-mined correspondence between service behavior features and service traffic features in the data transmission service and the user type.

In practical application, one user type may be determined according to the service behavior characteristics, another user type may be determined according to the service traffic characteristics, and a situation that the two determined user types are inconsistent may occur, so that at this time, the user type of the data transmission service for the operator may be determined by comprehensive consideration.

As already mentioned above, a user may have a variety of service behavior characteristics and service traffic characteristics, which in practice may be caused by different wishes of the user. For example, taking the service traffic characteristics as an example, if traffic alternation occurs, that is, the number of days of traffic usage in the month is >0, and the number of days of traffic usage in the month is <5, it may be stated that the user has no strong will of usage for some reasons, and then the user's will of usage may be motivated by recommending internet resources for the user. The user's will may be motivated by, for example, a policy to give away or promote internet resources such as data transmission traffic. If a zero traffic user is present, and the user may be lost, then the user may be attempted to be recovered by way of a number of telephone promotions.

Therefore, in order to provide corresponding service policies for different user features in time, in an embodiment, the method may further include: and recommending corresponding internet resources for the user according to the extracted service behavior characteristics and/or service flow characteristics.

Specifically, the internet resources may include internet resources in mobile communication services such as data transmission resources and mobile communication resources, as described above, for example, for the service traffic characteristics, if the extracted service traffic characteristics reflect that the traffic of the user is alternated, the user may be precisely marketed by recommending some data transmission package and other internet resources.

In practical application, a first characteristic condition and a second characteristic condition may also be preset for the service behavior characteristic and the service traffic characteristic, for example, a decrease of a certain service behavior characteristic exceeds 20% in two consecutive periods, and the like. The recommended internet resources may also include presenting telephone charges, discounting traffic, presenting call duration, and the like.

In practical application, the method for recommending internet resources may also be determined for a user of the "home network master card", for example, the first characteristic condition and the second characteristic condition may be that a certain business behavior characteristic rises by more than 20% in consecutive periods, and the like, and the business service policy may include promoting a sales promotion package, promoting a user level, and the like.

As shown in fig. 2, a schematic diagram of a method applied to identify a "different network master card" user based on the data processing method provided by this embodiment may be used for identifying the "different network master card" user. Firstly, the terminal data can be obtained through crawling on the network by the Scarpay framework, the terminal of the 'dual card dual standby' is identified, and the terminal data is stored in a local terminal library by the model identification. Secondly, business behavior data and business flow data of a large number of users, such as calls and data, can be collected, user types can be obtained through telephone investigation or experience analysis by engineering personnel, and user samples are created. Therefore, feature extraction can be carried out on the service behavior data and the service flow data of the user to obtain service behavior features and/or service flow features for describing the user type, and the service behavior features and the service flow features corresponding to the user type of the 'different network master card' are mined out by means of K-means clustering, a FARIMA model and the like. In practical application, verification can be performed, and the corresponding relation between the service behavior characteristic and the service flow characteristic and the user of the different network main card can be repeatedly optimized. In practical application, the use information of the user on the mobile communication service of the own operator on the dual-card dual-standby terminal can be collected, and whether the user is the user of the different-network main card or not is determined by means of feature extraction and matching with the corresponding relation.

The method provided by the embodiment can obtain the mobile communication service use data of the target operator on the terminal of the user, and then respectively extract the characteristics of the service behavior data and/or the service flow data in the use data to obtain the service behavior characteristics and/or the service flow characteristics, so that the user type of the target operator on the terminal of the user can be determined according to the corresponding relationship between the service behavior characteristics and/or the flow behavior characteristics and the user type, which are mined in advance.

Example 2

Based on the same concept, embodiment 2 of the present invention further provides a data processing apparatus, which can obtain the type of a user from a use perspective in a process that the user uses a mobile communication service of an operator. It is assumed that the execution subject of the method may be a server. The schematic structural diagram of the device is shown in fig. 3, and the device comprises: a data acquisition unit 202, a feature extraction unit 204, and a type determination unit 206, wherein,

a data obtaining unit 202, configured to obtain mobile communication service usage data of a target operator on a terminal by a user; the mobile communication service use data comprises service behavior data and/or service flow data, and the terminal supports the mobile communication service of simultaneously providing at least two SIM cards;

a feature extraction unit 204, configured to perform feature extraction on the service behavior data and/or the service traffic data to obtain a service behavior feature and/or a service traffic feature for describing a user type;

the type determining unit 206 may determine the user type of the user according to the corresponding relationship between the service behavior feature and/or the service traffic feature and the user type.

In an embodiment, the data obtaining unit 202 may further be configured to:

before the user on the terminal acquires the mobile communication service use data of a target operator, crawling a specified website through a Scaray frame to acquire terminal data, and storing the terminal data supporting the mobile communication service of simultaneously providing at least two SIM cards into a local terminal library;

a data acquisition unit 202 operable to:

and acquiring the mobile communication service use data of the target operator on the terminal of the user in the local terminal library.

In an embodiment, the type determining unit 206 may be configured to:

and determining whether the user type of the user is a user with the target operator as a non-main operator according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

In an embodiment, the data obtaining unit 202 may be configured to:

and acquiring mobile communication service use data of a user on a terminal aiming at a target operator in a plurality of service periods.

In one embodiment, the apparatus further includes a resource recommending unit operable to:

and recommending corresponding internet resources for the user according to the service behavior characteristics and/or the service flow characteristics.

In an embodiment, the data obtaining unit 202 may be configured to:

acquiring specific mobile communication service use data of a target operator on a terminal of a user;

a type determination unit 206, which may be configured to:

and determining the user type of the user aiming at the specific mobile communication service according to the corresponding relation between the service behavior characteristics and/or the service flow characteristics and the user type.

As can be seen from the arrangement provided in the above embodiments,

the method comprises the steps of firstly obtaining mobile communication service use data of a target operator by a user on a terminal, then respectively extracting characteristics of service behavior data and/or service flow data in the use data to obtain service behavior characteristics and/or service flow characteristics, and accordingly determining the user type of the target operator on the terminal according to the corresponding relationship between the service behavior characteristics and/or the flow behavior characteristics and the user type mined in advance.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the data processing execution device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

The method executed by the data processing apparatus according to the embodiment of the present invention shown in fig. 3 may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

An embodiment of the present invention further provides a computer-readable storage medium, which stores one or more programs, where the one or more programs include instructions, which, when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the method performed by the data processing apparatus in the embodiment shown in fig. 3, and are specifically configured to perform:

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above system is described as being divided into various units by functions, and described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in a plurality of software and/or hardware when implementing the invention.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

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

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that 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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A data processing method, comprising:

2. The method of claim 1, wherein before the obtaining the user usage data for the mobile communication service of the target operator on the terminal, the method further comprises:

crawling the appointed website through a Scarpay framework, acquiring terminal data, and storing the terminal data supporting mobile communication services of providing at least two SIM cards to a local terminal library;

the acquiring of the mobile communication service use data of the user on the terminal aiming at the target operator comprises the following steps:

3. The method of claim 1, wherein determining the user type of the user according to the correspondence between the service behavior characteristics and/or the service traffic characteristics and the user type comprises:

4. The method of claim 1, wherein obtaining mobile communication service usage data of a user at a terminal for a target operator comprises:

5. The method of claim 1, wherein the method further comprises:

6. The method of claim 1, wherein the method further comprises:

and pre-mining the service behavior characteristics corresponding to different user types in a K-means clustering mode.

7. The method of claim 1, wherein the method further comprises:

and (4) excavating the service flow characteristics corresponding to different user types in advance through a FARIMA regression model.

8. A data processing apparatus for determining a user type, comprising: a data acquisition unit, a feature extraction unit, and a type determination unit, wherein,

9. An electronic device, comprising:

a processor; and

10. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to: