CN116070018A - Big data analysis method and system based on mobile terminal - Google Patents
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Abstract
The invention provides a big data analysis method and a big data analysis system based on a mobile terminal, and relates to the technical field of mobile data analysis. First, user-generated movement data is acquired from a mobile terminal and preprocessed. Including communication mode data, internet surfing behavior data, payment behavior data, etc. And then, performing feature learning on the mobile data by using the deep neural network structure. And pre-clustering the learned characteristic representation by using a K-means algorithm to obtain a clustering center. And then, determining the label of each cluster center based on a preset user portrait label system to obtain user labels in all aspects, and constructing a user portrait. And finally, pushing corresponding service or consultation contents for the user according to the user portrait. According to the method and the system, the mobile data generated by the user are comprehensively analyzed and counted in a mode of feature extraction and cluster analysis, so that the user portrait is more accurately depicted, and personalized network service is provided for the user.
Description
Technical Field
The invention relates to the technical field of mobile data analysis, in particular to a big data analysis method and system based on a mobile terminal.
Background
With the rapid development of network technology, hundreds of millions of mobile users are increasingly using mobile terminals to access networks, such as mobile phones, ipads, tablets, and other tools, to meet the demands of work, life, leisure, entertainment, and the like. The internet surfing time of the user is changed into fragmentation from the previous immobilization mode of surfing by using the PC end, and the surfing place is changed from the original single immobilization mode into diversified. Network applications are becoming more complex and the amount of data is also becoming larger. The mobile internet surfing data are important data for reflecting user attributes, wherein the mobile internet surfing data comprise marketing key information such as user terminals, consumption capability, position information, internet surfing service preferences and the like, and target marketing users are screened out by matching own service of operators with the user attributes, so that the operators can be helped to accurately match service and products, operation channels are carefully selected, and the fine operation of end-to-end service content of the operators is realized. When the internet surfing mode has the mobile characteristics, how to accurately understand the information requirement of the mobile user from the rapidly expanding information so as to provide personalized information service for the mobile user becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a big data analysis method and a big data analysis system based on a mobile terminal, which more accurately delineate user figures by fusing feature extraction and cluster analysis, thereby providing personalized network services for users.
Embodiments of the present invention are implemented as follows:
in a first aspect, an embodiment of the present application provides a big data analysis method based on a mobile terminal, including:
mobile data generated by a user are acquired from a mobile terminal and preprocessed, wherein the mobile data comprise communication mode data and internet surfing behavior data;
carrying out cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects, and constructing user portraits;
and pushing corresponding service or consultation contents to the user according to the user portrait.
Based on the first aspect, in some embodiments of the present invention, the step of acquiring the movement data generated by the user from the mobile terminal and performing the preprocessing includes:
converting the non-numerical type mobile data into numerical type mobile data, and performing standardization processing;
and carrying out exception processing on the mobile data after standardized processing, and deleting the sensitive data.
Based on the first aspect, in some embodiments of the present invention, the step of performing cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects, and constructing a user portrait includes:
performing feature learning on the mobile data by using a deep neural network structure;
pre-clustering the learned characteristic representation by using a K-means algorithm to obtain a clustering center;
based on a preset user portrait label system, determining labels of each cluster center, obtaining user labels of all aspects, and constructing a user portrait.
Based on the first aspect, in some embodiments of the present invention, the step of pre-clustering the learned feature representation by using a K-means algorithm to obtain a cluster center includes:
k clustering centers are selected from the learned feature representation, and similarity results between the rest features and the clustering centers are obtained through calculation;
clustering is carried out according to the similarity result, and the position of the iterative clustering center is continuously updated;
stopping when the cluster center is not changed or reaches the preset maximum iteration number, and determining the final cluster center.
Based on the first aspect, in some embodiments of the invention, further comprising:
acquiring user communication information according to the communication mode data, wherein the user communication information comprises communication duration, geographic position, contact number and frequency;
establishing a Gantt chart according to the communication information to obtain time and space distribution characteristics of a user;
and determining the user attribute according to the time and space distribution characteristics.
Based on the first aspect, in some embodiments of the invention, further comprising: and visually displaying the user portrait.
In a second aspect, an embodiment of the present application provides a big data analysis system based on a mobile terminal, including:
the preprocessing module is used for acquiring mobile data generated by a user from the mobile terminal and preprocessing the mobile data, wherein the mobile data comprises communication mode data and internet surfing behavior data;
the user portrait module is used for carrying out cluster analysis on the mobile data by utilizing a K-means algorithm based on deep learning to obtain user labels of all aspects and constructing user portraits;
and the service module is used for pushing corresponding service or consultation content for the user according to the user portrait.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory for storing one or more programs; a processor. The method as described in any one of the first aspects is implemented when the one or more programs are executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the first aspects above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the embodiment of the application provides a big data analysis method and a big data analysis system based on a mobile terminal. Including communication mode data, internet surfing behavior data, payment behavior data, etc. And then, performing feature learning on the mobile data by using the deep neural network structure. And pre-clustering the learned characteristic representation by using a K-means algorithm to obtain a clustering center. And then, determining the label of each cluster center based on a preset user portrait label system to obtain user labels in all aspects, and constructing a user portrait. And finally, pushing corresponding service or consultation contents for the user according to the user portrait. According to the method and the system, the mobile data generated by the user are comprehensively analyzed and counted in a mode of feature extraction and cluster analysis, so that the user portrait is more accurately depicted, and personalized network service is provided for the user.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram illustrating steps of an embodiment of a big data analysis method based on a mobile terminal according to the present invention;
FIG. 2 is a block diagram of steps for creating a user portrait in an embodiment of a big data analysis method based on a mobile terminal provided by the present invention;
FIG. 3 is a schematic flow chart of cluster analysis in an embodiment of a big data analysis method based on a mobile terminal according to the present invention;
FIG. 4 is a block diagram of a big data analysis system based on a mobile terminal according to the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 1. a memory; 2. a processor; 3. a communication interface; 11. a preprocessing module; 12. a user portrayal module; 13. and a service module.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Examples
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a block diagram illustrating a big data analysis method based on a mobile terminal according to an embodiment of the present application, where the method includes the following steps:
step S1: and acquiring mobile data generated by a user from the mobile terminal and preprocessing the mobile data, wherein the mobile data comprises communication mode data and internet surfing behavior data.
In the above steps, firstly, mobile data generated in the running process of the mobile terminal system can be recorded and collected in a mode of ROM embedded points and APP embedded points, and the mobile data comprises communication mode data and internet surfing behavior data. The communication mode data mainly comprise data such as communication duration, geographic position, contact number and frequency. By analyzing the communication mode data, the social roles, social modes and social scales of the users can be known, and the construction of the user portraits is promoted. In addition, the obtained internet surfing behavior data is analyzed through a deep packet inspection (DeepPacketInspection, DPI) technology, so that corresponding user behavior data can be obtained, wherein the corresponding user behavior data comprise APP use times, use types, use time, browsing content and the like. The deep packet inspection technology is based on the traditional IP packet inspection technology, that is, on the basis of inspection and analysis of packet elements contained between the second layer and the fourth layer of OSI, adds protocol identification, packet content inspection and deep decoding to the application layer data, so as to obtain deeper user behavior data.
After the mobile data is obtained, the data needs to be preprocessed, and further clustering analysis can be performed to construct the user portraits. Specifically, the preprocessing of data mainly includes: 1. converting the non-numerical type mobile data into numerical type mobile data, and performing standardization processing. When K-means cluster analysis is carried out subsequently, the K-means cluster algorithm can only process numerical data, so that non-numerical mobile data are required to be converted into numerical data, and then unified processing is required. And then, carrying out standardized processing on the data by using a Z-score standardized method, removing dimension influence, carrying out exception processing on the data, deleting sensitive data, error data, invalid data and the like, so that the data distribution is more reasonable, and ensuring that the data participating in clustering is high-quality and effective data.
Step S2: and carrying out cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects, and constructing user portraits.
In the above steps, the thought of the K-means algorithm based on deep learning is to firstly reduce the dimension and then cluster. Firstly, using a neural network to reduce the dimension, and then using a K-means clustering algorithm to cluster in a low-dimension space. By means of the feature extraction and cluster analysis fusion method, comprehensive analysis statistics is carried out on mobile data generated by users, so that high-dimensional data are more convenient and better in effect during clustering, and user portraits are more accurately depicted. Specifically, referring to fig. 2, the steps mainly include:
step S2-1: and performing feature learning on the mobile data by using the deep neural network structure.
In the above steps, feature learning may be performed using an Automatic Encoder (AE), a boltzmann limited machine, or the like, for example. Wherein an Auto Encoder (AE) is a self-supervised neural network model that learns implicit features of input data by encoding; and simultaneously, reconstructing the learned new features into the original input data by utilizing decoding. Typically comprising three layers of neural networks: an input layer, an encoding layer, and a decoding layer. The input is reconstructed through a three-layer neural network, so that the hidden layer learns a good representation of the input. Therefore, the hidden deep layer characteristics are extracted from the communication mode data and the internet surfing behavior data of the user by utilizing the deep neural network structure, and then clustering analysis is carried out.
Step S2-2: and pre-clustering the learned characteristic representation by using a K-means algorithm to obtain a clustering center.
In the above steps, K cluster centers are selected from the learned feature representations, and the similarity result between the rest features and the cluster centers is obtained by calculation. And then clustering is carried out according to the similarity result, and the position of the iterative clustering center is continuously updated. And stopping when the cluster center is not changed or the preset maximum iteration number is reached, and determining the final cluster center.
Specifically, in the calculation process, euclidean distance is used as an index for measuring the similarity between data, the similarity is inversely proportional to the distance between the data, and the larger the similarity is, the smaller the distance is. Referring to fig. 3, the data is divided into a plurality of clusters according to the distance between the data by pre-designating the initial cluster number and the initial cluster center, and the positions of the cluster centers are continuously updated according to the similarity between the data objects and the cluster centers, so as to continuously reduce the error square sum (SumofSquaredError, SSE) of the cluster-like clusters, and when the SSE is no longer changed or the objective function converges, or when the cluster center is no longer changed or reaches the preset maximum iteration number, the clustering is ended, thereby obtaining the final result.
Step S2-3: based on a preset user portrait label system, determining labels of each cluster center, obtaining user labels of all aspects, and constructing a user portrait.
In the above steps, the preset user portrait tag system mainly constructs the user portrait of the user comprehensively from four aspects of social attribute, behavioral attribute, interest attribute and psychological attribute of the user. The social attribute labels comprise a user gender label, an age label, a region label and the like; the behavior attribute labels comprise a user liveness label, a purchase label, a use habit label and the like; the interest attribute labels comprise sports labels, music labels, automobile labels, game labels, cosmetic labels and the like; psychological attribute labels include personality labels, status labels, and the like. The user portrayal is a labeled user model abstracted according to the information of the user social attribute, living habit, consumption behavior and the like. The process of creating user portraits is to add corresponding labels, which are called labeling in the field of data mining. Tags are highly refined identification of features obtained by analysis of user information. According to the embodiment of the invention, the mobile data of the user terminal is subjected to cluster analysis by the K-means algorithm based on deep learning, so that the social attribute, social characteristic, interest and like information of the user are mined from the massive data of the user, a jointed portrait is established for the user, and personalized service and accurate marketing to the user are promoted.
Step S3: and pushing corresponding service or consultation contents to the user according to the user portrait.
Based on the first aspect, in some embodiments of the invention, further comprising: and acquiring user communication information according to the communication mode data, wherein the user communication information comprises communication duration, geographic position, contact number, frequency and the like. And then, establishing a Gantt chart according to the communication information, and performing visual display to obtain the time and space distribution characteristics of the user. And then determining the user attribute according to the time and space distribution characteristics.
In the above embodiment, the communication mode data can characterize when and where the user has contacted who, and the communication mode data can be characterized from both the underlying data type and the underlying data object by visualizing the communication mode data. At the basic data type level, the communication mode data has typical space-time characteristics due to the fact that the communication mode data comprises communication time and communication places, a time and space type visualization method (such as a time line diagram and a time flow diagram) can be adopted to express time, a point, line and plane map is adopted to express space information, and a geographic track is adopted to display the mobility of a space. In this embodiment, the Gantt chart may be used to display social attributes of the user, where the abscissa represents a date and the ordinate represents a place, so as to identify social attributes of the user according to characteristics of time and space. For example, a user has a large moving distance, a large number of access points, a long conversation time and a large number of contacts. Based on this it can be speculated that the user may be an outdoor worker engaged in contact communication in a larger metropolitan area.
Based on the first aspect, in some embodiments of the invention, further comprising: and visually displaying the user portrait.
In this embodiment, social attributes of the user obtained through visual analysis are combined with labels such as user behaviors and interests obtained through a clustering algorithm, so that a more comprehensive user image is depicted. And the user portrait is visually displayed, so that a service provider can more intuitively and accurately know information of various aspects of the user, and personalized service and accurate marketing of the user are promoted.
Based on the same inventive concept, the invention also provides a big data analysis system based on the mobile terminal, please refer to fig. 4, and fig. 4 is a block diagram of the big data analysis system based on the mobile terminal according to the embodiment of the present application. The system comprises:
a preprocessing module 11, configured to acquire mobile data generated by a user from a mobile terminal and perform preprocessing, where the mobile data includes communication mode data and internet surfing behavior data;
the user portrayal module 12 is used for carrying out cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects and constructing a user portrayal;
and the service module 13 is used for pushing corresponding service or consultation content to the user according to the user portrait.
Referring to fig. 5, fig. 5 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 1, a processor 2 and a communication interface 3, wherein the memory 1, the processor 2 and the communication interface 3 are electrically connected with each other directly or indirectly so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 1 may be used for storing software programs and modules, such as program instructions/modules corresponding to a mobile terminal-based big data analysis system provided in the embodiments of the present application, and the processor 2 executes the software programs and modules stored in the memory 1, thereby executing various functional applications and data processing. The communication interface 3 may be used for communication of signaling or data with other node devices.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments and that other specific embodiments may be utilized without departing from the spirit or essential characteristics of the present application
The form realizes the present application. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. A big data analysis method based on a mobile terminal, comprising:
mobile data generated by a user are acquired from a mobile terminal and preprocessed, wherein the mobile data comprise communication mode data and internet surfing behavior data;
carrying out cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects, and constructing user portraits;
and pushing corresponding service or consultation content for the user according to the user portrait.
2. The mobile terminal-based big data analysis method of claim 1, wherein the step of acquiring the user-generated movement data from the mobile terminal and preprocessing comprises:
converting the non-numerical type mobile data into numerical type mobile data, and performing standardization processing;
and carrying out exception processing on the mobile data after standardized processing, and deleting the sensitive data.
3. The big data analysis method based on the mobile terminal as claimed in claim 1, wherein the step of performing cluster analysis on the mobile data by using a K-means algorithm based on deep learning to obtain user labels of all aspects and constructing a user portrait comprises:
performing feature learning on the mobile data by using a deep neural network structure;
pre-clustering the learned characteristic representation by using a K-means algorithm to obtain a clustering center;
based on a preset user portrait label system, determining labels of each cluster center, obtaining user labels of all aspects, and constructing a user portrait.
4. A mobile terminal based big data analysis method according to claim 3, wherein the step of pre-clustering the learned feature representation using a K-means algorithm to obtain a cluster center comprises:
k clustering centers are selected from the learned feature representation, and similarity results between the rest features and the clustering centers are obtained through calculation;
clustering is carried out according to the similarity result, and the position of the iterative clustering center is continuously updated;
stopping when the cluster center is not changed or reaches the preset maximum iteration number, and determining the final cluster center.
5. The mobile terminal-based big data analysis method of claim 1, further comprising:
acquiring user communication information according to the communication mode data, wherein the user communication information comprises communication duration, geographic position, contact number and frequency;
establishing a Gantt chart according to the communication information to obtain time and space distribution characteristics of a user;
and determining the user attribute according to the time and space distribution characteristics.
6. The mobile terminal-based big data analysis method of claim 1, further comprising: and visually displaying the user portrait.
7. A mobile terminal-based big data analysis system, comprising:
the preprocessing module is used for acquiring mobile data generated by a user from the mobile terminal and preprocessing the mobile data, wherein the mobile data comprises communication mode data and internet surfing behavior data;
the user portrait module is used for carrying out cluster analysis on the mobile data by utilizing a K-means algorithm based on deep learning to obtain user labels of all aspects and constructing user portraits;
and the service module is used for pushing corresponding service or consultation content for the user according to the user portrait.
8. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 1-6 is implemented when the one or more programs are executed by the processor.
9. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-6.
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| CN202211662468.8A CN116070018A (en) | 2022-12-23 | 2022-12-23 | Big data analysis method and system based on mobile terminal |
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| CN202211662468.8A CN116070018A (en) | 2022-12-23 | 2022-12-23 | Big data analysis method and system based on mobile terminal |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116956097A (en) * | 2023-09-18 | 2023-10-27 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116956097A (en) * | 2023-09-18 | 2023-10-27 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
| CN116956097B (en) * | 2023-09-18 | 2023-12-12 | 湖南华菱电子商务有限公司 | Expert portrait analysis method and system based on K-means |
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