WO2018137104A1 - Procédé et système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées - Google Patents
Procédé et système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées Download PDFInfo
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- WO2018137104A1 WO2018137104A1 PCT/CN2017/072375 CN2017072375W WO2018137104A1 WO 2018137104 A1 WO2018137104 A1 WO 2018137104A1 CN 2017072375 W CN2017072375 W CN 2017072375W WO 2018137104 A1 WO2018137104 A1 WO 2018137104A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
Definitions
- the present invention relates to the field of Internet technologies, and in particular, to a user behavior analysis method and system based on big data mining.
- user behavior analysis refers to the statistical and analysis of real-time and historical user behavior generated by users accessing network services (including accessing and browsing web pages, performing interactive operations, using APP, etc.). information.
- network service access times access frequency, access time, active time, user input keywords, user click links, user interaction operations (such as adding attention, canceling attention, scoring) , save as a bookmark, add to the shopping cart, take out the shopping cart, form an order, cancel an order, pay, refund, etc.).
- the most effective means is to record all user behavior information brought by all actions of users, and to analyze and analyze all user behavior information.
- Big data technology is an information processing technology that targets all data resources of any system and discovers the correlation between data representation. It has been widely used in Internet process optimization, targeted message and advertisement push, and user personalized service. And improvement, etc., has become a strong back-office support behind network services. Based on the big data platform, the analysis and utilization of all user behavior information is realized, which adapts to the characteristics of large user behavior information, complex and diversified data formats, and high computing speed requirements, which can meet the actual needs of various types of network services.
- the embodiment of the invention provides a non-intelligent air conditioning monitoring method and system based on the Internet of Things, which can monitor various parameter information of the non-intelligent air conditioner, and adjust the non-intelligent air conditioner according to the parameter information, and can check whether the temperature and humidity setting of the air conditioner is reasonable. Or automatically control the air conditioner switch according to the time period to avoid unnecessary waste of cold source.
- the first aspect of the embodiments of the present invention discloses a user behavior analysis method based on big data mining, including:
- a user behavior data ontology model is established and stored in the database.
- the method further includes
- the user behavior data ontology model is analyzed to find out the user's latest interest data.
- the user behavior data includes a user behavior main body, an occurrence time, a generated page, a scrolling page up and down, a moving or clicking mouse, a page staying time, collecting, printing, saving, accessing the same page, Copy and paste text operations, current user search criteria, and search keyword-related titles.
- the pre-processing includes: removing incomplete data, deleting duplicate data, pictures, and page animation; printing, collecting, saving, and downloading operations on the page, and after converting, converting the The corresponding data format is saved in the database;
- the data aggregation includes: filtering and integrating the correct and invalid user behavior information by using a rule-based user behavior aggregation algorithm.
- the establishing a user behavior data ontology model specifically includes:
- the OWL-DL description language is used to build the user behavior data ontology model, and the ontology model is decomposed.
- the database uses an open source non-relational distributed database.
- the second aspect of the embodiment of the present invention discloses a user behavior analysis system based on big data mining, including:
- An acquisition unit for collecting user behavior data An acquisition unit for collecting user behavior data
- a pre-processing unit for performing pre-processing and aggregation on user behavior data using a parallel computing model
- the modeling unit is configured to establish a user behavior data ontology model according to the aggregated user behavior data, and store the data in the database.
- system further includes:
- the analysis unit is configured to analyze the user behavior data ontology model to find out the latest interest data of the user.
- the user behavior data includes a user behavior main body, an occurrence time, a generated page, a scrolling page up and down, a moving or clicking mouse, a page staying time, collecting, printing, saving, accessing the same page, Copy and paste text operations, current user search criteria, and search keyword-related titles.
- the pre-processing includes: removing incomplete data, deleting duplicate data, pictures, and page animation; printing, collecting, saving, and downloading operations on the page, and after converting, converting the The corresponding data format is saved in the database;
- the data aggregation includes: filtering and integrating the correct and invalid user behavior information by using a rule-based user behavior aggregation algorithm.
- the modeling unit is specifically configured to: establish an ontology model of the user behavior data by using an OWL-DL description language, and decompose the ontology model, where the database adopts an open source non-relational distributed database. .
- the user behavior data is collected; the user behavior data is preprocessed and aggregated by using a parallel computing model; and the user behavior data ontology model is established according to the aggregated user behavior data, and stored in the database.
- the implementation of the embodiments of the present invention combines the powerful processing capability of the cloud computing technology with the large-scale data storage capability, the ontology and its analysis, and the knowledge discovery method, analyzes the massive user behavior data in real time, and timely acquires the user interest, thereby realizing Effective and accurate user push.
- FIG. 1 is a schematic flowchart of a user behavior analysis method based on big data mining according to a first embodiment of the present invention
- FIG. 3 is a schematic structural diagram of a terminal device according to a third embodiment of the present invention.
- the embodiment of the invention provides a user behavior analysis method based on big data mining, which combines the powerful processing capability of cloud computing technology with large-scale data storage capability, ontology and its analysis, and knowledge discovery method to analyze massive user behavior data in real time. Get user interest in a timely manner to achieve effective and accurate user push.
- FIG. 1 is a user based on big data mining disclosed in the first embodiment of the present invention. Schematic diagram of the behavior analysis method. The method for analyzing user behavior based on big data mining shown in FIG. 1 may include the following steps:
- the user behavior data includes a user behavior main body, an occurrence time, a generated page, a scrolling page up and down, a moving or clicking mouse, a page staying time, collecting, printing, saving, accessing the same page number, copying and pasting text.
- the pre-processing includes: removing incomplete data, deleting duplicate data, pictures, and page animation; printing, collecting, saving, and downloading operations on the page, and converting the data into corresponding data after obtaining The format is saved in the database.
- the data aggregation includes: filtering and integrating the correct and invalid user behavior information by using a rule-based user behavior aggregation algorithm.
- the user behavior data ontology model is established by using the OWL-DL description language, and the ontology model is decomposed, and the database adopts an open source non-relational distributed database.
- the aggregated user behavior data is added to the user behavior data ontology model, and the user behavior data ontology model data stored in the database is analyzed to find the user's latest interest data.
- the user behavior data is collected; the user behavior data is preprocessed and aggregated by using a parallel operation model; and the user behavior data ontology model is established according to the aggregated user behavior data, and stored in the database.
- the implementation of the embodiments of the present invention combines the powerful processing capability of the cloud computing technology with the large-scale data storage capability, the ontology and its analysis, and the knowledge discovery method, analyzes the massive user behavior data in real time, and timely acquires the user interest, thereby realizing Effective and accurate user push.
- the system embodiment of the present invention is used to perform the method for implementing the first embodiment of the method of the present invention.
- the system embodiment of the present invention is used to perform the method for implementing the first embodiment of the method of the present invention.
- the method related to the embodiment of the present invention is shown, and specific calculation details are not disclosed. Please refer to Embodiments 1 to 2 of the present invention.
- FIG. 2 is a structural diagram of a user behavior analysis system based on big data mining disclosed in a second embodiment of the present invention. As shown in Figure 2, the system can include:
- the collecting unit 201 is configured to collect user behavior data.
- the user behavior data includes a user behavior main body, an occurrence time, a generated page, a scrolling page up and down, a moving or clicking mouse, a page staying time, a favorite, a print, a save, a visit to the same page number, a copy and paste text operation, and a current user search.
- Conditions search for the title of the keyword.
- the pre-processing unit 202 is configured to perform pre-processing and aggregation on the user behavior data by using a parallel computing model.
- the pre-processing includes: removing incomplete data, deleting duplicate data, pictures, page animation; printing, collecting, saving, and downloading operations on the page, and after converting, converting the data into a corresponding data format and saving the same in a database;
- the data aggregation includes: filtering and integrating the correct and invalid user behavior information by using a rule-based user behavior aggregation algorithm.
- the modeling unit 203 is configured to establish a user behavior data ontology model according to the aggregated user behavior data, and store the data in the database.
- the user behavior data ontology model is established by using the OWL-DL description language, and the ontology model is decomposed, and the database adopts an open source non-relational distributed database.
- the analyzing unit 204 is configured to analyze the user behavior data ontology model to find the latest interest data of the user.
- the aggregated user behavior data is added to the user behavior data ontology model, and the user behavior data ontology model data stored in the database is analyzed to find the user's latest interest data.
- the acquisition unit collects user behavior data; the pre-processing unit uses a parallel operation model to perform pre-processing and aggregation on the user behavior data; the modeling unit establishes a user behavior data ontology model according to the aggregated user behavior data. And stored in the database.
- FIG. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention. As shown in FIG. 3, for the convenience of description, only the parts related to the embodiments of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.
- the terminal may include a processor 301, a memory 302, a collector 303, the processor 301, a memory 302, and a transmitter 303 connected by a communication bus 304.
- each step method flow may be implemented based on the structure of the terminal device.
- Both the application layer and the operating system kernel can be considered as part of the abstraction structure of the processor 301.
- the processor 301 performs the following operations by calling program code stored in the memory 302:
- a user behavior data ontology model is established and stored in the database.
- the user behavior data includes a user behavior main body, an occurrence time, a generated page, a scrolling page up and down, a moving or clicking mouse, a page staying time, a favorite, a print, a save, a visit to the same page number, a copy and paste text operation, and a current user search.
- Conditions search for the title of the keyword.
- the pre-processing includes: removing incomplete data, deleting duplicate data, pictures, page animation; printing, collecting, saving, and downloading operations on the page, and after converting, converting the data into a corresponding data format and saving the same in a database;
- the data aggregation includes: filtering and integrating the correct and invalid user behavior information by using a rule-based user behavior aggregation algorithm.
- the collector 303 collects user behavior data; the processor 301 performs preprocessing and aggregation on the user behavior data by using a parallel computing model; and establishes a user behavior data ontology model according to the aggregated user behavior data. And stored in the database. It can be seen that implementing the embodiments of the present invention, the powerful processing capability of the cloud computing technology and the large-scale data storage capability, the ontology and its points The combination of analysis and knowledge discovery methods analyzes massive user behavior data in real time and acquires user interest in time to achieve effective and accurate user push.
- processor 301 is further configured to perform the following operations by calling program code stored in the memory 302:
- the user behavior data ontology model is analyzed to find out the user's latest interest data.
- the embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium can store a program, and the program includes a part or all steps of a monitoring method of any one of the foregoing method embodiments.
- the insufficiency of the method of the embodiment of the present invention may be adjusted, merged, or deleted according to actual needs.
- the unit of the terminal in the embodiment of the present invention may be integrated, further divided or deleted according to actual needs.
- the disclosed system may be implemented in other manners, for example, the system embodiment described above is illustrative, for example, the division of the unit is A logical function partitioning may be implemented in an actual manner. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
- the mutual coupling or direct coupling or communication connection shown or discussed may be an inductive or communication connection through some interface, device or unit, and may be electrical or otherwise.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. . Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
- each functional unit in various embodiments of the present invention may be integrated in one processing unit. It is also possible that each unit physically exists alone, or two or more units may be integrated in one unit.
- the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
- each unit included is only divided according to functional logic, but is not limited to the above division, as long as the corresponding The functions of the functional units are only for the purpose of facilitating mutual differentiation and are not intended to limit the scope of the present invention.
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- RAM Random Access Memory
- PROM Programmable Read-Only Memory
- Erasable Programmable Read Only Memory Erasable Programmable Read Only Memory
- EPROM One-time Programmable Read-Only Memory
- OTPROM One-time Programmable Read-Only Memory
- EEPROM Electronically-Erasable Programmable Read-Only Memory
- CD-ROM Compact Disc Read-Only Memory
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Abstract
Selon des modes de réalisation, la présente invention concerne un procédé et un système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées. Le procédé comprend les étapes qui consistent : à collecter des données de comportement d'utilisateur; à prétraiter et agréger les données de comportement d'utilisateur au moyen d'un modèle de calcul parallèle; à établir un modèle d'ontologie de données de comportement d'utilisateur en fonction des données de comportement d'utilisateur agrégées, et à mémoriser le modèle d'ontologie de données de comportement d'utilisateur dans une base de données. En conséquence, grâce à l'exécution des modes de réalisation de la présente invention, la très grande capacité de traitement d'une technologie de cloud computing est combinée avec une capacité de mémorisation de données à grande échelle, une ontologie et une analyse de ces données ainsi qu'un procédé de découverte de connaissances, ce qui permet d'analyser des données de comportement d'utilisateur de masse en temps réel, de connaître au fil du temps ce qui intéresse un utilisateur, et de mettre ainsi en œuvre une poussée efficace et précise vers l'utilisateur.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2017/072375 WO2018137104A1 (fr) | 2017-01-24 | 2017-01-24 | Procédé et système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées |
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| PCT/CN2017/072375 WO2018137104A1 (fr) | 2017-01-24 | 2017-01-24 | Procédé et système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées |
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| WO2018137104A1 true WO2018137104A1 (fr) | 2018-08-02 |
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| PCT/CN2017/072375 WO2018137104A1 (fr) | 2017-01-24 | 2017-01-24 | Procédé et système d'analyse de comportement d'utilisateur basés sur une exploration de mégadonnées |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111460046A (zh) * | 2020-03-06 | 2020-07-28 | 合肥海策科技信息服务有限公司 | 一种基于大数据的科技信息聚类方法 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793465A (zh) * | 2013-12-20 | 2014-05-14 | 武汉理工大学 | 基于云计算的海量用户行为实时分析方法及系统 |
| CN104462213A (zh) * | 2014-12-05 | 2015-03-25 | 成都逸动无限网络科技有限公司 | 一种基于大数据的用户行为分析方法及系统 |
| CN105447186A (zh) * | 2015-12-16 | 2016-03-30 | 汉鼎信息科技股份有限公司 | 一种基于大数据平台的用户行为分析系统 |
| US20160092774A1 (en) * | 2014-09-29 | 2016-03-31 | Pivotal Software, Inc. | Determining and localizing anomalous network behavior |
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- 2017-01-24 WO PCT/CN2017/072375 patent/WO2018137104A1/fr active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793465A (zh) * | 2013-12-20 | 2014-05-14 | 武汉理工大学 | 基于云计算的海量用户行为实时分析方法及系统 |
| US20160092774A1 (en) * | 2014-09-29 | 2016-03-31 | Pivotal Software, Inc. | Determining and localizing anomalous network behavior |
| CN104462213A (zh) * | 2014-12-05 | 2015-03-25 | 成都逸动无限网络科技有限公司 | 一种基于大数据的用户行为分析方法及系统 |
| CN105447186A (zh) * | 2015-12-16 | 2016-03-30 | 汉鼎信息科技股份有限公司 | 一种基于大数据平台的用户行为分析系统 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111460046A (zh) * | 2020-03-06 | 2020-07-28 | 合肥海策科技信息服务有限公司 | 一种基于大数据的科技信息聚类方法 |
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