CN107895026A - A kind of implementation method of campus user portrait - Google Patents
A kind of implementation method of campus user portrait Download PDFInfo
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- CN107895026A CN107895026A CN201711143522.7A CN201711143522A CN107895026A CN 107895026 A CN107895026 A CN 107895026A CN 201711143522 A CN201711143522 A CN 201711143522A CN 107895026 A CN107895026 A CN 107895026A
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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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
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
Abstract
The present invention provides a kind of generation method of User portrait, applied to campus and internet crossing domain, except traditional data information and campus data messages such as the name of collection User, photo, age, family, educational background, technical ability, also gather the internet data information of student, such as linking Internet time, IP, log duration, browsing content etc..On this basis, words and deeds and change of the student particularly on network are further analyzed with big data means, forms the personal portrait and colony's portrait, the multidimensional learning state that student is presented, economic scene, animation etc. of student.
Description
Art
The crossing domain of campus and Internet technology is belonged to, was both used as main portrait base by the use of the essential information of traditional student
Plinth, and the further supplement drawn a portrait using behavior of the student in internet, and pass through data analysis, proposed algorithm, data
Visualization, shows the portrait of student.
Background technology
As shown in figure 1, the Back ground Information that user's portrait analysis in industry at present is generally based on user is labelled,
Such as utilize the name of user, photo, age, family status, income, work, educational background, address, marital status, technical ability, hobby
Etc. information, by gathering these information, user feature analysis is then carried out, matching symbol shares the label at family, finally by visual
The method of change is presented.
But there are problems in this traditional way：First, how to obtain user base information.Second, how to protect
Demonstrate,prove the authenticity of user profile.Information from different channels is inconsistent, and the chaotic problems of information influence whether analysis knot
Fruit.3rd, how the information of real-time update user.Over time, change occurs in the information of user, then how real
The information of Shi Gengxin user is also key issue.Due to it is above-mentioned the problem of it is difficult to ensure that the accuracy of data, can be caused in label
Match and deviation occur.
Industry problems faced, at the same be also campus problems faced, this traditional industry in campus, how correctly
The personal portrait for identifying student is a difficult point, and present most school still takes collects student's using traditional mode
Personal information, the simple artificial mark for judging, judging due to each teacher or counsellor is then carried out by the information of collection
Accurate inconsistent, there is difference in the result for causing to judge.
See this, I take charge of for user portrait model, research it is a kind of can meet campus to student the needs of drawing a portrait, and can
Solves the variety of problems that campus faces.
The content of the invention
I take charge of now to campus data, services on the basis of, it is quick, effective integration is big by the solution of internet
Students ' behavior data are measured, and then on big data service, establish the model for User portrait, reaches and is ensureing data
On the basis of quality, matching meets the label of student, finally by graphic exhibition.
The present invention provides a kind of method for realizing campus user portrait, including：
Data analysis phase, extract and learn work, educational administration, finance, consumption, scientific data and internet data, carry out data analysis
And excavation, excavate the inclined feature of user；
Read phase is solved, the business of problem student is understood and studied by various kinds of schools administrative staff, furthers investigate custodian
The item that member is concerned about, it is that student group is tagged, label is artificial defined highly refined signature identification；
Modelling phase, with reference to the actual demand of school administrator, the data entity of correlation is found out, is advised centered on data entity
About data dimension type and incidence relation, form the modeling systems for meeting client's actual conditions；
In the dimensional analysis stage, data dimension centered on multi-dimensional data entity, is being carried out caused by school life's study by student
Decompose and enumerate；
Application stage, for the demand of different role personnel, design use function of each role personnel in user's portrait instrument
And operating process.
Further, the internet data includes network access duration, access frequency, access time, access website with
And its content.
Further, the data analysis phase includes generation data source, data modeling, Data Mart and visualization point
Analyse four-stage.
Further, the multi-dimensional data entity includes personal reading, personal consumption, individual results, individual have dinner and
Network log.
Further, the application stage includes recommending all kinds of problem students to related custodian by commending system
Member, the business structure of commending system are as follows：First layer is to recommend business activity layer, and recommendation results are showed user；The second layer
It is proposed algorithm layer, including user's portrait recommends and situation recommending；Third layer is index level, and to student data, student is all kinds of goes through
Index is established in Records of the Historian record, improves inquiry velocity；4th layer is data Layer, storage student, students ' behavior data and the basic number of recommendation
According to.
Brief description of the drawings
Fig. 1 be at this stage user draw a portrait schematic diagram.
Fig. 2 is big data analysis system Organization Chart.
Fig. 3 is personal reading portrait schematic diagram.
Fig. 4 (a) -4 (b) is personal consumption portrait schematic diagram.
Fig. 5 (a) -5 (b) is individual results portrait schematic diagram.
Fig. 6 is network log portrait schematic diagram.
Fig. 7 is the schematic diagram that abnormal behaviour is judged according to portrait.
Embodiment
By building big data analysis system, the data of conformity of business operation system, point of the structure one using student as dimension
Analyse theme.On this basis, further analyze university student with big data means and be particularly its words and deeds and change on network,
The personal portrait of formation student and colony's portrait, the health status of various dimensions presentation student, learning state, economic scene, life shape
State, state of mind, safe condition, provided for students' educational management Service Promotion and personnel training decision-making more solid
Data supporting.The target of construction mainly includes following two aspects：
First, lifts the quality of data, and the student for after is personal and colony's portrait provides reliable data source, meanwhile, it is school
A data assets with break-up value are provided, it is convenient to carry out data analysis and data mining from now on.
2nd, describes student individual and colony's portrait, and side entirely is carried out in school behavior to individual students by individual's portrait
Position, various dimensions are portrayed, by colony draw a portrait reflection particular student colony the characteristics of, support many condition combined sorting colony；
Data analysis step：The step can be realized by building big data analysis system.Specific Technical Architecture such as Fig. 2 institutes
Show.
Big data analysis system is main in two sub-sections：A part is the acquisition of traditional data.Extract business datum source number
According to（Including learning the data such as work, educational administration, finance, consumption and scientific research）, central database is synchronized to by processing, cleaning way, most
Afterwards by the way that data are carried out with the division on different dimensions, unified, integrated, high quality a data warehouse, this portion are formed
The Technical Architecture divided is fairly simple, using relational data library storage.Another part data source is from internet
Data, including network access duration, access frequency, access time, the data such as website and its content are accessed, fully understand student
Network behavior, the Technical Architecture of this part is mainly the Hadoop Development Frameworks using main flow, and using ETL, Flume daily records are adopted
The various ways such as collection, Python reptiles, data storage is collected, carry out task distribution using the MapReduce in Hadoop, use
ZooKeeper carries out resources regulation, with reference to Hive/Pig off-line calculation and rationally efficiently locating in line computation for Spark
Multi-source, various dimensions are managed, substantial amounts of real time data is simultaneously excavated to data, rationally effectively solves the demand student of school
Portrait.
From data source to it is final show be divided into it is following several layers of：
1. data source：Including from the analyze data source of each operation system and medium, its carrier includes database, file, big
Data platform etc..
Data modeling：According to user's portrait modeling systems, configuration data model.
Data Mart：Each Data Mart is the detail data that light weight modeling is carried out based on a theme, and data are according to row
The mode of storage, by Efficient Compression, label is accomplished fluently, is stored in disk.When needing to calculate, line number is entered using internal memory calculating
According to calculating, and every machine node can calculate simultaneously, eventually result is sent to visual analyzing layer and done show.
Visual analyzing：All kinds of method for visualizing are used to show final result for user, user can also pass through mobile terminal
To access system.Visualized Analysis System provides system monitoring, authority multiple management, multidimensional data analysis, etc. function, also props up
Hold from service formula Report Form Design and data analysis.
By being analyzed user behavior data and being excavated, the inclined feature of user is excavated, progressively sketches the contours of the picture of user
Picture.User's portrait generally by business experience and establishes method that model is combined to realize, user's portrait is more in this programme
Bias toward the judgement of business experience.Using student all kinds of multidimensional data analysis combination school administrators in school business experience
User's portrait of student is sketched out, such portrait is more the warp provided by business personnel due to being closely related with business
Test to describe user preference.
The focus work of user's portrait is exactly to be beaten " label " for user, and the usually manual defined height essence of a label
The signature identification of refining, such as age, sex, consumption, user preference, labels by all of user in general, just finally
The solid " portrait " of the user can be sketched the contours of.Specifically, when being drawn a portrait for user, it is necessary to the following four stage：
1. understand：The business of problem student is understood by various kinds of schools administrative staff for the program and the research of CROSS REFERENCE,
User's portrait of individual students is built, the item that further investigation administrative staff are concerned about is effectively tagged for student group.
Modeling：User is drawn a portrait and carries out data modeling, with reference to the actual demand of school administrator, finds out related data
Entity, conventions data dimension type and incidence relation centered on data entity, form the modeling body for meeting client's actual conditions
System.
Dimension is decomposed：Data dimension centered on multi-dimensional data entity, is being carried out caused by school life's study by student
Decompose and enumerate.According to relevance principle, the data dimension related to final purpose is chosen, avoids producing excessive hash and does
Disturb analysis process.
Using：For the demand (such as counsellor, institute director, learning teacher etc. at work) of different role personnel, each angle is designed
Use function and application/operating process of the color personnel in user's portrait instrument.
The construction drawn a portrait by user, we are capable of each student of understanding of more reasonable science, for some problems
There can be the specific aim more strengthened.
Label is built to the data of user by establishing model, realizes that user draws a portrait, is realized further according to proposed algorithm to student
Precision management（We build commending system based on being drawn a portrait by user（Recommend all kinds of problem students to related custodian
Member））, model will consider precision and stability, carry out sufficiently modification, perfect.The business structure of commending system is as follows：
First layer is to recommend business activity layer, and recommendation results are showed user.
The second layer is proposed algorithm layer, including user draws a portrait recommendation, situation recommending etc..
Third layer is index level, establishes index to student data, all kinds of historical records of student, improves inquiry velocity.
4th layer is data Layer, storage student, students ' behavior data and recommendation basic data such as recommended models.
Calculated in real time using storm according to user and corresponding business scenario, provide recommendation results；To a large amount of samples
Notebook data carries out offline machine learning calculating using Spark, produces model, determines for user's portrait weight and calculates in real time.
Extensive batch processing is calculated using Hadoop mapReduce.Search to student can also use user's portrait and commodity to draw
As carrying out result displaying.The behavioral data of student is changing, and the recommendation information of student is also changing, and user, which draws a portrait, needs timing to enter
Row modification, such as two weeks or one month.Table, which is had, in Hbase student's label preserves data, and according to these data machines
Learning training algorithm model, model result are stored in Hbase, take the data of nearly one month to bring model into when specific recommend
Calculated, a variety of recommendation results arrive optimal recommendation results according to after rule calculating, then are shown to user with presentation engine.
The intermediate result of calculating is stored in hbase.
We can take multiclass machine learning method to build each class model herein, while we can be according to all kinds of machines
The advantage and disadvantage of study carry out the screening of model, if in order to prevent over-fitting, can add regularization term；Such as fruit instant feature
Screening, can use stepwise logistic regression；Logistic regression precision under big data quantity can decline, can be substantial amounts of special by adding
Sign（Such as the mode of dummy variable）To improve precision；Random forest, each tree are replaced using this boosting methods of GBRT
What is practised is the residual error of upper one tree, effectively lift scheme.
Finally we will more accurately service all kinds of Early-warning Models using the network data combination text mining crawled.Text
This analysis is to utilize natural language processing（NLP）One kind of the text datas such as technical Analysis text document, social media, webpage should
With.With the high speed development of ecommerce, digital marketing and big data technology, the file management of data-driven, Consumer's Experience pipe
Reason has become enterprise core competence, and text analyzing is then the crucial application of Consumer's Experience management.And to traditional text text
The text data that these relative increments of shelves are little, total amount is stable is analyzed, then highlights its knowledge, information, value and excavate, especially
It is simplification to mass text, marking, more educated, then is structure expert system, artificial intelligence, the basis of knowledge mapping.Cause
This can strengthen the accuracy of model using all kinds of text mining methods in the present case, while can provide problem student and more have
The possibility problem of body, provided for school administrator and more enrich complete student's problem.
As shown in figure 3, personal read portrait mainly reflection student in school reading conditions, class of being read to student can be passed through
Type, reading total amount, books to be gone back etc., show that student's reading conditions during school are reported.
Such as Fig. 4（a）With 4（b）Shown, personal consumption portrait mainly reflects consumption of the student in school, by learning
Raw all-purpose card consumer record, and the level of consumption of daily life, comprehensive assessment draw the report in school consumption of student.
Such as Fig. 5（a）With 5（b）Shown, individual results portrait is the achievement situation for reflecting student, can be by student's academic year
The multi dimensional analysis such as point, class's ranking, ranking of subject, show that student's individual results are drawn a portrait.
Individual have dinner portrait be reflection student's dining situation, according to the consumer record of student's all-purpose card, understand student just
Meal time, place, the daily custom etc. of having dinner of reflection student.
As shown in fig. 6, network log is drawn a portrait, including but not limited to student accesses the time of internet, ip, and browse
Related content, log duration etc..Including but not limited to student accesses the time of internet, ip, and the related content browsed, steps on
Record duration etc..
I is taken charge of by scale-model investigation of being drawn a portrait to the student in campus, solves campus because data quality problem causes user to draw
As the problem of inaccurate, by this solution, we can help more campuses to realize that student's portrait is researched and analysed, enter one
Step finds whether the behavior of student abnormal situation occurs, as shown in Figure 7.
Claims (5)
- A kind of 1. method for realizing campus user portrait, it is characterised in that including：Data analysis phase, extract and learn work, educational administration, finance, consumption, scientific data and internet data, carry out data analysis And excavation, excavate the inclined feature of user；Read phase is solved, the business of problem student is understood and studied by various kinds of schools administrative staff, furthers investigate custodian The item that member is concerned about, it is that student group is tagged, label is artificial defined highly refined signature identification；Modelling phase, with reference to the actual demand of school administrator, the data entity of correlation is found out, is advised centered on data entity About data dimension type and incidence relation, form the modeling systems for meeting client's actual conditions；In the dimensional analysis stage, data dimension centered on multi-dimensional data entity, is being carried out caused by school life's study by student Decompose and enumerate；Application stage, for the demand of different role personnel, design use function of each role personnel in user's portrait instrument And operating process.
- 2. according to the method for claim 1, it is characterised in that the internet data includes network access duration, accesses Frequency, access time, access website and its content.
- 3. according to the method for claim 2, it is characterised in that the data analysis phase includes generation data source, data Modeling, Data Mart and visual analyzing four-stage.
- 4. according to the method for claim 1, it is characterised in that the multi-dimensional data entity includes personal reading, individual Consumption, individual results, individual has dinner and network log.
- 5. according to the method described in claim any one of 1-4, it is characterised in that the application stage includes passing through commending system Recommend all kinds of problem students as follows to related administrative staff, the business structure of commending system：First layer is to recommend business activity Recommendation results, are showed user by layer；The second layer is proposed algorithm layer, including user's portrait recommends and situation recommending；Third layer It is index level, index is established to student data, all kinds of historical records of student, improves inquiry velocity；4th layer is data Layer, storage Student, students ' behavior data and recommendation basic data.
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| CN108829721A (en) * | 2018-05-08 | 2018-11-16 | 浪潮软件集团有限公司 | Scientific and technological user portrait construction method and system based on data model |
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| CN109192310A (en) * | 2018-07-25 | 2019-01-11 | 同济大学 | A kind of undergraduate psychological behavior unusual fluctuation scheme Design method based on big data |
| CN109347903A (en) * | 2018-08-28 | 2019-02-15 | 浙江工业大学 | A kind of campus message pushing and optimizing method based on generalized information system |
| CN109460440A (en) * | 2018-09-18 | 2019-03-12 | 平安科技（深圳）有限公司 | A kind of portrait processing method, device and equipment based on weighted value |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108829721A (en) * | 2018-05-08 | 2018-11-16 | 浪潮软件集团有限公司 | Scientific and technological user portrait construction method and system based on data model |
| CN109192310A (en) * | 2018-07-25 | 2019-01-11 | 同济大学 | A kind of undergraduate psychological behavior unusual fluctuation scheme Design method based on big data |
| CN109189905A (en) * | 2018-08-15 | 2019-01-11 | 苏州至纤至悉信息科技有限公司 | A kind of big data behavior pattern multi-dimensional intelligent matching pushing software |
| CN109347903A (en) * | 2018-08-28 | 2019-02-15 | 浙江工业大学 | A kind of campus message pushing and optimizing method based on generalized information system |
| CN109347903B (en) * | 2018-08-28 | 2021-02-26 | 浙江工业大学 | Campus message pushing optimization method based on GIS (geographic information System) |
| CN109460440A (en) * | 2018-09-18 | 2019-03-12 | 平安科技（深圳）有限公司 | A kind of portrait processing method, device and equipment based on weighted value |
| CN109460440B (en) * | 2018-09-18 | 2023-10-27 | 平安科技（深圳）有限公司 | Image processing method, device and equipment based on weight value |
| CN109766000A (en) * | 2018-12-25 | 2019-05-17 | 重庆和贯科技有限公司 | A kind of wisdom education system and method based on virtual reality |
| CN112311612A (en) * | 2019-07-29 | 2021-02-02 | 腾讯科技（深圳）有限公司 | Family portrait construction method and device and storage medium |
| CN110781221A (en) * | 2019-09-27 | 2020-02-11 | 同济大学 | Estimation decision support system architecture for concealed property of executed person in court |
| CN110750733A (en) * | 2019-10-15 | 2020-02-04 | 赛尔网络有限公司 | Service recommendation method, device, equipment and medium based on school identity portrait |
| CN111046263A (en) * | 2019-11-22 | 2020-04-21 | 广东机电职业技术学院 | Student learning interest portrait generation system, method and device and storage medium |
| WO2021098187A1 (en) * | 2019-11-22 | 2021-05-27 | 广东机电职业技术学院 | Student learning interest profile generation system, method and device, and storage medium |
| CN112256667A (en) * | 2020-09-16 | 2021-01-22 | 珠海市新德汇信息技术有限公司 | Multi-biological characteristic normalization method |
| CN112256667B (en) * | 2020-09-16 | 2024-03-22 | 珠海市新德汇信息技术有限公司 | Multi-biological characteristic normalization method |
| CN112465543A (en) * | 2020-11-25 | 2021-03-09 | 宁波阶梯教育科技有限公司 | User portrait generation method, equipment and computer storage medium |
| CN112671709A (en) * | 2020-11-25 | 2021-04-16 | 紫光云技术有限公司 | User portrait visualization method based on college network behavior log |
| CN112597157A (en) * | 2020-12-16 | 2021-04-02 | 光大兴陇信托有限责任公司 | Method and system for storing and managing submission information |
| CN112686462A (en) * | 2021-01-06 | 2021-04-20 | 广州视源电子科技股份有限公司 | Student portrait-based anomaly detection method, device, equipment and storage medium |
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Address after: 510000 No. 16 Keyun Road, Tianhe District, Guangzhou City, Guangdong Province, 1 501 Applicant after: Link Polytron Technologies Inc. Address before: 510000 No. 16 Keyun Road, Tianhe District, Guangzhou City, Guangdong Province, 1 501 Applicant before: LIANYI TECHNOLOGY Co.,Ltd. |
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