CN102946319B - Networks congestion control information analysis system and analytical method thereof - Google Patents
Networks congestion control information analysis system and analytical method thereof Download PDFInfo
- Publication number
- CN102946319B CN102946319B CN201210374292.6A CN201210374292A CN102946319B CN 102946319 B CN102946319 B CN 102946319B CN 201210374292 A CN201210374292 A CN 201210374292A CN 102946319 B CN102946319 B CN 102946319B
- Authority
- CN
- China
- Prior art keywords
- data
- user
- behavior
- congestion control
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of networks congestion control information analysis system and analytical method thereof, system comprises the networks congestion control data acquisition module connected successively, networks congestion control data preprocessing module, networks congestion control data memory module, networks congestion control data analysis module, analysis result display module; Method comprises the collection of data, the preliminary treatment of data, the conversion of data, and the steps such as the analysis of data and the display of data, the present invention can obtain networks congestion control information easily and fast, ensure that the integrality of user behavior information, continuity and validity.
Description
Technical field
The invention belongs to Analysis of Network Information field, particularly a kind of networks congestion control information analysis system and analytical method thereof.
Background technology
Along with the development of the Internet and the universal of computer, the quantity rapid development of the network user, network behavior becomes one of most important social phenomenon in human behavior gradually.Understand and analyze the behavior of the network user in depth, the influencing factor of user website usage behavior could be found, contribute to improving and optimizing network information service, improve the efficiency of information management and service.
In the scope of behavioural information, behavior refers to activity as action, operation or event and entity produces under specific situation and environment in virtual or actual tissue active sequences.The feature that has of user behavior in a network environment that what the present invention mainly studied is.
Network behavior quantitatively or qualitatively can represent with the incidence relation of the statistical nature of some characteristic quantity or characteristic quantity.User is concluded the business by e-commerce website, in the operation process of these websites, have accumulated in a large number about the data message of customer action, these behavioral datas are further studied, the general modfel in user website usage behavior and rule can be found, and then find possibility Problems existing in webpage and web sites function design, thus find the direction of website Improvement and perfection.
Document 1: Chinese patent CN101188521A, Ning Hui, Zhang Tao. a kind of method of digging user behavioral data and Website server .2008.5 disclose a kind of method and Website server of digging user behavioral data, web log file data are preserved by Website server, read described web log file data, and to described web log file data analysis, this method need not arrange statistical server separately, save hardware resource and cost.But this method cannot realize the analysis to network user's dynamic behaviour.Data due to web log file data record have certain specification, essential record access time of user, accession page, user ID, and access IP etc., cannot obtain more required information.Such as, the behavioural information of the user at website registration failure can not be obtained by means of only web log file data.
Document 2: Chinese patent CN102238045A, Xie Yongkai. a kind of wireless Internet user's behavior prediction system .2011.11 discloses a kind of prognoses system of wireless Internet user behavior, this system is by being positioned at cellphone subscriber's behavioral data acquisition module of client, collect the user behavior data of cellphone subscriber's running time, and be sent to server, be positioned at cellphone subscriber's behavioural analysis prediction module of server end, to user behavior modeling, carry out user behavior analysis and prediction according to the user behavior data that the user behavior data acquisition module of client is collected.This invention and the present invention have certain consistency on thought and method, but also existing defects: this invention is carried out cluster analysis to the user behavior data collected and obtained user grouping, the relation between user grouping with corresponding behavior is set up by correlation rule, so different clustering methods may make the result of grouping inconsistent, cause and analyze the inaccurate of conclusion, this invention is mainly for mobile phone wireless Internet user simultaneously, and the operation system of smart phone supported is limited.
Summary of the invention
The object of the present invention is to provide one can obtain networks congestion control information easily and fast, and networks congestion control information analysis system and the analytical method of the integrality of user behavior information, continuity and validity can be ensure that.
The technical solution realizing the object of the invention is:
A kind of networks congestion control information analysis system, comprise the networks congestion control data acquisition module connected successively, networks congestion control data preprocessing module, networks congestion control data memory module, networks congestion control data analysis module, analysis result display module.
A kind of networks congestion control information analysis method, comprises the following steps:
Step one: networks congestion control data acquisition module utilizes the program of burying be integrated in wherein to obtain website user's behavioral data, and be recorded in web log file data, then whether the data collected in the unit interval are judged extremely, finally by data syn-chronization to local data base; Wherein bury a program to collect result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data whether abnormal program code forms;
Step 2: networks congestion control data preprocessing module identifies web log file data, filters out the user behavior data for user behavior information analysis;
Step 3: the user behavior data of extraction converted to the behavioral data towards behavioural characteristic space by behavior sequence analysis, be stored in networks congestion control data memory module;
Step 4: networks congestion control data analysis module calls the user behavior data in networks congestion control data memory module, uses the user's behavior pattern mining method of default to analyze user behavior data;
Step 5: user behavior information analysis be the results are shown in user terminal displays device interface by analysis result display module.
The present invention compared with prior art, its remarkable advantage:
1, obtain networks congestion control information easily and fast, comprise the behavioural information of Static and dynamic, ensure that the integrality of user behavior information, continuity and validity;
2, analyze forward and the negative sense behavioural information of each network user, fully excavate networks congestion control characteristic sum pattern;
3, data mining algorithm calculated off-line, result of calculation is clear, objective, to facilitate enterprise to sum up business rule further, carries out optimization and the adjustment of website structure.
Below in conjunction with accompanying drawing, the present invention is described in further detail.
Accompanying drawing explanation
Fig. 1 is the structural representation according to networks congestion control information analysis system of the present invention.
Fig. 2 is that a program diagram is buried in the JS page front end of registering behavioural information analytical system according to the website user of the embodiment of the present invention.
Fig. 3 is that a program diagram is buried in the Java rear end of registering behavioural information analytical system according to the website user of the embodiment of the present invention.
Fig. 4 registers behavioural information according to the website user of the embodiment of the present invention to collect and storage organization schematic diagram.
Fig. 5 registers behavior sequence schematic diagram according to the website user of the embodiment of the present invention.
Fig. 6 is the flow chart registering behavioural information analytical system according to the website user of the embodiment of the present invention.
Embodiment
For making the object of embodiments of the invention, technical scheme and advantage clearly, simplicity of explanation is done to some terms related in the present invention below.
Bury a program: whether abnormal program code forms to collect result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data.
Basic data: be bury the data for user behavior information analysis that a program catches.Basic data belongs to primary data, and needing through native system according to rule extraction is intermediate layer data.
Intermediate layer data: be the business information combination used for native system according to the rule extraction preset from basic data, intermediate layer data belong to semi-finished product data, also need to solve analysis result through native system according to the algoritic module preset.
User: the user of access websites.
Goal behavior: refer to the behavior relevant to the performance of enterprise.
A kind of networks congestion control information analysis system of the present invention, this system comprises the networks congestion control data acquisition module connected successively, networks congestion control data preprocessing module, networks congestion control data memory module, networks congestion control data analysis module, analysis result display module.
See Fig. 1, show the structural representation according to a kind of networks congestion control information analysis system of the present invention, specifically comprise with lower module.
User behavior data acquisition module 101: at webpage embedded cover JavaScript script, when the user accesses a web page, triggers statistics script and obtains visit data, and rear end java applet judges that whether data structure is abnormal, and both data is merged.
Website user's behavioral data pretreatment module 102: the daily record data of daily record data storage rule to data-base recording according to presetting identifies, screens, classifies and gather.
Website user's behavioral data memory module 103: the networks congestion control data through data prediction are stored in UEAM system data library unit with the form of standard.
Website user's behavioral data analysis module 104: the data mining algorithm according to default is analyzed daily record data, and the user behavior analysis result drawn is stored in analysis result memory cell.
Analysis result display module 105: data analysis be the results are shown in system manager's terminal display interface.
A kind of networks congestion control information analysis method, comprises the following steps:
Step one: networks congestion control data acquisition module utilizes the program of burying be integrated in wherein to obtain website user's behavioral data, and be recorded in web log file data, then whether the data collected in the unit interval are judged extremely, finally by data syn-chronization to local data base; Wherein bury a program to collect result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data whether abnormal program code forms;
When obtaining website user's behavioral data, make use of and bury a program, when user logs in client browser, the program code of trigger collection user behavior data, when user's access websites page, for user creates a session and page number; When user fills in relevant information as requested, the program code collecting user behavior data can automatic recording user behavioral data; Meanwhile, Operation system setting user minimum input data volume, when the user behavior data collected reaches the minimum input data volume of user, is sent to Web server by the page overall data be recorded to packing; Then the user behavior data collected in the unit interval is synchronized to local data base.
When the user behavior data collected in unit interval is synchronized to local data base, whether abnormal program carries out warehouse-in judgement to be used for monitor user ' behavioral data collection result by rear end, whether the data that judgement collects and mean value exist exception, if exist abnormal, then the data collected are cast out, replace with mean value, otherwise directly by the data syn-chronization that collects in local data base; Described exception is that wherein difference rate can set by judging with the difference rate of mean value;
Step 2: networks congestion control data preprocessing module identifies web log file data, filters out the user behavior data for user behavior information analysis;
Web log file data are identified, screened, namely selects useful user behavior data, get rid of useless user behavior data; The networks congestion control data directly collected there will be following several situation: the page elements name that the page elements name of (1) front-end user interface display and rear end are recorded in daily record data is inconsistent; As page elements is called FullName, and the field of logdata record is userName; (2) content relating to user security privacy cannot collect record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thus cause same page elements information repeatedly repeat record; After user fill in email address, change again email address, so filled in behavior for twice of user and all can go on record, thus caused this information of mailbox to occur 2 records.Therefore, identify, screen useful user behavior data time eliminate the page elements relating to user security privacy, and construct the contrast list of the page elements recorded in page elements and daily record data, and for the phenomenon repeating to record, the principle that the information taking to record for the last time is as the criterion.
Step 3: the user behavior data of extraction converted to the behavioral data towards behavioural characteristic space by behavior sequence analysis, be stored in networks congestion control data memory module;
The method that forward builds behavior sequence is have employed, i.e. the method for time window when building user behavior sequence; A time window rolled is set, according to the order of occurrence of each target, user behavior is moved to right from the left side in behavior coordinate.
Step 4: networks congestion control data analysis module calls the user behavior data in networks congestion control data memory module, uses the user's behavior pattern mining method of default to analyze user behavior data;
When carrying out user's behavior pattern mining to networks congestion control data, mainly have employed support and calculating and these two kinds of methods of correlation analysis;
It is that excavate the behavior pattern that those probably cause ownership goal frequently to occur, computing formula is by calculating page elements to the support of goal behavior that support calculates:
Wherein a
irepresentative of consumer behavior, D representative of consumer behavioral data collection, comprising target data set D
twith non-targeted data set
Meanwhile, although low in order to capture those frequencies of occurrences, can produce the behavior of significant impact to goal behavior, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein D
trefer to target data set,
refer to non-targeted data set, T is target,
be non-targeted, Contrast>0 here, arranges a threshold alpha, if Contrast> is α, a is described
ithe impact of target T is greater than non-targeted
impact; If Contrast< is α, then on the contrary;
In order to weigh the related intimate degree between user behavior, carry out correlation analysis.
Carrying out correlation analysis is utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between page elements; If P<0, think negative correlation; If 0<P<0.2, think uncorrelated; If 0.2<P<0.4, think weak positive correlation; If 0.4<P<0.6, think medium positive correlation; If 0.6<P<0.8, think strong positive correlation; If P>0.8, think extremely strong positive correlation.
Step 5: user behavior information analysis be the results are shown in user terminal displays device interface by analysis result display module.
Embodiment 1 one kinds of network users retrieve behavior information analysis method
Step one: networks congestion control data acquisition module utilizes some program of burying be integrated in wherein to obtain a website user and retrieve behavioral data, wherein buries a program and collects result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data whether abnormal program code forms, when user is according to demand retrieving information, the automatic recording user name of program code of the collection user behavior data write by java applet and the information of user search, if user search has arrived information needed, then the packing of the user retrieval behavior data of record is sent to website web server, if user does not retrieve information needed, when user leaves the page, the user retrieval behavior data of record are sent to website web server, front end java applet have collected the user retrieval behavior data of a week, request is sent to server, now start rear end collecting for monitor user ' behavioral data the warehouse-in that whether abnormal result program code carry out data and judge by JavaScript programming, the data gathered in this week and average data weekly before contrast, judge whether the difference rate of data compared with average data gathered exceeds the domain of walker preset, if exceeded, then replace with mean value, then by data syn-chronization to local data base, otherwise directly by the data syn-chronization that collects to local data base.
Step 2: networks congestion control data preprocessing module identifies web log file data, filters out the user retrieval behavior data for user behavior information analysis;
Web log file data are identified, screened, namely selects useful user retrieval behavior data, get rid of useless user retrieval behavior data; The network user directly collected retrieves behavioral data and there will be following several situation: the page elements name that the page elements name of (1) front-end user interface display and rear end are recorded in daily record data is inconsistent; (2) content relating to user security privacy cannot collect record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thus cause same page elements information repeatedly repeat record; After user have input a term, again term is changed, so fill in behavior for twice of user and all can go on record, thus cause the retrieving information of primary retrieval behavior to repeat situation about recording.Therefore, identify, screen useful user behavior data time eliminate the page elements relating to user security privacy, and construct the contrast list of the page elements recorded in page elements and daily record data, and for the phenomenon repeating to record, the principle that the information taking to record for the last time is as the criterion.
Step 3: by behavior sequence analysis, the user retrieval behavior data transaction extracted is become towards the behavioral data in behavioural characteristic space, be stored in networks congestion control data memory module;
The method that forward builds behavior sequence is have employed, i.e. the method for time window when building user retrieval behavior sequence; A time window rolled is set, according to the order of occurrence of each target, user retrieval behavior is moved to right from the left side in behavior coordinate.
Step 4: networks congestion control data analysis module calls the user retrieval behavior data in networks congestion control data memory module, uses the user's behavior pattern mining method of default to user retrieval behavior data analysis;
When retrieving behavioral data to the network user and carrying out user's behavior pattern mining, mainly have employed support and calculate and these two kinds of methods of correlation analysis;
It is that excavate the behavior pattern that those probably cause ownership goal frequently to occur, computing formula is by calculating retrieval behavior to the support of searched targets that support calculates:
Wherein a
irepresentative of consumer retrieval behavior, D representative of consumer retrieval behavior data set, comprising target data set D
twith non-targeted data set
In like manner can draw data set D
twith
in cause the support Support (a of the behavior pattern of user search success and failure
i/ D
t) and
be respectively:
At data set D
tmiddle support is higher, illustrate the behavior or the impact of behavior sequence on target larger; At data set
middle support is lower, illustrate the behavior or the impact of behavior sequence on target larger.
Meanwhile, although low in order to capture those frequencies of occurrences, can produce the behavior of significant impact to goal behavior, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein D
trefer to target data set,
refer to non-targeted data set, T is target,
be non-targeted, Contrast>0 here, arranges a threshold alpha, if Contrast> is α, a is described
ithe impact of target T is greater than non-targeted
impact; If Contrast< is α, then on the contrary;
In order to weigh the related intimate degree between user retrieval behavior, carry out correlation analysis, so that find the successful optimal path of retrieval.
Carrying out correlation analysis is utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between page elements; If P<0, think negative correlation; If 0<P<0.2, think uncorrelated; If 0.2<P<0.4, think weak positive correlation; If 0.4<P<0.6, think medium positive correlation; If 0.6<P<0.8, think strong positive correlation; If P>0.8, think extremely strong positive correlation.
Step 5: user retrieval behavior information analysis be the results are shown in user terminal displays device interface by analysis result display module.
In order to set forth the object, technical solutions and advantages of the present invention more clearly, below in conjunction with specific embodiments and the drawings, the present invention will be described in detail.
2-6 by reference to the accompanying drawings:
Embodiment 2 one kinds of network user register behavioural information analytical methods
Step one: networks congestion control data acquisition module utilizes some program of burying be integrated in wherein to obtain a website user and register behavioral data, wherein buries a program and collects result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data whether abnormal program code forms, when user accesses the English enrollment page 201 of MIC, then can create a Session and produce PageId204, if user's refresh page, then re-create PageId205, (user name is comprised when user fills in related registration information as requested, password, area, mailbox, Business Name etc.), by the program code of the collection user behavior data of JavaScript programming automatic recording user registration behavioral data 206, if user completes the data input of predetermined amount, then by the page overall data PageInfo208 of record, packing is sent to web server 209, if user closes MIC enrollment page, front page overall data 208 closed in record, and data packing is sent to web server 209 and is recorded in web log file data, the user that front end JavaScript program have collected one day registers behavioral data, request 301 is sent to server, what now start that rear end write by java applet collects for monitor user ' behavioral data the warehouse-in that whether abnormal result program code carry out data and judges 302, the average data of the data gather this day and before every day contrasts, judge whether the difference rate of data compared with average data gathered exceeds the domain of walker preset, if exceeded, then replace 305 with mean value, then by data syn-chronization to local data base 304, otherwise directly by the data syn-chronization that collects to local data base 304.
Step 2: networks congestion control data preprocessing module identifies web log file data, the user filtered out for user behavior information analysis registers behavioral data;
Web log file data are identified, screened, namely selects useful user behavior data, get rid of useless user behavior data; The networks congestion control data directly collected there will be following several situation: the page elements name that the page elements name of (1) front-end user interface display and rear end are recorded in daily record data is inconsistent; As page elements is called FullName, and the field of logdata record is userName; (2) content relating to user security privacy cannot collect record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thus cause same page elements information repeatedly repeat record; After user fill in email address, change again email address, so filled in behavior for twice of user and all can go on record, thus caused this information of mailbox to occur 2 records.Therefore, identify, screen useful user behavior data time eliminate the page elements relating to user security privacy, and construct the contrast list of the page elements recorded in page elements and daily record data, and for the phenomenon repeating to record, the principle that the information taking to record for the last time is as the criterion.
Step 3: the user of extraction registered behavioral data by behavior sequence analysis and convert behavioral data towards behavioural characteristic space to, be stored in networks congestion control data memory module;
The method that forward builds behavior sequence is have employed, i.e. the method for time window when building user and registering behavior sequence; Arrange a time window rolled, according to the order of occurrence of each target, behavior of user being registered moves to right from the left side in behavior coordinate.
Step 4: the user that networks congestion control data analysis module calls in networks congestion control data memory module registers behavioral data, uses the user's behavior pattern mining method of default to register behavioral data analysis to user;
When carrying out user's behavior pattern mining to network user register behavioral data, mainly have employed support and calculating and these two kinds of methods of correlation analysis;
It is that excavate the behavior pattern that those probably cause ownership goal frequently to occur, computing formula is by calculating the support of page elements to the behavior of submission to that support calculates:
Wherein a
irepresentative of consumer registration behavior, D representative of consumer registration behavioral data collection, comprising target data set D
twith non-targeted data set
In like manner can draw data set D
twith
in cause the support Support (a of user registration success and failed behavior pattern
i/ D
t) and
be respectively:
At data set D
tmiddle support is higher, illustrate the behavior or the impact of behavior sequence on target larger; At data set
middle support is lower, illustrate the behavior or the impact of behavior sequence on target larger.
Meanwhile, although low in order to capture those frequencies of occurrences, can produce the behavior of significant impact to goal behavior, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein D
trefer to target data set,
refer to non-targeted data set, T is target,
be non-targeted, Contrast>0 here, arranges a threshold alpha, if Contrast> is α, a is described
ithe impact of target T is greater than non-targeted
impact; If Contrast< is α, then on the contrary;
In order to weigh the related intimate degree of page element between two, carrying out correlation analysis, so that adjustment page layout, having optimized interface.
Carrying out correlation analysis is utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between page elements; If P<0, think negative correlation; If 0<P<0.2, think uncorrelated; If 0.2<P<0.4, think weak positive correlation; If 0.4<P<0.6, think medium positive correlation; If 0.6<P<0.8, think strong positive correlation; If P>0.8, think extremely strong positive correlation.
Step 5: what user was registered behavioural information analysis by analysis result display module the results are shown in user terminal displays device interface.
Fig. 2 is that a program diagram is buried in the JavaScript page front end of registering behavioural information analytical system according to the website user of the embodiment of the present invention.
User accesses the English enrollment page 201 of MIC, then can create a Session and produce PageId204, if user's refresh page, then re-create PageId205, when user fills in related registration information as requested, JavaScript program automatic recording user registration behavioral data 206, if user completes the data input of predetermined amount, then by the page overall data PageInfo208 of record, packing is sent to web server 209, if user closes MIC enrollment page, front page overall data 208 closed in record, and data packing is sent to web server 209.
Fig. 3 is that a program diagram is buried in the Java rear end of registering behavior monitoring system according to the website user of the embodiment of the present invention.
Front end JavaScript buries a program and have collected a certain amount of user and register behavioral data, request 301 is sent to server, now start rear end Java to bury the warehouse-in that a program carries out data and judge 302, judge whether data and the mean value of collection exist exception, if exist abnormal, then replace 305 with mean value, then by data syn-chronization to local data base 304, otherwise directly by the data syn-chronization that collects to local data base 304.
See Fig. 4, show and register behavioural information collection and storage organization schematic diagram according to the website user of the embodiment of the present invention.
User's open any browser access MIC website 401, system judges user whether as maiden visit MIC website 402, if user is not maiden visit MIC, does not then gather the registration behavioural information of user, if user is maiden visit MIC, then trigger " burying a program " 403, then implant to user browser and start JS page front end and " bury a program " 404, 405 differentiate for the user behavior data amount of burying a program record, the data volume preset if reach, then the packing of the registration behavioral data of user will be sent to website Web server and be stored in local log database 406 by JS program, if the data volume of record does not reach the data volume preset, then identify and judge whether user leaves enrollment page 407, if user have left enrollment page, whether further judgement user closes browser 408, if user browser is closed, then when user again open any browser time 410, the packing of the registration behavioral data of user is sent website Web server and to be stored in local log database 406, if user does not leave enrollment page, or although user have left enrollment page but is not closed by browser, then waiting system Preset Time 409, after Preset Time, the packing of the registration behavioral data of user is sent website Web server and to be stored in local log database 406,
Application server extracts web log file file 411, the Java data of burying rear end in a programmed acquisition local data base to include in Data Warehouse for Enterprises 412 in, by analysis, after process, merge Java end data and JavaScript daily record data 413, and by the data of edit stored in UEAM system database 414, then system completes the collection 415 that a time user registers behavioral data.
The embodiment of the present invention have employed the method that forward builds behavior sequence, i.e. the method for time window when identification, screening, extraction data.The goal behavior that the embodiment of the present invention is paid close attention to be user registration success whether, therefore enter enrollment page from user and leave enrollment page to user and just constitute a complete time window, if target is user's " succeed in registration T " and " registration failure
" wherein, because register flow path exists two enrollment pages, therefore, to succeed in registration T if two sub-goals are respectively the page one
1to succeed in registration T with the page two
2unsuccessful with the page one
unsuccessful with the page two
arrange a time window rolled and carry out housing choice behavior, time window moves to right from the left side according in the behavior that the occurs in coordinate of each target.No matter whether user registration success, as long as he leaves enrollment page, so, a series of behaviors of user are all said and are placed in a group.Fig. 5 is MIC user and registers behavior sequence schematic diagram.Wherein, T
irepresent goal behavior, registration behavior sequence is broken down into two sub-behavior sequences, and sub-goal behavior is respectively T
1and T
2; a
irepresent i-th control and fill in behavior.
See Fig. 6, show the flow chart registering behavior monitoring system according to the website user of the embodiment of the present invention.
System manager logs in 601 by browser terminal, monitoring system returns a http response, keeper's browser shows user and register behavior monitoring system queries interface, wait for operation requests 603, eject prompting operation dialog box 604, the need of the data in invoke user registration behavioral data memory module? if keeper selects "No", then exit monitoring system, and welcome the page that reuses at user browser terminal demonstration.If user selects "Yes", first the data in data memory module are called in user and register behavioral data analysis module 605, then the choice box 606 of data analysis algorithm type is ejected, after keeper clicks, by the selection instruction Input Monitor Connector system 607 that keeper inputs, carry out data analysis according to the algorithm that keeper selects and calculate 608, the result of data analysis is shown to terminal browser interface 609, eject prompting operation dialog box 610, whether is continuation inquired about? keeper selects "Yes", returns 606; Keeper selects "No", then exit monitoring system, and at the page that keeper's browser terminal display welcome reuses.
For 606, the user's behavior pattern mining method adopted in an embodiment of the present invention mainly contains two, and namely support calculates and correlation analysis.These two kinds of methods mainly achieve the calculating of support of page elements to the behavior of submission to, and the analysis of correlation between each element of the page.
In an embodiment of the present invention, support calculates and will excavate those behavior patterns probably causing the frequent generation of user registration success or registration failure exactly.User constitutes a data set D in all behaviors of the English enrollment page of MIC, and it has two subsets, namely causes the user behavior data collection D of user registration success
twith the user behavior data collection causing user's registration failure
support Support (a of the behavior pattern causing goal behavior to occur in whole data set D
i/ D) computing formula is:
In like manner can draw data set D
twith
in cause the support Support (a of user registration success and failed behavior pattern
i/ D
t) and
be respectively:
At data set D
tmiddle support is higher, illustrate the behavior or the impact of behavior sequence on target larger; At data set
middle support is lower, illustrate the behavior or the impact of behavior sequence on target larger.
Meanwhile, the embodiment of the present invention has also done comparative analysis to the support that same behavior is concentrated in different pieces of information, although low to capture those frequencies of occurrences, can produce the behavior of significant impact to goal behavior.Data set D
twith
in behavior a
isupport contrast ratio C ontrast be:
A threshold alpha is set, if Contrast> is α, then behavior a is described
ithe impact of goal behavior T is greater than goal behavior
impact; If Contrast< is α, then behavior a is described
ito goal behavior
impact be greater than impact on goal behavior T; The embodiment of the present invention arranges threshold alpha=2, finds behavior a different target behavior being produced to material impact
i.
In order to analyze the incidence relation between each control of enrollment page further, the embodiment of the present invention has done correlation analysis to each element of MIC enrollment page.Correlation analysis refers to be analyzed two or more variable element possessing correlation, thus weighs the related intimate degree of Two Variables factor.
By the description of above embodiment, those skilled in the art can be well understood to the present invention and can realize by the mode of software combined with hardware platform.Based on this, what technical scheme of the present invention contributed to background technology all or part ofly can embody with the form of software product, this computer software product can be stored in storage medium, as ROM/RAM, magnetic disc, CD etc., comprise the method some part described in of some instructions in order to use a computer equipment (can be personal computer, server, or the network equipment etc.) to perform each embodiment of the present invention or embodiment.
The present invention can analyze various user behavior information.
In sum, a kind of networks congestion control information analysis system of the present invention and analytical method gather networks congestion control information and store, the mining algorithm Sum fanction arranged in advance by system draws data results, be shown to enterprise finally by analysis result to be further analyzed, thus sum up certain business rule.Website is not only helped to find user behavior feature timely, and the institutional framework that made website further perfect, improve the reasonability of website structure.
Claims (5)
1. a networks congestion control information analysis method, is characterized in that, comprises the following steps:
Step one: networks congestion control data acquisition module utilizes the program of burying be integrated in wherein to obtain website user's behavioral data, and be recorded in web log file data, then whether the data collected in the unit interval are judged extremely, finally by data syn-chronization to local data base; Wherein bury a program to collect result by the program code of collection user behavior data and rear end that are embedded into browser end for monitor user ' behavioral data whether abnormal program code forms; When obtaining website user's behavioral data, make use of and bury a program, when user logs in client browser, the program code of trigger collection user behavior data, when user's access websites page, for user creates a session and page number; When user fills in relevant information as requested, the program code collecting user behavior data can automatic recording user behavioral data; Meanwhile, Operation system setting user minimum input data volume, when the user behavior data collected reaches the minimum input data volume of user, is sent to Web server by the page overall data be recorded to packing; Then the user behavior data collected in the unit interval is synchronized to local data base;
Step 2: networks congestion control data preprocessing module identifies web log file data, filters out the user behavior data for user behavior information analysis; Web log file data are identified, screened, namely selects useful user behavior data, get rid of useless user behavior data; The networks congestion control data directly collected there will be following several situation: the page elements name that the page elements name of (1) front-end user interface display and rear end are recorded in daily record data is inconsistent; (2) content relating to user security privacy cannot collect record, and can produce some irrelevant gibberish; (3) users can go on record to filling in of same page elements at every turn, thus cause same page elements information repeatedly repeat record; Therefore, identify, screen useful user behavior data time eliminate the page elements relating to user security privacy, and construct the contrast list of the page elements recorded in page elements and daily record data, and for the phenomenon repeating to record, the principle that the information taking to record for the last time is as the criterion;
Step 3: the user behavior data of extraction converted to the behavioral data towards behavioural characteristic space by behavior sequence analysis, be stored in networks congestion control data memory module;
Step 4: networks congestion control data analysis module calls the user behavior data in networks congestion control data memory module, uses the user's behavior pattern mining method of default to analyze user behavior data; When carrying out user's behavior pattern mining to networks congestion control data, mainly have employed support and calculating and these two kinds of methods of correlation analysis;
It is that excavate the behavior pattern that those probably cause ownership goal frequently to occur, computing formula is by calculating page elements to the support of goal behavior that support calculates:
Wherein a
irepresentative of consumer behavior, D representative of consumer behavioral data collection, comprising target data set D
twith non-targeted data set
Meanwhile, although low in order to capture those frequencies of occurrences, can produce the behavior of significant impact to goal behavior, we have also done comparative analysis to the support that same behavior is concentrated in different pieces of information;
Wherein D
trefer to target data set,
refer to non-targeted data set, T is target,
be non-targeted, Contrast>0 here, arranges a threshold alpha, if Contrast> is α, a is described
ithe impact of target T is greater than non-targeted
impact; If Contrast< is α, then on the contrary;
In order to find out the related intimate degree between user behavior, carrying out correlation analysis, so that adjustment page layout, having optimized interface;
Step 5: user behavior information analysis be the results are shown in user terminal displays device interface by analysis result display module.
2. a kind of networks congestion control information analysis method according to claim 1, is characterized in that, step 3 have employed the method that forward builds behavior sequence when building user behavior sequence, i.e. the method for time window; A time window rolled is set, according to the order of occurrence of each target, user behavior is moved to right from the left side in behavior coordinate.
3. a kind of networks congestion control information analysis method according to claim 1, it is characterized in that, when the user behavior data collected in unit interval is synchronized to local data base, whether abnormal program carries out warehouse-in judgement to be used for monitor user ' behavioral data collection result by rear end, whether the data that judgement collects and mean value exist exception, if exist abnormal, then the data collected are cast out, replace with mean value, otherwise directly by the data syn-chronization that collects in local data base.
4. a kind of networks congestion control information analysis method according to claim 1, is characterized in that, carrying out correlation analysis is utilize SPSS software, and employing Pearson correlation coefficient P judges the degree of relevancy between page elements; If P<0, think negative correlation; If 0<P<0.2, think uncorrelated; If 0.2<P<0.4, think weak positive correlation; If 0.4<P<0.6, think medium positive correlation; If 0.6<P<0.8, think strong positive correlation; If P>0.8, think extremely strong positive correlation.
5. a kind of networks congestion control information analysis method according to claim 3, is characterized in that, described exception is that wherein difference rate can set by judging with the difference rate of mean value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210374292.6A CN102946319B (en) | 2012-09-29 | 2012-09-29 | Networks congestion control information analysis system and analytical method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210374292.6A CN102946319B (en) | 2012-09-29 | 2012-09-29 | Networks congestion control information analysis system and analytical method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102946319A CN102946319A (en) | 2013-02-27 |
| CN102946319B true CN102946319B (en) | 2015-12-16 |
Family
ID=47729228
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210374292.6A Expired - Fee Related CN102946319B (en) | 2012-09-29 | 2012-09-29 | Networks congestion control information analysis system and analytical method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102946319B (en) |
Families Citing this family (76)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103227821B (en) | 2013-04-03 | 2015-07-01 | 腾讯科技（深圳）有限公司 | Method and device for processing position data of target user |
| CN104348650B (en) * | 2013-08-05 | 2019-07-16 | 腾讯科技（深圳）有限公司 | Monitoring method, service apparatus and the system of website |
| CN104348817B (en) * | 2013-08-07 | 2018-09-28 | 深圳市腾讯计算机系统有限公司 | The guard method of user account number and device |
| CN104376027B (en) * | 2013-08-16 | 2018-07-10 | 上海未来宽带技术股份有限公司 | Information acquisition and analysis system and its method based on social media |
| US9215252B2 (en) * | 2013-09-27 | 2015-12-15 | Intel Corporation | Methods and apparatus to identify privacy relevant correlations between data values |
| CN103729397B (en) * | 2013-10-28 | 2017-03-08 | 公安部第三研究所 | Based on the method that time locus realizes electronic evidence data analysis |
| CN103646093A (en) * | 2013-12-18 | 2014-03-19 | 北京博雅立方科技有限公司 | Data processing method and platform for search engines |
| CN103763124B (en) * | 2013-12-26 | 2017-04-05 | 孙伟力 | A kind of Internet user's behavior analysiss early warning system and method |
| CN105227615B (en) * | 2014-07-03 | 2019-09-20 | 阿里巴巴集团控股有限公司 | A kind of method and device of distributed system data transmitting |
| CN105335409B (en) * | 2014-07-31 | 2019-02-01 | 华为技术有限公司 | A kind of determination method, equipment and the network server of target user |
| CN104268665A (en) * | 2014-08-28 | 2015-01-07 | 广东电网公司电力科学研究院 | User behavior analysis method of management system |
| CN104268217B (en) * | 2014-09-25 | 2017-08-01 | 张文铸 | A kind of determination method and device of user behavior temporal correlation |
| CN105550184B (en) * | 2014-10-31 | 2019-11-19 | 阿里巴巴集团控股有限公司 | A kind of information acquisition method and device |
| CN104410533A (en) * | 2014-12-17 | 2015-03-11 | 乐山师范学院 | Network user behavior identification system |
| CN106156178A (en) * | 2015-04-17 | 2016-11-23 | 阿里巴巴集团控股有限公司 | A kind of data processing method and equipment |
| CN106294101B (en) * | 2015-05-19 | 2019-08-23 | 阿里巴巴集团控股有限公司 | The page gets test method and device ready |
| CN105301615A (en) * | 2015-10-14 | 2016-02-03 | 公安部第三研究所 | GPS positioning information-based companion analysis method |
| CN105389704B (en) * | 2015-11-16 | 2020-01-10 | 小米科技有限责任公司 | Method and device for judging authenticity of user |
| CN105868256A (en) * | 2015-12-28 | 2016-08-17 | 乐视网信息技术（北京）股份有限公司 | Method and system for processing user behavior data |
| CN105844107B (en) * | 2016-03-31 | 2019-10-15 | 百度在线网络技术（北京）有限公司 | Data processing method and device |
| CN105930363B (en) * | 2016-04-12 | 2020-04-21 | 深圳云之家网络有限公司 | HTML5 webpage-based user behavior analysis method and device |
| CN106095895B (en) * | 2016-06-07 | 2019-12-10 | 百度在线网络技术（北京）有限公司 | Information pushing method and device |
| CN106209429A (en) * | 2016-06-29 | 2016-12-07 | 北京小米移动软件有限公司 | Collecting method and device |
| CN106202280B (en) * | 2016-06-29 | 2020-06-23 | 联想(北京)有限公司 | Information processing method and server |
| CN107566148B (en) * | 2016-07-01 | 2021-08-03 | 北京京东尚科信息技术有限公司 | Method, system and device for analyzing terminal application operation data and terminal |
| CN106202374A (en) * | 2016-07-07 | 2016-12-07 | 无锡天脉聚源传媒科技有限公司 | A kind of data processing method and device |
| CN106294650B (en) * | 2016-08-03 | 2019-08-20 | 北京金和网络股份有限公司 | Neologisms method for digging a little is buried based on search |
| CN106294651A (en) * | 2016-08-03 | 2017-01-04 | 北京金和网络股份有限公司 | Decision data method for digging a little is buried based on search |
| CN106383869B (en) * | 2016-09-05 | 2021-02-19 | Tcl科技集团股份有限公司 | Method and device for acquiring user behavior information |
| CN106559425B (en) * | 2016-11-18 | 2019-07-26 | 暴风集团股份有限公司 | The operation method and numbering-off system of numbering-off system |
| CN106776895B (en) * | 2016-11-29 | 2019-05-14 | 天津大学 | Interpersonal relationships based on person-to-person session information automates portrait method |
| CN106599295A (en) * | 2016-12-27 | 2017-04-26 | 四川中电启明星信息技术有限公司 | Multi-track visual analyzing evidence-collecting method for user behaviors and system |
| CN106844588A (en) * | 2017-01-11 | 2017-06-13 | 上海斐讯数据通信技术有限公司 | A kind of analysis method and system of the user behavior data based on web crawlers |
| CN108734437A (en) * | 2017-04-13 | 2018-11-02 | 普天信息技术有限公司 | A kind of operation system optimization method and device |
| CN107220745B (en) * | 2017-04-24 | 2021-03-09 | 北京红马传媒文化发展有限公司 | Method, system and equipment for identifying intention behavior data |
| CN107196788B (en) * | 2017-05-02 | 2020-12-18 | 创新先进技术有限公司 | Method and device for processing buried point data, server and client |
| CN107229992A (en) * | 2017-05-03 | 2017-10-03 | 北京中维百视科技有限公司 | A kind of optimization method based on shared component reference pattern |
| CN107169796A (en) * | 2017-05-12 | 2017-09-15 | 深圳市浩天投资有限公司 | A kind of analysis method of user behavior data, system and computer-readable recording medium |
| CN107577706B (en) * | 2017-07-28 | 2019-09-20 | 平安科技（深圳）有限公司 | User behavior data processing method, device and computer readable storage medium |
| CN107402875A (en) * | 2017-07-31 | 2017-11-28 | 深圳市金立通信设备有限公司 | A kind of log recording method, terminal and computer-readable recording medium |
| CN107612882B (en) * | 2017-08-03 | 2020-09-29 | 奇安信科技集团股份有限公司 | User behavior identification method and device based on intermediate log |
| CN107577587A (en) * | 2017-08-28 | 2018-01-12 | 江西博瑞彤芸科技有限公司 | Integrate the generation method of form |
| CN107506476A (en) * | 2017-09-08 | 2017-12-22 | 上海炫萌网络科技有限公司 | User behavior data collects and surveys system and analysis method |
| CN107562930B (en) * | 2017-09-15 | 2020-06-19 | 广州快信信息科技有限公司 | Method and device for processing operation behavior data |
| CN107679897A (en) * | 2017-09-25 | 2018-02-09 | 北京京东尚科信息技术有限公司 | A kind of security risk control method and device |
| CN109561117B (en) * | 2017-09-26 | 2022-02-25 | 北京国双科技有限公司 | Data acquisition method and device |
| CN107704605B (en) * | 2017-10-13 | 2021-04-09 | 北京中教在线科技有限公司 | User behavior recording method based on local storage |
| CN107861655B (en) * | 2017-11-01 | 2020-07-07 | 平安科技（深圳）有限公司 | Control matching method and device, computer equipment and storage medium |
| CN107800591B (en) * | 2017-11-07 | 2022-01-21 | 南威软件股份有限公司 | Unified log data analysis method |
| CN107995283B (en) * | 2017-11-29 | 2019-06-14 | 上海恺英网络科技有限公司 | A kind of data bury the method, equipment and system of point analysis |
| CN108595450B (en) * | 2017-11-29 | 2021-06-04 | 五八有限公司 | Buried point database construction method, buried point database and buried point query method |
| CN108304308A (en) * | 2018-02-07 | 2018-07-20 | 平安普惠企业管理有限公司 | User behavior monitoring method, device, computer equipment and storage medium |
| CN108304704B (en) * | 2018-02-07 | 2021-02-09 | 平安普惠企业管理有限公司 | Authority control method and device, computer equipment and storage medium |
| CN108900563B (en) * | 2018-03-30 | 2021-05-18 | 世纪龙信息网络有限责任公司 | Data transmission method and device, computer equipment and storage medium |
| CN108932188A (en) * | 2018-05-03 | 2018-12-04 | 北京奇虎科技有限公司 | A kind of data processing method and device |
| CN108920343B (en) * | 2018-05-03 | 2022-06-10 | 北京奇虎科技有限公司 | Data processing method and device |
| CN108874641A (en) * | 2018-06-01 | 2018-11-23 | 平安科技（深圳）有限公司 | A kind of analysis method that burying point data and device |
| CN108990089B (en) * | 2018-06-21 | 2022-02-22 | 中国铁道科学研究院集团有限公司通信信号研究所 | Multi-detection window joint detection analysis method for mobile communication network |
| CN109088751B (en) * | 2018-07-25 | 2021-08-03 | 深圳市酷开网络科技股份有限公司 | Application data processing method, innovation service platform and storage medium |
| CN108958899A (en) * | 2018-08-14 | 2018-12-07 | 安徽云才信息技术有限公司 | A kind of implementation method of micro services framework distributed transaction |
| CN110971476A (en) * | 2018-09-29 | 2020-04-07 | 珠海格力电器股份有限公司 | Method and system for analyzing file downloading behavior and intelligent terminal |
| CN111105259B (en) * | 2018-10-29 | 2024-04-02 | 北京奇虎科技有限公司 | User identification method, device, electronic equipment and storage medium |
| CN109688207B (en) * | 2018-12-11 | 2022-06-03 | 北京云中融信网络科技有限公司 | Log transmission method and device and server |
| CN109902253A (en) * | 2019-01-18 | 2019-06-18 | 中国平安人寿保险股份有限公司 | Page data acquisition method and device, electronic equipment, storage medium |
| CN109783339A (en) * | 2019-01-23 | 2019-05-21 | 上海逸涵信息科技有限公司 | A kind of user behavior data acquisition system |
| CN109933705B (en) * | 2019-03-22 | 2021-10-19 | 国家电网有限公司 | Big data platform operation and maintenance management system |
| CN110347906A (en) * | 2019-05-20 | 2019-10-18 | 拉扎斯网络科技（上海）有限公司 | User behavior display method and device, electronic equipment and storage medium |
| CN110322250A (en) * | 2019-05-22 | 2019-10-11 | 深圳壹账通智能科技有限公司 | The recognition methods of inactive users courses of action, device, equipment and storage medium |
| CN112084107A (en) * | 2019-06-14 | 2020-12-15 | 北京奇虎科技有限公司 | Method and device for realizing page experiment |
| CN111061477A (en) * | 2019-11-19 | 2020-04-24 | 腾讯科技（深圳）有限公司 | Menu generation method, device, equipment and medium based on SPSS application program |
| CN110909063B (en) * | 2019-11-28 | 2020-11-17 | 蜂助手股份有限公司 | User behavior analysis method and device, application server and storage medium |
| CN111930078B (en) * | 2020-06-21 | 2024-04-19 | 中国舰船研究设计中心 | Network testing device for nuclear control system |
| CN113722196A (en) * | 2021-08-27 | 2021-11-30 | 北京八分量信息科技有限公司 | Decentralized-based user behavior information analysis system |
| CN114138605A (en) * | 2021-11-30 | 2022-03-04 | 深圳市风行趋势科技有限公司 | User behavior data acquisition method and device, computer equipment and storage medium |
| CN114625442A (en) * | 2022-03-22 | 2022-06-14 | 平安普惠企业管理有限公司 | Cold start recommendation method and device, electronic equipment and readable storage medium |
| CN114884741B (en) * | 2022-06-02 | 2024-05-24 | 上海企源科技股份有限公司 | Secure access and control system and method in distributed cloud environment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7165105B2 (en) * | 2001-07-16 | 2007-01-16 | Netgenesis Corporation | System and method for logical view analysis and visualization of user behavior in a distributed computer network |
| CN101266610A (en) * | 2008-04-25 | 2008-09-17 | 浙江大学 | Web active user website accessing mode on-line excavation method |
| CN101582817A (en) * | 2009-06-29 | 2009-11-18 | 华中科技大学 | Method for extracting network interactive behavioral pattern and analyzing similarity |
-
2012
- 2012-09-29 CN CN201210374292.6A patent/CN102946319B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7165105B2 (en) * | 2001-07-16 | 2007-01-16 | Netgenesis Corporation | System and method for logical view analysis and visualization of user behavior in a distributed computer network |
| CN101266610A (en) * | 2008-04-25 | 2008-09-17 | 浙江大学 | Web active user website accessing mode on-line excavation method |
| CN101582817A (en) * | 2009-06-29 | 2009-11-18 | 华中科技大学 | Method for extracting network interactive behavioral pattern and analyzing similarity |
Non-Patent Citations (1)
| Title |
|---|
| 面向网络用户行为模式发现的数据挖掘技术探索;李亭枫;《中国优秀硕士学位论文全文数据库信息科技辑》;20110415(第4期);第1-65页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102946319A (en) | 2013-02-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102946319B (en) | Networks congestion control information analysis system and analytical method thereof | |
| US20240037225A1 (en) | Systems and methods for detecting resources responsible for events | |
| JP5551704B2 (en) | Evaluating online marketing efficiency | |
| US7664732B2 (en) | Method of managing websites registered in search engine and a system thereof | |
| CN107797894B (en) | APP user behavior analysis method and device | |
| CN110428127B (en) | Automatic analysis method, user equipment, storage medium and device | |
| Heymann et al. | Turkalytics: analytics for human computation | |
| CN105069087B (en) | The web information flow method excavated based on web log data | |
| US20100153431A1 (en) | Alert triggered statistics collections | |
| US20100088354A1 (en) | Method and System for Log File Analysis Based on Distributed Computing Network | |
| US7908239B2 (en) | System for storing event data using a sum calculator that sums the cubes and squares of events | |
| CN107800591A (en) | A kind of analysis method of unified daily record data | |
| CN106294101A (en) | The page gets method of testing and device ready | |
| CN112633573B (en) | Prediction method of active state and determination method of activity threshold | |
| CN107483443B (en) | Advertisement information processing method, client, storage medium and electronic device | |
| CN102254004A (en) | Method and system for modeling Web in weblog excavation | |
| CN112486708B (en) | Page operation data processing method and processing system | |
| US20150026660A1 (en) | Methods for building application intelligence into event driven applications through usage learning, and systems supporting such applications | |
| CN106779946A (en) | A kind of film recommends method and device | |
| CN107592305A (en) | A kind of anti-brush method and system based on elk and redis | |
| CN107832333A (en) | Method and system based on distributed treatment and DPI data structure user network data fingerprint | |
| CN110728301A (en) | Credit scoring method, device, terminal and storage medium for individual user | |
| CN111046083A (en) | Data analysis method and system and big data platform | |
| CN112035205A (en) | Data processing method, device, equipment and storage medium | |
| CN104731937A (en) | User behavior data processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151216 Termination date: 20210929 |