CN110490638A - A kind of click event stochastic method and system - Google Patents
A kind of click event stochastic method and system Download PDFInfo
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- CN110490638A CN110490638A CN201910655661.0A CN201910655661A CN110490638A CN 110490638 A CN110490638 A CN 110490638A CN 201910655661 A CN201910655661 A CN 201910655661A CN 110490638 A CN110490638 A CN 110490638A
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- G—PHYSICS
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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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
Abstract
The invention discloses a kind of click event stochastic method and systems, wherein the described method includes: big data platform, which receives, clicks event data；The big data platform is based on the click event data statistical phenomeon and converts situation.The big data platform is based on the step of click event data statistical phenomeon conversion situation, it include: that the big data platform pre-processes the click event data according to targeted particle size, obtain the number of clicks of each event type, wherein, institute according to granularity include: one of application version number, network type, network operator, FTP client FTP type and FTP client FTP version or a variety of.The big data platform real-time reception of the embodiment of the present invention clicks event data, and carries out real-time big data analysis calculating, can be improved conversion ratio statistical efficiency, quickly analyzes the pros and cons of correlative factor, and adjustable strategies improve order rate.
Description
Technical field
The present invention relates to field of computer technology, in particular to a kind of click event stochastic method and system.
Background technique
Conversion ratio (conversion rate) refers to the access times and total access times for the goal activity that user carries out
Ratio.It is a series of that the action that user carries out can be user's login, user's registration, user's subscription, user's downloading, user's purchase etc.
User behavior, therefore conversion ratio is the concept of a broad sense.It is simply that when guest access ordering page,
Visitor is converted to resident user, it is understood that for the conversion of visitor to user.When guest access ordering page, visitor is turned
Chemical conversion resident user promotes into consumption user in turn again, and resulting rate of consumption is referred to as order conversion ratio.Conversion ratio is higher,
Illustrate that operation level is higher, is the main performance assessment criteria of operation.
But the statistical efficiency of conversion ratio compares lag at present, can not timely analyze and determine out user behavior, so that nothing
Method quickly makes Developing Tactics.
Summary of the invention
In order to solve problems in the prior art, the embodiment of the invention provides a kind of click event stochastic method and systems.
The technical solution is as follows:
In a first aspect, providing a kind of click event stochastic method, which comprises
Big data platform, which receives, clicks event data；
The big data platform is based on the click event data statistical phenomeon and converts situation；
Wherein, the click event data include authentication field, product coding, timestamp, application version number, using packet name
Title, client ip address, client affiliated area, network type, network operator, FTP client FTP type, FTP client FTP
One of version, client identification are a variety of；
The big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform pre-processes the click event data according to targeted particle size, obtains each event type
Number of clicks, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP class
One of type and FTP client FTP version are a variety of.
Optionally, the event data of clicking includes the click event number that back-end server is sent to the big data platform
According to wherein the back-end server receives the click event data from client.
Optionally, the client generates the click event after receiving the object event by user behavior triggering
Data, and the click event data is sent to the back-end server.
Optionally, the back-end server includes an at least proxy server and an at least data server；
The proxy server receives the click event data that the client is sent, and is based on load balancing
The click event data is distributed to the different data servers；
The click event data received is sent to the big data platform by the data server.
Optionally, if during the back-end server sends the click event data to big data platform, data
Transmission failure then sends the click event data of the transmission failure to the big data platform again.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform is based on each event type in the data statistics different time intervals obtained after the pretreatment
Number of clicks Long-term change trend and event conversion situation Long-term change trend.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform sets up user behavior chain using at least two event types；
Turned based on the event between each event type in user behavior chain described in the data statistics obtained after the pretreatment
Change situation.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform compares the event conversion situation of same user behavior chain different time intervals, or comparison is not
Event with user behavior chain converts situation.
Second aspect provides a kind of conversion ratio statistical system, and the system comprises big data platforms, client, and
The back-end server being connected between the big data platform and the client；
The client is sent to the back-end server clicks event data, the back-end server receive it is described
After clicking event data, Xiang Suoshu big data platform sends the click event data, described in the big data platform receives
Event data is clicked, and situation is converted based on the click event data statistical phenomeon；
Wherein, the click event data include authentication field, product coding, timestamp, application version number, using packet name
Title, client ip address, client affiliated area, network type, network operator, FTP client FTP type, FTP client FTP
One of version, client identification are a variety of；
The big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform pre-processes the click event data according to targeted particle size, obtains each event type
Number of clicks, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP class
One of type and FTP client FTP version are a variety of.
Optionally, the event data of clicking includes the click event number that back-end server is sent to the big data platform
According to wherein the back-end server receives the click event data from client.
Optionally, the client generates the click event after receiving the object event by user behavior triggering
Data, and the click event data is sent to the back-end server.
Optionally, the back-end server includes an at least proxy server and an at least data server；
The proxy server receives the click event data that the client is sent, and is based on load balancing
The click event data is distributed to the different data servers；
The click event data received is sent to the big data platform by the data server.
Optionally, if during the back-end server sends the click event data to big data platform, data
Transmission failure then sends the click event data of the transmission failure to the big data platform again.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform is based on each event type in the data statistics different time intervals obtained after the pretreatment
Number of clicks Long-term change trend and event conversion situation Long-term change trend.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform sets up user behavior chain using at least two event types；
Turned based on the event between each event type in user behavior chain described in the data statistics obtained after the pretreatment
Change situation.
Optionally, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform compares the event conversion situation of same user behavior chain different time intervals, or comparison is not
Event with user behavior chain converts situation.
The third aspect, provides a kind of big data platform, and the big data platform includes data processing module and display mould
Block, the data processing module is based on the click event stochastic method as described in above-mentioned first aspect to the click event received
Data are counted, and the event conversion situation that statistics obtains is sent to the display module, and the display module is to described
Event change over condition is shown.
The big data platform real-time reception of the embodiment of the present invention clicks event data, and carries out real-time big data analysis meter
It calculates, so as to improve the statistical efficiency of conversion ratio, quickly analyzes the pros and cons of correlative factor, adjustable strategies improve order rate,
And the click event data that big data platform receives includes the various factors of influence event conversion ratio, such as application version
Number, network type, network operator, FTP client FTP type and FTP client FTP version etc., with quickly analyze it is various because
Pros and cons of the element to the conversion of the event of promotion, timely adjustable strategies improve order rate.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is a kind of structural block diagram for clicking event statistics system provided in an embodiment of the present invention；
Fig. 2 is a kind of flow chart for clicking event stochastic method provided in an embodiment of the present invention；
Fig. 3 is the comparison signal of the conversion situation of same user behavior chain different time intervals provided in an embodiment of the present invention
Figure.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is described in further detail.
The embodiment of the invention provides a kind of click event stochastic method, this method can be applied to system shown in FIG. 1
In.The system includes at least a client, big data platform, and is connected between the big data platform and the client
Back-end server.
Wherein, client provides user interface, and receives user's operation.Client is being received by user's operation row
It after the object event of triggering, generates and clicks event data, and rear end server sends and clicks event data, wherein target
Event may include the pre-set button or the default page of entrance clicked in the page.
Back-end server sends the click event data after receiving click event data, to big data platform,
So that the big data platform is based on the click event data statistical phenomeon and converts situation.
Wherein, back-end server can be server cluster, including an at least proxy server and an at least data service
Device, to support high concurrent request data.Proxy server connects client, and data server connects big data platform.Agency's clothes
Device of being engaged in receives the click event data that client is sent, and is then based on load balancing and will click on event data and is distributed to difference
Data server, such as carry out data distribution according to poll rule and to avoid single server load too high pass through more
Data server shares click event data equally, also facilitates dilatation, if data server can not carry instantly, extending transversely to add
Add a data server.The click event data received is sent to big data platform again by data server.It can be with
Understand, in some scenes without agency service, data server can be connect with client, direct reception client end hair
The click event sent.
The back-end server of the embodiment of the present invention will click on thing after the click event data for receiving client transmission
Number of packages is sent to big data platform according to real-time report is carried out, and big data platform carries out real-time big data analysis calculating, so as to
Conversion ratio statistical efficiency is improved, the pros and cons of correlative factor are quickly analyzed, changes decision, improves order rate.
It is detailed to a kind of click event stochastic method progress provided in an embodiment of the present invention below in conjunction with specific embodiment
Thin explanation, content can be as follows.
Referring to Fig. 2, for a kind of flow chart for clicking event stochastic method provided in an embodiment of the present invention, this method is specifically wrapped
Include following steps.
Step 201, client rear end server, which is sent, clicks event data.
In an implementation, the App (Application, cell phone software) or Website page of business service can provided in advance
In pre-set button or the default page it is tagged (flag), click (onclick) event, the click can be added in label
The function that event is realized is that rear end server reports click event data.When user behavior triggers object event, such as user
When clicking the pre-set button in the page or entering the default page, client can receive the behaviour of user behavior triggering object event
Make, then trigger click event, generate and click event data, clicks event number to realize that client rear end server is sent
According to.Further, client can report click event data by POST request rear end server.When default in the page
When button is clicked or presets the page and be opened, client can trigger click event immediately, be reported with rear end server
Event data is clicked, so the real-time of conversion ratio analysis statistics can be further increased.It is worth noting that, thing is clicked in addition
The pre-set button or the page of part can be selected according to practical application request, comprising user's registration submitting button, order submitting button, into
Enter to push page etc., so as to obtain the corresponding data of target service, it is to be understood that, can needle in application scenes
Behavior is entered to all pages and button clicks behavior and all adds click event, to collect complete user behavior data.
Clicking event data includes click information and the client-side information.The click information includes authentication field, produces
One of product coding, timestamp, application version number and application packet title are a variety of.Wherein, authentication field is for verifying number
According to legitimacy, MD5 Message Digest 5 ciphertext can be used, the clear text format that ciphertext uses can add often for product coding
Measure feature code and timestamp.Product coding is the coding of the product ordered shown in App or website.Timestamp can be with
To click the system time to give the correct time in event data.Application version number is application version number used by App or website.The visitor
Family client information include network type, network operator, FTP client FTP type, FTP client FTP version, in client identification
It is one or more.Wherein, network type includes LTE (Long Term Evolution, long term evolution) type, GPRS
(General Packet Radio Service, general packet radio service) type etc..FTP client FTP include iOS system and
Android system.If FTP client FTP is Android system, client identification includes SIM (Subscriber Identification
Module, user identity identification) card IMSI and client IMEI (International Mobile Subscriber
Identification Number, international mobile subscriber identity).If FTP client FTP is iOS system, client identification packet
Include ADID (advertisingIdentifier, advertisement identifier) and UUID (Unique Device Identifier, equipment
Unique identification).Client identification can also include MDN (Mobile Directory Number, Mobile Directory Number)
Or the encryption string of MDN.
Step 202, back-end server receives the click event data that client is sent.
The click event data that back-end server is sent by increasing interface client.
In a particular application, back-end server can be server cluster, including more proxy servers, such as two
Nginx server and more data servers, to support high concurrent request data.Proxy server receives what client was sent
Event data is clicked, load balancing is then based on and will click on event data and be distributed to different data servers, such as by
Data distribution is carried out according to poll rule, so that single server load too high is avoided, by more data servers come equal stand
Event data is hit, dilatation is also facilitated, if data server can not carry instantly, one data server of addition extending transversely is
It can.
Step 203, back-end server sends the click event data to big data platform.
Back-end server determines the corresponding event type of click event data after receiving click event data,
Such as into ordering page behavior, click order behavior, confirmation payment behavior, send identifying code behavior, complete order behavior etc.
Deng.After determining to click the corresponding event type of event data, it is marked, may then based on preset data format
The click event data received is handled, and carries out real-time report, is pushed to big data platform, to improve conversion ratio point
Analyse the real-time of statistics.For example, data format can be with are as follows:
'[$time_local]$token$imsi$adid$packageName$carrier$ip$region$
eventName$time
Each field is same a line, is separated with space, is indicated for empty value use-.Wherein, time_local indicates local service
Device time, token indicate that the encryption string of MDN, imsi indicate that the IMSI of SIM card, adid indicate the ADID of iOS system,
PackageName indicates to apply packet title, carrier expression network operator, the IP address of ip expression client, region table
Show that the affiliated area of client, eventName indicate event type, call time in time expression click event data.
It should be noted that data format shown in above-mentioned is exemplary illustration, the embodiment of the present invention is not taken rear end
Business device reports data format used by clicking event data specifically to be limited.
Back-end server acquires in real time clicks event data, while output journal, facilitates tracking.
If data transmission fails enable weight during back-end server sends click event data to big data platform
Test-run a machine system sends the click event data of transmission failure to big data platform again, it is ensured that data are not lost, and guarantee conversion ratio
Statistical accuracy.
Step 204, the big data platform, which receives, clicks event data, and is based on the click event data statistical phenomeon
Convert situation.
The click event data that big data platform meeting real-time reception back-end server is reported, and carry out at real time data
Reason.Big data platform can pre-process the click event data according to targeted particle size, obtain the point of each event type
Hit number, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP type with
And FTP client FTP version.Big data platform can be with prefixed time interval when counting the number of clicks of each event type
Unit is counted, such as counts each event type number of clicks hourly.Big data platform is converged in real time according to granularity
Total statistics can quickly analyze influence of the different factors to conversion ratio under each granularity.For example, the application version packet that App is used
Application version a and application version b are included, when being that granularity pre-processes click event data with application version number, is united respectively
The number of clicks of the corresponding each event type of application version a and the number of clicks of the corresponding each event type of application version b are counted,
To analyze influence of the different application version to conversion ratio.The embodiment of the present invention turns the event of promotion by quickly analysis various factors
The pros and cons of change, can timely adjustable strategies, improve order rate.
The big data platform can be based on each event in the data statistics different time intervals obtained after the pretreatment
The Long-term change trend of Long-term change trend and event the conversion situation of the number of clicks of type.Such as statistics 2019-06-17 to 2019-
Daily login user number between 06-23, into ordering page number, click Number of Orders and complete Number of Orders etc.
Deng to analyze the Long-term change trend of the number of clicks of each event type.Correspondingly, the conversion ratio between each event type may include
2019-06-17 is to, daily into the ratio between ordering page number and login user number, click is ordered between 2019-06-23
The ratio between number and login user number is purchased, Number of Orders is clicked and enters the ratio between ordering page number, is completed
Ratio between Number of Orders and login user number completes Number of Orders and enters the ratio between ordering page number, with
And complete Number of Orders and click the ratio between Number of Orders, become with analyzing the trend of the conversion situation between all types of events
Change.The Long-term change trend that situation is converted by the Long-term change trend and event that count the number of clicks of each event type, can analyze
Which more active period out, to carry out event promotion in the more active period.And by counting each event type
Between conversion ratio, whether reasonable, in order to ordering page cloth if can also analyze currently used ordering page layout
The improvement of office.For example, the ratio between ordering page number and login user number is lower, it may illustrate ordering page layout not
Rationally.
In an implementation, it customized user behavior chain, the i.e. any several event types of selection can be used to set up user in advance
Behavioral chain.Big data platform utilizes at least two events class after at least two event types for getting user's selection
Type sets up user behavior chain, is then based on each event type in user behavior chain described in the data statistics obtained after the pretreatment
Between event convert situation, such as by user log in, into ordering page, click order and complete order form user's row
For chain.
Big data platform also compares the conversion situation of same user behavior chain different time intervals, to analyze different time area
Between influence to conversion ratio, determine the more active period.Such as shown in figure 3, by entering the behavior of the business page, clicking and order
Behavior, confirmation payment behavior and the user behavior chain for ordering result prompt behavior establishment, compare the user behavior chain at three
Event in time interval converts situation, and three time intervals are 2019-06-17 respectively to the section 2019-06-23,2019-
06-17 is to the section 2019-06-23 and 2019-06-17 to the section 2019-06-23.
Big data platform can also compare the event conversion situation of different user behavioral chain, to analyze different ordering channels
Influence to conversion ratio.
Big data platform can convert situation with the event that page graphical representation counts, to facilitate operation maintenance personnel at any time
It checks.
The big data platform real-time reception of the embodiment of the present invention clicks event data, and carries out real-time big data analysis meter
It calculates, so as to improve the statistical efficiency of conversion ratio, quickly analyzes the pros and cons of correlative factor, adjustable strategies improve order rate,
And the click event data that big data platform receives includes the various factors of influence event conversion ratio, such as application version
Number, network type, network operator, FTP client FTP type and FTP client FTP version etc., with quickly analyze it is various because
Pros and cons of the element to the conversion of the event of promotion, timely adjustable strategies improve order rate.
Based on inventive concept identical with above method embodiment, the embodiment of the invention also provides a kind of conversion ratio statistics
System, the system comprises big data platforms, client, and are connected between the big data platform and the client
Back-end server；
The client is sent to the back-end server clicks event data, the back-end server receive it is described
After clicking event data, Xiang Suoshu big data platform sends the click event data, described in the big data platform receives
Event data is clicked, and situation is converted based on the click event data statistical phenomeon；
Wherein, the click event data include authentication field, product coding, timestamp, application version number, using packet name
Title, client ip address, client affiliated area, network type, network operator, FTP client FTP type, FTP client FTP
One of version, client identification are a variety of；
The big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform pre-processes the click event data according to targeted particle size, obtains each event type
Number of clicks, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP class
One of type and FTP client FTP version are a variety of.
Preferably, the event data of clicking includes the click event number that back-end server is sent to the big data platform
According to wherein the back-end server receives the click event data from client.
Preferably, the client generates the click event after receiving the object event by user behavior triggering
Data, and the click event data is sent to the back-end server.
Preferably, the back-end server includes an at least proxy server and an at least data server；
The proxy server receives the click event data that the client is sent, and is based on load balancing
The click event data is distributed to the different data servers；
The click event data received is sent to the big data platform by the data server.
Preferably, if during the back-end server sends the click event data to big data platform, data
Transmission failure then sends the click event data of the transmission failure to the big data platform again.
Preferably, the big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform is based on each event type in the data statistics different time intervals obtained after the pretreatment
Number of clicks Long-term change trend and event conversion situation Long-term change trend.
Preferably, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform sets up user behavior chain using at least two event types；
Turned based on the event between each event type in user behavior chain described in the data statistics obtained after the pretreatment
Change situation.
Preferably, the big data platform is based on the step of click event data statistical phenomeon conversion situation, also wraps
It includes:
The big data platform compares the event conversion situation of same user behavior chain different time intervals, or comparison is not
Event with user behavior chain converts situation.
The big data platform real-time reception of the embodiment of the present invention clicks event data, and carries out real-time big data analysis meter
It calculates, so as to improve the statistical efficiency of conversion ratio, quickly analyzes the pros and cons of correlative factor, adjustable strategies improve order rate,
And the click event data that big data platform receives includes the various factors of influence event conversion ratio, such as application version
Number, network type, network operator, FTP client FTP type and FTP client FTP version etc., with quickly analyze it is various because
Pros and cons of the element to the conversion of the event of promotion, timely adjustable strategies improve order rate.
It should be understood that conversion ratio statistical system provided by the above embodiment and click event stochastic method embodiment category
In same design, specific implementation process is detailed in embodiment of the method, and which is not described herein again.
The embodiment of the invention also provides a kind of big data platform, the big data platform includes data processing module and shows
Show module, the data processing module unites to the click event data received based on above-mentioned click event stochastic method
Meter, and the event conversion situation that statistics obtains is sent to the display module, the display module converts feelings to the event
Condition is shown.
The data processing module includes processor and memory, and at least one instruction, extremely is stored in the memory
A few Duan Chengxu, code set or instruction set, at least one instruction, an at least Duan Chengxu, the code set or instruction
Collection is loaded by the processor and is executed to realize above-mentioned click event stochastic method.
Those of ordinary skill in the art will appreciate that realizing that all or part of the steps of above-described embodiment can pass through hardware
It completes, relevant hardware can also be instructed to complete by program, the program can store in a kind of computer-readable
In storage medium, storage medium mentioned above can be read-only memory, disk or CD etc..
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (17)
1. a kind of click event stochastic method, which is characterized in that the described method includes:
Big data platform, which receives, clicks event data；
The big data platform is based on the click event data statistical phenomeon and converts situation；
Wherein, the click event data include authentication field, product coding, timestamp, application version number, using packet title,
Client ip address, client affiliated area, network type, network operator, FTP client FTP type, FTP client FTP version
Originally, one of client identification or a variety of；
The big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform pre-processes the click event data according to targeted particle size, obtains the point of each event type
Hit number, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP type with
And one of FTP client FTP version or a variety of.
2. the method according to claim 1, wherein the click event data includes back-end server to described
The click event data that big data platform is sent, wherein the back-end server receives the click event number from client
According to.
3. according to the method described in claim 2, it is characterized in that, the client is receiving the mesh triggered by user behavior
After mark event, the click event data is generated, and send the click event data to the back-end server.
4. according to the method described in claim 2, it is characterized in that, the back-end server include an at least proxy server and
An at least data server；
The proxy server receives the click event data that the client is sent, and is based on load balancing for institute
It states click event data and is distributed to the different data servers；
The click event data received is sent to the big data platform by the data server.
5. according to the method described in claim 2, it is characterized in that,
If during the back-end server sends the click event data to big data platform, data transmission fails, then
Again the click event data of the transmission failure is sent to the big data platform.
6. the method according to claim 1, wherein the big data platform is united based on the click event data
Meter event converts the step of situation, comprising:
Point of the big data platform based on each event type in the data statistics different time intervals obtained after the pretreatment
Hit the Long-term change trend of number and the Long-term change trend of event conversion situation.
7. the method according to claim 1, wherein the big data platform is united based on the click event data
Meter event converts the step of situation, further includes:
The big data platform sets up user behavior chain using at least two event types；
Feelings are converted based on the event between each event type in user behavior chain described in the data statistics obtained after the pretreatment
Condition.
8. the method according to claim 1, wherein the big data platform is united based on the click event data
Meter event converts the step of situation, further includes:
The big data platform compares the event conversion situation of same user behavior chain different time intervals, or the different use of comparison
The event of family behavioral chain converts situation.
9. a kind of click event statistics system, which is characterized in that the system comprises big data platforms, client, and connection
Back-end server between the big data platform and the client；
The client sends to the back-end server and clicks event data, and the back-end server is receiving the click
After event data, Xiang Suoshu big data platform sends the click event data, and the big data platform receives the click
Event data, and situation is converted based on the click event data statistical phenomeon；
Wherein, the click event data include authentication field, product coding, timestamp, application version number, using packet title,
Client ip address, client affiliated area, network type, network operator, FTP client FTP type, FTP client FTP version
Originally, one of client identification or a variety of；
The big data platform is based on the step of click event data statistical phenomeon conversion situation, comprising:
The big data platform pre-processes the click event data according to targeted particle size, obtains the point of each event type
Hit number, wherein institute according to granularity include: application version number, network type, network operator, FTP client FTP type with
And one of FTP client FTP version or a variety of.
10. system according to claim 9, which is characterized in that the click event data includes back-end server to institute
The click event data for stating big data platform transmission, wherein the back-end server receives the click event number from client
According to.
11. system according to claim 10, which is characterized in that the client is being received by user behavior triggering
After object event, the click event data is generated, and send the click event data to the back-end server.
12. system according to claim 10, which is characterized in that the back-end server includes an at least proxy server
An at least data server；
The proxy server receives the click event data that the client is sent, and is based on load balancing for institute
It states click event data and is distributed to the different data servers；
The click event data received is sent to the big data platform by the data server.
13. system according to claim 10, which is characterized in that
If during the back-end server sends the click event data to big data platform, data transmission fails, then
Again the click event data of the transmission failure is sent to the big data platform.
14. system according to claim 9, which is characterized in that the big data platform is based on the click event data
Statistical phenomeon converts the step of situation, comprising:
Point of the big data platform based on each event type in the data statistics different time intervals obtained after the pretreatment
Hit the Long-term change trend of number and the Long-term change trend of event conversion situation.
15. system according to claim 9, which is characterized in that the big data platform is based on the click event data
Statistical phenomeon converts the step of situation, further includes:
The big data platform sets up user behavior chain using at least two event types；
Feelings are converted based on the event between each event type in user behavior chain described in the data statistics obtained after the pretreatment
Condition.
16. system according to claim 9, which is characterized in that the big data platform is based on the click event data
Statistical phenomeon converts the step of situation, further includes:
The big data platform compares the event conversion situation of same user behavior chain different time intervals, or the different use of comparison
The event of family behavioral chain converts situation.
17. a kind of big data platform, which is characterized in that the big data platform includes data processing module and display module, institute
Data processing module is stated based on click event stochastic method as described in any of the claims 1 to 8 to the click event received
Data are counted, and the event conversion situation that statistics obtains is sent to the display module, and the display module is to described
Event change over condition is shown.
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