CN110310158A - The working method of accurate matching consumption data during user network behavioural analysis - Google Patents
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
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- G06Q30/00—Commerce
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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- G06Q30/0643—Graphical representation of items or shoppers
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention proposes a kind of working methods of matching consumption data accurate during user network behavioural analysis, include the following steps: S1, by obtaining user network behavioral data, form behavioral data aggregate set, and carry out parsing operation；S2 crawls behavioral data progress data in parsing operating process, carries out noise reduction process to behavioral data；S3, the behavioral data after noise reduction process link optimal user network behavior by fine matching method.
Description
Technical field
The present invention relates to accurate during computer data excavation applications more particularly to a kind of user network behavioural analysis
Match the working method of consumption data.
Background technique
In recent years, with the continuous development of internet, huge numbers of families have been come into shopping online.Various electric business podium levels go out
Not poor, each platform has respective characteristic, on the one hand, the market segments gradual perfection of electric business platform meets various demands
Electric business platform is complete, and the optional space enlargement of user forms the user behavior storing data of magnanimity；On the other hand, it uses
Family in addition to be concerned about goods themselves source, also begin to focus on the source platform whether have buy accordingly it is preferential, how in magnanimity
Data in carry out the matching of user behavior track, and the behavioral data after matching is directed toward discount link to completing to purchase
Buy behavior.This just needs those skilled in the art and solves corresponding technical problem.
Summary of the invention
The present invention is directed at least solve the technical problems existing in the prior art, one kind is especially innovatively proposed
In order to realize above-mentioned purpose of the invention, the present invention provides accurate during a kind of user network behavioural analysis
The working method for matching consumption data, includes the following steps:
S1 forms behavioral data aggregate set, and carry out parsing operation by obtaining user network behavioral data；
S2 crawls behavioral data progress data in parsing operating process, carries out noise reduction process to behavioral data；
S3, the behavioral data after noise reduction process link optimal user network behavior by fine matching method.
Preferably, the S1 includes:
S1-1 configures the electric business platform access domain name that need to be covered, and obtains the commodity searched in user network behavioral data
Keyword scans in a certain electric business platform, uses asynchronous call search result；
S1-2 after the completion of search, crawls the commodity data details of inhomogeneity now, extracts user using Scrapy frame
Structural data in network behavior data use beautiful soup syntax parsing netpage user's network behavior data；
Then S1-3 is completed using document object model to search result in user network behavioral data
Parsing, after being parsed polymerization return user network behavioral data as a result, server-side arrange polymerization return as a result, in client
End is accordingly shown.
Preferably, the S2 includes:
S2-1, since initial URL, Scheduler can be handed over to Downloader and be used during data crawl
Network behavior data downloading in family can give Spider and carry out behavioral data analysis after downloading；
S2-2, Spider are analyzed there are two types of the results come: one is the link for needing further to grab, the chains of crawl
Scheduler can be transmitted back to by connecing；Another kind is the behavioral data for needing to save, and is sent in Item Pipeline, to behavior
Data carry out post-processing；
S2-3, in addition, installation data grabs intermediate control in the channel Item Pipeline that behavior data flow is moved, into
A walking advance as data grabber processing.
Preferably, the S3 includes:
S3-1, client realize the search of commodity by using Retrofit network request frame, then use Glide
Picture loading frame realizes the displaying of merchandise preview picture；
S3-2, user in client by purchase operation immediately, realize the specifications of consumer behavior data, quantity, price,
The confirmation in time limit, the amount of money by stages by stages, server-side complete corresponding consumption data according to the extractive process of user network behavioral data
Direction operation.
Preferably, the commodity data details include:
Price, link, picture, information source, Merchant name, commercial specification, picture and text details,
Preferably, the S2-1 further include:
S2-A, engine open a website (open adomain), find handle the website Spider and to this
Spider requests first URL (s) to be crawled；
S2-B, engine got from Spider the URL that first to be crawled and scheduler (Scheduler) with
Request scheduling；
S2-C, engine is to the next URL to be crawled of scheduler request.
Preferably, the S2-1 further include:
S2-D, scheduler return to next URL to be crawled to engine, and URL is passed through downloading intermediate request by engine
Request is transmitted to downloader Downloader；
S2-E, once user behavior data page-downloading finishes, downloader generates the Response of the page, and
It is sent to engine by the intermediate response that returns of downloading；
S2-F, engine receive Response from downloader and are sent to Spider by Spider intermediate input direction
Processing.
Preferably, the S2-1 further include:
S2-G, Spider processing Response simultaneously return to the Item that crawls and (follow-up) new Request to drawing
It holds up；
S2-H, the Item crawled that engine returns to Spider return to Spider to ItemPipeline
Request is to scheduler；
S2-I, from S2-B repeatedly until more request, engine do not close the website in scheduler.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
Overall network data are subjected to traversal covering scanning by aggregated data, and pass through web crawlers for network data
Data grabber is carried out, after tripartite's data are carried out polymerization arrangement, the comprehensive crawl for forming data is analyzed, and by total data
Effectively shown.
Additional aspect and advantage of the invention will be set forth in part in the description, partially will from the following description
Become obvious, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect of the invention and advantage will become from the description of the embodiment in conjunction with the following figures
It obtains obviously and is readily appreciated that, in which:
Fig. 1 is general illustration of the present invention；
Fig. 2 is operation schematic diagram of the present invention；
Fig. 3 is the specific embodiment of the invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to
The embodiment of attached drawing description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.
As illustrated in fig. 1 and 2, the whole network polymerize: the electric business platform access domain name that configuration need to cover, and the commodity for uploading search close
Key word scans in the electric business platform preset, uses asynchronous call search result；After the completion of search, inhomogeneity is crawled
Now commodity details (price, link, picture, source, Merchant name, commercial specification, picture and text details), use Scrapy frame
Frame extracts structural data, using beautiful soup syntax parsing webpage, and uses document object model
Complete parsing to search result, polymerization returns the result after being parsed, server-side arrange that polymerization returns as a result, in client
End is accordingly shown.
Data crawl: first since initial URL, Scheduler can be handed over to Downloader and be downloaded, under
Spider can be given after load to be analyzed, Spider is analyzed there are two types of the results come: one is need further to grab
Link, such as the link of " the lower one page " of analysis, these things can be transmitted back to Scheduler before；Another kind is to need to save
Data, they are then sent to the there Item Pipeline, that is data to be carried out with post-processing (detailed analysis is filtered, deposited
Storage etc.) place.In addition, various middlewares can also be installed in the channel of data flowing, necessary processing is carried out.
Consumption data matching: client is realized the search of commodity, is passed through by using Retrofit network request frame
Using Glide picture loading frame, the displaying of merchandise preview picture is realized；User can be operated in client by purchase immediately,
Realize the specifications of selected commodity, quantity, price, the confirmation in time limit, the amount of money by stages by stages, confirm it is errorless after, submit order, clothes
The calculating and lower single process for the achievable corresponding total amount of the orders by stages for buying commodity in end of being engaged in.
With polymerization technique and the whole network commodity can business model by stages combination, real comprehensive mainstream electric business platform
On the basis of commodity, buying behavior can be completed on an APP without other tripartite's platforms, realize the whole network commodity
Can search for, is browsable, the purchase that can place an order, can payable by installment function.
As shown in figure 3,1. engines open a website (open adomain), the Spider of the processing website is found simultaneously
First URL (s) to be crawled is requested to the spider.
2. engine gets the URL that first to be crawled and in scheduler (Scheduler) from Spider with Request
Scheduling.
3. engine is to the next URL to be crawled of scheduler request.
4. scheduler returns to next URL to be crawled to engine, URL is passed through downloading middleware (request by engine
(request) direction) it is transmitted to downloader (Downloader).
5. once downloader generates the Response of the page, and it is intermediate by downloading page-downloading finishes
Part (returning to the direction (response)) is sent to engine.
6. engine receives Response from downloader and is sent to by Spider middleware (input direction)
Spider processing.
The Item and (follow-up) that 7.Spider processing Response and return crawl new Request is to engine.
8. the Item that engine crawls (Spider is returned) returns to Spider to ItemPipeline
Request is to scheduler.
9. (from second step) is repeatedly until more request, engine do not close the website in scheduler.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective,
The scope of the present invention is defined by the claims and their equivalents.
Claims (8)
1. the working method of accurate matching consumption data during a kind of user network behavioural analysis, which is characterized in that including such as
Lower step:
S1 forms behavioral data aggregate set, and carry out parsing operation by obtaining user network behavioral data；
S2 crawls behavioral data progress data in parsing operating process, carries out noise reduction process to behavioral data；
S3, the behavioral data after noise reduction process link optimal user network behavior by fine matching method.
2. the working method of accurate matching consumption data during user network behavioural analysis according to claim 1,
It is characterized in that, the S1 includes:
S1-1 configures the electric business platform access domain name that need to be covered, and it is crucial to obtain the commodity searched in user network behavioral data
Word scans in a certain electric business platform, uses asynchronous call search result；
S1-2 after the completion of search, crawls the commodity data details of inhomogeneity now, extracts user network row using Scrapy frame
For the structural data in data, beautiful soup syntax parsing netpage user's network behavior data are used；
Then S1-3 completes the parsing to search result in user network behavioral data using document object model,
Polymerization returns to user network behavioral data as a result, server-side arranges that polymerization returns as a result, carrying out in client after being parsed
It is corresponding to show.
3. the working method of accurate matching consumption data during user network behavioural analysis according to claim 1,
It is characterized in that, the S2 includes:
S2-1, since initial URL, Scheduler can be handed over to Downloader and carry out user network during data crawl
The downloading of network behavioral data can give Spider and carry out behavioral data analysis after downloading；
S2-2, Spider are analyzed there are two types of the results come: one is the link for needing further to grab, the link of crawl can quilt
Pass Scheduler back；Another kind is the behavioral data for needing to save, and is sent in Item Pipeline, is carried out to behavioral data
Post-processing；
S2-3, in addition, behavior data flow move the channel Item Pipeline in installation data grab intermediate control, carry out into
One walking is data grabber processing.
4. the working method of accurate matching consumption data during user network behavioural analysis according to claim 1,
It is characterized in that, the S3 includes:
S3-1, client realize the search of commodity by using Retrofit network request frame, then use Glide picture
Loading frame realizes the displaying of merchandise preview picture；
S3-2, user, by purchase operation immediately, realize specification, the quantity, price, by stages phase of consumer behavior data in client
The confirmation of limit, the by stages amount of money, server-side complete the direction of corresponding consumption data according to the extractive process of user network behavioral data
Operation.
5. the working method of accurate matching consumption data during user network behavioural analysis according to claim 2,
It is characterized in that, the commodity data details include:
Price, link, picture, information source, Merchant name, commercial specification, picture and text details.
6. the working method of accurate matching consumption data during user network behavioural analysis according to claim 3,
It is characterized in that, the S2-1 further include:
S2-A, engine open a website (open adomain), find the Spider for handling the website and ask to the spider
Seek first URL (s) to be crawled；
S2-B, engine get the URL that first to be crawled and in scheduler (Scheduler) from Spider with Request
Scheduling；
S2-C, engine is to the next URL to be crawled of scheduler request.
7. the working method of accurate matching consumption data during user network behavioural analysis according to claim 6,
It is characterized in that, the S2-1 further include:
S2-D, scheduler return to next URL to be crawled to engine, and engine turns URL by downloading intermediate request request
Issue downloader Downloader；
S2-E, once user behavior data page-downloading finishes, downloader generates the Response of the page, and is led to
It crosses the intermediate response that returns of downloading and is sent to engine；
S2-F, engine is from receiving Response and be sent to from Spider by Spider intermediate input direction in downloader
Reason.
8. the working method of accurate matching consumption data during user network behavioural analysis according to claim 6,
It is characterized in that, the S2-1 further include:
The Item and (follow-up) that S2-G, Spider processing Response and return crawl new Request is to engine；
S2-H, the Item crawled that engine returns to Spider give the Spider Request returned to ItemPipeline
Scheduler；
S2-I, from S2-B repeatedly until more request, engine do not close the website in scheduler.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111552872A (en) * | 2020-04-15 | 2020-08-18 | 携程旅游网络技术（上海）有限公司 | Method and system for restoring user behavior, electronic device and storage medium |
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