CN109408816A - A kind of internet data analysis Web vector graphic method - Google Patents
A kind of internet data analysis Web vector graphic method Download PDFInfo
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- CN109408816A CN109408816A CN201811176536.3A CN201811176536A CN109408816A CN 109408816 A CN109408816 A CN 109408816A CN 201811176536 A CN201811176536 A CN 201811176536A CN 109408816 A CN109408816 A CN 109408816A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000004458 analytical method Methods 0.000 claims abstract description 68
- 230000010076 replication Effects 0.000 claims abstract description 4
- 239000000284 extract Substances 0.000 claims description 16
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 238000012216 screening Methods 0.000 claims description 11
- 230000006698 induction Effects 0.000 claims description 10
- 238000010224 classification analysis Methods 0.000 claims description 7
- 230000013011 mating Effects 0.000 claims description 3
- 241001269238 Data Species 0.000 abstract description 5
- 230000009977 dual effect Effects 0.000 abstract description 3
- 230000001939 inductive effect Effects 0.000 abstract description 3
- 238000000605 extraction Methods 0.000 description 5
- 239000000470 constituent Substances 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
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Abstract
The invention discloses a kind of internet data analysis Web vector graphic methods, the following steps are included: S1 web page text obtains module: the usage behavior data of replication application webpage, and it according to fixed format modifies with fixed interval, it can also be installed in server end, the laggard row format of usage behavior data for collecting Web page application program changes.Internet data analysis Web vector graphic method, by the way that three summary datas are arranged, so that the ratio of data increases, improve the analytical precision and analysis rate of data, the analysis of event correlation data is set, so that data analysis is further accurate, it is conducive to provide effective game data improvement project simultaneously, increase the practicability of the internet data analysis Web vector graphic method, classification and whole dual inductive method are taken simultaneously, comparison after being conducive to data analysis, while reducing the pressure of system operations.
Description
Technical field
The present invention relates to internet data analysis technical fields, specially a kind of internet data analysis Web vector graphic side
Method.
Background technique
In present network big data era, the update of data is more and more quicker, thus for data analysis and
Preservation is more and more important, and the data needs from different industries different event are disaggregatedly concluded, and is then analyzed,
For the analysis of data can make we to future event occur probability calculate, so for data processing especially
It is important,
It in the case that especially current online game is prevailing, needs to analyze the data of game, successively improve
The developing direction of game, but often present game data analysis processing is not in time, usually when analyzing game data, only adopts
It is analyzed with one-stop data, is easy error, it, can be because the error of data mobile terminal produces when especially starting to collect extraction game data
Raw abnormal data, so that the data processing inaccuracy in later period, while data withdrawal ratio is very little, it is not easy to it most compares, makes data
Analytical precision reduces, and concludes unreasonable.
Summary of the invention
The purpose of the present invention is to provide a kind of internet data analysis Web vector graphic methods, to solve above-mentioned background skill
The problem of being proposed in art.
To achieve the above object, the invention provides the following technical scheme: a kind of internet data analysis Web vector graphic side
Method, comprising the following steps:
S1 web page text obtains module: the usage behavior data of replication application webpage, and according to fixed format between fixation
It modifies every the time, server end can also be installed in, collect the laggard row format of usage behavior data of Web page application program
Change.
S2 webpage obtains module again: the web page text data of acquisition being extracted again, guarantee the complete of text data
Property.
S3 weight screening module: the complete data of acquisition are carried out screening and extract significant data, unnecessary data
It is rejected, avoids the conclusion and analysis that influence the later period.
S4 web data analysis module: the data that sorted generalization is arranged carry out whole analysis, then whole analysis
Data carry out classification analysis again, are convenient for control global analysis data and classification analysis data, carry out final analysis, and make
Constituent class and whole analysis table.
S5 web data concludes module: the significant data extracted being carried out sorted generalization arrangement, and corresponding table is made
Lattice use convenient for analysis later.
S6 webpage stored off-line module: transferring the data that webpage summarizes and analyzes, and carries out modeling storage according to default storage model
It deposits, analysis module data can be transferred when necessary and supplemented.
Preferably, web page text described in step S1 obtain module include step S101 obtain target webpage data text and
Step S102 obtains the characteristic of every part of webpage in target webpage, every part of net in acquisition target webpage described in step S102
The characteristic of page includes that S1021 extracts summary data.
Preferably, weight screening module described in step S3 includes that step S301 extracts the first summary data, step S302 is mentioned
The second summary data, step S303 is taken to extract third summary data and step S304 extraction abnormal data.
Preferably, web data analysis module described in step S4 includes step S401 analysis the first summary data, step
S402 analyzes the second summary data and step S403 analyzes third summary data.
Preferably, the first summary data described in step S401 carries out the analysis of any active ues data, event correlation data respectively
Analysis is lost Users'Data Analysis, and corresponding table is made, and wherein the analysis of event correlation data includes the data point that Add User
Users'Data Analysis is analysed and is lost, the second summary data described in step S402 carries out the analysis of any active ues data respectively, event is closed
Join data analysis, be lost Users'Data Analysis, and corresponding table is made, wherein the analysis of event correlation data includes Adding User
Data analysis and be lost Users'Data Analysis, third summary data described in step S403 carry out respectively the analysis of any active ues data,
The analysis of event correlation data is lost Users'Data Analysis, and corresponding table is made, and wherein the analysis of event correlation data includes new
Increase Users'Data Analysis and be lost Users'Data Analysis, then carries out final analysis by three tables, and generate final table
Lattice, while retaining original three tables.
Preferably, it includes that step S501 concludes whole summary data and step that web data described in step 5, which concludes module,
S502 concludes abnormal data, concludes the data integrally made a summary and the corresponding table of mating imparting.
Preferably, the whole summary data of conclusion described in step S501 carries out the conclusion of any active ues data, event correlation respectively
Data induction is concluded with user data is lost, and wherein event correlation Data induction includes Add User Data induction and loss user
Data induction.
Compared with prior art, the beneficial effects of the present invention are:
Module is obtained again by the way that webpage is arranged, and is effectively avoided mobile terminal when extracting data and is generated abnormal data, makes to count
According to analysis inaccuracy, the analytical precision of data is improved, together so that the ratio of data increases by three summary datas of setting
When retain and extract abnormal data so that when analysis data, the effective loophole for improving system, point of setting event correlation data
Analysis, is conducive to the reason of analyzing data fluctuations, so that data analysis is further accurate, while being conducive to provide effective game
Data improvement project, increases the practicability of the internet data analysis Web vector graphic method, while taking classification and entirety
Dual inductive method, the comparison after being conducive to data analysis, so that effectively raising the accuracy of data analysis.
Detailed description of the invention
Fig. 1 is the structural schematic diagram that Web vector graphic method is used in internet data of the present invention analysis；
Fig. 2 is the flow chart that internet data of the present invention analysis obtains screening module with Web vector graphic method；
Fig. 3 is the flow chart that Web vector graphic method web data analysis module is used in internet data of the present invention analysis；
Fig. 4 is the flow chart that module is concluded in internet data of the present invention analysis with Web vector graphic method web data.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1-4 is please referred to, the present invention provides a kind of technical solution: a kind of internet data analysis Web vector graphic method,
The following steps are included:
S1 web page text obtains module: the usage behavior data of replication application webpage, and according to fixed format between fixation
It modifies every the time, server end can also be installed in, collect the laggard row format of usage behavior data of Web page application program
Change, specifically: it includes that step S101 obtains the data text of target webpage again by step that step S1 web page text, which obtains module,
S102 obtains the characteristic of every part of webpage in target webpage, and wherein step S102 obtains every part of webpage in target webpage
Characteristic includes that S1021 extracts summary data.
S2 webpage obtains module again: the web page text data of acquisition being extracted again, guarantee the complete of text data
Property.
S3 weight screening module: the complete data of acquisition are carried out screening and extract significant data, unnecessary data
It is rejected, avoids the conclusion and analysis that influence the later period, have are as follows: step S3 weight screening module includes that step S301 extracts the
One summary data, step S302 extract the second summary data, step S303 extracts third summary data and step S304 extraction is different
Regular data.
S4 web data analysis module: the data that sorted generalization is arranged carry out whole analysis, then whole analysis
Data carry out classification analysis again, are convenient for control global analysis data and classification analysis data, carry out final analysis, and make
Constituent class and whole analysis table, specifically: step S4 web data analysis module includes the first abstract of step S401 analysis
Data, step S402 analyze the second summary data and step S403 analyzes third summary data, the first summary data of step S401
The analysis of any active ues data is carried out respectively, the analysis of event correlation data, is lost Users'Data Analysis, and corresponding table is made,
Wherein the analysis of event correlation data includes Add User data analysis and loss Users'Data Analysis, the abstract number of step S402 second
According to progress any active ues data analysis respectively, the analysis of event correlation data, it is lost Users'Data Analysis, and corresponding table is made
Lattice, wherein the analysis of event correlation data is analyzed including the data that Add User and is lost Users'Data Analysis, and step S403 third is plucked
It wants data to carry out the analysis of any active ues data respectively, the analysis of event correlation data, be lost Users'Data Analysis, and is made corresponding
Table, wherein the analysis of event correlation data includes Add User data analysis and loss Users'Data Analysis, then passes through three tables
Lattice carry out final analysis, and generate final table, while retaining original three tables.
S5 web data concludes module: the significant data extracted being carried out sorted generalization arrangement, and corresponding table is made
Lattice use convenient for analysis later, specifically: it includes that step S501 concludes whole abstract number that step 5 web data, which concludes module,
Abnormal data is concluded according to step S502, concludes the data integrally made a summary and the corresponding table of mating imparting, wherein step
The whole summary data of S501 conclusion carries out the conclusion of any active ues data, event correlation Data induction and loss user data respectively and returns
It receives, wherein event correlation Data induction includes Add User Data induction and loss user data conclusion.
S6 webpage stored off-line module: transferring the data that webpage summarizes and analyzes, and carries out modeling storage according to default storage model
It deposits, analysis module data can be transferred when necessary and supplemented.
Specifically used method: it first passes through web page text and obtains the data text that module obtains target webpage, while again to mesh
Every page of the characteristic marked in webpage carries out data acquisition, then extracts summary data again after obtaining characteristic, simultaneously
It reuses weight screening module and classification extraction, and respectively the first summary data, the second summary data, is carried out to summary data
Three summary datas and abnormal data restart web data analysis module respectively to the first summary data, the second summary data,
Three summary datas are analyzed, and obtain the data form of any active ues, event correlation data form, be lost user data
Table, wherein the data of event correlation be divided into Add User data and be lost user data, and respectively carry out aggregate analysis and
Classification analysis, and table is made and carries out last comparative analysis, restart web data and conclude module, so as to analysis data
Whole and classification is concluded, while individually being concluded exception and being provided, and the detection of the classified storage and system vulnerability in later period is conducive to,
For the later period improvement and increase convenience be provided, finally carry out the stored off-line of webpage, integrated convenient for the later period of data.
The present invention provides a kind of internet data analyses to be had the advantages that with Web vector graphic method
Module is obtained again by the way that webpage is arranged, and is effectively avoided mobile terminal when extracting data and is generated abnormal data, makes to count
According to analysis inaccuracy, by the way that three summary datas are arranged, so that the ratio of data increases, improve the analytical precision of data and divide
Rate is analysed, while retaining extraction abnormal data, so that when analysis data, event correlation is arranged in the effective loophole for improving system
The analysis of data so that data analysis is further accurate, while being conducive to provide effective game data improvement project, and increasing should
The practicability of Web vector graphic method is used in internet data analysis, while taking classification and whole dual inductive method, is conducive to
Comparison after data analysis, while reducing the pressure of system operations.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (7)
1. a kind of internet data analysis Web vector graphic method, it is characterised in that: the following steps are included:
S1 web page text obtain module: the usage behavior data of replication application webpage, and according to fixed format with fixed intervals when
Between modify, server end can also be installed in, the laggard row format of usage behavior data for collecting Web page application program changes；
S2 webpage obtains module again: the web page text data of acquisition being extracted again, guarantee the integrality of text data；
S3 weight screening module: the complete data of acquisition are carried out screening and extract significant data, unnecessary data are carried out
It rejects, avoids the conclusion and analysis that influence the later period；
S4 web data analysis module: the data that sorted generalization is arranged carry out whole analysis, then whole analysis data
Classification analysis is carried out again, control global analysis data and classification analysis data is convenient for, carries out final analysis, and is made point
Class and whole analysis table；
S5 web data concludes module: the significant data extracted being carried out sorted generalization arrangement, and corresponding table is made, just
It is used in analysis later；
S6 webpage stored off-line module: transferring the data that webpage summarizes and analyzes, and carries out modeling storage according to default storage model, must
Analysis module data can be transferred when wanting to be supplemented.
2. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step S1 institute
Stating web page text and obtaining module includes that step S101 is obtained in the data text and step S102 acquisition target webpage of target webpage
Every part of webpage characteristic, the characteristic of every part of webpage in acquisition target webpage described in step S102 includes S1021
Extract summary data.
3. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step S3 institute
Stating weight screening module includes that step S301 extracts the first summary data, step S302 extracts the second summary data, step S303
It extracts third summary data and step S304 extracts abnormal data.
4. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step S4 institute
Stating web data analysis module includes that step S401 analyzes the first summary data, step S402 analyzes the second summary data and step
S403 analyzes third summary data.
5. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step S401
First summary data carries out the analysis of any active ues data respectively, the analysis of event correlation data, is lost Users'Data Analysis, and
Corresponding table is made, wherein the analysis of event correlation data includes Add User data analysis and loss Users'Data Analysis, step
Second summary data described in rapid S402 carries out the analysis of any active ues data respectively, the analysis of event correlation data, is lost user data
Analysis, and corresponding table is made, wherein the analysis of event correlation data includes Add User data analysis and loss user data
It analyzes, third summary data described in step S403 carries out the analysis of any active ues data respectively, the analysis of event correlation data, is lost and uses
User data analysis, and corresponding table is made, wherein the analysis of event correlation data is analyzed and is lost including the data that Add User and uses
User data analysis, then final analysis is carried out by three tables, and generate final table, while retaining original three tables
Lattice.
6. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step 5 institute
Stating web data and concluding module includes that step S501 concludes whole summary data and step S502 conclusion abnormal data, is concluded whole
The data of abstract and the corresponding table of mating imparting.
7. a kind of internet data analysis Web vector graphic method according to claim 1, it is characterised in that: step S501
The whole summary data of the conclusion carries out the conclusion of any active ues data, event correlation Data induction and loss user data respectively and returns
It receives, wherein event correlation Data induction includes Add User Data induction and loss user data conclusion.
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| CN201811176536.3A CN109408816A (en) | 2018-10-10 | 2018-10-10 | A kind of internet data analysis Web vector graphic method |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105577431A (en) * | 2015-12-11 | 2016-05-11 | 青岛云成互动网络有限公司 | User information identification and classification method based on internet application and system thereof |
| CN106776567A (en) * | 2016-12-22 | 2017-05-31 | 金蝶软件（中国）有限公司 | A kind of internet big data analyzes extracting method and system |
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2018
- 2018-10-10 CN CN201811176536.3A patent/CN109408816A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105577431A (en) * | 2015-12-11 | 2016-05-11 | 青岛云成互动网络有限公司 | User information identification and classification method based on internet application and system thereof |
| CN106776567A (en) * | 2016-12-22 | 2017-05-31 | 金蝶软件（中国）有限公司 | A kind of internet big data analyzes extracting method and system |
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