CN109739917A - A kind of integration method of magnanimity isomeric data - Google Patents
A kind of integration method of magnanimity isomeric data Download PDFInfo
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- CN109739917A CN109739917A CN201811636483.9A CN201811636483A CN109739917A CN 109739917 A CN109739917 A CN 109739917A CN 201811636483 A CN201811636483 A CN 201811636483A CN 109739917 A CN109739917 A CN 109739917A
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000010354 integration Effects 0.000 title claims abstract description 13
- 238000005243 fluidization Methods 0.000 claims abstract description 12
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000009434 installation Methods 0.000 claims 2
- 230000006870 function Effects 0.000 description 2
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Abstract
A kind of integration method of magnanimity isomeric data includes the following steps: 1) to carry out the acquisition of full dose data and be put in storage；2) it carries out incremental data acquisition and is put in storage；3) the full dose data in database are taken out；4) the full dose data are broken up as lattice data；5) the incremental data fluidization treatment；6) find that rule finds the meaning that the respective data of each school represent automatically by artificial discovery and system；7) the indices data after artificial discovery and system being found automatically rule are stored in database；8) the Database Publishing for having indices data to related education department；9) related education department carries out selection subscription information；10) initialization process；11) increment process；12) data are saved in specified data library；13) after subscribing to successfully, school's situation feedback and counter-measure suggestion are supplied to related education department.Data acquisition of the present invention meets real-time with subscription, improves subscription and checks efficiency and flexibility.
Description
Technical field
The present invention relates to field of communication technology more particularly to a kind of integration methods of magnanimity isomeric data.
Background technique
School is child's learning knowledge, receives the place of education, the function that the talent is conveyed for society is carry, so right
There is critical significance for the culture of the talent in fact in the supervision of school, and a portion function in relation to education department is exactly
Supervision and guidance to school, to the supervision and management of school be often will according to each school it is owned have school's items
The data of the database of situation extract to analyze, but each school is stored in meaning representated by the data of respective database
Be it is different, this is difficult to achieve the effect that unified management.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of integration methods of magnanimity isomeric data, solve related religion
When educating each school of supervision, department, each school is stored in hardly possible brought by the difference of meaning representated by the data of respective database
The problem of to be managed collectively.
In order to solve above scheme, the present invention in the following way: a kind of integration method of magnanimity isomeric data, including such as
Lower step:
1) it carries out the acquisition of full dose data and is put in storage, the full dose data include each school indices data over the years；
2) it carries out incremental data acquisition and is put in storage, the incremental data includes each school's same day indices data；
3) the full dose data in database are taken out；
4) the full dose data are broken up as lattice data；
5) the incremental data fluidization treatment；
6) by artificial discovery combing, wherein rule and system find that regular two methods find the respective number of each school automatically
According to the meaning of representative；
7) artificial discovery combing, wherein rule and system find that the indices data after rule are stored in database automatically；
8) the Database Publishing for having indices data to related education department；
9) related education department carries out selection subscription information；
10) initialization process, the initialization process are the data of the related data that need to subscribe to related education department from storage
It is screened in library, is then reduced into row data, then fluidization treatment is carried out to row data, deposit is subscribed to data flow, ordered described in acquirement
The full dose data in data flow are read, data are handled in the subscription data flow, are converted to needed for related education department
Data；
11) increment process, the increment process are to collect the incremental data fluidized, and data flow is subscribed in deposit, are sequentially obtained
Data in the subscription data flow are handled data in the subscription data flow, and being converted to related education department needs
The data wanted transmit the data for not needing to calculate same as before；
12) treated, data are saved in related education department's specified data library；
13) after subscribing to successfully, school's situation feedback and counter-measure suggestion are pointedly supplied to related education department.
In above-mentioned technical proposal, it is preferred that the indices data include school's title, school place, number of student,
Teacher's number, student Ge Ke total marks of the examination, student attendance, teacher turns out for work, situation of graduating, school finance takes in situation, department sets
Standby facility situation, school's land use situation, evaluation of teacher's situation.
In above-mentioned technical proposal, it is preferred that school's situation feedback include school's title, school place, number of student,
Teacher's number, student Ge Ke total marks of the examination, student attendance, teacher turns out for work, situation of graduating, school finance takes in situation, department sets
Standby facility situation, school's land use situation, evaluation of teacher's situation.
In above-mentioned technical proposal, it is preferred that the counter-measure suggestion includes regarding to which aspect of which school
It examines, which school to carry out economic aid to, which aspect of which school is instructed.
In above-mentioned technical proposal, it is preferred that it includes according to each which aspect to which school, which inspect,
The student Ge Ke total marks of the examination in indices data described in school compare, and are ranked up from top to bottom, to coming
End and with rank the first the student Ge Ke examination attainment discrepancy it is larger when, to come end school suggest view
It examines.
In above-mentioned technical proposal, it is preferred that described includes according to each school which school's progress economic aid
Indices data in school finance income situation compare, be ranked up from top to bottom, to coming end
And with rank the first the school finance income situation gap it is larger when, to come end school suggest economic aid.
In above-mentioned technical proposal, it is preferred that carrying out guidance to which aspect of which school includes according to each institute, school
The student Ge Ke total marks of the examination in the indices data stated compare, and are ranked up from top to bottom, to coming end
And with rank the first the student Ge Ke examination attainment discrepancy it is larger when, to the school for coming end be proposed with about
The guidance to improve results.
The present invention carries out data acquisition session first, to each school indices data (namely full dose number over the years
According to) be acquired and be put in storage, each school's same day indices data (namely incremental data) acquisition is taken out in database
Full dose data, then the full dose data in data list structure are broken up, the direct fluidization treatment of incremental data is passed through into people for lattice data
Wherein rule and system find that regular two methods find the meaning that the respective data of each school represent automatically for work discovery combing,
Artificial discovery combing, wherein rule and system finds the deposit database of the indices data after rule automatically, there being items
The Database Publishing of achievement data is to related education department, and related education department carries out selection subscription information, in relation to education department
Required data lattice data can be independently selected, then carry out initialization task, the related data that related education department need to be subscribed to
It is screened from the database of storage, is then reduced into row data, then fluidization treatment is carried out to row data, data are subscribed in deposit
Stream obtains the full dose data subscribed in data flow, handles in a stream data, data needed for being converted to user,
Then increment task is carried out, the incremental data fluidized is collected, data flow is subscribed in deposit, is sequentially obtained and is subscribed in data flow
Data are in a stream handled data, are converted to the data of user's needs, to the data for not needing to calculate, same as before
Then treated full dose data and incremental data are saved in related education department's specified data library, subscribed to successfully by transmission
Afterwards, school's situation feedback and counter-measure suggestion are pointedly supplied to related education department.
Beneficial effects of the present invention:
1, full-range data are carried out fluidization treatment by the present invention, are handled in stream the calculating of data, are made school's indices
Data acquisition meets real-time with subscription, and related education department can obtain required data within the second.
2, after the present invention breaks up full dose data, related education department can only subscribe to required data, complete without subscribing to
Data are measured, subscribing efficiency and flexibility is improved, improves and check efficiency.
Detailed description of the invention
Fig. 1 is the method for the present invention flow diagram.
Specific embodiment
Below by specific embodiment, the present invention is described further,
Embodiment 1: for feeding back tri- school's Senior Chinese Achievement Tests of A, B, C, it is assumed that A school Senior is averaged Chinese language
Achievement be 100 points, B school Senior be averaged Chinese Achievement Test be 90 points, C school Senior be averaged Chinese Achievement Test be 60 points,
The following steps are included:
1) high three average Chinese Achievement Tests, progress full dose data acquisitions are simultaneously put in storage over the years in importing tri- school's databases of A, B, C；
Described can be 2010 to 2018 over the years.
Full dose data acquisition be respectively to have 2010 to 2018 A, B, C, tri- schools high three be averaged Chinese language at
The database of achievement carries out data importing.
2) the last high three average Chinese Achievement Test in tri- databases of A, B, C is imported, is 100 points respectively, 90 points, 60
Point, it carries out incremental data acquisition and is put in storage, because tri- schools of A, B, C are each to have respective database, each number by oneself
It is difference according to the data that meaning is averaged Chinese Achievement Tests as high three that represent that inventory has, so needing further to locate data
Reason, it is assumed that the data that A school database represents high three average Chinese Achievement Tests are " grade ", it is assumed that B school database represents high by three
The data of average Chinese Achievement Test are " score ", it is assumed that C school database, which represents the data that high three are averaged Chinese Achievement Tests, is
Data are unified for " achievement " by " achievement ".
3) the full dose data in database are taken out；
4) the full dose data are broken up as lattice data, lattice data are one of formats of data minimum unit；
5) the incremental data fluidization treatment；
6) by artificial discovery combing, wherein rule and system find that regular two methods find tri- schools of A, B, C respectively automatically
Database in represent meaning as the data of high three average Chinese Achievement Tests be respectively " grade ", " score ",
"achievement"；
7) artificial discovery combing wherein rule and system find rule automatically after tri- schools of A, B, C represent meaning as Gao Sanping
The data of equal Chinese Achievement Test are stored in database；
8) tri- schools of A, B, C represent meaning as the data of high three average Chinese Achievement Tests Database Publishing to the related Ministry of Education
Door；
9) related education department carries out selection subscription information；
10) initialization process, the initialization process are the number for the high three average Chinese Achievement Tests that need to subscribe to related education department
It is screened according to from the database of storage, is then reduced into row data, then fluidization treatment is carried out to row data, data are subscribed in deposit
Stream obtains the full dose data subscribed in data flow, handles in the subscription data flow data, be converted to related
Data needed for education department " achievement "；
11) increment process, the increment process are to collect the incremental data fluidized, and data flow is subscribed in deposit, are sequentially obtained
Data in the subscription data flow are handled data in the subscription data flow, and being converted to related education department needs
The data " achievement " wanted transmit the data for not needing to calculate same as before；
12) treated, data are saved in related education department's specified data library；
13) after subscribing to successfully, school's situation feedback and counter-measure suggestion are pointedly supplied to related education department, it is described
Counter-measure suggestion includes that be averaged to tri- schools of A, B, C high three height of Chinese Achievement Test compares, and is arranged from top to bottom
Sequence, to come end and be more than 30 timesharing with the student Ge Ke examination attainment discrepancy to rank the first, to coming end
The school of tail suggests inspecting, and A school 100 divides, and B school 90 divides, and C school 60 divides, so suggesting inspecting to C school.
Embodiment 2: for feeding back tri- school finance's income situations of A, B, C, it is assumed that A school finance takes in situation and is
1000000, B school finance income situation are that 900,000, C school finance income situation is 600,000, comprising the following steps:
1) fiscal revenues situation, progress full dose data acquisition are simultaneously put in storage over the years in importing tri- school's databases of A, B, C；
Described can be 2010 to 2018 over the years.
Full dose data acquisition is respectively to there being 2010 to 2018 A, B, C, tri- school finances' income situations
Database carries out data importing.
2) the last fiscal revenues situation in tri- databases of A, B, C is imported, is 1,000,000,900,000,600,000 respectively, into
Row incremental data is acquired and is put in storage, because tri- schools of A, B, C are each to have respective database, each data inventory by oneself
Represent meaning as the data of fiscal revenues situation be it is different, so needing that data are further processed, it is assumed that A school
The data that database represents fiscal revenues situation are " finance ", it is assumed that B school database represents the data of fiscal revenues situation
It is " income ", it is assumed that the data that C school database represents fiscal revenues situation are " proceeds ", and data are unified for " wealth
Political affairs income "；
3) the full dose data in database are taken out；
4) the full dose data are broken up as lattice data, lattice data are one of formats of data minimum unit；
5) the incremental data fluidization treatment；
6) by artificial discovery combing, wherein rule and system find that regular two methods find tri- schools of A, B, C respectively automatically
Database in represent meaning as fiscal revenues situation data be respectively " finance ", " income ", " proceeds "；
7) artificial discovery is combed tri- schools of A, B, C after wherein rule and system find rule automatically and represents meaning as finance receipts
Enter the data deposit database of situation；
8) tri- schools of A, B, C represent meaning as the data of fiscal revenues situation Database Publishing to related education department；
9) related education department carries out selection subscription information；
10) initialization process, the initialization process be the fiscal revenues situation that need to subscribe to related education department data from
It being screened in the database of storage, is then reduced into row data, then fluidization treatment is carried out to row data, data flow is subscribed in deposit,
The full dose data in the subscription data flow are obtained, data are handled in the subscription data flow, are converted to related religion
Data needed for educating department " fiscal revenues "；
11) increment process, the increment process are to collect the incremental data fluidized, and data flow is subscribed in deposit, are sequentially obtained
Data in the subscription data flow are handled data in the subscription data flow, and being converted to related education department needs
The data " fiscal revenues " wanted transmit the data for not needing to calculate same as before；
12) treated, data are saved in related education department's specified data library；
13): after subscribing to successfully, school's situation feedback and counter-measure suggestion being pointedly supplied to related education department, institute
Stating counter-measure suggestion includes comparing to the height of tri- school finance's income situations of A, B, C, is ranked up from top to bottom,
To coming end and when being more than 300,000 with the fiscal revenues situation gap to rank the first, to the school for coming end
It is recommended that economic aid, A school 1,000,000, B school 900,000, C school 600,000, so suggesting economic aid to C school.
Claims (7)
1. a kind of integration method of magnanimity isomeric data, which comprises the steps of:
1) it carries out the acquisition of full dose data and is put in storage, the full dose data include each school indices data over the years；
2) it carries out incremental data acquisition and is put in storage, the incremental data includes each school's same day indices data；
3) the full dose data in database are taken out；
4) the full dose data are broken up as lattice data；
5) the incremental data fluidization treatment；
6) by artificial discovery combing, wherein rule and system find that regular two methods find the respective number of each school automatically
According to the meaning of representative；
7) artificial discovery combing, wherein rule and system find that the indices data after rule are stored in database automatically；
8) the Database Publishing for having indices data to related education department；
9) related education department carries out selection subscription information；
10) initialization process, the initialization process are the data of the related data that need to subscribe to related education department from storage
It is screened in library, is then reduced into row data, then fluidization treatment is carried out to row data, deposit is subscribed to data flow, ordered described in acquirement
The full dose data in data flow are read, data are handled in the subscription data flow, are converted to needed for related education department
Data；
11) increment process, the increment process are to collect the incremental data fluidized, and data flow is subscribed in deposit, are sequentially obtained
Data in the subscription data flow are handled data in the subscription data flow, and being converted to related education department needs
The data wanted transmit the data for not needing to calculate same as before；
12) treated, data are saved in related education department's specified data library；
13) after subscribing to successfully, school's situation feedback and counter-measure suggestion are pointedly supplied to related education department.
2. a kind of integration method of magnanimity isomeric data according to claim 1, which is characterized in that the indices number
According to include school's title, school place, number of student, teacher's number, student Ge Ke total marks of the examination, student attendance, teacher turn out for work,
Graduation situation, school finance take in situation, department's installations and facilities situation, school's land use situation, evaluation of teacher's situation.
3. a kind of integration method of magnanimity isomeric data according to claim 1, which is characterized in that school's situation is anti-
Feedback include school's title, school place, number of student, teacher's number, student Ge Ke total marks of the examination, student attendance, teacher turn out for work,
Graduation situation, school finance take in situation, department's installations and facilities situation, school's land use situation, evaluation of teacher's situation.
4. a kind of integration method of magnanimity isomeric data according to claim 1, which is characterized in that the counter-measure is built
View include to which school which aspect inspected,
Economic aid is carried out to which school, which aspect of which school is instructed.
5. a kind of integration method of magnanimity isomeric data according to claim 4, which is characterized in that described to which school
Which aspect inspect include student Ge Ke total marks of the examination in the indices data according to each school
Compare, be ranked up from top to bottom, to come end and with rank the first the student Ge Ke examination get poor results
Away from it is larger when, to come end school suggest inspect.
6. a kind of integration method of magnanimity isomeric data according to claim 4, which is characterized in that described to which school
Carrying out economic aid includes that the school finance income situation in the indices data according to each school carries out pair
Than, be ranked up from top to bottom, to come end and with rank the first the school finance income situation gap it is larger
When, economic aid is suggested to the school for coming end.
7. a kind of integration method of magnanimity isomeric data according to claim 4, which is characterized in that which school which
It includes that the student Ge Ke total marks of the examination in the indices data according to each school carry out that a little aspects, which carry out guidance,
Comparison, be ranked up from top to bottom, to come end and with rank the first the student Ge Ke examination attainment discrepancy compared with
When big, the school for coming end is proposed with about the guidance to improve results.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113098978A (en) * | 2021-04-21 | 2021-07-09 | 上海微盟企业发展有限公司 | Data transmission method, device and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101836428A (en) * | 2007-08-31 | 2010-09-15 | 帕姆公司 | Accessing subscribed content with a mobile computing device |
| CN102654762A (en) * | 2010-10-14 | 2012-09-05 | 因文西斯系统公司 | Achieving lossless data streaming in a scan based industrial process control system |
| CN103617585A (en) * | 2013-11-06 | 2014-03-05 | 梧州学院 | Data sharing platform-based data processing method |
-
2018
- 2018-12-29 CN CN201811636483.9A patent/CN109739917A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101836428A (en) * | 2007-08-31 | 2010-09-15 | 帕姆公司 | Accessing subscribed content with a mobile computing device |
| CN102654762A (en) * | 2010-10-14 | 2012-09-05 | 因文西斯系统公司 | Achieving lossless data streaming in a scan based industrial process control system |
| CN103617585A (en) * | 2013-11-06 | 2014-03-05 | 梧州学院 | Data sharing platform-based data processing method |
Non-Patent Citations (1)
| Title |
|---|
| 公安部治安管理局等: "国外中小学校园安全保卫", 群众出版社, pages: 142 - 147 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113098978A (en) * | 2021-04-21 | 2021-07-09 | 上海微盟企业发展有限公司 | Data transmission method, device and medium |
| CN113098978B (en) * | 2021-04-21 | 2023-04-07 | 上海微盟企业发展有限公司 | Data transmission method, device and medium |
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