CN109087224A - A method of the individual demand based on examinee carries out college entrance will recommendation and prediction - Google Patents
A method of the individual demand based on examinee carries out college entrance will recommendation and prediction Download PDFInfo
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Abstract
The method of the present invention relates to a kind of individual demand progress college entrance will recommendation and prediction based on examinee, comprising the following steps: step 1) carries out professional coding to major name；Step 2 stores major name and professional code into database；Step 3), the database that the corresponding relationship of physical examination restricted code and physical examination description is stored in server end；Step 4), according to physical examination coding with it is corresponding limitation profession code storage to server end database；Step 6) increases the API for obtaining user's physical examination data；Personalized factor is arranged in step 7)；The personalized factor data of the personalized factor of the user's specification of step 7) and customization is passed to front end by API by step 8)；The method of the present invention is advanced scientific, easy to use, relative to existing artificial investigation mode, is conducive to improve applications for university efficiency, accuracy.
Description
Technical field
The method of the present invention relates to a kind of individual demand progress college entrance will recommendation and prediction based on examinee, belongs to meter
Calculation machine programmatics field.
Background technique
Examinee needs to make a report on corresponding aspiration after the completion of college entrance examination at present, and college entrance will is often more troublesome in making a report on,
Need the school from magnanimity, selection fits well on oneself school and profession according to their own situation in profession, while selecting
When also need the physical examination result according to examinee, personal interest factor to go to select, therefore, people place hope on using it is a kind of more
It goes to be selected for simple, quick, effective mode, the school being suitble in order to better choice and profession.
Summary of the invention
The purpose of the present invention is to above-mentioned existing drawback, provide it is a kind of based on the individual demand of examinee into
Row college entrance will recommends and the method for prediction, is conducive to improve applications for university efficiency, accuracy.
The object of the present invention is achieved like this, and a kind of individual demand based on examinee, which carries out college entrance will, to be recommended and pre-
Survey method, characterized in that the following steps are included:
The major name of college and university admission in the works is carried out professional coding according to the major name of national publication by step 1)；
Furthermore by analyzing the major name of college and university admission in the works, College Enrollment specialized information in the works is extracted, specially
Industry information includes on Oral Requiremen, household register requirement, orientation requires, gender requires, whether intercollegiate cooperation is professional, whether authorizes overseas
Position, whether high charge, funding information, whether Normal Specialty, foreign language language requirement, whether continuation-bachelor-and-master profession, whether this
Continuous academic program that involves postgraduate and doctoral study profession, additional examination subject requirement；
Specialized information, professional tuition fee, the length of schooling of professional code and College Enrollment in the works that College Enrollment is extracted in the works are believed
Breath, admission number are stored together as format data into database；
Step 2 stores the major name of national publication and professional code into database, retrieves these professional generations by API
Code and major name, the expert data for meeting querying condition is returned according to the professional levels structure of tree or list is returned
It returns, professional levels structure is divided into first level discipline, second level class and three-level profession；Search condition is divided into according to bachelor degree or professorship
Profession, the professional code of professional code or part, major name or part major name, return the result, and expert data includes special
Industry title, subject, learns class, specialized information at professional code；
Step 3), Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication, by physical examination limited code and body
The corresponding relationship of inspection description is stored in the database of server end, and increases an API and return to all physical examination code and correspondence
Physical examination description；
Step 4), the disease for Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication are accordingly limited and to be entered oneself for the examination
Major name, the major name that limitation is entered oneself for the examination carries out professional coding according to the major name of national publication, according to physical examination code
With the database of the professional code storage of corresponding limitation to server end；
Step 6) increases the API for obtaining user's physical examination data, by the API by the physical examination data transmission of user to front end, user
Carry out accordingly select after by the physical examination data transmission of user to server end and store into database；
Personalized factor is arranged in step 7), and personalized factor includes the personalized factor of standard and the personalized factor of customization；
The personalized factor of standard includes:
(1) household economy；
(2) professional basic demand: have that the profession, colleges and universities' special plan profession, colleges and universities' orientation plan of household register requirement be professional, this large company
Profession, the profession of Sino-foreign Cooperative Education for reading profession, this continuous academic program that involves postgraduate and doctoral study profession, running a school jointly；
(3) additional examination subject: it is divided into additional examination spoken language, high performance athlete, the fine arts；
(4) gender requirement；
The personalized factor of standard in step 1) from enrollment plan extract specialized information item it is corresponding；
The personalized factor of customization includes languages requirement, Scientific basis, study hope, Obtained employment orientation, hobby, ability spy
Long, personality feature；User can according to need the personalized factor for increasing new customization, and the personalized factor of each customization is by one
A or many levels customization individual character subitem compositions, and the personalized factor of these customizations and its level are stored to database
In, each user includes that the personalized factor of all standards and the personality data of customization select and be stored in database
In；It is pre- using corresponding default system if user does not have the personalized factor data of standard or the personality data of customization
The value of definition；
The personality factors subitem that system defines each customization is related to the profession of step 2 one or more national publication, personalized
Factor subitem and associated profession are also stored into database；
Step 8), before the personalized factor data of the personalized factor of the user's specification of step 7) and customization is passed to by API
End, and user is transmitted to server end by API to the preferential or refusal selection of these personalized factors and is stored to data
Library；
Step 9), that user is calculated according to the physical examination data of step 6) user and the physical examination data and professional relationship of step 4) is all
Refusal second level class and three-level profession, for the class being rejected, then all three-level professions of the class are also all refused；
Step 10), according to the selection of each subitem of the customized personalized factor of step 8) and the personalization of step 6)
Factor and professional relationship calculate all preferential and refusal second level class and three-level profession；
Step 11), the corresponding profession progress according to the selection of step 8) user's specification personalization personality factors, to corresponding school
During probability calculation, matched using the specialized information of step 2 extracted, determine the profession whether be it is preferential or
Refusal profession；
Step 12), in the admission probability for the corresponding profession of corresponding school for calculating user, whether first look at the profession in step
9) class or profession of refusal, if wherein, marking the profession is that user's physical examination limits item limitation profession and refuses this specially
Otherwise industry is checked whether in the preferential of step 10) or refusal profession, while calculating the school according to the calculation of step 11)
Whether corresponding profession is preferential or refusal is professional and does corresponding label, if refused in calculating process, the profession
For refusal profession, while calculating the probability of the profession, and by API will whether user's physical examination limit item limitation it is professional, whether user
Preferential or refusal profession is transmitted to front end；
Step 13), aspiration recommend in front end invocation step 2) in API obtain customizing according to user for data and step 10)
Personalized subitem calculate preferential refusal profession merge and return to front end；User according to their own needs to learn class or
Specific major name preferentially selects or refuses, if a class is refused, affiliated three-level profession is also all refused；
Step 14) is transmitted to server end by preferential or refusal the expert data that API selects user in step 13)；
Step 15), server-side processes volunteer recommend when, if it find that some school it is a certain profession coding in step 9)
Refusal profession then indicates that the profession is that user's physical examination limits item profession, else if finding that a certain profession coding of some school exists
Then the profession is labeled user's actively refusal in second level class or the three-level profession of the refusal of step 14), step 11)；Otherwise such as
The a certain profession of some school of fruit belongs to the second level class that user preferentially selects or three-level profession, then is preferential by the major setting
Profession；
Aspiration recommends to include school information and specialized information in the result returned；School information include school's code, school's title,
Enroll probability, the preferential score of school, the preferential score of highest profession, the corresponding professional code of highest preferential score and profession
City where title, enrollment plan number, school, the province where school, the category pipe of school, network address, address information；Profession letter
Breath includes profession code, whether major name, professional prior information, profession admission probability, whether user's physical examination limitation is professional, use
Householder is dynamic preferential or refusal is professional；Aspiration recommends the specialized information that above-mentioned school and its enrollment are returned by API；
The data that step 15) returns are shown that the content of display includes school in front end by step 16 according to list of schools
Information, admission plan, school information include school's code, school's title, admission probability, the preferential score of school, school's attribute,
Address, and the colleges and universities specialized information API in the aspiration recommendation for having specialized information entrance to pass through step 17) checks the special of specified universities and colleges
Industry details and school's admission information entrance check that specified school enrolls the detailed analysis of data over the years；
Colleges and universities specialized information API in step 17, aspiration recommendation, calculates the admission probability of all enrollments profession of a certain colleges and universities
And admission probability is returned to, while returning to professional code, major name, length of schooling, tuition fee, quota；User is clearly refused
Profession is also marked so that front end carries out corresponding information alert；The calculation method of preferential refusal profession is referring to step 15).
The front end is browser, Android App, iOS App or Desktop App.
The step 12), 13) the middle preferential or refusal profession selected carries out corresponding when carrying out profession probabilistic forecasting
Preferential or refusal label, carries out corresponding operation when to show result.
Calculate the admission probability of all enrollments profession of a certain colleges and universities, comprising the following steps:
Step 1), examination admission score distribution over the years, admission plan and the admission meter in this year saved according to server end
It draws and examination mark is distributed, the examination mark in this year are converted to the admission score conversion formula of the first three years respective batch；
Step 2, the effective admission lowest fractional for obtaining school or professional past three year, if the school or profession are not pass by
3 years whole admission scores, then admission probability can not be calculated by returning, if corresponding school or profession have the whole of past three year
Admission score then enters in next step；
Step 3), according to the acceptance cut-off point of respective batch past three year, calculate the admission of the past three year school or profession
It is poor to divide；Calculation formula is that practical admission score-admission controls score line, while calculating the admission point of examinee's current year respective batch
Difference, calculation formula are as follows: examinee's score-admission controls score line；
Step 4), the admission point that the admission point difference of past three year in step 3) is converted to this year by step 1) are poor；
Step 5) calculates biennial bearing mode according to the admission point difference of past three year corresponding school or profession, and calculating mode is to count
Maximum, the minimum value of admission point difference after calculating the conversion of past three year, if a certain year converts point difference as past three year conversion
Maximum admission point afterwards is poor, then the year is good year, if a certain year converts point difference as the minimum admission after past three year conversion
It is poor to divide, then the year is off year；Share 6 kinds of combinations, in large, medium and small, size, middle size, it is medium and small it is large and small in it is large and small big-and-middle；
If step 6), the admission point difference of current year are bigger or smaller than minimum value than the maximum value in step 5), according to the admission of current year
Point difference number and biennial bearing assign a specific admission probability predetermined；Between the probability of maximum value and median
Calculation method: being divided into 5 grades for maximum value and median, and every grade assigns corresponding probability, is searched according to point difference of current year corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability；It is worth the probability calculation with minimum value between
Method: being divided into 5 grades for median and minimum value, and every grade assigns corresponding probability, is searched according to the admission of current year point difference corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability.
The method of the present invention is advanced scientific, easy to use, will be in the enrollment plan of college entrance examination relative to existing artificial investigation mode
Professional verbal description be converted into formalization data (professional code, if having on Oral Requiremen, household register requirement, orientation require, learn
Expense, gender require, whether intercollegiate cooperation profession, whether authorize overseas degree, whether high charge, funding information, whether teacher
Model profession, foreign language language requirement etc.), while sufficiently with reference to the physical examination result of examinee, personal interest factor.
Using computer, mobile phone or other mobile devices and situation when college entrance will makes a report on analysis according to examinee is carried out,
Profession (school) automatic screening that examinee cannot make a report on is come out, when aspiration is made a report on or volunteers prediction, no longer display cannot be made a report on
Profession or give specific prompt when showing relevant speciality the profession be not suitable for corresponding examinee due to its condition.Using model
It encloses and includes: the fields such as internet, mobile app, desktop application.
Specific embodiment
A kind of individual demand progress college entrance will recommendation and prediction technique based on examinee, characterized in that including following
Step:
The major name of college and university admission in the works is carried out professional coding according to the major name of national publication by step 1)；
Furthermore by analyzing the major name of college and university admission in the works, College Enrollment specialized information in the works is extracted, specially
Industry information includes on Oral Requiremen, household register requirement, orientation requires, gender requires, whether intercollegiate cooperation is professional, whether authorizes overseas
Position, whether high charge, funding information, whether Normal Specialty, foreign language language requirement, whether continuation-bachelor-and-master profession, whether this
Continuous academic program that involves postgraduate and doctoral study profession, additional examination subject requirement；
Specialized information, professional tuition fee, the length of schooling of professional code and College Enrollment in the works that College Enrollment is extracted in the works are believed
Breath, admission number are stored together as format data into database；
Step 2 stores the major name of national publication and professional code into database, retrieves these professional generations by API
Code and major name, the expert data for meeting querying condition is returned according to the professional levels structure of tree or list is returned
It returns, professional levels structure is divided into first level discipline, second level class and three-level profession；Search condition is divided into according to bachelor degree or professorship
Profession, the professional code of professional code or part, major name or part major name, return the result, and expert data includes special
Industry title, subject, learns class, specialized information at professional code；
Step 3), Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication, by physical examination limited code and body
The corresponding relationship of inspection description is stored in the database of server end, and increases an API and return to all physical examination code and correspondence
Physical examination description；
Step 4), the disease for Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication are accordingly limited and to be entered oneself for the examination
Major name, the major name that limitation is entered oneself for the examination carries out professional coding according to the major name of national publication, according to physical examination code
With the database of the professional code storage of corresponding limitation to server end；
Step 6) increases the API for obtaining user's physical examination data, by the API by the physical examination data transmission of user to front end, user
Carry out accordingly select after by the physical examination data transmission of user to server end and store into database；
Personalized factor is arranged in step 7), and personalized factor includes the personalized factor of standard and the personalized factor of customization；
The personalized factor of standard includes:
(1) household economy；
(2) professional basic demand: have that the profession, colleges and universities' special plan profession, colleges and universities' orientation plan of household register requirement be professional, this large company
Profession, the profession of Sino-foreign Cooperative Education for reading profession, this continuous academic program that involves postgraduate and doctoral study profession, running a school jointly；
(3) additional examination subject: it is divided into additional examination spoken language, high performance athlete, the fine arts；
(4) gender requirement；
The personalized factor of standard in step 1) from enrollment plan extract specialized information item it is corresponding；
The personalized factor of customization includes languages requirement, Scientific basis, study hope, Obtained employment orientation, hobby, ability spy
Long, personality feature；User can according to need the personalized factor for increasing new customization, and the personalized factor of each customization is by one
A or many levels customization individual character subitem compositions, and the personalized factor of these customizations and its level are stored to database
In, each user includes that the personalized factor of all standards and the personality data of customization select and be stored in database
In；It is pre- using corresponding default system if user does not have the personalized factor data of standard or the personality data of customization
The value of definition；
The personality factors subitem that system defines each customization is related to the profession of step 2 one or more national publication, personalized
Factor subitem and associated profession are also stored into database；
Step 8), before the personalized factor data of the personalized factor of the user's specification of step 7) and customization is passed to by API
End, and user is transmitted to server end by API to the preferential or refusal selection of these personalized factors and is stored to data
Library；
Step 9), that user is calculated according to the physical examination data of step 6) user and the physical examination data and professional relationship of step 4) is all
Refusal second level class and three-level profession, for the class being rejected, then all three-level professions of the class are also all refused；
Step 10), according to the selection of each subitem of the customized personalized factor of step 8) and the personalization of step 6)
Factor and professional relationship calculate all preferential and refusal second level class and three-level profession；
Step 11), the corresponding profession progress according to the selection of step 8) user's specification personalization personality factors, to corresponding school
During probability calculation, matched using the specialized information of step 2 extracted, determine the profession whether be it is preferential or
Refusal profession；
Step 12), in the admission probability for the corresponding profession of corresponding school for calculating user, whether first look at the profession in step
9) class or profession of refusal, if wherein, marking the profession is that user's physical examination limits item limitation profession and refuses this specially
Otherwise industry is checked whether in the preferential of step 10) or refusal profession, while calculating the school according to the calculation of step 11)
Whether corresponding profession is preferential or refusal is professional and does corresponding label, if refused in calculating process, the profession
For refusal profession, while calculating the probability of the profession, and by API will whether user's physical examination limit item limitation it is professional, whether user
Preferential or refusal profession is transmitted to front end；
Step 13), aspiration recommend in front end invocation step 2) in API obtain customizing according to user for data and step 10)
Personalized subitem calculate preferential refusal profession merge and return to front end；User according to their own needs to learn class or
Specific major name preferentially selects or refuses, if a class is refused, affiliated three-level profession is also all refused；
Step 14) is transmitted to server end by preferential or refusal the expert data that API selects user in step 13)；
Step 15), server-side processes volunteer recommend when, if it find that some school it is a certain profession coding in step 9)
Refusal profession then indicates that the profession is that user's physical examination limits item profession, else if finding that a certain profession coding of some school exists
Then the profession is labeled user's actively refusal in second level class or the three-level profession of the refusal of step 14), step 11)；Otherwise such as
The a certain profession of some school of fruit belongs to the second level class that user preferentially selects or three-level profession, then is preferential by the major setting
Profession；
Aspiration recommends to include school information and specialized information in the result returned；School information include school's code, school's title,
Enroll probability, the preferential score of school, the preferential score of highest profession, the corresponding professional code of highest preferential score and profession
City where title, enrollment plan number, school, the province where school, the category pipe of school, network address, address information；Profession letter
Breath includes profession code, whether major name, professional prior information, profession admission probability, whether user's physical examination limitation is professional, use
Householder is dynamic preferential or refusal is professional；Aspiration recommends the specialized information that above-mentioned school and its enrollment are returned by API；
The data that step 15) returns are shown that the content of display includes school in front end by step 16 according to list of schools
Information, admission plan, school information include school's code, school's title, admission probability, the preferential score of school, school's attribute,
Address, and the colleges and universities specialized information API in the aspiration recommendation for having specialized information entrance to pass through step 17) checks the special of specified universities and colleges
Industry details and school's admission information entrance check that specified school enrolls the detailed analysis of data over the years；
Colleges and universities specialized information API in step 17, aspiration recommendation, calculates the admission probability of all enrollments profession of a certain colleges and universities
And admission probability is returned to, while returning to professional code, major name, length of schooling, tuition fee, quota；User is clearly refused
Profession is also marked so that front end carries out corresponding information alert；The calculation method of preferential refusal profession is referring to step 15)；
Calculate the admission probability of all enrollments profession of a certain colleges and universities, comprising the following steps:
Step 1), examination admission score distribution over the years, admission plan and the admission meter in this year saved according to server end
It draws and examination mark is distributed, the examination mark in this year are converted to the admission score conversion formula of the first three years respective batch；
Step 2, the effective admission lowest fractional for obtaining school or professional past three year, if the school or profession are not pass by
3 years whole admission scores, then admission probability can not be calculated by returning, if corresponding school or profession have the whole of past three year
Admission score then enters in next step；
Step 3), according to the acceptance cut-off point of respective batch past three year, calculate the admission of the past three year school or profession
It is poor to divide；Calculation formula is that (practical admission score subtracts admission control score to practical admission score-admission control score line
Line), while calculating poor, the calculation formula of admission point of examinee's current year respective batch are as follows: examinee's score-admission control score line (is examined
Number estranged subtracts admission control score line)；
Step 4), the admission point that the admission point difference of past three year in step 3) is converted to this year by step 1) are poor；
Step 5) calculates biennial bearing mode according to the admission point difference of past three year corresponding school or profession, and calculating mode is to count
Maximum, the minimum value of admission point difference after calculating the conversion of past three year, if a certain year converts point difference as past three year conversion
Maximum admission point afterwards is poor, then the year is good year, if a certain year converts point difference as the minimum admission after past three year conversion
It is poor to divide, then the year is off year；Share 6 kinds of combinations, in large, medium and small, size, middle size, it is medium and small it is large and small in it is large and small big-and-middle；
If step 6), the admission point difference of current year are bigger or smaller than minimum value than the maximum value in step 5), according to the admission of current year
Point difference number and biennial bearing assign a specific admission probability predetermined；Between the probability of maximum value and median
Calculation method: being divided into 5 grades for maximum value and median, and every grade assigns corresponding probability, is searched according to point difference of current year corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability；It is worth the probability calculation with minimum value between
Method: being divided into 5 grades for median and minimum value, and every grade assigns corresponding probability, is searched according to the admission of current year point difference corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability.
Further, the front end is browser, Android App, iOS App or Desktop App；The step
12), 13) the middle preferential or refusal profession that selects when carry out profession probabilistic forecasting carry out corresponding preferential or refusal mark,
Corresponding operation is carried out when to show result.
Claims (4)
1. a kind of individual demand based on examinee carries out college entrance will recommendation and prediction technique, characterized in that including following step
It is rapid:
The major name of college and university admission in the works is carried out professional coding according to the major name of national publication by step 1)；
Furthermore by analyzing the major name of college and university admission in the works, College Enrollment specialized information in the works is extracted, specially
Industry information includes on Oral Requiremen, household register requirement, orientation requires, gender requires, whether intercollegiate cooperation is professional, whether authorizes overseas
Position, whether high charge, funding information, whether Normal Specialty, foreign language language requirement, whether continuation-bachelor-and-master profession, whether this
Continuous academic program that involves postgraduate and doctoral study profession, additional examination subject requirement；
Specialized information, professional tuition fee, the length of schooling of professional code and College Enrollment in the works that College Enrollment is extracted in the works are believed
Breath, admission number are stored together as format data into database；
Step 2 stores the major name of national publication and professional code into database, retrieves these professional generations by API
Code and major name, the expert data for meeting querying condition is returned according to the professional levels structure of tree or list is returned
It returns, professional levels structure is divided into first level discipline, second level class and three-level profession；Search condition is divided into according to bachelor degree or professorship
Profession, the professional code of professional code or part, major name or part major name, return the result, and expert data includes special
Industry title, subject, learns class, specialized information at professional code；
Step 3), Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication, by physical examination limited code and body
The corresponding relationship of inspection description is stored in the database of server end, and increases an API and return to all physical examination code and correspondence
Physical examination description；
Step 4), the disease for Physical Examination instruction of being recruited student according to the gerneral institutes of higher education of national publication are accordingly limited and to be entered oneself for the examination
Major name, the major name that limitation is entered oneself for the examination carries out professional coding according to the major name of national publication, according to physical examination code
With the database of the professional code storage of corresponding limitation to server end；
Step 6) increases the API for obtaining user's physical examination data, by the API by the physical examination data transmission of user to front end, user
Carry out accordingly select after by the physical examination data transmission of user to server end and store into database；
Personalized factor is arranged in step 7), and personalized factor includes the personalized factor of standard and the personalized factor of customization；
The personalized factor of standard includes:
(1) household economy；
(2) professional basic demand: have that the profession, colleges and universities' special plan profession, colleges and universities' orientation plan of household register requirement be professional, this large company
Profession, the profession of Sino-foreign Cooperative Education for reading profession, this continuous academic program that involves postgraduate and doctoral study profession, running a school jointly；
(3) additional examination subject: it is divided into additional examination spoken language, high performance athlete, the fine arts；
(4) gender requirement；
The personalized factor of standard in step 1) from enrollment plan extract specialized information item it is corresponding；
The personalized factor of customization includes languages requirement, Scientific basis, study hope, Obtained employment orientation, hobby, ability spy
Long, personality feature；User can according to need the personalized factor for increasing new customization, and the personalized factor of each customization is by one
A or many levels customization individual character subitem compositions, and the personalized factor of these customizations and its level are stored to database
In, each user includes that the personalized factor of all standards and the personality data of customization select and be stored in database
In；It is pre- using corresponding default system if user does not have the personalized factor data of standard or the personality data of customization
The value of definition；
The personality factors subitem that system defines each customization is related to the profession of step 2 one or more national publication, personalized
Factor subitem and associated profession are also stored into database；
Step 8), before the personalized factor data of the personalized factor of the user's specification of step 7) and customization is passed to by API
End, and user is transmitted to server end by API to the preferential or refusal selection of these personalized factors and is stored to data
Library；
Step 9), that user is calculated according to the physical examination data of step 6) user and the physical examination data and professional relationship of step 4) is all
Refusal second level class and three-level profession, for the class being rejected, then all three-level professions of the class are also all refused；
Step 10), according to the selection of each subitem of the customized personalized factor of step 8) and the personalization of step 6)
Factor and professional relationship calculate all preferential and refusal second level class and three-level profession；
Step 11), the corresponding profession progress according to the selection of step 8) user's specification personalization personality factors, to corresponding school
During probability calculation, matched using the specialized information of step 2 extracted, determine the profession whether be it is preferential or
Refusal profession；
Step 12), in the admission probability for the corresponding profession of corresponding school for calculating user, whether first look at the profession in step
9) class or profession of refusal, if wherein, marking the profession is that user's physical examination limits item limitation profession and refuses this specially
Otherwise industry is checked whether in the preferential of step 10) or refusal profession, while calculating the school according to the calculation of step 11)
Whether corresponding profession is preferential or refusal is professional and does corresponding label, if refused in calculating process, the profession
For refusal profession, while calculating the probability of the profession, and by API will whether user's physical examination limit item limitation it is professional, whether user
Preferential or refusal profession is transmitted to front end；
Step 13), aspiration recommend in front end invocation step 2) in API obtain customizing according to user for data and step 10)
Personalized subitem calculate preferential refusal profession merge and return to front end；User according to their own needs to learn class or
Specific major name preferentially selects or refuses, if a class is refused, affiliated three-level profession is also all refused；
Step 14) is transmitted to server end by preferential or refusal the expert data that API selects user in step 13)；
Step 15), server-side processes volunteer recommend when, if it find that some school it is a certain profession coding in step 9)
Refusal profession then indicates that the profession is that user's physical examination limits item profession, else if finding that a certain profession coding of some school exists
Then the profession is labeled user's actively refusal in second level class or the three-level profession of the refusal of step 14), step 11)；Otherwise such as
The a certain profession of some school of fruit belongs to the second level class that user preferentially selects or three-level profession, then is preferential by the major setting
Profession；
Aspiration recommends to include school information and specialized information in the result returned；School information include school's code, school's title,
Enroll probability, the preferential score of school, the preferential score of highest profession, the corresponding professional code of highest preferential score and profession
City where title, enrollment plan number, school, the province where school, the category pipe of school, network address, address information；Profession letter
Breath includes profession code, whether major name, professional prior information, profession admission probability, whether user's physical examination limitation is professional, use
Householder is dynamic preferential or refusal is professional；Aspiration recommends the specialized information that above-mentioned school and its enrollment are returned by API；
The data that step 15) returns are shown that the content of display includes school in front end by step 16 according to list of schools
Information, admission plan, school information include school's code, school's title, admission probability, the preferential score of school, school's attribute,
Address, and the colleges and universities specialized information API in the aspiration recommendation for having specialized information entrance to pass through step 17) checks the special of specified universities and colleges
Industry details and school's admission information entrance check that specified school enrolls the detailed analysis of data over the years；
Colleges and universities specialized information API in step 17, aspiration recommendation, calculates the admission probability of all enrollments profession of a certain colleges and universities
And admission probability is returned to, while returning to professional code, major name, length of schooling, tuition fee, quota；User is clearly refused
Profession is also marked so that front end carries out corresponding information alert；The calculation method of preferential refusal profession is referring to step 15).
2. a kind of individual demand based on examinee according to claim 1 carries out college entrance will recommendation and prediction technique,
It is characterized in that the front end is browser, Android App, iOS App or Desktop App.
3. a kind of individual demand based on examinee according to claim 1 carries out college entrance will recommendation and prediction technique,
It is characterized in that the preferential or refusal profession of the step 12), 13) middle selection, when carrying out profession probabilistic forecasting, progress is corresponding
It is preferential or refusal label, corresponding operation is carried out when to show result.
4. a kind of individual demand based on examinee according to claim 1 carries out college entrance will recommendation and prediction technique,
It is characterized in that calculating the admission probability of all enrollments profession of a certain colleges and universities, comprising the following steps:
Step 1), examination admission score distribution over the years, admission plan and the admission meter in this year saved according to server end
It draws and examination mark is distributed, the examination mark in this year are converted to the admission score conversion formula of the first three years respective batch；
Step 2, the effective admission lowest fractional for obtaining school or professional past three year, if the school or profession are not pass by
3 years whole admission scores, then admission probability can not be calculated by returning, if corresponding school or profession have the whole of past three year
Admission score then enters in next step；
Step 3), according to the acceptance cut-off point of respective batch past three year, calculate the admission of the past three year school or profession
It is poor to divide；Calculation formula is that practical admission score-admission controls score line, while calculating the admission point of examinee's current year respective batch
Difference, calculation formula are as follows: examinee's score-admission controls score line；
Step 4), the admission point that the admission point difference of past three year in step 3) is converted to this year by step 1) are poor；
Step 5) calculates biennial bearing mode according to the admission point difference of past three year corresponding school or profession, and calculating mode is to count
Maximum, the minimum value of admission point difference after calculating the conversion of past three year, if a certain year converts point difference as past three year conversion
Maximum admission point afterwards is poor, then the year is good year, if a certain year converts point difference as the minimum admission after past three year conversion
It is poor to divide, then the year is off year；Share 6 kinds of combinations, in large, medium and small, size, middle size, it is medium and small it is large and small in it is large and small big-and-middle；
If step 6), the admission point difference of current year are bigger or smaller than minimum value than the maximum value in step 5), according to the admission of current year
Point difference number and biennial bearing assign a specific admission probability predetermined；Between the probability of maximum value and median
Calculation method: being divided into 5 grades for maximum value and median, and every grade assigns corresponding probability, is searched according to point difference of current year corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability；It is worth the probability calculation with minimum value between
Method: being divided into 5 grades for median and minimum value, and every grade assigns corresponding probability, is searched according to the admission of current year point difference corresponding
The probability of class, this point of poor admission probability are as follows: elementary probability ~ class probability.
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| CN109919375A (en) * | 2019-03-01 | 2019-06-21 | 北京慧辰资道资讯股份有限公司 | A kind of method and device for predicting the crucial student of examination based on big data |
| CN110796576A (en) * | 2019-10-16 | 2020-02-14 | 湖北美和易思教育科技有限公司 | High vocational education enrollment consultation management platform |
| CN112069407A (en) * | 2020-09-07 | 2020-12-11 | 南京松数科技有限公司 | Examinee college entrance examination voluntary reporting recommendation system based on historical data |
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| CN110796576A (en) * | 2019-10-16 | 2020-02-14 | 湖北美和易思教育科技有限公司 | High vocational education enrollment consultation management platform |
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