Data processing method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides a data processing method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the following steps: collecting admission data of each batch and each professional group of each higher school over the years; according to the professional admission data of each batch and each professional group of each college over the years, calculating the lowest collocation score, the highest collocation score and the average collocation score of each batch and each professional group of each college over the years; the lowest collocation score refers to the lowest-order normalized score in the admission data over the years, the highest collocation score refers to the highest-order normalized score in the admission data over the years, and the average collocation score refers to the average-order normalized score in the admission data over the years; and generating a volunteer prediction result for the target examinee according to the score of the target examinee, the lowest parity score, the highest parity score and the average parity score. According to the method, the schools and the specialized groups matched with the scores of the examinees are accurately judged, the time and energy of information collection are saved, and the admission probability is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data processing method, device, electronic equipment and medium. Background Art

[0002] With the reform of the new college entrance examination system, the college entrance examination volunteer application has changed from the traditional choice of colleges and universities to the choice of professional groups, and the complexity and difficulty of the college entrance examination volunteer application has also increased. A college and university professional group is a college that can set up one or more college and university professional groups, and each college and university professional group can contain different numbers of majors. The subject selection requirements of each major in the same college and university professional group must be the same. Candidates can directly choose to volunteer for a certain professional group of a certain school according to their own wishes, and professional adjustments are limited to adjustments within the same professional group. When filling in the application, candidates need to choose a specific professional group to fill in, rather than directly choosing a school. The traditional volunteer application method mainly relies on the candidates and parents' understanding of colleges and majors and the analysis of admission data over the years. However, candidates and parents need to spend a lot of time and energy to collect and organize information such as the enrollment plans of various colleges and universities and the admission scores over the years. The process is cumbersome and it is easy to miss important information. Even if enough information is obtained, how to scientifically analyze these data is also a difficult problem. There are large differences in admission data from different years and batches, and it is difficult for candidates and parents to draw accurate conclusions by directly comparing and analyzing them.

[0003] Due to the lack of sufficient and effective information, scientific analysis tools and methods, candidates and parents often rely on experience and intuition when filling out their applications, and the accuracy and rationality of their decisions are difficult to guarantee. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present invention provide a data method, device, electronic device and medium.

[0005] In a first aspect, an embodiment of the present invention provides a data processing method, including:

[0006] Collect the admission data of each batch and each professional group of each college over the years;

[0007] According to the professional admission data of each batch and each professional group of each college over the years, the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years are calculated; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years;

[0008] Based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years, a volunteer prediction result is generated for the target candidates.

[0009] Optionally, the step of calculating the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years according to the admission data of each batch and each professional group of each college over the years includes:

[0010] Convert the admission data of each batch and each professional group of each college over the years into first-order data;

[0011] Map the first ranking data to the minimum admission control score of each batch in the current year to obtain the second ranking data;

[0012] Based on the second ranking data, calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each university over the years.

[0013] Optionally, mapping the first ranking data to the minimum admission control score of each batch in the current year to obtain the second ranking data includes:

[0014] Based on the minimum control score for admission of each batch in the current year, the equivalent scores corresponding to each score in the first ranking data are calculated, and based on the equivalent scores corresponding to each score in the first ranking data, the second ranking data is generated.

[0015] Optionally, the step of calculating the equivalent scores corresponding to the scores of each rank in the first ranking data based on the minimum admission control score of each batch in the current year includes:

[0016] For each ranking in the first ranking data, calculate the difference between the score of the ranking and the corresponding historical minimum admission control score;

[0017] Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the ranking score.

[0018] Optionally, based on the scores of the subject groups selected by the target candidates and the lowest scores, highest scores and average scores of each batch and each major group of each university in previous years, a volunteer prediction result is generated for the target candidates, including:

[0019] The lowest equivalent scores, highest equivalent scores and average equivalent scores of each batch and each professional group of each college over the years, and the minimum and maximum values ​​of the lowest equivalent scores, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores of each batch and each professional group;

[0020] Based on the scores of the subject groups selected by the target candidates and the minimum and maximum values ​​of the lowest equivalent scores of each batch and each professional group, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores, a volunteer prediction result is generated for the target candidates.

[0021] Optionally, generating volunteer prediction results for the target candidate based on the scores of the subject group selected by the target candidate and the minimum and maximum values ​​of the lowest equivalent scores of each batch and each professional group, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores, includes:

[0022] In the case where the score of the subject group selected by the target candidate is greater than the minimum value of the lowest equivalent score of the to-be-selected major group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the to-be-selected major group, the to-be-selected major group is determined as the available major group;

[0023] In the case where the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the candidate professional group, and the score of the subject group selected by the target candidate is less than the maximum value of the average equivalent score of the candidate professional group, the candidate professional group is determined to be a safe professional group;

[0024] When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum highest parity score of the selected major group, the selected major group is determined as the backup major group.

[0025] Optionally, when the score of the subject group selected by the target candidate is greater than the maximum average score of the selected professional group, and the score of the subject group selected by the target candidate is less than the maximum highest score of the selected professional group, determining the selected professional group as the backup professional group includes:

[0026] When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the target value, the selected major group is determined as the backup major group, and the target value is the sum of the maximum value of the highest parity score of the selected major group and the designated reference value.

[0027] Optionally, the method further includes: calculating the line difference data of each batch according to the admission data of each batch of each college in previous years and the minimum control score of each batch in previous years;

[0028] The method of generating a volunteer prediction result for the target candidate based on the scores of the subject groups selected by the target candidate and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years includes: generating a volunteer prediction result for the target candidate based on the scores of the subject groups selected by the target candidate, the linear difference data and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years.

[0029] In a second aspect, an embodiment of the present invention provides a data processing device, including:

[0030] The data collection module is used to collect the admission data of each batch and each professional group of each college over the years;

[0031] A data processing module is used to calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each college and university over the years according to the professional admission data of each batch and each professional group of each college and university over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years;

[0032] The matching module is used to generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years.

[0033] Optionally, the data processing module is used to: convert the admission data of each batch and each professional group of each college over the years into first ranking data; map the first ranking data to the minimum control score for admission of each batch in the current year to obtain second ranking data; and calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each college over the years based on the second ranking data.

[0034] Optionally, the data processing module is used to: calculate the equivalent scores corresponding to each score in the first ranking data based on the minimum control score for admission of each batch in the current year, and generate the second ranking data based on the equivalent scores corresponding to each score in the first ranking data.

[0035] Optionally, the data processing module is used to: for each ranking in the first ranking data, calculate the difference between the score of the ranking and its corresponding historical minimum admission control score;

[0036] Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the ranking score.

[0037] Optionally, the data processing module is used to: determine the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group in each university over the years, and determine the minimum and maximum values ​​of the lowest parity scores, the minimum and maximum values ​​of the average parity scores, and the minimum and maximum values ​​of the highest parity scores of each batch and each professional group; and generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the minimum and maximum values ​​of the lowest parity scores, the minimum and maximum values ​​of the average parity scores, and the minimum and maximum values ​​of the highest parity scores of each batch and each professional group.

[0038] Optionally, the matching module is used to:

[0039] In the case where the score of the subject group selected by the target candidate is greater than the minimum value of the lowest equivalent score of the to-be-selected major group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the to-be-selected major group, the to-be-selected major group is determined as the available major group;

[0040] In the case where the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the candidate professional group, and the score of the subject group selected by the target candidate is less than the maximum value of the average equivalent score of the candidate professional group, the candidate professional group is determined to be a safe professional group;

[0041] When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum highest parity score of the selected major group, the selected major group is determined as the backup major group.

[0042] Optionally, the matching module is used to: when the score of the subject group selected by the target candidate is greater than the maximum average parity score of the to-be-selected major group, and the score of the subject group selected by the target candidate is less than a target value, determine that the to-be-selected major group is a backup major group, and the target value is the sum of the maximum value of the highest parity score of the to-be-selected major group and a specified reference value.

[0043] Optionally, the processing module is further used to: calculate the line difference data of each batch according to the admission data of each batch of each college in previous years and the minimum control score of each batch in previous years;

[0044] The matching module is also used to generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates, the line difference data, and the lowest parity scores, highest parity scores, and average parity scores of each batch and each professional group in each university over the years.

[0045] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory is used to store computer programs; and the processor is used to implement the data processing method of any embodiment of the present invention when executing the program stored in the memory.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method of any embodiment of the present invention.

[0047] The technical solution provided by the embodiment of the present invention brings at least the following beneficial effects:

[0048] The data processing method provided by the embodiment of the present invention collects the admission data of each batch and each professional group of each college over the years; according to the professional admission data of each batch and each professional group of each college over the years, calculates the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years; according to the scores of the subject groups selected by the target candidates and the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years, the volunteer prediction results are generated for the target candidates. The method can effectively solve the problems of information acquisition difficulties, complex data analysis and high decision uncertainty in the traditional volunteer filling method, accurately judge the schools and professional groups that match the candidates' scores, save time and energy in collecting information, help candidates and parents formulate more scientific and reasonable volunteer filling strategies, and improve the probability of admission. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0050] Figure 1 A schematic diagram showing a flow chart of a data processing method provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram showing a flow chart of a data processing method according to an embodiment of the present invention;

[0052] Figure 3 A schematic flow chart showing a data processing method according to another embodiment of the present invention;

[0053] Figure 4A schematic diagram showing the structure of a data processing device provided by an embodiment of the present invention is shown;

[0054] Figure 5 A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0056] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0057] Figure 1 FIG. 1 is a flow chart of a data processing method provided by an embodiment of the present invention. Figure 1 As shown, the data processing method includes:

[0058] Step S101: Collect the admission data of each batch and each professional group of each college over the years.

[0059] Among them, the enrollment batches can be divided into undergraduate batches and junior college batches, for example. Undergraduate batches are divided into undergraduate advance batches, undergraduate first batches and undergraduate second batches. Junior college batches can be divided into junior college advance batches, junior college first batches and junior college second batches. Admission data, for example, includes the number of admissions, admission scores, places of origin, and schools of origin. As an optional example, this embodiment can collect admission data of each batch and each professional group of each college in the past three years. In an optional embodiment, the enrollment plan can also be collected, for example, including: enrollment regulations, admission rules, professional settings, enrollment scale, enrollment categories, etc. The admission data of each batch and each professional group of each college over the years are screened according to the collected enrollment plan, for example, the admission data corresponding to the professional group not in the enrollment plan is screened out.

[0060] Step S102: Calculate the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college and university over the years according to the professional admission data of each batch and each professional group of each college and university over the years.

[0061] Among them, the lowest equivalent score refers to the normalized score of the lowest ranking in the admission data of previous years. The lowest ranking refers to the ranking of the candidate in the country corresponding to the lowest score admitted to a certain professional group in a certain batch of a certain school. The normalized score of the lowest ranking refers to the equivalent score corresponding to the score of the lowest ranking. Optionally, based on the minimum control score for admission of the current year, the scores of the lowest rankings in previous years can be equated to determine the equivalent scores corresponding to the scores of the lowest rankings in previous years. The equivalent scores are used to indicate how many points the scores of the lowest rankings in previous years are equivalent to in the current year. For example, the lowest ranking score of a certain professional group in a certain batch last year was score 1. The score 1 was equated to determine that its corresponding equivalent score was score 2, that is, score 1 of a certain professional group in a certain batch last year was equivalent to score 2 this year.

[0062] In an optional embodiment, the lowest ranking score in all previous years is equivalent, and the step of determining the equivalent score corresponding to the lowest ranking score in all previous years includes:

[0063] Calculate the difference between the lowest ranking score in previous years and the lowest admission control score in previous years;

[0064] Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the lowest ranking score in all previous years.

[0065] For example, the lowest score of a certain professional group in a certain batch last year was 600, and the minimum control score for admission to the batch last year was 590. The difference between the lowest score of a certain professional group in a certain batch last year and the minimum control score for admission to the batch last year was 10. This year's minimum control score for admission is 595, so the equivalent score corresponding to the lowest score last year is 605=595+10.

[0066] The highest equivalent score refers to the normalized score of the highest ranking in the admission data of previous years. The highest ranking refers to the national ranking of the candidate corresponding to the highest score admitted to a certain batch of a certain professional group in a certain school. The normalized score of the highest ranking refers to the equivalent score corresponding to the score of the highest ranking. Optionally, based on the current year's maximum admission control score, the scores of the highest rankings in previous years can be equated to determine the equivalent scores corresponding to the scores of the highest rankings in previous years. The equivalent scores are used to indicate how many points the scores of the highest rankings in previous years are equivalent to in the current year. The process of calculating the normalized score of the highest ranking can refer to the calculation of the normalized score of the lowest ranking. To avoid repetition, it will not be repeated here.

[0067] The average equivalent score refers to the normalized score of the average ranking in the data of previous years. The average ranking refers to the ranking of candidates in the country corresponding to the average score of a certain batch of a certain professional group in a certain school. The normalized score of the average ranking refers to the equivalent score corresponding to the score of the average ranking. Optionally, based on the current year's maximum admission control score, the scores of the average rankings of previous years can be equated to determine the equivalent scores corresponding to the scores of the average rankings of previous years. The equivalent scores are used to indicate how many points the scores of the average rankings of previous years are equivalent to in the current year. The process of calculating the normalized score of the average ranking can refer to the calculation of the normalized score of the lowest ranking. To avoid repetition, it will not be repeated here.

[0068] Step S103: Generate volunteer prediction results for the target candidate based on the scores of the subject groups selected by the target candidate and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years.

[0069] Optionally, you can select schools and major groups for target candidates based on the Risk-Moderate-Safe Strategy. The Risk-Moderate-Safe Strategy indicates that based on the comparison between the candidate's score and the admission data of previous years, three major group selection strategies are provided: "Sprint", "Safe", and "Safe". This helps candidates formulate a reasonable volunteer application plan. The Risk-Moderate-Safe Strategy is to select a target that can be sprinted, a safe target (a target with a higher admission efficiency), and a safe target from the optional schools and major groups.

[0070] As an optional embodiment, step S103 may generate volunteer prediction results for the target examinee according to the following process:

[0071] According to the lowest equivalent scores, highest equivalent scores and average equivalent scores of each batch and each professional group in each university over the years, determine the minimum and maximum values ​​of the lowest equivalent scores, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores of each batch and each professional group;

[0072] Based on the scores of the subject groups selected by the target candidates and the minimum and maximum values ​​of the lowest equivalent scores of each batch and professional group, the minimum and maximum values ​​of the average equivalent scores, the minimum and maximum values ​​of the highest equivalent scores, the minimum and minimum values ​​of the average equivalent scores, and the minimum and minimum values ​​of the highest equivalent scores, a volunteer prediction result is generated for the target candidates.

[0073] Specifically, when the score of the subject group selected by the target candidate is greater than the minimum value of the lowest par score of the selected major group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest par score of the selected major group, the selected major group is determined as the available major group.

[0074] When the score of the subject group selected by the target candidate is greater than the maximum value of the lowest parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum value of the average parity score of the selected major group, the selected major group is determined to be a safe major group.

[0075] When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum highest parity score of the selected major group, the selected major group is determined as the backup major group.

[0076] For example, calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group in each university in the past three years, and determine the minimum and maximum values ​​of the lowest parity score, the minimum and maximum values ​​of the average parity score, and the minimum and maximum values ​​of the highest parity score of each batch and each professional group from the lowest parity score, the highest parity score and the average parity score in the past three years. Then compare the target candidate's score with the minimum and minimum values ​​of the lowest parity score of each professional group, the minimum value of the average parity score of each professional group, and the minimum value of the highest parity score of each professional group, and recommend sprint goals, safe goals and bottom-line goals for the target candidate to generate volunteer prediction results.

[0077] The data processing method provided by the embodiment of the present invention collects the admission data of each batch and each professional group of each college over the years; according to the professional admission data of each batch and each professional group of each college over the years, calculates the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years; according to the scores of the subject groups selected by the target candidates and the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years, the volunteer prediction results are generated for the target candidates. The method can effectively solve the problems of information acquisition difficulties, complex data analysis and high decision uncertainty in the traditional volunteer filling method, accurately judge the schools and professional groups that match the candidates' scores, save time and energy in collecting information, help candidates and parents formulate more scientific and reasonable volunteer filling strategies, and improve the probability of admission.

[0078] Figure 2 FIG. 2 is a flow chart of a data processing method provided by another embodiment of the present invention. Figure 2 As shown, the method includes:

[0079] Step S201: Collect the admission data of each batch and each professional group of each college over the years.

[0080] In an optional embodiment, after collecting the current year's enrollment plan of each college and university, as well as the admission data of each batch and each professional group of each college and university over the years, the collected data needs to be pre-processed to remove duplicate data and abnormal data to ensure the accuracy and validity of all data. For example, the enrollment plan and admission data are accurately matched according to key conditions such as colleges, majors, batches, and subject groups to construct a comprehensive data set for candidates to query.

[0081] Step S202: Convert the admission data of each batch and each professional group of each college over the years into the first-order data. Optionally, the admission data of each batch and each professional group of each college over the years can be converted into the first-order data based on a one-point-one-file table. Among them, the one-point-one-file table lists the number of people with each score (in units of 1 point). The ranking of any score can be known in detail through this table. The first-order data indicates the college entrance examination scores of each rank.

[0082] Step S203: Map the first ranking data to the minimum admission control score of each batch in the current year to obtain the second ranking data. For example, based on the minimum admission control score of each batch in the current year, the equivalent scores corresponding to the scores of each ranking in the first ranking data can be calculated to obtain the second ranking data. Specifically, based on the minimum admission control score of the current year, the scores of each ranking can be equivalent to determine the equivalent score corresponding to the ranking score, and the equivalent score is used to indicate how many points the ranking score is equivalent to in the current year.

[0083] In an optional embodiment, the steps of equating the scores of each rank in previous years and determining the equivalent scores corresponding to the scores of the rank include:

[0084] For a certain ranking, calculate the difference between the score of that ranking and its corresponding historical minimum admission control score;

[0085] Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the score of this ranking.

[0086] Step S204: According to the second ranking data, determine the lowest equivalent score, highest equivalent score and average equivalent score of each batch and each professional group of each college in the past years. The lowest equivalent score refers to the equivalent score corresponding to the lowest ranking score in the admission data of the past years, the highest equivalent score refers to the equivalent score corresponding to the highest ranking score in the admission data of the past years, and the average equivalent score refers to the equivalent score corresponding to the average ranking score in the data of the past years.

[0087] Step S205: Generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group in various universities over the years.

[0088] Specifically, the minimum and maximum values ​​of the lowest parity score, the minimum and maximum values ​​of the average parity score, and the minimum and maximum values ​​of the highest parity score for each batch and each professional group can be determined based on the lowest parity scores, highest parity scores, and average parity scores of each batch and each professional group in each university over the years.

[0089] Then, when the score of the subject group selected by the target candidate is greater than the minimum value of the lowest parity score of the selected professional group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest parity score of the selected professional group, the selected professional group is determined as the available professional group.

[0090] When the score of the subject group selected by the target candidate is greater than the maximum value of the lowest parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum value of the average parity score of the selected major group, the selected major group is determined to be a safe major group.

[0091] If the score of the subject group selected by the target candidate is greater than the maximum average score of the selected major group, and the score of the subject group selected by the target candidate is less than the target value, the selected major group is determined as the backup major group, and the target value is the sum of the maximum value of the highest score of the selected major group and the specified reference value. The specified reference value is a positive number, such as 8, 10, 15, 16.5, etc.

[0092] Step S206: Output the volunteer prediction result generated for the target candidate. The volunteer prediction result can fully reflect the candidate's score level, ranking and the gap with the college admission line, providing a scientific and reasonable volunteer filling reference for the candidate and his parents.

[0093] The data processing method provided by the embodiment of the present invention calculates the professional admission data of each batch and each professional group of each college over the years, and obtains the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years. The lowest parity score, the highest parity score and the average parity score over the years provide a quantitative analysis basis for the subsequent professional group selection, and improve the scientificity and accuracy of the professional group selection. Furthermore, through the strategy of rushing, stabilizing and guaranteeing, personalized professional group suggestions are provided for target candidates, helping candidates to formulate scientific and reasonable volunteer reporting strategies, thereby increasing the probability of admission, reducing the risk of slippage and withdrawal, and saving time and energy.

[0094] Figure 3 FIG. 2 is a flow chart of a data processing method provided by another embodiment of the present invention. Figure 3As shown, the method includes:

[0095] Step S301: Collect the admission data of each batch and each professional group of each college over the years.

[0096] Step S302: According to the professional admission data of each batch and each professional group of each college over the years, calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each college over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years. Figure 1 or Figure 2 The illustrated embodiments are not described in detail here to avoid repetition.

[0097] Step S303: Calculate the line difference data of each batch based on the admission data of each batch of each college and the minimum control score of each batch of each batch over the years. The difference between the admission score and the minimum control score of each major group in each batch each year is calculated, and the difference is used as the line difference data.

[0098] Step S304: Generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates, the line difference data, and the lowest parity scores, highest parity scores, and average parity scores of each batch and each professional group of each university over the years.

[0099] The process of selecting a professional group for target candidates can be referenced according to the lowest parity scores, highest parity scores and average parity scores of each batch and professional group of each university over the years. Figure 1 or Figure 2 The illustrated embodiments are not described in detail here to avoid repetition.

[0100] Calculate the difference between the score of the subject group selected by the target candidate and the minimum admission control score of each batch in the current year. Select a professional group for the target candidate based on the difference and the line difference data. For example, if the difference between the score of the subject group selected by the target candidate and the minimum admission control score of the first batch of undergraduates in the current year is 20, then based on the line difference data, determine the professional group with a difference of 20 between the highest admission score of the first batch of undergraduates in previous years and the minimum admission control score, and the professional group with a difference of 20 between the lowest admission score of the first batch of undergraduates and the minimum admission control score, and the professional group with a difference of 20 between the highest admission score of the first batch of undergraduates in previous years and the minimum admission control score, and the professional group with a difference of 20 between the lowest admission score of the first batch of undergraduates and the minimum admission control score as the professional group selected for the target candidate.

[0101] The target candidate's selected major group is determined based on the lowest parity score, highest parity score and average parity score of each batch and each major group in each university over the years, as well as the major group selected for the target candidate based on the line difference data, to generate a volunteer prediction result for the target candidate.

[0102] The data processing method provided by the embodiment of the present invention integrates the linear difference data and the ranking data to generate volunteer prediction results for the target candidates, provides a basis for multi-dimensional data analysis and quantitative analysis, accurately determines the schools and major groups that match the candidates' scores, and helps the candidates to formulate scientific and reasonable volunteer filling strategies, thereby increasing the probability of admission and reducing the risk of slippage and withdrawal.

[0103] The data processing method provided by the embodiment of the present invention may also include: in response to receiving a query keyword input by a target candidate; based on the query keyword, determining the content corresponding to the query keyword, and outputting the content corresponding to the query keyword. The query keyword includes, for example, colleges, batches, subject groups, majors, etc. Through the query keyword, the user can view the enrollment situation of a certain major group of any college in the current year and the admission situation in previous years.

[0104] The data processing method provided by the embodiment of the present invention can be applied to the college entrance examination volunteer filling simulation system. The system receives the college entrance examination scores output by the user through a visual interactive interface, and in response to detecting the operation of the user clicking the volunteer prediction control, generates volunteer prediction results for the user according to the method provided by any embodiment of the present invention. Optionally, the college entrance examination volunteer filling simulation system also includes a query control, through which query keywords input by the user are received, such as colleges, batches, subject groups, majors, etc. for query. Through the query control, the user can view the enrollment situation of a certain professional group of any university in the current year and the admission situation in previous years.

[0105] Figure 4 FIG. 2 shows a schematic diagram of the structure of a data processing device provided by an embodiment of the present invention. Figure 4 As shown, the data processing device 400 includes:

[0106] The data collection module 401 is used to collect the admission data of each batch and each major group of each college over the years;

[0107] The data processing module 402 is used to calculate the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college and university over the years according to the professional admission data of each batch and each professional group of each college and university over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years;

[0108] The matching module 403 is used to generate a volunteer prediction result for the target candidate based on the score of the subject group selected by the target candidate and the lowest parity score, highest parity score and average parity score of each batch and each professional group of each university in previous years.

[0109] The data processing device provided by the embodiment of the present invention collects the admission data of each batch and each professional group of each college over the years; according to the professional admission data of each batch and each professional group of each college over the years, calculates the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years; according to the scores of the subject groups selected by the target candidates and the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years, the volunteer prediction results are generated for the target candidates. The method can effectively solve the problems of information acquisition difficulties, complex data analysis and high decision uncertainty in the traditional volunteer filling method, save time and energy in collecting information, accurately judge the schools and professional groups that match the candidates' scores, help candidates and parents formulate more scientific and reasonable volunteer filling strategies, and improve the probability of admission.

[0110] Optionally, the data processing module is used to: convert the admission data of each batch and each professional group of each college over the years into first ranking data; map the first ranking data to the minimum control score for admission of each batch in the current year to obtain second ranking data; and calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each college over the years based on the second ranking data.

[0111] Optionally, the data processing module is used to: calculate the equivalent scores corresponding to each score in the first ranking data based on the minimum control score for admission of each batch in the current year, and generate the second ranking data based on the equivalent scores corresponding to each score in the first ranking data.

[0112] Optionally, the data processing module is used to: for each ranking in the first ranking data, calculate the difference between the score of the ranking and its corresponding historical minimum admission control score;

[0113] Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the ranking score.

[0114] Optionally, the data processing module is used to: determine the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group in each university over the years, and determine the minimum and maximum values ​​of the lowest parity scores, the minimum and maximum values ​​of the average parity scores, and the minimum and maximum values ​​of the highest parity scores of each batch and each professional group; and generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the minimum and maximum values ​​of the lowest parity scores, the minimum and maximum values ​​of the average parity scores, and the minimum and maximum values ​​of the highest parity scores of each batch and each professional group.

[0115] Optionally, the matching module is used to:

[0116] In the case where the score of the subject group selected by the target candidate is greater than the minimum value of the lowest equivalent score of the to-be-selected major group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the to-be-selected major group, the to-be-selected major group is determined as the available major group;

[0117] In the case where the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the candidate professional group, and the score of the subject group selected by the target candidate is less than the maximum value of the average equivalent score of the candidate professional group, the candidate professional group is determined to be a safe professional group;

[0118] When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum highest parity score of the selected major group, the selected major group is determined as the backup major group.

[0119] Optionally, the matching module is used to: when the score of the subject group selected by the target candidate is greater than the maximum average parity score of the to-be-selected major group, and the score of the subject group selected by the target candidate is less than a target value, determine that the to-be-selected major group is a backup major group, and the target value is the sum of the maximum value of the highest parity score of the to-be-selected major group and a specified reference value.

[0120] Optionally, the processing module is further used to: calculate the line difference data of each batch according to the admission data of each batch of each college in previous years and the minimum control score of each batch in previous years;

[0121] The matching module is also used to generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates, the line difference data, and the lowest parity scores, highest parity scores, and average parity scores of each batch and each professional group in each university over the years.

[0122] The above device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the data processing method provided by the embodiment of the present invention.

[0123] Figure 5 FIG. 1 is a schematic diagram showing the structure of an electronic device according to an embodiment of the present invention. Figure 5 As shown, the electronic device includes:

[0124] Processor 501, communication interface 502, memory 503 and communication bus 504, wherein the processor 501, communication interface 502, memory 503 communicate with each other via the communication bus 504.

[0125] Memory 503, used for storing computer programs;

[0126] The processor 501 is used to execute the program stored in the memory 503, and implements the following steps:

[0127] Collect the admission data of each batch and each professional group of each college over the years;

[0128] According to the professional admission data of each batch and each professional group of each college over the years, the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years are calculated; the lowest parity score refers to the normalized score of the lowest rank in the admission data over the years, the highest parity score refers to the normalized score of the highest rank in the admission data over the years, and the average parity score refers to the normalized score of the average rank in the data over the years;

[0129] Based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years, a volunteer prediction result is generated for the target candidates.

[0130] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0131] The communication interface is used for communication between the above terminal and other devices.

[0132] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0133] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0134] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the data processing method described in any one of the above embodiments.

[0135] In another embodiment of the present invention, a computer program product including instructions is provided. When the computer program product is run on a computer, the computer executes the data processing method described in any one of the above embodiments.

[0136] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0137] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0138] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A data processing method, characterized in that: include: Collect the admission data of each batch and each professional group of each college over the years; According to the professional admission data of each batch and each professional group of each college over the years, calculate the lowest equivalent score, the highest equivalent score and the average equivalent score of each batch and each professional group of each college over the years; The lowest parity score refers to the normalized score of the lowest ranking in the admission data of previous years, the highest parity score refers to the normalized score of the highest ranking in the admission data of previous years, and the average parity score refers to the normalized score of the average ranking in the data of previous years; Based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years, a volunteer prediction result is generated for the target candidates.

2. The method according to claim 1, characterized in that According to the admission data of each batch and each professional group of each college over the years, the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years are calculated, including: Convert the admission data of each batch and each professional group of each college over the years into first-order data; Map the first ranking data to the minimum admission control score of each batch in the current year to obtain the second ranking data; Based on the second ranking data, calculate the lowest parity score, highest parity score and average parity score of each batch and each professional group of each university over the years.

3. The method according to claim 2, characterized in that Mapping the first ranking data to the minimum admission control score of each batch in the current year to obtain the second ranking data includes: Based on the minimum control score for admission of each batch in the current year, the equivalent scores corresponding to each score in the first ranking data are calculated, and based on the equivalent scores corresponding to each score in the first ranking data, the second ranking data is generated.

4. The method according to claim 3, characterized in that The step of calculating the equivalent scores corresponding to the scores of each rank in the first ranking data based on the minimum control score for admission of each batch in the current year includes: For each ranking in the first ranking data, calculate the difference between the score of the ranking and the corresponding historical minimum admission control score; Calculate the sum of the current year's minimum admission control score and the difference, and use the sum of the current year's minimum admission control score and the difference as the equivalent score corresponding to the ranking score.

5. The method according to any one of claims 1 to 4, characterized in that: Based on the scores of the subject groups selected by the target candidates and the lowest, highest and average scores of each batch and each major group of the colleges and universities over the years, the volunteer prediction results are generated for the target candidates, including: The lowest equivalent scores, highest equivalent scores and average equivalent scores of each batch and each professional group of each college over the years, and the minimum and maximum values ​​of the lowest equivalent scores, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores of each batch and each professional group; Based on the scores of the subject groups selected by the target candidates and the minimum and maximum values ​​of the lowest equivalent scores of each batch and each professional group, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores, a volunteer prediction result is generated for the target candidates.

6. The method according to claim 5, characterized in that The method of generating volunteer prediction results for the target candidate based on the scores of the subject group selected by the target candidate and the minimum and maximum values ​​of the lowest equivalent scores of each batch and each professional group, the minimum and maximum values ​​of the average equivalent scores, and the minimum and maximum values ​​of the highest equivalent scores, includes: In the case where the score of the subject group selected by the target candidate is greater than the minimum value of the lowest equivalent score of the to-be-selected major group, and the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the to-be-selected major group, the to-be-selected major group is determined as the available major group; In the case where the score of the subject group selected by the target candidate is greater than the maximum value of the lowest equivalent score of the candidate professional group, and the score of the subject group selected by the target candidate is less than the maximum value of the average equivalent score of the candidate professional group, the candidate professional group is determined to be a safe professional group; When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the maximum highest parity score of the selected major group, the selected major group is determined as the backup major group.

7. The method according to claim 6, characterized in that In the case where the score of the subject group selected by the target candidate is greater than the maximum average score of the selected professional group, and the score of the subject group selected by the target candidate is less than the maximum highest score of the selected professional group, determining the selected professional group as the backup professional group includes: When the score of the subject group selected by the target candidate is greater than the maximum average parity score of the selected major group, and the score of the subject group selected by the target candidate is less than the target value, the selected major group is determined as the backup major group, and the target value is the sum of the maximum value of the highest parity score of the selected major group and the designated reference value.

8. The method according to claim 1, characterized in that The method further comprises: Calculate the line difference data of each batch based on the admission data of each batch of each college and university over the years and the minimum control score of each batch of admission over the years; The method generates volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the lowest scores, highest scores and average scores of the various batches and professional groups of the various colleges and universities over the years, including: Based on the scores of the subject groups selected by the target candidates, the line difference data and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years, a volunteer prediction result is generated for the target candidates.

9. A data processing device, characterized in that: include: The data collection module is used to collect the admission data of each batch and each professional group of each college over the years; A data processing module is used to calculate the lowest parity score, the highest parity score and the average parity score of each batch and each professional group of each college over the years according to the professional admission data of each batch and each professional group of each college over the years; The lowest parity score refers to the normalized score of the lowest ranking in the admission data of previous years, the highest parity score refers to the normalized score of the highest ranking in the admission data of previous years, and the average parity score refers to the normalized score of the average ranking in the data of previous years; The matching module is used to generate volunteer prediction results for the target candidates based on the scores of the subject groups selected by the target candidates and the lowest parity scores, highest parity scores and average parity scores of each batch and each professional group of each university in previous years.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, used to implement the method described in any one of claims 1-88 when executing a program stored in a memory.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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