Intelligent college entrance examination volunteer application recommendation method based on big data

Abstract

The embodiment of this specification provides a method for intelligent college entrance examination volunteer application recommendation based on big data, including: calculating the number of students affected by the target major of the target college in the corresponding province and city in previous years, the total number of students nationwide in previous years, and the total number of students nationwide in the current year; calculating the ranking influence coefficient according to the enrollment ranking interval of the target major of the target college in the corresponding province and city in previous years, the total number of students taking the college entrance examination in the corresponding province and city in previous years, and the total number of students taking the college entrance examination in the corresponding province and city in the current year; calculating the enrollment score interval of the corresponding province and city in the current year according to the number influence coefficient and the ranking influence coefficient; when the college entrance examination score of the user is within the enrollment score interval of the corresponding province and city in the current year, recommending the user to apply for the target major of the target college. This improves the success rate of college entrance examination volunteer application.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of intelligent volunteer filling, and in particular, to a method for recommending intelligent college entrance examination volunteer filling based on big data. Background Art

[0002] Filling out the college entrance examination application form is a problem that every high school student must face. The correct way of filling out the application form is one of the prerequisites for successfully entering the university of your choice. Most high school students currently only use the admission scores of previous years to roughly estimate whether they can enter the target school when filling out the college entrance examination application form. However, due to the different number of admissions and test scores each year, this method of filling out the application form is not reliable.

[0003] Therefore, there is an urgent need for an intelligent college entrance examination application recommendation method based on big data, which can help candidates more accurately measure whether they can enter the target school and improve the success rate of application. Summary of the invention

[0004] The purpose of the embodiments of this specification is to provide an intelligent college entrance examination volunteer application recommendation method based on big data to improve the success rate of volunteer application.

[0005] To achieve the above objectives, on the one hand, the embodiments of this specification provide a method for intelligent college entrance examination volunteer application recommendation based on big data, including:

[0006] Obtain the user's province, city, target institution, and subject information;

[0007] Screening all majors of the target institution according to the subject information to determine a target major that can be selected by the user;

[0008] Based on the province or city where the user is located, determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years, where the enrollment ranking range is the ranking range of students enrolled in the target major of the target institution in the corresponding province or city;

[0009] The population impact coefficient is calculated based on the number of students enrolled in the target major of the target institution in the corresponding province and city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year;

[0010] The ranking influence coefficient is calculated based on the enrollment ranking range of the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year;

[0011] According to the number of people influence coefficient and the ranking influence coefficient, the admission score range of the corresponding province and city in the current year is calculated;

[0012] When the college entrance examination score of the user is within the admission score range of the corresponding province or city in the current year, the user is recommended to apply for the target major of the target institution.

[0013] Preferably, the calculation of the number of students affected by the target major of the target institution in the corresponding province or city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year further includes:

[0014] Divide the number of students enrolled in the corresponding province or city in previous years by the total number of students enrolled nationwide in previous years to calculate the enrollment ratio of the corresponding province or city in previous years;

[0015] Multiply the total number of admissions nationwide in the current year by the admissions ratio to calculate the first expected number of admissions for the corresponding province or city in the current year as the population impact coefficient.

[0016] Preferably, the ranking influence coefficient is calculated based on the enrollment ranking range of the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year, further comprising:

[0017] The proportion of first place in previous years is calculated by dividing the number of first place candidates ranked at the top of the enrollment ranking interval of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years;

[0018] The proportion of the second place in previous years is calculated by dividing the last second place in the enrollment ranking range of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years;

[0019] The first ranking ratio and the second ranking ratio are used as ranking influence coefficients.

[0020] Preferably, the calculation of the admission score range of the corresponding province or city in the current year based on the number of students and the ranking influence coefficient further includes:

[0021] The first ranking of the corresponding province or city in the current year is obtained by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the first ranking ratio;

[0022] The second ranking of the corresponding province or city in the current year is obtained by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the second ranking ratio;

[0023] Calculate the difference between the second ranking and the first ranking to obtain the second expected enrollment number;

[0024] Based on the first expected number of students to be admitted, the second expected number of students to be admitted, the first ranking, the second ranking, the first place and the second place, the admission score range for the corresponding province and city in the current year is obtained.

[0025] Preferably, obtaining the admission score range of the corresponding province or city in the current year based on the first estimated number of students to be admitted, the second estimated number of students to be admitted, the first ranking, the second ranking, the first place and the second place further includes:

[0026] The college entrance examination scores ranked first and second in the provincial ranking of the corresponding province and city in the current year are used to obtain the first score and the second score;

[0027] Adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain an adjusted first score;

[0028] Adjusting the second score according to the second expected number of students enrolled and the second ranking to obtain an adjusted second score;

[0029] According to the adjusted first score and the adjusted second score, the admission score range of the corresponding province and city in the current year is obtained.

[0030] Preferably, adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further comprises:

[0031] Calculate the first difference between the first estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0032] If the first expected number of students to be enrolled is less than the actual number of students to be enrolled;

[0033] According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N positions after the first position are taken as the first target scores, where N is the first number difference;

[0034] Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio;

[0035] The first score is multiplied by the first ratio to obtain a first adjustment value, and the first adjustment value is subtracted from the first score to obtain an adjusted first score.

[0036] Preferably, adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further comprises:

[0037] Calculate the first difference between the first estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0038] If the first expected number of students to be enrolled is greater than the actual number of students to be enrolled;

[0039] According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N places before the first ranking are taken as the first target scores, where N is the first number difference;

[0040] Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio;

[0041] The first score is multiplied by the first ratio to obtain a first adjustment value, and the first score is added to the first adjustment value to obtain an adjusted first score.

[0042] Preferably, adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further comprises:

[0043] Calculate the difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0044] If the second estimated enrollment number is less than the actual enrollment number;

[0045] According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places behind the second ranking are taken as the second target scores, where M is the second number difference;

[0046] Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking to obtain a second ratio;

[0047] The second score is multiplied by the second ratio to obtain a second adjustment value, and the second adjustment value is subtracted from the second score to obtain an adjusted second score.

[0048] Preferably, adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further comprises:

[0049] Calculate the difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0050] If the second estimated number of students enrolled is greater than the actual number of students enrolled;

[0051] According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places before the second ranking are taken as the second target scores, where M is the second number difference;

[0052] Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking to obtain a second ratio;

[0053] The second score is multiplied by the second ratio to obtain a second adjustment value, and the second score is added to the second adjustment value to obtain an adjusted second score.

[0054] On the other hand, the embodiment of this specification provides an intelligent college entrance examination volunteer application recommendation device based on big data, the device comprising:

[0055] The acquisition module is used to obtain the user's province, city, target school, and subject information;

[0056] A first determination module is used to screen all majors of the target institution according to the subject information and determine a target major that can be selected by the user;

[0057] The second determination module is used to determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years based on the province or city where the user is located, and the enrollment ranking range is the ranking range of students enrolled in the target major of the target institution in the corresponding province or city;

[0058] The first calculation module is used to calculate the number of students affected by the target major of the target institution in the corresponding province and city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year;

[0059] The second calculation module is used to calculate the ranking influence coefficient according to the enrollment ranking range of the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year;

[0060] A third calculation module is used to calculate the admission score range of the corresponding province or city in the current year according to the number of people influence coefficient and the ranking influence coefficient;

[0061] The recommendation module is used to recommend the user to fill in the target major of the target institution when the user's college entrance examination score is within the admission score range of the corresponding province or city in the current year.

[0062] It can be seen from the technical solutions provided in the above embodiments of this specification that the embodiments of this specification calculate the number of students affected by the target major of the target institution in the corresponding province and city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year; calculate the ranking influence coefficient based on the enrollment ranking range of the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of students taking the college entrance examination in the corresponding province and city in the current year; and then calculate the admission score range of the corresponding province and city in the current year. Judging that the user's college entrance examination score is within the admission score range of the corresponding province and city in the current year can improve the success rate of the user's volunteer application.

[0063] In order to make the above and other purposes, features and advantages of the present specification more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 A flowchart of a method for intelligent college entrance examination volunteer application recommendation based on big data provided in an embodiment of this specification is shown;

[0066] Figure 2 A schematic diagram of a process for calculating the number of people influence coefficient provided in an embodiment of this specification is shown;

[0067] Figure 3 A schematic diagram of a process for calculating a ranking influence coefficient provided in an embodiment of this specification is shown;

[0068] Figure 4 A schematic diagram of a process for calculating the admission score range of the corresponding province or city in the current year provided in an embodiment of this specification is shown;

[0069] Figure 5 A schematic diagram showing the flow chart of the admission score ranges for the corresponding provinces and cities in the current year provided in the embodiments of this specification;

[0070] Figure 6 A first flow chart of obtaining an adjusted first score provided in an embodiment of this specification is shown;

[0071] Figure 7 A second flow chart of obtaining an adjusted first score provided in an embodiment of this specification is shown;

[0072] Figure 8 A schematic diagram of a first process for obtaining an adjusted second score provided in an embodiment of this specification is shown;

[0073] Fig. 9 A second flow chart of obtaining an adjusted second score provided in an embodiment of this specification is shown;

[0074] Fig.10 A schematic diagram of the module structure of an intelligent college entrance examination volunteer application device based on big data provided in an embodiment of this specification is shown;

[0075] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of this specification is shown.

[0076] Description of the accompanying symbols:

[0077] 100, acquisition module; 200, first determination module; 300, second determination module; 400, first calculation module; 500, second calculation module; 600, third calculation module; 700, recommendation module. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of this article to clearly and completely describe the technical solutions in the embodiments of this article. Obviously, the described embodiments are only part of the embodiments of this article, not all of the embodiments. Based on the embodiments of this article, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this article.

[0079] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the acquisition, storage, use, processing, etc. of data in the technical solutions described in the embodiments of this application are in compliance with the provisions of relevant laws and regulations.

[0080] like Figure 1 This application discloses a method for intelligent college entrance examination volunteer application recommendation based on big data, including:

[0081] S101: Obtain the user's province, city, target institution, and subject information;

[0082] S102: Screening all majors of the target institution according to the subject information to determine a target major that can be selected by the user;

[0083] S103: Based on the province or city where the user is located, determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years, where the enrollment ranking range is the ranking range of students enrolled in the target major of the target institution in the corresponding province or city;

[0084] S104: Calculate the population impact coefficient based on the number of students enrolled in the target major of the target institution in the corresponding province or city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year;

[0085] S105: Calculate the ranking influence coefficient based on the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year;

[0086] S106: Calculate the admission score range of the corresponding province or city in the current year based on the number of students and the ranking influence coefficient;

[0087] S107: When the college entrance examination score of the user is within the admission score range of the corresponding province or city in the current year, the user is recommended to apply for the target major of the target institution.

[0088] The user's province and city, target school and subject information can all be obtained by the user entering into the system. The subject information is the user's subject selection. Since the college entrance examination adopts the 3+1+2 model, in addition to Chinese and mathematics, students are required to choose one from history and physics, and two from geography, biology, chemistry, and politics. The specific subject information is the specific subject the user takes the exam for.

[0089] For the majors in the target colleges and universities, since some majors will restrict the test subjects of candidates, for example, computer majors must choose physics, so based on the user's subject information, it is necessary to determine the corresponding target major to be selected, where the target major is the major that the user is allowed to apply for.

[0090] The target major of the target institution can be obtained through official information, including the number of students enrolled in the target major of the target institution in the corresponding province and city in previous years, the enrollment ranking range, and the enrollment score range. The enrollment ranking range is the ranking range of the students enrolled in the target major of the target institution in the corresponding province and city. For example, the total number of college entrance examination candidates in the corresponding province and city is 30,000, and the students enrolled in the target major of the target institution are ranked 2000-2200, with a ranking range of 2000-2200. The enrollment score range is the college entrance examination scores of the students enrolled in the target major of the target institution. For example, if the college entrance examination scores of the enrolled students are between 680 and 700 points, the enrollment score range is 680-700 points.

[0091] like Figure 2 In the embodiment of this specification, the number of students enrolled in the target major of the target college in the corresponding province and city in previous years, the total number of students enrolled nationwide in previous years and the total number of students enrolled nationwide in the current year, the number of students affected by the number of students further includes:

[0092] S201: Divide the number of students enrolled in the corresponding province or city in previous years by the total number of students enrolled nationwide in previous years to calculate the enrollment ratio of the corresponding province or city in previous years;

[0093] S202: Multiply the total number of students enrolled nationwide in the current year by the enrollment ratio to calculate the first estimated number of students enrolled in the corresponding province or city in the current year as the number impact coefficient.

[0094] For example, the target major of the target university has recruited a total of 200 students in the corresponding province and city in previous years, and the target major of the target university has recruited a total of 10,000 students nationwide in previous years. The enrollment ratio of the corresponding province and city in previous years is 1:5. The target major of the target university has recruited a total of 15,000 students nationwide in the current year. The first estimated number of students to be enrolled in the target major of the target university in the corresponding province and city in the current year is 300. The number impact coefficient is calculated based on the proportion of the number of students enrolled in the target major of the target university in the corresponding province and city to the national number of students enrolled, and it takes into account the impact of the number of students enrolled on the filling of applications.

[0095] like Figure 3 The ranking influence coefficient calculated based on the target major of the target institution in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year further includes:

[0096] S301: Divide the first place ranking in the enrollment ranking interval of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years to calculate the first place ratio in previous years;

[0097] S302: Calculate the proportion of the second place in previous years by dividing the last second place in the enrollment ranking interval of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years;

[0098] S303: Taking the first ranking ratio and the second ranking ratio as ranking influence coefficients.

[0099] For example, the target major of the target college has a ranking range of 2000-2200 in the corresponding province and city in previous years. It should be noted that the two endpoints of the ranking range are the minimum and maximum rankings of the target major of the target college among all the candidates recruited in the corresponding province and city in previous years. For example, candidate A ranks 2000th, candidate B ranks 2200th, and the rankings of other candidates are distributed between 2000-2200, then the ranking range is 2000-2200. The total number of college entrance examination candidates for the target major of the target college in the corresponding province and city in previous years is 30,000, of which the first place ratio is 1:15 and the second place ratio is 11:150. The ranking impact coefficient is based on the proportion of the target major of the target college in the corresponding province and city to the total number of college entrance examination candidates in the corresponding province and city, and takes into account the impact of the ranking of candidates in the province and city on the volunteer application.

[0100] The previous years in the embodiments of this specification may refer to any year in the past, such as last year or the year before last, or may refer to many years in the past, such as the past three years or the past five years. If the previous years refer to many years in the past, the number of students enrolled in the target major of the target institution in the corresponding province and city in previous years, the total number of students enrolled nationwide in previous years, etc. may be the average number of students enrolled in the past years, the average total number of students enrolled, etc. The same is true for other concepts related to previous years in this specification and will not be repeated here.

[0101] like Figure 4 In the embodiment of this specification, the calculation of the enrollment score range of the corresponding province or city in the current year based on the number of people influence coefficient and the ranking influence coefficient further includes:

[0102] S401: Obtain the first ranking of the corresponding province or city in the current year by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the first ranking ratio;

[0103] S402: Obtain the second ranking of the corresponding province or city in the current year by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the second ranking ratio;

[0104] S403: Calculate the difference between the second ranking and the first ranking to obtain a second expected number of students to be enrolled;

[0105] S404: According to the first estimated number of students to be admitted, the second estimated number of students to be admitted, the first ranking, the second ranking, the first place and the second place, the admission score range of the corresponding province or city in the current year is obtained.

[0106] The difference between the second ranking and the first ranking can be used to obtain the second expected number of admissions. However, since the two end values ​​of the admissions ranking range are obtained based on the minimum and maximum rankings of all candidates recruited in previous years, there are actually many candidates within the admissions ranking range who do not belong to the target majors of the target colleges and universities and are recruited in the corresponding provinces and cities in previous years. Therefore, the second expected number of admissions calculated based on this must be greater than the first expected number of admissions.

[0107] like Figure 5 Further, the enrollment score ranges of the corresponding provinces and cities in the current year obtained based on the first expected enrollment number, the second expected enrollment number, the first ranking, the second ranking, the first place and the second place further include:

[0108] S501: Obtain a first score and a second score by taking the college entrance examination scores ranked first and second in the provincial ranking of the corresponding province or city in the current year;

[0109] S502: adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain an adjusted first score;

[0110] S503: adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain an adjusted second score;

[0111] S504: Obtain the admission score range of the corresponding province or city in the current year based on the adjusted first score and the adjusted second score.

[0112] The college entrance examination scores ranked first and second in the provincial ranking of the corresponding province and city in the current year (i.e., the first score and the second score) can be used as the two end values ​​of the admission score range of the target major of the target institution in the corresponding province and city in the current year. The above method is based on ranking prediction to obtain the admission score range of the target major of the target institution in the corresponding province and city in the current year, but because the actual number of students enrolled in the target major of the target institution in the corresponding province and city in the current year is different from the first estimated number of students enrolled and the second estimated number of students enrolled, the first score and the second score need to be adjusted.

[0113] like Figure 6 Specifically, the adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further includes:

[0114] S601: Calculate the first difference between the first estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0115] S602: If the first estimated number of students to be enrolled is less than the actual number of students to be enrolled;

[0116] S603: according to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N positions after the first position are taken as the first target scores, where N is the first number difference;

[0117] S604: Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio;

[0118] S605: multiply the first score by the first ratio to obtain a first adjustment value, and subtract the first adjustment value from the first score to obtain an adjusted first score.

[0119] The first score is greater than the second score. For example, the first score is 700 points and the second score is 680 points. When adjusting the first score, if the first expected number of students is less than the actual number of students, it means that the target major of the target institution has expanded enrollment in the corresponding province in the current year, and the first score may need to be adjusted downward. Due to the different difficulty of the exam each year, the current year's exam score cannot be adjusted downward based on the exam scores of previous years, but the score can be indirectly adjusted downward based on the ranking. Specifically, for example, the first expected number of students is 300, the actual number of students is 400, and N is 100. Then the first place (in previous years in the corresponding province) will be adjusted downward. The first target score is the first ranking from the top of the admission ranking range of the province or city (for example, 2000th), 100 places behind, that is, 2100th. The first score difference between 2100th (for example, 730 points) and 2000th is calculated. The first score difference is 10 points, for example. The ratio of 10 points to the score of 2000th is calculated, and the first ratio is approximately 0.013. The first score of 700 points is multiplied by the first ratio of 0.013, resulting in a first score of approximately 9.5 points. The first score of 700 points minus 9.5 points, and the adjusted first score is 690.5 points.

[0120] like Figure 7 , adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further includes:

[0121] S701: Calculate the first difference between the first estimated number of students and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0122] S702: If the first estimated number of students to be enrolled is greater than the actual number of students to be enrolled;

[0123] S703: according to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N places before the first ranking are set as the first target scores, where N is the first number difference;

[0124] S704: Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio;

[0125] S705: multiply the first score by the first ratio to obtain a first adjustment value, and add the first score to the first adjustment value to obtain an adjusted first score.

[0126] Similarly, if the first estimated number of admissions is greater than the actual number of admissions, it means that the target major of the target institution has reduced enrollment in the corresponding province in the current year. In this case, the first score may need to be adjusted upward. The specific adjustment method can be referred to the previous description and will not be repeated in this manual.

[0127] like Figure 8 The adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further includes:

[0128] S801: Calculate the second difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0129] S802: If the second estimated number of students to be enrolled is less than the actual number of students to be enrolled;

[0130] S803: according to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places behind the second ranking are set as the second target scores, where M is the second number difference;

[0131] S804: Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking, to obtain a second ratio;

[0132] S805: multiply the second score by the second ratio to obtain a second adjustment value, and subtract the second adjustment value from the second score to obtain an adjusted second score.

[0133] The first score is greater than the second score, for example, the first score is 700 points and the second score is 680 points. When adjusting the second score, if the second expected number of admissions is less than the actual number of admissions, it means that the target major of the target institution has expanded enrollment in the corresponding province in the current year, and the second score may need to be lowered. The specific adjustment method can be referred to the previous description and will not be repeated in this manual.

[0134] like Fig. 9 The adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further includes:

[0135] S901: Calculate the second difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year;

[0136] S902: If the second estimated number of students to be enrolled is greater than the actual number of students to be enrolled;

[0137] S903: according to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places before the second ranking are set as the second target scores, where M is the second number difference;

[0138] S904: Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking, to obtain a second ratio;

[0139] S905: multiply the second score by the second ratio to obtain a second adjustment value, and add the second score to the second adjustment value to obtain an adjusted second score.

[0140] Similarly, if the second estimated number of admissions is greater than the actual number of admissions, it means that the target major of the target institution has reduced enrollment in the corresponding province in the current year. In this case, the second score may need to be adjusted upward. The specific adjustment method can be referred to the previous description and will not be repeated in this manual.

[0141] The first score is adjusted by the first expected number of admissions, and the second score is adjusted by the second expected number of admissions. The reason is that the first score is the highest score among the admissions scores, and the second score is the lowest score among the admissions scores. The second expected number of admissions is based on the minimum and maximum rankings of all candidates admitted in previous years. The calculated second expected number of admissions must be greater than the first expected number of admissions, that is, the second expected number of admissions can be considered as the maximum number of admissions for the target school and the target major in the corresponding province and city in the current year. The more admissions, the lower the corresponding admissions score. Therefore, the second score is adjusted by the second expected number of admissions, and the first score is adjusted by the first expected number of admissions.

[0142] The interval formed by the second score and the first score is the enrollment score range of the target major of the target institution in the corresponding province and city in the current year. If the user's college entrance examination score is within the interval, it is recommended that the user apply for the target major of the target institution. If it is within the interval, it is not recommended that the user apply for the target major of the target institution, thereby increasing the success rate of application.

[0143] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this specification are all information and data authorized by the user or fully authorized by all parties. The acquisition, storage, use, and processing of data in the technical solutions described in the embodiments of this application are in compliance with the provisions of relevant laws and regulations.

[0144] Based on the above-mentioned intelligent college entrance examination volunteer application recommendation method based on big data, the embodiment of this specification also provides an intelligent college entrance examination volunteer application recommendation device based on big data.

[0145] Specifically, Fig.10 This is a schematic diagram of the module structure of an embodiment of an intelligent college entrance examination volunteer application recommendation device based on big data provided by the embodiment of this specification, referring to Fig.10 As shown, an intelligent college entrance examination volunteer application recommendation device based on big data provided in an embodiment of this specification includes: an acquisition module 100, a first determination module 200, a second determination module 300, a first calculation module 400, a second calculation module 500, a third calculation module 600, and a recommendation module 700.

[0146] The acquisition module 100 is used to obtain the user's province, city, target school, and subject information;

[0147] The first determination module 200 is used to screen all majors of the target institution according to the subject information and determine a target major that can be selected by the user;

[0148] The second determination module 300 is used to determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years based on the province or city where the user is located, wherein the enrollment ranking range is the ranking range of the students enrolled by the target major of the target institution in the corresponding province or city;

[0149] The first calculation module 400 is used to calculate the number of students affected by the target major of the target college in the corresponding province or city in previous years, the total number of students enrolled nationwide in previous years, and the total number of students enrolled nationwide in the current year;

[0150] The second calculation module 500 is used to calculate the ranking influence coefficient according to the enrollment ranking interval of the target major of the target college in the corresponding province and city in previous years, the total number of college entrance examination candidates in the corresponding province and city in previous years, and the total number of college entrance examination candidates in the corresponding province and city in the current year;

[0151] The third calculation module 600 is used to calculate the admission score range of the corresponding province or city in the current year according to the number of people influence coefficient and the ranking influence coefficient;

[0152] The recommendation module 700 is used to recommend the user to fill in the target major of the target college when the college entrance examination score of the user is within the admission score range of the corresponding province or city in the current year.

[0153] The beneficial effects of the system provided above are consistent with the beneficial effects of the method provided above, and are not described in detail in the embodiments of this specification.

[0154] This embodiment provides a computer device, whose internal structure diagram can be as follows: Fig.11 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection.

[0155] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0156] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0157] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0158] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0160] It should also be understood that in the embodiments of this article, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0161] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this article.

[0162] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0163] In the several embodiments provided herein, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this article.

[0165] Specific embodiments are used in this article to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for general technicians in this field, according to the ideas of this article, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on this article.

Claims

1. An intelligent college entrance examination volunteer application recommendation method based on big data, characterized in that: include: Obtain the user's province, city, target institution, and subject information; Screening all majors of the target institution according to the subject information to determine a target major that can be selected by the user; Based on the province or city where the user is located, determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years, where the enrollment ranking range is the ranking range of students enrolled in the target major of the target institution in the corresponding province or city; Divide the number of students enrolled in the corresponding province or city in previous years by the total number of students enrolled nationwide in previous years to calculate the enrollment ratio of the corresponding province or city in previous years; The total number of students enrolled in the country in the current year is multiplied by the enrollment ratio to calculate the first estimated number of students enrolled in the corresponding province or city in the current year as the population impact coefficient; The proportion of first place in previous years is calculated by dividing the number of first place candidates ranked at the top of the enrollment ranking interval of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years; The proportion of the second place in previous years is calculated by dividing the last second place in the enrollment ranking range of the corresponding province or city in previous years by the total number of college entrance examination candidates in the corresponding province or city in previous years; The first ranking ratio and the second ranking ratio are used as ranking influence coefficients; According to the number of people influence coefficient and the ranking influence coefficient, the admission score range of the corresponding province and city in the current year is calculated; When the college entrance examination score of the user is within the admission score range of the corresponding province or city in the current year, the user is recommended to apply for the target major of the target institution.

2. The method according to claim 1, characterized in that: The calculation of the enrollment score range of the corresponding province or city in the current year based on the number of people influence coefficient and the ranking influence coefficient further includes: The first ranking of the corresponding province or city in the current year is obtained by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the first ranking ratio; The second ranking of the corresponding province or city in the current year is obtained by multiplying the total number of college entrance examination candidates in the corresponding province or city in the current year by the second ranking ratio; Calculate the difference between the second ranking and the first ranking to obtain the second expected enrollment number; Based on the first expected number of students to be admitted, the second expected number of students to be admitted, the first ranking, the second ranking, the first place and the second place, the admission score range for the corresponding province and city in the current year is obtained.

3. The method according to claim 2, characterized in that The enrollment score ranges of the corresponding provinces and cities in the current year obtained according to the first estimated enrollment number, the second estimated enrollment number, the first ranking, the second ranking, the first place and the second place further include: The college entrance examination scores ranked first and second in the provincial ranking of the corresponding province and city in the current year are used to obtain the first score and the second score; Adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain an adjusted first score; Adjusting the second score according to the second expected number of students enrolled and the second ranking to obtain an adjusted second score; According to the adjusted first score and the adjusted second score, the admission score range of the corresponding province and city in the current year is obtained.

4. The method according to claim 3, characterized in that The adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further includes: Calculate the first difference between the first estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year; If the first expected number of students to be enrolled is less than the actual number of students to be enrolled; According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N positions after the first position are taken as the first target scores, where N is the first number difference; Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio; The first score is multiplied by the first ratio to obtain a first adjustment value, and the first adjustment value is subtracted from the first score to obtain an adjusted first score.

5. The method according to claim 4, characterized in that The adjusting the first score according to the first expected number of students to be enrolled and the first ranking to obtain the adjusted first score further includes: Calculate the first difference between the first estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year; If the first expected number of students to be enrolled is greater than the actual number of students to be enrolled; According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the N places before the first ranking are taken as the first target scores, where N is the first number difference; Calculate, according to a first score difference between the first target score and the score corresponding to the first ranking, a ratio of the first score difference to the score corresponding to the first ranking to obtain a first ratio; The first score is multiplied by the first ratio to obtain a first adjustment value, and the first score is added to the first adjustment value to obtain an adjusted first score.

6. The method according to claim 3, characterized in that The adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further comprises: Calculate the difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year; If the second estimated enrollment number is less than the actual enrollment number; According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places behind the second ranking are taken as the second target scores, where M is the second number difference; Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking to obtain a second ratio; The second score is multiplied by the second ratio to obtain a second adjustment value, and the second adjustment value is subtracted from the second score to obtain an adjusted second score.

7. The method according to claim 6, characterized in that The adjusting the second score according to the second expected number of students to be enrolled and the second ranking to obtain the adjusted second score further comprises: Calculate the difference between the second estimated number of students enrolled and the actual number of students enrolled in the target major of the target institution in the corresponding province or city in the current year; If the second estimated number of students enrolled is greater than the actual number of students enrolled; According to the provincial rankings of the corresponding provinces and cities in previous years, the scores corresponding to the rankings M places before the second ranking are taken as the second target scores, where M is the second number difference; Calculate, according to a second score difference between the second target score and the score corresponding to the second ranking, a ratio of the second score difference to the score corresponding to the second ranking to obtain a second ratio; The second score is multiplied by the second ratio to obtain a second adjustment value, and the second score is added to the second adjustment value to obtain an adjusted second score.

8. An intelligent college entrance examination volunteer application recommendation device based on big data, characterized in that: The device comprises: The acquisition module is used to obtain the user's province, city, target school, and subject information; A first determination module is used to screen all majors of the target institution according to the subject information and determine a target major that can be selected by the user; The second determination module is used to determine the number of students enrolled and the enrollment ranking range of the target major of the target institution in the corresponding province or city in previous years based on the province or city where the user is located, and the enrollment ranking range is the ranking range of students enrolled in the target major of the target institution in the corresponding province or city; The first calculation module is used to divide the number of students enrolled in the corresponding province and city in previous years by the total number of students enrolled nationwide in previous years to calculate the enrollment ratio of the corresponding province and city in previous years; multiply the total number of students enrolled nationwide in the current year by the enrollment ratio to calculate the first expected number of students enrolled in the corresponding province and city in the current year as the number of students impact coefficient; The second calculation module is used to calculate the first-ranked ratio in previous years by dividing the first-ranked ratio in the enrollment ranking interval of the corresponding province and city in previous years by the total number of college entrance examination candidates in the corresponding province and city in previous years; calculate the second-ranked ratio in previous years by dividing the second-ranked ratio in the enrollment ranking interval of the corresponding province and city in previous years by the total number of college entrance examination candidates in the corresponding province and city in previous years; and use the first-ranked ratio and the second-ranked ratio as ranking influence coefficients; A third calculation module is used to calculate the admission score range of the corresponding province or city in the current year according to the number of people influence coefficient and the ranking influence coefficient; The recommendation module is used to recommend the user to fill in the target major of the target institution when the user's college entrance examination score is within the admission score range of the corresponding province or city in the current year.

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