Employment monitoring method and system based on big data

Abstract

The invention discloses an employment monitoring method and system based on big data, and belongs to the technical field of employment monitoring. Comprising the following steps: step (1), collecting graduate destination related data from each specialty of a school monthly; (2) carrying out each professional employment rate analysis on the graduate destination related data obtained in the step (1); step (3), comparing the data with the same professional employment rate data of the last year; step (4), pushing information for graduates whose pending information is yes; step (5), determining a pending rate; step (6), determining the probability of abnormality of the pending rate of graduates in the current year; and step (7), the graduate pending rate abnormal probability is pushed to the administrator in real time until the last month when the graduate is in school, and the pending rate is displayed to the administrator in real time. The employment information is pushed according to the specialty and gender orientation of graduates, the employment abnormal probability is estimated in advance, the employment rate is improved, and the pending rate is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of employment monitoring, and more specifically, relates to an employment monitoring method and system based on big data. Background Art

[0002] With the development of informatization, school majors and colleges are becoming more and more complex. The teachers who specialize in employment guidance are not in the corresponding colleges, but generally in independent management departments. This makes it difficult for the data display to specifically allow the college to associate the previous employment data when visualizing the employment data of each college, which makes the unemployment rate of the current year too high. If the school's administrative department counts the employment rate, it will ignore the historical employment rate and abnormal probability of the corresponding major, especially the inability to accurately achieve employment guidance through the school's unified professional employment rate. The current employment information management system has problems such as single structure and function and lagging application. At the same time, the graduation month of general graduates is from September of the previous year to June of the current year. During this period of time, graduates can find a job, but the current employment monitoring method does not pay attention to and does not identify the unemployment rate in advance, and does not implement training and push for some students based on their gender using some employed personnel training information, thereby further increasing the employment rate.

[0003] Therefore, it is necessary to prepare an employment monitoring method and system based on big data. Summary of the invention

[0004] In order to solve the above problems, the technical solution adopted by the present invention is as follows: A method for employment monitoring based on big data, characterized in that it comprises the following steps: Step (1): Collecting data on the destinations of graduates from each major of the school on a monthly basis. The data on the destinations of graduates include college information, employment information, unemployed information, contact information, gender information, and major information. The employment information includes further study, corporate employment, and public service. The unemployed information includes yes and no. Step (2): Analyze the employment rate of each major based on the professional information of the graduate destination-related data obtained in step (1); Step (3): Compare the employment rate analysis data of each major obtained in step (2) with the employment rate data of the same major in the previous year. If the employment rate analysis data of each major obtained in step (2) is lower than or equal to the employment rate of the major in the previous year, execute step (4); if the employment rate analysis data of each major obtained in step (2) is higher than the employment rate of the same major in the previous year, execute step (5); Step (4): Pushing information to graduates whose unemployed information is yes, ranking the employment information of previous graduates of the same gender and the same major in the previous year according to the number of people who went to each company, and selecting the top three companies to push information to the graduates, the push information includes the contact information of the previous graduates who are employed by each company, and provides online training courses for employment in the same major; Step (5): Determine the unemployment rate, which is the ratio of the number of graduates with unemployment information for each major to the total number of graduates for each major. Compare the unemployment rate of the current year with the final unemployment rate of graduates of the same major in the previous year. If the unemployment rate is greater than or equal to the unemployment rate of the previous year, execute step (6); if the unemployment rate is less than the unemployment rate of the previous year, execute step (7); Step (6): Determine the abnormal probability of the unemployment rate of graduates in the current year, where the abnormal probability is P, i represents the major, j represents the final unemployment rate data of the graduates in the previous year, P(j) represents the historical abnormal probability of the unemployment rate of the graduates in the previous year of the corresponding major, and P(i) represents the abnormal probability of the unemployment rate of graduates in the current year.

[0005] P(x) is the marginal probability of data x, indicates employment or unemployed, x indicates male or female, ; Step (7): Push the abnormal probability of the unemployment rate of graduates to the administrator in real time until the last month after graduation to determine whether the unemployment rate is 0%. If it is not 0%, continue to send the recruitment data of the top three companies to the unemployed graduates; if it is 0%, stop monitoring and display the unemployment rate to the administrator in real time.

[0006] In some specific embodiments, in step (2), the employment rate analysis of each major is the ratio of the number of people employed in each major to the total number of people in the major.

[0007] In some specific embodiments, the public office includes public institutions and civil servants.

[0008] In some specific implementations, the online employment training course is an employment training course recorded by previous graduates of the same major who are employed in the top three companies.

[0009] In some specific implementations, in step (5), the final unemployment rate of the graduates of the previous year is the ratio of the number of unemployed graduates of the previous year to the total number of graduates of the same major after leaving school.

[0010] In some specific implementations, the graduate destination-related data is obtained by an administrator downloading the template required by the students and sending it to the graduates of the corresponding majors, collecting information by major, and the administrator centrally imports the information into the platform.

[0011] In some specific embodiments, the professional employment online training course includes a professional learning content list, an employment direction list and salary packages.

[0012] In some specific implementations, if the number of companies with the same gender as the graduate's major in the previous year is 1 or 2, then the display will be repeated in sequence until 3 companies are reached.

[0013] A system for employment monitoring based on big data, including an employment monitoring method based on big data: Information acquisition module: used to obtain data on the destinations of graduates from various majors of the school; Professional employment rate analysis module: used to analyze the employment rate of each major; Information push module: used to push employment information to graduates; Data display module: displays corresponding data to administrators in real time.

[0014] Beneficial Effects Compared with the prior art, the present invention has the following beneficial effects: (1) The preparation method of the employment monitoring method based on big data of the present invention can use the training information of some employed personnel to implement training and push for students of different genders and majors, thereby further increasing the employment rate. With the professional employment rate as the goal, the employment rate can be accurately identified, and the historical data of each major can be comprehensively judged, which is more accurate and more accurate than calculating for colleges or schools. In addition, the abnormal probability of the unemployment rate of graduates can be pushed in real time based on the historical employment situation throughout the employment year (from September of the previous year to June of the current year), so that the administrator can issue early warnings to each college in real time, further improving the employment rate of each major.

[0015] (2) The employment monitoring system based on big data of the present invention can be safely operated through identification, push and real-time display. DETAILED DESCRIPTION

[0016] The more detailed description of the embodiments of the present invention below is not intended to limit the scope of the claimed invention, but is merely for illustration and not to limit the description of the features and characteristics of the present invention, so as to propose the best mode for performing the present invention, and to be sufficient to enable those skilled in the art to implement the present invention. However, it should be understood that various modifications and variations may be made without departing from the scope of the present invention as defined by the appended claims. The detailed description should be considered only as illustrative, not restrictive, and if there are any such modifications and variations, they will all fall within the scope of the present invention described herein. In addition, the background technology is intended to illustrate the current status and significance of the research and development of the present technology, and is not intended to limit the present invention or the application field of the present application and the present invention.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs; the terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with specific embodiments.

[0019] A big data-based employment monitoring method for monitoring university professional employment includes the following steps: Step (1): Collect the data related to the destination of graduates from each major of the school on a monthly basis. The data related to the destination of graduates includes college information, employment information, unemployed information, contact information, gender information and major information. The employment information includes further study, corporate employment and public office, and the unemployed information includes yes and no; the information acquisition module is used to obtain the data related to the destination of graduates from each major of the school; the administrator downloads the template required by the students and sends it to the graduates of the corresponding major, collects information by major, and imports it into the platform by the administrator. By counting the above information, it is convenient to judge the employment situation corresponding to the major and gender in the future. Taking gender into consideration is to facilitate the subsequent push to enterprises, and can be pushed in a targeted manner to increase the employment rate. Because when enterprises are currently employed, different types of work will have different recruitment requirements due to gender. Considering the above factors, the employment rate can be improved.

[0020] Step (2): Analyze the employment rate of each major based on the professional information of the graduate destination data obtained in step (1); when analyzing the professional employment rate, the main statistics are the comparison of the number of employed people in the major and the total number of people in the same major in the current year, and the real-time employment rate of each major in the school is displayed in real time, so as to make an accurate judgment. This module is mainly a professional employment rate analysis module, which is used to analyze the employment rate of each major.

[0021] Step (3): Compare the employment rate analysis data of each major obtained in step (2) with the employment rate data of the same major in the previous year. If the employment rate analysis data of each major obtained in step (2) is lower than or equal to the employment rate of the major in the previous year, execute step (4); if the employment rate analysis data of each major obtained in step (2) is higher than the employment rate of the same major in the previous year, execute step (5). By comparing with the previous year, the main consideration is that if the previous 2 or 3 years are combined, the employment gap may be large because employment will change and the employment environment will change. Choosing the previous year can not only have closer data, but also another related factor is that when the school is recruiting students, the gender ratio of majors in adjacent years will not change suddenly, which is helpful for the subsequent push of information to effectively combine gender information, thereby more accurately improving the employment rate of the current year.

[0022] Step (4): Push information to graduates whose unemployed information is yes, rank the employment information of previous graduates of the same gender and the same major in the previous year according to the number of people who went to each company, and select the top three companies to push the information to the graduates. The push information includes the contact information of the previous graduates who have been employed by each company, and provides online training courses for employment in the same major; if the number of companies with the same gender of the graduates of the corresponding gender in the previous year is 1 or 2, then repeat the display until 3 companies. When it is possible to push, the information of 3 employed graduates can be displayed in real time. The information can be a mobile phone number or an email address to facilitate mutual communication. The administrator will list the appropriate graduate representatives in the database of the corresponding company in advance, do a good job in preliminary communication, and the student is willing to do the corresponding volunteer service.

[0023] Step (5): Determine the unemployment rate, which is the ratio of the number of graduates with unemployment information for each major to the total number of graduates for each major. Compare the unemployment rate of the current year with the final unemployment rate of graduates of the same major in the previous year. If the unemployment rate is greater than or equal to the unemployment rate of the previous year, execute step (6); if the unemployment rate is less than the unemployment rate of the previous year, execute step (7); At present, many colleges and universities only consider the employment rate. However, when the employment rate is close to 95%, 96%, 97%, 98%, etc., it is easy to ignore the existence of 5%, 4%, 3%, 2%, etc., and thus ignore the unemployed students. When only the employment rate is increased, it may be easy to ignore the unemployment rate data because the employment rate is close to 100%, thus ignoring the unemployed students. Therefore, by calculating the unemployment rate at this time, we can accurately identify the proportion of unemployed students, so as to make targeted breakthroughs and reduce the unemployment rate.

[0024] Step (6): Determine the abnormal probability of the unemployment rate of graduates in the current year, where the abnormal probability is P, i represents the major, j represents the final unemployment rate data of the graduates in the previous year, P(j) represents the historical abnormal probability of the unemployment rate of the graduates in the previous year of the corresponding major, and P(i) represents the abnormal probability of the unemployment rate of graduates in the current year.

[0025] P(x) is the marginal probability of data x, indicates employment or unemployed, x indicates male or female, ; Determine the probability that the abnormal unemployment rate of graduates in the current year will continue until June of the graduation year, and it can be continuously updated, which will help administrators to make predictions in advance or notify contacts of corresponding majors, and further carry out targeted management and promote employment for the number of unemployed people.

[0026] Suppose we have a joint probability distribution table about student employment, which contains two variables: gender (male or female) and employment status (employed or unemployed). We want to calculate the marginal probability of gender.

[0027] Gender: Male; Employment status: employed 0.4, unemployed 0.1; Gender: Female; Employment status: employed 0.3, unemployed 0.2.

[0028] We can calculate the marginal probability of being a male student: P(male) = P(male, employed) + P(male, unemployed) = 0.4 + 0.1 = 0.5.

[0029] Similarly, we can calculate the marginal probability for female students: P(female) = P(female, employed) + P(female, unemployed) = 0.3 + 0.2 = 0.5.

[0030] If the abnormal probability of the previous year is 0.4, then P (abnormal probability of the major in the current year, male) = 0.4 / 0.5 = 0.8, and the abnormal probability of the current year is very high, which is displayed to the administrator in real time. The administrator can issue real-time warnings to the person in charge of each major based on the abnormal probability, so that the corresponding management personnel can follow up in real time and reduce the unemployment rate.

[0031] Step (7): Push the abnormal probability of the unemployment rate of graduates to the administrator in real time until the last month after graduation to determine whether the unemployment rate is 0%. If it is not 0%, continue to send the recruitment data of the top three companies to the unemployed graduates; if it is 0%, stop monitoring and display the unemployment rate to the administrator in real time. By processing the unemployment rate, we can have better measures to improve the unemployment rate compared to the initial sprint employment rate, improve the overall employment rate, and reduce the unemployment rate.

[0032] In some specific embodiments, the employment rate analysis of each major is the ratio of the number of people employed in each major to the total number of people in the major.

[0033] In some specific embodiments, the public office includes public institutions and civil servants.

[0034] In some specific implementations, the online employment training course is an employment training course recorded by previous graduates of the same major who are employed by the top three companies.

[0035] The main reason for choosing to work in the top three companies with the same major is that, for positions such as public office, the examination time is generally fixed and the historical employment ratio is relatively low. Therefore, choosing the top three companies can quickly target unemployed students and thus quickly solve the unemployment problem.

[0036] In some specific implementations, the graduate destination-related data is obtained by an administrator downloading the template required by the students and sending it to the graduates of the corresponding majors, collecting information by major, and the administrator centrally imports the information into the platform.

[0037] In some specific embodiments, the professional employment online training course includes a list of professional learning contents, a list of employment directions and salary packages.

[0038] In some specific implementations, if the number of companies with the same gender as the graduates' major in the previous year is 1 or 2, then the display will be repeated in sequence until 3 companies are reached.

[0039] A system for employment monitoring based on big data includes an employment monitoring method based on big data: first, an information acquisition module is used to obtain data related to the destinations of graduates from various majors of a school, and then a professional employment rate analysis is performed, mainly using a professional employment rate analysis module: used to analyze the employment rate of each major, and at the same time, a real-time information push module is needed: used to push employment information to graduates, and finally, a data display module is needed to display corresponding data to an administrator in real time.

[0040] The units described above as separate components may or may not be physically separate. The components shown may or may not be physical units; 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 solution of this embodiment.

[0041] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit. Each unit may be independently used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated unit may be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0042] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0043] The present invention has been described in detail above in conjunction with specific exemplary embodiments. However, it should be understood that various modifications and variations may be made without departing from the scope of the present invention as defined by the appended claims. The detailed description should be considered only as illustrative, not restrictive, and any such modifications and variations, if any, will fall within the scope of the present invention described herein. In addition, the background art is intended to illustrate the current status and significance of the research and development of the present technology and is not intended to limit the present invention or the application field of the present application and the present invention.

[0044] More specifically, although exemplary embodiments of the present invention have been described herein, the present invention is not limited to these embodiments, but includes any and all embodiments that are recognizable by those skilled in the art based on the foregoing detailed description, such as combinations between the various embodiments, adaptive changes and / or replacements. The limitations in the claims may be interpreted broadly based on the language used in the claims and are not limited to the examples described in the foregoing detailed description or during the implementation of the application, which examples should be considered non-exclusive. Any steps listed in any method or process claim may be performed in any order and are not limited to the order presented in the claim. Therefore, the scope of the present invention should be determined solely by the attached claims and their legal equivalents, rather than by the description and examples given above.

[0045] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An employment monitoring method based on big data, characterized in that: The steps include: Step (1): Collecting data on the destinations of graduates from each major of the school on a monthly basis. The data on the destinations of graduates include college information, employment information, unemployed information, contact information, gender information, and major information. The employment information includes further study, corporate employment, and public service. The unemployed information includes yes and no. Step (2): Analyze the employment rate of each major based on the professional information of the graduate destination-related data obtained in step (1); Step (3): Compare the employment rate analysis data of each major obtained in step (2) with the employment rate data of the same major in the previous year. If the employment rate analysis data of each major obtained in step (2) is lower than or equal to the employment rate of the major in the previous year, execute step (4); if the employment rate analysis data of each major obtained in step (2) is higher than the employment rate of the same major in the previous year, execute step (5); Step (4): Pushing information to graduates whose unemployed information is yes, ranking the employment information of previous graduates of the same gender and the same major in the previous year according to the number of people who went to each company, and selecting the top three companies to push information to the graduates, the push information includes the contact information of the previous graduates who are employed by each company, and provides online training courses for employment in the same major; Step (5): Determine the unemployment rate, which is the ratio of the number of graduates with unemployment information for each major to the total number of graduates for each major. Compare the unemployment rate of the current year with the final unemployment rate of graduates of the same major in the previous year. If the unemployment rate is greater than or equal to the unemployment rate of the previous year, execute step (6); if the unemployment rate is less than the unemployment rate of the previous year, execute step (7); Step (6): Determine the abnormal probability of the unemployment rate of graduates in the current year, where the abnormal probability is P, i represents the major, j represents the final unemployment rate data of the graduates in the previous year, P(j) represents the historical abnormal probability of the unemployment rate of the graduates in the previous year of the corresponding major, and P(i) represents the abnormal probability of the unemployment rate of graduates in the current year. , P(x) is the marginal probability of data x, indicates employment or unemployed, x indicates male or female, ; Step (7): Push the abnormal probability of the unemployment rate of graduates to the administrator in real time until the last month after graduation to determine whether the unemployment rate is 0%. If it is not 0%, continue to send the recruitment data of the top three companies to the unemployed graduates; if it is 0%, stop monitoring and display the unemployment rate to the administrator in real time.

2. The employment monitoring method based on big data according to claim 1 is characterized in that: In step (2), the employment rate of each major is analyzed as the ratio of the number of people employed in each major to the total number of people in that major.

3. The employment monitoring method based on big data according to claim 1, characterized in that: The public offices mentioned include public institutions and civil servants.

4. The employment monitoring method based on big data according to claim 1 is characterized in that: The online employment training courses are recorded for the previous graduates of the same major who are employed in the top three companies.

5. The employment monitoring method based on big data according to claim 1 is characterized in that: In step (5), the final unemployment rate of the previous year's graduates is the ratio of the number of unemployed graduates to the total number of graduates of the same major after leaving school.

6. The employment monitoring method based on big data according to claim 1, characterized in that: The administrator downloads the template required by the students and sends it to the graduates of the corresponding majors. The administrator collects information by major and imports it into the platform in a centralized manner.

7. The employment monitoring method based on big data according to claim 1, characterized in that: The professional employment online training course includes a professional learning content list, an employment direction list and salary benefits.

8. The employment monitoring method based on big data according to claim 1, characterized in that: If there are 1 and 2 companies with the same gender as the graduate's major in the previous year, the list will be displayed repeatedly until 3 companies are reached.

9. A system for employment monitoring based on big data, characterized in that: The method for monitoring employment based on big data as claimed in any one of claims 1 to 8 includes: Information acquisition module: used to obtain data on the destinations of graduates from various majors of the school; Professional employment rate analysis module: used to analyze the employment rate of each major; Information push module: used to push employment information to graduates; Data display module: displays corresponding data to administrators in real time.