Human resource data analysis method based on big data

Abstract

The invention relates to the technical field of human resources, and discloses a human resource data analysis method based on big data, and the method comprises the steps: obtaining target post demand data; obtaining pre-selected personnel data according to the target post demand data; according to the pre-selected personnel data and the target post demand data, through a quantity comparison strategy, forming quantity judgment data; and determining the final selection personnel of the target post according to the first final selection personnel data, the second final selection personnel data or the third final selection personnel data. According to the human resource data analysis method based on the big data, after all job seekers are comprehensively selected according to the requirements of the target post, the applicants which are matched with the post and can more quickly adapt to the post work can be accurately selected as the personnel which can enter the target post finally, the working efficiency is improved, and the labor intensity is reduced. And the human resource management cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resources, and in particular to a human resources data analysis method based on big data. Background Art

[0002] Human resource management refers to a series of human resource policies and corresponding management activities of an enterprise. These activities mainly include the formulation of enterprise human resource strategies, recruitment and selection of employees, training and development, performance management, salary management, employee mobility management, employee relationship management, employee safety and health management, etc., that is, a series of activities such as planning, organizing, commanding, controlling and coordinating the acquisition, development, maintenance and utilization of human resources by enterprises using modern management methods, and ultimately achieving the management behavior of achieving the development goals of the enterprise;

[0003] When companies recruit and select employees, they usually screen based on basic information, such as education and major. This makes it difficult to further determine the practical experience, professional skills and learning ability of applicants based on whether they have relevant work experience, historical salary levels, relevant school competition experience and school grades. As a result, it is impossible to accurately select applicants who match the position and can adapt to the job more quickly, thereby saving human resource management costs. Summary of the invention

[0004] The present invention provides a human resources data analysis method based on big data to solve the problems mentioned in the above background technology.

[0005] The present invention provides the following technical solution: a human resources data analysis method based on big data, comprising:

[0006] Obtain target job demand data;

[0007] The target position demand data includes the number of target position personnel required and target position demand items;

[0008] Obtain pre-selected personnel data based on target job demand data;

[0009] Based on the data of pre-selected personnel and the data of target job requirements, the quantity determination data is formed through the quantity comparison strategy;

[0010] The quantity determination data includes first quantity determination data, second quantity determination data and third quantity determination data;

[0011] If the quantity determination data is the first quantity determination data, the first final selection personnel data is formed;

[0012] If the quantity determination data is the second quantity determination data, the first screening strategy is executed to form the second final selection personnel data;

[0013] If the quantity determination data is the third quantity determination data, the second screening strategy is executed to form the third final selection personnel data;

[0014] The final candidates for the target position are determined based on the data of the first final candidates, the data of the second final candidates or the data of the third final candidates.

[0015] As an optional solution of the human resources data analysis method based on big data of the present invention, wherein: the acquisition of pre-selected personnel data according to the target position demand data is specifically as follows:

[0016] Obtain the number of personnel required for the target position and set it as the target number;

[0017] Set the number of pre-selected personnel, and the number of pre-selected personnel> the target number;

[0018] Obtain the requirements of the target position;

[0019] According to the number of pre-selected personnel and the requirements of the target position, obtain the set of pre-selected personnel, recorded as A1...An, where n = the number of pre-selected personnel;

[0020] The elements in the pre-selected personnel set are extracted sequentially and identified as pre-selected personnel.

[0021] As an optional solution of the human resources data analysis method based on big data of the present invention, the quantity comparison strategy includes:

[0022] Get all pre-selected people;

[0023] Get the graduation time of the pre-selected candidates and set it as the pre-selected graduation time;

[0024] Get the current time;

[0025] The time range between the pre-selected graduation time and the current time is set as the target time range;

[0026] If the pre-selected personnel have a time of leaving the company within the target time range, the pre-selected personnel will be determined as the first personnel;

[0027] If the pre-selected person does not have any resignation time within the target time range, the pre-selected person will be determined as the second person;

[0028] Obtain all first persons to form a first person set;

[0029] Obtain all second persons to form a second person set;

[0030] Obtain the number of elements in the first personnel set, and set it as the first number;

[0031] If the first quantity = the target quantity, it is determined as the first quantity determination data;

[0032] All the first candidates will be determined as the final candidates;

[0033] If the first quantity is less than the target quantity, it is determined as the second quantity determination data;

[0034] If the first quantity>the target quantity, it is determined as the third quantity determination data.

[0035] As an optional solution of the human resources data analysis method based on big data of the present invention, the first screening strategy includes:

[0036] Get the second person set;

[0037] Obtain the number of elements in the second personnel set and set it as the second number;

[0038] Extract the second person who contains the first data from the second person set and define it as the third person;

[0039] Obtain all third-party personnel to form a third-party personnel set;

[0040] Get the number of elements in the third person set and set it as the third number;

[0041] Calculate the first difference quantity, the first difference quantity = target quantity - first quantity;

[0042] If the third number = the first difference number, all the first persons and all the third persons in the set of the third persons are determined as the final selected persons;

[0043] If the third quantity is less than the first difference quantity, the first determination strategy is executed;

[0044] If the third quantity>the first difference quantity, the second determination strategy is executed.

[0045] As an optional solution of the human resources data analysis method based on big data of the present invention, the first determination strategy includes:

[0046] Get the second person set;

[0047] Get the third person set;

[0048] Calculate the fourth personnel set, the fourth personnel set = the second personnel set - the third personnel set;

[0049] Get the element in the fourth person set and set it as the first target element;

[0050] Obtain second data of the first target element;

[0051] Executing the first data analysis strategy for each first target element, and obtaining average sub-data corresponding to each first target element to form an average sub-data set;

[0052] Match the elements in the fourth person set to the elements in the average sub-data set one by one to form a first set;

[0053] Arrange the elements in the first set from large to small according to the values ​​of the average sub-data to form a first sequence set;

[0054] Get the first difference quantity;

[0055] Get the third quantity;

[0056] Calculate the second difference quantity, the second difference quantity = the first difference quantity - the third quantity;

[0057] Taking the first element in the first sequence set as the starting element, extract the elements of the second difference number and set them as the first target element;

[0058] Get the fourth person under each first target element and set it as the first target person;

[0059] Then all first persons, all third persons and all first target persons are determined as the final selected persons.

[0060] As an optional solution of the human resources data analysis method based on big data of the present invention, the first data analysis strategy includes:

[0061] Obtain all sub-data corresponding to the first target element and define them as second sub-data to form a second sub-data set, which is recorded as D1...Dn;

[0062] Get the number of elements in the second sub-data set and set it as the number of elements;

[0063] Calculate average sub-data, average sub-data=second sub-data D1+...+second sub-data Dn.

[0064] As an optional solution of the human resources data analysis method based on big data of the present invention, the second determination strategy includes:

[0065] Get the third person set;

[0066] Obtain all sub-data in the first data corresponding to each element in the third person set as first sub-data, and form a third number of first sub-data sets respectively;

[0067] Execute the second data analysis strategy for each first sub-data set, and obtain the highest score corresponding to each first sub-data set to form a highest score set;

[0068] Match the elements in the third person set with the elements in the highest score set one by one to form a determination set;

[0069] Arrange the elements in the judgment set from large to small according to the highest score value to form a score order set;

[0070] Take the first element in the score order set as the starting element, extract the elements with the first difference number, and set them as the second target element;

[0071] Get the third person under each second target element and define it as the second target person;

[0072] All first-target persons and all second-target persons are determined as finalists.

[0073] As an optional solution of the human resources data analysis method based on big data of the present invention, the second data analysis strategy includes:

[0074] Obtaining a first element corresponding to each element in the first sub-data set as a first determination element, and forming a first determination element set;

[0075] The first determination factor is divided into three levels, namely the first level, the second level and the third level;

[0076] Obtaining a score corresponding to each element in the first determination factor set as a first score, thereby forming a first score set;

[0077] The scores of the first level, second level and third level are set to 3, 2 and 1 respectively;

[0078] Obtaining a second factor corresponding to each element in the first sub-data set as a second determination factor to form a second determination factor set;

[0079] The second determination factor is divided into three levels, namely the first level, the second level and the third level;

[0080] Obtaining the score corresponding to each element in the second determination factor set as the second score, and forming a second score set;

[0081] The scores of the first level, second level and third level are set to 3, 2 and 1 respectively;

[0082] Match the elements in the first score set to the elements in the second score set one by one to form a score set;

[0083] Calculate the total score corresponding to each element in the score set, the total score = the first score + the second score;

[0084] Arrange all total scores from large to small, and set the total score with the largest value as the highest score.

[0085] As an optional solution of the human resources data analysis method based on big data of the present invention, the second screening strategy includes:

[0086] Get the first personnel set;

[0087] Get the first quantity;

[0088] Obtain third data corresponding to each element in the first person set to form a third data set;

[0089] Acquire all sub-data corresponding to each element in the third data set, define them as third sub-data, and form a first number of third sub-data sets;

[0090] Execute the third data analysis strategy on each third sub-data set, obtain the second condition element corresponding to each third sub-data set, and form a second condition element set;

[0091] Match the elements in the first person set with the elements in the second condition element set one by one to form a second set;

[0092] Sort the elements in the second set from small to large according to the values ​​of the second condition elements to form a second sequence set;

[0093] Get the target number;

[0094] Take the first element in the second sequence set as the starting element, extract the target number of elements, and set them as the third target element;

[0095] Get the first person under each third target element and set it as the third target person;

[0096] All third target persons will be designated as finalists.

[0097] As an optional solution of the human resources data analysis method based on big data of the present invention, the third data analysis strategy includes:

[0098] Obtain the first element corresponding to each element in the third sub-data set and define it as the first conditional element;

[0099] Arrange all first condition elements from the beginning to the end in time, and set the last first condition element as the target first condition element;

[0100] Obtaining an element in the third sub-data set corresponding to the target first condition element as a determination element;

[0101] The second element corresponding to the determination element is obtained and defined as the second condition element.

[0102] The present invention has the following beneficial effects:

[0103] 1. This human resources data analysis method based on big data can comprehensively evaluate all job seekers according to the needs of the target position, determine the pre-selected candidates who are most suitable for the target position, and then further compare the pre-selected candidates to screen out the job seekers who meet the required number of personnel for the target position, that is, the final selected candidates, as the personnel who can finally enter the target position.

[0104] 2. This human resources data analysis method based on big data gives priority to those with relevant work experience when screening pre-selected candidates. Pre-selected candidates with relevant work experience can adapt to the work of the target position more quickly, reduce employee induction training costs, and improve work efficiency. If the number of personnel with relevant work experience exceeds the number of personnel required for the target position, among the personnel with relevant work experience, those with lower salaries in the previous company can be given priority as the final candidates, as they are less likely to leave due to salary issues.

[0105] 3. This human resources data analysis method based on big data, when there is no person with relevant work experience among the pre-selected candidates, or the number of people with relevant work experience is less than the number of people required for the target position, can select pre-selected candidates who have experience in participating in relevant skill competitions during school from among the pre-selected candidates without relevant work experience, and give priority to pre-selected candidates with better competition results as final-selected candidates, who have strong professional skills, rich practical experience, and teamwork ability, can better and faster integrate into the work of the target position, adapt to the work rhythm of the target position more quickly, and improve work efficiency. If there is no person with experience in participating in relevant skill competitions during school, or the number of people with experience in participating in relevant skill competitions during school cannot meet the number of people required for the target position, the person with better academic performance in school can be selected as the final-selected candidate, who has strong learning ability and good learning attitude, can learn the skills required for the target position more quickly, adapt to the work of the target position more quickly, and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 This is a block diagram of the human resources data analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0107] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0108] For example, see Figure 1 , a human resources data analysis method based on big data, comprising:

[0109] Obtain target job demand data;

[0110] The target position demand data includes the number of target position personnel required and target position demand items;

[0111] According to the target position demand data, obtain the pre-selected personnel data, and conduct a comprehensive evaluation of all the target position application resumes based on the target position demand items, such as skills, experience, educational background, etc. and the number of target position personnel required, and preliminarily screen out the job seekers who are most suitable for the target position and the number of pre-selected personnel, namely the pre-selected personnel;

[0112] Based on the data of pre-selected personnel and the data of target position requirements, a quantity determination data is formed through a quantity comparison strategy to determine whether the number of personnel with work experience among the pre-selected personnel meets the number of personnel requirements for the target position;

[0113] The quantity determination data includes first quantity determination data, second quantity determination data and third quantity determination data;

[0114] If the quantity determination data is the first quantity determination data, the first final selection personnel data is formed;

[0115] If the quantity determination data is the second quantity determination data, the first screening strategy is executed to form the second final selection personnel data;

[0116] If the quantity determination data is the third quantity determination data, the second screening strategy is executed to form the third final selection personnel data;

[0117] The final candidates for the target position are determined based on the first final candidate data, the second final candidate data or the third final candidate data. The final candidates are job seekers who can eventually get the target position of the enterprise.

[0118] Through the above-mentioned human resources data analysis method based on big data, it is possible to comprehensively evaluate all job seekers according to the needs of the target position, determine the pre-selected candidates who are most suitable for the target position, and then further compare the pre-selected candidates to screen out the job seekers who meet the personnel requirements of the target position, namely the final selected candidates, as the personnel who can eventually enter the target position; pre-selected candidates with relevant work experience are given priority as final selected candidates. Pre-selected candidates with relevant work experience can adapt to the work of the target position more quickly and reduce the cost of employee on-the-job training. If the number of pre-selected candidates with relevant work experience exceeds the demand, the personnel with lower salary in the previous company can be given priority from the pre-selected candidates with relevant work experience as the final selected candidates. The risk of leaving the company due to salary issues may be If the number of pre-selected candidates with relevant work experience is lower than the demand, the pre-selected candidates who have experience in participating in relevant skill competitions during school can be selected from the pre-selected candidates without relevant work experience, and the pre-selected candidates with better competition results will be given priority as the final selected candidates. They have strong professional skills, rich practical experience, and teamwork ability, and can better and faster integrate into the work of the target position and adapt to the work rhythm of the target position more quickly. If the number of candidates with experience in participating in relevant skill competitions during school cannot meet the number of candidates for the target position, the candidates with better grades in school can be selected as the final selected candidates. They have strong learning ability and good learning attitude, and can learn the skills required for the target position more quickly and adapt to the work of the target position more quickly.

[0119] Embodiment 2: This embodiment is an improvement made on the basis of embodiment 1. The human resources data analysis method based on big data obtains pre-selected personnel data according to target position demand data, specifically:

[0120] Get the number of personnel required for the target position and set it as the target number, for example, the target number is 1;

[0121] Set the number of pre-selected candidates, and the number of pre-selected candidates > the target number. The number of pre-selected candidates is the number of job seekers initially screened from all resumes submitted to the target position. The number of pre-selected candidates can be set according to actual needs. For example, the number of pre-selected candidates can be set to twice the target number, that is, the number of pre-selected candidates can be 2;

[0122] Obtain the requirements of the target position;

[0123] According to the number of pre-selected candidates and the requirements of the target position, a set of pre-selected candidates is obtained, which is recorded as A1...An, where n = the number of pre-selected candidates. The pre-selected candidates are selected by comprehensive evaluation based on the requirements of the target position, such as skills, experience, educational background, etc. and the number of personnel required for the target position, and the optimal number of pre-selected candidates are initially screened from all resumes submitted to the target position to form a set of pre-selected candidates. Then, all pre-selected candidates in the set of pre-selected candidates are further screened to select the target number of candidates with better conditions and more suitable for the target position.

[0124] The elements in the pre-selected personnel set are extracted sequentially and identified as pre-selected personnel.

[0125] Embodiment 3: This embodiment is an improvement made on the basis of embodiment 2. The human resources data analysis method based on big data and the quantity comparison strategy include:

[0126] Get all pre-selected people;

[0127] Get the graduation time of the pre-selected candidates and set it as the pre-selected graduation time;

[0128] Get the current time;

[0129] The time range between the pre-selected graduation time and the current time is set as the target time range;

[0130] If the pre-selected person has a time of leaving the job within the target time range, the pre-selected person will be determined as the first person, and the first person will be the pre-selected person with work experience;

[0131] If the pre-selected candidate does not have any resignation time within the target time range, the pre-selected candidate will be determined as the second candidate, and the second candidate will be the pre-selected candidate without work experience;

[0132] Obtain all first persons to form a first person set;

[0133] Obtain all second persons to form a second person set;

[0134] Obtain the number of elements in the first personnel set, and set it as the first number;

[0135] If the first quantity = the target quantity, it means that the number of the first personnel, that is, the number of pre-selected personnel with work experience, is the same as the target quantity, which can just meet the personnel demand of the target position, and it is determined as the first quantity determination data;

[0136] All the first candidates will be determined as the final candidates;

[0137] If the first number is less than the target number, it means that the number of the first personnel, i.e. the pre-selected personnel with work experience, is less than the target number. In order to meet the number of personnel required for the target position, other job seekers need to be screened out from the pre-selected personnel, which is determined as the second number determination data;

[0138] If the first number is greater than the target number, it indicates that the number of the first personnel, ie, the pre-selected personnel with work experience, exceeds the target number, and it is necessary to further screen the target number of job seekers from the first personnel, which is determined as the third number determination data.

[0139] Embodiment 4: This embodiment is an improvement made on the basis of embodiment 3. The human resources data analysis method based on big data and the quantity comparison strategy include:

[0140] Get all pre-selected people;

[0141] Get the graduation time of the pre-selected candidates and set it as the pre-selected graduation time;

[0142] Get the current time;

[0143] The time range between the pre-selected graduation time and the current time is set as the target time range;

[0144] If the pre-selected person has a time of leaving the job within the target time range, the pre-selected person will be determined as the first person, and the first person will be the pre-selected person with work experience;

[0145] If the pre-selected candidate does not have any resignation time within the target time range, the pre-selected candidate will be determined as the second candidate, and the second candidate will be the pre-selected candidate without work experience;

[0146] Obtain all first persons to form a first person set;

[0147] Obtain all second persons to form a second person set;

[0148] Obtain the number of elements in the first personnel set, and set it as the first number;

[0149] If the first quantity = the target quantity, it means that the number of the first personnel, that is, the number of pre-selected personnel with work experience, is the same as the target quantity, which can just meet the personnel demand of the target position, and it is determined as the first quantity determination data;

[0150] All the first candidates will be determined as the final candidates;

[0151] If the first number is less than the target number, it means that the number of the first personnel, i.e. the pre-selected personnel with work experience, is less than the target number. In order to meet the number of personnel required for the target position, other job seekers need to be screened out from the pre-selected personnel, which is determined as the second number determination data;

[0152] If the first number is greater than the target number, it indicates that the number of the first personnel, ie, the pre-selected personnel with work experience, exceeds the target number, and it is necessary to further screen the target number of job seekers from the first personnel, which is determined as the third number determination data.

[0153] In this embodiment, the first determination strategy includes:

[0154] Get the second person set;

[0155] Get the third person set;

[0156] Calculate the fourth person set, the fourth person set = the second person set - the third person set, the elements in the fourth person set are the remaining elements after removing the elements in the third person set from the second person set;

[0157] Get the element in the fourth person set and set it as the first target element;

[0158] Obtaining second data of the first target element, the second data being academic performance;

[0159] Executing the first data analysis strategy for each first target element, and obtaining average sub-data corresponding to each first target element to form an average sub-data set;

[0160] Match the elements in the fourth person set to the elements in the average sub-data set one by one to form a first set;

[0161] Arrange the elements in the first set from large to small according to the values ​​of the average sub-data to form a first sequence set;

[0162] Get the first difference quantity;

[0163] Get the third quantity;

[0164] Calculate the second difference quantity, the second difference quantity = the first difference quantity - the third quantity;

[0165] Taking the first element in the first sequence set as the starting element, extract the elements of the second difference number and set them as the first target element;

[0166] Get the fourth person under each first target element and set it as the first target person;

[0167] Then all first persons, all third persons and all first target persons are determined as the final selected persons.

[0168] In this embodiment, the first data analysis strategy includes:

[0169] Obtain all sub-data corresponding to the first target element, which are defined as second sub-data, to form a second sub-data set, recorded as D1...Dn, where the second sub-data is the grade points of each semester of the fourth person in school, and the grade points of each semester are the ratio of the sum of the grade points of the courses taken in each semester to the number of courses taken in each semester;

[0170] Get the number of elements in the second sub-data set and set it as the number of elements;

[0171] Calculate average sub-data, average sub-data=second sub-data D1+...+second sub-data Dn.

[0172] When the sum of the number of second candidates with experience in participating in competitions in school and the number of pre-selected candidates with work experience is lower than the target number, candidates with better academic performance in school can be selected from other second candidates as final candidates. They have strong learning ability and good learning attitude, can learn the skills required for the target position more quickly, and adapt to the work of the target position more quickly.

[0173] In this embodiment, the second determination strategy includes:

[0174] Get the third person set;

[0175] Obtain all sub-data in the first data corresponding to each element in the third personnel set as first sub-data, and form a third number of first sub-data sets respectively. The first sub-data set is a set formed by all competitions participated by the third personnel under the element at school;

[0176] Execute the second data analysis strategy for each first sub-data set, and obtain the highest score corresponding to each first sub-data set to form a highest score set;

[0177] Match the elements in the third person set with the elements in the highest score set one by one to form a determination set;

[0178] Arrange the elements in the judgment set from large to small according to the highest score value to form a score order set;

[0179] Take the first element in the score order set as the starting element, extract the elements with the first difference number, and set them as the second target element;

[0180] Get the third person under each second target element and define it as the second target person;

[0181] All first-target persons and all second-target persons are determined as finalists.

[0182] In this embodiment, the second data analysis strategy includes:

[0183] Obtaining a first factor corresponding to each element in the first sub-data set as a first determination factor to form a first determination factor set, wherein the first determination factor is the event level;

[0184] The first determination factor is divided into three levels, namely the first level, the second level and the third level, and the first level, the second level and the third level are national level, provincial level and school level respectively;

[0185] Obtaining a score corresponding to each element in the first determination factor set as a first score, thereby forming a first score set;

[0186] The scores of the first level, second level and third level are set to 3, 2 and 1 respectively;

[0187] Obtain the second factor corresponding to each element in the first sub-data set, define it as the second determination factor, and form a second determination factor set, where the second determination factor is the level of the award obtained;

[0188] The second judging factor is divided into three levels, namely the first level, the second level and the third level, and the first level, the second level and the third level are respectively the first prize, the second prize and the third prize;

[0189] Obtaining the score corresponding to each element in the second determination factor set as the second score, and forming a second score set;

[0190] The scores of the first level, second level and third level are set to 3, 2 and 1 respectively;

[0191] Match the elements in the first score set to the elements in the second score set one by one to form a score set;

[0192] Calculate the total score corresponding to each element in the score set, the total score = the first score + the second score;

[0193] Arrange all total scores from large to small, and set the total score with the largest value as the highest score.

[0194] When the sum of the number of second candidates with experience in participating in competitions at school and the number of pre-selected candidates with work experience exceeds the target number, candidates with better performance in the competition can be further selected from the third candidates as final candidates. They have strong professional skills, rich practical experience, and teamwork ability, and can better and faster integrate into the work of the target position and adapt to the work rhythm of the target position more quickly.

[0195] Embodiment 5: This embodiment is an improvement made on the basis of embodiment 4. The human resources data analysis method based on big data, the second screening strategy includes:

[0196] Get the first personnel set;

[0197] Get the first quantity;

[0198] Obtain third data corresponding to each element in the first personnel set to form a third data set, where the third data set is work experience;

[0199] Obtain all sub-data corresponding to each element in the third data set, define them as third sub-data, and form a third sub-data set of the first quantity, wherein the third sub-data set is a set formed by the work experience of all companies in the work experience;

[0200] Execute the third data analysis strategy on each third sub-data set, obtain the second condition element corresponding to each third sub-data set, and form a second condition element set;

[0201] Match the elements in the first person set with the elements in the second condition element set one by one to form a second set;

[0202] Sort the elements in the second set from small to large according to the values ​​of the second condition elements to form a second sequence set;

[0203] Get the target number;

[0204] Take the first element in the second sequence set as the starting element, extract the target number of elements, and set them as the third target element;

[0205] Get the first person under each third target element and set it as the third target person;

[0206] All third target persons will be designated as finalists.

[0207] In this embodiment, the third data analysis strategy includes:

[0208] Obtain the first element corresponding to each element in the third sub-data set, and define it as the first condition element, where the first condition element is the time of leaving the job;

[0209] Arrange all first condition elements from the beginning to the end in time, and set the last first condition element as the target first condition element;

[0210] Obtaining an element in the third sub-data set corresponding to the target first condition element as a determination element;

[0211] The second element corresponding to the determination element is obtained and defined as the second condition element, and the second condition element is the salary under the determination element.

[0212] When the number of people with relevant work experience exceeds the number of people required for the target position, those with relevant work experience who had lower salaries in their previous companies can be given priority as finalists because they are less likely to face the risk of leaving due to salary issues.

[0213] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the human resources data analysis method based on big data in the aforementioned method embodiment.

[0214] It should be noted that more specific examples of computer-readable storage media may include portable computer disks, hard disks, erasable programmable read-only memories (EPROMs or flash memories), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present disclosure, computer-readable storage media may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device, and the computer-readable medium may be contained in the electronic device; or it may exist independently without being assembled into the electronic device.

[0215] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can implement the solution provided by the method embodiment.

[0216] Computer program code for performing operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, and may be executed entirely on a user's computer, partially on a user's computer, as a stand-alone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0217] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0218] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A human resources data analysis method based on big data, characterized by: include: Obtain target job demand data; The target position demand data includes the number of target position personnel required and target position demand items; Obtain pre-selected personnel data based on target job demand data; Based on the data of pre-selected personnel and the data of target job requirements, the quantity determination data is formed through the quantity comparison strategy; The quantity determination data includes first quantity determination data, second quantity determination data and third quantity determination data; If the quantity determination data is the first quantity determination data, the first final selection personnel data is formed; If the quantity determination data is the second quantity determination data, the first screening strategy is executed to form the second final selection personnel data; If the quantity determination data is the third quantity determination data, the second screening strategy is executed to form the third final selection personnel data; The final candidates for the target position are determined based on the data of the first final candidates, the data of the second final candidates or the data of the third final candidates.

2. The human resources data analysis method based on big data according to claim 1 is characterized by: The method of obtaining the pre-selected personnel data according to the target position demand data is as follows: Obtain the number of personnel required for the target position and set it as the target number; Set the number of pre-selected personnel, and the number of pre-selected personnel> the target number; Obtain the requirements of the target position; According to the number of pre-selected personnel and the requirements of the target position, obtain the set of pre-selected personnel, recorded as A1...An, where n = the number of pre-selected personnel; The elements in the pre-selected personnel set are extracted sequentially and identified as pre-selected personnel.

3. The human resources data analysis method based on big data according to claim 2 is characterized by: The quantity comparison strategy includes: Get all pre-selected people; Get the graduation time of the pre-selected candidates and set it as the pre-selected graduation time; Get the current time; The time range between the pre-selected graduation time and the current time is set as the target time range; If the pre-selected personnel have a time of leaving the company within the target time range, the pre-selected personnel will be determined as the first personnel; If the pre-selected person does not have any resignation time within the target time range, the pre-selected person will be determined as the second person; Obtain all first persons to form a first person set; Obtain all second persons to form a second person set; Obtain the number of elements in the first personnel set, and set it as the first number; If the first quantity = the target quantity, it is determined as the first quantity determination data; All the first candidates will be determined as the final candidates; If the first quantity is less than the target quantity, it is determined as the second quantity determination data; If the first quantity>the target quantity, it is determined as the third quantity determination data.

4. The human resources data analysis method based on big data according to claim 3 is characterized by: The first screening strategy includes: Get the second person set; Obtain the number of elements in the second personnel set and set it as the second number; Extract the second person who contains the first data from the second person set and define it as the third person; Obtain all third-party personnel to form a third-party personnel set; Get the number of elements in the third person set and set it as the third number; Calculate the first difference quantity, the first difference quantity = target quantity - first quantity; If the third number = the first difference number, all the first persons and all the third persons in the set of the third persons are determined as the final selected persons; If the third quantity is less than the first difference quantity, the first determination strategy is executed; If the third quantity>the first difference quantity, the second determination strategy is executed.

5. The human resources data analysis method based on big data according to claim 4 is characterized by: The first determination strategy includes: Get the second person set; Get the third person set; Calculate the fourth personnel set, the fourth personnel set = the second personnel set - the third personnel set; Get the element in the fourth person set and set it as the first target element; Obtain second data of the first target element; Executing the first data analysis strategy for each first target element, and obtaining average sub-data corresponding to each first target element to form an average sub-data set; Match the elements in the fourth person set to the elements in the average sub-data set one by one to form a first set; Arrange the elements in the first set from large to small according to the values ​​of the average sub-data to form a first sequence set; Get the first difference quantity; Get the third quantity; Calculate the second difference quantity, the second difference quantity = the first difference quantity - the third quantity; Taking the first element in the first sequence set as the starting element, extract the elements of the second difference number and set them as the first target element; Get the fourth person under each first target element and set it as the first target person; Then all first persons, all third persons and all first target persons are determined as the final selected persons.

6. The human resources data analysis method based on big data according to claim 5 is characterized by: The first data analysis strategy includes: Obtain all sub-data corresponding to the first target element and define them as second sub-data to form a second sub-data set, which is recorded as D1...Dn; Get the number of elements in the second sub-data set and set it as the number of elements; Calculate average sub-data, average sub-data=second sub-data D1+...+second sub-data Dn.

7. The human resources data analysis method based on big data according to claim 4 is characterized by: The second determination strategy includes: Get the third person set; Obtain all sub-data in the first data corresponding to each element in the third person set as first sub-data, and form a third number of first sub-data sets respectively; Execute the second data analysis strategy for each first sub-data set, and obtain the highest score corresponding to each first sub-data set to form a highest score set; Match the elements in the third person set with the elements in the highest score set one by one to form a determination set; Arrange the elements in the judgment set from large to small according to the highest score value to form a score order set; Take the first element in the score order set as the starting element, extract the elements with the first difference number, and set them as the second target element; Get the third person under each second target element and define it as the second target person; All first-target persons and all second-target persons are determined as finalists.

8. The human resources data analysis method based on big data according to claim 7 is characterized by: The second data analysis strategy includes: Obtaining a first element corresponding to each element in the first sub-data set as a first determination element, and forming a first determination element set; The first determination factor is divided into three levels, namely the first level, the second level and the third level; Obtaining a score corresponding to each element in the first determination factor set as a first score, thereby forming a first score set; The scores of the first level, second level and third level are set to 3, 2 and 1 respectively; Obtaining a second factor corresponding to each element in the first sub-data set as a second determination factor to form a second determination factor set; The second determination factor is divided into three levels, namely the first level, the second level and the third level; Obtaining the score corresponding to each element in the second determination factor set as the second score, and forming a second score set; The scores of the first level, second level and third level are set to 3, 2 and 1 respectively; Match the elements in the first score set to the elements in the second score set one by one to form a score set; Calculate the total score corresponding to each element in the score set, the total score = the first score + the second score; Arrange all total scores from large to small, and set the total score with the largest value as the highest score.

9. The human resources data analysis method based on big data according to claim 3 is characterized by: The second screening strategy comprises: Get the first personnel set; Get the first quantity; Obtain third data corresponding to each element in the first person set to form a third data set; Acquire all sub-data corresponding to each element in the third data set, define them as third sub-data, and form a first number of third sub-data sets; Execute the third data analysis strategy on each third sub-data set, obtain the second condition element corresponding to each third sub-data set, and form a second condition element set; Match the elements in the first person set with the elements in the second condition element set one by one to form a second set; Sort the elements in the second set from small to large according to the values ​​of the second condition elements to form a second sequence set; Get the target number; Take the first element in the second sequence set as the starting element, extract the target number of elements, and set them as the third target element; Get the first person under each third target element and set it as the third target person; All third target persons will be designated as finalists.

10. The human resources data analysis method based on big data according to claim 9 is characterized by: The third data analysis strategy includes: Obtain the first element corresponding to each element in the third sub-data set and define it as the first conditional element; Arrange all first condition elements from the beginning to the end in time, and set the last first condition element as the target first condition element; Obtaining an element in the third sub-data set corresponding to the target first condition element as a determination element; The second element corresponding to the determination element is obtained and defined as the second condition element.

Images