Personal occupational growth path design method and system based on big data analysis

Abstract

The invention discloses a personal occupational growth path design method and system based on big data analysis, and belongs to the technical field of big data analysis, and the method comprises the steps: 1, collecting occupational related data, and obtaining the basic data of the personal occupational growth path design; step 2, setting position scoring indexes, classifying occupational related data, and establishing an occupational growth path database; 3, inputting personal data, obtaining a matching data set based on a matching algorithm, and determining a skill set of each position; and step 4, designing a personal occupational growth path based on correlation analysis of occupational related data, and according to the scheme, position data matched in a workplace is acquired and analyzed through a big data analysis technology, so that young employees can clearly know position requirements and correctly evaluate their ability.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data analysis, and specifically relates to a method and system for designing a personal career growth path based on big data analysis. Background Art

[0002] With the rapid development of information technology, especially the widespread application of big data and artificial intelligence technology, the demand for high-skilled talents in all walks of life is growing, especially high-end technical positions such as algorithm development engineers, which have become the key force to promote corporate innovation and upgrading. However, the current talent big data center still has many deficiencies in functions and services, and it is difficult to meet the urgent needs of accurately connecting personal career planning with industrial development.

[0003] There is an urgent need to provide a design method for career growth path planning based on data analysis through technological innovation and model upgrading. This method should be able to deeply explore and display the detailed paths and standards for talent training, and provide young employees with accurate career development advice. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for designing a personal career growth path based on big data analysis, so as to solve the technical problem of insufficient intelligence in the career planning analysis process in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for designing personal career growth paths based on big data analysis, including:

[0007] Step 1: Collect career-related data to obtain basic data for designing personal career growth paths;

[0008] Step 2: Set job scoring indicators, classify career-related data, and establish a career growth path database;

[0009] Step 3: Input personal data, obtain matching data sets based on matching algorithms, and determine the skill sets for each position;

[0010] Step 4: Design personal career growth path based on correlation analysis of career-related data.

[0011] Furthermore, step one specifically includes the following process:

[0012] Collect basic occupational information through the company's personnel files, resume information, and relevant skill certificates: employees' current and past working hours, workload, and basic salary information; skill specialty information: the number and types of professional skills related to the position.

[0013] Furthermore, the calculation formula for the position scoring index in step 2 is as follows:

[0014]

[0015] Among them, α is the time change factor, which changes with the working time. The longer the working time in the current position, the smaller the α value. α is a virtual setting, C represents the basic salary, T represents the working time, M represents the workload, k represents the kth employee, i represents the position i of the kth employee, p(i) represents the position development information score, k1, k2, k3 represent the weight values ​​of basic salary, working time, and workload respectively, and k1+k2+k3=1;

[0016] The specific calculation method for job development information scoring is as follows:

[0017] The company's historical data is processed through machine learning algorithms to calculate the average resignation rate η, the promotion rate λ and the personnel growth rate δ of each position within I year. The calculation formula of the position development information score p(i) is as follows:

[0018]

[0019] Different data sets are divided based on the positions of different employees in different age groups. Each data set contains the employee positions, gender, work address, position scoring indicators and professional skills information within an age group. A career growth path database is established based on all data sets.

[0020] Furthermore, in step 3, the process of inputting personal data and obtaining a matching data set based on a matching algorithm includes:

[0021] The matching algorithm is calculated based on the individual's current skill reserve, and a matching threshold requirement is set. Data with a matching degree greater than the threshold is retained in the matching data set. The calculation formula of the matching algorithm is as follows:

[0022]

[0023] Where x represents an individual, y i represents the i-th employee, n(x|y i ) represents x and y i The number of same skills, n(y i ) represents y i The total number of skills possessed.

[0024] Furthermore, in step 3, a matching data set is obtained based on a matching algorithm, and the process of determining the skill set for each position includes:

[0025] Through the query function of the database, the career-related data of employees at different ages in the above matching data set are collected and set as the career growth data set. The data that meets the requirements is retained in the career growth data set. Based on personal requirements, the weight values ​​k1, k2, and k3 of basic salary, working hours, and workload are set to obtain the size of the position scoring index of employees at different ages in the career growth data set. According to the size of the position scoring index, the skill specialty information of employees with the largest N position scoring indexes of different positions is screened out in turn, and the resulting set is:

[0026] G={Ta1,…,Ta k ,…,Ta N};

[0027] Among them k Represents the skill set of the kth employee, marks the skill set of each employee and sorts them by learning time, expressed as:

[0028] Ta k ={a1,b1,…,a i ,b i ,c i ,…,a n ,b n ,c n ,d n};

[0029] Where a, b, c, and d represent different skills, and i represents the skills that an employee possesses in the i-th position.

[0030] Furthermore, step 4 specifically includes the following process:

[0031] Analyze the skill sets of all the above employees, set the skill frequency threshold f for each position, and obtain the necessary skill set for each position by counting the frequency of the same skills possessed by each employee in different positions. The calculation formula is as follows:

[0032]

[0033] where Y i (t) represents the frequency of occurrence of skill t in the ith position, r i (t) represents the number of people with skill t in the ith position. i When (t) is greater than or equal to f, skill t is set as the necessary skill required for position i, and the necessary skills for each position are set as the first learning goal. Then, the second learning goal is determined by the skill importance index and skill importance judgment. After determining the first and second learning goals, input your own interests and hobbies, and determine the third learning goal through the matching algorithm;

[0034] The first, second and third learning goals are taken as a round of learning content. The first goal is set as the primary learning goal, accounting for z% of the daily learning time; the second goal is set as the secondary learning goal, accounting for g% of the daily learning time; the third goal is set as the general learning goal, accounting for e% of the daily learning time, z>g>e. Whenever a learning goal is completed, the daily learning time of the goal is evenly divided into the unfinished learning goals. When all learning goals are completed, the round of learning content is completed;

[0035] Afterwards, based on the personal skill set obtained from completing a round of target learning, redefine the learning goals and carry out the next round of learning until the personal skill set and experience meet the requirements for job promotion. After being promoted to a new position, repeat the above steps based on the new position requirements and personal skill set, and redefine the growth method for promotion to other positions.

[0036] Furthermore, the specific process of determining the second learning goal through the skill importance index and skill importance judgment includes:

[0037] According to the position scoring index, the skill sets of each employee are sorted at different position stages, and employee A and employee B are selected. It is required that the skill set of employee A is included in the skill set of employee B in the same position, and the position scoring index of employee B is greater than that of A. The skill importance index is calculated. The calculation formula is as follows:

[0038]

[0039] Where V i represents the skill importance index of position i, and sets the skill importance index threshold v. When V i When it is greater than or equal to v, the skill importance is judged. If there is a skill that passes the skill importance judgment, the skill is set to belong to the second learning goal. The skill importance judgment method is as follows:

[0040]

[0041] Where t represents the skill t that employee B has but employee A does not have.

[0042] Furthermore, the specific process of determining the third learning goal through the matching algorithm includes:

[0043] Through the matching algorithm, select employees in the matching data set that match their additional information. The calculation formula of the matching algorithm is as follows:

[0044]

[0045] Where x represents an individual, y i represents the yth employee, l(x|yi ) represents x and y i The number of common interests and hobbies, l(y i ) represents y i The total number of hobbies, set the threshold H, when L(x|y i ) is greater than or equal to H to determine employee y i Matches itself, It represents the skill set that the employee has in position i. Based on this skill set, the employee selects the skills that do not belong to the first and second learning goals and that he or she does not yet possess as the third learning goal according to his or her own wishes.

[0046] A personal career growth path design system based on big data analysis, including:

[0047] Career-related data collection module: used to collect career-related data and obtain basic data for designing personal career growth paths;

[0048] Career growth path database establishment module: used to set job scoring indicators, classify career-related data, and establish a career growth path database;

[0049] Data matching module: used to input personal data, obtain matching data sets based on matching algorithms, and determine the skill sets for each position;

[0050] Personal career growth path design module: used to design personal career growth path based on correlation analysis of career-related data.

[0051] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0052] 1. The present invention sets job scoring indicators based on basic occupation information, job development information, and additional information. By setting scoring indicators, the career development direction and learning goals of employees are clarified, and skill learning plans are designed based on the skill set of employees with high job scoring indicators.

[0053] 2. The present invention helps new employees to clearly understand their career development direction and required skills by matching data of employees with similar growth starting points, sorting out career goals and planning growth paths, which helps them maintain motivation and concentration in learning and improve their competitiveness;

[0054] 3. The present invention designs a personal career growth path based on the correlation analysis of career-related data. By setting the first, second and third learning goals, core skills are systematically learned and mastered. A segmented learning plan is formulated according to the learning difficulty of different learning goals, which encourages young employees to continue learning and practicing, and thereby achieve personal growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flowchart of a method for designing a personal career growth path based on big data analysis is shown;

[0057] Figure 2 A structural diagram of a personal career growth path design system based on big data analysis is shown;

[0058] Figure 3 A design diagram showing a personal career growth path. DETAILED DESCRIPTION

[0059] 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.

[0060] Example 1

[0061] like Figure 1 , Figure 3 As shown, the present embodiment proposes a method for designing a personal career growth path based on big data analysis, which specifically includes the following steps:

[0062] Step 1. Collect career-related data to obtain basic data for designing personal career growth paths; collect career-related data of the same occupation in different companies through personnel files, resume information, and relevant skill certificates, and divide career-related data into types: basic employee information, basic occupation information, skill specialty information, additional information, and position development information. Basic information includes: employee age, gender, work address, position title, and academic and professional information; basic occupation information includes: employee current and past position working hours, workload, and basic salary information; skill specialty information includes: professional skills that employees possess that can improve the work efficiency and work quality of current or past positions; additional information includes: employees' current interests and hobbies; position development information includes: employees' competitiveness for current or past positions, difficulty of promotion, and development trends. The collected data will be cleaned, integrated, and normalized.

[0063] Step 2: Set job scoring indicators, classify career-related data, and establish a career growth path database; classify career-related data according to the basic information of employees, match career-related data of different employees at different age groups based on the age information in the basic information of employees, and set job scoring indicators based on basic career information, job development information, and additional information. The calculation formula of job scoring indicators is as follows:

[0064]

[0065] Among them, α is the time change factor, which changes with working time. The longer the working time in the current position, the smaller the α value. α is a virtual setting, C represents basic salary, T represents working time, M represents workload, k represents the kth employee, i represents the position i of the kth employee, p(i) represents the position development information score, k1, k2, k3 represent the weight values ​​of basic salary, working time and workload respectively, and k1+k2+k3=1.

[0066] The specific calculation method for job development information scoring is as follows:

[0067] The company's historical data is processed through machine learning algorithms to calculate the average resignation rate η, the promotion rate λ and the personnel growth rate δ of each position within I year, quantify the position competitiveness, promotion difficulty and development trend, and the calculation formula of the position development information score p(i) is as follows:

[0068]

[0069] Different data sets are divided based on the positions of different employees in different age groups. Each data set contains the employee positions, gender, work address, position scoring indicators and professional skill information within an age group. A career growth path database is established based on all data sets, and indexes are created for key fields such as position, age, and skills to improve query efficiency. Full-text indexes are used to support fuzzy queries on text fields such as job descriptions and career path descriptions, which are summarized in the career growth path database.

[0070] Step 3: Input personal basic information, obtain matching data set based on matching algorithm, and determine the skill set of each position; input personal data for which career growth path design is required, and match data with similar information through data analysis algorithm according to personal basic information such as academic degree, age, work address, and current position, i.e., data in the career growth path database with career-related data of employees of the same age group similar to personal basic information, calculate matching algorithm according to personal current skill reserve, and set matching degree threshold E, retain data with matching degree greater than or equal to E in matching data set, and the calculation formula of matching algorithm is as follows:

[0071]

[0072] Q(x,y k ) represents x and y k where x represents an individual and y represents k represents the kth employee, n(x|y k ) represents x and y k The number of same skills, n(y k ) represents y k The total number of skills possessed; in the resulting matching data set, all employees in the same age group have similar positions, work addresses, and skills.

[0073] Through the query function of the database, the career-related data of employees at different ages in the above matching data set are collected and set as the career growth data set. Preliminary screening is carried out through the hard requirements of the position, such as educational threshold, professional requirements, age and gender restrictions, and the data that meets the requirements are retained in the career growth data set. Based on personal requirements, the weight values ​​k1, k2, and k3 of basic salary, working hours, and workload are set to obtain the size of the position scoring index of employees at different age stages in the career growth data set. According to the size of the position scoring index, the skill specialty information of employees with the largest N position scoring indicators of different positions is screened out in turn, and the resulting set is:

[0074] G={Ta1,…,Ta k ,…,Ta N};

[0075] Among them k Represents the skill set of the kth employee, marks the skill set of each employee and sorts them by learning time, expressed as:

[0076] Ta k ={a1,b1,…,a i ,b i ,c i ,…,a n ,bn ,c n ,d n};

[0077] Where a, b, c, and d represent different skills, and i represents the skills that an employee has in the i-th position;

[0078] Through data analysis, we obtained the regular promotion paths of the above employees from entry-level positions to senior positions, including the required skills, years of experience, and educational background requirements.

[0079] Step 4: Design a personal career growth path based on the correlation analysis of career-related data; analyze the skill sets of all the above employees, set the skill frequency threshold f for each position, and obtain the necessary skill set for each position by counting the frequency of the same skills possessed by each employee in different positions. The calculation formula is as follows:

[0080]

[0081] where Y i (t) represents the frequency of occurrence of skill t in the ith position, r i (t) represents the number of people with skill t in the ith position. i When (t) is greater than or equal to f, skill t is set as the necessary skill required for position i, and the necessary skills of each position are set as the first learning goal, which is the primary learning skill before promotion to the position. If the skill is included in the personal skill set or after the skill is learned, the second learning goal is started. The steps for obtaining the second learning goal are as follows:

[0082] According to the position scoring index, the skill sets of each employee are sorted at different position stages, and employee A and employee B are selected. It is required that the skill set of employee A is included in the skill set of employee B in the same position, and the position scoring index of employee B is greater than that of A. The skill importance index is calculated. The specific calculation formula is as follows:

[0083]

[0084] Where V i represents the skill importance index of position i, and sets the skill importance index threshold v. When V i When it is greater than or equal to v, the skill importance is judged. If there is a skill that passes the skill importance judgment, the skill is set to belong to the second learning goal. The skill importance judgment method is as follows:

[0085]

[0086] Where t represents the skill t that employee B has but employee A does not have.

[0087] After determining the first and second learning goals, enter your additional information and use the matching algorithm to select employees in the matching data set that match your additional information. The calculation formula of the matching algorithm is as follows:

[0088]

[0089] Where x represents an individual, y i represents the yth employee, l(x|y i ) represents x and y i The number of common interests and hobbies, l(y i ) represents y i The total number of hobbies, set the threshold H, when L(x|y i ) is greater than or equal to H to determine employee y i Matches itself, It represents the skill set that the employee has in position i. Based on this skill set, the employee selects the skills that do not belong to the first and second learning goals and that he or she does not yet possess as the third learning goal according to his or her own wishes.

[0090] The first, second and third learning objectives are used as a round of learning content. For the first learning objective, a skill improvement plan is formulated: a detailed skill learning plan is formulated, including daily or weekly learning time, learning content, and practical projects. You can seek help from senior employees to quickly master key technologies; participate in training and practical application: utilize internal and external training resources of the company, participate in relevant skill training courses or seminars, obtain the latest skill trends and cutting-edge knowledge, actively apply learned skills in daily work, and consolidate and deepen understanding by solving practical problems.

[0091] For the second learning goal, read: buy or borrow relevant books, online courses to learn; simulate: conduct simulation tests with predecessors or colleagues to improve practical operation ability; practice: regularly seek feedback from mentors or colleagues, understand your own shortcomings, and conduct targeted practice;

[0092] For the third learning goal, through communication: briefly understand the relevant content through communication with predecessors or colleagues; conceptual understanding: roughly understand the relevant concepts through online search;

[0093] By testing the mastery of knowledge and skills in specific fields for each learning target, when the accuracy of the test is greater than or equal to the accuracy threshold of the corresponding learning target, it is judged that the learning of the learning target is completed, and the accuracy thresholds of the first, second and third learning targets are set to μ1, μ2, and μ3 respectively, and μ1>μ2>μ3.

[0094] like Figure 3As shown, after completing a round of learning content, based on the personal skill set that has completed a round of target learning, redefine the learning goals and proceed to the next round of learning until the personal skill set and experience meet the requirements for job promotion. After being promoted to a new position, repeat the above steps based on the new position requirements and personal skill set, and redefine the growth method for promotion to other positions.

[0095] Example 2

[0096] like Figure 2 As shown, the present embodiment proposes a personal career growth path design system based on big data analysis, which specifically includes the following contents:

[0097] Career-related data collection module: used to collect career-related data and obtain basic data for designing personal career growth paths; collect career-related data of the same occupation in different companies through personnel files, resume information, and relevant skill certificates, and divide career-related data into types: basic employee information, basic occupation information, skill specialty information, additional information, and position development information. Basic information includes: employee age, gender, work address, position title, and academic and professional information; basic occupation information includes: employee current and past position working hours, workload, and basic salary information; skill specialty information includes: professional skills that employees possess that can improve the work efficiency and work quality of current or past positions; additional information includes: employee current interests and hobbies; position development information includes: employee competitiveness for current or past positions, difficulty of promotion, and development trends. The collected data will be cleaned, integrated, and normalized.

[0098] Career growth path database establishment module: set job scoring indicators, classify career-related data, and establish a career growth path database; classify career-related data according to the basic information of employees, match career-related data information of different employees in different age groups based on the age information in the basic information of employees, set job scoring indicators based on basic career information, job development information, and additional information. The calculation formula of job scoring indicators is as follows:

[0099]

[0100] Among them, α is the time change factor, which changes with working time. The longer the working time in the current position, the smaller the α value. α is a virtual setting, C represents basic salary, T represents working time, M represents workload, k represents the kth employee, i represents the position i of the kth employee, p(i) represents the position development information score, k1, k2, k3 represent the weight values ​​of basic salary, working time and workload respectively, and k1+k2+k3=1.

[0101] The specific calculation method for job development information scoring is as follows:

[0102] The company's historical data is processed through machine learning algorithms to calculate the average resignation rate η, the promotion rate λ and the personnel growth rate δ of each position within I year, quantify the position competitiveness, promotion difficulty and development trend, and the calculation formula of the position development information score p(i) is as follows:

[0103]

[0104] Different data sets are divided based on the positions of different employees in different age groups. Each data set contains the employee positions, gender, work address, position scoring indicators and professional skill information within an age group. A career growth path database is established based on all data sets, and indexes are created for key fields such as position, age, and skills to improve query efficiency. Full-text indexes are used to support fuzzy queries on text fields such as job descriptions and career path descriptions, which are summarized in the career growth path database.

[0105] Data matching module: Step 3: Input personal basic information, obtain matching data set based on matching algorithm, and determine the skill set of each position; input personal data for which career growth path design is required, and match data with similar information through data analysis algorithm according to personal basic information such as academic degree, age, work address, and current position, i.e., data in the career growth path database with career-related data of employees of the same age group similar to personal basic information, calculate matching algorithm according to personal current skill reserve, set matching degree threshold E, and retain data with matching degree greater than or equal to E in matching data set. The calculation formula of matching algorithm is as follows:

[0106]

[0107] Q(x,y k ) represents x and y k where x represents an individual and y represents k represents the kth employee, n(x|y k ) represents x and y k The number of same skills, n(y k ) represents y k The total number of skills possessed; in the resulting matching data set, all employees in the same age group have similar positions, work addresses, and skills.

[0108] Through the query function of the database, the career-related data of employees at different ages in the above matching data set are collected and set as the career growth data set. Preliminary screening is carried out through the hard requirements of the position, such as educational threshold, professional requirements, age and gender restrictions, and the data that meets the requirements are retained in the career growth data set. Based on personal requirements, the weight values ​​k1, k2, and k3 of basic salary, working hours, and workload are set to obtain the size of the position scoring index of employees at different age stages in the career growth data set. According to the size of the position scoring index, the skill specialty information of employees with the largest N position scoring indicators of different positions is screened out in turn, and the resulting set is:

[0109] G={Ta1,…,Ta k ,…,Ta N};

[0110] Among them k Represents the skill set of the kth employee, marks the skill set of each employee and sorts them by learning time, expressed as:

[0111] Ta k ={a1,b1,…,a i ,b i ,c i ,…,a n ,b n ,c n ,d n};

[0112] Where a, b, c, and d represent different skills, and i represents the skills that an employee has in the i-th position;

[0113] Through data analysis, we obtained the regular promotion paths of the above employees from entry-level positions to senior positions, including the required skills, years of experience, and educational background requirements.

[0114] Personal career growth path design module: used to design personal career growth paths based on correlation analysis of career-related data; analyze the skill sets of all the above employees, set the skill frequency threshold f for each position, and obtain the necessary skill sets for each position by counting the frequency of the same skills possessed by each employee in different positions. The calculation formula is as follows:

[0115]

[0116] where Y i (t) represents the frequency of occurrence of skill t in the ith position, r i (t) represents the number of people with skill t in the ith position. iWhen (t) is greater than or equal to f, skill t is set as the necessary skill required for position i, and the necessary skills of each position are set as the first learning goal, which is the primary learning skill before promotion to the position. If the skill is included in the personal skill set or after the skill is learned, the second learning goal is started. The steps for obtaining the second learning goal are as follows:

[0117] According to the position scoring index, the skill sets of each employee are sorted at different position stages, and employee A and employee B are selected. It is required that the skill set of employee A is included in the skill set of employee B in the same position, and the position scoring index of employee B is greater than that of A. The skill importance index is calculated. The specific calculation formula is as follows:

[0118]

[0119] Where V i represents the skill importance index of position i, and sets the skill importance index threshold v. When V i When it is greater than or equal to v, the skill importance is judged. If there is a skill that passes the skill importance judgment, the skill is set to belong to the second learning goal. The skill importance judgment method is as follows:

[0120]

[0121] Where t represents the skill t that employee B has but employee A does not have.

[0122] After determining the first and second learning goals, enter your additional information and use the matching algorithm to select employees in the matching data set that match your additional information. The calculation formula of the matching algorithm is as follows:

[0123]

[0124] Where x represents an individual, y i represents the yth employee, l(x|y i ) represents x and y i The number of common interests and hobbies, l(y i ) represents y i The total number of hobbies, set the threshold H, when L(x|y i ) is greater than or equal to H to determine employee y i Matches itself, It represents the skill set that the employee has in position i. Based on this skill set, the employee selects the skills that do not belong to the first and second learning goals and that he or she does not yet possess as the third learning goal according to his or her own wishes.

[0125] The first, second and third learning objectives are used as a round of learning content. For the first learning objective, a skill improvement plan is formulated: a detailed skill learning plan is formulated, including daily or weekly learning time, learning content, and practical projects. You can seek help from senior employees to quickly master key technologies; participate in training and practical application: utilize internal and external training resources of the company, participate in relevant skill training courses or seminars, obtain the latest skill trends and cutting-edge knowledge, actively apply learned skills in daily work, and consolidate and deepen understanding by solving practical problems.

[0126] For the second learning goal, read: buy or borrow relevant books, online courses to learn; simulate: conduct simulation tests with predecessors or colleagues to improve practical operation ability; practice: regularly seek feedback from mentors or colleagues, understand your own shortcomings, and conduct targeted practice;

[0127] For the third learning goal, through communication: briefly understand the relevant content through communication with predecessors or colleagues; conceptual understanding: roughly understand the relevant concepts through online search;

[0128] The first, second and third learning goals are taken as a round of learning content. The first goal is set as the primary learning goal, accounting for z% of the daily learning time; the second goal is set as the secondary learning goal, accounting for g% of the daily learning time; the third goal is set as the general learning goal, accounting for e% of the daily learning time, z>g>e. Whenever a learning goal is completed, the daily learning time of the goal is evenly divided into the unfinished learning goals. When all learning goals are completed, the round of learning content is completed;

[0129] Afterwards, based on the personal skill set obtained from completing a round of target learning, redefine the learning goals and carry out the next round of learning until the personal skill set and experience meet the requirements for job promotion. After being promoted to a new position, repeat the above steps based on the new position requirements and personal skill set, and redefine the growth method for promotion to other positions.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0131] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for designing a personal career growth path based on big data analysis, characterized in that: include: Step 1: Collect career-related data to obtain basic data for designing personal career growth paths; Step 2: Set job scoring indicators, classify career-related data, and establish a career growth path database; Step 3: Input personal data, obtain matching data sets based on matching algorithms, and determine the skill sets for each position; Step 4: Design personal career growth path based on correlation analysis of career-related data.

2. According to the method for designing a personal career growth path based on big data analysis according to claim 1, it is characterized in that: Step 1 specifically includes the following process: Collect basic occupational information through the company's personnel files, resume information, and relevant skill certificates: employees' current and past working hours, workload, and basic salary information; skill specialty information: the number and types of professional skills related to the position.

3. The method for designing a personal career growth path based on big data analysis according to claim 1 is characterized in that: The calculation formula for the job scoring index in step 2 is as follows: Among them, α is the time change factor, which changes with the working time. The longer the working time in the current position, the smaller the α value. α is a virtual setting, C represents the basic salary, T represents the working time, M represents the workload, k represents the kth employee, i represents the position i of the kth employee, p(i) represents the position development information score, k1, k2, k3 represent the weight values ​​of basic salary, working time, and workload respectively, and k1+k2+k3=1; The specific calculation method for job development information scoring is as follows: The company's historical data is processed through machine learning algorithms to calculate the average resignation rate η, the promotion rate λ and the personnel growth rate δ of each position within I year. The calculation formula of the position development information score p(i) is as follows: Different data sets are divided based on the positions of different employees in different age groups. Each data set contains the employee positions, gender, work address, position scoring indicators and professional skills information within an age group. A career growth path database is established based on all data sets.

4. The method for designing a personal career growth path based on big data analysis according to claim 1 is characterized in that: Step 3: Input personal data. The process of obtaining matching data sets based on matching algorithms includes: The matching algorithm is calculated based on the individual's current skill reserve, and a matching threshold requirement is set. Data with a matching degree greater than the threshold is retained in the matching data set. The calculation formula of the matching algorithm is as follows: Where x represents an individual, y i represents the i-th employee, n(x|y i ) represents x and y i The number of same skills, n(y i ) represents y i The total number of skills possessed.

5. The method for designing a personal career growth path based on big data analysis according to claim 4 is characterized in that: Step 3: Based on the matching algorithm, the matching data set is obtained. The process of determining the skill set for each position includes: Through the query function of the database, the career-related data of employees at different ages in the above matching data set are collected and set as the career growth data set. The data that meets the requirements is retained in the career growth data set. Based on personal requirements, the weight values ​​k1, k2, and k3 of basic salary, working hours, and workload are set to obtain the size of the position scoring index of employees at different ages in the career growth data set. According to the size of the position scoring index, the skill specialty information of employees with the largest N position scoring indexes of different positions is screened out in turn, and the resulting set is: G={Ta1,…,Ta k ,…,Of N }; Among them k Represents the skill set of the kth employee, marks the skill set of each employee and sorts them by learning time, expressed as: Your k ={a1,b1,…,a i ,b i ,c i ,…,a n ,b n ,c n ,d n }; Where a, b, c, and d represent different skills, and i represents the skills that an employee possesses in the i-th position.

6. The method for designing a personal career growth path based on big data analysis according to claim 1 is characterized in that: Step 4 specifically includes the following processes: Analyze the skill sets of all the above employees, set the skill frequency threshold f for each position, and obtain the necessary skill set for each position by counting the frequency of the same skills possessed by each employee in different positions. The calculation formula is as follows: where Y i (t) represents the frequency of occurrence of skill t in the ith position, r i (t) represents the number of people with skill t in the ith position. i When (t) is greater than or equal to f, skill t is set as the necessary skill required for position i, and the necessary skills for each position are set as the first learning goal. Then, the second learning goal is determined by the skill importance index and skill importance judgment. After determining the first and second learning goals, input your own interests and hobbies, and determine the third learning goal through the matching algorithm; The first, second and third learning goals are taken as a round of learning content. The first goal is set as the primary learning goal, accounting for z% of the daily learning time; the second goal is set as the secondary learning goal, accounting for g% of the daily learning time; the third goal is set as the general learning goal, accounting for e% of the daily learning time, z>g>e. Whenever a learning goal is completed, the daily learning time of the goal is evenly divided into the unfinished learning goals. When all learning goals are completed, the round of learning content is completed; Afterwards, based on the personal skill set obtained from completing a round of target learning, redefine the learning goals and carry out the next round of learning until the personal skill set and experience meet the requirements for job promotion. After being promoted to a new position, repeat the above steps based on the new position requirements and personal skill set, and redefine the growth method for promotion to other positions.

7. The method for designing a personal career growth path based on big data analysis according to claim 6 is characterized in that: The specific process of determining the second learning goal through skill importance indicators and skill importance judgment includes: According to the position scoring index, the skill sets of each employee are sorted at different position stages, and employee A and employee B are selected. It is required that the skill set of employee A is included in the skill set of employee B in the same position, and the position scoring index of employee B is greater than that of A. The skill importance index is calculated. The calculation formula is as follows: Where V i represents the skill importance index of position i, and sets the skill importance index threshold v. When V i When it is greater than or equal to v, the skill importance is judged. If there is a skill that passes the skill importance judgment, the skill is set to belong to the second learning goal. The skill importance judgment method is as follows: Where t represents the skill t that employee B has but employee A does not have.

8. The method for designing a personal career growth path based on big data analysis according to claim 6 is characterized in that: The specific process of determining the third learning goal through the matching algorithm includes: Through the matching algorithm, select employees in the matching data set that match their additional information. The calculation formula of the matching algorithm is as follows: Where x represents an individual, y i represents the yth employee, l(x|y i ) represents x and y i The number of common interests and hobbies, l(y i ) represents y i The total number of hobbies, set the threshold H, when L(x|y i ) is greater than or equal to H to determine employee y i Matches itself, It represents the skill set that the employee has in position i. Based on this skill set, the employee selects the skills that do not belong to the first and second learning goals and that he or she does not yet possess as the third learning goal according to his or her own wishes.

9. A personal career growth path design system based on big data analysis, used to implement a personal career growth path design method based on big data analysis as described in any one of claims 1 to 8, characterized in that: include: Career-related data collection module: used to collect career-related data and obtain basic data for designing personal career growth paths; Career growth path database establishment module: used to set job scoring indicators, classify career-related data, and establish a career growth path database; Data matching module: used to input personal data, obtain matching data sets based on matching algorithms, and determine the skill sets for each position; Personal career growth path design module: used to design personal career growth path based on correlation analysis of career-related data.

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