Talent recommendation method and system based on man-post adaptation model, equipment and medium

Abstract

The invention relates to the technical field of big data, and discloses a talent recommendation method and system based on a person and post adaptation model, and the method comprises the steps: collecting talent data, carrying out the classification and summarization of the talent data, and generating a talent portrait; internal post data of the enterprise is analyzed, and a post demand portrait is generated; establishing a person and post adaptation model, and performing quantitative scoring to obtain a person and post adaptation matching degree score of the talent portrait and the post demand portrait; integrating the person and post adaptation model into a decision-making auxiliary information module, and generating and displaying a talent lifting list according to the matching degree score; and analyzing the internal personnel structure of the enterprise in combination with the talent lifting list. The system comprises a data collection module, a man-post adaptation model establishment module, a decision-making auxiliary information module and an enterprise personnel analysis module. The invention further discloses electronic equipment and a computer readable storage medium. The method is used for providing the talent extraction recommendation list from multiple dimensions and assisting in organizing personnel work decisions.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a talent recommendation method and system, device, and medium based on a person-job adaptation model, which are used to provide a talent promotion recommendation list from multiple dimensions to assist in organizational personnel work decision-making. Background Art

[0002] The existing talent selection is mainly based on human judgment, combined with information systems to query conditions to obtain a list of recommended personnel; or to a certain extent, the information system can be used to realize conditional warnings in talent selection (such as warnings of the impact period of penalties) and score talents based on their abilities, experience, assessments, rewards and punishments. The existing technology faces several major challenges in selecting outstanding talents through information systems to assist decision-making based on the principle of scientific selection and employment:

[0003] The scope of traditional selection based on qualifications is often relatively broad, and there are many qualified talents, making it difficult for the system to quantitatively analyze the strengths and weaknesses of each talent; some existing quantitative analysis standards for talent portraits have fixed formulas and single standards, which can easily lead to the problem that talents with high education, long working time and long experience always rank at the top; the traditional evaluation system has a single indicator and low flexibility, which can easily lead to the concentration of talents of the same type in the same department, which is not conducive to the flow and growth of talents, and is not conducive to the improvement of the comprehensive ability of talents; the traditional evaluation system is prone to the problem of balancing work experience and the age preference of young talents, and often either overemphasizes the experience advantage of old talents or overemphasizes the promotion of young talents; for the selection needs of young talents, consideration should be given to the differences between different ages. The score weighting coefficient of age group is used instead of simply scoring by work experience and age. The traditional query mode is prone to one-sidedness in terms of the talent’s major and work experience, and it is easy to overlook some talents whose majors and work experience cannot be completely matched but are relevant or close, as well as some talents who grow up later and are good at areas that are quite different from their majors. How to calculate the relevance of multiple work experiences of talents in different units and positions to the proposed promotion position: how to analyze the problems when there are difficulties in entering the talent’s historical work information, changes in the historical unit name, changes in job division, etc. In addition to considering the academic level, the talent’s multiple learning experiences or professional skills and professional titles also need to consider factors such as school rankings, and how to calculate the relevance when studying in different majors for undergraduate, master’s and doctoral degrees. In addition to considering personal factors such as the major studied, expertise, work experience, rewards and punishments, assessments, etc., it is also necessary to consider the combination of indicators such as personality type, quality, and ability when the leadership team is overall staffed; the system should provide references for talent recommendation strategies from different dimensions. For example, when focusing on talent training plans, some talents will inevitably lack work experience in certain fields, resulting in low quantitative scores, but auxiliary decision-making reminders should be provided when recommending suggestions; when conducting promotion analysis for multiple vacant positions, some outstanding talents may be ranked first in many positions according to quantitative scores at the same time. While considering the priority of the same positions, recommendations should be made in accordance with the principle of talent and overall optimization to give full play to the overall capabilities of talents.

[0004] The present invention provides a talent recommendation method and system, device, and medium based on a person-job adaptation model, which provide a talent promotion recommendation list from multiple dimensions to assist in organizational personnel work decision-making. Summary of the invention

[0005] The present invention provides a talent recommendation method and system, device, and medium based on a person-job adaptation model, which provide a talent promotion recommendation list from multiple dimensions to assist in organizational personnel work decision-making.

[0006] The present invention is implemented by the following technical solution: a talent recommendation method based on a person-job adaptation model, comprising the following steps:

[0007] Step S1, collecting talent data, classifying and aggregating the talent data, and generating a talent portrait;

[0008] Step S2, analyzing the internal job data of the enterprise to generate a job demand profile;

[0009] Step S3, establishing a person-job matching model, performing quantitative scoring based on the talent data and the internal job data of the enterprise, and obtaining a person-job matching score of the talent portrait and the job requirement portrait through person-job matching recommendation calculation and comprehensive weighting method;

[0010] Step S4, integrating the person-job matching model into the decision-making auxiliary information module, and generating and displaying a talent promotion list according to the matching score;

[0011] Step S5: Analyze the internal personnel structure of the enterprise based on the person-job matching model and the decision-making auxiliary information module in combination with the talent promotion list.

[0012] In order to better implement the present invention, further, step S1 includes:

[0013] The talent data includes but is not limited to the talent's age data, education experience data, historical work experience data, personal quality data, ability assessment data and assessment supervision data.

[0014] In order to better implement the present invention, further, step S2 includes:

[0015] The internal position data of the enterprise includes but is not limited to the qualification data of each position within the enterprise, professional requirements data, work division data, talent training plan data, defined position number data, defined position number / position staffing data of each unit and department, position level vacancy data and staffing requirements data;

[0016] The staffing requirement data include but are not limited to job number business domain data, study professional field data, basic qualification data, personality trait data and quality and ability data.

[0017] In order to better implement the present invention, further, step S3 includes:

[0018] The person-job matching model uses a dynamic weighting mechanism calculation method, a multi-field scoring attenuation mechanism calculation method, a matching degree calculation formula, and a configurable calculation method for model parameters to quantitatively score the talent data and the internal job data of the enterprise.

[0019] In order to better implement the present invention, further, step S4 includes:

[0020] The decision-making auxiliary information module combines the algorithm scalability method and the recommendation strategy differentiation display method to generate and display the talent promotion list.

[0021] In order to better implement the present invention, further, the method for analyzing the internal personnel structure of the enterprise in step S5 includes: analyzing the structural changes of the staff of the department before and after the adjustment, and recommending, planning and adjusting the internal personnel structure of the enterprise in combination with the talent promotion list.

[0022] The present invention also provides a talent recommendation system based on a person-job matching model, comprising a data collection module, a person-job matching model establishment module, a decision-making auxiliary information module and an enterprise personnel analysis module, wherein:

[0023] The data collection module is used to collect talent data, classify and summarize the talent data, and generate talent portraits; it is used to analyze the internal job data of the enterprise and generate job demand portraits;

[0024] A person-job matching model building module is used to perform quantitative scoring based on the talent data and the internal job data of the enterprise, and obtain the person-job matching score of the talent profile and the job requirement profile through person-job matching recommendation calculation and comprehensive weighting method;

[0025] A decision-making auxiliary information module, used to integrate the person-job matching model into the decision-making auxiliary information module, and generate and display a talent promotion list according to the matching score;

[0026] The enterprise personnel analysis module is used to analyze the internal personnel structure of the enterprise according to the person-job adaptation model and the decision-making auxiliary information module in combination with the talent promotion list.

[0027] The present invention also provides an electronic device, which includes a processor and a memory; the processor includes the talent recommendation system based on the person-job matching model recorded in the second aspect above.

[0028] The present invention also provides a computer-readable storage medium, which includes instructions; when the instructions are executed on the electronic device described in the third aspect, the electronic device executes the method described in the first aspect.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] The present invention provides a talent recommendation method and system, equipment and medium based on a person-job matching model. The present invention aims at how enterprises and units, when selecting talents, can analyze the matching degree of talents based on data of multiple dimensions such as age, education, professional direction, historical work experience, personal quality, ability assessment, and assessment supervision, and factors such as job qualifications, work division, job professional requirements, and department leadership team training plans through an information system, and conduct quantitative evaluation and comprehensive weighted scoring to establish a mathematical model. By comparing the impact of the leadership team adjustment on the unit structure before and after, the present invention finally provides a talent promotion recommendation list from multiple dimensions to assist in organizational personnel work decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention is further described in conjunction with the following drawings and embodiments, and all the concepts and innovations of the present invention should be regarded as the disclosed contents and the protection scope of the present invention.

[0032] Figure 1 A talent recommendation method based on a person-job adaptation model and a method flow chart of a system, device, and medium provided in an embodiment of the present application;

[0033] Figure 2 A talent recommendation method and system, device, and medium based on a person-job adaptation model provided in an embodiment of the present application; a schematic diagram of a person-job adaptation and recommendation calculation process in the person-job adaptation model;

[0034] Figure 3 A talent recommendation method and system, device, and medium based on a person-job adaptation model provided in an embodiment of the present application; a schematic diagram b of a person-job adaptation recommendation calculation process in a person-job adaptation model;

[0035] Figure 4 A schematic diagram of a quantitative scoring process of work experience matching in a talent recommendation method and system, device, and medium based on a person-job adaptation model provided in an embodiment of the present application a;

[0036] Figure 5 A schematic diagram b of a quantitative scoring process of work experience matching in a talent recommendation method and system, device, and medium based on a person-job adaptation model provided in an embodiment of the present application;

[0037] Figure 6 A schematic diagram of a talent recommendation method and system, device, and medium for quantitative scoring of educational experience matching based on a person-job adaptation model provided in an embodiment of the present application a;

[0038] Figure 7 Schematic diagram b of the quantitative scoring process of educational experience matching in a talent recommendation method and system, equipment, and medium based on a person-job adaptation model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0040] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Embodiment 1:

[0042] A talent recommendation method, system, device, and medium based on a person-job matching model in this embodiment, such as Figure 1 As shown, the talent recommendation methods include:

[0043] Multi-dimensional data collection: Data is collected and extracted based on multiple dimensions such as talent's age, educational background (education, major), historical work experience, personal qualities, ability assessment, and assessment supervision. Data is then classified and summarized according to the professional caliber and business domain required by the user to form a profile of the candidate and compare it with the corresponding relationship with the job match.

[0044] Job demand analysis: digitize and structure the qualifications, professional requirements, work division, talent training plans, etc. for each position within the enterprise.

[0045] Person-job matching model: A mathematical model is established to conduct quantitative scoring based on the above dimensions and job requirements, and the matching score of person-job matching is obtained through a comprehensive weighted method.

[0046] Decision support module: Integrate the model into the information module, and generate a talent promotion recommendation list from multiple dimensions based on the model's recommendation score.

[0047] Structural impact analysis: Analyze the structural changes of the department's staff before and after the adjustment, and provide structured suggestions for the organization's human resources department to ensure that the recommended list not only meets the current job requirements, but also conforms to the overall department planning.

[0048] Embodiment 2:

[0049] This embodiment is further optimized on the basis of Embodiment 1. The person-job adaptation model uses a dynamic weighting mechanism calculation method, a multi-field score attenuation mechanism calculation method, a matching degree calculation formula and a configurable calculation method for model parameters. At the same time, an algorithm scalability method is combined in the decision-making auxiliary information module, and a recommendation strategy differentiated display method is used.

[0050] The dynamic weighting mechanism calculation method is to flexibly deal with the balance between experience and age through dynamic weighting calculation based on age group, academic qualifications, etc.

[0051] The calculation method of the multi-field scoring attenuation mechanism is based on the gradual attenuation of sub-scores such as academic qualifications, work experience and professional titles over time, ensuring that the scoring results are close to the current actual level.

[0052] The configurability of the matching degree calculation formula and model parameters is calculated by supporting flexible configuration of the weights of each dimension through the scoring formula, so that enterprises can adjust the focus of different stages according to their needs.

[0053] The algorithm scalability method supports the adaptability of different positions and departmental needs by introducing flexible switch configurations, thereby improving the versatility of the decision-making auxiliary information module.

[0054] Differentiated display method of recommendation strategy: Labels or icons are used on the recommendation interface to enable users to quickly identify the talent advantages in different recommendation dimensions.

[0055] The other parts of this embodiment are the same as those of Embodiment 1, and thus will not be described in detail.

[0056] Embodiment 3:

[0057] This embodiment is further optimized on the basis of the above-mentioned embodiment 1 or 2. In addition to the basic parameter definitions related to talents, according to the needs of the person-job adaptation model and the talent recommendation algorithm, professional field classification and business domain classification should also be configured. These configurations may define different professional types and business domain divisions according to the nature of the user unit, and can be quickly re-divided based on some national standard definitions.

[0058] Set the number of positions within the enterprise, define the number of positions / positions for each unit and department, the vacancies of the positions, and the requirements for positions. This includes the business domain of the position, the field of study, the basic qualifications (necessary conditions for the position, including the length of time, assessment, supervision, etc.), personality traits, quality and ability, etc.;

[0059] The parameters of the person-job matching model in the person-job matching model include the weight of each dimension in the total score; the attenuation coefficient for multiple academic qualifications (bachelor's, master's and doctoral degrees); the age attenuation coefficient; the work experience attenuation coefficient; the addition and subtraction rules for rewards, penalties and assessments, etc.

[0060] The other parts of this embodiment are the same as any of the above embodiments 1-2, so they are not repeated here.

[0061] Embodiment 4:

[0062] This embodiment is further optimized based on any one of the above embodiments 1-3. Figure 2-Figure 3 As shown in the figure, the process of calculating the optimal matching of people to positions in the person-job matching model refers to selecting which people are suitable for the positions and recommending the most suitable people, which specifically includes the following processes:

[0063] Necessary condition screening: The system will pre-screen whether the talent meets the basic requirements of the position. Those who do not meet the necessary conditions will not enter the next step of evaluation.

[0064] The sub-indicators are quantified and weighted. The sub-indicators usually include education experience, work experience, assessment results, rewards and punishments, personnel quality, ability, personality (personality is usually not scored and is only used as a reference for later team matching). Separate detailed algorithm models are designed for each of these parts. After calculating each sub-indicator, the final score is formed by adding up the weights in the total score. Taking into account different selection criteria, work experience is usually included in the total score as a whole, and is also included in the most advantageous business domain to convert a total score into the most advantageous field.

[0065] Age factor: Set an age weighting coefficient based on the age preferences of different positions and the corresponding management levels, or give priority to a certain age group based on the age range of team members.

[0066] Reference factors: When recommending talents, we will combine the needs of the team leadership team, such as personality, ability and other diversity, to form a better combination of reference indicators for the recommendation plan.

[0067] Selection algorithm for multiple optimal positions and global comprehensive selection: When a person is the best candidate for multiple positions, the corresponding position of the person can be determined according to the priority of the position or the principle of maximum advantage; other positions are adjusted in turn to ensure that when multiple positions are recommended, there is no duplication of the top-ranked candidate.

[0068] Some people may not be ranked as priority candidates due to lack of specific experience, but if the corporate department marks them as talents to be trained in specific areas, they will receive additional reminders based on the training direction.

[0069] The system generates multiple recommendation plans for positions, and stores and outputs the calculation results. Enterprise departments can determine candidates based on actual conditions and job requirements:

[0070] Comprehensive evaluation recommendation: Filter the top candidates by total score.

[0071] Single matching recommendation: Display candidates who have outstanding performance in a single business domain.

[0072] Training potential recommendation: For candidates with preset training directions, if they lack experience in the business domain but meet the job qualification requirements, they will be additionally marked as recommended for training.

[0073] Team matching recommendation: Evaluate the personality and ability of candidates and select the most suitable candidates for the overall team combination.

[0074] Finally, in the result UI of the decision-making auxiliary information module, icons are marked to indicate that a single person meets multiple recommendation options, or different candidates are displayed in separate columns. And through global judgment, it is ensured that each candidate is only recommended as the first choice for the best position, forming a global optimal recommendation list, which can be exported or further adjusted by the human resources department.

[0075] The other parts of this embodiment are the same as any one of the above embodiments 1-3, so they are not repeated here.

[0076] Embodiment 5:

[0077] This embodiment is further optimized based on any one of the above embodiments 1-4. Figure 4-Figure 5 As shown in the figure, the person-job matching model performs quantitative scoring based on talent data and internal job data of the enterprise. In the quantitative scoring process of work experience matching, various coefficients and weights are included, among which:

[0078] Positions correspond to multiple business domains: In reality, each position may involve multiple divisions of labor and requires familiarity with multiple related businesses. For example, production management requires not only managing production, but also understanding process quality and being familiar with the supply chain. Therefore, the work experience of each position corresponds to the growth of personnel's experience in multiple business capabilities;

[0079] Discontinuation attenuation coefficient: If you have some business experience in the past, but do not engage in it for a long time, you will gradually lose the ability value of these work experiences;

[0080] Age decay coefficient: some positions are more suitable for people of a certain age group, or have more potential for training considering age factors;

[0081] Position level coefficient: For certain key positions or important roles, the accumulated business work experience will be increased in addition to the normal accumulated time;

[0082] The weight of the business domain in the new position: the importance of each business experience in the new position;

[0083] The other parts of this embodiment are the same as any one of the above embodiments 1-4, so they are not repeated here.

[0084] Embodiment 6:

[0085] This embodiment is further optimized on the basis of any one of the above embodiments 1-5. The person-job matching model performs quantitative scoring based on talent data and internal job data of the enterprise. The quantitative scoring also includes a quantitative scoring process for educational experience matching, a quantitative evaluation process for personal character, quality, and ability assessment, a quantitative evaluation process for assessment, and a quantitative evaluation process for rewards and penalties.

[0086] like Figure 6-Figure 7 As shown in the figure, the quantitative scoring process of educational experience matching explains how a person's work experience is broken down into experiences in multiple business domains based on the model, and then quantified after matching with the positions to be verified.

[0087] The specific process of quantifying educational experience matching includes:

[0088] This process matches personnel's educational experience with job requirements, and quantifies and evaluates multiple educational experiences based on academic level, school grade, major category, combined with job matching, time decay coefficient, and multiple learning decay coefficient;

[0089] Professional position matching: Different positions have different requirements for professional and technical fields, and the range can also be set according to the actual situation of the position. Some may be suitable for science and engineering majors, while some professional fields may require higher professionalism in machinery and electronics;

[0090] Time decay coefficient: The education level of young cadres who have just graduated is more indicative of their personal ability, while the educational level of older personnel will have a lower impact on their personal ability;

[0091] Attenuation coefficient for multiple degrees: If a person is studying for multiple degrees at the same time, the core learning areas of the person are determined from high to low. This also prevents the scores from being too high after the sum of multiple degrees. Therefore, the weight of the second and third degrees can be set to gradually decay.

[0092] Professional titles, expertise and specialties converted into academic qualifications: If some personnel have low academic qualifications but obtain professional titles or intermediate or senior certificates in a specific professional field through other means, such professional title evaluation can be equated with a certain academic qualification level in a certain professional field for calculation;

[0093] The quantitative evaluation process of personal character, quality and ability assessment includes:

[0094] The quality and ability model combines the personnel assessment and daily evaluation of the enterprise unit, and weights and totals the matching of the various qualities and abilities of the cadres with the positions to form this score;

[0095] The degree of quality and ability matching mainly refers to which aspects of the person's personality, quality, and ability characteristics the position values ​​more. For example, some positions focus on innovation, while some positions focus on rigor.

[0096] The quantitative evaluation process of the assessment specifically includes: Additional addition and subtraction based on the assessment results.

[0097] The quantitative evaluation process for rewards and penalties specifically includes: additional addition and subtraction based on the personnel’s rewards and penalties in recent years.

[0098] The present invention also provides a talent recommendation system based on a person-job matching model that matches the method, including a data collection module, a person-job matching model building module, a decision-making auxiliary information module and an enterprise personnel analysis module.

[0099] The present invention also provides an electronic device, which includes a processor and a memory; the processor includes the talent recommendation system based on the person-job matching model described above.

[0100] Embodiment 7:

[0101] The present invention also provides a computer-readable storage medium, which includes instructions; when the instructions are executed on the electronic device described in the above embodiment, the electronic device executes the method described in the above embodiment. Optionally, the computer-readable storage medium may be a memory.

[0102] The processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0103] The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0104] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0105] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0108] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one device or distributed on multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0109] In addition, each functional module in each embodiment of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.

[0110] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing a computer program instruction on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or may contain one or more servers, data centers and other data storage devices that can be integrated with a medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0111] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A talent recommendation method based on a person-job matching model, characterized in that: The following steps are involved: Step S1, collecting talent data, classifying and aggregating the talent data, and generating a talent portrait; Step S2, analyzing the internal job data of the enterprise to generate a job demand profile; Step S3, establishing a person-job matching model, performing quantitative scoring based on the talent data and the internal job data of the enterprise, and obtaining a person-job matching matching score between the talent portrait and the job requirement portrait through a person-job matching recommendation calculation method and a comprehensive weighting method; Step S4, integrating the person-job matching model into the decision-making auxiliary information module, and generating and displaying a talent promotion list according to the matching score; Step S5, analyzing the internal personnel structure of the enterprise according to the person-job adaptation model and the decision-making auxiliary information module in combination with the talent promotion list.

2. According to the talent recommendation method based on the person-job matching model of claim 1, it is characterized in that: The step S1 comprises: The talent data includes but is not limited to the talent's age data, education experience data, historical work experience data, personal quality data, ability assessment data and assessment supervision data.

3. According to the talent recommendation method based on the person-job matching model of claim 1, it is characterized in that: The step S2 comprises: The internal position data of the enterprise includes, but is not limited to, the qualification data of each position within the enterprise, professional requirements data, work division data, talent training plan data, defined position number data, defined position number / position staffing data of each unit and department, position level vacancy data and staffing requirements data; The staffing requirement data include but are not limited to job number business domain data, study professional field data, basic qualification data, personality trait data and quality and ability data.

4. The talent recommendation method based on the person-job matching model according to claim 1 is characterized in that: The step S3 comprises: The person-job matching model uses a dynamic weighting mechanism calculation method, a multi-field scoring attenuation mechanism calculation method, a matching degree calculation formula, and a configurable calculation method for model parameters to quantitatively score the talent data and the internal job data of the enterprise.

5. The talent recommendation method based on the person-job matching model according to claim 1 is characterized in that: The step S4 comprises: The decision-making auxiliary information module combines the algorithm scalability method and the recommendation strategy differentiation display method to generate and display the talent promotion list.

6. The talent recommendation method based on the person-job matching model according to claim 1 is characterized in that: The method for analyzing the internal personnel structure of an enterprise in step S5 includes: Analyze the structural changes of the department's staff before and after the adjustment, and recommend, plan and adjust the company's internal personnel structure based on the talent promotion list.

7. A talent recommendation system based on a person-job matching model, characterized in that: It includes data collection module, job matching model building module, decision-making auxiliary information module and enterprise personnel analysis module, among which: The data collection module is used to collect talent data, classify and summarize the talent data, and generate talent portraits; it is used to analyze the internal job data of the enterprise and generate job demand portraits; A person-job matching model building module is used to perform quantitative scoring based on the talent data and the internal job data of the enterprise, and obtain the person-job matching score of the talent profile and the job requirement profile through person-job matching recommendation calculation and comprehensive weighting method; A decision-making auxiliary information module, used to integrate the person-job matching model into the decision-making auxiliary information module, and generate and display a talent promotion list according to the matching score; The enterprise personnel analysis module is used to analyze the internal personnel structure of the enterprise according to the person-job adaptation model and the decision-making auxiliary information module in combination with the talent promotion list.

8. An electronic device, characterized in that: It comprises a processor and a memory; the processor is used to run the talent recommendation system based on the person-job adaptation model as described in claim 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes instructions; when the instructions are executed on the electronic device as claimed in claim 8, the electronic device is caused to execute the method as claimed in any one of claims 1-6.

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