Talent management method, system and device, electronic equipment and storage medium

Abstract

According to the talent management method and system, the talent management device, the electronic equipment and the storage medium provided by the invention, the to-be-selected data is subjected to data mining processing to obtain the target data; wherein the to-be-selected data comprises identification data corresponding to the to-be-selected targets, and the target data is used for determining a first target in the to-be-selected targets; performing data breeding processing according to the target data to obtain potential data, and performing shunting processing on the first target according to the potential data to obtain a shunted first target; performing data checking processing according to the target data and the potential data to obtain checking data; and performing data analysis processing according to the target data, the potential data and the inventory data to obtain culture data. Compared with the prior art, an integrated data closed loop is formed by breaking through a talent management process closed loop of talent modeling, talent evaluation, talent checking and talent cultivation, the current situation can be better organized through talent data insight, and the talent management effectiveness is maximized.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a talent management method and system, device, electronic device and storage medium. Background Art

[0002] In the traditional talent management process of enterprises, recruitment, training, assessment, incentives and other links are usually managed manually. This method has problems such as low efficiency, information asymmetry, and waste of human resources. With the expansion of enterprise scale and the intensification of market competition, traditional talent management methods can no longer meet the needs of modern enterprises.

[0003] At present, the development trend of talent management system is mainly reflected in the following aspects: first, the comprehensiveness and integration of system functions; second, the intelligent and precise data analysis; third, the personalized and convenient user experience. However, the talent management system in existing technologies often focuses on the management of single links such as talent recruitment, training, and performance, and lacks comprehensive and systematic analysis and utilization of talent data. These systems are difficult to help enterprises gain insight into the current situation of the organization from a macro perspective, and they are even more unable to gain insight into future talent development trends from data.

[0004] Therefore, how to better understand the current situation of the organization through talent data, gain insight into the future from talent data, and truly maximize the effectiveness of talent management is an urgent problem that needs to be solved. Summary of the invention

[0005] The present disclosure provides a talent management method and system, device, electronic device and storage medium, the main purpose of which is to solve the problem of how to better understand the current situation of the organization through talent data, to see the future from talent data, and to truly maximize the effectiveness of talent management.

[0006] According to a first aspect of the present disclosure, a talent management method is provided, comprising:

[0007] Performing data mining on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected;

[0008] Performing data selection processing according to the target data to obtain potential data corresponding to each of the first targets, and performing diversion processing on the first targets according to the potential data to obtain diverted first targets;

[0009] Performing data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after the diversion;

[0010] Data analysis and processing are performed based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0011] Optionally, performing data mining on the data to be selected to obtain target data includes:

[0012] The data to be selected is subjected to data mining processing according to a preset selection standard and a first preset evaluation algorithm to obtain the target data.

[0013] Optionally, performing data selection processing according to the target data to obtain potential data corresponding to each of the first targets includes:

[0014] Acquire business data of the business to be processed; wherein the business to be processed is the business that the target to be selected needs to process;

[0015] A breeding standard is determined according to the business data, and data breeding processing is performed on the target data according to the breeding standard and a second preset evaluation algorithm to obtain the potential data.

[0016] Optionally, performing diversion processing on the first target according to the potential data to obtain the diverted first target includes:

[0017] The first target is diverted according to the potential data through a preset data insight algorithm to obtain the diverted first target; wherein the diverted first target is used to establish a dynamic database.

[0018] Optionally, performing data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after diversion includes:

[0019] An inventory standard is determined according to a preset inventory range, and a data inventory process is performed on the target data and the potential data according to the inventory standard and a third preset evaluation algorithm to obtain the inventory data.

[0020] Optionally, performing data analysis and processing according to the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after diversion includes:

[0021] The training standards are determined according to the preset training objectives, and the target data, the potential data and the inventory data are analyzed and processed according to the training standards and a fourth preset evaluation algorithm to obtain the training data.

[0022] According to a second aspect of the present disclosure, a talent management system is provided, comprising: a selection module, a training module, an inventory module and a development module.

[0023] The selection module is used to perform data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected;

[0024] The breeding module is used to perform data breeding processing according to the target data to obtain potential data corresponding to each of the first targets, and to perform diversion processing on the first targets according to the potential data to obtain the diverted first targets;

[0025] The inventory module is used to perform data inventory processing according to the target data and the potential data to obtain the inventory data corresponding to the first target after the diversion;

[0026] The development module is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0027] According to a third aspect of the present disclosure, there is provided a talent management apparatus, comprising:

[0028] A mining unit, used for performing data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected;

[0029] A breeding unit, used for performing data breeding processing according to the target data to obtain potential data corresponding to each of the first targets;

[0030] A diversion unit, configured to perform diversion processing on the first target according to the potential data to obtain a diverted first target;

[0031] An inventory unit, configured to perform data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after the diversion;

[0032] The analysis unit is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0033] Optionally, the mining unit is further used to perform data mining processing on the data to be selected according to a preset selection criterion and a first preset evaluation algorithm to obtain the target data.

[0034] Optionally, the breeding unit comprises:

[0035] A selection module is used to obtain business data of the business to be processed; wherein the business to be processed is the business that the target to be selected needs to process;

[0036] A determination module, used to determine breeding criteria according to the business data;

[0037] A breeding module is used to perform data breeding processing on the target data according to the breeding criteria and a second preset evaluation algorithm to obtain the potential data.

[0038] Optionally, the diversion unit is also used to divert the first target according to the potential data through a preset data insight algorithm to obtain the first target after diversion; wherein the first target after diversion is used to establish a dynamic database.

[0039] Optionally, the inventory unit is further used to determine an inventory standard according to a preset inventory range, and perform data inventory processing on the target data and the potential data according to the inventory standard and a third preset evaluation algorithm to obtain the inventory data.

[0040] Optionally, the analysis unit is also used to determine the training standards according to the preset training objectives, and perform data analysis and processing on the target data, the potential data and the inventory data according to the training standards and a fourth preset evaluation algorithm to obtain the training data.

[0041] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0042] at least one processor; and

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0045] According to a fifth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0046] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0047] The talent management method and system, device, electronic device and storage medium provided by the present disclosure perform data mining on the selected data to obtain target data; wherein the selected data includes identification data corresponding to each selected target, and the target data is used to determine the first target among the selected targets; data selection is performed according to the target data to obtain potential data corresponding to each first target, and the first target is diverted according to the potential data to obtain the first target after diversion; data inventory is performed according to the target data and the potential data to obtain the inventory data corresponding to the first target after diversion; data analysis is performed according to the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after diversion. Compared with the related art, the embodiment of the present disclosure opens up the talent management process closed loop of talent modeling (data mining), talent assessment (talent selection), talent inventory and talent training, and at the same time, the talent data (data to be selected) of the whole process is retained in real time to form an integrated data closed loop, which can better understand the current situation of the organization through talent data and maximize the effectiveness of talent management.

[0048] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0050] Figure 1 A flow chart of a talent management method provided by an embodiment of the present disclosure;

[0051] Figure 2 A logical schematic diagram of a talent management method provided by an embodiment of the present disclosure;

[0052] Figure 3 A schematic diagram of execution logic of a talent management system provided by an embodiment of the present disclosure;

[0053] Figure 4 A schematic diagram of the structure of a talent management device provided by an embodiment of the present disclosure;

[0054] Figure 5 A schematic diagram of the structure of another talent management device provided by an embodiment of the present disclosure;

[0055] Figure 6 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0057] The talent management method, apparatus, electronic device, and storage medium of the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0058] Figure 1 A flowchart of a talent management method provided in an embodiment of the present disclosure.

[0059] like Figure 1 As shown, the method comprises the following steps:

[0060] Step 101 , performing data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected.

[0061] In the embodiments of the present disclosure, the data to be selected is talent data that needs to be processed, and its content includes but is not limited to: interview data, resume data, etc. The targets to be selected include but are not limited to: talents to be selected, etc. Specifically, the embodiments of the present disclosure do not limit the data to be selected and the targets to be selected.

[0062] Among them, the first goal includes but is not limited to: selecting talents, through talent selection, sorting out selection criteria around the required talents; customizing assessment tools to scientifically and objectively identify people; using behavioral interview guides to help interviewers fully explore talent information; locking in key talents and improving selection accuracy.

[0063] Step 102, performing data selection processing according to the target data to obtain potential data corresponding to each of the first targets, and performing diversion processing on the first targets according to the potential data to obtain the diverted first targets.

[0064] In the disclosed embodiment, the potential data is the data of the development potential of the first target, and the diversion processing of the first target includes but is not limited to: classifying the first target to build a database corresponding to different first targets. Through high-potential selection and cultivation, combined with the business status, clarify the high-potential standards; with the help of exclusive assessment, comprehensively identify high-potential; based on data insights, divert and develop talents; establish a dynamic high-potential pool, and the talent reserve is clearly visible.

[0065] Step 103: Perform data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after the diversion.

[0066] In the disclosed embodiments, a talent inventory is conducted to determine the scope of the inventory and to sort out talent standards; scientific assessment is used to implement a refined inventory; inventory insights are generated to understand the current status of talent; and the inventory results are implemented to help business growth.

[0067] Step 104, performing data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0068] In the disclosed embodiments, through leadership diagnosis and development, the purpose is clarified and talent standards are formulated; assessment tools are selected to implement talent diagnosis; talent data is analyzed to clarify the training direction; targeted talent diversion training is carried out to improve training results.

[0069] Specifically, in order to facilitate the understanding of the embodiment of the present disclosure, the embodiment of the present disclosure provides a logical schematic diagram of a talent management method, such as Figure 2 shown.

[0070] The talent management method provided by the present disclosure performs data mining on the selected data to obtain target data; wherein the selected data includes identification data corresponding to each selected target, and the target data is used to determine the first target among the selected targets; data selection is performed according to the target data to obtain potential data corresponding to each first target, and the first target is diverted according to the potential data to obtain the first target after diversion; data inventory is performed according to the target data and the potential data to obtain the inventory data corresponding to the first target after diversion; data analysis is performed according to the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after diversion. Compared with the related art, the embodiment of the present disclosure opens up the talent management process closed loop of talent modeling (data mining), talent assessment (talent selection), talent inventory and talent training, and at the same time, the talent data (data to be selected) of the whole process is retained in real time to form an integrated data closed loop, which can better understand the current situation of the organization through talent data and maximize the effectiveness of talent management.

[0071] In one possible implementation of the embodiment of the present disclosure, when data mining is performed on the data to be selected, it can also be implemented in but not limited to the following manner: data mining is performed on the data to be selected according to preset selection criteria and a first preset evaluation algorithm to obtain the target data.

[0072] In the disclosed embodiment, the preset selection criteria are a custom set criteria, including a series of quantitative and qualitative indicators to evaluate the ability, potential, adaptability and fit of the target to be selected with the organization. The first preset evaluation algorithm is a custom set algorithm, for example: a structured interview scoring algorithm, including: behavioral scoring method (Behavioral Scoring): evaluate the ability and potential of the candidate through the behavioral examples of the candidate, situational scoring method (Situational Scoring): evaluate the performance of the candidate in a specific simulation situation. Psychological assessment algorithm, including: (Myers-Briggs Type Indicator, MBTI): based on Jung's psychological type theory, evaluate the individual's psychological preferences, Big Five Personality Traits: evaluate the five personality traits of a person, namely openness, responsibility, extroversion, agreeableness, and neuroticism. Ability assessment algorithm, including: Knowledge Tests: evaluate the candidate's mastery of knowledge in a specific field, skill assessment (Skill Assessments): evaluate skill level through actual operation or simulated tasks. The potential assessment algorithm includes: Nine Box Model: Matrix analysis of employees' capabilities and potentials to identify high-potential talents, Talent Pipeline Analysis: Assessment of the reserve and development of talents at different levels, etc. Specifically, the present disclosure embodiment does not limit the preset selection criteria and the first preset assessment algorithm.

[0073] In one implementable method of the embodiment of the present disclosure, when performing data selection and breeding processing on the target data, it can also be implemented by but not limited to the following methods: obtaining business data of the business to be processed; wherein the business to be processed is the business that needs to be processed by the target to be selected; determining the breeding standard based on the business data, and performing data selection and breeding processing on the target data based on the breeding standard and a second preset evaluation algorithm to obtain the potential data.

[0074] In the disclosed embodiment, the selection and training standard is a custom set standard used to ensure that talents can continue to grow in the organization, realize their potential, and contribute value to the organization, including but not limited to: personal development plans, including: formulating personalized career development paths and setting short-term and long-term career development goals based on personal career plans and organizational needs. Capacity improvement, including: identifying key capability gaps, such as leadership, professional skills, innovative thinking, etc., and formulating improvement plans, providing training and learning opportunities, including internal training, external courses, online learning, etc. The second preset evaluation algorithm is a custom set algorithm. Specifically, for the content of the second preset evaluation algorithm, please refer to the description of the first preset evaluation algorithm mentioned above, so it will not be repeated here.

[0075] In one implementable manner of the embodiment of the present disclosure, when the first target is diverted, it can also be implemented in but not limited to the following manner: the first target is diverted according to the potential data through a preset data insight algorithm to obtain the diverted first target; wherein the diverted first target is used to establish a dynamic database.

[0076] In the embodiment of the present disclosure, the preset data insight algorithm is a custom-set algorithm, which is a series of algorithms for extracting useful information, patterns and insights from a large amount of data, including but not limited to: descriptive statistical analysis, exploratory data analysis (EDA), text analysis and natural language processing (NLP), etc. Specifically, the embodiment of the present disclosure does not limit the preset data insight algorithm.

[0077] In one implementable method of the embodiment of the present disclosure, when performing data inventory processing based on target data and potential data, it can also be implemented in but not limited to the following method: determining the inventory standard based on a preset inventory range, and performing data inventory processing on the target data and the potential data according to the inventory standard and a third preset evaluation algorithm to obtain the inventory data.

[0078] In the embodiment of the present disclosure, the inventory standard is a custom set standard used to evaluate and classify talents, including but not limited to: ability and skills, performance, career planning, etc. Specifically, the embodiment of the present disclosure does not limit the inventory standard.

[0079] The third preset evaluation algorithm is a custom-set algorithm. Specifically, for the content of the third preset evaluation algorithm, please refer to the description of the first preset evaluation algorithm mentioned above, so it will not be repeated here.

[0080] In one implementable manner of the embodiment of the present disclosure, when performing data analysis and processing, it can also be implemented in but not limited to the following manner: determining the training standards according to the preset training objectives, and performing data analysis and processing on the target data, the potential data and the inventory data according to the training standards and the fourth preset evaluation algorithm to obtain the training data.

[0081] In the embodiments of the present disclosure, the training standard is a custom set standard used to ensure that the direction and effect of talent training meet the needs of the organization, including but not limited to: job competency model, knowledge and skill improvement, learning and development methods, etc. Specifically, the embodiments of the present disclosure do not limit the training standard.

[0082] The fourth preset evaluation algorithm is a custom-set algorithm. Specifically, for the content of the fourth preset evaluation algorithm, please refer to the description of the first preset evaluation algorithm mentioned above, so it will not be repeated here.

[0083] Corresponding to the above-mentioned talent management method, the present invention also proposes a talent management system, including: a selection module, a breeding module, an inventory module and a development module.

[0084] The selection module is used to perform data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected;

[0085] The breeding module is used to perform data breeding processing according to the target data to obtain potential data corresponding to each of the first targets, and to perform diversion processing on the first targets according to the potential data to obtain the diverted first targets;

[0086] The inventory module is used to perform data inventory processing according to the target data and the potential data to obtain the inventory data corresponding to the first target after the diversion;

[0087] The development module is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0088] In the embodiment of the present disclosure, in order to facilitate understanding of the functions of the system described in the embodiment of the present disclosure, the embodiment of the present disclosure provides an execution logic diagram of the talent management system, as shown in FIG. Figure 3As shown in the figure, the talent management system is a full closed-loop process talent management system. The full closed-loop process talent management system takes the business as the starting point and the end as the beginning, and opens up the talent management process closed loop of talent modeling, talent assessment, and talent inventory. At the same time, the talent data of the whole process is retained in real time to form an integrated data closed loop, so that enterprises can better understand the current situation of the organization through talent data, and gain insight into the future from the inventory data, so as to truly maximize the effectiveness of talent management. Efficient modeling methods help enterprises quickly and flexibly establish talent standards that are closely linked to the business. Flexible combination of factors such as ability, potential, and motivation to form customized assessment tools. Dynamic and detailed analysis of multi-dimensional data such as potential, ability, and performance to formulate data-driven talent strategies. Multiple data dashboards retain talent data in real time, present data analysis results from multiple angles, and discover business insights from talent data.

[0089] In summary, the embodiments of the present disclosure can achieve the following effects:

[0090] The disclosed embodiment opens up a closed loop of the talent management process of talent modeling (data mining), talent assessment (talent selection), talent inventory, and talent training. At the same time, the talent data of the entire process (data to be selected) is retained in real time to form an integrated data closed loop. This can provide better insights into the current status of the organization through talent data and maximize the effectiveness of talent management.

[0091] Corresponding to the talent management method described above, the present invention further provides a talent management device. Since the device embodiment of the present invention corresponds to the method embodiment described above, details not disclosed in the device embodiment can be referred to the method embodiment described above, and will not be described in detail in the present invention.

[0092] Figure 4 A schematic diagram of a talent management device provided by an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, including:

[0093] A mining unit 41 is used to perform data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected;

[0094] A breeding unit 42, configured to perform data breeding processing according to the target data to obtain potential data corresponding to each of the first targets;

[0095] A diversion unit 43, configured to perform diversion processing on the first target according to the potential data to obtain a diverted first target;

[0096] An inventory unit 44, configured to perform data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after diversion;

[0097] The analysis unit 45 is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

[0098] The talent management device provided by the present disclosure performs data mining on the selected data to obtain target data; wherein the selected data includes identification data corresponding to each selected target, and the target data is used to determine the first target among the selected targets; data selection is performed according to the target data to obtain potential data corresponding to each first target, and the first target is diverted according to the potential data to obtain the first target after diversion; data inventory is performed according to the target data and the potential data to obtain the inventory data corresponding to the first target after diversion; data analysis is performed according to the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after diversion. Compared with the related art, the embodiment of the present disclosure opens up the talent management process closed loop of talent modeling (data mining), talent assessment (talent selection), talent inventory and talent training, and at the same time, the talent data (data to be selected) of the whole process is retained in real time to form an integrated data closed loop, which can better understand the current situation of the organization through talent data and maximize the effectiveness of talent management.

[0099] Furthermore, in a possible implementation of the embodiment of the present disclosure, the mining unit 41 is also used to perform data mining processing on the data to be selected according to a preset selection criterion and a first preset evaluation algorithm to obtain the target data.

[0100] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the breeding unit 42 includes:

[0101] The selection module 421 is used to obtain the business data of the business to be processed; wherein the business to be processed is the business that the target to be selected needs to process;

[0102] A determination module 422, configured to determine a breeding standard according to the business data;

[0103] The breeding module 423 is used to perform data breeding processing on the target data according to the breeding standard and the second preset evaluation algorithm to obtain the potential data.

[0104] Furthermore, in a possible implementation of the embodiment of the present disclosure, the diversion unit 43 is also used to divert the first target according to the potential data through a preset data insight algorithm to obtain the first target after diversion; wherein the first target after diversion is used to establish a dynamic database.

[0105] Furthermore, in a possible implementation of the embodiment of the present disclosure, the inventory unit 44 is also used to determine the inventory standard according to a preset inventory range, and perform data inventory processing on the target data and the potential data according to the inventory standard and a third preset evaluation algorithm to obtain the inventory data.

[0106] Furthermore, in a possible implementation of the embodiment of the present disclosure, the analysis unit 45 is also used to determine the training standards according to the preset training objectives, and perform data analysis and processing on the target data, the potential data and the inventory data according to the training standards and a fourth preset evaluation algorithm to obtain the training data.

[0107] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principle is the same, which is no longer limited in the embodiment of the present disclosure.

[0108] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0109] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0110] like Figure 6As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 to a RAM (Random Access Memory) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0111] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for talent management. For example, in some embodiments, the method for talent management may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the aforementioned talent management method in any other appropriate manner (for example, by means of firmware).

[0113] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0118] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0119] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A talent management method, characterized in that: include: Performing data mining on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected; Performing data selection processing according to the target data to obtain potential data corresponding to each of the first targets, and performing diversion processing on the first targets according to the potential data to obtain diverted first targets; Performing data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after the diversion; Data analysis and processing are performed based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

2. The method according to claim 1, characterized in that The data to be selected is subjected to data mining to obtain target data including: The data to be selected is subjected to data mining processing according to a preset selection standard and a first preset evaluation algorithm to obtain the target data.

3. The method according to claim 1, characterized in that The data selection and breeding process is performed according to the target data to obtain the potential data corresponding to each of the first targets, including: Acquire business data of the business to be processed; wherein the business to be processed is the business that the target to be selected needs to process; A breeding standard is determined according to the business data, and data breeding processing is performed on the target data according to the breeding standard and a second preset evaluation algorithm to obtain the potential data.

4. The method according to claim 1, characterized in that: The first target is diverted according to the potential data to obtain the diverted first target, comprising: The first target is diverted according to the potential data through a preset data insight algorithm to obtain the diverted first target; wherein the diverted first target is used to establish a dynamic database.

5. The method according to claim 1, characterized in that The data inventory processing is performed according to the target data and the potential data to obtain the inventory data corresponding to the first target after the diversion, including: An inventory standard is determined according to a preset inventory range, and a data inventory process is performed on the target data and the potential data according to the inventory standard and a third preset evaluation algorithm to obtain the inventory data.

6. The method according to claim 1, characterized in that The data analysis and processing is performed according to the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion, including: The training standards are determined according to the preset training objectives, and the target data, the potential data and the inventory data are analyzed and processed according to the training standards and a fourth preset evaluation algorithm to obtain the training data.

7. A talent management system, characterized in that: include: Selection module, breeding module, inventory module and development module, The selection module is used to perform data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected; The breeding module is used to perform data breeding processing according to the target data to obtain potential data corresponding to each of the first targets, and to perform diversion processing on the first targets according to the potential data to obtain the diverted first targets; The inventory module is used to perform data inventory processing according to the target data and the potential data to obtain the inventory data corresponding to the first target after the diversion; The development module is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

8. A talent management device, characterized in that: include: A mining unit, used for performing data mining processing on the data to be selected to obtain target data; wherein the data to be selected includes identification data corresponding to each target to be selected, and the target data is used to determine the first target among the targets to be selected; A breeding unit, used for performing data breeding processing according to the target data to obtain potential data corresponding to each of the first targets; A diversion unit, configured to perform diversion processing on the first target according to the potential data to obtain a diverted first target; An inventory unit, configured to perform data inventory processing according to the target data and the potential data to obtain inventory data corresponding to the first target after the diversion; The analysis unit is used to perform data analysis and processing based on the target data, the potential data and the inventory data to obtain the training data corresponding to the first target after the diversion.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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