Human resource data processing method and system based on cloud computing

Abstract

The embodiment of the invention provides a human resource data processing method and system based on cloud computing, and relates to the technical field of human resource data processing.The method comprises the steps that target data are obtained, and the target data comprise target hot data, target temperature data and / or target cold data; generating a target prediction result according to the target data; and according to the target prediction result, executing the target operation to enable the stability of the target tissue to be greater than or equal to the preset stability. The human resource data processing precision and processing efficiency can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of human resources data processing, and in particular to a human resources data processing method and system based on cloud computing. Background Art

[0002] Human resources data processing is primarily used to support an organization's human resource management and decision-making processes by collecting, storing, analyzing and interpreting information related to its employees.

[0003] In the related technologies, there are problems such as poor accuracy and low efficiency in human resources data processing.

[0004] Therefore, a new technical solution is urgently needed to solve the above technical problems. Summary of the Invention

[0005] According to an embodiment of the present application, a human resources data processing method and system based on cloud computing are provided, which can improve the accuracy and efficiency of human resources data processing.

[0006] In a first aspect of the present application, a human resources data processing method based on cloud computing is proposed, comprising:

[0007] Acquire target data, wherein the target data includes: target hot data, target warm data, and / or target cold data;

[0008] Generate target prediction results based on target data;

[0009] According to the target prediction result, the target operation is performed to make the stability of the target tissue greater than or equal to the preset stability.

[0010] In some feasible implementations, generating a target prediction result based on the target data includes:

[0011] Generate a target map based on the target data, wherein the target map includes: an organizational relationship map, an employee skills map, and / or a business participation map;

[0012] Generate target prediction results based on the target map;

[0013] Among them, the target prediction results include: turnover tendency prediction results.

[0014] In some feasible implementations, when it is determined that the turnover tendency rate of the target employee is greater than or equal to a preset turnover tendency rate according to the turnover tendency prediction result;

[0015] The above method further includes:

[0016] Based on the target map, determine the target employee's importance to the target organization, the target employee's skill breadth, and / or the target employee's depth of involvement in the target business;

[0017] Target actions are performed to achieve a target organizational stability greater than or equal to a preset stability based on importance, skill breadth, and / or depth of involvement.

[0018] In some feasible implementations, performing target operations based on importance, skill breadth, and / or engagement depth to ensure that the stability of the target organization is greater than or equal to a preset stability includes:

[0019] Identify target job groups based on importance, skill breadth, and / or depth of involvement;

[0020] Based on the target job group, adjust the target employee's job to make the target organization's stability greater than or equal to the preset stability.

[0021] In some feasible implementations, the above-mentioned target operation is performed based on the importance, skill breadth, and / or involvement depth to make the stability of the target organization greater than or equal to the preset stability, and / or further includes:

[0022] Determine target salary ranges based on importance, skill breadth, and / or depth of involvement;

[0023] Based on the target salary adjustment range, adjust the target employee's salary so that the stability of the target organization is greater than or equal to the preset stability.

[0024] In some feasible implementations, determining the target salary increase range based on importance, skill breadth, and / or involvement depth includes:

[0025] Identify target groups based on importance, breadth of skills, and / or depth of involvement;

[0026] Determine the target salary increase range based on the target candidate group;

[0027] The similarity between each element in the target candidate group and the target employee is greater than or equal to a preset similarity.

[0028] In some feasible implementations, the importance is determined according to the following formula:

[0029] C network =α·PageRank+β·Betweenness+γ·EigenVector

[0030] Among them, C networkis the importance of the target employee to the target organization; PageRank is the importance of the target employee's corresponding target node in the target graph for information flow; Betweenness is the frequency of occurrence of the target employee's corresponding target node on the shortest path between all other nodes in the target graph; EigenVector is the importance of the eigenvector of the adjacency matrix corresponding to the target node; α is the first weight coefficient; β is the second weight coefficient; γ is the third weight coefficient.

[0031] In some feasible implementations, the skill breadth is determined according to the following formula:

[0032]

[0033] Among them, C skill is the breadth of skills; T1 is the training cycle of target employees; T2 is the average on-the-job cycle in the market; K total1 K is the corresponding knowledge amount of the target employee; total2 The total amount of knowledge corresponding to the target organization.

[0034] In some feasible implementations, the participation depth is determined according to the following formula:

[0035]

[0036] Among them, C business P is the depth of participation; RC is the target business revenue; E is the target employee’s participation in the target business; D R is the total revenue of the target organization; Core c The target employee's contribution to core technology.

[0037] In a second aspect of the present application, a human resources data processing system based on cloud computing is proposed, comprising:

[0038] an acquisition unit, configured to acquire target data, wherein the target data includes: target hot data, target warm data, and / or target cold data;

[0039] A generation unit, used to generate a target prediction result based on the target data;

[0040] The execution unit is used to execute a target operation according to the target prediction result so that the stability of the target tissue is greater than or equal to a preset stability.

[0041] Embodiments of the present application provide a cloud computing-based human resources data processing method and system, wherein the method includes: obtaining target data, wherein the target data includes target hot data, target warm data, and / or target cold data; generating a target prediction result based on the target data; and performing a target operation based on the target prediction result to ensure that the stability of the target organization is greater than or equal to a preset stability. This application can improve the accuracy and efficiency of human resources data processing.

[0042] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0044] Figure 1 A schematic diagram of a process for processing human resources data based on cloud computing provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the structure of a human resources data processing system based on cloud computing provided in an embodiment of the present application;

[0046] Figure 3 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0048] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0049] In a first aspect of the embodiments of the present application, a human resources data processing method based on cloud computing is proposed. Figure 1A schematic diagram of a human resources data processing method 100 based on cloud computing provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method 100 includes:

[0050] Step S1: Acquire target data, wherein the target data includes: target hot data, target warm data, and / or target cold data.

[0051] It should be noted that the target data mentioned above includes employee profile data, performance records, salary history data, attendance log data, business project collaboration records, resume data, communication data, employee behavior data, and document data. The communication data mentioned above may include communication partners, the hierarchical relationships between communication partners, and documents generated during the communication process. The document data mentioned above may include the sender and recipient of the document, the business to which the document belongs, and document modification records.

[0052] For example, the target hot data may include target data with an access frequency greater than or equal to a first preset threshold. The target warm data may include target data with an access frequency less than the first preset threshold and greater than or equal to a second preset threshold. The target cold data may include target data with an access frequency less than the second preset threshold.

[0053] Step S2: Generate target prediction results based on target data.

[0054] For example, a target prediction result can be generated based on the target hot data, target warm data, and / or target cold data. The target prediction result can include a job transfer prediction result and / or a turnover tendency prediction result. The job transfer prediction result can include a job transfer probability result, etc. The turnover tendency prediction result can include a turnover tendency index result, etc.

[0055] It should be noted that the above-mentioned target prediction results can be generated based on target hot data, target temperature data, and / or target cold data according to the generation accuracy requirements of the target prediction results and / or the response speed requirements of the target prediction results.

[0056] Exemplarily, when the generation accuracy requirement of the target prediction result is greater than or equal to the first preset accuracy, and the response speed requirement of the target prediction result is less than or equal to the first response speed, the above-mentioned target prediction result can be generated based on the target hot data, target temperature data, and target cold data.

[0057] Exemplarily, when the generation accuracy requirement of the target prediction result is less than the first preset accuracy and greater than or equal to the second preset accuracy, and the response speed requirement of the target prediction result is greater than the first response speed and less than or equal to the second response speed, the above-mentioned target prediction result can be generated based on the target thermal data and target temperature data.

[0058] Exemplarily, when the target prediction result generation accuracy requirement is less than the second preset accuracy, and the target prediction result response speed requirement is greater than the second response speed, the target prediction result can be generated based on the target hot data.

[0059] In some feasible implementations, the above-mentioned generation of target prediction results based on target data includes: generating a target map based on the target data, wherein the target map includes: an organizational relationship map, an employee skill map, and / or a business participation map; generating a target prediction result based on the target map.

[0060] Exemplarily, an organizational relationship map, an employee skill map, and / or a business participation map can be generated based on the above-mentioned target hot data, target warm data, and / or target cold data, so as to generate job transfer prediction results and / or turnover tendency prediction results, etc. based on the above-mentioned organizational relationship map, employee skill map, and / or business participation map.

[0061] It should be noted that, based on the generation accuracy requirements of the organizational relationship map, employee skill map, and / or business participation map, and / or the response speed requirements of the organizational relationship map, employee skill map, and / or business participation map, the above-mentioned organizational relationship map, employee skill map, and / or business participation map can be generated based on target hot data, target warm data, and / or target cold data to generate job transfer prediction results, and / or turnover tendency prediction results, etc.

[0062] Exemplarily, when the generation accuracy requirement of the organizational relationship map, employee skills map, and / or business participation map is greater than or equal to a third preset accuracy, and the response speed requirement of the organizational relationship map, employee skills map, and / or business participation map is less than or equal to a third response speed, the above-mentioned organizational relationship map, employee skills map, and / or business participation map can be generated based on target hot data, target warm data, and target cold data to generate job transfer prediction results, and / or turnover tendency prediction results, etc.

[0063] Exemplarily, when the generation accuracy requirement of the organizational relationship map, employee skill map, and / or business participation map is less than the third preset accuracy and greater than or equal to the fourth preset accuracy, and the response speed requirement of the organizational relationship map, employee skill map, and / or business participation map is greater than the third response speed and less than or equal to the fourth response speed, the above-mentioned organizational relationship map, employee skill map, and / or business participation map can be generated based on the target hot data and target warm data to generate job transfer prediction results and / or turnover tendency prediction results, etc.

[0064] Exemplarily, when the generation accuracy requirement of the organizational relationship map, employee skill map, and / or business participation map is less than the fourth preset accuracy, and the response speed requirement of the organizational relationship map, employee skill map, and / or business participation map is greater than the fourth response speed, the above-mentioned organizational relationship map, employee skill map, and / or business participation map can be generated based on target hot data to generate job transfer prediction results, and / or turnover tendency prediction results, etc.

[0065] It should be noted that step S2 can be implemented based on cloud computing. Specifically, it can include: processing and analyzing the data based on cloud computing to determine the target prediction results; and generating the organizational relationship map, employee skills map, and / or business participation map based on the AI / ML services provided by the target cloud platform.

[0066] Therefore, the above method can realize the accurate and automatic generation of organizational relationship maps, employee skill maps, and / or business participation maps based on target hot data, target warm data, and / or target cold data, and the accurate and automatic generation of target prediction results based on the organizational relationship map, employee skill map, and / or business participation map, which is conducive to improving the automatic generation efficiency and generation accuracy of target prediction results such as turnover tendency prediction results, thereby improving the accuracy and processing efficiency of human resources data.

[0067] Step S3: Based on the target prediction result, perform a target operation to make the stability of the target tissue greater than or equal to a preset stability.

[0068] For example, based on job transfer prediction results and / or turnover tendency prediction results, target operations can be executed to ensure that the stability of the target organization is greater than or equal to a preset stability level. The target operations can include target salary increase operations and / or target job adjustment operations. The preset stability level can be set based on actual needs and circumstances.

[0069] It should be noted that the target operation can be automatically executed according to the target user settings, or content related to the target operation can be pushed to the target user, and the corresponding target operation can be executed according to the target user's final decision.

[0070] Specifically, when the target user sets the target operation to be automatically executed, or the target user determines to execute the corresponding target operation, a salary adjustment email and / or a position adjustment announcement can be automatically sent.

[0071] Based on this, the present application provides a cloud computing-based human resources data processing method and system, wherein the method includes: obtaining target data, wherein the target data includes: target hot data, target warm data, and / or target cold data; generating a target prediction result based on the target data; and executing a target operation based on the target prediction result so that the stability of the target organization is greater than or equal to a preset stability. The present application can realize the automatic and efficient generation of target prediction results based on target hot data, target warm data, and / or target cold data; and accurately and automatically executing the target operation based on the target prediction result so that the stability of the target organization is greater than or equal to the preset stability, which is conducive to improving the accuracy and efficiency of human resources data processing, and improving the execution accuracy and efficiency of personnel change operations during the personnel change operations of the target enterprise.

[0072] In some feasible implementations, when it is determined that the target employee's turnover tendency rate is greater than or equal to a preset turnover tendency rate based on the turnover tendency prediction result; the above method also includes: determining the importance of the target employee to the target organization, the target employee's skill breadth, and / or the target employee's depth of participation in the target business based on the target map; based on the importance, skill breadth, and / or participation depth, performing target operations to make the stability of the target organization greater than or equal to the preset stability.

[0073] For example, if the turnover tendency prediction result determines that the target employee's turnover tendency rate is greater than or equal to a preset turnover tendency rate, the importance of the target employee to the target organization can be determined based on the organizational relationship map. The importance of the target employee to the target organization can be used to determine the target employee's network influence in the organizational relationship map.

[0074] For example, if the turnover tendency prediction result indicates that the target employee's turnover tendency rate is greater than or equal to a preset turnover tendency rate, the target employee's skill breadth can be determined based on the employee's skill profile. The target employee's skill breadth can represent the number of skills in the same field and / or the number of cross-field skills the target employee possesses.

[0075] For example, if the turnover tendency prediction result determines that the target employee's turnover tendency rate is greater than or equal to a preset turnover tendency rate, the target employee's involvement depth in the target business can be determined based on the business involvement map. The target employee's involvement depth in the target business can be determined based on the target employee's decision-making participation rate for the target business project.

[0076] In some feasible implementations, target salary adjustment operations and / or target position adjustment operations can be performed based on the above-mentioned importance, the above-mentioned skill breadth, and / or the depth of participation, so that the stability of the target organization is greater than or equal to the preset stability.

[0077] Therefore, the above method can achieve the situation where, based on the turnover tendency prediction result, it is determined that the turnover tendency rate of the target employee is greater than or equal to the preset turnover tendency rate, and then, based on the organizational relationship map, the employee skill map, and / or the business participation map, the importance of the target employee to the target organization, the breadth of the target employee's skills, and / or the depth of participation of the target employee in the target business can be accurately and automatically determined; based on the importance, breadth of skills, and / or depth of participation, the target operation is automatically executed to make the stability of the target organization greater than or equal to the preset stability, which is conducive to further improving the accuracy and efficiency of human resources data processing, and further improving the execution accuracy and efficiency of personnel change operations during the personnel change operations of the target enterprise.

[0078] In some feasible embodiments, the above-mentioned execution of target operations based on importance, skill breadth, and / or participation depth to make the stability of the target organization greater than or equal to the preset stability includes: determining the target job group based on importance, skill breadth, and / or participation depth; and adjusting the positions of the target employees based on the target job group to make the stability of the target organization greater than or equal to the preset stability.

[0079] Illustratively, the target job group may include: a first target job group, a second target job group, and / or a third target job group.

[0080] Specifically, when the attributes of the target employee are determined to be high importance, broad skills, and deep involvement based on the above-mentioned importance, skill breadth, and / or participation depth, the target position group corresponding to the target employee is determined to be the first target position group. According to the first target position group, the position of the target employee is adjusted so that the stability of the target organization is greater than or equal to the preset stability, wherein the first target position group may include: a preset number of position elements with equity or option incentives to increase the job satisfaction and loyalty of the target employee.

[0081] Specifically, when the target employee's attributes are determined to be high importance, narrow skills, and deep participation based on the above-mentioned importance, skill breadth, and / or participation depth, the target position group corresponding to the target employee is determined to be the second target position group. According to the second target position group, the target employee's position is adjusted so that the stability of the target organization is greater than or equal to the preset stability, wherein the second target position group may include: a preset number of position elements with project decision-making power, so as to increase the target employee's job satisfaction and expand the target employee's development space.

[0082] Specifically, when the target employee's attributes are determined to be medium importance, broad skills, and shallow participation based on the above-mentioned importance, skill breadth, and / or participation depth, the target job group corresponding to the target employee is determined to be the third target job group. According to the third target job group, the target employee's position is adjusted so that the stability of the target organization is greater than or equal to the preset stability, wherein the third target job group may include: a preset number of job elements involving core technology research and development, so as to increase the target employee's job satisfaction and expand the target employee's development space.

[0083] Therefore, the above method can realize the automatic and accurate determination of the target job group based on importance, skill breadth, and / or participation depth; automatically adjust the target employee's position based on the target job group, thereby realizing the automatic generation of the target employee's job promotion path, reducing the risk of loss of key talents in the target enterprise, and helping to further improve the accuracy and efficiency of human resources data processing, and further improve the execution accuracy and efficiency of personnel change operations during the personnel change operations of the target enterprise.

[0084] In some feasible implementations, the above-mentioned target operation is performed based on importance, skill breadth, and / or participation depth to make the stability of the target organization greater than or equal to the preset stability, and / or, further includes: determining a target salary adjustment range based on importance, skill breadth, and / or participation depth; adjusting the salary of the target employee based on the target salary adjustment range to make the stability of the target organization greater than or equal to the preset stability.

[0085] Exemplarily, the labor market value corresponding to the target employee can be determined based on the above-mentioned importance, the above-mentioned skill breadth, and / or the above-mentioned participation depth to determine the above-mentioned target salary adjustment range. Based on the above-mentioned target salary adjustment range, the salary of the target employee can be dynamically adjusted in a step-by-step manner to ensure that the stability of the target organization is greater than or equal to the preset stability.

[0086] Therefore, the above method can realize the automatic dynamic determination of the target salary adjustment range based on importance, skill breadth, and / or participation depth; based on the target salary adjustment range, the salary of the target employee can be automatically and dynamically adjusted so that the stability of the target organization is greater than or equal to the preset stability, which is conducive to further reducing the risk of loss of key talents in the target enterprise.

[0087] In some feasible implementations, the above-mentioned determination of the target salary increase range based on importance, skill breadth, and / or participation depth includes: determining a target candidate group based on importance, skill breadth, and / or participation depth; determining a target salary increase range based on the target candidate group; wherein the similarity between each element in the target candidate group and the target employee is greater than or equal to a preset similarity.

[0088] Exemplarily, multiple target APIs are mobilized to obtain relevant data from multiple human resources websites, and the target candidate group is determined based on the importance, skill breadth, and / or participation depth. The target salary adjustment range is determined based on the salary range of the corresponding elements of the target candidate group. The salary of the target employee is adjusted according to the target salary adjustment range so that the stability of the target organization is greater than or equal to the preset stability. The target APIs may include: LinkedIn API, GitHub API, and / or Stack Overflow API, etc. The similarity between each element in the target candidate group and the target employee is greater than or equal to the preset similarity. It should be noted that the preset similarity is positively correlated with the accuracy requirement for determining the target salary adjustment range, that is, the higher the accuracy requirement for determining the target salary adjustment range, the higher the preset similarity.

[0089] Therefore, the above method can achieve accurate and automated matching of the target candidate group based on importance, skill breadth, and / or participation depth; accurately and automatically determine the target salary adjustment range based on the target candidate group, which is conducive to improving the accuracy and efficiency of the automated determination of the target salary adjustment range; and improving the execution accuracy and efficiency of the target employee salary adjustment operation.

[0090] It should be noted that, when it is determined that the target difference is greater than the maximum value of the target salary increase range of the preset ratio, a target candidate is selected to replace the target employee based on the above-mentioned target candidate group. The above-mentioned target difference is the difference between the ideal salary of the above-mentioned target employee and the maximum value of the above-mentioned target salary increase range. The above-mentioned preset ratio can be set according to the actual situation, for example, it can be set to 10%. The above-mentioned target candidate can be determined based on the time of arrival, the similarity between the target candidate and the target employee, and / or the expected salary of the target candidate. Specifically, a candidate with an earlier arrival time, a higher similarity with the target employee, and / or a lower expected salary can be selected as the above-mentioned target candidate.

[0091] In some feasible implementations, the importance is determined according to the following formula:

[0092] C networ =α·PageRank+β·Betweenness+γ·EigenVector(1)

[0093] Among them, C network is the importance of the target employee to the target organization; PageRank is the importance of the target employee's corresponding target node in the target graph for information flow; Betweenness is the frequency of occurrence of the target employee's corresponding target node on the shortest path between all other nodes in the target graph; EigenVector is the importance of the eigenvector of the adjacency matrix corresponding to the target node; α is the first weight coefficient; β is the second weight coefficient; γ is the third weight coefficient.

[0094] Therefore, the above method can realize the accurate and automatic determination of the importance of the target employee to the target organization based on formula (1), according to the importance of the target employee to the target organization, the importance of the information flow of the target employee's corresponding target node in the target graph, the frequency of occurrence of the target employee's corresponding target node on the shortest path between all other nodes in the target graph, and the importance of the target node's corresponding adjacency matrix eigenvector, and provide accurate data support for executing the target operation based on the above importance, the above skill breadth, and / or the above participation depth so that the stability of the target organization is greater than or equal to the preset stability, which is conducive to further improving the accuracy and efficiency of human resources data processing, and further improving the execution accuracy and efficiency of personnel change operations in the process of personnel change operations of the target enterprise.

[0095] In some feasible implementations, the skill breadth is determined according to the following formula:

[0096]

[0097] Among them, C skill is the breadth of skills; T1 is the training cycle of target employees; T2 is the average on-the-job cycle in the market; K total1 K is the corresponding knowledge amount of the target employee; total2 The total amount of knowledge corresponding to the target organization.

[0098] Therefore, the above method can realize the accurate and automatic determination of the target employee's skill breadth based on formula (2) according to the target employee's training cycle, the market average on-the-job cycle, the target employee's corresponding knowledge amount and the target organization's corresponding total knowledge amount, and provide accurate data support for executing the target operation based on the above importance, the above skill breadth, and / or the above participation depth so that the stability of the target organization is greater than or equal to the preset stability, which is conducive to further improving the accuracy and efficiency of human resources data processing, and further improving the execution accuracy and efficiency of personnel change operations in the target enterprise's personnel change operations.

[0099] In some feasible implementations, the participation depth is determined according to the following formula:

[0100]

[0101] Among them, C business P is the depth of participation; RC is the target business revenue; E is the target employee’s participation in the target business; D R is the total revenue of the target organization; Core c The target employee's contribution to core technology.

[0102] It should be noted that the contribution of the above-mentioned target employees to core technology is c It can be determined based on the target employee’s core patent contribution and / or core code contribution.

[0103] Therefore, the above method can realize the accurate and automatic determination of the depth of participation of the target employee in the target business based on formula (3) according to the target business revenue, the target employee's participation in the target business, the total revenue of the target organization and the target employee's contribution to the core technology, and provide accurate data support for executing the target operation based on the above importance, the above skill breadth, and / or the above participation depth so that the stability of the target organization is greater than or equal to the preset stability, which is conducive to further improving the accuracy and efficiency of human resources data processing, and further improving the execution accuracy and efficiency of personnel change operations in the personnel change operation process of the target enterprise.

[0104] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0105] The above is an introduction to the method embodiment. The following is a device embodiment to further illustrate the solution described in this application.

[0106] According to a second aspect of the embodiments of the present application, a human resources data processing system based on cloud computing is proposed. Figure 2 This is a structural diagram of a human resources data processing system 200 based on cloud computing provided in an embodiment of the present application. Figure 2 The system 200 shown includes an acquisition unit 210 , a generation unit 220 and an execution unit 230 .

[0107] An acquisition unit 210 is configured to acquire target data, wherein the target data includes: target hot data, target warm data, and / or target cold data;

[0108] A generating unit 220 is configured to generate a target prediction result based on the target data;

[0109] The execution unit 230 is configured to execute a target operation according to the target prediction result so that the stability of the target tissue is greater than or equal to a preset stability.

[0110] Figure 3 This is a structural diagram of an electronic device 300 provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or server are also stored. The CPU 301, ROM 302 and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0111] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0112] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-mentioned functions defined in the system of the present application are executed.

[0113] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0115] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0116] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A human resources data processing method based on cloud computing, characterized in that: include: Acquiring target data, wherein the target data includes: target hot data, target warm data, and / or target cold data; generating a target prediction result according to the target data; According to the target prediction result, a target operation is performed to make the stability of the target tissue greater than or equal to a preset stability.

2. The human resources data processing method based on cloud computing according to claim 1, characterized in that: Generating a target prediction result according to the target data includes: Generate a target map based on the target data, wherein the target map includes: an organizational relationship map, an employee skills map, and / or a business participation map; Generating the target prediction result according to the target map; The target prediction results include: turnover tendency prediction results.

3. The human resources data processing method based on cloud computing according to claim 2, characterized in that: If it is determined based on the turnover tendency prediction result that the turnover tendency rate of the target employee is greater than or equal to the preset turnover tendency rate; The method further comprises: Determining, based on the target map, the importance of the target employee to the target organization, the breadth of the target employee's skills, and / or the depth of the target employee's involvement in the target business; Based on the importance, the skill breadth, and / or the involvement depth, target operations are performed to make the stability of the target organization greater than or equal to a preset stability.

4. The human resources data processing method based on cloud computing according to claim 3, characterized in that: The performing of the target operation to make the stability of the target organization greater than or equal to the preset stability according to the importance, the skill breadth, and / or the participation depth includes: Determine a target position group based on the importance, the skill breadth, and / or the involvement depth; According to the target position group, the positions of the target employees are adjusted so that the stability of the target organization is greater than or equal to a preset stability.

5. The human resources data processing method based on cloud computing according to claim 4, characterized in that: The target operation is performed based on the importance, the skill breadth, and / or the participation depth to make the stability of the target organization greater than or equal to a preset stability, and / or further comprising: Determining a target salary increase range based on the importance, the skill breadth, and / or the depth of involvement; According to the target salary adjustment range, the salary of the target employee is adjusted so that the stability of the target organization is greater than or equal to a preset stability.

6. The human resources data processing method based on cloud computing according to claim 5, characterized in that: Determining the target salary increase range based on the importance, the skill breadth, and / or the involvement depth includes: Determining a target candidate group based on the importance, skill breadth, and / or involvement depth; Determine the target salary increase range based on the target candidate group; The similarity between each element in the target candidate group and the target employee is greater than or equal to a preset similarity.

7. The human resources data processing method based on cloud computing according to any one of claims 3 to 6, characterized in that: The importance is determined according to the following formula: C network =α·PageRank+β·Betweenness+γ·EigenVector Among them, C network is the importance of the target employee to the target organization; PageRank is the importance of the information flow of the target node corresponding to the target employee in the target graph; Betweenness is the frequency of occurrence of the target node corresponding to the target employee on the shortest path between all other nodes in the target graph; EigenVector is the importance of the eigenvector of the adjacency matrix corresponding to the target node; α is the first weight coefficient; β is the second weight coefficient; γ is the third weight coefficient.

8. The method for processing human resources data based on cloud computing according to claim 7, characterized in that: The skill breadth is determined according to the following formula: Among them, C skill is the skill breadth; T1 is the training period of the target employee; T2 is the average period of employment in the market; K total1 K is the knowledge amount corresponding to the target employee; total2 The total amount of knowledge corresponding to the target organization.

9. The human resources data processing method based on cloud computing according to claim 8, characterized in that: The participation depth is determined according to the following formula: Among them, C business is the participation depth; P RC is the target business revenue; E is the target employee’s participation in the target business; D R The total revenue of the target organization; c The amount of contribution made by the target employee to the core technology.

10. A human resources data processing system based on cloud computing, characterized in that: include: an acquiring unit, configured to acquire target data, wherein the target data includes: target hot data, target warm data, and / or target cold data; A generating unit, configured to generate a target prediction result based on the target data; An execution unit is used to execute a target operation according to the target prediction result so that the stability of the target tissue is greater than or equal to a preset stability.

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