Method, device and equipment for generating career analysis report based on educational experience

Abstract

The present application relates to a method, device and equipment for generating a career analysis report based on educational experience. The method comprises: constructing a career mobility network model in the dimension of educational experience based on the original career data obtained from the online career network, capturing the personnel mobility between occupations due to educational experience, and extracting features from the original career data according to the weights of the edges in the model, obtaining the total personnel mobility characteristics, personnel retention characteristics and circulation hub characteristics of each occupation and generating a career analysis report. The present method can capture the personnel mobility relationship between occupations by integrating career data to construct a career mobility network model in the dimension of educational experience, and extract multi-dimensional personnel mobility characteristics based on the career mobility network model and generate an analysis report, thereby improving the comprehensiveness and accuracy of career development analysis, which is of great significance for guiding job seekers in employment and guiding organizational human resource management.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and apparatus for generating a career analysis report based on educational experience. Background Art

[0002] In the knowledge economy, new knowledge and technologies are constantly emerging and merging, creating new work methods and career paths. Occupations are becoming increasingly diverse, and the job market is becoming increasingly complex and volatile. Job seekers and organizational managers need to use scientific methods to stay informed about the development of various occupations and stay abreast of job market trends. Current assessments and analyses of some occupations only consider the basic characteristics of the occupation itself, without factoring in the fluctuations in inter-occupational mobility. Consequently, job seekers and organizational managers lack an understanding of inter-occupational mobility relationships and career development, leading to incomplete and inaccurate career development analyses. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device and equipment for generating career analysis reports based on educational experience, which can integrate career data in the employment market, explore the personnel flow between occupations, and improve the comprehensiveness and accuracy of career development analysis results, in order to address the above technical problems.

[0004] A method for generating a career analysis report based on educational experience, the method comprising:

[0005] Obtaining the career data set from the online career network and preprocessing it to obtain the original career data;

[0006] The occupations in the original occupational data are used as nodes, the personnel flow between occupations is used as edges, and the number of personnel flows between occupations based on educational experience is used as the weight of the edge to construct an occupational mobility network model based on educational experience.

[0007] The original occupational data is feature extracted according to the weights of the edges in the occupational mobility network model to obtain the total personnel mobility characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation. By recording the extracted characteristics of each occupation, an occupational analysis report is generated.

[0008] In one embodiment, a career dataset from an online career network is obtained and preprocessed to obtain raw career data, including:

[0009] Obtain an occupational dataset from an online occupational network, calculate the frequency of occurrence of each occupation in the occupational dataset, and designate occupations with a frequency greater than 100 as common occupations.

[0010] The names of common occupations are calibrated to obtain preliminary standardized occupations. Based on the preliminary standardized occupations, the occupation names and occupation name stems of non-standardized occupations in the occupational dataset are fully matched to obtain standardized occupations.

[0011] The original occupation data were obtained by mapping the standardized occupations with the occupational classification of the Occupational Information Network.

[0012] In one embodiment, the original occupational data includes the number of occupations, the number of employees, the education level of the employees, the mobility history of the employees, the industries in which the employees are employed, and the regions in which the employees are employed.

[0013] In one embodiment, the career mobility network model of the educational experience dimension is represented as follows: in, Represents a node set, each node represents an occupation, ε represents an edge set, and each edge e(u,v)∈ε represents a transition from occupation to occupation at time t. To career The flow of people between Represents a weight set, the weight corresponding to each edge Indicates the occupational status based on educational experience at time t To career The amount of personnel movement between them.

[0014] In one embodiment, the weight w corresponding to each edge in the career mobility network model of educational experience dimension is t EA (u,v) is expressed as:

[0015]

[0016]

[0017] Among them, HC EA (p) represents the calculation factor of educational experience, edu(p) = {1, 2, 3, 4} represents the educational level, and α represents the control parameter of the curve steepness.

[0018] In one embodiment, the total flow of personnel is characterized by

[0019] Total t (v)=IN t (v)+OUT t (v)

[0020] Among them, Total t (v) represents the total personnel turnover characteristics of occupation v at time t, represents the number of people flowing into occupation v based on their educational experience at time t, represents the number of people leaving occupation v based on their educational experience at time t, represents the number of personnel flows from occupation u to occupation v based on educational experience at time t, It represents the number of personnel flows from occupation v to occupation z based on educational experience at time t.

[0021] In one embodiment, the staff retention feature is represented by

[0022]

[0023] Among them, Retain t (v) represents the personnel retention characteristics of occupation v at time t, and ε0 represents the smoothing factor. In one embodiment, the circulation hub degree characteristics are expressed as

[0024] Center t (v)=Flow t (v)×Path t (v)

[0025]

[0026]

[0027] Among them, Flow t (v) represents the traffic proportion of occupation v at time t, Path t (v) represents the critical path proportion of occupation v at time t, represents the number of times occupation v appears in the shortest path from occupation x to occupation y, g xy Represents the total number of shortest paths from occupation x to occupation y, where x and y are arbitrary occupations.

[0028] A device for generating a career analysis report based on educational experience, the device comprising:

[0029] The data preprocessing module is used to obtain the career data set in the online career network and preprocess it to obtain the original career data;

[0030] The occupational mobility network construction module is used to construct an occupational mobility network model based on educational experience, using the occupations in the original occupational data as nodes, the personnel flow between occupations as edges, and the number of personnel flows between occupations based on educational experience as the edge weights;

[0031] The occupational analysis report generation module is used to extract features from the original occupational data based on the weights of the edges in the occupational flow network model, obtain the total personnel flow characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation, and generate an occupational analysis report by recording the extracted characteristics of each occupation.

[0032] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Obtaining the career data set from the online career network and preprocessing it to obtain the original career data;

[0034] The occupations in the original occupational data are used as nodes, the personnel flow between occupations is used as edges, and the number of personnel flows between occupations based on educational experience is used as the weight of the edge to construct an occupational mobility network model based on educational experience.

[0035] The original occupational data is feature extracted according to the weights of the edges in the occupational mobility network model to obtain the total personnel mobility characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation. By recording the extracted characteristics of each occupation, an occupational analysis report is generated.

[0036] The above-mentioned method, device, and apparatus for generating a career analysis report based on educational experience construct a career mobility network model based on educational experience based on raw career data obtained from an online career network. The model consists of edges and nodes, with each node representing an occupation, and edges between nodes representing the flow of personnel between different occupations. The weight corresponding to each edge represents the amount of personnel mobility between occupations due to educational experience. This career mobility network model can capture the personnel mobility between occupations due to educational experience, thus preventing job seekers and organizational managers from having insufficient understanding of the personnel mobility relationship between occupations. Furthermore, based on the weights of the edges in the model, feature extraction is performed on the raw career data to obtain the total personnel mobility characteristics, personnel retention characteristics, and circulation hub characteristics for each occupation, and generate a career analysis report. The total personnel mobility characteristics recorded in the report represent the total number of personnel inflows and outflows of a certain occupation, which helps to understand the attractiveness and development opportunities of the occupation. The personnel retention characteristics represent the ratio of the number of personnel inflows to the number of personnel outflows of a certain occupation, which helps to understand the stability of the occupation. The circulation hub characteristics represent the proportion of a certain occupation in the critical path and flow ratio in the career mobility network model, which helps to understand the cross-disciplinary capabilities of the occupation.

[0037] Therefore, compared with the existing technology, the method of generating a career analysis report based on educational experience proposed in this application can capture the personnel flow relationship between occupations by integrating career data to construct a career mobility network model in the educational experience dimension, and extract multi-dimensional personnel mobility characteristics based on the career mobility network model and generate an analysis report, thereby improving the comprehensiveness and accuracy of career development analysis, which is of great significance for guiding job seekers in employment and guiding organizational human resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of a method for generating a career analysis report based on educational experience in one embodiment;

[0039] Figure 2 A structural block diagram of an apparatus for generating a career analysis report based on educational experience in one embodiment;

[0040] Figure 3 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0042] In one embodiment, Figure 1 As shown, a method for generating a career analysis report based on educational experience is provided, comprising the following steps:

[0043] Step S1: Obtain and pre-process the occupational data set from the online occupational network to obtain raw occupational data. Specifically, the raw occupational data includes the number of occupations, the number of employees, the education level of employees, the mobility history of employees, the industry in which employees work, and the region in which employees work.

[0044] It is understandable that since the occupations in the career data set of online career networks (such as LinkedIn) are filled in by users, there is a lack of unified standards for occupational names, and there are many types of occupations. Therefore, preprocessing is required to obtain original occupational data with standardized occupational names and unified occupational types.

[0045] In step S2, the occupations in the original occupational data are used as nodes, the personnel flow between occupations is used as edges, and the number of personnel flows between occupations based on educational experience is used as the weight of the edge to construct an occupational flow network model based on educational experience.

[0046] It can be understood that compared with the career mobility network model that only considers the change in the number of people, this application takes the number of personnel flows between occupations based on educational experience as the weight of the edge when constructing the career mobility network. Since human capital is closely related to the educational experience of personnel, the career mobility network model of educational experience dimension constructed by this method not only reflects the number of personnel flows between occupations, but also reflects the flow of human capital between occupations.

[0047] Step S3, extracting features from the original occupational data according to the weights of the edges of the occupational flow network model, obtaining the total personnel flow characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation, and generating an occupational analysis report by recording the extracted characteristics of each occupation.

[0048] It can be understood that the total personnel flow characteristic represents the total number of personnel inflow and outflow of a certain occupation, which helps to understand the attractiveness and development opportunities of the occupation. The personnel retention characteristic represents the ratio of the number of personnel inflow to the number of personnel outflow of a certain occupation, which helps to understand the stability of the occupation. The circulation hub degree characteristic represents the proportion of key paths and flow of a certain occupation in the occupational flow network model, which helps to understand the cross-domain capabilities of the occupation.

[0049] In one embodiment, a career dataset from an online career network is obtained and preprocessed to obtain raw career data, including:

[0050] First, obtain an occupational dataset from an online professional network (such as LinkedIn). By counting the occurrence frequency of each occupation in the occupational dataset, designate occupations with an occurrence frequency higher than 100 as common occupations.

[0051] Then, the names of common occupations are calibrated to obtain preliminary standardized occupations. Based on the preliminary standardized occupations, the occupation names and occupation name stems of the non-standardized occupations in the occupational dataset are fully matched to obtain standardized occupations. Specifically, the first step is the full match of occupation names. When the name of a non-standardized occupation contains all the words in the name of a preliminary standardized occupation, the non-standardized occupation is converted into the corresponding preliminary standardized occupation; when the name of a non-standardized occupation matches multiple preliminary standardized names, the non-standardized occupation is converted into a preliminary standardized occupation with the maximum full match. The second step is the full match of occupation name stems, that is, matching the name of the non-standardized occupation with the name of the stemmed preliminary standardized occupation, and converting the non-standardized occupation into a preliminary standardized occupation with the maximum full match of two or more matching words.

[0052] Finally, the original occupation data were obtained by mapping the standardized occupations with the occupational classification of the Occupational Information Network (O*NET).

[0053] As you can understand, the Occupational Information Network (O*NET) provides a unified classification of occupations. This application maps standardized occupations obtained from LinkedIn to the O*NET classification, thereby utilizing O*NET's occupational classification information. Multiple LinkedIn occupations may correspond to a single O*NET occupational classification, and occupations that cannot be mapped to the O*NET classification are excluded. By standardizing and mapping the occupational dataset, we can remove some useless data, improve the accuracy of the occupational data, and provide a data foundation for the subsequent construction of the occupational mobility network model.

[0054] In one embodiment, the career mobility network model of the educational experience dimension is represented as follows: in, Represents a node set, each node represents an occupation, ε represents an edge set, and each edge e(u,v)∈ε represents a transition from occupation to occupation at time t. To career The flow of people between Represents a weight set, the weight corresponding to each edge Indicates the occupational status based on educational experience at time t To career The number of people moving between Expressed as:

[0055]

[0056]

[0057] Among them, HC EA (p) represents the calculation factor of educational experience, which is obtained by simulating the sigmoid function. edu(p) = {1, 2, 3, 4} represents the educational level. edu(p) = 1 indicates that the employee’s highest educational level is other, edu(p) = 2 indicates that the employee’s highest educational level is a bachelor’s degree, edu(p) = 3 indicates that the employee’s highest educational level is a master’s degree, and edu(p) = 4 indicates that the employee’s highest educational level is a doctorate. α represents the control parameter of the curve steepness.

[0058] In one embodiment, the total flow of personnel is characterized by

[0059] Total t (v)=IN t (v)+OUT t (v)

[0060] Among them, Total t (v) represents the total personnel turnover characteristics of occupation v at time t, represents the number of people flowing into occupation v based on their educational experience at time t, represents the number of people leaving occupation v based on their educational experience at time t, represents the number of personnel flows from occupation u to occupation v based on educational experience at time t, It represents the number of personnel flows from occupation v to occupation z based on educational experience at time t.

[0061] In one embodiment, the staff retention feature is represented by

[0062]

[0063] Among them, Retain t (v) represents the personnel retention characteristics of occupation v at time t, and ε0 represents the smoothing factor with an extremely small value.

[0064] It is understandable that when the inflow of personnel in a profession is far greater than the outflow of personnel, it can be considered that the profession has a strong ability to retain or attract human capital, and the profession is more stable.

[0065] In one embodiment, the distribution hub degree feature is expressed as

[0066] Center t (v)=Flow t (v)×Path t (v)

[0067]

[0068]

[0069] Among them, Flow t (v) represents the traffic proportion of occupation v at time t, Path t (v) represents the critical path proportion of occupation v at time t, represents the number of times occupation v appears in the shortest path from occupation x to occupation y, g xy Represents the total number of shortest paths from occupation x to occupation y, where x and y are arbitrary occupations.

[0070] It can be understood that the shortest path between two occupations refers to the path with the fewest edges connecting the two occupations in the constructed occupational mobility network model. A larger critical path ratio and flow ratio indicate a higher degree of circulation hubness and a stronger capacity for occupational exchange of human capital.

[0071] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0072] In one embodiment, Figure 2 As shown, a device for generating a career analysis report based on educational experience is provided, comprising: a data preprocessing module 201, a career mobility network construction module 202, and a career analysis report generation module 203, wherein:

[0073] The data preprocessing module 201 is used to obtain the occupational data set in the online occupational network and preprocess it to obtain the original occupational data;

[0074] The occupational mobility network construction module 202 is used to construct an occupational mobility network model based on educational experience by using occupations in the original occupational data as nodes, personnel flows between occupations as edges, and the number of personnel flows between occupations based on educational experience as edge weights;

[0075] The occupational analysis report generation module 203 is used to extract features from the original occupational data based on the weights of the edges in the occupational flow network model, obtain the total personnel flow characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation, and generate an occupational analysis report by recording the extracted characteristics of each occupation.

[0076] The specific limitations of the device for generating a career analysis report based on educational experience can be found in the limitations of the method for generating a career analysis report based on educational experience above and will not be repeated here. Each module in the device for generating a career analysis report based on educational experience can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the corresponding operations of each of the modules.

[0077] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for generating a career analysis report based on educational experience is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0078] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0079] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0080] Obtaining the career data set from the online career network and preprocessing it to obtain the original career data;

[0081] The occupations in the original occupational data are used as nodes, the personnel flow between occupations is used as edges, and the number of personnel flows between occupations based on educational experience is used as the weight of the edge to construct an occupational mobility network model based on educational experience.

[0082] The original occupational data is feature extracted according to the weights of the edges in the occupational mobility network model to obtain the total personnel mobility characteristics, personnel retention characteristics and circulation hub degree characteristics of each occupation. By recording the extracted characteristics of each occupation, an occupational analysis report is generated.

[0083] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating a career analysis report based on educational experience, characterized in that: The method comprises: Obtaining the career data set from the online career network and preprocessing it to obtain the original career data; The occupations in the original occupational data are used as nodes, the personnel flow between occupations is used as edges, and the number of personnel flows between occupations based on educational experience is used as the weight of the edge to construct an occupational flow network model based on educational experience. Extracting features from the original occupational data based on the weights of the edges in the occupational mobility network model to obtain total personnel mobility features, personnel retention features, and circulation hub degree features for each occupation; and generating an occupational analysis report by recording the extracted features of each occupation; The total flow characteristics are expressed as follows: in, Indicates occupation exist t The total flow characteristics of personnel at each moment, Indicates t Always based on the flow of occupations caused by educational experience The number of personnel, Indicates t Occupational outflow based on educational experience The number of personnel, Indicates t Always based on the career path of educational experience To career The number of people moving between Indicates t Always based on the career path of educational experience To career the number of personnel movements between The staff retention characteristics are expressed as: in, Indicates occupation exist t The staff retention characteristics at different times, represents the smoothing factor; The distribution hub degree characteristic is expressed as: in, Indicates occupation exist t Traffic share at the time, Indicates occupation exist t The critical path ratio at the moment, Indicates occupation In the profession x To career y The number of times it appears in the shortest path, Indicates occupation x To career y The total number of shortest paths, x 、 y For any occupation.

2. The method according to claim 1, characterized in that Obtain and preprocess the career dataset from the online career network to obtain the original career data, including: Obtaining an occupational dataset from an online occupational network, counting the occurrence frequency of each occupation in the occupational dataset, and designating occupations with an occurrence frequency greater than 100 in the occupational dataset as common occupations; Calibrate the names of the common occupations to obtain preliminary standardized occupations, and perform a complete match between occupation names and occupation name stems on the non-standardized occupations in the occupation data set based on the preliminary standardized occupations to obtain standardized occupations; The original occupation data is obtained by mapping the standardized occupations with the occupation classification of the occupation information network.

3. The method according to claim 1 or 2, characterized in that The original occupational data includes the number of occupations, the number of employees, the educational background of the employees, the mobility experience of the employees, the industries in which the employees are employed, and the regions in which the employees are located.

4. The method according to claim 1, wherein The career mobility network model of the educational experience dimension is expressed as ,in, Represents a node set, each node Indicates a profession, Represents a set of edges, each edge Indicates t Always from the profession To career The flow of people between Represents a weight set, the weight corresponding to each edge Indicates t Always based on the career path of educational experience To career The amount of personnel movement between them.

5. The method according to claim 4, characterized in that The weight corresponding to each edge in the career mobility network model of the educational experience dimension Expressed as: in, represents the calculation factor of educational experience, Indicates the level of education, A control parameter that represents the steepness of the curve.

6. A device for generating a career analysis report based on educational experience, characterized in that: The device comprises: The data preprocessing module is used to obtain the career data set in the online career network and preprocess it to obtain the original career data; A career mobility network construction module is used to construct a career mobility network model based on educational experience, using the careers in the original career data as nodes, the personnel mobility between careers as edges, and the number of personnel mobility between careers caused by educational experience as edge weights; A job analysis report generation module is used to extract features from the original job data based on the weights of the edges in the job flow network model to obtain the total personnel flow characteristics, personnel retention characteristics, and circulation hub degree characteristics of each job, and generate a job analysis report by recording the extracted characteristics of each job; The total flow characteristics are expressed as follows: in, Indicates occupation exist t The total flow characteristics of personnel at each moment, Indicates t Always based on the flow of occupations caused by educational experience The number of personnel, Indicates t Occupational outflow based on educational experience The number of personnel, Indicates t Always based on the career path of educational experience To career The number of people moving between Indicates t Always based on the career path of educational experience To career the number of personnel movements between The staff retention characteristics are expressed as: in, Indicates occupation exist t The staff retention characteristics at different times, represents the smoothing factor; The distribution hub degree characteristic is expressed as: in, Indicates occupation exist t Traffic share at the time, Indicates occupation exist t The critical path ratio at the moment, Indicates occupation In the profession x To career y The number of times it appears in the shortest path, Indicates occupation x To career y The total number of shortest paths, x 、 y For any occupation.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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