Method, device and equipment for generating a professional analysis report based on social capital

Abstract

The application relates to a method, device and equipment for generating a professional analysis report based on social capital. The method comprises the following steps: obtaining the personnel flow relationship between professions caused by social capital in a professional data set, constructing a professional flow network model of the social capital dimension, extracting the personnel flow total amount feature, personnel retention feature and flow hub degree feature of each profession in the original professional data through the professional flow network model of the social capital dimension, recording the profession features, and generating a professional analysis report. The method can capture the personnel flow relationship between professions caused by social capital according to the professional flow network model of the social capital dimension, extract multi-dimensional personnel flow features according to the professional flow network model, and generate an analysis report, so that the comprehensiveness and accuracy of professional development analysis can be improved, and the method has important significance for guiding job seekers to employment and guiding organization human resource management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a method, device and equipment for generating a career analysis report based on social capital. BACKGROUND

[0002] In the era of knowledge economy, new knowledge and technology emerge and integrate constantly, creating new work methods and career paths. The career is more and more diverse, and the employment market is more and more complex and changeable. Job seekers and organization managers need to understand the development of each career in a timely manner and follow the trend of the employment market by using scientific means. At present, the evaluation and analysis of some careers only consider the basic situation of the career itself, without considering the number of personnel flow between careers, so there is a technical problem that the job seekers and organization managers lack understanding of the personnel flow relationship between careers and the development of careers, resulting in incomplete and inaccurate career development analysis. SUMMARY

[0003] Therefore, it is necessary to provide a method, device and equipment for generating a career analysis report based on social capital to solve the above technical problems.

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

[0005] Obtaining a career data set in an online career network, standardizing and mapping the career data set to obtain original career data;

[0006] According to the personnel flow relationship between careers caused by social capital in the original career data, a career flow network model in the social capital dimension is constructed; wherein the career flow network model in the social capital dimension includes nodes, edges and weights corresponding to each edge, the nodes represent the careers in the original career data, the edges represent the personnel flow between careers, and the weights corresponding to each edge represent the number of personnel flow between careers caused by social capital;

[0007] According to the weights of the edges in the career flow network model in the social capital dimension, the original career data is feature extracted to obtain the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each career, and the generated career analysis report is generated by recording the extracted features of each career.

[0008] In one embodiment, obtaining a career data set in an online career network, standardizing and mapping the career data set to obtain original career data, comprises:

[0009] Obtaining a career data set in an online career network, counting the frequency of occurrence of each career in the career data set, and specifying the careers with a frequency of occurrence higher than 100 in the career data set as common careers;

[0010] The names of common occupations are calibrated to obtain preliminary standardized occupations, and the non-standardized occupations in the occupation data set are sequentially matched with the occupation names and occupation name stems according to the preliminary standardized occupations to obtain standardized occupations;

[0011] The standardized occupations are mapped with the occupation classification of the occupation information network to obtain original occupation data.

[0012] In one of the embodiments, the original occupation data includes the number of occupations, the number of employees, the education of personnel, the flow experience of personnel, the industry of personnel, and the region of personnel.

[0013] In one of the embodiments, the occupation flow network model in the social capital dimension is represented as wherein, represents a node set, and each node represents an occupation, and ε represents an edge set, and each edge e(u,v) ∈ ε represents the flow of personnel between occupations at t time from occupation to occupation represents a weight set, and each edge corresponds to a weight represents the number of flows of personnel between occupations at t time based on social capital from occupation to occupation

[0014] In one of the embodiments, in the occupation flow network model in the social capital dimension, the weight corresponding to each edge is represented as:

[0015]

[0016] HC SC (p)=degree SN (p)

[0017] wherein, HC SC (p) represents a calculation factor of social capital, and degree SN (p) represents the social capital obtained by employee p in the social relationship network.

[0018] In one of the embodiments, the total amount of personnel flow feature is represented as

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

[0020] wherein, Total t (v) represents the total amount of personnel flow of occupation v at t time, denotes the number of people flowing into occupation v at time t based on social capital, denotes the number of people flowing out of occupation v at time t based on social capital, denotes the number of people flowing from occupation u to occupation v at time t based on social capital, denotes the number of people flowing from occupation v to occupation z at time t based on social capital.

[0021] In one of the embodiments, the personnel retention feature is represented as

[0022]

[0023] where Retain t (v) denotes the personnel retention feature of occupation v at time t, and ε0 denotes a smoothing factor.

[0024] In one of the embodiments, the circulation hub feature is represented as

[0025] Center t (v) = Flow t (v) × Path t (v)

[0026]

[0027]

[0028] where Flow t (v) denotes the flow proportion of occupation v at time t, Path t (v) denotes the key path proportion of occupation v at time t, denotes the number of times occupation v appears in the shortest path from occupation x to occupation y, g xy denotes the total number of shortest paths from occupation x to occupation y, x and y are any occupation.

[0029] An apparatus for generating an occupation analysis report based on social capital, the apparatus comprising:

[0030] an occupation data acquisition module, configured to acquire an occupation data set in an online occupation network, and perform standardization and mapping processing on the occupation data set to obtain original occupation data;

[0031] A professional flow network construction module is configured to construct a social capital dimension professional flow network model according to the personnel flow relationship between professions caused by social capital in the original professional data; wherein the social capital dimension professional flow network model includes nodes, edges, and weights corresponding to each edge, the nodes represent professions in the original professional data, the edges represent personnel flow between professions, and the weights corresponding to each edge represent the number of personnel flow between professions caused by social capital.

[0032] A professional analysis report generation module is configured to perform feature extraction on the original professional data according to the weights of the edges in the social capital dimension professional flow network model, obtain personnel flow total amount features, personnel retention features, and flow hub degree features of each profession, record the features of each profession obtained through extraction, and generate a professional analysis report.

[0033] A computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0034] Obtain a professional data set in an online professional network, standardize and map the professional data set, and obtain original professional data;

[0035] Construct a social capital dimension professional flow network model according to the personnel flow relationship between professions caused by social capital in the original professional data; wherein the social capital dimension professional flow network model includes nodes, edges, and weights corresponding to each edge, the nodes represent professions in the original professional data, the edges represent personnel flow between professions, and the weights corresponding to each edge represent the number of personnel flow between professions caused by social capital;

[0036] Perform feature extraction on the original professional data according to the weights of the edges in the social capital dimension professional flow network model, obtain personnel flow total amount features, personnel retention features, and flow hub degree features of each profession, record the features of each profession obtained through extraction, and generate a professional analysis report.

[0037] The aforementioned method, apparatus, and equipment for generating career analysis reports based on social capital acquire, standardize, and map career datasets to obtain raw career data. Based on the inter-career mobility relationships caused by social capital within this raw data, a social capital-based career mobility network model is constructed. This model captures the inter-career mobility driven by social capital, preventing job seekers and organizational managers from having insufficient understanding of these relationships. Furthermore, the model extracts characteristics of total mobility, retention, and hub-and-spoke characteristics for each career from the raw data. By recording these extracted characteristics, a career analysis report is generated. This method captures inter-career mobility relationships driven by social capital using a social capital-based career mobility network model, extracts multi-dimensional mobility characteristics, and generates analysis reports. This improves the comprehensiveness and accuracy of career development analysis, which is significant for guiding job seekers and organizational human resource management. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a method for generating a career analysis report based on social capital in one embodiment;

[0039] Figure 2 This is a structural block diagram of a method for generating a career analysis report based on social capital in one embodiment;

[0040] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] In one embodiment, such as Figure 1 As shown, a method for generating career analysis reports based on social capital is provided, including the following steps:

[0043] Step S1: Obtain the occupational dataset from the online occupational network, and perform standardization and mapping processing on the occupational dataset to obtain the original occupational data.

[0044] It is understandable that since the occupations in the occupation datasets of online professional networks (such as LinkedIn) are filled in by users, the occupation names lack a unified standard and need to be standardized. Furthermore, since there are many types of occupations in online professional networks, they need to be classified uniformly through mapping.

[0045] In step S2, a social capital dimension occupational flow network model is constructed according to the occupational flow relationship between occupations caused by social capital in the original occupational data; the social capital dimension occupational flow network model includes nodes, edges, and weights corresponding to each edge, the nodes represent occupations in the original occupational data, the edges represent the personnel flow between occupations, and the weight corresponding to each edge represents the number of personnel flow between occupations caused by social capital.

[0046] It can be understood that, compared with the occupational flow network model considering only the number of changes, the number of personnel flow between occupations caused by social capital is taken as the weight of the edge when constructing the occupational flow network in the present application. Social capital refers to the basic resources that individuals have in social structures and can be regarded as a soft skill. Social capital is an important factor affecting human capital flow. By constructing the social capital dimension occupational flow network model, the personnel flow and human capital flow between occupations caused by social capital can be effectively measured.

[0047] In step S3, the original occupational data is feature extracted according to the weight of the edge in the social capital dimension occupational flow network model, and the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each occupation are obtained. By recording the extracted features of each occupation, an occupational analysis report is generated.

[0048] It can be understood that the total personnel flow feature 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 feature 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 flow hub degree feature represents the proportion of key paths and flow in the occupational flow network model, which helps to understand the cross-field ability of the occupation.

[0049] In one embodiment, an occupational data set in an online occupational network is obtained, and the occupational data set is standardized and mapped to obtain original occupational data, including:

[0050] The occupational data set in the online occupational network is obtained, the frequency of each occupation in the occupational data set is counted, and the occupation with a frequency higher than 100 in the occupational data set is specified as a common occupation.

[0051] The names of common occupations are calibrated to obtain preliminary standardized occupations, and the non-standardized occupations in the occupation data set are sequentially matched with the occupation names and the occupation name stems of the preliminary standardized occupations to obtain standardized occupations. Specifically, the first step is complete matching of the occupation name. When the name of the non-standardized occupation contains all the words of the name of the preliminary standardized occupation, the non-standardized occupation is converted into the corresponding preliminary standardized occupation. When the name of the non-standardized occupation matches multiple preliminary standardized names, the non-standardized occupation is converted into the largest complete matching preliminary standardized occupation. The second step is complete matching of the occupation name stem, that is, the name of the non-standardized occupation is matched with the name of the preliminary standardized occupation after stemming, and the non-standardized occupation is converted into the largest complete matching preliminary standardized occupation with two or more matching words.

[0052] The original occupation data is obtained by mapping the standardized occupation to the occupation classification of the occupation information network. The original occupation data includes the number of occupations, the number of employees, the education level of personnel, the flow experience of personnel, the industry of personnel, and the region of personnel

[0053] It can be understood that the occupation information network (O*NET) classifies occupations uniformly. The standardized occupation obtained from LinkedIn is mapped to the O*NET classification, so that the occupation classification information of O*NET can be used. Multiple LinkedIn occupations may correspond to one O*NET occupation classification, and occupations that cannot be mapped to the O*NET classification are excluded. Through standardization and mapping processing of the occupation data set, some useless data can be removed, the accuracy of the occupation data is improved, and a data basis is provided for subsequent construction of the occupation flow network model.

[0054] In one embodiment, the occupation flow network model of the social capital dimension is represented as wherein, represents a node set, each node represents an occupation, and ε represents an edge set, each edge e(u,v)∈ε represents the flow of personnel from occupation to occupation at time t, represents a weight set, and each edge corresponds to a weight represents the number of personnel flows from occupation to occupation based on social capital at time t, The formula of

[0055]

[0056] HC SC(p) = degree SN (p)

[0057] wherein, HC SC (p) represents the calculation factor of social capital, degree SN (p) represents the social capital obtained by the employee p in the social relationship network.

[0058] In one of the embodiments, the total amount of personnel flow feature is represented as

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

[0060] wherein, Total t (v) represents the total amount of personnel flow feature of the occupation v at the time t, represents the number of personnel flowing into the occupation v at the time t based on the social capital, represents the number of personnel flowing out of the occupation v at the time t based on the social capital, represents the number of personnel flowing from the occupation u to the occupation v at the time t based on the social capital, represents the number of personnel flowing from the occupation v to the occupation z at the time t based on the social capital.

[0061] In one of the embodiments, the personnel retention feature is represented as

[0062]

[0063] wherein, Retain t (v) represents the personnel retention feature of the occupation v at the time t, and ε0represents a smoothing factor with a minimum value. It can be understood that when the personnel inflow of an occupation is much larger than the personnel outflow, it can be considered that the occupation has strong retention ability or attraction ability for human capital, and the stability of the occupation is stronger.

[0064] In one of the embodiments, the flow center feature is represented as

[0065] Center t (v) = Flow t (v) x Path t (v)

[0066]

[0067]

[0068] wherein, Flow t(v) represents the proportion of the flow of occupation v at time t, Path t (v) represents the proportion of the key path 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, x and y are any occupations.

[0069] It can be understood that the shortest path between two occupations refers to the path with the least number of edges connecting the two occupations in the constructed occupational flow network model. The greater the proportion of the key path and the flow, the higher the degree of flow hub and the stronger the ability of human capital exchange in the occupation.

[0070] It should be understood that, although Figure 1 the steps in the flowchart of the method are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, Figure 1 at least part of the steps in the method can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0071] In one embodiment, as shown in Figure 2 , an apparatus for generating an occupational analysis report based on social capital is provided, comprising: an occupational data acquisition module 201, an occupational flow network construction module 202, and an occupational analysis report generation module 203, wherein:

[0072] The occupational data acquisition module 201 is configured to acquire an occupational data set in an online occupational network, and to obtain original occupational data by standardizing and mapping the occupational data set;

[0073] The occupational flow network construction module 202 is configured to construct a social capital dimension occupational flow network model according to the personnel flow relationship between occupations caused by social capital in the original occupational data; wherein the social capital dimension occupational flow network model includes nodes, edges, and weights corresponding to each edge, the nodes represent occupations in the original occupational data, the edges represent personnel flow between occupations, and the weights corresponding to each edge represent the number of personnel flow between occupations caused by social capital;

[0074] The professional analysis report generation module 203 is configured to perform feature extraction on the original professional data according to the weights of the edges in the professional flow network model of the social capital dimension, to obtain the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each profession, and to generate a professional analysis report by recording the extracted features of each profession.

[0075] The specific limitations of the device for generating a professional analysis report based on social capital can be seen in the limitations of the method for generating a professional analysis report based on social capital described above, which will not be repeated here. The various modules in the device for generating a professional analysis report based on social capital described above can be realized by software, hardware and combinations thereof, in whole or in part. The various modules described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the various modules.

[0076] In one embodiment, a computer device is provided, which can be a terminal, and its internal structure diagram can be as shown in Figure 3 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured 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 operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a method for generating a professional analysis report based on social capital. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0077] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0078] In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0079] Obtaining a career dataset in an online career network, performing standardization and mapping processing on the career dataset to obtain original career data;

[0080] According to the personnel flow relationship between careers caused by social capital in the original career data, a career flow network model in the social capital dimension is constructed; wherein the career flow network model in the social capital dimension includes nodes, edges and weights corresponding to each edge, the nodes represent the careers in the original career data, the edges represent the personnel flow between careers, and the weight corresponding to each edge represents the number of personnel flow between careers caused by social capital;

[0081] According to the weight of the edge in the career flow network model in the social capital dimension, the original career data is feature extracted to obtain the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each career, and by recording the extracted features of each career, a career analysis report is generated.

[0082] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0083] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for generating career analysis reports based on social capital, characterized in that, The method comprises: obtaining a professional data set in an online professional network, performing standardization and mapping processing on the professional data set to obtain original professional data; Based on the inter-occupational personnel mobility relationships caused by social capital in the original occupational data, an occupational mobility network model based on social capital is constructed. This model includes nodes, edges, and a weight for each edge. Nodes represent occupations in the original occupational data, edges represent inter-occupational personnel mobility, and the weight for each edge represents the number of personnel movements between occupations caused by social capital. The occupational mobility network model based on social capital is represented as follows: ,in, Represents a set of nodes, each node Indicates a profession, Represents the set of edges, each edge Indicates in t Always from professional To professional Personnel movement between them This represents the set of weights, where each edge corresponds to a weight. Indicates in t The career path is always based on social capital. To professional The number of people moving between them; in the occupational mobility network model based on the social capital dimension, the weight corresponding to each edge. Represented as: ； ； wherein, represents a calculated factor of social capital, represents the number of employees p social capital obtained in a social relationship network; extracting features from the original professional data according to the weight of the edge in the professional flow network model of the social capital dimension, obtaining the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each profession, generating a professional analysis report by recording the extracted features of each profession; the total personnel flow feature is expressed as: ； in, Indicate occupation exist t Characteristics of total population movement at any given time. Indicates in t Inflow of occupations based on social capital Number of personnel Indicates in t Outflow of occupations based on social capital Number of personnel Indicates in t The career path is always based on social capital. To professional The number of people moving between them Indicates in t The career path is always based on social capital. To professional The number of people moving between them; the personnel retention feature is expressed as: ； wherein representing a profession at t moment in time, representing a smoothing factor; the flow hub degree feature is expressed as: ； ； ； wherein, representing a profession in t the proportion of traffic at the moment, representing a profession in t the proportion of critical path at the moment, representing a profession in the profession x the number of times of appearing in the shortest path from the profession y to the profession representing a profession x the total number of the shortest path from the profession y to the profession x , y is any profession.

2. The method of claim 1, wherein, obtaining a professional data set in an online professional network, performing standardization and mapping processing on the professional data set to obtain original professional data, comprising: obtaining a professional data set in an online professional network, counting the frequency of occurrence of each profession in the professional data set, and specifying the profession with a frequency of occurrence higher than 100 in the professional data set as a common profession; calibrating the name of the common profession to obtain a preliminary standardized profession, and performing complete matching of the profession name and the profession name stem of the non-standardized profession in the professional data set according to the preliminary standardized profession to obtain a standardized profession; obtaining original professional data by mapping the standardized profession and the profession classification of the profession information network.

3. The method according to claim 1 or 2, characterized in that, The original professional data includes the number of professions, the number of employees, the education of personnel, the flow experience of personnel, the industry of personnel, and the region of personnel.

4. A device for generating career analysis reports based on social capital, characterized in that, The device is based on the method for generating a professional analysis report based on social capital according to any one of claims 1-3, and the device comprises: a professional data acquisition module for obtaining a professional data set in an online professional network, performing standardization and mapping processing on the professional data set to obtain original professional data; a professional flow network construction module for constructing a professional flow network model of the social capital dimension according to the personnel flow relationship between professions caused by social capital in the original professional data; wherein the professional flow network model of the social capital dimension comprises nodes, edges and the weight corresponding to each edge, the node represents the profession in the original professional data, the edge represents the personnel flow between professions, and the weight corresponding to each edge represents the number of personnel flows between professions caused by social capital; a professional analysis report generation module for extracting features from the original professional data according to the weight of the edge in the professional flow network model of the social capital dimension, obtaining the total personnel flow feature, the personnel retention feature and the flow hub degree feature of each profession, and generating a professional analysis report by recording the extracted features of each profession. 5.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-4 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method according to any one of claims 1 to 3. The processor executes the computer program to realize the steps of the method according to any one of claims 1 to 3.

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