A post-level organization structure portrait automatic generation method and system

Abstract

The application discloses a post-level organization structure portrait automatic generation method and system, the method comprises the following steps: generating a post-level organization structure frame based on a unit, an internally provided organization and a post-level organization subject; further generating preset attribute parameters based on associated information of an employee information database, and adding corresponding additional information to different organization subjects from a tail node of the organization structure in a reverse and layer-by-layer manner, so that a post-level organization portrait is formed, and the information comprehensiveness of the organization structure and the wide application scenarios of the organization structure are improved.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to a method and system for automatically generating job-level organizational profiles. Background Technology

[0002] Currently, in the daily operation and management of large enterprises, due to the large number of employees and the wide variety of tasks handled by each department, in order to facilitate the query of the enterprise's organizational structure, they usually summarize data such as departments and personnel to generate an organizational chart for the enterprise's employees to view.

[0003] However, existing organizational charts can only represent a limited range of information and have limited application functions. They can only show the hierarchical relationships between various organizations and cannot display more comprehensive information or be flexibly applied to various application scenarios. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and system for automatically generating job-level organizational profiles, effectively improving the comprehensiveness of information covered in organizational profiles and their flexible applicability to various application scenarios. The technical solution of this invention is as follows:

[0005] Firstly, a method for automatically generating job-level organizational profiles is provided, including the following steps:

[0006] (1) Generate an organizational chart framework, the generation method including:

[0007] Obtain an organizational data pool, which includes multi-dimensional information on different organizational entities, which are divided into three levels: units, internal departments, and positions.

[0008] Based on the affiliation relationships between different institutional entities, an organizational chart is generated in a forward order according to the units, internal departments, and positions. This forward generation process includes: generating a first relationship line based on the affiliation relationships between the three levels of institutional entities: units, internal departments, and positions; generating a second relationship line based on the affiliation relationships between units; and forming an organizational chart framework based on different institutional entities and the first and second relationship lines between them.

[0009] (2) Analyze the nodes of the organization chart and generate additional information. The generation method includes:

[0010] Obtain an employee information database, which includes basic information about different employees and the main information of the organizations to which the employees belong;

[0011] Based on the employee information database, related information is obtained to generate preset attribute parameters. Then, from the end node of the organizational chart, corresponding additional information is added to different organizational entities layer by layer to form a job-level organizational profile. The preset attribute parameters and additional information are determined in real time based on client demand data.

[0012] In some implementations, in the organizational profile, each job-related organizational entity node is also associated with the job information table data and the employee personal information table data of the actual employees working in that job.

[0013] In some implementations, the step of generating preset attribute parameters based on associated information obtained from the employee information database and adding corresponding supplementary information to different organizational entities layer by layer from the end node of the organizational chart includes:

[0014] Generate the first preset attribute parameters of the parent node based on the association information of the current node;

[0015] Based on other nodes that belong to the same parent node as the current node, generate the second preset attribute parameters of the parent node based on the node's association information;

[0016] The first and second preset attribute parameters are combined to form the preset attribute parameters of the parent node to which the current node belongs, and corresponding additional information is added to the parent node.

[0017] In some implementations, the step of generating preset attribute parameters by obtaining associated information from the employee information database includes:

[0018] Based on the employee's affiliated organization information, calculate the total number of employees under different organizations, or...

[0019] Based on employee qualification data and historical performance data, analyze individual employee performance scores, and add corresponding comprehensive performance scores to different organizational entities layer by layer from the end node of the organizational chart.

[0020] In some embodiments, the method for automatically generating job-level organizational profiles further includes:

[0021] When a request to display an organizational chart is received from a client, the job-level organizational structure profile is displayed, including additional information for each node, i.e., each organizational entity.

[0022] When a client requests an organization chart display based on search terms, the system receives the search terms entered by the client. Based on the original complete organization chart data, the system searches for nodes related to the search terms, hides nodes unrelated to the search terms in the original complete organization chart, and re-executes the method of analyzing organization chart nodes and generating additional information based on the unhidden nodes to form an organization chart based on search terms.

[0023] In some implementations, the step of searching for nodes related to the search terms and hiding nodes unrelated to the search terms in the original complete organizational profile includes the following steps:

[0024] (61) For all nodes of the original complete organizational profile, perform a correlation analysis with the search terms, obtain and locate all nodes related to the search terms, and record them as the first candidate nodes;

[0025] (62) All subordinate nodes of the first candidate node are denoted as the second candidate node, and all superior nodes of the first candidate node are denoted as the third candidate node.

[0026] (63) For the second candidate node and the third candidate node, perform the steps of (62) above until all the second candidate nodes and the third candidate nodes are obtained;

[0027] (64) The first candidate node, the second candidate node, and the third candidate node are used as nodes related to the search terms.

[0028] In some embodiments, (61) includes:

[0029] The first set of related terms is generated based on the node name. For nodes of job-related organizations, the second set of related terms is also generated by combining job information table data.

[0030] Semantic association analysis based on node-related word sets and search terms;

[0031] When the semantic association analysis results indicate that the relevance is greater than a preset threshold, the node is determined to be related to the search term and is recorded as the first candidate node.

[0032] In some implementations, the generation of the related word set of the node is based on the generation of the related word set from source words, which include the name of the node and keywords in the job information that characterize the job function attributes;

[0033] The method for generating the related word set of the node includes:

[0034] A source document database is formed based on the texts of regular or irregular work reports from different organizations and the texts of announcements related to company organizations.

[0035] Keywords were initially extracted from the source documents in the database and denoted as the first keyword set;

[0036] Semantic association analysis is performed based on the first keywords in the first keyword set. Based on the semantic association, the first keywords in the first keyword set are classified to obtain multiple categories. The keywords in each category are denoted as the second keyword set.

[0037] Based on the matching of the source word in multiple sets of second keywords corresponding to the source document database, all sets of second keywords containing the source word are determined.

[0038] Based on the analysis of words in all second keyword sets containing the source word, the relevant word set of the source word is determined.

[0039] In some implementations, the semantic association analysis between the node-based related word set and the search terms includes:

[0040] The correlation between each word in the related word set of the node and the word vector of the search term is calculated and denoted as the first correlation degree.

[0041] Based on the first degree of association of each word in the related word set of the node, calculate the distance between any two first degrees of association, and obtain the cumulative value of the association distance of each word in the related word set of the node;

[0042] The correction coefficient for the relevance of each word in the related word set is determined based on the cumulative relevance distance value of each word in the related word set.

[0043] After correcting the first correlation degree based on the correction coefficient, the correlation degree of each word in the related word set is determined and denoted as the second correlation degree. The sum of the second correlation degrees corresponding to all words in the related word set is used as the correlation analysis result between the related word set and the search term of the node.

[0044] Secondly, an automatic generation system for job-level organizational structure profiles is provided. This system includes an organizational chart framework generation module and an organizational chart node additional information analysis and generation module.

[0045] The organization chart framework generation module includes:

[0046] The organizational data pool acquisition unit is used to acquire an organizational data pool, which includes multi-dimensional information of different organizational entities. The organizational entities are divided into three levels: units, internal departments, and positions.

[0047] The organization chart framework generation unit is used to generate an organization chart in a forward order based on the affiliation relationships between different organizational entities, in the order of unit, internal department, and position. This forward generation process includes: generating a first relationship line based on the affiliation relationships between the three levels of organizational entities (unit, internal department, and position); generating a second relationship line based on the affiliation relationships between units; and forming an organization chart framework based on different organizational entities and the first and second relationship lines between them.

[0048] The organizational chart node additional information analysis and generation module includes:

[0049] An employee information database acquisition unit is used to acquire an employee information database, which includes basic information of different employees and the main information of the organization to which the employees belong;

[0050] The node-attached information analysis and generation unit is used to obtain related information from the employee information database, generate preset attribute parameters, and add corresponding additional information to different organizational entities layer by layer from the end node of the organizational chart to form a job-level organizational profile.

[0051] The present invention provides a method and system for automatically generating job-level organizational structure profiles, which has the following beneficial effects: The present invention generates a job-level organizational structure chart framework based on three levels of organizational entities: unit, internal departments, and job positions. On the basis of the organizational structure chart framework, additional information is added to each node on the organizational structure chart, improving the information comprehensiveness and application scenarios of the organizational structure chart. Compared with traditional organizational structure charts, the job-level organizational structure profiles provided by the present invention can not only express the connection relationships and hierarchical relationships between different organizational entities, but also express richer feature information of different organizational entities. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for automatically generating a job-level organizational structure profile in an embodiment of this application.

[0053] Figure 2 This is a schematic diagram of the process of adding additional information to each node in reverse, layer by layer, in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram illustrating the method for processing organizational chart display requests based on search terms in an embodiment of this application;

[0055] Figure 4 This is a schematic diagram illustrating the method for determining nodes related to search terms in the original organizational chart in an embodiment of this application;

[0056] Figure 5 This is a schematic diagram of the structure of an automatic generation system for job-level organizational profiles in an embodiment of this application. Detailed Implementation

[0057] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0058] This application provides a method for automatically generating job-level organizational profiles, including the following steps:

[0059] (1) Generate an organizational chart framework, the generation method including:

[0060] (11) Obtain an organizational data pool, the data pool including multi-dimensional information of different organizational entities, the organizational entities being divided into three levels of organizational entities: units, internal departments, and positions;

[0061] (12) Generate an organizational chart in the order of unit, internal department, and position according to the affiliation between different organizational entities. This forward generation process includes: generating the first relationship line based on the affiliation between the three levels of organizational entities: unit, internal department, and position; generating the second relationship line based on the affiliation between units; and forming the organizational chart framework based on different organizational entities and the first and second relationship lines between different organizational entities.

[0062] (2) Analyze the nodes of the organization chart and generate additional information. The generation method includes:

[0063] (21) Obtain an employee information database, which includes basic information of different employees and the main information of the organization to which the employees belong;

[0064] (22) Generate preset attribute parameters based on the employee information database and add corresponding additional information to different organizational entities layer by layer from the end node of the organization chart to form a job-level organizational profile. The preset attribute parameters and additional information are determined in real time based on the client's demand data.

[0065] In this embodiment, a job-level organizational chart framework is generated based on a three-tiered organizational structure: unit, internal department, and position. A first relationship line is generated based on the hierarchical relationships between these three levels, and a second relationship line is generated based on the hierarchical relationships between units. Based on the first and second relationship lines, the relationships between all units, internal departments, and positions are displayed on the organizational structure profile. For example, in a power company, units include provincial power companies, municipal power companies, and county power companies. A second relationship line is generated between companies based on their affiliation. Each provincial, municipal, and county power company has its own internal departments, such as the Development and Construction Department, Human Resources Department, and Safety Supervision Department. It can be understood that the internal departments of provincial, municipal, and county power companies are not the same or completely the same. Each internal department has a variety of positions, and the types of positions under different internal departments are also different. For example, the marketing department of a county power company has positions such as: team leader, deputy team leader, meter installation and connection operator, and data collection and maintenance operator of the metering team; and team leader, deputy team leader, market development and business expansion application, and business acceptance officer of the sales team.

[0066] It is understood that in the organizational structure profile of this application, the nodes of unit-type institutional entities include two types of institutional entities: internal departments and lower-level units.

[0067] In this embodiment, based on the organizational chart framework, additional information is added to each node on the organizational chart to improve its comprehensiveness and applicability. In different application scenarios, the additional information to be added to each node can be flexibly set according to the needs of the application scenario. Compared to traditional organizational charts, the job-level organizational profile provided in this application can express not only the connection relationships and hierarchical relationships between different organizational entities, but also richer characteristic information about different organizational entities, such as the total number of employees in different positions, departments, and units, employee comprehensive work performance scores, and the work efficiency of the organizational entity. For example, analyzing the rationality of subordinate positions within an internal organization and adding the analysis results as additional information to the nodes of that internal organization can intuitively show the current status and existing problems of the overall job and personnel planning work of the provincial power company. The aforementioned preset attribute parameters and additional information are determined in real time based on client-side demand data. Of course, the associated information obtained from the employee information database is used to generate the preset attribute parameters, and the associated information obtained from the employee information database is determined according to the required attribute parameters and additional information. There is a functional calculation relationship between the associated information obtained from the employee information database and the attribute parameters and additional information.

[0068] It is understandable that in step (1) above, the organizational chart is generated in the order of unit, internal department, and position. In step (2) above, the order is reversed. From the end node of the organizational chart, corresponding additional information is added to different organizational entities layer by layer. In step (2), the additional information of the node is analyzed starting from the end node and passed to the upper level. The upper level node combines the additional information of all lower level nodes to conduct a comprehensive analysis to obtain feature data that can comprehensively represent the additional information of all lower level nodes as the additional information corresponding to the upper level node.

[0069] Furthermore, in the organizational profile of this application embodiment, each job-type organizational main node is also associated with the job information table data and the employee personal information table data of the actual employees working in the job.

[0070] It is understood that the organizational structure portrait in this application embodiment can not only intuitively display the organizational structure diagram and display more information about the main body of the organization at the nodes, but also, in order to improve the applicability of the organizational structure portrait in this application to multiple application scenarios, it is also associated with job information table data and employee personal information table data of the actual employees in the job. When the client submits various detailed information requests such as job information display request and employee personal information display request of the actual employees in the job on the organizational structure portrait display page, it can directly respond to the client.

[0071] Furthermore, in step (22) above, preset attribute parameters are generated based on the associated information obtained from the employee information database, and corresponding additional information is added layer by layer from the end node of the organization chart to different organizational entities, including:

[0072] (221) Generate the first preset attribute parameters of the parent node based on the association information of the current node;

[0073] (222) Based on other nodes that belong to the same parent node as the current node, generate the second preset attribute parameters of the parent node based on the node's association information;

[0074] (223) The first preset attribute parameter and the second preset attribute parameter are combined as the preset attribute parameter of the parent node to which the current node belongs, and the corresponding additional information is added to the parent node.

[0075] In this embodiment of the application, for a parent node, additional information of the parent node is obtained by merging the preset attribute parameters of all its subordinate parent nodes.

[0076] Furthermore, in the above step (22), the relevant information obtained from the employee information database is used to generate preset attribute parameters, including: based on the employee's affiliated organization information, the total number of subordinate employees of different organizations is counted, or based on the employee's qualification data and historical performance data, the employee's individual performance score is analyzed, and corresponding comprehensive performance scores are added to different organizations layer by layer from the end node of the organization chart.

[0077] In this embodiment, when adding additional information to each node on the organizational chart framework, this includes adding the total number of employees under that node's organizational entity and the overall performance of that node's organizational entity. Of course, for job-based organizational entities, this performance score can be obtained by summing individual employee performance scores. When analyzing the comprehensive performance score of higher-level nodes in reverse layer by layer, the comprehensive performance of different organizational entities can be further precisely analyzed by combining the summarized individual employee performance scores with the organizational entity's own characteristic data. For example, for unit-type organizational entities, comprehensive performance analysis can also be conducted by combining data related to their work deployment plans for subordinate units and their efforts to improve the power supply service level of subordinate units.

[0078] Furthermore, the method for automatically generating job-level organizational profiles according to an embodiment of this application further includes the following steps:

[0079] (3) Upon receiving the client's request to display the organizational chart, the job-level organizational structure profile is displayed, and the displayed information includes additional information for each node, i.e., each organizational entity.

[0080] (4) Upon receiving a request from the client to display an organization chart based on search terms:

[0081] (41) Receive search terms input by the client;

[0082] (42) Based on the original complete organizational profile data, search for nodes related to the search terms, hide nodes unrelated to the search terms in the original complete organizational profile, and re-execute the method of analyzing organizational chart nodes and generating additional information based on the unhidden nodes to form an organizational profile based on the search terms.

[0083] This application embodiment also provides a method for regenerating organizational profiles related to client-side search terms. If this organizational profile is designated as the second organizational profile, and the original complete organizational profile data is designated as the first organizational profile, then the second organizational profile is an organizational profile generated based on client requirements. The organizational profile generation method in this application embodiment improves the flexibility and practicality of organizational profiles. It should be noted that for the second organizational profile, the additional information on each node is recalculated based on the relevant information of its non-hidden child nodes, and is not directly copied from the additional information of the corresponding nodes in the first organizational profile.

[0084] Furthermore, in step (42) above, the process of searching for nodes related to the search terms and hiding nodes unrelated to the search terms in the original complete organizational profile includes the following steps:

[0085] (61) For all nodes of the original complete organizational profile, perform a correlation analysis with the search terms, obtain and locate all nodes related to the search terms, and record them as the first candidate nodes;

[0086] (62) All subordinate nodes of the first candidate node are denoted as the second candidate node, and all superior nodes of the first candidate node are denoted as the third candidate node.

[0087] (63) For the second candidate node and the third candidate node, perform the steps of (62) above until all the second candidate nodes and the third candidate nodes are obtained;

[0088] (64) The first candidate node, the second candidate node, and the third candidate node are used as nodes related to the search terms.

[0089] In this embodiment, the generation of organizational profiles based on actual client application needs is achieved based on the correlation analysis results between client search terms and nodes. Furthermore, considering the relationship between the first and second relationship lines in the organizational chart, nodes related to the search terms (first candidate nodes) are retained. Further, multiple retained nodes derived from the belonging relationship, such as second and third candidate nodes, are added based on the first and second relationship lines. The first, second, and third candidate nodes, as nodes related to the search terms, form the organizational profile based on actual client application needs. Of course, in the above step (62), if the first candidate node has no superior node, then the first candidate node does not have a third candidate node; if the first candidate node has no subordinate node, then the first candidate node does not have a second candidate node.

[0090] Furthermore, step (61) above includes the following steps:

[0091] (611) Generate a first set of related terms based on the name of the node. For nodes of the main body of the job category, a second set of related terms is also generated by combining the job information table data.

[0092] (612) Perform semantic association analysis based on the related word set and search terms of nodes;

[0093] (613) When the semantic association analysis results indicate that the association is greater than the preset threshold, the node is determined to be related to the search term and is recorded as the first candidate node.

[0094] In this embodiment, when analyzing the relevance between nodes and search terms, the analysis is based on the semantic features of their node names. Furthermore, to enhance the representational capability of node name semantic features, this embodiment generates a first set of related terms based on the node name. Semantic association analysis is then performed between multiple words in this set and the search term, improving the comprehensiveness of the first candidate nodes. Further, to further enhance the representational capability of node name semantic features, this embodiment can also combine other data to represent the semantic features of node names. For example, for nodes representing job-related organizational entities, in addition to the first set of related terms generated based on the node name, a second set of related terms can be generated in conjunction with job information table data. Semantic association analysis is then performed between the words in the first and second sets and the search term. The aforementioned semantic association analysis can be determined based on the correlation analysis between word vector codes.

[0095] Furthermore, in the above step (611), the generation of the related word set of the node is based on the generation of the related word set from the source words, wherein the source words include the name of the node and the keywords in the job information that represent the job function attributes;

[0096] Specifically, the method for generating the relevant word set of the node in step (611) includes:

[0097] (6111) A source document database is formed based on the texts of regular or irregular work reports from different institutional entities and the texts of announcements related to the company's institutional entities;

[0098] (6112) Based on the source documents in the database, keywords are initially extracted and denoted as the first keyword set;

[0099] (6113) Perform semantic association analysis based on the first keywords in the first keyword set, classify the first keywords in the first keyword set based on semantic association, obtain multiple categories, and record the keywords of each category as the second keyword set;

[0100] (6114) Based on the matching of the source word in multiple sets of second keywords corresponding to the source document database, determine all sets of second keywords containing the source word;

[0101] (6115) Analyze the words in all second keyword sets containing the source word to determine the related word set of the source word.

[0102] In this embodiment, when generating a related word set for a node, the source documents are regular or irregular work reports and announcements related to company entities. All related words corresponding to the node's structural entity are fully explored, and the semantic relevance of the keywords in the source documents is used for classification. Keywords with high semantic relevance in the source documents are grouped into one category, and in some cases, these categories can be used as related word sets. In this embodiment, multiple source documents are used for second keyword set analysis. The same keyword can be grouped into different second keyword sets in different source documents, and multiple related word sets can be found for the same keyword. In this embodiment, when multiple related word sets exist for the same source word, the words in these sets are analyzed and filtered. Specifically, the analysis based on words in all second keyword sets containing the source word to determine the related word set includes: counting the frequency of each word in all second keyword sets containing the source word, and forming a related word set based on a preset number of words with high frequency.

[0103] Furthermore, in step (612) above, semantic association analysis is performed based on the relevant word set of the node and the search terms, including the following steps:

[0104] (6121) Calculate the correlation between the word vector of each word in the related word set of the node and the word of the search term, and denote it as the first correlation degree;

[0105] (6122) Calculate the distance between any two first correlation degrees based on the first correlation degree corresponding to each word in the related word set of the node, and obtain the cumulative value of the correlation degree distance corresponding to each word in the related word set of the node;

[0106] (6123) The correction coefficient for the relevance of each word in the related word set is determined based on the cumulative relevance distance value of each word in the related word set of the node;

[0107] (6124) After correcting the first correlation degree based on the correction coefficient, determine the correlation degree of each word in the related word set, and denot it as the second correlation degree. The sum of the second correlation degrees corresponding to all words in the related word set is used as the correlation analysis result between the related word set and the search term of the node.

[0108] In this embodiment, based on the semantic relevance distance between each word in the relevant word set and the search term, the cumulative relevance distance value corresponding to each word in the relevant word set is calculated. The cumulative relevance distance value corresponding to each word in the relevant word set measures the degree of influence of the relevance between the word and the search term on the final relevance analysis result. It can be understood that if the cumulative relevance distance value corresponding to a word in the relevant word set is large, the degree of influence of the relevance of the word in the relevant word set on the final relevance analysis result is smaller. The correction coefficient represents the degree of influence mentioned above. If the cumulative relevance distance value corresponding to a word in the relevant word set is large, the corresponding correction coefficient is smaller.

[0109] This application provides an automatic generation system for organizational structure profiles at the job level, including an organizational chart framework generation module and an organizational chart node additional information analysis and generation module.

[0110] The organization chart framework generation module includes:

[0111] The organizational data pool acquisition unit is used to acquire an organizational data pool, which includes multi-dimensional information of different organizational entities. The organizational entities are divided into three levels: units, internal departments, and positions.

[0112] The organization chart framework generation unit is used to generate an organization chart in a forward order based on the affiliation relationships between different organizational entities, in the order of unit, internal department, and position. This forward generation process includes: generating a first relationship line based on the affiliation relationships between the three levels of organizational entities (unit, internal department, and position); generating a second relationship line based on the affiliation relationships between units; and forming an organization chart framework based on different organizational entities and the first and second relationship lines between them.

[0113] The organizational chart node additional information analysis and generation module includes:

[0114] An employee information database acquisition unit is used to acquire an employee information database, which includes basic information of different employees and the main information of the organization to which the employees belong;

[0115] The node-attached information analysis and generation unit is used to obtain related information from the employee information database, generate preset attribute parameters, and add corresponding additional information to different organizational entities layer by layer from the end node of the organizational chart to form a job-level organizational profile.

[0116] Specific limitations regarding the automatic generation system for job-level organizational structure profiles can be found in the limitations of the automatic generation method for job-level organizational structure profiles mentioned above, and will not be repeated here. Each module and unit in the aforementioned automatic generation system for job-level organizational structure profiles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules and units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each unit.

[0117] This invention is not limited to the specific embodiments described above. Any modifications made by those skilled in the art based on the above concept without creative effort are within the scope of protection of this invention.

Claims

1. A method for automatically generating job-level organizational structure profiles, characterized in that, Includes the following steps: (1) Generate an organizational chart framework, the generation method including: Obtain an organizational data pool, which includes multi-dimensional information on different organizational entities, which are divided into three levels: units, internal departments, and positions. Based on the affiliation relationships between different institutional entities, an organizational chart is generated in a forward order according to the units, internal departments, and positions. This forward generation process includes: generating a first relationship line based on the affiliation relationships between the three levels of institutional entities: units, internal departments, and positions; generating a second relationship line based on the affiliation relationships between units; and forming an organizational chart framework based on the different institutional entities and the first and second relationship lines between them. (2) Analyze the nodes of the organization chart and generate additional information. The generation method includes: Obtain an employee information database, which includes basic information of different employees and the main information of the organizations to which the employees belong; Based on the employee information database, related information is obtained to generate preset attribute parameters. From the end node of the organizational chart, corresponding additional information is added to different organizational entities layer by layer to form a job-level organizational profile. The preset attribute parameters and additional information are determined in real time based on client demand data. The method for automatically generating job-level organizational profiles further includes: Upon receiving a request from a client to display an organizational chart, the job-level organizational profile is displayed, including additional information for each node, i.e., each organizational entity. Upon receiving a request from a client to display an organizational chart based on search terms, the client's search terms are received. Based on the original complete organizational profile data, nodes related to the search terms are searched, nodes unrelated to the search terms are hidden in the original complete organizational profile, and the method of analyzing organizational chart nodes and generating additional information is re-executed based on the unhidden nodes to form an organizational profile based on search terms. The method of searching for nodes related to the search term and hiding nodes unrelated to the search term in the original complete organizational profile includes the following steps: (61) For all nodes in the original complete organizational profile, perform a correlation analysis with the search term to obtain and locate all nodes related to the search term, and record them as first candidate nodes; (62) Record all subordinate nodes of the first candidate node as second candidate nodes, and record the superior nodes of the first candidate node as third candidate nodes; (63) Perform the steps of (62) above on the second candidate nodes and the third candidate nodes until all second candidate nodes and third candidate nodes are obtained; (64) Use the first candidate nodes, second candidate nodes, and third candidate nodes as nodes related to the search term. The (61) includes: generating a first related word set based on the name of the node; for nodes of the main body of the job category, it also includes generating a second related word set by combining the job information table data; performing semantic association analysis based on the related word set of the node and the search term; when the semantic association analysis result indicates that the association is greater than a preset threshold, determining that the node is related to the search term and recording it as the first candidate node; The semantic association analysis between the node-based related word set and the search term includes: calculating the association between the word vectors of each word in the node-based related word set and the search term based on their similarity, denoted as the first association degree; calculating the distance between any two first association degrees corresponding to each word in the node-based related word set to obtain the cumulative distance value of the association degree corresponding to each word in the node-based related word set; determining the correction coefficient of the association degree corresponding to each word in the related word set based on the cumulative distance value of the association degree corresponding to each word in the related word set; determining the association degree of each word in the related word set after correcting the first association degree based on the correction coefficient, denoted as the second association degree; and accumulating the sum of the second association degrees corresponding to all words in the related word set as the association analysis result between the node-based related word set and the search term.

2. The method for automatically generating job-level organizational structure profiles according to claim 1, characterized in that, In the organizational profile, each job-related organizational node is also associated with the job information table data and the employee personal information table data of the actual employees working in that job.

3. The method for automatically generating job-level organizational structure profiles according to claim 1, characterized in that, The process of generating preset attribute parameters based on associated information obtained from the employee information database and adding corresponding supplementary information to different organizational entities layer by layer from the end node of the organizational chart includes: Generate the first preset attribute parameters of the parent node based on the association information of the current node; Based on other nodes that belong to the same parent node as the current node, generate the second preset attribute parameters of the parent node based on the node's association information; The first and second preset attribute parameters are combined to form the preset attribute parameters of the parent node to which the current node belongs, and corresponding additional information is added to the parent node.

4. The method for automatically generating job-level organizational structure profiles according to claim 1, characterized in that, The step of generating preset attribute parameters based on the associated information obtained from the employee information database includes: Based on the employee's affiliated organization information, calculate the total number of employees under different organizations, or... Based on employee qualification data and historical performance data, analyze individual employee performance scores, and add corresponding comprehensive performance scores to different organizational entities layer by layer from the end node of the organizational chart.

5. The method for automatically generating job-level organizational structure profiles according to claim 4, characterized in that, The generation of the related word set of the node is based on the generation of source words, which include the name of the node and keywords in the job information that represent the job function attributes; The method for generating the related word set of the node includes: A source document database is formed using regular or irregular work reports from different organizations and announcements related to company organizations as source documents. Keywords were initially extracted from the source documents in the database and denoted as the first keyword set; Semantic association analysis is performed based on the first keywords in the first keyword set. Based on the semantic association, the first keywords in the first keyword set are classified to obtain multiple categories. The keywords in each category are denoted as the second keyword set. Based on the matching of the source word in multiple sets of second keywords corresponding to the source document database, all sets of second keywords containing the source word are determined. Based on the analysis of words in all second keyword sets containing the source word, the relevant word set of the source word is determined.

6. A system for automatically generating job-level organizational profiles according to any one of claims 1-5, characterized in that, Includes an organization chart framework generation module and an organization chart node additional information analysis and generation module: The organization chart framework generation module includes: The organizational data pool acquisition unit is used to acquire an organizational data pool, which includes multi-dimensional information of different organizational entities. The organizational entities are divided into three levels: units, internal departments, and positions. The organization chart framework generation unit is used to generate an organization chart in a forward order based on the affiliation relationships between different organizational entities, in the order of unit, internal department, and position. This forward generation process includes: generating a first relationship line based on the affiliation relationships between the three levels of organizational entities (unit, internal department, and position); generating a second relationship line based on the affiliation relationships between units; and forming an organization chart framework based on different organizational entities and the first and second relationship lines between them. The organizational chart node additional information analysis and generation module includes: An employee information database acquisition unit is used to acquire an employee information database, which includes basic information of different employees and the main information of the organization to which the employees belong. The node-attached information analysis and generation unit is used to obtain related information from the employee information database, generate preset attribute parameters, and add corresponding additional information to different organizational entities layer by layer from the end node of the organizational chart to form a job-level organizational profile.

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