Human resource information release management system based on cloud collaboration

Abstract

The invention relates to the technical field of human resource management and cloud computing, in particular to a human resource information release management system based on cloud collaboration, and aims to solve the problems that in the prior art, multi-dimensional ability vectors of employees cannot be constructed, behavior characteristics cannot be fused to generate individual ability representations, and the performance is poor. End-to-end automatic analysis from behavior data to matching decision is difficult to realize, and objectivity, accuracy and intelligent level of talent configuration are reduced. According to the method, multi-dimensional ability vector construction of employees is executed through a behavior situation ability adaptation module, individual ability representation is generated by fusing behavior characteristics, a structured demand vector is generated by combining post demands, and the person-post adaptation degree is output through vector similarity calculation, so that end-to-end automatic analysis from behavior data to matching decision is realized; and objectivity, accuracy and intelligent level of talent configuration are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human resource management and cloud computing, more particularly, to a human resource information release management system based on cloud collaboration. BACKGROUND

[0002] Traditional human resource information management relies on localized systems, forming data islands that make information synchronization difficult, integration time-consuming and error-prone, and the single release channel makes cross-border enterprise information asymmetric; the cloud system lacks collaboration mechanisms and security protection, leading to inefficient process management and difficulty in cross-border compliance; at the same time, traditional management is difficult to extract data value, resulting in recruitment and training being disconnected from demand and strategic decision-making being subjective and blind.

[0003] The patent application with publication number CN114595912A discloses a human resource information comprehensive management system based on intelligent management, including an employee attendance and examination unit, an employee salary and welfare unit, a human resource training management unit, a cloud human resource database, a central processing server, a personnel basic information management unit, an enterprise roster management module, a human resource application unit, a talent recruitment unit, and an information display unit; the human resource information comprehensive management system based on intelligent management improves the accuracy of query and retrieval, uses classified input of human resource information to make the input of human resource information more efficient, and at the same time, the classified input and classified storage cooperate to facilitate the management of human resource information, effectively realizing the orderly and comprehensive management of human resource information, saving time, facilitating talent management and recruitment, and other human resource work, and greatly improving work efficiency;

[0004] However, the above-mentioned reference patent improves the accuracy and efficiency of information query and retrieval through classified input and storage, realizes the orderly and comprehensive management of human resource information, saves management time, facilitates talent management and recruitment, and significantly improves work efficiency, but cannot construct a multi-dimensional capability vector of employees, cannot integrate behavior characteristics to generate individual capability representation, is difficult to realize end-to-end automatic analysis from behavior data to matching decision, reduces the objectivity, precision and intelligence level of talent allocation; at the same time, it cannot realize the aggregation analysis of organizational strategic goals and personnel capability factors, is difficult to realize the dynamic correction of post elements, cannot complete the whole cycle management from intelligent generation to closed-loop optimization, and reduces the precision of post design and talent matching efficiency.

[0005] Therefore, we propose a human resource information release management system based on cloud collaboration to solve the above problems. SUMMARY

[0006] The present application aims to provide a cloud-based collaborative human resource information release management system, which solves the problems of the prior art that cannot construct a multi-dimensional employee capability vector, cannot fuse behavior characteristics to generate individual capability representation, is difficult to realize end-to-end automated analysis from behavior data to matching decision, reduces the objectivity, precision and intelligent level of talent allocation, cannot realize the aggregated analysis of organizational strategic objectives and personnel capability factors, is difficult to realize dynamic modification of post elements, cannot complete the whole-cycle management from intelligent generation to closed-loop optimization, and reduces the precision of post design and talent matching efficiency.

[0007] The present application aims to provide a cloud-based collaborative human resource information release management system, which solves the problems of the prior art that cannot construct a multi-dimensional employee capability vector, cannot fuse behavior characteristics to generate individual capability representation, is difficult to realize end-to-end automated analysis from behavior data to matching decision, reduces the objectivity, precision and intelligent level of talent allocation, cannot realize the aggregated analysis of organizational strategic objectives and personnel capability factors, is difficult to realize dynamic modification of post elements, cannot complete the whole-cycle management from intelligent generation to closed-loop optimization, and reduces the precision of post design and talent matching efficiency.

[0008] A cloud-based collaborative human resource information release management system applied to a human information cloud platform, comprising:

[0009] A data acquisition and processing module for acquiring organizational behavior log data from the human resource management domain, the business process management domain, the collaborative office platform, the software development tool chain and the post information release platform, preprocessing the acquired organizational behavior log data, and generating structured behavior characteristic data;

[0010] A behavior situation capability adaptation module for constructing a personnel capability factor vector based on the structured behavior characteristic data generated from the organizational behavior log data, performing similarity calculation in combination with a preset post scene demand vector, and outputting a post adaptation degree value;

[0011] A post generation optimization module for generating a post name, job responsibilities, skill requirements and performance evaluation methods based on the aggregated analysis result of the preset organizational objectives and the personnel capability factor vector, and triggering automatic optimization and adjustment of the post content in combination with the post exposure, post click volume and post application volume after the post is released;

[0012] A post release feedback module for performing channel adaptability analysis on the generated post information, generating multi-language and multi-style post scripts adapted to different platforms, pushing to an external recruitment platform, and monitoring post exposure, click and application behaviors.

[0013] As a preferred embodiment of the present application, the process of the behavior situation capability adaptation module for constructing a personnel capability factor vector based on the structured behavior characteristic data generated from the organizational behavior log data comprises:

[0014] Obtaining organizational behavior log data of any employee within a work cycle as input, the organizational behavior log data including operation timestamp, operation type, operation duration, message sending frequency, file editing times, code submission times, post exposure volume, post click volume and post application volume;

[0015] The following behavior features are extracted and calculated from the organization behavior log data to generate structured behavior feature data: daily login times, total system operation time length, document editing time length proportion, code development time length proportion, system configuration time length proportion, data query time length proportion, communication initiation times, average response time delay, cross-functional communication breadth, technical activity intensity, post click rate, application conversion rate, task completion quantity, concurrent task average, task completion rate, and attendance rate;

[0016] Normalization processing is performed on the structured behavior feature data to obtain standardized behavior feature data, and six capability scores are calculated based on the standardized behavior feature data, each capability being calculated by weighted linear combination, and the six capability scores being arranged in order to form a capability factor vector.

[0017] As a preferred embodiment of the present application, the process of the behavior situation capability adaptation module combining the preset post scene demand vector to perform similarity calculation and output the post adaptation degree value includes:

[0018] The employee capability factor vector and the post responsibility text are obtained, keywords are extracted from the post responsibility text to generate a post demand vector, and the keyword extraction rule is based on a preset post capability dictionary;

[0019] The post responsibility text is subjected to the following operations: word segmentation and matching of the capability terms in the dictionary, statistics of the occurrence frequency of each type of capability term, taking the frequency as the initial weight, and performing normalization processing on the six types of initial weights;

[0020] The post demand vector is a six-dimensional vector, the dimension order of the employee capability factor vector and the post demand vector is verified to be consistent, and the six items of the capability factor vector are arranged in order;

[0021] The six items of the post demand vector are aligned in the same order, the cosine similarity of the employee capability factor vector and the post demand vector is calculated, the calculated cosine similarity is taken as the post adaptation degree value, and the post adaptation degree value is output.

[0022] As a preferred embodiment of the present application, the process of the post generation optimization module based on the aggregation analysis result of the preset organization target and the personnel capability factor vector and generating the post name, work responsibility, skill requirement, and performance evaluation method includes:

[0023] The organization strategic target original data and the personnel capability factor vector set are obtained, normalization processing is performed on the organization strategic target original data to generate a normalized organization strategic target vector, the post capability factor weight coefficient is obtained, for each post capability factor, an adaptation value thereof under the current organization strategic target is generated, and the adaptation value is calculated by a target mapping function;

[0024] Based on the post ability factor weight coefficient, the personnel ability factor vector and the adaptation value, the post element content is calculated, and based on the post element content P, a corresponding template in a preset post template library is matched;

[0025] Each template in the template library is associated with a P threshold interval, a threshold interval to which the P belongs is matched, a corresponding template is called to generate a post name, a job responsibility, a skill requirement and a performance evaluation method, and the post name, the job responsibility, the skill requirement and the performance evaluation method are output.

[0026] As a preferred embodiment of the present application, the post generation optimization module triggers the content optimization process in combination with the post exposure, the post click volume and the post application volume after the post is published, which includes:

[0027] The operation index data after the post is published is obtained, the operation index data includes actual values of the following five items: the post click volume, the number of applicants, the matching success rate, the average response time and the candidate quality score, the operation target value set by the organization is obtained, the operation target value is generated by the human resource strategy of the organization and includes five target values corresponding to the post click volume, the number of applicants, the matching success rate, the average response time and the candidate quality score, a deviation vector is generated, the deviation vector is a five-dimensional vector arranged in order;

[0028] The overall deviation is calculated, the overall deviation is the Euclidean norm of the deviation vector, the deviation trigger threshold is obtained, it is judged whether the overall deviation is greater than or equal to the deviation trigger threshold, if the judgment result is yes, the post element content optimization process is triggered.

[0029] As a preferred embodiment of the present application, the post generation optimization module performs the automatic optimization and adjustment of the post content, which includes:

[0030] The original post element content and the deviation vector are obtained, the operation deviation weight and the content stability weight are obtained, based on the original post element content, the deviation vector, the operation deviation weight and the content stability weight, a target function is constructed, the target function is a loss function, the value of which is composed of the weighted sum of the following two parts: the square sum of the operation deviation and the square difference between the original post element content and the optimized post element content;

[0031] The optimized post element content is initialized, the initial value of the optimized post element content is equal to the original post element content, the learning rate is set, and the following iteration steps are repeatedly executed:

[0032] S1: the gradient of the loss function at the current optimized post element content is calculated;

[0033] S2: the optimized post element content is updated, and it is judged whether the termination condition is met, the termination condition includes that the operation deviation is less than the termination threshold and the absolute value of the change of the post element content in this update is less than the change threshold.

[0034] S3: If both termination conditions are met, end the iteration and generate the post element content after optimization.

[0035] As a preferred embodiment of the present application, the process of the post release feedback module performing channel adaptability analysis on the generated post information and generating multi-language and multi-style post scripts adapted to different platforms includes:

[0036] Obtain the post element content and the recruitment platform preference set, and perform the following operations on each text content: convert the text content into a word vector sequence, calculate the arithmetic mean of each column of the word vector sequence, generate a semantic vector, perform element-by-element multiplication of the semantic vector and a preset weight vector, sum the multiplication result, and generate a feature value of this dimension;

[0037] Arrange the feature values of the seven dimensions in order to form a numerical representation of the post element content, and obtain a preference weight set corresponding to each recruitment platform;

[0038] Calculate the adaptability score, calculate the semantic similarity, generate the adaptability score and the semantic similarity as additional attributes of the post element content, obtain the source language post element content, generate the target language post element content based on the source language post element content and a preset bilingual dictionary;

[0039] Verify the numerical representation of the target language post element content, if the verification is passed, output the target language post element content, obtain the specified style type, generate the stylized post element content based on the source language post element content and the specified style type, verify the numerical representation of the stylized post element content, if the verification is passed, output the stylized post element content.

[0040] As a preferred embodiment of the present application, the process of the post release feedback module pushing the post element content to an external recruitment platform and monitoring the post exposure, click and application behavior includes:

[0041] Obtain the post element content and the target recruitment platform, perform field mapping conversion on the eight fields of the post element content according to the interface specification of the target recruitment platform, the field mapping conversion is completed through a preset mapping relationship table, and a data structure adapted to the target platform is generated;

[0042] Send the converted post element content through an API interface, receive the status code returned by the API interface, determine whether the status code is a preset success status code, if the determination result is no, perform a retry operation, and record an error log;

[0043] Get the post release state, which is obtained through a state query interface and takes values of: published, failed, and pending review. If the post release state is failed, mark the post element content as a repairable state, start a user behavior data collection task, and collect three behavior indicators of the post on the target platform: exposure, clicks, and applications.

[0044] Perform data collection according to the target platform and a specified time interval, write the collected exposure, clicks, and applications into a data warehouse, calculate the click rate, application rate, and total conversion rate, generate the click rate, application rate, and total conversion rate, and output the click rate, application rate, and total conversion rate.

[0045] Compared with the prior art, the advantages of the present application are:

[0046] (1) In the present application, the employee multi-dimensional capability vector is constructed by the behavior context capability adaptation module, the individual capability representation is generated by fusing the behavior characteristics, the structured demand vector is generated by combining the post demand, the person-post adaptation degree is output by vector similarity calculation, the end-to-end automatic analysis from behavior data to matching decision is realized, and the objectivity, accuracy, and intelligent level of talent allocation are improved.

[0047] (2) In the present application, the organization strategy target and personnel capability factor are aggregated and analyzed by the post generation optimization module, the post name, responsibility, skill requirement, and performance evaluation method are automatically generated, the target deviation is calculated based on the click volume, application volume, matching success rate, response time, and candidate quality score after release, the automatic optimization is triggered when the threshold is exceeded, the weighted loss function is constructed by combining the operation effectiveness and content stability, the dynamic correction of post elements is realized by iterative adjustment, the whole cycle management from intelligent generation to closed-loop optimization is completed, and the accuracy of post design and talent matching efficiency are improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The system block diagram of example one in the present application;

[0049] Figure 2 The system block diagram of example two in the present application;

[0050] Figure 3 The iterative step flow chart of updating post element content in the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings of the embodiments of the present application; obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments; based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0052] Embodiment one: as shown in Figure 1 and Figure 3 The present application proposes a human resource information release management system based on cloud collaboration, applied to a human information cloud platform, comprising:

[0053] A data acquisition and processing module is configured to collect organization behavior log data from the human resource management domain, the business process management domain, the collaborative office platform, the software development tool chain and the post information release platform, and to preprocess the collected organization behavior log data, the preprocessing operation including missing value filling, abnormal value detection and correction, timestamp standardization, operation type coding and aggregation of behavior frequency and duration within a fixed time window to generate structured behavior feature data.

[0054] The data acquisition and processing module uniformly collects multi-source behavior logs of human resources, business processes, collaborative offices, development tool chains and recruitment platforms, improves data quality and consistency through missing value filling, abnormal value correction, timestamp standardization and operation type coding, aggregates behavior frequency and duration within a fixed time window, and generates structured feature data, effectively breaking data silos and providing high-quality, scalable data support for intelligent post generation and organization optimization.

[0055] The behavior context capability adaptation module generates structured behavior feature data based on organization behavior log data, constructs a personnel capability factor vector, and performs similarity calculation combined with a preset post scene demand vector to output a post adaptation degree value.

[0056] The process of the behavior context capability adaptation module generating structured behavior feature data based on organization behavior log data and constructing a personnel capability factor vector includes:

[0057] Obtain organization behavior log data of any employee within a work cycle as input, the organization behavior log data including operation timestamp, operation type, operation duration, message sending frequency, file editing times, code submission times, post exposure, post click volume and post application volume.

[0058] Extract and calculate the following behavior features from the organization behavior log data to generate structured behavior feature data: daily login times, system operation total duration, document editing duration proportion, code development duration proportion, system configuration duration proportion, data query duration proportion, communication initiation times, average response delay, cross-functional communication breadth, technical activity intensity, post click rate, application conversion rate, task completion quantity, concurrent task mean, task completion rate and attendance rate.

[0059] The mean-variance normalization processing is performed on the structured behavior characteristic data to obtain standardized behavior characteristic data, and six ability scores are calculated based on the standardized behavior characteristic data. Each ability is calculated by weighted linear combination, the weight coefficient is a non-negative real number, the sum of all weight coefficients corresponding to each ability is equal to 1, and the weight coefficient is determined by linear regression model training based on historical matching data, without using expert evaluation, rule setting or other methods. The ability factor score is calculated according to the following formula:

[0060] Where a ij is the score of the ith employee on the jth ability factor, f′ im is the value of the ith employee on the mth standardized behavior characteristic, w jm is the weight coefficient of the mth behavior characteristic to the jth ability factor, m ranges from 1 to n, and j ranges from 1 to 6.

[0061] The six abilities are: task execution ability, communication and cooperation ability, time management ability, problem solving ability, technical or professional ability, and initiative and innovation ability. The six ability scores are arranged in order to form an ability factor vector.

[0062] The process of the behavior situation ability adaptation module combining the preset post scene demand vector to perform similarity calculation and output the post adaptation degree value includes:

[0063] The employee ability factor vector and the post responsibility text are obtained, the keywords are extracted from the post responsibility text, the post demand vector is generated, and the keyword extraction rule is based on the preset post ability dictionary. The dictionary includes the following six types of ability terms: task execution, communication and cooperation, time management, problem solving, technical or professional ability, and initiative and innovation.

[0064] The following operations are performed on the post responsibility text: word segmentation and matching of ability terms in the dictionary, statistics of the occurrence frequency of each type of ability term, and the frequency as the initial weight. The six types of initial weights are normalized to make the sum equal to 1. The normalization method is: each weight is divided by the range (maximum value-minimum value) after subtracting the minimum value. If the range is 0, each is set to 1 / 6. The six normalized weights form the post demand vector.

[0065] The post demand vector is a six-dimensional vector, each item is a non-negative real number, and the sum of the six items is equal to 1. It is verified that the dimension order of the employee ability factor vector and the post demand vector is consistent. The six items of the ability factor vector are arranged in order: task execution ability, communication and cooperation ability, time management ability, problem solving ability, technical or professional ability, and initiative and innovation ability.

[0066] The six items of the post demand vector are aligned in the same order, the cosine similarity of the employee ability factor vector and the post demand vector is calculated, the calculated cosine similarity is taken as the post adaptation degree value, and the cosine similarity is calculated according to the following formula:

[0067] Wherein sim(A i ,S) is the similarity of the ability factor vector of the i th employee and the post demand vector, a ij is the score of the i th employee on the j th ability factor, s j is the weight of the post demand vector in the j th dimension, the numerator is the sum of the product of the corresponding components of the two vectors, and the denominator is the product of the Euclidean norms of the two vectors.

[0068] The post adaptation degree value is a real number in the interval [0, 1], and the post adaptation degree value is output.

[0069] The behavior situation ability adaptation module constructs the employee multi-dimensional ability factor vector based on the organization behavior log, automatically extracts core abilities such as task execution, communication and cooperation, time management, problem solving, professional ability and innovation initiative through data-driven mode, extracts key ability characteristics combined with post responsibility text and generates quantitative demand vector, uses similarity calculation to output adaptation degree value, realizes end-to-end automatic analysis from behavior data to human-post matching, and improves the objectivity, precision and intelligent level of talent allocation.

[0070] The post generation optimization module generates post name, work responsibility, skill requirement and performance evaluation method based on the preset organization target and the aggregation analysis result of the personnel ability factor vector, and triggers the automatic optimization and adjustment of the post content combined with the post exposure, post click volume and post application volume after the post is released;

[0071] The process of generating post optimization module based on the preset organization target and the aggregation analysis result of the personnel ability factor vector and generating post name, work responsibility, skill requirement and performance evaluation method includes:

[0072] The original data of organizational strategic objectives and the set of personnel capability factor vectors are obtained, the original data of organizational strategic objectives include the original values of the following six indicators: revenue growth, market expansion, technological innovation, customer satisfaction, operational efficiency and compliance safety, minimum-maximum normalization processing is performed on the original data of organizational strategic objectives to generate a normalized organizational strategic objective vector, the normalized organizational strategic objective vector is a six-dimensional vector, each item is in the range of [0, 1], the post capability factor weight coefficient is obtained, the post capability factor weight coefficient is a six-dimensional non-negative real number vector, the sum of the six items is equal to 1, the weight coefficient is generated by linear regression model training from historical post matching data, without using expert scoring, questionnaire survey or other methods, the post capability factor includes task execution capability, communication and cooperation capability, time management capability, problem solving capability, technical or professional capability and initiative and innovation capability, for each post capability factor, its adaptation value under the current organizational strategic objective is generated, the adaptation value is calculated by a target mapping function, the target mapping function is a linear weighted function, and the mapping weight is generated by historical data training;

[0073] Based on the post capability factor weight coefficient, the personnel capability factor vector and the adaptation value, the post element content is calculated, the post element content is calculated as follows:

[0074] Where P is the output value of the post element content, w j is the weight of the jth capability factor, c j is the quantitative value of the jth capability factor, φ j (G) is the output of the jth capability factor corresponding to the target mapping function, G is the organizational strategic objective vector, and j is in the range of 1 to 6, based on the post element content P, the corresponding template in the preset post template library is matched;

[0075] Each template in the template library is associated with a P threshold interval, the P threshold interval to which P belongs is matched, and the corresponding template is called to generate the post name, job responsibilities, skill requirements and performance evaluation method, and the post name, job responsibilities, skill requirements and performance evaluation method are output.

[0076] The post generation optimization module triggers the content optimization process in combination with the post exposure, post click volume and post application volume after the post is published, which includes:

[0077] Obtain operational metrics data after job posting. These metrics include actual values ​​for the following five items: job clicks, number of applicants, match success rate, average response time, and candidate quality score. Obtain the operational target values ​​set by the organization. These target values ​​are generated by the organization's human resources strategy and include five target values, corresponding to job clicks, number of applicants, match success rate, average response time, and candidate quality score, respectively. Generate a deviation vector, which is a five-dimensional vector arranged in the following order: the difference between the actual value of job clicks and the target value, the difference between the actual value of the number of applicants and the target value, the difference between the actual value of the match success rate and the target value, the difference between the actual value of the average response time and the target value, and the difference between the actual value of the candidate quality score and the target value.

[0078] Calculate the overall deviation, which is the Euclidean norm of the deviation vector. The calculation method is: square the five differences respectively, sum them, and then take the square root of the sum. The overall deviation is a non-negative real number. Obtain the deviation trigger threshold, which is a positive real number. Determine whether the overall deviation is greater than or equal to the deviation trigger threshold. If the determination result is yes, trigger the job element content optimization process.

[0079] The process of automatically optimizing and adjusting job content in the job generation and optimization module includes:

[0080] Obtain the original job element content and deviation vector, and obtain the operational deviation weight and content stability weight. The operational deviation weight and content stability weight are non-negative real numbers. Based on the original job element content, deviation vector, operational deviation weight, and content stability weight, construct an objective function, which is a loss function. Its value consists of the weighted sum of the following two parts: the square of the operational deviation (i.e., the sum of the squares of each component of the deviation vector) and the squared difference between the original job element content and the optimized job element content. The loss function is calculated using the following formula:

[0081] L(P′)=α·||ΔM|| 2 +β·(PP′) 2 Where L(P′) is the loss function value, P is the original job element content, P′ is the optimized job element content, and ||ΔM|| 2 α is the square of the operational deviation, which is the sum of the squares of the components of the deviation vector. α is the operational deviation weight, greater than or equal to 0. β is the content stability weight, greater than or equal to 0. (PP′) 2 This represents the squared difference between the original content and the optimized content.

[0082] Initialize the optimized job element content. The initial value of the optimized job element content is equal to the original job element content. Set the learning rate to a positive real number. Repeat the following iterative steps:

[0083] S1: Calculate the gradient of the loss function at the current optimized job element content. The gradient is determined by the partial derivative of the loss function with respect to P′.

[0084] S2: Updated and optimized job element content. The update of job element content is calculated according to the following formula:

[0085] Where P (t) Let η be the job element content for the t-th iteration, and η be the learning rate. For the loss function in P (t) The gradient at point P is calculated using the partial derivative of the loss function with respect to P′, and the partial derivative is 2α·(||ΔM||). 2 )′+2β·(P′-P), the calculation process is executed by the algorithm module to determine whether the termination conditions are met. The termination conditions include: the operational deviation is less than the termination threshold, the termination threshold is a positive real number, the absolute value of the change in the content of the job element updated this time is less than the change threshold, and the change threshold is a positive real number.

[0086] S3: If both termination conditions are met, end the iteration and generate the optimized job element content;

[0087] The job creation and optimization module automatically generates job titles, responsibilities, skill requirements, and performance appraisal methods based on the aggregation analysis of organizational strategic goals and personnel capability factors, achieving dynamic alignment between job design and organizational development direction. Through normalized strategic indicators and data-driven capability weights, combined with a target mapping function and a pre-set template library, it ensures the scientific generation of job content. After publication, it continuously collects operational data such as clicks, application volume, matching success rate, response time, and candidate quality to calculate the deviation vector between actual performance and goals. The overall deviation is assessed using the Euclidean norm, and an optimization mechanism is automatically triggered when it exceeds a threshold. A weighted loss function incorporating operational effectiveness and content stability is constructed, and job elements are dynamically adjusted through a gradient iteration method, enhancing job attractiveness while avoiding frequent changes. This achieves full-cycle autonomous evolution of job creation from intelligent generation to closed-loop optimization, significantly improving the accuracy, adaptability, and talent matching efficiency of job design.

[0088] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that:

[0089] like Figure 2 As shown, the job posting feedback module is used to perform channel compatibility analysis on the generated job information, generate multilingual and multi-style job descriptions adapted to different platforms, push them to external recruitment platforms, and monitor job exposure, clicks, and application behavior.

[0090] The job posting feedback module performs channel compatibility analysis on the generated job information and generates multilingual and multi-style job descriptions adapted to different platforms. This process includes:

[0091] Obtain the job requirements and recruitment platform preferences. The job requirements include the following seven text items: job title, job responsibilities, job requirements, salary and benefits, work location, company introduction and recruitment highlights. Perform the following operations on each text item: convert the text content into a word vector sequence, calculate the arithmetic mean of each column of the word vector sequence to generate a semantic vector, perform element-wise multiplication of the semantic vector with a preset weight vector, sum the multiplication results, and generate the feature value of this dimension.

[0092] The seven-dimensional feature values ​​are arranged in order to form a numerical representation of the job element content. The preference weight set corresponding to each recruitment platform is obtained. The preference weight set is a seven-dimensional non-negative real number vector, and the sum of the seven terms is equal to 1. The preference weight set is generated in the following way: the weighted sum of the click volume and application volume of the historical job on the platform is used as the target variable, and the seven-dimensional feature values ​​are used as independent variables. Linear regression calculation is performed, and the regression coefficients are normalized to generate the preference weight set.

[0093] The suitability score is calculated as follows: the seven feature values ​​of the job element content are multiplied by their corresponding preference weights, and the seven products are summed to generate the suitability score. Semantic similarity is calculated as follows: the job description text is converted into a semantic vector, the platform recommended format text is converted into a semantic vector, the dot product of the two semantic vectors is calculated, the Euclidean norm of the two semantic vectors is calculated, and the dot product is divided by the product of the two norms to generate the semantic similarity. The suitability score and semantic similarity are generated as additional attributes of the job element content. The source language job element content is obtained, and based on the source language job element content and a preset bilingual dictionary, the target language job element content is generated. The generation method is to perform bilingual dictionary matching and replacement on the words in the source language text while maintaining the text structure.

[0094] Verify the numerical representation of the job element content in the target language. The verification method is as follows: calculate the feature values ​​of the seven texts of the job element content in the target language, and compare the seven generated feature values ​​with the seven feature values ​​of the job element content in the source language item by item. If all are equal, the verification passes; otherwise, the verification fails. If the verification passes, output the job element content in the target language. Obtain the specified style type. Based on the job element content in the source language and the specified style type, generate stylized job element content. The generation rules are as follows: if the style type is formal, replace informal expressions with preset formal vocabulary; if the style type is concise, delete redundant modifiers; if the style type is creative, insert preset creative phrases; if the style type is benefits, strengthen the description of salary and benefits. Verify the numerical representation of the stylized job element content. The verification method is as follows: calculate the feature values ​​of the seven texts of the stylized job element content, and compare the seven generated feature values ​​with the seven feature values ​​of the job element content in the source language item by item. If all are equal, the verification passes; otherwise, the verification fails. If the verification passes, output the stylized job element content.

[0095] The job posting feedback module pushes job details to external recruitment platforms and monitors job exposure, clicks, and application activity. This process includes:

[0096] Obtain the job information and target recruitment platform. The job information includes the following eight text items: job title, job description, job requirements, salary and benefits, work location, company introduction, recruitment highlights and benefits. According to the interface specifications of the target recruitment platform, perform field mapping transformation on the eight fields of the job information. The field mapping transformation is completed through a preset mapping relationship table to generate a data structure adapted to the target platform.

[0097] Send the converted job information via API interface, receive the status code returned by API interface, determine whether the status code is the preset success status code, if the result is no, perform a retry operation and record the error log;

[0098] Get the job posting status. The job posting status is obtained through the status query interface, and the values ​​are: posted, failed, and pending review. If the job posting status is failed, mark the job content as pending repair. Start the user behavior data collection task to collect three behavioral indicators of the job on the target platform: exposure (the number of times the job is displayed), clicks (the number of times the job is clicked), and applications (the number of times the job receives applications).

[0099] Data collection is performed on the target platform and within the specified time interval. The collected exposure, clicks, and applications are written into the data warehouse. Click-through rate, application rate, and total conversion rate are calculated, and the click-through rate, application rate, and total conversion rate are generated and output.

[0100] The job posting feedback module quantifies job elements and combines platform preference weights and semantic similarity to achieve intelligent adaptation and precise delivery of job content across multiple languages, styles, and recruitment channels. It employs a feature value consistency verification mechanism to ensure that translated and rewritten content maintains semantic integrity and information density. It supports API-based automated posting, status monitoring, and exception retries to improve process reliability. It comprehensively collects behavioral data such as exposure, clicks, and applications and calculates conversion rates, providing measurable and traceable closed-loop feedback for talent attraction effectiveness. With high adaptability, strong robustness, and good scalability, it significantly improves the dissemination efficiency of recruitment content and the quality of job matching.

[0101] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A cloud-based collaborative human resources information publishing and management system, applied to a human resources information cloud platform, characterized in that: include: The data acquisition and processing module is used to collect organizational behavior log data from the human resources management domain, business process management domain, collaborative office platform, software development toolchain and job information publishing platform, and to preprocess the collected organizational behavior log data to generate structured behavioral feature data. The behavioral context capability adaptation module constructs a personnel capability factor vector based on structured behavioral feature data generated from organizational behavior log data. It then calculates the similarity by combining the vector with the preset job scenario requirements and outputs the job fit value. The job creation and optimization module generates job titles, job responsibilities, skill requirements, and performance appraisal methods based on the aggregated analysis results of preset organizational goals and personnel capability factor vectors. It also triggers automatic optimization and adjustment of job content by combining the job exposure, clicks, and application volume after the job is posted. The job posting feedback module is used to perform channel compatibility analysis on the generated job information, generate multilingual and multi-style job descriptions adapted to different platforms, push them to external recruitment platforms, and monitor job exposure, clicks, and application behavior.

2. The cloud-based collaborative human resources information publishing and management system according to claim 1, characterized in that, The process by which the behavioral context capability adaptation module generates structured behavioral feature data based on organizational behavior log data and constructs personnel capability factor vectors includes: The organizational behavior log data of any employee during the work cycle is used as input. The organizational behavior log data includes operation timestamp, operation type, operation duration, message sending frequency, number of file edits, number of code submissions, job exposure, job clicks, and number of job applications. The following behavioral characteristics are extracted and calculated from organizational behavior log data to generate structured behavioral characteristic data: daily login frequency, total system operation time, document editing time percentage, code development time percentage, system configuration time percentage, data query time percentage, number of communication initiations, average response latency, cross-functional communication breadth, technical activity intensity, job click rate, application conversion rate, number of tasks completed, average number of concurrent tasks, task completion rate, and attendance rate. The structured behavioral feature data is normalized to obtain standardized behavioral feature data. Based on the standardized behavioral feature data, six ability scores are calculated. Each ability is calculated by weighted linear combination. The six ability scores are arranged in order to form an ability factor vector.

3. The cloud-based collaborative human resources information publishing and management system according to claim 2, characterized in that, The process by which the behavioral context capability adaptation module calculates similarity and outputs a job suitability score based on a preset job scenario requirement vector includes: Obtain employee competency factor vectors and job description texts, extract keywords from the job description texts to generate job requirement vectors, and use keyword extraction rules based on a pre-defined job competency dictionary; Perform the following operations on the job description text: segment and match the ability terms in the dictionary, count the frequency of occurrence of each type of ability term, use the frequency as the initial weight, and perform normalization processing on the six types of initial weights; The job requirement vector is a six-dimensional vector. It is verified that the dimension order of the employee competency factor vector is consistent with that of the job requirement vector, and the six items of the competency factor vector are arranged in order. The six items of the job requirement vector are aligned in the same order. The cosine similarity between the employee ability factor vector and the job requirement vector is calculated. The calculated cosine similarity is used as the job fit value, and the job fit value is output.

4. The cloud-based collaborative human resources information publishing and management system according to claim 1, characterized in that, The process by which the job generation and optimization module generates job titles, job responsibilities, skill requirements, and performance appraisal methods based on the aggregated analysis results of preset organizational goals and personnel capability factor vectors includes: Obtain the original data of organizational strategic goals and the set of personnel capability factor vectors. Perform normalization processing on the original data of organizational strategic goals to generate a normalized organizational strategic goal vector. Obtain the weight coefficients of job capability factors. For each job capability factor, generate its adaptation value under the current organizational strategic goals. The adaptation value is calculated through the target mapping function. Based on the job competency factor weight coefficient, personnel competency factor vector and fit value, calculate the job element content, and match the corresponding template in the preset job template library based on the job element content P. Each template in the template library is associated with a threshold interval P. Matching the threshold interval to which P belongs, the corresponding template is called to generate the job title, job responsibilities, skill requirements and performance appraisal method, and outputs the job title, job responsibilities, skill requirements and performance appraisal method.

5. A cloud-based collaborative human resources information publishing and management system according to claim 4, characterized in that, The process by which the job creation and optimization module triggers content optimization based on job posting exposure, click-through rates, and application volume includes: Obtain operational metrics data after job posting. These metrics include actual values ​​for the following five items: job clicks, number of applicants, matching success rate, average response time, and candidate quality score. Obtain the operational target values ​​set by the organization. These target values ​​are generated by the organization's human resources strategy and include five target values, corresponding to job clicks, number of applicants, matching success rate, average response time, and candidate quality score, respectively. Generate a deviation vector, which is a five-dimensional vector arranged in order. Calculate the overall deviation, which is the Euclidean norm of the deviation vector. Obtain the deviation trigger threshold and determine whether the overall deviation is greater than or equal to the deviation trigger threshold. If the result is yes, trigger the job element content optimization process.

6. The cloud-based collaborative human resources information publishing and management system according to claim 5, characterized in that, The process of automatically optimizing and adjusting job content by the job generation and optimization module includes: Obtain the original job element content and deviation vector, obtain the operational deviation weight and content stability weight, and construct an objective function based on the original job element content, deviation vector, operational deviation weight and content stability weight. The objective function is a loss function, and its value is composed of the weighted sum of the following two parts: the sum of the squares of the operational deviation and the squared difference between the original job element content and the optimized job element content. Initialize the optimized job element content. The initial value of the optimized job element content is equal to the original job element content. Set the learning rate and repeat the following iterative steps: S1: Calculate the gradient of the loss function at the current optimized job element content; S2: Update and optimize the job element content, and determine whether the termination conditions are met. The termination conditions include that the operational deviation is less than the termination threshold and that the absolute value of the change in the job element content of this update is less than the change threshold. S3: If both termination conditions are met, the iteration ends and the optimized job element content is generated.

7. The cloud-based collaborative human resources information publishing and management system according to claim 1, characterized in that, The process by which the job posting feedback module performs channel compatibility analysis on the generated job information and generates multilingual and multi-style job descriptions adapted to different platforms includes: Obtain the job requirements and recruitment platform preferences, and perform the following operations for each piece of text content: convert the text content into a word vector sequence, calculate the arithmetic mean of each column of the word vector sequence to generate a semantic vector, perform element-wise multiplication of the semantic vector with a preset weight vector, sum the multiplication results, and generate the feature value for this dimension. The feature values ​​of the seven dimensions are arranged in order to form a numerical representation of the job elements, and the set of preference weights corresponding to each recruitment platform is obtained. Calculate the suitability score and semantic similarity, and generate the suitability score and semantic similarity as additional attributes of the job element content. Obtain the job element content in the source language, and generate the job element content in the target language based on the job element content in the source language and a preset bilingual dictionary. Verify the numerical representation of the job element content in the target language. If the verification passes, output the job element content in the target language. Obtain the specified style type. Based on the job element content in the source language and the specified style type, generate stylized job element content. Verify the numerical representation of the stylized job element content. If the verification passes, output the stylized job element content.

8. A cloud-based collaborative human resources information publishing and management system according to claim 7, characterized in that, The process by which the job posting feedback module pushes job information to external recruitment platforms and monitors job exposure, clicks, and application behavior includes: Obtain the job requirements and target recruitment platform. Based on the interface specifications of the target recruitment platform, perform field mapping transformation on the eight fields of the job requirements. The field mapping transformation is completed through a preset mapping relationship table to generate a data structure adapted to the target platform. Send the converted job information via API interface, receive the status code returned by API interface, determine whether the status code is the preset success status code, if the result is no, perform a retry operation and record the error log; Get the job posting status. The job posting status is obtained through the status query interface, and the values ​​are: posted, failed, and pending review. If the job posting status is failed, mark the job elements as pending repair. Start the user behavior data collection task to collect three behavioral indicators of the job on the target platform: exposure, clicks, and applications. Data collection is performed on the target platform and within the specified time interval. The collected exposure, clicks, and applications are written into the data warehouse. Click-through rate, application rate, and total conversion rate are calculated, and the click-through rate, application rate, and total conversion rate are generated and output.

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