A cloud computing-based intelligent employment data processing method and system

Abstract

This application relates to a cloud-based intelligent employment data processing method and system. The method integrates multi-source heterogeneous data through a cloud computing architecture and constructs a standardized dataset; it utilizes distributed feature extraction technology to generate a dynamic feature set with time dimensions and industry labels; it combines parallel profile construction and dynamic matching degree matrix calculation to achieve accurate mapping between professional skills and job requirements; and it further optimizes the matching model by incorporating real-time industry trend features; finally, by detecting changes in industry demand, it generates structured data that supports personalized job recommendations and skills enhancement suggestions based on the dynamic matching model. This addresses the problems of poor real-time performance, limited dimensions, and delayed response in traditional methods, significantly improving the dynamic adaptability and analytical accuracy of employment data processing, and helping employment service platforms provide timely and accurate personalized job recommendations and skills enhancement suggestions to job seekers.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a smart employment data processing method and system based on cloud computing. Background Technology

[0002] In the field of digital employment services, existing platforms generally adopt a rule-based job matching mechanism, which uses keyword matching and static weight calculation to initially connect job seekers with positions. Such systems typically rely on manually labeled resume parsing models and fixed-cycle updates to the job database, making it difficult to adapt to rapidly evolving industry skill requirements and job seekers' skill development trajectories.

[0003] Due to a lack of ability to integrate and process heterogeneous data from multiple sources such as social media, real-time recruitment updates, and enterprise technical documents, traditional systems are often limited to structured fields such as education and years of work experience when building job requirement profiles, and are unable to deeply explore emerging skill indicators and implicit ability requirements.

[0004] Meanwhile, static matching models lack the ability to dynamically perceive emerging skill requirements when analyzing job requirements, resulting in the inability to identify rapidly iterating skill requirements from the enterprise side in a timely manner, exacerbating the structural mismatch between job seekers' skills and market demands.

[0005] More importantly, existing methods use a centralized storage architecture when processing massive amounts of job seeker data, which causes the real-time matching response speed to drop sharply as the data scale increases, making it difficult to support the accurate recommendation needs of tens of millions of concurrent users.

[0006] The aforementioned shortcomings make it difficult for current employment service platforms to provide dynamically optimized decision support in a market environment of rapid technological change. Summary of the Invention

[0007] Therefore, it is necessary to provide a cloud-based intelligent employment data processing method and system to address the aforementioned technical issues.

[0008] Firstly, this application provides a cloud computing-based intelligent employment data processing method, including:

[0009] S1: Acquire multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; among which, multi-source heterogeneous data includes job seeker ability data and job description data from the job market;

[0010] S2: Perform distributed feature extraction on the standardized dataset to generate a dynamic feature set that includes time dimension and industry classification labels;

[0011] S3: Construct professional competence profiles and job requirement profiles in parallel based on dynamic feature sets, and calculate the matching degree matrix between the professional competence profiles and job requirement profiles;

[0012] S4: Extract trend features from industry trend reports, integrate these trend features into the matching degree matrix for dynamic updates, and generate a dynamic matching model.

[0013] S5: By detecting changes in industry demand, structured data is generated based on a dynamic matching model to support personalized job recommendations and skills enhancement suggestions;

[0014] S4 includes:

[0015] S41: Extract the frequency change rate of each skill term from the industry trend report. When the frequency change rate of a skill term exceeds the preset change rate threshold, mark the skill feature corresponding to the skill term as a high-impact feature.

[0016] S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate a demand topic distribution. ;in, Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , Indicates the number of demand topics;

[0017] S43: Adjust the distribution of demand themes based on high-impact characteristics to obtain a dynamic distribution of demand themes;

[0018] S44: Generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as a dynamic matching model.

[0019] Secondly, this application also provides a cloud computing-based intelligent employment data processing system, including:

[0020] The data standardization module is used to acquire multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; among which, multi-source heterogeneous data includes job seeker ability data and job description data from the job market;

[0021] The dynamic feature extraction module is used to perform distributed feature extraction on a standardized dataset to generate a dynamic feature set that includes a time dimension and industry classification labels.

[0022] The dual-profile matching module is used to construct professional competence profiles and job requirement profiles in parallel based on dynamic feature sets, and to calculate the matching degree matrix between the professional competence profiles and job requirement profiles.

[0023] The trend fusion and update module is used to extract trend features from industry trend reports, integrate the trend features into the matching degree matrix for dynamic updates, and generate a dynamic matching model.

[0024] The job recommendation module is used to generate structured data that supports personalized job recommendations and skills enhancement suggestions by detecting changes in industry demand and using a dynamic matching model.

[0025] The trend fusion update module includes:

[0026] The high-impact feature extraction unit is used to extract the frequency change rate of each skill word from the industry trend report. When the frequency change rate of a skill word exceeds the preset change rate threshold, the skill feature corresponding to the skill word is marked as a high-impact feature.

[0027] The demand topic distribution generation unit is used to perform LDA topic modeling on job data in a dynamic feature set to generate a demand topic distribution. ;in, Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , Indicates the number of demand topics;

[0028] The demand theme distribution adjustment unit is used to adjust the demand theme distribution based on high-impact characteristics to obtain a dynamic demand theme distribution.

[0029] The dynamic matching model generation unit is used to generate a dynamic matching matrix based on the dynamic demand topic distribution, and the dynamic matching matrix is ​​used as the dynamic matching model.

[0030] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a cloud computing-based smart employment data processing method as described in the first aspect.

[0031] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a cloud computing-based smart employment data processing method as described in the first aspect.

[0032] The aforementioned cloud-based intelligent employment data processing method and system integrates multi-source heterogeneous data and constructs a standardized dataset through a cloud computing architecture; it utilizes distributed feature extraction technology to generate a dynamic feature set with time dimensions and industry labels; it combines parallel profile construction and dynamic matching degree matrix calculation to achieve accurate mapping between professional skills and job requirements; and it further optimizes the matching model by incorporating real-time industry trend features; finally, by detecting changes in industry demand, it generates structured data that supports personalized job recommendations and skills enhancement suggestions based on the dynamic matching model. This addresses the problems of poor real-time performance, limited dimensions, and delayed response in traditional methods, significantly improving the dynamic adaptability and analytical accuracy of employment data processing, and helping employment service platforms provide timely and accurate personalized job recommendations and skills enhancement suggestions to job seekers. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a cloud-based smart employment data processing method provided by the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of a cloud computing-based smart employment data processing system provided by the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] refer to Figure 1 The document presents a flowchart illustrating a cloud-based smart employment data processing method provided in this application, which includes the following steps:

[0038] S1: Obtain multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; among which, multi-source heterogeneous data includes job seeker ability data and job description data in the job market.

[0039] Specifically, this method relies on a cloud computing architecture to collect heterogeneous data from various sources with different structures and characteristics. This data mainly covers two categories: first, job seeker competency data, including but not limited to detailed information such as educational background, professional skills, work experience, project experience, and certificates and honors; second, job description data from the employment market, such as specific descriptions of job responsibilities, requirements, skill preferences, work location, and salary. Through the cloud computing architecture, it can efficiently connect and interact with various data sources, enabling real-time data acquisition and dynamic updates.

[0040] To ensure consistency and accuracy in subsequent processing, the acquired multi-source heterogeneous data undergoes standardization processing, which includes, but is not limited to, the following aspects:

[0041] 1) Data format standardization: Convert data of different formats into a unified standard format. For example, standardize the date format to "YYYY-MM-DD" and the numerical format to a specific number of decimal places, so that subsequent data processing and analysis operations can be carried out smoothly and avoid errors or compatibility issues caused by format differences.

[0042] 2) Encoding standardization: For text data, a unified character encoding standard, such as UTF-8, is adopted to ensure that text information from different languages ​​and sources can be correctly displayed and processed under the same encoding system, avoiding garbled characters or unrecognizable characters, thereby ensuring the integrity and availability of the data.

[0043] 3) Data cleaning and denoising: Identify and process noisy data such as missing values, erroneous values, and outliers. For example, missing values ​​can be imputed using the mean, median, or data imputation based on machine learning algorithms; erroneous and outliers can be detected and corrected by setting reasonable data ranges and validation rules to improve data quality and reliability and ensure the accuracy of subsequent analysis results.

[0044] 4) Data Normalization: Numerical data is normalized to fall within a specific range, such as [0,1] or [-1,1]. Methods that can be used include Min-Max normalization and Z-Score normalization. This helps improve the performance and convergence speed of certain distance-based machine learning algorithms, enabling data of different dimensions and magnitudes to be compared and analyzed on the same scale, thereby improving the effectiveness and stability of the entire data processing system.

[0045] After the above standardization process, the originally messy and inconsistent multi-source heterogeneous data is organized into a standardized data set with a regular structure and uniform format.

[0046] S2: Perform distributed feature extraction on the standardized dataset to generate a dynamic feature set that includes time dimension and industry classification labels.

[0047] Specifically, for the generated standardized dataset, distributed feature extraction technology is employed, utilizing distributed computing resources under a cloud computing architecture. By dividing the data into multiple subsets and processing them in parallel across multiple computing nodes, the efficiency and speed of feature extraction are improved. For example, distributed computing frameworks such as Hadoop or Spark can be used to distribute feature extraction tasks to various nodes in the cluster. Each node independently performs feature extraction operations on its assigned subset, and finally, the extraction results from each node are integrated and summarized to obtain a complete dynamic feature set.

[0048] In the feature extraction process, we not only focus on the intrinsic characteristics of the data itself, but also incorporate the time dimension and industry classification labels to capture the dynamic characteristics of data changes over time and across industries. Specifically:

[0049] 1) Text Feature Extraction: For text-based data such as job applicant ability descriptions and job requirement descriptions, natural language processing techniques are used for feature extraction. For example, the Bag of Words model, TF-IDF (Term Frequency-Inverse Document Frequency), or more advanced deep learning methods based on word embeddings, such as Word2Vec and BERT, are used to convert text information into numerical feature vectors to represent important features such as keywords and semantic information in the text content. This allows for the extraction of relevant feature information about job applicants and job positions from the text level.

[0050] 2) Structured Data Feature Extraction: For structured data such as educational background, years of work experience, and salary level, numerical features can be directly extracted or appropriately encoded and transformed as features. For example, years of work experience can be directly used as a numerical feature, and for the education field, it can be mapped to different numerical codes, such as "high school=1, bachelor's degree=2, master's degree=3, doctorate=4", etc., to facilitate calculation and analysis in the feature space. These structured features can intuitively reflect the job seeker's and the job's requirements and matching degree in certain aspects.

[0051] 3) Incorporation of Time-Dimensional Features: Considering that job seekers' skills development and job requirements change over time, each extracted feature is assigned a timestamp or time identifier to construct a time-series feature. For example, this involves recording the skill certificates obtained by job seekers at different time periods, the start and end dates of their work experience, and the changes in job requirements at different posting periods. By analyzing these time-series features, the dynamic evolution trends of skills and job requirements can be captured, providing richer dynamic information for subsequent matching models and enabling them to better adapt to dynamic market changes.

[0052] 4) Industry Category Tagging Integration: Based on the job seeker's industry and the industry of the position, the extracted features are tagged with corresponding industry categories. This helps to differentiate the feature analysis and processing in subsequent processing, taking into account the characteristics and needs of different industries, thus improving the accuracy and relevance of the matching. For example, the information technology industry may focus more on features such as programming skills and project experience; while the finance industry may focus more on financial knowledge, risk assessment, and other abilities. Guided by industry category tags, the feature extraction and matching process can be more aligned with actual industry needs, thereby enhancing the professionalism and effectiveness of the entire smart employment data processing system.

[0053] Through the above distributed feature extraction process, a dynamic feature set containing time dimension and industry classification labels is generated, providing a comprehensive, dynamic and detailed feature foundation for the subsequent construction of professional competence profiles and job requirement profiles.

[0054] S3: Construct professional competence profiles and job requirement profiles in parallel based on dynamic feature sets, and calculate the matching degree matrix between the professional competence profiles and job requirement profiles.

[0055] Specifically, based on the generated dynamic feature set, professional competency profiles and job requirement profiles are constructed in parallel. The specific construction process is as follows:

[0056] This process integrates and abstracts various dynamic characteristics of job seekers to form a comprehensive and three-dimensional profile of their professional capabilities. This includes a comprehensive analysis and weighting of multi-dimensional characteristics such as skill features, experience, and educational background. For example, weights are assigned according to the industry's emphasis on different skills, with higher weights given to core skills and relatively lower weights to auxiliary skills. Then, the feature values ​​of each dimension are multiplied by their corresponding weights and summed to obtain a vector representation that comprehensively reflects the job seeker's professional capabilities—the professional capability profile. This profile not only reflects the job seeker's current skill level but also demonstrates their growth trajectory and development trend through changes in features over time, providing employers with deeper insights into job seeker capabilities.

[0057] Similarly, the dynamic characteristics of job descriptions are integrated and abstracted to construct a job requirement profile. This involves extracting and weighting the skill characteristics, work experience characteristics, and educational requirements of the job. Based on the general market requirements and industry standards for the job, the weights of each characteristic are determined. For example, for senior technical positions, work experience and professional skills may have higher weights, while for entry-level positions, educational background and learning ability may have relatively more weights. Through weighted calculation, a comprehensive feature vector of job requirements is obtained, i.e., the job requirement profile. This profile accurately depicts the core needs and preferences of the job, providing a clear target guide for subsequent matching with professional competency profiles.

[0058] Based on the completed professional competency profile and job requirement profile, a matching degree matrix is ​​calculated between the two. The matching degree matrix is ​​a two-dimensional matrix, where rows represent job seekers and columns represent job positions. Each element in the matrix represents the matching degree value between the corresponding job seeker and the job position. The calculation method is as follows:

[0059] Appropriate similarity metrics should be employed to calculate the degree of similarity between the professional competency profile and the job requirement profile. Common similarity metrics include cosine similarity, Euclidean distance, and Jaccard similarity coefficient. For example, cosine similarity measures the directional similarity of two vectors by calculating the cosine of the angle between them; the closer the value is to 1, the more similar the two vectors are, indicating a higher degree of match between the job seeker and the job. Euclidean distance measures the straight-line distance between two vectors in space; the smaller the distance, the higher the match. Based on the actual application scenario and data characteristics, the most suitable similarity metric should be selected to accurately reflect the matching relationship between the job seeker and the job.

[0060] For each job seeker and each job position, a similarity metric is calculated between their professional competence profile and the job requirement profile. These values ​​are then filled into the corresponding positions in the matching matrix to generate a complete matching matrix. This matrix comprehensively and intuitively displays the distribution of matching degrees between all job seekers and job positions.

[0061] S4: Extract trend features from industry trend reports, integrate these features into the matching degree matrix for dynamic updates, and generate a dynamic matching model.

[0062] Specifically, based on industry trend reports, in-depth analysis is conducted on information regarding industry development dynamics, technological innovation, and changes in talent demand to extract representative and predictive trend characteristics. These trend characteristics can include key information such as the rise of emerging skills, the decline of traditional skills, and shifts in industry requirements for talent qualities. For example, by mining and analyzing data from numerous industry analysis reports, technical forum discussions, and corporate recruitment trends, trends such as the increasing demand for specific professionals in currently popular programming languages, artificial intelligence technology application areas, and the green energy industry can be identified. These characteristics can reflect the industry's future development direction and key talent needs, providing forward-looking guidance for the dynamic updating of matching models.

[0063] The extracted trend features are organically integrated into the previous matching matrix, enabling dynamic updates to the matching matrix and generating a dynamic matching model that can adapt to industry changes. The specific process is as follows:

[0064] Determine a reasonable method for fusing trend features to organically integrate them with the original matching matrix. For example, trend features can be added as a new dimension to the professional competence profile and job requirement profile, and the similarity between the fused profiles can be recalculated to update the matching matrix. Alternatively, the weights of the original features can be adjusted based on the trend features, increasing the weight of features that align with industry development trends and decreasing the weight of those that do not, thereby reflecting the impact of industry changes on the matching relationship. This allows the matching model to more sensitively capture market dynamics and improve its adaptability and predictive accuracy.

[0065] Establish a real-time or periodic matching matrix update mechanism to ensure the dynamic matching model always keeps pace with the latest industry changes. When new industry trend reports are released or significant market changes occur, promptly extract trend characteristics and update the matching matrix according to a predetermined fusion strategy. Simultaneously, considering the timeliness and stability of data, smoothing techniques can be employed to avoid drastic fluctuations in the matching model due to isolated data points, ensuring the model's robustness and reliability. This allows the model to continuously provide accurate and effective matching services to job seekers and employers in a dynamically changing environment.

[0066] S5: By detecting changes in industry demand, structured data is generated based on a dynamic matching model to support personalized job recommendations and skills enhancement suggestions.

[0067] Specifically, by continuously monitoring industry dynamics and changes in market demand, the system utilizes big data analytics to track and analyze massive amounts of recruitment data, company updates, and industry news in real time, enabling timely detection of new changes and trends in industry talent needs. For example, when it detects a sharp increase in the number of job postings in a particular emerging technology field, and a significant rise in the frequency of specific skill requirements appearing in the descriptions, the system can quickly identify this signal of changing industry demand and use it as a crucial basis for triggering dynamic matching model updates and personalized service generation. This ensures that the system can respond to market changes in a timely manner and provide users with timely and effective decision support.

[0068] Based on the updated dynamic matching model, we provide job seekers with accurate and personalized job recommendations. The specific implementation is as follows:

[0069] Based on the matching scores between job seekers and various positions calculated by the dynamic matching model, the positions are sorted from highest to lowest. This is further refined by considering the job seeker's personal preferences, geographical limitations, salary expectations, and other filtering criteria to create a list of high-quality job recommendations that meet the job seeker's needs. For example, for a job seeker located in Beijing, expecting a salary of 15-20K, proficient in Python programming, and interested in artificial intelligence, the system will prioritize recommending highly matched positions located in Beijing, with salaries that meet the requirements, and related to Python programming and AI technologies. This increases the job seeker's chances of finding a suitable position and saves them time and effort sifting through a vast amount of job information.

[0070] Personalized job recommendations are presented to job seekers in an intuitive and user-friendly manner, such as displaying key information like job title, company name, salary range, and work location in a list format, along with links to detailed job descriptions for further information. The system also allows job seekers to provide feedback on the recommendations, such as marking their interest, disinterest, or application status. Based on this feedback, the recommendation algorithm and model parameters are further optimized to continuously improve the recommendation service and enhance personalization, thereby increasing user engagement and satisfaction.

[0071] In addition to job recommendations, the system also provides job seekers with targeted skills enhancement suggestions based on a dynamic matching model and industry trend analysis, helping them better adapt to market demands and improve their competitiveness in the job market. The specific implementation is as follows:

[0072] By comparing job seekers' professional competency profiles with the requirements of target positions, the system accurately identifies gaps and deficiencies in the job seeker's skills, knowledge, and experience. For example, if a job seeker is applying for a data analyst position that requires proficiency in data mining algorithms and data visualization tools, but the job seeker currently only possesses basic data processing skills, the system will automatically identify data mining algorithms and data visualization tools as the job seeker's weakness. This provides a basis for subsequent skills improvement suggestions, ensuring the accuracy and practicality of the recommendations and helping the job seeker clarify the direction and focus of their improvement.

[0073] To address identified skill gaps, the system integrates a wealth of learning resources, such as online courses, training materials, and practical projects, to recommend the most suitable resources for job seekers' learning and improvement, and to plan reasonable learning paths. For example, for job seekers who need to improve their data mining skills, the system can recommend a series of courses from basic theoretical learning to practical project applications, including explanations of data mining algorithm principles, tutorials on using Python data mining libraries (such as Scikit-learn), and real-world e-commerce data mining projects. This helps job seekers systematically learn and master new skills, while also providing learning progress tracking and learning effectiveness evaluation functions to motivate job seekers to continuously learn and improve, ultimately achieving the goals of skill enhancement and successful employment, providing comprehensive support and guarantees for job seekers' career development.

[0074] The aforementioned cloud-based intelligent employment data processing method integrates multi-source heterogeneous data and constructs a standardized dataset through a cloud computing architecture; it utilizes distributed feature extraction technology to generate a dynamic feature set with time dimensions and industry labels; it combines parallel profile construction and dynamic matching degree matrix calculation to achieve accurate mapping between professional skills and job requirements; and it further optimizes the matching model by incorporating real-time industry trend features; finally, by detecting changes in industry demand, it generates structured data that supports personalized job recommendations and skills enhancement suggestions based on the dynamic matching model. This method addresses the problems of poor real-time performance, limited dimensions, and delayed response in traditional methods, significantly improving the dynamic adaptability and analytical accuracy of employment data processing, and helping employment service platforms provide job seekers with timely and accurate personalized job recommendations and skills enhancement suggestions.

[0075] In an optional embodiment, S4 includes the following steps:

[0076] S41: Extract the frequency change rate of each skill term from the industry trend report. When the frequency change rate of a skill term exceeds the preset change rate threshold, mark the skill feature corresponding to the skill term as a high-impact feature.

[0077] Specifically, from industry trend reports, text analysis and information extraction methods in natural language processing are used to identify and extract the frequency of occurrence and the rate of change of each skill term. By comparing the number of times skill terms appear in the report over different time periods, their rate of change is calculated to quantify the changing trend of skill term popularity in the industry.

[0078] A preset threshold for the rate of change is set, which can be determined based on industry characteristics, market fluctuations, and historical data experience. When the frequency of change of a skill term exceeds this threshold, it indicates a significant change in the demand or importance of that skill in the industry, which may have a significant impact on the job market. At this point, the skill feature corresponding to the skill term is marked as a high-impact feature, so that it can be given special attention and processing in subsequent matching model adjustments. This ensures that the model can adapt to dynamic changes in the industry in a timely manner, improving its sensitivity and responsiveness to emerging skill demands.

[0079] S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate a demand topic distribution. ;in, Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , This indicates the number of demand topics.

[0080] Specifically, Latent Dirichlet Allocation (LDA) topic modeling is performed on the job data in the dynamic feature set. LDA is a probabilistic topic model capable of mining latent topic structures from large-scale document collections. In this step, the algorithm treats the job data as a collection of documents generated from a mixture of different topics, and estimates the demand topic distribution for each job through iterative training. ,in Indicates the number of demand topics. Indicates the first One position.

[0081] Demand Theme Distribution Description of the The degree of preference for different themes for each job position. Among them, Indicates the first The first position Demand weight for each demand topic Demand weights reflect the importance that the position places on specific topic-related skills and requirements. The probability distribution estimated by the LDA model can comprehensively depict the multidimensional demand structure of the position.

[0082] S43: Based on the high-impact characteristics, adjust the distribution of demand themes to obtain a dynamic distribution of demand themes.

[0083] Specifically, the previously generated demand theme distribution is adjusted based on the identified high-impact features. Specifically, for demand themes related to high-impact features, their weight is appropriately increased or their proportion in the theme distribution is adjusted to reflect the importance and changing demand of these themes under current industry trends. For example, if an emerging skill is marked as a high-impact feature, and this skill corresponds to a specific demand theme, then during the adjustment, the weight of this demand theme in the job demand theme distribution can be increased, making the job demand profile more inclined to emphasize this emerging skill area, thereby better aligning with the market's urgent need for this skill.

[0084] Specific algorithms or mathematical models are employed to adjust the distribution of demand topics. This may involve recalculating probability distributions, updating matrix factorization, or utilizing online learning algorithms in machine learning to enable the model to quickly adapt to new changes in feature weights. These methods ensure that the adjusted distribution of demand topics accurately and promptly reflects the impact of industry trends on job requirements, providing a foundation for generating dynamic matching models that better reflect actual market conditions and improving the system's matching accuracy and effectiveness in dynamic environments.

[0085] S44: Generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as a dynamic matching model.

[0086] Specifically, based on the adjusted demand theme distribution, the matching relationship between professional competency profiles and job requirement profiles is recalculated, generating a new matching matrix, namely the dynamic matching matrix. This matrix reflects the impact of the latest industry trends on job matching, more accurately reflecting which professional competencies and job requirements have a higher degree of fit in the current market environment. Compared with the original matching matrix, the dynamic matching matrix incorporates the perception and response to dynamic changes in the industry, which can better guide job seekers in personalized job recommendations and skills enhancement plans, while helping employers to more accurately screen suitable talent.

[0087] The generated dynamic matching matrix serves as the core component of the updated dynamic matching model, replacing the previous model. In practical applications, the system will use this dynamic matching model to provide job seekers with job recommendations that better align with actual market needs, and to offer employers more precise talent matching support. By regularly extracting information from industry trend reports and updating the dynamic matching model, the system can maintain sensitivity and adaptability to market dynamics, continuously improve service quality and user satisfaction, promote the efficient operation of the job market and the rational allocation of talent resources, and drive the healthy development and innovative progress of the entire industry.

[0088] In one optional embodiment, S1 includes the following steps:

[0089] S11: Obtain skill feature sets from job search platforms This serves as data on job seekers' abilities; each skill characteristic... This includes skill codes, skill weights, and skill proficiency. For skill serial numbers, This represents the total number of skills.

[0090] Specifically, by establishing a stable connection and data interaction interface with job search platforms, the system obtains a set of skill characteristics stored on the platform. This collection serves as a core source of job seeker competency data, encompassing detailed information on each job seeker's various skills.

[0091] gather Each skill feature It is a comprehensive data structure containing three key elements: skill code, skill weight, and skill proficiency. The skill code is a unique identifier for a specific skill; for example, the programming language Python might be assigned the specific code "PY". The skill weight reflects the relative importance of the skill within the job seeker's overall competence, typically ranging from 0 to 1, with higher values ​​indicating greater importance to the job seeker's professional image. Skill proficiency is a quantitative assessment of the job seeker's mastery of the skill, which can be expressed through level classifications (e.g., beginner, intermediate, advanced) or numerical scores (e.g., 1-10). These are skill serial numbers used to distinguish different skills recorded by the same job seeker. The total number of skills indicates the complete number of skill items that the job seeker currently has registered in the system.

[0092] S12: Obtain a set of job descriptions from recruitment platforms using distributed web crawlers. This serves as job description data in the employment market; among which, each job description... This includes job ID, skill keyword set, and industry classification. For job serial number, This represents the total number of positions.

[0093] Specifically, by leveraging a distributed web crawler system, massive amounts of job description information are proactively and selectively scraped from various recruitment platforms, thereby constructing a set of job descriptions. This collection forms the basic framework for job description data in the employment market.

[0094] gather Each job description It consists of three parts: Job ID, Skill Keyword Set, and Industry Category. The Job ID is a unique identifier used to accurately identify each job posting, facilitating precise location and differentiation within a large dataset. The Skill Keyword Set is a collection of words reflecting the core skills required for the job; these keywords are directly related to the key abilities required of job seekers and serve as an important reference for the system in matching job applicants to candidates. The Industry Category clearly defines the specific industry sector to which the job belongs, helping the system implement more precise and personalized matching strategies based on the characteristics and needs of different industries in subsequent processing. These are job serial numbers, used to sequentially arrange and distinguish different job records. The total number of job postings represents the total number of job postings collected by the system at present.

[0095] S13: Store the skill feature set and job description set to a distributed storage cluster, and generate a sharded index with timestamps and industry tags.

[0096] Specifically, the skill feature set and job description set are integrated and stored in a high-efficiency, stable distributed storage cluster. This cluster consists of multiple servers, and the data is divided into multiple data blocks and distributed across different server nodes, thereby achieving high scalability and high availability of data storage.

[0097] During storage, the system shards the data according to specific rules and generates a shard index with timestamps and industry tags for each shard. The timestamp accurately records the generation or update time of the shard data; the industry tag clearly defines the industry category to which the shard data belongs, enabling the system to quickly focus on industry-specific data during data retrieval and processing.

[0098] S14: Perform Z-Score normalization on numeric fields in the sharded index to generate normalized numeric fields.

[0099] Specifically, for numeric fields in the sharded index, the Z-Score standardization method from statistics is used for data transformation. This method is based on the mean and standard deviation of the original data. By calculating the difference between each numeric point and the mean and dividing it by the standard deviation, it transforms the data into standard normal distribution data with a mean of 0 and a standard deviation of 1.

[0100] The system automatically performs batch Z-Score standardization on the numeric fields identified in the sharded index. First, it calculates the mean and standard deviation of each numeric field; then, it substitutes the original values ​​in each field into the Z-Score formula to generate the corresponding standardized values; finally, it replaces the original values ​​with these standardized values ​​and stores them back in the sharded index to ensure that the numeric data is comparable and stable in subsequent processing.

[0101] S15: Perform TF-IDF vectorization on text fields in the sharded index to generate vectorized text features.

[0102] Specifically, for text fields in the sharded index, the TF-IDF (Term Frequency-Inverse Document Frequency) vectorization method is used to transform them into numerical feature vectors. TF-IDF is a statistical method for measuring the importance of words in text. It considers both the frequency (TF) of a word in a single text and the inverse document frequency (IDF) of that word in the entire text set. TF represents the ratio of the number of times a word appears in the text to the total number of words in the text, while IDF measures the generality of a word, and is taken as the logarithm of the ratio of the total number of texts in the text set to the number of texts containing that word.

[0103] The specific steps of the system to perform TF-IDF vectorization processing on text fields are as follows: First, the text is preprocessed, including stop word removal and stemming; then, the TF value of each word in the text is calculated; next, the IDF value of each word is calculated in combination with the entire text set; finally, the TF value and IDF value are multiplied to obtain the TF-IDF weight of each word, thereby constructing a vectorized text feature vector that can reflect the text features, so that text data can be effectively applied in numerical computation and machine learning algorithms.

[0104] S16: Perform imputation processing on missing fields in the sharded index to generate an imputed data set.

[0105] Specifically, the system comprehensively scans each field in the sharded index, and accurately identifies fields with missing values ​​by setting data integrity rules and NOT NULL constraints. For numeric fields, if the value is empty or exceeds a reasonable range, it is determined to be missing; for text fields, if the text content is an empty string or contains only invalid characters, it is determined to be missing.

[0106] Based on the data type and business semantics of the missing fields, the system intelligently selects an appropriate imputation method. For numerical missing fields, methods such as mean imputation, median imputation, or predictive imputation based on machine learning algorithms are commonly used. For textual missing fields, strategies such as mode imputation, fixed value imputation, or imputation based on related fields can be used. The system automatically executes the selected imputation operation, filling the missing fields with reasonable substitute values ​​and generating an imputed dataset, ensuring data integrity and avoiding deviations or errors in subsequent processing due to missing values.

[0107] S17: Integrate the interpolated dataset with the standardized numerical fields and vectorized text features to generate a standardized dataset.

[0108] Specifically, a unified data integration framework is constructed based on the imputed dataset. Within this framework, the relationships and mapping rules among the various data components are clearly defined, ensuring that different types of data can be organically integrated within the same framework.

[0109] Standardized numerical fields and vectorized text features are integrated one-to-one into the corresponding positions or associated records of the imputed dataset according to preset data structures and format requirements. Through data linking, field mapping, and other technical means, scattered data of different natures are integrated into a complete, coherent, and logically consistent whole in the integrated dataset, ultimately generating a standardized dataset. This provides unified, standardized, and comprehensive data support for subsequent operations such as feature extraction, profile construction, and matching calculations, ensuring the smooth progress and efficient execution of the entire smart employment data processing workflow.

[0110] In an optional embodiment, S2 includes the following steps:

[0111] S21: Perform sharding on the standardized dataset using the MapReduce framework to generate sharded datasets with time windows.

[0112] Specifically, the MapReduce distributed computing framework is used to shard standardized datasets. The MapReduce framework can divide large datasets into multiple smaller subsets and process them in parallel on different nodes in the cluster, thereby greatly improving data processing efficiency.

[0113] During the sharding process, a specific time window is set for each shard dataset. Setting a time window helps capture the dynamic changes of data over different time periods. For example, time windows can be set by week, month, or quarter, so that each shard dataset contains data within a specific time range, providing a foundation for subsequent analysis of the timeliness and evolution trends of the data.

[0114] S22: Calculate the skill ability density for each data segment and generate a dynamic ability feature set based on the skill ability density; the formula for calculating the skill ability density is:

[0115] ;

[0116] in For the first The importance rating of each skill This is a function that indicates core skills.

[0117] Specifically, skill density The calculation formula is: .in, For the first The importance score of each skill reflects the relative importance of that skill in the overall skill system. Its value can be determined based on industry standards, enterprise needs surveys or expert evaluations. This is a core skill indicator function used to determine the first... Is this skill a core skill? If it is a core skill, then The value is 1 if it is true, and 0 otherwise. ,Will The value is set to 0.

[0118] Based on the calculated skill ability density This generates a dynamic competency feature set. This feature set not only includes the original skill characteristics but also incorporates the dynamic changes in the importance and core nature of skills across different time windows and industry contexts, thus enabling a more accurate depiction of the dynamic evolution of job seekers' abilities.

[0119] S23: Calculate the job skill update rate for each shard dataset, and generate a dynamic job feature set based on the job skill update rate.

[0120] Specifically, the job skills update rate is calculated by comparing the set of job skills keywords in the current time window with that in the previous time window. It is an indicator reflecting the degree to which job skills requirements change over time. Specifically, it first counts the number of newly added and disappeared skill keywords in the current time window; then, combined with the total number of job skills keywords, it calculates the job skills update rate. This update rate reflects the speed and extent of dynamic changes in job skills requirements between different time windows.

[0121] The calculation process for the job skills update rate is as follows:

[0122] 1) Determine the time window: Choose a suitable time window, such as a month or a quarter, as the basis for comparison.

[0123] 2) Collect skill keyword data: Collect skill keyword sets for all positions in the current time window and the previous time window respectively.

[0124] 3) Calculate the number of newly added skill keywords: The number of newly added skill keywords refers to the number of skill keywords that appear in the current time window but did not appear in the previous time window.

[0125] 4) Calculate the number of disappearing skill keywords: The number of disappearing skill keywords refers to the number of skill keywords that appeared in the previous time window but did not appear in the current time window.

[0126] 5) Calculate the total number of skill keywords: The total number of skill keywords refers to the sum of the skill keyword sets for all positions within the current time window.

[0127] 6) Calculate the job skills update rate: The job skills update rate can be calculated using the following formula:

[0128] ;

[0129] This formula represents the ratio of the sum of newly added and disappeared skill keywords to the total number of skill keywords, reflecting the degree of change in job skill requirements.

[0130] Based on the calculated job skills update rate, a dynamic job feature set is generated. This feature set, in addition to containing the original job description information, further reflects the dynamic updating characteristics of job skill requirements. It can promptly capture the evolutionary trends of job demands driven by market changes and technological developments, providing more practical job feature descriptions for dynamic matching of jobs and job seekers.

[0131] S24: Aggregate the dynamic capability feature set and the dynamic job feature set by industry tags to generate a dynamic feature set.

[0132] Specifically, the dynamic competency feature set and the dynamic job feature set are aggregated according to industry labels, which means integrating and summarizing the dynamic competency features and dynamic job features from different data segments within the same industry. This approach takes into account the unique requirements and differences in skill and job features across different industries, making the final dynamic feature set more industry-specific and professional.

[0133] After aggregation by industry tags, a final dynamic feature set is generated. This dynamic feature set comprehensively integrates the dynamic characteristic information of job seekers' abilities and job requirements under different industry backgrounds. It not only includes static descriptions of skills and job characteristics, but more importantly, reflects the dynamic changing trends of these characteristics over time, industry, and other factors. This provides a high-quality, dynamic data foundation for subsequent steps in cloud-based smart employment data processing methods, such as constructing professional ability profiles and job requirement profiles, calculating matching degree matrices, and generating dynamic matching models. This helps improve the matching accuracy and dynamic adaptability of the entire smart employment system, thereby better serving job seekers and employers, promoting the efficient operation of the employment market, and the rational allocation of talent.

[0134] In an optional embodiment, S21 includes the following steps:

[0135] S211: Perform sliding window segmentation on the standardized dataset according to a preset time window to generate a window-sliced ​​dataset.

[0136] Specifically, the appropriate time window length can be set according to specific business needs and data update frequency, such as a week, a month, or a quarter. This time window will serve as the basic unit for sliding window segmentation, used to partition the standardized dataset.

[0137] A sliding window technique is used to segment a standardized dataset. Specifically, starting from the beginning of the dataset's timeframe, data is extracted according to a pre-defined time window length, generating the first window-splitter dataset. Then, the window slides with a step size (which may be the same as or different from the time window length) to extract the next window-splitter dataset, until the entire time range of the dataset is covered. For example, if the time window length is one month and the step size is one week, the data for each month will be divided into four window-splitter datasets, each containing one week's worth of data, with some overlap between adjacent splits. This sliding window segmentation method helps capture the dynamic changes in data over different time periods, while smoothing out data fluctuations and improving the stability and accuracy of subsequent analysis.

[0138] S212: Calculate the skill-job co-occurrence matrix for each window segment dataset, and generate association strength data based on the skill-job co-occurrence matrix; the association strength data represents the association strength between skills and jobs.

[0139] Specifically, for each window-segmented dataset, the co-occurrence of skills and job positions is statistically analyzed, and a skill-job co-occurrence matrix is ​​constructed. Rows in the matrix represent different skills, columns represent different job positions, and elements in the matrix indicate the frequency or intensity of the skill's appearance in that job position. For example, if a skill appears frequently in the description of a job position, the corresponding matrix element value will be larger, indicating a higher degree of association between the skill and the job position.

[0140] Based on the constructed skill-job co-occurrence matrix, association strength data is generated. This data can be the result of normalizing co-occurrence frequencies, or a numerical value calculated using other statistical methods that reflects the strength of the association between skills and jobs. For example, the relative importance of a skill within a job can be obtained by dividing the co-occurrence frequency by the total number of skills in that job; alternatively, more complex statistical indicators, such as Point Mutual Information (PMI), can be used to measure the strength of the association between skills and jobs. This association strength data can quantitatively represent the correlation between skills and jobs, providing a foundation for subsequent feature extraction and analysis.

[0141] S213: Perform singular value decomposition on the correlation strength data to generate low-dimensional feature vectors.

[0142] Specifically, Singular Value Decomposition (SVD) is performed on the generated association strength data matrix. SVD is a commonly used matrix factorization technique that can decompose the original data matrix into the product of three matrices, i.e. ,in and It is an orthogonal matrix. It is a diagonal matrix, with the elements on the diagonal being singular values. By selecting the left and right singular vectors corresponding to the larger singular values, a low-dimensional approximation of the original data matrix can be achieved while preserving the main features and information in the data.

[0143] After performing singular value decomposition (SVD), based on a preset dimensionality reduction ratio or threshold, the k largest singular values ​​and their corresponding singular vectors are selected to construct low-dimensional feature vectors. For example, if the original correlation strength data matrix has a dimension of m×n, after SVD decomposition, the left singular vectors corresponding to the k largest singular values ​​are selected to form an m×k low-dimensional feature vector matrix, or the right singular vectors are selected to form an n×k low-dimensional feature vector matrix, depending on the focus and requirements of the analysis. These low-dimensional feature vectors can reduce the data dimensionality while preserving the correlation characteristics between skills and positions to the greatest extent, improving the efficiency of data processing and the generalization ability of the model.

[0144] S214: Associate the low-dimensional feature vectors with the windowed dataset according to the time window to generate a sharded dataset with time windows.

[0145] Specifically, the generated low-dimensional feature vectors are associated with the corresponding window-sliced ​​datasets according to time windows. In other words, each window-sliced ​​dataset is merged or linked with its corresponding low-dimensional feature vector, so that each data record in the window-sliced ​​dataset is associated with a corresponding feature value in the low-dimensional feature vector. For example, additional columns can be added to the window-sliced ​​dataset to store the various feature values ​​of the low-dimensional feature vectors, thereby enriching the feature dimensions of the dataset and providing more comprehensive and representative feature information for subsequent analysis and model training.

[0146] After the aforementioned association operations, a sharded dataset with time windows is generated. This dataset not only contains the original standardized data but also incorporates low-dimensional feature vector information obtained through singular value decomposition, and each data record is clearly identified to its corresponding time window. This sharded dataset with time windows can better reflect the dynamic changes of data over time, and at the same time, the expressive power of low-dimensional feature vectors improves the efficiency and accuracy of data in model training and prediction. In subsequent cloud-based smart employment data processing methods, this sharded dataset with time windows will serve as important input data to construct dynamic ability feature sets and dynamic job feature sets, thereby achieving dynamic characterization and accurate matching of job seekers' abilities and job requirements, improving the performance and user experience of the entire smart employment system, and promoting the efficient operation of the job market and the rational allocation of human resources.

[0147] In an optional embodiment, S3 includes the following steps:

[0148] S31: Construct a professional competency profile, including:

[0149] DBSCAN clustering analysis is performed on the skill data in the dynamic feature set to generate professional competency clusters. .

[0150] Calculate the central vector of each professional competency cluster within the professional competency cluster set, and use the central vector as a professional competency profile; the formula for calculating the central vector is:

[0151] ;

[0152] in, Indicates the first The central vector of a cluster of professional capabilities; Indicates the first Each professional competency cluster includes the aforementioned skill characteristics. , , Indicates the number of professional skill clusters; Indicates the first Skill characteristics described in each professional competency cluster Quantity; Indicate skill characteristics The TF-IDF vector representation.

[0153] Specifically, DBSCAN clustering analysis is performed on the skill data in the dynamic feature set. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based spatial clustering algorithm that can effectively discover clusters of arbitrary shapes and is robust to noisy data. In this step, the algorithm groups skill data points according to their density distribution in the feature space, generating professional competency clusters. ,in This indicates the number of professional skill clusters.

[0154] For each generated professional capability cluster Calculate its center vector The formula for calculating the center vector is: .in, Indicates the first Skill characteristics within a professional competency cluster Quantity; Indicate skill characteristics of The vector representation, which comprehensively considers the frequency of occurrence (TF) of a skill in a document and its inverse document frequency (IDF) in the overall corpus, reflects the importance and uniqueness of the skill. The center vector is obtained by calculating the average of all skill feature vectors within the cluster. This vector, serving as a professional competency profile, can accurately depict the core features and skill distribution of the professional competency cluster, providing a precise competency representation for subsequent matching calculations.

[0155] S32: Distribute demand themes This serves as a profile of job requirements; among them... Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , This indicates the number of demand topics.

[0156] S33: Obtain the topic skill mapping matrix, calculate the cosine similarity between the professional ability clusters and the demand topic distribution based on the topic skill mapping matrix, and generate an initial matching degree matrix based on the cosine similarity. The formula for calculating the cosine similarity is:

[0157] ;

[0158] in, This is a topic-skill mapping matrix, representing the relationship between each requirement topic and skills; This represents the product of the topic skill mapping matrix and the transpose of the demand topic distribution.

[0159] Specifically, thematic skill mapping matrix It is a matrix representing the relationship between each demand topic and skill. This matrix is ​​constructed using statistical analysis or machine learning methods, where each element... Indicates the first The first demand topic and the first The strength of the correlation between skills can be measured, for example, by calculating the correlation between the distribution of demand topics and the feature vectors of skills, or by training with supervised learning algorithms, to accurately reflect the intrinsic connection between topics and skills.

[0160] Based on the topic-skill mapping matrix M, the cosine similarity between professional ability clusters and the distribution of required topics is calculated. The formula for calculating the cosine similarity is: .in, It is the first The central vector of a cluster of professional capabilities; It is the first The distribution of job requirements by topic; Represents the topic skill mapping matrix Distribution of demand themes Multiplying by the transpose of the vector yields a vector mapping the topic distribution to the skill space. Cosine similarity measures the similarity between professional skill clusters and job requirements at the skill topic level; the closer the value is to 1, the more similar the two are. Based on the calculated cosine similarity, an initial matching degree matrix is ​​generated. The rows of this matrix represent professional skill clusters, the columns represent job positions, and the element values ​​are the corresponding similarity scores. This provides basic data support for subsequent matching optimization, helping the system to initially understand the matching relationship between professional skills and job requirements.

[0161] S34: When the similarity value in the initial matching matrix is ​​lower than the preset similarity threshold, the initial matching matrix is ​​optimized to generate an optimized matching matrix, which serves as the matching matrix between the professional competence profile and the job requirement profile, including:

[0162] Extract feature weight vectors from the topic skill mapping matrix ,in Indicates the first Feature weights of each skill Indicates the number of skills.

[0163] The feature weight vector is analyzed using the gradient descent method. Perform iterative optimization to generate an optimized weight vector.

[0164] The topic skill mapping matrix is ​​updated based on the optimized weight vector, and an optimized matching degree matrix is ​​generated based on the updated topic skill mapping matrix.

[0165] Specifically, when the similarity value in the initial matching matrix is ​​lower than the preset similarity threshold, it indicates that the current matching may be insufficient, and the initial matching matrix needs to be optimized. First, feature weight vectors are extracted from the topic-skill mapping matrix. ,in Indicates the first Feature weights of each skill This represents the number of skills. The feature weight vector reflects the importance of different skills in the topic mapping.

[0166] Gradient descent is used to apply the feature weight vector Perform iterative optimization. Gradient descent is a commonly used optimization algorithm that calculates the gradient of the objective function (e.g., the matching error function) with respect to the weight vector, updates the weight values ​​along the negative gradient direction, and gradually decreases the objective function value to find the optimal weight vector. In each iteration, the weight vector is adjusted based on the preset learning rate and gradient information. The values ​​of each element enable the optimized weight vector to better reflect the actual contribution of skills in matching, thereby improving the accuracy of the matching.

[0167] Update the topic skill mapping matrix based on the optimized feature weight vector. Substitute the new weight values ​​into the matrix. The corresponding positions in the matrix are then used to replace the original weight values, resulting in an updated topic skill mapping matrix. Based on this updated matrix, the cosine similarity between the professional skill clusters and the job requirement profiles is recalculated, generating an optimized matching matrix. Compared to the initial matching matrix, this optimized matching matrix more accurately reflects the degree of matching between professional skills and job requirements, effectively improving the quality and reliability of the matching. It recommends jobs that better match job seekers' abilities and helps employers select talent that better fits their job requirements, promoting efficient matching in the job market and the rational flow of talent resources.

[0168] In an optional embodiment, S5 includes the following steps:

[0169] S51: When the job skill update rate exceeds the preset update rate threshold, perform matrix decomposition update on the dynamic matching matrix to generate the updated matching matrix.

[0170] Specifically, the system monitors the job skills update rate in real time. When the rate exceeds a preset update rate threshold, it triggers the dynamic matching matrix update process. The preset update rate threshold is determined based on industry dynamics, market changes, and historical data to judge whether job skills changes have reached a point where the matching model needs to be updated.

[0171] Perform matrix factorization updates on the dynamic matching matrix. Matrix factorization is a mathematical method that decomposes a matrix into a product of multiple matrices, and it is widely used in recommender systems and matching models. The specific steps are as follows:

[0172] 1) Original matrix decomposition: Decompose the original dynamic matching matrix into the product of two or more matrices, such as the user feature matrix and the job feature matrix. These matrices represent the weights and attributes of users and jobs in different feature dimensions, respectively.

[0173] 2) Update the user feature matrix: Based on the latest job skill updates and user feedback data, adjust the corresponding elements in the user feature matrix to reflect users' mastery of new skills and changes in their needs.

[0174] 3) Update the job feature matrix: Combine job skill updates with company recruitment dynamics to update the job feature matrix to include the latest job skill requirements and preferences.

[0175] 4) Reconstruct the dynamic matching matrix: Multiply the updated user feature matrix and job feature matrix again to obtain the updated dynamic matching matrix. This matrix can better reflect the matching relationship between users and jobs in the current market environment, providing a more accurate basis for subsequent personalized recommendations and skills improvement suggestions.

[0176] S52: Perform decision tree analysis on the updated matching matrix to generate a set of personalized job recommendations and skills enhancement suggestions.

[0177] Specifically, a decision tree analysis algorithm is applied to the updated matching matrix. A decision tree is a supervised learning method that uses a tree structure to represent the decision-making process and prediction results. In this step, the algorithm uses user features, job features, and matching results from the matching matrix as training data to learn and build a decision tree model.

[0178] Decision rules are extracted from the trained decision tree model to generate a set of personalized job recommendations and skills enhancement suggestions. Each rule represents the job recommendation or skills enhancement suggestion to be given under specific user and job characteristics. For example, a rule can be expressed as: "If a user possesses skills A and B and has X years of work experience, then job Y is recommended, and skills C should be enhanced." These rule sets, by integrating information from the updated matching matrix and the decision logic of the decision tree model, can provide users with accurate and personalized job recommendations and skills enhancement guidance, helping them better adapt to market changes and improve their competitiveness.

[0179] S53: Convert the set of rules for personalized job recommendations and skills enhancement suggestions into a structured data format to generate structured data that supports personalized job recommendations and skills enhancement suggestions.

[0180] Specifically, the generated set of personalized job recommendations and skills enhancement suggestions rules will be converted into a structured data format. Structured data formats can include relational database tables, JSON, XML, etc. These formats facilitate data exchange and sharing between different systems and applications, and are easy to process and analyze subsequently.

[0181] During the transformation process, each rule is broken down into multiple structured fields, such as user characteristic fields, job characteristic fields, recommended job fields, and skills enhancement suggestion fields, which are then populated and stored according to a preset structured format. The generated structured data will serve as the core data source supporting personalized job recommendations and skills enhancement suggestions, and will be integrated into the smart employment service platform. When users use the platform, the system will quickly retrieve and match relevant job recommendations and skills enhancement suggestions from the structured data based on the user's specific characteristics and needs, presenting them to the user in an intuitive and user-friendly manner. This helps users make more informed career choices and plans, improves user experience and service quality, and promotes efficient matching in the job market and the rational flow of talent.

[0182] The aforementioned cloud-based intelligent employment data processing method constructs a multi-source heterogeneous data integration and standardized processing system under a cloud computing architecture. It employs distributed feature extraction technology to generate dynamic feature sets by fusing time dimensions and industry classification labels. Based on parallel clustering analysis and topic modeling, it constructs dual profiles of professional skills and job requirements. Combining cosine similarity matching and dynamic weight optimization, it achieves accurate correlation mapping and continuously updates the matching model by incorporating real-time industry trend features. Finally, it generates structured data supporting personalized job recommendations and skills enhancement suggestions through decision tree analysis. This method addresses the shortcomings of traditional methods in terms of data processing timeliness, multi-dimensional correlation analysis, and dynamic adaptability, significantly improving the matching efficiency and accuracy of the job market and talent cultivation. It achieves dynamic optimization across the entire chain from data collection to decision output, helping employment service platforms provide job seekers with timely and accurate personalized job recommendations and skills enhancement suggestions.

[0183] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0184] Based on the same inventive concept, this application also provides a system for implementing the cloud computing-based smart employment data processing method described above. The solution provided by this system is similar to the implementation scheme described in the above method; therefore, the specific limitations of one or more cloud computing-based smart employment data processing system embodiments provided below can be found in the above-described limitations of a cloud computing-based smart employment data processing method, and will not be repeated here.

[0185] In one exemplary embodiment, such as Figure 2 As shown, a cloud-based intelligent employment data processing system 20 is provided, comprising:

[0186] The data standardization module 21 is used to acquire multi-source heterogeneous data through cloud computing architecture, perform standardization processing on the multi-source heterogeneous data, and generate a standardized data set; among which, the multi-source heterogeneous data includes job seeker ability data and job description data in the job market.

[0187] The dynamic feature extraction module 22 is used to perform distributed feature extraction on a standardized dataset to generate a dynamic feature set containing time dimension and industry classification labels.

[0188] The dual-profile matching module 23 is used to construct professional competence profiles and job requirement profiles in parallel based on dynamic feature sets, and to calculate the matching degree matrix between the professional competence profiles and job requirement profiles.

[0189] The trend fusion and update module 24 is used to extract trend features based on industry trend reports, integrate the trend features into the matching degree matrix for dynamic updates, and generate a dynamic matching model.

[0190] The Employment Recommendation Module 25 is used to generate structured data that supports personalized job recommendations and skills enhancement suggestions based on a dynamic matching model by detecting changes in industry demand.

[0191] The trend fusion update module 24 includes:

[0192] The high-impact feature extraction unit is used to extract the frequency change rate of each skill word from the industry trend report. When the frequency change rate of a skill word exceeds a preset change rate threshold, the skill feature corresponding to the skill word is marked as a high-impact feature.

[0193] The demand topic distribution generation unit is used to perform LDA topic modeling on job data in a dynamic feature set to generate a demand topic distribution. ;in, Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , This indicates the number of demand topics.

[0194] The demand theme distribution adjustment unit is used to adjust the demand theme distribution based on high-impact characteristics to obtain a dynamic demand theme distribution.

[0195] The dynamic matching model generation unit is used to generate a dynamic matching matrix based on the dynamic demand topic distribution, and the dynamic matching matrix is ​​used as the dynamic matching model.

[0196] Optionally, data standardization module 21 includes:

[0197] The job seeker ability data acquisition unit is used to obtain a set of skill features from the job search platform. This serves as data on job seekers' abilities; each skill characteristic... This includes skill codes, skill weights, and skill proficiency. For skill serial numbers, This represents the total number of skills.

[0198] The job description data acquisition unit is used to obtain a set of job descriptions from recruitment platforms through distributed web crawlers. This serves as job description data in the employment market; among which, each job description... This includes job ID, skill keyword set, and industry classification. For job serial number, This represents the total number of positions.

[0199] The data storage and index generation unit is used to store skill feature sets and job description sets to a distributed storage cluster and generate sharded indexes with timestamps and industry tags.

[0200] The numeric field normalization unit is used to perform Z-Score normalization on numeric fields in a sharded index to generate normalized numeric fields.

[0201] The text field vectorization unit is used to perform TF-IDF vectorization processing on text fields in the sharded index to generate vectorized text features.

[0202] The missing field imputation unit is used to perform imputation processing on missing fields in the sharded index and generate an imputed data set.

[0203] The data integration unit is used to integrate the imputed data set with standardized numerical fields and vectorized text features to generate a standardized data set.

[0204] Optionally, the dynamic feature extraction module 22 includes:

[0205] The time window sharding processing unit is used to perform sharding processing on a standardized dataset using the MapReduce framework, generating sharded datasets with time windows.

[0206] The dynamic capability feature extraction unit is used to calculate the skill capability density of each data segment and generate a dynamic capability feature set based on the skill capability density; the formula for calculating the skill capability density is:

[0207] ;

[0208] in For the first The importance rating of each skill This is a function that indicates core skills.

[0209] The dynamic job feature extraction unit is used to calculate the job skill update rate for each segment of the dataset and generate a dynamic job feature set based on the job skill update rate.

[0210] The feature aggregation unit is used to aggregate the dynamic capability feature set and the dynamic job feature set according to industry tags to generate a dynamic feature set.

[0211] Optionally, the time window segmentation processing unit includes:

[0212] The sliding window segmentation subunit is used to perform sliding window segmentation on the standardized dataset according to a preset time window, generating a window-sliced ​​dataset.

[0213] The association strength calculation subunit is used to calculate the skill-job co-occurrence matrix for each window segment dataset, and generate association strength data based on the skill-job co-occurrence matrix; the association strength data represents the association strength between skills and jobs.

[0214] The feature dimensionality reduction subunit is used to perform singular value decomposition on the correlation strength data to generate low-dimensional feature vectors.

[0215] The time window association subunit is used to associate low-dimensional feature vectors with window-sliced ​​datasets according to time windows, generating sliced ​​datasets with time windows.

[0216] Optionally, the dual-profile matching module 23 includes:

[0217] The professional competency profile building unit is used to construct professional competency profiles, including:

[0218] DBSCAN clustering analysis is performed on the skill data in the dynamic feature set to generate professional competency clusters. .

[0219] Calculate the central vector of each professional competency cluster within the professional competency cluster set, and use the central vector as a professional competency profile; the formula for calculating the central vector is:

[0220] ;

[0221] in, Indicates the first The central vector of a cluster of professional capabilities; Indicates the first Each professional competency cluster includes the aforementioned skill characteristics. , , Indicates the number of professional skill clusters; Indicates the first Skill characteristics described in each professional competency cluster Quantity; Indicate skill characteristics The TF-IDF vector representation.

[0222] The job requirement profile building unit is used to build job requirement profiles, including:

[0223] Perform LDA topic modeling on job data in the dynamic feature set to generate a demand topic distribution. This serves as a profile of job requirements; among them, Indicates the first The distribution of job requirements by topic Indicates the first The first position Demand weight for each demand topic , This indicates the number of demand topics.

[0224] The initial matching degree matrix calculation unit is used to obtain the topic skill mapping matrix, calculate the cosine similarity between the professional ability clusters and the distribution of demand topics based on the topic skill mapping matrix, and generate the initial matching degree matrix based on the cosine similarity. The formula for calculating the cosine similarity is:

[0225] ;

[0226] in, This is a topic-skill mapping matrix, representing the relationship between each requirement topic and skills; This represents the product of the topic skill mapping matrix and the transpose of the demand topic distribution.

[0227] The matching degree matrix optimization unit is used to optimize the initial matching degree matrix when the similarity value in the initial matching degree matrix is ​​lower than a preset similarity threshold, generating an optimized matching degree matrix as the matching degree matrix between the professional ability profile and the job requirement profile. This includes:

[0228] Extract feature weight vectors from the topic skill mapping matrix ,in Indicates the first Feature weights of each skill Indicates the number of skills.

[0229] The feature weight vector is analyzed using the gradient descent method. Perform iterative optimization to generate an optimized weight vector.

[0230] The topic skill mapping matrix is ​​updated based on the optimized weight vector, and an optimized matching degree matrix is ​​generated based on the updated topic skill mapping matrix.

[0231] Optional, employment advice module 25 includes:

[0232] The matching matrix update unit is used to perform matrix decomposition and update the dynamic matching matrix when the job skill update rate exceeds the preset update rate threshold, and generate the updated matching matrix.

[0233] The rule generation unit is used to perform decision tree analysis on the updated matching matrix to generate a set of personalized job recommendations and skills enhancement suggestions.

[0234] The structured data generation unit is used to convert the rule set of personalized job recommendations and skills enhancement suggestions into a structured data format, generating structured data that supports personalized job recommendations and skills enhancement suggestions.

[0235] Embodiments of this application also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the aforementioned method embodiments.

[0236] Embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0237] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0238] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A cloud computing-based intelligent employment data processing method, characterized in that, The method comprises: S1: obtaining multi-source heterogeneous data through a cloud computing architecture, standardizing the multi-source heterogeneous data, and generating a standardized data set; wherein the multi-source heterogeneous data comprises job seeker ability data and employment market job description data; S2: performing distributed feature extraction on the standardized data set to generate a dynamic feature set containing time dimension and industry classification labels; S3: constructing professional ability portraits and job demand portraits in parallel based on the dynamic feature set, and calculating a matching degree matrix between the professional ability portraits and the job demand portraits; S4: extracting change trend features from an industry trend report, integrating the change trend features into the matching degree matrix for dynamic updating, and generating a dynamic matching model; S5: generating structured data supporting personalized job recommendation and skill improvement suggestions based on the dynamic matching model by detecting changes in industry demand; Wherein, the S4 comprises: S41: extracting the frequency change rate of each skill word from the industry trend report, and marking the skill feature corresponding to the skill word as a high-impact feature when the frequency change rate of the skill word exceeds a preset change rate threshold; S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate a demand topic distribution ; wherein, represents the demand topic distribution of the jth job, represents the demand weight of the ith demand topic of the jth job, , represents the number of demand topics;​​​ S43: adjusting the demand theme distribution according to the high-impact feature to obtain a dynamic demand theme distribution; S44: generating a dynamic matching matrix based on the dynamic demand theme distribution, and taking the dynamic matching matrix as the dynamic matching model; Wherein, the S3 comprises: S31: constructing a professional ability portrait, comprising: performing DBSCAN clustering analysis on the skill data in the dynamic feature set to generate a professional competency cluster set ; calculating the center vector of each professional ability cluster in the professional ability cluster set, and taking the center vector as the professional ability portrait; the calculation formula of the center vector is: ； wherein, denotes the center vector of the th professional competence cluster; denotes the th professional competence cluster comprising the skill feature , , denotes the number of professional competence clusters; denotes the number of the skill feature in the th professional competence cluster; denotes the TF-IDF vector representation of the skill feature ; S32: distribute the demand topics according to the demand topic distribution as the post demand portrait; wherein, represents the demand topic distribution of the post, represents the demand weight of the demand topic of the post, , represents the number of demand topics; S33: obtaining a theme-skill mapping matrix, calculating the cosine similarity between the professional ability cluster set and the demand theme distribution based on the theme-skill mapping matrix, and generating an initial matching degree matrix according to the cosine similarity, wherein the calculation formula of the cosine similarity is: ； wherein, is the topic-skill mapping matrix, representing the relationship between each demand topic and skill; represents the topic-skill mapping matrix multiplied by the transpose of the demand topic distribution; S34: when the cosine similarity in the initial matching degree matrix is lower than a preset similarity threshold, optimizing the initial matching degree matrix to generate an optimized matching degree matrix as the matching degree matrix between the professional ability portrait and the job demand portrait, comprising: extracting a feature weight vector from the subject skill mapping matrix wherein denotes a feature weight of an i th skill, denotes the number of skills, ; by a gradient descent method on the feature weight vector performing an iterative optimization to generate an optimized weight vector; updating the theme-skill mapping matrix according to the optimization weight vector, and generating the optimized matching degree matrix based on the updated theme-skill mapping matrix.

2. The method of claim 1, wherein, The S1 comprises: S11: obtaining a skill feature set from a job platform , as the job seeker ability data; wherein each skill feature includes a skill code, a skill weight, and a skill proficiency, is a skill serial number, is a total number of skills, ; S12: Obtain a set of job descriptions from a recruitment platform through a distributed crawler , as the job market job description data; wherein each job description includes a job ID, a set of skill keywords, and an industry classification, is the job number, is the total number of jobs, ; S13: storing the skill feature set and the job description set to a distributed storage cluster to generate a sharded index with timestamp and industry label; S14: performing Z-Score standardization processing on the numerical fields in the sharded index to generate standardized numerical fields; S15: performing TF-IDF vectorization processing on the text fields in the sharded index to generate vectorized text features; S16: performing imputation processing on the missing fields in the sharded index to generate an imputed data set; S17: integrating the imputed data set with the standardized numerical fields and the vectorized text features to generate the standardized data set.

3. The method of claim 2, wherein, The S2 comprises: S21: performing sharding processing on the standardized data set through a MapReduce framework to generate a time-windowed sharded data set; S22: calculating a skill capability density of each sharded data set, and generating a dynamic capability feature set based on the skill capability density; wherein the calculation formula of the skill capability density is: ； wherein is the importance score of the th skill, is an indicator function for core skills; S23: calculating a post skill update rate of each sharded data set, and generating a dynamic post feature set based on the post skill update rate; S24: aggregating the dynamic capability feature set and the dynamic post feature set according to an industry label to generate the dynamic feature set.

4. The method of claim 3, wherein, The S21 comprises: S211: performing sliding window segmentation on the standardized data set according to a preset time window to generate a windowed sharded data set; S212: calculating a skill-post co-occurrence matrix of each windowed sharded data set, and generating correlation strength data according to the skill-post co-occurrence matrix; the correlation strength data represents the correlation strength between skills and posts; S213: performing singular value decomposition on the correlation strength data to generate a low-dimensional feature vector; S214: associating the low-dimensional feature vector with the windowed sharded data set according to a time window to generate the time-windowed sharded data set.

5. The method of claim 3, wherein, The S5 comprises: S51: when the post skill update rate exceeds a preset update rate threshold, performing matrix decomposition update on the dynamic matching matrix to generate an updated matching matrix; S52: performing decision tree analysis on the updated matching matrix to generate a personalized post recommendation and skill improvement suggestion rule set; S53: converting the personalized post recommendation and skill improvement suggestion rule set into a structured data format to generate the structured data supporting personalized post recommendation and skill improvement suggestion.

6. A cloud computing-based smart employment data processing system, characterized by, The system comprises: a data standardization module configured to obtain multi-source heterogeneous data through a cloud computing architecture, perform standardization processing on the multi-source heterogeneous data, and generate a standardized data set; wherein the multi-source heterogeneous data comprises job seeker capability data and employment market post description data; a dynamic feature extraction module configured to perform distributed feature extraction on the standardized data set to generate a dynamic feature set comprising a time dimension and an industry classification label; a dual-portrait matching module configured to construct a professional capability portrait and a post demand portrait in parallel based on the dynamic feature set, and calculate a matching degree matrix between the professional capability portrait and the post demand portrait; a trend fusion update module configured to extract a change trend feature from an industry trend report, and dynamically update the matching degree matrix by fusing the change trend feature into the matching degree matrix to generate a dynamic matching model; an employment suggestion module configured to generate structured data supporting personalized post recommendation and skill improvement suggestion according to the dynamic matching model by detecting industry demand changes; The trend fusion update module comprises: a high-impact feature extraction unit configured to extract a frequency change rate of each skill word from the industry trend report, and mark a skill feature corresponding to the skill word as a high-impact feature when the frequency change rate of the skill word exceeds a preset change rate threshold. a demand topic distribution generation unit configured to perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distributions ; wherein, denotes a demand topic distribution of the jth job, denotes a demand weight of the ith demand topic for the jth job, , denotes a number of demand topics;​​​ The demand topic distribution adjustment unit is configured to adjust the demand topic distribution according to the high-impact feature, to obtain a dynamic demand topic distribution. The dynamic matching model generation unit is configured to generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as the dynamic matching model. The double portrait matching module includes: The professional competence portrait construction unit is configured to construct a professional competence portrait, including: performing DBSCAN clustering analysis on the skill data in the dynamic feature set to generate a professional competency cluster set ; The professional competence portrait construction unit is configured to calculate a center vector of each professional competence cluster in the professional competence cluster set, and use the center vector as the professional competence portrait; and a calculation formula of the center vector is: ； wherein, represents a center vector of the th professional competence cluster; represents the th professional competence cluster comprising the skill feature , , represents the number of professional competence clusters; represents the number of the skill feature in the th professional competence cluster; represents a TF-IDF vector representation of the skill feature ; The post demand portrait construction unit is configured to construct the demand theme distribution as the post demand portrait; wherein, indicates the demand theme distribution of the post, indicates the demand weight of the demand theme of the post, , indicates the number of demand themes; The initial matching degree matrix calculation unit is configured to obtain a topic-skill mapping matrix, calculate a cosine similarity between the professional competence cluster set and the demand topic distribution based on the topic-skill mapping matrix, and generate an initial matching degree matrix according to the cosine similarity, where a calculation formula of the cosine similarity is: ； wherein, is the topic-skill mapping matrix, representing the relationship between each demand topic and skill; represents the topic-skill mapping matrix multiplied by the transpose of the demand topic distribution; The matching degree matrix optimization unit is configured to, when the cosine similarity in the initial matching degree matrix is lower than a preset similarity threshold, optimize the initial matching degree matrix to generate an optimized matching degree matrix, which is used as the matching degree matrix between the professional competence portrait and the job demand portrait, including: extracting a feature weight vector from the subject skill mapping matrix wherein denotes a feature weight of an i th skill, denotes the number of skills, ; by a gradient descent method on the feature weight vector performing an iterative optimization to generate an optimized weight vector; The matching degree matrix optimization unit is configured to, when the cosine similarity in the initial matching degree matrix is lower than a preset similarity threshold, optimize the initial matching degree matrix to generate an optimized matching degree matrix, which is used as the matching degree matrix between the professional competence portrait and the job demand portrait, including: 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The matching degree matrix optimization unit is configured to, when the cosine similarity in the initial matching degree matrix is lower than a preset similarity threshold, optimize the initial matching degree matrix to generate an optimized matching degree matrix, which is used as the matching degree matrix between the professional competence portrait and the job demand portrait, including:

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The matching degree matrix optimization unit is configured to, when the cosine similarity in the initial matching degree matrix is lower than a preset similarity threshold, optimize the initial matching degree matrix to generate an optimized matching degree matrix, which is used as the matching degree matrix between the professional competence portrait and the job demand portrait, including: The processor executes the computer program to implement the method in any one of claims 1 to 5. The computer program is executed by the processor to implement the method in any one of claims 1 to 5.

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