Intelligent employment data processing method and system based on cloud computing

Abstract

The invention relates to an intelligent employment data processing method and system based on cloud computing. The method comprises the following steps: integrating multi-source heterogeneous data through a cloud computing architecture and constructing a standardized data set; generating a dynamic feature set with a time dimension and an industry label by using a distributed feature extraction technology; parallel portrait construction and dynamic matching degree matrix calculation are combined to realize accurate mapping of professional ability and post requirements, and then a matching model is optimized by fusing real-time industry trend features; and finally, detecting industry demand changes, and according to the dynamic matching model, generating structured data supporting personalized post recommendation and skill improvement suggestions. The problems that a traditional method is poor in real-time performance, single in dimension and lagged in response are solved, the dynamic adaptive capacity and analysis precision of employment data processing are remarkably improved, and an employment service platform is helped to provide timely and accurate personalized post recommendation and skill improvement suggestions for job seekers.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a cloud computing-based smart employment data processing method and system. Background Art

[0002] In the field of digital employment services, existing platforms generally use a rule-based job matching mechanism, initially matching job seekers with positions through keyword matching and static weight calculation. These systems typically rely on manually annotated resume parsing models and periodic job database updates, making them difficult to adapt to the rapidly evolving industry skill requirements and job seekers' growth trajectory.

[0003] Due to the lack of the ability to integrate and process multi-source heterogeneous data such as social media, real-time recruitment dynamics, and corporate technical documents, traditional systems are often limited to structured fields such as education and years of work experience when building job demand profiles, and are unable to deeply explore emerging skill indicators and implicit ability requirements.

[0004] At the same time, the static matching model lacks the ability to dynamically perceive emerging skill demands when analyzing job requirements, resulting in the inability to timely identify the rapidly iterating skill requirements on the enterprise side, exacerbating the structural mismatch between job seekers' skills and market demand.

[0005] More critically, existing methods use a centralized storage architecture when processing massive amounts of job seeker data, resulting in a sharp drop in real-time matching response speed as the data scale grows, making it difficult to support the precise recommendation needs of tens of millions of concurrent users.

[0006] The above-mentioned defects make it difficult for current employment service platforms to provide dynamically optimized decision-making support in a market environment where technology is changing rapidly. Summary of the Invention

[0007] Based on this, it is necessary to provide a cloud computing-based smart employment data processing method and system to address the above technical problems.

[0008] In a first aspect, the present application provides a method for processing smart employment data based on cloud computing, comprising: S1: Obtain multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; the multi-source heterogeneous data includes job seeker ability data and job market job description data; S2: Perform distributed feature extraction on the standardized data set to generate a dynamic feature set containing time dimension and industry classification labels; S3: Construct professional capability profiles and job requirement profiles in parallel based on the dynamic feature set, and calculate the matching matrix between the professional capability profiles and job requirement profiles; S4: Extract the change trend characteristics based on the industry trend report, integrate the change trend characteristics into the matching matrix for dynamic update, and generate a dynamic matching model; S5: By detecting changes in industry demand, we generate structured data based on dynamic matching models to support personalized job recommendations and skills improvement suggestions; Among them, S4 includes: S41: extracting 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, marking the skill feature corresponding to the skill word as a high-impact feature. S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of demand topics; S43: Adjust the demand topic distribution according to the high-impact features to obtain a dynamic demand topic distribution; S44: Generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as a dynamic matching model.

[0009] Secondly, this application also provides a cloud computing-based smart employment data processing system, including: The data standardization module is used to obtain multi-source heterogeneous data through cloud computing architecture, perform standardization processing on the multi-source heterogeneous data, and generate a standardized data set; the multi-source heterogeneous data includes job seeker ability data and job market job description data; Dynamic feature extraction module, used to perform distributed feature extraction on standardized data sets and generate dynamic feature sets containing time dimensions and industry classification labels; The dual-profile matching module is used to construct professional capability profiles and job requirement profiles in parallel based on dynamic feature sets, and calculate the matching matrix between professional capability profiles and job requirement profiles; The trend fusion update module is used to extract the change trend characteristics based on the industry trend report, integrate the change trend characteristics into the matching matrix for dynamic update, and generate a dynamic matching model; The employment recommendation module is used to detect changes in industry demand and generate structured data based on a dynamic matching model to support personalized job recommendations and skills improvement suggestions; Among them, the trend fusion update module includes: A 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. The demand topic distribution generation unit is used to perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of demand topics; A demand topic distribution adjustment unit is used to adjust the demand topic distribution according to high-impact features to obtain a dynamic demand topic distribution; The dynamic matching model generation unit is used to generate a dynamic matching matrix based on the dynamic demand topic distribution and use the dynamic matching matrix as a dynamic matching model.

[0010] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements a cloud computing-based smart employment data processing method as in the first aspect.

[0011] In a fourth aspect, the present 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 in the first aspect.

[0012] The aforementioned cloud computing-based intelligent employment data processing method and system integrates multi-source heterogeneous data through a cloud computing architecture and constructs a standardized data set. It utilizes distributed feature extraction technology to generate dynamic feature sets with time dimensions and industry labels. It combines parallel profiling with dynamic matching matrix calculation to accurately map professional capabilities to job requirements, and then optimizes the matching model by incorporating real-time industry trend features. Finally, by detecting changes in industry demand, the system generates structured data supporting personalized job recommendations and skills improvement suggestions based on the dynamic matching model. This method addresses the problems of traditional methods, such as poor real-time performance, single dimensions, and delayed response, 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 improvement suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A flowchart of a cloud computing-based smart employment data processing method provided by the present invention; Figure 2 A schematic structural diagram of a cloud computing-based smart employment data processing system provided by the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0016] refer to Figure 1 , which presents a flow chart of a cloud computing-based smart employment data processing method provided by this application, the method comprising the following steps: S1: Obtain multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; among them, the multi-source heterogeneous data includes job seeker ability data and job market job description data.

[0017] Specifically, this method leverages a cloud computing architecture to collect a wide range of heterogeneous data from diverse sources, with varying structures and characteristics. This data primarily encompasses two categories: first, candidate competency data, including but not limited to detailed information such as a candidate's education, professional skills, work experience, project experience, certifications, and honors; and second, job description data, including job responsibilities, job requirements, preferred skills, location, and salary packages. This cloud computing architecture enables efficient integration and interaction with various data sources, enabling real-time data acquisition and dynamic updates.

[0018] To ensure the consistency and accuracy of subsequent processing, the acquired multi-source heterogeneous data is standardized. This process includes but is not limited to the following aspects: 1) Data format unification: Convert data in different formats into a unified standard format. For example, standardize the date format to "YYYY-MM-DD" and the numeric format to a specific number of decimal places. This will facilitate subsequent data processing and analysis operations and avoid errors or compatibility issues caused by format differences.

[0019] 2) Encoding standardization: For text data, a unified character encoding standard, such as UTF-8, is adopted to ensure that text information in different languages ​​and from different sources can be correctly displayed and processed under the same encoding system, avoiding garbled characters or unrecognizable characters, thereby ensuring data integrity and availability.

[0020] 3) Data cleaning and denoising: Identify and address missing values, erroneous values, outliers, and other noise in the data. For example, missing values ​​can be addressed by filling in the mean or median, or by using machine learning algorithms for data interpolation. For erroneous and outlier values, set reasonable data ranges and validation rules to detect and correct them, improving data quality and reliability and ensuring the accuracy of subsequent analysis results.

[0021] 4) Data Normalization: Normalize numerical data 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 enhancing the effectiveness and stability of the entire data processing system.

[0022] After the above standardization processing steps, the originally messy, multi-source heterogeneous data with different formats are organized into a standardized data set with regular structure and unified format.

[0023] S2: Perform distributed feature extraction on the standardized data set to generate a dynamic feature set containing time dimension and industry classification labels.

[0024] Specifically, distributed feature extraction technology is employed for the generated standardized data set, leveraging the distributed computing resources within a cloud computing architecture. By segmenting the data into multiple sub-datasets and processing them in parallel across multiple computing nodes, the efficiency and speed of feature extraction are improved. For example, a distributed computing framework such as Hadoop or Spark can be employed to distribute feature extraction tasks to individual nodes in the cluster. Each node independently performs feature extraction on its assigned sub-dataset, and the extraction results from each node are then integrated and aggregated to produce a complete dynamic feature set.

[0025] During feature extraction, we not only focus on the intrinsic characteristics of the data itself, but also specifically incorporate the time dimension and industry classification labels to capture the dynamic characteristics of data that change over time and industry. Specifically: 1) Text Feature Extraction: For text-based data such as applicant skill descriptions and job requirements, natural language processing techniques are used for feature extraction. For example, using bag-of-words models, TF-IDF (Term Frequency-Inverse Document Frequency), or more advanced deep learning word embedding methods such as Word2Vec and BERT, text information is converted into numerical feature vectors to represent key features such as key words and semantic information in the text content. This allows for the extraction of relevant feature information about the applicant and the position at the text level.

[0026] 2) Structured Data Feature Extraction: For structured data such as education background, years of work experience, and salary level, directly extract numerical features or perform appropriate encoding conversions as features. For example, years of work experience can be used as a numerical feature, while educational qualifications can be mapped to different numerical codes, such as "high school = 1, bachelor's degree = 2, master's = 3, doctorate = 4," to facilitate calculation and analysis in the feature space. These structured features can intuitively reflect the requirements and compatibility between the job applicant and the position in certain aspects.

[0027] 3) Incorporating Time-Dimensional Features: Considering that job seekers' skills development and job requirements change over time, we assign a timestamp or time identifier to each extracted feature to construct time series features. For example, we record the skill certificates obtained by job seekers over different time periods, the start and end dates of their work experience, and the changes in job requirements during different posting periods. By analyzing these time series features, we can capture the dynamic evolution of skills and job requirements, providing subsequent matching models with richer dynamic information, enabling them to better adapt to market dynamics.

[0028] 4) Integration of industry classification labels: Extracted features are labeled with corresponding industry classification labels based on the job seeker's industry and the industry to which the position belongs. This facilitates differentiated analysis and processing of features in subsequent processing, tailored to the characteristics and needs of different industries, improving the accuracy and specificity of matching. For example, in the information technology industry, features such as programming skills and project experience may be more emphasized; in the financial industry, capabilities such as financial knowledge and risk assessment may be more closely considered. Guided by industry classification labels, the feature extraction and matching process can be more closely aligned with the actual needs of the industry, thereby enhancing the professionalism and effectiveness of the entire smart employment data processing system.

[0029] Through the above-mentioned distributed feature extraction process, a dynamic feature set containing time dimensions and industry classification labels is generated, providing a comprehensive, dynamic and detailed feature foundation for the subsequent construction of professional capability portraits and job requirement portraits.

[0030] S3: Based on the dynamic feature set, professional ability profiles and job requirement profiles are constructed in parallel, and the matching matrix between the professional ability profiles and job requirement profiles is calculated.

[0031] Specifically, based on the generated dynamic feature set, professional capability profiles and job requirement profiles are constructed in parallel. The specific construction process is as follows: The dynamic characteristics of job applicants are integrated and abstracted to form a comprehensive, three-dimensional portrait of their professional capabilities. This includes comprehensive analysis and weighting of multi-dimensional characteristics such as the applicant's skills, experience, and educational background. For example, weights are set based on the importance of different skills in the industry, with core skills given higher weights and auxiliary skills given relatively lower weights. The characteristic values ​​of each dimension are then multiplied and summed with their corresponding weights to obtain a vector representation that comprehensively reflects the applicant's professional capabilities, namely the professional capability portrait. This portrait not only reflects the applicant's current ability level, but also demonstrates the growth trajectory and development trend of their capabilities through changes in characteristics over time, providing employers with deeper insights into the applicant's capabilities.

[0032] Similarly, the dynamic features of job descriptions are integrated and abstracted to construct a job requirement profile. This involves extracting and weighting the required skills, work experience, and educational qualifications. Weights are assigned to each feature based on the general market requirements and industry standards for the position. For example, for senior technical positions, work experience and professional skills may be weighted more heavily, while for entry-level positions, educational qualifications and learning ability may be more important. This weighted calculation yields a comprehensive feature vector of job requirements, known as the job requirement profile. This profile accurately captures the core requirements and preferences of the position, providing a clear goal orientation for subsequent matching with the professional competency profile.

[0033] After constructing the professional competency profile and job requirement profile, calculate the matching matrix between the two. The matching matrix is ​​a two-dimensional matrix, where rows represent job seekers and columns represent jobs. Each element in the matrix represents the matching value between the corresponding job seeker and job. The calculation method is as follows: Use an appropriate similarity metric 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 the 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 linear distance between two vectors in space; the smaller the distance, the higher the match. Based on the actual application scenario and data characteristics, select the most appropriate similarity metric to accurately reflect the match between the job seeker and the job.

[0034] For each job applicant and each position, we calculate the similarity measure between their professional ability profile and the position requirement profile, and then fill these values ​​into the corresponding positions of the matching matrix to generate a complete matching matrix. This matrix comprehensively and intuitively displays the matching degree distribution between all job applicants and positions.

[0035] S4: Extract the change trend characteristics according to the industry trend report, integrate the change trend characteristics into the matching matrix for dynamic update, and generate a dynamic matching model.

[0036] Specifically, based on industry trend reports, we conduct in-depth analysis of industry development dynamics, technological innovation, and changes in talent demand, extracting 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 shifting industry requirements for talent quality. For example, by mining and analyzing data from a large number of industry analysis reports, hot topics in technical forums, and corporate recruitment trends, we can identify trend characteristics such as currently popular programming languages, application areas of artificial intelligence technology, and the growing demand for specific professional talent in the development of the green energy industry. These characteristics can reflect the industry's future development direction and talent demand priorities, providing forward-looking guidance for the dynamic updating of matching models.

[0037] The extracted trend characteristics are organically integrated into the previous matching matrix to achieve dynamic updates of the matching matrix, thereby generating a dynamic matching model that can adapt to the dynamic changes in the industry. The specific process can be as follows: Determine a reasonable way to integrate trend-based features so they can be organically integrated with the existing matching matrix. For example, trend-based features can be added as new dimensions to the professional competency and job requirement profiles, and the similarity between the integrated profiles can be recalculated to update the matching matrix. Alternatively, adjust the existing feature weights based on trend-based features, increasing the weight of features that align with industry development trends and decreasing the weight of features that do not. This reflects the impact of industry changes on matching relationships, enabling the matching model to more sensitively capture market dynamics and improve its adaptability and predictive accuracy.

[0038] Establish a real-time or regular matching matrix update mechanism to ensure the dynamic matching model remains current with the latest industry changes. When new industry trend reports are released or significant market shifts occur, timely extract trend characteristics and update the matching matrix according to the pre-defined fusion strategy. Furthermore, considering the timeliness and stability of data, technical measures such as smoothing can be employed to avoid significant fluctuations in the matching model caused by individual data fluctuations, ensuring the robustness and reliability of the model and enabling it to continue providing accurate and effective matching services for job seekers and employers in a dynamically changing environment.

[0039] S5: By detecting changes in industry demand, structured data that supports personalized job recommendations and skill improvement suggestions is generated based on a dynamic matching model.

[0040] Specifically, by continuously monitoring industry dynamics and market demand changes, and leveraging big data analytics to track and analyze massive amounts of recruitment data, corporate dynamics, industry news, and other information in real time, the system can promptly identify new changes and trends in the industry's demand for talent. For example, if it detects a sharp increase in the number of job openings in a particular emerging technology field, and a significant increase in the frequency of specific skill requirements appearing in job descriptions, the system can quickly identify this signal of changing industry demand and use it as an important basis for triggering dynamic matching model updates and personalized service generation, ensuring that the system can respond to market changes immediately and provide users with timely and effective decision support.

[0041] Based on the updated dynamic matching model, we provide job seekers with accurate and personalized job recommendation services. The specific implementation methods are as follows: Based on the dynamic matching model's calculated match scores between job seekers and various positions, the positions are ranked from highest to lowest. Furthermore, the system considers the job seeker's personal preferences, location, salary expectations, and other screening criteria to further refine a list of high-quality job recommendations that meet the job seeker's needs. For example, for a Beijing-based job seeker with a salary expectation of 15,000-20,000 RMB, proficiency in Python programming, and an interest in artificial intelligence, the system will prioritize highly compatible positions located in Beijing, offering suitable salary packages, and related to Python programming and AI technologies. This increases the job seeker's chances of securing their desired position and saves them the time and effort of sifting through the vast amount of job listings.

[0042] Personalized job recommendations are presented to job seekers in an intuitive and user-friendly manner, such as a list displaying key information such as job title, company name, salary range, and location, along with links to detailed job descriptions for further information. The system also allows job seekers to provide feedback on recommended results, such as marking them as interested, not interested, or having applied. Based on this feedback, the recommendation algorithm and model parameters are further optimized, enabling continuous improvement and personalization of the recommendation service, thereby increasing job seekers' engagement with and satisfaction with the system.

[0043] In addition to job recommendations, the system also provides job seekers with targeted skills improvement suggestions based on dynamic matching models and industry trend analysis, helping them better adapt to market demands and improve their employment competitiveness. The specific approach is as follows: By comparing the applicant's professional competency profile with the target position's requirements, the system accurately identifies gaps and deficiencies in the applicant's skills, knowledge, and experience. For example, if a data analyst position requires proficiency in data mining algorithms and data visualization tools, but the applicant currently possesses only basic data processing capabilities, the system will automatically identify these skills as weaknesses. This will provide a basis for subsequent skill improvement recommendations, ensuring their accuracy and practicality, and helping the applicant identify the areas and priorities for improvement.

[0044] Based on 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 to improve their learning and plan a reasonable learning path. 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. These courses include explanations of the principles of data mining algorithms, tutorials on how to use Python data mining libraries (such as Scikit-learn), and real-life e-commerce data mining projects. These courses help job seekers systematically learn and master new skills. The system also provides learning progress tracking and learning effect evaluation functions, encouraging job seekers to continue learning and progress, ultimately achieving the goals of skill improvement and successful employment, and providing comprehensive support and guarantees for job seekers' career development.

[0045] The aforementioned cloud computing-based intelligent employment data processing method integrates multi-source heterogeneous data through a cloud computing architecture and constructs a standardized data set. It utilizes distributed feature extraction technology to generate dynamic feature sets with time dimensions and industry labels. It combines parallel profiling with dynamic matching matrix calculation to accurately map professional capabilities to job requirements, and then optimizes the matching model by incorporating real-time industry trend features. Finally, by detecting changes in industry demand, the dynamic matching model generates structured data that supports personalized job recommendations and skills improvement suggestions. This method addresses the problems of traditional methods, such as poor real-time performance, single dimensions, and delayed response, 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 improvement suggestions.

[0046] In an optional embodiment, S4 includes the following steps: S41: extracting 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, marking the skill feature corresponding to the skill word as a high-impact feature.

[0047] Specifically, we use text analysis and information extraction methods within natural language processing to identify and extract the frequency of occurrence and rate of change of each skill term from industry trend reports. By comparing the number of skill term appearances in reports over different time periods and calculating their rate of change, we can quantify the changing popularity of skill terms within the industry.

[0048] Set a preset change rate threshold, which can be determined based on industry characteristics, market fluctuations, and historical data experience. When the frequency change rate of a skill term exceeds this threshold, it indicates a significant shift in the demand or importance of the skill within the industry, potentially having a significant impact on the job market. In this case, the skill feature corresponding to the skill term is marked as a high-impact feature, giving it special attention and processing in subsequent matching model adjustments. This ensures the model can promptly adapt to industry dynamics and improves its sensitivity and responsiveness to emerging skill demands.

[0049] S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of requirement topics.

[0050] Specifically, LDA (Latent Dirichlet Allocation) topic modeling is performed on the job data in the dynamic feature set. LDA is a probabilistic topic model that can mine potential topic structures from large document collections. In this step, the algorithm treats the job data as a collection of documents generated by a mixture of different topics, and estimates the distribution of required topics for each job through iterative training. ,in represents the number of demand topics, Indicates the positions.

[0051] Demand topic distribution Description The preference of each position for different required topics. Indicates the Position No. The demand weight of each demand topic, The demand weight reflects the importance that the position places on the skills and requirements related to a specific topic. The probability distribution estimated by the LDA model can comprehensively characterize the multidimensional demand structure of the position.

[0052] S43: Adjust the demand topic distribution according to the high-impact features to obtain a dynamic demand topic distribution.

[0053] Specifically, based on the marked high-impact features, the previously generated demand topic distribution is adjusted. Specifically, for those demand topics related to high-impact features, their weights are appropriately increased or their proportion in the topic distribution is adjusted to reflect the importance and demand changes of these topics 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 topic, then during the adjustment, the weight of this demand topic in the job demand topic distribution can be increased, so that the job demand profile is more inclined to pay attention to this emerging skill area, thereby better matching the market's urgent demand for this skill.

[0054] Specific algorithms or mathematical models are used to adjust the demand topic distribution. This can involve recalculating the probability distribution, updating matrix decompositions, or leveraging 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 demand topic distribution 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.

[0055] S44: Generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as a dynamic matching model.

[0056] Specifically, based on the adjusted demand theme distribution, the matching relationship between professional competency profiles and job requirement profiles is recalculated to generate a new matching matrix, the Dynamic Matching Matrix. This matrix reflects the impact of the latest industry trends on job matching and more accurately reflects which professional competencies are most compatible with which job requirements in the current market environment. Compared to the original matching matrix, the Dynamic Matching Matrix incorporates awareness and response to industry dynamics, better guiding job seekers in personalized job recommendations and skill development planning, while also helping employers more accurately identify suitable talent.

[0057] The generated dynamic matching matrix serves as the core component of the updated dynamic matching model, replacing the previous matching model. In practical applications, the system will use this dynamic matching model to provide job seekers with job recommendation services that better meet actual market needs and offer employers more accurate talent matching support. By regularly extracting information from industry trend reports and updating the dynamic matching model, the system can maintain its sensitivity and adaptability to market dynamics, continuously improving service quality and user satisfaction, promoting the efficient operation of the employment market and the rational allocation of talent resources, and promoting the healthy development and innovative progress of the entire industry.

[0058] In an optional embodiment, S1 includes the following steps: S11: Obtaining skill feature sets from job search platforms , as the job seeker ability data; where each skill feature Including skill code, skill weight and skill proficiency, is the skill number, The total number of skills.

[0059] Specifically, through a stable connection and data interaction interface with the job search platform, the skill feature set stored in the platform is obtained. This collection serves as the core source of candidate competency data, covering details of all skills possessed by each candidate.

[0060] gather Each skill feature in It is a comprehensive data structure that contains 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 may be assigned a specific code "PY"; the skill weight reflects the relative importance of the skill in the overall ability system of the job seeker. Its value range is usually set in the interval [0,1]. The higher the value, the more critical the skill is to the job seeker's professional image; skill proficiency is a quantitative assessment of the job seeker's proficiency in mastering the skill, which can be reflected through level division (such as elementary, intermediate, advanced) or score assessment (such as 1-10 points). Among them, Skill serial number, used to distinguish different skills recorded for the same job seeker. The total number of skills, indicating the complete number of skills currently registered by the job seeker in the system.

[0061] S12: Obtaining a collection of job descriptions from recruitment platforms using distributed crawlers , as the job description data of the job market; each job description Including job ID, skill keyword set and industry classification, is the position number, The total number of positions.

[0062] Specifically, with the help of a distributed crawler system, we actively and specifically crawl massive amounts of job description information from various recruitment platforms, thereby building a job description collection. ,This collection constitutes the basic structure of job market job description data.

[0063] gather Each job description in It consists of three parts: job ID, skill keyword set and industry classification. Job ID is a unique identification code that accurately identifies each recruitment position, making it easier to accurately locate and distinguish in a large data set; skill keyword set is a set of words that can reflect the core skill requirements of the position. These keywords are directly related to the key abilities that job seekers need to possess, and are an important reference for the system to match people with jobs; industry classification clarifies the specific industry field to which the position belongs, which helps the system implement more accurate and personalized matching strategies in subsequent processing based on the characteristics and needs of different industries. Among them, It is the position serial number, used to arrange and distinguish different position records in sequence. The total number of positions, indicating the overall number of recruitment positions collected by the current system.

[0064] S13: Store the skill feature set and job description set in a distributed storage cluster and generate a shard index with a timestamp and industry label.

[0065] Specifically, the skill profile set and job description set are integrated and stored in an efficient and stable distributed storage cluster. This cluster consists of multiple servers, and the data is divided into multiple data blocks and stored in different server nodes, thus achieving high scalability and high availability of data storage.

[0066] During storage, the system shards data according to specific rules and generates a shard index for each data shard, complete with a timestamp and industry tag. The timestamp precisely records the moment the shard data was generated or updated; the industry tag clearly identifies the industry to which the shard data belongs, allowing the system to quickly focus on data related to a specific industry during data retrieval and processing.

[0067] S14: Perform Z-Score normalization on the numeric fields in the shard index to generate normalized numeric fields.

[0068] Specifically, for numeric fields in shard indexes, we use the statistical Z-Score normalization method to convert data. This method is based on the mean and standard deviation of the original data. By calculating the difference between each numerical point and the mean and dividing it by the standard deviation, it converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0069] The system automatically performs batch Z-Score normalization on numeric fields identified in the sharded index. First, the mean and standard deviation of each numeric field are calculated. Then, the original values ​​in each field are substituted into the Z-Score formula to generate the corresponding normalized values. Finally, these normalized values ​​replace the original values ​​and are stored back in the sharded index, ensuring the comparability and stability of the numeric data during subsequent processing.

[0070] S15: Perform TF-IDF vectorization processing on the text fields in the shard index to generate vectorized text features.

[0071] Specifically, for text fields in sharded indexes, the TF-IDF (Term Frequency-Inverse Document Frequency) vectorization method is used to convert them into numerical feature vectors. TF-IDF is a statistical method used to measure the importance of words in text. It comprehensively considers the term's frequency (TF) in a single document and the term's inverse document frequency (IDF) in the entire text collection. TF represents the ratio of the number of times a term appears in a document to the total number of words in the document, while IDF measures the commonality of a term, calculated as the logarithm of the ratio of the total number of documents in a text collection to the number of documents containing the term.

[0072] The specific steps for the system to perform TF-IDF vectorization processing on text fields are as follows: first, preprocess the text, including removing stop words, extracting stems, and other operations; then, count the TF value of each word in the text; then, calculate the IDF value of each word based on the entire text collection; finally, multiply the TF value by the IDF value to obtain the TF-IDF weight of each word, thereby constructing a vectorized text feature vector that can reflect the text characteristics, so that text data can be effectively applied in numerical calculations and machine learning algorithms.

[0073] S16: Perform interpolation processing on the missing fields in the shard index to generate an interpolated data set.

[0074] Specifically, the system comprehensively scans every field in the shard index and, by setting data integrity rules and non-null constraints, accurately identifies fields with missing values. Numeric fields are considered missing if the value is empty or outside the acceptable range; text fields are considered missing if the text is an empty string or contains only invalid characters.

[0075] Based on the data type and business semantics of the missing fields, the system intelligently selects an appropriate interpolation method. For missing numeric fields, methods such as mean interpolation, median interpolation, or predictive interpolation based on machine learning algorithms are often used. For missing text fields, strategies such as mode interpolation, fixed value interpolation, or inference based on associated fields can be used. The system automatically performs the selected interpolation operation, filling the missing fields with reasonable alternative values ​​and generating the interpolated data set, ensuring data integrity and preventing deviations or errors in subsequent processing due to missing values.

[0076] S17: Integrate the interpolated data set with the standardized numerical fields and vectorized text features to generate a standardized data set.

[0077] Specifically, we build a unified data integration framework based on the interpolated data set. In this framework, we clarify the association and mapping rules of each data part to ensure that different types of data can be organically integrated under the same framework.

[0078] Standardized numerical fields and vectorized text features are integrated one by one into the corresponding positions or associated records of the interpolated data set according to the preset data structure and format requirements. Through technical means such as data linking and field mapping, the dispersed and heterogeneous data are formed into a complete, coherent, and logically consistent whole in the integrated data set, ultimately generating a standardized data set. 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 process.

[0079] In an optional embodiment, S2 includes the following steps: S21: Perform sharding processing on the standardized data set through the MapReduce framework to generate a sharded data set with a time window.

[0080] Specifically, the standardized data set is sharded using the MapReduce distributed computing framework. The MapReduce framework can split large data sets into multiple small subsets and process them in parallel on different nodes in the cluster, greatly improving data processing efficiency.

[0081] During the sharding process, a specific time window is set for each sharded dataset. This helps capture the dynamic changes in data over different time periods. For example, you can set a weekly, monthly, or quarterly time window so that each sharded dataset contains data within a specific time range, providing a foundation for subsequent analysis of data timeliness and evolution trends.

[0082] S22: Calculate the skill capability density of each sharded data set and generate a dynamic capability feature set based on the skill capability density. The calculation formula for skill capability density is: ; in For the The importance rating of each skill, Indicates the function of core skills.

[0083] Specifically, skill density The calculation formula is: .in, For the The importance score of each skill reflects the relative importance of the skill in the overall skill system. Its value can be determined based on industry standards, enterprise demand research, or expert evaluation; Is the core skill indicator function, used to determine the Is the skill a core skill? As a core skill, The value is 1, otherwise it is 0. ,Will Set the value of to 0.

[0084] Based on calculated skill capability density , generating a dynamic capability feature set. This feature set not only contains the original skill feature information, but also incorporates the dynamic changes in the importance and coreness of skills in different time windows and industry contexts, thereby more accurately portraying the dynamic evolution of job seekers' capabilities.

[0085] S23: Calculate the job skill update rate of each sharded data set, and generate a dynamic job feature set based on the job skill update rate.

[0086] Specifically, the job skill update rate is calculated by comparing the difference between the job skill keyword set in the current time window and the previous time window. It is an indicator that reflects the degree to which job skill requirements change over time. Specifically, the number of newly added skill keywords and the number of disappeared skill keywords in the current time window are counted. Then, combined with the total number of job skill keyword sets, the job skill update rate is calculated. This update rate reflects the speed and degree of dynamic change in job skill requirements between different time windows.

[0087] The calculation process of job skill renewal rate is as follows: 1) Determine the time window: Select an appropriate time window, such as a month or a quarter, as the basis for comparison.

[0088] 2) Collect skill keyword data: Collect skill keyword sets for all positions in the current time window and the previous time window.

[0089] 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.

[0090] 4) Calculate the number of disappeared skill keywords: The number of disappeared 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.

[0091] 5) Calculate the total number of skill keywords: The total number of skill keywords refers to the sum of the skill keyword sets of all positions in the current time window.

[0092] 6) Calculate the job skills renewal rate: The job skills renewal rate can be calculated using the following formula: ; This formula represents the ratio of the sum of the number of new and disappeared skill keywords to the total number of skill keywords, reflecting the degree of change in job skill requirements.

[0093] Based on the calculated job skill update rate, a dynamic job feature set is generated. This feature set, while incorporating the original job description information, further reflects the dynamic updating of job skill requirements. This allows for timely capture of evolving job requirements driven by market changes and technological development, providing job feature descriptions that are more tailored to actual needs for dynamic matching of jobs and job seekers.

[0094] S24: Aggregate the dynamic capability feature set and the dynamic position feature set according to industry labels to generate a dynamic feature set.

[0095] Specifically, the dynamic capability and job feature sets are aggregated by industry tag, integrating and summarizing the dynamic capability and job feature sets from different sharded datasets within the same industry. This approach takes into account the unique requirements and differences in skills and job features across different industries, making the resulting dynamic feature sets more industry-specific and professional.

[0096] After aggregation by industry tag, the final dynamic feature set is generated. This dynamic feature set comprehensively integrates the dynamic feature information of job seekers' abilities and job requirements in different industry contexts. It not only contains static descriptions of skills and job characteristics, but more importantly, reflects the dynamic changes in these characteristics over time, industry, and other factors. This provides a high-quality, dynamic data foundation for subsequent steps in cloud computing-based smart employment data processing methods, such as the construction of professional ability and job requirement profiles, matching matrix calculation, and dynamic matching model generation. 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.

[0097] In an optional embodiment, S21 includes the following steps: S211: Perform sliding window segmentation on the standardized data set according to a preset time window to generate a window segmentation data set.

[0098] Specifically, you can set an appropriate time window length based on specific business needs and data update frequency, such as one week, one month, or one quarter. This time window will serve as the basic unit of sliding window segmentation for sharding the standardized data set.

[0099] Sliding window technology is used to perform segmentation operations on standardized data sets. Specifically, starting from the starting time point of the data set, data is intercepted according to the preset time window length to generate the first window shard dataset; then, the window is sliding according to a certain step size (which can be the same as or different from the time window length) to continue intercepting the next window shard dataset until the time range of the entire data set 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 shard datasets, each shard dataset contains one week of data, and there is a certain degree of data overlap between adjacent shards. This sliding window segmentation method helps to capture the dynamic changes in data in different time periods, while smoothing data fluctuations and improving the stability and accuracy of subsequent analysis.

[0100] S212: Calculate the skill-position co-occurrence matrix of each window segmented data set, and generate association strength data based on the skill-position co-occurrence matrix; the association strength data represents the association strength between skills and positions.

[0101] Specifically, for each window-sliced ​​dataset, the co-occurrence of skills and job titles is counted to construct a skill-job co-occurrence matrix. The rows of the matrix represent different skills, and the columns represent different job titles. The elements in the matrix indicate the frequency or intensity of the skill's occurrence in that job title. For example, if a skill appears frequently in a job title description, the corresponding matrix element value is large, indicating a high correlation between the skill and the job title.

[0102] Based on the constructed skill-job co-occurrence matrix, association strength data is generated. This association strength data can be the result of normalizing the co-occurrence frequency or calculated using other statistical methods to reflect the strength of the association between skills and jobs. For example, the co-occurrence frequency can be divided by the total number of skills in the job to obtain the relative importance of the skill within the job as the association strength. More complex statistical indicators, such as pointwise mutual information (PMI), can also 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.

[0103] S213: Perform singular value decomposition on the correlation strength data to generate a low-dimensional feature vector.

[0104] Specifically, singular value decomposition (SVD) is performed on the generated correlation strength data matrix. SVD is a commonly used matrix decomposition technique that can decompose the original data matrix into the product of three matrices, namely ,in and is an orthogonal matrix, It is a diagonal matrix with singular values ​​on the diagonal. 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 retaining the main features and information in the data.

[0105] After performing singular value decomposition (SVD), the first k largest singular values ​​and their corresponding singular vectors are selected based on a preset dimensionality reduction ratio or threshold to construct a low-dimensional feature vector. For example, if the original correlation strength data matrix has an m×n dimension, after SVD decomposition, the left singular vectors corresponding to the first k 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 needs of the analysis. These low-dimensional feature vectors can maximize the preservation of the correlation characteristics between skills and positions while reducing the data dimension, improving data processing efficiency and model generalization.

[0106] S214: Associating the low-dimensional feature vector with the windowed sharded dataset according to the time window to generate a sharded dataset with the time window.

[0107] Specifically, the generated low-dimensional feature vectors are associated with the corresponding window-sliced ​​datasets according to the time window. Specifically, each window-sliced ​​dataset is merged or linked with the low-dimensional feature vector corresponding to that window, so that each data record in the window-sliced ​​dataset is associated with the corresponding eigenvalue in the low-dimensional feature vector. For example, based on the window-sliced ​​dataset, several columns can be added to store the individual eigenvalues ​​of the low-dimensional feature vectors, thereby enriching the feature dimension of the dataset and providing more comprehensive and representative feature information for subsequent analysis and model training.

[0108] After the above-mentioned association operation, a sharded dataset with a time window is generated. This dataset not only contains the original standardized data, but also integrates the low-dimensional feature vector information obtained by singular value decomposition, and each data record clearly identifies the time window to which it belongs. This sharded dataset with a time window can better reflect the dynamic change characteristics of the data in the time series, and at the same time, with the help of the expressive power of the low-dimensional feature vector, it improves the efficiency and accuracy of the data in model training and prediction. In the subsequent cloud computing-based smart employment data processing method, this sharded dataset with a time window will be used as important input data to construct a dynamic ability feature set and a dynamic job feature set, thereby realizing the dynamic characterization and precise 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 employment market and the rational allocation of talent resources.

[0109] In an optional embodiment, S3 includes the following steps: S31: Build a professional capability profile, including: Perform DBSCAN cluster analysis on the skill data in the dynamic feature set to generate professional ability clusters .

[0110] Calculate the central vector of each professional ability cluster in the professional ability cluster set and use the central vector as the professional ability profile; the calculation formula of the central vector is: ; in, Indicates the The central vector of each professional competence cluster; Indicates the Professional ability clusters, including the skill characteristics , , represents the number of professional competence clusters; Indicates the Skill characteristics described in each professional competency cluster the number of Indicates skill characteristics TF-IDF vector representation of .

[0111] Specifically, DBSCAN cluster 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 any shape and is robust to noisy data. In this step, the algorithm groups the skill data points according to their density distribution in the feature space to generate professional ability clusters. ,in Indicates the number of professional ability clusters.

[0112] For each generated professional capability cluster , calculate its center vector The calculation formula of the center vector is: .in, Indicates the Skill characteristics in professional ability clusters the number of Indicates skill characteristics of The vector represents the skill's importance and uniqueness by taking into account the skill's frequency of occurrence (TF) in the document and the inverse document frequency (IDF) in the entire corpus. The center vector is obtained by calculating the average value of all skill feature vectors in the cluster. ,As a professional ability portrait, this vector can accurately depict the core characteristics and skill distribution of the professional ability cluster, and provide accurate ability representation for subsequent matching calculations.

[0113] S32: Distribute the demand topics As a job requirement portrait; among them, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of requirement topics.

[0114] S33: Obtain a subject-skill mapping matrix, calculate the cosine similarity between the professional ability clusters and the demand subject distribution based on the subject-skill mapping matrix, and generate an initial matching matrix based on the cosine similarity. The calculation formula of the cosine similarity is: ; in, The subject-skill mapping matrix represents the relationship between each required subject and skill; Represents the multiplication of the topic-skill mapping matrix and the transpose of the demand-topic distribution.

[0115] Specifically, the subject skills mapping matrix It is a matrix that represents the relationship between each demand topic and skills. The matrix is ​​constructed by statistical analysis or machine learning methods, where each element Indicates the The first demand topic and the For example, the correlation between the demand topic distribution and the skill feature vector can be calculated, or it can be obtained by training with a supervised learning algorithm to accurately reflect the intrinsic connection between the topic and the skill.

[0116] Based on the subject-skill mapping matrix M, the cosine similarity between the professional ability clusters and the demand topic distribution is calculated. The formula for calculating cosine similarity is: .in, It is The central vector of each professional competence cluster; It is The distribution of demand topics for each position; Represents the subject-skill mapping matrix Distribution of demand topics Multiplying the transpose of yields a vector that maps the topic distribution to the skill space. Cosine similarity measures the similarity between professional competency clusters and job requirements at the skill theme level; the closer the value is to 1, the more similar they are. Based on the calculated cosine similarity, an initial matching matrix is ​​generated. The rows of this matrix represent professional competency clusters, the columns represent job positions, and the element values ​​represent the corresponding similarities. This matrix provides basic data support for subsequent matching optimization, helping the system gain a preliminary understanding of the matching relationship between professional competencies and job requirements.

[0117] S34: When the similarity value in the initial matching matrix is ​​lower than a preset similarity threshold, the initial matching matrix is ​​optimized to generate an optimized matching matrix as a matching matrix between the professional capability profile and the job requirement profile, including: Extract feature weight vectors from the subject-skill mapping matrix ,in Indicates the The feature weight of each skill, Indicates the number of skills.

[0118] The feature weight vector is calculated by gradient descent method. Perform iterative optimization to generate an optimized weight vector.

[0119] The subject-skill mapping matrix is ​​updated according to the optimized weight vector, and an optimized matching degree matrix is ​​generated based on the updated subject-skill mapping matrix.

[0120] 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, the feature weight vector is extracted from the subject skill mapping matrix. ,in Indicates the The feature weight of each skill, Represents the number of skills. This feature weight vector reflects the importance of different skills in topic mapping.

[0121] Gradient descent method is used to adjust the feature weight vector Perform iterative optimization. Gradient descent is a commonly used optimization algorithm that calculates the gradient of the objective function (such as the matching error function) with respect to the weight vector, updates the weight value along the negative direction of the gradient, and gradually reduces the objective function value to find the optimal weight vector. In each iteration, the weight vector is adjusted according to the preset learning rate and gradient information. The optimized weight vector can better reflect the actual contribution of skills in matching and improve the accuracy of matching.

[0122] Update the topic-skill mapping matrix based on the optimized feature weight vector . Substitute the new weight values ​​into the matrix , replacing the original weight values ​​at the corresponding positions in the subject-skill mapping matrix to obtain an updated subject-skill mapping matrix. Then, based on the updated subject-skill mapping matrix, the cosine similarity between the professional competency clusters and the job requirement profiles is recalculated to generate an optimized matching matrix. Compared to the initial matching matrix, this optimized matching matrix can more accurately reflect the degree of match between professional competencies and job requirements, effectively improving the quality and reliability of matching. It can recommend positions that better match job seekers' abilities, while also helping employers screen talents that better meet job requirements, promoting efficient matching in the job market and the rational flow of talent resources.

[0123] In an optional embodiment, S5 includes the following steps: S51: When the job skill update rate exceeds a preset update rate threshold, matrix decomposition update is performed on the dynamic matching matrix to generate an updated matching matrix.

[0124] Specifically, the job skill update rate is monitored in real time. When it exceeds a preset update rate threshold, the dynamic matching matrix update process is triggered. The preset update rate threshold is determined based on industry dynamics, market changes, and historical data to determine whether job skill changes have reached a level that requires an update to the matching model.

[0125] Perform matrix factorization updates on the dynamic matching matrix. Matrix factorization is a mathematical method that decomposes a matrix into the product of multiple matrices. It is widely used in recommendation systems and matching models. The specific steps can be as follows: 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.

[0126] 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 the user's mastery of new skills and changes in demand.

[0127] 3) Update the job characteristics matrix: Based on job skill update information and corporate recruitment dynamics, update the job characteristics matrix to include the latest job skill requirements and preferences.

[0128] 4) Reconstruct the dynamic matching matrix: Multiply the updated user feature matrix and the job feature matrix again to obtain an 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 skill improvement suggestions.

[0129] S52: Perform decision tree analysis on the updated matching matrix to generate a set of personalized job recommendations and skill improvement suggestion rules.

[0130] 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 predict outcomes. In this step, the algorithm uses the user characteristics, job characteristics, and matching results in the matching matrix as training data to learn and build a decision tree model.

[0131] Decision rules are extracted from the trained decision tree model to generate a set of rules for personalized job recommendations and skill improvement suggestions. Each rule represents a job recommendation or skill improvement suggestion based on specific user and job characteristics. For example, a rule might be expressed as: "If the user possesses skills A and B and has X years of work experience, then recommend job Y and improve skill C." These rule sets combine the information in the updated matching matrix with the decision logic of the decision tree model to provide users with accurate and personalized job recommendations and skill improvement guidance, helping them better adapt to market changes and enhance their competitiveness.

[0132] S53: Convert the personalized job recommendation and skill improvement suggestion rule set into a structured data format to generate structured data that supports personalized job recommendation and skill improvement suggestion.

[0133] Specifically, the generated personalized job recommendation and skill improvement suggestion rule set is 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 facilitate subsequent data processing and analysis.

[0134] During the conversion process, each rule is broken down into multiple structured fields, such as user characteristics, job characteristics, recommended jobs, and skill improvement suggestions, which are then populated and stored according to a pre-set structured format. The generated structured data will serve as the core data source supporting personalized job recommendations and skill improvement suggestions, and will be integrated into the smart employment service platform. When users use the platform, the system will quickly retrieve and match corresponding job recommendations and skill improvement suggestions from the structured data based on their specific characteristics and needs, presenting them to the user in an intuitive and user-friendly manner. This will help users make more informed career choices and plans, improve user experience and service quality, and promote efficient matching in the job market and the rational flow of talent.

[0135] The above-mentioned cloud computing-based intelligent employment data processing method, by building a multi-source heterogeneous data integration and standardization processing system under the cloud computing architecture, uses distributed feature extraction technology to fuse the time dimension and industry classification labels to generate a dynamic feature set, builds a dual portrait of professional capabilities and job requirements based on parallel clustering analysis and topic modeling, combines cosine similarity matching with dynamic weight optimization to achieve accurate association mapping, and integrates real-time industry trend characteristics to continuously update the matching model. Finally, through decision tree analysis, it generates structured data that supports personalized job recommendations and skills improvement suggestions. This method addresses the shortcomings of traditional methods in data processing timeliness, multi-dimensional association analysis, and dynamic adaptability, significantly improves the matching efficiency and accuracy of the employment market and talent training, and realizes dynamic optimization of the entire link from data collection to decision output, helping employment service platforms provide job seekers with timely and accurate personalized job recommendations and skills improvement suggestions.

[0136] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0137] Based on the same inventive concept, embodiments of the present application also provide a system for implementing the aforementioned cloud computing-based smart employment data processing method. The implementation solution provided by this system is similar to the implementation solution described in the aforementioned 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-mentioned limitations of the cloud computing-based smart employment data processing method, and will not be further elaborated here.

[0138] In an exemplary embodiment, Figure 2 As shown, a cloud computing-based smart employment data processing system 20 is provided, comprising: The data standardization module 21 is used to obtain multi-source heterogeneous data through cloud computing architecture, standardize the multi-source heterogeneous data, and generate a standardized data set; wherein the multi-source heterogeneous data includes job seeker ability data and job market job description data.

[0139] The dynamic feature extraction module 22 is used to perform distributed feature extraction on the standardized data set to generate a dynamic feature set including a time dimension and an industry classification label.

[0140] The dual-portrait matching module 23 is used to construct the professional ability portrait and the job requirement portrait in parallel based on the dynamic feature set, and calculate the matching matrix between the professional ability portrait and the job requirement portrait.

[0141] The trend fusion and updating module 24 is used to extract the change trend characteristics according to the industry trend report, integrate the change trend characteristics into the matching matrix for dynamic updating, and generate a dynamic matching model.

[0142] The employment recommendation module 25 is used to detect changes in industry demand and generate structured data that supports personalized job recommendations and skill improvement suggestions based on a dynamic matching model.

[0143] The trend fusion update module 24 includes: 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.

[0144] The demand topic distribution generation unit is used to perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of requirement topics.

[0145] The demand topic distribution adjustment unit is used to adjust the demand topic distribution according to high-impact features to obtain a dynamic demand topic distribution.

[0146] The dynamic matching model generation unit is used to generate a dynamic matching matrix based on the dynamic demand topic distribution and use the dynamic matching matrix as a dynamic matching model.

[0147] Optionally, the data standardization module 21 includes: Job seeker capability data acquisition unit, used to obtain skill feature sets from the job search platform , as the job seeker ability data; where each skill feature Including skill code, skill weight and skill proficiency, is the skill number, The total number of skills.

[0148] Job description data acquisition unit, used to obtain job description sets from the recruitment platform through distributed crawlers , as the job description data of the job market; each job description Including job ID, skill keyword set and industry classification, is the position number, The total number of positions.

[0149] The data storage and index generation unit is used to store the skill feature set and job description set in a distributed storage cluster and generate a shard index with a timestamp and industry label.

[0150] The numeric field normalization unit is used to perform Z-Score normalization on numeric fields in the shard index to generate standardized numeric fields.

[0151] The text field vectorization unit is used to perform TF-IDF vectorization processing on text fields in the shard index to generate vectorized text features.

[0152] The missing field interpolation unit is used to perform interpolation processing on the missing fields in the shard index and generate a post-interpolation data set.

[0153] The data integration unit is used to integrate the interpolated data set with the standardized numerical fields and vectorized text features to generate a standardized data set.

[0154] Optionally, the dynamic feature extraction module 22 includes: The time window sharding processing unit is used to perform sharding processing on the standardized data set through the MapReduce framework to generate a sharded data set with a time window.

[0155] The dynamic capability feature extraction unit is used to calculate the skill capability density of each sharded data set and generate a dynamic capability feature set based on the skill capability density. The calculation formula for skill capability density is: ; in For the The importance rating of each skill, Indicates the function of core skills.

[0156] The dynamic job feature extraction unit is used to calculate the job skill update rate of each sharded data set and generate a dynamic job feature set based on the job skill update rate.

[0157] The feature aggregation unit is used to aggregate the dynamic capability feature set and the dynamic position feature set according to industry labels to generate a dynamic feature set.

[0158] Optionally, the time window sharding processing unit includes: The sliding window segmentation subunit is used to perform sliding window segmentation on the standardized data set according to a preset time window to generate a window segmentation data set.

[0159] The association strength calculation subunit is used to calculate the skill-position co-occurrence matrix of each window segment data set and generate association strength data based on the skill-position co-occurrence matrix; the association strength data represents the association strength between skills and positions.

[0160] The feature dimensionality reduction subunit is used to perform singular value decomposition on the correlation strength data to generate a low-dimensional feature vector.

[0161] The time window association subunit is used to associate the low-dimensional feature vector with the window sharded dataset according to the time window to generate a sharded dataset with a time window.

[0162] Optionally, the dual-portrait matching module 23 includes: The professional capability profile construction unit is used to construct professional capability profiles, including: Perform DBSCAN cluster analysis on the skill data in the dynamic feature set to generate professional ability clusters .

[0163] Calculate the central vector of each professional ability cluster in the professional ability cluster set and use the central vector as the professional ability profile; the calculation formula of the central vector is: ; in, Indicates the The central vector of each professional competence cluster; Indicates the Professional ability clusters, including the skill characteristics , , represents the number of professional competence clusters; Indicates the Skill characteristics described in each professional competency cluster the number of Indicates skill characteristics TF-IDF vector representation of .

[0164] The job requirement profile building unit is used to build job requirement profiles, including: Perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution , as a job requirement portrait; among them, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of requirement topics.

[0165] The initial matching degree matrix calculation unit is used to obtain the subject skill mapping matrix, calculate the cosine similarity between the professional ability clusters and the demand subject distribution based on the subject skill mapping matrix, and generate the initial matching degree matrix based on the cosine similarity. The calculation formula of the cosine similarity is: ; in, The subject-skill mapping matrix represents the relationship between each required subject and skill; Represents the multiplication of the topic-skill mapping matrix and the transpose of the demand-topic distribution.

[0166] The matching matrix optimization unit is used to optimize the initial matching matrix when the similarity value in the initial matching matrix is ​​lower than the preset similarity threshold, and generate an optimized matching matrix as the matching matrix between the professional ability profile and the job requirement profile, including: Extract feature weight vectors from the subject-skill mapping matrix ,in Indicates the The feature weight of each skill, Indicates the number of skills.

[0167] The feature weight vector is calculated by gradient descent method. Perform iterative optimization to generate an optimized weight vector.

[0168] The subject-skill mapping matrix is ​​updated according to the optimized weight vector, and an optimized matching degree matrix is ​​generated based on the updated subject-skill mapping matrix.

[0169] Optional, Employment Advice Module 25 includes: The matching matrix updating unit is used to perform matrix decomposition update on the dynamic matching matrix when the job skill update rate exceeds a preset update rate threshold to generate an updated matching matrix.

[0170] The rule generation unit is used to perform decision tree analysis on the updated matching matrix and generate a set of rules for personalized job recommendations and skill improvement suggestions.

[0171] The structured data generation unit is used to convert the personalized job recommendation and skill improvement suggestion rule set into a structured data format, and generate structured data that supports personalized job recommendation and skill improvement suggestion.

[0172] An embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0173] An embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0174] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0175] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A cloud computing-based smart employment data processing method, characterized in that: The methods include: S1: Acquire multi-source heterogeneous data through a cloud computing architecture, perform standardization on the multi-source heterogeneous data, and generate a standardized data set; wherein the multi-source heterogeneous data includes job seeker ability data and job market job description data; S2: Perform distributed feature extraction on the standardized data set to generate a dynamic feature set including a time dimension and industry classification labels; S3: Constructing a professional capability profile and a job requirement profile in parallel based on the dynamic feature set, and calculating a matching matrix between the professional capability profile and the job requirement profile; S4: extracting change trend features according to the industry trend report, integrating the change trend features into the matching matrix for dynamic updating, and generating a dynamic matching model; S5: By detecting changes in industry demand, structured data supporting personalized job recommendations and skill improvement suggestions is generated based on the dynamic matching model; Wherein, the S4 includes: S41: extracting the frequency change rate of each skill word from the industry trend report, and when the frequency change rate of the skill word exceeds a preset change rate threshold, marking the skill feature corresponding to the skill word as a high-impact feature; S42: Perform LDA topic modeling on the job data in the dynamic feature set to generate demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of demand topics; S43: Adjusting the demand topic distribution according to the high-impact features to obtain a dynamic demand topic distribution; S44: Generate a dynamic matching matrix based on the dynamic demand topic distribution, and use the dynamic matching matrix as the dynamic matching model.

2. The method according to claim 1, characterized in that Said S1 comprises: S11: Obtaining skill feature sets from job search platforms , as the job seeker ability data; wherein each skill feature Including skill code, skill weight and skill proficiency, is the skill number, is the total number of skills; S12: Obtaining a collection of job descriptions from recruitment platforms using distributed crawlers , as the job market job description data; wherein each job description Including job ID, skill keyword set and industry classification, is the position number, is the total number of positions; S13: Storing the skill feature set and the job description set in a distributed storage cluster, and generating a shard index with a timestamp and industry label; S14: Perform Z-Score normalization processing on the numerical field in the shard index to generate a normalized numerical field; S15: Perform TF-IDF vectorization processing on the text field in the shard index to generate vectorized text features; S16: Perform interpolation processing on the missing fields in the shard index to generate an interpolated data set; S17: Integrate the interpolated data set with the standardized numerical field and the vectorized text feature to generate the standardized data set.

3. The method according to claim 2, characterized in that The S2 includes: S21: performing sharding processing on the standardized data set through a MapReduce framework to generate a sharded data set with a time window; S22: Calculate the skill capability density of each of the sharded data sets, and generate a dynamic capability feature set based on the skill capability density; wherein the calculation formula for the skill capability density is: ； in For the The importance rating of each skill, Indicates the function of core skills; S23: Calculate the job skill update rate of each of the sharded data sets, and generate a dynamic job feature set based on the job skill update rate; S24: Aggregate the dynamic capability feature set and the dynamic position feature set according to industry labels to generate the dynamic feature set.

4. The method according to claim 3, characterized in that The S21 includes: S211: performing sliding window segmentation on the standardized data set according to a preset time window to generate a window segmentation data set; S212: Calculating the skill-position co-occurrence matrix of each window segmented data set, and generating association strength data based on the skill-position co-occurrence matrix; the association strength data represents the association strength between skills and positions; S213: performing singular value decomposition on the association strength data to generate a low-dimensional feature vector; S214: Associating the low-dimensional feature vector with the window segmented dataset according to the time window to generate the segmented dataset with the time window.

5. The method according to claim 3, characterized in that The S3 includes: S31: Build a professional capability profile, including: Perform DBSCAN cluster analysis on the skill data in the dynamic feature set to generate professional ability clusters ; Calculate the center vector of each professional ability cluster in the professional ability cluster set, and use the center vector as the professional ability profile; the calculation formula of the center vector is: ； in, Indicates the The central vector of each professional competence cluster; Indicates the Professional ability clusters, including the skill characteristics , , represents the number of professional competence clusters; Indicates the Skill characteristics described in each professional competency cluster the number of Indicates skill characteristics TF-IDF vector representation of; S32: Distribute the required topics As the job requirement portrait; among them, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of demand topics; S33: Obtain a subject-skill mapping matrix, calculate the cosine similarity between the professional ability clusters and the demand subject distribution based on the subject-skill mapping matrix, and generate an initial matching matrix based on the cosine similarity, wherein the calculation formula of the cosine similarity is: ； in, The subject-skill mapping matrix represents the relationship between each required subject and skill; represents the multiplication of the subject-skill mapping matrix and the transpose of the demand-topic distribution; S34: When the similarity value in the initial matching matrix is ​​lower than a preset similarity threshold, the initial matching matrix is ​​optimized to generate an optimized matching matrix as the matching matrix between the professional ability profile and the job requirement profile, including: Extract feature weight vectors from the subject-skill mapping matrix ,in Indicates the The feature weight of each skill, Indicates the number of skills; The feature weight vector is calculated by gradient descent method. Perform iterative optimization to generate an optimized weight vector; The subject-skill mapping matrix is ​​updated according to the optimized weight vector, and the optimized matching degree matrix is ​​generated based on the updated subject-skill mapping matrix.

6. The method according to claim 3, characterized in that The S5 includes: S51: When the job 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 set of personalized job recommendation and skill improvement suggestion rules; S53: Convert the personalized job recommendation and skill improvement suggestion rule set into a structured data format to generate the structured data supporting personalized job recommendation and skill improvement suggestion.

7. A cloud computing-based smart employment data processing system, characterized in that: The system comprises: A data standardization module is used 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 includes job seeker ability data and job market job description data; A dynamic feature extraction module, configured to perform distributed feature extraction on the standardized data set to generate a dynamic feature set including a time dimension and an industry classification label; A dual-profile matching module is used to construct a professional capability profile and a job requirement profile in parallel based on the dynamic feature set, and calculate a matching matrix between the professional capability profile and the job requirement profile; A trend fusion update module is used to extract change trend features based on industry trend reports, integrate the change trend features into the matching matrix for dynamic update, and generate a dynamic matching model; An employment recommendation module, configured to detect changes in industry demand and generate structured data supporting personalized job recommendations and skills improvement suggestions based on the dynamic matching model; Wherein, the trend fusion update module includes: a high-impact feature extraction unit, configured to extract a frequency change rate of each skill word from the industry trend report, and mark 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; The demand topic distribution generating unit is used to perform LDA topic modeling on the job data in the dynamic feature set to generate a demand topic distribution ;in, Indicates the The distribution of demand topics for each position, Indicates the Position No. The demand weight of each demand topic, , Indicates the number of demand topics; a demand topic distribution adjustment unit, configured to adjust the demand topic distribution according to the high-impact features to obtain a dynamic demand topic distribution; A dynamic matching model generating 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.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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