Intelligent matching recruitment management system based on large model

Abstract

The invention relates to the technical field of intelligent recruitment management, and discloses an intelligent matching recruitment management system based on a large model. The system comprises a talent database construction module which collects multi-dimensional ability characteristics of job seekers and enterprise post demand data and generates a three-dimensional recruitment data set containing ability, post and time dimensions; the personnel post adaptation field modeling module performs dynamic integrating degree analysis on the data set, constructs a three-dimensional personnel post adaptation field, and identifies adaptation field critical point distribution and a flowing path; the key feature screening module extracts core adaptive features meeting a threshold value; the large model matching engine module inputs the core adaptive features into a pre-training large model generator, introduces adaptive field topology and feature continuity constraints, and outputs optimized dynamic adaptive field distribution; the environment variable compensation module fuses the dynamic indexes of the labor market to generate compensation parameters, and corrects the core adaptation features; and the strategy generation module generates a post matching strategy set and a talent optimization path to adapt to the requirements of the recruitment market.

Description

Technical Field

[0001] This invention relates to the field of intelligent recruitment management technology, specifically to an intelligent matching recruitment management system based on a large model. Background Technology

[0002] In the current recruitment landscape, traditional recruitment management systems generally rely on single-dimensional data for job matching. They mostly compare job seekers' basic information, such as education and work experience, with the basic requirements of the position, failing to comprehensively capture the multidimensional abilities of job seekers and the deeper needs of the company. With the rapid changes in the labor market, job requirements exhibit a dynamic adjustment trend. Traditional systems lack effective integration of time-dimensional data, failing to reflect the changing patterns of job requirements over time and the growth trajectory of job seekers' abilities. This results in matching results that often lag behind the actual market situation.

[0003] Traditional job-person matching processes often lack in-depth analysis of fit relationships, making it difficult to identify key features affecting matching effectiveness and frequently resulting in insufficient matching accuracy. Even when some systems attempt to incorporate more data dimensions, they lack effective modeling methods to construct models that accurately reflect the job-person fit, thus failing to accurately determine the degree of compatibility between individuals and positions. Furthermore, traditional systems often overlook the impact of dynamic labor market conditions during matching, such as changes in industry talent supply and demand and regional employment trends. These environmental variables are not incorporated into the matching process, making the generated matching strategies ill-suited to market fluctuations and failing to provide practical guidance for decision-making by both companies and job seekers.

[0004] At the technology application level, traditional recruitment management systems rarely integrate advanced artificial intelligence technologies, especially large-scale modeling. Existing systems' matching algorithms are mostly based on traditional machine learning models. These models are prone to problems such as insufficient feature extraction and weak generalization ability when dealing with complex, multi-dimensional data, making them ill-suited for handling massive amounts of complex recruitment data. As the scale of recruitment continues to expand, the amount of data grows exponentially, posing a significant challenge to the processing capacity and efficiency of traditional systems. They are unable to quickly process massive amounts of data and generate optimized matching results, severely impacting recruitment efficiency and user experience. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent matching recruitment management system based on a large model to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent matching recruitment management system based on a large model, the system comprising:

[0007] The talent database construction module is used to collect multidimensional ability characteristic data of job seekers and job demand data of enterprises to generate a three-dimensional recruitment dataset containing ability dimension, job dimension and time dimension.

[0008] The talent-job fit field modeling module is used to perform dynamic fit analysis on the three-dimensional recruitment dataset, construct a three-dimensional talent-job fit field, and identify the critical point distribution and flow path of the fit field through gradient field calculation.

[0009] The key feature filtering module is used to extract core adaptation features that meet the stability threshold and the correlation threshold from the three-dimensional talent job matching field. The core adaptation features include the ability gradient change rate, the job demand convergence amount, and the human-job interaction fluctuation coefficient.

[0010] The large model matching engine module is used to input the core adaptation features into the pre-trained large model generator, introduce adaptation field topological constraints and feature continuity constraints into the discriminator loss function, and output the optimized dynamic adaptation field distribution.

[0011] The environmental variable compensation module is used to integrate the labor market dynamic index with the dynamic adaptation field distribution to generate market environment compensation parameters, and to correct and compensate the capability gradient change rate, job demand convergence amount and human-job interaction fluctuation coefficient.

[0012] The strategy generation module is used to generate a set of job matching strategies and talent optimization paths based on the revised core adaptation features.

[0013] Preferably, the talent database construction module includes:

[0014] The data cleaning unit is used to perform noise filtering and standardization alignment on the raw job seeker ability data and company job data.

[0015] The 3D matrix generation unit integrates the cleaned data into a 3D data cube according to capability tags, job category tags, and timestamps.

[0016] The dynamic update unit updates the capability dimension weights and job dimension structure of the three-dimensional recruitment dataset incrementally according to a preset period.

[0017] Preferably, the human-job fit field modeling module includes:

[0018] The gradient field calculation unit calculates the rate of change of the capability requirement gradient of the three-dimensional recruitment dataset along the capability dimension.

[0019] The critical point identification unit locates the capability saturation point and job vacancy in the adaptation field by detecting the second derivative extremum.

[0020] The flow path generation unit constructs a talent capability flow topology network and job demand diffusion paths based on gradient direction fields.

[0021] Preferably, the key feature filtering module includes:

[0022] The stability analysis unit calculates the standard deviation threshold of the rate of change of the capability gradient in the time dimension.

[0023] The correlation calculation unit determines the correlation strength coefficient between the convergence of job requirements and the ability dimension through a cross-correlation algorithm.

[0024] The feature fusion unit selects critical points that simultaneously satisfy the standard deviation threshold and the association strength threshold as core adaptation features.

[0025] Preferably, the large model matching engine module includes:

[0026] Topological constraint unit, which adds a gradient field difference measure between the optimized dynamic fitting field and the original fitting field to the discriminator loss function;

[0027] The continuous constraint unit constructs regularization constraint terms through the time dimension derivative of the core adaptation feature;

[0028] The adversarial training unit iteratively adjusts the parameters of the large model generator until the topology and capability flow path of the dynamic adaptation field converge.

[0029] Preferably, the environmental variable compensation module includes:

[0030] The market index mapping unit establishes a linear compensation model between the labor supply and demand index and the convergence of job demand.

[0031] The thermal correction unit dynamically scales and corrects the rate of change of the ability gradient based on salary fluctuation data;

[0032] Friction coefficient adaptation unit, based on the calculation benchmark of adjusting the fluctuation coefficient of human-job interaction according to the industry flow barrier coefficient.

[0033] Preferably, the strategy generation module includes:

[0034] The path planning unit generates a skill enhancement path for job seekers based on the revised rate of change of the ability gradient.

[0035] The job allocation unit outputs a job structure adjustment plan for the enterprise based on the corrected job demand convergence.

[0036] The interaction optimization unit uses the modified human-job interaction fluctuation coefficient to design the parameters of the interview evaluation process.

[0037] Preferably, the system further includes:

[0038] The closed-loop optimization module is used to collect actual job matching result data, calculate the matching deviation coefficient, and dynamically adjust the weight parameters of the large model generator.

[0039] Preferably, the closed-loop optimization module further includes:

[0040] The deviation acquisition unit acquires the difference matrix between the actual onboarding data and the predicted fit field in real time.

[0041] The parameter adjustment unit adjusts the weights of the feature extraction layer of the large model generator based on the Euclidean distance of the difference matrix.

[0042] Preferably, the closed-loop optimization module further includes a threshold recalibration unit for recalculating the stability threshold and correlation threshold based on historical matching deviations.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By constructing a three-dimensional recruitment dataset that includes competency, job, and time dimensions, the limitations of traditional recruitment systems' single-dimensional data processing are broken. It can comprehensively collect and integrate job seekers' multi-dimensional competency characteristics and corporate job requirements data, while incorporating time dimension information to fully present the changes in job seekers' competencies over time and the dynamic adjustment process of job requirements. This allows subsequent person-job matching to be based on a more comprehensive and accurate data foundation, enabling matching analysis to cover all key aspects of the person-job relationship and avoiding matching biases caused by missing data dimensions.

[0045] The system's person-job fit modeling module constructs a three-dimensional talent-job fit field by dynamically analyzing the fit of a three-dimensional recruitment dataset. It then uses gradient field calculations to identify the distribution of critical points and flow paths within the fit field, achieving a deep characterization of the person-job fit relationship. This modeling approach intuitively and accurately reflects the fit between people and jobs, clearly presenting the spatial distribution and changing trends of the fit. It helps staff accurately grasp the key nodes and potential directions of change in the person-job matching process, providing a clear analytical basis for subsequent feature selection and matching optimization. Compared to traditional systems that can only perform simple data comparisons, this significantly improves the depth of understanding of the person-job fit relationship.

[0046] The key feature screening module extracts core fitting features that meet the stability and correlation thresholds from the three-dimensional talent-job matching field. It focuses on key indicators such as the rate of change of ability gradient, the convergence of job requirements, and the fluctuation coefficient of person-job interaction, effectively eliminating the interference of redundant data and irrelevant features. This allows subsequent large-scale model matching to focus on the core factors that have an important impact on the matching results, reducing the interference of invalid data on the matching process, improving the pertinence and efficiency of matching analysis, and enabling the matching model to more accurately capture the key logic of person-job matching.

[0047] The large-scale model matching engine module inputs core fitting features into a pre-trained large-scale model generator and introduces fitting field topological constraints and feature continuity constraints into the discriminator loss function, fully leveraging the advantages of large-scale models in handling complex data and uncovering deep patterns. The pre-trained large-scale model possesses powerful feature learning and data processing capabilities, enabling it to deeply explore the intrinsic relationships between core fitting features. The introduction of fitting field topological constraints and feature continuity constraints further ensures the rationality and stability of the generated dynamic fitting field distribution, avoiding problems such as overfitting or feature fragmentation during model training, thus outputting optimized results that better reflect actual human-job fit.

[0048] The environmental variable compensation module integrates dynamic labor market indices and dynamic matching field distributions to generate market environment compensation parameters. This parameters correct and compensate for core matching characteristics, incorporating dynamic changes in the labor market into the consideration of job-person matching. By combining real-time market environment information such as industry talent supply and demand fluctuations and regional employment trends, the core matching characteristics can dynamically reflect the actual market situation. This makes the subsequently generated job matching strategy set and talent optimization path more market-adaptable, effectively addressing dynamic changes in the labor market and providing enterprises and job seekers with decision support more aligned with the current market environment.

[0049] The strategy generation module generates a set of job matching strategies and talent optimization paths based on the revised core adaptation features, providing diversified and targeted solutions for both companies and job seekers. Companies can quickly filter job seekers who highly match the job requirements using the matching strategy set, while clarifying the direction of job optimization. Job seekers, on the other hand, can clearly understand the gap between their own abilities and those of their target positions based on the talent optimization path, and develop personalized skills enhancement plans. This achieves a two-way improvement in both corporate recruitment efficiency and job seeker success rates, promoting the efficient operation of the recruitment market. Attached Figure Description

[0050] Figure 1 This is a sequence diagram of the intelligent matching recruitment management system based on a large model as described in this invention;

[0051] Figure 2A flowchart illustrating the work involved in building the talent database module;

[0052] Figure 3 A flowchart illustrating the operation of the key feature filtering module;

[0053] Figure 4 A flowchart illustrating how the large model matching engine module works;

[0054] Figure 5 A flowchart illustrating the operation of the environmental variable compensation module. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 This invention provides an intelligent matching recruitment management system based on a large model, the system comprising:

[0057] The talent database construction module is used to collect multidimensional ability characteristic data of job seekers and job demand data of enterprises to generate a three-dimensional recruitment dataset containing ability dimension, job dimension and time dimension.

[0058] The talent-job fit field modeling module is used to perform dynamic fit analysis on the three-dimensional recruitment dataset, construct a three-dimensional talent-job fit field, and identify the critical point distribution and flow path of the fit field through gradient field calculation.

[0059] The key feature filtering module is used to extract core adaptation features that meet the stability threshold and the correlation threshold from the three-dimensional talent job matching field. The core adaptation features include the ability gradient change rate, the job demand convergence amount, and the human-job interaction fluctuation coefficient.

[0060] The large model matching engine module is used to input the core adaptation features into the pre-trained large model generator, introduce adaptation field topological constraints and feature continuity constraints into the discriminator loss function, and output the optimized dynamic adaptation field distribution.

[0061] The environmental variable compensation module is used to integrate the labor market dynamic index with the dynamic adaptation field distribution to generate market environment compensation parameters, and to correct and compensate the capability gradient change rate, job demand convergence amount and human-job interaction fluctuation coefficient.

[0062] The strategy generation module is used to generate a set of job matching strategies and talent optimization paths based on the revised core adaptation features.

[0063] Example 1: See Figure 2 The talent database construction module is responsible for processing raw recruitment data and forming a structured three-dimensional dataset. This module includes a data cleaning unit, a three-dimensional matrix generation unit, and a dynamic update unit. The data cleaning unit receives raw job seeker ability data and company job data from multiple channels. Job seeker ability data typically includes structured fields such as skill certificate names and levels, project names and durations, and years of work experience, as well as unstructured text content such as resume descriptions and self-evaluations. Company job data includes job descriptions, a list of job requirements, salary ranges, and work locations. This unit first performs noise filtering, identifying and removing invalid characters or garbled text, and correcting inconsistent formats, such as standardizing date representation to YYYY-MM-DD format and unifying various salary units to RMB yuan per year. Then, it performs standardization and alignment processing, for example, mapping skill descriptions from different sources, such as "proficient in Java programming" and "has Java development experience," to a unified "Java language application" ability tag; and mapping "project management ability" and "ability to lead a team" to a "project management" ability tag. The 3D matrix generation unit receives cleaned and standardized data and integrates it according to a predefined 3D label system. The capability dimension label system covers multiple categories such as technical capabilities, business capabilities, and soft skills, with each category further subdivided into specific labels, such as programming languages ​​and framework tools under technical capabilities. The job dimension label system is based on industry standard job classifications, including job categories such as R&D and marketing, industry categories such as internet and finance, and job levels such as junior and senior. The time dimension is based on the timestamp of data collection and is typically aggregated weekly or monthly. This unit places each data point in a position uniquely determined by the capability label, job label, and time point, and assigns a corresponding data value, such as the number of job seekers with "Python programming" skills in a specific week, or the demand for "machine learning engineer" positions in that week, thus forming a 3D data cube. The dynamic update unit initiates an incremental update process according to a preset update cycle, such as starting at midnight every day. This unit acquires new job seeker resume data and new job data, processes them through the data cleaning unit, and then the 3D matrix generation unit integrates the new data into the existing 3D data cube, updating the values ​​of the corresponding data points. In addition, the dynamic update unit analyzes recent data trends. For example, if it finds that the growth rate of job demand corresponding to a certain ability tag is consistently higher than the threshold, it will automatically increase the weight coefficient of that ability tag in the overall ability dimension; or if it detects that the frequency of emerging job types is increasing, it will create new job classification tags in the job dimension.

[0064] The job-person matching modeling module performs in-depth analysis of a 3D recruitment dataset to construct a job-person matching model. This module includes a gradient field calculation unit, a critical point identification unit, and a flow path generation unit. The gradient field calculation unit, for the 3D dataset, calculates the rate of change of data between adjacent ability labels along the ability dimension under fixed time points and fixed job categories. This calculation analyzes how the demand for jobs or the supply of job seekers changes when moving from one ability label to a neighboring label within the ability space. For example, it analyzes whether the demand for jobs increases or decreases when moving from "Python programming" to "machine learning" ability, and by what magnitude, thus forming a gradient vector field along the ability dimension. This field indicates the direction of the most significant changes in demand and supply within the ability space. The critical point identification unit further analyzes the gradient vector field output by the gradient field calculation unit, identifying its extreme points by calculating the rate of change of the gradient field along the ability dimension. Extreme points include points where the gradient change rate is positive and reaches a local maximum, representing capability saturation points where the supply of job seekers for a specific capability far exceeds the job demand; and points where the gradient change rate is negative and reaches a local minimum, representing job vacancies where the job demand for a specific capability is far from being met by the supply of job seekers. These critical points identify key areas of supply-demand imbalance in the job matching process. The flow path generation unit constructs two types of path models based on the gradient direction information provided by the gradient field calculation unit. The talent capability flow topology network describes the possible paths for job seekers to develop and migrate their capabilities in the capability dimension space to improve their match with job requirements. This network consists of a series of connected edges, each representing a feasible transition direction from one capability state to another. The direction of the edge is determined by the demand growth direction indicated by the gradient field, and the weight may depend on the difficulty or cost of the capability transition. The job demand diffusion path describes the direction in which companies may adjust or expand their recruitment needs to effectively fill job vacancies. For example, when there is a shortage of talent with a specific skill, companies may expand their demand to related skills or lower experience requirements. This path is guided by the direction of sufficient supply indicated by the gradient field. These path models collectively reveal the dynamic process and potential optimization directions of person-job matching.

[0065] The entire implementation process relies on large-scale data processing and computing capabilities. The data cleaning unit needs to integrate natural language processing techniques for text parsing and standardization. The 3D matrix generation unit requires an efficient multi-dimensional data indexing and storage mechanism, potentially utilizing distributed databases or data cube technology. The dynamic update unit needs to implement incremental computation algorithms to efficiently update the 3D dataset and related weights. Gradient field calculation involves a large amount of numerical computation, typically achieved using matrix operation libraries. Critical point identification requires optimized algorithms to efficiently locate extreme points in large multi-dimensional spaces. Flow path generation involves graph theory algorithms to construct and traverse topological networks. The collaborative work of these components forms the foundation for human-job fit field modeling.

[0066] Example 2: See Figure 3 The key feature screening module is responsible for extracting core fitting features with practical guiding significance from the 3D talent-job fit field model. This module receives 3D talent-job fit field data from the talent-job fit field modeling module. This fit field data is a structured multidimensional dataset containing values ​​of key indicators such as the rate of change of ability gradient, the convergence of job requirements, and the fluctuation coefficient of talent-job interaction at specific time points, specific job categories, and specific ability dimensions.

[0067] The stability analysis unit operates on the characteristic of the rate of change of the ability gradient. Within the three-dimensional talent-job matching field, for each fixed ability and job dimension point, this unit extracts the historical numerical sequence of the rate of change of the ability gradient over a time window along the time axis. The length of this time window is preset, for example, it could be data points from the past twelve weeks or three months. For each such spatiotemporal location, the stability analysis unit calculates the standard deviation of its rate of change of the ability gradient over that time series. The standard deviation is calculated based on the dispersion of the values ​​at each time point within the time series relative to the series mean. The system sets a stability threshold, which is typically an upper limit of the standard deviation. The task of the stability analysis unit is to identify all data points whose standard deviation of the rate of change of the ability gradient over the time dimension is lower than this preset stability threshold. These identified points indicate that the rate of change of the ability gradient at that location has been relatively stable in recent times, with fluctuations within an acceptable range, excluding interference from short-term sharp fluctuations.

[0068] The correlation calculation unit focuses on the correlation analysis between job demand convergence and other dimensional features. Within the three-dimensional talent-job matching field, this unit examines the correlation between the job demand convergence feature and various ability features on the ability dimension for each fixed job dimension point and time point. Specifically, for a specific job category at a specific time point, its job demand convergence is a numerical value, while the ability dimension displays the relevant values ​​of various abilities within that job category at that time point, such as the supply of job seekers or the popularity of each ability. The correlation calculation unit uses a cross-correlation algorithm to calculate the correlation strength coefficient between the job demand convergence and each ability feature value on the ability dimension. The cross-correlation algorithm quantifies the statistical linear correlation between two different variable sequences, and the calculated correlation strength coefficient value is typically between -1 and +1. The system sets a correlation threshold, which is usually a positive lower limit. The task of the correlation calculation unit is to filter out all job-ability-time combinations whose correlation strength coefficient values ​​are greater than or equal to this preset correlation threshold. These selected points indicate a significant correlation between the competency characteristics of the position and the convergence of job requirements, suggesting that changes in these competencies can strongly influence or reflect the job's demand status.

[0069] The feature fusion unit receives outputs from the stability analysis unit and the correlation calculation unit. The stability analysis unit outputs a set of all data points in three-dimensional space that meet the stability requirements—that is, points where the rate of change of the capability gradient fluctuates little over time. The correlation calculation unit outputs a set of all data points in three-dimensional space that meet the correlation requirements—that is, points where there is a strong correlation between capability features and job requirement convergence. The core operation of the feature fusion unit is to perform an intersection operation on these sets. It identifies data points that appear in both sets simultaneously. In other words, the feature fusion unit selects points that meet both the stability and correlation requirements. The feature values ​​corresponding to these selected points constitute the final core adaptation feature set. Specifically, this includes: the rate of change of the capability gradient at that point, the job requirement convergence at that point, and the person-job interaction fluctuation coefficient at that point. The person-job interaction fluctuation coefficient, as an inherent feature value of that location, is also included in the core adaptation feature set.

[0070] During implementation, the stability analysis unit needs to efficiently process large amounts of time-series data. For each capability-job combination point in the three-dimensional space, the time-series standard deviation of its capability gradient change rate needs to be calculated independently. This involves sliding window calculation, mean calculation, and variance calculation of the time-series data. In large-scale data scenarios, distributed computing frameworks or optimized time-series database functions are usually required to improve computational efficiency. The correlation calculation unit faces even greater computational challenges because it needs to calculate the correlation strength coefficient between the convergence of job demand and numerous capability feature values ​​in the capability dimension for each job-time combination point in the three-dimensional space. The computational cost of the cross-correlation algorithm increases linearly with the number of capability dimensions. To optimize performance, approximate calculation algorithms, feature selection techniques, or GPU acceleration may be necessary. The feature fusion unit operates relatively directly, mainly involving the intersection of sets, but efficient implementation relies on effective indexing and fast query capabilities for the three-dimensional space point set, such as using spatial index data structures or utilizing the join query function of a database. The core adaptation feature set is ultimately output to the subsequent large model matching engine module. The entire screening process is based on preset stability and relevance thresholds. The initial values ​​of these thresholds are usually set by domain knowledge or historical experience and can be adjusted later through the closed-loop optimization module.

[0071] Example 3: See Figure 4 The core task of the large model matching engine module is to optimize the core adaptation features output by the key feature filtering module to generate a dynamic adaptation field distribution that better meets actual matching needs. This module is implemented based on a pre-trained generative adversarial network architecture, which includes a generator, a discriminator, and specific constraint mechanisms.

[0072] The generator receives core fitting features as input data. These core fitting features include the rate of change of ability gradient, the convergence of job requirements, and the fluctuation coefficient of person-job interaction. They are a set of feature values ​​selected from the three-dimensional talent-job fitting field, possessing time stability and strong dimensional correlation. The generator itself is a deep neural network model, typically employing an encoder-decoder framework or a multilayer perceptron. The encoder is responsible for deep feature extraction and abstract representation of the input core fitting features, capturing the complex nonlinear relationships between features. The decoder, based on the learned feature representations, reconstructs or predicts the optimized fitting field distribution data. This output, called the dynamic fitting field distribution, aims to reflect the state of the talent-job fit relationship after large-scale model optimization.

[0073] The discriminator acts as the evaluator in this module. It receives two types of data: unoptimized raw 3D job fit field data and dynamically generated fit field distribution data from the generator. The discriminator is also a deep neural network, typically designed as a classifier structure. Its basic training objective is to distinguish whether the input data comes from the raw fit field or the optimized output of the generator. The discriminator improves its discriminative ability by minimizing its adversarial loss.

[0074] To guide the generator to produce optimized results that are physically reasonable and conform to actual matching rules, two important constraints are introduced into the discriminator's loss function: topological constraints and continuity constraints. Topological constraints aim to maintain consistency between the optimized dynamic adaptation field and the original adaptation field in key structural characteristics. Specifically, this is achieved by calculating the difference in gradient vectors between the original adaptation field data and the generator output data at various points in space. This difference measures the degree of deviation between the two fields in the direction and intensity of local changes. Continuity constraints focus on the smoothness of the core adaptation features' changes over time. This constraint constructs a regularization term by calculating the first derivative of the core adaptation features in the time dimension, requiring that the changing trends of these features in the generator's output dynamic adaptation field be continuous over time, avoiding unrealistic abrupt changes or discontinuities.

[0075] The discriminator's total loss function therefore consists of three parts: a basic adversarial loss term, a topological constraint term, and a continuity constraint term. Its mathematical expression is as follows:

[0076] L total =L adv +λ1L top +λ2L con

[0077] Where: L adv L represents the adversarial loss of the discriminator, measuring its ability to distinguish between the real original fit field and the dynamically generated fit field. top The topological constraint loss term is calculated as a measure of the difference in gradient fields between the original adapted field and the dynamically adapted field output by the generator (e.g., the norm sum of the gradient vector differences), L. con λ1 represents the continuity constraint loss term, which is calculated as a penalty term (such as regularization based on the magnitude of the derivative) of the first derivative (rate of change) of the core adaptation feature in the time dimension of the dynamic adaptation field output by the generator. λ1 and λ2 are hyperparameters used to adjust the weights of the topology constraint term and the continuity constraint term in the total loss, respectively, and control the influence of these two constraints on the final optimization result.

[0078] The adversarial training unit is responsible for executing the module's training process. This process is iterative. In each training iteration, the generator first generates a dynamic adaptation field distribution based on the current core adaptation feature input. The discriminator simultaneously receives the original adaptation field data samples and the generator's output samples, and calculates the distribution based on the total loss function L, which includes the three losses mentioned above. total The discriminant loss is calculated, and then the discriminator's network parameters are updated using the backpropagation algorithm. Next, with the discriminator parameters fixed, the generator attempts to generate a dynamic fit field distribution that better "deceives" the discriminator (i.e., makes the discriminator mistakenly identify it as the original fit field). The generator's optimization objective is to minimize the discriminator's ability to distinguish its generated samples, while implicitly being affected by the topological constraint loss L in the discriminator. top and continuity constraint loss L con Guided by backpropagation, the generator's network parameters are updated. This process is repeated iteratively.

[0079] The termination conditions for continuous training are mainly based on two convergence metrics: first, the discriminator has difficulty effectively distinguishing between generated samples and real samples, manifested in the adversarial loss L. adv The training process terminates when the following convergence conditions are met: 1) the dynamic adaptation field distribution of the generator output stabilizes at a relatively low level; 2) the topological features (such as the number and location distribution of critical points) and the pattern of capacity flow paths no longer change significantly.

[0080] Ultimately, the fully trained generator was used in practical applications. When new core adaptation features are input, the generator outputs an optimized dynamic adaptation field distribution. This distribution integrates information from the original adaptation field and, through deep learning and constraint optimization of a large model, better characterizes the potential, optimized dynamic adaptation relationship between talent capabilities and job requirements, providing a foundation for subsequent environmental variable compensation and strategy generation. The implementation of the entire module relies on deep neural network model training frameworks such as TensorFlow or PyTorch and requires sufficient computing resources for efficient iterative training. The customized loss function design in the discriminator is crucial to ensuring that the generated results conform to business logic constraints.

[0081] Example 4: See Figure 5 The environmental variable compensation module is responsible for integrating the dynamic changes in the external labor market into the system and correcting the core adaptation features. This module receives dynamic adaptation field distribution data output from the large model matching engine module, and simultaneously accesses multiple external market data sources to generate market environment compensation parameters, and adjusts the capability gradient change rate, job demand convergence amount, and person-job interaction fluctuation coefficient accordingly.

[0082] The market index mapping unit first accesses dynamic labor market index data released by authoritative institutions. This data typically includes industry-specific job vacancy rates, industry talent supply-demand ratios, regional recruitment activity indices, and macroeconomic unemployment rate trends. For each industry or job category of interest, this unit establishes a linear compensation model between these market indices and the corresponding job demand convergence in the dynamic adaptation field distribution. The model is trained using multiple linear regression on historical data. For example, for data engineer positions in the internet industry, the model might find that for every 1% increase in the job vacancy rate, the demand convergence needs to increase by 0.5 units in the model; for every 10-point decrease in the recruitment activity index, the demand convergence needs to decrease by 0.3 units. During system operation, this unit acquires the latest market index values ​​in real time, substitutes them into the trained linear model, and calculates the compensation amount for the job demand convergence at each job-skill combination point. This compensation amount can be positive or negative, used to increase or decrease the original demand convergence value.

[0083] The thermal correction unit focuses on the impact of salary changes on skill value. This unit accesses real-time updated industry salary data, such as the average salary growth rate for specific skills, quarterly changes in median job salaries, and reports on emerging skill premiums. Based on this salary fluctuation data, the unit dynamically scales and corrects the rate of change of skill gradients in the dynamic fitting field distribution. The core logic is that salary changes directly reflect the market's recognition of skill value. When the market salary for a specific skill shows a significant increase, the relative value and influence of that skill in the talent market increases. At this time, the thermal correction unit amplifies the rate of change of that skill in the gradient field, increasing its weight in the fitting field. Conversely, if the salary for a skill stagnates or declines, the impact of its gradient rate of change is reduced accordingly. This scaling operation is non-linear and is usually based on a preset mapping relationship or rule. For example, when the salary growth rate exceeds a threshold X, the gradient rate of change is multiplied by a factor greater than 1; when the growth rate is below a threshold Y, it is multiplied by a factor less than 1. See Table 1.

[0084] Table 1: Data table of thermodynamic correction for capacity gradient change rate.

[0085]

[0086] The Friction Coefficient Adaptation Unit addresses barriers to talent mobility. This unit receives data on industry or job mobility barriers, such as the difficulty assessment coefficient for cross-industry transitions (derived from industry reports or expert surveys), regional mobility cost indices (e.g., differences in living costs and ease of migration between major cities), and the difficulty coefficient for obtaining specific professional certifications. This barrier data collectively forms the basis for adjusting the calculation benchmark of the person-job interaction fluctuation coefficient. The core operation of the Friction Coefficient Adaptation Unit is to adjust the calculation logic or numerical range of the person-job interaction fluctuation coefficient based on the barrier coefficient. Higher industry mobility barriers mean greater resistance to talent moving into that industry or transitioning to different jobs within it, increasing friction in the actual person-job matching process. Therefore, for industries or jobs with high barrier coefficients, this unit will increase the benchmark value of the person-job interaction fluctuation coefficient or amplify its fluctuation range, shifting its numerical range towards a higher level to more accurately reflect the difficulty and instability of the matching process. For example, for high-barrier industries like medical device R&D that require rigorous professional certification, the calculation benchmark for the person-job interaction fluctuation coefficient might be set 30% higher than that for internet customer service positions.

[0087] The compensation module integrates the calculation results from the above three units. The market index mapping unit outputs the compensation amount for the convergence of job demand, the thermal correction unit outputs the correction factor for the rate of change of capability gradient, and the friction coefficient adaptation unit outputs the benchmark adjustment amount or range adjustment parameter for the fluctuation coefficient of human-job interaction. The compensation module integrates and applies these compensation parameters to numerically correct the three core adaptation features in the input dynamic adaptation field distribution:

[0088] Corrected capacity gradient rate of change = Original capacity gradient rate of change × Thermodynamic correction factor;

[0089] Corrected job demand convergence = Original job demand convergence + Market index compensation;

[0090] The corrected personnel-job interaction fluctuation coefficient = original personnel-job interaction fluctuation coefficient × (1 + barrier adjustment coefficient) or application scope mapping function.

[0091] These revised core adaptation features more accurately incorporate the impact of the current external market environment.

[0092] Based on the revised core adaptation feature values, the strategy generation module generates an executable decision scheme.

[0093] The path planning unit analyzes the revised rate of change in the ability gradient. This characteristic value reflects the changing trend of the relative value of various abilities in the suitable market after market correction (positive values ​​represent increased demand and value; negative values ​​represent saturated or declining demand). This unit generates a personalized skills enhancement path map for job seekers. This path map clearly indicates the gap between the job seeker's current skill set and the abilities required for the target position. Based on the magnitude and direction of the revised rate of change in the ability gradient, it prioritizes the enhancement of abilities with the greatest potential for value improvement (i.e., the largest positive gradient change rate) and high relevance to the target position, while suggesting reducing investment in abilities with significantly decreased value (i.e., large negative gradient change rates). The path map not only lists the recommended skills to learn but also includes a reasonable order of ability development and recommended learning resource directions.

[0094] The job configuration unit analyzes the convergence of job demand after correction. This feature quantifies the true intensity and concentration of demand for core competencies for specific positions within the company after market correction. This unit provides management with recommendations for job structure adjustment. Recommendations include: suggesting increasing the staffing or recruitment priority for core competencies with significantly increased convergence after correction; suggesting reducing or merging positions corresponding to competencies with excessively low or continuously declining convergence after correction; and reassessing and adjusting the weighting of different competency elements in the job competency model based on changes in convergence. These recommendations may manifest as specific recruitment plan adjustment tables, job description update suggestions, or organizational structure fine-tuning diagrams.

[0095] The Interaction Optimization Unit utilizes a modified person-job interaction volatility coefficient. This coefficient reflects the degree of friction and volatility risk in the matching process between talent capabilities and job requirements within a specific industry or job category after market adjustments. Based on the magnitude of this coefficient, this unit designs or optimizes specific parameters of the interview assessment process. For positions with a high person-job interaction volatility coefficient (meaning a complex matching process, high uncertainty, and strong friction), this unit tends to recommend increasing the number of interview rounds for a more comprehensive assessment, introducing more diversified assessment methods (such as scenario simulations and skills practice tests), extending the assessment period to observe stability, or strengthening the depth of background investigation. For positions with a low volatility coefficient (a relatively smooth matching process), it may recommend streamlining the assessment process, shortening the number of interview rounds, and focusing on core competency assessment. The output is an interview process optimization guide containing specific parameter settings for the recruitment team to implement.

[0096] Example 5: The closed-loop optimization module enables the system to continuously improve itself. This module runs after the strategy generation module. By collecting actual job matching result data, it calculates the deviation between the prediction and the actual results, and dynamically adjusts the core parameters of the system accordingly, forming a feedback closed loop. The module includes a deviation acquisition unit, a parameter adjustment unit, and a threshold recalibration unit.

[0097] The deviation acquisition unit is responsible for obtaining actual job matching result data. This unit extracts data in real time from the enterprise's human resources system and job seeker application tracking system. The acquired data mainly includes detailed information about the job the job seeker ultimately joined, such as the specific job title, department, job level, hiring date, and probationary period evaluation results; and employee competency data actually hired by the enterprise, such as employee skills assessment reports after joining the company, scores of core competency items reflected in performance appraisals, and information on key projects participated in. This actual result data serves as the objective verification basis for the system's predicted matching effect. The deviation acquisition unit aligns and compares the actual result data with the dynamic adaptation field distribution data used by the strategy generation module for prediction. The alignment operation is based on the same dimensional system: competency dimension labels, job dimension labels, and corresponding time points. For each location point in the three-dimensional space, this unit calculates the difference between the actual observed value and the predicted value at that time point. For example, the difference between the actual demand and the predicted demand for a certain competency label for a certain job category at a certain time point, or the difference between the actual proportion of job seekers with a certain competency who are hired for a certain position and the predicted proportion. These differences are organized into a difference matrix. This matrix has the same structure as the dynamic adaptation field distribution, but the value of each element is no longer the adaptation field eigenvalue, but rather the prediction deviation value at that location point. The difference matrix quantifies the overall deviation pattern and local deviation intensity of the system prediction in the capability, job, and time dimensions.

[0098] The parameter adjustment unit receives the difference matrix calculated by the deviation acquisition unit as input. This unit first calculates the global Euclidean distance of the difference matrix. The Euclidean distance is calculated by summing the squares of the deviation values ​​of all elements in the difference matrix and then taking the square root, resulting in a scalar value. This Euclidean distance represents the degree of deviation between the predicted dynamic adaptation field distribution and the actual matching result in the overall space, and is the core metric for the system's prediction accuracy. The core task of the parameter adjustment unit is to dynamically adjust the weight parameters of the feature extraction layer of the generator in the large model matching engine module based on this deviation metric and the internal structure of the difference matrix. The feature extraction layer is the front end of the generator's deep neural network, responsible for extracting abstract high-level feature representations from the core adaptation features of the input. The parameter adjustment unit analyzes the difference matrix to identify the distribution pattern of prediction deviations along the feature dimensions. For feature dimensions with persistently large or significant prediction deviations, such as regions corresponding to certain specific ability labels or job categories, this unit applies a significant adjustment update to the weight parameters corresponding to that feature dimension in the feature extraction layer. The adjustment aims to reduce the prediction deviation along that feature dimension. For feature dimensions with small prediction bias or relatively accurate predictions, their corresponding weight parameters are fine-tuned to avoid unnecessary fluctuations. Weight adjustments are performed using specific optimization algorithms, such as gradient descent or its variants. The adjusted weight parameters are then updated in the generator of the large model matching engine module. When the generator processes new core fitting features next time, it will use the updated weight parameters for feature extraction and dynamic fitting field prediction, aiming to generate a predicted distribution that more closely approximates the actual future matching results.

[0099] The threshold recalibration unit focuses on optimizing the threshold parameters of the key feature selection module. This unit periodically collects and stores historical matching deviation data calculated by the deviation acquisition unit. This historical data typically includes difference matrix records at multiple time points within a time window, such as weekly prediction deviation data over the past six months. The threshold recalibration unit performs statistical analysis on this historical deviation data. The analysis includes calculating the average level, variance, and deviation distribution characteristics of the prediction deviations corresponding to the core fitting features selected under the current stability and correlation thresholds used by the key feature selection module in practical applications. For example, it analyzes whether the capability gradient change rate feature points selected by the original stability threshold truly exhibit low volatility in actual matching results; and whether the correlation of feature points selected by the original correlation threshold is truly significant in the actual matching process.

[0100] Based on the statistical analysis of these historical deviation data, the threshold recalibration unit performs threshold recalculation. If historical data analysis shows that the actual capability gradient change rate of certain feature points selected under the original stability threshold fluctuates more than expected in subsequent time, the system will recalculate and lower the upper limit of the standard deviation threshold to make the selection criteria more stringent. If historical data shows that the corresponding capability features of certain feature points selected under the original correlation threshold have insufficient correlation strength with the convergence of job requirements in actual matching, the system will recalculate and increase the lower limit requirement of the correlation strength coefficient. Recalculation is usually based on statistical methods, such as setting quantiles at the target confidence level. The new stability threshold and new correlation threshold obtained after recalculation are updated in real time to the corresponding units of the key feature selection module. Subsequently, the key feature selection module will use the updated threshold parameters to perform core fitting feature selection operations, hoping to select a feature set with greater predictive stability and significant correlation in practical applications.

[0101] The entire closed-loop optimization module operates automatically at a preset cycle. The deviation acquisition unit continuously or periodically acquires the latest actual data. The parameter adjustment unit may fine-tune the weights after each acquisition of new deviation data, or perform batch adjustments after accumulating a certain amount of new data. The threshold recalibration unit operates at a longer time cycle, such as performing threshold recalculation monthly or quarterly. This continuous deviation monitoring, parameter updating, and threshold optimization mechanism enables the system to continuously adapt to changes in the labor market and the shortcomings of its own predictive model, gradually improving the accuracy of matching predictions and the effectiveness of strategy recommendations. The implementation of the module relies on a stable interface with actual recruitment result data sources, efficient large-scale matrix operation capabilities, and robust parameter optimization algorithms.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A large-scale intelligent matching recruitment management system, characterized in that, include: The talent database construction module is used to collect multidimensional ability characteristic data of job seekers and job demand data of enterprises to generate a three-dimensional recruitment dataset containing ability dimension, job dimension and time dimension. The talent-job fit field modeling module is used to perform dynamic fit analysis on the three-dimensional recruitment dataset, construct a three-dimensional talent-job fit field, and identify the critical point distribution and flow path of the fit field through gradient field calculation. The key feature filtering module is used to extract core adaptation features that meet the stability threshold and the correlation threshold from the three-dimensional talent job matching field. The core adaptation features include the ability gradient change rate, the job demand convergence amount, and the human-job interaction fluctuation coefficient. The large model matching engine module is used to input the core adaptation features into the pre-trained large model generator, introduce adaptation field topological constraints and feature continuity constraints into the discriminator loss function, and output the optimized dynamic adaptation field distribution. The environmental variable compensation module is used to integrate the labor market dynamic index with the dynamic adaptation field distribution to generate market environment compensation parameters, and to correct and compensate the capability gradient change rate, job demand convergence amount and human-job interaction fluctuation coefficient. The strategy generation module is used to generate a set of job matching strategies and talent optimization paths based on the revised core adaptation features.

2. The intelligent matching recruitment management system based on a large model according to claim 1, characterized in that, The talent database construction module includes: The data cleaning unit is used to perform noise filtering and standardization alignment on the raw job seeker ability data and company job data. The 3D matrix generation unit integrates the cleaned data into a 3D data cube according to capability tags, job category tags, and timestamps. The dynamic update unit updates the capability dimension weights and job dimension structure of the three-dimensional recruitment dataset incrementally according to a preset period.

3. The intelligent matching recruitment management system based on a large model according to claim 2, characterized in that, The human-job fit field modeling module includes: The gradient field calculation unit calculates the rate of change of the capability requirement gradient of the three-dimensional recruitment dataset along the capability dimension. The critical point identification unit locates the capability saturation point and job vacancy in the adaptation field by detecting the second derivative extremum. The flow path generation unit constructs a talent capability flow topology network and job demand diffusion paths based on gradient direction fields.

4. The intelligent matching recruitment management system based on a large model according to claim 3, characterized in that, The key feature filtering module includes: The stability analysis unit calculates the standard deviation threshold of the rate of change of the capability gradient in the time dimension. The correlation calculation unit determines the correlation strength coefficient between the convergence of job requirements and the ability dimension through a cross-correlation algorithm. The feature fusion unit selects critical points that simultaneously satisfy the standard deviation threshold and the association strength threshold as core adaptation features.

5. The intelligent matching recruitment management system based on a large model according to claim 4, characterized in that, The large model matching engine module includes: Topological constraint unit, which adds a gradient field difference measure between the optimized dynamic fitting field and the original fitting field to the discriminator loss function; The continuous constraint unit constructs regularization constraint terms through the time dimension derivative of the core adaptation feature; The adversarial training unit iteratively adjusts the parameters of the large model generator until the topology and capability flow path of the dynamic adaptation field converge.

6. The intelligent matching recruitment management system based on a large model according to claim 5, characterized in that, The environmental variable compensation module includes: The market index mapping unit establishes a linear compensation model between the labor supply and demand index and the convergence of job demand. The thermal correction unit dynamically scales and corrects the rate of change of the ability gradient based on salary fluctuation data; Friction coefficient adaptation unit, based on the calculation benchmark of adjusting the fluctuation coefficient of human-job interaction according to the industry flow barrier coefficient.

7. The intelligent matching recruitment management system based on a large model according to claim 6, characterized in that, The strategy generation module includes: The path planning unit generates a skill enhancement path for job seekers based on the revised rate of change of the ability gradient. The job allocation unit outputs a job structure adjustment plan for the enterprise based on the corrected job demand convergence. The interaction optimization unit uses the modified human-job interaction fluctuation coefficient to design the parameters of the interview evaluation process.

8. The intelligent matching recruitment management system based on a large model according to claim 7, characterized in that, Also includes: The closed-loop optimization module is used to collect actual job matching result data, calculate the matching deviation coefficient, and dynamically adjust the weight parameters of the large model generator.

9. The intelligent matching recruitment management system based on a large model according to claim 8, characterized in that, The closed-loop optimization module further includes: The deviation acquisition unit acquires the difference matrix between the actual onboarding data and the predicted fit field in real time. The parameter adjustment unit adjusts the weights of the feature extraction layer of the large model generator based on the Euclidean distance of the difference matrix.

10. The intelligent matching recruitment management system based on a large model according to claim 9, characterized in that, The closed-loop optimization module also includes a threshold recalibration unit, which is used to recalculate the stability threshold and correlation threshold based on historical matching deviations.

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