Automatic semantic tag model method and system based on talent skill AI

Abstract

The invention provides an automatic semantic tag model method and system based on talent skill AI, and relates to the field of information retrieval and clustering. Automatic generation and real-time demand matching of talent skill semantic tags are achieved through nonlinear dynamic modeling and stability control, skill-time frequency domain mapping is combined with a Gevrey smoothing operator, accurate separation of high-frequency noise and low-frequency core features is achieved while skill relevance is reserved, the method is superior to a traditional fixed threshold filtering method, and the method is suitable for large-scale popularization and application. An evolution equation containing a fractional order derivative is constructed, a cross-domain skill synergistic effect (such as' AI + medical 'composite skills) is quantified, and the characterization limitation of an existing linear model on a nonlinear coupling relation is broken through. Through resonance parameter exclusion and local dependency constraint, the problem of model distortion caused by extreme data or parameter resonance in a traditional method is solved, a frequency domain mathematical solution is converted into a physical interpretable semantic tag, and two-dimensional analysis of a skill weight and a demand trend is supported.

Description

Technical Field

[0001] The present invention relates to the fields of information retrieval and clustering, and in particular to a talent skill AI-based automated semantic labeling model method and system. Background Art

[0002] With the rapid development of the digital economy, companies are increasingly in need of dynamic tracking and precise matching of talent skills. In scenarios such as recruitment management, talent pool construction, and career training planning, the traditional skill labeling system based on static keyword matching has become unable to cope with the challenges of rapid skill iteration and cross-domain integration. Current talent data generally has multi-dimensional heterogeneous features (such as text resumes, time-series data on job requirements, and implicit skill associations), while redundant descriptions, ambiguous terms, and high-frequency demand noise lead to distorted skill profiles. Corporate human resources systems often face pain points such as delayed core skill identification, low matching of multi-talent candidates, and rigid label update mechanisms, making it difficult to support real-time, intelligent decision-making needs.

[0003] Existing technologies primarily employ label generation methods that combine rule-based keyword extraction with statistical analysis, or utilize shallow machine learning models for skill clustering analysis. Some approaches construct skill knowledge graphs to depict static associations, or introduce time series models to predict trends in individual skills. At the data processing level, fixed threshold filtering or simple sliding averages are typically used to eliminate noise; label optimization often relies on manually set weights or linear regression models to adjust label priorities. While these methods can achieve basic skill classification, they are limited by linear assumptions and local data processing mechanisms.

[0004] However, existing technologies suffer from three core flaws: First, the coupling between high-frequency noise filtering and low-frequency core feature extraction is insufficient, making it difficult for traditional frequency-domain analysis methods to eliminate short-term interference while preserving skill relevance. Second, skill evolution modeling lacks the ability to depict nonlinear dynamics, making it impossible to quantify cross-domain synergies (such as the value transition of compound skills in "blockchain + supply chain"). Third, the label generation mechanism lacks parameter stability control and is easily affected by extreme data or resonance effects, leading to model distortion. This directly results in poor timeliness and interpretability of talent skill labels, making them difficult to adapt to rapidly changing market demands. Summary of the Invention

[0005] In response to the needs raised in the above background technology, an embodiment of the present invention provides a talent skill AI automated semantic labeling model method and system, which aims to achieve automatic generation of talent skill semantic labels and real-time demand matching through nonlinear dynamic modeling and stability control.

[0006] A method for an AI-based automated semantic labeling model for talent skills, including the following steps:

[0007] Step 1: Data cleaning and core feature extraction

[0008] First, collect multi-dimensional talent skill data, including skill descriptions (such as "Python programming", "data analysis") and time dimension information (such as skill update frequency, market demand change cycle); the data may be in the form of text (resume, job description), time series (skill popularity trend), etc.

[0009] Identify multi-dimensional talent skill data and eliminate redundant or erroneous information (such as repeated skill descriptions and vague terms such as "familiarity with office software").

[0010] The data is then mapped into the "skill-time" frequency domain space, distinguishing high-frequency noise (such as temporary fluctuations in skill demand) from low-frequency core features (such as long-term stable professional skills).

[0011] The filter retains low-frequency core features (such as the strong correlation between "machine learning" and "deep learning") and weakens short-term interference (such as the accidental skill requirements in a job advertisement).

[0012] Step 2: Modeling the Dynamic Evolution of Skills

[0013] Based on the data processed in step 1, a skill evolution equation is constructed, including linear diffusion terms, nonlinear coupling terms, and external incentive terms. The linear diffusion term simulates the direct relationship between skills (such as the natural association between "Java development" and "Spring framework"); the nonlinear coupling term characterizes the cross-domain synergy effect (such as the value superposition of "AI+medical" compound skills); the external incentive term introduces real-time market demand data (such as corporate recruitment needs) to drive model updates; by solving the equation, the steady-state distribution of skill labels is predicted (such as the increase in the weight of "cloud computing" skills within a certain period of time), and the intensity of interaction between skills is quantified (such as the driving force of "blockchain" on "financial technology").

[0014] Step 3: Model iterative optimization

[0015] For the skill evolution equation, the initial linear solution is generated by ignoring the complex coupling between skills and only generating initial labels based on independent skill weights (such as calculating the weights of "Python" and "SQL" separately). The skill evolution equation is then subjected to nonlinear correction iteration, including error calculation, coupling term injection, and convergence verification.

[0016] Step 4: Parameter tuning and stability control

[0017] The iteratively optimized model eliminates resonant parameters by identifying and eliminating parameter combinations that cause model distortion (such as over-amplifying the "programming" skill, which causes the weights of other skills to return to zero). For example, the maximum weight of a single skill is limited to avoid the model outputting extreme results.

[0018] The model then imposes local dependency constraints to ensure that skill modifications only affect related areas (e.g., adjusting the "cloud computing" label does not affect the weight of "graphic design"); and uses exponential decay rules to limit accidental interference between remote skills.

[0019] Step 5: Semantic tag generation and matching

[0020] Based on the stable parameters and robust solutions selected in step 4, we first verify the global convergence of the iterative solution (ensuring that the skill weights of high-frequency noise decay exponentially and that core skills such as "machine learning" remain stable and prominent). Furthermore, we convert the frequency domain mathematical solution into physically interpretable semantic labels—each frequency component corresponds to a specific skill (such as "Python-Advanced"), its amplitude quantifies the skill weight, and its phase reflects the demand trend (such as quarterly fluctuations). Finally, by dynamically matching the company's real-time demand data (such as recruitment job descriptions), we generate a structured tag library (such as "Cloud Architect: Weight 0.9, Demand Trend ↑") and dynamically update the tag weights based on external stimuli (such as new job demand data).

[0021] Furthermore: the error calculation includes comparing the deviation between the initial prediction and the actual data (such as underestimating the demand for the "AI+medical" combination skills).

[0022] Further: the coupling item injection includes gradually adding skill synergy effects (such as the joint weight correction of "data analysis" and "business insights").

[0023] Furthermore: the convergence verification is to ensure that the error is reduced in each iteration through residual compression (such as the revised model is more in line with the actual market demand).

[0024] Further: A talent skills AI automated semantic labeling model system, comprising:

[0025] The data collection and skill feature extraction module is used to collect multi-dimensional talent skill data, including skill descriptions, time dimension information, market demand trends, etc., and identify and eliminate redundant or erroneous data, remove noise, and filter high-frequency noise by mapping the data to the "skill-time" frequency domain space, retaining low-frequency core features, and using filtering algorithms to extract key skill features.

[0026] The skill evolution modeling module is used to construct the skill evolution equation, including linear diffusion terms, nonlinear coupling terms and external excitation terms; it predicts skill trends and quantifies the interactions between skills by solving the equation.

[0027] The label optimization and iteration module is used to generate initial labels, perform nonlinear correction iteration based on the skill evolution equation, calculate errors, inject coupling terms, and perform convergence verification.

[0028] The parameter tuning and stability control module is used to eliminate parameter combinations that cause model distortion to ensure system stability, while limiting accidental interference between remote skills and ensuring that skill corrections only affect related areas.

[0029] The semantic label generation and matching module is used to convert iterative solutions into physically interpretable semantic labels, dynamically adjust label weights based on the company's real-time demand data, and ultimately form a structured label library and provide query, analysis, and matching functions.

[0030] Further: the terminal device may include: a processor, a storage medium and a bus, the storage medium stores machine-readable instructions executable by the processor, when the terminal device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to execute the steps of the deep learning model training method described in the above embodiments.

[0031] Further: A storage medium storing a computer program, wherein the computer program executes the steps of the above method when executed by a processor.

[0032] Further: A computer program product, comprising a computer program, wherein the computer program executes the method described above when executed by a processor.

[0033] Beneficial effects of the present invention: The present invention combines the Gevrey smoothing operator with the "skill-time" frequency domain mapping to achieve accurate separation of high-frequency noise and low-frequency core features while retaining skill correlation. It is superior to the traditional fixed threshold filtering method, constructs an evolution equation containing fractional derivatives, quantifies the synergistic effect of cross-domain skills (such as "AI+medical" composite skills), and breaks through the limitations of existing linear models in characterizing nonlinear coupling relationships. Through resonance parameter exclusion and local dependency constraints, the model distortion problem caused by extreme data or parameter resonance in traditional methods is solved, and the frequency domain mathematical solution is converted into a physically interpretable semantic label, supporting the two-dimensional analysis of skill weights and demand trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flow chart of the method of the present invention is shown.

[0036] Figure 2A schematic diagram of the composition of the system of the present invention is shown. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the drawings in the present invention only serve the purpose of illustration and description and are not used to limit the scope of protection of the present invention. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present invention illustrate the operations implemented according to some embodiments of the present invention. It should be understood that the operations of the flowchart can be implemented out of sequence, and steps that have no logical context relationship can be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of the present invention, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0038] In addition, the embodiments described in the present invention are only some of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0039] It should be noted that the term "including" will be used in the embodiments of the present invention to indicate the presence of the features declared thereafter, but does not exclude the addition of other features. It should also be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of the present invention, it should also be noted that the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0040] The following is a detailed description of this case with reference to the relevant drawings in the specification.

[0041] See Figure 1 , which shows the relevant steps of the method of the present invention, specifically including:

[0042] 1. Data preprocessing and multi-dimensional feature extraction

[0043] The purpose of this section is to build the basic structure of the high-dimensional semantic space by cleaning the raw data and extracting core features.

[0044] 1.1. Collect raw talent skill data and record it as P(x,θ), where x represents the skill dimension and θ represents the time dimension. Raw talent skill data can be viewed as a complex signal formed by the interweaving of the time dimension and the skill dimension, containing high-frequency noise (such as redundant descriptions and ambiguous words) and low-frequency core features (such as professional terms and skill associations).

[0045] First, the original data P(x, θ) is modeled as a two-dimensional function (Equation 1). The original talent skill data P(x, θ) is mapped to the frequency domain through Fourier transform to identify the characteristic components of different frequencies.

[0046]

[0047] In formula (1), it is assumed that P(x,θ) is periodic and is decomposed into a superposition of different frequency components through two-dimensional Fourier transform.

[0048] In the frequency domain, each component is identified by a frequency pair (n, k), corresponding to the multiples of the fundamental frequency of the skill dimension x and the time dimension θ respectively; the frequency domain coefficient Represents the intensity of the corresponding frequency component, which is calculated by inner product (i.e. projected onto the Fourier basis function e i(n·x+kθ) superior);

[0049] High-frequency components (larger |n|, |k|) correspond to rapidly changing parts of the data, such as noise (redundant descriptions, ambiguous words, etc.);

[0050] Low-frequency components (smaller |n|, |k|) correspond to the core features of the data, such as stable professional terminology and skill relevance.

[0051] 1.2. Apply the Gevrey smoothing operator (similar to a "filter") and introduce the cutoff frequency N and exponential decay factor Double processing is performed on high frequency components (for example, frequency components above the threshold N are multiplied by ), in order to retain the core features of the data corresponding to the low-frequency component, the process is shown in formula (2):

[0052]

[0053] In formula (2), N is the cutoff frequency, and the truncation process is achieved by limiting the high-frequency components (|n|,|k|<N) through the cutoff frequency; Gevrey weight is applied c controls the smoothing intensity to achieve exponential decay;

[0054] This process is similar to extracting clear contours from a blurred image, ultimately obtaining skill data with low complexity and high signal-to-noise ratio. Provide a "clean canvas" for subsequent modeling.

[0055] 2. Constructing Nonlinear Dynamic Equations

[0056] The dynamic distribution of talent skill labels is analogized to the wave phenomenon in high-dimensional space. Borrowing the framework of the nonlinear Schrödinger equation in quantum mechanics, the Schrödinger equation is used to describe the evolution of wave functions. Here, skill diffusion, nonlinear coupling and external excitation terms are introduced to adapt it to the talent skill model.

[0057] Based on the skill data obtained after noise reduction Construct a nonlinear dynamic evolution equation (analogous to the Schrödinger equation), as shown in formula (3):

[0058]

[0059] In formula (3), is the time evolution term, which represents the change of skill distribution over time (or frequency parameter ω). ω controls the cycle frequency and reflects the periodic fluctuation of skill demand (such as technology iteration cycle);

[0060] -Δu is the diffusion term. The Laplace operator Δu describes the diffusion of skills in the spatial dimension (such as the skill tree) and simulates the correlation between skills (such as the coupled propagation of "programming" and "algorithms");

[0061] u is a linear term, which represents the spontaneous growth or decay of skills, such as the stability of basic skills;

[0062] D α (|u| 2 u) is a nonlinear term, and the fractional derivative D α (See Equation 4) to characterize the non-local coupling between skills, such as cross-domain synergy (such as the composite skills of "AI + medical care"), whose intensity is controlled by α;

[0063] is an external incentive term. The preprocessed skill data P is used as external input to drive the system evolution (such as the enterprise demand data injection model). ε is a small parameter used to control the intensity of external incentives.

[0064] By establishing a nonlinear dynamic equation to describe the evolution of skill label distribution u(x,θ), and integrating linear diffusion (direct correlation between skills), nonlinear coupling (synergistic effect of cross-domain skill combination), external incentives and other factors (the impact of real-time data on skill distribution), dynamic evolution modeling is achieved; at the same time, through the fractional derivative D α and the nonlinear term |u| 2u, quantifies the dynamic coupling strength between skills, simulates real scenarios more accurately, and solves the problem that traditional labeling models are difficult to handle nonlinear relationships between skills (such as the value superposition of compound skills); the steady-state solution of the equation (obtained through iterative approximation) corresponds to structured semantic labels, and each frequency component (n, k) represents a label primitive (such as "Python-Advanced"), whose amplitude and phase reflect the skill weight and time evolution trend, realizing intelligent matching of talent skills and needs, and ultimately realizing automatic label generation.

[0065] At the same time, for the fractional derivative D α Also defined, formula (4) defines the fractional derivative D α The operational form of is:

[0066]

[0067] In formula (4), Represents the regularization frequency to avoid high-frequency explosion.

[0068] The derivation process of formula (4) is described in detail below.

[0069] First, any function f(x) can be expanded into a Fourier series (Equation 4.1):

[0070]

[0071] In formula (4.1), is the Fourier coefficient, which represents the amplitude of frequency component n;

[0072] Secondly, the Fourier representation of integer-order derivatives is to convert the traditional integer-order derivatives into In the Fourier domain, this corresponds to multiplication by (in) k , the process is shown in formula (4.2):

[0073]

[0074] Finally, the fractional derivative D α The concept of integer-order derivatives is generalized. To avoid the divergence problem of high-frequency components (large |n|), the regularization frequency is introduced. Instead of the original |n|, the final fractional derivative is defined in the Fourier domain as Equation (4).

[0075] The role of formula (4) in the model includes characterizing the nonlocal coupling effect, the fractional derivative D α pass <n> α Different frequency components are weighted to simulate long-range correlations between skills (such as the cross-domain collaboration between "blockchain" and "finance"). α is the derivative order. When α = 0, only linear terms are retained, degenerating into a linear model, and the skills are independent and uncoupled. When α > 0, nonlinear interactions of remote skill combinations are allowed, such as the cross-domain coupling between "blockchain" and "finance".

[0076] Equation (4) is used to construct the term D in the nonlinear Schrödinger equation α (|u| 2 u) quantifies the dynamic coupling strength of skill labels to solve the problem that traditional models have difficulty handling complex skills (such as "AI+medical").

[0077] 3. Iterative Approximation and Error Correction

[0078] Since the nonlinear dynamic evolution equation of formula (3) contains complex nonlinear terms and is difficult to solve directly, we first process the linear part to obtain a basic solution, and then gradually add the influence of nonlinear terms through iteration to gradually approach the true solution.

[0079] 3.1. First, linearize the nonlinear dynamic evolution equation and ignore the nonlinear term D in equation (3). α (|u| 2 u), we get the linear equation (6):

[0080]

[0081] 3.2. Then, the linear equation (6) is transformed into the frequency domain by Fourier transform. A two-dimensional Fourier transform is performed on the spatial dimension x and the time dimension θ, where the time evolution term Corresponding to the frequency domain The diffusion term -Δu corresponds to the frequency domain The linear term u remains unchanged as Then the linear equation (6) becomes (6.1) in the frequency domain:

[0082]

[0083] 3.3. Based on formula (6.1), solve the initial approximate solution (formula 5):

[0084]

[0085] Formula (5) represents the initial estimate of the skill label distribution when only linear effects are considered. This solution only reflects the linear characteristics of skill labels (such as independent skill weights).

[0086] 3.4. The initial solution only considers linear terms and ignores the nonlinear coupling between skills (such as the synergistic effect of compound skills). Equation (7) corrects the deviation of the solution by successively adding the influence of nonlinear terms and obtains the corrected solution.

[0087]

[0088] The derivation process of formula (7) is described in detail below:

[0089] The residual for the nonlinear equation (Equation 3) can be defined as shown in (Equation 8):

[0090]

[0091] In formula (8), F(u) is the residual function of the equation, which measures the current solution u j The error; then in the iterative process, assuming the current solution is u j , the nonlinear term is locally linearly approximated by Taylor expansion (F(u j +v j )≈F(u j )+T j v j =0), where T j is F(u) at u j The linearization operator (Jacobian matrix) at v j is the correction amount;

[0092] By solving v j Make the residual approach zero, that is: T j v j =-F(u j ); thus obtaining the correction amount: Finally, the correction amount is added to the current solution to obtain the updated solution (Equation 7);

[0093] 3.5. In order to solve the correction value v in the iterative process j , this step defines the linearization operator (Equation 9):

[0094] T j =D+εΛS j 6(9)

[0095] In formula (9):

[0096] D=diag(-kw+|n| 2 +1,kw+|n| 2 +1) represents the diagonal linear term, corresponding to the linear term in the original equation (time evolution term diffusion term -Δu, linear term u);

[0097] Λ= <n> α Represents fractional derivative weights, which quantify the non-local coupling between skills (such as cross-domain synergy effects) by introducing fractional derivative weights;

[0098] S j Represents the off-diagonal part, which is the nonlinear coupling matrix and the linearized contribution from the nonlinear term;

[0099] Through the inverse operation Calculate the current approximate solution u j The error between the true solution and the solution of the nonlinear equation is gradually approached;

[0100] 3.6. During the iteration process, the current solution u j The residual F(u j ) characterizes the deviation between the approximate solution and the true solution. In order to ensure the convergence of the iterative process, combined with the properties of the Gevrey class function (Fourier coefficient exponential decay), the Gevrey norm of the residual is defined as By induction, it can be shown that after each iteration, the residual norm is compressed into an exponential form (Equation 10):

[0101]

[0102] In formula (10), M>1 is the amplification factor, and c controls the decay rate.

[0103] The initial solution u0 is generated by linear response (that is, only the linear terms in the equation are retained and complex coupling is ignored), which is equivalent to predicting the skill distribution with a "simplified model".

[0104] Then, in each iteration, the linearization operator T is constructed j , the diagonal part D reflects the inherent attributes of the skill label (such as the independent value of the skill), and the non-diagonal part ΛS j The dynamic impact between skills is quantified (such as the dependency weight of "cloud computing" on "distributed systems").

[0105] By inversion Calculate the correction value v j (similar to "error compensation"), gradually reducing the prediction error from Compress to higher precision.

[0106] This process is like using a "multi-level magnifying glass" to correct the model details layer by layer, and ultimately approach the actual skill distribution.

[0107] 4. Parameter Screening and Stability Assurance

[0108] The purpose of this section is to eliminate unstable parameter combinations and ensure the robustness of the model. In the parameter space (such as skill weight ω, coupling strength α), some parameters may cause the equation to "resonate" (for example, over-emphasis on a single skill leads to model distortion). To this end, the non-resonance condition ((min|±kw+|n| 2 +1|)>N δ )Filter parameters:

[0109] If a set of parameters makes the denominator -kw+|n| 2 +1 is too small (close to zero), it is judged as a "dangerous parameter" and removed from the effective set Λ j+1 Excluded.

[0110] At the same time, the Green function estimation (exponential decay of matrix inversion) is used to ensure that the correction value v j The local dependence of (i.e., the correction amount of the remote skill label decays rapidly) is shown in formula (11):

[0111]

[0112] This step is similar to installing a "shock absorber" for the model to filter out interference factors that cause system instability.

[0113] The parameter elimination method is applied to screen the model parameters (such as skill weight ω), eliminate the unstable combination that leads to resonance (such as over-enhancement of a single skill), and combine the Green function (Equation 12) to estimate the local dependence of the correction amount, and output the robust parameter set Λ j+1 and the corresponding solution.

[0114]

[0115] In formula (12), the linearized operator inverse matrix The exponential decay of the off-diagonal elements ensures local dependence; ξ = (n, k), ξ′ = (n′, k′) represents the index of different frequency components; |ξ-ξ′| = |nn′| + |kk′| represents the frequency distance;

[0116] By Expanded into a Neumann series Using non-resonance conditions and Gevrey smoothness, we demonstrate that the contribution of higher-order terms decays exponentially. The above formula represents a "local correction" of skill labels: corrections to high-frequency labels do not affect low-frequency components, ensuring iterative stability.

[0117] 5. Global Convergence and Semantic Label Generation

[0118] The purpose of this section is to verify the global validity of the model and output structured labels, through the Gevrey norm convergence condition Verify the consistency of the iterative solution.

[0119] Since each frequency component (n,k) corresponds to a semantic label primitive (such as "Python-Advanced"). Indicates skill weight, phase Reflect the time evolution trend; then by reconstructing u(x,θ), the abstract frequency domain solution is transformed into an interpretable skill label distribution to achieve dynamic matching of talent skills and demand. The process is shown in formula (13):

[0120]

[0121] At the same time, in order to ensure the exponential decay of the high-frequency components of the iterative solution and avoid the divergence of the model due to nonlinear coupling or external excitation, the weighted Fourier coefficients and Eliminate unstable parameter combinations (such as resonant frequency) to ensure model robustness. The process is shown in formula (14):

[0122]

[0123] Where C represents the convergence constant, which depends on the dimensions d and α;

[0124] If the exponentially weighted sum of the high-frequency components is controllable, the solution is determined to be globally valid.

[0125] The Whitney continuation theorem is then used to smoothly extend the solution on the discrete parameter set to the continuous space, ensuring the adaptability of the model to any ω (such as changes in enterprise demand).

[0126] In the final output periodic solution u(x,θ), each frequency component (n,k) corresponds to a semantic label primitive (such as "Python Programming - Advanced"), and its amplitude Characterize label weight, phase Reflects the time evolution phase.

[0127] This process is equivalent to "compiling" the solution of the wave equation into an interpretable label system, realizing the automatic matching of talent skills and job requirements.

[0128] See Figure 2 Based on the method of the present invention, this embodiment correspondingly proposes an AI-based automated semantic labeling model system for talent skills to realize the collection, processing, semantic label generation and application of talent skills data, specifically including the following modules:

[0129] The data collection and skill feature extraction module is used to collect multi-dimensional talent skill data, including skill descriptions, time dimension information, market demand trends, etc., and identify and eliminate redundant or erroneous data, remove noise, and filter high-frequency noise by mapping the data into the "skill-time" frequency domain space, retaining low-frequency core features, and using filtering algorithms to extract key skill features.

[0130] The skill evolution modeling module is used to construct the skill evolution equation, including linear diffusion terms, nonlinear coupling terms and external excitation terms; it predicts skill trends and quantifies the interactions between skills by solving the equation.

[0131] The label optimization and iteration module is used to generate initial labels, perform nonlinear correction iteration based on the skill evolution equation, calculate errors, inject coupling terms, and perform convergence verification.

[0132] The parameter tuning and stability control module is used to eliminate parameter combinations that cause model distortion to ensure system stability, while limiting accidental interference between remote skills and ensuring that skill corrections only affect related areas.

[0133] The semantic label generation and matching module is used to convert iterative solutions into physically interpretable semantic labels, dynamically adjust label weights based on the company's real-time demand data, and ultimately form a structured label library and provide query, analysis, and matching functions.

[0134] Furthermore, this embodiment also proposes a terminal device, including: a processor, a storage medium and a bus, the storage medium stores machine-readable instructions executable by the processor, when the terminal device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the deep learning model training method described in the above embodiments.

[0135] For ease of description, only one processor is described in the above terminal device. However, it should be noted that in some embodiments, the terminal device in the present invention may also include multiple processors, and thus the steps performed by one processor described in the present invention may also be performed jointly or individually by multiple processors.

[0136] This embodiment further provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of the above method are executed.

[0137] This embodiment further provides a computer program product, including a computer program, wherein the computer program executes the above-described method when executed by a processor.

[0138] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.< / n> < / n>

Claims

1. A talent skills AI automated semantic labeling model method, characterized by: The specific steps include: Step 1: Collect multi-dimensional talent skill data, including skill descriptions and time dimension information; identify the multi-dimensional talent skill data and remove redundant or erroneous information; then map the de-duplicated multi-dimensional talent skill data into the "skill-time" frequency domain, distinguish high-frequency noise from low-frequency core features, and use a filter to retain the low-frequency core features; Step 2: Construct a skill evolution equation based on the retained low-frequency core features. The skill evolution equation includes a linear diffusion term, a nonlinear coupling term, and an external excitation term. Step 3: For the skill evolution equation, the initial labels are generated based only on the independent skill weights by ignoring the complex coupling between skills, thus achieving the initial linear solution. Based on the initial linear solution, the skill evolution equation is subjected to nonlinear correction iteration to obtain an iterative solution, including error calculation, coupling term injection, and convergence verification. Step 4: For the iterative solution obtained after iterative optimization, resonant parameters are eliminated by identifying and eliminating parameter combinations that cause model distortion, thereby obtaining stable parameters. Local dependency constraints are also imposed on the model to ensure that skill corrections only affect relevant areas, and exponential decay rules are used to limit accidental interference between remote skills. Step 5: Verify the global convergence of the iterative solution, and then convert the iterative solution into physically interpretable semantic labels; finally, generate a structured label library by dynamically matching the enterprise's real-time demand data, and dynamically update the label weights based on external incentives.

2. The method according to claim 1, characterized in that The linear diffusion term simulates the direct connection between skills; the nonlinear coupling term characterizes the cross-domain synergy effect; and the external incentive term introduces real-time market demand data to drive model updates.

3. The method according to claim 1, characterized in that The error calculation involves comparing the deviation of the initial prediction to the actual data.

4. The method according to claim 1, wherein The coupling term injection includes gradually adding skill synergy effects.

5. The method according to claim 1, wherein The convergence verification is to ensure that the error is reduced at each iteration through residual compression.

6. A talent skills AI automated semantic labeling model system, characterized by: include: The data collection and skill feature extraction module collects multi-dimensional talent skill data, identifies and removes redundant or erroneous data, and removes noise. By mapping the data into the "skill-time" frequency domain, it filters out high-frequency noise, retains low-frequency core features, and uses filtering algorithms to extract key skill features. Skill evolution modeling module, which is used to construct the skill evolution equation, including linear diffusion terms, nonlinear coupling terms, and external excitation terms; by solving the equation, it predicts skill trends and quantifies the interactions between skills; The label optimization and iteration module is used to generate initial labels, perform nonlinear correction iteration based on the skill evolution equation, calculate errors, inject coupling terms, and perform convergence verification. Parameter tuning and stability control module, which is used to eliminate parameter combinations that cause model distortion to ensure system stability, while limiting accidental interference between remote skills and ensuring that skill corrections only affect related areas; The semantic label generation and matching module is used to convert iterative solutions into physically interpretable semantic labels, dynamically adjust label weights based on the company's real-time demand data, and ultimately form a structured label library and provide query, analysis, and matching functions.

7. A terminal device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the terminal device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, executes the steps of the method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method according to any one of claims 1 to 5.

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