A resume recommendation method based on natural language processing

Abstract

This invention relates to the field of big data processing technology and discloses a resume recommendation method based on natural language processing, comprising: collecting resume data streams and job requirement data; extracting semantic event tuples with associated duration parameters from project experience, mapping them to a multi-dimensional capability space and constructing an initial capability temporal tensor; acquiring resource topology data of enterprise entities to establish resource boundary constraints, and generating a modulated capability temporal tensor through projection modulation; when the gradient of the tensor exceeds the upper limit of the business evolution rate, correcting the intensity value using a decay coefficient to generate a corrected capability evolution feature trajectory; and calculating the discrete Friesian distance between the trajectory and the job standard benchmark curve. This invention, by introducing enterprise resource topology as a verification dimension, breaks the isolated semantic mapping pattern, effectively solves the data disconnect problem in talent evaluation, and enhances the logical authenticity and profile accuracy of the evaluation model.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, specifically to a resume recommendation method based on natural language processing. Background Technology

[0002] Currently, in talent allocation and management processes, resume recommendation systems based on unstructured text mainly employ keyword matching or vector space models. These methods calculate the semantic similarity between candidate resumes and job descriptions to achieve initial screening and ranking of talent data. However, this approach exhibits significant technical limitations in practical applications. This is because existing natural language processing logic typically treats resume text as a discrete and assumed set of true facts, ignoring the physical time span of the actions and organizational resource constraints. Consequently, the system only parses the actions and results described in the text, without verifying the underlying logical truthfulness.

[0003] When the claimed output in a resume exceeds the limits of human cognitive capacity or physical working hours, existing models still assign high matching scores based on semantic weights. This results in processing results containing a large amount of high-entropy noise. Furthermore, due to the lack of cross-validation of organizational boundary data, the system cannot distinguish the candidate's true technical contributions under different scales of resource support, ultimately reducing the engineering confidence of the evaluation model. From simply relying on textual semantic similarity matching to introducing complex graph structures or reinforcement learning strategies for control, existing technologies still have shortcomings in verifying the authenticity of underlying data logic. For example, Chinese invention patent application with publication number CN117786124A... This paper proposes a public opinion event handling recommendation method based on GNN policy space reinforcement. It utilizes knowledge graphs and graph neural networks to mine deep connections between nodes and optimizes the policy space to make the recommendation results closer to human intentions. Its core logic is based on the idealized assumption that the input data is the facts. In actual human resource assessment scenarios, resume descriptions are accompanied by physical working hour limits. This method lacks hard constraints on the physical time span of behavior and organizational resource boundaries, such as computing power, data volume, and collaboration density. As a result, the assessment results are in a vacuum of environmental data and cannot identify floating data that is detached from the actual support capabilities of the organization, thus reducing the engineering confidence of the assessment model.

[0004] Therefore, how to construct a resume processing model with physical spatiotemporal constraints and resource boundary verification capabilities, so as to achieve accurate quantification and authenticity restoration of talent data, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention proposes a resume recommendation method based on natural language processing, comprising the following steps: Acquire the candidate resume data stream to be processed and the target job requirement data; Using the semantic role labeling model, multiple semantic event tuples are extracted from the project experience text block of the candidate's resume data stream. Each semantic event tuple includes a background feature vector, a behavior feature vector, a result feature vector, and a time span parameter representing the duration of the semantic event. Based on the pre-defined job capability ontology library, semantic event tuples are mapped to a multi-dimensional capability space to construct an initial capability temporal tensor. Each element in the initial capability temporal tensor represents the initial intensity value of each capability dimension under each time slice. The background feature vector is parsed to extract the entity identifier of the candidate's enterprise, and the resource distribution feature data of the enterprise entity, including the scale of organizational computing power, the volume of business data and the density of project collaboration network, is obtained. Based on the resource boundary constraints defined by the resource distribution feature data, the initial intensity value is projected and modulated to generate the modulated capability time series tensor. The gradient of the modulated capability temporal tensor on the time axis is calculated. When the gradient exceeds the upper limit of the business evolution rate, which represents the logical speed limit of a single subject acquiring a specific skill per unit time, the intensity value is corrected using a preset attenuation coefficient to generate the corrected capability evolution characteristic trajectory. Calculate the discrete Fréchet distance between the corrected capability evolution feature trajectory and the target job standard baseline curve, and output resume recommendation results based on the discrete Fréchet distance.

[0006] Preferably, in the step of projecting and modulating the initial intensity value, the calculation logic of the modulated intensity value is as follows: The resource modulation coefficient ρ is determined using the resource support abundance mapped from the background feature vector; the initial intensity value is then used... The product of the modulated value and the resource modulation coefficient ρ determines the modulated intensity value. The calculation formula is: Wherein, ρ is a dimensionless coefficient determined based on the industry position score, asset size and topological distribution of the collaborative network nodes of the enterprise entity, and the value of ρ ranges from 0.5 to 1.5.

[0007] Preferably, in the step of extracting multiple semantic event tuples, a preset named entity recognition algorithm is used to separate organizational entity words from the background feature vector and use the organizational entity words as enterprise entity identifiers.

[0008] Preferably, obtaining resource distribution characteristic data includes: based on the enterprise entity identifier, initiating a query request to a pre-built enterprise asset database through an asynchronous application programming interface to obtain structured data representing the enterprise entity's industry ranking, server cluster size, and number of business node connections.

[0009] Preferably, the step of projecting and modulating the initial intensity value includes: projecting the initial intensity value onto a dynamic feature space composed of the organization's computing power scale, business data volume, and project collaboration network density, in order to identify and correct anomalous outlier data that deviates from the support of resource distribution feature data.

[0010] Preferably, the generation step of the modified capability evolution feature trajectory further includes: detecting whether there are overlapping time periods in multiple semantic event tuples on the time axis; if there are overlapping time periods, calculating the total capability strength value within the overlapping time period; if the total capability strength value exceeds the upper limit of the single subject's output efficiency bandwidth, then performing normalization compression processing on the strength value within the overlapping time period.

[0011] Preferably, before calculating the discrete Fraser distance, the method further includes: sampling the corrected capability evolution feature trajectory at equal intervals on the time axis to generate multiple sets of sequentially arranged capability feature points.

[0012] Preferably, the job competency ontology stores feature keywords, competency dimension weights, and coordinate mapping rules in a multi-dimensional competency space corresponding to different industry jobs.

[0013] Preferably, the method for generating the target job standard baseline curve includes: simulating a standard growth path in a multi-dimensional capability space based on the job qualification requirements in the target job demand data, so as to generate a dynamic evolution model.

[0014] Preferably, the resume recommendation results based on discrete Friesian distance include: calculating the matching score between candidates and target positions, arranging the resumes of multiple candidates in descending order of matching score, and pushing the sorted resume data stream to the recruitment management terminal.

[0015] The beneficial effects of this invention are: 1. In resume recommendation using natural language processing, a mechanism for calibrating the authenticity of unstructured data based on physical spatiotemporal constraints is constructed. This mechanism maps action feature vectors in the resume data stream with associated time span parameters, establishing a quantitative correlation between semantic complexity and physical time consumption. A human cognitive output limit threshold is introduced as a hard constraint at the computational level. When the workload density claimed by a candidate exceeds the objective physical limit, the system automatically reduces the weight of related events through a nonlinear collapse correction mechanism. This suppresses exaggerated or false information descriptions at the underlying logic of data processing. This verification method based on physical conservation laws effectively solves the technical bottleneck of traditional semantic analysis in identifying the disconnect between text descriptions and objective physical spatiotemporal constraints, thereby improving the objective confidence of candidate feature extraction.

[0016] 2. To achieve cross-modulation of multi-source heterogeneous data constrained by organizational resources and environment, the system extracts enterprise entity identifiers by parsing background feature vectors and asynchronously obtains the resource topology vector of the entity within the corresponding time span. This anchors isolated individual behavior descriptions within the real organizational productivity boundary. By calculating the fit coefficient between action complexity and enterprise resource carrying capacity limit, the system can forcibly modulate the initial semantic weights, automatically identify and filter floating data that is detached from the actual resource support of the organization. This mechanism breaks the limitations of one-way semantic mapping, ensuring that capability strength calculation no longer relies solely on empty textual declarations, but is based on the real business physical boundary supported by big data, thus enhancing the robustness of the talent evaluation model under complex working conditions.

[0017] 3. This invention establishes a capability growth trajectory evaluation model based on discrete Fraser distance. Unlike conventional methods that treat skills as static labels, this invention utilizes timeline slicing to transform unstructured text into continuous capability time-series tensors and generates dynamic trajectory curves reflecting the evolution trend of candidates' capabilities. By calculating the discrete Fraser distance between the candidate's capability curve and the target position's standard benchmark curve, it captures the differences in the depth and timeliness of skill mastery. This matching algorithm based on morphological similarity prevents the recommendation system from being interfered with by simple keyword stuffing, thereby identifying high-quality candidates with long-term accumulation and reasonable growth logic, reducing the trial-and-error costs in the human resource screening process. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the core process of resume recommendation in this invention, which integrates spatiotemporal constraints and evolutionary trajectories. Figure 2 This is a logical architecture diagram of resume recommendation based on multi-source data interaction and component collaboration in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] A resume recommendation method based on natural language processing includes the following steps: Acquire the candidate resume data stream to be processed and the target job requirement data; Using the semantic role labeling model, multiple semantic event tuples are extracted from the project experience text block of the candidate's resume data stream. Each semantic event tuple includes a background feature vector, a behavior feature vector, a result feature vector, and a time span parameter representing the duration of the semantic event. Based on the pre-defined job capability ontology library, semantic event tuples are mapped to a multi-dimensional capability space to construct an initial capability temporal tensor. Each element in the initial capability temporal tensor represents the initial intensity value of each capability dimension under each time slice. The background feature vector is parsed to extract the entity identifier of the candidate's enterprise, and the resource distribution feature data of the enterprise entity, including the scale of organizational computing power, the volume of business data and the density of project collaboration network, is obtained. Based on the resource boundary constraints defined by the resource distribution feature data, the initial intensity value is projected and modulated to generate the modulated capability time series tensor. The gradient of the modulated capability temporal tensor on the time axis is calculated. When the gradient exceeds the upper limit of the business evolution rate, which represents the logical speed limit of a single subject acquiring a specific skill per unit time, the intensity value is corrected using a preset attenuation coefficient to generate the corrected capability evolution characteristic trajectory. Calculate the discrete Fréchet distance between the corrected capability evolution feature trajectory and the target job standard baseline curve, and output resume recommendation results based on the discrete Fréchet distance.

[0022] Preferably, in the step of projecting and modulating the initial intensity value, the calculation logic of the modulated intensity value is as follows: The resource modulation coefficient ρ is determined using the resource support abundance mapped from the background feature vector; the initial intensity value is then used... The product of the modulated value and the resource modulation coefficient ρ determines the modulated intensity value. The calculation formula is: , where ρ is a dimensionless coefficient determined based on the enterprise entity's industry position score, asset size, and the topological distribution of collaborative network nodes, and the value of ρ ranges from 0.5 to 1.5.

[0023] Preferably, in the step of extracting multiple semantic event tuples, a preset named entity recognition algorithm is used to separate organizational entity words from the background feature vector and use the organizational entity words as enterprise entity identifiers.

[0024] Preferably, obtaining resource distribution characteristic data includes: based on the enterprise entity identifier, initiating a query request to a pre-built enterprise asset database through an asynchronous application programming interface to obtain structured data representing the enterprise entity's industry ranking, server cluster size, and number of business node connections.

[0025] Preferably, the step of projecting and modulating the initial intensity value includes: projecting the initial intensity value onto a dynamic feature space composed of the organization's computing power scale, business data volume, and project collaboration network density, in order to identify and correct anomalous outlier data that deviates from the support of resource distribution feature data.

[0026] Preferably, the generation step of the modified capability evolution feature trajectory further includes: detecting whether there are overlapping time periods in multiple semantic event tuples on the time axis; if there are overlapping time periods, calculating the total capability strength value within the overlapping time period; if the total capability strength value exceeds the upper limit of the single subject's output efficiency bandwidth, then performing normalization compression processing on the strength value within the overlapping time period.

[0027] Preferably, before calculating the discrete Fraser distance, the method further includes: sampling the corrected capability evolution feature trajectory at equal intervals on the time axis to generate multiple sets of sequentially arranged capability feature points.

[0028] Preferably, the job competency ontology stores feature keywords, competency dimension weights, and coordinate mapping rules in a multi-dimensional competency space corresponding to different industry jobs.

[0029] Preferably, the method for generating the target job standard baseline curve includes: simulating a standard growth path in a multi-dimensional capability space based on the job qualification requirements in the target job demand data, so as to generate a dynamic evolution model.

[0030] Preferably, the resume recommendation results based on discrete Friesian distance include: calculating the matching score between candidates and target positions, arranging the resumes of multiple candidates in descending order of matching score, and pushing the sorted resume data stream to the recruitment management terminal.

[0031] Example 1: In a big data processing scenario involving the screening of massive historical resumes for high-concurrency distributed system architect positions, the data processing system receives a large volume of resume text containing high-throughput architectural design descriptions. Existing static vector similarity models map such text descriptions to discrete sets of facts, detaching them from the organizational resource constraints and physical time span of the behavior, resulting in floating text data lacking objective physical computing power and business data volume support. This disrupts the feature space and reduces the precision of person-job matching. To address the technical contradiction between static feature extraction and objective resource carrying capacity, this resume recommendation method based on natural language processing extracts background feature vectors. The system consists of multiple semantic event tuples, including behavioral feature vectors, result feature vectors, and time span parameters representing duration. Based on a pre-defined job capability ontology, these semantic event tuples are mapped to a multi-dimensional capability space to construct an initial capability temporal tensor. Background feature vectors are parsed to extract organizational entity words as enterprise entity identifiers. An asynchronous application programming interface (API) is used to initiate a query request to a pre-defined enterprise asset database to obtain structured data containing server cluster size and business node connection counts as resource distribution feature data. The resource support abundance mapped from the background feature vectors is used to determine the resource modulation coefficient ρ. The initial intensity value in the initial capability temporal tensor is then used... The product of the modulated value and the resource modulation coefficient ρ determines the modulated intensity value. The specific mathematical formula is as follows: ,in Let ρ be the initial capability strength value for each time slice in the multidimensional capability space, and ρ be a dimensionless coefficient ranging from 0.5 to 1.5, determined based on the enterprise entity's industry position score, asset size, and the topological distribution of collaborative network nodes. The capability strength value after projection modulation is the resource boundary constraint logic that anchors isolated textual semantic features to external objective entity scale features and cross-modulates them.

[0032] To address the high-entropy noise interference problem in time-series data streams, this method calculates the gradient of the modulated capability time-series tensor on the time axis. It compares this gradient with the upper limit of the business evolution rate, which represents the logical speed limit of a single entity acquiring a specific skill per unit time. Simultaneously, it detects overlapping periods of multiple semantic event tuples on the time axis and calculates the total capability strength value within these overlapping periods. When the gradient exceeds the upper limit of the business evolution rate or the total capability strength value exceeds the upper limit of the single entity's output efficiency bandwidth, it normalizes the compression strength value using a preset attenuation coefficient. This corrects outlier data that deviates from the support of resource distribution characteristic data, outputting a corrected capability evolution characteristic trajectory constrained by physical working hours. The system samples the corrected capability evolution characteristic trajectory at equal intervals on the time axis to generate multiple sets of sequentially arranged capability feature points. It then calculates the relationship between these sets of sequentially arranged capability feature points and the target job standard benchmark curve generated based on target job requirement data. The Discrete Fraser Distance (DFS) is used to calculate the matching score between candidates and target positions. Multiple candidates' resumes are then arranged in descending order of matching score and pushed to the recruitment management terminal. The output is an objective, quantitative evaluation indicator constrained by both organizational resource boundaries and physical cognitive limits. The data processing system receives target position requirement data and extracts skill requirement tags and expected proficiency achievement time points. Based on a pre-defined job competency ontology, the skill requirement tags are mapped to feature coordinate components in a multi-dimensional competency space. Using the expected proficiency achievement time point as the time axis benchmark, corresponding expected competency strength values ​​are set. The system sequentially connects these expected competency strength values ​​along the time axis in the multi-dimensional competency space using a cubic spline interpolation algorithm, outputting a continuously distributed geometric trajectory of the expected competency space over time. This trajectory is written to local memory and used as the standard benchmark curve for matching calculations of the target position.

[0033] Example 2: For redundant resume data streams containing features of exaggeration and logical inconsistencies, this experimental platform is deployed on a distributed data processing node with 128 computing cores and 512GB of memory to verify the stability of the data feature space and the boundary condition response logic. The test data uses a de-identified general open-source human resources historical trajectory dataset, which contains 10,000 standard architect resume texts and 2,000 interference samples containing high-entropy noise. The noise features include at least three conflicting architecture-dominated events with tens of millions of concurrent connections on the same time axis, as well as fictitious enterprise entity names without actual registration information. It is determined that the sampling period of the initial capability time series tensor on the time axis is constrained by the technical limitations of feature capture resolution and tensor operation memory overhead. Based on the set sampling parameter decision model, when the average time span of semantic event tuples approaches the quarterly cycle and the data stream event density is high, in order to avoid the signal feature aliasing phenomenon defined by the Nyquist sampling theorem, the sampling period tends to the lower limit of the set value range. Applying this parameter decision model, the system outputs a time slice sampling period of 30 days for input quarterly cycle span events.

[0034] The experiment set up three parallel processing branches to verify the synergistic effect of resource boundary constraints and physical working time constraints. Control group 1 applied static semantic vector similarity mapping, extracting static semantic vector similarity but removing time span and resource environment verification parameters; control group 2 applied time axis slice cumulative mapping, including time span parameters but removing enterprise resource topology cross-modulation steps; the experimental group adopted the complete data processing method claimed in this invention. When processing interference samples injected with noise, the system extracted a specific time window with a duration span of 180 days from the candidate data stream. Within this time window, five semantic event tuples labeled as core architecture reconstruction were stacked. Control group 1 extracted background feature vectors and behavioral features. The vectors are combined to calculate the Euclidean distance matching degree, and the initial capability strength value is output as 450.5. The control group 2 sums the above event features within the time slice, and the total capability strength value is output as 380.2. The experimental group analyzes the background feature vector to separate the enterprise entity identifier, and obtains the structured data of the enterprise entity through asynchronous requests. The server cluster size is 15 nodes, and the business data volume is 10GB per day. The system compares the resource distribution feature data with the preset interval and determines that the resource support abundance is within the low threshold range. Based on the parameter mapping relationship, the resource modulation coefficient ρ is determined to be 0.4. The experimental group calculates the product of the initial strength value and the resource modulation coefficient ρ, and outputs the projected modulated capability strength value. It is 34.0.

[0035] The experimental group synchronously monitored the gradient of the capability temporal tensor within a set time window. In tests with interference samples containing 2 to 5 consecutively increasing concurrent semantic event tuples, the algorithm logic determined that the total capability strength value reached the upper limit of the single-entity output efficiency bandwidth when the number of concurrent events reached 3. A preset attenuation coefficient was applied to normalize and compress the total capability strength value. The measured corrected capability evolution characteristic trajectory exhibited nonlinear saturation characteristics. Specifically, when the number of concurrent events was 1 and 2, the characteristic trajectory amplitude increased linearly; when the number of concurrent events reached 3 or more, the corrected strength value stopped increasing and stably converged to a value range of 28.5. The compression step prevented the divergence of intensity values ​​under conditions of no resource support. The system calculated the discrete Fréchet distance between the corrected capability evolution characteristic trajectory of all samples and the target job standard baseline curve and output a sorted queue. The system output the precision index of person-job matching. The precision rate of control group 1 was 62.4%, the precision rate of control group 2 was 71.8%, and the precision rate of the experimental group was 88.5%. The experimental data showed that when the spatial scale modulation set by the resource distribution characteristic data and the temporal gradient constraint set by the business evolution rate acted synchronously, the calculation logic of the matching score eliminated the interference of high entropy noise and made the feature vector mapping of the semantic space converge within the quantitative boundary of the objective entity resources.

[0036] Example 3: In big data processing scenarios involving automated matching and screening of candidate data streams containing dense high-level technical descriptions, the discrete semantic labels output by the basic natural language processing module face engineering application challenges such as the lack of a quantitative scale for feature vector mapping and the unclear source of resource boundary adjustment parameters. The data acquisition and preprocessing module extracts project experience text blocks from the candidate resume data stream and calls a preset semantic role labeling model to extract background feature vectors. behavioral feature vector Result feature vector In addition to the semantic event tuples with the time span parameter Δt, the system queries the locally stored job competency ontology knowledge base and combines the behavioral feature vectors. The core verb features are mapped to a pre-defined standard action weight lookup table. A standard semantic weight value uniquely corresponding to each core verb is extracted. The product of the standard semantic weight value and the time span parameter Δt is used to generate a specific capability dimension, and the initial intensity value for the corresponding time slice is calculated. Output semantic event tuples to the initial capability temporal tensor The numerical transformation results of basic data elements are used to analyze behavioral feature vectors to extract core verbs. The corresponding standard semantic weights W are retrieved from a pre-set standard action weight lookup table. The weight lookup table is constructed based on the absolute frequency of core verbs in job descriptions and the co-occurrence probability of promotion events in the benchmark database. The extracted feature values ​​are linearly mapped to the numerical range of 0.1 to 1.0 to determine the weights W. The initial intensity value is the product of the standard semantic weights W and the time span parameter Δt, which transforms the unstructured semantic description into a numerical representation of each time slice in the multidimensional capability space.

[0037] System Analysis Background Feature Vector Extract the enterprise entity identifier and send an asynchronous request to the enterprise asset database, receiving information including the number of server cluster nodes. The system extracts the number of pre-set industry benchmark nodes from the structured resource distribution characteristic data and the system extracts the number of pre-set industry benchmark nodes from the memory. Utilizing the number of server cluster nodes Number of industry benchmark nodes The ratio determines the relative scale of resource distribution, and the resource modulation coefficient ρ is calculated based on the relative scale. The specific calculation formula is as follows: Where ρ is a dimensionless resource modulation coefficient, To obtain the number of server cluster nodes of the target enterprise entity, To set the industry standard benchmark number of nodes, when the calculated ρ value is greater than the upper limit of 1.5, the system performs a truncation operation and forcibly sets it to 1.5, extracting the initial capability time series tensor. Initial intensity value in It calculates the product of the modulated signal and the resource modulation coefficient ρ, and outputs the modulated intensity value, which is absolutely constrained by the scale of physical computing power. Obtain the number of server cluster nodes of the target enterprise entity through an asynchronous application programming interface. Synchronous read memory preset industry standard reference node number ;in The value is taken from the 95th percentile of the distribution of server nodes in similar industries in the benchmark database; the resource modulation coefficient ρ is calculated using the following logic: When the calculated ρ is greater than 1.5, a truncation operation is performed to set ρ to 1.5; Modulated intensity value Initial strength value Multiplying the resource modulation coefficient ρ, the semantic mapping intensity dimension is scaled and modulated using the external entity's computing power scale, anchoring the generated feature trajectory to the organizational productivity boundary. The data processing system obtains the code hosting platform interaction logs of the target enterprise entity through an asynchronous application programming interface, extracts the total number of independent developer nodes that triggered code merging actions and the total number of merge request connections within a specific time period, calculates the mathematical ratio of the total number of merge request connections to the total number of independent developer nodes, and determines it as the project collaboration network density. The system simultaneously extracts the organizational computing power scale parameter including the total number of server processor cores and the business data volume parameter representing the average daily throughput of the database. It calls the preset extreme value normalization function to map the organizational computing power scale parameter, business data volume parameter, and project collaboration network density to a dimensionless numerical range of 0.1 to 1.0, respectively. The arithmetic mean of the three mapped values ​​is calculated and determined as a comprehensive resource feature variable. The system inputs this comprehensive resource feature variable into the core calculation model of the resource modulation coefficient ρ to perform subsequent projection modulation operations.

[0038] The data processing module calculates the modulated capability time-series tensor on the time axis. gradient of change The system reads the upper limit of the business evolution rate, which represents the physical limit of a single subject's learning of specific skills. When the gradient change is detected in real time Greater than the upper limit of business evolution rate At that time, the system triggers a logical confidence correction step to calculate the upper limit of the business evolution rate. With the gradient of change The ratio of the two values ​​is used to determine the dynamic attenuation coefficient. The system utilizes the dynamic attenuation coefficient and the modulated intensity value. Multiplication performs a normalization compression operation, forcing the slope of feature trajectories that exceed the limits of real time and space to converge to the upper limit of the business evolution rate. Within the defined security threshold range, a modified capability evolution characteristic trajectory constrained by objective physical evolution laws is generated; the gradient of the modulated capability temporal tensor on the time axis is calculated; and the upper limit of the service evolution rate is read. ; Detected changing gradient Greater than the upper limit At that time, a logical confidence correction step is triggered, based on the upper limit of the business evolution rate. With the gradient of change The ratio determines the dynamic attenuation coefficient; the dynamic attenuation coefficient is then used in conjunction with the modulated intensity value. Multiplication performs normalized compression, converging the slope of the feature trajectory exceeding the spatiotemporal limits to the upper limit of the business evolution rate. Within a defined safety threshold, the system generates capability evolution feature trajectories that are modified by physical evolution laws. The system samples the modified capability evolution feature trajectories at equal intervals according to time sequence to generate a set of capability feature points arranged in sequence. The system calculates the discrete Friesian distance between the set of capability feature points and the target job standard benchmark curve generated based on the target job requirement data. The system calculates the matching score based on the reciprocal of the discrete Friesian distance. The system arranges the candidate data stream in order of the matching score and pushes it to the recruitment management terminal. This data processing procedure anchors the semantic tensor transformation of natural language within a quantitative evaluation framework of objective enterprise computing power scale and time physical limits.

[0039] Example 4: In the scenario of configuring an initial big data processing environment for cross-industry general technical positions, the data processing system accesses a benchmark database containing a large number of historical resumes, parses project experience text blocks with positive performance tags to extract a set of core verbs, and constructs a two-dimensional feature matrix representing behavioral complexity by calculating the absolute word frequency of each core verb in the job description and the co-occurrence probability of promotion events. The system extracts the principal component eigenvalues ​​of the two-dimensional feature matrix and linearly maps the extracted principal component eigenvalues ​​to a numerical range of 0.1 to 1.0. The mapped values ​​are bound to the core verbs and stored in local memory to fill the standard action weight lookup table, thereby establishing a quantitative mapping correspondence between semantic action tags and numerical weights.

[0040] The system synchronously aggregates continuous growth time span parameters under the same skill dimension in the benchmark database, extracts quantile values ​​with a cumulative probability density of 95%, and calculates the benchmark evolution slope by dividing the preset standard proficiency increment by this quantile value. The system writes this benchmark evolution slope into the configuration register as the upper limit of the business evolution rate. Before accessing a specific enterprise operating environment, the system injects a baseline semantic event tuple data stream into the local server cluster and monitors the system response latency to measure the physical concurrency limit of the computing nodes. The system then determines the measured limit value as the industry benchmark number of nodes. And replace the initial preset reference value to obtain the environmental adaptation constant required to calculate the resource modulation coefficient ρ.

[0041] Example 5: In a big data processing scenario involving redundant resume data streams with time-span discontinuities, the data processing system faces engineering challenges related to the candidate's corrected capability evolution trajectory and the target job's standard benchmark curve, including time-axis sampling node misalignment and data sparsity-induced distance metric distortion. The data processing system acquires the sampling point sequence P of the corrected capability evolution trajectory and the benchmark point sequence Q of the target job's standard benchmark curve. It reads the time interval difference between two adjacent sampling points in sequence P, extracts the preset capability maintenance half-life constant from memory, and triggers timing completion when the time interval difference exceeds the capability maintenance half-life constant. Logically, virtual evaluation nodes are generated at equal intervals along the time axis within the interval of time interval difference at a preset sampling frequency. The product of the intensity value of the preceding adjacent sampling point and the preset time decay factor is calculated and determined as the intensity value of the virtual evaluation node. The aligned capability feature point set is output. The system constructs a dynamic programming cost matrix of dimension M×N in memory, where M is the total number of nodes in the aligned capability feature point set and N is the total number of nodes in the reference point sequence Q. The system calculates the Euclidean distance between the i-th node in the aligned capability feature point set and the j-th node in the reference point sequence Q in the multidimensional capability space and writes it into the corresponding coordinate position of the dynamic programming cost matrix.

[0042] The system recursively updates the maximum path distance in the dynamic programming cost matrix layer by layer based on the state transition equation of discrete Friesian distance. This recursive operation continues until the coordinates (M,N) are reached, the minimum value on the current mapping path is extracted and determined as the discrete Friesian distance, the reciprocal of the discrete Friesian distance is calculated to generate the matching score, the candidate resume data stream is arranged in descending order of the matching score and pushed to the recruitment management terminal. The control logic that integrates the underlying dynamic programming evaluation process with the temporal completion procedure prevents the divergence of algorithm measurement caused by non-continuous work experience, so that the spatiotemporal tensor evolution path of the candidate's unstructured resume mapping converges within a continuous mathematical space measurement scale.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A resume recommendation method based on natural language processing, characterized in that, Includes the following steps: Acquire the candidate resume data stream to be processed and the target job requirement data; Using the semantic role labeling model, multiple semantic event tuples are extracted from the project experience text block of the candidate's resume data stream. Each semantic event tuple includes a background feature vector, a behavior feature vector, a result feature vector, and a time span parameter representing the duration of the semantic event. Based on the pre-defined job capability ontology library, semantic event tuples are mapped to a multi-dimensional capability space to construct an initial capability temporal tensor. Each element in the initial capability temporal tensor represents the initial intensity value of each capability dimension under each time slice. The background feature vector is parsed to extract the entity identifier of the candidate's enterprise, and the resource distribution feature data of the enterprise entity, including the scale of organizational computing power, the volume of business data and the density of project collaboration network, is obtained. Based on the resource boundary constraints defined by the resource distribution feature data, the initial intensity value is projected and modulated to generate the modulated capability time series tensor. The gradient of the modulated capability temporal tensor on the time axis is calculated. When the gradient exceeds the upper limit of the business evolution rate, which represents the logical speed limit of a single subject acquiring a specific skill per unit time, the intensity value is corrected using a preset attenuation coefficient to generate the corrected capability evolution characteristic trajectory. Calculate the discrete Fréchet distance between the corrected capability evolution feature trajectory and the target job standard baseline curve, and output resume recommendation results based on the discrete Fréchet distance.

2. The resume recommendation method based on natural language processing according to claim 1, characterized in that, In the step of projecting and modulating the initial intensity value, the calculation logic of the modulated intensity value is as follows: The resource modulation coefficient ρ is determined using the resource support abundance mapped from the background feature vector; the initial intensity value is then used... The product of the modulated value and the resource modulation coefficient ρ determines the modulated intensity value. The calculation formula is: , where ρ is a dimensionless coefficient determined based on the enterprise entity's industry position score, asset size, and the topological distribution of collaborative network nodes, and the value of ρ ranges from 0.5 to 1.

5.

3. The resume recommendation method based on natural language processing according to claim 1, characterized in that, In the step of extracting multiple semantic event tuples, a preset named entity recognition algorithm is used to separate organizational entity words from the background feature vector and use the organizational entity words as enterprise entity identifiers.

4. The resume recommendation method based on natural language processing according to claim 1, characterized in that, Obtaining resource distribution characteristic data includes: based on the enterprise entity identifier, initiating a query request to a pre-built enterprise asset database through an asynchronous application programming interface to obtain structured data representing the enterprise entity's industry ranking, server cluster size, and number of business node connections.

5. The resume recommendation method based on natural language processing according to claim 1, characterized in that, The steps of projecting and modulating the initial intensity value include: projecting the initial intensity value onto a dynamic feature space composed of the organization's computing power scale, business data volume, and project collaboration network density, in order to identify and correct anomalous outlier data that deviates from the support of resource distribution feature data.

6. The resume recommendation method based on natural language processing according to claim 1, characterized in that, The steps for generating the modified capability evolution feature trajectory also include: detecting whether there are overlapping time periods for multiple semantic event tuples on the time axis; if there are overlapping time periods, calculating the total capability strength value within the overlapping time period; if the total capability strength value exceeds the upper limit of the bandwidth of a single subject's output efficiency, then performing normalization compression processing on the strength value within the overlapping time period.

7. The resume recommendation method based on natural language processing according to claim 1, characterized in that, Before calculating the discrete Fraser distance, the method also includes sampling the modified capability evolution feature trajectory at equal intervals on the time axis to generate multiple sets of sequentially arranged capability feature points.

8. The resume recommendation method based on natural language processing according to claim 1, characterized in that, The job competency ontology stores feature keywords, competency dimension weights, and coordinate mapping rules in a multi-dimensional competency space for different industry jobs.

9. The resume recommendation method based on natural language processing according to claim 1, characterized in that, The generation of the target job standard baseline curve includes: simulating a standard growth path in a multi-dimensional capability space based on the job qualification requirements in the target job demand data, in order to generate a dynamic evolution model.

10. The resume recommendation method based on natural language processing according to claim 1, characterized in that, The resume recommendation results based on discrete Friesian distance include: calculating the matching score between candidates and target positions, arranging the resumes of multiple candidates in descending order of matching score, and pushing the sorted resume data stream to the recruitment management terminal.

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