Human resource intelligent matching visualization system based on gear meshing theory

Abstract

The invention relates to the technical field of semantic recognition and data analysis, and discloses a human resource intelligent matching visualization system based on a gear meshing theory, which comprises a semantic structure analysis module, a polar coordinate topological mapping module, a meshing logical operation module and a data interaction terminal, dividing the extracted semantic feature tag into rigid constraint and flexible preference attributes by the system, and respectively mapping the rigid constraint and the flexible preference attributes into envelope functions with step type or gradual change type geometric boundaries in a polar coordinate space; a matching degree score is generated by executing geometric interference detection and effective overlapping area integral operation in a full-angle range, and uniform operation of binary logic blocking and continuous numerical value weight is realized in a single mathematical model by utilizing a geometric gradient coding mechanism, so that the matching degree score is obtained. The problems that when a traditional vector model processes mixed constraint conditions, calculation links are split, and precision is insufficient are solved, and logic consistency and operation efficiency of heterogeneous data retrieval are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a visual system for intelligent matching of human resources based on gear engagement theory, belonging to the technical field of semantic recognition and data analysis. BACKGROUND

[0002] In the current existing semantic retrieval and intelligent matching technology system, constructing a vector space model based on a deep learning algorithm is the mainstream paradigm for processing unstructured text data. Such technology maps discrete text symbols into high-dimensional continuous dense vectors, quantifies the semantic correlation strength between different text objects using the cosine similarity or Euclidean distance between vectors, breaks through the rigid matching limitations of traditional keywords, identifies synonyms, near synonyms, and implicit context semantic relationships, and is widely used in information recommendation, knowledge graph construction, and resource matching scenarios. However, in the context of complex constraint condition accurate matching, the target data feature attributes present a logical dimensionality duality, and both rigid constraints and flexible preferences that only serve as optimization references are involved. When the existing vector space model performs feature embedding, the binary truncated attribute rigid logic and the continuous linear attribute flexible weight are mixed and compressed into the same vector dimension. This homogenization mapping causes the bottom layer of the retrieval model to lose the ability to recognize the logical topology of features, and the system cannot distinguish between structural mismatches caused by missing core features and numerical deviations caused by differences in secondary features at the level of simply numerical similarity integration. When processing large-scale heterogeneous data, the confusion of logical dimensions causes high-value retrieval results to be mixed with invalid objects that do not meet the basic threshold conditions, resulting in reduced precision and reduced result usability.

[0003] In addition to the limitations of the bottom layer data representation structure, existing technology matching logic specific control methods have deficiencies. For example, the Chinese invention patent with the authorization announcement number CN116993311B discloses a human resource information retrieval system based on information matching technology. The system introduces an adjustment factor to weight and correct the matching coefficient, and generates a target degree ranking in combination with the job seeker's expectation degree. The core algorithm is limited to linear weighting or product scalar algebraic operation methods, and the processing logic is based on numerical accumulation or multiplication correction. It is impossible to construct a rigid logic barrier with strict geometric limits within a single mathematical model. When encountering missing key hard qualifications such as professional qualifications or education threshold, the matching score value decreases, and the non-logical link is physically blocked, causing invalid candidates with higher secondary feature scores to be mixed into the final recommendation queue, resulting in low signal-to-noise ratio of the retrieval results and reduced precision. To solve the above problems, existing technologies usually adopt a rule filter in series before vector retrieval or increase the feature dimension strategy, which destroys the continuity and integrity of the semantic calculation model, resulting in fragmented calculation links. Increasing the vector dimension causes the curse of dimensionality, which causes the matrix operation algorithm to consume exponentially, and cannot fundamentally solve the problem of unified representation of Boolean logic and fuzzy logic in the same mathematical space.

[0004] Therefore, the technical problem to be solved by the present application is to establish a single mathematical model to simultaneously accurately encode and calculate semantic feature logic attributes and similarity attributes, and to realize efficient and accurate retrieval of heterogeneous data containing mixed constraint conditions. SUMMARY

[0005] To solve the problems in the background art, the technical solution of the present application is as follows: a visual system for intelligent matching of human resources based on gear engagement theory, comprising:

[0006] A semantic structure analysis module is configured to receive post text data and candidate object resume data, extract a plurality of independent semantic feature labels, define constraint attribute categories of the semantic feature labels based on a pre-set industry logical graph, the constraint attribute categories including rigid constraint attributes representing binary logic blocking and flexible preference attributes representing linear logic weight, and perform normalized quantization processing on the semantic intensity of each semantic feature label.

[0007] A polar coordinate topology mapping module is configured to construct a polar coordinate operation space capable of simultaneously carrying Boolean logic and continuous numerical logic, and map each semantic feature label into a geometric envelope function in different angle sectors in the space; wherein for the rigid constraint attributes, a step-type geometric boundary with a first gradient slope is generated based on the quantized numerical values thereof; and for the flexible preference attributes, a gradual-type geometric boundary with a second gradient slope is generated based on the quantized numerical values thereof, the absolute value of the first gradient slope being greater than a pre-set logical judgment threshold, and the absolute value of the second gradient slope being less than the logical judgment threshold.

[0008] An engagement logic operation module is configured to perform matching degree evaluation operations, traverse the angle sectors corresponding to the rigid constraint attributes, judge whether the radial polar values of the object envelope function and the target envelope function satisfy the geometric inclusion condition to generate an interference veto signal, and calculate the effective overlapping area integral of the object envelope function and the target envelope function in the full angle range to generate a matching degree score.

[0009] A data interaction terminal is configured to output a retrieval result based on the interference veto signal or the matching degree score.

[0010] Preferably, the engagement logic operation module further comprises a phase self-adaptive calibration unit configured to apply a rotation bias sequence within a pre-set angle range to the object envelope function before performing the matching degree evaluation operation; for each bias angle in the rotation bias sequence, a geometric interference coefficient value between the object envelope function and the target envelope function is calculated; the rotation angle corresponding to the minimum geometric interference coefficient value is selected as a reference phase angle, and the polar angle coordinate of the object envelope function in the polar coordinate operation space is corrected based on the reference phase angle to eliminate systematic phase misplacement caused by differences in semantic classification standards.

[0011] Preferably, the meshing logic operation module further includes a potential energy analysis unit, used to retrieve the polar radius value of the object envelope function in the neighboring sectors of the gap region based on a preset semantic association graph when a gap region not filled by the object envelope function is detected in a specific angular sector of the target envelope function, and to determine the semantic elastic modulus of the gap region based on the path distance between the neighboring sectors and the gap region in the semantic association graph. ; combined with the radial depth of the gap region With semantic elastic modulus Calculate the change in energy value Its calculation rules satisfy the formula ;in, For polar angle variables, The angle range of the gap region; when the change in situation energy value When the learning cost is less than a preset threshold, the gap region is identified as a potential matching region and the matching is based on the variable energy value. The matching score is adjusted by weighting.

[0012] Preferably, the semantic structure parsing module further includes an information granularity quantification unit, which is used to statistically analyze the entity density index of each semantic feature label within the source text context window, and convert the entity density index into a surface roughness parameter representing the information confidence using a reverse mapping function; when calculating the effective overlap area integral, the meshing logic operation module simultaneously reads the surface roughness parameter within the corresponding angular sector, calculates the friction loss value based on the surface roughness parameter, and subtracts the friction loss value from the matching score. The friction loss value is positively correlated with the surface roughness parameter to reduce the weighting influence of the confidence semantic feature on the retrieval results.

[0013] Preferably, the system also includes a supply and demand distribution sensing unit, used to statistically analyze the capability value distribution density of the candidate object database within the angle sector corresponding to the specified rigid constraint attribute; the polar coordinate topology mapping module is also configured with boundary correction logic based on supply and demand distribution: when the capability value distribution density is lower than a preset scarcity threshold, a geometric correction operation is triggered to correct the step-type geometric boundary of the target envelope function within the corresponding angle sector into a trapezoidal boundary with a specific tilt angle. The magnitude of the tilt angle is positively correlated with the reciprocal of the capability value distribution density, adaptively expanding the geometric reception tolerance in a scarce data environment.

[0014] Preferably, the polar coordinate topology mapping module dynamically calculates the width of the angular sector occupied by each semantic feature label in the polar coordinate computation space based on the weight coefficient of each semantic feature label in the preset job model; the higher the weight coefficient, the larger the corresponding angular sector width, so that the high-weight features occupy a larger geometric integral domain in the calculation of the effective overlapping area integral.

[0015] Preferably, the meshing logic operation module further includes an interference fit analysis unit, which is used to identify the overflow region where the radial radius value of the object envelope function exceeds the radial radius value of the target envelope function, calculate the geometric area of ​​the overflow region, and combine the geometric area with the semantic attributes of the corresponding sector to generate a talent reserve index that characterizes the redundancy of talent capabilities.

[0016] Preferably, the data interaction terminal also includes a dynamic evolution rendering unit, which is used to acquire the historical data of the candidate object at different time points, call the polar coordinate topology mapping module to generate the corresponding time series geometric envelope function group, and render the time series geometric envelope function group in a concentric layered manner, and characterize the growth trajectory of the candidate object through the radial expansion trend of the geometric boundaries at each level.

[0017] Preferably, the meshing logic operation module includes a pre-screening submodule and a fine calculation submodule. The pre-screening submodule performs Boolean interference detection only based on the step geometric boundary corresponding to the rigid constraint attribute to quickly eliminate candidate object data items that trigger interference rejection signals. The fine calculation submodule performs effective overlapping area integration calculations across the entire angular range only for candidate object data items that have passed the pre-screening submodule.

[0018] Preferably, the pre-set industry logic graph in the semantic structure parsing module includes a skill mutual exclusion rule base; the system also includes a logic conflict verification unit, which is used to detect whether there are logically mutually exclusive semantic feature tags in the same candidate object data item according to the skill mutual exclusion rule base during the semantic feature tag extraction stage; when logical mutual exclusion is detected, an abnormal confidence mark is generated and the subsequent polar coordinate topology mapping process is blocked to filter out false data sources with factual contradictions.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. A semantic feature geometric topology mapping mechanism is constructed in polar coordinate space, which encodes discrete Boolean logic constraints and continuous fuzzy matching preferences into envelope function boundary slope gradient features. The system maps rigid attributes to step geometric boundaries with infinite slope gradients and flexible attributes to finite slope gradient gradients. This data structure design allows computers to process complex semantic retrieval tasks without switching between independent processes of logical filtering and similarity ranking. It directly enforces hard constraints at the physical level and completes non-core dimension quantization weighting through a single geometric overlap area integration operation. This mechanism solves the problems of computational link fragmentation and computational redundancy when traditional vector space models process mixed logical conditions, and improves the consistency of logic and computational efficiency in heterogeneous data processing.

[0021] 2. Before performing high-dimensional feature similarity integration operations, a circuit breaker mechanism based on geometric topological relationships is constructed using radial distance comparison logic in polar coordinates. The meshing logic operation module first traverses the extreme radius values ​​within the rigid attribute range of the object's envelope function and compares them with the boundary threshold of the target envelope function. If a radial interference state is detected where the extreme radius value is less than the boundary threshold, the system triggers a rejection signal and terminates subsequent calculations. This mechanism utilizes geometric comparison operations with extremely low computational complexity to achieve physical-level elimination of invalid candidates in the preprocessing stage of massive data retrieval. This avoids the unnecessary computational power consumption caused by matrix multiplication of the entire data in traditional deep learning models, and reduces the hardware load and response latency of large-scale semantic analysis systems.

[0022] 3. By introducing a mapping relationship between information density and geometric boundary morphology, a confidence compensation logic to resist semantic noise is constructed. The semantic feature parsing module quantitatively analyzes the entity density in the context of specific feature labels in the source text, and the reverse mapping is the high-frequency perturbation attribute of the geometric envelope function boundary. When the meshing operation module calculates the overlapping area, it simultaneously calculates the friction loss value based on the perturbation attribute and performs subtraction correction. This mechanism transforms the text information entropy into geometric texture features that can participate in integral operations, enabling the system to automatically distinguish data objects with the same feature value but different confidence levels based on the level of detail in the description. This does not rely on manual rule intervention, reduces the signal-to-noise ratio data matching weight, and improves the accuracy and credibility of the retrieval results in real and complex contexts. Attached Figure Description

[0023] Fig. 1 This is the system logic architecture and data flow diagram of the gear meshing theory of this invention;

[0024] Fig. 2 This is a nonlinear quantization relationship diagram between the semantic elasticity modulus and the skill path distance of this invention;

[0025] Fig. 3 This is a logical view of user interaction and function call in the intelligent matching scenario of this invention. Detailed Implementation

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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.

[0027] This invention provides a visualization system for intelligent human resource matching based on gear meshing theory, comprising a semantic structure parsing module, a polar coordinate topology mapping module, a meshing logic operation module, and a data interaction terminal. These modules are connected via a data bus, forming a processing link from unstructured text input to geometric matching result output. The system aims to uniformly handle rigid constraints and flexible preferences in human resource matching scenarios within a single mathematical model through geometric topology transformation. The semantic structure parsing module receives target job text data containing job requirements and candidate object text data containing resume information. To address the issue of unstructured features in the text data, this module incorporates named entities. The recognition unit employs an algorithm architecture combining a bidirectional long short-term memory network and a conditional random field, namely BiLSTM-CRF, to extract entities such as skill terms, certificate names, educational requirements, and years of work experience from the source text, generating several independent semantic feature labels. The attribute definition unit queries the attribute category of each semantic feature label based on a pre-set industry logic graph. Nodes marked as mandatory or disqualifying items in the graph, such as professional qualification certificates or minimum educational qualifications, are defined as rigid constraint attributes; nodes marked as bonus items or quantifiable levels, such as programming language proficiency, are defined as flexible preference attributes. The quantification unit maps the semantic strength of each semantic feature label to an interval... For values ​​within a Boolean type, given their rigidity, they are assigned a value as soon as they exist. If missing, assign a value. For continuous flexible features, the corresponding numerical values ​​are mapped based on the weight levels of the modifiers in the graph.

[0028] The polar coordinate topology mapping module constructs a polar coordinate computation space capable of supporting both binary logic and continuous numerical logic, mapping semantic feature labels to geometric envelope functions in polar coordinates. The system will The circumference is divided into Each sector is an independent angular sector, and each sector corresponds to a semantic feature label. The sector width is determined by the weight coefficient of that feature in the job model. In the radial dimension, for the target job text data, the system generates a target envelope function that is concave towards the center of the circle. For candidate object text data, the system generates an outwardly convex object envelope function. During this process, the system performs gradient encoding based on attribute categories. For rigid constraint attributes, it generates an attribute with a first gradient slope. The step-type geometric boundary, which is represented as a rectangular tooth groove or tooth profile in polar coordinates, satisfies... ,in The preset logical judgment threshold is used; for flexible preference attributes, a second gradient slope is generated. The gradually changing geometric boundary, characterized by an involute or trapezoidal tooth profile, satisfies... The meshing logic operation module performs a computational geometry-based matching evaluation. This module includes a pre-screening submodule and a fine-computation submodule. The pre-screening submodule traverses all angular sectors corresponding to rigid constraint attributes, compares the radial polar radius values ​​of the object envelope function and the target envelope function, and if a match is detected in any rigid sector... If the system determines that the object is in a state of top-cutting interference, it generates an interference rejection signal and terminates subsequent calculations, thereby eliminating candidate objects that do not meet the core threshold conditions. The fine calculation submodule then calculates the effective overlap area integral of the object envelope function and the target envelope function across the entire angular range for the pre-screened data, generating a matching score. The calculation formula is: ,in The angle weighting function is used; the meshing logic operation module also includes a phase adaptive calibration unit, which is used to eliminate phase misalignment caused by differences in semantic classification standards. This unit sets a rotation bias sequence before performing the matching degree calculation. Rotate the object envelope function in sequence. The system calculates the corresponding geometric interference coefficient, selects the rotation angle with the smallest geometric interference coefficient as the reference phase angle, and corrects the polar coordinates of the object envelope function based on this angle.

[0029] The meshing logic operation module also includes a potential energy analysis unit, used to quantify the cross-skill transferability of candidate objects. When an unfilled gap region is detected in a specific flexible attribute sector of the target envelope function, this unit retrieves the polar radius value of the object's envelope function in the neighboring sectors of the gap region based on the semantic association graph. The system calculates the path distance between the skill nodes in the neighboring sectors and the skill nodes in the gap region in the graph, and determines the semantic elastic modulus of the gap region accordingly. The shorter the path distance, the higher the semantic elasticity modulus. The smaller the radial depth of the system's combined gap region, the better. With semantic elastic modulus Calculate the change in energy value The formula is ,like If the learning cost is less than the preset threshold, the system identifies the gap region as a potential matching region and adjusts the matching score accordingly. The semantic structure parsing module also includes an information granularity quantification unit, which handles the confidence differences in text information. This unit calculates the entity density index of each semantic feature label within the source text context window, i.e., the character proportion of numerical values, proper nouns, and technical parameters, and uses a reverse mapping function to convert the entity density index into a surface roughness parameter. When calculating the effective overlapping area integral, the system simultaneously reads the surface roughness parameters of the corresponding angular sector and calculates the friction loss value. ,in To address the contact pressure function, the friction loss value is subtracted from the matching score. The system also includes a supply and demand distribution sensing unit, used to adjust the retrieval strategy in a scarce data environment. This unit statistically analyzes the capability value distribution density of the candidate object database within the angle sector corresponding to the specified rigid constraint attribute. When this density is lower than a preset scarcity threshold, a geometric correction operation is triggered. The polar coordinate topology mapping module corrects the step-shaped geometric boundary of the target envelope function within the corresponding angle sector to a trapezoidal boundary with a specific tilt angle. The tilt angle is positively correlated with the reciprocal of the capability value distribution density, thereby expanding the geometric reception tolerance. The data interaction terminal outputs the retrieval results based on the matching score and interference state, and is equipped with a dynamic evolution rendering unit. This unit acquires the historical data of the candidate object at different time points, generates a time-series geometric envelope function group, and renders it in a concentric layered manner. The radial expansion of the geometric boundary at each level represents the growth trajectory of the candidate object.

[0030] Example 1: In a recruitment scenario for a senior system architect position, the target position sets Java development experience as a rigid constraint and Python data analysis ability as a flexible preference. Candidates possess a strong C++ background but lack explicit Java project experience, and have exceptional expertise in Python. In this situation, existing retrieval systems based on vector space models typically determine a mismatch due to the lack of feature vectors in the Java dimension, leading to missed opportunities for high-potential talent. This invention, upon system startup, uses a semantic structure parsing module based on a pre-defined industry logic graph, defining Java development experience as a rigid constraint and Python data analysis ability as a flexible preference. The polar coordinate topology mapping module then maps these features to a polar coordinate computation space, generating a gradient slope with infinitely large gradients for Java sectors. The rectangular groove geometry boundary is used to generate sectors with finite gradient slopes in Python. The gradient geometric boundary, during the pre-screening phase of the meshing logic operation module, the system detects the object envelope function of the candidate object. The extreme radius value in the Java sector does not satisfy the target envelope function. The boundary conditions create a physical gap.

[0031] At this point, the potential energy analysis unit triggers a neighborhood scan procedure, retrieving a candidate object with a high extreme radius distribution in the C sector adjacent to the Java sector. The system calculates the path distance between C++ and Java in the semantic association graph, determining the semantic elastic modulus of the gap region. The low level indicates low resistance to skill transfer, and the system combines the gap depth with this semantic elasticity modulus. The calculated variable energy value If the learning cost is less than the preset threshold, and the supply and demand distribution sensing unit's statistical database detects that the distribution density of candidate objects with Java+Python composite capabilities in the current market is lower than the scarcity threshold, a geometric correction operation is automatically triggered. This corrects the rectangular boundary of the target envelope function in the Java sector to a trapezoidal boundary with a specific tilt angle. During the fine calculation phase, the meshing logic operation module performs an effective overlap area integration operation based on the corrected geometric boundary, without generating an interference rejection signal, and outputs a matching score containing high potential weights. In the visualization interface, the data interaction terminal renders Java sectors as a plastic-filled state with dotted lines and Python sectors as a capacity overflow state.

[0032] Example 2: To verify the performance, logical consistency, and adaptability of the semantic retrieval system proposed in this invention when processing large-scale heterogeneous data, this example constructs a control test platform. The test platform is built based on a computing cluster, configured with an Intel Xeon Gold 6248R processor (3.0GHz, 24 cores), 256GB DDR4 memory, and CentOS 7.8 operating system. The test dataset contains 10,000 de-identified technical resume texts and 500 standard job requirement descriptions. Approximately 15% of noisy data, including spelling errors, non-standard formatting, and non-standard terminology, is retained in the dataset to simulate real-world scenarios. The experiment is designed with two comparison groups: the control group uses a vector retrieval model based on the BERT-BiLSTM architecture, mapping the text to 768-dimensional vectors and sorting them based on cosine similarity; the experimental group uses the system of this invention. The experiment inputs the same job requirements and candidate resume streams into both models via scripts. In the system of this invention, the reference phase angle calibration is set... The rotation bias sequence with a step size of In the calculation of semantic elastic modulus, the spectral damping coefficient Set as The experiment examined three working conditions: a single rigid constraint scenario (the job only contains a single hard threshold), a mixed multi-constraint scenario (the job contains a complex combination of rigid and flexible conditions), and a sparse data environment (the proportion of qualified candidates in the database is less than 0.5%). The evaluation indicators included: precision, the proportion of people who meet the job requirements in the search results; recall, the proportion of all qualified candidates in the database that were retrieved; and logical consistency error rate, the proportion of the top 10 retrieved candidates who lacked rigid conditions but were ranked higher due to high flexible scores, as shown in Table 1.

[0033] Table 1: Experimental Data on Search Performance Comparison

[0034]

[0035] Referring to Table 1, in the mixed multi-constraint scenario, the logical consistency error rate of the control group was 22.6%, while that of the experimental group was 1.5%. The precision rate of the experimental group was 92.4%, which was higher than that of the control group. In the sparse data environment, the recall rate of the control group was 42.1%, while that of the experimental group was 78.3%. The average response latency of the experimental group was lower than that of the control group.

[0036] Example 3: This example combines Figs. 1 to 3 This document describes a visualization system for intelligent human resource matching based on gear meshing theory, such as... Fig. 1 As shown, the data stream contains job text data and candidate resume data. It enters the semantic structure parsing module, which performs rigid constraint and flexible preference classification based on the skill mutual exclusion rules and weight levels in the pre-set industry logic graph. On the other hand, it receives the surface roughness parameters generated after the entity density index is calculated by the information granularity quantification unit. The parsed data is then transmitted to the polar coordinate topology mapping module. This module combines the capability value distribution density statistically obtained by the supply and demand distribution perception unit and the triggered scarcity threshold geometric correction instruction to construct a unified space of Boolean and continuous logic and generate step-type or gradual geometric boundaries. The data enters the meshing logic operation module. At this stage, the system not only performs pre-screening based on geometric interference detection and fine calculation based on effective overlap area integration, but also calls the phase adaptive calibration unit to perform rotation bias sequence trial calculation to eliminate systematic phase misalignment, and calls the potential energy analysis unit to calculate the variable potential energy of the gap region and perform weighted correction based on semantic elastic modulus. Finally, the matching degree score, interference signal, and growth trajectory visualization results processed by the dynamic evolution rendering unit are output through the data interaction terminal.

[0037] like Fig. 2 As shown, the horizontal axis represents the distance of the skill path. The value ranges from 0 to 9, and the vertical axis represents the semantic elastic modulus. The values ​​range from 0 to 600, and the graph contains three lines corresponding to different damping coefficients. The curves showing the changes are as follows: A solid line with a value of 0.3 =0.5 dashed line and A dotted line with a value of 0.7, varying with the skill path distance. With the increase in [something], all three curves show an exponential growth trend with different slopes, indicating that the greater the semantic distance between skills, the greater the corresponding elastic modulus. The larger; such as Fig. 3As shown, the system user, i.e., HR or recruiter, is the main executor. They initiate interaction by inputting job title and resume data. The core use case is to perform intelligent semantic retrieval. This use case is associated with multiple sub-functional modules, including semantic structure parsing and associated logical conflict verification, polar coordinate topology mapping and associated supply and demand distribution boundary correction, and meshing logic operation. Among them, the meshing logic operation is further associated with phase adaptive calibration, potential energy analysis, and confidence friction correction functions. After the retrieval is completed, the user executes the use case to obtain the visual retrieval results. This use case relies on the support of dynamic evolution rendering function, thus forming a complete business closed loop from data input to visual output.

[0038] Example 4: This example elaborates on semantic elastic modulus. Calibration and variable energy values The calculation procedure, initial state definition procedure: the system loads the pre-set skill knowledge graph. , where nodes Represents a skill entity, edge This represents the co-occurrence or derivation relationship between skills, for any two skill nodes. Its path distance Defined as the shortest path length connecting two nodes in the graph, the input data is the set of unfilled sectors for the target job. and the global polar radius distribution of the object envelope function Process judgment quantification procedure: for Each gap sector (corresponding to a skill) The system performs the following calculation steps: Neighborhood scan: in Search for extreme radius values (Normalized threshold) set of strong skills Modulus calculation: For Each skill Calculate its to path distance Select the minimum distance Parameter calibration: semantic elastic modulus The value is derived from the formula It is confirmed that, among them, The reference modulus constant (calibrated as) ), The damping coefficient of the spectrum (calibrated through regression analysis of historical job transfer data) is... Potential energy integral: when the given potential energy is determined After the value is obtained, the system operates within the angular range of the notched sector. Integral operation ,like If so, the gap is determined to be fillable.

[0039] Example 5: To systematically address issues such as semantic mutual exclusion logic judgment, information confidence quantification, and system stability boundaries encountered in practical engineering deployments, this example provides a targeted technical remediation solution. This solution introduces a logic matrix, a quantization mapping function, and an exception handling procedure. A logic conflict verification unit is embedded in the semantic structure parsing module. This unit constructs a mutual exclusion matrix based on industry rules. , dimension ,in The total number of identifiable semantic feature labels, matrix elements Value , Indicate feature label and Mutually exclusive To ensure compatibility, the matrix is ​​constructed based on a pre-set expert knowledge base. At runtime, the system extracts a set of all semantic feature labels for candidate objects. Iterate through all the label pairs in the collection. And query the mutual exclusion matrix If detected The system generates a confidence anomaly flag, sets the matching score of the candidate object to zero, and records the conflict type in the log.

[0040] To distinguish between empty keyword stuffing and detailed capability descriptions, this embodiment describes surface roughness parameters. The quantization mapping procedure utilizes a pre-built entity dictionary. Textual descriptions of specific skills in a candidate's resume Perform word segmentation and statistics, and calculate entity density. A mapping relationship is constructed using the inverse Sigmoid function. ,in Set as , Set as In the meshing integral calculation, a friction loss term is introduced, which reduces the effective overlapping area to a infinitesimal value. Revised to , Set as ; Regarding semantic elastic modulus The system loads a pre-set skill knowledge graph for calibration and calculation. When processing the target job vacancy sector, the system uses the candidate object envelope function. Search for extreme radius values A collection of powerful skills ,for Each skill Calculate the target skill Calculate the path distance and select the minimum value. Semantic elastic modulus By function Sure, Set as , Set as The system is in the angle range of the gap sector. Internal execution potential energy integration ,like If the value is less than a preset threshold, the gap is determined to be fillable.

[0041] Example 6: To ensure the surface roughness parameters of the present invention The mapping relationship between the confidence level and text information exhibits stable engineering stability across different data sources and application scenarios. This embodiment provides a standardized parameter calibration and optimization procedure, and through controlled engineering experiments, determines the sensitivity coefficient in the inverse Sigmoid function. With density median threshold The optimal value is determined to achieve accurate quantification of text features at different granularities. The system constructs a benchmark corpus containing multiple gradients, which covers five levels of skill description samples ranging from extremely vague to extremely detailed. For each sample, a confidence score is manually labeled. As the gold standard, an offline calibration process is performed to calculate the entity density of each sample. The roughness is calculated by traversing all possible parameter combinations within a preset parameter space using a grid search algorithm. And evaluate its comparison with human scoring. The correlation coefficient is used to select the parameter combination that maximizes the Pearson correlation coefficient as the system's default configuration. For example, in the calibration of IT technical positions, the system should determine... and At that time, the friction loss value output by the system best reflects the real differences in information quality. This calibration procedure ensures that the mathematical model is based on an objective mapping of empirical data.

[0042] To address the adaptability issue of the system when facing entirely new industry sectors, this embodiment further elaborates on the pre-deployment calibration procedure. Before applying the system to the new sector, a sample set of job descriptions for that sector is imported. The system automatically generates a histogram of entity density distribution in this sample set and calculates its statistical characteristics. Based on these distribution characteristics, the system adaptively adjusts... The value of this parameter is adjusted to align with the average information density level of that specific domain. For example, if the average entity density is high in the financial sector, the system automatically adjusts the value upwards. To prevent loss of discriminative power due to excessively low standards, the system uses a small amount of labeled data in the field for fine-tuning tests to verify the retrieval accuracy after parameter adjustment. This pre-calibration process serves as a standard step in system initialization, ensuring the universality and effectiveness of the gear meshing model in different semantic environments and eliminating the risk of performance fluctuations caused by domain differences.

[0043] Example 7: To eliminate engineering uncertainties that may exist in the geometric correction logic of the supply and demand distribution sensing unit under different industry data distributions, and to prevent the rigid constraint from failing due to improper parameter calibration of the trapezoidal boundary, this example establishes an offline chamfer angle calibration procedure based on edge benefit analysis. The system extracts a set of benchmark test samples from the historical recruitment database. This sample set includes recalled samples (marked as positive samples) that were manually reviewed and determined to meet the ability requirements despite lacking certification, and rejected samples (marked as negative samples) that seriously do not meet the hard requirements. For a specific rigid attribute interval, the system calculates the global capability value distribution density. In the simulation environment The step size is used to tilt the rectangular boundary of the sector corresponding to the target envelope function by an angle. from Gradually increase to In each iteration, the recall gain of positive samples is recorded. The false alarm growth rate compared with negative samples The system defines the effective engineering threshold point as: Angle value at time By studying different densities Below Perform nonlinear regression fitting to establish the control function. ,in and As an empirical constant for a specific field, this procedure ensures that the geometric deformation of the chamfering operation is always within a range of risk-controlled and profit-maximizing.

[0044] To address the potential random phase shift issue in the initial stages of polar coordinate mapping for heterogeneous data sources, this embodiment specifies a global phase synchronization operation procedure based on semantic anchors. Before performing any micro-perturbation optimization or meshing calculations, the system performs coarse alignment. The system pre-sets a set of anchor semantic tags with cross-domain applicability. Typically, rigid features such as highest education level or years of work experience are selected. For any input candidate data, the system identifies those belonging to... The characteristic sectors are identified, and the angle bisectors of these sectors in polar coordinates are calculated. The system constructs a rotation matrix. Rotate the entire object envelope function so that By aligning with the zero-degree reference line of the polar coordinate system, this step eliminates systematic phase errors caused by different encoding orders in the data source, ensuring that the subsequent phase adaptive calibration unit always operates within the effective linear convergence domain, thereby guaranteeing the reproducibility of the matching results.

[0045] Example 8: This example illustrates the system initialization phase graph construction procedure and the underlying geometric precision control logic. The attribute category definition of each node in the industry logic graph is based on the conditional probability statistical procedure of historical recruitment logs. The system backtracks the recruitment process data for the past 36 natural months within a preset time window. For each semantic feature label, the conditional rejection rate of the resume screening stage is calculated. When the probability of a resume being marked as unqualified due to missing feature labels exceeds the preset rigid confidence threshold of 0.98, the label is automatically marked as a node representing a rigid constraint of binary logic blocking. When the probability is between 0.05 and 0.98 and the Pearson correlation coefficient between the frequency of the label and the final salary level is greater than 0.6, it is marked as a node representing a flexible preference of linear logic weights. The statistical procedure makes the constraint attribute category classification based on the distribution of real historical recruitment decision data.

[0046] To prevent geometric boundary aliasing distortion caused by insufficient sampling rate, the polar coordinate topological mapping discretization process sets the minimum resolution of the angle sector based on Shannon's sampling theorem. The system statistically analyzes the maximum gradient change rate of each semantic feature label through normalization and quantization. The angle discretization sampling frequency is set to 2.5 times the frequency corresponding to the maximum gradient change rate. The 360-degree circle is divided into no less than 1024 discrete sampling points. For rigid constraint attributes with step-type boundaries, no less than 5 transition interpolation points are automatically inserted at gradient abrupt changes. This ensures that the impact of the Gibbs phenomenon caused by the step boundary on the accuracy of the matching degree score calculation is controlled within 0.1% error, guaranteeing the numerical stability of geometric interferometry detection under discrete calculation environment.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A visual system for human resource intelligent matching based on gear engagement theory, characterized by, The method comprises the following steps: a semantic structure analysis module is configured to receive job text data and candidate object resume data, extract a plurality of independent semantic feature labels, define constraint attribute categories of the semantic feature labels based on a pre-set industry logic graph, the constraint attribute categories including rigid constraint attributes representing binary logic blocking and flexible preference attributes representing linear logic weight, and perform normalized quantization processing on semantic intensity of the semantic feature labels; a polar coordinate topology mapping module is configured to construct a polar coordinate operation space capable of simultaneously carrying Boolean logic and continuous numerical logic, and map the semantic feature labels into geometric envelope functions in different angle sectors in the space; for the rigid constraint attributes, a step-type geometric boundary with a first gradient slope is generated based on quantized numerical values thereof; for the flexible preference attributes, a gradual-type geometric boundary with a second gradient slope is generated based on quantized numerical values thereof, the absolute value of the first gradient slope being greater than a pre-set logic judgment threshold, and the absolute value of the second gradient slope being less than the logic judgment threshold; an engagement logic operation module is configured to perform a matching degree evaluation operation, traverse the angle sector corresponding to the rigid constraint attributes, judge whether radial polar values of the object envelope function and the target envelope function satisfy a geometric inclusion condition to generate an interference veto signal, and calculate an effective overlapping area integral of the object envelope function and the target envelope function in a full angle range to generate a matching degree score; a data interaction terminal is configured to output a search result based on the interference veto signal or the matching degree score.

2. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, characterized in that, The engagement logic operation module further comprises a phase self-adaptive calibration unit configured to, before performing the matching degree evaluation operation, apply a rotation bias sequence in a pre-set angle range to the object envelope function; for each bias angle in the rotation bias sequence, a geometric interference coefficient value between the object envelope function and the target envelope function is calculated; a rotation angle corresponding to a state in which the geometric interference coefficient value is at a minimum is selected as a reference phase angle, and the polar angle coordinate of the object envelope function in the polar coordinate operation space is corrected based on the reference phase angle.

3. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, characterized in that, The engagement logic operation module further comprises a potential potential energy analysis unit, configured to, when it is detected that the target envelope function has a gap region not filled by the object envelope function in a certain angle sector, retrieve the polar radius value of the object envelope function in the neighborhood sector of the gap region based on a preset semantic correlation graph, and determine the semantic elastic modulus of the gap region according to the path distance of the neighborhood sector and the gap region in the semantic correlation graph ; combined with the radial depth of the gap region and the semantic elastic modulus , the deformation potential value is calculated , and the calculation rule satisfies the formula ; wherein, is the polar angle variable, is the angle interval of the gap region; when the deformation potential value is less than a preset learning cost threshold, the gap region is identified as a potential matching region, and the matching degree score is weighted and corrected based on the deformation potential value .

4. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, characterized in that, The semantic structure analysis module further comprises an information granularity quantization unit configured to count an entity density index of each semantic feature label in a source text context window, convert the entity density index into a surface roughness parameter representing information confidence using an inverse mapping function; the engagement logic operation module reads the surface roughness parameter in the corresponding angle sector synchronously when calculating the effective overlapping area integral, and calculates a friction loss value based on the surface roughness parameter, subtracts the friction loss value from the matching degree score, and the friction loss value and the surface roughness parameter are in a positive correlation relationship.

5. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, wherein, The system further comprises a supply-demand distribution perception unit configured to count a distribution density of the candidate object database in an angle sector corresponding to the specified rigid constraint attribute; the polar coordinate topology mapping module is further configured with a boundary correction logic based on the supply-demand distribution: when the distribution density of the capability value is lower than a preset scarcity threshold, a geometric correction operation is triggered to correct the step-type geometric boundary of the target envelope function in the corresponding angle sector to a trapezoidal boundary with a specific inclination angle, and the size of the inclination angle is positively correlated with the reciprocal of the distribution density of the capability value, thereby adaptively expanding the geometric receiving tolerance in a scarce data environment.

6. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, characterized in that, The polar coordinate topology mapping module dynamically calculates the width of the angle sector occupied by each semantic feature label in the polar coordinate operation space according to the weight coefficient of each semantic feature label in the preset post model; The higher the weight coefficient, the greater the corresponding allocated angle sector width, so that the high-weight feature occupies a larger geometric integral domain in the calculation of the effective overlapping area integral.

7. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, wherein, The engagement logic operation module further comprises an interference fit analysis unit configured to identify an overflow region of the object envelope function in which the radial polar radius value exceeds the radial polar radius value of the target envelope function, calculate the geometric area of the overflow region, and combine the geometric area with the semantic attribute of the corresponding sector to generate a talent echelon reserve index representing the redundancy of the talent capability.

8. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, wherein, The data interaction terminal further comprises a dynamic evolution rendering unit configured to obtain the history data of the candidate object at different time nodes, call the polar coordinate topology mapping module to generate a corresponding time series of geometric envelope functions, and render the time series of geometric envelope functions in a concentric layering manner to represent the growth trajectory of the candidate object through the radial expansion trend of each level of geometric boundary.

9. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, wherein, The engagement logic operation module comprises a pre-screening submodule and a fine calculation submodule, the pre-screening submodule performs Boolean interference detection based only on the step-type geometric boundary corresponding to the rigid constraint attribute, and quickly eliminates candidate object data items that trigger interference veto signals; The fine calculation submodule only performs effective overlapping area integral operation in the full angle range for the candidate object data items that pass through the pre-screening submodule.

10. The visual system for human resource intelligent matching based on gear engagement theory according to claim 1, wherein, The preset industry logic graph in the semantic structure analysis module comprises a skill mutual exclusion rule base; the system further comprises a logic conflict checking unit configured to detect whether there are logically exclusive semantic feature labels in the same candidate object data item during the semantic feature label extraction stage according to the skill mutual exclusion rule base; When a logical conflict is detected, a confidence abnormality flag is generated and the subsequent polar coordinate topology mapping process is blocked to filter out false data sources with factual contradictions.

Images