A precise enterprise matching system and method based on worker skills

Abstract

The present invention belongs to the field of enterprise management technology, and specifically relates to a precise enterprise matching system and method based on worker skills. By systematically analyzing the skill chain of the industrial worker's skill improvement process, a skill data collection business chain is constructed; key management services such as data security are proposed to realize key security capabilities such as data privacy security and precise access control in the data center; the theory of hybrid skill evaluation models under complex data ecosystems such as industrial worker skill data is studied, a skill evaluation model for enterprise employee skills and abilities is constructed, and a fine-grained multi-dimensional description of employee abilities is completed; the knowledge graph construction technology of industrial worker abilities is explored, and a multi-field, multi-dimensional comprehensive talent management framework is designed to achieve precise matching of the personalized needs of industrial workers and enterprises, and meet the precise management and control and accurate decision-making of relevant functional departments.

Description

Technical Field

[0001] The present invention belongs to the field of enterprise management technology, and specifically relates to an enterprise precise matching system and method based on worker skills. Background Art

[0002] In current enterprise employment management and job allocation processes, traditional matching methods rely primarily on internal trainees' resumes, skills certifications, or subjective evaluations. These methods struggle to accurately reflect their comprehensive capabilities in real-world work scenarios, particularly their implicit skills (such as communication, teamwork, and problem-solving). Existing systems attempt to assist with job recommendations by collecting task completion data from internal trainees and modeling their behavior.

[0003] However, most of these methods only focus on explicit indicators (such as task completion time and quantity), lack in-depth exploration of behavioral patterns and semantic-level job requirement matching, resulting in inaccurate matching results and an inability to meet the personalized employment needs of enterprises. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for accurately matching enterprises based on worker skills to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for accurately matching enterprises based on worker skills, comprising the following steps:

[0006] The system collects behavioral data from internally trained talent from various sources, including vocational skills competitions, internal company training records, and learning progress on online learning platforms, to generate behavioral profiles. Based on these multi-source data, the system identifies and analyzes key behavioral patterns and constructs a comprehensive and dynamically updated skills knowledge base. Behavioral profiles and skill evaluation models are used to semantically correlate behavioral patterns with job requirements to generate matching feature vectors. Based on these matching feature vectors, job suitability scores are calculated and matching priorities are dynamically adjusted. Recommended job sequences are generated based on matching priorities and fed back into the next cycle's behavioral records to optimize subsequent matching results. The recommended job sequences and their suitability scores are presented to users in charts or lists for quick understanding and selection.

[0007] Preferably, behavioral data of internally trained personnel is collected from multiple channels, such as vocational skills competitions, internal corporate training records, and learning progress on online learning platforms, to generate behavioral profiles, including:

[0008] Collect the interaction logs generated by internal trainees during the task completion process and organize them into a time series behavioral event stream;

[0009] Conduct frequency statistics and sequence analysis on the behavioral event stream to construct a behavioral feature matrix, in which each element represents the number of occurrences of the corresponding internal training talent in the behavioral category;

[0010] Based on the behavioral feature matrix, the behavioral activity value of each internal training talent is calculated, and the behavioral activity value is combined with the task completion time to generate a comprehensive behavioral portrait.

[0011] Preferably, the interaction logs generated by internal trainees during the task completion process are collected and organized into a time series of behavioral event streams, including:

[0012] Identify and collect all digital interaction records of internal trainees during their tasks, parse the digital interaction records, mark the time of occurrence of each record, and form a set of tuples arranged in chronological order, each tuple containing the behavior content and a timestamp;

[0013] Classify the interaction records of the same task stage according to the task stages corresponding to the behaviors in the tuple set, and form a behavior event stream based on the task stage;

[0014] The time intervals between adjacent records were calculated for behavioral events in each task stage, and the behavioral dynamic patterns within each stage were constructed.

[0015] Preferably, frequency statistics and sequence analysis are performed on the behavior event stream to construct a behavior feature matrix, including:

[0016] Counting the number of times each behavior category appears in the behavior event stream to generate an original frequency vector;

[0017] Extracting sequential segments from the behavior sequence in the behavior event stream to generate a set of behavior combination segments with a fixed length;

[0018] Based on the original frequency vector and the set of behavior combination fragments, the association strength of each behavior category is calculated, and the frequency data and the association strength are combined to construct a behavior feature matrix.

[0019] Preferably, a behavioral profiling and skill evaluation model based on multi-source data is used to identify and analyze key behavioral patterns and build a comprehensive and dynamically updated skill knowledge base, including:

[0020] Perform cluster analysis on internal training talents based on the behavioral profiles, grouping individuals with similar behavioral characteristics into one group to form a classification result;

[0021] Extracting commonalities from the behavioral event streams within each group of the classification results to generate a representative behavioral sequence;

[0022] Cross-comparison is performed between the representative behavior sequences and the job task stages to screen out high-frequency corresponding behavior segments;

[0023] All the behavior fragments are aggregated and stored in a structured manner to form a skill knowledge base for subsequent matching.

[0024] Preferably, cluster analysis is performed on the internal training talents based on the behavioral profile, and individuals with similar behavioral characteristics are grouped together to form a classification result, including:

[0025] Extracting numerical features from the behavioral profile to form a multidimensional feature vector, and calculating the difference between the feature vectors;

[0026] Set the initial classification center point, and iteratively update the classification of each internal training talent so that the feature vector belongs to the group corresponding to the center point with the smallest difference;

[0027] When the average deviation between the members in each group and the corresponding center point tends to be stable, the final classification result is confirmed.

[0028] Preferably, semantically associating the behavior pattern with the job requirements to generate a matching feature vector includes:

[0029] Extract core behavioral keywords from job description texts to form a set of demand words;

[0030] For each word in the demand word set, search for behavior fragments with similar semantics in the skill knowledge base and establish a preliminary mapping relationship;

[0031] The association strength between each behavior segment and the corresponding word is calculated based on the mapping relationship, and all the association strengths are arranged in the order of job requirements and combined into a numerical matching feature vector.

[0032] Preferably, the job suitability score is calculated based on the matching feature vector, and the matching priority is dynamically adjusted, including:

[0033] For each position's matching feature vector, calculate the total score based on the weight of each dimension as the initial score;

[0034] Comparing the initial score with the actual task completion efficiency in the historical matching records to calculate a deviation value;

[0035] Adjust the matching priority of the current position according to the deviation value, and perform dynamic correction in combination with the adjustment constant;

[0036] All positions are sorted according to the updated matching priority to form a dynamically adjusted position recommendation queue.

[0037] Preferably, a recommended job sequence is output based on the matching priority and fed back into the behavior record of the next cycle to optimize subsequent matching results, including:

[0038] Generate an ordered list of recommended positions based on the matching priority, indicating the order of position recommendations;

[0039] Compare the recommended job list with the job information actually accepted and completed by the internal trainees, and calculate the matching gap value;

[0040] Update the associated tags in the behavior record according to the matching gap value, and mark the job selection tendency in the behavior data of the next cycle;

[0041] The updated behavioral records with tendency labels will be incorporated into the next round of portrait generation process to enhance the fit between job recommendations and individual behavioral characteristics.

[0042] On the other hand, the present invention proposes a precise enterprise matching system based on worker skills, comprising:

[0043] The behavioral profile generation module is used to collect behavioral data of internal trainees from multiple channels, such as vocational skills competitions, internal corporate training records, and learning progress on online learning platforms, and generate behavioral profiles.

[0044] Behavior pattern extraction module, used to identify and analyze key behavior patterns based on multi-source data behavior profiling and skill evaluation models, and build a comprehensive and dynamically updated skill knowledge base;

[0045] Job feature mapping module, used to semantically associate behavioral patterns with job requirements and generate matching feature vectors;

[0046] The suitability evaluation and ranking module is used to calculate the job suitability score based on the matching feature vector and dynamically adjust the matching priority;

[0047] The job recommendation feedback optimization module is used to output the recommended job sequence according to the matching priority and feed it back to the behavior record of the next cycle to optimize the subsequent matching results.

[0048] Technical effects and advantages of the present invention: The system and method for accurately matching enterprises based on worker skills proposed in the present invention have the following advantages over the existing technology:

[0049] This invention extracts key behavioral patterns by analyzing historical behavioral records, performs semantic mapping based on job requirements, generates computable matching feature vectors, and dynamically adjusts the fitness score to achieve a deep, structured match between internally trained personnel and positions. It can effectively identify and quantify the implicit skills of internally trained personnel, establish a correlation between them and job requirements, and significantly improve the accuracy and practicality of job matching, helping companies achieve efficient job matching and enhance organizational effectiveness and employee satisfaction.

[0050] In addition, by systematically analyzing the skill chain of the industrial workers' skill improvement process, a skill data collection business chain is constructed; key management services such as data security are proposed to realize key security capabilities such as data privacy security and precise access control in data centers; the theory of hybrid skill evaluation models under complex data ecosystems such as industrial workers' skill data is studied, and a skill evaluation model for enterprise employees' skills and abilities is constructed to complete a fine-grained and multi-dimensional description of employee capabilities; the knowledge graph construction technology of industrial workers' capabilities is explored, and a multi-field and multi-dimensional comprehensive talent management framework is designed to achieve accurate matching of the personalized needs of industrial workers and enterprises, and meet the precise management and control and accurate decision-making of relevant functional departments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of the enterprise precise matching method based on internal training talent skills of the present invention;

[0052] Figure 2 This is a module diagram of the enterprise precise matching system based on worker skills of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] The present invention provides Figure 1 The method shown here, which accurately matches employees based on their skills, can effectively quantify the implicit skills of internally trained personnel and integrate them into the job matching model, thereby improving the accuracy and adaptability of job recommendations. By analyzing historical behavior records to extract key behavior patterns, combining them with job requirements for semantic mapping, generating computable matching feature vectors, and dynamically adjusting the fitness score, it achieves a deep, structured match between internally trained personnel and positions. The details are as follows:

[0055] In this embodiment, a method for accurately matching enterprises based on worker skills includes the following steps:

[0056] Step 1: Collect behavioral data from internal training personnel through various channels, including vocational skills competitions, internal training records, and learning progress on online learning platforms, and generate behavioral profiles. This includes:

[0057] Collect the interaction logs generated by internal trainees during the task completion process and organize them into a time series of behavioral event streams, specifically including:

[0058] Identify and collect all digital interaction records of internal trainees during their tasks, including but not limited to clicks, inputs, and page switching behaviors;

[0059] Parse digital interaction records and mark each record with a timestamp. , forming a set of tuples arranged in time order ,in represents the i-th interaction record;

[0060] According to each record in the tuple collection Corresponding task phase Classify the interaction records belonging to the same task stage so that they are grouped together to form a behavioral event flow based on the task stage. ,in represents the set of behavioral events in the jth task stage;

[0061] For each mission stage Calculate relative time difference between behavioral events in ,in It is the timestamp of two consecutive interaction records in the same task stage, which is used to construct the behavioral dynamic pattern within each stage.

[0062] Perform frequency statistics and sequence analysis on the behavior event flow to construct the behavior feature matrix M, where Indicates the number of times the a-th internal trainee has performed the b-th behavior; specifically:

[0063] Count the number of times each behavior category appears in the behavior event stream to obtain the original frequency vector ,in Indicates the number of occurrences of type x behavior;

[0064] Extract sequential fragments from the behavior sequence in the behavior event stream to generate a set of behavior combination fragments of length L ;

[0065] Based on the original frequency vector G and the behavior combination fragment set Q, a weighted behavior association strength value is established ,in represents the weight of the mth segment, indicating the importance or representativeness of the segment in the overall behavior pattern. Represents the number of times the segment contains the xth type of behavior, quantifying the frequency of the target behavior within the segment. This formula calculates the weighted association strength of a behavior type (such as "teamwork" or "task planning") within a behavior sequence, reflecting the co-occurrence frequency and sequential relationship between that behavior and other behaviors. By assigning weights to behavior segments and counting the number of target behaviors within them, we can capture the temporal dependencies and contextual structure between behaviors, thereby more comprehensively characterizing the behaviors.

[0066] Combine the frequency data with the correlation strength to construct the behavior feature matrix M, where α is a moderating factor, used to balance the influence of frequency and sequence. This formula combines two dimensions of behavioral information into a unified feature value: the frequency of the behavior and its structural impact within the behavioral sequence. By introducing a moderating factor, we can balance the two and generate a more representative comprehensive behavioral feature value for subsequent analysis tasks such as matching and clustering.

[0067] It represents the original frequency statistics of the b-th type of behavior, that is, the number of times this type of behavior appears in the entire group or individual behavior event stream. The weighted behavior association strength value of the b-th behavior reflects the co-occurrence and sequential relationship between the behavior and other behaviors in the behavior sequence, and is used to capture the contextual pattern between behaviors.

[0068] Calculate the behavior activity value based on the behavior feature matrix M ,in Represents the importance weight of the x-th type of behavior; represents the number of times the a-th in-house trainee appears in the x-th behavior category. This formula uses a weighted aggregation method to multiply the characteristic values ​​of an in-house trainee in each behavior category by their corresponding importance weights, and then accumulates them to obtain a comprehensive score reflecting their overall behavioral activity. This score not only considers the type and frequency of behaviors, but also the actual value of each behavior to the company's needs, making it a key component of behavioral profiling.

[0069] The behavioral activity value Time to complete the task Combine to generate a comprehensive behavioral profile , used to measure the intensity of behavioral output per unit time.

[0070] Step 2: Identify and analyze key behavioral patterns based on behavioral profiling and skill evaluation models from multi-source data to build a comprehensive and dynamically updated skill knowledge base. This includes:

[0071] Based on behavioral profiling, cluster analysis is performed on internal training talents, and individuals with similar behavioral characteristics are grouped together to obtain a behavioral group set. ; Specifically include:

[0072] Extract numerical features from behavioral profiles to form multidimensional feature vectors , used to represent the behavior performance of the i-th internal training talent;

[0073] Use distance measurement method to calculate the difference between vectors , used to measure the degree of behavioral variation between different internal trainees; this formula is used to calculate the degree of behavioral variation between two internal trainees. By taking the absolute value of the difference in each dimension of the multidimensional feature vector and summing it, a quantitative behavioral distance index is obtained, which measures the similarities or differences between individuals. This distance value serves as the core basis for cluster analysis, determining which internal trainees should be grouped together.

[0074] in, : The value of the feature vector of the i-th internal training talent in the kk-th dimension. : The value of the feature vector of the j-th internal training talent in the k-th dimension. : The absolute difference between two internally trained talents in a certain characteristic dimension. : Accumulate all feature dimensions.

[0075] Set the initial classification center point By iteratively updating the category of each internal training talent, each feature vector Belong to it The group corresponding to the smallest center point;

[0076] When the average deviation of each group member from the corresponding center point When the clustering results are stable, the formula is used to evaluate the average deviation between the members of a group and its center point. By calculating the distance between each member and the center of the group and taking the average value, we can determine whether the current clustering result is stable. When the value tends to converge, it means that the grouping has reached a relatively stable state and can be used as the clustering termination condition. Confirm the final grouping result and generate the behavior group set . represents the total number of data points associated with the p-th object; Represents the measurement value of the pth object on all n data points the sum of; Represents a measurement or observation value of the nth data point for the pth object.

[0077] For each group of behavior Extract commonalities from the internal behavioral event stream and generate representative behavioral sequences ; Specifically include:

[0078] Collect belonging to the same group The behavioral event stream of all internal training talents constitutes a group behavior set ; Collection of group behavior Compare the behavior sequences in the data and identify the segments that appear repeatedly in the time sequence. ; for each repeat segment Calculate occurrence density ,in is the number of times the segment appears in the group, is the total time covered; this formula measures the frequency of occurrence of a particular behavioral segment within a group. By dividing the number of occurrences of a segment by the time covered, we can determine its representativeness. High-density segments reflect prevalent behavioral patterns within the group and can be used to extract key behavioral sequences.

[0079] According to the density Screen the segments with density higher than the set threshold θ and splice them into a continuous sequence in chronological order as the representative behavior sequence of the group .

[0080] Representative behavior sequence and job task stage Perform cross-comparison to filter out high-frequency corresponding behavior fragments ; Specifically include:

[0081] Representative behavior sequence Each job task stage Match the behavior sets in the content and find the overlapping behavior segments between the two; for each stage Statistical overlapping behavior fragments in The location distribution of the occurrence and calculation of coverage ratio ,in To match the total length of the fragment, for This formula measures the matching ratio between a certain behavior pattern and a specific job task stage. By calculating the ratio of the length of the overlapping segments to the length of the entire behavior sequence, we can determine the importance of each behavior segment to the job stage. The higher the ratio, the more representative the behavior is of the required competencies for the job.

[0082] According to coverage ratio Determine the importance of the behavior segment and select Exceeding the set benchmark value The fragments are taken as the key related parts of this stage; the key related parts are sorted according to the job task stage to form a set of behavior fragments , which is used to build the skill knowledge base later. Aggregate and store in a structured manner to form a skill knowledge base Z for subsequent matching.

[0083] Step 3: Semantically associate the behavior pattern with the job requirements to generate a matching feature vector; specifically, this includes:

[0084] Extract core behavioral keywords from job description texts to form a demand word set ; For each word in the demand word set K , search for behavior fragments with similar semantics in the skill knowledge base , establish a preliminary mapping relationship; specifically including:

[0085] Each word in the demand word set K Convert to the corresponding semantic vector representation ; For each semantic vector , traverse all behavior fragments in the skill knowledge base , calculate its The similarity score between ,in for This formula measures the semantic similarity score between job requirement words and behavioral segments. Keywords in job descriptions are converted into semantic vectors and then dot-producted with the semantic vectors of each behavioral segment in the skill knowledge base to determine the degree of match between the two in the semantic space. A larger value indicates closer semantic similarity.

[0086] Select behavioral segments whose similarity scores are higher than the set threshold ε , as the corresponding word Candidate matching fragments; construct a preliminary mapping table between words and behavior fragments based on the candidate matching fragments This formula is used to construct a preliminary mapping table between job keywords and behavioral fragments. By setting a semantic similarity threshold ε, behavioral fragments with semantic similarities to the keywords are selected as candidate matches, forming a set of "word → behavioral fragment" mappings. This forms the basis for subsequently establishing more refined association strengths.

[0087] Calculate each behavior fragment according to the mapping relationship and corresponding words The association strength value ,in is the co-occurrence frequency, is the semantic similarity score, is the balancing factor; this formula calculates the comprehensive correlation strength between each job keyword and its corresponding behavioral segment. It combines two dimensions of information: co-occurrence frequency, which reflects how often the keyword and behavioral segment co-occur in historical data; and semantic similarity, which reflects the degree of semantic similarity between the two. The balancing factor controls the weighting of semantic information, making the final result more adaptable and robust.

[0088] All the association strength values Arrange in order of job requirements and combine into numerical matching feature vectors .

[0089] Step 4: Calculate the job suitability score based on the matching feature vector and dynamically adjust the matching priority; specifically,

[0090] Matching feature vector corresponding to each position , calculate the weighted sum of each dimension value to get the initial score ,in The xth dimension is the job emphasis weight of the feature, reflecting the importance of the behavior in this dimension in job requirements; The value of the xth dimension in the matching feature vector represents the strength of that behavior within the current internal trainee's behavioral profile. This formula is used to calculate the initial fit score for a position. By assigning a corresponding weight to each item in the matching feature vector (representing a specific behavior or skill), multiplying it by its actual fit value, and summing the results, a comprehensive score is generated, reflecting the overall fit between the current internal trainee and the position requirements. This score not only considers the strength of the behavioral fit but also incorporates the position's emphasis on different behaviors through weighting, making the score more practical and instructive.

[0091] The initial score S is compared with the actual task completion efficiency E in the historical matching records to obtain the deviation value Δ=|S−E|; the initial score is compared with the actual task completion efficiency of similar matches in the historical records, and the absolute difference is calculated as the error feedback signal for subsequent dynamic adjustment of the matching strategy.

[0092] Adjust the matching priority of the current position according to the deviation value Δ ,in The original priority is δ, and the adjustment constant is δ. Based on the deviation value obtained in the previous step, the adjustment constant and the original priority are combined to adjust the recommended priority of the position. If the deviation is large, the priority is reduced; if the deviation is small or even zero, the priority remains unchanged or slightly increased.

[0093] Sort all positions according to the updated matching priority P to form a dynamically adjusted position recommendation queue; specifically, it includes:

[0094] Collect updated matching priority values ​​corresponding to all positions , forming a priority set Q; sorting the priority set Q in descending order to generate a sorted index sequence ,in Indicates the kth highest priority corresponding to the job number; extract the description information of the corresponding job according to the sorting index sequence I and combine it into an ordered list ; The ordered list L is used as the final output job recommendation queue and provided to the scheduling system or user interface in sequence.

[0095] Step 5: Output the recommended job sequence based on the matching priority and feed it back into the behavior record of the next cycle to optimize the subsequent matching results; specifically, it includes:

[0096] Generate an ordered list of recommended positions based on matching priority ,in Represents the i-th recommended position; compare the recommended position list R with the position information actually accepted and completed by the internal training talents, and calculate the matching gap value ,in In the recommended order, For actual selection of location;

[0097] In actual application scenarios, the system generates a sequence of recommended positions based on matching priorities and outputs them sequentially to the user or scheduling system. However, in-house trainees may ultimately choose positions lower down the list, or even positions not recommended at all, rather than those ranked higher. This discrepancy between the "recommended order" and the "actual position chosen" reflects issues with the recommendation system's accuracy.

[0098] By calculating the absolute deviation between the recommended position and the actual position chosen for each recommended position and summing this across all positions, we can obtain an overall matching gap value. A larger value indicates a greater deviation between the recommendation and actual behavior; conversely, a smaller value indicates a more accurate recommendation. This metric can be used to dynamically adjust subsequent recommendation strategies, update individual behavior profiles, and optimize the position feature mapping process, thereby improving the system's long-term matching accuracy.

[0099] Update the associated labels in the behavior record based on the matching gap value ΔG, and mark the job selection tendency in the next cycle's behavior data; incorporate the updated behavior record with the tendency label into the next round of portrait generation process to enhance the fit between job recommendations and individual behavior characteristics.

[0100] The recommended job sequences and their suitability scores are presented to users in charts or lists, making them easy to understand and select. Interactive tools also allow users to personalize the recommendations (e.g., filter criteria, adjust weights, etc.), and the recommendation algorithm is continuously optimized through user feedback. For example, users can mark jobs they are not interested in, and the system will adjust future recommendations accordingly. Users can also view the specific requirements and matching reasons for each job, enhancing transparency and user experience.

[0101] On the other hand, the present invention proposes a precise enterprise matching system based on worker skills, such as Figure 2 Shown, including:

[0102] The behavioral profile generation module is used to collect behavioral data of internal trainees from multiple channels, such as vocational skills competitions, internal corporate training records, and learning progress on online learning platforms, and generate behavioral profiles.

[0103] Behavior pattern extraction module, used to identify and analyze key behavior patterns based on multi-source data behavior profiling and skill evaluation models, and build a comprehensive and dynamically updated skill knowledge base;

[0104] The job feature mapping module is used to semantically associate behavioral patterns with job requirements and generate matching feature vectors;

[0105] The suitability evaluation and ranking module is used to calculate the job suitability score based on the matching feature vector and dynamically adjust the matching priority;

[0106] The job recommendation feedback optimization module is used to output the recommended job sequence according to the matching priority and feed it back to the behavior record of the next cycle to optimize the subsequent matching results.

[0107] In addition, when executed, the above modules are also used to implement other steps of the above-mentioned enterprise precise matching method based on worker skills, which will not be described one by one here.

[0108] In summary, the present invention extracts key behavioral patterns by analyzing historical behavioral records, performs semantic mapping based on job requirements, generates computable matching feature vectors, and dynamically adjusts the fitness score to achieve a deep, structured match between internally trained personnel and positions. This method can effectively identify and quantify the implicit skills of internally trained personnel, establish a correlation between them and job requirements, and significantly improve the accuracy and practicality of job matching, helping enterprises achieve efficient job matching and enhance organizational effectiveness and employee satisfaction.

[0109] By systematically analyzing the skill chain of the industrial workers' skill improvement process, a skill data collection business chain is constructed; key management services such as data security are proposed to realize key security capabilities such as data privacy security and precise access control in data centers; the theory of hybrid skill evaluation models under complex data ecosystems such as industrial workers' skill data is studied, and a skill evaluation model for enterprise employees' skills and abilities is constructed to complete a fine-grained and multi-dimensional description of employee capabilities; the knowledge graph construction technology of industrial workers' capabilities is explored, and a multi-field and multi-dimensional comprehensive talent management framework is designed to achieve accurate matching of the personalized needs of industrial workers and enterprises, and meet the precise management and control and accurate decision-making of relevant functional departments.

[0110] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for accurately matching enterprises based on worker skills, characterized in that: The following steps are involved: Collect behavioral data of internal trainees from multiple channels, including vocational skills competitions, internal corporate training records, and learning progress on online learning platforms, to generate behavioral profiles; Based on behavioral profiling and skill evaluation models from multi-source data, we identify and analyze key behavioral patterns and build a comprehensive and dynamically updated skill knowledge base. This includes: clustering internal trainees based on the behavioral profiling, grouping individuals with similar behavioral characteristics to form classification results; extracting commonalities from the behavioral event streams within each group of classification results to generate representative behavioral sequences; cross-comparing the representative behavioral sequences with job task phases to identify high-frequency corresponding behavioral segments; and aggregating and structurally storing all the behavioral segments to form a skill knowledge base for subsequent matching. Performing cluster analysis on the internal trainees based on the behavioral profiles, grouping individuals with similar behavioral characteristics to form a classification result, including: extracting numerical features from the behavioral profiles to form multidimensional feature vectors, and calculating the difference between each feature vector; setting an initial classification center point, and iteratively updating the classification of each internal trainee so that the feature vector belongs to the group corresponding to the center point with the smallest difference; and confirming the final classification result when the average deviation value of the members in each group from the corresponding center point tends to be stable; Semantically associating behavioral patterns with job requirements to generate matching feature vectors, including: extracting core behavioral keywords from job description texts to form a set of requirement words; for each word in the set of requirement words, searching for semantically similar behavioral segments in the skill knowledge base to establish a preliminary mapping relationship; calculating the association strength between each behavioral segment and the corresponding word based on the mapping relationship, arranging all the association strengths in order of job requirements, and combining them into a numerical matching feature vector; Calculating job suitability scores based on matching feature vectors and dynamically adjusting matching priorities includes: calculating a total score for each matching feature vector corresponding to each job based on the weights of each dimension as an initial score; comparing the initial score with the actual task completion efficiency in historical matching records to calculate a deviation value; adjusting the matching priority of the current job based on the deviation value, and dynamically correcting it in combination with an adjustment constant; and sorting all jobs according to the updated matching priority to form a dynamically adjusted job recommendation queue. Output a recommended job sequence based on the matching priority and feed it back into the behavioral records of the next cycle to optimize subsequent matching results, including: generating an ordered list of recommended jobs based on the matching priority to indicate the order of job recommendations; comparing the recommended job list with the job information actually accepted and completed by the internal trainees to calculate the matching gap value; updating the associated tags in the behavioral records based on the matching gap value, marking the job selection tendency in the behavioral data of the next cycle; incorporating the updated behavioral records with tendency tags into the next round of portrait generation process to enhance the fit between job recommendations and individual behavioral characteristics; The recommended job sequence and its suitability score are presented to users in the form of a chart or list to facilitate their quick understanding and selection.

2. The enterprise precise matching method based on worker skills according to claim 1 is characterized in that: Generate behavioral profiles, including: Collect the interaction logs generated by internal trainees during the task completion process and organize them into a time series behavioral event stream; Conduct frequency statistics and sequence analysis on the behavioral event stream to construct a behavioral feature matrix, in which each element represents the number of occurrences of the corresponding internal training talent in the behavioral category; Based on the behavioral feature matrix, the behavioral activity value of each internal training talent is calculated, and the behavioral activity value is combined with the task completion time to generate a comprehensive behavioral portrait.

3. The enterprise precise matching method based on worker skills according to claim 2 is characterized in that: Collect the interaction logs generated by internal trainees during the task completion process and organize them into a time series of behavioral event streams, including: Identify and collect all digital interaction records of internal trainees during their tasks, parse the digital interaction records, mark the time of occurrence of each record, and form a set of tuples arranged in chronological order, each tuple containing the behavior content and a timestamp; Classify the interaction records of the same task stage according to the task stages corresponding to the behaviors in the tuple set, and form a behavior event stream based on the task stage; The time intervals between adjacent records were calculated for behavioral events in each task stage, and the behavioral dynamic patterns within each stage were constructed.

4. The enterprise precise matching method based on worker skills according to claim 2 is characterized in that: Perform frequency statistics and sequence analysis on the behavioral event stream to construct a behavioral feature matrix, including: Counting the number of times each behavior category appears in the behavior event stream to generate an original frequency vector; Extracting sequential segments from the behavior sequence in the behavior event stream to generate a set of behavior combination segments with a fixed length; Based on the original frequency vector and the set of behavior combination fragments, the association strength of each behavior category is calculated, and the frequency data and the association strength are combined to construct a behavior feature matrix.

5. A precise enterprise matching system based on worker skills for implementing the method according to any one of claims 1 to 4, characterized in that: include: The behavioral profile generation module is used to collect behavioral data of internal trainees from multiple channels, including vocational skills competitions, internal corporate training records, and learning progress on online learning platforms, and generate behavioral profiles. Behavior pattern extraction module, used to identify and analyze key behavior patterns based on multi-source data behavior profiling and skill evaluation models, and build a comprehensive and dynamically updated skill knowledge base; The job feature mapping module is used to semantically associate behavioral patterns with job requirements and generate matching feature vectors; The suitability evaluation and ranking module is used to calculate the job suitability score based on the matching feature vector and dynamically adjust the matching priority; The job recommendation feedback optimization module is used to output the recommended job sequence based on the matching priority and feed it back to the behavior record of the next cycle to optimize the subsequent matching results. The recommended job sequence and its suitability score are displayed to the user in the form of a chart or list to facilitate the user's quick understanding and selection.

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