Blockchain-based talent storage and screening management system for education and training industry

Abstract

The application discloses a talent storage and screening management system based on a blockchain in the teaching and training industry, relates to the technical field of talent management in the teaching and training industry and blockchain application, and comprises the following modules: a blockchain-based chain storage, a portrait construction, an update, a screening and reporting, and a privacy screening module, talent data is encrypted and stored, a portrait is constructed and updated, a talent node is a practitioner terminal or an agent who joins the blockchain, a key pair is generated after registration, is used for data signature, portrait viewing, response verification request, and push of proof information, and all-process operation records are stored on the chain; the application stores and encrypts multidimensional data, generates a special chain capacity portrait through three-dimensional capacity quantitative analysis, guarantees data authenticity and privacy security, and improves talent data credibility; meanwhile, an intelligent contract is deployed to realize automatic portrait updating, hash processing and interactive verification are adopted in the screening link, a process is simplified, cost is reduced, talent management standardization in the teaching and training industry is promoted, and high-quality development is facilitated.

Description

Technical Field

[0001] This invention relates to the field of talent management and blockchain application technology in the education and training industry, specifically to a blockchain-based talent storage and screening management system for the education and training industry. Background Technology

[0002] With the rapid development of the education and training industry and the increasing frequency of talent mobility, the industry's need for assessment of talent's professional competence, practical skills, and suitability is becoming more urgent. Talent information encompasses multiple dimensions, including qualifications, work performance, and feedback. The authenticity and completeness of this information directly affect the accuracy of talent assessment and selection. Meanwhile, the widespread adoption of digital technology is driving various industries towards digital and intelligent transformation. Blockchain technology, with its core characteristics of distributed storage, immutability, and traceability, has demonstrated significant advantages in areas such as data storage, privacy protection, and trust building. It has gradually been applied to data management scenarios across multiple industries. The education and training industry urgently needs to leverage mature blockchain technology to build a standardized and trustworthy talent information management system. This system will enable the standardized storage, effective verification, and reasonable application of talent data, meeting the industry's demand for transparent and efficient talent management and addressing many pain points in traditional talent information management models.

[0003] Traditional talent information management in the education and training industry often adopts a centralized storage model, with data scattered across different institutions or platforms. The lack of unified standards and interoperability mechanisms leads to severe fragmentation of talent information, low efficiency in cross-institutional information verification, and an inability to effectively share and collaboratively apply talent information. Data authenticity is difficult to guarantee, and some talents may engage in fraudulent practices such as falsifying qualifications or exaggerating achievements. Traditional verification methods rely on manual review or endorsement from a single institution, lacking multi-dimensional cross-verification mechanisms, making it difficult to effectively identify false information and posing significant risks to talent selection for education and training institutions. Talent evaluation systems are often limited to a single dimension, failing to comprehensively reflect a talent's overall capabilities. Furthermore, evaluation results lack objective quantitative evidence, are highly subjective, and struggle to form scientific talent assessment standards. In addition, traditional talent profiles are mostly statically generated, failing to incorporate the latest work data and skill development information, leading to discrepancies between the profile and reality, affecting the accuracy of selection. Moreover, insufficient talent privacy protection measures pose a risk of raw data leakage during the selection process, harming the legitimate rights and interests of talents. All these problems hinder the standardized and efficient development of talent management in the education and training industry. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a blockchain-based talent storage and screening management system for the education and training industry. The system encrypts and cross-verifies multi-dimensional talent data with third parties, achieving distributed permanent storage on a consortium blockchain. It generates personalized on-chain capability profiles through a three-dimensional capability quantification algorithm, comprehensively showcasing the overall qualities of talent. By relying on smart contracts to bind compliant new data to the blockchain, the profiles are automatically and dynamically updated. Employing a screening condition hashing process and interactive verification mode, it protects talent privacy while meeting the screening needs of institutions. The entire process is recorded on the blockchain, ensuring data authenticity and traceability, and transparent and efficient processes. This effectively solves problems such as fragmented talent information, high risk of fraud, and inaccurate screening in the education and training industry, providing standardized and reliable technical support for talent management in the industry.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a blockchain-based talent storage and screening management system for the education and training industry, comprising:

[0006] On-chain evidence storage module: The multi-dimensional data of education and training talents submitted by talent nodes are encrypted and hashed. At the same time, the data is verified by third-party verification nodes. After the verified data is pushed to the consortium chain consensus node to achieve on-chain consensus, the data is permanently stored in the consortium chain and a certificate of evidence is generated.

[0007] Profile building module: retrieves evidence-stored data on the consortium blockchain, preprocesses and standardizes it, calculates the comprehensive entropy value of the talent's three-dimensional capabilities using the three-dimensional capability entropy value algorithm, converts it into three-dimensional capability index scores, integrates and generates a unique on-chain capability profile, and stores it on the consortium blockchain after encryption.

[0008] Profile Update Module: Deploy a dynamic update trigger smart contract at the bottom layer of the consortium blockchain, bind the contract trigger conditions to the compliant on-chain results of new talent data. When the new data is compliantly on-chain, the contract is automatically triggered to retrieve the original profile data and new data parameters, use the profile update entropy correction algorithm to calculate the updated comprehensive entropy value, generate the updated profile and record the log, and encrypt and overwrite the original on-chain data.

[0009] Screening and Reporting Module: Provides an entry point for educational institutions to submit screening requests, standardizes and parses the original screening conditions, transforms the parameters, hashes the transformed parameters, submits only the hash value to the consortium blockchain to complete synchronization and consensus, generates a unique screening identifier and stores it in association;

[0010] Privacy Screening Module: The consortium blockchain matches the corresponding talent nodes according to the hash value of the screening parameters and pushes the hash value. The talent nodes retrieve their latest profile and conduct interactive verification with the consensus nodes. Only the proof information is pushed without exposing the original data and privacy information. The consensus nodes summarize the verification results and reach a consensus, which will be directed to the education and training institution nodes that initiated the screening. The entire process is recorded on the blockchain for evidence storage.

[0011] The talent node refers to a client terminal or software agent that joins the consortium blockchain network and represents a single practitioner in the education and training industry. It is configured to: generate and securely store its asymmetric encryption key pair after completing the consortium blockchain identity registration; digitally sign the multi-dimensional data it submits based on the key pair; receive, decrypt, and view the on-chain capability profile bound to its unique identifier; respond to the verification request initiated by the privacy screening module, generate a zero-knowledge proof or signature proof locally using its private key for a specific statement of its own capability profile, and push the proof as verification information to the consensus node.

[0012] The consortium blockchain consensus nodes consist of multiple pre-permitted independent servers or validators operated by institutions, jointly running a consensus algorithm to maintain the consistency of the consortium blockchain ledger state; they are configured to: jointly sort and confirm data upload requests verified by third-party verification nodes; execute and verify the logic of the dynamic update triggering smart contract; in the privacy screening module, receive screening parameter hash values ​​from education and training institution nodes, coordinate the interactive verification process with target talent nodes, summarize the independent verification results of each consensus node on the proofs submitted by the talent nodes, and reach a consensus on the final screening matching result through the consensus mechanism;

[0013] The term "education and training institution node" refers to the client terminal or management backend of a certified education and training enterprise or employer that has joined the consortium blockchain network. It is configured to: obtain data submission and screening request permissions after completing consortium blockchain identity registration; provide endorsement signatures for teaching performance, evaluations, and other data of personnel with whom it has an employment or contractual relationship; construct and submit talent screening requests through the screening and submission module; receive a list of anonymized matching results that meets the conditions and has passed consensus through the privacy screening module, and initiate further recruitment or contact processes based on this list.

[0014] Furthermore, the multi-dimensional data of education and training personnel in the on-chain evidence storage module includes academic certificates, professional qualification certificates, skills training completion certificates, and personal honor certificates uploaded independently by talent nodes, as well as teaching hour statistics, course completion rates, student rating details, cross-institutional entry and exit certificates, and job competency evaluation reports submitted with endorsement from education and training institution nodes. All data is submitted in PDF or image format, and the digital signature includes a unique node identifier, submission timestamp, and data summary information. Data without a valid digital signature is directly rejected and will not be allowed to enter the verification process.

[0015] Furthermore, the third-party verification nodes in the on-chain evidence storage module consist of official verification nodes from the education authorities, verification nodes from the education and training industry associations, and nodes from two qualified third-party data verification institutions. A multi-level cross-verification mechanism is adopted, requiring that the same type of data be verified by at least two different types of verification nodes before it can be pushed to the consensus node. Qualification certificate data is verified in real time by connecting to the official databases of the education authorities and vocational skills assessment centers. Teaching performance and cross-institutional work records require secondary endorsement and confirmation from the original institution nodes, and student evaluations require verification of the correspondence between the evaluator and the teaching record.

[0016] Furthermore, the mathematical expression for the 3D capability entropy algorithm in the portrait construction module is: ,in These are the three-dimensional capability weight coefficients, and their sum is 1. For professional competence entropy value, The entropy value of teaching ability. To adapt to the capability entropy value, For entropy reduction optimization coefficients, The average hash digest. As a preset hash base value, The confidence coefficient for single-class data verification. The total number of on-chain data types is given. The calculation steps are as follows: First, determine the weight coefficients corresponding to the three types of capabilities and ensure that the sum of the three is 1. Calculate the single-dimensional entropy values ​​of professional ability, teaching ability, and adaptation ability respectively. Then, calculate the ratio of the mean hash digest to the preset hash benchmark value, multiply this ratio by the entropy reduction optimization coefficient, and simultaneously calculate the product of the verification credibility coefficients of all single-type data. Finally, subtract the above product term from the weighted sum of the entropy values ​​of the three types of capabilities to obtain the comprehensive entropy value of the three-dimensional capabilities of the talent. The purpose of this calculation is to transform the multi-dimensional discrete data of talent into a unified quantitative comprehensive indicator, and then map it to a three-dimensional capability indicator score, providing a core quantitative basis for the generation of a dedicated on-chain capability profile.

[0017] Furthermore, the scores of the three-dimensional capability indicators in the profile construction module are calculated by weighting the sub-indicators. Each of the three-dimensional capability indicators contains four sub-indicators with corresponding preset weighting rules. The professional capability sub-indicators are qualification level, skill certification, knowledge reserve, and industry qualification annual review status. The preset weighting rules are: qualification level accounts for 30%, skill certification accounts for 25%, knowledge reserve accounts for 25%, and industry qualification annual review status accounts for 20%. The teaching capability sub-indicators are teaching hours, student satisfaction, curriculum development results, and teaching score improvement rate. The preset weighting rules are: teaching hours account for 20%, student satisfaction accounts for 30%, curriculum development results account for 25%, and teaching score improvement rate accounts for 25%. The adaptability sub-indicators are cross-institutional adaptability, job matching rate, cross-domain teaching ability, and institutional evaluation feedback. The preset weighting rules are: cross-institutional adaptability accounts for 25%, job matching rate accounts for 25%, cross-domain teaching ability accounts for 20%, and institutional evaluation feedback accounts for 30%. Each sub-indicator is standardized and quantified from 0 to 100 points, and the corresponding three-dimensional capability indicator scores are obtained by weighting and summing according to the corresponding weights.

[0018] Furthermore, the specific steps for generating a unique on-chain capability profile in the profile construction module are as follows: Based on the talent's unique identifier, retrieve the corresponding multi-dimensional data of the talent that has been notarized on the consortium blockchain; preprocess and standardize the retrieved full data; calculate the comprehensive entropy value of the talent's three-dimensional capabilities and simultaneously convert the comprehensive entropy value into a three-dimensional capability index score; then integrate the talent's unique identifier, the three-dimensional capability index score, the on-chain data number on which the profile is based, and the profile generation timestamp to form the basic profile data; encrypt the basic profile data; after encryption, assign a unique on-chain storage address to the profile; then push the encrypted basic profile data to the consortium blockchain consensus node to achieve consensus; after consensus is passed, complete the distributed storage on the consortium blockchain; and simultaneously generate a profile storage identifier and bind it to the talent's unique identifier.

[0019] Furthermore, the dedicated on-chain capability profile in the profile construction module includes three parts: basic identification information, capability quantification information, and related traceability information. The basic identification information includes the talent's unique identifier, the sub-field of education and training to which the profile belongs, the profile generation timestamp, and the most recent update timestamp. The capability quantification information includes the scores of the three-dimensional capability core indicators, the scores of each sub-indicator, the basis for weighted calculation, the comprehensive entropy value, and the parameters involved in the calculation. The related traceability information includes the on-chain data number on which the profile generation is based, the corresponding verification node identifier, and the algorithm execution node identifier. The profile also includes a data validity marker to indicate whether the profile is currently the latest version. After being encrypted and stored, it is bound to the talent's unique identifier. Only the talent node can decrypt and view the complete content using its own private key, while other nodes can only obtain the profile hash digest for verification.

[0020] Furthermore, the mathematical expression for the image update entropy correction algorithm in the image update module is: ,in To update the overall entropy value, The overall entropy value before updating, The time decay coefficient, The interval for adding new data to the blockchain. To add a comprehensive entropy value to the data, This is the trigger coefficient for the smart contract; it is first calculated using the algorithm of the profile building module during execution. Then, substitute the values ​​into the formula to complete the entropy correction. The calculation steps are as follows: First, obtain the comprehensive entropy value before the update, the time decay coefficient, the interval period for new data to be added to the chain, the comprehensive entropy value of the newly added data, and the smart contract trigger coefficient; calculate the product of the comprehensive entropy value before the update and the time decay adjustment term, and at the same time calculate the product of the comprehensive entropy value of the newly added data and the dynamic weight adjustment term; add the two calculation results to obtain the updated comprehensive entropy value. The purpose of this calculation is to combine the time decay effect and the dynamic weight of the newly added data to correct the original comprehensive entropy value, generate a comprehensive indicator that fits the latest ability status of talents, support the generation of the updated ability profile, and ensure the timeliness and accuracy of the profile.

[0021] Furthermore, the contract triggering conditions in the profile update module include five distinct scenarios, and the contract is automatically triggered when any one of these scenarios is met. The first scenario is when newly added qualification certificates or professional skills certification documents are verified and uploaded to the blockchain. The second scenario is when newly added teaching performance reaches a cumulative total of 50 class hours, and the corresponding class hour statistics and course completion rate data are submitted and verified and uploaded to the blockchain by a single educational institution node. The third scenario is when newly added non-duplicate student reviews reach a cumulative total of 30, and each review completes the verification of the correspondence between the reviewer and the teaching record and is uploaded to the blockchain. The fourth scenario is when newly added cross-institutional employment or resignation certificates are endorsed by the original institution node and verified by a third-party verification node before being uploaded to the blockchain. The fifth scenario is when newly added skills training completion certificates or industry qualification annual review documents are verified and uploaded to the blockchain. Before triggering any of these scenarios, it must be confirmed that the new data has been verified by a third-party verification node. New data that does not meet the compliance requirements will not trigger the contract.

[0022] Furthermore, during interactive verification in the privacy screening module, talent nodes generate proof information after completing local preprocessing of their latest on-chain capability profile. The proof information includes a unique talent identifier, a screening identifier, a proof validity marker, a proof generation timestamp, and a verification result marker. The consensus node takes no more than 3 seconds to verify the proof information submitted by a single talent node. Abnormal verification results generated during the verification process are divided into three categories: verification failure, timeout without verification, and invalid proof information. Abnormal verification results need to be marked separately and stored in the consortium blockchain, and the abnormal results are simultaneously fed back to the corresponding talent node. The feedback content includes the abnormal type, the verification node identifier, and the abnormal occurrence timestamp.

[0023] Compared with existing technologies, this blockchain-based talent storage and screening management system for the education and training industry has the following advantages:

[0024] I. This invention achieves distributed permanent storage of talent data on a consortium blockchain through multi-dimensional data encryption hashing and third-party cross-verification mechanisms, ensuring data authenticity and immutability. It addresses the problems of fragmented talent information and high risk of falsification in the traditional education and training industry. Based on standardized data cleaning and three-dimensional capability quantification analysis, it integrates information related to talent's profession, teaching, and suitability to generate a unique on-chain capability profile, comprehensively presenting the talent's overall qualities and breaking the limitations of single-dimensional evaluation. The profile is encrypted and bound to the talent's unique identifier; only authorized nodes can decrypt and view the complete content, ensuring privacy and security while enabling information traceability and verification. This provides an objective and comprehensive basis for talent evaluation, enhances the credibility and application value of industry talent data, promotes the standardization and transparency of talent information management, and reduces information verification costs and trust risks.

[0025] Second, this invention deploys dynamically updated smart contracts, binding contract triggering with the compliant on-chain results of new data. This enables automatic iterative optimization of competency profiles, ensuring that the profiles always reflect the latest talent status and avoiding the lag caused by static evaluations. The screening process employs original condition hashing and interactive verification modes, transmitting only proof information without exposing original data or privacy information. While meeting the screening needs of educational institutions, it maximizes the protection of talent privacy and security. The entire process is recorded and stored on the blockchain, ensuring transparency and fairness in the screening process and eliminating any underhanded operations. At the same time, it simplifies the screening process, improves the matching efficiency between talent and institutions, helps educational institutions quickly locate suitable talent, reduces recruitment and screening costs, promotes the rational flow of talent and efficient allocation of resources in the educational industry, provides technical support for upgrading the industry's talent management system, and contributes to the high-quality development of the industry.

[0026] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0028] Figure 1This is the execution process of the core module of a blockchain-based talent storage and screening management system for the education and training industry.

[0029] Figure 2 The overall architecture of a blockchain-based talent storage and screening management system for the education and training industry;

[0030] Figure 3 Flowchart for dynamically updating the profile. Detailed Implementation

[0031] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0032] Example 1:

[0033] A scene depicting a training institution recruiting full-time math instructors.

[0034] Education and training industry practitioners independently upload multi-dimensional data, including mathematics-related academic certificates, teacher qualification certificates, mathematics subject skills training completion certificates, and personal teaching honor certificates, through talent nodes. All data is submitted in PDF format, and the talent node digitally signs the data using its own asymmetric encryption key. The signature includes the node's unique identifier, submission timestamp, and data digest information, ensuring the integrity and traceability of the submitted data. The education and training institution where the practitioner previously worked submits mathematics teaching hour statistics, course completion rates, student rating details, resignation certificates, and job competency evaluation reports through the institution node, endorsing and signing the data. This data is submitted in image format with the institution node's digital signature, strengthening the data's authority and credibility. The on-chain evidence module pushes this data to third-party verification nodes. These third-party verification nodes consist of official verification nodes from education authorities, verification nodes from education and training industry associations, and two third-party data verification agency nodes with corresponding qualifications. A multi-layered cross-verification mechanism is used; the same type of data must be verified by at least two different types of verification nodes before being pushed to the consensus node, ensuring the data's authenticity from multiple dimensions. Teacher qualification certificates and academic certificates are verified in real-time against the official database of the education department to prevent the influx of false qualification information; teaching hour statistics and resignation certificates are secondarily endorsed and confirmed by the original institution node to ensure the accuracy of work experience-related data; student rating details verify the correspondence between evaluators and teaching records to avoid false evaluations affecting data validity. All data, after verification, is pushed to the consortium blockchain consensus node. After the consensus node completes sorting and consensus confirmation, it performs distributed permanent notarization on the consortium blockchain and generates notarization certificates, achieving secure data storage and immutability. Figure 1 As shown.

[0035] The profile building module retrieves the talent's stored data from the consortium blockchain, performs preprocessing and standardization cleaning to remove invalid and redundant information and unify the data format, providing a standardized data foundation for subsequent capability assessment. The module calculates scores according to preset three-dimensional capability sub-indicators and weighting rules. Professional capability sub-indicators include qualification level, skill certification, knowledge reserves, and industry qualification annual review status, weighted at 30%, 25%, 25%, and 20% respectively, comprehensively considering the talent's professional foundation and compliance. Teaching capability sub-indicators include teaching hours, student satisfaction, curriculum development achievements, and teaching improvement rate, weighted at 20%, 30%, 25%, and 25% respectively, accurately reflecting the talent's teaching practice effectiveness. Adaptability sub-indicators include cross-institutional adaptability, job matching rate, cross-domain teaching ability, and institutional evaluation feedback, weighted at 25%, 25%, 20%, and 30% respectively, comprehensively assessing the talent's fit with the job. Each sub-indicator is standardized and quantified from 0-100 points, then weighted and summed to obtain the three-dimensional capability index score, making the capability assessment more quantifiable and comparable. Simultaneously, the comprehensive entropy value is calculated using a three-dimensional capability entropy algorithm. The mathematical expression for the three-dimensional capability entropy algorithm is: ,in These are the three-dimensional capability weight coefficients, and their sum is 1. For professional competence entropy value, The entropy value of teaching ability. To adapt to the capability entropy value, For entropy reduction optimization coefficients, The average hash digest. As a preset hash base value, The confidence coefficient for single-class data verification. To accommodate the total number of data types on the blockchain, the system integrates basic identification information such as the talent's unique identifier, the specific field of mathematics education and training, the profile generation timestamp, the quantification of abilities, the scores of the three-dimensional core ability indicators, the scores of each sub-indicator, the weighted calculation basis and comprehensive entropy value, the associated traceability information, the on-chain data number, and the verification node identifier. This generates a unique on-chain ability profile, marked with a data validity tag indicating the latest version. After encryption, this profile is bound to the talent's unique identifier and stored on the consortium blockchain. Only the designated talent node can decrypt and view the complete content using its private key. This ensures both a comprehensive presentation of talent ability information and the security and confidentiality of personal information. Figure 3 As shown.

[0036] During their tenure at the new educational institution, this talent accumulated 50 additional math teaching hours. The institution submitted corresponding hourly statistics and course completion rate data, and endorsed and signed the data. After verification by a third-party verification node, the data was compliantly uploaded to the blockchain, meeting the second trigger scenario of the dynamic update smart contract in the profile update module. The contract was automatically triggered, ensuring that the profile could be promptly incorporated with the latest teaching performance data. The system retrieves the talent's original on-chain capability profile data and the parameters of the newly added teaching performance data. First, it calculates the comprehensive entropy value of the newly added data using the algorithm of the profile construction module. Then, it uses the profile update entropy value correction algorithm to correct the original comprehensive entropy value. The mathematical expression of the profile update entropy value correction algorithm is: ,in To update the overall entropy value, The overall entropy value before updating, The time decay coefficient, The interval for adding new data to the blockchain. To add a comprehensive entropy value to the data, This is the trigger coefficient for the smart contract; it is calculated before execution. Then, the entropy value is corrected by substituting it into the formula, so that the updated comprehensive entropy value can accurately reflect the latest changes in talent capabilities. An updated exclusive on-chain capability profile is generated, the update log is recorded and encrypted to overwrite the original on-chain data, and the data validity mark of the updated profile is updated to the latest version, ensuring that the most comprehensive and accurate capability information of the talent is used in the subsequent screening process.

[0037] Educational institutions specializing in K-12 mathematics education need to recruit full-time mathematics teachers. These institutions log into the system through an institutional node and submit screening requests via the screening and submission module. The initial screening criteria include: meeting professional competence standards (qualification level, skills certification including mathematics-related certifications, and sufficient knowledge); teaching ability (student satisfaction rate of at least 85 points, teaching improvement rate of at least 20%); and suitability (job matching rate of at least 80%, and positive institutional feedback), clearly demonstrating a recruitment need orientation. The system standardizes and transforms the initial screening criteria to better align with the system's matching logic. The transformed parameters are hashed, and only the hash value is submitted to the consortium blockchain for synchronization and consensus, preventing the leakage of screening criteria. A unique screening identifier is generated and stored, providing a basis for subsequent matching result traceability.

[0038] The consortium blockchain matches corresponding talent nodes based on the hash value of the screening parameters and pushes the hash value to ensure the accuracy of the screening match. Upon receiving the matched talent node, it retrieves its latest on-chain capability profile and uses its private key locally to generate a zero-knowledge proof for specific statements in its capability profile that meet the screening criteria. This proof is then pushed to the consensus node as verification information. The verification information includes the talent's unique identifier, screening identifier, proof validity marker, proof generation timestamp, and verification result marker, proving that it meets the screening criteria without exposing raw data or privacy information. The consensus node coordinates the interactive verification process between talent nodes and institutional nodes, completing the verification of the submitted proof information within 3 seconds, improving screening efficiency. It aggregates the independent verification results from each consensus node and reaches a consensus, without any verification failures, timeouts, or invalid proof information, ensuring the reliability of the matching results. Subsequently, the anonymized matching result of the talent is synchronized to the educational institution node that initiated the screening, balancing the institution's recruitment needs with talent privacy protection. After receiving the anonymized result, the institutional node initiates further recruitment contact processes. All process records are stored on the blockchain to ensure the screening process is compliant and traceable.

[0039] In summary, in the scenario of recruiting full-time math instructors for educational institutions, the system operates smoothly throughout the entire process, relying on the core characteristics of blockchain. Multi-dimensional data on practitioners is digitally signed and verified through multiple cross-validation mechanisms to ensure authenticity and trustworthiness. It is then permanently stored on a consortium blockchain to guarantee security and immutability. The profile building module, after data cleaning and standardization, uses a three-dimensional capability entropy algorithm to generate a quantified and comprehensive on-chain capability profile. Simultaneously, dynamic updates trigger smart contracts and profile update entropy correction algorithms, enabling real-time profile iteration. The screening process, through parameter hashing and interactive privacy verification, achieves accurate matching without exposing the original data, ultimately outputting anonymized results. The entire process balances data authenticity, privacy security, and screening efficiency, providing reliable support for recruitment by educational institutions.

[0040] Example 2:

[0041] A scenario where educational institutions select part-time lecturers from different fields (Chinese language and history).

[0042] Educational professionals with teaching abilities in both Chinese language and history can independently upload data such as their Chinese language and literature degree certificates, history-related professional qualification certificates, dual-subject skills training completion certificates, and cross-disciplinary teaching honor certificates through talent nodes. The data is submitted in PDF format and includes the talent node's digital signature, a unique node identifier, a submission timestamp, and a data summary, ensuring the integrity and traceability of their personal qualification data. Multiple educational institutions that have collaborated with these professionals submit their teaching hours statistics, course completion rates, student rating details, cross-institutional cooperation certificates, and job competency evaluation reports for both Chinese language and history through the institution's node, with each submitting their endorsement and signature. This data is submitted in image format and includes the institution's digital signature, enriching the dimensions of the talent's ability data and enhancing its credibility. The on-chain evidence module pushes this data to third-party verification nodes. These nodes operate using a multi-layered cross-verification mechanism; the same type of data must be verified by at least two different types of verification nodes before being pushed to the consensus node, ensuring data authenticity through multiple channels. Academic certificates and professional qualification certificates are verified in real-time against official databases of education departments and vocational skills assessment centers to prevent fraudulent qualifications; cross-institutional cooperation certificates are endorsed and confirmed a second time by the original cooperating institution's node to ensure the accuracy of cross-institutional employment data; student rating details are verified against the evaluator and the corresponding subject's teaching records to ensure the authenticity and validity of the evaluation data. After all data passes verification, it is pushed to the consortium blockchain consensus node. The consensus node completes sorting and consensus confirmation, and then performs distributed permanent notarization on the consortium blockchain, generating a notarization certificate to achieve secure storage and immutability of dual-discipline related data. Figure 2 As shown.

[0043] The profile building module retrieves the talent's stored data from the consortium blockchain, performs preprocessing and standardization cleaning to remove invalid information and unify data format, providing standardized data support for capability assessment. The module calculates scores based on preset three-dimensional capability sub-indicators and weighting rules. Professional capability sub-indicators are weighted according to qualification level (30%), skills certification (25%), knowledge reserves (25%), and industry qualification annual review status (20%). Skills certification includes dual-discipline related certifications, comprehensively assessing the talent's professional foundation in both fields. Teaching capability sub-indicators are weighted according to teaching hours (20%), student satisfaction (30%), curriculum development achievements (25%), and teaching score improvement rate (25%), covering data related to teaching in both disciplines and accurately reflecting the teaching effectiveness in both fields. Adaptability sub-indicators are weighted according to cross-institutional adaptability (25%), job matching rate (25%), cross-domain teaching ability (20%), and institutional evaluation feedback (30%), emphasizing cross-domain teaching ability and collaborative adaptability. Each sub-indicator is standardized and quantified using a score of 0-100, then weighted and summed to obtain a three-dimensional capability index score. This makes the dual-domain capability assessment more quantitative. Simultaneously, a three-dimensional capability entropy algorithm is used to calculate the comprehensive entropy value. The mathematical expression for the three-dimensional capability entropy algorithm is: ,in These are the three-dimensional capability weight coefficients, and their sum is 1. For professional competence entropy value, The entropy value of teaching ability. To adapt to the capability entropy value, For entropy reduction optimization coefficients, The average hash digest. As a preset hash base value, The confidence coefficient for single-class data verification. To accommodate the total number of data types on the blockchain, the system integrates basic identification information (unique talent identifier), cross-disciplinary education and training sub-fields (Chinese and History), profile generation and latest update timestamps, competency quantification information (three-dimensional competency core indicator scores, sub-indicator scores, weighted calculation basis and comprehensive entropy value), related traceability information (on-chain data number), verification node identifiers, and algorithm execution node identifiers) to generate a unique on-chain competency profile. This profile includes a data validity marker, is encrypted, and bound to the unique talent identifier for storage. Only talent nodes can decrypt and view the complete content using their private key, thus comprehensively presenting competencies across both domains while ensuring personal privacy and security.

[0044] The talent profile now includes 30 unique student evaluations covering both Chinese language and history. Each evaluation has been verified to match the evaluator with the corresponding teaching record before being uploaded to the blockchain in compliance with regulations. This fulfills the third trigger scenario for the dynamic update smart contract in the profile update module, ensuring that the latest teaching feedback is promptly integrated into the profile. The system retrieves the talent's original profile data and the parameters of the 30 newly added student evaluations. First, the algorithm in the profile construction module calculates the comprehensive entropy value of the new data. Then, the profile update entropy correction algorithm is used to correct the original comprehensive entropy value. The mathematical expression for the profile update entropy correction algorithm is as follows: ,in To update the overall entropy value, The overall entropy value before updating, The time decay coefficient, The interval for adding new data to the blockchain. To add a comprehensive entropy value to the data, The smart contract trigger coefficient ensures that the comprehensive entropy value accurately reflects the capability changes brought about by the new evaluation, generates an updated on-chain capability profile, records the update log and encrypts and overwrites the original data, and updates the data validity mark of the updated profile to the latest version, ensuring that the profile can reflect the latest level of talent dual-domain teaching in real time.

[0045] A comprehensive educational institution plans to offer interdisciplinary courses in humanities and history and needs to select part-time lecturers from both fields. The institution logs into the system through its institutional node and submits a screening request via the screening submission module. The initial screening criteria include: professional competence (possessing relevant qualifications and skills certifications in Chinese language and history, passing annual industry qualification reviews); teaching ability (student satisfaction rate of no less than 90 points, course development achievements including interdisciplinary results); and suitability (interdisciplinary teaching ability of no less than 85 points, and inter-institutional suitability of no less than 75 points), clearly defining the competency requirements for interdisciplinary part-time lecturers. The system standardizes and transforms the initial screening criteria, ensuring they align with the system's matching logic. The transformed parameters are hashed, and the hash value is submitted to the consortium blockchain for synchronization and consensus, preventing the leakage of screening criteria. A unique screening identifier is generated and stored, providing support for tracing the matching results.

[0046] The consortium blockchain matches corresponding talent nodes based on the hash value of the screening parameters and pushes the hash value to achieve precise matching. Upon receiving the talent node with dual-domain teaching capabilities, it retrieves its latest on-chain capability profile and uses its private key locally to generate a signed proof for specific statements that meet the screening criteria. This proof is then pushed to the consensus node as verification information. The verification information includes the talent's unique identifier, screening identifier, proof validity marker, proof generation timestamp, and verification result marker, proving its compliance without disclosing original data or privacy information. The consensus node completes the verification of this proof information within 3 seconds, improving screening efficiency. It then aggregates the verification results from all consensus nodes and reaches a consensus, ensuring the reliability of the matching results as there are no abnormal verification results. Subsequently, the anonymized matching result of the talent is synchronized to the educational institution node that initiated the screening, balancing the institution's recruitment needs with talent privacy. After receiving the result, the institution node initiates a part-time cooperation negotiation process based on the result. All operation records are stored on the blockchain to ensure the compliance and traceability of the screening and subsequent cooperation processes.

[0047] In summary, the system fully adapts to the needs of cross-disciplinary talent assessment in the scenario of educational institutions selecting part-time lecturers in Chinese language and history. Data such as the practitioner's dual-discipline qualifications and teaching performance are verified by multiple verification nodes and endorsed by the institution, and then encrypted and stored on the blockchain for secure storage and traceability. In the profile construction stage, three-dimensional ability indicators are quantified according to preset weighting rules, and combined with a three-dimensional ability entropy algorithm to integrate dual-discipline ability information to form a unique profile. After a new student evaluation triggers a smart contract, the profile is dynamically optimized through a profile update entropy correction algorithm. During the screening process, parameters are standardized and hashed, and interactive verification is used to achieve accurate matching of cross-disciplinary abilities. This ensures the privacy of talent is not compromised and provides institutions with an efficient and compliant solution for screening cross-disciplinary talent. The entire process is traceable and the data is reliable.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A blockchain-based talent storage and screening management system for the education and training industry, characterized in that: The system includes: On-chain evidence storage module: The multi-dimensional data of education and training talents submitted by talent nodes are encrypted and hashed. At the same time, the data is verified by third-party verification nodes. After the verified data is pushed to the consortium chain consensus node to achieve on-chain consensus, the data is permanently stored in the consortium chain and a certificate of evidence is generated. Profile building module: retrieves evidence-stored data on the consortium blockchain, preprocesses and standardizes it, calculates the comprehensive entropy value of the talent's three-dimensional capabilities using the three-dimensional capability entropy value algorithm, converts it into three-dimensional capability index scores, integrates and generates a unique on-chain capability profile, and stores it on the consortium blockchain after encryption. Profile Update Module: Deploy a dynamic update trigger smart contract at the bottom layer of the consortium blockchain, bind the contract trigger conditions to the compliant on-chain results of new talent data. When the new data is compliantly on-chain, the contract is automatically triggered to retrieve the original profile data and new data parameters, use the profile update entropy correction algorithm to calculate the updated comprehensive entropy value, generate the updated profile and record the log, and encrypt and overwrite the original on-chain data. Screening and Reporting Module: Provides an entry point for educational institutions to submit screening requests, standardizes and parses the original screening conditions, transforms the parameters, hashes the transformed parameters, submits only the hash value to the consortium blockchain to complete synchronization and consensus, generates a unique screening identifier and stores it in association; Privacy Screening Module: The consortium blockchain matches the corresponding talent nodes according to the hash value of the screening parameters and pushes the hash value. The talent nodes retrieve their latest profiles and conduct interactive verification with the consensus nodes. Only the proof information is pushed without exposing the original data and privacy information. The consensus nodes summarize the verification results and reach a consensus, which will be directed to the education and training institution nodes that initiated the screening. The entire process is recorded on the blockchain for evidence storage.

2. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The on-chain evidence storage module contains multi-dimensional data on education and training personnel, including academic certificates, professional qualification certificates, skills training completion certificates, and personal honor certificates uploaded independently by the personnel nodes, as well as teaching hour statistics, course completion rates, student rating details, cross-institutional employment and resignation certificates, and job competency evaluation reports submitted by the education and training institution nodes. All data is submitted in PDF or image format, and the digital signature includes a unique node identifier, submission timestamp, and data summary information. Data without a valid digital signature is directly rejected and will not be allowed to proceed to the verification stage.

3. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The on-chain evidence storage module consists of official verification nodes from education authorities, verification nodes from education and training industry associations, and two third-party data verification institutions with relevant qualifications. It adopts a multi-level cross-verification mechanism, requiring that the same type of data be verified by at least two different types of verification nodes before it can be pushed to the consensus node. Qualification certificate data is verified in real time by connecting to the official databases of education authorities and vocational skills assessment centers. Teaching performance and cross-institutional work records require secondary endorsement and confirmation from the original institution node, and student evaluations require verification of the correspondence between the evaluator and the teaching record.

4. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The mathematical expression for the 3D capability entropy algorithm in the portrait construction module is: ,in These are the three-dimensional capability weight coefficients, and their sum is 1. For professional competence entropy value, The entropy value of teaching ability. To adapt to the capability entropy value, For entropy reduction optimization coefficients, The average hash digest. As a preset hash base value, The confidence coefficient for single-class data verification. This represents the total number of data types on the chain.

5. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The scores of the three-dimensional capability indicators in the profile construction module are calculated by weighting the sub-indicators. Each of the three-dimensional capability indicators contains four sub-indicators with corresponding preset weighting rules. The professional capability sub-indicators are qualification level, skill certification, knowledge reserve, and industry qualification annual review status. The preset weighting rules are: qualification level accounts for 30%, skill certification accounts for 25%, knowledge reserve accounts for 25%, and industry qualification annual review status accounts for 20%. The teaching capability sub-indicators are teaching hours, student satisfaction, curriculum development results, and teaching score improvement rate. The preset weighting rules are: teaching hours account for 20%, student satisfaction accounts for 30%, curriculum development results account for 25%, and teaching score improvement rate accounts for 25%. The adaptability sub-indicators are cross-institutional adaptability, job matching rate, cross-domain teaching ability, and institutional evaluation feedback. The preset weighting rules are: cross-institutional adaptability accounts for 25%, job matching rate accounts for 25%, cross-domain teaching ability accounts for 20%, and institutional evaluation feedback accounts for 30%. Each sub-indicator is standardized and quantified from 0 to 100 points, and the corresponding three-dimensional capability indicator scores are obtained by weighting and summing according to the corresponding weights.

6. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The specific steps for generating a unique on-chain capability profile in the profile construction module are as follows: Based on the talent's unique identifier, retrieve the corresponding multi-dimensional data of the talent that has been notarized on the consortium blockchain. Perform preprocessing and standardization cleaning on the retrieved full data. After completion, calculate the comprehensive entropy value of the talent's three-dimensional capabilities and simultaneously convert the comprehensive entropy value into a three-dimensional capability index score. Then, integrate the talent's unique identifier, the three-dimensional capability index score, the on-chain data number on which the profile is based, and the profile generation timestamp to form the basic profile data. Encrypt the basic profile data. After encryption, assign a unique on-chain storage address to the profile. Then, push the encrypted basic profile data to the consortium blockchain consensus node to achieve consensus. After consensus is passed, complete the distributed storage on the consortium blockchain and generate a profile storage identifier, which is then bound and associated with the talent's unique identifier.

7. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The dedicated on-chain capability profile in the profile construction module includes three parts: basic identification information, capability quantification information, and related traceability information. The basic identification information includes a unique talent identifier, the sub-field of education and training to which the profile belongs, the timestamp of the profile generation, and the timestamp of the most recent update; the ability quantification information includes the scores of the three-dimensional core ability indicators, the scores of each sub-indicator, the basis for weighted calculation, the comprehensive entropy value, and the parameters involved in the calculation; the associated traceability information includes the on-chain data number on which the profile generation is based, the corresponding verification node identifier, and the algorithm execution node identifier; the profile also includes a data validity marker, used to indicate whether the profile is currently the latest version, which is encrypted and stored and bound to the unique talent identifier. Only the talent node can decrypt and view the complete content through its own private key, while other nodes can only obtain the profile hash digest for verification.

8. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The mathematical expression for the image update entropy correction algorithm in the image update module is: ,in To update the overall entropy value, To update the overall entropy value, The time decay coefficient, The interval for adding new data to the blockchain. To add a comprehensive entropy value to the data, This is the trigger coefficient for smart contracts.

9. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, The contract triggering conditions in the profile update module include five distinct scenarios, and the contract will be automatically triggered if any one of these scenarios is met. The first scenario is when newly added qualification certificates or professional skills certification documents are verified and uploaded to the blockchain. The second scenario is when newly added teaching performance reaches a cumulative total of 50 class hours, and the corresponding class hour statistics and course completion rate data are submitted and verified and uploaded to the blockchain by a single educational institution node. The third scenario is when newly added non-duplicate student reviews reach a cumulative total of 30, and each review completes the verification of the correspondence between the reviewer and the teaching record and is uploaded to the blockchain. The fourth scenario is when newly added cross-institutional entry or exit certificates are endorsed by the original institution node and verified by a third-party verification node before being uploaded to the blockchain. The fifth scenario is when newly added skills training completion certificates or industry qualification annual review documents are verified and uploaded to the blockchain. Before triggering any of these scenarios, it must be confirmed that the new data has been verified by a third-party verification node. New data that does not meet the compliance requirements will not trigger the contract.

10. The blockchain-based talent storage and screening management system for the education and training industry according to claim 1, characterized in that, During interactive verification in the privacy screening module, the talent node generates proof information after completing local preprocessing of its latest on-chain capability profile. The proof information includes the talent's unique identifier, screening identifier, proof validity marker, proof generation timestamp, and verification result marker. The consensus node takes no more than 3 seconds to complete the verification of the proof information submitted by a single talent node; abnormal verification results generated during the verification process are divided into three categories: verification failure, timeout without verification, and invalid proof information; abnormal verification results need to be marked separately and stored in the consortium blockchain, and at the same time, the abnormal results are synchronously fed back to the corresponding talent node. The feedback content includes the abnormal type, verification node identifier, and abnormal occurrence timestamp.

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