A blockchain technology-based continuous learning data traceability method and device

Abstract

The application provides a continuous learning data tracing method and device based on a blockchain technology, which is applied to a distributed system platform based on the blockchain technology, participants of the platform include learners, evaluators and tracers, the method comprises the following steps: receiving education experience information of the learners, executing a second smart contract to obtain a matching pair of a learner identity credential signature and an evaluator identity credential signature; receiving evaluation data input by the evaluators, and saving the evaluation data in a blockchain; receiving an evaluation query request of the tracers, executing a third smart contract to verify the evaluation query request; executing a fourth smart contract to verify data, and sending the matching evaluation data to the tracers. The data of continuous learning of the learners is saved on the blockchain through the blockchain technology, so that the process and result of continuous learning of the learners can be mastered, the learning results of the learners at various stages are sequentially and continuously managed, and the learning situation of the learners can be objectively known as a whole.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a method and device for tracing continuous learning data based on blockchain technology. Background Art

[0002] From conception to death, individuals undergo a variety of capacity development and skill assessments throughout their lifespan, including both knowledge and education assessments from schools and competency assessments from various institutions. These educational assessment data come from a wide range of sources and are dispersed across various institutional systems. Most current system architectures suffer from severe "information silos," preventing the sharing of large amounts of data.

[0003] Understanding an individual's lifelong development requires continuous data support. This continuous assessment data allows us to understand an individual's ongoing learning process and potential. Currently, individual educational assessment data is fragmented and disorganized, lacking the continuity of ongoing learning data. Summary of the Invention

[0004] The present invention provides a continuous learning data tracing method and device based on blockchain technology, which is used to solve the defects of the existing technology that the educational assessment data of individual learners are scattered and disordered, and lack of continuity management of learning data, and realize the continuity management of learning data of individual learners.

[0005] The present invention provides a continuous learning data tracing method based on blockchain technology, which is applied to a distributed system platform based on blockchain technology. A second smart contract, a third smart contract, and a fourth smart contract are deployed on the platform. Participants of the platform include learners, evaluators, and tracers. The method includes:

[0006] Receive the educational experience information input by the learner, execute the second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature;

[0007] Receive assessment data input by the assessor and store the assessment data in the blockchain; wherein the assessment data is the assessment score corresponding to the learner's educational experience information;

[0008] Receive the learner's evaluation query request from the tracer, and execute the third smart contract to verify the evaluation query request;

[0009] According to the verified evaluation query request, the corresponding evaluation data is matched in the blockchain, the fourth smart contract is executed to perform distributed data verification, and the matched evaluation data is sent to the tracer.

[0010] According to a continuous learning data tracing method based on blockchain technology provided by the present invention, a first smart contract is also deployed on the platform, with the learner, evaluator, and tracer as participants; the educational experience information input by the learner is received, and the first smart contract also includes:

[0011] Receive a registration request from a participant, where the registration request includes the participant type and basic information of the participant;

[0012] The first smart contract is executed to verify the registration request. In response to the verification result of the participant's registration request, an identity credential signature is sent to the participant if the verification passes, wherein the participant's identity credential signature is used to represent its identity information; and re-registration information is sent to the participant if the verification fails.

[0013] According to a continuous learning data tracing method based on blockchain technology provided by the present invention, a fifth smart contract is further deployed on the platform, and the verification result of the response to the participant's registration request further includes:

[0014] The fifth smart contract is executed to assign an initial evaluation value to the evaluator, wherein the initial evaluation value of the evaluator is obtained by evaluating the evaluator's basic information according to authority, credibility, and importance.

[0015] According to a continuous learning data tracing method based on blockchain technology provided by the present invention, a sixth smart contract is further deployed on the platform; the method of receiving a tracer's evaluation query request for a learner and executing the third smart contract to verify the evaluation query request also includes:

[0016] Granting learners preset management permissions, so that the learners can view the evaluation data associated with the learners themselves published by the evaluators, evaluate the credibility of the evaluators based on the evaluation data, and manage the weight of the evaluation data that can be traced by the tracers;

[0017] Receive the learner's first evaluation value data of the evaluator, execute the fifth smart contract to confirm the first evaluation value data, and realize the correction of the evaluator's evaluation value;

[0018] In response to the first evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the learner who submitted the evaluation value data.

[0019] According to a continuous learning data tracing method based on blockchain technology provided by the present invention, the method includes receiving a tracer's evaluation query request for a learner, executing a third smart contract to verify the evaluation query request, and then further comprising:

[0020] Granting the tracer preset management authority, so that the tracer can manage the weight of the evaluator according to the evaluation data through the management authority;

[0021] Receive the second evaluation value data of the evaluator from the tracer, execute the fifth smart contract to confirm the second evaluation value data, and realize the correction of the evaluator's evaluation value;

[0022] In response to the second evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the tracer who submitted the evaluation value data.

[0023] According to a continuous learning data tracing method based on blockchain technology provided by the present invention, the step of receiving evaluation data input by an evaluator and storing the evaluation data in a blockchain specifically includes:

[0024] Encrypt evaluation data;

[0025] The encrypted evaluation data, the identity certificate signature of the evaluator who published the evaluation data, and the identity certificate signature of the learner associated with the evaluation data are converted into block structure data in a preset format and sent to the blockchain platform;

[0026] In response to the consensus mechanism's approval of the block structure data, if the block structure data is approved, the block structure data is stored in the blockchain; otherwise, a questionable evaluation data message is sent to the evaluator;

[0027] In response to the successful preservation of the block structure data, the sixth smart contract is executed and the evaluator is rewarded.

[0028] According to a blockchain-based continuous learning data tracing method provided by the present invention, the method matches corresponding evaluation data in the blockchain according to the verified evaluation query request, executes a fourth smart contract to perform distributed data verification, and sends the matched evaluation data to the tracer, specifically including:

[0029] Match the corresponding block structure data in the blockchain according to the evaluation query request;

[0030] Determine the identity certificate signature of the evaluator corresponding to the matched block result data;

[0031] Send a data verification request to the corresponding evaluator based on the evaluator's identity certificate signature;

[0032] In response to the encrypted verification data digest sent by the reviewer, when the verification data digest matches the digest of the evaluation data in the block structure data, the evaluation data in the block structure data is sent to the tracer; otherwise, the matching of the block structure data in the blockchain is performed again.

[0033] The present invention also provides a continuous learning data tracing device based on blockchain technology, which is applied to a distributed system platform based on blockchain technology. A second smart contract, a third smart contract, and a fourth smart contract are deployed on the platform. Participants of the platform include learners, evaluators, and tracers. The device includes:

[0034] A learner information receiving unit is used to receive the educational experience information input by the learner, execute the second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature;

[0035] An evaluator information receiving unit, configured to receive evaluation data input by the evaluator and store the evaluation data in the blockchain; wherein the evaluation data is the evaluation score corresponding to the learner's educational experience information;

[0036] A tracer information receiving unit, configured to receive a tracer's evaluation query request regarding a learner and execute a third smart contract to verify the evaluation query request;

[0037] The traceability data matching unit is used to match the corresponding evaluation data in the blockchain according to the verified evaluation query request, execute the fourth smart contract to perform distributed data verification, and send the matched evaluation data to the tracer.

[0038] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the processor executes the program, the steps of the continuous learning data tracing method based on blockchain technology as described above are implemented.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for continuous learning data tracing based on blockchain technology.

[0040] The continuous learning data tracing method and device based on blockchain technology provided by the present invention uses blockchain technology to save the evaluation data on the learner's continuous learning published by the evaluator on the blockchain, which is convenient for systematically grasping the process and results of the learner's continuous learning, and orderly and systematically managing the learning results of the learner at each stage, thereby facilitating the overall and objective understanding and judgment of the learner's learning situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of the continuous learning data tracing method based on blockchain technology provided by the present invention;

[0043] Figure 2 yes Figure 1 A method flow chart of the participant registration process before step 110;

[0044] Figure 3 yes Figure 1 Flowchart of the method for managing learner permissions before step 130;

[0045] Figure 4 yes Figure 1 Flowchart of a method for managing tracer authority after step 130;

[0046] Figure 5 yes Figure 1 Specific method flow chart of step 120;

[0047] Figure 6 yes Figure 1 Specific method flow chart of step 140;

[0048] Figure 7 yes Figure 1 A schematic diagram of the relationship between the corresponding participants;

[0049] Figure 8 This is a schematic diagram of the structure of the blockchain platform provided by the present invention from the perspective of learners;

[0050] Figure 9 This is a schematic diagram of the structure of the blockchain platform provided by the present invention from the perspective of the evaluator;

[0051] Figure 10 It is a structural diagram of the block structure data provided by the present invention;

[0052] Figure 11 This is a schematic diagram of the structure of the blockchain platform provided by the present invention from the perspective of the tracer;

[0053] Figure 12 This is a schematic diagram of the reward token circulation mechanism in the blockchain platform provided by the present invention;

[0054] Figure 13This is a schematic diagram of the structure of the continuous learning data tracing device based on blockchain technology provided by the present invention;

[0055] Figure 14 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] The traceability method of the embodiment of the present invention involves three types of participants: learners, evaluators, and tracers. Participants need to register on a distributed system platform based on blockchain technology (hereinafter referred to as the platform) before operation. Different participants use smart contracts in the distributed system platform based on blockchain technology (hereinafter referred to as the platform) to implement operations with different permissions, thereby realizing continuous learning data sharing, data authentication, data storage, data traceability and other functions.

[0058] The smart contracts in the embodiments of the present invention are deployed on the blockchain platform. They are both automatically executable computer programs and system participants. They can receive and process data according to pre-defined rules, respond to received information, and send information externally. The smart contract programs involved in the embodiments of the present invention include at least a registration and authentication contract, a user association contract, a traceability query contract, a data verification contract, a user evaluation contract, and a token reward contract.

[0059] like Figure 1 As shown, an embodiment of the present invention provides a continuous learning data tracing method based on blockchain technology, which is applied to a distributed system platform based on blockchain technology. The participants of the platform include: learners, evaluators, and tracers. The second smart contract, the third smart contract, and the fourth smart contract are deployed on the platform. The method includes:

[0060] Step 110: Receive the educational experience information input by the learner, execute the second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature;

[0061] In the embodiment of the present application, the learner can add and update the educational evaluation institutions experienced in the individual development process at any time in step 110, wherein the educational experience information includes each school education experience and its school, and each extracurricular quality training experience and its institution. The educational experience information input by the learner triggers the user-associated smart contract, that is, the second smart contract, thereby realizing the association of the learner with each corresponding evaluator (school or institution).

[0062] In the embodiment of the present application, the platform is also deployed with a first smart contract; as shown in Figure 2 The receiving of the educational experience information input by the learner in step 110 further includes:

[0063] Step 210: receiving a registration request of a participant, wherein the registration request includes a participant type and basic information of the participant;

[0064] Specifically, the learner submits a registration request to the online client of the platform, wherein the basic information of the learner includes but is not limited to name, gender, ID number, age, etc., and the identity verification keyword of the learner is the ID number. The evaluator submits a registration request to the online client of the platform, wherein the basic information of the evaluator includes but is not limited to name, unified social credit code, residence, legal representative, school (or institution) category, etc., and the identity verification keyword of the evaluator is the unified social credit code. When the traceability person submits a registration request to the online client of the platform, the basic information and the identity verification keyword provided are different according to the type of the traceability person. If the type of the traceability person is individual, the basic information submitted in the registration request is similar to that provided by the learner, and the identity verification keyword is the ID number of the traceability person. If the type of the traceability person is organization, the basic information submitted in the registration request is similar to that provided by the evaluator, and the identity verification keyword is the unified social credit code of the traceability person.

[0065] Step 220: executing the first smart contract to verify the registration request, and in response to the verification result of the registration request of the participant, sending an identity credential signature to the participant when the verification is passed, wherein the identity credential signature of the participant is used to represent the identity information thereof; and sending re-registration information to the participant when the verification is not passed.

[0066] Specifically, before each type of participant (learner, evaluator and traceability person) sends data to the platform or receives data from the platform, the participant needs to be registered on the platform, and the participant who passes the registration is given the permission to operate on the platform.

[0067] Specifically, the registration authentication smart contract, that is, the first smart contract, is triggered according to the participant's registration request. A key is created for the participant who has passed the authentication. The key consists of a pair of public and private keys. A series of hash algorithms and encoding algorithms are used to encrypt the participant's identity authentication keyword and public key to generate an identity credential signature for the participant as its unique identification in the platform; the private key is a random number generated by the platform, which is used for the digital signature of login or verification.

[0068] In this embodiment of the present invention, the public key and the authentication keyword are combined into raw data and a digital digest is generated through a hash function, which can be used as the participant's unique credential. As shown in formulas (1) and (2), x is the public key and t is the authentication keyword converted to hexadecimal.

[0069] Hash (original data) = summary information (1)

[0070] Original data = x & t (2)

[0071] Specifically, the SHA-256 hash algorithm is used to generate the hash value of the public key and authentication keyword. To improve the readability and robustness of the summary, the final participant identity credential summary is generated through Base58Check encoding. This credential summary will be saved in the corresponding identity credential dictionary according to the participant type.

[0072] In the embodiment of the present invention, a fifth smart contract is further deployed on the platform corresponding to step 220, and the verification result of the response to the participant's registration request further includes:

[0073] The fifth smart contract is executed to assign an initial evaluation value to the evaluator, wherein the initial evaluation value of the evaluator is obtained by evaluating the evaluator's basic information according to authority, credibility, and importance.

[0074] The evaluation smart contract, also known as the fifth smart contract, is triggered for the evaluator who has passed the verification. The evaluation value of the evaluator is initialized based on the basic information of the evaluator. The initial evaluation value is the initial value obtained after comprehensive consideration of the evaluator's authority, credibility, and importance.

[0075] Step 120: Receive the evaluation data input by the evaluator and save the evaluation data in the blockchain; wherein the evaluation data is the evaluation score corresponding to the learner's educational experience information;

[0076] In an embodiment of the present invention, in step 120, the assessment data input by the assessor is informationally bound to the learner, wherein the assessor is partially bound by passive association based on the educational experience information input by the learner, and the other binding is that the assessor submits the learner information in its own system to the platform, actively triggering the user association smart contract to achieve active association and binding with the learner. The associated "learner identity credential signature-assessor identity credential signature" matching pair is saved in the user association dictionary through the user association smart contract. Passive association of the learner with the assessor means that the learner triggers the user association smart contract without filling in the educational background, and finds the corresponding learner's record in the identity credential dictionary based on the learner-related information in the assessment data submitted by the assessor, thereby obtaining the learner's identity credential signature, and completing the storage of the associated "learner identity credential signature-assessor identity credential signature" in the user association dictionary.

[0077] In an embodiment of the present invention, in step 120, specifically, the assessment data input into the platform by the assessor includes but is not limited to the learner's name, ID number, subject, score, assessment time, and auxiliary information (including certificates, activity photos, activity videos, etc.).

[0078] Step 130: Receive the learner's evaluation query request from the tracer, and execute the third smart contract to verify the evaluation query request;

[0079] Specifically, the tracer submits an assessment data query request, wherein the request sent mainly includes the signature of the learner's identity certificate to be queried, and the query time period can also be specified. The third smart contract is also the query smart contract.

[0080] In the embodiment of the present invention, a sixth smart contract is also deployed on the platform, such as Figure 3 As shown, corresponding to step 130 of receiving the tracer's evaluation query request about the learner, executing the third smart contract to verify the evaluation query request, the process also includes:

[0081] Step 310: Granting the learner preset management permissions, so that the learner can view the assessment data associated with the learner published by the assessor, evaluate the assessor's credibility based on the assessment data, and manage the weight of the assessment data that can be traced by the tracer.

[0082] Specifically, learners are granted administrative authority to display their learning data on the platform. Learners can trigger a query smart contract to obtain and view all personal assessment data published by their associated assessors. Learners can manage the weight of traceable information within a certain range of authority. For example, learners can select the display status of assessment data provided by assessors with lower evaluation scores, and independently determine whether to display this assessment data to tracers. However, due to administrative authority restrictions, learners cannot select the display status of assessment data submitted by assessors with higher evaluation scores, such as schools and the Ministry of Education. Such data must be viewable by tracers.

[0083] Step 320: Receive the learner's first evaluation value data of the evaluator, execute the fifth smart contract to confirm the first evaluation value data, and realize the correction of the evaluator's evaluation value;

[0084] Specifically, after obtaining the evaluation data, the learner can trigger the evaluation smart contract, that is, the fifth smart contract, to give a reference value score for the credibility of the associated evaluator.

[0085] Step 330: In response to the first evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the learner who submitted the evaluation value data.

[0086] In the embodiment of the present invention, Figure 4 As shown, corresponding to step 130, the process of receiving the tracer's evaluation query request for the learner and executing the third smart contract to verify the evaluation query request may further include:

[0087] Step 410: granting the tracer a preset management authority, so that the tracer can manage the weight of the evaluator's importance according to the evaluation data through the management authority;

[0088] Step 420: Receive the second evaluation value data of the evaluator from the tracer, execute the fifth smart contract to confirm the second evaluation value data, and realize the correction of the evaluator's evaluation value;

[0089] Step 430: In response to the second evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the tracer who submitted the evaluation value data.

[0090] Step 140: Match the corresponding evaluation data in the blockchain according to the verified evaluation query request, execute the fourth smart contract to perform distributed data verification, and send the matched evaluation data to the tracer.

[0091] Specifically, the fourth smart contract is a verification smart contract.

[0092] In the embodiment of the present application, the evaluation data received by the evaluator is saved in the blockchain corresponding to step 120, as shown in the following table, which specifically includes: Figure 5

[0093] Step 510: encrypt the evaluation data;

[0094] Specifically, the evaluation data is encrypted by a hash algorithm.

[0095] Step 520: form a block structure data in a preset form by associating the encrypted evaluation data with the identity certificate signature of the evaluator who publishes the evaluation data and the identity certificate signature of the learner associated with the evaluation data, and send it to the blockchain platform;

[0096] The blockchain platform in this step is the "platform based on blockchain technology" described in the foregoing of the embodiment of the present application, that is, the abbreviation of the platform based on blockchain technology in the embodiment of the present application is "platform".

[0097] Step 530: in response to the approval result of the consensus mechanism on the block structure data, save the block structure data in the blockchain when the approval is passed, otherwise send the evaluation data suspicious information to the evaluator;

[0098] Step 540: in response to the successful saving of the block structure data, execute the sixth smart contract to give the evaluator a reward.

[0099] The evaluator successfully submits a block structure data to the platform, triggers the reward mechanism smart contract, that is, the sixth smart contract, and obtains the reward token (Token) of the evaluation value.

[0100] In the embodiment of the present application, the block structure of the block structure data includes a block header and a block body, wherein the block header contains basic field information, and the block body is a Merkle tree form of evaluation data record digest.

[0101] The block header includes:

[0102] Block identification (block ID), as a unique identifier in the blockchain;

[0103] Hash value of the parent block, realizing the chain connection between block data;

[0104] Merkle root, realizing the integration of all evaluation records in the block layer by layer in pairs, and finally containing all information in the block header through a hash value;

[0105] Timestamp, recording the time of block generation, accurate to milliseconds;

[0106] Evaluator identity certificate signature, used to identify the evaluator who generates the block; ​

[0107] The learner identity certificate signature is used to identify the learner related to the data in this block.

[0108] The block body primarily stores evaluation data records. Network-wide verified evaluation data is represented by a Merkle tree. The Merkle tree first hashes the evaluation data, obtaining a corresponding hash value for each evaluation data item. The tree then hashes the evaluation data hash values ​​in pairs, and so on. The final hash value is the Merkle root stored in the block header.

[0109] The above-mentioned assessment data format can be composed of a JSON object, mainly including the learner's name, ID number, score, subject, assessment agency name, assessment agency unified social credit code, assessment time, and auxiliary information array. The auxiliary information mainly stores information such as pictures, videos, and large files, including the auxiliary information type, the address of the source file stored in the distributed server, and the hash value of the encrypted source file.

[0110] In the embodiment of the present invention, the corresponding evaluation data is matched in the blockchain according to the evaluation query request that has passed the verification in step 140, the fourth smart contract is executed to perform distributed data verification, and the matched evaluation data is sent to the tracer, such as Figure 6 As shown, specifically including:

[0111] Step 610: Match the corresponding block structure data in the blockchain according to the evaluation query request;

[0112] Specifically, the retrospective query smart contract is triggered according to the evaluation query request, and the block structure data of all relevant evaluation data that matches are queried on the blockchain according to the learner credentials and / or the query time period.

[0113] Specifically, the smart contract receives the learner identity credential signature and time period to be traced, and matches the learner identity credential signature with the sender identity credential signature:

[0114] If the match is consistent, all relevant data of the learner within the traceable time period on the blockchain will be returned.

[0115] If the match is inconsistent, and if the sender is confirmed to be the tracer, the relevant data of the learner on the blockchain that is not marked as not displayed will be returned.

[0116] Step 620: Determine the identity certificate signature of the evaluator corresponding to the matched block result data;

[0117] Step 630: Sending a data verification request to the corresponding evaluator based on the evaluator's identity certificate signature;

[0118] Specifically, the data verification smart contract is triggered, and a data verification request for the block ID information of the corresponding block structure data is sent to the corresponding evaluator based on the matched evaluator's identity certificate signature.

[0119] By searching the evaluator's server for the learner's evaluation data of the corresponding evaluation time, forming the above-mentioned evaluation data format, and performing hash calculation to obtain the data hash value, which is the encrypted verification data summary.

[0120] Step 640: In response to the encrypted verification data digest sent by the reviewer, when the verification data digest matches the digest of the evaluation data in the block structure data, the evaluation data in the block structure data is sent to the tracer; otherwise, the block structure data in the blockchain is re-matched.

[0121] Specifically, the hash value of the rightmost evaluation data node in the block body Merkle tree in the block is obtained, which is the summary of the evaluation data in the block structure data. This hash value is compared with the hash value obtained in step 630. If they are consistent, the evaluation data passes the data verification, that is, the match is successful.

[0122] Specifically, the evaluator generates a summary by hashing the relevant evaluation result information of the corresponding block ID distributed in its server, and matches it with the evaluation data summary in the block structure data. If the match is successful, the platform returns the evaluation data in the block structure data to the tracer.

[0123] In this embodiment of the present invention, the evaluator can obtain reward tokens after verifying the validity of the block. Learners and tracers can obtain reward tokens mainly by submitting valid evaluation information about the evaluator.

[0124] The rules for the flow and use of the tokens obtained by each participant are as follows:

[0125] Learners use tokens to gain permission to edit the display of data provided by higher-level evaluators;

[0126] Reviewers use tokens to get higher evaluation values;

[0127] Tracers use Tokens to obtain more and wider evaluation data query permissions.

[0128] In this embodiment of the present invention, after step 640, the tracer, having obtained the assessment data, can sort the learner's assessment data based on data integrity, the assessor's authority, and the credibility of the assessment data, and edit the assessment data weights. Furthermore, when the tracer edits the assessment data weights, the evaluation smart contract can be triggered to evaluate the assessor and assign a reference score based on their importance.

[0129] In this embodiment of the present invention, the evaluator's evaluation value is updated according to preset update rules based on the reference scores of the learner and the tracer. Depending on the evaluation value, the evaluator has different evaluation permissions over the learner's evaluation data and scope. For example, if a learner cancels the display of data submitted by a particular evaluator, and a certain number of learners have performed this operation, the evaluator's credibility score will be lowered. Tracers rank the evaluation data. If the ranking of the evaluator's data is improved by the tracer when the cumulative number reaches a certain level, the evaluator's importance score will be increased; otherwise, the evaluator's importance score will be lowered.

[0130] In the embodiment of the present invention, by establishing a blockchain, the basic education subject assessment data and quality education assessment data in the lifelong development process of an individual are completely recorded and associated, so that learners, assessors and tracers can all perform corresponding operations on the blockchain platform, realizing decentralized data storage, ensuring that the data cannot be tampered with, having data sharing functions, and achieving information intercommunication and interconnection.

[0131] The tracing method provided by the embodiment of the present invention is described in detail below with reference to specific examples.

[0132] like Figure 7 As shown, there are three types of participants (users) in this method. Different participants implement operations with different permissions through smart contracts in a distributed system platform based on blockchain technology (referred to as the platform).

[0133] Take middle school student X as an example. Figure 8 As shown, the following operations can be performed on the platform:

[0134] Middle school student X registered through the platform's client and obtained usage rights. The registration information included name, gender, ID number, age, and other information.

[0135] The smart contract generates a key pair when it confirms the new registered user through the ID number. It then encrypts and encodes the public key and the ID number converted into hexadecimal to generate the identity credential of middle school student X, which serves as the address for receiving assessment data.

[0136] Learners can actively establish a relationship with evaluators by entering their educational experience. For example, a middle school student named X may enter information such as attending Beijing A Primary School from September 2014 to June 2020, attending Beijing B Middle School from September 2020 to present, and participating in a piano competition organized by Beijing Institution C in August 2019. The smart contract matches the school information with the evaluator information on the platform. If a match is successful, the evaluator's identity credential signature and the learner's identity credential signature are extracted and stored as an association pair.

[0137] Learners can view all associated pairs, and the evaluators can view their assessment data published on the blockchain. For example, if a student X is successfully associated with Primary School A, the signatures of X's identity credentials and Primary School A's identity credentials are matched with the block header information on the blockchain. The matching result is the scores for all subjects submitted by the primary school for X. If a student X is successfully associated with Institution C, the student can view X's competition results and the competition process in the supplementary information provided by the institution on the blockchain.

[0138] Learners have a degree of autonomy over the traceability of their assessment data and can block the traceability of data provided by assessors with low evaluation scores. However, the school learning manager, as the most critical information in continuous learning data, cannot be blocked. For example, if the event organized by institution C is a regional private event and institution C has a low evaluation score, middle school student X can choose not to display the piano competition assessment data it published in the traceability display information of its own assessment data.

[0139] After learners obtain the evaluation data and perform display editing, they can evaluate the credibility of the evaluator based on their personal experience or evaluation data. The smart contract will update the evaluator's evaluation value based on the learner's above operations.

[0140] Take Beijing B Middle School as an example. Figure 9 As shown, the following operations can be performed on the platform:

[0141] Middle School B registered through the platform's client and obtained usage rights. The registration information included the school name, unified social credit code, address, legal representative, institution type, etc.

[0142] The smart contract generates a key pair if it confirms the user as a new registered user through the unified social credit code. It then encrypts and encodes the public key and the unified social credit code converted into hexadecimal to generate the identity credential of Middle School B as the evaluator, which serves as the address for receiving and sending evaluation data.

[0143] Based on the information filled in by the evaluator, the smart contract gives the newly registered user an initial evaluation value, including authority, credibility, importance, etc., from aspects such as management department and institution category, combined with evaluation rules.

[0144] The assessor submits the student information they manage to the platform, primarily including the student's name, ID number, and enrollment date. The smart contract automatically associates the assessor with the learner based on this information. For example, if assessor B enters information about middle school student X, including her name, ID number, and enrollment date of September 2020, the smart contract will successfully match the information and establish a connection between Middle School B and student X.

[0145] The evaluator can submit evaluation data regularly. For example, evaluator B submits the test scores of the 2020.9-2021.7 semester, which includes the Chinese score of student X as 95 points. According to the evaluation data JSON format requirements, the evaluation data record is generated.

[0146] The evaluation data is hashed and encrypted, and as shown in Figure 10 , combined with the school identity certificate signature of B Middle School, the student X identity certificate signature, the timestamp and other information, a block structure data is formed.

[0147] The block structure data is authenticated through the consensus mechanism in the blockchain network and saved to the blockchain. For the data saved to the blockchain, student X can view the evaluation data submitted by evaluator B associated with him.

[0148] After the block structure data is successfully published, the smart contract will send a reward Token to B Middle School according to the rules.

[0149] Taking drawing quality education agency D as an evaluator as an example, it can perform the following operations on the platform:

[0150] The operation process of agency D is similar to the above process of B Middle School. The difference is:

[0151] Agency D has submitted an association with student X, such as student X participating in the kindergarten drawing competition organized by agency D in 2013. Although student X did not fill in the information about agency D in the education experience, agency D can actively establish an association with student X through this step. And student X can also edit whether the evaluation data provided by agency D has the right to display to the traceability person.

[0152] In addition to submitting student X's performance in the competition, the evaluation data provided by agency D also includes auxiliary information such as award-winning drawings, and these picture files and the address information stored in agency D will be recorded in the evaluation data.

[0153] Taking the Ministry of Education as a traceability person as an example, as shown in Figure 11 , it can perform the following operations on the platform:

[0154] The Ministry of Education registers through the client of the platform and obtains the use right. The registration information includes the name, the unified social credit code, the residence, the person in charge, the agency category and other information.

[0155] The smart contract confirms that the new registered user is the unified social credit code, then it generates a key pair, and then encrypts and encodes the public key and the unified social credit code converted to hexadecimal, to generate the identity certificate of the traceability person as the address for receiving the evaluation data.

[0156] The Ministry of Education can query assessment data by submitting a retrospective request. For example, if the Ministry of Education wants to query the continuous learning data of middle school student X before junior high school, it will submit the learner's identity certificate signature and time period (2008.1.1 to 2020.8.31) to the platform.

[0157] Based on the submitted traceability request and the display permissions specified by middle school student X, the smart contract returns all eligible block structure data. For example, the block structure data for middle school student X from January 1, 2008, to August 31, 2020, includes all basic education assessment data provided by School A, competition assessment data provided by Institution C, and quality-oriented education assessment data provided by Institution D. Since middle school student X marked Institution C's data as not required for display to the tracer, the returned data includes assessment data from School A and Institution D, but not Institution C's assessment data.

[0158] The smart contract verifies the returned block structure data in sequence before displaying it to the tracer. For example, to verify that middle school student X won the 2013 painting competition at institution D, the smart contract first retrieves student X's score from institution D's node server, constructs a field of the same type as the block structure data, and generates a digest of this data through hash encryption. Next, the smart contract retrieves the relevant block structure data digest from the blockchain based on the signature of institution D's identity credential, the signature of student X's identity credential, and the timestamp. Finally, the two encrypted digests are compared; if they match, the data passes verification. This process continues in this manner, completing the verification of all block structure data.

[0159] When the Ministry of Education obtains the continuous learning data of middle school student X, it can weight and rank the data based on the presentation of the data. For example, if the Ministry of Education wants to understand the learning progress of middle school student X in quality education during his preschool years, it can increase the weight of the assessment data of middle school student X provided by institution D.

[0160] The Ministry of Education can evaluate the importance and authority of the data provided by the evaluator based on the data provided by the evaluator. The smart contract will update the evaluator's evaluation value based on the above operations of the Ministry of Education.

[0161] Due to the high authority of the Ministry of Education, the assessment data of more learners can be traced at a time. However, for tracers with lower authority, the amount of data that can be traced at a time is limited. Tokens can be used to exchange for more traces.

[0162] In the embodiment of the present invention, the evaluation value of the evaluator includes an authority score, a credibility score, and an importance score corresponding to the authority, credibility, and importance of the evaluator, respectively.

[0163] The authority score is determined based on the evaluator's registration management department, institution type, and department affiliation. The basic principle is that education-related categories are ranked higher than other categories, public institutions are ranked higher than other categories, and affiliations are ranked lower by ministry, province, city, and district. This principle is used to determine the authority score.

[0164] The initial value of the credibility score is 100 points.

[0165] The initial value of the important score is determined according to the identity of the evaluator. The important score for basic education (such as schools, Ministry of Education) is full marks, and the initial value of the important score for quality education is 60 points.

[0166] After obtaining their own assessment data, learners can manage permissions on the platform for assessment data provided by assessors with authority scores below 60 in the quality education category. They can also choose whether to display their provided assessment data to tracers. When a certain amount of learners have chosen not to display a particular assessor's data to tracers, the smart contract will review the assessment data submitted by the assessor. If the data quality is determined to be insufficient and a large number of learners are not shown, the assessor's credibility score will be deducted.

[0167] When reviewing assessment data, tracers prioritize the importance of quality education assessment data based on their own needs. If the tracer is an institution, they can only rank assessment data submitted by assessors whose authority scores are at least 10 points lower than their own. Tracers' actions regarding data importance ranking will be recorded. When a certain amount of tracers have modified the importance of data submitted by a particular assessor, the smart contract will review the tracer's information and the modified data. If it is confirmed that the importance of the data submitted by the assessor has been increased by a large number of tracers, the assessor's importance score will be increased; otherwise, the assessor's importance score will be deducted.

[0168] like Figure 12 As shown, based on the above evaluation method of the evaluator and the characteristics of the blockchain, tokens are used to incentivize various participants. The circulation method of tokens is as follows:

[0169] ① How to obtain tokens for different participants:

[0170] Evaluators can receive token rewards when they successfully submit evaluation data blocks and assist in verifying blocks;

[0171] Learners can receive token rewards after their submitted evaluator credibility evaluation is recognized;

[0172] The tracer can receive token rewards after the reviewer's importance evaluation submitted by the tracer is recognized.

[0173] ②How different participants use tokens:

[0174] Learners can request the management rights to the assessment data provided by assessors with an authority score of 60 or higher in the quality education category through token transactions. This right can be obtained through token transactions with the assessor.

[0175] Tracers can request higher traceability rights through token transactions, so that they can query more learners' continuous learning data at a time. Tracers can obtain this right through token transactions with learners and evaluators.

[0176] Reviewers can request to increase their ratings, primarily to enhance their authority, through token transactions. Reviewers publish their request and associated tokens on the blockchain, and the request must be approved by a majority of nodes before the rating can be increased.

[0177] The following describes a continuous learning data tracing device based on blockchain technology provided by an embodiment of the present invention. The continuous learning data tracing device based on blockchain technology described below and the continuous learning data tracing method based on blockchain technology described above can refer to each other. Figure 13 As shown, an embodiment of the present invention provides a continuous learning data tracing device based on blockchain technology, which is applied to a distributed system platform based on blockchain technology. The participants of the platform include: learners, evaluators, and tracers. The second smart contract, the third smart contract, and the fourth smart contract are deployed on the platform. The device includes:

[0178] The learner information receiving unit 1310 is used to receive the educational experience information input by the learner, execute the second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature;

[0179] The evaluator information receiving unit 1320 is used to receive the evaluation data input by the evaluator and store the evaluation data in the blockchain; wherein the evaluation data is the evaluation score corresponding to the learner's educational experience information;

[0180] The tracer information receiving unit 1330 is used to receive a tracer's evaluation query request about a learner and execute a third smart contract to verify the evaluation query request;

[0181] The traceability data matching unit 1340 is used to match the corresponding evaluation data in the blockchain according to the verified evaluation query request, execute the fourth smart contract to perform distributed data verification, and send the matched evaluation data to the tracer.

[0182] In the embodiment of the present invention, a first smart contract is further deployed on the platform, and the device further includes:

[0183] A registration request receiving unit, configured to receive a registration request from a participant, wherein the registration request includes the participant type and basic information of the participant;

[0184] An identity credential signature generation unit is configured to execute the first smart contract to verify the registration request, and in response to the verification result of the participant's registration request, send an identity credential signature to the participant if the verification passes, wherein the participant's identity credential signature is used to represent its identity information; and send re-registration information to the participant if the verification fails.

[0185] In an embodiment of the present invention, a fifth smart contract is further deployed on the platform, and the device further includes an evaluation unit, which is configured to execute the fifth smart contract to assign an initial evaluation value to the evaluator, wherein the initial evaluation value of the evaluator is obtained by evaluating the evaluator's basic information according to authority, credibility, and importance.

[0186] In the embodiment of the present invention, a sixth smart contract is further deployed on the platform; and the device further includes:

[0187] A learner authority granting unit is used to grant a learner preset management authority, so that the learner can view the evaluation data associated with the learner published by the evaluator through the management authority, evaluate the credibility of the evaluator based on the evaluation data, and perform weight management on the evaluation data that can be traced by the tracer;

[0188] A learner evaluation unit, configured to receive a first evaluation value data of the learner on the evaluator, execute a fifth smart contract to confirm the first evaluation value data, and implement correction of the evaluator's evaluation value;

[0189] The learner reward unit is configured to reward the learner who submitted the evaluation value data in response to the first evaluation value data satisfying the sixth smart contract execution condition.

[0190] In an embodiment of the present invention, the device further includes:

[0191] The tracer authority granting unit is used to grant the tracer preset management authority, so that the tracer can manage the weight of the evaluator's importance according to the evaluation data through the management authority;

[0192] The tracer evaluation unit is used to receive the second evaluation value data of the evaluator from the tracer, execute the fifth smart contract to confirm the second evaluation value data, and realize the correction of the evaluator's evaluation value;

[0193] The tracer reward unit is configured to reward the tracer who submitted the evaluation value data in response to the second evaluation value data satisfying the sixth smart contract execution condition.

[0194] In this embodiment of the present invention, the evaluator information receiving unit 1320 includes:

[0195] Evaluation data encryption subunit, used to encrypt evaluation data;

[0196] The block structure data generation subunit is used to form block structure data according to a preset format by combining the encrypted evaluation data, the identity certificate signature of the evaluator who published the evaluation data, and the identity certificate signature of the learner associated with the evaluation data, and send the block structure data to the blockchain platform;

[0197] A block structure data verification subunit is configured to respond to the consensus mechanism's approval of the block structure data and, if approved, save the block structure data in the blockchain; otherwise, send a questionable evaluation data message to the evaluator;

[0198] The block reward sub-unit is used to execute the sixth smart contract in response to the successful preservation of the block structure data, and to reward the evaluator.

[0199] In this embodiment of the present invention, the tracing data matching unit 1340 includes:

[0200] The block structure data matching subunit is used to match the corresponding block structure data in the blockchain according to the evaluation query request;

[0201] The evaluator matching subunit is used to determine the evaluator's identity certificate signature corresponding to the matched block result data;

[0202] The evaluator verification request sending subunit is used to send a data verification request to the corresponding evaluator based on the evaluator's identity certificate signature;

[0203] The verification request verification subunit is configured to respond to the encrypted verification data digest sent by the reviewer. When the verification data digest matches the digest of the evaluation data in the block structure data, the reviewer is sent the evaluation data in the block structure data to the tracer; otherwise, the block structure data in the blockchain is re-matched.

[0204] The following combination Figure 14 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention is described as follows: Figure 14As shown, the electronic device may include: a processor (Processor) 1410, a communication interface (CommunicationsInterface) 1420, a memory (Memory) 1430 and a communication bus 1440, wherein the processor 1410, the communication interface 1420, and the memory 1430 communicate with each other through the communication bus 1440. The processor 1410 can call the logic instructions in the memory 1430 to execute a continuous learning data tracing method based on blockchain technology, which is applied to a distributed system platform based on blockchain technology. The participants of the platform include: learners, evaluators and tracers. The second smart contract, the third smart contract and the fourth smart contract are deployed on the platform. The method includes: receiving educational experience information input by the learner, executing the second smart contract, matching and associating the learner's educational experience information with the evaluator, and returning a matching pair of the learner's identity credential signature and the evaluator's identity credential signature; receiving assessment data input by the evaluator, and storing the assessment data in the blockchain; wherein the assessment data is about the assessment score corresponding to the learner's educational experience information; receiving an assessment query request about the learner from the tracer, executing the third smart contract to verify the assessment query request; matching the corresponding assessment data in the blockchain according to the verified assessment query request, executing the fourth smart contract to perform distributed data verification, and sending the matched assessment data to the tracer.

[0205] In addition, the logic instructions in the above-mentioned memory 1430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0206] On the other hand, an embodiment of the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions. When the program instructions are executed by a computer, the computer is capable of executing the continuous learning data tracing method based on blockchain technology provided by the above-mentioned methods, which is applied to a distributed system platform based on blockchain technology. Participants in the platform include: learners, evaluators, and tracers. A second smart contract, a third smart contract, and a fourth smart contract are deployed on the platform. The method comprises: receiving educational experience information input by a learner, executing the second smart contract, matching and associating the learner's educational experience information with the evaluator, and returning a matching pair of the learner's identity credential signature and the evaluator's identity credential signature; receiving assessment data input by the evaluator, and storing the assessment data in a blockchain; wherein the assessment data is assessment scores corresponding to the learner's educational experience information; receiving an assessment query request regarding the learner from a tracer, executing the third smart contract to verify the assessment query request; matching corresponding assessment data in the blockchain according to the verified assessment query request, executing the fourth smart contract to perform distributed data verification, and sending the matched assessment data to the tracer.

[0207] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the above-mentioned continuous learning data tracing methods based on blockchain technology, which are applied to a distributed system platform based on blockchain technology. Participants of the platform include: learners, evaluators and tracers. A second smart contract, a third smart contract and a fourth smart contract are deployed on the platform. The method includes: receiving educational experience information input by the learner, executing the second smart contract, matching and associating the learner's educational experience information with the evaluator, and returning a matching pair of the learner's identity credential signature and the evaluator's identity credential signature; receiving assessment data input by the evaluator, and storing the assessment data in the blockchain; wherein the assessment data is about the assessment score corresponding to the learner's educational experience information; receiving an assessment query request about the learner from the tracer, executing the third smart contract to verify the assessment query request; matching the corresponding assessment data in the blockchain according to the verified assessment query request, executing the fourth smart contract to perform distributed data verification, and sending the matched assessment data to the tracer.

[0208] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0209] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A continuous learning data tracing method based on blockchain technology, characterized in that: Applied to a distributed system platform based on blockchain technology, a second smart contract, a third smart contract, and a fourth smart contract are deployed on the platform, and participants of the platform include learners, evaluators, and tracers. The method includes: Receive the educational experience information input by the learner, execute the second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature; the educational experience information includes various on-campus educational experiences and their schools, and various off-campus quality training experiences and their institutions; Receive assessment data input by the assessor and store the assessment data in the blockchain; wherein the assessment data is the assessment score corresponding to the learner's educational experience information; Receive the learner's evaluation query request from the tracer, and execute the third smart contract to verify the evaluation query request; Match the corresponding evaluation data in the blockchain according to the verified evaluation query request, execute the fourth smart contract to perform distributed data verification, and send the matched evaluation data to the tracer; The receiving of evaluation data input by the evaluator and storing the evaluation data in the blockchain specifically includes: Encrypt the evaluation data using hash algorithm; The encrypted evaluation data, the identity certificate signature of the evaluator who published the evaluation data, and the identity certificate signature of the learner associated with the evaluation data are converted into block structure data in a preset format and sent to the blockchain platform; In response to the consensus mechanism's approval of the block structure data, if the block structure data is approved, the block structure data is stored in the blockchain; otherwise, a questionable evaluation data message is sent to the evaluator; In response to the successful saving of the block structure data, the sixth smart contract is executed to reward the evaluator; the block structure of the block structure data includes a block header and a block body, the block header contains basic field information, the identity credential signature of the evaluator who published the evaluation data, and the identity credential signature of the learner associated with the evaluation data, and the block body is a summary of the evaluation data record in the form of a Merkle tree; The Merkle tree first hashes the evaluation data, obtaining a corresponding hash value for each evaluation data. Then, the hash values ​​of the evaluation data are hashed in groups of two, and so on. The final hash value is the Merkle root stored in the block header. The process of matching the corresponding evaluation data in the blockchain according to the verified evaluation query request, executing the fourth smart contract to perform distributed data verification, and sending the matched evaluation data to the tracer specifically includes: Match the corresponding block structure data in the blockchain according to the evaluation query request; Determine the identity certificate signature of the evaluator corresponding to the matched block result data; Send a data verification request to the corresponding evaluator based on the evaluator's identity certificate signature; In response to the encrypted verification data digest sent by the reviewer, when the verification data digest matches the digest of the evaluation data in the block structure data, the evaluation data in the block structure data is sent to the tracer; otherwise, the matching of the block structure data in the blockchain is performed again.

2. The continuous learning data tracing method based on blockchain technology according to claim 1 is characterized in that: The platform also deploys a first smart contract, which includes the learner, evaluator, and tracer as participants; The receiving of the educational experience information input by the learner also includes: Receive a registration request from a participant, where the registration request includes the participant type and basic information of the participant; The first smart contract is executed to verify the registration request. In response to the verification result of the participant's registration request, an identity credential signature is sent to the participant if the verification passes, wherein the participant's identity credential signature is used to represent its identity information; and re-registration information is sent to the participant if the verification fails.

3. The continuous learning data tracing method based on blockchain technology according to claim 2 is characterized in that: A fifth smart contract is also deployed on the platform, which responds to the verification result of the participant's registration request and further includes: The fifth smart contract is executed to assign an initial evaluation value to the evaluator, wherein the initial evaluation value of the evaluator is obtained by evaluating the evaluator's basic information according to authority, credibility, and importance.

4. The continuous learning data tracing method based on blockchain technology according to claim 3 is characterized in that: The platform also deploys a sixth smart contract; the platform receives a tracer's assessment query request for a learner and executes the third smart contract to verify the assessment query request, which also includes: Granting learners preset management permissions, so that the learners can view the evaluation data associated with the learners themselves published by the evaluators, evaluate the credibility of the evaluators based on the evaluation data, and manage the weight of the evaluation data that can be traced by the tracers; Receive the learner's first evaluation value data of the evaluator, execute the fifth smart contract to confirm the first evaluation value data, and realize the correction of the evaluator's evaluation value; In response to the first evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the learner who submitted the evaluation value data.

5. The continuous learning data tracing method based on blockchain technology according to claim 4 is characterized in that: The step of receiving the learner's evaluation query request from the tracer and executing the third smart contract to verify the evaluation query request further includes: Granting the tracer preset management authority, so that the tracer can manage the weight of the evaluator according to the evaluation data through the management authority; Receive the second evaluation value data of the evaluator from the tracer, execute the fifth smart contract to confirm the second evaluation value data, and realize the correction of the evaluator's evaluation value; In response to the second evaluation value data satisfying the sixth smart contract execution condition, a reward is given to the tracer who submitted the evaluation value data.

6. A continuous learning data tracing device based on blockchain technology, characterized in that: Applied to a distributed system platform based on blockchain technology, the platform is deployed with a second smart contract, a third smart contract, and a fourth smart contract. The participants of the platform include learners, evaluators, and tracers. The device includes: A learner information receiving unit is configured to receive educational experience information input by the learner, execute a second smart contract, match the learner's educational experience information with the evaluator's, and return a matching pair of the learner's identity credential signature and the evaluator's identity credential signature; the educational experience information includes various on-campus educational experiences and their respective schools, and various off-campus quality training experiences and their respective institutions; An evaluator information receiving unit, configured to receive evaluation data input by the evaluator and store the evaluation data in the blockchain; wherein the evaluation data is the evaluation score corresponding to the learner's educational experience information; A tracer information receiving unit, configured to receive a tracer's evaluation query request regarding a learner and execute a third smart contract to verify the evaluation query request; A traceability data matching unit is used to match the corresponding evaluation data in the blockchain according to the verified evaluation query request, execute the fourth smart contract to perform distributed data verification, and send the matched evaluation data to the tracer; The evaluator information receiving unit is specifically used to: Encrypt the evaluation data using hash algorithm; The encrypted evaluation data, the identity certificate signature of the evaluator who published the evaluation data, and the identity certificate signature of the learner associated with the evaluation data are converted into block structure data in a preset format and sent to the blockchain platform; In response to the consensus mechanism's approval of the block structure data, if the block structure data is approved, the block structure data is stored in the blockchain; otherwise, a questionable evaluation data message is sent to the evaluator; In response to the successful saving of the block structure data, the sixth smart contract is executed to reward the evaluator; the block structure of the block structure data includes a block header and a block body, the block header contains basic field information, the identity credential signature of the evaluator who published the evaluation data, and the identity credential signature of the learner associated with the evaluation data, and the block body is a summary of the evaluation data record in the form of a Merkle tree; The Merkle tree first hashes the evaluation data, obtaining a corresponding hash value for each evaluation data. Then, the hash values ​​of the evaluation data are hashed in groups of two, and so on. The final hash value is the Merkle root stored in the block header. The traceability data matching unit includes: The block structure data matching subunit is used to match the corresponding block structure data in the blockchain according to the evaluation query request; The evaluator matching subunit is used to determine the evaluator's identity certificate signature corresponding to the matched block result data; The evaluator verification request sending subunit is used to send a data verification request to the corresponding evaluator based on the evaluator's identity certificate signature; The verification request verification subunit is configured to respond to the encrypted verification data digest sent by the reviewer. When the verification data digest matches the digest of the evaluation data in the block structure data, the reviewer is sent the evaluation data in the block structure data to the tracer; otherwise, the block structure data in the blockchain is re-matched.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the continuous learning data tracing method based on blockchain technology are implemented as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the continuous learning data tracing method based on blockchain technology as described in any one of claims 1 to 5 are implemented.

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