Student archive data management method and system

Abstract

The invention discloses a student archive data management method and system, and relates to the technical field of education informatization management, and the method comprises the specific steps: S100, collecting structured and unstructured data and generating a data chain, S200, splitting the data chain, S300, carrying out the simulation verification of the data chain, S400, changing the data chain, and S500, cloning the data chain. According to the student archive data management method, potential development paths of students can be accurately simulated according to development parameters input by authorized users, various possible development paths of the students can be simulated by adjusting probability distribution of related nodes in a prediction network, the deviation degree of each path and current dominant chain data is calculated, and the student archive data management efficiency is improved. Therefore, teaching contents and methods are adjusted in a targeted manner according to potential development requirements of students, data evolution paths of the students are completely recorded based on a chain structure of the timestamps, and data clone backtracking of any node is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of educational informatization management, and specifically to a method and system for managing student file data. Background Art

[0002] In the context of today's digital education, student file data, as a key information set reflecting the learning and growth process of students, the efficiency and scientific nature of its management are becoming increasingly important. With the continuous advancement of educational informatization, schools have accumulated a vast amount of student data, covering various aspects such as grades, attendance, classroom performance, interests and specialties, etc. These data are not only records of students' past study and life, but also valuable resources for providing a basis for personalized education and educational decision-making. How to effectively integrate, analyze and utilize these data has become an important issue faced by the education field.

[0003] Currently, there are obvious deficiencies in the existing student file management technology in terms of data integration. For structured data and unstructured data, they often adopt a separate storage method, making the data lack internal connections and it is difficult to form a coherent and comprehensive student growth portrait. Moreover, in terms of data prediction, traditional methods are based on simple statistical analysis, unable to deeply explore the potential development trends of students, let alone simulate various possibilities under different development parameters. For example, it is difficult to accurately construct a knowledge graph based on students' library search records and predict their academic interest trends. At the same time, in terms of data security and linkage, it is difficult for the existing technology to achieve a tight binding and efficient verification of the data chain, and the ability to respond to the risk of data tampering is weak.

[0004] In summary, the current student file data management technology has defects in data integration, predictive analysis and security linkage, and cannot meet the requirements of educational modernization for the in-depth utilization and secure management of student file data. Developing an innovative method and system for managing student file data to achieve efficient data fusion, accurate prediction and secure and reliable linkage management has important practical significance. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology, and provides a method and system for managing student file data, which can accurately simulate the potential development path of students according to the development parameters input by authorized users, simulate multiple possible development paths of the student by adjusting the probability distribution of relevant nodes in the prediction network, and calculate the deviation degree of each path from the current explicit chain data, so as to adjust teaching content and methods targeted at the potential development needs of students, and based on the timestamp-based chain structure, completely record the data evolution path of students, supporting data cloning and backtracking at any node.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for managing student file data, and the specific steps of the method are as follows: S100. Collect the structured data and unstructured data of students, generate a unique data chain with a timestamp through spatio-temporal coding, where adjacent data units are connected by hash pointers to form a traceable continuous sequence; S200. Split the data chain into a visible chain and an invisible chain, and the two chains are bound by an entangled key; S300. In response to the simulation instructions of an authorized user, input development parameters into the invisible chain, generate a predicted path through simulation, and each path is marked with the deviation degree from the data in the visible chain; S400. When the data in the visible chain changes, synchronously trigger the path recalculation of the invisible chain based on the entangled key, generate an updated set of predicted paths and update the audit log; S500. When cloning the data chain at any historical time node, synchronously verify the consistency of the digital signature of the visible chain and the hash fingerprint of the invisible chain, generate a clone with a read-only label and a quantum watermark, and write the operation record of the clone into the blockchain for evidence preservation.

[0007] Further, the structured data in S100 includes grade and attendance data, which are used to form structured data units. For grade data, it is classified and extracted according to subject, exam type, and grade value, and attendance data is classified according to date and attendance status; The unstructured data includes classroom videos and work files, which are used to form unstructured data units. For classroom videos, image recognition technology is used to identify the facial expressions and classroom interaction behaviors of students in the video. The image recognition technology extracts the behavioral feature data of each student in the classroom through frame-by-frame analysis of the video frames. For work files, when it is a text-based work file, the features of keywords and themes are extracted, and when it is an image-based work, the visual features of color, shape, and composition are extracted.

[0008] Furthermore, in S100, a timestamp is added to each classified and recognized data unit through spatio-temporal coding, the content, classification information, and timestamp of the data unit are integrated and encoded, and a unique hash value is generated according to the type and time sequence of the data; The hash values of adjacent data units are constructed into hash pointers, and the hash value of the previous data unit is used as part of the hash pointer of the next data unit to form a continuous and traceable sequence. When tracing back data, start from the latest data unit and gradually trace back to each previous data unit through the hash pointer to obtain the complete growth process of the student.

[0009] Furthermore, the S200 obtains the unique student identity information, including the student ID number and the hash value of the ID card number, and generates entangled pairs A and B through the entangled key. The entangled pair A is the explicit chain verification key, and the entangled pair B is the implicit chain verification key, where: The explicit chain key A stores the authenticated determined data in the school local server and attaches a digital signature; The implicit chain key B encapsulates the prediction data set derived from the explicit chain and merges and attaches the hash fingerprint, and an asymmetric verification channel is established between the two chains. When the explicit chain data is read, the transmission verification of the implicit chain is triggered.

[0010] Furthermore, the specific process of the transmission verification is as follows: Hash value generation: Calculate the hash values of the explicit chain key A and the implicit chain key B respectively using the hash function to obtain the hash values HA and HB; Associated storage: Store HA together with the explicit chain data in the school local server, and HB is distributed and stored in the education chain nodes along with the fragments of the implicit chain key B; Trigger condition: The user issues a viewing request and a request to modify the data of the two chains; Verification detection: Extract the explicit chain key A and its corresponding hash value HA from the school server, and at the same time request the education chain to reconstruct the implicit chain key B and its corresponding hash value HB, and recalculate the hash value of the currently extracted explicit chain key A locally, denoted as H , calculate the hash value of the reconstructed implicit chain key B, denoted as H , compare HA with H 、HB with H , when HA = H and HB = H , then it is determined that the key verification is passed. When any group of hash values does not match, that is, HA ≠ H , then it is determined that there is a risk in the explicit chain key A, trigger an alarm, and reject the request.

[0011] Furthermore, the working principle of the S300 is as follows: Input parameter parsing: The authorized user inputs the development parameters, including the extracurricular practice duration 、disciplinary strengthening direction ; Initial data extraction: Extract the basic prediction data of the current student from the implicit chain ; Simulation execution: Construct a prediction network , where the node set contains student feature variables, and the edge set represents the causal relationship between variables, and according to the input parameters and , adjust the probability distribution of nodes in the network , where represents the probability of obtaining the new predicted state and under the current parameters based on the previous state , represents the conditional probability of a node in the state of its secondary node . Through continuous iterative calculation, a series of predicted paths are generated; Deviation calculation: For each predicted path , calculate its deviation from the current data of the dominant chain and , where is the data dimension, represents the data value of the predicted path in the th dimension, represents the value of the current data of the dominant chain in the th dimension. The smaller the deviation , the closer the predicted path is to the current actual situation.

[0012] Furthermore, the working principle of the S400 is as follows: Dominant chain change monitoring: Monitor the data of the dominant chain in real time. When a change in the data in the dominant chain is detected, obtain the changed data ; Quantum entanglement key trigger: Since the dominant chain and the recessive chain are bound by an entanglement key, a change in the dominant chain data notifies the recessive chain to perform path recalculation; Recalculation parameter adjustment: According to the changed data of the dominant chain, re-evaluate the parameters and of the simulation, and adjust the probability distribution in the prediction network ; Path recalculation: Perform simulation execution again, using the new parameters and , as well as the basic prediction data in the recessive chain, to generate an updated set of predicted paths ; Update the quantum audit log: Record the current dominant chain data change situation, the recalculation process, and the information of the generated new set of predicted paths in the audit log. The log record format is , where is the data before the change of the dominant chain.

[0013] On the other hand, a student file data management system, the components of which include: a data acquisition module, a data encoding and processing module, a double-chain storage module, a simulation and verification module, and an audit and logging module; The data acquisition module obtains the structured and unstructured data of students, divides them into data units, generates a unique data chain with a timestamp, and adjacent data units are connected through hash pointers; The data encoding and processing module performs quantum key generation operations, generates entangled pairs and encodes them into verification keys for the explicit chain and the implicit chain; The double-chain storage module stores the explicit chain in the school local server and adds a digital signature, and distributes and stores the implicit chain in the education chain nodes and adds a hash fingerprint; The simulation and verification module responds to the development parameters input by the authorized user, processes the implicit chain data using the prediction network, generates a prediction path and calculates the deviation from the current data of the explicit chain, and performs double-chain binding verification when the user requests to view and modify the double-chain data; The audit and logging module monitors the changes of the explicit chain data in real time. When a change is detected, it triggers the recalculation of the implicit chain path, records the data change situation, the recalculation process and the new prediction path set into the quantum audit log, and when cloning the data chain, verifies the consistency of the explicit chain digital signature and the implicit chain hash fingerprint, and writes the clone operation record into the blockchain for evidence storage, for the whole process audit and traceability of data operations.

[0014] Compared with the prior art, the student file data management method and system have the following beneficial effects: First, the student file data management method of the present invention can accurately simulate the potential development path of students according to the development parameters input by the authorized user. By adjusting the probability distribution of relevant nodes in the prediction network, multiple possible development paths of the student can be simulated, and the deviation of each path from the current explicit chain data can be calculated, so as to adjust the teaching content and methods targeted at the potential development needs of students, and based on the timestamp-based chain structure, the data evolution path of students is completely recorded, supporting data cloning and backtracking at any node.

[0015] Second, the present invention utilizes entangled keys to bind the explicit chain and the implicit chain, achieving efficient and secure data protection. When the data on the explicit chain changes, based on entanglement, it quickly triggers the recalculation of the implicit chain path. When the student data is updated, it can promptly notify the implicit chain, re-evaluate the simulation parameters, generate an updated set of predicted paths, and record them in the quantum audit log. This mechanism ensures that the data maintains the accuracy of file prediction and the consistency of the data chain under any change circumstances. Meanwhile, the full-process quantum audit log and blockchain evidence preservation further enhance the security and traceability of the data, providing an all-round and multi-level security protection system for the management of file data and ensuring the reliability and integrity of student file data.

[0016] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the examination and research of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is an operation flowchart of a method for managing student file data; Figure 2 It is a schematic diagram of the system composition of a system for managing student file data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and their effects according to the present invention as follows.

[0020] Embodiment 1 This embodiment provides the specific process of applying a method for managing student file data to a system for managing student file data. By elaborating in detail on the entire process from data collection, coding processing, double-chain storage, simulation prediction to audit traceability, it demonstrates the efficiency of this invention in file management. It utilizes space-time coding, quantum entanglement key binding, and prediction network simulation to achieve in-depth integration, accurate prediction, and secure management of student file data, providing strong support for personalized education and educational decision-making. As Figure 2As shown, the components of the system include: a data acquisition module, a data encoding and processing module, a double-chain storage module, a simulation and verification module, and an audit and log module. The data acquisition module is responsible for obtaining structured data (grades, attendance) and unstructured data (classroom videos, work files) of students from multiple sources. Through interfaces deployed in the school's educational administration system, classroom monitoring devices, and file upload platforms, it collects various types of data in real time and divides them into data units according to specific rules to prepare for subsequent processing. The data encoding and processing module uses spatio-temporal encoding to generate a unique data chain with timestamps for the collected data units, and adjacent data units are connected by hash pointers; it performs quantum key generation operations to generate a pair of entangled pairs and encodes them as verification keys for the explicit chain and the implicit chain; it preprocesses the implicit chain data to extract basic prediction data for subsequent simulation analysis. The double-chain storage module stores the explicit chain in the school's local server, which contains authenticated determined data and digital signatures; it divides the implicit chain into multiple segments and stores them distributedly in the educational chain nodes, and at the same time stores the hash fingerprints of the implicit chain and related key segments to ensure the security and dispersion of data storage. The simulation and verification module responds to the development parameters (extra-curricular practice duration, subject strengthening direction) input by authorized users, uses simulation based on the prediction network to process the implicit chain data, generates a prediction path and calculates the deviation from the current data of the explicit chain; when the user requests to view or modify the double-chain data, it performs double-chain binding verification and determines the relevance and security of the data chain by comparing the hash values. The audit and log module monitors the changes in the explicit chain data in real time. When a change is detected, it triggers the recalculation of the implicit chain path and records the data change situation, the recalculation process, and the new prediction path set in the quantum audit log; when cloning the data chain, it verifies the consistency of the digital signature of the explicit chain and the hash fingerprint of the implicit chain, and writes the clone operation record into the blockchain for evidence, realizing the full-process audit and traceability of data operations.

[0021] In a specific implementation, first, data collection is carried out, covering a variety of data sources. For structured data, the achievement data comes from the school's educational administration system, which details the scores of each student's exam, including the subject, exam type (such as monthly exam, mid-term exam, final exam, etc.). The achievement data is classified and extracted according to the subject, exam type, and score value to form structured data units. For example, "Mathematics - Final Exam - 85 points" constitutes an achievement data unit. The attendance data is obtained from the school's access control punching system and classroom attendance records and is classified according to the date and attendance status (such as present, absent, late, etc.) as an attendance data unit. For unstructured data, classroom videos are recorded in real-time by surveillance cameras in the classroom. Image recognition technology is used to analyze each frame of the video to extract features such as students' facial expressions and classroom interaction behaviors. The image recognition technology is trained with a large amount of labeled classroom video data to enable it to accurately identify behavior features such as raising hands to speak, group discussions, and changes in concentration. For work files, if they are text-based works such as compositions and papers, keywords, themes, etc. are extracted; if they are image-based works such as paintings and photographic works, visual features such as color, shape, and composition are extracted. The data collected and preprocessed enters the data encoding link. Space-time encoding is used to add an accurate timestamp to each classified and identified data unit. The timestamp is accurate to the second, recording the exact moment when the data is generated. The content, classification information, and timestamp of the data unit are integrated and encoded. According to the type and time sequence of the data, a hash value is assigned to each data unit after space-time encoding, and the hash values of adjacent data units are constructed into hash pointers. The hash value of the previous data unit is used as part of the hash pointer of the next data unit. For example, if the hash value of data unit A is Hash(A) and the hash value of data unit B is Hash(B), then the hash pointer of data unit B contains part of the information of Hash(A), forming a continuous and traceable sequence. In this way, when data needs to be traced back, starting from the latest data unit, each previous data unit can be gradually traced through the hash pointer to obtain the complete growth process of the student.

[0022] Obtain the unique identity information of students, such as student ID numbers and the hash values of ID cards. Generate entangled pairs A and B through an entanglement key generator. Entangled pair A is used as the explicit chain verification key and stored in the school's local server. It is stored together with the authenticated and determined data (such as verified data like grades and attendance) and appended with a digital signature. The private key is used to sign the data, and the public key is used for verification during authentication to ensure the integrity and authenticity of the data. Entangled pair B is used as the implicit chain verification key, encapsulating the predicted data set derived from the explicit chain and appended with a hash fingerprint. The implicit chain data includes prediction data based on students' learning behaviors, interests, hobbies, etc., and predictions of potential development directions. The hash fingerprint is used to verify the integrity of the implicit chain data. An asymmetric verification channel is established between the two chains. When the explicit chain data is read, it triggers the transmission verification of the implicit chain. The transmission verification process is as follows: Use a hash function to calculate the hash values of the explicit chain key A and the implicit chain key B respectively, obtaining hash values HA and HB. HA is stored together with the explicit chain data in the school's local server, while HB is distributed and stored in the education chain nodes along with the fragments of the implicit chain key B. When a user issues a request to view and modify the data of the two chains, extract the explicit chain key A and its corresponding hash value HA from the school server, and at the same time request the education chain to reconstruct the implicit chain key B and its corresponding hash value HB. Recalculate the hash value of the currently extracted explicit chain key A locally, denoted as , calculate the hash value of the reconstructed implicit chain key B, denoted as , compare HA with , and HB with . When HA and HB , it is determined that the key verification passes; when any set of hash values does not match, that is, HA , it is determined that there is a risk with the explicit chain key A, trigger an alarm, and reject the request. For example, as an authorized user, a teacher hopes to understand the potential development path of a student after increasing the extracurricular mathematics tutoring time and strengthening the physics subject direction. The teacher enters the development parameters in the system, extracurricular practice time , subject strengthening direction . The system extracts the basic prediction data of the current student from the implicit chain , which includes information such as the student's past learning behavior patterns and knowledge mastery levels . Construct a prediction network . The node set includes student characteristic variables, such as academic performance, interests, hobbies, study time, etc. The edge set represents the causal relationship between variables. According to the input parameters and , adjust the probability distribution of the nodes in the network, specifically calculated through the formula , where Indicates under the current parameters and to obtain the probability of the new predicted state based on the previous state . Indicates the conditional probability of node in the state of its secondary node . By continuously iterative calculation, a series of predicted paths are generated. For each predicted path , calculate its deviation from the current data of the dominant chain. The formula is , where is the data dimension, represents the data value of the predicted path in the th dimension, represents the value of the current data of the dominant chain in the th dimension. For example, in the dimension of academic performance, the predicted value of the math score in the predicted path is , and the current math score in the dominant chain is . Taking the square root of the sum of the squared differences of all dimensions, the deviation is obtained. The smaller the deviation, the closer the predicted path is to the current actual situation.

[0023] In the daily teaching process, the data of the dominant chain such as the students' scores will change. Monitor the data of the dominant chain in real time. When it is detected that the data in the dominant chain has changed, obtain the changed data . Since the dominant chain and the recessive chain are bound by the entanglement key, a change in the data of the dominant chain notifies the recessive chain to recalculate the path. According to the changed data of the dominant chain, re-evaluate the simulated parameters and , adjust the probability distribution in the prediction network . The formula is . Perform the simulation execution again, using the new parameters and , as well as the basic prediction data in the recessive chain, to generate an updated set of predicted paths . Record the situation of the data change of the dominant chain this time, the recalculation process, and the information of the generated new set of predicted paths in the audit log. The log record format is , where is the data before the change of the dominant chain. In this way, the audit log completely records the data change and the update of the predicted path, which is convenient for subsequent query and analysis.

[0024] When the school needs to clone the data chain of students at specific historical time nodes for scenarios such as further education recommendation and evaluation for excellence, it starts the data chain cloning process, synchronously verifies the consistency of the digital signature of the explicit chain and the hash fingerprint of the implicit chain, obtains the explicit chain data and its digital signature from the school local server, and uses the corresponding public key for verification to ensure that the explicit chain data has not been tampered with. At the same time, it obtains the implicit chain data and its hash fingerprint from the education chain node, recalculates the hash value of the implicit chain data and compares it with the stored hash fingerprint. If the verification passes, the system generates a clone with read-only tags and quantum watermarks. The read-only tags ensure that the clone data cannot be modified casually, and the quantum watermarks mark the source and time information of the clone, enhancing the traceability of the data. The operation records of the clone are written into the blockchain for evidence storage. The distributed ledger feature of the blockchain ensures the immutability and queryability of the operation records, realizing the full-process audit and traceability of data operations. In summary, this embodiment details the complete operation process of the student file data management method, from the fine processing of structured and unstructured data in data collection to the formation of a traceable data chain through data encoding; from the double-chain splitting and key binding to ensure data security to the simulation and prediction to provide a basis for educational decision-making; and then to the explicit chain change processing and data chain cloning verification. The whole process forms an efficient, secure, and accurate closed-loop for student file management. Through the application of space-time encoding, quantum entanglement key binding, and prediction networks, it effectively solves the problems of difficult data integration, insufficient predictive analysis, and poor security linkage in traditional student file management, providing an innovative solution for information-based file management in the education field.

[0025] Embodiment 2 As Figure 1 shown, the specific process of a student file data management method for file data management provided in this embodiment is as follows: (1)Data collection and preprocessing Structured data collection: Obtain students' grades from the school's educational administration system, covering grade information of different subjects and different types of exams; collect students' attendance data from the school's attendance system, including attendance, absence, lateness, early leave, etc. Unstructured data collection: Use classroom monitoring equipment to record students' classroom performance videos, recording students' interactions and participation in class; collect students' work files, such as compositions, paintings, research reports, etc., which reflect students' learning achievements and abilities. Data preprocessing: Unify the formats and clean the structured and unstructured data, remove duplicate and incorrect data, and ensure the accuracy and consistency of the data. (2)Data encoding and data chain generation Spatiotemporal coding: Add a timestamp to each collected data unit to mark the specific time when the data was generated, and convert the data unit into a unified coding format to make the data uniquely identified; Data chain construction: Hash pointers are used to connect adjacent data units to form a continuous and traceable data chain; (3) Double chain splitting and key binding Dual chain splitting: Split the generated data chain into an explicit chain and a hidden chain. The explicit chain stores authenticated data, while the hidden chain encapsulates predicted data derived from the explicit chain. Key generation: Based on the student's unique identity information, an entangled pair is generated and its partial encoding is respectively used as the explicit chain verification key and the implicit chain verification key; Key storage: The explicit chain key is stored in the school's local server and managed together with the explicit chain data. The implicit chain key is divided into multiple fragments and distributedly stored in the education chain nodes to improve data security and reliability. (4) Double-chain viewing and modification Double-chain review: When the association between the dominant chain and the recessive chain after the split needs to be verified, the user initiates a request to modify the double-chain data; Hash value calculation: Use the hash function to calculate the hash values of the explicit chain key A and the implicit chain key B respectively to obtain the hash values HA and HB; Verification test: Extract the explicit chain key A and its corresponding hash value HA from the school server, and request the education chain to reconstruct the implicit chain key B and its corresponding hash value HB, and recalculate the hash value of the currently extracted explicit chain key A locally, recorded as H , calculate the hash value of the reorganized implicit chain key B, denoted as H , compared with HA and H , HB and H , when HA=H And HB=H When the key verification is passed, the user is allowed to view and modify the key and is marked as an authorized user. When any set of hash values does not match, that is, HA≠H When , it is determined that the explicit chain key A is at risk, an alarm is triggered, and the request is rejected; (5) Simulation prediction and deviation calculation Parameter input: Authorized users input development parameters to simulate students’ future development; Simulation prediction: Based on the input parameters and the predicted data in the implicit chain, the student's development is simulated. By analyzing the student's historical data and potential trends, different prediction paths are generated to show the student's development direction in different situations; Deviation calculation: For each predicted path, calculate its deviation from the current data of the dominant chain. The deviation reflects the degree of difference between the predicted result and the actual situation. (6) Data change processing and audit log update Change monitoring: Real-time monitoring of explicit chain data changes. When data changes are found, the content and time of the changes are recorded. Implicit chain update: Since the explicit chain and the implicit chain are bound by an entangled key, changes in the explicit chain data will trigger the recalculation of the implicit chain path. Based on the new explicit chain data, the student's potential development trend is re-evaluated and the predicted data in the implicit chain is updated; Audit log records: Record changes to explicit chain data, implicit chain update processes, and generated new prediction paths in audit logs. Audit logs are used for subsequent query, analysis, and tracing to ensure transparency in data management. (7) Data chain cloning and verification Clone request: When a data chain of a historical time node needs to be cloned, the user initiates a clone request and specifies the time range for the clone; Verification operation: Synchronously verify the consistency of the explicit chain digital signature and the implicit chain hash fingerprint. The digital signature is used to ensure the integrity and authenticity of the explicit chain data, and the hash fingerprint is used to verify the accuracy of the implicit chain data. Clone generation: If the verification passes, a clone with a read-only tag and a quantum watermark is generated. The read-only tag prevents the clone data from being modified at will, and the quantum watermark is used to mark the source and identity information of the clone.

[0026] Operation record storage: The operation records of the clone are written into the blockchain for storage, and the tamper-proof nature of the blockchain is used to ensure the security and reliability of the operation records.

[0027] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for managing student file data, characterized in that, The specific steps of this method are as follows: S100. Collect the structured data and unstructured data of students, generate a unique data chain with timestamps through spatio-temporal coding, where adjacent data units are connected by hash pointers to form a traceable continuous sequence; S200. Split the data chain into an explicit chain and an implicit chain, and the two chains are bound by an entangled key; S300. In response to the simulation instructions of an authorized user, input development parameters into the implicit chain, generate a predicted path through simulation, and mark the deviation degree of each path from the data in the explicit chain; S400. When the data in the explicit chain changes, trigger the recalculation of the path of the implicit chain synchronously based on the entangled key, generate an updated set of predicted paths and update the audit log; S500. When cloning the data chain at any historical time node, synchronously verify the consistency of the digital signature of the explicit chain and the hash fingerprint of the implicit chain, generate a clone with a read-only label and a quantum watermark, and write the operation record of the clone into the blockchain for evidence storage.

2. The method for managing student file data according to claim 1, wherein, The structured data in S100 includes grades and attendance data, which are used to form structured data units. For grade data, it is classified and extracted according to subject, exam type, and grade value, and attendance data is classified according to date and attendance status; The unstructured data includes classroom videos and work files, which are used to form unstructured data units. For classroom videos, image recognition technology is used to identify the facial expressions and classroom interaction behaviors of students in the video. The image recognition technology extracts the behavioral feature data of each student in the classroom through frame-by-frame analysis of the video frames. For work files, when it is a text-based work file, keywords and theme features are extracted, and when it is an image-based work, visual features such as color, shape, and composition are extracted.

3. A method for managing student file data according to claim 1, characterized in that, In S100, each classified and recognized data unit is added a timestamp through spatio-temporal coding, and the content, classification information, and timestamp of the data unit are integrated and encoded to generate a unique hash value according to the type and time sequence of the data; The hash values of adjacent data units are constructed into hash pointers. The hash value of the previous data unit is used as part of the hash pointer of the next data unit to form a continuous and traceable sequence. When tracing back data, start from the latest data unit and gradually trace back to each previous data unit through the hash pointer to obtain the complete growth process of the student.

4. A method for managing student file data according to claim 1, characterized in that, In S200, obtain the unique identity information of the student, including the student ID number and the hash value of the ID card number, generate entangled pairs A and B through the entangled key. Entangled pair A is the verification key for the explicit chain, and entangled pair B is the verification key for the implicit chain, where: The explicit chain key A stores the authenticated determined data in the school local server and attaches a digital signature; The implicit chain key B encapsulates the predicted data set derived from the explicit chain and attaches a hash fingerprint, and an asymmetric verification channel is established between the two chains. When the data in the explicit chain is read, the transmission verification of the implicit chain is triggered.

5. A method for managing student file data according to claim 4, characterized in that, The specific process of the transmission verification is as follows: Hash value generation: Use a hash function to calculate the hash values of the explicit chain key A and the implicit chain key B respectively to obtain hash values HA and HB; Associated storage: Store HA and explicit chain data together on the school's local server, while HB is distributed and stored on educational chain nodes along with fragments of the implicit chain key B; Trigger condition: The user issues a viewing request and a request to modify the double-chain data; Verification Detection: Extract the explicit chain key A and its corresponding hash value HA from the school server, and at the same time request the educational chain to recombine the implicit chain key B and its corresponding hash value HB. Recalculate the hash value of the currently extracted explicit chain key A locally, denoted as H , calculate the hash value of the recombined implicit chain key B, denoted as H , compare HA with H , HB with H , when HA = H and HB = H , then it is determined that the key verification is passed. When any set of hash values does not match, that is, HA ≠ H , then it is determined that there is a risk with the explicit chain key A, trigger an alarm, and reject the request.

6. The method for managing student file data according to claim 1, characterized in that, The working principle of S300 is as follows: Input parameter parsing: The authorized user inputs development parameters, including the duration of extracurricular practice , the direction of subject strengthening ; Initial data extraction: Extract the basic prediction data of the current student from the implicit chain ; Simulated execution: Construct a prediction network , where the node set contains student feature variables, and the edge set represents the causal relationship between variables. According to the input parameters and , adjust the probability distribution of the nodes in the network , where represents the probability of obtaining the new prediction state and based on the previous state under the current parameters . represents the conditional probability of the node in the state of its secondary node . Through continuous iterative calculation, a series of prediction paths are generated; Deviation calculation: For each predicted path , calculate its deviation from the current data of the dominant chain . And , where is the data dimension, represents the data value of the predicted path in the th dimension, represents the value of the current data of the dominant chain in the th dimension.

7. A method for managing student file data according to claim 6, characterized in that, The working principle of S400 is as follows: Dominant chain change monitoring: Real-time monitoring of dominant chain data. When a change in the data in the dominant chain is detected, obtain the changed data ; Entangled key trigger: Since the explicit chain and the implicit chain are bound by an entangled key, a change in the explicit chain data notifies the implicit chain to perform a path recalculation; Recalculation Parameter Adjustment: Based on the data changed by the dominant chain , re-evaluate the parameters of the simulation and , adjust the probability distribution in the prediction network ; ; Path recalculation: Perform simulation execution again, using new parameters and , as well as the basic prediction data in the implicit chain , to generate an updated set of predicted paths ; Update the quantum audit log: Record the current explicit chain data change situation, the recalculation process, and the information of the generated new prediction path set into the audit log, and the log record format is , where is the data before the explicit chain change.

8. A student file data management system, applicable to the student file data management method according to any one of claims 1-7, characterized in that, The components of this system include: a data acquisition module, a data encoding and processing module, a double-chain storage module, a simulation and verification module, and an audit and log module; The data acquisition module obtains the structured and unstructured data of students, divides them into data units, generates a unique data chain with a timestamp, and adjacent data units are connected by hash pointers; The data encoding and processing module performs quantum key generation operations, generates entangled pairs and encodes them into verification keys for the explicit chain and the implicit chain; The double-chain storage module stores the explicit chain on the school's local server and adds a digital signature, and distributes and stores the implicit chain on educational chain nodes and adds a hash fingerprint; The simulation and verification module responds to the development parameters input by an authorized user, processes the implicit chain data using a prediction network, generates a predicted path and calculates the deviation from the current data of the explicit chain, and performs double-chain binding verification when the user requests to view and modify the double-chain data; The audit and log module monitors the changes in the explicit chain data in real time. When a change is detected, it triggers a recalculation of the implicit chain path, records the data change situation, the recalculation process, and the new predicted path set in the quantum audit log, and verifies the consistency of the explicit chain digital signature and the implicit chain hash fingerprint during data chain cloning, and writes the clone operation record into the blockchain for whole-process audit and traceability of data operations.

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