Job analysis and correction method and system based on user upload, terminal and medium

Abstract

The application relates to a homework analysis and correction method and system based on user uploading, a terminal and a medium, and relates to the technical field of intelligent education. The method comprises the following steps: obtaining identity verification information of a user terminal; after the identity verification information passes identity verification, obtaining a user authorization token in response to an analysis instruction; obtaining homework data uploaded by the user terminal within a token validity period corresponding to the user authorization token, wherein the homework data is transmitted through an encrypted transmission protocol; obtaining an analysis algorithm according to the type of the homework data; obtaining a custom correction rule corresponding to the user terminal; correcting the homework data in combination with the analysis algorithm and the custom correction rule to obtain a correction result; and sending the correction result to the user terminal. The application has the effect of protecting the data privacy of users.

Description

Technical Field

[0001] This application relates to the field of intelligent education technology, and in particular to a method, system, intelligent terminal, and storage medium for analyzing and grading homework based on user uploads. Background Technology

[0002] With the continuous development of educational informatization, homework grading systems are gradually being widely used. Teachers can access some teaching materials online, and students can submit their homework online.

[0003] The homework grading system based on this technology consists of a homework management module, an answer preset module, a grading module, and a result feedback module. In the homework management module, teachers select homework from the system's built-in resource library to assign to students, or upload a small number of assignments according to the system's specific format. After completing the homework, students submit it online. The system grades it based on preset answers through a simple text comparison. If the answer matches the preset exactly, it is judged as correct; otherwise, it is judged as incorrect. Finally, the grading result is fed back to the student.

[0004] The aforementioned technologies have poor semantic recognition capabilities for assignments, which affects the accuracy of assignment grading. Summary of the Invention

[0005] To improve the accuracy of homework grading, this application provides a method, system, smart terminal, and storage medium for analyzing and grading homework based on user uploads.

[0006] Firstly, this application provides a method for analyzing and grading user-uploaded assignments, employing the following technical solution: A method for analyzing and grading assignments based on user uploads, comprising: Obtain the user terminal's authentication information; After the authentication information is successfully authenticated, a user authorization token is obtained in response to the acquisition analysis command; Within the validity period of the user authorization token, the job data uploaded by the user terminal is obtained, and the job data is transmitted through an encrypted transmission protocol; Based on the type of the work data, an analysis algorithm is derived; Obtain the custom batch modification rules corresponding to the user terminal; The analysis algorithm and the custom grading rules are combined to grade the job data and obtain the grading results. The correction results are sent to the user terminal.

[0007] By adopting the above technical solutions, when grading job data, semantic recognition and image recognition are combined with custom grading rules to make the grading results more accurate. Furthermore, a dual verification mechanism using identity verification information and user authorization tokens ensures that only legitimate users can submit job data within the authorized validity period, effectively preventing unauthorized access and identity theft. Job data is transmitted using an encrypted transmission protocol to prevent theft or tampering during transmission, significantly improving data confidentiality and integrity. Combining analysis algorithms with user-defined grading rules ensures the specificity of the grading process while avoiding privacy risks caused by the generality of algorithms. The overall process strengthens user privacy protection at multiple stages.

[0008] Optionally, the quantity information corresponding to the job data can be obtained from the analysis instructions; Determine whether the number of data packets in the job data is consistent with the quantity information; If not, extract each data packet from the job data; Perform a signature verification operation on the data packet to obtain a set of verified data packets; Obtain the set of job rosters corresponding to the job data; By comparing the verification pass set with the job roster set, the missing job data is obtained; Based on the missing job data, a secondary data transmission request is sent to the user terminal.

[0009] By employing the above technical solution, and comparing the number of data packets with the verification set, it is possible to identify job data that may be lost during transmission and request the user to retransmit it, thus avoiding the leakage of privacy information or misjudgment during the grading process due to incomplete data. The signature verification mechanism ensures the credibility and integrity of the source of each job data, preventing forged or tampered data from entering the grading system, thereby establishing a privacy protection barrier at the data receiving stage and enhancing the security and reliability of the entire grading process.

[0010] Optionally, the sending time of the secondary data transmission request can be obtained; Based on the sending time and the user authorization token, the validity of the transmission time is verified, and the verification result is obtained; If the verification result is valid, the missing job data is processed, and a second data transmission request is sent to the user terminal. If the verification result is invalid, a temporary authorization request is sent to the user terminal according to the transmission time.

[0011] By adopting the above technical solution, and verifying the sending time of missing job data and the validity of the authorization token, it is ensured that data request operations are within a safe time frame, preventing the unauthorized transmission of private data due to timeouts or token expiration. Predicting the transmission backup time and sending a temporary authorization request allows for data retransmission without extending the fixed token validity period. This ensures process flexibility while avoiding privacy risks caused by excessive permissions, further enhancing the granularity and security of data control.

[0012] Optionally, the terminal validity period and server validity period for obtaining the user authorization token; If the difference between the effective duration of the terminal and the effective duration of the server is greater than a preset duration threshold, a clock calibration request is sent to the user terminal. In response to receiving an acceptance signal for the clock calibration request, the clock is synchronized with that of the user terminal to obtain a synchronized clock; The transmission time is updated based on the synchronization clock.

[0013] By adopting the above technical solution, the time difference between the user terminal and the system is calibrated through a clock synchronization mechanism, avoiding errors in authorization token judgment or invalid data reception time due to time asynchrony, and preventing data access during unauthorized time slots. The updated transmission time can more accurately reflect the actual data transmission status, ensuring the effective execution of subsequent verification logic, strengthening privacy protection during data transmission, and reducing security risks caused by system time deviations.

[0014] Optionally, the communication records of the user terminal can be obtained; Extract the average communication duration of the user terminal from the communication records; Obtain the communication connection delay with the user terminal; The data transmission delay duration is generated based on the communication connection delay. Obtain the transmission duration of the missing job data; The transmission reserve time is obtained by summing the average communication duration, the data transmission delay duration, and the transmission duration.

[0015] By adopting the above technical solution, a more realistic corrected transmission backup time is calculated by comprehensively considering average communication duration, connection status, and transmission latency. This backup time is then added to the temporary authorization request, enabling users to complete data retransmission within a reasonable timeframe and preventing abnormal access due to network conditions. This mechanism ensures a good user experience while strictly controlling the timeliness of temporary authorizations, preventing permission retention or abuse, and enhancing privacy control during data transmission.

[0016] Optionally, the analysis algorithm is used to revise the job data to obtain initial revision results; Convert the custom batching rules into code logic; The initial correction result is processed according to the code logic to obtain the correction result.

[0017] By adopting the above technical solution, and converting user-defined grading rules into code logic and processing the initial grading results, the accuracy of grading is ensured while avoiding user privacy leaks due to human intervention or rule disclosure. This automated processing mechanism respects user grading preferences while eliminating the risk of information exposure caused by human access to data, achieving a balance between efficiency and privacy security in the grading process.

[0018] Optionally, if the type of the assignment data is text data, the analysis algorithm is used to identify the assignment data to obtain the answer content; the matching degree between the answer content and the standard answer is calculated to obtain the initial grading result; If the type of the assignment data is mathematical data, the assignment data is converted into computable symbols; the analysis algorithm is called to perform logical verification on the computable symbols to obtain the initial grading result; If the type of the task data is image data, feature data is obtained from the image data; the analysis algorithm is called to match and analyze the feature data to obtain the initial correction result.

[0019] By adopting the above technical solutions, corresponding analysis algorithms are used to process different types of assignment data, such as text, mathematical data, and images. All recognition, conversion, and matching operations are completed internally, preventing the original data from being leaked or obtained by third-party tools. By processing sensitive content through structured and symbolic methods, the possibility of directly exposing user privacy information is reduced, improving grading efficiency while strengthening the confidentiality and security of data use.

[0020] Secondly, this application provides a user-uploaded assignment analysis and grading system, which adopts the following technical solution: A user-uploaded assignment analysis and grading system includes: The acquisition module is used to acquire authentication information, analysis instructions, and custom batch modification rules. A memory for storing the program of the user-uploaded job analysis and grading method; The processor and memory can load and execute the program to implement the user-uploaded job analysis and grading method.

[0021] By adopting the above technical solution, a dual verification mechanism using identity verification information and user authorization tokens ensures that only legitimate users can submit job data within the authorization period, effectively preventing unauthorized access and identity theft. Job data is transmitted using an encrypted transmission protocol to prevent theft or tampering during transmission, significantly improving data confidentiality and integrity. Combining analysis algorithms with user-defined grading rules ensures the grading process is targeted while avoiding privacy risks caused by the generality of algorithms. The overall process strengthens user privacy protection at multiple stages.

[0022] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described in any one of the above.

[0023] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates the protection of user data privacy, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described user-uploaded job analysis and grading methods.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. A dual verification mechanism using identity verification information and user authorization tokens ensures that only legitimate users can submit job data within the authorized validity period, effectively preventing unauthorized access and identity theft. Job data is transmitted using an encrypted transmission protocol to prevent theft or tampering during transmission, significantly improving data confidentiality and integrity. Combining analysis algorithms with user-defined grading rules ensures the grading process is targeted while avoiding privacy risks caused by the generality of algorithms. The entire process strengthens user privacy protection at multiple stages. 2. By comparing the number of data packets with the verification set, potential lost job data during transmission can be identified, and the user can be requested to retransmit the data. This prevents privacy leaks or misjudgments during the grading process due to incomplete data. The signature verification mechanism ensures the credibility and integrity of the source of each job data, preventing forged or tampered data from entering the grading system. This establishes a privacy protection barrier at the data reception stage, enhancing the security and reliability of the entire grading process. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a user-uploaded job analysis and grading method provided in an embodiment of this application.

[0026] Figure 2This is a flowchart illustrating a method for verifying job data provided in an embodiment of this application.

[0027] Figure 3 This is a flowchart illustrating a method for retransmitting missing job data provided in an embodiment of this application.

[0028] Figure 4 This is a flowchart illustrating a clock update method provided in an embodiment of this application.

[0029] Figure 5 This is a flowchart illustrating a method for calculating transmission time provided in an embodiment of this application.

[0030] Figure 6 This is a flowchart illustrating a method for generating correction results provided in an embodiment of this application.

[0031] Figure 7 This is a schematic diagram of a user-uploaded job analysis and grading system provided in an embodiment of this application. Detailed Implementation

[0032] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0033] This application discloses a method for analyzing and grading user-uploaded assignments. This method can be executed by a server; please refer to [reference needed]. Figure 1 The method includes: Step S101: Obtain the user terminal's authentication information.

[0034] Authentication information is used to verify the identity of the user terminal. Authentication information includes at least one of the following: user terminal account, password, name, ID, and serial number.

[0035] Optionally, after obtaining the user terminal's authentication information, the authentication information is compared with existing identity information. If the existing identity information contains matching authentication information, the authentication information is considered to have passed verification; otherwise, the existing identity information does not contain matching authentication information, the authentication information is considered to have failed verification.

[0036] Optionally, in response to a trigger operation on the user terminal, the user terminal displays a login interface, which includes an account input field and a password input field. In response to the login operation on the user terminal, the system retrieves the entered account from the account input field and the entered password from the password input field, and combines the entered account and password into authentication information. The user terminal then sends the authentication information to the server.

[0037] Step S102: After the authentication information is successfully authenticated, the user authorization token is obtained in response to the acquisition analysis instruction.

[0038] Analysis commands are used to authorize the server to send job data using user terminals.

[0039] A user authorization token is an encrypted string representing access permissions. It indicates that the server can use job data provided by the user's terminal.

[0040] For example, the user terminal sends an analysis command to the server. The server generates a user authorization token based on the analysis command.

[0041] The user authorization token has a time limit and is only valid within its validity period. Furthermore, the token validity period can be a preset fixed value or set based on the amount of data transmitted by the user terminal. For example, the token validity period could be a fixed 15 minutes. Alternatively, the token validity period could be positively correlated with the amount of job data transmitted by the user terminal; if the user terminal is transmitting job data for 50 jobs, the token validity period would be 5 minutes; if the user terminal is transmitting job data for 80 jobs, the token validity period would be 8 minutes.

[0042] Step S103: Within the validity period of the user authorization token, obtain the job data uploaded by the user terminal. The job data is transmitted via an encrypted transmission protocol.

[0043] Assignment data is related to the assignment type. For example, for text-based and math / physics assignments, assignment data includes character data; for image-based assignments, assignment data includes image data.

[0044] Optionally, the encrypted transport protocol can be SSL (Secure Sockets Layer) or TLS (Transport Layer Security).

[0045] Step S104: Obtain the analysis algorithm based on the type of job data.

[0046] There is a one-to-one correspondence between the analysis algorithms and the types of job data. The server pre-stores an algorithm mapping table, which records the mapping relationship between the types of job data and the analysis algorithms. The server can retrieve the analysis algorithm corresponding to the type of job data through the algorithm mapping table.

[0047] Step S105: Obtain the custom batch modification rules corresponding to the user terminal.

[0048] Customized grading rules are personalized grading rules created by the user. These rules can be uploaded to the server in real time by the user's terminal, or they can be pre-stored on the server.

[0049] For example, for the same assignment, user terminal A's custom grading rule setting for writing step 1 can earn 4 points, while user terminal B's custom grading rule setting for writing step 1 can earn 6 points.

[0050] Optionally, custom grading rules can also set the content of the grading results. For example, custom grading rules can add error annotations, score details, and knowledge point association suggestions to the grading results.

[0051] Step S106: Combine the analysis algorithm and the custom grading rules to grade the job data and obtain the grading results.

[0052] Optionally, the process of grading the job data includes at least semantic recognition processing and image recognition processing. Semantic recognition processing can extract the semantic meaning of the text portion of the job data, while image recognition processing can extract the content of the image portion of the job data.

[0053] In this application, job data exists in encrypted form in the server's memory and is automatically deleted after the grading results are obtained, without being written to persistent storage media. This ensures user privacy and security.

[0054] The grading results should at least include the assignment score and the location of the incorrect answers. Grading results may also include annotations of incorrect answers, detailed score breakdowns, and suggestions for linking related knowledge points.

[0055] Step S107: Send the correction results to the user terminal.

[0056] The user terminal can display the correction results in text or chart format.

[0057] By adopting the above technical solutions, when grading job data, semantic recognition and image recognition are combined with custom grading rules to make the grading results more accurate. Furthermore, a dual verification mechanism using identity verification information and user authorization tokens ensures that only legitimate users can submit job data within the authorized validity period, effectively preventing unauthorized access and identity theft. Job data is transmitted using an encrypted transmission protocol to prevent theft or tampering during transmission, significantly improving data confidentiality and integrity. Combining analysis algorithms with user-defined grading rules ensures the specificity of the grading process while avoiding privacy risks caused by the generality of algorithms. The overall process strengthens user privacy protection at multiple stages.

[0058] In the following embodiments, after the server obtains the job data, the job data can be verified to ensure that the job data is received completely. Therefore, this application discloses a method for verifying job data. (Refer to...) Figure 2 The method includes: Step S201: Obtain the quantity information corresponding to the job data from the analysis instructions.

[0059] The quantity information indicates the number of work assignments.

[0060] For example, the user terminal displays an interactive control for uploading job information. In response to input on the interactive control, quantity information is obtained. This uploaded job information comprises relevant information about the job data that the user terminal needs to upload, including quantity information and job type.

[0061] Step S202: Determine whether the number of data packets in the job data matches the quantity information.

[0062] In this application, when a user terminal sends assignment data, it packages the data for different assignments into different data packets. For example, student A's assignment is packaged into data packet 1, and student B's assignment is packaged into another data packet 2. Therefore, the number of data packets can represent the number of assignments actually received by the server.

[0063] Furthermore, the user terminal will add an additional packet identifier to the data packet, which can uniquely identify the data packet.

[0064] Step S203: If not, extract each data packet from the job data.

[0065] If the number of data packets in the job data is inconsistent with the quantity information, it indicates that the job data received by the server is missing, and it is necessary to first identify each data packet in the job data.

[0066] In some other implementations, if the number of data packets in the interrupted job data matches the quantity information, then the analysis algorithm steps continue to be derived based on the type of the job data.

[0067] Step S204: Perform a signature verification operation on the data packet to obtain the set of data packets that have passed verification.

[0068] In this embodiment, the signature verification operation is used to verify the integrity and authenticity of data packets. The verified set records data packets that have passed the signature verification operation. Furthermore, the verified set can be represented by a data packet identifier.

[0069] Step S205: Obtain the set of job rosters corresponding to the job data.

[0070] The job roster collection records the data packet identifiers of each data packet within the job data.

[0071] Step S206: Compare and verify the data of missing jobs by comparing the data with the set of job rosters.

[0072] By comparing the verified set and the job list set, the data packets that are not in the job list set are identified from the verified set, and the missing job data is obtained.

[0073] Step S207: Based on the missing job data, send a secondary data transmission request to the user terminal.

[0074] The secondary data transmission request is used to request the user terminal to resend the missing job data.

[0075] By employing the above technical solution, and comparing the number of data packets with the verification set, it is possible to identify job data that may be lost during transmission and request the user to retransmit it, thus avoiding the leakage of privacy information or misjudgment during the grading process due to incomplete data. The signature verification mechanism ensures the credibility and integrity of the source of each job data, preventing forged or tampered data from entering the grading system, thereby establishing a privacy protection barrier at the data receiving stage and enhancing the security and reliability of the entire grading process.

[0076] In the following embodiments, when requesting the user terminal to retransmit missing job data, the confidentiality of the missing job data also needs to be considered. Therefore, this application discloses a method for retransmitting missing job data. (Refer to...) Figure 3 The method includes: Step S301: Obtain the sending time of the secondary data transmission request.

[0077] The sending time is the moment when the secondary data transmission request is sent from the server.

[0078] Step S302: Based on the sending time and the user authorization token, predict the validity of the transmission time and obtain the verification result.

[0079] Transmission time refers to the moment when the user terminal transmits missing job data to the server.

[0080] Step S303: If the verification result is valid, execute the step of sending a secondary data transmission request to the user terminal based on the missing job data.

[0081] If the verification result is valid, it means that the user terminal can send the missing job data within the validity period of the user authorization token. Therefore, we can directly execute the missing job data and send a second data transmission request to the user terminal.

[0082] Step S304: If the verification result is invalid, send a temporary authorization request to the user terminal according to the transmission time.

[0083] A temporary authorization request is used to extend the validity period of a user authorization token. For example, a transmission time is added to the temporary authorization request, extending the validity period of the user authorization token to the specified time.

[0084] By adopting the above technical solution, and verifying the sending time of missing job data and the validity of the authorization token, it is ensured that data request operations are within a safe time frame, preventing the unauthorized transmission of private data due to timeouts or token expiration. Predicting the transmission backup time and sending a temporary authorization request allows for data retransmission without extending the fixed token validity period. This ensures process flexibility while avoiding privacy risks caused by excessive permissions, further enhancing the granularity and security of data control.

[0085] This application discloses a clock update method. (Refer to...) Figure 4 The method includes: Step S401: Obtain the terminal validity period and server validity period of the user authorization token.

[0086] Terminal validity duration refers to the validity period of the user authorization token on the user's terminal.

[0087] Server validity period refers to the duration for which the user authorization token remains valid on the server.

[0088] Optionally, after the user authorization token is generated, the server will send the user authorization token to the user terminal. If the user authorization token on the server expires, the server will stop processing the job data; if the user authorization token on the user terminal expires, the user terminal will send a stop processing command to the server, and the server will stop processing the job data after receiving the stop processing command.

[0089] Step S402: If the difference between the terminal's effective duration and the server's effective duration is greater than a preset duration threshold, a clock calibration request is sent to the user terminal.

[0090] The preset duration threshold is a pre-defined empirical value, and technicians can adjust the specific value of the preset duration threshold according to actual needs. For example, the preset duration threshold is set to 30 seconds.

[0091] If the difference between the terminal's effective duration and the server's effective duration is greater than the preset duration threshold, it indicates that there is a large deviation between the clocks of the user terminal and the server. It is necessary to calibrate the system clocks of both devices before recalculating.

[0092] The clock calibration request is used to calibrate the system clock of user terminals and servers.

[0093] In some other embodiments, if the difference between the terminal's effective duration and the server's effective duration is not greater than a preset duration threshold, the step of sending a second data transmission request to the user terminal based on the missing job data continues.

[0094] Step S403: In response to receiving an acceptance signal for a clock calibration request, synchronize the clock with the user terminal to obtain a synchronized clock.

[0095] Optionally, after receiving the clock calibration request, the user terminal will send an accept signal to the server.

[0096] Step S404: Update the transmission time based on the synchronization clock.

[0097] By adopting the above technical solution, the time difference between the user terminal and the system is calibrated through a clock synchronization mechanism, avoiding errors in authorization token judgment or invalid data reception time due to time asynchrony, and preventing data access during unauthorized time slots. The updated transmission time can more accurately reflect the actual data transmission status, ensuring the effective execution of subsequent verification logic, strengthening privacy protection during data transmission, and reducing security risks caused by system time deviations.

[0098] This application discloses a method for calculating transmission time. (Refer to...) Figure 5 The method includes: Step S501: Obtain the communication records of the user terminal.

[0099] Communication records include at least one of the following: communication duration between the user terminal and the server, communication time, and communication content size.

[0100] Step S502: Extract the average communication duration of the user terminal from the communication records.

[0101] Optionally, the communication duration of each communication can be extracted from the communication records to obtain a set of communication durations. The mean of the communication durations in the set of communication durations can be calculated to obtain the average communication duration.

[0102] Step S503: Obtain the communication connection delay with the user terminal.

[0103] Communication connection latency is used to represent the delay factors between a user terminal and a server. Communication connection latency includes at least one of the following: transmission latency, propagation latency, processing latency, and queuing latency. Transmission latency is the time it takes to push missing job data from the user terminal onto the transmission link. Propagation latency is the delay duration for transmitting missing job data. Processing latency refers to the time it takes for network devices to check for missing job data. Queuing latency refers to the time it takes for missing job data to wait in the network device's egress queue before being sent onto the link.

[0104] Step S504: Generate data transmission delay duration based on communication connection delay.

[0105] For example, the sum of the sending delay, propagation delay, processing delay, and queuing delay is calculated to obtain the data transmission delay duration.

[0106] Step S505: Obtain the transmission duration of the missing job data.

[0107] Transmission time refers to the time required for the missing job data to be transmitted. Transmission time is positively correlated with the amount of missing job data.

[0108] Step S506: Obtain the transmission time based on the sum of the average communication duration, data transmission delay duration, and transmission duration.

[0109] For example, the average communication duration, data transmission delay duration, and the sum of the transmission duration are calculated to obtain the total delay duration. The sum of the total delay duration and the transmission time is calculated to obtain the transmission time.

[0110] By adopting the above technical solution, a more realistic corrected transmission backup time is calculated by comprehensively considering average communication duration, connection status, and transmission latency. This backup time is then added to the temporary authorization request, enabling users to complete data retransmission within a reasonable timeframe and preventing abnormal access due to network conditions. This mechanism ensures a good user experience while strictly controlling the timeliness of temporary authorizations, preventing permission retention or abuse, and enhancing privacy control during data transmission.

[0111] This application discloses a method for generating correction results. (Refer to...) Figure 6 The method includes: Step S601: Use the analysis algorithm to correct the job data and obtain the initial correction results.

[0112] Optionally, if the assignment data is text data, an analysis algorithm is used to identify the assignment data and obtain the answer content. The matching degree between the answer content and the standard answer is calculated to obtain the initial grading results.

[0113] Optionally, if the assignment data is mathematical data, the assignment data is converted into computable symbols. An analysis algorithm is then invoked to perform logical verification on the computable symbols to obtain the initial grading results.

[0114] Optionally, if the assignment data is image data, feature data can be extracted from the image data. An analysis algorithm is then invoked to match and analyze the feature data, yielding initial grading results.

[0115] Step S602: Convert the custom batching rules into code logic.

[0116] Code logic refers to executable computer code.

[0117] Optionally, a code generation module can be set up within the server, which can generate corresponding executable code based on custom batching rules.

[0118] Step S603: Process the initial correction result according to the code logic to obtain the correction result.

[0119] By adopting the above technical solution, and converting user-defined grading rules into code logic and processing the initial grading results, the accuracy of grading is ensured while avoiding user privacy leaks due to human intervention or rule disclosure. This automated processing mechanism respects user grading preferences while eliminating the risk of information exposure caused by human access to data, achieving a balance between efficiency and privacy security in the grading process.

[0120] This application also provides a user-upload-based job analysis and grading system, which includes a user terminal module, a data transmission and access control module, a dynamic analysis engine module, an intelligent grading process module, and a management and monitoring module.

[0121] User terminal module: This module allows users to independently upload assignment data, including various types of assignments such as text, mathematical data, and image data. It features data preprocessing capabilities, such as local noise reduction and text extraction for image-based assignments, reducing the amount of raw image data uploaded. Users can also set personalized grading rules in this module and upload these rules along with the assignment data.

[0122] Data transmission and access control module: Employs encrypted transmission protocols (such as SSL / TLS) to transmit user-uploaded job data and grading rules to the system server. Before data transmission, users must authenticate and actively trigger an "analysis command" to generate a user authorization token. The system only grants data processing permissions within the token's validity period; after the token expires, all data operations are automatically terminated. Data exists in encrypted form during transmission and in the system server's memory, and is automatically deleted after processing, without being written to persistent storage media.

[0123] The dynamic analysis engine module employs different analysis algorithms for different types of assignments. For text-based assignments, a locally deployed lightweight NLP model is used to identify the matching degree between the answer content and the standard answer in real time. For mathematical assignments, a symbolic parsing module converts the formulas and solution steps uploaded by the bank into computable symbols, and calls the algorithm library to complete logical verification. For image-based assignments, deep feature matching and analysis are performed based on the preprocessed feature data from the user's terminal. Simultaneously, this module can parse personalized grading rules uploaded by users, converting the rules into executable code logic to achieve dynamic and personalized assignment analysis.

[0124] The intelligent grading process module: Based on the analysis results from the dynamic analysis engine module and combined with user-defined grading rules, this module intelligently grades assignments. It generates grading results such as incorrect question annotations, detailed scores, and suggestions for linking knowledge points, returning them to the user in a format decryptable on the user's terminal. Furthermore, this module allows users to correct and annotate the grading results and records user feedback. It optimizes subsequent analysis model parameters based on this feedback, but does not involve the retention of original assignment data.

[0125] Management and Monitoring Module: This module provides real-time monitoring of the system's operational status, including data transmission, analysis processes, and user operations. It also manages user authorization tokens and system resource allocation to ensure stable and secure system operation.

[0126] Based on the same inventive concept, embodiments of this application provide a user-uploaded assignment analysis and grading system, including: The acquisition module 701 is used to acquire authentication information, analysis instructions, and custom batch modification rules.

[0127] Memory 702 is used to store a program for analyzing and grading user-uploaded jobs.

[0128] Processor 703 enables the processor to load and execute programs in memory and implement a method for analyzing and grading user-uploaded jobs.

[0129] By adopting the above technical solution, a dual verification mechanism using identity verification information and user authorization tokens ensures that only legitimate users can submit job data within the authorization period, effectively preventing unauthorized access and identity theft. Job data is transmitted using an encrypted transmission protocol to prevent theft or tampering during transmission, significantly improving data confidentiality and integrity. Combining analysis algorithms with user-defined grading rules ensures the grading process is targeted while avoiding privacy risks caused by the generality of algorithms. The overall process strengthens user privacy protection at multiple stages.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0131] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed based on a user-uploaded job analysis and grading method.

[0132] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0133] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor based on a user-uploaded job analysis and grading method.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0135] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for analyzing and grading assignments based on user uploads, characterized in that, The method includes: Obtain the user terminal's authentication information; After the authentication information is successfully authenticated, a user authorization token is obtained in response to the acquisition analysis command; Within the validity period of the user authorization token, the job data uploaded by the user terminal is obtained, and the job data is transmitted through an encrypted transmission protocol; Based on the type of the work data, an analysis algorithm is derived; Obtain the custom batch modification rules corresponding to the user terminal; The analysis algorithm and the custom grading rules are combined to grade the job data and obtain the grading results. The correction result is sent to the user terminal; After obtaining the job data uploaded by the user terminal, the process further includes: Obtain the quantity information corresponding to the operation data from the analysis instructions; Determine whether the number of data packets in the job data is consistent with the quantity information; If not, extract each data packet from the job data; Perform a signature verification operation on the data packet to obtain a set of verified data packets; Obtain the set of job rosters corresponding to the job data; By comparing the verification pass set with the job roster set, the missing job data is obtained; Based on the missing job data, a secondary data transmission request is sent to the user terminal. Before sending a secondary data transmission request to the user terminal based on the missing job data, the method further includes: Obtain the sending time of the secondary data transmission request; Based on the sending time and the user authorization token, the validity of the transmission time is verified, and the verification result is obtained; If the verification result is valid, the step of sending a secondary data transmission request to the user terminal based on the missing job data is executed. If the verification result is invalid, a temporary authorization request is sent to the user terminal according to the transmission time.

2. The method for analyzing and grading assignments based on user uploads according to claim 1, characterized in that, The method further includes: The terminal validity period and server validity period of the user authorization token are obtained; If the difference between the effective duration of the terminal and the effective duration of the server is greater than a preset duration threshold, a clock calibration request is sent to the user terminal. In response to receiving an acceptance signal for the clock calibration request, the clock is synchronized with that of the user terminal to obtain a synchronized clock; The transmission time is updated based on the synchronization clock.

3. The method for analyzing and grading user-uploaded assignments according to claim 1, characterized in that, The method further includes: Obtain the communication records of the user terminal; Extract the average communication duration of the user terminal from the communication records; Obtain the communication connection delay with the user terminal; The data transmission delay duration is generated based on the communication connection delay. Obtain the transmission duration of the missing job data; The transmission time is obtained by summing the average communication duration, the data transmission delay duration, and the transmission duration.

4. The method for analyzing and grading user-uploaded assignments according to claim 1, characterized in that, The process of combining the analysis algorithm and the custom grading rules to grade the job data and obtain grading results includes: The analysis algorithm is used to correct the job data to obtain initial correction results; Convert the custom batching rules into code logic; The initial correction result is processed according to the code logic to obtain the correction result.

5. The method for analyzing and grading user-uploaded assignments according to claim 4, characterized in that, The step of using the analysis algorithm to correct the job data and obtain initial correction results includes: When the type of the assignment data is text data, the analysis algorithm is used to identify the assignment data to obtain the answer content; the matching degree between the answer content and the standard answer is calculated to obtain the initial grading result; If the type of the assignment data is mathematical data, the assignment data is converted into computable symbols; the analysis algorithm is called to perform logical verification on the computable symbols to obtain the initial grading result; If the type of the task data is image data, feature data is obtained from the image data; the analysis algorithm is called to match and analyze the feature data to obtain the initial correction result.

6. A user-uploaded assignment analysis and grading system, characterized in that, The system is used to execute the user-uploaded job analysis and grading method as described in any one of claims 1 to 5, the system comprising: The acquisition module is used to acquire authentication information, analysis instructions, and custom batch modification rules. A memory for storing the program of the user-uploaded job analysis and grading method; The processor and memory can load and execute the program to implement the user-uploaded job analysis and grading method.

7. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 5.

Images