Educational resource sharing method based on big data

Abstract

The invention relates to the technical field of resource sharing, in particular to an educational resource sharing method based on big data, which comprises the following steps: S1, collecting and integrating educational resource data from an educational platform and a textbook resource library by using a big data technology; s2, storing the collected educational resource data in a database, wherein the database supports efficient retrieval, updating and management of the resource data; when the method is used, the problem that modification suggestions cannot be put forward when a user has wrong data is solved, the legality and format correctness of annotated contents submitted by the user are improved, the data quality of educational resources is continuously optimized, and it is ensured that the updating process of the data has transparency and traceability; the data quality of the platform and the trust of the user are further improved, the sharing efficiency of educational resources is improved, the problem can be quickly processed and solved when the user puts forward an objection through regular inspection and a timely feedback mechanism, and the use experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource sharing, and in particular to a method for sharing educational resources based on big data. Background Art

[0002] Resource sharing refers to the process of sharing resources among multiple individuals, organizations or systems to achieve common goals or improve efficiency. These resources can be physical, informational, human or financial resources. Effective resource sharing can bring significant benefits at different levels, such as reducing costs, improving productivity, promoting innovation and cooperation, etc.

[0003] The patent application number is CN202311266212.X, which states in its specification that "the present invention discloses an educational resource sharing method based on an educational big data platform, which relates to the field of resource sharing technology and solves the technical problem of not using a feature extraction method to display shared resources. It first confirms similar resources based on resource header data, and then hides the corresponding duplicate data in similar resources. After hiding, the corresponding feature data is confirmed. Subsequently, the corresponding front-end features are selected from the feature data to generate marked resources. According to the marked resource confirmation display page, the feature part of the corresponding resource is extracted, and then the different feature parts are combined. The above technology has the advantages of fast sharing rate and privacy protection of shared resources, but there are word errors, semantic errors and data errors in shared resources. When word errors, semantic errors and data errors occur, users who call shared resources cannot make timely corrections due to lack of permission, resulting in the inability to further improve the accuracy of shared resources.

[0004] To sum up, developing an educational resource sharing method based on big data is still a key issue that needs to be urgently addressed in the field of resource sharing technology. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art that although the above-mentioned technology has the advantages of fast sharing speed and protection of the privacy of shared resources, there will be word errors, semantic errors and data errors in the shared resources. When word errors, semantic errors and data errors occur, users who call shared resources cannot make timely corrections due to lack of authority, resulting in the inability to further improve the accuracy of shared resources.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides an educational resource sharing method based on big data, comprising the following steps: S1, using big data technology to collect and integrate educational resource data from an educational platform and a teaching resource library;

[0008] S2. Store the collected educational resource data in a database that supports efficient retrieval, updating, and management of resource data;

[0009] S3. When users find erroneous data when using educational resource data, they can highlight the erroneous data through the annotation function provided by the database and attach the modified content;

[0010] S4. When the number of annotations for the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently among the highlighted annotations and replaces the erroneous data based on the modification content;

[0011] S5. After replacing the erroneous data, the erroneous data record in the database is updated, and a notification of the correction of the erroneous data is issued through the push notification function of the database.

[0012] Furthermore, in step S1, the method for collecting and integrating educational resource data from the educational platform and teaching material resource library using big data technology is as follows:

[0013] The big data-based educational resource sharing management system interacts with online education platforms through API interfaces to obtain journal content, paper content, and popular science texts. It also obtains textbook content from textbook publishers' databases. It uses a big data processing framework to clean, standardize, and deduplicate the collected data. It then uses ETL tools to integrate data from different platforms and map it to a unified database model. The ETL tool formula is: where Q final Represents the data set that is finally loaded into the unified database after integration. Indicates the platform P i The original data on Extract, transform, and load operations performed, Indicates that from platform P i The extracted data set, i is the platform number.

[0014] Furthermore, in step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating and management of resource data as follows:

[0015] The database adopts a microservice architecture. Under the microservice architecture, each service independently manages its own data storage. Services communicate through APIs. The database establishes indexes on key fields, uses B-tree indexes to speed up range queries and sorting operations, and uses an integrated search engine to process full-text retrieval and fuzzy queries on unstructured data. The independent database service is set as S i′ , search engine formula: where Z range It is a range query, Z search It is a full-text search. k Indicates the B-tree index established on the key field k, X i′ Indicates that the service S i′ Data in the managed database, X final It's a database.

[0016] Furthermore, in step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating and management of resource data as follows:

[0017] Use query cache to cache frequently used SQL query results, use batch insert and batch update operations to update data, and combine incremental backup with full backup strategies to perform regular database backups. Implement regular cleanup strategies to automatically delete useless and expired data, and use database integrity check tools to regularly perform data consistency and integrity checks. The integrity check tool formula is as follows:

[0018] where Z cached is the cached SQL query result, U′ barch It is a batch insert and batch update operation. backup It is an incremental backup, C backup It is a full backup, X clean It is to clear out useless and expired data, X inconsistent The inconsistent data set becomes consistent after repair.

[0019] Furthermore, in step S3, when the user finds erroneous data when using the educational resource data, the user highlights the erroneous data through the annotation function provided by the database and attaches the modified content.

[0020] In the display interface of the database, a labeling button is provided. When the user clicks the button, the identity is authenticated first. When the authenticated user finds erroneous data, he can click the button to label the data and fill in the modified content in the pop-up input box. After the modified content is submitted, the big data-based educational resource sharing management system stores the modified content in the labeling table in the database to record the user's highlighted annotations and modification suggestions. Let the user identity be W and the authentication result be V. The authentication process: V = validate(W), where validate(W) is the verification function. If the user authentication passes, V = True, otherwise V = False. The labeling button process is triggered: L = trigger_label(E, R), where E is the erroneous data and R is the modification content entered by the user. trigger_label(E, R) indicates the labeling function triggered by the user. The modification content submission and storage formula is: T label =T label ∪(R,W,t), where T label It is a database table that stores highlighted annotations and modification content, R is the submitted modification content, W is the user who submitted the annotation, and t is the timestamp of the annotation submission.

[0021] Furthermore, in step S3, when the user finds erroneous data when using the educational resource data, the user highlights the erroneous data through the annotation function provided by the database and attaches the modified content.

[0022] The front-end submits the annotation information and modified content to the back-end interface by sending an HTTP request, and can obtain and display the submitted annotations and modified content through the interface. In addition, the front-end and back-end jointly perform double verification to check the format of the modified content submitted by the user. The formula for double verification between the front-end and back-end is: Validate_front(R front ) is the format check function of the front end, Validate_back(R back ) is the format verification function of the backend, R front It is the modified content of the front-end verification, R back It is the modified content of the back-end verification, front =True and O back = True, modify the content and continue submitting.

[0023] Furthermore, in step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner:

[0024] The frequency of the erroneous data is counted based on the highlighted annotations in the annotation table. When the same erroneous data is highlighted more than three times, the educational resource sharing management system based on big data queries all the modifications related to the erroneous data and counts the frequency of each modification. The educational resource sharing management system based on big data extracts the modification with the highest frequency and displays it to the database administrator for review. The database administrator checks whether the modification meets the correction requirements and decides whether to approve the modification for replacement. After the database administrator confirms the modification, the modification is applied to the corresponding erroneous data field to replace the original erroneous data. The frequency statistics formula is: Where 1(R i =R) is the indicator function, F R (E) represents the occurrence frequency of error data E corresponding to modified content R.

[0025] Furthermore, in step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner:

[0026] Design an automated process. When a highlighted markup appears more than three times, automatically analyze the frequency of highlighted marks and extract the most frequently appearing modifications. Replace the original erroneous data with the most frequently appearing modifications. Regularly check the effectiveness of the erroneous data corrections. At the same time, record the changes to the erroneous data in the database, save the data before and after the modification, administrator information, and modification time. The process formula for automatically extracting the most frequently appearing modifications is as follows: where R top is the most frequently modified content, E new Check the formula regularly for incorrect data after updating: Then make corrections, where C check (E′) represents the number of inconsistent or erroneous records, E i ′ represents the i″th data record in the data set E′, E′ new Indicates the corrected data record, 1(E i ′≠E′ new ) is an indicator function, and n″ represents the number of records in the dataset.

[0027] Furthermore, in step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and a method of publishing the erroneous data correction notification through the push notification function of the database is as follows:

[0028] After replacing the erroneous data, the educational resource sharing management system based on big data will first update the erroneous data record in the database. After the update is completed, the educational resource sharing management system based on big data will issue a correction notice through the push notification function. The correction notice includes the description of the erroneous data, the modified new data, the administrator information, the modification time and the correction reason. The correction notice can be sent to the user via email, SMS and platform message. The updated data record formula is: H error (E)→H error (E new ), where H error (E) indicates the record of the original error data E in the database, which is updated to H after replacement error (E new ), E new This is the updated error data.

[0029] Furthermore, in step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and a method of publishing the erroneous data correction notification through the push notification function of the database is as follows:

[0030] The educational resource sharing management system based on big data will regularly check and collect user feedback. If the user disagrees with the revised content or believes that the revision is inaccurate, the administrator will make another revision and further optimize the data based on the feedback. The optimization process formula is: J error (E old )→J error (E final ), where J error (E old ) is the data before modification, E final It is the final data after processing user feedback.

[0031] Beneficial effects

[0032] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0033] Beneficial effects:

[0034] When used, the present invention is helpful in solving the problem that users cannot propose modification suggestions when erroneous data occurs, is helpful in improving the legality and format correctness of the annotation content submitted by users, is helpful in continuously optimizing the data quality of educational resources, is helpful in ensuring that the data update process is transparent and traceable, further improving the data quality and user trust of the platform, and is helpful in improving the sharing efficiency of educational resources. Through regular inspections and timely feedback mechanisms, when users raise objections, the problems can be quickly processed and resolved, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1This is a flow chart of an educational resource sharing method based on big data of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0038] The present invention is described in further detail below with reference to the accompanying drawings:

[0039] Example:

[0040] like Figure 1 As shown, the present invention provides an educational resource sharing method based on big data, comprising the following steps: S1, using big data technology to collect and integrate educational resource data from an educational platform and a teaching resource library;

[0041] Furthermore, in step S1, the method for collecting and integrating educational resource data from the educational platform and the teaching material resource library using big data technology is as follows:

[0042] The big data-based educational resource sharing management system interacts with online education platforms through API interfaces to obtain journal content, paper content, and popular science texts. It also obtains textbook content from textbook publishers' databases. It uses a big data processing framework to clean, standardize, and deduplicate the collected data. It then uses ETL tools to integrate data from different platforms and map it to a unified database model. The ETL tool formula is: where Q final Represents the data set that is finally loaded into the unified database after integration. Indicates the platform P i The original data on Extract, transform, and load operations performed, Indicates that from platform P i The extracted data set, i is the platform number;

[0043] In this embodiment, the education resource sharing management system based on big data technology interacts with the online education platform and the teaching material resource library through the API interface to collect educational resources including journal content, paper content, popular science texts, and teaching material content. Through the big data processing framework, the collected data are cleaned, standardized, and deduplicated to ensure the accuracy and consistency of the data. Subsequently, the ETL tool is used to integrate the data from different platforms and map them to a unified database model. This process ensures that data from multiple platforms can be seamlessly connected and form a unified, structured data set, effectively improving the utilization and sharing of the data. In addition, the cleaning and standardization steps eliminate duplicate data and inconsistent data formats, thereby improving data quality and providing a reliable data foundation for subsequent education resource management and query. Through this series of processes, education resource data can be efficiently integrated and managed, further optimizing resource sharing and utilization efficiency, and contributing to the development of informatization in the field of education.

[0044] S2. Store the collected educational resource data in a database that supports efficient retrieval, updating, and management of resource data;

[0045] Furthermore, in step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating and management of resource data as follows:

[0046] The database adopts a microservice architecture. Under the microservice architecture, each service independently manages its own data storage. Services communicate through APIs. The database establishes indexes on key fields, uses B-tree indexes to speed up range queries and sorting operations, and uses an integrated search engine to process full-text retrieval and fuzzy queries on unstructured data. The independent database service is set as S i′ , search engine formula: where Z range It is a range query, Z search It is a full-text search. k Indicates the B-tree index established on the key field k, X i′ Indicates that the service S i′ Data in the managed database, X final It's a database.

[0047] Furthermore, in step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating and management of resource data as follows:

[0048] Use query cache to cache frequently used SQL query results, use batch insert and batch update operations to update data, and combine incremental backup with full backup strategies to perform regular database backups. Implement regular cleanup strategies to automatically delete useless and expired data, and use database integrity check tools to regularly perform data consistency and integrity checks. The integrity check tool formula is as follows:

[0049] where Z cached is the cached SQL query result, U′ barch It is a batch insert and batch update operation. backup It is an incremental backup, C backup It is a full backup, X clean It is to clear out useless and expired data, X inconsistent The inconsistent data set becomes consistent after repair;

[0050] In this embodiment, educational resource data is stored in the database through a microservice architecture. Each microservice independently manages its data storage, and services communicate with each other through APIs. To improve the efficiency of data retrieval and update, the database establishes a B-tree index on key fields and uses an integrated search engine to process full-text retrieval and fuzzy queries on unstructured data. In addition, the query cache mechanism caches commonly used SQL query results, reducing the query response time. At the same time, data updates are performed through batch insert and batch update operations, greatly improving data processing efficiency. Incremental backup and full backup strategies are combined, and database backups are performed regularly to ensure data security. In addition, cleanup strategies are implemented regularly to automatically delete useless and expired data to ensure data cleanliness and efficiency. The database also regularly uses integrity checking tools to perform data consistency and integrity checks to ensure data accuracy and reliability. This series of measures is conducive to achieving efficient data management and maintenance, not only improving data processing speed, but also enhancing data reliability and availability, effectively supporting the sharing and application of educational resources.

[0051] S3. When users find erroneous data when using educational resource data, they can highlight the erroneous data through the annotation function provided by the database and attach the modified content;

[0052] Furthermore, in step S3, when the user finds erroneous data when using the educational resource data, the user highlights the erroneous data through the annotation function provided by the database and attaches the modified content.

[0053] In the display interface of the database, a labeling button is provided. When the user clicks the button, the identity is authenticated first. When the authenticated user finds erroneous data, he can click the button to label the data and fill in the modified content in the pop-up input box. After the modified content is submitted, the big data-based educational resource sharing management system stores the modified content in the labeling table in the database to record the user's highlighted annotations and modification suggestions. Let the user identity be W and the authentication result be V. The authentication process: V = validate(W), where validate(W) is the verification function. If the user authentication passes, V = True, otherwise V = False. The labeling button process is triggered: L = trigger_label(E, R), where E is the erroneous data and R is the modification content entered by the user. trigger_label(E, R) indicates the labeling function triggered by the user. The modification content submission and storage formula is: T label =T label ∪(R,W,t), where T label It is a database table that stores highlighted annotations and modification content, R is the submitted modification content, W is the user who submitted the annotation, and t is the timestamp of the annotation submission.

[0054] Furthermore, in step S3, when the user finds erroneous data when using the educational resource data, the user highlights the erroneous data through the annotation function provided by the database and attaches the modified content.

[0055] The front-end submits the annotation information and modified content to the back-end interface by sending an HTTP request, and can obtain and display the submitted annotations and modified content through the interface. In addition, the front-end and back-end jointly perform double verification to check the format of the modified content submitted by the user. The formula for double verification between the front-end and back-end is: Validate_front(R front ) is the format check function of the front end, Validate_back(R back ) is the format verification function of the backend, R front It is the modified content of the front-end verification, R back It is the modified content of the back-end verification, front =True and O back =True, modify the content and continue submitting;

[0056] In this embodiment, when the user discovers erroneous data while using educational resource data, he or she can mark it through the marking function provided by the database. The specific process includes the user first performing identity authentication. After the verification is passed, the user can click the marking button to highlight the erroneous data and attach the modified content. After the modified content is submitted, these modified contents are stored in the marking table in the database, and the marked content, modification suggestions, submitting user and timestamp are recorded. A dual verification mechanism is adopted for the front-end and back-end. The front-end performs preliminary verification through the format verification function, and the back-end performs format verification on the submitted content. Only the modified content that passes the verification can continue to be submitted and stored. This mechanism is conducive to improving the legality and format correctness of the annotation content submitted by users, avoiding invalid and erroneous data correction, and improving data quality. At the same time, this marking and feedback mechanism enhances the user's sense of participation and trust in the platform, and helps to continuously optimize the data quality of educational resources.

[0057] S4. When the number of annotations for the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently among the highlighted annotations and replaces the erroneous data based on the modification content;

[0058] Furthermore, in step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner:

[0059] The frequency of the erroneous data is counted based on the highlighted annotations in the annotation table. When the same erroneous data is highlighted more than three times, the educational resource sharing management system based on big data queries all the modifications related to the erroneous data and counts the frequency of each modification. The educational resource sharing management system based on big data extracts the modification with the highest frequency and displays it to the database administrator for review. The database administrator checks whether the modification meets the correction requirements and decides whether to approve the modification for replacement. After the database administrator confirms the modification, the modification is applied to the corresponding erroneous data field to replace the original erroneous data. The frequency statistics formula is: Where 1(R i =R) is the indicator function, F R (E) represents the occurrence frequency of error data E corresponding to modified content R.

[0060] Furthermore, in step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner:

[0061] Design an automated process. When a highlighted markup appears more than three times, automatically analyze the frequency of highlighted marks and extract the most frequently appearing modifications. Replace the original erroneous data with the most frequently appearing modifications. Regularly check the effectiveness of the erroneous data corrections. At the same time, record the changes to the erroneous data in the database, save the data before and after the modification, administrator information, and modification time. The process formula for automatically extracting the most frequently appearing modifications is as follows: where R top is the most frequently modified content, E new Check the formula regularly for incorrect data after updating: Then make corrections, where C check (E′) represents the number of inconsistent or erroneous records, E i ′ represents the i″th data record in the data set E′, E′ new Indicates the corrected data record, 1(E i ′≠E′ new ) is an indicator function, n″ represents the number of records in the dataset;

[0062] In this embodiment, when the number of annotations of erroneous data exceeds 3 times, the education resource sharing management system based on big data extracts the most frequently occurring modifications through frequency statistical analysis. The specific method is that the system performs frequency statistics on the highlighted annotations in the annotation table, determines the number of occurrences of the modified content, and displays the most frequently occurring modifications to the database administrator for review. After the administrator reviews and confirms that the modified content meets the correction requirements, it is applied to the erroneous data field to replace the original erroneous data. By designing an automated process, when the highlighted annotations exceed 3 times, the most frequently occurring modifications are automatically extracted and the data is replaced. The system regularly checks the correction effect and saves the modification records, including the data before and after the modification, administrator information, and modification time. This process improves the efficiency and accuracy of erroneous data correction through automation and frequency statistics, which helps to ensure the transparency and traceability of the data update process, and further improves the data quality and user trust of the platform.

[0063] S5. After replacing the erroneous data, the erroneous data record in the database is updated, and a notification of the erroneous data correction is issued through the push notification function of the database;

[0064] Furthermore, in step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and a method of publishing the erroneous data correction notification through the push notification function of the database is as follows:

[0065] After replacing the erroneous data, the educational resource sharing management system based on big data will first update the erroneous data record in the database. After the update is completed, the educational resource sharing management system based on big data will issue a correction notice through the push notification function. The correction notice includes the description of the erroneous data, the modified new data, the administrator information, the modification time and the correction reason. The correction notice can be sent to the user via email, SMS and platform message. The updated data record formula is: H error (E)→H error (E new ), where H error (E) indicates the record of the original error data E in the database, which is updated to H after replacement error (E new ), E new This is the updated error data.

[0066] Furthermore, in step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and a method of publishing the erroneous data correction notification through the push notification function of the database is as follows:

[0067] The educational resource sharing management system based on big data will regularly check and collect user feedback. If the user disagrees with the revised content or believes that the revision is inaccurate, the administrator will make another revision and further optimize the data based on the feedback. The optimization process formula is: J error (E old )→J error (E final ), where J error (E old ) is the data before modification, E final It is the final data after processing user feedback;

[0068] In this embodiment, each data correction is subject to administrator review and user feedback, effectively avoiding the impact of erroneous data on the teaching resource management system. The big data-based educational resource sharing management system records all data modification operations, including modification time, administrator information, and data content before and after modification, ensuring that the data history is traceable and convenient for review and monitoring. The correction notification function is issued to users to improve the real-time response capability of the platform. At the same time, regular error data checks and user feedback mechanisms enable the system to be continuously optimized, improving the sharing efficiency of educational resources. Through regular checks and timely feedback mechanisms, when users raise objections, they can quickly handle and resolve problems, thereby improving the user experience.

[0069] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for sharing educational resources based on big data, characterized in that: The following steps are involved: S1. Use big data technology to collect and integrate educational resource data from educational platforms and teaching resource libraries; S2. Store the collected educational resource data in a database that supports efficient retrieval, updating, and management of resource data; S3. When users find erroneous data when using educational resource data, they can highlight the erroneous data through the annotation function provided by the database and attach the modified content; S4. When the number of annotations for the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently among the highlighted annotations and replaces the erroneous data based on the modification content; S5. After replacing the erroneous data, the erroneous data record in the database is updated, and a notification of the correction of the erroneous data is issued through the push notification function of the database.

2. The method for sharing educational resources based on big data according to claim 1, characterized in that: In step S1, the method of collecting and integrating educational resource data from the educational platform and teaching resource library using big data technology is as follows: The big data-based educational resource sharing management system interacts with online education platforms through API interfaces to obtain journal content, paper content, and popular science texts. It also obtains textbook content from textbook publishers' databases. It uses a big data processing framework to clean, standardize, and deduplicate the collected data. It then uses ETL tools to integrate data from different platforms and map it to a unified database model. The ETL tool formula is: where Q final Represents the data set that is finally loaded into the unified database after integration. Indicates that the platform P i The original data on Extract, transform, and load operations performed, Indicates that from platform P i The extracted data set, i is the platform number.

3. The method for sharing educational resources based on big data according to claim 2, characterized in that: In step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating, and management of resource data as follows: The database adopts a microservice architecture. Under the microservice architecture, each service independently manages its own data storage. Services communicate through APIs. The database establishes indexes on key fields, uses B-tree indexes to speed up range queries and sorting operations, and uses an integrated search engine to process full-text retrieval and fuzzy queries on unstructured data. The independent database service is set as S i′ , search engine formula: where Z range It is a range query, Z search It is a full-text search. k Indicates the B-tree index established on the key field k, X i′ Indicates that the service S i′ Data in the managed database, X final It's a database.

4. The method for sharing educational resources based on big data according to claim 3, characterized in that: In step S2, the collected educational resource data is stored in a database. The database supports efficient retrieval, updating, and management of resource data as follows: Use query cache to cache frequently used SQL query results, use batch insert and batch update operations to update data, and combine incremental backup with full backup strategies to perform regular database backups. Implement regular cleanup strategies to automatically delete useless and expired data, and use database integrity check tools to regularly perform data consistency and integrity checks. The integrity check tool formula is as follows: where Z cached is the cached SQL query result, U′ barch It is a batch insert and batch update operation. backup It is an incremental backup, C backup It is a full backup, X clean It is to clear out useless and expired data, X inconsistent The inconsistent data set becomes consistent after repair.

5. The method for sharing educational resources based on big data according to claim 4, characterized in that: In step S3, when the user uses the educational resource data and finds that there is erroneous data, the user uses the annotation function provided by the database to highlight the erroneous data and attach the modified content. In the display interface of the database, a labeling button is provided. When the user clicks the button, the identity is authenticated first. When the authenticated user finds erroneous data, he can click the button to label the data and fill in the modified content in the pop-up input box. After the modified content is submitted, the big data-based educational resource sharing management system stores the modified content in the labeling table in the database to record the user's highlighted annotations and modification suggestions. Let the user identity be W and the authentication result be V. The authentication process: V = validate(W), where validate(W) is the verification function. If the user authentication passes, V = True, otherwise V = False. The labeling button process is triggered: L = trigger_label(E, R), where E is the erroneous data and R is the modification content entered by the user. trigger_label(E, R) indicates the labeling function triggered by the user. The modification content submission and storage formula is: T label =T label ∪(R,W,t), where T label It is a database table that stores highlighted annotations and modification content, R is the submitted modification content, W is the user who submitted the annotation, and t is the timestamp of the annotation submission.

6. The method for sharing educational resources based on big data according to claim 5, characterized in that: In step S3, when the user uses the educational resource data and finds that there is erroneous data, the user uses the annotation function provided by the database to highlight the erroneous data and attach the modified content. The front-end submits the annotation information and modified content to the back-end interface by sending an HTTP request, and can obtain and display the submitted annotations and modified content through the interface. In addition, the front-end and back-end jointly perform double verification to check the format of the modified content submitted by the user. The formula for double verification between the front-end and back-end is: Validate_front(R front ) is the format check function of the front end, Validate_back(R back ) is the format verification function of the backend, R front It is the modified content of the front-end verification, R back It is the modified content of the back-end verification. front =True and O back = True, modify the content and continue submitting.

7. The method for sharing educational resources based on big data according to claim 6, characterized in that: In step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner: The frequency of the erroneous data is counted based on the highlighted annotations in the annotation table. When the same erroneous data is highlighted more than three times, the educational resource sharing management system based on big data queries all the modifications related to the erroneous data and counts the frequency of each modification. The educational resource sharing management system based on big data extracts the modification with the highest frequency and displays it to the database administrator for review. The database administrator checks whether the modification meets the correction requirements and decides whether to approve the modification for replacement. After the database administrator confirms the modification, the modification is applied to the corresponding erroneous data field to replace the original erroneous data. The frequency statistics formula is: Where 1(R i =R) is the indicator function, F R (E) represents the occurrence frequency of error data E corresponding to modified content R.

8. The method for sharing educational resources based on big data according to claim 7, characterized in that: In step S4, when the number of annotations of the erroneous data is greater than 3, the database administrator extracts the modification content that appears most frequently in the highlighted annotations, and replaces the erroneous data according to the modification content in the following manner: Design an automated process. When a highlighted markup appears more than three times, automatically analyze the frequency of highlighted marks and extract the most frequently appearing modifications. Replace the original erroneous data with the most frequently appearing modifications. Regularly check the effectiveness of the erroneous data corrections. At the same time, record the changes to the erroneous data in the database, save the data before and after the modification, administrator information, and modification time. The process formula for automatically extracting the most frequently appearing modifications is as follows: where R top is the most frequently modified content, E new Check the formula regularly for incorrect data after updating: C check (E′)>0, then make corrections, where C check (E′) represents the number of inconsistent or erroneous records, E i ′ represents the i″th data record in the data set E′, E′ new Indicates the corrected data record, 1(E i ′≠E′ new ) is an indicator function, and n″ represents the number of records in the dataset.

9. The method for sharing educational resources based on big data according to claim 8, characterized in that: In step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and the method of publishing the erroneous data correction notification through the push notification function of the database is as follows: After replacing the erroneous data, the educational resource sharing management system based on big data will first update the erroneous data record in the database. After the update is completed, the educational resource sharing management system based on big data will issue a correction notice through the push notification function. The correction notice includes the description of the erroneous data, the modified new data, the administrator information, the modification time and the correction reason. The correction notice can be sent to the user via email, SMS and platform message. The updated data record formula is: H error (E)→H error (E new ), where H error (E) indicates the record of the original error data E in the database, which is updated to H after replacement error (E new ), E new This is the updated error data.

10. The method for sharing educational resources based on big data according to claim 8, characterized in that: In step S5, after replacing the erroneous data, the erroneous data record in the database is updated, and the method of publishing the erroneous data correction notification through the push notification function of the database is as follows: The educational resource sharing management system based on big data will regularly check and collect user feedback. If the user disagrees with the revised content or believes that the revision is inaccurate, the administrator will make further revisions and further optimize the data based on the feedback. The optimization process formula is: J error (E old )→J error (E final ), where J error (E old ) is the data before modification, E final It is the final data after processing user feedback.

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