An information literacy knowledge tree platform system

Abstract

The present invention discloses an information literacy knowledge tree platform system, which relates to the field of educational technology and solves the problem that the specific confirmation of conversion logic is not carried out for different integration situations, resulting in a low associated integration rate and failure to achieve a better resource integration effect. The present invention unifies the formats of teaching resources acquired within a limited period, gives priority to specific classifications based on corresponding formats, confirms the corresponding number of categories, and executes different conversion processing logics based on the specific verification of the number of categories and the number of preset processes in the system to complete the format unification process corresponding to several teaching resources. In this way, it can be effectively guaranteed that under different number verification states, the format of the resource conversion can reach the fastest state, be in the optimal solution, and achieve the fastest processing method, thereby reducing the integration time of several educational resources when integrating and improving the practicality of the platform.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to an information literacy knowledge tree platform system. Background Art

[0002] Information literacy education is increasingly valued in the fields of library and information science, higher education, etc.; however, existing information literacy education resources are relatively scattered and lack a unified knowledge management and sharing platform, which makes it difficult for users to efficiently acquire, edit and learn information literacy-related knowledge; therefore, there is a need for a platform system that can integrate information literacy education resources and provide convenient editing and learning functions.

[0003] The educational resources associated with its information literacy knowledge tree platform have inconsistent formats. When integrating most of the educational resources with inconsistent formats, the platform directly converts the educational resources in the corresponding formats into relevant formats that can be read and written by the system. It does not specifically confirm the conversion logic for different integration situations, resulting in a low integration rate and failure to achieve a good resource integration effect, and its integration time is slow. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an information literacy knowledge tree platform system, which solves the problem that the conversion logic is not specifically confirmed for different integration situations, resulting in a low associated integration rate and the inability to achieve a good resource integration effect.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an information literacy knowledge tree platform system, comprising:

[0006] The periodic resource acquisition terminal acquires the teaching resources associated with the limited period and transmits the teaching resources acquired within the limited period to the resource integration terminal;

[0007] The resource integration end unifies the format of the acquired teaching resources, first confirming the total number of individual teaching resources, and then determining the format unification logic based on the number of conversion processes preset in the system and the system's preset read and write formats. The specific method is to unify the formats of the confirmed groups of teaching resources.

[0008] Determine the teaching resources acquired during this limited period, classify the teaching resources in different formats and record them as format classification areas, and mark the number of the determined format classification areas as G;

[0009] Then confirm the number of conversion processes H preset in the system;

[0010] If G≤H:

[0011] S11. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data i The relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i , based on the different time T associated with different format classification areas i , select T i max is recorded as the characteristic time value of the format to be converted;

[0012] S12. Randomly select another read / write format as the format to be converted, and use the same processing method as step S11 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format;

[0013] S13, converting the multiple groups of teaching resources confirmed within the limited period into an execution format to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing end;

[0014] If G>H:

[0015] S21. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data i The relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i ;

[0016] S22, the confirmed several groups of formats G iPerform random combination to confirm the format set, so that the number of format sets after random combination is consistent with the number of conversion processes H preset in the system, and confirm the collection time of different format sets, which is the different time T in this format set. i The sum of the values of the different set times associated with several different format sets is used, and the maximum value of the set time is selected as the characteristic time value of this format set;

[0017] Different random combination processes are associated with different sets of formats. The same method as above is used to process other sets of formats. The characteristic time values associated with the corresponding sets of formats are confirmed. The combination process with the smallest characteristic time value is recorded as the standard process, and the characteristic time value associated with the standard process is simultaneously recorded as the characteristic time value associated with the format to be converted this time.

[0018] S23. Randomly select another read / write format as the format to be converted, and use the same processing method as steps S21-S22 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format;

[0019] S24: converting the multiple groups of teaching resources confirmed within the limited period into an execution format, combining them according to the confirmed standard process during the format conversion to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing terminal;

[0020] The graph classification processing end performs knowledge graph classification on several groups of teaching resources after the format is unified. Based on multiple branch knowledge points preset in the knowledge graph, the knowledge points to which the corresponding teaching resources belong are confirmed. The knowledge points to which they belong have been marked in advance in the corresponding teaching resources. The knowledge points to which they belong are compared with the preset branch knowledge points to confirm the specific location of the corresponding teaching resources in the knowledge graph and store them.

[0021] Preferably, the knowledge graph is a preset graph, and several different branch knowledge points are set in the knowledge graph. Based on the set branch knowledge points and the knowledge points to which the teaching resources belong, the associated teaching resources are classified so that the teaching resources of the corresponding knowledge points are stored at different knowledge point positions in the corresponding knowledge graph.

[0022] Preferably, it also includes:

[0023] The resource output optimization end restores the format of the associated teaching resources for the branch knowledge points selected by external personnel in the knowledge graph, adjusts the output computing power during the restoration process, and optimizes the output rate. The specific methods are as follows:

[0024] Based on the knowledge points selected by external stakeholders, identify the teaching resources associated with such knowledge points, restore the format of the associated teaching resources, and monitor the format restoration process associated with different teaching resources to confirm the real-time format restoration rate associated with the corresponding process;

[0025] Performing an average processing on the format recovery rate associated with the corresponding process in real time, determining the average rate, then determining the remaining educational resources of the teaching resources corresponding to the corresponding process, determining the data capacity of the remaining educational resources, and determining the recovery time associated with the corresponding remaining educational resources based on the determined data capacity and the average rate;

[0026] Based on the different recovery times T associated with different recovery processes k , where k represents different recovery processes, based on multiple recovery processes in real time, from T k In the recovery process associated with max, k The restoration process associated with min transfers computing power, completes the restoration process of several groups of teaching resources, and displays the restored groups of teaching resources.

[0027] The present invention provides an information literacy knowledge tree platform system. Compared with the existing technology, it has the following advantages:

[0028] Beneficial effects:

[0029] The present invention unifies the formats of the teaching resources acquired within a limited period, giving priority to confirming the corresponding classification number based on the specific classification of the corresponding format, and executing different conversion processing logics based on the specific verification of the classification number and the number of preset processes in the system to complete the format unification process of the corresponding several teaching resources. In this way, it can effectively ensure that under different number verification states, the format of the resource conversion can reach the fastest state, be in the optimal solution, and achieve the fastest processing method, thereby reducing the integration time of several educational resources when integrating them and improving the practicality of the platform.

[0030] Subsequently, when extracting resources, the recovery process of each teaching resource is monitored based on the multiple groups of teaching resources associated with the corresponding branch knowledge points, and the computing power associated with different recovery processes is reallocated based on the actual progress monitored. Based on the actual allocation processing process, the recovery time associated with each teaching resource is ensured to be relatively consistent, so as to achieve better teaching resource output effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0032] Figure 2 Schematic diagram of the structure of the knowledge tree platform of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 are within the scope of protection of the present invention.

[0034] First embodiment

[0035] See also Figure 1 , the present application provides an information literacy knowledge tree platform system, including a periodic resource acquisition end, a resource integration end, a graph classification processing end, and a resource output optimization end, wherein the periodic resource acquisition end, the resource integration end, the graph classification processing end, and the resource output optimization end are all electrically connected from the output node to the input node in sequence;

[0036] The periodic resource acquisition terminal acquires the teaching resources associated with a limited period, where the limited period is a preset period, which is set by relevant operators based on experience, and transmits the teaching resources acquired within the limited period to the resource integration terminal, where the teaching resources can be uploaded by relevant management parties;

[0037] The resource integration end unifies the format of the acquired teaching resources, first confirming the total number of individual teaching resources, and then determining the format unification logic based on the number of conversion processes preset in the system and the system's preset read and write formats. The specific method of unification is as follows:

[0038] Determine the teaching resources acquired during this limited period, classify the teaching resources in different formats and record them as format classification areas, and mark the number of the determined format classification areas as G;

[0039] Then confirm the number of conversion processes H preset in the system. Specifically, the preset conversion processes have been preset in this system in advance by relevant operators based on the system's computing power. The associated conversion processes can uniformly convert content in related formats to obtain converted content in the same format;

[0040] If G≤H:

[0041] S11. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data iThe relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i , based on the different time T associated with different format classification areas i , select T i Max is recorded as the characteristic time value of the format to be converted. Specifically, each different conversion process performs related conversions between different formats. The existing historical data can be confirmed from the cloud data, and the associated rate data can be confirmed from the historical data. Thus, the time characteristics associated with each group of conversion processes can be confirmed. Then, based on the multiple groups of time characteristics associated with the corresponding formats to be converted, the characteristic time value associated with the corresponding formats to be converted is selected, thereby performing specific confirmation of the formats to be converted.

[0042] S12. Randomly select another read / write format as the format to be converted, and use the same processing method as step S11 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format;

[0043] S13, converting the multiple groups of teaching resources confirmed within the limited period into an execution format to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing end;

[0044] If G>H:

[0045] S21. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data i The relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i ;

[0046] S22, the confirmed several groups of formats G iPerform random combination (two sets of formats can be a combination, or three sets of formats can be a combination, both are random combination forms) to confirm the format set, so that the number of format sets after random combination is consistent with the number of conversion processes H preset in the system, and confirm the collection time of different format sets, which is the different time T in this format set. i The sum of the values of the different set times associated with several different format sets is used, and the maximum value of the set time is selected as the characteristic time value of this format set;

[0047] Different random combination processes are associated with different sets of formats. The same method as above is used to process other sets of formats. The characteristic time values associated with the corresponding sets of formats are confirmed. The combination process with the smallest characteristic time value is recorded as the standard process, and the characteristic time value associated with the standard process is simultaneously recorded as the characteristic time value associated with the format to be converted this time.

[0048] S23. Randomly select another read / write format as the format to be converted, and use the same processing method as steps S21-S22 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format;

[0049] S24: converting the multiple groups of teaching resources confirmed within the limited period into an execution format, combining them according to the confirmed standard process during the format conversion to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing terminal;

[0050] Specifically, since the number of corresponding classification formats is relatively larger than the number of corresponding conversion processes, in order to enable multiple groups of different classification formats to achieve a faster conversion method and fully reduce the conversion time, there are multiple groups of combination methods. In order to confirm the optimal solution, it is necessary to confirm the standard process associated with the corresponding format to be converted, and then confirm the formats to be converted one by one, so that the best format to be converted and the best standard process can be locked, so as to achieve the fastest conversion method, reduce the conversion time, and achieve better conversion effect.

[0051] Second embodiment

[0052] Among them, the graph classification processing end performs knowledge graph classification on several groups of teaching resources after the format is unified, and confirms the knowledge points to which the corresponding teaching resources belong based on multiple knowledge points preset in the knowledge graph. The knowledge points to which they belong have been marked in advance in the corresponding teaching resources. The knowledge points to which they belong are compared with the preset knowledge points to confirm the specific location of the corresponding teaching resources in the knowledge graph and store them. The knowledge graph is a preset graph, which is prepared in advance by relevant operators, and several different branch knowledge points are set in the knowledge graph. Based on the set branch knowledge points and the knowledge points to which the teaching resources belong, the associated teaching resources are classified so that the teaching resources of the corresponding knowledge points are stored at different knowledge point positions in the corresponding knowledge graph.

[0053] The resource output optimization end restores the format of the associated teaching resources for the knowledge points selected by external personnel in the knowledge graph, adjusts the output computing power during the restoration process, and optimizes the output rate. The specific adjustment methods are:

[0054] Based on the knowledge points selected by external stakeholders, identify the teaching resources associated with such knowledge points, restore the format of the associated teaching resources, and monitor the format restoration process associated with different teaching resources to confirm the real-time format restoration rate associated with the corresponding process;

[0055] Performing an average processing on the format recovery rate associated with the corresponding process in real time, determining the average rate, then determining the remaining educational resources of the teaching resources corresponding to the corresponding process, determining the data capacity of the remaining educational resources, and determining the recovery time associated with the corresponding remaining educational resources based on the determined data capacity and the average rate;

[0056] Based on the different recovery times T associated with different recovery processes k , where k represents different recovery processes, based on multiple recovery processes in real time, from T k In the recovery process associated with max, k The restoration process associated with min transfers computing power, completes the restoration process of several groups of teaching resources, and displays the restored groups of teaching resources. As the restoration process continues, its T k The recovery process associated with max and T k The restoration processes associated with min will change. During the change process, the specific time for each group of restoration processes to complete the restoration will be relatively close, which can achieve a better display effect and facilitate the corresponding personnel to view the teaching resources, avoiding the situation where some teaching resources have been restored but some are still not completed, which will affect the specific usage effect of the relevant personnel.

[0057] Third embodiment

[0058] Combine Figure 2 The information literacy knowledge tree platform system also has multiple functional modules, namely entry editing function, resource uploading function and course building function. The entry editing function includes: adding entries, modifying entries and deleting entries. Users can edit entries in the knowledge tree, including adding, modifying and deleting. It supports users to upload various types of information literacy education resources, such as documents (PDF, Word, etc.), videos, audio, pictures, etc. The edited entries will be reviewed for format and content to ensure their legality and validity and avoid the spread of bad information. After passing the review, they will be updated to the knowledge tree for other users to view and learn.

[0059] Resource upload functions include uploading resources, linking resources to nodes, and deleting resources. Users can upload educational resources related to information literacy (such as documents, videos, and audio) to the platform and link them to corresponding knowledge nodes. These resources will serve as supplementary materials for the knowledge tree, providing users with richer learning content. Resource classification and tagging functions are provided to facilitate users to search and filter resources by type and topic.

[0060] Course component functions include selecting knowledge points, selecting resources, and building courses. Users can select relevant knowledge points and resources from the knowledge tree based on their teaching needs to build personalized information literacy courses. Courses can include multiple chapters and knowledge points. Users can save their built courses to the system for easy viewing, modification, and sharing.

[0061] Based on the information literacy knowledge framework provided by experts in the field of information literacy, an information literacy knowledge tree consisting of multiple knowledge nodes is constructed. Each knowledge node represents a knowledge point or concept related to information literacy, and a systematic knowledge tree is formed through the association between nodes. An intuitive and easy-to-use knowledge tree display interface is designed to support users to browse and navigate the information literacy knowledge system in a graphical manner. Users are allowed to expand or collapse the sub-nodes of a node as needed to better understand the hierarchy and association of knowledge points.

[0062] By building an information literacy knowledge tree, we achieve a systematic and structured display of knowledge, making it easier for users to quickly understand the overall framework and core content of information literacy.

[0063] Resource sharing: The platform provides entry editing and resource uploading functions, allowing users to easily share and update information literacy education resources, promoting the dissemination and sharing of knowledge;

[0064] Personalized learning: The platform supports users to build personalized information literacy courses based on their needs and interests, and provides users with independent learning functions to improve the pertinence and efficiency of learning.

[0065] The specific steps are as follows:

[0066] Constructing an information literacy knowledge tree: Based on the knowledge framework provided by experts in the field of information literacy, determine the main nodes and relationships of the knowledge tree. For example, information literacy can be divided into several main nodes, such as information awareness, information knowledge, information ability, and information ethics. Under each main node, further subdivide specific subnodes to form a systematic knowledge tree structure. To achieve an intuitive display of the knowledge tree, design a user-friendly graphical interface. This interface allows users to browse and navigate the information literacy knowledge system in a graphical manner. Users can expand or collapse the subnodes of a node as needed to better understand the hierarchy and relationships of knowledge points.

[0067] Developing and editing entries: Users can choose to upload various types of information literacy education resources, such as documents (PDF, Word, etc.), videos, audio, images, etc., to enrich the content of the entry. The system will review the format and content of the resources uploaded by users to ensure the legality and validity of the resources and avoid the spread of negative information. After editing is completed, the system will update the entry and synchronize it to the knowledge tree in real time for other users to view and learn. Users can also add, modify, and delete entries.

[0068] Development resource upload: Users upload educational resources related to information literacy to the platform and associate them with corresponding knowledge nodes. These resources will serve as supplementary materials for the knowledge tree, providing users with richer learning content. Resource classification and tagging functions are provided to achieve effective resource management. Users can classify and tag resources according to type and subject, making it easier for other users to search and filter based on these classifications and tags. When uploading resources, the system will review the format and content of the resources to ensure their legality and validity. Once the review is passed, the resources will be saved in the system and associated with the corresponding knowledge node.

[0069] Develop course components: Users select relevant knowledge points and resources from the knowledge tree to build personalized information literacy courses. When building a course, users can select multiple chapters and knowledge points, and sort and combine them according to their own teaching needs. The system provides an intuitive course editing interface to facilitate users to adjust and modify courses. After completing the course building, users can save the built course to the system for viewing, modification and sharing at any time.

[0070] Among them, concepts related to information include: data, big data, information, knowledge, intelligence, wisdom, intelligence, artificial intelligence and the DI KW pyramid;

[0071] Concepts related to information literacy include: literacy, computer literacy, network literacy, information literacy, information communication and technology literacy, media literacy, media and information literacy, data literacy, digital skills, digital literacy and skills, digital capabilities, digital IQ, artificial intelligence literacy, algorithm literacy and element literacy.

[0072] The specific structure of the information literacy knowledge system includes: theory, methods, tools, operations, applications, ethics and law, and security.

[0073] The theoretical part specifically includes: information-related concepts, information literacy-related concepts, information retrieval-related concepts, types of information retrieval, the history of information retrieval, and models of information retrieval;

[0074] The methods section specifically includes: Boolean logic, position restriction, exact search, truncation search, case-sensitive search, and field restriction search;

[0075] Tools include: search engines, social media, AI GC tools, free comprehensive information retrieval systems, commercial comprehensive information retrieval systems, professional information retrieval systems, commercial comprehensive information retrieval systems, commercial and professional academic resource retrieval platforms, and free professional information retrieval systems;

[0076] The operation section includes: Chinese specific type of literature search and foreign specific type of literature search;

[0077] The application section includes: information selection and evaluation, digital processing and processing tools, data analysis tools, reference management tools, digital life, digital learning, digital work, digital innovation, and how to use AI to help you work;

[0078] The ethics and law section includes: online etiquette, information cocoon, information gap, digital divide, intellectual property rights, etc.;

[0079] The security section includes: personal information protection, privacy protection, telecommunications fraud, digital addiction, and cyber violence.

[0080] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0081] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An information literacy knowledge tree platform system, characterized by: include: The periodic resource acquisition terminal acquires the teaching resources associated with the limited period and transmits the teaching resources acquired within the limited period to the resource integration terminal; The resource integration end unifies the format of the acquired teaching resources, first confirming the total number of individual teaching resources, and then determining the format unification logic based on the number of conversion processes preset in the system and the system's preset read and write formats. The specific method is to unify the formats of the confirmed groups of teaching resources. Determine the teaching resources acquired during this limited period, classify the teaching resources in different formats and record them as format classification areas, and mark the number of the determined format classification areas as G; Then confirm the number of conversion processes H preset in the system; If G≤H: S11. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data i The relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i , based on the different time T associated with different format classification areas i , select T i max is recorded as the characteristic time value of the format to be converted; S12. Randomly select another read / write format as the format to be converted, and use the same processing method as step S11 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format; S13, converting the multiple groups of teaching resources confirmed within the limited period into an execution format to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing end; If G>H: S21. Randomly select a group of read-write formats from the system preset read-write formats and record them as formats to be converted. The different formats associated with different format classification areas are recorded as G i , where i represents different format classification areas, and the corresponding format G is confirmed from the cloud data i The relevant rate data to be converted to the format to be converted is averaged among the confirmed groups of relevant rate data to determine the average rate V i , and then based on the total data volume R of teaching resources in the corresponding format classification area i , using R i ÷V i =T i Confirm the time T required for the corresponding format classification area to complete the conversion of the format to be converted i ; S22, the confirmed several groups of formats G i Perform random combination to confirm the format set, so that the number of format sets after random combination is consistent with the number of conversion processes H preset in the system, and confirm the collection time of different format sets, which is the different time T in this format set. i The sum of the values of the different set times associated with several different format sets is used, and the maximum value of the set time is selected as the characteristic time value of this format set; Different random combination processes are associated with different sets of formats. The same method as above is used to process other sets of formats. The characteristic time values associated with the corresponding sets of formats are confirmed. The combination process with the smallest characteristic time value is recorded as the standard process, and the characteristic time value associated with the standard process is simultaneously recorded as the characteristic time value associated with the format to be converted this time. S23. Randomly select another read / write format as the format to be converted, and use the same processing method as steps S21-S22 to lock the characteristic time value associated with the corresponding format to be converted. Based on the different characteristic time values associated with different formats to be converted, the format to be converted with the smallest characteristic time value is selected as the execution format; S24: converting the multiple groups of teaching resources confirmed within the limited period into an execution format, combining them according to the confirmed standard process during the format conversion to complete format unification, and transmitting the multiple groups of teaching resources after format unification to the graph classification processing terminal; The graph classification processing end performs knowledge graph classification on several groups of teaching resources after the format is unified. Based on multiple branch knowledge points preset in the knowledge graph, the knowledge points to which the corresponding teaching resources belong are confirmed. The knowledge points to which they belong have been marked in advance in the corresponding teaching resources. The knowledge points to which they belong are compared with the preset branch knowledge points to confirm the specific location of the corresponding teaching resources in the knowledge graph and store them.

2. The information literacy knowledge tree platform system according to claim 1, characterized in that: The limited period is a preset period, and its teaching resources are uploaded by the relevant management party.

3. The information literacy knowledge tree platform system according to claim 1, characterized in that: The knowledge graph is a preset graph, and several different branch knowledge points are set in the knowledge graph. Based on the set branch knowledge points and the knowledge points to which the teaching resources belong, the associated teaching resources are classified so that the teaching resources of the corresponding knowledge points are stored at different knowledge point positions in the corresponding knowledge graph.

4. The information literacy knowledge tree platform system according to claim 1, characterized in that: Also includes: On the resource output optimization side, the format of the associated teaching resources is restored for the branch knowledge points selected by external personnel in the knowledge graph, and the output computing power is adjusted during the restoration process to optimize the output rate.

5. The information literacy knowledge tree platform system according to claim 4, characterized in that: The resource output optimization end adjusts the output computing power during the recovery process in the following specific ways: Based on the knowledge points selected by external stakeholders, identify the teaching resources associated with such knowledge points, restore the format of the associated teaching resources, and monitor the format restoration process associated with different teaching resources to confirm the real-time format restoration rate associated with the corresponding process; Performing an average processing on the format recovery rate associated with the corresponding process in real time, determining the average rate, then determining the remaining educational resources of the teaching resources corresponding to the corresponding process, determining the data capacity of the remaining educational resources, and determining the recovery time associated with the corresponding remaining educational resources based on the determined data capacity and the average rate; Based on the different recovery times T associated with different recovery processes k , where k represents different recovery processes, based on multiple recovery processes in real time, from T k In the recovery process associated with max, k The restoration process associated with min transfers computing power, completes the restoration process of several groups of teaching resources, and displays the restored groups of teaching resources.

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