Information quality knowledge tree platform system

Abstract

The invention discloses an information quality knowledge tree platform system, relates to the technical field of education, and solves the problems that the associated integration rate is relatively low and a relatively good resource integration effect cannot be achieved due to the fact that specific confirmation of conversion logic is not carried out aiming at different integration conditions. According to the method, when format unification is carried out on the teaching resources obtained in the limited period, the corresponding classification number is confirmed preferentially based on the specific classification of the corresponding format, and different conversion processing logics are executed based on the specific checking condition of the classification number and the preset process number in the system; by adopting the mode, the resource conversion format can be effectively guaranteed to reach the fastest state, be in the optimal solution and reach the fastest processing mode under different number checking states, the integration time of the plurality of educational resources during integration is shortened, and the practicability of the platform is improved.

Description

Technical Field

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

[0002] Information literacy education has been increasingly emphasized in fields such as library and information science, higher education, etc. However, the existing information literacy education resources are relatively scattered, and there is a lack of a unified knowledge management and sharing platform, which makes it difficult for users to efficiently obtain, edit, and learn information literacy-related knowledge. Therefore, a platform system that can integrate information literacy education resources and provide convenient editing and learning functions is needed.

[0003] Regarding the educational resources associated with the information literacy knowledge tree platform, due to the inconsistent formats, when the platform integrates most of the educational resources with inconsistent formats, it directly converts the educational resources in the corresponding formats into the relevant formats that can be read and written by this system, without specifically confirming the conversion logic for different integration situations, resulting in a relatively low integration rate of the associated resources and not achieving a good resource integration effect, and the integration time is slow. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an information literacy knowledge tree platform system, which solves the problem that the specific conversion logic is not confirmed for different integration situations, resulting in a relatively low integration rate of the associated resources and not achieving a good resource integration effect.

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

[0006] A periodic resource acquisition end, which acquires the teaching resources associated within a limited period and transmits the teaching resources acquired within the limited period to the resource integration end;

[0007] A resource integration end, which unifies the formats of the acquired teaching resources, first confirms the total number of monomers of the teaching resources, and then determines the format unification logic based on the number of preset conversion processes and the preset read-write formats within the system, and unifies the formats of the confirmed several groups of teaching resources. The specific method is as follows:

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

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

[0010] If G≤H:

[0011] S11. Randomly select a set of read-write formats from the system-predefined read-write formats and denote it as the format to be converted. Denote the different formats associated with different format classification areas as G i , where i represents different format classification areas, and confirm the relevant rate data for conversion to the format to be converted from the cloud data. Perform mean processing on the confirmed several groups of relevant rate data to confirm the mean rate V i . Then, based on the total amount of resource data R i of the teaching resources in the corresponding format classification area, use R i divided by V i = T i to confirm the time T i required for the corresponding format classification area to complete the conversion to the format to be converted. Based on the different times T i associated with different format classification areas, select T i max and denote it as the characteristic time value of this format to be converted; i

[0012] S12. Then randomly select other read-write formats and denote them as the formats to be converted. Adopt the same processing method as in step S11 to lock the characteristic time values associated with the corresponding formats to be converted. Based on the different characteristic time values associated with different formats to be converted, take the format to be converted with the minimum characteristic time value as the execution format;

[0013] S13. Convert the several groups of teaching resources confirmed within this defined period to the execution format to complete format unification, and transmit the several groups of teaching resources after format unification to the graph classification processing end;

[0014] If G > H:

[0015] S21. Randomly select a set of read-write formats from the system-predefined read-write formats and denote it as the format to be converted. Denote the different formats associated with different format classification areas as G i , where i represents different format classification areas, and confirm the relevant rate data for conversion to the format to be converted from the cloud data. Perform mean processing on the confirmed several groups of relevant rate data to confirm the mean rate V i . Then, based on the total amount of resource data R i of the teaching resources in the corresponding format classification area, use R i divided by V i = T i to confirm the time T i required for the corresponding format classification area to complete the conversion to the format to be converted; i ;

[0016] S22. The 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 H of preset conversion processes in the system, confirm the set time of different format sets, and the set time is the sum value of different times T within this format set. Based on the different set times associated with several different format sets, select the maximum value of the set time as the characteristic time value of this format set; i For the sum value of, based on the different set times associated with several different format sets, select the maximum value of the set time as the characteristic time value of this format set;

[0017] Different random combination processes are associated with different multiple groups of format sets. Process other multiple groups of format sets in the same way as above, confirm the characteristic time values associated with the corresponding multiple groups of format sets, record the combination process with the smallest characteristic time value as the standard process, and synchronously record the characteristic time value associated with the standard process as the characteristic time value associated with the format to be converted this time;

[0018] S23. Randomly select other read-write formats as the format to be converted, adopt the same processing method as in steps S21 - S22, lock the characteristic time value associated with the corresponding format to be converted, and based on the different characteristic time values associated with different formats to be converted, use the format to be converted with the minimum characteristic time value as the execution format;

[0019] S24. Convert several groups of teaching resources confirmed within this limited period to the execution format, and perform combination according to the confirmed standard process during format conversion to complete format unification, and transmit the several groups of teaching resources after format unification to the knowledge graph classification processing end;

[0020] The knowledge graph classification processing end classifies several groups of teaching resources after format unification. Based on multiple branch knowledge points preset in the knowledge graph, confirm the knowledge points to which the corresponding teaching resources belong. The knowledge points to which they belong have been pre - marked in the corresponding teaching resources. Compare the knowledge points to which they belong with the preset branch knowledge points to confirm the specific positions of the corresponding teaching resources in the knowledge graph and store them.

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

[0022] Preferably, it further includes:

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

[0024] Based on the knowledge points selected by external relevant personnel, confirm the teaching resources associated with such knowledge points, restore the format of the associated teaching resources, monitor the format restoration processes associated with different teaching resources, and confirm the format restoration rate associated with the corresponding processes in real time;

[0025] Perform mean processing on the format restoration rate associated with the corresponding process in real time to confirm the mean rate, then determine the remaining educational resources of the corresponding teaching resources for the corresponding process, determine the data capacity of the remaining educational resources, and based on the determined data capacity and the mean rate, confirm the restoration time associated with the corresponding remaining educational resources;

[0026] Based on the different restoration times T associated with different restoration processes k , where k represents different restoration processes. Based on multiple groups of restoration processes carried out in real time, transfer computing power from the restoration process associated with T k max to the restoration process associated with T k min to complete the restoration process of several groups of teaching resources, and display the restored several groups of teaching resources.

[0027] The present invention provides an information literacy knowledge tree platform system. Compared with the prior art, it has the following

[0028] Beneficial effects:

[0029] When the present invention unifies the format of the teaching resources obtained within a limited period, it first confirms the corresponding number of classifications based on the specific classification of the corresponding format, and based on the specific verification situation between the number of classifications and the number of preset processes in the system, executes different conversion processing logics to complete the format unification process of the corresponding several teaching resources. By using this method, it can effectively ensure that in different number verification states, the format of resource conversion can reach the fastest state, be in the optimal solution, achieve the fastest processing method, reduce the integration time of several educational resources during integration, and improve the practicability of the platform;

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

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

[0032] Figure 2 is a schematic diagram of the structure of the knowledge tree platform of the present invention. Detailed implementation mode

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] First embodiment

[0035] Please refer to Figure 1 , this 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. Among them, the periodic resource acquisition end, the resource integration end, the graph classification processing end, and the resource output optimization end are electrically connected in sequence from the output node to the input node;

[0036] Among them, the periodic resource acquisition end acquires the teaching resources associated within a limited period. The limited period is a preset period, which is determined by relevant operators according to experience, and transmits the teaching resources acquired within the limited period to the resource integration end. The teaching resources can be uploaded by relevant management parties;

[0037] Among them, the resource integration end unifies the formats of the acquired teaching resources. First, it confirms the total number of monomers of the teaching resources, and then determines the format unification logic based on the number of preset conversion processes in the system and the preset read-write format in the system, and unifies the formats of the confirmed several groups of teaching resources. The specific method of unification is as follows:

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

[0039] Then confirm the number H of preset conversion processes in the system. Specifically, the preset conversion processes have been preset in this system in advance, and are preset by relevant operators in advance according to the holding computing power of the system. The associated conversion processes can uniformly convert the content of relevant formats to obtain the conversion content of the same format;

[0040] If G≤H:

[0041] S11. Randomly select a group of read-write formats from the preset read-write formats in the system and record them as the format to be converted, and record the different formats associated with different format classification areas as G i , where i represents different format classification areas, and confirm the corresponding format G from the cloud data iFor the relevant rate data to be converted to the target format, perform mean processing on several groups of confirmed relevant rate data to confirm the mean rate V i Then, based on the total amount of resource data R i of the teaching resources in the corresponding format classification area i Use R i ÷V i = T i Confirm the time T required to complete the conversion to the target format in the corresponding format classification area i Based on the different times T i associated with different format classification areas i Select T i max as the characteristic time value of this target format. Specifically, here, each group of different conversion processes performs relevant conversions between different formats. Historical data that existed in the past 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 several groups of time characteristics associated with the target format to be converted, select the characteristic time value associated with the target format to be converted to specifically confirm the target format;

[0042] S12. Then, randomly select another read / write format as the target format to be converted, and use the same processing method as in step S11 to lock the characteristic time value associated with the target format to be converted. Based on the different characteristic time values associated with different target formats to be converted, use the target format with the minimum characteristic time value as the execution format;

[0043] S13. Convert several groups of teaching resources confirmed within this defined period to the execution format to complete format unification, and transfer the several groups of teaching resources after format unification to the atlas classification processing end;

[0044] If G > H:

[0045] S21. Randomly select a group of read / write formats from the system-predefined read / write formats and record it as the target format to be converted. Denote the different formats associated with different format classification areas as G i where i represents different format classification areas. Confirm the relevant rate data for the conversion of the corresponding format G i to the target format. Perform mean processing on the several groups of confirmed relevant rate data to confirm the mean rate V i Then, based on the total amount of resource data R i of the teaching resources in the corresponding format classification area i Use R i ÷V i = T i Confirm the time T required to complete the conversion to the target format in the corresponding format classification area i ;

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

[0047] Different random combination processes are associated with different multiple groups of format sets. Process other multiple groups of format sets in the same way as above, confirm the characteristic time values associated with the corresponding multiple groups of format sets, record the combination process with the smallest characteristic time value as the standard process, and synchronously record the characteristic time value associated with the standard process as the characteristic time value associated with the format to be converted this time;

[0048] S23. Then randomly select other read-write formats as the format to be converted, and use the same processing method as in 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, use the format to be converted with the minimum characteristic time value as the execution format;

[0049] S24. Convert several groups of teaching resources confirmed within this defined period to the execution format, and perform combination according to the confirmed standard process during format conversion to complete format unification, and transmit the several groups of teaching resources after format unification to the atlas classification processing end;

[0050] Specifically, since the number of corresponding classification formats is relatively more 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 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 each format to be converted one by one, so as to lock the best format to be converted and the best standard process, so as to achieve the fastest conversion method, reduce the conversion time, and achieve a better conversion effect.

[0051] Second Embodiment

[0052] Among them, the knowledge graph classification processing end classifies several groups of teaching resources after format unification. Based on multiple preset knowledge points in the knowledge graph, it confirms the knowledge points to which the corresponding teaching resources belong. The knowledge points to which they belong have been pre-marked in the corresponding teaching resources. It compares the knowledge points to which they belong with the preset knowledge points, confirms the specific positions of the corresponding teaching resources in the knowledge graph, and stores them. The knowledge graph is a preset graph, drawn up in advance by relevant operators, and there are several different branch knowledge points set in the knowledge graph. Based on the set branch knowledge points and the knowledge points to which the teaching resources belong, it classifies the associated teaching resources, so that the teaching resources corresponding to the knowledge points are stored at different knowledge point positions in the corresponding knowledge graph.

[0053] Among them, the resource output optimization end restores the format of the teaching resources associated with the knowledge points selected by external personnel in the knowledge graph, and adjusts the output computing power and optimizes the output rate during the restoration process. The specific method of adjustment is as follows:

[0054] Based on the knowledge points selected by external relevant personnel, it confirms the teaching resources associated with such knowledge points, restores the format of the associated teaching resources, and monitors the format restoration processes associated with different teaching resources to confirm the format restoration rate associated with the corresponding processes in real time;

[0055] It processes the average value of the format restoration rate associated with the corresponding processes in real time to confirm the average rate, then determines the remaining educational resources of the corresponding teaching resources for the corresponding processes, determines the data capacity of the remaining educational resources, and based on the determined data capacity and the average rate, confirms the restoration time associated with the corresponding remaining educational resources;

[0056] Based on the different restoration times T associated with different restoration processes k , where k represents different restoration processes. Based on multiple groups of restoration processes carried out in real time, from the restoration processes associated with T k max to the restoration processes associated with T k min, it transfers computing power, completes the restoration process of several groups of teaching resources, and displays the restored several groups of teaching resources. Among them, as the restoration process continues, the restoration processes associated with T k max and the restoration processes associated with T k min will both change. During the change process, the specific times restored by each group of restoration processes are relatively close, so as to achieve a better display effect, facilitate the corresponding personnel to view the teaching resources, and avoid the situation that some teaching resources have been restored while some teaching resources are still not completed, which will affect the specific use effect of relevant personnel.

[0057] Third Embodiment

[0058] Combined with Figure 2 , the information literacy knowledge tree platform system also has multiple functional modules, namely the entry editing function, the resource uploading function, and the course building function. The entry editing function includes: adding entries, modifying entries, and deleting entries. Users can edit the entries in the knowledge tree, including operations such as adding, modifying, and deleting. It supports users to upload various types of information literacy education resources, such as documents (PDF, Word, etc.), videos, audios, pictures, etc. The edited entries will be reviewed for format and content to ensure their legality and effectiveness, and to 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] Among them, the resource uploading function includes: uploading resources, associating resources to nodes, and deleting resources. Users can upload education resources related to information literacy (such as documents, videos, audios, etc.) to the platform and associate them to the corresponding knowledge nodes. These resources will serve as supplementary materials for the knowledge tree, providing users with richer learning content. It provides resource classification and tagging functions to facilitate users to search and filter according to the type and theme of resources.

[0060] Among them, the course component function includes: selecting knowledge points, selecting resources, and building courses. Users can select relevant knowledge points and resources from the knowledge tree according to teaching needs to build personalized information literacy courses, and the courses can include multiple chapters and knowledge points. It supports users to save the built courses to the system for viewing, modifying, and sharing at any time.

[0061] According to the information literacy knowledge framework provided by information literacy field experts, construct an information literacy knowledge tree containing multiple knowledge nodes. Each knowledge node represents a knowledge point or concept related to information literacy, and a systematic knowledge tree is formed through the association relationships between nodes; design an intuitive and easy-to-use knowledge tree display interface to support users to browse and navigate the information literacy knowledge system in a graphical way; allow users to expand or collapse the sub-nodes of the nodes according to needs to better understand the levels and associations of knowledge points;

[0062] By constructing the information literacy knowledge tree, the systematic and structured display of knowledge is realized, which is convenient 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, enabling users to conveniently share and update information literacy education resources, promoting the spread and sharing of knowledge;

[0064] Learning personalization: The platform supports users to build personalized information literacy courses according to their own needs and interests, and provides users with autonomous learning functions, improving the pertinence and efficiency of learning.

[0065] The specific operation steps are as follows:

[0066] Construct an information literacy knowledge tree: Determine the main nodes and association relationships of the knowledge tree according to the knowledge framework provided by experts in the field of information literacy; for example, divide information literacy into several main nodes such as information awareness, information knowledge, information ability, and information ethics; under each main node, further subdivide specific sub-nodes to form a systematic knowledge tree structure; to achieve an intuitive display of the knowledge tree, design a graphical interface that is easy for users to operate; this interface supports users to browse and navigate the information literacy knowledge system in a graphical way; users can expand or collapse the sub-nodes of the nodes according to their needs to better understand the levels and association relationships of knowledge points.

[0067] Develop entry editing: Users can choose to upload various types of information literacy education resources, such as documents (PDF, Word, etc.), videos, audios, pictures, 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 effectiveness of the resources and avoid the spread of bad information; after the 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, and allow users to perform operations such as adding, modifying, and deleting the entry.

[0068] Develop resource upload: Users upload education resources related to information literacy to the platform and associate them with the corresponding knowledge nodes; these resources will serve as supplementary materials for the knowledge tree to provide users with richer learning content; provide resource classification and tagging functions to achieve effective management of resources; users can classify and tag resources according to the type and theme of the resources, which is convenient for other users to search and filter according to these classifications and tags; when uploading resources, the system will review the format and content of the resources to ensure the legality and effectiveness of the resources; after the review is passed, the resources will be saved in the system and associated with the corresponding knowledge nodes.

[0069] Develop course assembly: Users select relevant knowledge points and resources from the knowledge tree to assemble personalized information literacy courses; when assembling courses, users can select multiple chapters and knowledge points and sort and combine them according to their teaching needs; the system provides an intuitive course editing interface to facilitate users to adjust and modify the courses; after completing the course assembly, users can save the assembled courses in the system for viewing, modifying, and sharing at any time.

[0070] Among them, the concepts related to information include: data, big data, information, knowledge, intelligence, wisdom, intelligence, artificial intelligence, and the DIKW 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, algorithmic literacy, and elemental literacy.

[0072] Among them, the specific structure of the information literacy knowledge system includes: theory section, method section, tool section, operation section, application section, ethics and law section, and security section, etc.;

[0073] And the theory section specifically includes: concepts related to information, concepts related to information literacy, concepts related to information retrieval, types of information retrieval, history of information retrieval, and models of information retrieval;

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

[0075] The tool section includes: 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, free professional information retrieval systems;

[0076] The operation section includes: retrieval of specific types of Chinese literature and retrieval of specific types of foreign literature;

[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: netiquette, information cocoons, information gaps, digital divides, intellectual property rights, etc.;

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

[0080] Some of the data in the above formulas are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0081] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced 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 in that: 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 confirms the total number of teaching resources, and then determines the format unification logic based on the number of conversion processes preset in the system and the system's preset reading and writing formats, and unifies the formats of the confirmed groups of teaching resources; At the graph classification processing end, knowledge graph classification is performed 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 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 specific method of unifying the formats of several groups of teaching resources at the resource integration end is: Determine the teaching resources acquired in 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, and record different formats associated with different format classification areas 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, the confirmed several groups of relevant rate data are averaged, and the average rate V is determined i , and then based on the total amount of resource data 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 adopt the same processing method as step S11 to lock the characteristic time value associated with the corresponding format to be converted, and 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 used as the execution format; S13. Convert several groups of teaching resources confirmed within this limited period into an execution format to complete format unification, and transmit the several groups of teaching resources after format unification to the graph classification processing terminal.

3. The information literacy knowledge tree platform system according to claim 2, characterized in that: 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, and record different formats associated with different format classification areas 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, the confirmed several groups of relevant rate data are averaged, and the average rate V is determined i , and then based on the total amount of resource data 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 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 to select the maximum value of the set time as the characteristic time value of the current format set; Different random combination processes are associated with different multiple sets of format sets. The other multiple sets of format sets are processed in the same manner as above, and the characteristic time values ​​associated with the corresponding multiple sets of format sets 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 adopt the same processing method as steps S21-S22 to lock the characteristic time value associated with the corresponding format to be converted, and 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 used as the execution format; S24. Convert several groups of teaching resources confirmed within this limited period into an execution format, and combine them according to the confirmed standard process during the format conversion to complete the format unification, and transmit the several groups of teaching resources after the format unification to the graph classification processing terminal.

4. 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.

5. The information literacy knowledge tree platform system according to claim 1, characterized in that: The knowledge graph is a preset graph, and a number of 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 of the corresponding knowledge graph.

6. 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.

7. The information literacy knowledge tree platform system according to claim 6, characterized in that: The resource output optimization end adjusts the output computing power in the recovery process in the following specific ways: Based on the knowledge points selected by external relevant personnel, confirm the teaching resources associated with such knowledge points, and restore the format of the associated teaching resources, and monitor the format restoration process associated with different teaching resources to confirm the format restoration rate associated with the corresponding process in real time; Performing average processing on the format recovery rate associated with the corresponding process in real time, confirming 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 confirming 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 sets of 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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