Teaching resource library management system and method

Abstract

The invention provides a teaching resource library management system and method. Resource metadata of teaching resources of a target professional can be automatically generated; determining a plurality of metadata samples; extracting digital characteristics of all resource information in each metadata sample, generating a cloud droplet sample of each metadata sample through a cloud model, and determining a sample membership degree of each metadata sample to the cloud droplet sample according to the digital characteristics of each metadata sample; determining a fluctuation trend of each piece of resource information in the corresponding metadata sample, and generating a distribution mode of all the resource information in each metadata sample according to all the fluctuation trends; and determining a matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution modes, and storing and distributing the teaching resources of the target professional according to the matching degree. By adopting the scheme of the invention, the multi-source heterogeneous metadata can be subjected to difference calibration, so that the storage efficiency of teaching resources is improved.

Description

Technical Field

[0001] The present application relates to the technical field of resource library management, and more specifically, to a teaching resource library management system and method. Background Art

[0002] Resource library management aims to efficiently store, maintain, share and utilize various resources (such as data, documents, knowledge, etc.) to improve resource utilization efficiency and management effectiveness. Core technologies include data storage, indexing and retrieval, security management, version control and the application of artificial intelligence. It is widely used in enterprises, scientific research, education and government, and supports data analysis, document management, knowledge sharing and other functions. With the increase in data volume and technological development, resource library management will become more intelligent, secure and efficient in the future.

[0003] Teaching resource library management aims to improve teaching efficiency and optimize the use of educational resources by classifying, storing, indexing, retrieving and sharing teaching resources. It includes the scientific classification and storage of teaching content such as lesson plans, textbooks, and courseware, as well as learning materials, test question banks and other resources. By establishing a flexible indexing system and using intelligent recommendation technology, it ensures that teachers and students can efficiently find the required resources. In addition, the resource library also needs to implement version control to ensure the consistency and accuracy of resources, and achieve safe sharing through permission management to promote teaching collaboration and the extensive use of resources. With the development of technology, intelligent and personalized teaching resource libraries will further enhance educational effects. When storing the teaching resources of mechanical and design manufacturing majors in the teaching resource library, the existing technology Usually, after extracting the metadata of teaching resources, the metadata is stored in the resource library as the description information of the teaching resources. However, this method often leads to high differences in metadata due to the different sources, formats and standards of the teaching resources of mechanical and design manufacturing majors (i.e., multi-source heterogeneity of metadata), which makes the system unable to perform effective keyword matching (for example, some resources may adopt subject classification standards, while others may adopt different classification methods such as course type and teaching objectives). As a result, all resources cannot be matched and stored according to the same standards, which leads to a decrease in the storage efficiency of the resource library. Therefore, how to calibrate the differences of multi-source heterogeneous metadata to improve the storage efficiency of teaching resources has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a teaching resource library management system and method, which can improve the storage efficiency of teaching resources when the metadata differences are high.

[0005] In a first aspect, the present application provides a teaching resource library management method, comprising the following steps: After the teaching resources of the target major are uploaded to the teaching resource library, resource metadata of the teaching resources of the target major are automatically generated; Determine a plurality of metadata samples of the resource metadata, the metadata samples including a plurality of resource information; Extracting digital features of all resource information in each metadata sample, generating a cloud droplet sample of each metadata sample through a cloud model, and determining the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample; Determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution pattern of all resource information in each metadata sample according to all the fluctuation trends; The matching degree between the resource metadata and the teaching resources is determined based on all sample memberships and all distribution patterns, and the teaching resources of the target major are stored and allocated according to the matching degree.

[0006] In some embodiments, extracting digital features of all resource information in each metadata sample specifically includes: selecting a metadata sample as a selected metadata sample; determining the average amount of information for all resource information in the selected metadata sample; Determine the resource proportion of each resource information in the selected metadata sample; Determine the digital characteristics of all resource information in the selected metadata sample through the proportion of all resources and the average amount of information; Continue to determine the digital characteristics of all resource information in the remaining metadata sample.

[0007] In some embodiments, determining the sample membership of each metadata sample to the cloud droplet sample according to the digital feature of each metadata sample specifically includes: selecting a metadata sample as a selected metadata sample; obtaining the average information content in the digital features of the selected metadata sample and the cloud droplet sample of the selected metadata sample; determining the average amount of information and the amount of resource fluctuation between the cloud droplet samples; Determining the sample membership of the selected metadata sample and the corresponding cloud droplet sample through the resource fluctuation amount and the digital feature; Continue to determine the sample membership of the remaining metadata samples and the corresponding cloud droplet samples.

[0008] In some embodiments, determining the fluctuation trend of each resource information in the corresponding metadata sample specifically includes: Obtaining resource information entropy and average information content in the digital features; selecting a metadata sample as a selected metadata sample; Determine the information offset of each resource information in the selected metadata sample by using the average information amount; Determine the fluctuation trend of each resource information in the selected metadata sample through all information offsets and the resource information entropy; Continue to determine the fluctuation trend of each resource information in the remaining metadata samples, so as to obtain the fluctuation trend of each resource information in the corresponding metadata sample.

[0009] In some embodiments, generating a distribution pattern of all resource information in each metadata sample according to all fluctuation trends specifically includes: selecting a metadata sample as a selected metadata sample; Obtain the fluctuation trend of each resource information in the selected metadata sample; Determine the distribution probability of each resource information in the selected metadata sample through all fluctuation trends; generating a distribution pattern of all resource information in the selected metadata sample according to all distribution probabilities; Continue to generate distribution patterns of all resource information in the remaining metadata samples.

[0010] In some embodiments, determining the matching degree of the resource metadata through all sample memberships and all distribution patterns specifically includes: Determine the resource contribution of each metadata sample to the resource metadata through all sample memberships and all distribution patterns; The matching degree of the resource metadata is determined according to all resource contributions.

[0011] In some embodiments, the target major is mechanical and design manufacturing major.

[0012] In a second aspect, the present application provides a teaching resource library management system, comprising: A generation module, used for automatically generating resource metadata of the teaching resources of the target major after the teaching resources of the target major are uploaded to the teaching resource library; A processing module, configured to determine a plurality of metadata samples of the resource metadata, wherein the metadata samples include a plurality of resource information; The processing module is further used to extract digital features of all resource information in each metadata sample, generate a cloud droplet sample of each metadata sample through a cloud model, and determine the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample; The processing module is further used to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution pattern of all resource information in each metadata sample according to all fluctuation trends; An execution module is used to determine the matching degree between the resource metadata and the teaching resources based on all sample memberships and all distribution patterns, and to store and allocate the teaching resources of the target major according to the matching degree.

[0013] In a third aspect, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned teaching resource library management method when executing the computer program.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned teaching resource library management method are implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the teaching resource library management system and method provided by the present application, after receiving the teaching resources of the mechanical and design and manufacturing major and uploading them to the teaching resource library, the resource metadata of the teaching resources is automatically generated; multiple metadata samples of the resource metadata are determined, and the metadata samples include multiple resource information; the digital features of all resource information in each metadata sample are extracted, and cloud droplet samples of each metadata sample are generated through a cloud model, and the sample membership of each metadata sample to the cloud droplet sample is determined according to the digital features of each metadata sample; the fluctuation trend of each resource information in the corresponding metadata sample is determined, and the distribution pattern of all resource information in each metadata sample is generated according to all fluctuation trends; the matching degree between the resource metadata and the teaching resource is determined based on all sample memberships and all distribution patterns, and the teaching resources of the target major are stored and allocated according to the matching degree.

[0016] It can be seen that in this application, first, after the teaching resources of the target major are uploaded to the teaching resource library, the resource metadata of the teaching resources of the target major is automatically generated, and multiple metadata samples of the metadata are determined, so as to classify and analyze different types of resource metadata; this can identify the characteristics of each resource in the metadata, thereby providing more comprehensive sample information for subsequent processing, and helping to unify and standardize different types of resources (that is, to perform difference calibration on multi-source heterogeneous metadata); then, after determining the digital characteristics of each metadata sample, a cloud droplet sample of the metadata sample is generated through a cloud model. The cloud model can be processed in the case of incomplete determination or uncertainty, especially when facing the diversity of metadata. The fuzziness of the cloud model can adapt to different standards and classification methods, which improves the flexibility and efficiency of matching. Subsequently, the membership of each metadata sample to the cloud droplet sample (sample membership) is determined, and the distribution pattern of all resource information is determined through the fluctuation trend, so that the system can understand the characteristics of each resource more accurately and optimize the storage method of resources by integrating different distribution patterns and memberships; finally, resource storage allocation is performed according to the matching degree, which can ensure that all resources are reasonably allocated according to their similarity and belongingness; this not only solves the problem of inconsistent standards, but also improves storage efficiency; in summary, the scheme can perform difference calibration on multi-source heterogeneous metadata to improve the storage efficiency of teaching resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a teaching resource library management method according to some embodiments of the present application; Figure 2 is a schematic diagram of a process for determining digital features according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for setting a storage threshold according to some embodiments of the present application; Figure 4 It is a structural diagram of a teaching resource library management system according to some embodiments of the present application; Figure 5 It is an internal structure diagram of a computer device for implementing a teaching resource library management method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution in this embodiment, the technical solution in this embodiment will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is a flow chart of a teaching resource library management method according to some embodiments of the present application. The teaching resource library management method 100 mainly includes the following steps: In step 101, after the teaching resources of the target major are uploaded to the teaching resource library, resource metadata of the teaching resources of the target major are automatically generated.

[0020] In specific implementation, after defining the basic structure of resource metadata of the target professional teaching resources, natural language processing technology or file parsing tools can be used to automatically extract the content of resource files to generate metadata, and the metadata can be used as the resource metadata in this application; for example: for text resources, the title, author, keywords, abstract and other contents of the document are analyzed by text extraction tools; computer vision technology (such as image recognition and object detection) is used to analyze images and video resources, automatically identify file contents, and extract descriptive metadata (for example, video title, explanation content, etc.) from them; for audio and video resources, speech recognition technology is used to convert speech into text, and then metadata such as keywords, themes, and key sentences are extracted through text analysis.

[0021] It should be noted that the target major described in this application is the major of mechanical and design manufacturing. The basic structure of the resource metadata of the teaching resources of the major of mechanical and design manufacturing includes: title (such as the basic mechanical design course presentation document), author, resource type (such as design drawings, textbooks), subject type (such as mechanical engineering, design manufacturing and automation control), keywords (such as mechanical design, 3D printing, computer-aided design), applicable target groups (such as mechanical engineering undergraduates, engineering and technical personnel) and additional information, etc. In other embodiments, other methods of definition can be used, which is not limited here.

[0022] In step 102, a plurality of metadata samples of the resource metadata are determined, wherein the metadata samples include a plurality of resource information.

[0023] In specific implementation, multiple metadata samples of resource metadata can be determined according to different types of teaching resources (such as text resources, multimedia resources, experimental data resources, and design resources, etc.), that is, the metadata of different types of teaching resources are used as a metadata sample to obtain multiple metadata samples of the resource metadata. In other embodiments, other methods can be used to determine, which is not limited here.

[0024] It should be noted that the metadata in the prior art includes character-based resource information and numeric-based resource information. For the character-based resource information, in this application, first, stop words (such as common meaningless words like "of", "is", "in", "and", etc.) in the character-based resource information are removed, and then, using the text feature extraction method in the prior art, a unique digital identifier is assigned to each independent word, so that after converting the character-based information into digital representation, the conversion result is used as the resource information in this application. The numeric-based resource information is not processed, that is: the numeric-based resource information is directly used as the resource information in this application; in addition, the resource information in this application is a value used to describe the resource characteristics (such as: video duration, image size, resource name, etc.) in the metadata sample.

[0025] In step 103, extract the digital features of all resource information in each metadata sample, generate the cloud droplet sample of each metadata sample through the cloud model, and determine the sample membership degree of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample.

[0026] In some embodiments, refer to Figure 2 As shown, this figure is a schematic flow diagram of extracting digital features shown in some embodiments of this application. The extraction of the digital features of all resource information in each metadata sample can be implemented by the following steps: First, in 1031, select a metadata sample as the selected metadata sample; Then, in 1032, determine the average information amount of all resource information in the selected metadata sample; Subsequently, in 1033, determine the resource proportion of each resource information in the selected metadata sample; Secondly, in 1034, determine the digital features of all resource information in the selected metadata sample through all resource proportions and the average information amount; Finally, in 1035, continue to determine the digital features of all resource information in the remaining metadata samples.

[0027] When specifically implemented, the determination of the average information amount of all resource information in the selected metadata sample can be implemented in the following manner, that is: taking the average value of all resource information in the selected metadata sample as the average information amount. In other embodiments, other methods can also be used to determine it, which is not limited here; in addition, as a preferred embodiment, the determination of the resource proportion of each resource information in the selected metadata sample can be implemented in the following manner, that is: first, determine the sum of all resource information in the selected metadata sample, and then, taking the ratio of each resource information to the sum of the resource information as the resource proportion of the corresponding resource information, so as to obtain the resource proportion of each resource information in the selected metadata sample.

[0028] It should be noted that the average amount of information described in this application refers to the average value of all resource information in the metadata sample, and the resource information refers to a numerical value used to describe the resource characteristics in the metadata sample; the average amount of information can be used to reflect the information richness in the corresponding metadata sample. The larger the average amount of information, the higher the information richness in the corresponding metadata sample, and the smaller the average amount of information, the lower the information richness in the corresponding metadata sample; in addition, the resource proportion, that is, the proportion of the amount of information of the resource information in the corresponding metadata sample, the larger the resource proportion, the higher the proportion of the amount of information of the resource information in the corresponding metadata sample, and the smaller the resource proportion, the lower the proportion of the amount of information of the resource information in the corresponding metadata sample.

[0029] In specific implementation, the digital features of various resource information in the selected metadata sample can be determined by all resource proportions and the average amount of information in the following manner, namely: first, determine the logarithmic function value of each resource proportion with base 10, then add the corresponding results of all resource proportions and take the negative result as the resource information entropy of the selected metadata sample, finally, use the resource information entropy and the average amount of information as vector elements to form a vector as the digital features of all resource information in the selected metadata sample of this application. In other embodiments, other methods can also be used to determine, which is not limited here.

[0030] It should be noted that the resource information entropy described in the present application is a quantitative indicator of the distribution uncertainty of the resource information in the metadata sample, and the resource information entropy can be used to characterize the degree of confusion of the resource information in the corresponding metadata sample. The larger the resource information entropy, the higher the degree of confusion of the resource information in the corresponding metadata sample, and the smaller the resource information entropy, the lower the degree of confusion of the resource information in the corresponding metadata sample; in addition, the digital feature is a feature vector that comprehensively considers the resource proportion and the amount of information, and is used to represent the digital information of the metadata sample, wherein the feature vector includes resource information entropy and the average amount of information.

[0031] It should be noted that the present application uses the digital features as input parameters, and then uses the normal distribution method in the prior art combined with the cloud model to generate multiple random numbers as corresponding cloud droplet data points. As a preferred embodiment, the cloud droplet data points closest to the center of the normal distribution can be selected as the cloud droplet samples in the present application, thereby generating cloud droplet samples for each metadata sample. In other embodiments, other methods can be used to determine, which are not limited here; in addition, the cloud droplet data points are resource information that satisfies the normal distribution under the constraints of the digital features.

[0032] In some embodiments, determining the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample may be implemented by the following steps: selecting a metadata sample as a selected metadata sample; obtaining the average information content in the digital features of the selected metadata sample and the cloud droplet sample of the selected metadata sample; determining the average amount of information and the amount of resource fluctuation between the cloud droplet samples; Determining the sample membership of the selected metadata sample and the corresponding cloud droplet sample through the resource fluctuation amount and the digital feature; Continue to determine the sample membership of the remaining metadata samples and the corresponding cloud droplet samples.

[0033] In specific implementation, determining the resource fluctuation amount between the average amount of information and the cloud droplet sample can be achieved in the following manner, namely: first, determining the difference between the average amount of information and the cloud droplet sample, and then using the square value of the result as the resource fluctuation amount between the average amount of information and the cloud droplet sample. In other embodiments, other methods can also be used for determination, which are not limited here.

[0034] In specific implementation, the sample membership of the selected metadata sample and the corresponding cloud droplet sample can be determined by the resource fluctuation amount and the digital feature in the following manner, namely: first, the resource information entropy in the digital feature is obtained, then, the result of comparing the resource fluctuation amount with twice the resource information entropy is negative, and finally, the result obtained is used as the exponentiation of the natural constant e as the sample membership of the selected metadata sample to the corresponding cloud droplet sample.

[0035] It should be noted that, in the present application, the resource fluctuation amount is used to characterize the degree of deviation between the corresponding resource information in the metadata sample and the cloud droplet sample. The larger the resource fluctuation amount, the higher the degree of deviation between the corresponding resource information in the metadata sample and the cloud droplet sample. The smaller the resource fluctuation amount, the lower the degree of deviation between the corresponding resource information in the metadata sample and the cloud droplet sample. In addition, the sample membership refers to the degree of membership between the metadata sample and the cloud droplet sample, which reflects the similarity between the metadata sample and the cloud droplet sample. The larger the sample membership, the higher the similarity between the metadata sample and the cloud droplet sample. The smaller the sample membership, the lower the similarity between the metadata sample and the cloud droplet sample.

[0036] In step 104, the fluctuation trend of each resource information in the corresponding metadata sample is determined, and the distribution pattern of all resource information in each metadata sample is generated according to all the fluctuation trends.

[0037] In some embodiments, determining the fluctuation trend of each resource information in the corresponding metadata sample may be implemented by the following steps: Obtaining resource information entropy and average information content in the digital features; selecting a metadata sample as a selected metadata sample; Determine the information offset of each resource information in the selected metadata sample by using the average information amount; Determine the fluctuation trend of each resource information in the selected metadata sample through all information offsets and the resource information entropy; Continue to determine the fluctuation trend of each resource information in the remaining metadata samples, so as to obtain the fluctuation trend of each resource information in the corresponding metadata sample.

[0038] In specific implementation, the information offset of each resource information in the selected metadata sample can be determined by the average information amount in the following manner, namely: the difference between each resource information in the selected metadata sample and the average information amount is used as the information offset of the corresponding resource information, thereby obtaining the information offset of each resource information in the selected metadata sample. In other embodiments, other methods can also be used to determine it, which will not be repeated here.

[0039] It should be noted that the information offset described in this application refers to the quantitative value of the position difference between the resource information and other resource information in the metadata sample in the overall distribution, and the resource information refers to the numerical value used to describe the resource characteristics in the metadata sample; the information offset can be used to represent the degree of deviation of the resource information from the average amount of information in the corresponding metadata sample. The larger the information offset, the higher the degree of deviation of the resource information from the average amount of information in the corresponding metadata sample, and the smaller the information offset, the lower the degree of deviation of the resource information from the average amount of information in the corresponding metadata sample. Therefore, the fluctuation trend described in this application is based on the information offset and the resource information entropy to describe the change of the resource information in the entire metadata sample, that is: The fluctuation trend is a quantitative value of the change of the resource information in the entire metadata sample. The larger the fluctuation trend, the greater the change of the resource information in the entire metadata sample, and the smaller the fluctuation trend, the smaller the change of the resource information in the entire metadata sample. As a preferred embodiment, determining the fluctuation trend of each resource information in the selected metadata sample by all information offsets and the resource information entropy can be implemented in the following manner, namely: first, select a resource information in the selected metadata sample, then compare the information offset of the resource information with the resource information entropy, and finally, subtract the comparison result from the natural constant 1 as the fluctuation trend of the resource information, repeat the above steps to determine the fluctuation trend of the remaining resource information in the selected metadata sample.

[0040] In some embodiments, generating a distribution pattern of all resource information in each metadata sample according to all fluctuation trends may be implemented by the following steps: selecting a metadata sample as a selected metadata sample; Obtain the fluctuation trend of each resource information in the selected metadata sample; Determine the distribution probability of each resource information in the selected metadata sample through all fluctuation trends; generating a distribution pattern of all resource information in the selected metadata sample according to all distribution probabilities; Continue to generate distribution patterns of all resource information in the remaining metadata samples.

[0041] In specific implementation, determining the probability density of each resource information in the selected metadata sample through all fluctuation trends can be achieved in the following manner, namely: first, select a resource information in the selected metadata sample, then compare the square of the information offset corresponding to the resource information with the result of twice the square value of the entropy of the resource information, then negate the comparison result and use it as the exponent of the natural constant e, finally, multiply the obtained result by the reciprocal of the square root of 2π, and then multiply the result by the fluctuation trend of the resource information as the distribution probability of the resource information in the selected metadata sample, repeat the above steps to determine the distribution probability of the remaining resource information in the selected metadata sample; as a preferred embodiment, generating the distribution pattern of all resource information in the selected metadata sample according to all distribution probabilities can be achieved in the following manner, namely: taking the result after adding the distribution probabilities of all resource information in the selected metadata sample as the distribution pattern of all resource information in the selected metadata sample, and in other embodiments, other methods can also be used for determination, which are not limited here.

[0042] It should be noted that the distribution probability described in this application is used to describe the probability of a certain resource information appearing in a metadata sample. The larger the distribution probability, the higher the frequency of occurrence of the resource information in the metadata sample, and the smaller the distribution probability, the lower the frequency of occurrence of the resource information in the metadata sample. In addition, the distribution pattern refers to the overall distribution pattern generated by the distribution probability of all resource information, which describes the overall distribution characteristics of all resource information in the metadata sample.

[0043] In step 105, the matching degree between the resource metadata and the teaching resources is determined based on all sample memberships and all distribution patterns, and the teaching resources of the target major are stored and allocated according to the matching degree.

[0044] In some embodiments, determining the matching degree between the resource metadata and the teaching resource based on all sample memberships and all distribution patterns can be implemented by the following steps: Determine the resource contribution of each metadata sample to the resource metadata through all sample memberships and all distribution patterns; The matching degree between the resource metadata and the teaching resource is determined according to all resource contributions.

[0045] In specific implementation, determining the resource contribution of each metadata sample to the resource metadata through all sample memberships and all distribution patterns can be implemented in the following manner, namely: first, selecting a metadata sample, then obtaining the sample membership of the metadata sample and the distribution pattern corresponding to the metadata sample, then multiplying the sample membership and the distribution pattern as the resource contribution of the metadata sample to the resource metadata, repeating the above steps to determine the resource contribution of the remaining metadata samples to the resource metadata; as a preferred embodiment, determining the matching degree between the resource metadata and the teaching resource based on all resource contributions can be implemented in the following manner, namely: first, summing up all resource contributions, then summing up all sample memberships, and finally, comparing the result of summing up all resource contributions with the result of summing up all sample memberships, and using the obtained result as the matching degree between the resource metadata and the teaching resource.

[0046] It should be noted that the resource contribution degree described in this application is used to reflect the contribution degree of the metadata sample to the resource metadata. The greater the resource contribution degree, the higher the contribution degree of the metadata sample to the resource metadata, and the smaller the resource contribution degree, the lower the contribution degree of the metadata sample to the resource metadata. In addition, the matching degree is a quantitative value of the data quality in the resource metadata. Specifically, the matching degree reflects the matching degree between the resource metadata and the teaching resources of the target major (that is, the integrity, consistency and validity of the data). The greater the matching degree, the higher the matching degree between the resource metadata and the teaching resources of the target major, and the smaller the matching degree, the lower the matching degree.

[0047] Preferably, after setting multiple storage thresholds of the matching degree of the teaching resource library, the teaching resources of the mechanical and design manufacturing major can be divided into multiple levels, and different storage strategies can be adopted, that is, resources of different qualities are allocated to storage devices or areas of different levels; Figure 3As shown, this figure is a schematic diagram of the process of setting the storage threshold shown in some embodiments of the present application; in specific implementation, the first storage threshold and the second storage threshold of the matching degree of the teaching resource library are first set, and when the matching degree of the resource metadata of the teaching resources of the mechanical and design manufacturing major is greater than the first storage threshold, the corresponding resource metadata is stored in a storage area with a faster response speed, that is: a high-priority storage area (for example, a cloud storage area); when the matching degree of the resource metadata of the teaching resources of the mechanical and design manufacturing major is less than or equal to the first storage threshold and greater than the second storage threshold, the corresponding resource metadata is stored in a medium-priority storage area (for example, a traditional hard disk array or a low-latency storage device); when the matching degree of the resource metadata of the teaching resources of the mechanical and design manufacturing major is less than or equal to the second storage threshold, the corresponding resource metadata is stored in a low-priority storage area (for example, a storage device with lower cost), such as tape storage, cold storage or low-frequency access cloud storage, thereby realizing storage allocation of the teaching resources of the mechanical and design manufacturing major.

[0048] As a preferred embodiment, the first storage threshold and the second storage threshold in the present application can be set according to the distribution of resource metadata matching degrees. For example: after obtaining the matching degrees of all teaching resources in the teaching resource library, the numerical value corresponding to the first 25% of all matching degrees can be selected as the first storage threshold; the numerical value corresponding to the last 25% of all matching degrees can be selected as the second storage threshold. In other embodiments, other methods can also be used for setting, which are not limited here.

[0049] It should be noted that the first storage threshold in the present application is greater than the second storage threshold. In addition, the present application allocates teaching resources to different storage areas through the matching degree, which can effectively reduce the waste of storage space, thereby making the management of the teaching resource library of the mechanical and design and manufacturing majors more flexible, and the resource storage location can be adjusted according to teaching needs, resource quality and access frequency; for example, when the quality of certain low-quality resources is improved after revision in the future, they can be migrated to a higher priority storage area to ensure that the update and improvement of teaching resources are responded to in a timely manner.

[0050] In addition, in another aspect of the present application, in some embodiments, the present application provides a teaching resource library management system, referring to Figure 4 , which is a schematic diagram of the structure of a teaching resource library management system according to some embodiments of the present application, the teaching resource library management system 200 includes: a generating module 201, a processing module 202 and an executing module 203, which are described as follows: The generation module 201 in this application is mainly used to automatically generate resource metadata of the teaching resources of the target major after the teaching resources of the target major are uploaded to the teaching resource library; Processing module 202, in this application, processing module 202 is mainly used to determine multiple metadata samples of the resource metadata, and the metadata samples include multiple resource information; In addition, the processing module 202 in the present application is also used to extract the digital features of all resource information in each metadata sample, generate a cloud droplet sample of each metadata sample through a cloud model, and determine the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample; In addition, the processing module 202 in the present application is also used to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution pattern of all resource information in each metadata sample according to all fluctuation trends; Execution module 203. In the present application, execution module 203 is mainly used to determine the matching degree between the resource metadata and the teaching resources based on all sample affiliations and all distribution patterns, and to store and allocate the teaching resources of the target major according to the matching degree.

[0051] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned teaching resource library management method.

[0052] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device for implementing a teaching resource library management method according to some embodiments of the present application. The teaching resource library management method in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0053] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the teaching resource library management method in the present application.

[0054] The communication bus 302 is used to transmit information between the above components.

[0055] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0056] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The teaching resource library management method in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0057] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0058] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0059] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0060] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned teaching resource library management method is implemented.

[0061] In summary, in the teaching resource library management system and method disclosed in the embodiment of the present application, after the teaching resources of the target major are uploaded to the teaching resource library, the resource metadata of the teaching resources of the target major are automatically generated; multiple metadata samples of the resource metadata are determined, and the metadata samples include multiple resource information; the digital features of all resource information in each metadata sample are extracted, and a cloud droplet sample of each metadata sample is generated through a cloud model, and the sample membership of each metadata sample to the cloud droplet sample is determined according to the digital features of each metadata sample; the fluctuation trend of each resource information in the corresponding metadata sample is determined, and the distribution pattern of all resource information in each metadata sample is generated according to all fluctuation trends; the matching degree between the resource metadata and the teaching resources is determined based on all sample memberships and all distribution patterns, and the teaching resources of the target major are stored and allocated according to the matching degree; the multi-source heterogeneous metadata can be differentially calibrated to improve the storage efficiency of teaching resources.

[0062] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0063] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A teaching resource library management method, characterized in that: The steps include: After the teaching resources of the target major are uploaded to the teaching resource library, resource metadata of the teaching resources of the target major are automatically generated; Determine a plurality of metadata samples of the resource metadata, the metadata samples including a plurality of resource information; Extracting digital features of all resource information in each metadata sample, generating a cloud droplet sample of each metadata sample through a cloud model, and determining the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample; Determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution pattern of all resource information in each metadata sample according to all the fluctuation trends; The matching degree between the resource metadata and the teaching resources is determined based on all sample memberships and all distribution patterns, and the teaching resources of the target major are stored and allocated according to the matching degree.

2. The method according to claim 1, characterized in that The digital features of all resource information extracted from each metadata sample specifically include: selecting a metadata sample as a selected metadata sample; determining the average amount of information for all resource information in the selected metadata sample; Determine the resource proportion of each resource information in the selected metadata sample; Determine the digital characteristics of all resource information in the selected metadata sample through the proportion of all resources and the average amount of information; Continue to determine the digital characteristics of all resource information in the remaining metadata sample.

3. The method according to claim 1, characterized in that Determining the sample membership of each metadata sample to the cloud droplet sample according to the digital feature of each metadata sample specifically includes: selecting a metadata sample as a selected metadata sample; obtaining the average information content in the digital features of the selected metadata sample and the cloud droplet sample of the selected metadata sample; determining the average amount of information and the amount of resource fluctuation between the cloud droplet samples; Determining the sample membership of the selected metadata sample and the corresponding cloud droplet sample through the resource fluctuation amount and the digital feature; Continue to determine the sample membership of the remaining metadata samples and the corresponding cloud droplet samples.

4. The method according to claim 1, characterized in that Determining the fluctuation trend of each resource information in the corresponding metadata sample specifically includes: Obtaining resource information entropy and average information content in the digital features; selecting a metadata sample as a selected metadata sample; Determine the information offset of each resource information in the selected metadata sample by using the average information amount; Determine the fluctuation trend of each resource information in the selected metadata sample through all information offsets and the resource information entropy; Continue to determine the fluctuation trend of each resource information in the remaining metadata samples, so as to obtain the fluctuation trend of each resource information in the corresponding metadata sample.

5. The method according to claim 1, characterized in that The distribution patterns of all resource information in each metadata sample generated based on all fluctuation trends specifically include: selecting a metadata sample as a selected metadata sample; Obtain the fluctuation trend of each resource information in the selected metadata sample; Determine the distribution probability of each resource information in the selected metadata sample through all fluctuation trends; generating a distribution pattern of all resource information in the selected metadata sample according to all distribution probabilities; Continue to generate distribution patterns of all resource information in the remaining metadata samples.

6. The method according to claim 1, characterized in that Determining the matching degree of the resource metadata through all sample memberships and all distribution patterns specifically includes: Determine the resource contribution of each metadata sample to the resource metadata through all sample memberships and all distribution patterns; The matching degree of the resource metadata is determined according to all resource contributions.

7. The method according to claim 1, characterized in that The target major is machinery and design and manufacturing.

8. A teaching resource library management system, characterized in that: include: A generation module, used for automatically generating resource metadata of the teaching resources of the target major after the teaching resources of the target major are uploaded to the teaching resource library; A processing module, configured to determine a plurality of metadata samples of the resource metadata, wherein the metadata samples include a plurality of resource information; The processing module is further used to extract digital features of all resource information in each metadata sample, generate a cloud droplet sample of each metadata sample through a cloud model, and determine the sample membership of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample; The processing module is further used to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution pattern of all resource information in each metadata sample according to all fluctuation trends; An execution module is used to determine the matching degree between the resource metadata and the teaching resources based on all sample memberships and all distribution patterns, and to store and allocate the teaching resources of the target major according to the matching degree.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the teaching resource library management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the teaching resource library management method according to any one of claims 1 to 7 are implemented.

Images