A teaching resource library management system and method

Abstract

The present application provides a teaching resource library management system and method, which can automatically generate resource metadata of teaching resources for a target major; determine a plurality of metadata samples; extract digital features of all resource information in each metadata sample, generate cloud droplet samples of each metadata sample through a cloud model, and determine the sample membership degree of each metadata sample to the cloud droplet samples 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; determine the matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution patterns, and perform storage allocation for the teaching resources of the target major according to the matching degree. By adopting the solution of the present application, differential calibration can be performed on multi-source heterogeneous metadata to improve the storage efficiency of teaching resources.

Description

Technical Field

[0001] This application relates to the technical field of resource library management. More specifically, this application relates 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.), improve the usage efficiency and management effect of resources. The 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 other fields, supporting functions such as data analysis, document management, and knowledge sharing. With the increase in data volume and the development of technology, future resource library management will be more intelligent, secure, and efficient.

[0003] Teaching resource library management classifies, stores, indexes, retrieves, and shares teaching resources, aiming to improve teaching efficiency and optimize the utilization of educational resources. It includes the scientific classification and storage of teaching contents such as lesson plans, textbooks, and courseware, as well as resources such as learning materials and test question banks. By establishing a flexible indexing system and using intelligent recommendation technology, it ensures that teachers and students can efficiently find the required resources. Moreover, the resource library also needs to implement version control to ensure the consistency and accuracy of resources, and achieve secure sharing through permission management, promoting teaching collaboration and the wide utilization of resources. With the development of technology, intelligent and personalized teaching resource libraries will further improve the educational effect. When storing the teaching resources of the mechanical and design manufacturing major in the teaching resource library, in the prior art, after extracting the metadata of the teaching resources, the metadata is usually stored in the resource library as the descriptive information of the teaching resources. However, this method often has a high difference in its metadata due to different sources, formats, and standards of the teaching resources of the mechanical and design manufacturing major (i.e., multi-source heterogeneity of metadata), resulting in ineffective keyword matching by the system (for example, some resources may adopt the subject classification standard, while some may adopt different classification methods such as course type and teaching objective), so that all resources cannot be matched and stored according to the same standard, thereby leading to a decline in the storage efficiency of the resource library. Therefore, how to calibrate the differences in 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] This application provides a teaching resource library management system and method, which can improve the storage efficiency of teaching resources in the case of high metadata differences.

[0005] In the first aspect, this application provides a teaching resource library management method, including the following steps:

[0006] 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;

[0007] Determine multiple metadata samples of the resource metadata, where the metadata samples include multiple resource information;

[0008] 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;

[0009] 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;

[0010] Determine the matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution patterns, and perform storage allocation on the teaching resources of the target major according to the matching degree.

[0011] In some embodiments, extracting the digital features of all resource information in each metadata sample specifically includes:

[0012] Select a metadata sample as the selected metadata sample;

[0013] Determine the average information amount of all resource information in the selected metadata sample;

[0014] Determine the resource proportion of each resource information in the selected metadata sample;

[0015] Determine the digital features of all resource information in the selected metadata sample through all the resource proportions and the average information amount;

[0016] Continue to determine the digital features of all resource information in the remaining metadata samples.

[0017] In some embodiments, determining the sample membership degree of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample specifically includes:

[0018] Select a metadata sample as the selected metadata sample;

[0019] Obtain the average information amount in the digital features of the selected metadata sample and the cloud droplet sample of the selected metadata sample;

[0020] Determine the resource fluctuation amount between the average information amount and the cloud droplet sample;

[0021] Determine the sample membership degree of the selected metadata sample to the corresponding cloud droplet sample through the resource fluctuation amount and the digital features;

[0022] Continue to determine the sample membership degrees of the remaining metadata samples and the corresponding cloud droplet samples.

[0023] In some embodiments, determining the fluctuation trend of each resource information in the corresponding metadata sample specifically includes:

[0024] Obtain the resource information entropy and the average amount of information in the digital features;

[0025] Select a metadata sample as the selected metadata sample;

[0026] Determine the information offset amounts of each resource information in the selected metadata sample through the average amount of information;

[0027] Determine the fluctuation trend of each resource information in the selected metadata sample through all the information offset amounts and the resource information entropy;

[0028] 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.

[0029] In some embodiments, generating the distribution patterns of all resource information in each metadata sample according to all the fluctuation trends specifically includes:

[0030] Select a metadata sample as the selected metadata sample;

[0031] Obtain the fluctuation trend of each resource information in the selected metadata sample;

[0032] Determine the distribution probabilities of each resource information in the selected metadata sample through all the fluctuation trends;

[0033] Generate the distribution pattern of all resource information in the selected metadata sample according to all the distribution probabilities;

[0034] Continue to generate the distribution patterns of all resource information in the remaining metadata samples.

[0035] In some embodiments, determining the matching degree of the resource metadata through all the sample membership degrees and all the distribution patterns specifically includes:

[0036] Determine the resource contribution degrees of each metadata sample to the resource metadata through all the sample membership degrees and all the distribution patterns;

[0037] Determine the matching degree of the resource metadata according to all the resource contribution degrees.

[0038] In some embodiments, the target major is Mechanical and Design Manufacturing.

[0039] In a second aspect, the present application provides a teaching resource library management system, including:

[0040] A generation module, configured 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;

[0041] A processing module, configured to determine a plurality of metadata samples of the resource metadata, where the metadata samples include a plurality of resource information;

[0042] The processing module is further configured to extract digital features of all resource information in each metadata sample, generate cloud droplet samples of each metadata sample through a cloud model, and determine the sample membership degree of each metadata sample to the cloud droplet samples according to the digital features of each metadata sample;

[0043] The processing module is further configured to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate a distribution pattern of all resource information in each metadata sample according to all the fluctuation trends;

[0044] An execution module, configured to determine the matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution patterns, and perform storage allocation on the teaching resources of the target major according to the matching degree.

[0045] In a third aspect, the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above teaching resource library management method are implemented.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above teaching resource library management method are implemented.

[0047] The technical solutions provided by the embodiments disclosed in the present application have the following beneficial effects:

[0048] In the teaching resource library management system and method provided by this application, after receiving the teaching resources of the mechanical and design 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, the cloud droplet samples of each metadata sample are generated through the cloud model, and the sample membership degree 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 mode of all resource information in each metadata sample is generated according to all the fluctuation trends; based on all the sample membership degrees and all the distribution modes, the matching degree between the resource metadata and the teaching resources is determined, and the storage allocation of the teaching resources of the target major is carried out according to the matching degree.

[0049] 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, so as to provide more comprehensive sample information for subsequent processing, and help to unify and standardize different types of resources (that is: perform difference calibration on multi-source heterogeneous metadata); then, after determining the digital features of each metadata sample, the cloud droplet samples of the metadata samples are generated through the cloud model. The cloud model can process in the case of incomplete certainty or uncertainty. Especially when facing the diversity of metadata, the ambiguity of the cloud model can adapt to different standards and classification methods, improving the flexibility and efficiency of matching. Subsequently, the membership degree of each metadata sample to the cloud droplet sample (sample membership degree) is determined, and the distribution mode of all resource information is determined through the fluctuation trend, so that the system can more accurately understand the characteristics of each resource, and optimize the storage method of resources by integrating different distribution modes and membership degrees; finally, the resource storage allocation is carried out according to the matching degree, which can ensure that all resources are reasonably allocated according to their similarity and attribution; this not only solves the problem of inconsistent standards, but also improves the storage efficiency; in summary, this solution can perform difference calibration on multi-source heterogeneous metadata to improve the storage efficiency of teaching resources. Description of the Drawings

[0050] Figure 1 It is a schematic flowchart of a teaching resource library management method shown according to some embodiments of this application;

[0051] Figure 2 It is a schematic flowchart of determining digital features shown according to some embodiments of this application;

[0052] Figure 3It is a schematic flowchart of setting a storage threshold according to some embodiments of the present application;

[0053] Figure 4 It is a schematic structural diagram of a teaching resource library management system according to some embodiments of the present application;

[0054] Figure 5 It is an internal structural diagram of a computer device for implementing a teaching resource library management method according to some embodiments of the present application. Detailed implementation manners

[0055] To better understand the technical solutions in this embodiment, the technical solutions in this embodiment will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0056] Refer to Figure 1 , this figure is a schematic flowchart 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:

[0057] 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 is automatically generated.

[0058] Specifically, when implementing, after defining the basic structure of the resource metadata of the teaching resources of the target major, natural language processing technology or file parsing tools can be used to automatically extract the content of the resource file, thereby generating metadata, and using the metadata as the resource metadata in the present application; for example: for text resources, analyze the title, author, keywords, and abstract of the document through a text extraction tool; use computer vision technology (such as image recognition, object detection) to analyze image and video resources, automatically identify the content of the file, and extract descriptive metadata from it (for example, video title, explanation content, etc.); for audio and video resources, use speech recognition technology to convert speech into text, and then extract metadata such as keywords, themes, and key sentences through text analysis.

[0059] It should be noted that in the present application, the target major is the mechanical design and manufacturing major. The basic structure of the resource metadata of the teaching resources of the mechanical design and manufacturing major includes: title (such as a demonstration document for the mechanical design foundation course), author, resource type (such as design drawings, textbooks), subject type (such as mechanical engineering, design and manufacturing, and automation control), keywords (such as mechanical design, 3D printing, computer-aided design), applicable target group (such as mechanical engineering undergraduates, engineering and technical personnel), and additional information, etc. In other embodiments, other methods can also be used for definition, which will not be limited here.

[0060] In step 102, multiple metadata samples of the resource metadata are determined, and the metadata samples include multiple resource information.

[0061] In specific implementation, multiple metadata samples of the 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 is used as a metadata sample, so as to obtain multiple metadata samples of the resource metadata. In other embodiments, other methods can also be used to determine, which is not limited here.

[0062] 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, and 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 numerical value used to describe the resource characteristics (such as: video duration, image size, and resource name, etc.) in the metadata sample.

[0063] In step 103, the digital features of all resource information in each metadata sample are extracted, cloud droplets samples of each metadata sample are generated through a cloud model, and the sample membership degrees of each metadata sample to the cloud droplets samples are determined according to the digital features of each metadata sample.

[0064] In some embodiments, as shown in Figure 2 the 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:

[0065] First, in 1031, a metadata sample is selected as the selected metadata sample;

[0066] Then, in 1032, the average information amount of all resource information in the selected metadata sample is determined;

[0067] Subsequently, in 1033, the resource proportion of each resource information in the selected metadata sample is determined;

[0068] Secondly, in 1034, the digital features of all resource information in the selected metadata sample are determined through all resource proportions and the average information amount;

[0069] Finally, in 1035, continue to determine the digital features of all resource information in the remaining metadata samples.

[0070] When specifically implemented, the average amount of information of all resource information in the selected metadata sample can be determined in the following manner, that is: taking the average value of all resource information in the selected metadata sample as the average amount of information, and in other embodiments, other methods can also be used to determine it, which is not limited here; additionally, as a preferred embodiment, the resource proportion of each resource information in the selected metadata sample can be determined 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.

[0071] 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 the numerical value used to describe the resource characteristics in the metadata sample; the information richness of the corresponding metadata sample can be reflected by the average amount of information. The larger the average amount of information, the higher the information richness of the corresponding metadata sample, and the smaller the average amount of information, the lower the information richness of the corresponding metadata sample; additionally, the resource proportion 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.

[0072] When specifically implemented, 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, that is: first, determine the logarithmic function values with base 10 of each resource proportion, then, taking the negative result after adding the corresponding results of all resource proportions as the resource information entropy of the selected metadata sample, and finally, taking the vector composed of the resource information entropy and the average amount of information as vector elements respectively 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 it, which is not limited here.

[0073] It should be noted that the resource information entropy described in this application is a quantization index for the distribution uncertainty of resource information in the metadata sample. The degree of chaos of the resource information in the corresponding metadata sample can be characterized by the resource information entropy. The greater the resource information entropy, the higher the degree of chaos of the resource information in the corresponding metadata sample; the smaller the resource information entropy, the lower the degree of chaos of the resource information in the corresponding metadata sample. In addition, the digital feature is a feature vector that comprehensively considers the resource ratio and the amount of information, and is used to represent the digital information of the metadata sample. Among them, the feature vector includes the resource information entropy and the average amount of information.

[0074] It should be noted that in this application, the digital feature is used as an input parameter, and then the normal distribution method in the prior art is used to generate multiple random numbers as the corresponding cloud droplet data points in combination with the cloud model for the input parameter. As a preferred embodiment, the cloud droplet data point closest to the center of the normal distribution can be selected as the cloud droplet sample in this application, so as to generate the cloud droplet sample of each metadata sample. In other embodiments, other methods can also be used to determine it, which is not limited here. In addition, the cloud droplet data point is the resource information that satisfies the normal distribution under the constraint of the digital feature.

[0075] In some embodiments, the sample membership degree of each metadata sample to the cloud droplet sample can be determined according to the digital feature of each metadata sample by the following steps:

[0076] Select a metadata sample as the selected metadata sample;

[0077] Obtain the average amount of information in the digital feature of the selected metadata sample and the cloud droplet sample of the selected metadata sample;

[0078] Determine the resource fluctuation amount between the average amount of information and the cloud droplet sample;

[0079] Determine the sample membership degree of the selected metadata sample to the corresponding cloud droplet sample through the resource fluctuation amount and the digital feature;

[0080] Continue to determine the sample membership degree of the remaining metadata samples to the corresponding cloud droplet samples.

[0081] When specifically implemented, the resource fluctuation amount between the average amount of information and the cloud droplet sample can be implemented in the following way, that is: first, determine the difference between the average amount of information and the cloud droplet sample, and then use the square value of the obtained 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 to determine it, which is not limited here.

[0082] In specific implementation, determining the sample membership degree of the selected metadata sample and the corresponding cloud droplet sample based on the resource fluctuation amount and the digital feature can be achieved in the following manner, that is: First, obtain the resource information entropy in the digital feature. Then, take the negative of the result of dividing the resource fluctuation amount by twice the resource information entropy. Finally, take the result after taking the exponential of the obtained result with the natural constant e as the sample membership degree of the selected metadata sample to the corresponding cloud droplet sample.

[0083] It should be noted that in this application, the resource fluctuation amount is used to characterize the deviation degree between the corresponding resource information in the metadata sample and the cloud droplet sample. The larger the resource fluctuation amount, the higher the deviation degree between the corresponding resource information in the metadata sample and the cloud droplet sample; the smaller the resource fluctuation amount, the lower the deviation degree between the corresponding resource information in the metadata sample and the cloud droplet sample. In addition, the sample membership degree refers to the membership degree between the metadata sample and the cloud droplet sample, reflecting the similarity between the metadata sample and the cloud droplet sample. The larger the sample membership degree, the higher the similarity between the metadata sample and the cloud droplet sample; the smaller the sample membership degree, the lower the similarity between the metadata sample and the cloud droplet sample.

[0084] In step 104, 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.

[0085] In some embodiments, determining the fluctuation trend of each resource information in the corresponding metadata sample can be achieved through the following steps:

[0086] Obtain the resource information entropy and the average amount of information in the digital feature;

[0087] Select a metadata sample as the selected metadata sample;

[0088] Determine the information offset amount of each resource information in the selected metadata sample through the average amount of information;

[0089] Determine the fluctuation trend of each resource information in the selected metadata sample through all the information offset amounts and the resource information entropy;

[0090] 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.

[0091] 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 way, that is: 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, so as to obtain 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 elaborated here.

[0092] It should be noted that the information offset in this application refers to the quantization value of the position difference of the resource information and other resource information in the overall distribution in the metadata sample. The resource information refers to the numerical value used to describe the resource characteristics in the metadata sample. The deviation degree of the resource information relative to the average information amount in the corresponding metadata sample can be represented by the information offset. The larger the information offset, the higher the deviation degree of the resource information relative to the average information amount in the corresponding metadata sample. The smaller the information offset, the lower the deviation degree of the resource information relative to the average information amount in the corresponding metadata sample. Therefore, the fluctuation trend in this application is used to describe the change situation of the resource information in the entire metadata sample based on the information offset and the resource information entropy, that is: the fluctuation trend is the quantization value of the change situation 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. The smaller the fluctuation trend, the smaller the change of the resource information in the entire metadata sample. As a preferred embodiment, the fluctuation trend of each resource information in the selected metadata sample can be determined by all the information offsets and the resource information entropy in the following way, that is: first, select a resource information in the selected metadata sample, then, compare the information offset of this resource information with the resource information entropy, and finally, use the natural constant 1 minus the result of the comparison as the fluctuation trend of this resource information. Repeat the above steps to determine the fluctuation trend of the remaining resource information in the selected metadata sample.

[0093] In some embodiments, the distribution pattern of all resource information in each metadata sample can be generated according to all the fluctuation trends by the following steps:

[0094] Select a metadata sample as the selected metadata sample;

[0095] Obtain the fluctuation trend of each resource information in the selected metadata sample;

[0096] Determine the distribution probability of each resource information in the selected metadata sample through all the fluctuation trends;

[0097] Generate the distribution pattern of all resource information in the selected metadata sample according to all the distribution probabilities;

[0098] Continue to generate the distribution patterns of all resource information in the remaining metadata samples.

[0099] In specific implementation, to determine the probability density of each resource information in the selected metadata sample through all fluctuation trends, the following method can be adopted, that is: First, select a resource information in the selected metadata sample. Subsequently, compare the result of squaring the information offset corresponding to this resource information with the result of squaring twice the resource information entropy. Then, take the negative of the comparison result as the exponent of the natural constant e. Finally, multiply the obtained result by the reciprocal of the square root of 2π and multiply it by the fluctuation trend of this resource information, and the result is used as the distribution probability of this 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, to generate the distribution pattern of all resource information in the selected metadata sample according to all distribution probabilities, the following method can be adopted, that is: Take the sum of 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. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0100] It should be noted that the distribution probability in this application is used to describe the probability of a certain resource information appearing in the metadata sample. The larger the distribution probability, the higher the appearance frequency of the resource information in the metadata sample. The smaller the distribution probability, the lower the appearance frequency of the resource information in the metadata sample; In addition, the distribution pattern refers to the overall distribution pattern generated by the distribution probabilities of all resource information, which describes the overall distribution characteristics of all resource information in the metadata sample.

[0101] In step 105, based on all sample membership degrees and all distribution patterns, determine the matching degree between the resource metadata and the teaching resources, and store and allocate the teaching resources of the target major according to the matching degree.

[0102] In some embodiments, to determine the matching degree between the resource metadata and the teaching resources based on all sample membership degrees and all distribution patterns, the following steps can be adopted:

[0103] Determine the resource contribution degree of each metadata sample to the resource metadata through all sample membership degrees and all distribution patterns;

[0104] Determine the matching degree between the resource metadata and the teaching resources according to all resource contribution degrees.

[0105] In specific implementation, determining the resource contribution degree of each metadata sample to the resource metadata based on all sample membership degrees and all distribution patterns can be achieved in the following manner: First, select a metadata sample. Then, obtain the sample membership degree of this metadata sample and the distribution pattern corresponding to this metadata sample. Subsequently, use the result of multiplying the sample membership degree and the distribution pattern as the resource contribution degree of this metadata sample to the resource metadata. Repeat the above steps to determine the resource contribution degrees of the remaining metadata samples to the resource metadata. As a preferred embodiment, determining the matching degree between the resource metadata and the teaching resources based on all the resource contribution degrees can be achieved in the following manner: First, sum up all the resource contribution degrees. Then, sum up all the sample membership degrees. Finally, after comparing the result of summing up all the resource contribution degrees with the result of summing up all the sample membership degrees, use the obtained result as the matching degree between the resource metadata and the teaching resources.

[0106] It should be noted that in this application, the resource contribution degree is used to reflect the contribution degree of the metadata sample to the resource metadata. The larger the resource contribution degree, the higher the contribution degree of the metadata sample to the resource metadata; 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 (i.e., the integrity, consistency, and validity of the data). The larger the matching degree, the higher the matching degree between the resource metadata and the teaching resources of the target major; the smaller the matching degree, the lower the matching degree between the resource metadata and the teaching resources of the target major.

[0107] Preferably, after setting multiple storage thresholds for 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 different levels of storage devices or areas; reference Figure 3As shown in the figure, it is a schematic flowchart of setting a storage threshold in some embodiments of the present application. Specifically, when implementing, first set a first storage threshold and a second storage threshold for the matching degree of the teaching resource library. 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, store the corresponding resource metadata in a storage area with a faster response speed, that is, a high-priority storage area (such as 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, store the corresponding resource metadata in a medium-priority storage area (such as 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, store the corresponding resource metadata in a low-priority storage area (such as a storage device with a lower cost), such as tape storage, cold storage, or low-frequency access cloud storage, so as to realize the storage allocation of the teaching resources of the mechanical and design manufacturing major.

[0108] 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 the matching degree of the resource metadata. For example: after obtaining the matching degrees of all teaching resources in the teaching resource library, select the values corresponding to the top 25% of all matching degrees as the first storage threshold; select the values corresponding to the bottom 25% of all matching degrees as the second storage threshold. In other embodiments, other methods can also be used for setting, which are not limited here.

[0109] It should be noted that the first storage threshold in the present application is greater than the second storage threshold. In addition, by allocating teaching resources to different storage areas through the matching degree, the waste of storage space can be effectively reduced, and thus the management of the teaching resource library of the mechanical and design manufacturing major can be made more flexible, and the storage location of the resources can be adjusted according to teaching needs, the quality of the resources, and the access frequency. For example, when the quality of some low-quality resources is improved after being revised 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.

[0110] In addition, on the other hand of the present application, in some embodiments, the present application provides a teaching resource library management system. Refer to Figure 4 , this figure is a schematic structural diagram of the teaching resource library management system according to some embodiments of the present application. The teaching resource library management system 200 includes: a generation module 201, a processing module 202, and an execution module 203, which are described as follows:

[0111] The generation module 201 is mainly used in this application 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;

[0112] The processing module 202 is mainly used in this application to determine multiple metadata samples of the resource metadata, and the metadata samples include multiple resource information;

[0113] In addition, the processing module 202 in this application is also used to extract the digital features of all resource information in each metadata sample, generate cloud droplet samples 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;

[0114] In addition, the processing module 202 in this application is also used to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate the distribution mode of all resource information in each metadata sample according to all the fluctuation trends;

[0115] The execution module 203 is mainly used in this application to determine the matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution modes, and perform storage allocation on the teaching resources of the target major according to the matching degree.

[0116] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above teaching resource library management method.

[0117] In some embodiments, refer to Figure 5 , this figure is the internal structure diagram of a computer device for implementing the teaching resource library management method according to some embodiments of this application. The teaching resource library management method in the above embodiments can be implemented by Figure 5 The computer device shown, and this computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0118] The processor 301 can be a general central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the teaching resource library management method in this application.

[0119] The communication bus 302 is used to transmit information between the above components.

[0120] 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. It may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures 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 through the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0121] Among them, the memory 303 is used to store the program code for executing the solution of this application and is controlled by the processor 301 to execute. 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 embodiments may be implemented by one or more software modules in the program code in the processor 301 and the memory 303.

[0122] The communication interface 304 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0123] In a specific implementation, as an embodiment, the computer device may include multiple processors, and each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0124] The above computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a laptop 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 embodiments of the present application do not limit the type of the computer device.

[0125] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the teaching resource library management method described above is implemented.

[0126] In summary, in the teaching resource library management system and method disclosed in the embodiments of the present application, 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 is automatically generated; a plurality of metadata samples of the resource metadata are determined, and the metadata samples include a plurality of resource information; digital features of all resource information in each metadata sample are extracted, cloud droplet samples of each metadata sample are generated through a cloud model, and the sample membership degree 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 the fluctuation trends; the matching degree between the resource metadata and the teaching resources is determined based on all the sample membership degrees and all the distribution patterns, and the storage allocation of the teaching resources of the target major is performed according to the matching degree; the differences of multi-source heterogeneous metadata can be calibrated to improve the storage efficiency of the teaching resources.

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0128] Obviously, those skilled in the art can 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 equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A teaching resource library management method, characterized in that It includes 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 is automatically generated; Determine a plurality of metadata samples of the resource metadata, where the metadata samples include a plurality of resource information; 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 degree 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; Determine the matching degree between the resource metadata and the teaching resources based on all the sample membership degrees and all the distribution patterns, and perform storage allocation on the teaching resources of the target major according to the matching degree; Among them, determining the sample membership degree of each metadata sample to the cloud droplet sample according to the digital features of each metadata sample specifically includes: Select a metadata sample as the selected metadata sample; Obtain the average information amount in the digital features of the selected metadata sample and the cloud droplet sample of the selected metadata sample; Determine the resource fluctuation amount between the average information amount and the cloud droplet sample; Determine the sample membership degree of the selected metadata sample to the corresponding cloud droplet sample through the resource fluctuation amount and the digital features; Continue to determine the sample membership degrees of the remaining metadata samples to the corresponding cloud droplet samples.

2. The method according to claim 1, wherein Extracting the digital features of all resource information in each metadata sample specifically includes: Select a metadata sample as the selected metadata sample; Determine the average information amount of all resource information in the selected metadata sample; Determine the resource proportion of each resource information in the selected metadata sample; Determine the digital features of all resource information in the selected metadata sample through all the resource proportions and the average information amount; Continue to determine the digital features of all resource information in the remaining metadata samples.

3. The method according to claim 1, wherein Determining the fluctuation trend of each resource information in the corresponding metadata sample specifically includes: Obtain the resource information entropy and the average information amount in the digital features; Select a metadata sample as the selected metadata sample; Determine the information offset amount of each resource information in the selected metadata sample through the average information amount; Determine the fluctuation trend of each resource information in the selected metadata sample through all the information offset amounts 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.

4. The method according to claim 1, characterized in that, Generating the distribution pattern of all resource information in each metadata sample according to all the fluctuation trends specifically includes: Select a metadata sample as the 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 the fluctuation trends; Generate the distribution pattern of all resource information in the selected metadata sample according to all the distribution probabilities; Continue to generate the distribution patterns of all resource information in the remaining metadata samples.

5. The method according to claim 1, characterized in that, Determining the matching degree of the resource metadata through all sample membership degrees and all distribution patterns specifically includes: Determining the resource contribution degree of each metadata sample to the resource metadata through all sample membership degrees and all distribution patterns; Determining the matching degree of the resource metadata according to all resource contribution degrees.

6. The method according to claim 1, characterized in that, The target major is Mechanical and Design Manufacturing.

7. A teaching resource library management system, which manages the teaching resource library by using the method described in any one of claims 1 to 6, characterized in that, The teaching resource library management system includes: A generation module, configured 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; A processing module, configured to determine multiple metadata samples of the resource metadata, and the metadata samples include multiple resource information; The processing module is further configured to extract digital features of all resource information in each metadata sample, generate cloud droplet samples of each metadata sample through a 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; The processing module is further configured to determine the fluctuation trend of each resource information in the corresponding metadata sample, and generate distribution patterns of all resource information in each metadata sample according to all fluctuation trends; An execution module, configured to determine the matching degree between the resource metadata and the teaching resources based on all sample membership degrees and all distribution patterns, and perform storage allocation on the teaching resources of the target major according to the matching degree.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the teaching resource library management method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the teaching resource library management method according to any one of claims 1 to 6.

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