Teaching data storage method and system based on image processing

Abstract

The invention discloses a teaching data storage method and system based on image processing, relates to the technical field of big data, and mainly aims to solve the problem of low data storage efficiency of an existing smart campus platform. Comprising the steps that image teaching data uploaded by a user is acquired, and the image teaching data is acquired by binding at least one of text content, a user object and a teaching environment based on an image terminal; key features are extracted from the image teaching data, the number of image frames corresponding to the key features is counted, and the key features comprise pixel features, contour features, text features and face features; if the image frame number is greater than a preset frame threshold value, compressing the image teaching data according to the key features, and storing the compressed image teaching data according to a compression index; and if the image frame number is smaller than or equal to a preset frame threshold value, determining a storage position corresponding to the key feature, and storing the image teaching data according to the storage position.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a teaching data storage method and system based on image processing. Background Art

[0002] With the widespread adoption of big data technology, various industries are increasingly focusing on its application in different scenarios to better serve different users. In particular, platforms with a large number of users, such as schools and training institutions, require more precise data processing based on big data technology. For example, teaching data in smart campuses is updated daily and the volume is huge, which can be managed through big data processing.

[0003] At present, when storing teaching data, the smart campus platform usually stores it in the form of logs or stores it with timestamps by user units. However, with the diversification of the forms of teaching data, teaching data in the form of images will waste a lot of storage space when stored due to the non-uniform form and size of user uploads, and there is no unified image processing mechanism, which makes the data storage of the smart campus platform occupy a large amount of system resources, greatly reducing the data processing efficiency of the smart campus platform. Summary of the Invention

[0004] In view of this, the present invention provides a teaching data storage method and system based on image processing, the main purpose of which is to solve the problem of low data storage efficiency of the existing smart campus platform.

[0005] According to one aspect of the present invention, a teaching data storage method based on image processing is provided, comprising: Acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; Extracting key features from the image teaching data and counting the number of image frames corresponding to the key features, the key features including pixel features, contour features, text features, and face features; If the number of image frames is greater than a preset frame threshold, compressing the image teaching data according to the key feature, and storing the compressed image teaching data according to the compression index; If the number of image frames is less than or equal to a preset frame threshold, a storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location.

[0006] According to another aspect of the present invention, there is provided a teaching data storage system based on image processing, comprising: An acquisition module is used to acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; An extraction module is used to extract key features from the image teaching data and count the number of image frames corresponding to the key features, wherein the key features include pixel features, contour features, text features, and face features; a first storage module, configured to compress the image teaching data according to the key feature if the number of image frames is greater than a preset frame threshold, and store the compressed image teaching data according to a compression index; The second storage module is used to determine the storage location corresponding to the key feature if the number of image frames is less than or equal to a preset frame threshold, and store the image teaching data according to the storage location.

[0007] According to another aspect of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned teaching data storage method based on image processing.

[0008] According to another aspect of the present invention, there is provided a terminal comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned teaching data storage method based on image processing.

[0009] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages: The present invention provides a teaching data storage method and system based on image processing. Compared with the prior art, the embodiment of the present invention obtains image teaching data uploaded by users, and the image teaching data is collected by binding at least one of text content, user objects and teaching environment based on an image terminal; key features are extracted from the image teaching data, and the number of image frames corresponding to the key features is counted, and the key features include pixel features, contour features, text features, and face features; if the number of image frames is greater than a preset frame threshold, the image teaching data is compressed according to the key features, and the compressed image teaching data is stored according to the compression index; if the number of image frames is less than or equal to the preset frame threshold, the storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location, thereby realizing flexibility and convenience in storing teaching data in the form of images, reducing waste of storage space, and avoiding the data storage of the smart campus platform from occupying large system resources, thereby greatly improving the data processing efficiency of the smart campus platform.

[0010] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings: Figure 1 A flow chart of a teaching data storage method based on image processing provided by an embodiment of the present invention is shown; Figure 2 It shows a block diagram of a teaching data storage system based on image processing provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a terminal provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0012] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0013] The embodiment of the present invention provides a teaching data storage method based on image processing, such as Figure 1 As shown, the method includes: 101. Obtain image teaching data uploaded by the user.

[0014] In the embodiment of the present application, the current execution end, as the execution subject for executing the storage of teaching data, can be a teaching data processing service end, so as to obtain teaching data in the form of images in real time, that is, image teaching data. At this time, the image teaching data is collected based on the binding of the text content, user object and at least one of the teaching environment by the image terminal. The image terminal can be a mobile phone device of a student user or a teacher user, a mobile personal computer device, etc., and the teaching data to be uploaded is photographed by the camera of the image terminal. Among them, the text content can include but is not limited to the test paper text, homework text, etc., the user object can include but is not limited to students, teachers, etc., and the teaching environment can include but is not limited to the classroom environment, library environment, etc. For example, a student user can upload a photo of himself checking in in the classroom waiting for class by taking a selfie. For example, a teacher user can upload the student's corrected homework by taking a photo. This embodiment of the present application does not make specific limitations.

[0015] It should be noted that when the current execution end obtains the image teaching data uploaded by the user, it can be acquired in a real-time monitoring manner. That is, after each user uploads the image teaching data, it is immediately acquired to execute the method in step 102 for storage. In addition, the image terminal can download the corresponding smart campus application for the terminal device used by different users to act as the server end of the smart campus platform of the current execution end to upload data, thereby ensuring the integrity of data interaction.

[0016] In another embodiment of the present application, for further definition and explanation, the steps further include: After the data connection of the image terminal is completed, the image terminal is instructed to perform acquisition according to the acquisition time to obtain image teaching data.

[0017] In order to ensure that the image terminal can effectively and accurately collect teaching data, the current execution end needs to establish a data connection with the image terminal. The data connection method at this time may include but is not limited to logging into the smart teaching platform of the current execution end through the application of the image terminal, and ensuring that the camera permissions of the image terminal are open so that images can be taken at any time. This embodiment of the present application does not make specific restrictions. After completing the data connection with the image terminal, the current execution end pre-configures an acquisition time, for example, 5 seconds or 10 seconds, that is, within this acquisition time, instructs the image terminal to acquire multiple frames of images to obtain image teaching data, thereby ensuring that the acquired image teaching data contains multiple frames of images, so as to improve the recognition of features in the image teaching data and effectively establish a storage index.

[0018] 102. Extract key features from the image teaching data, and count the number of image frames corresponding to the key features.

[0019] In an embodiment of the present application, in order to construct a more efficient and accurate storage index to increase the speed of storing and querying teaching data, the current execution end extracts key features from the image teaching data. In this case, the key features include pixel features, contour features, text features, and facial features. Pixel features are used to characterize the content extracted based on pixels, including but not limited to pixels whose grayscale values ​​or color values ​​meet preset conditions. Contour features are used to characterize the content extracted based on image contours, including but not limited to special logo contours, building contours, etc. Text features are used to characterize the content extracted based on text or words, including but not limited to Chinese characters or other language characters, numbers, etc. Facial features are used to characterize the content extracted based on faces, including but not limited to the faces of different teachers or students. Furthermore, after extracting the key features, the number of image frames containing the key features in multiple frames is counted as the basis for storing the image teaching data. For example, if 10 frames of image teaching data are collected and the key facial features are extracted from all images in sequence, the specific number of image frames where the facial features can be extracted is counted, such as 2 frames, which is not specifically limited in this embodiment of the application.

[0020] In another embodiment of the present application, for further definition and explanation, the step of extracting key features from the image teaching data includes: Traverse all pixel points of the image teaching data according to the pixel position relationship, extract at least one pixel point that meets the pixel position relationship, and obtain pixel features; or, Recognize the image teaching data based on the contour recognition model that has completed model training to determine the contour features; or Recognize the image teaching data based on the text recognition model that has completed model training to determine text features; or The face recognition model that has completed model training is used to identify the image teaching data and determine facial features.

[0021] In order to achieve effective storage of teaching data in image form and thus speed up the query efficiency of teaching data, the current execution end adopts different extraction methods for different key features.

[0022] In some embodiments, for pixel features, the current execution end traverses all pixel points of the image teaching data according to the pixel position relationship, extracts at least one pixel point that meets the pixel position relationship, and obtains pixel features. At this time, the pixel position relationship includes adjacent pixel relationships, spaced pixel relationships, and connected pixel relationships, that is, including extracting pixel features that match grayscale values ​​or color values ​​according to adjacent positions, extracting pixel features that match grayscale values ​​or color values ​​according to spaced positions, and extracting pixel features that match grayscale values ​​or color values ​​according to the connection positions between multiple pixels. The embodiments of the present application do not make specific limitations.

[0023] In some embodiments, the current execution end identifies contour features from the image teaching data based on a trained contour recognition model. In this case, the contour recognition model is trained based on pre-labeled contour samples. Contour recognition models include, but are not limited to, convolutional neural networks (CNNs), encoder-decoder architectures, and transformers, and are not specifically limited in this embodiment.

[0024] In some embodiments, with respect to text features, the current execution end identifies the image teaching data based on a text recognition model that has completed model training to determine text features. In this case, the text recognition model is trained based on a preset text library. Among them, the text recognition model may include but is not limited to a hidden Markov model (HMM), a recurrent neural network (RNN), a semantic recognition model ‌BERT, etc., and this embodiment of the application does not make specific limitations.‌ In some embodiments, the current execution end identifies facial features based on a trained facial recognition model from the image teaching data. The facial recognition model is trained based on a facial database. The facial recognition model may include, but is not limited to, a Transformer-CNN hybrid, VGGFace, DeepFace, and the like, and is not specifically limited in this embodiment.

[0025] In another embodiment of the present application, for further definition and explanation, the step of counting the number of image frames corresponding to the key feature includes: Determining the total number of image frames of the image teaching data according to the acquisition time length; The number of image frames is counted from the number of image frames according to the image teaching data that identifies the key features.

[0026] In order to implement storage based on the number of image frames, thereby improving the storage query efficiency of image teaching data, the current execution end, when counting the number of image frames, first determines the total number of image frames of the collected image teaching data according to the length of the acquisition time. For example, the total number of image frames of the image teaching data collected within 3 seconds is 30 frames, which means that 30 frames of images are collected every second as image teaching data. Furthermore, after extracting the key features, the frame images from which the key features are extracted are counted. For example, if the frame images from which the key features are extracted are 10, then the number of frame images from the 30 frames that meet the requirements of the key features extraction is 10. This is not specifically limited in the embodiments of the present application.

[0027] 103. If the number of image frames is greater than a preset frame threshold, compress the image teaching data according to the key feature, and store the compressed image teaching data according to the compression index.

[0028] In the embodiment of the present application, a preset frame threshold is used as a basis for determining whether the image teaching data is compressed and stored. In this case, the preset frame threshold can be 5 frames or 7 frames, and can be configured to be less than the number of frames of all image teaching data collected during the collection time. The embodiment of the present application does not make specific restrictions. When the number of image frames is greater than the preset frame threshold, it means that there are many key features that can be extracted from the image teaching data. Therefore, the image teaching data is compressed according to the key features, so that the compressed image teaching data is stored according to the compression index. Among them, the compression index is generated based on the key features so that the corresponding storage location of the image teaching data can be quickly found in the subsequent query process.

[0029] In another embodiment of the present application, for further definition and explanation, the step of compressing the image teaching data according to the key features and storing the compressed image teaching data according to the compression index includes: If the key feature is a pixel feature, determining a compression object for the image teaching data according to the pixel feature, and constructing a first compression index according to the compression object to store the compressed image teaching data according to the first compression index, wherein the compression object is determined from pixel points of the image teaching data according to pixel position and pixel number; If the key feature is a contour feature, the image teaching data is extended according to the contour feature to obtain an extended contour, and a second compression index is constructed according to the extended contour to store the compressed image teaching data according to the second compression index, wherein the extended contour is obtained by extending the contour feature according to a preset extension length; If the key feature is a text feature, extracting keywords from the text feature, and constructing a third compression index based on the keywords, so as to store the compressed image teaching data according to the third compression index, wherein the keywords are determined according to the importance coefficient of the text feature; If the key feature is a facial feature, the user identity information is determined based on the facial feature, and a fourth compression index is constructed based on the user identity information to store the compressed image teaching data according to the fourth compression index. The user identity information is obtained by identifying and matching the facial feature.

[0030] In some embodiments, if the key feature is a pixel feature, in order to construct an index at a pixel angle to improve the diversity and effectiveness of the storage of image teaching data, the current execution end determines the compression object of the image teaching data according to the pixel feature, and constructs a first compression index according to the compression object. Among them, the compression object is the pixel point to be compressed. At this time, the compression object is determined from the pixel points of the image teaching data according to the pixel position and the number of pixels. Specifically, the pixel position is the position corresponding to the extracted pixel feature, and the number of pixels is the number corresponding to the extracted pixel feature. This embodiment of the application does not make specific restrictions. In addition, when determining the compression object, it can be based on pre-configured pixel positions, such as pixel features and the surrounding pixel points of the pixel features, as well as the number of pixels, such as 4 front, back, left, and right, or 8 front, back, left, right, left front, right front, left back, and right back, etc. This embodiment of the application does not make specific restrictions. In addition, when constructing the first compression index according to the compression object, specifically, the pixel point is extracted according to its position in the entire image teaching data to obtain the first compression index according to the extracted pixel position, and the compressed image teaching data is stored according to the first compression index. This embodiment of the present application does not make specific limitations.

[0031] In some embodiments, if the key feature is a contour feature, in order to construct an index based on the contour angle to improve the diversity and effectiveness of the storage of image teaching data, the current execution end extends the image teaching data according to the contour feature to obtain an extended contour, and constructs a second compressed index based on the extended contour. The extended contour can be obtained by extending the contour feature according to a preset extension length. For example, if the contour feature is the contour of a hammer made during practice, the hammer contour is extended and expanded according to a preset extension length of 2 mm. This is not specifically limited in this embodiment of the present application. Furthermore, the second compressed index is constructed based on the extended contour. Specifically, the shape of the extended contour feature is first identified, including but not limited to squares, rectangles, circles, irregular polygons, etc., and a corresponding compressed index is generated based on the identified shape and a preset shape sorting (e.g., square sorting 1, rectangle sorting 2, etc.). For example, irregular polygon 1, curve and line combination 1-2, i.e., the compressed index created by the contour feature is 1-2. The compressed image teaching data is stored according to the second compressed index. This is not specifically limited in this embodiment of the present application.

[0032] In some embodiments, if the key feature is a text feature, in order to construct an index from a text perspective to improve the diversity and effectiveness of the storage of image teaching data, the current execution end extracts keywords from the text feature and constructs a third compressed index based on the keywords. Since the extracted keywords are pre-trained based on natural language processing technology, the current execution end can construct an index according to the importance coefficient of the text feature when creating the third compressed index. Specifically, first determine the pinyin order of each character in the text feature as the first-level index, and calculate the number of keywords in the text feature. The importance coefficient is obtained by comparing the number of keywords counted with the total number in the entire keyword library as the second-level index. The first-level index and the second-level index are combined into a third compressed index to store the compressed image teaching data according to the third compressed index. This embodiment of the present application does not make specific limitations.

[0033] In some embodiments, if the key features are facial features, in order to construct an index from a facial perspective to improve the diversity and effectiveness of the storage of image teaching data, the current execution end determines user identity information based on the facial features and constructs a fourth compressed index based on the user identity information. The user identity information is obtained by identifying and matching the facial features, that is, a compressed index can be constructed for the facial features according to the user identity information. For example, if the user is a student, the student's major, class, student ID, and other information are retrieved to construct an index, so that the compressed image teaching data is stored according to the fourth compressed index. This embodiment of the present application does not impose any specific limitations.

[0034] It should be noted that when the current execution end stores the compressed image teaching data according to each compression index, the image teaching data is first compressed. During compression, the current execution end can choose to extract and compress the key features obtained by identification, and delete the non-key features to reduce storage redundancy. Furthermore, the compressed image teaching data containing key features is named according to the compression index and stored in the corresponding storage location. When the management user requests a query, the interpretations corresponding to different compression indexes can be displayed to the management user. For example, the interpretation content of the first compression index is the pixel positions corresponding to the front, back, left and right respectively, so that the management user can enter all the pixel features that need to be queried and retrieve the corresponding image teaching data from the corresponding storage location.

[0035] 104. If the number of image frames is less than or equal to a preset frame threshold, determine a storage location corresponding to the key feature, and store the image teaching data according to the storage location.

[0036] In the embodiment of the present application, a preset frame threshold is used as a basis for determining whether the image teaching data should be compressed and stored. In this case, the preset frame threshold can be 5 frames or 7 frames, and can be configured to be less than the number of frames of the total image teaching data collected during the acquisition time. This embodiment of the present application does not impose any specific restrictions. When the number of image frames is less than or equal to the preset frame threshold, it indicates that fewer key features can be extracted from the image teaching data. Therefore, the storage location corresponding to the key feature is directly determined, and the image teaching data is stored according to this storage location.

[0037] In another embodiment of the present application, for further definition and explanation, the step of determining a storage location corresponding to the key feature and storing the image teaching data according to the storage location includes: Determining a storage location from the teaching data storage according to the storage capacity of the storage, the storage update time, and the storage round; When a storage instruction of the storage location is monitored, the image teaching data is stored in the storage location.

[0038] In order to meet the storage validity of the storage space and increase storage efficiency, the current execution end first determines the storage location from the teaching data memory according to the memory storage capacity, memory update time and storage round. Among them, the memory storage capacity is the storage capacity of the data stored in each storage location in the teaching data memory, the memory update time is the time for updating the data in each storage location in the teaching data memory, and the storage round is the number of storage times completed by the teaching data memory. When determining the storage location, the storage location can be matched from all storage locations according to the preset matching strategy according to the memory storage capacity, memory update time and storage round. The preset matching strategy can be to use at least one of the largest memory storage capacity, the longest memory update time and the smallest storage round as a screening condition to determine whether each storage location meets the requirements. At this time, the storage location corresponding to the largest memory storage capacity, the longest memory update time and the smallest storage round is used as the optimal storage location, that is, the memory storage capacity is used as the optimal judgment condition, the storage update time is used as the secondary judgment condition, and the storage round is used as the final judgment condition. This embodiment of the application does not make specific limitations.

[0039] It should be noted that when a storage instruction of a storage location is monitored, the image teaching data can be stored in the determined storage location. In this case, the storage location is a fixed storage space pre-divided in the memory, and the present embodiment does not make specific restrictions. In addition, the storage instruction is triggered after the current execution end passes the identity authentication. For example, after the current execution end determines that the teacher user or student user has completed the real identity authentication, a storage instruction is generated. At this time, it can be a telephone number verification code verification or a face verification, and the present embodiment does not make specific restrictions.

[0040] An embodiment of the present invention provides a teaching data storage method based on image processing. Compared with the prior art, the embodiment of the present invention obtains image teaching data uploaded by users, and the image teaching data is collected by binding at least one of text content, user objects and teaching environment based on an image terminal; key features are extracted from the image teaching data, and the number of image frames corresponding to the key features is counted, and the key features include pixel features, contour features, text features, and face features; if the number of image frames is greater than a preset frame threshold, the image teaching data is compressed according to the key features, and the compressed image teaching data is stored according to the compression index; if the number of image frames is less than or equal to the preset frame threshold, the storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location, thereby realizing flexibility and convenience in storing teaching data in the form of images, reducing waste of storage space, and avoiding the data storage of the smart campus platform from occupying large system resources, thereby greatly improving the data processing efficiency of the smart campus platform.

[0041] Furthermore, as a response to the above Figure 1 The embodiment of the present invention provides a teaching data storage system based on image processing, such as Figure 2 As shown, the system includes: An acquisition module 21 is configured to acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; An extraction module 22 is used to extract key features from the image teaching data and count the number of image frames corresponding to the key features, wherein the key features include pixel features, contour features, text features, and face features; A first storage module 23 is configured to compress the image teaching data according to the key feature if the number of image frames is greater than a preset frame threshold, and store the compressed image teaching data according to a compression index; The second storage module 24 is configured to determine a storage location corresponding to the key feature if the number of image frames is less than or equal to a preset frame threshold, and store the image teaching data according to the storage location.

[0042] Furthermore, the extraction module is specifically used to traverse all pixel points of the image teaching data according to the pixel position relationship, extract at least one pixel point that meets the pixel position relationship, and obtain pixel features, wherein the pixel position relationship includes adjacent pixel relationship, separated pixel relationship, and connected pixel relationship; or, based on a contour recognition model that has completed model training, the image teaching data is identified to determine contour features, and the contour recognition model is trained based on pre-labeled contour samples; or, based on a text recognition model that has completed model training, the image teaching data is identified to determine text features, and the text recognition model is trained based on a preset text library; or, based on a face recognition model that has completed model training, the image teaching data is identified to determine facial features, and the face recognition model is trained based on a face database.

[0043] Furthermore, the first storage module is specifically used to, if the key feature is a pixel feature, determine the compression object of the image teaching data according to the pixel feature, and construct a first compression index according to the compression object, so as to store the compressed image teaching data according to the first compression index, and the compression object is determined from the pixel points of the image teaching data according to the pixel position and the number of pixels; if the key feature is a contour feature, extend the image teaching data according to the contour feature to obtain an extended contour, and construct a second compression index according to the extended contour, so as to store the compressed image teaching data according to the second compression index, and the extended contour is used to store the compressed image teaching data according to the second compression index. The extended contour is obtained by extending the contour feature according to a preset extension length; if the key feature is a text feature, keywords are extracted from the text feature, and a third compression index is constructed based on the keywords, so as to store the compressed image teaching data according to the third compression index, and the keywords are determined according to the importance coefficient of the text feature; if the key feature is a facial feature, user identity information is determined based on the facial feature, and a fourth compression index is constructed based on the user identity information, so as to store the compressed image teaching data according to the fourth compression index, and the user identity information is obtained by identifying and matching the facial feature.

[0044] Furthermore, the second storage module is specifically used to determine a storage location from the teaching data memory according to the memory storage capacity, memory update time and storage rounds; when a storage instruction of the storage location is monitored, the image teaching data is stored in the storage location.

[0045] Furthermore, the extraction module is also used to determine the total number of image frames of the image teaching data according to the length of acquisition time; and count the number of image frames from the number of image frames according to the image teaching data that identifies the key features.

[0046] Furthermore, the system further comprises: The acquisition module is used to instruct the image terminal to perform acquisition according to the acquisition time after completing the data connection of the image terminal to obtain image teaching data.

[0047] An embodiment of the present invention provides a teaching data storage system based on image processing. Compared with the prior art, the embodiment of the present invention obtains image teaching data uploaded by users, and the image teaching data is collected by binding at least one of text content, user objects and teaching environment based on an image terminal; key features are extracted from the image teaching data, and the number of image frames corresponding to the key features is counted, and the key features include pixel features, contour features, text features, and face features; if the number of image frames is greater than a preset frame threshold, the image teaching data is compressed according to the key features, and the compressed image teaching data is stored according to the compression index; if the number of image frames is less than or equal to the preset frame threshold, the storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location, thereby realizing flexibility and convenience in storing teaching data in the form of images, reducing waste of storage space, and avoiding the data storage of the smart campus platform from occupying large system resources, thereby greatly improving the data processing efficiency of the smart campus platform.

[0048] According to one embodiment of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the computer executable instruction can execute the teaching data storage method based on image processing in any of the above method embodiments.

[0049] Figure 3 A schematic structural diagram of a terminal provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the terminal.

[0050] like Figure 3 As shown, the terminal may include: a processor 302 , a communications interface 304 , a memory 306 , and a communication bus 308 .

[0051] The processor 302 , the communication interface 304 , and the memory 306 communicate with each other via a communication bus 308 .

[0052] The communication interface 304 is used to communicate with other devices such as clients or other servers.

[0053] The processor 302 is configured to execute the program 310 , and specifically to execute the relevant steps in the embodiment of the teaching data storage method based on image processing.

[0054] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0055] Processor 302 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the terminal may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0056] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, or may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0057] The program 310 may be specifically configured to cause the processor 302 to perform the following operations: Acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; Extracting key features from the image teaching data and counting the number of image frames corresponding to the key features, the key features including pixel features, contour features, text features, and face features; If the number of image frames is greater than a preset frame threshold, compressing the image teaching data according to the key feature, and storing the compressed image teaching data according to the compression index; If the number of image frames is less than or equal to a preset frame threshold, a storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location.

[0058] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0059] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A teaching data storage method based on image processing, characterized in that: include: Acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; Extracting key features from the image teaching data and counting the number of image frames corresponding to the key features, the key features including pixel features, contour features, text features, and face features; If the number of image frames is greater than a preset frame threshold, compressing the image teaching data according to the key feature, and storing the compressed image teaching data according to the compression index; If the number of image frames is less than or equal to a preset frame threshold, a storage location corresponding to the key feature is determined, and the image teaching data is stored according to the storage location.

2. The method according to claim 1, characterized in that The extracting key features from the image teaching data comprises: Traverse all pixel points of the image teaching data according to the pixel position relationship, extract at least one pixel point that meets the pixel position relationship, and obtain pixel features, wherein the pixel position relationship includes adjacent pixel relationship, separated pixel relationship, and connected pixel relationship; or Recognize the image teaching data based on a contour recognition model that has been trained to determine contour features, wherein the contour recognition model is trained based on pre-labeled contour samples; or Recognize the image teaching data based on a text recognition model that has been trained to determine text features, wherein the text recognition model is trained based on a preset text library; or The image teaching data is recognized based on a face recognition model that has completed model training to determine facial features. The face recognition model is obtained by training based on a face database.

3. The method according to claim 2, characterized in that The compressing the image teaching data according to the key features and storing the compressed image teaching data according to the compression index includes: If the key feature is a pixel feature, determining a compression object for the image teaching data according to the pixel feature, and constructing a first compression index according to the compression object to store the compressed image teaching data according to the first compression index, wherein the compression object is determined from pixel points of the image teaching data according to pixel position and pixel number; If the key feature is a contour feature, the image teaching data is extended according to the contour feature to obtain an extended contour, and a second compression index is constructed according to the extended contour to store the compressed image teaching data according to the second compression index, wherein the extended contour is obtained by extending the contour feature according to a preset extension length; If the key feature is a text feature, extracting keywords from the text feature, and constructing a third compression index based on the keywords, so as to store the compressed image teaching data according to the third compression index, wherein the keywords are determined according to the importance coefficient of the text feature; If the key feature is a facial feature, the user identity information is determined based on the facial feature, and a fourth compression index is constructed based on the user identity information to store the compressed image teaching data according to the fourth compression index. The user identity information is obtained by identifying and matching the facial feature.

4. The method according to claim 3, characterized in that Determining the storage location corresponding to the key feature and storing the image teaching data according to the storage location includes: Determining a storage location from the teaching data storage according to the storage capacity of the storage, the storage update time, and the storage round; When a storage instruction of the storage location is monitored, the image teaching data is stored in the storage location.

5. The method according to claim 4, characterized in that The counting of the number of image frames corresponding to the key features includes: Determining the total number of image frames of the image teaching data according to the acquisition time length; The number of image frames is counted from the number of image frames according to the image teaching data that identifies the key features.

6. The method according to claim 5, characterized in that The method further comprises: After the data connection of the image terminal is completed, the image terminal is instructed to perform acquisition according to the acquisition time to obtain image teaching data.

7. A teaching data storage system based on image processing, characterized in that: include: An acquisition module is used to acquire image teaching data uploaded by a user, wherein the image teaching data is collected by binding at least one of text content, user object, and teaching environment based on an image terminal; An extraction module is used to extract key features from the image teaching data and count the number of image frames corresponding to the key features, wherein the key features include pixel features, contour features, text features, and face features; a first storage module, configured to compress the image teaching data according to the key feature if the number of image frames is greater than a preset frame threshold, and store the compressed image teaching data according to a compression index; The second storage module is used to determine the storage location corresponding to the key feature if the number of image frames is less than or equal to a preset frame threshold, and store the image teaching data according to the storage location.

8. A storage medium storing at least one executable instruction, wherein the executable instruction enables a processor to execute an operation corresponding to the teaching data storage method based on image processing according to any one of claims 1 to 7.

9. A terminal comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the teaching data storage method based on image processing according to any one of claims 1 to 7.

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