A cloud-based collaborative aesthetic education curriculum management method, system, and equipment

Abstract

This invention discloses a cloud-based collaborative aesthetic education curriculum management method, system, and device, relating to the field of curriculum management technology. The system includes a multi-dimensional process characteristic indicator evaluation module, used to completely store the brushstroke event data stream generated during collaborative editing in a time sequence, forming a creative process record. From the obtained creative process record, multi-dimensional process characteristic indicators, consisting of brushstroke stability, color richness, and number of modification iterations, are extracted. Based on these multi-dimensional process characteristic indicators, an aesthetic literacy evaluation report for student users is generated. This invention calculates dimensional indicators reflecting techniques, colors, and modification habits based on brushstroke event sequences, enabling teachers to dynamically track the development trajectory of students' aesthetic literacy.

Description

Technical Field

[0001] This invention relates to the field of curriculum management technology, specifically to a cloud-based collaborative aesthetic education curriculum management method, system, and device. Background Technology

[0002] With the advancement of digital transformation in aesthetic education, the use of digital technology to co-create and share high-quality aesthetic education content and enhance students' aesthetic literacy and artistic expression has become a current development trend. Under this trend, a number of digital platforms and tools for aesthetic education have been put into use.

[0003] In practical use, these technical solutions still have some significant problems when dealing with specific scenarios in art education. Regarding resource sharing and copyright protection, most existing platforms use centralized storage combined with simple watermarks to identify complete resources. In art education, teachers often need to extract a section of a classic painting to explain color matching, or extract specific segments from teaching videos as technique demonstrations. Such partial referencing and adaptation lacks an effective tracking mechanism in the existing technology system. A single watermark only marks the original complete resource and cannot trace the source link of derived resource segments, making the copyright ownership of adapted materials unclear. Regarding remote teaching interaction, existing art education remote teaching systems mainly rely on live audio and video streaming or screen sharing. For subjects like painting, calligraphy, and handicrafts, which highly rely on real-time demonstrations of the creative process, the image compression and frame rate constraints associated with screen sharing cause a significant loss of key details such as brushstroke trajectory, changes in brush pressure, and speed. The demonstration images seen by students are often blurry and choppy, resulting in a significant gap between the teaching experience and face-to-face demonstrations. In terms of aesthetic education evaluation, most current systems focus on static scoring of students' final works or on comprehensive evaluation using structured data such as attendance and assignment completion. Behavioral data generated during the creative process, such as the frequency of student revisions, the stability of brushstrokes, and the richness of color usage, has not yet been systematically collected and utilized. This lack of process-oriented evaluation makes it difficult for teachers to fully grasp the development trajectory of students' aesthetic judgment and the formation process of their artistic expression abilities. Therefore, existing technologies still have significant shortcomings in areas such as resource sharing and tracing, remote creative interaction, and process-oriented evaluation. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud-based collaborative aesthetic education course management method, system, and device to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a cloud-based collaborative aesthetic education course management method, comprising: S1. Receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources. The aesthetic education teaching resources include text resources, image resources, and video resources. The metadata information includes resource number, resource type, subject classification, uploader identifier, and upload timestamp. The user terminal types include teacher user terminals and student user terminals. S2. The obtained aesthetic education teaching resources are segmented according to preset semantic unit rules to obtain multiple resource segments, and a corresponding segmented digital watermark is generated for each resource segment. The segmented digital watermark is embedded into the corresponding resource segment, and the watermark embedding record is synchronously written into the cloud traceability database. The segmented digital watermark is obtained through the resource number, resource segment number, uploader identifier, and watermark generation timestamp. S3. Receive a collaboration request for the target aesthetic education teaching resource initiated by the first user terminal. The collaboration request includes the collaboration operation type and the segment identifier of the target resource. Verify whether the first user terminal has the corresponding operation permission according to the preset permission gradient model. S4. When the collaboration request is verified and the corresponding collaboration operation type is a painting collaboration operation, receive the pen touch event data stream sent by the first user terminal. The pen touch event data stream includes pen touch type, coordinate sequence, pressure value sequence, velocity vector sequence and timestamp sequence. Based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, generate synchronization data packets corresponding to each other user terminal and send them respectively, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. S5. Store the brushstroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brushstroke stability index, color richness index, and number of modification iterations from the obtained creation process record; generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. S6. Upload the creative works completed by student users to the cloud virtual exhibition hall, and receive structured aesthetic evaluation cards submitted by viewers for the creative works. The structured aesthetic evaluation cards include dimensions of feeling description, associative inspiration, and improvement suggestions. Summarize the data of the structured aesthetic evaluation cards, generate evaluation results, and feed them back to the teacher user terminal.

[0006] Furthermore, the specific method for segmenting the obtained aesthetic education teaching resources according to preset semantic unit rules in S2 includes: When the resource type is an image resource, the image semantic segmentation rule is called, the contour boundary in the image is extracted based on the edge detection operator, the image is divided into the main body region and the background environment region according to the obtained contour boundary, and the obtained main body region is further divided into one or more independent composition element regions based on connected component analysis, and each independent composition element region is a resource segmentation segment. When the resource type is video, the video semantic segmentation rules are invoked to identify the shot transition boundaries and audio silence segments in the video. Combined with the preset teaching link keyword matching, the video is divided into introductory segments, appreciation segments, technique explanation segments, creative demonstration segments, and work display segments, with each segment serving as a resource segment. When the resource type is text, the text semantic segmentation rules are invoked to identify chapter titles or paragraph separators in the text and divide the text into several semantic paragraphs, with each semantic paragraph serving as a resource segmentation segment. In the process of generating corresponding segmented digital watermarks for each resource segment in S2, the corresponding resource number, resource segment sequence number, resource segment start position pointer, resource segment end position pointer, uploader identifier and watermark generation timestamp are concatenated into a string to be verified in a preset order. A hash algorithm is used to calculate a hash value of a fixed length on the obtained string to be verified, and the first preset number of bytes of the obtained hash value are taken as the corresponding segmented digital watermark.

[0007] Furthermore, S2 also includes a derived resource tracing sub-step: When the cloud server detects that a third user terminal performs a referencing or adaptation operation on an existing resource segment and generates a derived resource file, the derived resource file automatically inherits all field contents of the segment digital watermark of the original resource segment referenced, and adds a record consisting of the adaptor user identifier, adaptation operation type and adaptation timestamp to the corresponding metadata. The cloud server adds a new traceability record for the derived resource file in the cloud traceability database. This traceability record contains a pointer field pointing to the original resource segment traceability record, forming a reversible watermark derivative chain. When a copyright verification request for a specified resource is received, the cloud server starts from the source tracing record corresponding to the specified resource, traces back level by level along the corresponding watermark derivative chain until it locates the earliest uploaded original resource segment record, and returns the complete tracing path to the requester.

[0008] This invention employs a semantic segmentation algorithm to divide aesthetic education resources into independent segments based on their teaching semantics, embedding a segmented digital watermark into each segment. The watermark carries fields including resource number, segment sequence number, segment start pointer, segment end pointer, uploader identifier, and watermark generation timestamp. When a resource is partially referenced or a derivative resource is created due to adaptation, the derivative resource automatically inherits the watermark information of the original segment and establishes a pointer link in the source tracing database, thus constructing a reversibly traceable watermark derivation chain. Through this mechanism, common partial reference behaviors in aesthetic education, such as partial extraction of famous paintings and excerpts from teaching video clips, have a clear source tracing path, thus overcoming the shortcomings of existing technologies that can only trace complete files and are difficult to handle resource derivation scenarios. Based on this, the source tracing database can be used to generate a visualized resource evolution map, intuitively presenting version evolution relationships and contributor information, providing a technical basis for copyright confirmation and contribution quantification in multi-teacher collaborative lesson preparation scenarios. Furthermore, the derived resource tracing sub-step also includes an evolutionary map generation step: The cloud server constructs a resource evolution graph data structure based on the traceability records and corresponding pointer fields stored in the cloud traceability database. The resource evolution graph uses nodes to represent each resource segment version and directed edges to represent the reference or adaptation relationship between versions. Each directed edge carries the adapter user identifier and adaptation operation type as edge attributes. When a teacher's client requests to view the evolution history of a specified resource, the cloud server renders the corresponding resource evolution map as a visual tree diagram and presents it on the corresponding teacher's client interface.

[0009] Furthermore, in step S3, verifying whether the first user terminal has the corresponding operation permission according to the preset permission gradient model specifically includes: querying the locally stored permission configuration table to obtain the authorization level corresponding to the target resource number of the first user terminal that initiated the corresponding collaboration request. The authorization level is set according to the preset permission gradient model, and the permission gradient includes browsing level, citation level, adaptation level, collaborative editing level and dissemination level. The collaboration operation type of the obtained collaboration request is matched and determined with the obtained authorization level: If the operation type of the requested collaboration operation belongs to the set of operations allowed by the obtained authorization level, then the first user terminal is determined to have the corresponding operation permission, the collaboration request is verified, a collaboration permission token is generated and returned to the first user terminal, the collaboration permission token includes a session identifier, validity period and allowed operation range fields; otherwise, the first user terminal is determined not to have the corresponding operation permission, the collaboration request verification fails, a permission rejection response is returned and the current processing flow is terminated. The permission gradient model also includes a permission management mechanism, specifically: When the teacher's client of the first institution needs to share the target aesthetic education teaching resources to the user's client of the second institution, the teacher's client of the first institution initiates a certificate issuance request to the cloud server. The cloud server generates a usage certificate token and sends it to the user's client of the second institution. The usage certificate token includes the target resource number, the resource segment sequence number list, the authorization level, the usage scenario limitation description, the validity period start time, the validity period end time, the maximum number of uses limit, and the issuing institution identifier. Each time a user of the second institution accesses the target aesthetic education teaching resources, the user must include the obtained usage rights token in the request. The cloud server will respond after verifying the validity period and usage count of the token and update the usage count counter corresponding to the token. When the second institution further shares the target aesthetic education teaching resources with the third institution, the cloud server generates a sub-authorization token. The sub-authorization token contains all the authorized content of the parent authorization token, as well as a hash pointer pointing to the parent authorization token, forming a verifiable nested authorization chain.

[0010] Furthermore, in S4, each coordinate point in the coordinate sequence of the pen stroke event data stream represents the offset relative to the position of the previous coordinate point, and the starting coordinate point in the coordinate sequence represents the offset relative to the preset origin in the drawing interface. Each element in the pressure value sequence corresponds to the normalized pressure value at a coordinate point in the coordinate sequence, and the normalized pressure value belongs to the integer range [0, 1023]. Each element in the velocity vector sequence corresponds to a velocity vector at a coordinate point in the coordinate sequence, and the velocity vector consists of a horizontal velocity component and a vertical velocity component. Each element in the timestamp sequence corresponds to the timestamp of the stroke at the corresponding coordinate point within the coordinate sequence. The cloud server in S4 also supports switching between demonstration mode, patrol mode and collaborative creation mode. In the demonstration mode, only the teacher's client has the permission to send the pen touch event data stream, while all student clients have the permission to receive and render. In the demonstration mode, student clients can review the received pen touch events step by step. In the patrol mode, the teacher's client sends annotation stroke events on the shared drawing interface of the designated student's client in the form of a semi-transparent overlay layer. The annotation stroke events carry layer attribute identifiers. The student's client renders the annotation stroke events on an independent layer. The original stroke layer content of the student's client is not affected by the overlay of the annotation stroke events. In the collaborative creation mode, all participating user terminals have the permission to send pen touch event data streams. The cloud server sorts and merges the concurrently received pen touch event data streams according to the event timestamp and preset conflict resolution rules, so that the drawing content displayed on the drawing interface of each participating terminal remains consistent. The pre-defined conflict resolution rules in the collaborative creation mode are as follows: If multiple pen touch event data streams targeting the same spatial region are received within the same time tolerance window, they are sorted according to the user priority of the sending user terminal, with pen touch events from the teacher user terminal taking precedence over those from the student user terminal; if the user priorities are the same, they are sorted according to the order of the event timestamps; the absolute difference between any two time points within the same time tolerance window is less than or equal to the preset interval duration.

[0011] Furthermore, the calculation method for the pen stroke stability index in S5 is as follows: Let Pi(xi,yi) be the i-th coordinate point in the coordinate sequence corresponding to the pen stroke event; calculate the change in the vector angle formed by any three adjacent coordinate points; and let the change in the vector angle formed by the (i-1)-th, i-th, and (i+1)-th coordinate points in the coordinate sequence corresponding to the pen stroke event be denoted as... ; ; in, It represents the direction angle of the vector formed by the i-th coordinate point to the (i+1)-th coordinate point in the coordinate sequence corresponding to the pen stroke event; It represents the direction angle of the vector formed by the (i-1)th coordinate point to the ith coordinate point in the coordinate sequence corresponding to the pen stroke event; The formulas involved in calculating the stroke stability index are as follows: ; in, This represents the pen stroke stability index corresponding to the coordinate sequence of the pen stroke event. The smaller the index value, the more stable the pen stroke. k represents the number of coordinate points in the coordinate sequence corresponding to the pen stroke event. The calculation method for the color richness index is as follows: Extract all color value codes from the creation process record file, convert the obtained color value codes to the HSV color space, and only take the hue component H value. Divide the hue circle from 0° to 360° into M equal hue intervals, and count the frequency of color values ​​in each hue interval. The frequency of color values ​​in the j-th hue interval is denoted as Pj. The color richness index is denoted as CR. ; A higher color richness index value indicates a richer use of colors. The specific method for calculating the number of modification iterations is as follows: The shared drawing interface is divided into grid cells of preset size, and the resulting creation process record file is analyzed stroke by stroke: when a pen lifting event is detected, if the coordinate point of a subsequent pen movement event falls into a grid cell occupied by a previously completed pen stroke trajectory, it is determined that a valid modification iteration has occurred, and the counter is incremented by one; after traversing all pen stroke event data streams, the modification iteration count index is obtained.

[0012] This invention saves the entire data stream of brushstroke events generated during the collaborative session in chronological order as a creative process record file, and extracts three process characteristic indicators from it: brushstroke stability, color richness, and number of modification iterations. The aesthetic literacy evaluation report generated based on these indicators, to some extent, compensates for the shortcomings of existing aesthetic education evaluation systems that only focus on static work results, enabling teachers to grasp the development trajectory of students' aesthetic literacy relatively comprehensively from dimensions such as technique stability, color thinking, and creative iteration habits.

[0013] A cloud-based collaborative aesthetic education curriculum management system, the system comprising: The cloud resource aggregation and preprocessing module is used to receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources; The resource segmentation management module is used to segment the obtained aesthetic education teaching resources according to the preset semantic unit rules, obtain multiple resource segmentation segments, generate corresponding segmented digital watermarks for each resource segmentation segment, embed each segmented digital watermark into the corresponding resource segmentation segment, and synchronously write the watermark embedding record into the cloud traceability database. The remote collaboration management module is used to receive collaboration requests for target aesthetic education teaching resources initiated by the first user terminal. The collaboration request includes the type of collaboration operation requested and the segment identifier of the target resource. The module verifies whether the first user terminal has the corresponding operation permissions according to a preset permission gradient model. The cloud-based collaborative creation data synchronization module is used to receive the pen touch event data stream sent by the first user terminal when the collaboration request is verified and the corresponding collaboration operation type is painting-related collaboration operation; based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, it generates synchronization data packets for each other user terminal and sends them separately, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. The multi-dimensional process feature evaluation module is used to store the brush stroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brush stroke stability index, color richness index, and number of modification iterations from the obtained creation process record; and generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. The evaluation and feedback closed-loop module is used to upload the creative works completed by student users to the cloud virtual exhibition hall, receive the structured aesthetic evaluation cards submitted by viewers, summarize the data of the structured aesthetic evaluation cards, generate evaluation results and feed them back to the teacher user terminal.

[0014] A cloud-based collaborative aesthetic education curriculum management device, the device being used to implement a cloud-based collaborative aesthetic education curriculum management method.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) This invention uses semantic segmentation and segmented watermarking mechanisms to form a chain-like traceability record for both local citation and adaptation behaviors. Combined with the visualization of resource evolution graphs, it clearly presents the version contribution relationship and solves the problem of difficulty in tracing local citations of materials in aesthetic education. (2) The brush stroke event data stream defined in this invention replaces the transmission of complete image frames with structured data. Combined with three collaborative modes (demonstration, guidance, and collaborative creation), the precision of remote painting demonstration is close to that of offline teaching. The guidance mode uses semi-transparent layer annotations to avoid damaging the students' original brush strokes. The collaborative creation mode ensures that the corresponding painting content is consistent on the multi-terminal painting interface. (3) Based on the sequence of brush stroke events, this invention calculates indicators reflecting techniques, colors and modification habits, enabling teachers to dynamically track the development trajectory of students' aesthetic literacy. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a cloud-based collaborative aesthetic education curriculum management method according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This embodiment provides a cloud-based collaborative aesthetic education course management method, including: S1. Receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources. The aesthetic education teaching resources include text resources, image resources, and video resources. The metadata information includes resource number, resource type, subject classification, uploader identifier, and upload timestamp. The user terminal types include teacher user terminals and student user terminals. S2. The obtained aesthetic education teaching resources are segmented according to preset semantic unit rules to obtain multiple resource segments, and a corresponding segmented digital watermark is generated for each resource segment. The segmented digital watermark is embedded into the corresponding resource segment, and the watermark embedding record is synchronously written into the cloud traceability database. The segmented digital watermark is obtained through the resource number, resource segment number, uploader identifier, and watermark generation timestamp. The specific method for segmenting the obtained aesthetic education teaching resources according to preset semantic unit rules in S2 includes: When the resource type is an image resource, the image semantic segmentation rule is called, the contour boundary in the image is extracted based on the edge detection operator, the image is divided into the main body region and the background environment region according to the obtained contour boundary, and the obtained main body region is further divided into one or more independent composition element regions based on connected component analysis, and each independent composition element region is a resource segmentation segment. When the resource type is video, the video semantic segmentation rules are invoked to identify the shot transition boundaries and audio silence segments in the video. Combined with the preset teaching link keyword matching, the video is divided into introductory segments, appreciation segments, technique explanation segments, creative demonstration segments, and work display segments, with each segment serving as a resource segment. When the resource type is text, the text semantic segmentation rules are invoked to identify chapter titles or paragraph separators in the text and divide the text into several semantic paragraphs, with each semantic paragraph serving as a resource segmentation segment. In the process of generating corresponding segmented digital watermarks for each resource segment in S2, the corresponding resource number, resource segment sequence number, resource segment start position pointer, resource segment end position pointer, uploader identifier and watermark generation timestamp are concatenated into a string to be verified in a preset order. A hash algorithm is used to calculate a hash value of a fixed length on the obtained string to be verified, and the first preset number of bytes of the obtained hash value are taken as the corresponding segmented digital watermark.

[0019] S2 also includes a derived resource tracing sub-step: When the cloud server detects that a third user terminal performs a referencing or adaptation operation on an existing resource segment and generates a derived resource file, the derived resource file automatically inherits all field contents of the segment digital watermark of the original resource segment referenced, and adds a record consisting of the adaptor user identifier, adaptation operation type and adaptation timestamp to the corresponding metadata. The cloud server adds a new traceability record for the derived resource file in the cloud traceability database. This traceability record contains a pointer field pointing to the original resource segment traceability record, forming a reversible watermark derivative chain. When a copyright verification request for a specified resource is received, the cloud server starts from the source tracing record corresponding to the specified resource, traces back level by level along the corresponding watermark derivative chain until it locates the earliest uploaded original resource segment record, and returns the complete tracing path to the requester.

[0020] Specifically, the derived resource tracing sub-step further includes an evolutionary map generation step: The cloud server constructs a resource evolution graph data structure based on the traceability records and corresponding pointer fields stored in the cloud traceability database. The resource evolution graph uses nodes to represent each resource segment version and directed edges to represent the reference or adaptation relationship between versions. Each directed edge carries the adapter user identifier and adaptation operation type as edge attributes. When a teacher's client requests to view the evolution history of a specified resource, the cloud server renders the corresponding resource evolution map as a visual tree diagram and presents it on the corresponding teacher's client interface.

[0021] S3. Receive a collaboration request for the target aesthetic education teaching resource initiated by the first user terminal. The collaboration request includes the collaboration operation type and the segment identifier of the target resource. Verify whether the first user terminal has the corresponding operation permission according to the preset permission gradient model. The step S3, which verifies whether the first user terminal has the corresponding operation permission according to the preset permission gradient model, specifically includes: querying the locally stored permission configuration table to obtain the authorization level corresponding to the target resource number of the first user terminal that initiated the corresponding collaboration request. The authorization level is set according to the preset permission gradient model, and the permission gradient includes browsing level, citation level, adaptation level, collaborative editing level and dissemination level. The collaboration operation type of the obtained collaboration request is matched and determined with the obtained authorization level: If the operation type of the requested collaboration operation belongs to the set of operations allowed by the obtained authorization level, then the first user terminal is determined to have the corresponding operation permission, the collaboration request is verified, a collaboration permission token is generated and returned to the first user terminal, the collaboration permission token includes a session identifier, validity period and allowed operation range fields; otherwise, the first user terminal is determined not to have the corresponding operation permission, the collaboration request verification fails, a permission rejection response is returned and the current processing flow is terminated. The permission gradient model also includes a permission management mechanism, specifically: When the teacher's client of the first institution needs to share the target aesthetic education teaching resources to the user's client of the second institution, the teacher's client of the first institution initiates a certificate issuance request to the cloud server. The cloud server generates a usage certificate token and sends it to the user's client of the second institution. The usage certificate token includes the target resource number, the resource segment sequence number list, the authorization level, the usage scenario limitation description, the validity period start time, the validity period end time, the maximum number of uses limit, and the issuing institution identifier. Each time a user of the second institution accesses the target aesthetic education teaching resources, the user must include the obtained usage rights token in the request. The cloud server will respond after verifying the validity period and usage count of the token and update the usage count counter corresponding to the token. When the second institution further shares the target aesthetic education teaching resources with the third institution, the cloud server generates a sub-authorization token. The sub-authorization token contains all the authorized content of the parent authorization token, as well as a hash pointer pointing to the parent authorization token, forming a verifiable nested authorization chain.

[0022] S4. When the collaboration request is verified and the corresponding collaboration operation type is a painting collaboration operation, receive the pen touch event data stream sent by the first user terminal. The pen touch event data stream includes pen touch type, coordinate sequence, pressure value sequence, velocity vector sequence and timestamp sequence. Based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, generate synchronization data packets corresponding to each other user terminal and send them respectively, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. Specifically, in this embodiment, for other user terminals with network latency lower than the first preset threshold, a real-time forwarding mode is adopted, which forwards the received pen touch event data stream directly without caching; for other user terminals with network latency higher than or equal to the first preset threshold, a cache alignment mode is adopted, which merges and compresses multiple pen touch event data streams within a preset time window and sends them in batches; after receiving the synchronization data packet, other user terminals parse the corresponding pen touch event data and perform the corresponding pen touch rendering operation on the locally shared drawing interface, so that the drawing content displayed on the drawing interface of each participating terminal is consistent; In the S4, each coordinate point in the coordinate sequence of the pen stroke event data stream represents the offset relative to the position of the previous coordinate point, and the starting coordinate point in the coordinate sequence represents the offset relative to the preset origin in the drawing interface. Each element in the pressure value sequence corresponds to the normalized pressure value at a coordinate point in the coordinate sequence, and the normalized pressure value belongs to the integer range [0, 1023]. Each element in the velocity vector sequence corresponds to a velocity vector at a coordinate point in the coordinate sequence, and the velocity vector consists of a horizontal velocity component and a vertical velocity component. Each element in the timestamp sequence corresponds to the timestamp of the stroke at the corresponding coordinate point within the coordinate sequence. The cloud server in S4 also supports switching between demonstration mode, patrol mode and collaborative creation mode. In the demonstration mode, only the teacher's client has the permission to send the pen touch event data stream, while all student clients have the permission to receive and render. In the demonstration mode, student clients can review the received pen touch events step by step. In the patrol mode, the teacher's client sends annotation stroke events on the shared drawing interface of the designated student's client in the form of a semi-transparent overlay layer. The annotation stroke events carry layer attribute identifiers. The student's client renders the annotation stroke events on an independent layer. The original stroke layer content of the student's client is not affected by the overlay of the annotation stroke events. In the collaborative creation mode, all participating user terminals have the permission to send pen touch event data streams. The cloud server sorts and merges the concurrently received pen touch event data streams according to the event timestamp and preset conflict resolution rules, so that the drawing content displayed on the drawing interface of each participating terminal remains consistent. The pre-defined conflict resolution rules in the collaborative creation mode are as follows: If multiple pen touch event data streams targeting the same spatial region are received within the same time tolerance window, they are sorted according to the user priority of the sending user terminal, with pen touch events from the teacher user terminal taking precedence over those from the student user terminal; if the user priorities are the same, they are sorted according to the order of the event timestamps; the absolute difference between any two time points within the same time tolerance window is less than or equal to the preset interval duration.

[0023] S5. Store the brushstroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brushstroke stability index, color richness index, and number of modification iterations from the obtained creation process record; generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. The calculation method for the stroke stability index in S5 is as follows: Let Pi(xi,yi) be the i-th coordinate point in the coordinate sequence corresponding to the pen stroke event; calculate the change in the vector angle formed by any three adjacent coordinate points; and let the change in the vector angle formed by the (i-1)-th, i-th, and (i+1)-th coordinate points in the coordinate sequence corresponding to the pen stroke event be denoted as... ; ; in, It represents the direction angle of the vector formed by the i-th coordinate point to the (i+1)-th coordinate point in the coordinate sequence corresponding to the pen stroke event; It represents the direction angle of the vector formed by the (i-1)th coordinate point to the ith coordinate point in the coordinate sequence corresponding to the pen stroke event; The formulas involved in calculating the stroke stability index are as follows: ; in, This represents the pen stroke stability index corresponding to the coordinate sequence of the pen stroke event. The smaller the index value, the more stable the pen stroke. k represents the number of coordinate points in the coordinate sequence corresponding to the pen stroke event. The calculation method for the color richness index is as follows: Extract all color value codes from the creation process record file, convert the obtained color value codes to the HSV color space, and only take the hue component H value. Divide the hue circle from 0° to 360° into M equal hue intervals, and count the frequency of color values ​​in each hue interval. The frequency of color values ​​in the j-th hue interval is denoted as Pj. The color richness index is denoted as CR. ; A higher color richness index value indicates a richer use of colors. The specific method for calculating the number of modification iterations is as follows: The shared drawing interface is divided into grid cells of preset size, and the resulting creation process record file is analyzed stroke by stroke: when a pen lifting event is detected, if the coordinate point of a subsequent pen movement event falls into a grid cell occupied by a previously completed pen stroke trajectory, it is determined that a valid modification iteration has occurred, and the counter is incremented by one; after traversing all pen stroke event data streams, the modification iteration count index is obtained.

[0024] S6. Upload the creative works completed by student users to the cloud virtual exhibition hall, and receive structured aesthetic evaluation cards submitted by viewers for the creative works. The structured aesthetic evaluation cards include dimensions of feeling description, associative inspiration, and improvement suggestions. Summarize the data of the structured aesthetic evaluation cards, generate evaluation results, and feed them back to the teacher user terminal.

[0025] This embodiment also provides a cloud-based collaborative aesthetic education curriculum management system, the system comprising: The cloud resource aggregation and preprocessing module is used to receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources; The resource segmentation management module is used to segment the obtained aesthetic education teaching resources according to the preset semantic unit rules, obtain multiple resource segmentation segments, generate corresponding segmented digital watermarks for each resource segmentation segment, embed each segmented digital watermark into the corresponding resource segmentation segment, and synchronously write the watermark embedding record into the cloud traceability database. The remote collaboration management module is used to receive collaboration requests for target aesthetic education teaching resources initiated by the first user terminal. The collaboration request includes the type of collaboration operation requested and the segment identifier of the target resource. The module verifies whether the first user terminal has the corresponding operation permissions according to a preset permission gradient model. The cloud-based collaborative creation data synchronization module is used to receive the pen touch event data stream sent by the first user terminal when the collaboration request is verified and the corresponding collaboration operation type is painting-related collaboration operation; based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, it generates synchronization data packets for each other user terminal and sends them separately, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. The multi-dimensional process feature evaluation module is used to store the brush stroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brush stroke stability index, color richness index, and number of modification iterations from the obtained creation process record; and generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. The evaluation and feedback closed-loop module is used to upload the creative works completed by student users to the cloud virtual exhibition hall, receive the structured aesthetic evaluation cards submitted by viewers, summarize the data of the structured aesthetic evaluation cards, generate evaluation results and feed them back to the teacher user terminal.

[0026] This embodiment also provides a cloud-based collaborative aesthetic education curriculum management device, which is used to implement a cloud-based collaborative aesthetic education curriculum management method.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0028] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud-based collaborative aesthetic education curriculum management method, characterized in that, include: S1. Receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources. The aesthetic education teaching resources include text resources, image resources, and video resources. The metadata information includes resource number, resource type, subject classification, uploader identifier, and upload timestamp. The user terminal types include teacher user terminals and student user terminals. S2. The obtained aesthetic education teaching resources are segmented according to preset semantic unit rules to obtain multiple resource segments, and a corresponding segmented digital watermark is generated for each resource segment. The segmented digital watermark is embedded into the corresponding resource segment, and the watermark embedding record is synchronously written into the cloud traceability database. The segmented digital watermark is obtained through the resource number, resource segment number, uploader identifier, and watermark generation timestamp. S3. Receive a collaboration request for the target aesthetic education teaching resource initiated by the first user terminal. The collaboration request includes the collaboration operation type and the segment identifier of the target resource. Verify whether the first user terminal has the corresponding operation permission according to the preset permission gradient model. S4. When the collaboration request is verified and the corresponding collaboration operation type is a painting collaboration operation, receive the pen touch event data stream sent by the first user terminal. The pen touch event data stream includes pen touch type, coordinate sequence, pressure value sequence, velocity vector sequence and timestamp sequence. Based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, generate synchronization data packets corresponding to each other user terminal and send them respectively, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. S5. Store the brushstroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brushstroke stability index, color richness index, and number of modification iterations from the obtained creation process record; generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. S6. Upload the creative works completed by student users to the cloud virtual exhibition hall, receive the structured aesthetic evaluation cards submitted by viewers, summarize the data of the structured aesthetic evaluation cards, generate evaluation results and feed them back to the teacher user terminal.

2. The cloud-based collaborative aesthetic education curriculum management method according to claim 1, characterized in that, The specific method for segmenting the obtained aesthetic education teaching resources according to preset semantic unit rules in S2 includes: When the resource type is an image resource, the image semantic segmentation rule is called, the contour boundary in the image is extracted based on the edge detection operator, the image is divided into the main body region and the background environment region according to the obtained contour boundary, and the obtained main body region is further divided into one or more independent composition element regions based on connected component analysis, and each independent composition element region is a resource segmentation segment. When the resource type is video, the video semantic segmentation rules are invoked to identify the shot transition boundaries and audio silence segments in the video. Combined with the preset teaching link keyword matching, the video is divided into introductory segments, appreciation segments, technique explanation segments, creative demonstration segments, and work display segments, with each segment serving as a resource segment. When the resource type is text, the text semantic segmentation rules are invoked to identify chapter titles or paragraph separators in the text and divide the text into several semantic paragraphs, with each semantic paragraph serving as a resource segmentation segment. In the process of generating corresponding segmented digital watermarks for each resource segment in S2, the corresponding resource number, resource segment sequence number, resource segment start position pointer, resource segment end position pointer, uploader identifier and watermark generation timestamp are concatenated into a string to be verified in a preset order. A hash algorithm is used to calculate a hash value of a fixed length on the obtained string to be verified, and the first preset number of bytes of the obtained hash value are taken as the corresponding segmented digital watermark.

3. The cloud-based collaborative aesthetic education curriculum management method according to claim 1, characterized in that, S2 also includes a derived resource tracing sub-step: When the cloud server detects that a third user terminal performs a referencing or adaptation operation on an existing resource segment and generates a derived resource file, the derived resource file automatically inherits all field contents of the segment digital watermark of the original resource segment referenced, and adds a record consisting of the adaptor user identifier, adaptation operation type and adaptation timestamp to the corresponding metadata. The cloud server adds a new traceability record for the derived resource file in the cloud traceability database. This traceability record contains a pointer field pointing to the original resource segment traceability record, forming a reversible watermark derivative chain. When a copyright verification request for a specified resource is received, the cloud server starts from the source tracing record corresponding to the specified resource, traces back level by level along the corresponding watermark derivative chain until it locates the earliest uploaded original resource segment record, and returns the complete tracing path to the requester.

4. The cloud-based collaborative aesthetic education curriculum management method according to claim 3, characterized in that, The derived resource tracing sub-step also includes an evolutionary map generation step: The cloud server constructs a resource evolution graph data structure based on the traceability records and corresponding pointer fields stored in the cloud traceability database. The resource evolution graph uses nodes to represent each resource segment version and directed edges to represent the reference or adaptation relationship between versions. Each directed edge carries the adapter user identifier and adaptation operation type as edge attributes. When a teacher's client requests to view the evolution history of a specified resource, the cloud server renders the corresponding resource evolution map as a visual tree diagram and presents it on the corresponding teacher's client interface.

5. The cloud-based collaborative aesthetic education curriculum management method according to claim 1, characterized in that: The step S3, which verifies whether the first user terminal has the corresponding operation permission according to the preset permission gradient model, specifically includes: querying the locally stored permission configuration table to obtain the authorization level corresponding to the target resource number of the first user terminal that initiated the corresponding collaboration request. The authorization level is set according to the preset permission gradient model, and the permission gradient includes browsing level, citation level, adaptation level, collaborative editing level and dissemination level. The collaboration operation type of the obtained collaboration request is matched and determined with the obtained authorization level: If the operation type of the requested collaboration operation belongs to the set of operations allowed by the obtained authorization level, then the first user terminal is determined to have the corresponding operation permission, the collaboration request is verified, a collaboration permission token is generated and returned to the first user terminal, the collaboration permission token includes a session identifier, validity period and allowed operation range fields; otherwise, the first user terminal is determined not to have the corresponding operation permission, the collaboration request verification fails, a permission rejection response is returned and the current processing flow is terminated. The permission gradient model also includes a permission management mechanism, specifically: When the teacher's client of the first institution needs to share the target aesthetic education teaching resources to the user's client of the second institution, the teacher's client of the first institution initiates a certificate issuance request to the cloud server. The cloud server generates a usage certificate token and sends it to the user's client of the second institution. The usage certificate token includes the target resource number, the resource segment sequence number list, the authorization level, the usage scenario limitation description, the validity period start time, the validity period end time, the maximum number of uses limit, and the issuing institution identifier. Each time a user of the second institution accesses the target aesthetic education teaching resources, the user must include the obtained usage rights token in the request. The cloud server will respond after verifying the validity period and usage count of the token and update the usage count counter corresponding to the token. When the second institution further shares the target aesthetic education teaching resources with the third institution, the cloud server generates a sub-authorization token. The sub-authorization token contains all the authorized content of the parent authorization token, as well as a hash pointer pointing to the parent authorization token, forming a verifiable nested authorization chain.

6. The cloud-based collaborative aesthetic education curriculum management method according to claim 1, characterized in that, In the S4, each coordinate point in the coordinate sequence of the pen stroke event data stream represents the offset relative to the position of the previous coordinate point, and the starting coordinate point in the coordinate sequence represents the offset relative to the preset origin in the drawing interface. Each element in the pressure value sequence corresponds to the normalized pressure value at a coordinate point in the coordinate sequence, and the normalized pressure value belongs to the integer range [0, 1023]. Each element in the velocity vector sequence corresponds to a velocity vector at a coordinate point in the coordinate sequence, and the velocity vector consists of a horizontal velocity component and a vertical velocity component. Each element in the timestamp sequence corresponds to the timestamp of the stroke at the corresponding coordinate point within the coordinate sequence. The cloud server in S4 also supports switching between demonstration mode, patrol mode and collaborative creation mode. In the demonstration mode, only the teacher's client has the permission to send the pen touch event data stream, while all student clients have the permission to receive and render. In the demonstration mode, student clients can review the received pen touch events step by step. In the patrol mode, the teacher's client sends annotation stroke events on the shared drawing interface of the designated student's client in the form of a semi-transparent overlay layer. The annotation stroke events carry layer attribute identifiers. The student's client renders the annotation stroke events on an independent layer. The original stroke layer content of the student's client is not affected by the overlay of the annotation stroke events. In the collaborative creation mode, all participating user terminals have the permission to send pen touch event data streams. The cloud server sorts and merges the concurrently received pen touch event data streams according to the event timestamp and preset conflict resolution rules, so that the drawing content displayed on the drawing interface of each participating terminal remains consistent. The pre-defined conflict resolution rules in the collaborative creation mode are as follows: If multiple pen touch event data streams targeting the same spatial region are received within the same time tolerance window, they are sorted according to the user priority of the sending user terminal, with pen touch events from the teacher user terminal taking precedence over those from the student user terminal; if the user priorities are the same, they are sorted according to the order of the event timestamps; the absolute difference between any two time points within the same time tolerance window is less than or equal to the preset interval duration.

7. The cloud-based collaborative aesthetic education curriculum management method according to claim 1, characterized in that, The calculation method for the stroke stability index in S5 is as follows: Let Pi(xi,yi) be the i-th coordinate point in the coordinate sequence corresponding to the pen stroke event; calculate the change in the vector angle formed by any three adjacent coordinate points; and let the change in the vector angle formed by the (i-1)-th, i-th, and (i+1)-th coordinate points in the coordinate sequence corresponding to the pen stroke event be denoted as... ; ； in, It represents the direction angle of the vector formed by the i-th coordinate point to the (i+1)-th coordinate point in the coordinate sequence corresponding to the pen stroke event; It represents the direction angle of the vector formed by the (i-1)th coordinate point to the ith coordinate point in the coordinate sequence corresponding to the pen stroke event; The formulas involved in calculating the stroke stability index are as follows: ； in, This represents the pen stroke stability index corresponding to the coordinate sequence of the pen stroke event; k represents the number of coordinate points in the coordinate sequence corresponding to the pen stroke event. The calculation method for the color richness index is as follows: Extract all color value codes from the creation process record file, convert the obtained color value codes to the HSV color space, and only take the hue component H value. Divide the hue circle from 0° to 360° into M equal hue intervals, and count the frequency of color values ​​in each hue interval. The frequency of color values ​​in the j-th hue interval is denoted as Pj. The color richness index is denoted as CR. ； The specific method for calculating the number of modification iterations is as follows: The shared drawing interface is divided into grid cells of preset size, and the resulting creation process record file is analyzed stroke by stroke: when a pen lifting event is detected, if the coordinate point of a subsequent pen movement event falls into a grid cell occupied by a previously completed pen stroke trajectory, it is determined that a valid modification iteration has occurred, and the counter is incremented by one; after traversing all pen stroke event data streams, the modification iteration count index is obtained.

8. A cloud-based collaborative aesthetic education curriculum management system, employing the cloud-based collaborative aesthetic education curriculum management method described in any one of claims 1 to 7, characterized in that, The system includes: The cloud resource aggregation and preprocessing module is used to receive aesthetic education teaching resources uploaded from user terminals, standardize the format of the obtained aesthetic education teaching resources, and extract the metadata information of the obtained aesthetic education teaching resources; The resource segmentation management module is used to segment the obtained aesthetic education teaching resources according to the preset semantic unit rules, obtain multiple resource segmentation segments, generate corresponding segmented digital watermarks for each resource segmentation segment, embed each segmented digital watermark into the corresponding resource segmentation segment, and synchronously write the watermark embedding record into the cloud traceability database. The remote collaboration management module is used to receive collaboration requests for target aesthetic education teaching resources initiated by the first user terminal. The collaboration request includes the type of collaboration operation requested and the segment identifier of the target resource. The module verifies whether the first user terminal has the corresponding operation permissions according to a preset permission gradient model. The cloud-based collaborative creation data synchronization module is used to receive the pen touch event data stream sent by the first user terminal when the collaboration request is verified and the corresponding collaboration operation type is painting-related collaboration operation; based on the network status of other user terminals in the collaborative painting relationship corresponding to the target resource segment, it generates synchronization data packets for each other user terminal and sends them separately, so that each other user terminal generates corresponding painting content on the painting interface based on the corresponding synchronization data packets. The multi-dimensional process feature evaluation module is used to store the brush stroke event data stream generated during collaborative editing in a complete time sequence to form a creation process record; extract multi-dimensional process feature indicators consisting of brush stroke stability index, color richness index, and number of modification iterations from the obtained creation process record; and generate an aesthetic literacy evaluation report for student users based on the obtained multi-dimensional process feature indicators. The evaluation feedback closed-loop module is used to upload the creative works completed by student users to the cloud virtual exhibition hall, receive the structured aesthetic evaluation cards submitted by viewers, summarize the data of the structured aesthetic evaluation cards, generate evaluation results and feed them back to the teacher user terminal.

9. A cloud-based collaborative aesthetic education curriculum management device, characterized in that, The device is used to implement a cloud-based collaborative aesthetic education course management method as described in any one of claims 1 to 7.

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