An art enlightenment animation generation teaching system and method

Abstract

The application discloses an art enlightenment animation generation teaching system and method, which comprises the following steps: obtaining each to-be-displayed art knowledge feature and obtaining object cognitive feature; obtaining each target art knowledge segment feature and obtaining fused art teaching feature; determining no less than one to-be-displayed knowledge point and the corresponding art knowledge animation performance mode in the to-be-processed art popular science teaching information according to the fused art teaching feature; performing animation generation processing on the animation performance material range to obtain a target art enlightenment animation segment; and synthesizing the target art enlightenment animation segment and the to-be-processed art popular science teaching information to obtain a target art enlightenment animation teaching video. The application realizes the bidirectional adaptation of teaching content and learner cognitive level, forms an end-to-end automatic process from content analysis to video output, and solves the problems of the disconnection between art enlightenment teaching content and learner cognitive level and the dependence of animation production on professional personnel and low efficiency.

Description

Technical Field

[0001] This invention relates to the intersection of art education and computer animation generation technology. In particular, it relates to a teaching system and method for generating animations for art education beginners. Background Technology

[0002] Art education for young children is a crucial step in cultivating their aesthetic literacy and creativity. Its core objective is to help learners develop a preliminary understanding of basic art elements such as color, form, and composition through intuitive and vivid visual expression. With the deepening of quality education and the popularization of multimedia technology, art education for young children is gradually evolving from traditional classroom lectures to digital and visual methods, placing higher demands on the presentation and efficiency of teaching content.

[0003] Animation, as a medium that combines visual expressiveness and narrative coherence, naturally aligns with the teaching needs of art education for beginners. It can transform abstract art knowledge into dynamic visual presentations, effectively lowering the learning threshold for learners. In recent years, the rapid development of artificial intelligence and digital media technologies has provided new technological pathways for the automated generation of educational videos. For example, Chinese patent CN120897102B discloses a method for automatically generating lecturer videos based on AI speech synthesis and animation-driven technology. By performing structured parsing and semantic grouping of PPTs or text scripts, combined with speech synthesis technology and digital human-driven technology, it automatically generates lecturer teaching videos with expressive gestures and facial expressions, thereby improving the production efficiency of educational videos and reducing production costs to a certain extent.

[0004] However, the aforementioned approach is essentially a teaching video generation method centered on digital human explanations. Its animation-driven approach primarily serves the instructor's actions and facial expressions, without transforming art knowledge itself into targeted animated content. In the specific teaching scenario of art enlightenment, learners exhibit significant age and individual differences in their cognitive development levels regarding color perception, spatial understanding, and abstract thinking. Therefore, the presentation of teaching content needs to match the learners' cognitive abilities to achieve effective teaching.

[0005] Currently, most existing art teaching video or animation generation solutions adopt a unified content arrangement logic and a fixed animation presentation strategy, failing to incorporate the cognitive characteristics of the target learners into the animation content generation decision-making process. This results in a lack of effective adaptation between the generated art enlightenment teaching content and the learners' actual cognitive level, affecting the pertinence and effectiveness of art enlightenment teaching. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a teaching method for generating art enlightenment animations, so as to solve the problem that the existing art enlightenment teaching animation generation does not consider the cognitive characteristics of the target teaching objects, resulting in a disconnect between the teaching content and the learner's cognitive level.

[0007] The objective of this invention is achieved through the following technical solution: A method for generating art education animations, the method comprising: Content analysis is performed on the art science popularization teaching information to be processed to obtain the characteristics of each art knowledge to be displayed, and feature analysis is performed on the target teaching objects corresponding to the art science popularization teaching information to be processed to obtain the cognitive characteristics of the objects. Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the characteristics of each target art knowledge fragment feature and the object cognitive feature are fused to obtain fused art teaching features. Based on the integrated art teaching characteristics, at least one knowledge point to be displayed and its corresponding art knowledge animation interpretation method are determined in the art popular science teaching information to be processed. In the art animation material library associated with the art popular science teaching information to be processed, the range of animation interpretation materials for each of the at least one knowledge point to be displayed is obtained. Within each of at least one range of animated performance materials, based on the art knowledge animation performance method of the corresponding knowledge point to be displayed, the range of animated performance materials is processed to generate animation segments to obtain target art enlightenment animation segments. The target art enlightenment animation segments are then combined with the art science popularization teaching information to be processed to obtain target art enlightenment animated teaching videos.

[0008] As a preferred approach, before acquiring the integrated art teaching characteristics, the method further includes: performing content analysis on the reference art teaching case information to acquire the characteristics of each reference art knowledge; wherein, the reference art teaching case information includes pre-stored completed art enlightenment animation teaching works, which integrate three forms of expression: graphic design, animation motion design, and audiovisual language; Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object's cognitive features, the characteristics of each target art knowledge fragment and the object's cognitive features are fused to obtain fused art teaching features, including: Based on the features of each art knowledge to be displayed and the features of each reference art knowledge, the features of each target art knowledge fragment are obtained; Based on the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the target art knowledge fragment features and the object cognitive feature are fused to obtain fused art teaching features.

[0009] As a preferred method, within each of at least one range of animated performance materials, based on the art knowledge animation performance method of the corresponding knowledge point to be displayed, the range of animated performance materials is processed to generate animation, resulting in target art enlightenment animation clips, including: Perform the following operations on each of the at least one animation material range to obtain the target art enlightenment animation clip: For art knowledge animation interpretation methods that do not contain specified animation interpretation instructions, determine the animation image sequence obtained after animate rendering of a knowledge point to be displayed according to the art knowledge animation interpretation method as the target art enlightenment animation clip, and render and output the target art enlightenment animation clip in the animation material range of the knowledge point to be displayed. For art knowledge animation demonstration methods containing specified animation demonstration instructions, the target art enlightenment animation clip is obtained based on each target art animation element in a pre-established art animation element library that matches the specified animation demonstration instructions. Alternatively, the target art animation elements that match the specified animation demonstration instructions are extracted from the reference art teaching case information, and the target art enlightenment animation clip is rendered and output within the animation demonstration material range of the knowledge point to be displayed. The specified animation demonstration instructions include at least one of the following: specified graphic movement rhythm rules, camera transition methods, object deformation rules, and two-dimensional and three-dimensional mutual conversion methods.

[0010] As a preferred method, the target art-introduction animation clip is obtained based on each target art-animation element in a pre-established art-animation element library that matches the specified animation rendering instruction, including: Based on the element type identifier carried by the specified animation performance instruction, obtain the same type of art animation element that matches the element type identifier from the pre-established art animation element library; wherein, the element type identifier includes at least one of character design type, scene style type, color saturation type and motion rhythm type; A random art animation element of the same type is selected as the target art animation element. Alternatively, the target art / animation element can be obtained by combining multiple art / animation elements of the same type.

[0011] As a preferred approach, after obtaining the target art education animation clips, the following is also included: Based on the cognitive characteristics of the target audience, the adaptive rendering frame rate of the target art education animation segment is determined using the cognitive adaptation frame rate calculation formula. The target art education animation segment is then rendered and output according to the adaptive rendering frame rate so that the animation playback rhythm matches the cognitive acceptance ability of the target teaching audience. The formula for calculating the cognitive adaptation frame rate is as follows:

[0012] in, For adaptive rendering frame rate, the unit is frames per second (fps); The base rendering frame rate, measured in frames per second (fps). The cognitive level of the target learners is quantified, with a value ranging from 1 to 10. This represents the upper limit of the quantitative value of cognitive level. The cognitive decay index, ranging from 1.5 to 3.0, represents the non-linear decay rate of cognitive level in response to frame rate requirements. The information density of the knowledge point to be displayed is expressed in bits per frame. This represents the upper limit of information density, measured in bits per frame (bit / frame).

[0013] As a preferred method, the range of animation materials for each of the at least one knowledge point to be displayed is obtained from the art animation material library associated with the art science popularization teaching information to be processed, including: The art animation material library associated with the art popular science teaching information to be processed is used to extract material features to obtain the features of each animation material; wherein, the art animation material library includes character design materials, scene background materials, graphic symbol materials, color matching materials and sound effects and dubbing materials; Obtain the matching degree between each animation material feature and the integrated art teaching feature. Based on the material data fragments corresponding to the animation material features with a matching degree not less than a set threshold, obtain the animation performance material range for each of the at least one knowledge point to be displayed.

[0014] As a preferred method, based on the material data fragments corresponding to the animation material features with a matching degree of not less than a set threshold, the range of animation performance materials for each of the not less than one knowledge point to be displayed is obtained, including: Cluster the features of animation materials with a matching degree of not less than a set threshold to obtain at least one set of animation material features; For each of the not less than one set of animation material features, perform the following operations: obtain the material data interval corresponding to each animation material feature in the set of animation material features, where each material data interval includes not less than one material data segment; Based on the acquired data ranges of each material, the constraints of the range of animation performance materials are determined. By connecting the constraints, the range of animation performance materials for the corresponding knowledge points to be displayed is obtained from the art animation material library. The constraints include uniform color saturation constraints, visual matching constraints between characters and backgrounds, and animation duration constraints. The animation duration constraints set the duration of each art enlightenment animation science popularization segment to be between 30 seconds and 3 minutes.

[0015] As a preferred method, after obtaining the range of animation materials for each of the not less than one knowledge point to be displayed, the method further includes: Based on the teaching semantic segmentation information of each knowledge point to be displayed, the animation transition time of each knowledge point to be displayed is determined by the transition timing calculation formula. The transition is performed on the target art enlightenment animation segment of the corresponding knowledge point to be displayed according to the animation transition time, so that the animation screen switching rhythm is synchronized with the semantic advancement rhythm of the teaching content. The formula for calculating the transition timing is as follows:

[0016] in, For the first The animation transition times for each knowledge point to be presented are measured in seconds (s). For the first The start time of the animation for each knowledge point to be displayed is in seconds (s). For the first The total number of semantic segments contained in each knowledge point to be displayed; For the first The semantic length of each semantic segment. For animation rendering semantic throughput rate, For the first The teaching pace adjustment coefficient for each knowledge point to be presented ranges from 0.8 to 1.5.

[0017] As a preferred approach, the art enlightenment animation generation teaching method is implemented through an art animation generation network model. The art animation generation network model is trained by performing the following operations: using an iterative training method, the untrained art animation generation network model is trained based on the art popular science teaching information of each training sample labeled with standard animation generation until the iteration training terminates, obtaining the art animation generation network model that has completed the first training; using an iterative training method, the art popular science teaching information of each training sample labeled with teaching adaptability is trained based on the art popular science teaching information of each training sample until the iteration training terminates, obtaining the art animation generation network model that has completed the second training. The iterative training method involves training an untrained art animation generation network model based on art popularization teaching information from each training sample annotated with standard animation generation, until the iterative training terminates, thus obtaining the first completed art animation generation network model, including: Each iteration includes: based on at least one sample animation generation task, performing standard animation generation operations on the corresponding training sample art science popularization teaching information to obtain their respective sample generated art animation videos; wherein, the standard animation generation operations include regularizing and deforming the movement rhythm of the graphics to avoid uniform motion and make the picture vivid and interesting. Obtain the standard-annotated animation videos of the corresponding training sample art popular science teaching information, generate art animation videos and corresponding standard-annotated animation videos based on at least one sample, obtain the animation generation quality evaluation index generated in this iteration, and update each network parameter in the network model in sequence according to the animation generation quality evaluation index; wherein, the animation generation quality evaluation index includes the screen smoothness index, the transition naturalness index, and the audio-visual synchronization index, and the audio-visual synchronization index is obtained by adjusting the number of frames of the animation and the duration of the audio. The iterative training method involves training the art animation generation network model (which has undergone initial training) based on the art science teaching information of each training sample labeled with teaching adaptability, until the iterative training terminates, thus obtaining the art animation generation network model (which has undergone secondary training). This includes: Each iteration includes: based on at least one sample teaching adaptation task, performing teaching adaptation evaluation operations on the corresponding training sample art popular science teaching information, and obtaining the respective sample teaching adaptation evaluation results; wherein, the teaching adaptation evaluation operations include maintaining the uniformity of color saturation and the harmony of the picture, so that the use of color does not cause visual fatigue to the target teaching object. Obtain the standard teaching adaptation labels for each of the corresponding training sample art popular science teaching information. Based on the teaching adaptation evaluation results of at least one sample and the corresponding standard teaching adaptation labels, obtain the teaching adaptation evaluation index generated in this iteration. Then, based on the teaching adaptation evaluation index, update each network parameter in the network model in sequence.

[0018] As a preferred approach, the cognitive characteristics of the target audience include at least one of the following: the age stage characteristics of the target audience, the cognitive level characteristics of the target audience, and the basic art characteristics of the target audience; the animated presentation method of the art knowledge includes at least one of the following: motion graphics animation presentation method, two-dimensional vector animation presentation method, image sequence frame presentation method, and text special effects presentation method; the art popular science teaching information to be processed includes art popular science knowledge points for children, color cognition elements, and artistic expression ability training objectives. After receiving the target art and animation tutorial videos, it also includes: The target art education animation teaching videos are pushed to the learning terminals corresponding to the target learners through the online teaching platform to realize online art education; wherein, the push method includes point-to-point task distribution and point-to-multipoint information distribution.

[0019] An art education animation generation teaching system includes a processor and a memory that communicate with each other. The processor is used to read computer programs from the memory and execute them to implement the art education animation generation teaching method.

[0020] The present invention has at least the following beneficial effects: This invention analyzes the cognitive characteristics of the target learners and integrates these characteristics with the characteristics of art knowledge fragments, achieving a two-way adaptation between teaching content and learners' cognitive levels. Based on this, it determines matching animation methods for each knowledge point to be presented and retrieves corresponding material from a resource library, achieving refined animation generation at the knowledge point level. Finally, it automatically synthesizes the generated art enlightenment animation clips with the original teaching information into a teaching video, forming an end-to-end automated process from content analysis to video output. This effectively solves the problems of existing art enlightenment teaching content being disconnected from learners' cognitive levels and animation production relying on professionals and being inefficient. Attached Figure Description

[0021] To facilitate understanding of the technical details of this invention, the accompanying drawings involved in the embodiments are briefly described below. It should be noted that the following drawings only illustrate some embodiments of the invention and do not constitute a limitation on the scope of protection of this invention. Those skilled in the art can obtain other related drawings based on these drawings without any creative effort.

[0022] Figure 1 Flowchart of teaching methods for generating animations for art enlightenment; Figure 2 A flowchart for obtaining art science education information to be processed. Detailed Implementation

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following specific embodiments.

[0024] It should be noted that although detailed specific details are provided below to aid in a comprehensive understanding of the exemplary embodiments, those skilled in the art should understand that the exemplary embodiments can still be implemented even without some specific details. For example, to avoid excessive detail affecting the clarity of the example, the system structure can be shown in the form of a block diagram; at the same time, to maintain the simplicity of the example, unnecessary detailed descriptions of processes, structures, and technologies well-known in the art may be omitted.

[0025] like Figure 1 As shown, a method for generating art education animations is described, the method comprising: Content analysis is performed on the art science popularization teaching information to be processed to obtain the characteristics of each art knowledge to be displayed, and feature analysis is performed on the target teaching objects corresponding to the art science popularization teaching information to be processed to obtain the cognitive characteristics of the objects. Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the characteristics of each target art knowledge fragment feature and the object cognitive feature are fused to obtain fused art teaching features. Based on the integrated art teaching characteristics, at least one knowledge point to be displayed and its corresponding art knowledge animation interpretation method are determined in the art popular science teaching information to be processed. In the art animation material library associated with the art popular science teaching information to be processed, the range of animation interpretation materials for each of the at least one knowledge point to be displayed is obtained. Within each of at least one range of animated performance materials, based on the art knowledge animation performance method of the corresponding knowledge point to be displayed, the range of animated performance materials is processed to generate animation segments to obtain target art enlightenment animation segments. The target art enlightenment animation segments are then combined with the art science popularization teaching information to be processed to obtain target art enlightenment animated teaching videos.

[0026] The art education animation generation teaching method first deeply analyzes the input art science teaching information to extract core knowledge features, and simultaneously analyzes the individual characteristics of the target audience, such as their cognitive level. Then, it adaptively integrates the knowledge content with the audience's cognitive abilities, forming a fusion teaching feature that balances teaching accuracy and audience acceptance. Based on this fusion feature, the system accurately locates the knowledge points to be displayed and their appropriate animation presentation methods, and intelligently selects materials from a related material library that fit the current teaching context, ensuring that the selected materials are highly consistent with the teaching objectives in terms of style, difficulty, and emotional expression. Subsequently, customized animation generation processing is performed for each knowledge point within its corresponding material range, automatically completing rendering operations such as graphic movement, transitions, and audiovisual synchronization. Finally, the generated animation clips are seamlessly synthesized with the original teaching content, outputting art education animation teaching videos that are faithful to the art knowledge system and fully adapted to children's cognitive patterns, thus achieving the intelligent transformation from static teaching content to dynamic personalized teaching resources.

[0027] In a preferred embodiment, before obtaining the integrated art teaching features, the method further includes: performing content analysis on the reference art teaching case information to obtain the features of each reference art knowledge; wherein, the reference art teaching case information includes pre-stored completed art enlightenment animation teaching works, and the art enlightenment animation teaching works integrate three forms of expression: graphic design, animation motion design and audiovisual language; Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object's cognitive features, the characteristics of each target art knowledge fragment and the object's cognitive features are fused to obtain fused art teaching features, including: Based on the features of each art knowledge to be displayed and the features of each reference art knowledge, the features of each target art knowledge fragment are obtained; Based on the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the target art knowledge fragment features and the object cognitive feature are fused to obtain fused art teaching features.

[0028] Before acquiring the integrated art teaching characteristics, this method first introduces reference art teaching case information as an empirical foundation. This reference case information comes from pre-stored completed art enlightenment animation teaching works. These works themselves integrate three forms of expression: graphic design, animation motion design, and audiovisual language. Therefore, the reference art knowledge characteristics obtained from their content analysis essentially cover the design experience of mature works in multiple dimensions such as visual composition, dynamic expression, and narrative rhythm. Based on this, the system compares and correlates the art knowledge characteristics to be displayed extracted from the current teaching information with the aforementioned reference art knowledge characteristics, thereby screening and determining the characteristics of each target art knowledge segment. The significance of this step is to use the design paradigm of existing successful cases to guide and constrain the presentation of the current knowledge segments, avoiding the animation performance from deviating from the expression norms of art enlightenment teaching. Furthermore, the system matches the determined characteristics of each target art knowledge segment with the object cognitive characteristics of the target teaching objects, and performs fusion processing based on the adaptation relationship between the two, ultimately obtaining the integrated art teaching characteristics. Thus, the entire integration process forms a three-tiered progressive logic of "using mature case experience as a reference, current teaching content as the core, and the audience's cognitive level as a constraint," ensuring that the generated integration features not only possess the standardization of professional art expression but also fit the actual acceptance ability of the target teaching audience.

[0029] In a preferred embodiment, within each of at least one range of animated performance materials, an animation generation process is performed on the range of animated performance materials based on the art knowledge animation performance method of the corresponding knowledge point to be displayed, to obtain a target art enlightenment animation clip, including: Perform the following operations on each of the at least one range of animation materials to obtain the target art enlightenment animation clip: For art knowledge animation interpretation methods that do not contain specified animation interpretation instructions, determine the animation image sequence obtained after rendering an animation of a knowledge point to be displayed according to the art knowledge animation interpretation method as the target art enlightenment animation clip, and render and output the target art enlightenment animation clip based on the range of animation materials of the knowledge point to be displayed. For art knowledge animation demonstration methods containing specified animation demonstration instructions, the target art enlightenment animation clip is obtained based on each target art animation element in a pre-established art animation element library that matches the specified animation demonstration instructions. Alternatively, the target art animation elements that match the specified animation demonstration instructions are extracted from the reference art teaching case information, and the target art enlightenment animation clip is rendered and output within the animation demonstration material range of the knowledge point to be displayed. The specified animation demonstration instructions include at least one of the following: specified graphic movement rhythm rules, camera transition methods, object deformation rules, and two-dimensional and three-dimensional mutual conversion methods.

[0030] In the animation generation process, this method uniformly performs rendering operations on the animation rendering material range for each knowledge point to be displayed. However, different generation paths are adopted depending on whether the art knowledge animation rendering method carries a specified animation rendering instruction. For cases without a specified animation rendering instruction, the system directly performs conventional rendering on the material according to the animation rendering method corresponding to that knowledge point, determining the entire sequence of animation images generated frame by frame as the target art enlightenment animation clip for that knowledge point, and completing the rendering output within its corresponding material range. For cases containing a specified animation rendering instruction, the system no longer performs general rendering. Instead, it first parses the instruction content, which covers at least one of the specified graphic movement rhythm rules, camera transition methods, object deformation rules, and two-dimensional to three-dimensional conversion methods. Then, it retrieves the target art animation element matching the instruction from a pre-established art animation element library, or extracts the matching target art animation element from previously parsed reference art teaching case information. Finally, it uses the obtained animation element as the material basis to perform rendering output within its corresponding material range, thereby obtaining the target art enlightenment animation clip. Through the aforementioned branching mechanism, animations of general knowledge points can be generated efficiently, while knowledge points involving specific motion rhythms, transition techniques, or dimensional transformations can obtain more targeted and expressive animation elements. The two work together to ensure a balance between overall coherence and local expressiveness in the final animated teaching video.

[0031] In a preferred embodiment, the target art-introduction animation clip is obtained based on each target art animation element in a pre-established art animation element library that matches the specified animation performance instruction, including: Based on the element type identifier carried by the specified animation performance instruction, obtain the same type of art animation element that matches the element type identifier from the pre-established art animation element library; wherein, the element type identifier includes at least one of character design type, scene style type, color saturation type and motion rhythm type; A random art animation element of the same type is selected as the target art animation element. Alternatively, the target art / animation element can be obtained by combining multiple art / animation elements of the same type.

[0032] When retrieving target animation elements based on specified animation rendering instructions, this method first parses the element type identifier carried by the instruction. This identifier covers at least one of the following: character design type, scene style type, color saturation type, and motion rhythm type. Then, using this identifier as a search condition, it filters out matching animation elements of the same type from a pre-established animation element library, thus narrowing the search scope from the entire element library to a specific set of types directly related to the current rendering requirements. After obtaining the aforementioned animation elements of the same type, this method provides two parallel paths to ultimately determine the target animation element: first, randomly selecting one from the animation elements of the same type as the target animation element, suitable for scenarios where a single element can meet the rendering requirements; second, combining multiple animation elements of the same type to obtain the target animation element, suitable for scenarios requiring richer visual layers or more complex dynamic expressions. Through this two-level processing method of "precise retrieval by type identifier first, then flexible selection or combination by rendering complexity," the system can ensure a high degree of matching between animation elements and specified rendering instructions while balancing the differentiated needs of generation efficiency and expressiveness.

[0033] In a preferred embodiment, after obtaining the target art education animation clip, the method further includes: Based on the cognitive characteristics of the target audience, the adaptive rendering frame rate of the target art education animation segment is determined using the cognitive adaptation frame rate calculation formula. The target art education animation segment is then rendered and output according to the adaptive rendering frame rate so that the animation playback rhythm matches the cognitive acceptance ability of the target teaching audience. The formula for calculating the cognitive adaptation frame rate is as follows:

[0034] in, For adaptive rendering frame rate, the unit is frames per second (fps). The base rendering frame rate, measured in frames per second (fps). The cognitive level of the target learners is quantified, with a value ranging from 1 to 10. This represents the upper limit of the quantitative value of cognitive level. The cognitive decay index, ranging from 1.5 to 3.0, represents the non-linear decay rate of cognitive level in response to frame rate requirements. This represents the information density of the knowledge points to be displayed, measured in bits per frame. This represents the upper limit of information density, measured in bits per frame (bit / frame).

[0035] After obtaining the target art education animation clips, this method further introduces an adaptive adjustment mechanism for the playback rhythm. The playback speed of the animation is not fixed but should match the actual cognitive and comprehension abilities of the target learners. Specifically, the system determines the adaptive rendering frame rate based on the learners' cognitive characteristics. This frame rate is influenced by two dimensions: first, the learners' cognitive level; the lower the cognitive level, the slower the animation playback rhythm should be to give learners more time to observe and understand; second, the information density of the knowledge points to be presented; the higher the information density, the more knowledge is conveyed per unit frame, and the playback rhythm also needs to be appropriately reduced to avoid cognitive burden caused by information overload. Based on this, the system renders and outputs the target art education animation clips according to the determined adaptive rendering frame rate, so that the final animation playback rhythm is neither too fast, exceeding the learners' comprehension ability, nor too slow, diminishing their learning interest, thus achieving a dynamic adaptation between the animation's performance rhythm and the audience's cognitive level.

[0036] In a preferred embodiment, to ensure that the information capacity of the target art education animation clip is precisely matched with the real-time cognitive processing ability of the target learners, and to avoid single-frame information overload or information gaps under the adaptive rendering frame rate, the method further includes the following after obtaining the target art education animation clip and before rendering output: The target art enlightenment animation clips are subjected to frame-by-frame visual information density control operation; the control operation is based on the cognitive flux conservation formula to determine the maximum number of art elements allowed to be presented in each frame, and accordingly, the frames that exceed the threshold are subjected to element simplification or layered display processing, so that the instantaneous information input rate of the animation sequence is kept constant within the cognitive comfort zone of the target teaching object. The formula for the conservation of cognitive flux is:

[0037] in, For the first The maximum number of art elements allowed to be displayed in a frame; these art elements include character parts, background decorations, text labels, and graphic symbols. The cognitive throughput limit for the target learning object, measured in bits per second (bit / s), represents the total amount of visual art information that the brain can effectively process per unit of time, and is quantified by the cognitive level value in the cognitive characteristics of the target object. Obtained by linear mapping, the mapping relationship is as follows: in This represents the information processing rate base corresponding to a single cognitive level, measured in bits per second per level (bit / s / level). The value ranges from 0.6 to 1.0 for the conversion efficiency of art knowledge types; higher values ​​are used for figurative and realistic knowledge points, and lower values ​​are used for abstract concept knowledge points, reflecting the cognitive processing difficulty of different forms of art knowledge. The average information entropy of a single art element, measured in bits per element (bit / piece), is determined based on the historical statistical average of each material in the art animation material library. For the first The adaptive rendering frame rate used by the frame is measured in frames per second (fps), and the calculation result is directly taken from the aforementioned cognitive adaptive frame rate calculation formula. This is a floor function that ensures the number of elements is an integer.

[0038] Through the above formula, the present invention achieves the aforementioned macroscopic frame rate control. Transformed into microscopic single-frame element count constraints When the system detects that a frame's calculated value is affected by scene complexity... When the number of elements is less than the actual number to be rendered, element priority sorting is automatically triggered, retaining core teaching elements and moving secondary decorative elements to the next frame or converting them to a semi-transparent overlay state. This formula establishes a conservation relationship between "cognitive ability, frame rate, and image density" through a simple division relationship, ensuring that the animation generation process does not exceed children's cognitive bottlenecks at any time, significantly enhancing the feasibility and stability of the technical solution in teaching scenarios.

[0039] In a preferred embodiment, the range of animation materials for each of the at least one knowledge point to be displayed is obtained from the art animation material library associated with the art science popularization teaching information to be processed, including: The art animation material library associated with the art popular science teaching information to be processed is used to extract material features to obtain the features of each animation material; wherein, the art animation material library includes character design materials, scene background materials, graphic symbol materials, color matching materials and sound effects and dubbing materials; Obtain the matching degree between each animation material feature and the integrated art teaching feature. Based on the material data fragments corresponding to the animation material features with a matching degree not less than a set threshold, obtain the animation performance material range for each of the at least one knowledge point to be displayed.

[0040] In a preferred embodiment, based on the material data fragments corresponding to the animation material features with a matching degree of not less than a set threshold, the range of animation performance materials for each of the not less than one knowledge point to be displayed is obtained, including: Cluster the features of animation materials with a matching degree of not less than a set threshold to obtain at least one set of animation material features; For each of the not less than one set of animation material features, perform the following operations: obtain the material data interval corresponding to each animation material feature in the set of animation material features, where each material data interval includes not less than one material data segment; Based on the acquired data ranges of each material, the constraints of the range of animation performance materials are determined. By connecting the constraints, the range of animation performance materials for the corresponding knowledge points to be displayed is obtained from the art animation material library. The constraints include uniform color saturation constraints, visual matching constraints between characters and backgrounds, and animation duration constraints. The animation duration constraints set the duration of each art enlightenment animation science popularization segment to be between 30 seconds and 3 minutes.

[0041] In a preferred embodiment, to overcome the lack of emotional adaptability of the uniform color saturation constraint in different teaching contexts, and to ensure that the colors of the materials maintain visual harmony while accurately conveying the emotional connotations of art knowledge, when acquiring the animation performance material range for each of the not less than one knowledge point to be displayed, the uniform color saturation constraint is dynamically upgraded for emotional adaptation. Specifically, this includes: Based on the teaching semantics of the knowledge points to be displayed, a target emotional color reference vector is generated, and the matching score between the candidate materials and the target emotional color reference vector is calculated using the emotion-color alignment formula. Candidate materials with matching scores not less than a set emotion threshold are included in the range of animation performance materials, thereby replacing the original static saturation uniformity constraint.

[0042] The formula for emotion-color alignment is:

[0043] in, The value for emotional-color alignment is between 0 and 1; the closer the value is to 1, the better the color of the material matches the current teaching emotional needs, and the closer the value is to 0, the greater the deviation. The absolute value of the angle difference between the main hue of the candidate material and the target hue in the target emotional color reference vector, in degrees (°); divide by 180 to normalize, map the angle difference to the interval [0, 1], and eliminate the dimension of angle; This is the normalized difference between the saturation of the candidate material and the target saturation, ranging from 0 to 1, and is calculated as follows: ,in The saturation value of the candidate material. The target saturation value in the target emotional color baseline vector. This represents the maximum saturation value in the aforementioned art and animation asset library; This is the normalized difference between the brightness of the candidate material and the brightness of the target material, ranging from 0 to 1, and is calculated as follows: ,in The brightness value of the candidate material. The target brightness value in the target emotional color baseline vector. The maximum value of brightness in the aforementioned art and animation material library; Weighting of hue emotional sensitivity; The saturation level is weighted by the emotional sensitivity. The weights are for brightness and emotional sensitivity; the above three weights satisfy the constraints. The weights are dynamically allocated based on the art category of the knowledge points to be displayed: the weights are increased for knowledge points related to color recognition. The number of knowledge points related to emotional expression has increased. The knowledge points related to lighting and shadow techniques have increased. The target emotional color baseline vector is automatically generated by the teaching semantic parsing module based on the knowledge point text, and includes the target hue. Target saturation and target brightness Three components.

[0044] Through the above formula, this invention upgrades the previously fixed "uniform constraint on image color saturation" to a soft constraint of "emotional-color alignment" that can be dynamically adjusted according to the teaching content. For example, when teaching the knowledge point of "contrast between warm and cool colors," When automatically adjusted, the system prioritizes combinations of materials with significant hue differences but harmonious saturation; however, when teaching the concept of "ink painting aesthetics," When the brightness is increased, the system focuses on selecting materials with rich brightness levels and neutral gray hues. All input items of this formula are normalized, and the output is a standard score in the range of 0 to 1. This can be directly and seamlessly integrated with the aforementioned judgment logic of "the degree of matching is not less than the set threshold". Without increasing the computational burden on the system, it significantly improves the accuracy of the teaching emotional expression in the generated animation.

[0045] In a preferred embodiment, after obtaining the range of animation materials for each of the not less than one knowledge point to be displayed, the method further includes: Based on the teaching semantic segmentation information of each knowledge point to be displayed, the animation transition time of each knowledge point to be displayed is determined by the transition timing calculation formula. The transition is performed on the target art enlightenment animation segment of the corresponding knowledge point to be displayed according to the animation transition time, so that the animation screen switching rhythm is synchronized with the semantic advancement rhythm of the teaching content. The formula for calculating the transition timing is as follows:

[0046] in, For the first The animation transition times for each knowledge point to be presented are measured in seconds (s). For the first The start time of the animation for each knowledge point to be displayed is in seconds (s). For the first The total number of semantic segments contained in each knowledge point to be displayed; For the first The semantic length of each semantic segment, measured in semantic units (su). The semantic throughput rate for animation rendering, measured in semantic units per second (su / s). For the first The teaching pace adjustment coefficient for each knowledge point to be presented ranges from 0.8 to 1.5.

[0047] In a preferred embodiment, to overcome the problem of inconsistent subjective perception of brightness caused by relying solely on the arithmetic mean of physical brightness in the image brightness equalization adjustment, a perceptual brightness matching method is used instead of the traditional arithmetic mean alignment when performing the image brightness equalization adjustment. The human eye's perception of brightness exhibits a non-linear response characteristic; two images with the same physical brightness mean may still have significant differences in subjective perception of brightness. Therefore, brightness equalization must be performed in a perceptibly uniform color space, with the optimization goal of ensuring the continuity of subjective brightness between adjacent materials, in order to achieve true visual coherence.

[0048] Specifically, the screen brightness balance adjustment is performed in the following four steps: Step 1: Perceived Brightness Extraction. Compare the current candidate image with the original image of the previously confirmed image. Pixels are respectively sRGB to Color space conversion, extracting the color space of each pixel. Brightness component. Then, for all pixels in the entire image... The value is taken as the weighted median, with the weights determined based on the area ratio of the pixel's region. The center region of the image has a higher weight than the edge region, thus obtaining the perceived brightness of the current material. Perceived brightness compared to the previous material The reason for choosing the weighted median instead of the arithmetic mean is that the arithmetic mean is easily affected by extreme highlights or shadows in the image, causing it to deviate from the true brightness level of the main area. The weighted median, on the other hand, has both the ability to resist outlier interference and sensitivity to the center of gravity of the image composition, and can more accurately represent the overall brightness actually perceived by the viewer.

[0049] Step Two: Calculation of Brightness Compensation Coefficient. Based on the ratio of perceived brightness of adjacent materials, calculate the brightness compensation coefficient according to the perceived brightness matching formula:

[0050]

[0051] in, This is the brightness compensation coefficient, with a theoretical value range of 0.5 to 2.0; when When the current image is darker than the previous image, it needs to be brightened; when When the current image is too bright, it needs to be darkened; when... This indicates that the perceived brightness is the same for both, and no adjustment is needed. This is the perceived brightness value of the previously confirmed material, in units of... Brightness unit; The perceived brightness value of the current candidate material, in units of The unit of brightness; λ is the perceptual sensitivity adjustment index, ranging from 0.6 to 1.0; this index is used to non-linearly compress or expand the brightness ratio: a lower value (e.g., 0.6) is used when teaching younger children, making... The value is compressed to be closer to 1, and the compensation range is moderate to avoid visual discomfort caused by sudden changes in screen brightness. When teaching older students, a higher value (such as 1.0) is used to retain the complete brightness ratio mapping and enhance the recognition of light and dark contrast.

[0052] To prevent overcompensation caused by materials with extreme brightness differences, if the calculated... If it exceeds the preset range, it will be clamped to the boundary value, i.e. This step ensures the physical validity of the compensation coefficient, preventing overexposure or underexposure of the image due to individual abnormal materials.

[0053] Step 3: Application of compensation coefficient. After clamping... Apply multiplicative gain to the current candidate material Each pixel in space The channel, without changing Channel and Channels. This design ensures that brightness adjustments are made only in the lightness dimension, without introducing hue shift or saturation distortion. For channels located... The adjusted brightness value for the pixel at that location is:

[0054]

[0055] Because after multiplication May exceed The valid domain for brightness is [0, 100]. A clamping operation is required to ensure the validity of the value.

[0056] Step 4: Inverse conversion output. The clamped output... With the original , Recombined, Inverse conversion to sRGB yields a balanced brightness. Pixel values ​​are used for subsequent animation rendering. At this point, the current candidate material has achieved perceptual brightness alignment with the preceding material.

[0057] Through the aforementioned perceived brightness matching method, this invention upgrades the image brightness balance adjustment from a physical level of arithmetic mean alignment to a subjective brightness matching that conforms to the perceptual characteristics of the human eye. Because Space itself is a uniformly perceived space, its The numerical difference directly corresponds to the difference in brightness perceived by the human eye, therefore... By using a ratio as a benchmark and supplementing it with power-law mapping for adjustable sensitivity, the transition between light and dark areas of adjacent materials during switching can be made smooth and natural for the viewer. This method effectively avoids the problem of "consistent numerical values ​​but abrupt changes in perception" in traditional methods, improves the visual comfort of animated clips in educational settings and enhances the continuity of attention for young viewers. It also provides a perceptually consistent material basis for the regulation of visual information density in the aforementioned cognitive flux conservation formula.

[0058] In a preferred embodiment, the target art education animation clip and the art science education information to be processed are synthesized to obtain the target art education animation teaching video, including: Embed the target art enlightenment animation clip into the corresponding knowledge point position in the art science popularization teaching information to be processed to obtain the target art enlightenment animation teaching video. Alternatively, extract the remaining teaching information from the art popular science teaching information to be processed, excluding the at least one knowledge point to be displayed, and splice at least one of the remaining teaching information into the target art enlightenment animation clip to obtain the target art enlightenment animation teaching video. Alternatively, extract at least one target animation image sequence from the target art enlightenment animation clip, and splice the at least one target animation image sequence into the unprocessed art popular science teaching information where at least one knowledge point to be displayed has been deleted, to obtain the target art enlightenment animation teaching video.

[0059] In a preferred embodiment, such as Figure 2 As shown, the art science popularization teaching information to be processed is obtained in the following way: Obtain the initial art science popularization teaching information input for the target teaching task, and determine the initial art science popularization teaching information as the art science popularization teaching information to be processed; Alternatively, in response to the art knowledge extraction instruction, art knowledge points are extracted from the initial art science teaching information input for the target teaching task to obtain the art science teaching information to be processed; wherein, the art knowledge point extraction includes extracting the course name, unit name, topic content and art teaching resource direction from the course information table, and extracting the resource name, physical address of resource storage and teaching direction of art resources from the resource information table.

[0060] When acquiring art science education information to be processed, this method provides two parallel input paths based on the actual application scenario. The first is a direct transmission mode, where the system determines the initial art science education information input for the target teaching task as the art science education information to be processed, suitable for scenarios where the teaching content is already structured and does not require further analysis. The second is an instruction-driven knowledge point extraction mode. When the system receives an art knowledge extraction instruction, it performs a structured extraction operation on the same initial art science education information: on the one hand, it extracts course-dimensional information such as course name, unit name, topic content, and art teaching resource direction from the course information table; on the other hand, it extracts resource-dimensional information such as resource name, physical address of resource storage, and teaching direction of art resources from the resource information table. The results of the above two-dimensional extraction are integrated as the art science education information to be processed. Through the above dual-path mechanism, the system can be compatible with input data that already has a complete structure, and can also automatically extract knowledge points from raw or unstructured teaching information, thereby ensuring that the subsequent animation generation stage always obtains standardized and usable teaching information input.

[0061] In a preferred embodiment, the art enlightenment animation generation teaching method is implemented through an art animation generation network model. The art animation generation network model is trained by performing the following operations: using an iterative training method, the untrained art animation generation network model is trained based on the art popular science teaching information of each training sample labeled with standard animation generation until the iterative training terminates, and the art animation generation network model after the first training is obtained; using an iterative training method, the art popular science teaching information of each training sample labeled with teaching adaptability is trained based on the art popular science teaching information of each training sample until the iterative training terminates, and the art animation generation network model after the second training is obtained. The iterative training method involves training an untrained art animation generation network model based on art popularization teaching information from each training sample annotated with standard animation generation, until the iterative training terminates, thus obtaining the first completed art animation generation network model, including: Each iteration includes: based on at least one sample animation generation task, performing standard animation generation operations on the corresponding training sample art science popularization teaching information to obtain their respective sample generated art animation videos; wherein, the standard animation generation operations include regularizing and deforming the movement rhythm of the graphics to avoid uniform motion and make the picture vivid and interesting. Obtain the standard-annotated animation videos of the corresponding training sample art popular science teaching information, generate art animation videos and corresponding standard-annotated animation videos based on at least one sample, obtain the animation generation quality evaluation index generated in this iteration, and update each network parameter in the network model in sequence according to the animation generation quality evaluation index; wherein, the animation generation quality evaluation index includes the screen smoothness index, the transition naturalness index, and the audio-visual synchronization index, and the audio-visual synchronization index is obtained by adjusting the number of frames of the animation and the duration of the audio. The iterative training method involves training the art animation generation network model (which has undergone initial training) based on the art science teaching information of each training sample labeled with teaching adaptability, until the iterative training terminates, thus obtaining the art animation generation network model (which has undergone secondary training). This includes: Each iteration includes: based on at least one sample teaching adaptation task, performing teaching adaptation evaluation operations on the corresponding training sample art popular science teaching information, and obtaining the respective sample teaching adaptation evaluation results; wherein, the teaching adaptation evaluation operations include maintaining the uniformity of color saturation and the harmony of the picture, so that the use of color does not cause visual fatigue to the target teaching object. Obtain the standard teaching adaptation labels for each of the corresponding training sample art popular science teaching information. Based on the teaching adaptation evaluation results of at least one sample and the corresponding standard teaching adaptation labels, obtain the teaching adaptation evaluation index generated in this iteration. Then, based on the teaching adaptation evaluation index, update each network parameter in the network model in sequence.

[0062] In a preferred embodiment, the cognitive characteristics of the target learner include at least one of the following: the age stage characteristics of the target learner, the cognitive level characteristics of the target learner, and the basic art characteristics of the target learner; the animated presentation method of the art knowledge includes at least one of the following: motion graphics animation presentation method, two-dimensional vector animation presentation method, image sequence frame presentation method, and text special effects presentation method; the art popular science teaching information to be processed includes art popular science knowledge points for children, color cognition elements, and artistic expression ability training objectives. After receiving the target art and animation tutorial videos, it also includes: The target art education animation teaching videos are pushed to the learning terminals corresponding to the target learners through the online teaching platform to realize online art education; wherein, the push method includes point-to-point task distribution and point-to-multipoint information distribution.

[0063] An art education animation generation teaching system includes a processor and a memory that communicate with each other. The processor is used to read computer programs from the memory and execute them to implement the art education animation generation teaching method.

[0064] In a preferred embodiment, the art enlightenment animation generation teaching system further includes a teacher-end module and a student-end module that are communicatively connected to the processor. The teacher-end module is configured with a server connection creation unit and a teaching function unit, and the student-end module is configured with a learning function unit. The art enlightenment animation generation teaching system establishes a network connection between the teacher-end module and one or more student-end modules through the server connection creation unit. The teaching function unit of the teacher-side module includes: a file transfer subunit for transmitting art teaching files to the student-side module; a broadcast subunit for broadcasting art teaching audio and video streams to the student-side module; a message transmission subunit for sending teaching messages to the student-side module; and a drawing board subunit for realizing the drawing of two-dimensional vector graphics, image processing, and display of text fonts, sizes, and styles. The learning function units of the student-side module include: a hand-raising question subunit, used to send question requests to the teacher-side module; an artwork upload subunit, used to upload student artwork; and a resource download subunit, used to download art teaching resources shared by the teacher-side module. The art enlightenment animation generation and teaching system also includes a storage unit and a printing unit. The storage unit is used to locally store the target art enlightenment animation teaching video and lecture information, and the printing unit is used to print out the image frames and lecture information in the target art enlightenment animation teaching video.

[0065] At the system architecture level, the art enlightenment animation generation teaching system, in addition to the core generation processor, is equipped with teacher-side and student-side modules. These modules establish a network connection through a server-side connection unit, enabling a single teacher to conduct online art enlightenment teaching to one or more students simultaneously. On the teacher-side, the teaching function unit integrates four sub-functions: file transfer, audio and video broadcasting, teaching message transmission, and a drawing board. These functions handle tasks such as distributing art teaching files, real-time broadcasting of classroom audio and video streams, instant push notifications, and 2D vector graphics drawing and text style editing, providing teachers with a complete teaching toolchain from resource distribution to real-time demonstration. Correspondingly, the student-side learning function unit focuses on classroom interaction and self-study, featuring three sub-functions: raising hands to ask questions, uploading artwork, and downloading resources. This allows students to ask questions during class, submit their artwork to the teacher for feedback, and download shared art teaching resources after class for review and consolidation, thus forming a complete closed loop of teaching, learning, practice, and evaluation between the teacher and student ends. In addition, the system is also equipped with a storage unit and a printing unit. The former is used to store the generated target art enlightenment animation teaching videos and lecture information locally for later playback, while the latter supports printing out the image frames and lecture information in the animation video in paper form to meet the actual needs of offline archiving and classroom presentation.

[0066] In a preferred embodiment, the drawing board sub-unit includes: a pen creation sub-module for creating pens with set line widths and colors; a basic graphic drawing sub-module for drawing basic graphics such as straight lines, rectangles, rounded rectangles, and ellipses; a curve drawing sub-module for drawing Bézier curves; a brush creation sub-module for creating brushes with set fill styles; an image fill sub-module for performing monochrome fill, gradient fill, and texture fill on graphics; an image transformation sub-module for performing stretching, flipping, translation, rotation, and scaling transformations on the drawn image; and a text processing sub-module for setting the font, font size, and style of the displayed text. The drawing board unit uses different pressure sensors to obtain the length and width of the unit, with a pressure sensitivity level of 2048. Teachers can use the drawing board unit to correct and revise the artwork submitted by students online.

[0067] The drawing board sub-unit serves as the core drawing tool for teachers' instructional functions. It integrates a complete functional module chain from basic drawing to advanced editing: First, the brush creation sub-module and the paintbrush creation sub-module set the line width, color, and fill style of the lines, providing a parameter basis for subsequent drawing operations; then, the basic shape drawing sub-module and the curve drawing sub-module complete the drawing of regular shapes such as straight lines, rectangles, and ellipses, as well as Bézier curves, respectively; then, the image fill sub-module applies monochrome, gradient, or texture fill effects to the drawn shapes; on this basis, the image transformation sub-module can perform geometric transformations such as stretching, flipping, translating, rotating, and scaling on the drawing results, while the text processing sub-module is responsible for configuring the text font, font size, and style, thereby meeting the diverse annotation and demonstration needs in art teaching. Crucially, this drawing board unit boasts up to 2048 levels of pressure sensitivity, enabling it to adjust the length and width of the strokes in real time based on subtle changes in the pressure applied by the teacher. This allows for a natural transition in line thickness during online grading, closely resembling the effect of writing with pen and paper, thus supporting teachers in accurately and intuitively grading and correcting students' artwork online.

[0068] In a preferred embodiment, the teaching function unit of the teacher module further includes: a student selection subunit, used to determine the selected student based on the student module's request to raise their hand, using a random selection method or a location selection method; and a task distribution subunit, used to implement point-to-point task distribution from the teacher module to a student module and point-to-multipoint task distribution from the teacher module to more than one student module. The learning function units of the student-side module further include: a classroom interaction subunit, used to establish an interactive communication channel between the student-side module and the teacher-side module after obtaining the consent of the teacher-side module, the interactive communication channel being used to transmit art instruction information and classroom Q&A information; and a testing subunit, used to receive unit test questions issued by the teacher-side module, through which students conduct consolidation tests of their art enlightenment knowledge.

[0069] In terms of classroom interaction and task management, the teacher-side module's teaching function unit is further configured with student selection and task distribution sub-units. When a student initiates interaction by raising their hand, the teacher can choose to randomly or strategically select the student to be asked questions, ensuring fairness in classroom participation while also providing targeted attention to specific students. After selecting a student, the task distribution sub-unit supports point-to-point task distribution to a single student or simultaneous point-to-multipoint task distribution to multiple students, flexibly adapting to different teaching scenarios such as individual guidance and group practice. In conjunction with this, the student-side module's learning function unit adds a classroom interaction sub-unit and a testing sub-unit. The former, with the teacher's consent, establishes a dedicated interactive communication channel for real-time transmission of art instruction information and classroom Q&A content, enabling seamless online personalized tutoring between teachers and students. The latter receives unit test questions from the teacher, allowing students to complete consolidation tests of their art literacy knowledge in or after class. This organically links real-time interaction, task execution, and effectiveness verification, forming a closed-loop online art literacy teaching process.

[0070] In a preferred embodiment, the server connection creation unit includes: a server creation submodule, used to create a teaching server according to the port number input by the teacher-side module; and a client connection submodule, used to establish a network connection with the teaching server according to the server IP address and port number input by the student-side module. The art enlightenment animation generation teaching system also includes an administrator module, which is used to search, add, modify and delete teacher users and student users, as well as to upload, classify and manage the permissions of materials in the art animation material library.

[0071] Regarding network connection establishment, the server connection creation unit adopts a collaborative working mechanism between the server and the client: First, the server creation submodule dynamically creates a teaching server instance based on the port number input by the teacher. Then, the student terminal initiates a connection request through the client connection submodule based on the server's IP address and port number, thereby establishing a network path between the teacher and one or more student terminals, providing the underlying communication foundation for subsequent audio and video broadcasting, file transfer, and classroom interaction. In addition, the system is equipped with an independent administrator module, undertaking dual management functions for users and resources: In user management, the administrator can perform search, add, modify, and delete operations on teacher and student accounts, ensuring the accurate maintenance of information on teaching participants; in resource management, the administrator is responsible for uploading, classifying, and setting permissions for materials in the art and animation material library, ensuring the orderly organization of material resources and enabling differentiated access to materials for different roles through access control, thus balancing the flexibility of teaching operations with the security of data management at the system level.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art, based on their understanding of the basic inventive concept of the present invention, can make various changes, modifications, or equivalent substitutions accordingly. Therefore, the appended claims should be interpreted as covering the above preferred embodiments and all improvements and variations falling within the spirit and principles of the present invention. Any modifications, equivalent substitutions, and improvements made within this scope should be included within the protection scope of the present invention.

Claims

1. A method for generating animations for art education, characterized in that, The method includes: Content analysis is performed on the art science popularization teaching information to be processed to obtain the characteristics of each art knowledge to be displayed, and feature analysis is performed on the target teaching objects corresponding to the art science popularization teaching information to be processed to obtain the cognitive characteristics of the objects. Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the characteristics of each target art knowledge fragment feature and the object cognitive feature are fused to obtain fused art teaching features. Based on the integrated art teaching characteristics, at least one knowledge point to be displayed and its corresponding art knowledge animation interpretation method are determined in the art popular science teaching information to be processed. In the art animation material library associated with the art popular science teaching information to be processed, the range of animation interpretation materials for each of the at least one knowledge point to be displayed is obtained. Within each of at least one range of animated performance materials, based on the art knowledge animation performance method of the corresponding knowledge point to be displayed, the range of animated performance materials is processed to generate animation segments to obtain target art enlightenment animation segments. The target art enlightenment animation segments are then combined with the art science popularization teaching information to be processed to obtain target art enlightenment animated teaching videos.

2. The method for generating and teaching art enlightenment animations according to claim 1, characterized in that, Before acquiring the integrated art teaching characteristics, the process also includes: analyzing the content of reference art teaching case information to acquire the characteristics of each reference art knowledge; wherein, the reference art teaching case information includes pre-stored completed art enlightenment animation teaching works, which integrate three forms of expression: graphic design, animation motion design and audiovisual language; Based on the characteristics of each art knowledge to be displayed, the characteristics of each target art knowledge fragment are obtained. According to the matching relationship between each target art knowledge fragment feature and the object's cognitive features, the characteristics of each target art knowledge fragment and the object's cognitive features are fused to obtain fused art teaching features, including: Based on the features of each art knowledge to be displayed and the features of each reference art knowledge, the features of each target art knowledge fragment are obtained; Based on the matching relationship between each target art knowledge fragment feature and the object cognitive feature, the target art knowledge fragment features and the object cognitive feature are fused to obtain fused art teaching features.

3. The method for generating and teaching art enlightenment animations according to claim 2, characterized in that, The process involves generating animation from at least one set of animation materials, based on the art knowledge animation method for the corresponding knowledge point to be displayed, to obtain the target art enlightenment animation clip, including: Perform the following operations on each of the at least one animation material range to obtain the target art enlightenment animation clip: For art knowledge animation interpretation methods that do not contain specified animation interpretation instructions, determine the animation image sequence obtained after animate rendering of a knowledge point to be displayed according to the art knowledge animation interpretation method as the target art enlightenment animation clip, and render and output the target art enlightenment animation clip in the animation material range of the knowledge point to be displayed. For art knowledge animation demonstration methods containing specified animation demonstration instructions, the target art enlightenment animation clip is obtained based on each target art animation element in a pre-established art animation element library that matches the specified animation demonstration instructions. Alternatively, the target art animation elements that match the specified animation demonstration instructions are extracted from the reference art teaching case information, and the target art enlightenment animation clip is rendered and output within the animation demonstration material range of the knowledge point to be displayed. The specified animation demonstration instructions include at least one of the following: specified graphic movement rhythm rules, camera transition methods, object deformation rules, and two-dimensional and three-dimensional mutual conversion methods.

4. The method for generating art enlightenment animation teaching according to claim 3, characterized in that, After receiving the target art-inspired animation clips, it also includes: Based on the cognitive characteristics of the target audience, the adaptive rendering frame rate of the target art education animation segment is determined using the cognitive adaptation frame rate calculation formula. The target art education animation segment is then rendered and output according to the adaptive rendering frame rate so that the animation playback rhythm matches the cognitive acceptance ability of the target teaching audience. The formula for calculating the cognitive adaptation frame rate is as follows: ； in, For adaptive rendering frame rate; Based on the rendering frame rate; The cognitive level of the target learners is quantified, with a value ranging from 1 to 10. This represents the upper limit of the quantitative value of cognitive level. The cognitive decay index, ranging from 1.5 to 3.0, represents the non-linear decay rate of cognitive level in response to frame rate requirements. The information density of the knowledge points to be displayed. This represents the upper limit of information density.

5. The method for generating and teaching art enlightenment animations according to claim 1, characterized in that, The step of obtaining the range of animation materials for each of the at least one knowledge point to be displayed from the art animation material library associated with the art popular science teaching information to be processed includes: The art animation material library associated with the art popular science teaching information to be processed is used to extract material features to obtain the features of each animation material; wherein, the art animation material library includes character design materials, scene background materials, graphic symbol materials, color matching materials and sound effects and dubbing materials; Obtain the matching degree between each animation material feature and the integrated art teaching feature. Based on the material data fragments corresponding to the animation material features with a matching degree not less than a set threshold, obtain the animation performance material range for each of the at least one knowledge point to be displayed.

6. The method for generating art enlightenment animation teaching according to claim 5, characterized in that, The material data fragments corresponding to the animation material features with a matching degree of not less than a set threshold are used to obtain the animation performance material range for each of the not less than one knowledge point to be displayed, including: Cluster the features of animation materials with a matching degree of not less than a set threshold to obtain at least one set of animation material features; For each of the not less than one set of animation material features, perform the following operations: obtain the material data interval corresponding to each animation material feature in the set of animation material features, where each material data interval includes not less than one material data segment; Based on the acquired data ranges of each material, the constraints of the range of animation performance materials are determined. By connecting the constraints, the range of animation performance materials for the corresponding knowledge points to be displayed is obtained from the art animation material library. The constraints include uniform color saturation constraints, visual matching constraints between characters and backgrounds, and animation duration constraints. The animation duration constraints set the duration of each art enlightenment animation science popularization segment to be between 30 seconds and 3 minutes.

7. The method for generating and teaching art enlightenment animations according to claim 6, characterized in that, After obtaining the range of animation materials for each of the not less than one knowledge point to be displayed, it also includes: Based on the teaching semantic segmentation information of each knowledge point to be displayed, the animation transition time of each knowledge point to be displayed is determined by the transition timing calculation formula. The transition is performed on the target art enlightenment animation segment of the corresponding knowledge point to be displayed according to the animation transition time, so that the animation screen switching rhythm is synchronized with the semantic advancement rhythm of the teaching content. The formula for calculating the transition timing is as follows: ； in, For the first Animated transitions for each knowledge point to be presented. For the first The starting moment of the animation for each knowledge point to be presented. For the first The total number of semantic segments contained in each knowledge point to be displayed; For the first The semantic length of each semantic segment. Semantic throughput for animation rendering For the first The teaching pace adjustment coefficient for each knowledge point to be presented ranges from 0.8 to 1.

5.

8. The method for generating art enlightenment animation teaching according to claim 1, characterized in that, The art enlightenment animation generation teaching method is implemented through an art animation generation network model. The art animation generation network model is trained by performing the following operations: using an iterative training method, the untrained art animation generation network model is trained based on the art popular science teaching information of each training sample labeled with standard animation generation until the iterative training is terminated, and the art animation generation network model that has been trained for the first time is obtained. An iterative training approach is adopted, based on the art science teaching information of each training sample labeled with teaching adaptability, to train the art animation generation network model that has been trained for the first time until the iterative training ends, and then obtain the art animation generation network model that has been trained for the second time. The training process employs an iterative approach, using art and science education information from each training sample annotated with standard animation generation data to train an untrained art animation generation network model until the iterative training terminates, resulting in the first successfully trained art animation generation network model, including: Each iteration includes: based on at least one sample animation generation task, performing standard animation generation operations on the corresponding training sample art science popularization teaching information to obtain their respective sample generated art animation videos; wherein, the standard animation generation operations include regularizing and deforming the movement rhythm of the graphics to avoid uniform motion and make the picture vivid and interesting. Obtain the standard-annotated animation videos of the corresponding training sample art popular science teaching information, generate art animation videos and corresponding standard-annotated animation videos based on at least one sample, obtain the animation generation quality evaluation index generated in this iteration, and update each network parameter in the network model in sequence according to the animation generation quality evaluation index; wherein, the animation generation quality evaluation index includes the screen smoothness index, the transition naturalness index, and the audio-visual synchronization index, and the audio-visual synchronization index is obtained by adjusting the number of frames of the animation and the duration of the audio. The iterative training method involves training the art animation generation network model (which has undergone initial training) based on the art science teaching information of each training sample labeled with teaching adaptability, until the iterative training terminates, thus obtaining the art animation generation network model (which has undergone secondary training). This includes: Each iteration includes: based on at least one sample teaching adaptation task, performing teaching adaptation evaluation operations on the corresponding training sample art popular science teaching information, and obtaining the respective sample teaching adaptation evaluation results; wherein, the teaching adaptation evaluation operations include maintaining the uniformity of color saturation and the harmony of the picture, so that the use of color does not cause visual fatigue to the target teaching object. Obtain the standard teaching adaptation labels for each of the corresponding training sample art popular science teaching information. Based on the teaching adaptation evaluation results of at least one sample and the corresponding standard teaching adaptation labels, obtain the teaching adaptation evaluation index generated in this iteration. Then, based on the teaching adaptation evaluation index, update each network parameter in the network model in sequence.

9. The method for generating art enlightenment animation teaching according to claim 1, characterized in that, The cognitive characteristics of the target learners include at least one of the following: the age stage characteristics of the target learners, the cognitive level characteristics of the target learners, and the basic art characteristics of the target learners; The animated presentation methods for art knowledge include at least one of the following: motion graphics animation, two-dimensional vector animation, image sequence frame presentation, and text effects presentation; the art science popularization teaching information to be processed includes art science popularization knowledge points for children, color cognition elements, and artistic expression ability training objectives. After receiving the target art and animation tutorial videos, it also includes: The target art education animation teaching videos are pushed to the learning terminals corresponding to the target learners through the online teaching platform to realize online art education; wherein, the push method includes point-to-point task distribution and point-to-multipoint information distribution.

10. A system for generating and teaching art education animations, characterized in that, The method includes a processor and a memory that communicate with each other, the processor being used to read a computer program from the memory and execute it to implement the art enlightenment animation generation teaching method according to any one of claims 1-9.

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