Digital media art teaching system constructed by fusing virtual reality technology

Abstract

The invention relates to the technical field of digital media art teaching, and discloses a digital media art teaching system constructed by fusing a virtual reality technology. A teaching state evaluation unit of the system comprises a plurality of student creation state acquisition subunits corresponding to student terminals, and can synchronously acquire multi-dimensional state information in a plurality of student creation processes, and a teaching balance degree judgment unit determines a teaching balance degree based on an acquisition result; the virtual creation environment construction unit judges whether to start three-dimensional scene acquisition or not according to the teaching balance degree, and acquires adaptive virtual creation scene data when judging that the three-dimensional scene acquisition is started; and the teaching strategy decision-making unit generates an optimal teaching path plan based on the virtual creation scene data, and adjusts virtual teaching scene parameters according to the optimal teaching path plan. The system can realize accurate monitoring of student creation states, targeted construction of a virtual environment and dynamic optimization of teaching strategies, and assists in improving digital media art teaching quality and student creation ability.

Description

Technical Field

[0001] This invention relates to the field of digital media art teaching technology, specifically a digital media art teaching system that integrates virtual reality technology. Background Technology

[0002] With the rapid development of the digital media arts industry, the market demand for professionals with innovative abilities and practical skills is growing, and teaching models are gradually transforming towards digitalization and intelligence. Virtual reality technology, with its immersive and interactive characteristics, shows broad application prospects in digital media arts education. It can break the spatial and temporal limitations of traditional teaching, allowing students to conduct creative practice in virtual scenarios, thus enhancing their learning experience and creative efficiency.

[0003] Current solutions integrating virtual reality technology into digital media art education still have many shortcomings. Regarding monitoring student creative progress, most teaching systems can only collect single-dimensional student learning data, such as learning time and simple operation records, failing to comprehensively capture the detailed states of students during the creative process, including changes in creative thinking, adjustments in operating techniques, and technical difficulties encountered. Due to the lack of synchronous, multi-dimensional collection of multiple students' creative states, teachers struggle to accurately grasp each student's learning progress and ability level, making it impossible to determine whether the teaching process is balanced—some students may fall behind due to weak foundations, while others may be unable to further improve their abilities due to overly basic teaching content, making it difficult to accurately allocate teaching resources.

[0004] In constructing virtual creative environments, most existing systems adopt fixed 3D scene templates. Regardless of the student's creative status or whether the teaching balance is met, scene acquisition and loading are initiated according to a uniform process, lacking specificity and flexibility. In this model, the virtual scene is disconnected from the student's actual needs. When students are at different creative stages or have different skill levels, the fixed virtual scene cannot match their personalized learning needs, which may lead to students being unable to fully realize their creative potential in the virtual environment, or even reducing their learning enthusiasm due to the mismatch between the scene and their own abilities.

[0005] At the level of adjusting teaching strategies, due to a lack of in-depth analysis of student creative status data and effective integration with virtual creation scene data, teaching strategies often rely on teachers' experience, making it difficult to form a scientific and systematic optimal teaching path plan. Even when some systems attempt to adjust teaching strategies, they mostly remain at the level of simple content additions or subtractions, failing to adjust scene parameters in real time based on students' actual feedback in the virtual creation scene, such as scene complexity, interaction methods, and task difficulty. This results in a disconnect between teaching strategies and the virtual teaching scene, failing to form a closed loop of "status acquisition - environment adaptation - strategy adjustment," thus affecting teaching quality and student learning outcomes. These problems restrict the depth and breadth of virtual reality technology application in digital media art education, making it difficult to meet the current needs for high-quality talent cultivation. Summary of the Invention

[0006] The purpose of this invention is to provide a digital media art teaching system that integrates virtual reality technology to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a digital media art teaching system integrating virtual reality technology, the system comprising: The teaching status assessment unit includes multiple student creative status collection sub-units and a teaching balance judgment unit. The multiple student creative status collection sub-units correspond to multiple student terminals, and the teaching balance judgment unit is used to determine the teaching balance based on the output of the student creative status collection sub-units. The virtual creation environment construction unit is used to determine whether to start 3D scene acquisition based on the teaching balance of each student creation status acquisition subunit. When it is determined that 3D scene acquisition should be started, virtual creation scene data is acquired. The teaching strategy decision-making unit is used to generate an optimal teaching path plan based on the virtual creation scene data, and to adjust the virtual teaching scene parameters based on the optimal teaching path plan.

[0008] Preferably, the student creation status acquisition subunit includes a status monitoring panel set on the student terminal interface, and the status monitoring panel is displayed synchronously with the student creation interface; The status monitoring panel is equipped with multiple operation trajectory capture devices; The status monitoring panel is equipped with virtual teaching icons.

[0009] Preferably, the teaching balance judgment unit determines the teaching balance based on the output of the student creative status acquisition subunit, including: For each of the operation trajectory capturing devices, the input signal of the operation trajectory capturing device during the continuous teaching period is subjected to mode decomposition to generate characteristic mode components and trend components; The signal correction model generates a corrected continuous teaching period input signal based on the characteristic modal components and trend components. The teaching balance is determined based on the corrected continuous teaching period input signals corresponding to multiple operation trajectory capture devices.

[0010] Preferably, the teaching balance judgment unit determines the teaching balance based on the corrected continuous teaching time input signals corresponding to multiple operation trajectory capture devices, including: The corrected input signal of each of the aforementioned operation trajectory capture devices is sampled over a time period. Obtain the input signal values ​​of multiple sampled teaching moments; Based on the input signal values ​​of multiple operation trajectory capture devices at each of the sampling teaching moments, the single-point teaching balance parameter is calculated. The teaching balance is determined based on the single-point teaching balance parameter of multiple sampled teaching moments.

[0011] Preferably, the virtual creation environment construction unit determines whether to initiate 3D scene acquisition based on teaching balance, including: Calculate the difference in teaching balance between any two of the student creative status collection sub-units; Calculate the discrete parameters of teaching balance based on all differences in teaching balance; When the teaching balance discrete parameter is less than the preset discrete threshold and there is at least one teaching balance degree greater than the preset balance threshold, it is determined that the three-dimensional scene acquisition will be started.

[0012] Preferably, the virtual creation environment construction unit collects virtual creation scene data by collecting scene data under multiple observation poses; The teaching strategy decision-making unit generates the optimal teaching path plan based on the virtual creation scenario data, including: For each observation pose, determine the spatial relationship between the virtual teaching marker and the scene reference point; Generate spatial distribution characteristics of markers based on the spatial relationships of multiple observation poses; The optimal teaching path plan is generated based on the spatial distribution characteristics of the markers.

[0013] Preferably, the teaching strategy decision-making unit generates the optimal teaching path plan based on the spatial distribution characteristics of the markers, including: Multiple candidate teaching paths are generated based on the spatial distribution characteristics of the markers; Acquire interactive response data from student terminals during continuous teaching periods; Obtain environmental interference parameters for the corresponding teaching period; Based on the interactive response data and environmental interference parameters, multiple candidate teaching paths are screened to determine the optimal teaching path plan.

[0014] Preferably, the teaching dynamic compensation unit is used to generate a teaching parameter compensation scheme based on the interactive response data, environmental interference parameters, and optimal teaching path planning.

[0015] Preferably, the teaching strategy decision-making unit adjusts the virtual teaching scenario parameters based on the optimal teaching path planning, including: Pre-set teaching strategies are generated based on historical teaching records, and the pre-set teaching strategies include time-segmented scenario parameter configurations; When the deviation between the optimal teaching path plan and the preset teaching strategy for the corresponding time period exceeds the preset deviation threshold, the virtual teaching scenario parameters are adjusted and the preset teaching strategy is updated according to the optimal teaching path plan.

[0016] Preferably, the teaching parameter compensation scheme generated by the teaching dynamic compensation unit includes: The teaching strategy decision space is calibrated a second time based on the predicted interactive response data and predicted environmental disturbance parameters of the predicted teaching period. Generate a feature space for teaching compensation decisions; The optimal teaching path planning is optimized by parameter compensation based on the teaching compensation decision feature space.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This digital media art teaching system, built using virtual reality technology, achieves comprehensive and accurate data collection on the creative status of multiple students and a scientific assessment of teaching balance through a teaching status evaluation unit. Multiple student creative status collection sub-units correspond to multiple student terminals, simultaneously capturing multi-dimensional information about each student during the creative process, covering details of creative operations, expression of ideas, and feedback, moving beyond a single data dimension. Based on this collected information, the teaching balance assessment unit objectively analyzes differences in learning progress and ability levels among students, clearly revealing whether there are issues such as uneven resource allocation or imbalanced teaching pace. This allows teachers to intuitively understand the overall teaching situation and provides clear direction for subsequent teaching adjustments.

[0018] The virtual creation environment construction unit determines whether to initiate 3D scene acquisition based on the assessment of teaching balance, changing the traditional system's fixed-scene-initiation mode and making the construction of virtual creation scenes more targeted. When the teaching balance is determined to require adjustment, 3D scene acquisition is initiated to obtain virtual scene data that fits the current teaching needs and students' creative status, avoiding the problem of fixed scene templates being out of touch with students' actual needs. If the teaching balance is within a reasonable range, there is no need to initiate scene acquisition, reducing unnecessary resource consumption and ensuring that the virtual creation environment can both meet students' personalized creative needs and achieve efficient use of teaching resources. This allows students to create in virtual scenes that suit their abilities, fully stimulating their creative potential and enhancing their immersive learning experience.

[0019] The teaching strategy decision-making unit generates optimal teaching path plans based on virtual creation scenario data, breaking the limitations of traditional strategies that rely on teacher experience. By analyzing virtual scenario data, it can combine students' creative status with the balance of instruction to formulate teaching paths that align with overall teaching objectives while considering individual differences, clarifying the teaching focus, practical tasks, and guidance directions at different stages. Simultaneously, it adjusts virtual teaching scenario parameters based on the optimal teaching path plan, achieving deep integration of teaching strategies and virtual scenarios: the complexity, interaction logic, and resource allocation of the scenario are optimized in real time according to the progress of the teaching path, ensuring that the virtual teaching scenario always matches the teaching progress and students' abilities. For example, when the teaching path plan requires students to improve their complex scene modeling skills, the system can automatically adjust the richness and difficulty of the modeling elements in the virtual scene; when some students encounter difficulties in specific operational steps, more detailed guidance can be provided by adjusting the scene interaction methods. This dynamic adjustment mechanism creates a complete closed loop in the teaching process, allowing each student to learn in a suitable teaching pace and virtual environment, effectively solving potential progress imbalances in teaching, helping students gradually improve their digital media art creation abilities, and promoting an overall improvement in teaching quality.

[0020] The entire system's various units work together to form a complete teaching support system, from student status monitoring to environment construction and strategy adjustment. The teaching status assessment unit provides a basis for constructing the virtual creation environment, ensuring the rationality of scene construction; virtual creation scene data, in turn, supports teaching strategy decisions, making strategy adjustments more scientific; and the adjustment of teaching strategies, in turn, optimizes the focus of subsequent student status monitoring and the direction of virtual scene construction. These three aspects mutually promote each other, continuously improving the accuracy and effectiveness of teaching. In practical teaching applications, the system can adapt to teaching scenarios of different sizes. Whether it's small-class, precise teaching or large-class, collective teaching, it can ensure the fairness and efficiency of the teaching process by paying attention to the creative status of each student and dynamically allocating teaching resources, providing a practical solution for the innovation of digital media art teaching models. Attached Figure Description

[0021] Figure 1 This is a sequence diagram of the digital media art teaching system constructed by integrating virtual reality technology as described in this invention; Figure 2 Flowchart illustrating the working principle of the student creative status collection subunit; Figure 3 Flowchart illustrating the working principle of determining the teaching balance in the teaching balance assessment unit; Figure 4 A flowchart illustrating the working principle of virtual creation scenario data collection and optimal teaching path planning. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 This invention provides a digital media art teaching system that integrates virtual reality technology, the system comprising: Personalized teaching is achieved by monitoring students' creative status in real time and dynamically adjusting teaching strategies. The system includes a teaching status assessment unit, a virtual creation environment construction unit, and a teaching strategy decision-making unit. The teaching status assessment unit acquires student creative data through multiple student creative status collection sub-units and analyzes the teaching balance status through a teaching balance judgment unit. The virtual creation environment construction unit decides whether to initiate 3D scene acquisition based on the teaching balance judgment results and collects virtual creation scene data. The teaching strategy decision-making unit generates the optimal teaching path plan based on the collected scene data and adjusts the virtual teaching scene parameters to optimize teaching effectiveness.

[0024] Example 1: See Figure 2In the actual deployment of the digital media art teaching system, the specific implementation of the student creation status acquisition subunit described in Example 1 is as follows: The student terminal interface integrates a dynamically rendered status monitoring panel, which is strictly synchronized with the digital media creation software interface currently used by the student. This synchronization is achieved through the frame buffer sharing mechanism of the underlying graphics processing unit. When the student is painting, modeling, or editing animation in the main creation area, the status monitoring panel mirrors the same visual content in real time, including core parameters such as canvas size, color space, and layer structure. The panel uses a semi-transparent overlay technology, and its transparency can be dynamically adjusted within the range of 20% to 80% to avoid obscuring key content in the creation area.

[0025] Six sets of high-sensitivity operation trajectory capture devices are embedded in the edge area of ​​the status monitoring panel. Four of these are capacitive touch sensors located at the four corners of the panel, collecting real-time contact pressure data from students' fingers or styluses, with a pressure sensing accuracy of 0.1 Newtons. An optical gesture recognition module is integrated at the bottom center of the panel, capturing the three-dimensional motion trajectory of the hand during hovering operations via an infrared laser grid. An electromagnetic induction zone is located on the right edge of the panel, recording the pen tip tilt angle and rotation direction data at a sampling rate of 200Hz when students use a digital pen for creation. All raw signals generated by the capture devices are transmitted to a preprocessing module via a dedicated data channel, converting them into structured trajectory data packets containing timestamps, coordinate values, and operation types.

[0026] The virtual teaching signage system employs a layered rendering architecture. The base layer contains static positioning markers, such as the golden ratio grid and perspective guide boxes, presented as semi-transparent green lines with a width of 0.5 pixels. The dynamic layer automatically activates based on the teaching progress. For example, when students practice color composition, a circular hue wheel marker appears at the edge of the panel, its diameter dynamically scaling with the frequency of palette use. Interactive markers are draggable 3D controls, such as a virtual light source sphere, whose spatial position can be adjusted by multi-touch, with the sphere's surface displaying real-time light intensity values. The visibility of all markers is controlled by the teaching strategy engine. When the system detects that a student has timed out during the material mapping phase, it automatically overlays a UV unwrapping diagram marker in the corresponding area.

[0027] The data fusion of the operation trajectory capture device employs a spatiotemporal alignment algorithm. Raw data collected by each sensor is first clock-synchronized, using the teaching system's master clock as a reference, with alignment accuracy controlled within 5 milliseconds. Touch pressure data and electromagnetic handwriting data are uniformly transformed to the panel coordinate system using a coordinate transformation matrix, forming a 3D trajectory point cloud containing X / Y coordinates, pressure values, and timestamps. Gesture recognition data is then converted into control vectors for the virtual joystick using a skeletal joint mapping algorithm. The final generated operation trajectory feature vector includes 12-dimensional feature indicators such as the variance of operation point displacement per unit time, pressure change gradient, and interaction event trigger interval.

[0028] The dynamic response mechanism of virtual teaching markers comprises three layers of feedback logic. The basic response layer automatically switches marker types based on the student's current operating tool; for example, displaying a 3D mesh density reference line when switching to the carving knife tool. The advanced response layer analyzes the characteristics of the creative content; when a large area of ​​water is detected in the scene, a Fresnel reflection principle demonstration model is automatically loaded. The anomaly response layer monitors for abnormal operation trajectories; for example, if consecutive rapid erasing actions exceed a threshold, a historical operation playback window marker is overlaid in the corresponding area. The color coding of all markers follows teaching semantic rules: red markers indicate operation warnings, blue markers represent knowledge point prompts, and green markers indicate the correct operation path.

[0029] If a tracking device continuously loses data for more than 3 seconds, the system automatically activates the data interpolation compensation algorithm for adjacent sensors. If the panel rendering experiences a delay of more than 100 milliseconds, the system immediately initiates a degradation mode, switching the rendering precision of markers from vector graphics to bitmap caching to maintain a minimum level of instructional guidance. The synchronization status between the panel and the creation interface is checked every 16 milliseconds; if a frame rate difference exceeds 5%, the system automatically adjusts the priority of rendering pipeline resource allocation.

[0030] In a 3D modeling teaching scenario, when students are modeling a character's head, anatomical markers of muscle structures appear on the right side of the status monitoring panel. These markers are displayed with an X-ray perspective effect, and students can adjust their transparency using a two-finger pinch gesture, continuously adjusting from 0% (completely transparent) to 100% (opaque). Simultaneously, an operation trajectory capture device at the edge of the panel records the time distribution of the student's observation of the anatomical structures. When insufficient observation time is detected in the cheekbone area, a highlighted flashing marker is automatically superimposed on that area. During the modeling process, an electromagnetic induction zone continuously collects carving tool pressure data. When the pressure value consistently exceeds a set threshold, a pressure bar warning indicator is displayed next to the tool icon.

[0031] Upon first startup each day, a touch sensor baseline calibration procedure is automatically executed, displaying a nine-square calibration pattern on the panel. During weekly in-depth maintenance, the distortion correction process of the optical gesture recognition module is activated, updating camera parameters by recognizing ten predefined standard gestures. All calibration data is stored in a calibration history database, used to build a device condition degradation model. When a trend of declining sensor accuracy is detected, a maintenance warning notification is triggered in advance.

[0032] Example 2: See Figure 3 During the operation of the digital media art teaching system, the teaching balance judgment unit described in Example 2 achieves signal processing and balance analysis in the following way. When students create virtual sculptures in the 3D modeling course, six operation trajectory capture devices continuously generate data streams. Touch sensors located at the four corners of the status monitoring panel record the force change curves of students' finger presses, the bottom gesture recognition module captures the dynamic trajectory of hands pinching and scaling the model in the air, and the right electromagnetic induction area transmits the timing data of the digital pen's tilt angle. These raw signals are transmitted to the signal preprocessing module at a rate of 200 frames per second via a dedicated data bus.

[0033] The signal mode decomposition process is executed on a dedicated hardware acceleration card. Taking the tilt angle signal of the electromagnetic pen as an example, the signal first enters a three-stage cascaded filter bank. The first-stage high-pass filter removes inherent vibration noise from the device, the second-stage band-pass filter separates wrist tremor components, and the third-stage low-pass filter extracts the overall movement trend of the arm. The decomposed characteristic mode components contain three typical modes: the 0.5-2Hz frequency band reflects the fine control during local detail sculpting, the 2-5Hz frequency band corresponds to medium-scale shaping movements, and the components above 5Hz record sudden operations of rapid tool switching. The trend component is calculated by integration through a sliding time window, updating the average tilt azimuth angle of the pen every 30 seconds.

[0034] When the characteristic modal components detect continuous high-frequency jitter (such as tremors caused by student hand fatigue), the model automatically injects a smoothing filter algorithm to eliminate abnormal fluctuations while preserving the intended operation. For touch pressure signals, when the trend component shows that the pressure value is consistently below 20% of the historical average, the correction model initiates sensitivity gain compensation to amplify the weak touch signal to a recognizable range. All correction operations are performed in a dedicated digital signal processor, with processing latency controlled within 8 milliseconds to ensure real-time performance.

[0035] The system uses 5-minute intervals as the basic teaching unit, performing three random samplings within each unit. Sampling is driven by a teaching clock, avoiding breaks and prioritizing key teaching points (such as practical sessions after teacher demonstrations). In a sculpture lesson, at the 25-minute sampling point, the system acquires corrected signal values ​​from six student terminals: terminal A's pen pressure is 0.73 N (Newtons), terminal B's gesture zoom speed is 15 mm / s, terminal C's touch trajectory curvature is 0.18, and the data from the other terminals is recorded similarly. These values ​​are then fed into the real-time calculation engine.

[0036] First, the arithmetic mean of the six signal values ​​is calculated; for example, the average pen pressure is 0.68N. Then, the standard deviation is calculated; in this sample, the standard deviation is 0.12N. The balance parameter is generated by converting the coefficient of variation (standard deviation / mean). The conversion formula sets the coefficient of variation to 0-0.3, corresponding to a balance score of 100-80, and 0.3-0.6, corresponding to 80-60. In this sample, the coefficient of variation is 0.18, resulting in a single-point balance parameter of 86. Simultaneously, an operation type consistency factor is introduced; when 80% of students are detected performing similar operations (e.g., all performing surface smoothing), the balance parameter increases by 5%.

[0037] Sampling data from three consecutive teaching units (15 minutes each) were used to form a time series. The balance score was 82 points for Unit 1 (0-5 min), 79 points for Unit 2 (5-10 min), and 85 points for Unit 3 (10-15 min). The final teaching balance score was calculated using an exponentially weighted moving average, assigning higher weight to the most recent time period. With a decay factor of 0.6, the final balance score was calculated as follows: 85 × 0.6 + 79 × 0.24 + 82 × 0.16 = 83.2 points. This value was quantified as a percentage and stored in the teaching status database.

[0038] If the teaching balance score is consistently below 75 points for the first 10 minutes, the sampling frequency is automatically increased to once per minute. In one sampling, an abnormal deviation of the character skeleton rotation angle signal from the group mean was detected on terminal D. The system immediately initiated a signal source investigation, discovering a constant deviation of 0.5 degrees in the gyroscope of the terminal's digital pen. The teaching balance judgment unit automatically marked this terminal's data as a special sample, excluding the influence of outliers when calculating the group dispersion, thus avoiding misjudgments of teaching imbalance.

[0039] The mode decomposition filter bank undergoes phase calibration monthly, and the separation accuracy of each frequency band is verified using standard test signals. The parameters of the signal correction model are updated quarterly, and new compensation coefficients are trained based on historical data. The teaching balance algorithm adjusts weight allocation each semester, optimizing conversion rules based on teacher evaluation feedback. All adjustments are completed through a remote configuration center, without interrupting on-site teaching activities.

[0040] This implementation method constructs a balanced evaluation system adapted to different teaching scenarios through multi-level signal processing and dynamic sampling mechanisms. The conversion process from the original operation signal to the teaching balance not only preserves individual operation characteristics but also effectively extracts the commonalities of group learning states, providing an objective basis for the dynamic adjustment of the virtual teaching environment.

[0041] Example 3: See Figure 4 In the 3D scene acquisition decision-making process of the digital media art teaching system, the teaching balance discrete parameter calculation and scene acquisition triggering mechanism described in Example 3 are implemented as follows: The system continuously monitors the teaching balance data stream from different student terminals. For example, in a virtual scene design course, the balance score of eight student terminals is updated every 3 minutes, with a score range of 0-100. This data is transmitted to the balance analysis engine in real time through a distributed message queue. The engine maintains the teaching balance time series of the most recent 15 minutes for each terminal.

[0042] The calculation of the difference in teaching balance uses a dynamic matching algorithm based on time alignment. When the balance sequence of terminal A is [82,85,79,83,80] and the sequence of terminal B is [78,76,81,79,77], the system first performs dynamic time warping on the two sequences to find the optimal alignment path. The aligned sequence point pairs are (82,78), (85,76), (79,81), (83,79), and (80,77). The difference value is calculated using a modified Manhattan distance formula. ; in: This represents the difference in balance between terminals A and B, where n is the length of the aligned sequence (n=5 in this example). and These represent the i-th balance values ​​of terminals A and B after alignment. The time decay weighting coefficient is 0.9^i (where i ranges from 0 to n-1). This formula calculates the difference between terminals A and B to be 3.72. The system performs the same calculation on all terminal combinations (in this example, C(8,2)=28 pairs) to generate a difference matrix.

[0043] The calculation of the discrete parameters for teaching equilibrium employs a multi-stage aggregation strategy. First, the difference matrix is ​​normalized so that the sum of the differences between each terminal and other terminals is 1. Then, the Frobenius norm of the matrix is ​​calculated as an initial dispersion index. Finally, a course stage adjustment factor is introduced. (Values ​​range from 0.7 to 1.3, varying linearly with the course progress), resulting in the final discrete parameters: ; Where: D is the discrete parameter for teaching balance, and m is the total number of student terminals. This is the normalized difference value. When this parameter is below the preset threshold of 0.35 for two consecutive detection cycles, and at least one terminal's equalization score exceeds 85, the 3D scene acquisition process is triggered.

[0044] In the virtual character design course, when the system determines that the acquisition conditions are met, it first sends an acquisition preparation command to the target terminal (the terminal with the highest balance). The 6DoF positioning system built into the VR headset of this terminal then begins to initialize the environment map. Four wide-angle RGB-D cameras simultaneously acquire scene data from different directions (front, left, upper right, and lower rear), with each camera recording 1280×720 resolution depth and color images at 30fps. The spatial registration algorithm aligns the four video streams to a unified coordinate system, generating a sparse point cloud containing 32,000 feature points.

[0045] The spatial relationship analysis of virtual teaching markers employs a hierarchical recognition strategy. The system first establishes three reference planes in the scene's baseline coordinate system: the ground plane (fitted using the RANSAC algorithm), the workbench plane (determined by the point cloud of the student's operating area), and the virtual canvas plane (calculated based on the position of the creation software interface). Then, the markers are categorized: red-framed markers are projected onto the ground plane to calculate their coverage area, while blue rotating arrow markers are analyzed for their angle with the workbench plane. At each observation pose, the system records the polar coordinates (ρ, θ, φ) of the marker's center point relative to the nearest reference point, where ρ represents the distance, θ is the horizontal angle, and φ is the vertical angle.

[0046] In a character rigging tutorial, data collected from four poses showed that, from the frontal view, the average distance between the two rotating arrow markers and the pelvic reference point was 1.2 meters, and from the upper left view, the vertical distribution angle deviation of the spinal marker chain was 4.5 degrees. This spatial relationship data was converted into 7-dimensional feature vectors and input into the path planning engine. The engine generated three candidate paths based on these feature vectors: path A prioritizes pelvic region operations, path B focuses on spinal adjustments, and path C balances the training time for each part. Each path is accompanied by a spatial complexity score (level 1-5) and an expected operation intensity coefficient (0.1-0.9).

[0047] When the data quality index (calculated based on the number of feature points and signal-to-noise ratio) for a certain camera pose falls below 0.6, backup data from adjacent time periods is automatically used for completion. When a registration error greater than 5 cm occurs during spatial registration, a rapid recalibration process based on IMU data is triggered. For temporarily unidentifiable markers, the system retains their original point cloud data and marks them as "objects to be analyzed," and performs secondary identification in subsequent analysis in conjunction with student operation logs.

[0048] By quantifying the discrete characteristics of the teaching state, a precise scene acquisition trigger criterion was established. Multi-view spatial data analysis not only captured the geometric characteristics of the teaching environment but also revealed the topological relationships between virtual markers, providing a spatial relational basis for generating teaching paths that conform to the laws of 3D art creation. The time decay weight and course stage factor introduced into the formula enable the system to adaptively adjust its sensitivity to historical data and teaching progress.

[0049] Example 4: In the teaching implementation of the virtual scene lighting design course, the teaching strategy decision-making unit operates according to the following process. After the system completes the 3D scene acquisition, the spatial distribution characteristics of the markers are parsed into structured data. Three types of key markers were detected in this course: red spotlight icons (12), blue floodlight markers (8), and green light and shadow attenuation indicators (5). Based on the 3D coordinates of these markers, the path planning engine generates three candidate teaching paths, as shown in Table 1.

[0050] Table 1: Evaluation Parameters for Candidate Teaching Paths

[0051] Path A employs a "regional progression" strategy: first, the main light source group is deployed (4 red icons), then auxiliary light sources are added (blue markers), and finally, attenuation parameters are adjusted (green indicators). Path B implements a "type-centralized" approach: all spotlights are set up first, then the floodlights are addressed, and finally, attenuation is adjusted uniformly. Path C is designed as a "dynamic balance" approach: three types of light sources are arranged alternately, and attenuation is fine-tuned immediately after every two light settings are completed.

[0052] Data shows that in the last three lessons, the class's average completion rate for multi-step continuous operations was 83%, with the peak error rate in the parameter fine-tuning stage occurring 25 minutes after the start of the lesson. The environmental monitoring module reported real-time network latency fluctuations ranging from 120-180ms, classroom background noise levels maintained at around 65 decibels, and terminal GPU temperatures generally within the safe threshold of 72℃.

[0053] The multi-objective selection process operates within a decision matrix. Path A, with a spatial complexity level of 3 corresponding to medium navigation difficulty, has an operation intensity coefficient of 0.6 that matches the average operational ability of the class. Its 82% response matching rate indicates that the path's step settings align with students' operational habits. However, the interference sensitivity of 0.35 suggests that real-time rendering steps within the path may experience stuttering when network latency exceeds 150ms. Path B is marked as eliminated because its operation intensity coefficient of 0.8 exceeds the class's tolerance threshold (historical data shows that the error rate increases by 40% when it exceeds 0.75), and its high interference sensitivity (0.42) poses a risk in the current network environment. Path C, with its low spatial complexity (level 2) reducing cognitive load, an operation intensity of 0.5 allowing for adjustment, an 85% response matching rate reflecting that its step sequence matches the class's operational rhythm, and a low interference sensitivity of 0.28 demonstrating stability under the current environmental parameters, is selected as the optimal teaching path plan.

[0054] The system compares the optimal path with the preset teaching strategy library. The preset strategy configures the scene parameters for the 25-35 minute timeframe as follows: display 6 light source controls on the interface, material editor transparency is set to 50% by default, and error message frequency is set to once every 3 minutes. Path C requires displaying 9 light source controls during this timeframe, adjusting the material editor transparency to 70%, and reducing the error message frequency to once every 5 minutes. The deviation is calculated using a multi-dimensional vector angle method: preset strategy vector = [6, 50, 3]; path planning vector = [9, 70, 5]; deviation = arccos(dot product of two vectors / (product of magnitudes)) × 180 / π.

[0055] The calculated deviation angle reached 54°, exceeding the preset threshold of 45°. The system then performed parameter adjustments: the rendering engine expanded the light source control group to a three-column layout, the default transparency of the material editor was increased to 70%, and the error detection module extended the alarm interval to 5 minutes. At the same time, the configuration template for this course type in the preset strategy library was updated, a new "Medium Complexity Class" parameter group was added, and the adjusted scene configuration data was recorded.

[0056] During the path execution, the system continuously monitors the implementation status. When it detects that a student has completed 20% more progress than planned in the first 15 minutes, a dynamic acceleration mechanism is automatically activated: the advanced light source type, originally scheduled for the 18th minute, is introduced at the 12th minute. If three students are detected repeatedly performing actions during the shadow softening phase, an auxiliary guidance layer is immediately inserted, displaying a light scattering diagram at the edge of the interface. All real-time adjustment data is recorded in the strategy optimization log for improving path planning for similar courses in the future.

[0057] Through multi-dimensional path evaluation and dynamic strategy adjustment, an environmental parameter control mechanism adapted to real-time teaching conditions was constructed. The tabular data-driven screening process objectively reflects the technical characteristics and implementation conditions of different path schemes, while the deviation model based on vector space calculation enables quantitative management of the teaching strategy optimization process.

[0058] Example 5: In the teaching implementation of the 3D animation rendering course, the teaching dynamic compensation unit optimizes parameters through the following mechanism. When the optimal teaching path planning enters the skinning weight adjustment stage, the system starts the predictive analysis module. This module retrieves interactive response data from the same time period in the last three class periods, including twelve indicators such as vertex operation completion rate, number of weight slider adjustments, and error rollback frequency. The environmental monitor simultaneously collects classroom light intensity change curves, air conditioner vent temperature and humidity data, and network switch port traffic statistics. These historical data are processed by a time series decomposition engine to separate daily periodic fluctuations, course progress trend items, and random disturbance components.

[0059] The system generates interactive response data for predicted teaching sessions using an adaptive sliding window algorithm. Taking vertex operation completion rate as an example, the system identifies a regular pattern of declining completion rate in the class during the first 40-50 minutes of the lesson, with a decrease of approximately 15%-20% of the baseline value. Combined with the complexity coefficient of the day's course content (the current course has 1.2 times the baseline complexity), the prediction engine outputs a curve showing the future completion rate change, marking time intervals where the rate may fall below a warning value. Environmental interference prediction analyzes the air conditioning system's operating logs, revealing a periodic decrease in cooling power between 2:00 PM and 3:00 PM daily, based on which the potential increase in rendering computation latency is estimated.

[0060] The secondary calibration process of the instructional strategy decision space is executed on distributed computing nodes. The calibration engine divides the original decision space into 1024 multi-dimensional grid cells, each corresponding to an instructional strategy under a specific parameter combination. Based on prediction data, the system dynamically adjusts the weights of the grid cells: when the prediction indicates that the network latency may exceed 150ms in a certain period, the weight of strategy cells that rely on real-time data transmission is reduced; when the predicted ambient temperature exceeds 26℃, the failure probability of GPU load-sensitive strategies is increased. The calibrated decision space forms a three-dimensional probability distribution cloud map, with high-probability regions corresponding to robust instructional strategy combinations.

[0061] The construction of the teaching compensation decision feature space employs manifold learning technology. The system maps a set of teaching parameters (including interface response speed, prompt information density, and operation fault tolerance threshold) with 38 dimensions to a three-dimensional feature space through nonlinear dimensionality reduction. The first principal dimension represents the cognitive load level, determined by the complexity of interface elements and the number of operation steps. The second principal dimension reflects the strength of technical support, depending on the amount of real-time auxiliary resources invested. The third principal dimension indicates environmental adaptability, associated with the device performance buffer coefficient. Each coordinate point in the feature space corresponds to a teaching state fingerprint; for example, coordinates (0.7, -0.3, 1.2) represent a teaching scenario with moderate cognitive load, weak technical support, and strong environmental adaptability.

[0062] The parameter compensation optimization engine runs an optimization algorithm within the feature space. For each key node in the optimal teaching path planning, the system generates a compensation sphere within the feature space, with the sphere's radius dynamically shrinking as the course progresses. During the character expression binding teaching segment, if the feature coordinates (-0.2, 0.5, -0.8) are detected as falling into a high-risk area for operational errors, the engine immediately initiates a three-level compensation: The first level adjusts the teaching pace, splitting the originally planned continuous 12 expression mixing targets into three groups, inserting a 2-minute effect preview after each group's operation; the second level adds auxiliary prompts, overlaying muscle movement vector diagrams around key control points at the corners of the eyes and mouth; the third level modifies the task difficulty, changing the precise value input to interval slider control, allowing ±10% parameter deviation. After the compensation scheme is generated, it is injected into the teaching scene via a dynamic loader, with scene switching latency controlled within 300 milliseconds.

[0063] In the cloth simulation teaching unit, when a continuous negative drift of the feature space coordinates along the second dimension is detected (due to insufficient technical support), an automatic compensation scheme iteration is triggered: First, the allocation ratio of physics engine computing resources is increased, raising the number of cloth calculation iterations from the default 30 to 45; if the drift trend does not ease, a preparatory scheme is activated, adding a real-time tension heatmap to the right side of the interface; finally, the teaching assistant virtual character is activated to demonstrate the key parameter adjustment process in the scene. All compensation operations are recorded in the decision log, marking the triggering conditions, execution intensity, and duration.

[0064] A dynamic compensation mechanism for the teaching process was established through multi-level prediction and feature space mapping. From extracting patterns from historical data to analyzing real-time feature coordinates, the system develops the ability to predict changes in the teaching state. The three-level response system of the compensation strategy maintains the continuity of basic teaching while providing progressive solutions for abnormal states, enabling the virtual teaching environment to have adaptive adjustment characteristics. The dimensionality reduction representation of the decision feature space effectively simplifies the optimization process of complex parameters, while the dynamic loading mechanism ensures the seamless integration of compensation measures.

[0065] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital media art teaching system integrating virtual reality technology, characterized in that, include: The teaching status assessment unit includes multiple student creative status collection sub-units and a teaching balance judgment unit. The multiple student creative status collection sub-units correspond to multiple student terminals, and the teaching balance judgment unit is used to determine the teaching balance based on the output of the student creative status collection sub-units. The virtual creation environment construction unit is used to determine whether to start 3D scene acquisition based on the teaching balance of each student creation status acquisition subunit. When it is determined that 3D scene acquisition should be started, virtual creation scene data is acquired. The teaching strategy decision-making unit is used to generate an optimal teaching path plan based on the virtual creation scene data, and to adjust the virtual teaching scene parameters based on the optimal teaching path plan.

2. The digital media art teaching system according to claim 1, characterized in that: The student creation status acquisition subunit includes a status monitoring panel set on the student terminal interface, and the status monitoring panel is displayed synchronously with the student creation interface. The status monitoring panel is equipped with multiple operation trajectory capture devices; The status monitoring panel is equipped with virtual teaching icons.

3. The digital media art teaching system according to claim 2, characterized in that: The teaching balance judgment unit determines the teaching balance based on the output of the student creative status acquisition subunit, including: For each of the operation trajectory capturing devices, the input signal of the operation trajectory capturing device during the continuous teaching period is subjected to mode decomposition to generate characteristic mode components and trend components; The signal correction model generates a corrected continuous teaching period input signal based on the characteristic modal components and trend components. The teaching balance is determined based on the corrected continuous teaching period input signals corresponding to multiple operation trajectory capture devices.

4. The digital media art teaching system according to claim 3, characterized in that: The teaching balance judgment unit determines the teaching balance based on the corrected continuous teaching time input signals corresponding to multiple operation trajectory capture devices, including: The corrected input signal of each of the aforementioned operation trajectory capture devices is sampled over a time period. Obtain the input signal values ​​at multiple sampled teaching moments; Based on the input signal values ​​of multiple operation trajectory capture devices at each of the sampling teaching moments, the single-point teaching balance parameter is calculated. The teaching balance is determined based on the single-point teaching balance parameter of multiple sampled teaching moments.

5. The digital media art teaching system according to claim 4, characterized in that: The virtual creation environment construction unit determines whether to initiate 3D scene acquisition based on teaching balance, including: Calculate the difference in teaching balance between any two of the student creative status collection sub-units; Calculate the discrete parameters of teaching balance based on all differences in teaching balance; When the teaching balance discrete parameter is less than the preset discrete threshold and there is at least one teaching balance degree greater than the preset balance threshold, it is determined that the three-dimensional scene acquisition will be started.

6. The digital media art teaching system according to claim 2, characterized in that: The virtual creation environment construction unit collects virtual creation scene data, including collecting scene data under multiple observation poses; The teaching strategy decision-making unit generates the optimal teaching path plan based on the virtual creation scenario data, including: For each observation pose, determine the spatial relationship between the virtual teaching marker and the scene reference point; Generate spatial distribution characteristics of markers based on the spatial relationships of multiple observation poses; The optimal teaching path plan is generated based on the spatial distribution characteristics of the markers.

7. The digital media art teaching system according to claim 6, characterized in that: The teaching strategy decision-making unit generates the optimal teaching path plan based on the spatial distribution characteristics of the markers, including: Multiple candidate teaching paths are generated based on the spatial distribution characteristics of the markers; Acquire interactive response data from student terminals during continuous teaching periods; Obtain environmental interference parameters for the corresponding teaching period; Based on the interactive response data and environmental interference parameters, multiple candidate teaching paths are screened to determine the optimal teaching path plan.

8. The digital media art teaching system according to claim 7, characterized in that, Also includes: The teaching dynamic compensation unit is used to generate a teaching parameter compensation scheme based on the interactive response data, environmental interference parameters, and optimal teaching path planning.

9. The digital media art teaching system according to claim 8, characterized in that: The teaching strategy decision-making unit adjusts the parameters of the virtual teaching scenario based on the optimal teaching path planning, including: Pre-set teaching strategies are generated based on historical teaching records, and the pre-set teaching strategies include time-segmented scenario parameter configurations; When the deviation between the optimal teaching path plan and the preset teaching strategy for the corresponding time period exceeds the preset deviation threshold, the virtual teaching scenario parameters are adjusted and the preset teaching strategy is updated according to the optimal teaching path plan.

10. The digital media art teaching system according to claim 9, characterized in that: The teaching dynamic compensation unit generates a teaching parameter compensation scheme including: The teaching strategy decision space is calibrated a second time based on the predicted interactive response data and predicted environmental disturbance parameters of the predicted teaching period. Generate a feature space for teaching compensation decisions; The optimal teaching path planning is optimized by parameter compensation based on the teaching compensation decision feature space.

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