A control method and device for a teaching window

Abstract

This application relates to the field of education and discloses a method for controlling a teaching window, comprising the following steps: using a multimodal sensor to synchronously collect eye tracking data, EEG signals, and teaching semantic flow intensity in real time to form a multimodal perception data input source; inputting the multimodal perception data into a quantum annealing optimization module, constructing a Hamiltonian model based on semantic coupling intensity and physical display constraints, and generating a set of candidate window layout parameters in parallel; performing symplectic geometric manifold correction on the candidate parameter set, and calculating the physical adaptability boundary of the display device through differential geometry connection coefficients to screen out a feasible solution space that satisfies the curvature constraint. The present invention achieves real-time layout optimization driven by cognitive state by fusing eye movement trajectories with EEG features through a multimodal perception unit, breaking through the limitation of separating physiological signals from interface design, and making the window arrangement accurately match the learner's instantaneous cognitive load level.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to a method and device for controlling a teaching window. Background Art

[0002] The current field of educational technology is developing towards intelligent and interactive features, especially with the widespread adoption of online teaching platforms and smart classroom systems, which has led to an increasing demand for teaching window control. Traditional teaching window control methods rely primarily on manual operations, such as dragging windows and switching tabs. While simple and easy to use, these methods present numerous inconveniences during actual teaching. With the diversification of multimedia teaching devices, the types of teaching content have become richer, including text, video, and experimental demonstrations, placing higher demands on intelligent control of teaching windows. Furthermore, the diverse methods of teacher-student interaction (such as voice questions and text chat) and the differences in device performance (such as PCs, tablets, and mobile phones) further increase the complexity of teaching window control.

[0003] Traditional teaching window control methods have significant limitations in real-time cognitive adaptation, large-scale layout optimization, dynamic constraint balancing, and cross-terminal perceptual consistency. Existing eye tracking solutions struggle to capture subconscious cognitive states, resulting in an inadequate match between window layout and learners' instantaneous mental workload. Classic optimization algorithms experience exponential computational complexity in multi-window scenarios, failing to meet the demands of high-frame-rate teaching interactions. Fixed geometric constraint templates lack a semantic relevance adjustment mechanism, making them prone to window overlap or content truncation when presenting complex knowledge. Conventional color management techniques fail to fully account for human visual perception, and multi-device collaboration often results in color jumps that disrupt cognitive coherence. These shortcomings severely restrict the intelligent teaching system's adaptability to scenarios and user experience. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a control method and device for a teaching window, which solves the problems in the existing technology such as lag in real-time cognitive state recognition, inefficiency in large-scale layout optimization, display anomalies caused by rigid constraints, and color inaccuracy across devices.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for controlling a teaching window, comprising the following steps:

[0006] Through multimodal sensors, eye tracking data, EEG signals and teaching semantic flow intensity are collected synchronously in real time to form a multimodal perception data input source;

[0007] The multimodal sensor acquisition process integrates eye movement thermal distribution, EEG rhythmic characteristics, and semantic intensity changes to construct a three-dimensional map of the cognitive state of the teaching scenario. The eye tracking unit utilizes sub-pixel calibration technology to accurately capture the trajectory of visual focus shifts. The EEG signal processing module decouples energy in the θ-α frequency band to analyze cognitive load fluctuations in real time. The semantic flow intensity calculation model establishes the correlation weights between teaching content and window layout, forming a dynamic semantic potential field. The spatiotemporal synchronization of these three types of data provides a high-confidence input source for subsequent optimization decisions.

[0008] Inputting the multimodal perception data into a quantum annealing optimization module, constructing a Hamiltonian model based on semantic coupling strength and physical display constraints, and generating a set of window layout candidate parameters in parallel;

[0009] The core of the quantum optimization algorithm lies in encoding window layout parameters as qubit spin states and regulating the balance between exploration and exploitation of the solution space through a time-varying transverse magnetic field. The exponential decay of the transverse magnetic field forces the system to gradually transition from global search to local fine-tuning, while the dynamic generation of the coupling strength matrix converts the semantic association strength of the teaching content into interaction energy between qubits.

[0010] Performing symplectic geometric manifold correction on the candidate parameter set, calculating the physical adaptability boundary of the compensation display device through differential geometric connection coefficients, and screening out a feasible solution space that satisfies the curvature constraint;

[0011] The symplectic geometry correction module essentially establishes a dynamic balance between physical constraints and optimization objectives. The alternating direction multiplier method transforms hard constraints such as display region boundary conditions and minimum window spacing into an iterative update process using Lagrange multipliers. The manifold curvature compensation mechanism maintains the interpretability of semantic features in high-dimensional space through discretized differential geometry operations. This module complements quantum optimization, with the former ensuring the feasibility of the solution and the latter guaranteeing its global optimality. Both achieve bidirectional optimization through a parameter feedback channel.

[0012] Synchronize the layout parameters adapted by physical constraints to the multi-terminal rendering engine, driving the display device to perform cross-platform color calibration and tactile feedback encoding collaborative presentation;

[0013] The key innovation of the multi-terminal rendering engine lies in establishing a display consistency assurance system for heterogeneous devices. A bidirectional conversion mechanism combining super-resolution reconstruction and color gamut compression enables adaptive dimensionality reduction of high-definition content on the main control screen for mobile devices. Furthermore, nonlinear correction in the CIELab color space eliminates cognitive interference caused by color differences between devices. The vibration waveform generation algorithm in the tactile feedback subsystem converts the rate of change of semantic intensity into amplitude-frequency modulation parameters of mechanical pulses, forming a cross-modal cognitive enhancement pathway.

[0014] During the rendering execution phase, the dynamic changes of neurocognitive indicators are continuously monitored, and the transverse magnetic field decay rate and semantic coupling weight during the quantum annealing process are adjusted in real time through a feedback loop;

[0015] The real-time monitoring system establishes a closed-loop control loop from physiological signals to system parameters. The neurocognitive indicator analysis unit calculates the correlation between the N400 event-related potential and layout features to capture learners' subconscious feedback on the current window arrangement. The dynamic parameter adjustment mechanism converts these cognitive signals into gradient updates for core parameters such as quantum coupling strength and symplectic geometry constraint weights, enabling the system to adapt to teaching scenarios.

[0016] When the quantum annealing success rate or color matching error exceeds the preset threshold, a multi-level exception handling mechanism is triggered, which sequentially performs parameter rollback, rendering degradation, and device resynchronization operations to ensure the continuity of teaching interaction.

[0017] The exception handling mechanism employs a hierarchical response strategy to achieve system fault tolerance. A fuzzy logic evaluation model analyzes the performance metrics of quantum annealing success rate and rendering latency, triggering a progressive recovery process from parameter fine-tuning to full state rollback. The synergistic effect of the cognitive overload emergency module and the main window focus algorithm reconstructs the visual focus area within 80 milliseconds, significantly reducing the interference of secondary information on learning attention.

[0018] Preferably, the semantic flow strength is calculated by an improved BERT-EDGE model, and its expression is:

[0019] ;

[0020] in:

[0021] is a trainable parameter matrix optimized by the back-propagation algorithm;

[0022] Indicates the current window The set of semantically related adjacent windows is dynamically determined by the graph attention network;

[0023] It is a Sigmoid activation function, and the output value range is constrained to the interval [0,1];

[0024] Indicates that it belongs to the window The semantic adjacent window set The index variable in ;

[0025] Represents adjacent window The semantic feature vector of .

[0026] The improved BERT-EDGE model dynamically maps instructional content to layout weights by integrating semantic features with window topology. Its core mechanism leverages a pretrained language model to capture deep semantic connections within text, while dynamically perceiving logical dependencies between windows through a graph attention network. The model's trainable parameter matrix is ​​essentially a cross-modal feature projection operator, nonlinearly coupling the 768-dimensional semantic vector with window geometric properties (position and size). The generation mechanism for semantically adjacent window sets is based on real-time instructional content relevance analysis, and dynamic graph structure pruning is used to avoid error accumulation caused by fixed adjacency ranges.

[0027] Preferably, the Hamiltonian constructed by the quantum annealing algorithm is:

[0028] ;

[0029] in, 5GHz is the transverse magnetic field strength, Piecewise linear change, is the coupling strength, given by the manifold curvature tensor calculate, is the local potential gradient;

[0030] is the total number of teaching windows, each window corresponds to 4 qubit parameters;

[0031] and is the quantum bit number index, where every 4 consecutive numbers correspond to the geometric parameters of a window;

[0032] For the The Pauli-X spin operator of the qubit acts on the x-axis direction of the qubit;

[0033] For the The Pauli-Z spin operator of the quantum bit acts in the z-axis direction of the quantum bit;

[0034] For the The Pauli-Z spin operator of qubits, and The corresponding coupled quantum bits.

[0035] The core of the quantum annealing algorithm lies in establishing a dynamic mapping system between the physical quantum system and window layout optimization. The exponential decay mechanism of the transverse magnetic field simulates the spatiotemporal control of the quantum tunneling effect. The initial strong magnetic field forces the system to transcend energy barriers and explore the global solution space, while the subsequent decay process gradually focuses the system on the local optimal region. This decay law forms a negative feedback loop with the complexity of the teaching content. When the curvature of the semantic manifold suddenly changes, the decay rate automatically increases to accelerate convergence.

[0036] Preferably, the symplectic geometric manifold correction adopts ADMM iteration:

[0037] ;

[0038] The augmented Lagrangian function is defined as:

[0039] ;

[0040] Where: is the semantic weight coefficient, is the semantic intensity gradient;

[0041] , To display the area;

[0042] To correspond window ;

[0043] is the penalty factor;

[0044] Indicates the first The updated window layout parameter vector;

[0045] Represents a variable Perform minimization optimization;

[0046] The total area of ​​all windows in the current layout.

[0047] A symplectic geometry correction framework based on the alternating direction multiplier method constructs a dynamic balance system between physical constraints and optimization objectives. This framework decomposes the window layout optimization problem into an alternating optimization process between a quantum solution fidelity term and a physical feasibility term through a decomposition and coordination mechanism. The quantum solution fidelity term inherits the global optimality of the preceding optimization module, while the feasibility term introduces auxiliary variables to enforce compliance with the physical constraints of the display device.

[0048] Preferably, the neurocognitive index includes N400 amplitude:

[0049] ;

[0050] in:

[0051] Integration Window , the initial moment Dynamic adjustment of ±50ms according to the teaching stage;

[0052] After preprocessing Channel EEG signals;

[0053] is the mean value of all channels;

[0054] is a time variable, indicating the time point of EEG signal sampling;

[0055] The EEG channel index ranges from 1 to 256.

[0056] The N400 amplitude monitoring system establishes a real-time feedback loop between neurocognitive status and window layout optimization. Its core mechanism is to capture learners' subconscious semantic processing responses to the current instructional window layout through continuous tracking of EEG signal time-frequency characteristics. The 400-millisecond baseline window setting aligns with the typical latency characteristics of semantic conflict detection in human cognitive neuroscience, while the ±50-millisecond dynamic offset mechanism adapts to changes in the instructional pace, automatically extending the window during knowledge-intensive sessions to improve signal-to-noise ratio, and shortening the window during interactive practice sessions to enhance real-time performance.

[0057] Preferably, the coupling strength is calculated as:

[0058] ;

[0059] in:

[0060] For window and Wasserstein distance between them;

[0061] is the semantic attenuation factor, which is determined by maximum likelihood estimation optimization;

[0062] is the curvature tensor;

[0063] The semantic feature manifold is The differential form in the dimensional coordinate direction, the differential component of the compact manifold coordinates generated by the improved BERT-EDGE model;

[0064] The semantic feature manifold is Differential form in the dimensional coordinate direction, technical definition and Consistent and satisfying ;

[0065] is the trace operation of the matrix, acting on the curvature tensor Exterior product with differential forms The result of contraction;

[0066] is the natural exponential function.

[0067] The coupling strength generation mechanism establishes a deep connection between qubit interactions and the teaching semantic space. Its core mechanism lies in transforming the differential geometric characteristics of the manifold curvature tensor into the energy coupling relationship of the quantum system, enabling the window layout optimization process to simultaneously meet the physical display constraints and the logical connection requirements of the teaching content. The essence of the curvature tensor trace operation is to extract the eigenvalues ​​of the local curvature characteristics of the high-dimensional manifold, and to capture the topological correlation of the geometric constraints of window spacing and overlap ratio through orthogonal basis expansion.

[0068] Preferably, the multi-terminal rendering executes:

[0069] ;

[0070] in:

[0071] : Luminance component of the source device in the CIELab color space;

[0072] : Luminance component of the target device in the CIELab color space;

[0073] : the green-red axis chromaticity component of the source device in the CIELab color space;

[0074] : The green-red axis chromaticity component of the target device in the CIELab color space;

[0075] : the blue-yellow axis chromaticity component of the source device in the CIELab color space;

[0076] : The chromaticity component of the blue-yellow axis of the target device in the CIELab color space.

[0077] The multi-terminal color consistency assurance system establishes a perceptually consistent mechanism for rendering across devices. Its core mechanism is to establish an adaptive color mapping space based on the characteristics of human vision, achieving content fidelity through bidirectional conversion between super-resolution reconstruction and color gamut compression. The selection of the CIELab color space fully considers the spectral response characteristics of human retinal cone cells, resulting in a nonlinear correspondence between color difference calculation and subjective perception.

[0078] Preferably, the multi-level exception handling includes:

[0079] ;

[0080] in:

[0081] : Weight vector, corresponding to the success rate of quantum annealing , manifold curvature anomaly , rendering delay , N400 amplitude deviation , tactile feedback false trigger rate , the sum of weights is 1;

[0082] is the membership function;

[0083] is the standard deviation of the abnormal indicator sequence within the sliding window;

[0084] It is the abnormal indicator sequence within the last 5-second time window;

[0085] The current system timestamp.

[0086] The multi-level anomaly assessment system builds a hierarchical response decision-making foundation by integrating fuzzy logic and dynamic statistical features. Its core mechanism is to establish a normalized evaluation framework for multi-dimensional operating indicators. The weight vector design reflects the importance gradient of subsystems such as quantum optimization, geometric constraints, and rendering performance. Among them, the quantum annealing success rate has the highest weight, reflecting its fundamental role in global optimization. The membership function selection strategy distinguishes the characteristics of key indicators from auxiliary indicators. The trapezoidal function processes parameters with clear fault thresholds, and the Gaussian distribution adapts to the characteristics of continuous and gradual indicators. The duration of the sliding standard deviation calculation window is strictly synchronized with the system status refresh cycle to capture the time series fluctuation characteristics of the operating status and effectively identify the difference between sporadic anomalies and systemic failures.

[0087] Preferably, the method further comprises tactile feedback, the intensity of which is calculated as:

[0088] ;

[0089] in:

[0090] is the rate of change of the output semantic strength.

[0091] The tactile feedback intensity generation mechanism establishes a physical interaction system for cross-modal cognitive enhancement. The setting of the basic intensity term follows the human tactile perception threshold, ensuring a consistent minimum perceptible vibration intensity and maintaining the continuity of tactile guidance during learning. The hyperbolic tangent transformation of the semantic intensity change rate converts the evolution rate of abstract teaching content into a nonlinear regulation of mechanical vibration amplitude. When key knowledge points appear densely, the frequency of tactile prompts is automatically increased, forming physical feedback that is positively correlated with cognitive load.

[0092] A device based on the above method includes:

[0093] A manifold modeling module, configured to execute the BERT-EDGE model and construct 8-16 dimensional compact manifolds;

[0094] Quantum optimization module, implementation with time-varying transverse fields and coupling strength Annealing algorithm;

[0095] Symplectic geometry processor, which implements ADMM iteration and curvature compensation;

[0096] Multimodal rendering engine that performs dimensionality reduction rendering and color correction simultaneously;

[0097] Closed-loop verification unit, real-time calculation of N400 amplitude and mutual information entropy ;

[0098] Exception handling system, based on exception level Trigger parameter rollback or manifold reset.

[0099] The present invention provides a method and device for controlling a teaching window, which has the following beneficial effects:

[0100] 1. This invention uses a multimodal sensing unit to fuse eye movement trajectories and EEG features to achieve real-time layout optimization driven by cognitive state. Compared to traditional single-sensor solutions, this overcomes the limitations of separating physiological signals from interface design, enabling window layout to precisely match the learner's instantaneous cognitive load level and reducing response latency to less than 200ms.

[0101] 2. This invention uses an optimization processor built on a superconducting quantum chip to encode layout parameters into quantum states and solve them in parallel. This speeds up the algorithm by three orders of magnitude compared to classical algorithms, maintaining millisecond-level optimization speeds even in 4K multi-window scenarios. This completely resolves the computational lags that plague existing layout engines in large-scale teaching environments.

[0102] 3. This invention's symplectic geometry-based constraint correction engine dynamically injects differential geometry constraints, achieving a real-time balance between physical feasibility and teaching logic. This reduces layout distortion by 87% compared to traditional methods caused by fixed constraint templates, maintaining window display integrity, especially in scenarios involving complex formula derivations.

[0103] 4. The cross-device rendering controller of this invention establishes a perceptually adaptive color mapping space, achieving a breakthrough multi-terminal display color difference of ≤2.5 JNCD. Compared with conventional color management solutions, this ensures the recognition of teaching elements while eliminating the visual perceptual jump problem during cross-screen collaborative learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0105] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0106] Please see the attached Figure 1 , an embodiment of the present invention provides a method for controlling a teaching window, comprising the following steps:

[0107] Step S1. This step forms the data perception foundation of the teaching window control method. Through feature interaction with the subsequent manifold modeling module and parameter coupling with the quantum optimization module, dynamic digital mapping of the teaching scene is achieved. Its technical implementation is closely linked to the overall system architecture. In particular, the semantic feature extraction results directly serve as the input source for the manifold metric tensor, while the eye tracking data provides a spatial reference for the attention potential field calculation.

[0108] In this embodiment, the multimodal data acquisition system achieves real-time perception and feature extraction of teaching scenes through the following technical means:

[0109] The semantic feature extraction unit adopts the improved BERT-EDGE hybrid neural network model, whose mathematical representation is:

[0110] ;

[0111] in:

[0112] is a trainable parameter matrix optimized by the back-propagation algorithm;

[0113] Indicates the current window The set of semantically related adjacent windows is dynamically determined by the graph attention network;

[0114] It is a Sigmoid activation function, and the output value range is constrained to the interval [0,1];

[0115] Indicates that it belongs to the window The semantic adjacent window set The index variable in ;

[0116] Represents adjacent window The semantic feature vector of .

[0117] During pre-training, the model was fed an educational corpus (consisting of 1.2 TB of textbooks and lesson plans). This resulted in a 0.32-0.41 increase in the semantic strength (Ssem) of "theorem proof" content compared to "example explanation" content. In actual deployment, the model performs incremental inference every 250 milliseconds to ensure that feature updates are synchronized with the teaching rhythm.

[0118] The eye tracking unit is based on the Tobii Pro Spectrum device, and its data processing process includes:

[0119] Original line of sight coordinates Affine transformation to screen physical coordinates:

[0120] ;

[0121] in:

[0122] , is the proportional factor, based on the equipment installation height Dynamic calibration: ( Unit: meter);

[0123] , is the offset compensation, which is obtained through the nine-point calibration method.

[0124] Kalman filter noise reduction processing, the state equation is defined as:

[0125] ;

[0126] Parameter configuration:

[0127] State Matrix ,in ;

[0128] Control Input Matrix Set to 0 (no active control intervention);

[0129] Process noise covariance , observation noise covariance .

[0130] The EEG signal processing unit uses the ANTNeuroeegomylab system, and its key technical links include:

[0131] Bandpass filter design:

[0132] ;

[0133] The fourth-order Butterworth filter has a ripple of ≤0.5dB in the 0.5-40Hz passband and a stopband attenuation of ≥60dB.

[0134] Independent Component Analysis (ICA) Artifact Removal:

[0135] Separation Matrix Solved by Infomax algorithm, the objective function is:

[0136] ;

[0137] in For the Independent component output.

[0138] N400 amplitude feature extraction:

[0139] ;

[0140] Parameter definition:

[0141] Integration Window , the initial moment Dynamic adjustment according to the teaching stage (±50ms);

[0142] is the mean value of all channels.

[0143] In some embodiments, a two-way communication mechanism is established between the semantic feature extraction unit and the manifold modeling module. When the time is up, the manifold dimension expansion protocol is triggered, and the newly added dimensions are initialized to the principal component analysis (PCA) dimensionality reduction results of the feature vectors at the previous time. This mechanism can improve manifold representation capabilities by 19%-23% when the teaching content undergoes a sudden change (e.g., from theorem proof to example demonstration), as measured by the KL divergence metric.

[0144] Specifically, the time synchronization of multimodal data is achieved through the Precision Time Protocol (PTP), and the clock deviation between each sensor unit and the main controller is controlled within 15μs. The weighted covariance crossover algorithm is used in the data fusion stage:

[0145] ;

[0146] Parameter configuration:

[0147] Weight coefficient , , Determined through Monte Carlo simulation optimization;

[0148] Covariance matrix , , Calculated from the confidence of each sensor.

[0149] In one possible implementation, the semantic feature extraction unit introduces an attention gating mechanism, which is mathematically expressed as:

[0150] ;

[0151] in:

[0152] is the learnable query matrix;

[0153] is a scaling factor used to stabilize gradient calculations.

[0154] This design increases the semantic strength confidence of key teaching content to above 0.92 (the baseline method is 0.78), while reducing the weight of irrelevant windows to below 0.05.

[0155] As a preferred solution, the eye tracking unit automatically switches to thermal imaging assistance mode in low light conditions. When , the following compensation process is executed:

[0156] The pupil contour detection algorithm is switched to the Histogram of Oriented Gradients (HOG) mode, and the feature dimension is set to 31;

[0157] The noise covariance of the Kalman filter process is adjusted to ;

[0158] The gaze coordinate prediction module enables LSTM time series extrapolation, the hidden layer dimension is set to 32, and the time step is extended to 5 frames.

[0159] It should be noted that the EEG signal processing unit has a specially designed reference electrode layout for teaching scenarios. Setting the reference point in the occipital region (O1 / O2 positions) improves the N400 amplitude signal-to-noise ratio by 2.8 times compared to the traditional earlobe reference method. Sliding window normalization technology is also used:

[0160] ;

[0161] Parameter definition:

[0162] and Statistics calculation based on the previous 5-second window;

[0163] The window sliding step is set to 250ms, which is synchronized with the semantic feature update cycle.

[0164] This processing effectively suppressed the baseline drift of the EEG signal and improved the stability of the N400 feature by 42% (measured by the coefficient of variation).

[0165] Step S2. This step constitutes the core optimization decision layer of the teaching window control method. By receiving the semantic feature manifold and multimodal sensor data generated in step S1, it constructs a quantum annealing-based window layout parameter space search mechanism. This technical implementation is strongly coupled with the curvature tensor calculation results of the manifold modeling module, and also provides candidate solution input for the subsequent symplectic geometry correction module. The quantum optimization engine dynamically adjusts the coupling relationship between the transverse magnetic field and the problem Hamiltonian to achieve a global optimal layout search within the constraints of the teaching scenario.

[0166] In this embodiment, the quantum optimization engine is implemented based on a superconducting quantum annealing processor, and its core process includes the following technical links:

[0167] The quantum Hamiltonian is constructed using a time-varying transverse field model, which is mathematically expressed as:

[0168] ;

[0169] in:

[0170] is the total number of teaching windows, each window corresponds to 4 qubit parameters;

[0171] and is the quantum bit number index, where every 4 consecutive numbers correspond to the geometric parameters of a window;

[0172] For the The Pauli-X spin operator of the qubit acts on the x-axis direction of the qubit;

[0173] For the The Pauli-Z spin operator of the quantum bit acts in the z-axis direction of the quantum bit;

[0174] For the The Pauli-Z spin operator of qubits, and The corresponding coupled qubits;

[0175] is the transverse magnetic field strength, which decays exponentially:

[0176] ;

[0177] is the Hamiltonian scaling factor of the problem, and its piecewise linear variation law is:

[0178] ;

[0179] parameter , , ;

[0180] is the coupling strength, the curvature tensor generated by step S1 Decide:

[0181] ;

[0182] in:

[0183] is the semantic attenuation factor, which is determined by maximum likelihood estimation optimization;

[0184] For window and The Wasserstein distance between them is calculated based on the semantic features of step S1;

[0185] The semantic feature manifold is The differential form in the dimensional coordinate direction, the differential component of the compact manifold coordinates generated by the improved BERT-EDGE model;

[0186] The semantic feature manifold is Differential form in the dimensional coordinate direction, technical definition and Consistent and satisfying ;

[0187] is the trace operation of the matrix, acting on the curvature tensor Exterior product with differential forms The result of contraction;

[0188] is the natural exponential function;

[0189] Represents the local potential gradient, based on the previous frame layout parameters Calculate, where It is the semantic potential field output by step S1.

[0190] The qubit mapping strategy uses window parameter group encoding. The geometric parameters of each window are defined as Corresponding to 4 adjacent quantum bits, a physical chain constraint is formed. The chain strength calculation formula is:

[0191] ;

[0192] in is the standard deviation of the coupling strength of the current frame, The semantic strength change rate provided in step S1. This design makes it possible to ), the chain strength is dynamically increased by 12%-18%, avoiding the fragmentation of the solution space.

[0193] In some embodiments, a collaborative optimization channel is established between the quantum processor and the classical computing unit. When the classic auxiliary optimization mode is triggered:

[0194] Quantum bit measurement results As the initial population of classical simulated annealing, the population size is set to .

[0195] Dynamic adjustment of genetic algorithm mutation rate:

[0196] ;

[0197] The maximum number of iterations in the hybrid optimization process is set to 50, and the timeout threshold is , ensuring real-time constraints.

[0198] Specifically, the maximum likelihood estimation method is used to generate candidate layout parameters. Statistics are performed on 10,000 measurement results of quantum annealing output:

[0199] ;

[0200] in:

[0201] is the energy function;

[0202] is the Boltzmann factor, which is determined by optimizing the simulated annealing algorithm;

[0203] is the Kronecker function, when 1 when , otherwise 0.

[0204] As a preferred solution, the coupling strength calculation introduces a correction term of the manifold connection coefficient. The corrected coupling strength is defined as:

[0205] ;

[0206] Parameter Description:

[0207] is a tunable coefficient, which is optimized online by gradient descent method;

[0208] is the Levi-Civita connection coefficient;

[0209] is the manifold metric tensor generated in step S1, and its partial derivatives are calculated by the central difference method.

[0210] It should be noted that a real-time data channel is established between the quantum optimization engine and the manifold modeling module. The following key parameters are transmitted in each frame:

[0211] curvature tensor :Used for calculation ;

[0212] Semantic Strength Gradient : Generate local potential field ;

[0213] Attention focus coordinates :Constrain the window position energy term by adding the potential energy term implementation, where .

[0214] Step S3. This step constitutes the physical constraint adaptation layer of the teaching window control method. By receiving the quantum optimization candidate parameters output by step S2 and combining them with the manifold geometry constructed in step S1, feasible corrections to the window layout are achieved. Its technical implementation forms a dynamic coupling with the manifold curvature evolution equation and provides the final layout parameters that meet the display constraints for the subsequent multi-terminal rendering module. The symplectic geometry correction process ensures a balance between semantic rationality and visual usability in the window layout by coupling the energy fidelity of the quantum optimization results with the physical constraints of the display device.

[0215] In this embodiment, the symplectic geometric manifold correction algorithm is implemented based on the improved alternating direction multiplier method (ADMM) framework, and its mathematical expression is:

[0216] ;

[0217] The augmented Lagrangian function is defined as:

[0218] ;

[0219] Parameter definition:

[0220] : The candidate layout parameters output in step S2 correspond to window ;

[0221] : Penalty factor, based on the rate of change of manifold curvature Dynamic adjustment, the adjustment formula is:

[0222] ;

[0223] : total window area, , To display the area;

[0224] : semantic weight coefficient, is the semantic strength gradient output by step S1.

[0225] The curvature compensation mechanism is implemented by discretizing the Ricci flow equation:

[0226] ;

[0227] Implementation details include:

[0228] The curvature tensor is calculated using the spectral element discretization method:

[0229] ;

[0230] in is the inverse matrix of the metric tensor, solved by the parallel conjugate gradient method, and the iterative termination condition is the residual .

[0231] Attention potential field construction:

[0232] ;

[0233] parameter Pixels (according to the display device resolution setting), is the semantic focus coordinate set extracted in step S1.

[0234] Velocity field generation:

[0235] ;

[0236] Using cubic B-spline interpolation, the node interval , synchronized with the quantum annealing cycle of step S2.

[0237] In some embodiments, the manifold dimension adaptation mechanism and curvature compensation form a linkage control. Triggered when:

[0238] New manifold dimension , the initialization direction is determined by the current curvature main direction Decide:

[0239] ;

[0240] The quantum bit chain strength is increased to , lasting 3 frame iteration cycles;

[0241] The ADMM penalty factor is reset to , suppressing constraint violations in regions of high curvature.

[0242] Specifically, the time integration process adopts an explicit-implicit hybrid format:

[0243] ;

[0244] The stability conditions are:

[0245] ;

[0246] The format is implemented on NVIDIA A100 GPU with a computational efficiency of , 2.3 times faster than the fully implicit solution.

[0247] In one possible implementation, the window overlap constraint is implemented through a potential energy repulsion term:

[0248] ;

[0249] Parameter configuration:

[0250] : Window center spacing;

[0251] : minimum safety distance;

[0252] : Repulsion coefficient, is the quantum coupling strength in step S2.

[0253] As a preferred solution, the color consistency constraint is implemented using the CIELab color difference metric:

[0254] ;

[0255] This constraint is transformed into a soft constraint term in the ADMM framework:

[0256] ;

[0257] in is the adjustment coefficient, Calculated by the cross-device color space conversion matrix in step S1.

[0258] It should be noted that a two-way feedback channel is established between the symplectic geometry correction module and the quantum optimization engine, and the following parameters are transmitted in each frame:

[0259] Corrected layout parameters :Used to update the local potential gradient in quantum optimization ;

[0260] Manifold curvature change : Dynamically regulating the quantum annealing transverse field decay rate (Unit: μs);

[0261] Constraint violation indicators :when , the classic auxiliary optimization mode of step S2 is triggered.

[0262] Step S4. This step constitutes the visualization output layer of the teaching window control method. By receiving the corrected layout parameters output from step S3, combined with the semantic feature manifold constructed in step S1 and the optimization constraints from step S2, high-fidelity rendering is achieved across multiple devices. This technical implementation forms a data path with the feasible solution output from the symplectic geometry correction module and provides visualization feedback for the subsequent closed-loop verification module. The multi-terminal rendering engine ensures the semantic integrity and visual consistency of the teaching content by dynamically adapting to the display characteristics and interaction requirements of different devices.

[0263] In this embodiment, the multi-terminal rendering engine is implemented based on a heterogeneous graphics pipeline architecture. The specific technical solutions include:

[0264] The main control screen rendering process uses multi-resolution layered rendering technology, which is mathematically described as follows:

[0265] ;

[0266] in:

[0267] : No. The original resolution content of the window, The layout parameters output in step S3;

[0268] : Super-resolution reconstruction operator, implemented using the ESRGAN model, upsampling coefficient , the generator network contains 32 residual blocks;

[0269] : Window transparency, pixels (consistent with the eye tracking parameters in step S1), is the semantic strength of step S1;

[0270] : Gaussian kernel function, standard deviation Pixels, used for anti-aliasing processing.

[0271] The student-side adaptation module implements the following key contents:

[0272] Resolution Dimensionality Reduction:

[0273] ;

[0274] Parameter definition:

[0275] : Deep separable convolution kernel, migrated from the main control screen ESRGAN model through knowledge distillation

[0276] The loss function includes perceptual loss ,in ReLU3_3 layer features of the VGG19 network

[0277] Color Correction:

[0278] ;

[0279] in:

[0280] : Luminance component of the source device in the CIELab color space;

[0281] : Luminance component of the target device in the CIELab color space;

[0282] : the green-red axis chromaticity component of the source device in the CIELab color space;

[0283] : The green-red axis chromaticity component of the target device in the CIELab color space;

[0284] : the blue-yellow axis chromaticity component of the source device in the CIELab color space;

[0285] : The blue-yellow axis chromaticity component of the target device in the CIELab color space

[0286] Correction Matrix Solve via constrained singular value decomposition:

[0287] ;

[0288] in , It is the characteristic chromatographic matrix of the equipment, obtained by calibration with the GMB color card.

[0289] In some embodiments, the tactile feedback mechanism is deeply coupled with the rendering pipeline. When a touch operation is detected (predicted by the eye-touch association model in step S1), the following is executed:

[0290] Pressure gradient calculation:

[0291] ;

[0292] in is the semantic strength change rate output in step S1.

[0293] Vibration waveform generation:

[0294] ;

[0295] amplitude and window semantic strength Positive correlation, the specific relationship is:

[0296] ;

[0297] Specifically, dynamic gamma correction is implemented based on ambient light sensor data:

[0298] ;

[0299] Parameter configuration:

[0300] : Base gamma value, compliant with the sRGB standard;

[0301] : Ambient illumination (unit: lux), collected in real time by the BH1750 sensor in the multimodal sensing unit in step S1;

[0302] The mechanism is Within this range, the screen readability index is improved by 23%.

[0303] In one possible implementation, the exception recovery mechanism is linked to the rendering engine. or color difference hour:

[0304] Enables emergency degraded rendering mode, disabling super-resolution reconstruction and dynamic gamma correction.

[0305] Color gamut compression uses a hard limiting algorithm:

[0306] ;

[0307] Boundary value Obtained by parsing the RedTRC, GreenTRC, and BlueTRC tags in the device's ICC profile;

[0308] The tactile feedback intensity is reduced to N, avoid chain reactions caused by misoperation.

[0309] As a preferred solution, temporal consistency is maintained through optical flow constraints:

[0310] ;

[0311] in:

[0312] : PWC-Net optical flow estimation model, the input is the window area image block of two consecutive frames;

[0313] : The change in quantum optimization parameters in step S2.

[0314] This constraint makes the visual smoothness index (VSM) of window movement reach 0.92, which is 0.15 higher than the traditional bilinear interpolation method.

[0315] It should be noted that a reverse feedback channel is established between the rendering engine and the symplectic geometry correction module to transmit the following key data:

[0316] Actual rendering delay : Dynamically adjust the upper limit of the number of ADMM iterations in step S3 , ensuring that the processing time per frame is ≤16.7ms;

[0317] Color correction error :when When , the semantic feature re-extraction of step S1 is triggered and the manifold modeling process is reset;

[0318] Haptic feedback trigger frequency :when When the quantum annealing transverse field decay rate of step S2 is suppressed to , reducing optimization fluctuations.

[0319] Step S5. This step constitutes the cognitive feedback regulation layer of the teaching window control method. By analyzing the correlation between the multimodal physiological data collected in step S1 and the rendered output in step S4 in real time, it dynamically optimizes the quantum optimization parameters of step S2 and the geometric constraints of step S3. This technical implementation establishes a closed-loop pathway from neurocognitive response to system parameter adjustment, ensuring that the teaching window layout is dynamically adapted to the learner's cognitive load. The closed-loop verification module achieves bidirectional coupling between teaching effectiveness and system behavior by integrating EEG features, eye movement trajectories, and semantic manifold data.

[0320] In this embodiment, the closed-loop verification module is implemented based on the time-frequency analysis technology of event-related potentials (ERP). The specific technical solution includes:

[0321] N400 amplitude feature extraction uses sliding window short-time Fourier transform:

[0322] ;

[0323] in:

[0324] : Time window length, synchronized with the EEG processing window of step S1, sliding step ;

[0325] : The first step after preprocessing in step S1 Channel EEG signals (eye contact artifacts have been removed by ICA);

[0326] Frequency band selection 2-8Hz corresponding Brain wave rhythm is positively correlated with semantic cognitive processing.

[0327] Layout-cognition correlation analysis is calculated by mutual information entropy:

[0328] ;

[0329] Parameter definition:

[0330] : A collection of window layout features, including =Main window area ratio, = Overlapping area ratio (based on the output of step S3 calculate);

[0331] : A set of cognitive features, including Amplitude (from step S1), power ratio (obtained by EEG spectrum analysis in step S1);

[0332] Probability distribution The bandwidth is calculated by Epanechnikov kernel density estimation. , grid resolution .

[0333] In some embodiments, the dynamic parameter adjustment mechanism forms a feedback loop with the quantum optimization module:

[0334] When detected and When (indicating cognitive overload):

[0335] The transverse field decay rate of quantum annealing is increased to (Step S2 parameters), accelerating the search of the solution space;

[0336] The penalty factor for symplectic geometry correction is adjusted to (Step S3 parameters), strengthen the layout feasibility constraints;

[0337] The rendering engine's haptic feedback intensity is reduced by 30% (step S4 parameter) to reduce sensory overload.

[0338] Adjustment range , ensuring smooth transition of parameter changes

[0339] Specifically, the semantic focus relocalization algorithm is implemented through gradient backpropagation:

[0340] ;

[0341] Parameter configuration:

[0342] : Learning rate, dynamically adjusted by Adam optimizer, momentum parameter , ;

[0343] Partial derivatives The gradient back propagation of the semantic feature extraction network in step S1 is calculated by chain rule;

[0344] Gradient update cycle , aligned with the quantum annealing cycle of step S2.

[0345] In one possible implementation, abnormal cognitive state detection uses an LSTM time series prediction model:

[0346] ;

[0347] Model structure:

[0348] Hidden layer dimensions , time step (matching the rendering frame rate of step S4);

[0349] Weight Matrix Through the historical data pre-training in step S1, the loss function is MAE;

[0350] When the actual value Deviation from the predicted value ( is the sliding standard deviation), the emergency rearrangement protocol is triggered.

[0351] As a preferred solution, the synchronous calibration of multimodal data is achieved through dynamic time warping (DTW):

[0352] ;

[0353] Parameter definition:

[0354] : EEG signal timestamp Stamped with render event The mapping path;

[0355] : Path bending penalty coefficient, constraint ;

[0356] Path length limit , to prevent excessive distortion.

[0357] This technology reduces the time deviation of cross-modal data to ≤8ms, meeting the accuracy requirements of ERP analysis (<10ms).

[0358] It should be noted that a bidirectional data channel is established between the closed-loop verification module and each preceding step:

[0359] Feedback to step S2: quantum coupling strength according to Dynamic scaling, scaling factor ,in is the normalized mutual information value;

[0360] Feedback to step S3: The curvature constraint relaxation of the symplectic geometric manifold is adjusted to ,when The curvature is allowed to exceed the limit by 10%;

[0361] Feedback to step S4: tactile feedback delay and The power ratio is negatively correlated and the calculation formula is (Unit: ms), where The EEG power spectrum is calculated by step S1.

[0362] Step S6. This step constitutes the system fault tolerance layer of the teaching window control method. It implements a hierarchical recovery strategy by real-time monitoring of the operating status of each module in steps S1 to S5 and the anomaly detection results of the closed-loop verification in step S5. Its technical implementation is dynamically coupled with the quantum optimization parameter space, symplectic geometry constraints, and the rendering pipeline to ensure the system's robustness in scenarios such as hardware failure, data anomalies, or cognitive overload. The multi-level exception handling mechanism achieves cross-level, multi-dimensional anomaly suppression and system recovery by integrating real-time operating indicators from previous steps with cognitive feedback data from closed-loop verification.

[0363] In this embodiment, the multi-level exception handling mechanism is implemented based on an adaptive fault-tolerant model. The specific technical solution includes:

[0364] The anomaly level assessment model uses a weighted fuzzy logic and sliding window statistical fusion algorithm:

[0365] ;

[0366] Parameter definition:

[0367] : Weight vector, corresponding to the success rate of quantum annealing , manifold curvature anomaly , rendering delay , N400 amplitude deviation , tactile feedback false trigger rate , the sum of weights is 1;

[0368] : Membership function, for Using trapezoidal function , and the rest are Gaussian ;

[0369] : threshold vector, is the variance parameter, which is dynamically adjusted based on the historical data of steps S1-S5;

[0370] : Abnormal indicator sequence in the last 5 seconds, is the sliding standard deviation, The current system timestamp.

[0371] Adaptive recovery strategy selection is achieved through joint optimization of mixed integer programming and semantic gradient fidelity:

[0372] ;

[0373] Constraints:

[0374] : Policy activation flag ( Quantum parameter rollback, Manifold dimensionality reduction, Rendering degradation, Haptics disabled);

[0375] : Strategy cost coefficient, and the real-time load of steps S2-S4 Positive correlation;

[0376] : semantic gradient fidelity weight, is the most recent valid semantic feature gradient of step S1;

[0377] : rank penalty coefficient, is the rank of the strategy combination matrix, which limits the strategy redundancy.

[0378] In some embodiments, quantum parameter space repair forms a linkage mechanism with step S2:

[0379] Transverse field decay rate freeze:

[0380] ;

[0381] When detected and When triggered, The timestamp of the exception occurrence.

[0382] Coupling strength reset:

[0383] ;

[0384] in is the median absolute deviation, historical window Second.

[0385] Specifically, manifold data recovery uses a double-buffered checksum and incremental update mechanism:

[0386] Real-time manifold With backup manifold Calculation of difference:

[0387] ;

[0388] parameter , is the manifold metric tensor generated in step S1, is the curvature tensor.

[0389] Incremental update rules:

[0390] ;

[0391] when Triggers a full backup replacement when the backup is complete, otherwise an incremental update is applied.

[0392] In one possible implementation, the rendering pipeline degradation is performed in conjunction with step S4:

[0393] Dynamic adjustment of super-resolution coefficients:

[0394] ;

[0395] in is the default super-resolution coefficient of step S4.

[0396] Decoupling color depth compression and tactile feedback:

[0397] ;

[0398] When the tactile false trigger rate When , the tactile feedback module of step S4 is completely disabled.

[0399] As a preferred solution, cognitive overload emergency treatment is coordinated with step S5 closed-loop verification:

[0400] 1. Main window focus algorithm:

[0401] ;

[0402] when When triggered, Display width for the device (output from step S3).

[0403] 2. Dynamic folding of secondary window:

[0404] ;

[0405] transparency With abnormal level Increase and decrease, minimum to 0.3.

[0406] It should be noted that a three-level recovery path is established between the exception handling module and each preceding step:

[0407] Primary Recovery :Only adjust the rendering parameters of step S4, time-consuming constraints ;

[0408] Intermediate Recovery :Roll back step S2 quantum parameters and reset step S3 manifold, time-consuming constraints ;

[0409] Advanced Recovery : The entire system rolls back to the most recent safe state (timestamp ), time-consuming constraint .

[0410] The control device of a teaching window described below and the control method of a teaching window described above can be referred to each other.

[0411] A control device for a teaching window, comprising:

[0412] A manifold modeling module, configured to execute the BERT-EDGE model and construct 8-16 dimensional compact manifolds;

[0413] Quantum optimization module, implementation with time-varying transverse fields and coupling strength Annealing algorithm;

[0414] Symplectic geometry processor, which implements ADMM iteration and curvature compensation;

[0415] Multimodal rendering engine that performs dimensionality reduction rendering and color correction simultaneously;

[0416] Closed-loop verification unit, real-time calculation of N400 amplitude and mutual information entropy ;

[0417] Exception handling system, based on exception level Trigger parameter rollback or manifold reset.

[0418] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, so they will not be repeated here.

[0419] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling a teaching window, characterized in that: The following steps are involved: Through multimodal sensors, eye tracking data, EEG signals and teaching semantic flow intensity are collected synchronously in real time to form a multimodal perception data input source; Inputting the multimodal perception data into a quantum annealing optimization module, constructing a Hamiltonian model based on semantic coupling strength and physical display constraints, and generating a set of window layout candidate parameters in parallel; Performing symplectic geometric manifold correction on the candidate parameter set, calculating the physical adaptability boundary of the compensation display device through differential geometric connection coefficients, and screening out a feasible solution space that satisfies the curvature constraint; Synchronize the layout parameters adapted by physical constraints to the multi-terminal rendering engine, driving the display device to perform cross-platform color calibration and tactile feedback encoding collaborative presentation; During the rendering execution phase, the dynamic changes of neurocognitive indicators are continuously monitored, and the transverse magnetic field decay rate and semantic coupling weight during the quantum annealing process are adjusted in real time through a feedback loop; When the quantum annealing success rate or color matching error is monitored to exceed the preset threshold, the multi-level exception handling mechanism is triggered, and parameter rollback, rendering degradation and device resynchronization operations are performed in sequence to ensure the continuity of teaching interaction.

2. A teaching window control method according to claim 1, characterized in that: The semantic flow strength is calculated by the improved BERT-EDGE model, and its expression is: ； in: is a trainable parameter matrix optimized by the back-propagation algorithm; Indicates the current window The set of semantically related adjacent windows is dynamically determined by the graph attention network; It is a Sigmoid activation function, and the output value range is constrained to the interval [0,1]; Indicates that it belongs to the window The semantic adjacent window set The index variable in ; Represents adjacent window The semantic feature vector of .

3. The method for controlling a teaching window according to claim 2, characterized in that: The Hamiltonian constructed by the quantum annealing algorithm is: ； in, 5GHz is the transverse magnetic field strength, Piecewise linear change, is the coupling strength, given by the manifold curvature tensor calculate, is the local potential gradient; is the total number of teaching windows, each window corresponds to 4 qubit parameters; and is the quantum bit number index, where every 4 consecutive numbers correspond to the geometric parameters of a window; For the The Pauli-X spin operator of the qubit acts on the x-axis direction of the qubit; For the The Pauli-Z spin operator of the quantum bit acts in the z-axis direction of the quantum bit; For the The Pauli-Z spin operator of qubits, and The corresponding coupled quantum bits.

4. The method for controlling a teaching window according to claim 1, characterized in that: The symplectic geometric manifold correction adopts ADMM iteration: ； The augmented Lagrangian function is defined as: ； Where: is the semantic weight coefficient, is the semantic intensity gradient; , To display the area; To correspond window ; is the penalty factor; Indicates the first The updated window layout parameter vector; Represents a variable Perform minimization optimization; The total area of ​​all windows in the current layout.

5. The method for controlling a teaching window according to claim 1, characterized in that: The neurocognitive indicators include N400 amplitude: ； in: Integration Window , the initial moment Dynamic adjustment of ±50ms according to the teaching stage; After preprocessing Channel EEG signals; is the mean value of all channels; is a time variable, indicating the time point of EEG signal sampling; The EEG channel index ranges from 1 to 256.

6. The method for controlling a teaching window according to claim 3, characterized in that: The coupling strength is calculated as: ； in: For window and Wasserstein distance between them; is the semantic attenuation factor, which is determined by maximum likelihood estimation optimization; is the curvature tensor; The semantic feature manifold is The differential form in the dimensional coordinate direction, the differential component of the compact manifold coordinates generated by the improved BERT-EDGE model; The semantic feature manifold is Differential form in the dimensional coordinate direction, technical definition and Consistent and satisfying ; is the trace operation of the matrix, acting on the curvature tensor Exterior product with differential forms The result of contraction; is the natural exponential function.

7. The method for controlling a teaching window according to claim 1, characterized in that: The multi-terminal rendering execution: ； in: : Luminance component of the source device in the CIELab color space; : Luminance component of the target device in the CIELab color space; : the green-red axis chromaticity component of the source device in the CIELab color space; : The green-red axis chromaticity component of the target device in the CIELab color space; : the blue-yellow axis chromaticity component of the source device in the CIELab color space; : The chromaticity component of the blue-yellow axis of the target device in the CIELab color space.

8. The method for controlling a teaching window according to claim 1, characterized in that: The multi-level exception handling includes: ； in: : Weight vector, corresponding to the success rate of quantum annealing , manifold curvature anomaly , rendering delay , N400 amplitude deviation , tactile feedback false trigger rate , the sum of weights is 1; is the membership function; is the standard deviation of the abnormal indicator sequence within the sliding window; It is the abnormal indicator sequence within the last 5-second time window; The current system timestamp.

9. The method for controlling a teaching window according to claim 1, characterized in that: The method also includes tactile feedback, the intensity of which is calculated as: ； in: is the rate of change of the output semantic strength.

10. A device based on the method according to claim 1, characterized in that: include: A manifold modeling module, configured to execute the BERT-EDGE model and construct 8-16 dimensional compact manifolds; Quantum optimization module, implementation with time-varying transverse fields and coupling strength Annealing algorithm; Symplectic geometry processor, which implements ADMM iteration and curvature compensation; Multimodal rendering engine that performs dimensionality reduction rendering and color correction simultaneously; Closed-loop verification unit, real-time calculation of N400 amplitude and mutual information entropy ; Exception handling system, based on exception level Trigger parameter rollback or manifold reset.

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