Multi-modal fusion AI digital human live broadcast generation system

Abstract

The invention relates to the technical field of power system supply and demand interaction and artificial intelligence, and discloses a multi-modal fusion AI digital human live broadcast generation system, which comprises a multi-modal data acquisition module, a cross-modal tensor fusion module, a dynamic resource optimization module, a federal reinforcement learning module, a containerization deployment module and a deviation thermal verification module. The method comprises the following steps: acquiring various parameters of a digital human in real time, fusing multi-modal data by adopting non-negative tensor decomposition and an attention weighting mechanism, and generating a semantic vector conforming to biomechanical constraints; rendering parameters are dynamically optimized in combination with a privacy protection strategy of federal reinforcement learning, and self-adaptive resource scheduling under the low-bandwidth condition is achieved; the dynamic adaptation platform interface is deployed in a containerized mode, and the output deviation is corrected in real time based on thermodynamic diagram analysis, so that the problems of action distortion, physical constraint failure and privacy disclosure in traditional digital human live broadcast are solved, and live broadcast interaction vividness, real-time performance and safety are improved.

Description

Technical Field

[0001] The present invention relates to the fields of power system supply and demand interaction and artificial intelligence technology, and specifically to a multimodal fusion AI digital human live broadcast generation system. Background Art

[0002] Current digital human live streaming systems generally adopt a modeling method based on a single data source. Most solutions rely solely on cameras to capture facial expressions or voice input to drive animation, such as generating lip animation through facial key point tracking. This single-modality acquisition method causes the digital human's movements to be disconnected from environmental parameters. When the lighting suddenly changes or the network jitters, the system cannot adaptively adjust the rendering strategy, often resulting in problems such as stiff expressions or distorted movements.

[0003] In terms of cross-modal data processing, existing technologies mostly use simple feature splicing, such as directly inputting speech features and joint angle data into the neural network in series. However, this linear combination is difficult to capture the implicit correlation between the time and space dimensions, especially in multi-user concurrent scenarios. The time alignment errors of different modal data will accumulate and amplify, eventually leading to logical contradictions in the semantic vectors.

[0004] What's more serious is that traditional tensor decomposition methods do not take biomechanical constraints into consideration. Some schemes directly project motion capture data into low-dimensional space, which can easily cause physical anomalies such as joint over-limit or excessive material deformation. There was a case in which a digital human in a live broadcast caused strong discomfort to the audience because the elbow rotation angle exceeded the limit of the human body. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a multimodal fusion AI digital human live broadcast generation system, which aims to solve the problems of unnatural performance, insufficient data integration, physical constraint failure, unstable and unreasonable motion models, and user discomfort caused by physical anomalies in digital human live broadcast.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multimodal fusion AI digital human live broadcast generation system, comprising:

[0007] Multimodal data acquisition module, used to collect geometric data, physical parameters, user interaction data and environmental parameters of the digital human model in real time;

[0008] A cross-modal tensor fusion module generates a unified semantic vector based on the output of the data acquisition module through physical constraint embedding and attention weighting;

[0009] A dynamic resource optimization module generates rendering parameter optimization instructions based on the semantic vector and real-time environment parameters;

[0010] The federated reinforcement learning module synchronously processes user interaction data, updates the local policy model, and feeds personalized parameters back to the resource optimization module;

[0011] The containerized deployment module dynamically adapts the platform interface and loads the rendering engine in response to the instructions of the dynamic resource optimization module and the parameters of the federated reinforcement learning module;

[0012] The deviation thermal verification module monitors the output results of the containerized deployment module and triggers the parameter update of the tensor fusion module when output deviation is detected.

[0013] Preferably, the multimodal data includes:

[0014] Geometric data: 3D key point coordinates, joint angles;

[0015] Physical parameters: surface stiffness matrix, joint motion range matrix;

[0016] User interaction data: speech MFCC features, text BERT vectors;

[0017] Environmental parameters: GPU computing power, network bandwidth, and light intensity.

[0018] Preferably, the cross-modal tensor fusion module generates a semantic vector by the following steps:

[0019] Construct the fourth-order space-time tensor as follows:

[0020]

[0021] in, is a multimodal data tensor; T is the number of time frames; V is the speech feature dimension; A is the action parameter dimension; G is the geometric feature dimension;

[0022] The non-negative tensor decomposition formula is as follows:

[0023]

[0024] Among them, ||X|| is the norm of the multimodal data tensor; R is the decomposition rank of the tensor; a r is the rth time factor vector; b r is the rth speech factor vector; c r is the rth action factor vector; d r is the rth geometric factor vector; λ1 is the regularization coefficient; λ2 is the weight parameter; T is the number of time frames; Δt k is the time synchronization error of the kth frame; R k is the correlation coefficient between emotion and expression of the kth frame; M joint is the joint motion range matrix;

[0025] The cross-modal attention weighted formula is as follows:

[0026]

[0027] Among them, S is the unified semantic vector; a i is the hidden feature of the i-th time factor; b j is the jth speech factor hidden feature; t i is the temporal position encoding vector of the i-th frame; v j is the jth visual feature vector; W q is the query weight matrix; W k is the key weight matrix; d is the attention dimension.

[0028] Preferably, the objective function of the dynamic resource optimization module is:

[0029]

[0030] Among them, u is the rendering parameter vector, α represents the rendering quality error weight; β represents the audio clarity weight; γ represents the transmission delay weight; y render (k) represents the rendering quality evaluation vector of the kth frame; y ref represents the reference mass vector; y audio (k) represents the audio clarity index of the kth frame; y delay (k) is the end-to-end delay of the k-th frame;

[0031] The constraint formula is as follows:

[0032] C lip (k)≤20ms,R emotion (k)≥0.7;

[0033] Among them, C lip (k) is the lip synchronization delay of the kth frame; R emotion (k) is the emotional expression correlation index of the kth frame; 20ms is the maximum allowed value of lip synchronization delay; 0.7 is the minimum correlation threshold of emotional expression.

[0034] Preferably, the dynamic resource optimization module supports the following rendering mode switching:

[0035] Low load mode: disable ray tracing and enable 8-bit color compression;

[0036] High Precision Mode: Enables 16-bit floating point precision and Vulkan ray tracing.

[0037] Preferably, the protection mechanism of the federated reinforcement learning module includes:

[0038] User interaction data is stored locally using AES-256 encryption and is only decrypted during training.

[0039] Dynamic noise is added to the global model parameters during federated aggregation, and the noise intensity is positively correlated with the difference in local model parameters;

[0040] The consistency of the parameters of the participants is verified through a secure multi-party computing protocol to prevent malicious data injection.

[0041] Preferably, the strategy update of the federated reinforcement learning module includes:

[0042] Local policy updates:

[0043]

[0044] in, is the i-th local strategy parameter; R(S,a) is the reward function of state S and action a; η is the learning rate; is the gradient of the parameter θ; E[·] is the expected operation; β i is the regularization coefficient of the i-th strategy; π global is the i-th local strategy distribution; is the global policy distribution; KL(·‖·) is the local policy distribution and the global policy distribution π global The divergence between

[0045] Adaptive distillation strength:

[0046]

[0047] Among them, β i is the regularization coefficient of the i-th strategy; N i is the number of samples for the i-th strategy; 0.1 is the coefficient for the low sample case; 1000 is the threshold for the number of samples; log(·) is the natural logarithm function.

[0048] Preferably, the rule for the containerized deployment module to dynamically load the rendering engine is:

[0049]

[0050] Among them, Plugin load is the selected rendering plug-in; C GPU The floating-point computing capability of the current GPU; OpenGL ES 3.0 is the rendering plug-in selected when the GPU computing power is less than 3 TFLOPS; Vulkan 1.2 is the rendering plug-in selected when the GPU computing power is greater than or equal to 3 TFLOPS.

[0051] Preferably, the deviation thermal verification module triggers optimization through the following formula:

[0052]

[0053] Among them, H(x,y) is the value of the heat map at position (x,y); S real (x, y, k) is the true semantic value of the kth frame; S pred (x, y, k) is the predicted semantic value of the kth frame; W phys (x,y) is the physical constraint weight, which represents the material elastic coefficient at the position (x,y);

[0054] Trigger conditions:

[0055] If max(H)>0.1, the tensor decomposition parameters are forced to be updated.

[0056] Preferably, the dynamic resource optimization module performs the following operations under low bandwidth conditions (B<15 Mbps):

[0057] Voice-first transmission: The audio stream sampling rate is increased to 48kHz, and the video stream resolution is reduced to 720p;

[0058] Semantic vector compression: sparsely encode the semantic vector and retain the feature values whose absolute value is greater than the threshold;

[0059] Dynamic error correction switching: When the network packet loss rate exceeds 5%, forward error correction coding is enabled.

[0060] The present invention provides a multimodal fusion AI digital human live broadcast generation system. It has the following beneficial effects:

[0061] 1. This invention introduces a cross-modal tensor fusion module and uses non-negative tensor decomposition and attention weighting mechanisms to achieve deep fusion of multimodal features. This makes the generated unified semantic vector more consistent in semantic expression. Compared with the simple feature superposition of traditional technologies, this method helps to solve the problems of information redundancy and lack of accuracy, ensuring that the performance of digital humans in live broadcasts is more vivid and reasonable.

[0062] 2. The present invention uses a dynamic resource optimization module to intelligently adjust rendering resources according to real-time environmental parameters, supports switching between multiple rendering modes, optimizes the system's ability to allocate computing resources, improves overall performance, and ensures a high-quality live broadcast experience under low bandwidth and different computing power conditions.

[0063] 3. With the help of the federated reinforcement learning module, the present invention can continuously optimize strategies while protecting user privacy, thereby improving the ability of personalized interaction. Compared with traditional centralized learning methods, it can effectively prevent data leakage and malicious attacks, solve the problem of insufficient security, and enhance users' trust and willingness to use.

[0064] 4. The present invention adopts multimodal data acquisition technology to achieve comprehensive perception of the digital human model by acquiring geometric, physical, user interaction and environmental parameters in real time. In this way, the adaptability of the system in dynamic environments is greatly improved. Compared with the single data source acquisition method in the existing technology, this multi-dimensional integration further enhances the accuracy of real-time interaction and avoids the unsmooth interaction caused by insufficient data. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the system architecture of the present invention;

[0066] Figure 2 Schematic diagram of a multimodal data acquisition module of the present invention;

[0067] Figure 3 Schematic diagram of the cross-modal tensor fusion module of the present invention;

[0068] Figure 4 This is a schematic diagram of a dynamic resource optimization module of the present invention;

[0069] Figure 5 Schematic diagram of the federated reinforcement learning module of the present invention;

[0070] Figure 6 This is a schematic diagram of the containerized deployment module of the present invention;

[0071] Figure 7 Schematic diagram of the deviation thermal verification module of the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] Please see the attached Figure 1 -Attached Figure 7 An embodiment of the present invention provides a multimodal fusion AI digital human live broadcast generation system, including a multimodal data acquisition module, a cross-modal tensor fusion module, a dynamic resource optimization module, a federated reinforcement learning module, a containerized deployment module, and a deviation thermal verification module.

[0074] The following is a detailed description of each module in the system of the present invention, which comprehensively explains the specific implementation principles, technical details and processes of each module.

[0075] Multimodal data acquisition module

[0076] The multimodal data acquisition module of the present invention includes a geometric data acquisition unit, a physical parameter acquisition unit, a user interaction data acquisition unit, and an environmental parameter acquisition unit, and realizes synchronous data acquisition through heterogeneous sensor fusion. The specific implementation method is as follows:

[0077] The geometric data acquisition unit consists of an RGB-D camera and an inertial measurement unit. The coordinates of key points are extracted through a 3D reconstruction algorithm. The 3D reconstruction formula is:

[0078] P 3D =K -1 (d·(u,v,1) T );

[0079] Among them, P 3D is the three-dimensional coordinate matrix of the key points; K is the camera intrinsic parameter; d is the depth sensor measurement value; u, v are image coordinates; T is the transpose symbol, indicating the transpose operation of the vector;

[0080] The surface deformation data is calculated using the structured light phase unwrapping algorithm:

[0081]

[0082] Among them, φ unwrap is the phase value after unpacking; φ wrapped is the phase value of the package; 2π is the radian value of a complete cycle; φ coarse is the roughly estimated phase value; round is the rounding function;

[0083] The physical parameter acquisition unit includes a strain gauge array and a six-axis force sensor; the surface stiffness matrix is calculated using the following formula:

[0084]

[0085] Where K is the stiffness matrix; F is the applied pressure; Δx is the deformation; A eff is the effective action area;

[0086] The joint torque model is:

[0087] τ=J T ·F sensor ;

[0088] Where τ is the torque vector; J is the Jacobian matrix; F sensoris the original reading of the sensor; J T is the transpose of the Jacobian matrix;

[0089] The user interaction data acquisition unit performs speech and text feature extraction, and the features are calculated by Mel filter banks:

[0090]

[0091] Among them, E m is the energy of the mth filter; X(k) is the spectrum; H m (k) is a triangle filter;

[0092] Text encoding uses a multi-head attention mechanism:

[0093]

[0094] Among them, Attention(Q,K,V) is the output of the attention mechanism; Q is the query matrix; K is the key matrix; V is the value matrix; Q T is the transpose of the query matrix; QK T is the dot product of the query and the key; d k is the dimension of the key, used for scaling; softmax is the normalization function to ensure that the sum of the attention weights is 1;

[0095] The environmental parameter acquisition unit obtains the system status in real time. The computing power calculation formula is:

[0096] C GPU =N SM ·F clock ·N cores 2;

[0097] Among them, C GPU is the computing power of the GPU; N SM is the number of streaming multiprocessors; F clock is the clock frequency; N cores The number of cores for each streaming multiprocessor; 2 is the parallelism of operations for each core;

[0098] Network bandwidth is calculated using a sliding window:

[0099]

[0100] Where B is the total value or total amount; W is the range of the summation, that is, the number of items; s i is the value of item i; Δt is the time interval or unit time;

[0101] The data alignment and verification unit adopts IEEE1588 protocol synchronization, and the interpolation formula is:

[0102]

[0103] Among them, x t is the interpolated coordinate; is the coordinate at time t1; is the coordinate at time t2; t is the interpolation time; t1 is the known time point 1; t2 is the known time point 2;

[0104] The physical constraint verification formula is:

[0105] ||q t -q t-1 ||≤v max Δt;

[0106] Among them, q t is the state variable at time t; q t-1 is the state variable at time t; v max is the maximum speed; Δt is the time interval;

[0107] The bandwidth anomaly detection formula is:

[0108]

[0109] in, is the bandwidth variance; W is the window size; B i is the i-th bandwidth value; μ B is the bandwidth mean;

[0110] The output parameters of each unit strictly match the formula definition, without the introduction of additional parameters, ensuring data acquisition accuracy and system compatibility;

[0111] Cross-modal tensor fusion module

[0112] The cross-modal tensor fusion module of the present invention includes a spatiotemporal tensor construction unit, a non-negative tensor decomposition unit, a cross-modal attention weighting unit, a physical constraint embedding unit, and a feature dimension reduction unit. The above embodiment generates a unified semantic representation through joint optimization of multimodal features. The specific implementation is as follows:

[0113] The space-time tensor construction unit integrates multi-source input data streams to construct a fourth-order space-time tensor:

[0114]

[0115] in, is a multimodal data tensor; T is the number of time frames; V is the speech feature dimension; A is the action parameter dimension; G is the geometric feature dimension; a time window sliding mechanism is used to process non-uniform length data, with a window step size of 5 frames and a fill threshold of 10%;

[0116] The non-negative tensor decomposition unit performs multimodal feature decoupling, and the decomposition formula is:

[0117]

[0118] in, is a multimodal data tensor; a r is the rth time factor vector; b r is the rth speech factor vector; c r rth action factor vector; d r is the rth geometric factor vector; R is the decomposition rank; Δt k is the time synchronization error of the kth frame; R k The correlation coefficient between emotion and expression of the kth frame;

[0119] The cross-modal attention weighted unit calculates the spatiotemporal correlation weights. The attention mechanism formula is:

[0120]

[0121] Among them, S is the unified semantic vector; a i is the hidden feature of the i-th time factor; b j is the jth speech factor hidden feature; t i is the temporal position encoding vector of the i-th frame; v j is the jth visual feature vector; W q is the query weight matrix; W k is the key weight matrix; d is the attention dimension;

[0122] Position encoding is generated using chord functions:

[0123]

[0124] in, The temporal position code for the i-th even index position; is the temporal position code of the i-th odd index position; i is the position index of the current code; k is the number of coding layers; 10000 is the scaling factor of the position code; 2k is the index of the current layer;

[0125] Physical constraints are embedded into the unit to inject biomechanical constraints, and the correction formula is:

[0126]

[0127] in, is the output state after constraint adjustment; S is the original state matrix; M constrain is the motion range constraint mask matrix; b offset is the deformation compensation vector; its calculation formula is:

[0128] b offset =λ·K·Δx;

[0129] Among them, b offset is the deformation compensation vector; λ is the stiffness compensation coefficient; K is the surface stiffness matrix; Δx is the vertex deformation;

[0130] The feature dimensionality reduction unit compresses the semantic vector through a learnable matrix as follows:

[0131]

[0132] Among them, z is the final output result; W z is the output layer weight matrix; is the output state after constraint adjustment; b z is the output layer bias term;

[0133] The above implementation method realizes physical connection through a high-speed bus, which can achieve low-latency transmission and real-time processing of multimodal features, and the output semantic vector maintains interface compatibility with the downstream rendering engine, meeting the frame rate requirements of real-time rendering. After optimization, the error of the fusion process is controlled within the set threshold, the feature distribution meets the expected design requirements, and the overall operation of the system meets the constraints of lip synchronization delay and the correlation between emotional expression, ensuring the real-time interaction and expressiveness of the digital human live broadcast.

[0134] Dynamic resource optimization module

[0135] The dynamic resource optimization module of the present invention includes a resource optimization model construction unit, a constraint processing unit, a rendering mode switching unit, and a low-bandwidth adaptive unit. The above embodiment realizes dynamic allocation of rendering resources through real-time parameter adjustment. The specific implementation method is as follows:

[0136] The resource optimization model building unit establishes a multi-objective optimization function:

[0137]

[0138] Among them, u is the rendering parameter vector, α represents the rendering quality error weight; β represents the audio clarity weight; γ represents the transmission delay weight; y render (k) represents the rendering quality evaluation vector of the kth frame; y ref represents the reference mass vector; y audio (k) represents the audio clarity index of the kth frame; y delay (k) is the end-to-end delay of the k-th frame;

[0139] The constraint processing unit implements real-time constraint validation:

[0140] C lip (k)≤20ms,Remotion (k)≥0.7;

[0141] Among them, C lip (k) is the lip synchronization delay of the kth frame; R emotion (k) is the emotional expression correlation index of the kth frame; 20ms is the maximum allowed value of lip synchronization delay; 0.7 is the minimum correlation threshold of emotional expression;

[0142] The rendering mode switching unit dynamically selects the engine based on the GPU computing power:

[0143]

[0144] Among them, E render For the rendering interface type; C GPU is the computing power of the GPU; 3TFLOPS is the upper limit of the number of computing operations per second; OpenGLES3.0 is to meet the C GPU <3TFLOPS rendering interface; Vulkan1.2 is to meet C GPU Rendering interface at ≥3TFLOPS;

[0145] Low load mode disables ray tracing and enables 8-bit color compression, with the chroma sampling rate set to 4:2:0; high precision mode enables 16-bit floating point precision and ray tracing, with the subdivision surface level set to level 3;

[0146] The low-bandwidth adaptive unit executes the network optimization strategy. The bandwidth threshold trigger conditions are:

[0147]

[0148] Where B is the bandwidth; 15Mbps is the upper limit of bandwidth; f audio is the audio sampling rate; 48kHz is the upper limit of the audio sampling rate; 720p is the video resolution standard; z is the signal sequence; ||z||0 is the number of non-zero elements of the signal; ||z||2 is the second norm of the signal; τ is the constraint coefficient; 0.2 is the specific value of the constraint coefficient; FEC is the forward error correction mechanism; 5% is the packet loss rate threshold; when the packet loss rate is 5%, the FEC mechanism is triggered;

[0149] This implementation achieves efficient connectivity with upstream modules via a real-time data bus, ensuring the immediate generation and execution of optimization instructions. The system maintains high rendering quality and a smooth interactive experience even in low-bandwidth environments. Rendering mode switching is seamless, and various resource scheduling strategies effectively balance computing load and user experience. The constraints regarding lip synchronization accuracy and emotional expression relevance specified in the claims are fully met, validating the effectiveness of the dynamic resource optimization mechanism.

[0150] Federated Reinforcement Learning Module

[0151] The federated reinforcement learning module of the present invention includes a local model training unit, a parameter encryption unit, a federated aggregation unit, and a security verification unit. The above embodiment implements policy optimization under privacy protection through a distributed learning framework. The specific implementation is as follows:

[0152] The local model training unit performs personalized policy updates, and the objective function is:

[0153]

[0154] in, For the parameter θ i The optimal solution of E (s,a) is the expected return under state s and action a; D i is the experience replay pool; t is the time step; γ is the discount factor; r i (s t ,a t ) is in state s t and action a t The immediate reward under ;λ is the regularization parameter; is the parameter θ i With the target parameter θ G The squared difference of the two norms between

[0155] The parameter encryption unit uses a homomorphic encryption algorithm to protect gradient transmission. The encryption process is as follows:

[0156]

[0157] in, is the parameter θ i The gradient encryption value of g is the basic element in the encryption process; is the parameter θ i The gradient of r N is the random number r raised to the power of N; N is the modulus in the encryption algorithm; mod N 2 Indicates that the calculation result is modulo N 2 ; The decryption algorithm satisfies:

[0158]

[0159] in, is the parameter θ i The gradient of ; L is the decoding function; For the parameter θ i The result of encrypting the gradient of ; λ is the exponent in the encryption process; N is the modulus in the encryption algorithm; mod N 2 Indicates that the calculation result is modulo N 2 ;

[0160] The federation aggregation unit performs security parameter aggregation, and the dynamic noise injection formula is:

[0161]

[0162] in, is the target parameter in the k+1th iteration; m is the number of samples involved in the calculation; is the i-th parameter in the k-th iteration; (0,σ 2 Δf 2 ) has a mean of 0 and a variance of σ 2 Δf 2 Normal distribution noise; σ is the standard deviation of the noise; Δf 2 is the step size or update amount;

[0163] The security verification unit detects malicious clients. Verification conditions include:

[0164]

[0165] Among them, ||θ i -μ θ ||2 is the parameter θ i and mean μ θ The two-norm distance between θ is the mean of the parameter; σ θ is the standard deviation of the parameter; 3σ θ is the threshold value for parameter outlier detection; KL(D i ||D G ) is the data distribution D i With the target data distribution D G The Kullback-Leibler divergence between them; 0.3 is the threshold for data distribution consistency;

[0166] The above implementation method realizes distributed collaborative training through encrypted communication and secure aggregation mechanism, completes strategy optimization under the premise of strictly protecting the privacy of user's original data, and the dynamic noise injection and parameter verification mechanism effectively defends against model theft and poisoning attacks. The efficiency of federated learning meets the update frequency requirement of 10 minutes / time.

[0167] Containerized deployment module

[0168] The containerized deployment module of the present invention includes an interface dynamic adaptation unit, a rendering plug-in loading unit, and a resource allocation strategy unit. The above embodiment realizes the flexible deployment of cross-platform rendering engines through real-time environment perception. The specific implementation is as follows:

[0169] The interface dynamic adaptation unit performs multi-platform protocol conversion:

[0170]

[0171] Among them, P adapter is the output probability distribution of the adapter; is the minimization operation for index k; C API It is the feature representation of the API; is the target feature representation in the k-th iteration; is the difference in the two norms between the API feature and the target feature; OneHot means converting the index into a one-hot encoding;

[0172] The rendering plug-in loading unit dynamically selects the engine based on the GPU computing power:

[0173]

[0174] Among them, E render For the selection of rendering engine; E low It is a low-performance rendering engine; OpenGLES3.0 is a rendering option for low-performance GPUs; C GPU is the computing power of GPU; 3TFLOPS is the threshold of computing power; E high It is a high-performance rendering engine; Vulkan1.2 is a rendering option for high-performance GPUs;

[0175] Low-computing mode enables 8-bit color compression and texture MIPMAP hierarchical loading, and the chroma sampling rate is set to 4:2:0; high-computing mode enables 16-bit floating-point precision and ray tracing acceleration, and the subdivision surface level is set to Level 3.

[0176] The resource allocation policy unit implements container resource isolation:

[0177]

[0178] Among them, R alloc is the minimum value of resource allocation; λ is the weight factor of resource allocation; C max is the maximum resource capacity; B avail is the available resource amount; α is the proportional factor of the available resource amount; β is the proportional factor of the target performance; F target is the target performance indicator;

[0179] The above implementation method realizes dynamic adaptation of the rendering engine through real-time environmental perception and policy decision-making, optimizes computing resource utilization while ensuring cross-platform compatibility, and the interface protocol conversion mechanism effectively eliminates rendering differences between different terminal devices. The resource isolation strategy ensures that critical tasks obtain priority computing permissions. The system can achieve the target rendering performance indicators in heterogeneous hardware environments.

[0180] Deviation thermal verification module

[0181] The deviation thermal verification module of the present invention includes a thermal map generation unit, a physical constraint verification unit, and a parameter update trigger unit. Each unit realizes real-time correction of system output through multi-dimensional deviation quantification and dynamic threshold determination. The specific implementation method is as follows:

[0182] The heatmap generation unit calculates the semantic deviation distribution:

[0183]

[0184] Where H(x,y,k) is the thermal value of the kth frame at the screen coordinate (x,y); is the true value of the i-th semantic feature in the k-th frame; is the predicted value of the i-th semantic feature in the k-th frame; N is the total dimension of the semantic feature; w(x i ,y i ) is the coordinate (x i ,y i ); the physical constraint verification unit detects biomechanical violations:

[0185]

[0186] Where V(k) is the constraint violation at the kth frame; M is the total number of joints; θ j (k) is the angle measurement of the j-th joint at the k-th frame; is the maximum allowed angle of the jth joint; is an indicator function, which takes the value of 1 when the angle measurement of the j-th joint exceeds the maximum allowed angle, and 0 otherwise;

[0187] Parameter updates trigger the unit to perform dynamic optimization decisions:

[0188]

[0189] Among them, Trigger(k) is the trigger state; H(x,y,k) is the thermal value of the kth frame at the screen coordinate (x,y); τ H is the thermal deviation threshold; V(k) is the constraint violation value of the kth frame; τ V is the physical constraint violation threshold;

[0190] When Trigger(k)=1, a parameter reset instruction is sent to the cross-modal tensor fusion module:

[0191]

[0192] Among them, θ new is the updated parameter; θ old is the current parameter; η is the learning rate; is the gradient symbol; ||H(k)|| F is the Frobenius norm of the heat map at the kth frame; λ is the constraint violation penalty coefficient; V(k) is the constraint violation amount at the kth frame;

[0193] The above implementation method achieves precise positioning and correction of output deviations through quantitative analysis and dynamic decision-making mechanisms. The thermal model integrates spatial weights and semantic differences to effectively distinguish between random noise and systematic errors. Physical constraint verification strictly follows biomechanical limitations to avoid distortion of digital human movements.

[0194] In general, the present invention uses a multimodal data acquisition module to capture the geometric motion trajectory, surface physical properties and user interaction data of the digital human model in real time, and constructs a multi-dimensional perception matrix aligned in time and space; uses a cross-modal tensor fusion module to perform non-negative tensor decomposition and attention weighting on speech features, joint motion parameters and physical constraints to generate a semantic driving vector that conforms to biomechanics; based on the multi-objective Pareto frontier model of the dynamic resource optimization module, a dynamic balance between rendering quality, transmission delay and GPU power consumption is achieved; through the distributed strategy distillation framework of the federated reinforcement learning module, the collaborative optimization of personalized parameters is completed in an encrypted environment; with the help of the environmental perception interface adaptation technology of the containerized deployment module, millisecond-level dynamic loading of the cross-platform rendering engine is achieved; finally, the deviation thermal verification module integrates thermal field analysis and physical rule detection to trigger the online reconstruction of the semantic model and the adjustment of the rendering strategy.

[0195] 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 multimodal fusion AI digital human live broadcast generation system, characterized by: include: Multimodal data acquisition module, used to collect geometric data, physical parameters, user interaction data and environmental parameters of the digital human model in real time; A cross-modal tensor fusion module generates a unified semantic vector based on the output of the data acquisition module through physical constraint embedding and attention weighting; A dynamic resource optimization module generates rendering parameter optimization instructions based on the semantic vector and real-time environment parameters; The federated reinforcement learning module synchronously processes user interaction data, updates the local policy model, and feeds personalized parameters back to the resource optimization module; The containerized deployment module dynamically adapts the platform interface and loads the rendering engine in response to the instructions of the dynamic resource optimization module and the parameters of the federated reinforcement learning module; The deviation thermal verification module monitors the output results of the containerized deployment module and triggers the parameter update of the tensor fusion module when output deviation is detected.

2. A multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The multimodal data includes: Geometric data: 3D key point coordinates, joint angles; Physical parameters: surface stiffness matrix, joint motion range matrix; User interaction data: speech MFCC features, text BERT vectors; Environmental parameters: GPU computing power, network bandwidth, and light intensity.

3. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The cross-modal tensor fusion module generates a semantic vector through the following steps: Construct the fourth-order space-time tensor as follows: in, is a multimodal data tensor; T is the number of time frames; V is the speech feature dimension; A is the action parameter dimension; G is the geometric feature dimension; The non-negative tensor decomposition formula is as follows: Among them, ||X|| is the norm of the multimodal data tensor; R is the decomposition rank of the tensor; a r is the rth time factor vector; b r is the rth speech factor vector; c r is the rth action factor vector; d r is the rth geometric factor vector; λ1 is the regularization coefficient; λ2 is the weight parameter; T is the number of time frames; Δt k is the time synchronization error of the kth frame; R k is the correlation coefficient between emotion and expression of the kth frame; M joint is the joint motion range matrix; The cross-modal attention weighted formula is as follows: Among them, S is the unified semantic vector; a i is the hidden feature of the i-th time factor; b j is the jth speech factor hidden feature; t i is the temporal position encoding vector of the i-th frame; v j is the jth visual feature vector; W q is the query weight matrix; W k is the key weight matrix; d is the attention dimension.

4. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The objective function of the dynamic resource optimization module is: Among them, u is the rendering parameter vector, α represents the rendering quality error weight; β represents the audio clarity weight; γ represents the transmission delay weight; y render (k) represents the rendering quality evaluation vector of the kth frame; y ref represents the reference mass vector; y audio (k) represents the audio clarity index of the kth frame; y delay (k) is the end-to-end delay of the k-th frame; The constraint formula is as follows: C lip (k)≤20ms,R emotion (k)≥0.7; Among them, C lip (k) is the lip synchronization delay of the kth frame; R emotion (k) is the emotional expression correlation index of the kth frame; 20ms is the maximum allowed value of lip synchronization delay; 0.7 is the minimum correlation threshold of emotional expression.

5. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The dynamic resource optimization module supports the following rendering mode switching: Low load mode: disable ray tracing and enable 8-bit color compression; High Precision Mode: Enables 16-bit floating point precision and Vulkan ray tracing.

6. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The protection mechanism of the federated reinforcement learning module includes: User interaction data is stored locally using AES-256 encryption and is only decrypted during training. Dynamic noise is added to the global model parameters during federated aggregation, and the noise intensity is positively correlated with the difference in local model parameters; The consistency of the parameters of the participants is verified through a secure multi-party computing protocol to prevent malicious data injection.

7. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The strategy update of the federated reinforcement learning module includes: Local policy updates: in, is the i-th local strategy parameter; R(S,a) is the reward function of state S and action a; η is the learning rate; is the gradient of the parameter θ; E[·] is the expected operation; β i is the regularization coefficient of the i-th strategy; π global is the i-th local strategy distribution; is the global policy distribution; KL(·‖·) is the local policy distribution and the global policy distribution π global The divergence between Adaptive distillation strength: Among them, β i is the regularization coefficient of the i-th strategy; N i is the number of samples for the i-th strategy; 0.1 is the coefficient for the low sample case; 1000 is the threshold for the number of samples; log(·) is the natural logarithm function.

8. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The rules for dynamically loading the rendering engine in the containerized deployment module are: Among them, Plugin load is the selected rendering plug-in; C GPU The floating-point computing capability of the current GPU; OpenGL ES 3.0 is the rendering plug-in selected when the GPU computing power is less than 3 TFLOPS; Vulkan 1.2 is the rendering plug-in selected when the GPU computing power is greater than or equal to 3 TFLOPS.

9. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The deviation thermal verification module triggers optimization through the following formula: Among them, H(x,y) is the value of the heat map at position (x,y); S real (x, y, k) is the true semantic value of the kth frame; S pred (x, y, k) is the predicted semantic value of the kth frame; W phys (x,y) is the physical constraint weight, which represents the material elastic coefficient at the position (x,y); Trigger conditions: If max(H)>0.1, the tensor decomposition parameters are forced to be updated.

10. The multimodal fusion AI digital human live broadcast generation system according to claim 1, characterized in that: The dynamic resource optimization module performs the following operations under low bandwidth conditions B<15Mbps: Voice-first transmission: The audio stream sampling rate is increased to 48kHz, and the video stream resolution is reduced to 720p; Semantic vector compression: sparsely encode the semantic vector and retain the feature values whose absolute value is greater than the threshold; Dynamic error correction switching: When the network packet loss rate exceeds 5%, forward error correction coding is enabled.

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