Intelligent multi-mode virtual digital human interaction system based on AI language large model, interaction method and application

Abstract

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual digital humans, and more specifically relates to an intelligent multimodal virtual digital human interaction system, interaction method and application based on an AI language large model. Background Art

[0002] In recent years, artificial intelligence technology has entered a golden period of rapid development, which has laid a solid foundation for the booming rise of digital human technology. As a highly potential application branch of artificial intelligence, digital humans have begun to frequently appear in many industry fields and successfully attracted the attention of all sectors. However, upon in-depth exploration, it will be found that traditional digital human technology has many obvious shortcomings.

[0003] First, from the dimension of interaction modality, the functions of traditional digital humans show obvious limitations, and most of them can only achieve single-modal interaction forms. For example, the early widely used customer service digital humans often can only rely on simple voice interaction modes and mechanically respond to common questions according to preset scripts, completely lacking the ability to deeply analyze and flexibly respond to users' complex intentions. Once users pose special questions or personalized needs beyond the preset scope, these digital humans will immediately fall into a deadlock and cannot give satisfactory feedback, thus greatly damaging the user experience in actual application scenarios and limiting their practical value.

[0004] Second, focusing on the multimodal interaction level, existing digital human systems are full of loopholes. It is difficult for different interaction modalities to cooperate tacitly, and the synergy effect is forced and unnatural. For example, when a digital human conducts a voice conversation, the facial expressions and body movements that should complement it are often out of sync with the content conveyed by the voice, unable to accurately convey the corresponding emotional connotations and semantic information, making it difficult for users to immerse themselves in a smooth and natural interaction experience. Moreover, there are also many problems in the integration link after the information of each modality is collected. A large amount of multimodal data with potential value has not been fully explored and effectively utilized, directly resulting in the lack of coherence and consistency in the interaction process, seriously hindering the efficient communication between users and digital humans.

[0005] Third, in the face of the current explosive growth of massive knowledge information, traditional digital human systems are even more unable to cope. In key links such as knowledge acquisition, update and practical application, their performance is only mediocre. Specifically, traditional digital human systems are difficult to quickly and accurately extract the required knowledge from large-scale data resources and skillfully and correctly integrate this knowledge into the interaction process. In this way, in the face of users' urgent needs for professional, real-time and rich knowledge, especially in fields such as education, medical care and professional consulting that have strict requirements for the depth and breadth of knowledge reserves, the inherent defects of traditional digital human systems are infinitely magnified and simply cannot meet the actual needs.

[0006] In summary, in view of the fact that traditional digital human technology is deeply mired in the dilemmas of single function, multi-modal fusion, and the shortcoming of knowledge service, it has seriously hindered its wide penetration and in-depth development in various industries. Summary of the Invention

[0007] In order to solve the deficiencies of the existing technology, the purpose of the present invention is to provide an intelligent multi-modal virtual digital human interaction system, interaction method and application based on an AI language large model.

[0008] The present invention provides the following technical solutions: An intelligent multi-modal virtual digital human interaction system based on an AI language large model mainly includes: a high-fidelity face generation module;

[0009] The high-fidelity face generation module with adaptive feature fusion and voice drive uses the AdaAN network to achieve precise expression adjustment based on the time series modeling (LSTM + Self-Attention) of voice features. Among them, the mathematical model of AdaAN is as follows:

[0010] Let the voice feature be S and the face feature be F, then the deformation repair function T is defined as follows:

[0011] F′ = T(F, S) = W·F + b, W ∈ R N×N , b ∈ R N

[0012] Among them, S represents the voice feature, F represents the face feature, T represents the deformation repair function, W represents the adaptive weight matrix dynamically generated by CNN + Transformer to ensure the adaptive deformation of the face area, b represents the bias term optimized by the generator-discriminator adversarial training, and N represents the dimension.

[0013] Latent space navigation requires passing through the transformation matrix A to map the voice feature to the expression latent space to generate face expression features:

[0014] Z 表情 = A·Z 语音 + B

[0015] Among them, A and B are dynamically generated by the AdaAN network to ensure the coherence of expression changes under different voice inputs; Z 语音 represents the voice feature, Z 表情 represents the face expression feature, A represents the transformation matrix dynamically generated by the AdaAN network, and B represents the bias term optimized by the adversarial training.

[0016] In this way, the AdaAN module deeply fuses speech features and facial expression features, combines speech cloning technology and a deep learning model constructed based on a convolutional neural network, a recurrent neural network, or their variants and trained with a large amount of sample data to achieve high-fidelity facial generation and lip-syncing; introduces a spatio-temporal Transformer architecture, adopts a hierarchical attention mechanism to strengthen the spatio-temporal alignment of different modal features, and uses a bidirectional Transformer to process speech input to efficiently capture long-range dependencies and global spatial information; in the present invention, an expression generation technology based on bioelectric signal mapping is also used to establish an accurate mapping relationship between bioelectric signals and facial muscle movements through EEG (electroencephalogram) and EMG (electromyogram), and convert them into expression parameters:

[0017] P 表情 = f(EEG, EMG) = W EEG ·EEG + W EMG ·EMG + b

[0018] where W EEG , W EMG are obtained through neural network training, W EEG represents the EEG feature weight matrix trained end-to-end, W EMG represents the EMG feature weight matrix trained end-to-end, and b represents the dynamically calibrated bias term; an attention alignment mechanism is introduced to strengthen facial expression based on speech prosody features;

[0019] In addition to the high-fidelity facial generation module, the interactive system in the present invention further includes: an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interactive scene adaptation module, and a feedback optimization module;

[0020] The intelligent interaction module based on speech and knowledge base relies on a large-scale language model and a knowledge base construction method based on the RAG architecture to provide speech interaction answers; adopts a knowledge reasoning engine based on causal inference to analyze causal relationships; adopts a multi-modal knowledge graph fusion technology to unify multi-modal knowledge; uses a large amount of general text data in the pre-training stage, optimizes parameters for specific application fields in the fine-tuning stage, and dynamically updates the personalized knowledge graph during interaction to make the answers more professional and accurate; the knowledge base automatically accesses authoritative online databases and records citation sources, making it more timely;

[0021] For the intelligent interaction module, it further includes the following:

[0022] 1. Knowledge Base Based on RAG Architecture: Elasticsearch is used for full-text retrieval of the literature, speeches, and work content of the target person, and the retrieval response time is <200ms; the causal inference engine (such as the Structural Causal Model SCM) is combined to analyze the causal relationships in the multi-modal knowledge graph; the personalized knowledge graph is dynamically updated with an update frequency of hourly incremental indexing;

[0023] 2. Data Optimization Strategy: When screening data from the online database, the TF-IDF weighted algorithm is used to mark the priorities; the citation sources are classified (authoritative literature, social media, user-generated content, etc.), and the credibility weights are 0.5, 0.3, and 0.2 respectively;

[0024] 3. Model Fine-tuning: A large amount of general text (1TB corpus) is used in the pre-training stage, and domain adapters (Adapters) are used to inject parameters in the fine-tuning stage, and the fine-tuning takes less than 30 minutes.

[0025] Single-stage training optimization and efficient generation module, using single-stage training to optimize the digital human generation process, combining the AdaAN module to achieve adaptive alignment of feature regions, balancing generation quality and training efficiency; designing a loss function that comprehensively considers multiple metrics, using an adaptive learning rate strategy and a multi-objective optimization algorithm; adopting a meta-learning-based dynamic training strategy to automatically adjust training parameters; introducing adversarial distillation technology to transfer the knowledge of the teacher model; adopting a distributed training architecture and an efficient communication protocol to accelerate training;

[0026] In the specific implementation process of the present invention, single-stage training optimization combines the AdaAN module to achieve adaptive alignment of speech-expression features, and the alignment error loss function is:

[0027]

[0028] where, ||F′ - Z 表情 ||2 is the lip synchronization error term (calibrated by the OptiTrack motion capture system, P95 error <3ms), KL(P 表情 ||P gt ) is the KL divergence term in KL divergence, P gt is the data annotated by FACS experts, and λ = 0.7 is the weight coefficient;

[0029] In the specific implementation process of the present invention, based on the meta-learning-based dynamic training strategy, the learning rate is dynamically optimized through the MAML algorithm, and the formula is:

[0030] where, θ are the trainable parameters of the model, including all the weights and biases of the AdaAN network, α is the meta-learning rate (Meta-Learning Rate), which is dynamically adjusted through the MAML framework, Calculate the gradient of the comprehensive loss function, and freeze the parameters of non-critical layers during calculation (freezing ratio ≥ 30%);

[0031] In the specific implementation process of the present invention, through adversarial distillation, the teacher model (ResNet-101) transfers lip-sync knowledge to the student model (MobileNet-V3), and the distillation loss is

[0032] T tea Teacher model (ResNet-101 architecture): Input: 256×256 RGB facial image (frame rate 30fps); Output: Lip movement parameters (20-dimensional AU encoding); Pre-trained data: VoxCeleb2 dataset (1.2M video clips);

[0033] T stu (x): Student model (MobileNet-V3 architecture): Lightweight design: The number of channels is compressed to 1 / 4 of the teacher model (Base channel = 64); Computational complexity: FLOPS = 0.6G (compared with 7.8G of the teacher model);

[0034] MSE: Mean squared error loss function;

[0035] And / or,

[0036] Adopt lightweight technologies such as model compression and progressive channel pruning to reduce the number of model parameters and computational complexity.

[0037] In a specific implementation process, the present invention also adopts distributed acceleration, uses the NCCL communication protocol, and performs parallel training with 8 A100 GPUs, increasing the throughput by 12 times.

[0038] Efficient integration module for real-time streaming media technology, realizing synchronous audio and video transmission using FFMPEG and RTSP protocols; FFMPEG adopts hardware acceleration technology, and RTSP adopts dynamic bitrate adjustment strategy; Use 5G slicing technology to allocate exclusive network slices; Adopt audio and video traceability technology based on digital watermarking; Have an adaptive redundant transmission strategy to cope with network problems; Ensure smooth audio and video transmission;

[0039] Multi-modal data acquisition module, which collects user facial expressions, body movements, gestures, voice, and physiological state information to accurately perceive user intentions; The high-definition camera has multiple functions, and the high-sensitivity microphone array adopts multi-channel sound pickup and beamforming algorithms; External sensors are connected through standardized interfaces and support multiple types; Explore user intention acquisition technology based on brain-computer interface; Develop a multi-modal data fusion algorithm based on self-supervised learning; External sensors also include environmental sensors to collect interaction environment information;

[0040] The core processing module of the AI ​​large model processes multimodal data based on a super-large-scale neural network model; uses a variant of the attention mechanism to fuse data, and encodes and integrates data based on the content characteristics of the data and the location information of the data in time and space; combines multiple models including the Transformer model and the LSTM model for semantic analysis, sentiment recognition, and knowledge reasoning; builds an intelligent decision-making engine based on a quantum neural network; uses interpretable reasoning technology based on knowledge graphs to associate the decision-making process with the knowledge nodes of the knowledge graph;

[0041] The digital human image generation and driving module customizes the digital human image according to the application scenario and supports 2D and 3D presentation forms; the 2D image is based on vector graphics technology, and the 3D image adopts an advanced skeletal animation system; the image details are adjustable, and the driving output is strictly in accordance with the instructions; the hybrid model based on the generative adversarial network and the variational autoencoder is used to creatively generate the image; the action driving technology based on physical simulation is introduced; the physical rendering technology is used to enhance the visual texture; and a highly realistic digital human is generated;

[0042] The interactive scene adaptation module has multiple built-in typical scene templates, which can adjust the interaction according to feedback and environmental changes, and dynamically adjust the digital human behavior strategy; develop dynamic scene fusion technology based on reinforcement learning; use semantic communication technology to encode and decode voice and semantic information; digital humans have different performances in different scenes and can quickly switch behavior strategies; generate personalized recommendation words based on user data in marketing scenarios;

[0043] The feedback optimization module collects user feedback in real time and optimizes it using a reinforcement learning algorithm based on policy gradients; builds a reward function that comprehensively considers multiple indicators and regularly evaluates performance; uses a swarm intelligence-based optimization algorithm for collaborative optimization; develops active optimization technology based on user behavior prediction, and uses sentiment analysis technology to explore potential emotional tendencies, which can also improve user satisfaction.

[0044] Furthermore, in the adaptive feature fusion and voice-driven high-realistic face generation module, the deep learning model is trained with a large amount of sample data containing the correspondence between different voices and facial expressions, so that the generated facial expressions are highly matched with the voice in terms of lip movements and eye expressions;

[0045] In addition, the AdaAN network is provided with an anti-interference mechanism, which is expressed as follows:

[0046]

[0047] Where A is the state transfer matrix, based on the second-order muscle model, H is the observation matrix, which maps the electromyographic signal to the expression parameter space, K kis the Kalman gain matrix. Using the Sage-Husa adaptive algorithm, the process noise Q = 0.01I and the observation noise R = 0.1I are set, and z k represents the observed value of the EMG signal at the k-th moment, represents the estimated value of the facial expression parameters at the k-th moment (dimension is 32); and alignment is performed through spatio-temporal attention: lip error < 5ms, expression naturalness score > = 4.8 / 5.0.

[0048] Furthermore, in the intelligent interaction module based on speech and knowledge base, the data and optimization methods used in the pre-training and fine-tuning stages of the large language model enable it to have accuracy and professionalism in answering questions in specific fields.

[0049] Furthermore, the single-stage training optimization and efficient generation module comprehensively considers various indicators when designing the loss function and the optimization algorithm adopted, achieving a balance between generation quality and training efficiency.

[0050] Furthermore, in the efficient integration module of the real-time streaming media technology, FFMPEG uses hardware acceleration technology and RTSP uses a dynamic bitrate adjustment strategy to ensure smooth transmission and synchronization of audio and video under different network conditions.

[0051] Furthermore, the technical characteristics of the high-definition camera and high-sensitivity microphone array in the multi-modal data acquisition module enable it to accurately collect user information in complex environments.

[0052] Furthermore, the specific technologies and models adopted by the AI large model core processing module in the processes of fusion processing, semantic analysis, emotion recognition, and knowledge reasoning achieve effective processing of multi-modal data.

[0053] Furthermore, the digital human image generation and driving module supports 2D and 3D image generation technologies and the ways of image detail adjustment and driving output to meet different application requirements.

[0054] Furthermore, the interaction scenario adaptation module supports the switching methods of the performance and behavior strategies of the digital human in different scenarios, as well as the personalized recommendation function in the marketing scenario.

[0055] Furthermore, the methods adopted by the reinforcement learning algorithm, the indicators considered in constructing the reward function, and the ways of regularly evaluating the performance in the feedback optimization module improve the overall performance of the system.

[0056] Furthermore, in the adaptive feature fusion and voice-driven high-fidelity face generation module, the expression generation technology based on bioelectric signal mapping can monitor the dynamic changes of bioelectric signals in real time and update the expression parameters in real time according to preset thresholds and mapping rules to achieve a more natural and smooth expression transition. At the same time, this module has an adaptive anti-interference mechanism, which can still accurately extract effective signals for expression generation when the bioelectric signals are slightly interfered by external electromagnetic fields.

[0057] Furthermore, in the intelligent interaction module based on voice and knowledge base, during the process of automatically accessing the authoritative online database in the knowledge base, the TF-IDF weighted algorithm is used to mark the data priority, combined with the BERT semantic similarity calculation to remove duplicate content, screening and data cleaning techniques to screen and preprocess the data in the online database, remove duplicate, incorrect or irrelevant data, and classify and mark the citation sources at the same time for subsequent data traceability and credibility evaluation. And it can automatically adjust the priority and scope of data obtained from the online database according to the user's historical interaction records and preferences; specifically, based on the graph convolutional network, the credibility of authoritative literature, social media, and user-generated content is graded; the weights of the authoritative literature, the social media, and the user-generated content are 0.5, 0.3, and 0.2 respectively.

[0058] Furthermore, in the single-stage training optimization and efficient generation module, the dynamic training strategy based on meta-learning can automatically select appropriate meta-learning algorithms and parameters according to different training tasks and dataset characteristics, and monitor the convergence and performance indicators of the training in real time during the training process. When overfitting or underfitting phenomena are found in the training, it can automatically adjust the training parameters and optimization algorithms to ensure the stability and effectiveness of the training. At the same time, this module also has model compression and lightweight technologies to reduce the number of model parameters and computational complexity without affecting the generation quality;

[0059] Specifically, the model-agnostic meta-learning (MAML) framework is adopted to quickly adapt to new tasks through second-order gradient optimization, and train on multiple task sets to optimize the initial parameter θ so that it can adapt to new tasks through a small number of gradient updates: Through overfitting detection, calculate the validation set loss in real time and the training set loss ratio. If the ratio is greater than 1.5, it is determined that overfitting occurs, and the learning rate decay or the Dropout rate is increased. If the weak training loss rate is lower than the threshold (0.01 / epoch), the optimizer is automatically switched from Adam to Nesterov momentum SGD;

[0060] During the lightweight process, progressive channel pruning is adopted to gradually remove the channels with low contribution in the teacher model (ResNet-101) (the contribution is calculated by Taylor importance score). The compression rate can reach 75%. The 32-bit floating-point parameters are converted into 8-bit fixed-point numbers (dynamic range quantization), the model volume is reduced by 4 times, and the inference speed is increased by 2.3 times.

[0061] Furthermore, in the efficient integration module of the real-time streaming media technology, the audio-video traceability technology based on digital watermarking adopts an encryption and decentralized storage method. The digital watermark information is encrypted and then decentralized and stored in different frames and segments of the audio-video to improve the security and robustness of the digital watermark. At the same time, during the audio-video transmission process, the integrity and effectiveness of the digital watermark can be monitored in real time. Once it is found that the digital watermark is tampered with or lost, an alarm can be issued in time and corresponding repair measures can be taken. And this module also has an intelligent caching technology, which can automatically adjust the cache size and strategy of the audio-video according to the network condition and the performance of the user device to ensure the smooth playback of the audio-video.

[0062] Specifically, in the present invention, the AES-256-GCM mode is used to encrypt the watermark information, and the key is dynamically generated through the Diffie-Hellman key exchange to ensure that the encryption key for each segment of audio-video is unique; decentralized storage is carried out through a decentralized embedding strategy. The video part divides the watermark information into N parts and embeds it into the DCT intermediate frequency coefficients of the I frame (the positions from (5,5) to (7,7) of the 8×8 block), which has strong anti-compression performance. The watermark is embedded in the silent interval (energy < -40dB) of the Mel spectrum, and the spread spectrum technology is used to improve the robustness.

[0063] In addition, the HMAC-SHA256 signature is calculated every 10 frames and stored together with the watermark to perform real-time tampering verification and integrity verification.

[0064] Furthermore, in the multi-modal data acquisition module, the user intention acquisition technology based on the brain-computer interface adopts a multi-modal electroencephalogram signal acquisition and fusion analysis method, which can simultaneously acquire various electroencephalogram signals of the user's brain, such as electroencephalogram (EEG), magnetoencephalogram (MEG), etc., and perform fusion analysis on these signals through deep learning algorithms to improve the accuracy and reliability of user intention recognition. At the same time, this module also has an adaptive brain-computer interface calibration technology, which can automatically adjust the parameters and calibration models of the brain-computer interface according to the individual differences of the user and the changes in the usage environment to ensure the long-term stability and effectiveness of the brain-computer interface.

[0065] The adaptive brain-computer interface calibration technology refers to calculating the ratio of the power of the electroencephalogram signal frequency band (such as the α wave 8 - 12Hz) to the power of the baseline noise (30 - 45Hz), setting the threshold to 15dB, and using Kalman filtering and adaptive gain control for signal quality assessment and calibration.

[0066] Furthermore, in the core processing module of the AI large model, the intelligent decision-making engine based on the quantum neural network adopts the principles of quantum entanglement and quantum superposition. By creating entangled states through CNOT gates, it implements parallel search and dynamically adjusts the number of qubits. When processing complex multi-modal data, it can achieve parallel computing and fast search to improve the efficiency and accuracy of decision-making. At the same time, this engine also has adaptive qubit allocation and optimization technologies. According to the task complexity C (such as the depth of the decision tree), it allocates the number of qubits. This enables automatic adjustment of the number and distribution of qubits according to different decision-making tasks and data characteristics to give full play to the advantages of quantum computing. Moreover, this module also has quantum noise suppression and error correction technologies, which can effectively suppress the influence of quantum noise during the quantum computing process and improve the reliability of the calculation results. In a specific implementation, surface code error correction technology can be used to set the surface code layout with distance = 3, reducing the logical error rate from 10 -2 to 10 -5 .

[0067] Furthermore, in the digital human image generation and driving module, the hybrid model based on the generative adversarial network and variational autoencoder adopts a multi-stage generation and gradual refinement strategy during the process of creative image generation. It can gradually generate digital human images with different styles and characteristics according to the user's needs and application scenarios. At the same time, this model also has adaptive style transfer and fusion technologies, which can integrate different artistic styles and cultural elements into the digital human image to meet the personalized needs of users. Moreover, this module also has a physical simulation-based action driving technology, which can automatically generate actions and behaviors that conform to physical laws according to the environment where the digital human is located and the task requirements, to improve the realism and credibility of the digital human.

[0068] In a specific implementation, the multi-stage generation includes the following stages:

[0069] 1. Contour generation (VAE stage): The latent space dimension d = 128. Input the user's description text and output the basic contour point cloud.

[0070] 2. Detail refinement (GAN stage): Based on the StyleGAN2 architecture, add texture details (such as pores and hair) through adaptive instance normalization.

[0071] 3. Style transfer (AdaAN fusion): Transfer the artistic style features (such as Van Gogh's brushstrokes) to the digital human image through Gram matrix matching.

[0072] Furthermore, in the interactive scenario adaptation module, the dynamic scenario fusion technology based on reinforcement learning can automatically adjust the behavior strategy and scenario fusion method of the digital human according to the user's real-time feedback and environmental changes to achieve a more natural and smooth interaction experience. At the same time, this technology also has an adaptive reward function design and update mechanism, which can automatically adjust the parameters and weights of the reward function according to different scenario and task requirements to encourage the digital human to take more reasonable and effective actions. Moreover, this module also has the scenario perception and understanding ability based on semantic communication technology, which can analyze and understand the user's voice and semantic information in real time to accurately judge the user's intentions and needs, so as to provide more personalized and accurate services;

[0073] In a specific embodiment, the reward function is expressed as follows:

[0074] R(s,a) = w1·user satisfaction + w2·task completion rate - w3·response delay,

[0075] The dynamic update rule of the weights is expressed as follows:

[0076] where w1, w2, and w3 are weight coefficients, and their initial values are 0.5, 0.3, and 0.2 respectively. η is the learning rate, a fixed value of 0.01, is the loss function, is the mean square error, and t is the number of iterations. The weights are updated every 100 interactions.

[0077] Furthermore, in the feedback optimization module, the proactive optimization technology based on user behavior prediction uses deep learning and time series analysis methods to predict the user's future behaviors and needs according to the user's historical interaction records and behavior patterns, so as to take optimization measures in advance to improve the system's response speed and service quality. At the same time, this technology also has an adaptive model update and optimization mechanism, which can automatically adjust the parameters and structure of the prediction model according to the changes in user behavior and the evaluation results of system performance to ensure the accuracy and effectiveness of the prediction. Moreover, this module also has a user satisfaction evaluation and feedback mechanism based on sentiment analysis technology, which can monitor the user's emotional state and satisfaction in real time to promptly discover and solve the user's problems and needs.

[0078] Furthermore, the implementation method of this system is as follows:

[0079] S1. Multimodal data acquisition: Use high-definition cameras to collect users' facial expressions, body movements, and gesture information; collect voice information through high-sensitivity microphone arrays; use external sensors (such as physiological sensors, environmental sensors, etc.) to obtain users' physiological state information and interaction environment information; among them, high-definition cameras have functions such as autofocus, low-light compensation, and anti-shake, and can adapt to complex lighting and dynamic scenes; high-sensitivity microphone arrays use multi-channel sound pickup and beamforming algorithms to effectively reduce environmental noise interference; explore user intention acquisition technology based on brain-computer interfaces, adopt multi-modal electroencephalogram signal acquisition and fusion analysis methods, and simultaneously have an adaptive brain-computer interface calibration technology to improve the accuracy and stability of user intention recognition; develop a multi-modal data fusion algorithm based on self-supervised learning to perform preliminary fusion processing on the collected multi-modal data;

[0080] S2. Data transmission and preprocessing: Transmit the collected multi-modal data to the core processing module of the AI large model through wired or wireless networks; during the transmission process, encrypt the data to ensure data security; after reaching the core processing module, perform preprocessing operations such as noise reduction, duplicate removal, and normalization on the data to provide a high-quality data basis for subsequent processing;

[0081] S3. Core processing of the AI large model: Based on an ultra-large-scale neural network model, use a variant of the attention mechanism to fuse multi-modal data; combine multiple models (such as Transformer, LSTM, etc.) for semantic analysis, emotion recognition, and knowledge reasoning; build an intelligent decision-making engine based on quantum neural networks, and use the principles of quantum entanglement and quantum superposition to achieve parallel computing and fast search, improving decision-making efficiency and accuracy; at the same time, adopt an interpretable reasoning technology based on knowledge graphs to provide an interpretable basis for decision-making;

[0082] S4. Adaptive feature fusion and voice-driven high-fidelity face generation: Deeply fuse voice features and facial expression features through the AdaAN module, and use voice cloning technology and a deep learning model constructed based on convolutional neural networks, recurrent neural networks, or their variants and trained with a large amount of sample data to achieve high-fidelity face generation and audio-visual synchronization; introduce a spatio-temporal Transformer architecture to capture long-range dependencies and global spatial information; adopt an expression generation technology based on bioelectric signal mapping to monitor the dynamic changes of bioelectric signals in real time, and update expression parameters in real time according to preset thresholds and mapping rules to achieve natural and smooth expression transitions, and have an adaptive anti-interference mechanism; introduce an attention alignment mechanism to strengthen facial expression expressions for speech prosody features;

[0083] S5. Digital Human Image Generation and Driving: Customize digital human images according to application scenarios, supporting 2D and 3D presentation forms; 2D images are based on vector graphics technology, and 3D images adopt an advanced skeletal animation system with adjustable image details; Based on a hybrid model of generative adversarial networks and variational autoencoders, adopt a multi-stage generation and step-by-step refinement strategy to creatively generate images, with adaptive style transfer and fusion technology; Introduce a physics simulation-based motion driving technology to automatically generate physical-law-compliant actions and behaviors according to the environment and task requirements of the digital human; Use physics-based rendering technology to enhance visual texture, and the drive output strictly executes according to instructions;

[0084] S6. Intelligent Interaction Based on Speech and Knowledge Base: Rely on large-scale language models and a knowledge base construction method based on the RAG architecture to provide voice interaction answers; Adopt a knowledge reasoning engine based on causal inference to analyze causal relationships; Adopt multi-modal knowledge graph fusion technology to unify multi-modal knowledge; Use a large amount of general text data in the pre-training stage, optimize parameters for specific application fields in the fine-tuning stage, and dynamically update the personalized knowledge graph during interaction; The knowledge base automatically accesses authoritative online databases, preprocesses data using intelligent screening and data cleaning technology, records citation sources, and automatically adjusts the priority and scope of data acquisition according to the user's historical interaction records and preferences;

[0085] S7. Interaction Scenario Adaptation: Select built-in typical scenario templates according to the interaction scenario, and use reinforcement learning-based dynamic scenario fusion technology to automatically adjust the digital human behavior strategy and scenario fusion method according to the user's real-time feedback and environmental changes; Adopt an adaptive reward function design and update mechanism to encourage the digital human to take reasonable and effective actions; Use semantic communication technology to encode and decode voice and semantic information, realize scenario perception and understanding capabilities based on semantic communication technology, analyze and understand the user's voice and semantic information in real time, accurately judge the user's intentions and needs, and generate personalized recommendation scripts according to user data in the marketing scenario;

[0086] S8, Feedback Optimization and Real-time Streaming Media Transmission: Collect user feedback in real time, and optimize the system using a reinforcement learning algorithm based on policy gradients; construct a reward function that comprehensively considers multiple metrics and regularly evaluate the performance; use an optimization algorithm based on swarm intelligence for collaborative optimization, and adopt an active optimization technique based on user behavior prediction to predict future behaviors and needs according to the user's historical interaction records and behavior patterns, and take optimization measures in advance; use sentiment analysis technology to mine potential sentiment tendencies, and monitor the user's emotional state and satisfaction in real time; use the FFMPEG and RTSP protocols to achieve synchronous audio and video transmission. FFMPEG adopts hardware acceleration technology, and RTSP adopts a dynamic bitrate adjustment strategy; use 5G slicing technology to allocate exclusive network slices, and adopt an audio and video traceability technology based on digital watermarking. After encrypting the digital watermark information, it is stored dispersedly, and its integrity and effectiveness are monitored in real time; have an adaptive redundant transmission strategy and intelligent caching technology to cope with network problems and ensure smooth audio and video playback.

[0087] The present invention also provides the application of the above-mentioned interaction system or the above-mentioned interaction method in intelligent digital virtual human generation, online education interaction, medical consultation assistance, intelligent marketing recommendation, etc.

[0088] The technical effects and advantages of the present invention include:

[0089] By providing a high-fidelity face generation module with adaptive feature fusion and voice-driven, the present invention deeply fuses voice and facial expression features, and adopts a spatio-temporal Transformer architecture, an expression generation technology based on bioelectric signal mapping, and an attention alignment mechanism, bringing an interactive effect with highly matched facial expressions and voices, natural and smooth, and greatly enhancing the user's immersion;

[0090] By providing a multi-modal data acquisition module, the present invention comprehensively acquires information such as the user's facial expressions, body movements, gestures, voice, and physiological states, and adopts a multi-modal data fusion algorithm based on self-supervised learning, bringing a rich and well-fused data foundation, enabling the digital human to more accurately and comprehensively perceive the user's intentions;

[0091] By providing an interaction scenario adaptation module, the present invention adopts a dynamic scenario fusion technology and a semantic communication technology based on reinforcement learning, bringing the ability to automatically adjust the digital human's behavior strategy and scenario fusion method according to the user's real-time feedback and environmental changes, and achieving a natural and smooth interaction experience in different scenarios to meet the user's diverse interaction needs;

[0092] The present invention is provided with an intelligent interaction module based on speech and knowledge base, relying on large-scale language models and a knowledge base construction method based on the RAG architecture, adopting a knowledge reasoning engine based on causal inference and a multi-modal knowledge graph fusion technology, and optimizing the processing in the pre-training and fine-tuning stages, bringing the effect that the digital human has accuracy and professionalism in answering in specific fields, and can meet the high requirements for the depth and breadth of knowledge in fields such as education and medical care;

[0093] The present invention is provided with a mechanism for the knowledge base to automatically access authoritative online databases and adopt intelligent screening and data cleaning technologies, as well as a mechanism for automatically adjusting the priority and scope of data acquisition according to the user's historical interaction records and preferences, bringing timeliness, accuracy and personalization of knowledge, and providing more valuable knowledge services for users;

[0094] The present invention is provided with a single-stage training optimization and efficient generation module, which optimizes the digital human generation process by single-stage training, combines the AdaAN module, designs a comprehensive loss function, adopts an adaptive learning rate strategy and a multi-objective optimization algorithm, and a dynamic training strategy based on meta-learning, bringing a balance between generation quality and training efficiency, ensuring training stability and effectiveness, and at the same time realizing model compression and lightweight, reducing the demand for computing resources;

[0095] The present invention is provided with a digital human image generation and driving module, based on a hybrid model of generative adversarial network and variational autoencoder, adopting a multi-stage generation and gradual refinement strategy, an adaptive style transfer and fusion technology, and a physics simulation-based action driving technology, bringing a digital human image that meets different application requirements, has a high degree of realism and personalization, and the driving output strictly executes according to instructions;

[0096] The present invention is provided with an efficient integration module for real-time streaming media technology, using the FFMPEG and RTSP protocols, adopting hardware acceleration technology, dynamic bitrate adjustment strategy, 5G slicing technology, audio-video traceability technology based on digital watermark, adaptive redundant transmission strategy and intelligent caching technology, bringing smooth transmission and synchronization of audio-video under different network conditions, ensuring the security, traceability and stability of data transmission;

[0097] The present invention is provided with a feedback optimization module, using a reinforcement learning algorithm based on policy gradient, an optimization algorithm based on swarm intelligence, and an active optimization technology based on user behavior prediction, combined with sentiment analysis technology, bringing the effect of real-time collecting user feedback, optimizing system performance, improving response speed and service quality, timely discovering and solving user problems and needs, and enhancing user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0099] Figure 1 It is a schematic diagram of the overall architecture of the interaction system in the present invention.

[0100] Figure 2 It is a schematic diagram of the composition of the interaction system in the present invention.

[0101] Figure 3 It is a schematic diagram of the architecture of some component modules of the interaction system in the present invention.

[0102] Figure 4 It is a schematic diagram of the architecture of another part of the component modules of the interaction system in the present invention.

[0103] Figure 5 It is the overall end-to-end architecture diagram for the present invention to achieve audio-driven face generation.

[0104] Figure 6 It is a schematic diagram of post-processing optimization in the present invention.

[0105] Figure 7 It is a diagram of the real-time streaming media processing framework in the present invention.

[0106] Figure 8 It is a schematic diagram of the AdaAN network structure in the present invention. Detailed implementation manners

[0107] Combined with the following specific embodiments and drawings, the invention will be further described in detail. The processes, conditions, experimental methods, etc. for implementing the present invention, except for the specifically mentioned content below, are all common knowledge and well-known common sense in the art, and the present invention has no special restrictive content.

[0108] The present invention provides an intelligent multi-modal virtual digital human interaction system based on an AI language large model, as Figure 1 or Figure 2 shown, including: a high-fidelity face generation module, an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scenario adaptation module, and a feedback optimization module;

[0109] The system is based on an AI language large model, combined with multi-modal data collection, synchronous generation of voice and facial expressions, intelligent interaction, knowledge base support, and real-time feedback optimization technology, ensuring that the digital human can truly and naturally respond to the emotions and intentions of users, and dynamically adjust the interaction strategy according to different scenarios and environments. It provides users with a highly personalized virtual digital human interaction experience, achieving efficient and smooth interaction and emotional expression.

[0110] The present invention also provides a multi-modal virtual digital human interaction method, including:

[0111] S1. Collect the facial expressions, body movements, voice, and physiological information of the user through a high-definition camera, microphone array, and external sensors, explore the user's intention through a brain-computer interface, and fuse multi-modal data using self-supervised learning;

[0112] S2. Transmit the collected multi-modal data to the core processing module through an encrypted wired or wireless network, and perform preprocessing such as noise reduction, duplicate removal, and normalization to ensure data quality;

[0113] S3. Based on an ultra-large-scale neural network model, fuse multi-modal data, use the attention mechanism for semantic analysis, emotion recognition, and knowledge reasoning, construct an intelligent decision-making engine for a quantum neural network, and provide interpretable reasoning support;

[0114] S4. Fuse voice and facial expression features through the AdaAN module, use voice cloning and spatio-temporal Transformer technology to generate highly realistic facial expressions, and combine bioelectric signal mapping technology to achieve natural and smooth expression transitions;

[0115] S5. Customize the digital human image according to the scenario, support 2D and 3D presentations, use generative adversarial networks and variational autoencoders to generate the image, and combine physical simulation technology to drive the actions to enhance the visual texture;

[0116] S6. Provide voice answers using a large-scale language model and a knowledge base with the RAG architecture, optimize the answers by combining causal inference and a multi-modal knowledge graph, automatically update the personalized knowledge graph, and intelligently screen data;

[0117] S7. Select a template according to the interaction scenario, and use reinforcement learning technology to dynamically adjust the digital human behavior strategy; perceive the user's intention through semantic communication technology and generate personalized scripts in different scenarios;

[0118] S8. Collect user feedback in real time, optimize the system performance through reinforcement learning and swarm intelligence, predict and adjust user needs by combining emotion analysis technology, and at the same time use the FFMPEG and RTSP protocols to ensure audio-visual synchronization and smooth transmission.

[0119] Such as Figure 3As shown, the interaction scenario adaptation module further includes a scenario selection unit and a policy adjustment unit for selecting the interaction scenario and adjusting it according to the interaction policy to configure dynamic parameters; the AI large model core processing module further includes a retrieval and answer unit, a knowledge base construction unit, a fusion processing unit, a semantic analysis unit, an emotion recognition unit, and a knowledge reasoning unit, which is the most important part of data processing in the entire interaction system and performs various processes on the received data; the high-fidelity face generation module further includes a feature fusion unit and a face generation unit, which perform feature fusion on features such as voice and facial expressions, generate high-fidelity faces through an implementation rendering engine, and output them to the user terminal in the form of a video stream.

[0120] As Figure 4 shown, the external sensors include a light sensor, a temperature compensation circuit, a heart rate detection unit, and a galvanic skin response detection, which collect various indicators of the user and the environment, and together with a high-definition camera and a high-sensitivity microphone array, collect multi-modal data for the interaction system and output it to the multi-modal data collection module.

[0121] As Figure 5 shown, the overall end-to-end architecture diagram of the present invention for realizing audio-driven face generation fully demonstrates the entire process from multi-modal input to dynamic feature output, and the core innovation point is reflected in the AdaAN (Adaptive Attention Normalization) module marked by the red dotted box.

[0122] As Figure 6 shown, the post-processing optimization module further includes a key frame interpolation unit, a detail enhancement unit, and a temporal consistency constraint unit, which improve the lip-sync accuracy and facial texture details by performing motion compensation and super-resolution processing on the generated frame sequence; the quality evaluation unit realizes dynamic feedback of the generated quality and parameter tuning by jointly supervising the construction of an adversarial discriminator and the SSIM index.

[0123] As Figure 7 shown, the real-time streaming media processing framework module further includes a multi-channel input buffer unit, a distributed computing unit, and a low-latency rendering unit, where the streaming media input layer processes audio stream and video stream data in parallel through a dual-queue mechanism; the real-time processing engine is based on the CUDA-accelerated TensorRT inference framework to realize pipelined operation of encoding-inference-decoding; the edge computing node deploys a dynamic load balancing strategy to ensure real-time requirements through GPU resource monitoring and task sharding scheduling; the adaptive transmission unit automatically adjusts the bit rate and resolution parameters according to network bandwidth fluctuations to ensure a smooth interaction experience with an end-to-end delay of less than 80 ms.

[0124] Figure 8The multimodal feature processing flow of the present invention is briefly demonstrated, and the dynamic calibration of audiovisual features is achieved through a dual-channel architecture. The left path in the figure is the visual processing flow, and the source image is encoded by ResNet-50 to extract visual features, and the spatial dimension is compressed by global average pooling; the right path corresponds to the audio processing flow, and the original audio waveform is extracted through a bidirectional LSTM to extract timing features. After the bimodal features are cross-dimensionally fused at the splicing layer, the joint features are generated by a perceptron containing a multi-layer fully connected structure, and finally the parameter adaptive feature calibration is completed through a dynamic feature normalization module. The figure uses layered color blocks and flow arrows to clearly present the core process of feature cross-modal transfer, interactive fusion and nonlinear transformation.

[0125] Example 1: Multimodal data collection and preliminary processing

[0126] Visual information collection

[0127] The HD camera with advanced autofocus system can quickly focus in 0.1 seconds, ensuring that the subject is always clear and sharp. The low-light compensation technology uses an intelligent exposure algorithm, which can not only automatically adjust the exposure parameters, but also perform color correction according to the color temperature of the ambient light. Under lighting conditions as low as 5Lux, it can still output images with high color reproduction and rich details. The anti-shake function uses the built-in high-precision gyroscope and acceleration sensor to perform more than 1,000 shake detections per second. Through complex algorithm compensation, it can ensure stable and smooth images even in intense sports shooting scenes, effectively avoiding blur and shaking, so as to accurately capture the user's facial expressions, body movements and various subtle gesture information.

[0128] Voice information collection

[0129] The high-sensitivity microphone array consists of 6 carefully arranged high-sensitivity MEMS microphones. The layout has been optimized through multiple acoustic tests and can achieve 360-degree all-round sound pickup without dead angles. The beamforming algorithm uses a complex mathematical model to accurately adjust the phase and amplitude of the signal received by each microphone to form a highly directional beam. In noisy environments, such as crowded shopping malls, it can effectively suppress up to 20dB of environmental noise interference, significantly improve the signal-to-noise ratio of voice signals, and ensure that the collected voice is clear and accurate.

[0130] Physiological and environmental information collection

[0131] The physiological sensor adopts a comfortable wearable design, conforms to the human skin, and is connected to the system via Bluetooth 5.0. The data transmission rate is stable above 1 Mbps, ensuring real-time and stable data transmission. It can monitor the heart rate with high precision, with the error controlled within ±2 beats per minute; the blood pressure monitoring accuracy can reach ±5 mmHg; the monitoring resolution of the skin electrical response reaches 0.01 μS, accurately reflecting the changes in the user's physiological state. The environmental sensors are deployed at key positions in the interaction space, such as the four corners and the center of the room. Through ZigBee wireless communication technology, the environmental information such as temperature, humidity, and light intensity collected is transmitted to the system in real time at a rate of 250 kbps, ensuring the timeliness and accuracy of the data.

[0132] Exploration of Brain-Computer Interface

[0133] The electroencephalogram signal acquisition device uses a high-resolution electrode array, which contains 128 electrodes and can comprehensively collect the electrical activity signals of multiple regions of the cerebral cortex. The fusion analysis algorithm is based on deep learning models, such as the combination of convolutional neural network (CNN) and recurrent neural network (RNN), to perform deep feature extraction and fusion on various electroencephalogram (EEG), magnetoencephalogram (MEG) and other electroencephalogram signals. During the training process, millions of electroencephalogram data samples from thousands of different individuals are used to continuously optimize the model parameters, improving the accuracy and stability of user intention recognition. The adaptive brain-computer interface calibration technology automatically adjusts the parameters and calibration model of the brain-computer interface every 5 minutes by real-time monitoring of the changes in the user's electroencephalogram signals and considering factors such as electromagnetic interference in the usage environment, ensuring the stability and effectiveness of the brain-computer interface during long-term use.

[0134] Data Fusion Algorithm

[0135] The multi-modal data fusion algorithm based on self-supervised learning constructs a series of complex self-supervised learning tasks. For example, when predicting the missing part of an image, using the idea of generative adversarial network (GAN), the generator and the discriminator play against each other. The generator tries to generate the image content of the missing part, and the discriminator judges the authenticity of the generated content. In this way, the model can deeply learn the association and feature representation between image data and other modal data. In the task of generating text according to speech, a sequence-to-sequence model based on the attention mechanism is adopted, enabling the model to accurately capture the semantic information in the speech and convert it into the corresponding text, realizing the effective fusion of multi-modal data.

[0136] Data Transmission and Preprocessing

[0137] Data transmission adopts a combination of wired Gigabit Ethernet and wireless network Wi-Fi6. In wired transmission, Gigabit Ethernet transmits data at a stable rate of 1000Mbps, ensuring high-speed and stable data transmission. In a wireless network environment, the maximum transmission rate of Wi-Fi6 can reach 9.6Gbps, and it supports the multi-user, multiple-input, multiple-output (MU-MIMO) technology, which can connect multiple devices simultaneously to ensure the efficiency of data transmission. During the transmission process, the AES-256 encryption algorithm is used to encrypt data packet by packet, and the encryption key length reaches 256 bits, effectively preventing data from being stolen and tampered with. After reaching the core processing module, for image data, the bilateral filtering algorithm performs double weighting on the spatial distance between image pixels and the difference in pixel values, removing noise points while precisely retaining the edge information of the image. Even the fine textures and contours in the image can be completely retained. For voice data, the speech enhancement algorithm based on deep learning uses a deep neural network model and is trained on a large amount of voice data containing various background noises. It can accurately identify and remove background noises, significantly improving the clarity of the voice. Even for voices collected in a strong noise environment, clear and distinguishable content can be restored. The deduplication operation calculates the hash value of the data and uses an efficient hash table data structure to quickly compare and remove duplicate data records. The normalization operation adopts corresponding normalization methods according to the characteristics of different modal data, unifying the data into the numerical range of [-1, 1] or [0, 1] for convenient subsequent processing.

[0138] Embodiment 2: Core Processing of AI Large Model

[0139] Multi-modal Data Fusion

[0140] Based on an ultra-large-scale neural network model, a variant of the location-based attention mechanism is adopted. When calculating the attention weights, this mechanism not only considers the content features of the data but also encodes and incorporates the position information of the data in time and space. For example, when processing video and voice data, for each frame of the video and each time segment of the voice, corresponding position encodings are assigned, enabling the model to better capture the correlations of multi-modal data in the time and space dimensions. During the model training process, billions of samples containing various modal data are used, and the model parameters are continuously optimized through the backpropagation algorithm to improve the model's ability to fuse multi-modal data.

[0141] Semantic Analysis and Sentiment Recognition

[0142] Semantic analysis and sentiment recognition are combined with multiple models. In semantic analysis, the Transformer model deeply models the lexical relationships in the text through the multi-head attention mechanism. Each head focuses on different aspects of the text, such as semantic similarity of words, grammatical structure, etc., enabling accurate understanding of complex semantic structures. When dealing with long texts, positional encoding and masking mechanisms are adopted to effectively solve the long-distance dependence problem. In sentiment recognition, the LSTM model utilizes its ability to process time series data and can capture the emotional change trends in speech and text through memory units and gating mechanisms. During the training process, millions of speech and text data labeled with emotional tags are used to continuously adjust the model parameters to improve the accuracy of sentiment recognition.

[0143] Intelligent Decision Engine

[0144] An intelligent decision engine based on a quantum neural network is constructed, which uses the principles of quantum entanglement and quantum superposition to achieve parallel computing and fast search. The number of qubits can be dynamically adjusted between 100 and 1000 according to the complexity of the task. When dealing with complex multi-modal data, the quantum neural network can perform multiple computational paths simultaneously, greatly shortening the decision-making time. For example, in the face of a large amount of user data and complex decision-making scenarios, traditional neural networks may take seconds or even minutes to make a decision, while the quantum neural network can complete the decision in milliseconds. At the same time, an interpretable reasoning technique based on a knowledge graph is adopted to associate the decision-making process with the knowledge nodes in the knowledge graph. The knowledge graph contains rich domain knowledge and semantic relationships. By traversing and reasoning the knowledge graph, detailed interpretable bases are provided for decision-making, making the decision results more credible and persuasive.

[0145] Adaptive Feature Fusion and Speech-Driven High-Fidelity Facial Generation

[0146] The AdaAN module deeply fuses speech features and facial expression features. This module utilizes adaptive normalization technology to dynamically adjust the normalization parameters according to the distribution of speech and facial expression features, enabling better fusion of the two. The voice cloning technology adopts a variational autoencoder-based method to encode and decode the original speech. During the encoding process, the speech signal is mapped to a low-dimensional latent space to extract its key features; during the decoding process, a cloned voice highly similar to the original speech is generated based on the latent features, while preserving the prosody and emotional features of the speech. During the training process of the deep learning model, millions of sample data containing different correspondences between speech and facial expressions are used. These data cover different genders, ages, languages, and emotional expressions. Through learning these data, the model can precisely control details such as lip movement and eye expressions when generating facial expressions, making them highly match the speech. The spatio-temporal Transformer architecture is introduced. In the time dimension, the attention mechanism is used to capture the dependencies of speech and video data at different moments; in the space dimension, the changes in facial expressions in different regions are concerned. In this way, the long-term dependencies between speech and facial expressions can be effectively captured, making the generated facial expressions more natural and coherent. The expression generation technology based on bioelectric signal mapping is adopted. The bioelectric signal monitoring device can collect the electrical activity signals of facial muscles in real time with a sampling frequency of 1000Hz. By analyzing and processing the signals, the movement state of facial muscles can be accurately judged, and corresponding expression parameters can be generated. The adaptive anti-interference mechanism identifies and removes weak external electromagnetic interference signals through spectral analysis of bioelectric signals to ensure the accuracy of expression generation. The attention alignment mechanism is introduced. By calculating the attention weights between speech prosody features and facial expression features, the attention is concentrated on the facial expression regions related to speech prosody. For example, when the intonation of the speech rises, the model will automatically strengthen the surprised or excited expressions on the face, thus enhancing the expression of facial expressions to speech prosody.

[0147] The AdaAN network structure is as Figure 8 shown. Specifically,

[0148] Visual branch: The input image extracts spatial features layer by layer through the ResNet-50 network to obtain visual features;

[0149] Audio branch: The original audio data is input into a bidirectional LSTM network to obtain audio features;

[0150] The visual features are pooled and then concatenated with the audio features along the feature dimension to form joint features;

[0151] The concatenated features are processed by a multi-layer perceptron (MLP). The MLP contains fully connected layers, LeakyReLU activation functions, etc. to process the features;

[0152] Output calibrated visual features after dynamic feature normalization.

[0153] Embodiment 3: Digital human image generation and driving

[0154] Image customization and presentation

[0155] Customize the digital human image according to the application scenario, supporting 2D and 3D presentation forms. The 2D image is based on vector graphics technology, and the outline and details of the digital human are constructed through Bezier curves. During the construction process, high-precision graphics algorithms are used to ensure the smoothness and accuracy of the curves, enabling lossless scaling and editing. Users can freely adjust the details such as the facial features, hairstyle, and clothing of the digital human through the graphic editing tool. The parametric design allows each detail to be precisely controlled through specific parameters. The 3D image adopts an advanced skeletal animation system with hundreds of bones. Each bone has independent motion parameters and constraint conditions, enabling very delicate action performance. During the animation production process, a combination of motion capture technology and keyframe animation technology is adopted. First, real human motion data is obtained through motion capture, and then fine-tuning and optimization are performed through keyframe animation to make the actions of the digital human more natural and smooth.

[0156] Creative generation and style fusion

[0157] Based on a hybrid model of generative adversarial network and variational autoencoder, adopt a multi-stage generation and step-by-step refinement strategy to creatively generate images. In the first stage, the variational autoencoder encodes a large amount of digital human image data to obtain latent feature vectors, which contain the basic feature information of the digital human. Then, the generative adversarial network samples and generates in the latent feature space to generate a preliminary digital human image. In subsequent stages, by continuously refining the network structure and parameters of the generator and discriminator, the quality and details of the generated image are gradually improved. The adaptive style transfer and fusion technology can integrate different artistic styles and cultural elements into the digital human image. For example, when integrating the style of traditional Chinese ink painting into the clothing texture of the digital human image, by extracting and analyzing the color, brushstrokes, and texture features of the ink painting, using deep learning algorithms to transfer these features to the clothing texture of the digital human while maintaining the physical properties and wearing effects of the clothing, making the digital human image not only have traditional cultural characteristics but also meet modern aesthetic needs.

[0158] Motion driving and rendering

[0159] Introduce physics simulation-based action driving technology to automatically generate actions and behaviors that conform to physical laws according to the environment where the digital human is located and the task requirements. When simulating the walking of a digital human, physical factors such as gravity, friction, and inertia are considered. The motion trajectories and force conditions of each joint are calculated through a physics engine to make the walking action more natural and smooth. Use physics-based rendering technology to enhance the visual texture. This technology is based on the physical principle of light propagation and accurately simulates the interaction between light and the object surface, including phenomena such as light reflection, refraction, and scattering. During the rendering process, high-resolution texture maps and normal maps are used to increase the details and realism of the object surface. At the same time, global illumination and shadow algorithms are adopted to generate realistic light and shadow effects, making the visual performance of the digital human more vivid and real. The drive output strictly executes according to the instructions. By establishing an accurate action instruction mapping table, the instructions input by the user are accurately converted into the actions and behaviors of the digital human to ensure that the performance of the digital human meets the user's expectations.

[0160] Intelligent Interaction Based on Voice and Knowledge Base

[0161] Rely on large language models and a knowledge base construction method based on the RAG architecture to provide voice interaction answers. The large language model uses a vast amount of general text data of trillions of words in the pre-training stage, covering multiple fields such as history, science, culture, and technology. In the fine-tuning stage, for specific application fields (such as medical, financial, etc.), professional text data in this field is used to optimize the parameters, so that the answers in specific fields are accurate and professional. Adopt a knowledge reasoning engine based on causal inference to analyze causal relationships. For example, in the medical field, based on various information such as the patient's symptoms, medical history, and examination results, use a knowledge graph and causal reasoning algorithms to infer possible causes and treatment plans. Adopt multi-modal knowledge graph fusion technology to integrate knowledge in different modalities such as text, image, and voice into a single knowledge graph. During the construction of the knowledge graph, various technologies such as entity recognition, relation extraction, and semantic annotation are used to ensure the accuracy and integrity of the knowledge. Dynamically update the personalized knowledge graph during the interaction. According to the user's questions and feedback, continuously enrich and improve the content of the knowledge graph. The knowledge base is automatically connected to authoritative online databases, such as PubMed in the medical field and the Wind database in the financial field. Use intelligent screening and data cleaning technologies to preprocess the data, remove duplicate, incorrect, or irrelevant data, and at the same time classify and mark the citation sources for subsequent data traceability and credibility evaluation. And it can automatically adjust the priority and scope of data obtained from the online database according to the user's historical interaction records and preferences, improving the efficiency and quality of the interaction.

[0162] Example 4: Interaction Scenario Adaptation

[0163] Scene Template and Dynamic Fusion

[0164] Select built-in typical scenario templates according to the interaction scenario, such as education scenarios, marketing scenarios, customer service scenarios, etc. Each scenario template is carefully designed and optimized, including digital human images, actions, language styles, and interaction processes suitable for that scenario. In an education scenario, the digital human image may be a kind and approachable teacher, with natural and friendly actions and an easy-to-understand and patient language style; in a marketing scenario, the digital human image may be more fashionable and enthusiastic, with attractive actions and a language style that highlights the features and advantages of the product. Using dynamic scenario fusion technology based on reinforcement learning, the digital human continuously accumulates experience through interaction with the environment. In each interaction, the digital human selects different behavioral strategies according to the user's real-time feedback and environmental changes, such as the content, tone, and emotion of the user's questions, and adjusts its behavioral strategies according to the feedback of the reward function. The reward function is designed according to different scenarios and task requirements. For example, in an education scenario, the reward function may pay more attention to the user's understanding and mastery of knowledge; in a marketing scenario, the reward function may focus more on the user's purchase intention and conversion rate. By continuously optimizing the behavioral strategies, the digital human can achieve a more natural and smooth interaction experience.

[0165] Reward Function and Semantic Communication

[0166] Adopt an adaptive reward function design and update mechanism to automatically adjust the parameters and weights of the reward function according to different scenarios and task requirements. During the training process, use a large amount of simulated interaction data and actual user feedback data to continuously optimize the reward function through machine learning algorithms, so that it can accurately reflect the behavior effect of the digital human. Use semantic communication technology to encode and decode voice and semantic information to achieve scene perception and understanding capabilities based on semantic communication technology. Semantic communication technology extracts and encodes the semantic information in voice and text and converts it into a semantic representation that can be understood by a computer. In a marketing scenario, generate personalized recommendation scripts according to user data. For example, by analyzing data such as the user's browsing history, purchase records, and interest preferences, use natural language generation technology to generate product recommendation scripts that meet the user's needs and interests to improve marketing effectiveness.

[0167] Feedback Optimization and Real-Time Streaming Transmission

[0168] Collect user feedback in real time, including user evaluations, questions, operation behaviors, etc. Optimize the system using a reinforcement learning algorithm based on policy gradients. By continuously adjusting the system's parameters and behavior strategies, the system can better meet user needs. Construct a reward function that comprehensively considers multiple metrics, such as user satisfaction, interaction efficiency, answer accuracy, etc., and regularly evaluate the system performance. Adopt an optimization algorithm based on swarm intelligence for collaborative optimization, such as the particle swarm optimization algorithm. By simulating the foraging behavior of bird flocks, search for the optimal solution in the solution space. In the optimization process, each particle represents a system parameter configuration. By continuously adjusting the position and velocity of the particles, find the parameter configuration that maximizes the reward function to improve the overall performance of the system. Adopt an active optimization technology based on user behavior prediction. According to the user's historical interaction records and behavior patterns, use a deep learning model to predict the user's future behaviors and needs. For example, by analyzing the user's historical question content and browsing behaviors, predict the topics and information that the user may be interested in, and prepare relevant answers and content in advance to improve interaction efficiency and user satisfaction. Use sentiment analysis technology to mine potential sentiment tendencies and real-time monitor the user's emotional state and satisfaction. By analyzing the emotional words, tones, intonations, etc. in the user's voice and text, judge the user's emotional state, such as happy, dissatisfied, confused, etc., in order to promptly discover and solve the user's problems and needs. Use FFMPEG and RTSP protocols to achieve synchronous audio and video transmission. FFMPEG adopts hardware acceleration technology, such as using the CUDA acceleration library of NVIDIA GPUs to increase the video encoding speed by several times. During the encoding process, dynamically adjust the encoding parameters according to the complexity and change degree of the video content to ensure video quality and encoding efficiency. RTSP adopts a dynamic bitrate adjustment strategy. By real-time monitoring the change of network bandwidth and using a bandwidth prediction algorithm to predict the network bandwidth situation in the next period of time in advance, automatically adjust the bitrate of the video. When the network bandwidth is sufficient, increase the video bitrate to improve video quality; when the network bandwidth is tight, reduce the video bitrate to ensure smooth transmission and synchronization of audio and video. Use 5G slicing technology to allocate exclusive network slices to provide stable network guarantee for digital human interaction. 5G slicing technology divides the 5G network into multiple virtual slices according to the service requirements of digital human interaction, such as low latency, high bandwidth, etc. Each slice has independent network resources and service quality assurance. Adopt an audio and video traceability technology based on digital watermarking. After encrypting the digital watermark information, it is scattered and stored in different frames and segments of the audio and video. During the storage process, according to the content characteristics and structure of the audio and video, select the appropriate embedding position and embedding method to ensure the concealment and robustness of the digital watermark. Real-time monitor the integrity and effectiveness of the digital watermark. Once it is found that the digital watermark is tampered with or damaged, it can promptly trace the source and propagation path of the audio and video. Have an adaptive redundant transmission strategy and intelligent caching technology to cope with network problems and ensure smooth playback of audio and video.The adaptive redundancy transmission strategy sends redundant data at the sender side and performs error recovery based on the redundant data at the receiver side. During the generation of redundant data, the amount and coding method of redundant data are dynamically adjusted according to the packet loss rate and bit error rate of the network. When the network condition is poor, the proportion of redundant data is increased and more complex error correction coding is adopted to improve the reliability of data transmission; when the network condition is good, the redundant data is appropriately reduced to improve the transmission efficiency. The intelligent caching technology automatically adjusts the caching size and strategy of audio and video according to the network condition and the performance of the user device. By real-time monitoring of network bandwidth and latency, combined with the storage capacity and processing power of the user device, the caching space is dynamically allocated. When the network bandwidth fluctuates greatly, the caching capacity is increased to cache more audio and video data in advance to cope with possible network jams; when the network is stable, the caching is reasonably reduced to release device resources. At the same time, an intelligent prefetch algorithm is adopted to predict the content that the user may watch next according to the user's viewing history and current playback progress, and the relevant data is fetched and cached from the server in advance to further ensure the smooth playback of audio and video.

[0169] In practical applications, through these optimization measures, the digital human system can operate stably and efficiently in different network environments. For example, in an online education live broadcast, even if there are short-term fluctuations in the network in some areas, relying on the intelligent caching and redundancy transmission strategies, students can still smoothly watch the teaching content of the digital human teacher without any video freezing or audio interruption. In the online promotion activities of financial marketing, the real-time audio and video synchronous transmission and the interactive strategy adjustment based on user emotion analysis enable the digital human customer service to communicate with customers more effectively, improving the customer experience and business conversion rate.

[0170] System Scalability and Data Security

[0171] From the perspective of system scalability, the architecture design of the present invention fully considers the needs of future technological development and business growth. In terms of hardware, it supports the access of various types and specifications of sensors, facilitating the introduction of more advanced and accurate multi-modal data acquisition devices as sensor technology progresses. At the software level, a modular design is adopted, and each functional module, such as data acquisition, processing, fusion, and interaction modules, communicates through standardized interfaces. This enables new algorithms and models to be easily integrated into the system. For example, when a more efficient multi-modal data fusion algorithm or a more intelligent decision-making model appears, only the corresponding module needs to be replaced without large-scale modification of the entire system.

[0172] In addition, in terms of data security and privacy protection, in addition to using the AES-256 encryption algorithm during data transmission, during the data storage process, sensitive data is encrypted and stored in blocks, and an access control list (ACL) is used to strictly restrict the access rights of different users and programs to the data. Regularly conduct backup and recovery tests on the data to ensure the integrity and availability of the data. At the same time, comply with relevant data privacy regulations. When collecting and using user data, clearly inform users of the purpose and protection measures of the data, and obtain the explicit consent of the users to protect the legitimate rights and interests of the users.

[0173] For example, in the application in the medical field, sensitive information such as patients' physiological data will be strictly encrypted and stored, and only authorized medical personnel can access specific data. Moreover, the data backup strategy ensures that in case of hardware failures or other accidents, patients' data will not be lost, ensuring the continuity and reliability of medical services. In the financial industry, customers' financial information and transaction records, etc. are also subject to strict security protection to prevent financial risks caused by data leakage.

[0174] Advantages of this system:

[0175] In visual information collection, in addition to the characteristics of the high-definition camera itself, an object tracking algorithm based on deep learning is innovatively adopted. By training on a large amount of human behavior data in different scenarios, this algorithm can lock the target person in real time in a complex environment and intelligently adjust the shooting parameters to ensure that the target is always in the center of the picture and remains clear. For example, at a crowded event site, even if the target person moves frequently and is partially blocked, their facial expressions and movements can be accurately captured.

[0176] In terms of voice information collection, in order to further improve the accuracy of voice recognition, a personalized voice model training technology is introduced. The system will automatically generate a personalized voice recognition model according to the voice characteristics of each user, such as pronunciation habits, speech rate, intonation, etc. When the user first uses the system, through a short period of voice sample collection and analysis, a dedicated model can be quickly constructed, significantly improving the accuracy of voice recognition in specific user scenarios.

[0177] In the link of physiological and environmental information collection, the innovation of the physiological sensor lies in its function of adaptively adjusting the monitoring frequency. When it detects abnormal fluctuations in the user's physiological state, the sensor will automatically increase the monitoring frequency from the normal once per second to multiple times per second to more timely and accurately capture the changes in physiological parameters. For environmental sensors, a distributed collaborative sensing technology is adopted. The sensors communicate and cooperate with each other, enabling a more comprehensive and accurate perception of the environmental information in the interaction space, effectively avoiding information loss caused by the failure or blind area of a single sensor.

[0178] In the exploration of brain-computer interfaces, in addition to adopting advanced algorithms and calibration techniques, a brain electrical signal feature enhancement technology has been innovatively introduced. Through time-frequency analysis of the original brain electrical signals and combined with deep learning algorithms, more representative features are extracted, significantly improving the accuracy of user intention recognition. At the same time, in order to reduce the discomfort of users when using brain-computer interface devices, a new type of flexible electrode material has been developed, enabling the electrodes to better fit the scalp and reducing the impact on users' daily activities.

[0179] In multi-modal data fusion, the position-based attention mechanism variant is further optimized into a dynamic position attention mechanism. This mechanism can adjust the weights of position encoding in real time according to the dynamic changes of the data, more flexibly capturing the complex correlations of multi-modal data in the time and space dimensions. For example, when processing multi-modal data in a real-time video conference scenario, it can quickly adapt to dynamic factors such as the position movement of participants and the change of speaking order, achieving more efficient data fusion.

[0180] In semantic analysis and emotion recognition, in order to enhance the ability to understand complex semantics and emotions, a knowledge graph-enhanced semantic understanding model has been introduced. This model combines the prior knowledge in the knowledge graph with the text information, and through semantic association reasoning, can more accurately understand complex semantic expressions such as metaphors and puns in the text, and take into account the influence of context and background knowledge in emotion recognition, improving the accuracy of emotion recognition.

[0181] In the intelligent decision-making engine, the innovation of the quantum neural network lies in its adaptive quantum bit allocation technology. According to the real-time complexity and data volume of the decision-making task, it automatically adjusts the number and allocation method of quantum bits, maximizing the computational efficiency while ensuring the accuracy of the decision. At the same time, the knowledge graph-based interpretable reasoning technology is further optimized into an interactive interpretable reasoning. Users can query the specific knowledge nodes and reasoning paths relied on during the decision-making process by interacting with the system, enhancing the transparency and credibility of the decision result.

[0182] In image customization and presentation, in addition to vector graphics technology, image generation technology based on deep learning is introduced in the construction of 2D images. Users only need to input a simple text description, such as "a female image with a round face, big eyes, and long hair", and the system can automatically generate a 2D digital human image that meets the description and provide multiple styles and details for users to choose. In terms of 3D images, real-time physical simulation of clothing and hair effects is adopted. During the movement of the digital human, the clothing and hair will change dynamically in real time according to physical laws, such as fluttering in the wind and swaying with movements, greatly enhancing the realism and immersion of the digital human image.

[0183] In creative generation and style integration, the multi-stage generation and progressive refinement strategy combines the ideas of adversarial learning and reinforcement learning. When generating a digital human image, the generator not only has to confront the discriminator's judgment but also continuously optimize the generated image according to the reward mechanism of reinforcement learning to make it more in line with the user's creative needs and aesthetic standards. The adaptive style transfer and integration technology is further extended to cross-domain style integration, which can integrate style elements from different art forms, cultural backgrounds, and even different media (such as movies, animations, games, etc.) into the digital human image, creating a unique digital human image.

[0184] In motion driving and rendering, the motion driving technology based on physical simulation combines the motion optimization algorithm of reinforcement learning. During the interaction between the digital human and the environment, by continuously learning and optimizing the motion strategy, the digital human can generate more natural and reasonable motions. The physically based rendering technology introduces real-time global illumination and reflection probe technology, which can generate more realistic light and shadow effects during real-time rendering. Even in a complex lighting environment, the digital human can present a highly realistic visual texture.

[0185] In scene template and dynamic integration, the dynamic scene integration technology based on reinforcement learning is further optimized into multi-agent collaborative reinforcement learning. In complex interaction scenarios, such as multiplayer online games and remote collaborative office work, multiple digital humans can cooperate and coordinate their actions through collaborative reinforcement learning to achieve a more natural and smooth interaction experience. For example, in a multiplayer online game, digital human teammates can automatically adjust their tactics and cooperation strategies according to the game situation and player behavior, improving the fun and competitiveness of the game.

[0186] In the reward function and semantic communication, the adaptive reward function design and update mechanism combine transfer learning technology. Between different interaction scenarios, the system can use the method of transfer learning to quickly adjust the parameters and weights of the reward function to achieve a rapid adaptation to the new scenario. The semantic communication technology introduces semantic graph construction and reasoning technology, which can more deeply understand the semantic relationships in speech and text and achieve more accurate scene perception and understanding.

[0187] In feedback optimization and real-time streaming media transmission, the reinforcement learning algorithm based on policy gradient combines the method of deep reinforcement learning and can more effectively optimize system parameters and behavioral strategies. In the collaborative optimization of the optimization algorithm based on swarm intelligence, an adaptive weight adjustment mechanism is introduced, which dynamically adjusts its weight in collaborative optimization according to the performance of different optimization algorithms to improve the optimization efficiency. The active optimization technology based on user behavior prediction combines the idea of federated learning. On the premise of protecting user privacy, it improves the accuracy of user behavior prediction through collaborative learning among multiple user devices. The sentiment analysis technology combines multi-modal sentiment fusion analysis, which not only considers the sentiment information in speech and text, but also combines multi-modal information such as users' facial expressions and body movements to more comprehensively and accurately judge the users' emotional states.

[0188] The implementation method of this system is as follows:

[0189] S1. Multi-modal data collection: Use high-definition cameras to collect users' facial expressions, body movements, and gesture information; collect voice information through high-sensitivity microphone arrays; use external sensors (such as physiological sensors, environmental sensors, etc.) to obtain users' physiological state information and interaction environment information; among them, high-definition cameras have functions such as autofocus, low-light compensation, and anti-shake, and can adapt to complex light and dynamic scenes; high-sensitivity microphone arrays use multi-channel sound pickup and beamforming algorithms to effectively reduce environmental noise interference; explore user intention collection technology based on brain-computer interfaces, adopt multi-modal electroencephalogram signal collection and fusion analysis methods, and have an adaptive brain-computer interface calibration technology at the same time to improve the accuracy and stability of user intention recognition; develop a multi-modal data fusion algorithm based on self-supervised learning to perform preliminary fusion processing on the collected multi-modal data;

[0190] S2. Data transmission and preprocessing: Transmit the collected multi-modal data to the core processing module of the AI large model through wired or wireless networks; during the transmission process, encrypt the data to ensure data security; after reaching the core processing module, perform preprocessing operations such as noise reduction, duplicate removal, and normalization on the data to provide a high-quality data basis for subsequent processing;

[0191] S3. Core processing of the AI large model: Based on a super-large-scale neural network model, use a variant of the attention mechanism to fuse multi-modal data; combine multiple models (such as Transformer, LSTM, etc.) for semantic analysis, emotion recognition, and knowledge reasoning; build an intelligent decision-making engine based on quantum neural networks, and use the principles of quantum entanglement and quantum superposition to achieve parallel computing and fast search to improve decision-making efficiency and accuracy; at the same time, adopt an interpretable reasoning technology based on knowledge graphs to provide an interpretable basis for decision-making;

[0192] S4. Adaptive Feature Fusion and Speech-driven High-fidelity Facial Generation: Deeply fuse speech features and facial expression features through the AdaAN module. Utilize speech cloning technology and a deep learning model constructed based on convolutional neural networks, recurrent neural networks, or their variants and trained with a large amount of sample data to achieve high-fidelity facial generation and lip-sync. Introduce a spatio-temporal Transformer architecture to capture long-range dependencies and global spatial information. Adopt an expression generation technology based on bioelectric signal mapping to monitor the dynamic changes of bioelectric signals in real time, and update expression parameters in real time according to preset thresholds and mapping rules to achieve natural and smooth expression transitions, and have an adaptive anti-interference mechanism. Introduce an attention alignment mechanism to strengthen facial expression based on speech prosody features;

[0193] S5. Digital Human Image Generation and Driving: Customize digital human images according to application scenarios, supporting 2D and 3D presentation forms. The 2D image is based on vector graphics technology, and the 3D image adopts an advanced skeletal animation system with adjustable image details. Based on a hybrid model of generative adversarial networks and variational autoencoders, use a multi-stage generation and progressive refinement strategy to creatively generate images, with adaptive style transfer and fusion technology. Introduce a physics simulation-based motion driving technology to automatically generate actions and behaviors that conform to physical laws according to the environment and task requirements of the digital human. Adopt physics-based rendering technology to enhance visual texture, and the driving output strictly follows instructions;

[0194] S6. Intelligent Interaction Based on Speech and Knowledge Base: Rely on large language models and a knowledge base construction method based on the RAG architecture to provide speech interaction answers. Adopt a knowledge reasoning engine based on causal inference to analyze causal relationships. Adopt multi-modal knowledge graph fusion technology to unify multi-modal knowledge. Use a large amount of general text data in the pre-training stage, optimize parameters for specific application fields in the fine-tuning stage, and dynamically update the personalized knowledge graph during interaction. The knowledge base is automatically connected to authoritative online databases, and intelligent screening and data cleaning technologies are used to preprocess the data, record the citation sources, and automatically adjust the priority and scope of data acquisition according to the user's historical interaction records and preferences;

[0195] S7. Interaction Scenario Adaptation: Select built-in typical scenario templates according to the interaction scenario, and use a dynamic scenario fusion technology based on reinforcement learning to automatically adjust the digital human behavior strategy and scenario fusion method according to the user's real-time feedback and environmental changes. Adopt an adaptive reward function design and update mechanism to encourage the digital human to take reasonable and effective actions. Use semantic communication technology to encode and decode speech and semantic information to achieve scenario perception and understanding capabilities based on semantic communication technology, analyze and understand the user's speech and semantic information in real time, accurately judge the user's intentions and needs, and generate personalized recommendation scripts according to user data in the marketing scenario;

[0196] S8, Feedback Optimization and Real-time Streaming Media Transmission: Collect user feedback in real time, optimize the system using a reinforcement learning algorithm based on policy gradients; construct a reward function that comprehensively considers multiple metrics and regularly evaluate performance; use an optimization algorithm based on swarm intelligence for collaborative optimization, adopt an active optimization technique based on user behavior prediction, predict future behaviors and needs according to users' historical interaction records and behavior patterns, and take optimization measures in advance; use sentiment analysis technology to mine potential sentiment tendencies, and real-time monitor users' emotional states and satisfaction; use FFMPEG and RTSP protocols to achieve synchronous audio and video transmission, FFMPEG adopts hardware acceleration technology, and RTSP adopts a dynamic bitrate adjustment strategy; use 5G slicing technology to allocate exclusive network slices, adopt an audio and video traceability technology based on digital watermarking, encrypt the digital watermark information and store it dispersedly, and real-time monitor its integrity and effectiveness; have an adaptive redundant transmission strategy and intelligent caching technology to handle network problems and ensure smooth audio and video playback.

[0197] Scenario 1: Online Education Interaction

[0198] Background: Students conduct remote learning through virtual digital human teachers, and the system needs to perceive the learning state in real time and provide personalized guidance.

[0199] Process and Technology Application:

[0200] Data Collection (S1):

[0201] Brain-computer Interface: Real-time monitor students' EEG signals, detect the intensity of theta waves (4 - 8Hz), and calculate the concentration index through the ratio of the power of theta waves (4 - 8Hz) to the baseline noise power. Its expression is:

[0202] A th = 2.5

[0203] where P θ is the power of the theta wave frequency band, P noise is the baseline noise power of the system, and the concentration threshold is set as A th = 2.5. When A ≥ A th , it is determined to be in a high concentration state;

[0204] Camera and Microphone: Capture students' facial expressions (such as confusion, concentration), voice questions, and gesture actions (such as raising hands).

[0205] Core Processing (S3):

[0206] Quantum Decision Engine: If it detects A < A th (distraction), trigger the "insert interactive question" strategy, and the decision delay < 50ms.

[0207] Sentiment Analysis: Identify confused emotions (confidence > 85%) in speech through the Transformer model.

[0208] Adaptive Interaction (S7):

[0209] Dynamic Scene Fusion: Switch to the "Q&A Mode", and the digital human teacher generates a 3D anatomical model demonstration (bone animation delay < 85ms).

[0210] Reward Function Update: Adjust the teaching strategy weight according to the student's correct answer rate (w2 is increased from 0.3 to 0.5).

[0211] Effect Verification:

[0212] The student's concentration is increased by 35% (compared with traditional online courses); the knowledge point mastery rate is increased by 18% (statistical analysis through after-class tests).

[0213] Scenario 2: Medical Consultation Assistance

[0214] Background: The digital human doctor interacts with the patient and provides preliminary diagnosis suggestions by combining multi-modal data.

[0215] Process and Technology Application:

[0216] Multi-modal Data Collection (S1):

[0217] Physiological Sensors: Real-time monitoring of heart rate (error ±2 bpm), blood pressure (±5 mmHg);

[0218] Voice and Facial Expression Analysis: Identify micro-expressions when the patient describes pain (such as frowning intensity > 0.7).

[0219] Knowledge Base Interaction (S6):

[0220] RAG Retrieval: Match symptom-disease associations from authoritative databases such as PubMed (response time < 200ms);

[0221] Causal Reasoning: Construct a causal chain of symptoms (fever, cough) → etiology (flu / Covid-19) (accuracy 92%);

[0222] Decision-making and Feedback (S8):

[0223] Quantum Engine Diagnosis: Parallel computing of the probabilities of multiple etiologies (Covid-19 probability 85% vs. flu 68%);

[0224] Emotional Optimization: If the patient's anxiety is detected (voice tremor frequency > 10Hz), the softness of the digital human's intonation is increased by 30%.

[0225] Effect Verification:

[0226] The consistency of diagnostic recommendations with those of doctors in tertiary hospitals reached 89%; the patient satisfaction score was 4.6 / 5.0 (compared to 4.0 for traditional AI consultation).

[0227] Scenario 3: Intelligent Marketing Recommendations

[0228] Background: Digital human customer service interacts with users on e-commerce platforms to achieve accurate product recommendations.

[0229] Process and technology application:

[0230] User intent recognition (S1-S3):

[0231] Multimodal fusion: Combine user click behavior (gesture trajectory analysis), voice keywords ("cost-effective") and historical purchase records to generate the intent vector I∈R 128 .

[0232] Semantic communication: Encode the user query "thin and light notebook suitable for summer" into a semantic vector and retrieve matching products from the knowledge base.

[0233] Scene Adaptation (S7):

[0234] Dynamic reward mechanism: initial weights w1=0.5 (satisfaction), w2=0.3 (conversion rate), adjusted to w1=0.6, w2=0.4 according to real-time click rate.

[0235] Personalized script generation: Based on user preferences (such as “technology enthusiast”), parameter comparisons (CPU model, battery life) are added to the recommendation statements.

[0236] Real-time optimization (S8):

[0237] Cache and transmission: 5G slicing ensures smooth playback of high-definition product videos (bitrate adaptively adjusted to 8-12Mbps);

[0238] Watermark traceability: embed invisible digital watermarks to prevent recommended content from being tampered with (compression resistance > 90%).

[0239] Effect verification:

[0240] The conversion rate increased by 22% (compared to traditional recommendation algorithms); user interaction time increased by 40% (from 3 minutes to 4.2 minutes on average).

[0241] Compared with the existing related technologies, the technical solution of the present invention has the following advantages in terms of usage data comparison:

[0242] Adaptive feature fusion and speech driven module

[0243] Lip sync error:

[0244] Traditional technology: >5ms (RMS, OptiTrack motion capture calibration); This invention: <2.3ms (a 54% improvement);

[0245] · Facial expression generation latency:

[0246] Traditional technology: >200ms; This invention: <85ms (a 57.5% reduction);

[0247] · Accuracy of bioelectric signal mapping:

[0248] Traditional method: 85%; This invention: 92.4% (a 7.4 - percentage - point increase);

[0249] Multi - modal data acquisition and fusion

[0250] · Accuracy of user intention recognition:

[0251] Traditional fusion algorithm: 80%; This invention (self - supervised cross - modal contrast learning): 89.7% (a 9.7 - percentage - point increase);

[0252] · Stability of brain - computer interface signals:

[0253] Fixed - threshold calibration: SNR fluctuation range ±5dB; This invention's dynamic calibration: SNR fluctuation range ±1.2dB (a 35% improvement in stability);

[0254] Single - stage training optimization module

[0255] · Training efficiency:

[0256] Traditional multi - stage training: 10 hours / epoch; This invention's single - stage training: 2 hours / epoch (a 5 - fold increase in efficiency);

[0257] · Model compression effect:

[0258] Teacher model (ResNet - 101): Number of parameters 235M; Student model (MobileNet - V3): Number of parameters 58M (a 75% compression);

[0259] · Inference speed:

[0260] Traditional model: 45FPS; Lightweight model: 105FPS (a 133% increase).

[0261] · Knowledge retrieval response time:

[0262] Traditional database query: >500ms; This invention's RAG architecture: <200ms (a 60% speedup);

[0263] · Knowledge credibility grading:

[0264] Authoritative literature weight: 0.8 (accuracy > 95%); Social media weight: 0.5 (accuracy ~ 70%);

[0265] User-generated content weight: 0.3 (accuracy ~ 50%);

[0266] Real-time streaming media technology module

[0267] Anti-packet loss rate of the network:

[0268] Traditional redundancy strategy: 70% packet loss resistance; Adaptive redundancy of the present invention: 90% packet loss resistance (a 28.6% increase);

[0269] Watermark robustness:

[0270] Anti-compression rate (H.264 @ 10Mbps): > 90% (traditional LSB embedding is only 60%);

[0271] 5G slice transmission delay:

[0272] Public network: > 100ms; Dedicated slice: < 50ms (a 50% reduction).

[0273] The protected content of the present invention is not limited to the above embodiments. Without departing from the spirit and scope of the inventive concept, changes and advantages that can be conceived by those skilled in the art are included in the present invention, and the scope of protection is defined by the appended claims.

Claims

1. An intelligent multimodal virtual digital human interaction system based on the AI language large model, characterized in that, Including: A high-fidelity face generation module; the high-fidelity face generation module uses the AdaAN network, based on adaptive feature fusion, speech driving, and time series modeling of speech features, extracts speech-related feature information. The extracted speech features are processed by a deep neural network to ensure high spatio-temporal alignment between speech and facial expressions, collect bioelectrical signals and map the signals to facial muscle movements, generate final facial expressions, and interact with users.

2. The interactive system according to claim 1, wherein Also including: An intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scenario adaptation module, a feedback optimization module; The interaction system is based on an AI language large model, combines multi-modal data acquisition, synchronous generation of speech and facial expressions, intelligent interaction, knowledge base support, and real-time feedback optimization technologies to ensure that the digital human can truly and naturally respond to the emotions and intentions of users, and dynamically adjust interaction strategies according to different scenarios and environments, providing users with a highly personalized virtual digital human interaction experience, and achieving efficient and smooth interaction and emotional expression.

3. The interactive system according to claim 1, wherein The AdaAN network is defined by the following formula: F′ = T(F, S) = W·F + b, W ∈ R N×N , b ∈ R N , Where S represents speech features, F represents facial features, T represents a deformation repair function, W represents a dynamically generated adaptive weight matrix, b represents a bias term for generator-discriminator adversarial training optimization, and N represents the dimension; Map speech features to facial expression features through a transformation matrix: Z 表情 = A·Z 语音 + B, Among them, Z 语音 represents the voice feature, Z 表情 represents the facial expression feature, A represents the transformation matrix dynamically generated by the AdaAN network, and B represents the bias term optimized by adversarial training; and / or, Establish a mapping between bioelectrical signals and facial muscle movements, and transform it into an expression parameter representation as follows: P 表情 = f(EEG, EMG) = W EEG · EEG + W EMG · EMG + b, Among them, EEG represents electroencephalogram signals, EMG represents electromyogram signals, and W EEG represents the electroencephalogram feature weight matrix for end-to-end training, and W EMG represents the electromyogram feature weight matrix for end-to-end training, and b represents the dynamically calibrated bias term; and / or, The AdaAN network is provided with an anti-interference mechanism, which is expressed as the following formula: where A is the state transition matrix, based on the second-order muscle model, H is the observation matrix that maps the EMG signal to the expression parameter space, and K k is the Kalman gain matrix. Using the Sage-Husa adaptive algorithm, the process noise Q = 0.01I and the observation noise R = 0.1I are set. z k represents the observed value of the EMG signal at the k-th moment, and represents the estimated value of the facial expression parameters at the k-th moment; And perform alignment through spatio-temporal attention: lip error <5ms, expression naturalness score> = 4.8 / 5.

0.

4. The interactive system according to claim 2, wherein The intelligent interaction module provides speech interaction answers through a large language model and a knowledge base of the RAG architecture, combines causal inference to analyze causal relationships, integrates a multi-modal knowledge graph, dynamically updates a personalized knowledge graph, and accesses an online database; and / or, Optimize model parameters according to the actual field through pre-training and fine-tuning; and / or, Screen and clean the data in the online database, classify and label the citation sources, and personalized adjust the priority and scope of data acquisition; and / or, The training optimization and efficient generation module adopts single-stage training to optimize the generation process and combines the AdaAN module to achieve feature adaptive alignment; Design a comprehensive loss function, use an adaptive learning rate strategy and a multi-objective optimization algorithm; dynamically adjust training parameters based on meta-learning, and introduce adversarial distillation and distributed training to accelerate the process; and / or, The loss function of the alignment error is shown in the following formula: Among them, |F′-Z 表情 |2 is the lip synchronization error term calibrated by the OptiTrack motion capture system, KL(P 表情 ||P gt ) is the KL divergence term in P gt is the data annotated by FACS experts, and λ is the weight coefficient; and / or, The dynamic training strategy based on meta-learning dynamically optimizes the learning rate through the MAML algorithm, and the formula is: Among them, θ are the trainable parameters of the model, including all the weights and biases of the AdaAN network, and α is the meta-learning rate, which is dynamically adjusted through the MAML framework. The gradient of the comprehensive loss function, freezing the parameters of non-critical layers during calculation; and / or The adversarial distillation transfers lip-sync knowledge from the teacher model to the student model, and the distillation loss is expressed as Among them, T tea represents the teacher model, and T stu (x) represents the student model, and MSE represents the mean squared error loss function; and / or, Adopt lightweight technologies including model compression, distributed acceleration, and progressive channel pruning to reduce the number of model parameters and computational complexity.

5. The interactive system according to claim 2, wherein The efficient integration module uses the FFMPEG and RTSP protocols to ensure the synchronous transmission of audio and video, adopts hardware acceleration technology and dynamic bitrate adjustment, uses 5G slicing technology to allocate bandwidth for a dedicated network, adopts audio and video traceability technology based on digital watermarking, and has adaptive redundant transmission to cope with network problems; and / or, The audio and video traceability technology adopts an encryption and decentralized storage method, and real-time monitors the integrity and effectiveness of the digital watermark; and / or, The watermark information is encrypted in the AES-256-GCM mode, and the key is dynamically generated through the Diffie-Hellman key exchange to ensure that the encryption key for each segment of audio and video is unique; Decentralized storage is carried out through a decentralized embedding strategy. The video part divides the watermark information into N parts and embeds it into the DCT intermediate frequency coefficients of the I frame, and embeds the watermark in the silent interval of the Mel spectrum; and / or, The HMAC-SHA256 signature is calculated every 10 frames, stored together with the watermark, and tampering is verified in real time for integrity verification.

6. The interactive system according to claim 2, wherein The multi-modal data acquisition module acquires the user's facial expressions, body movements, gestures, voice and physiological information, and uses high-definition cameras and high-sensitivity microphone arrays; supports external sensor interfaces, adopts self-supervised learning to fuse multi-modal data, and acquires environmental information; and / or, Adopts a multi-modal electroencephalogram signal acquisition and fusion analysis method to simultaneously acquire multiple electroencephalogram signals of the user's brain and identify the user's intention; and / or, The AI large model core processing module processes multi-modal data based on a super-large-scale neural network, uses the attention mechanism and multi-model fusion for semantic analysis, emotion recognition and knowledge reasoning, constructs an intelligent decision-making engine for a quantum neural network, and adopts an interpretable reasoning technology for a knowledge graph; and / or, The intelligent decision-making engine adopts the principles of quantum entanglement and quantum superposition, creates an entangled state through the CNOT gate, implements parallel search, applies adaptive quantum bit allocation and optimization technology, allocates the number of quantum bits according to the task complexity, and improves the accuracy and efficiency of decision-making operation; and / or, The digital human image generation and driving module customizes 2D or 3D digital human images according to the application scenario, uses vector graphics and a skeletal animation system, combines a generative adversarial network and a variational autoencoder to generate images, and uses physical simulation-based action driving technology and physical rendering to enhance the visual effect; and / or, Adopts a multi-stage generation and gradual refinement strategy to generate digital human images; the multi-stage includes a contour generation stage, a detail refinement stage, and a style transfer stage.

7. The interactive system according to claim 2, characterized in that, The interaction scenario adaptation module has multiple built-in scenario templates, dynamically adjusts the digital human behavior strategy according to user feedback and environmental changes, realizes dynamic scenario fusion based on reinforcement learning technology, quickly switches performances in different scenarios, and generates personalized recommended words according to user data; and / or, Adopts a reward function and an update mechanism to motivate the digital human; and / or, The reward function is expressed as follows: R(s,a) = w1·user satisfaction + w2·task completion rate - w3·response delay, The dynamic update rule of the weight is expressed as follows: where w1, w2, w3 are weight coefficients, η is the learning rate, is the loss function, is the mean squared error, and t is the number of iterations.

8. The interactive system according to claim 2, wherein The feedback optimization module collects user feedback in real time, optimizes system performance using reinforcement learning, constructs a multi-index reward function, and regularly evaluates performance; uses swarm intelligence optimization algorithms for collaborative optimization, and optimizes system interaction based on user behavior prediction and sentiment analysis techniques; and / or, Adopts deep learning and time series analysis methods to predict users' future behaviors and needs based on their historical interaction records and behavior patterns, and optimize the system in advance.

9. A multimodal virtual digital human interaction method, characterized in that, The method is implemented through the interaction system described in any one of claims 1-8, including: S1. Collect users' facial expressions, body movements, voices, and physiological information through a high-definition camera, microphone array, and external sensors, explore the brain-computer interface to collect users' intentions, and use self-supervised learning to fuse multi-modal data; S2. Transmit the collected multi-modal data to the core processing module through an encrypted wired or wireless network, and perform preprocessing such as noise reduction, duplicate removal, and normalization to ensure data quality; S3. Based on a super-large-scale neural network model, fuse multi-modal data, use the attention mechanism for semantic analysis, emotion recognition, and knowledge reasoning, construct an intelligent decision-making engine for a quantum neural network, and provide interpretable reasoning support; S4. Fuse voice and facial expression features through the AdaAN module, use voice cloning and spatio-temporal Transformer technology to generate highly realistic facial expressions, and combine bioelectric signal mapping technology to achieve natural and smooth expression transitions; S5. Customize the digital human image according to the scenario, support 2D and 3D presentations, use generative adversarial networks and variational autoencoders to generate the image, and combine physical simulation technology to drive actions to enhance the visual texture; S6. Use a large-scale language model and a knowledge base with the RAG architecture to provide voice answers, optimize the answers by combining causal inference and multi-modal knowledge graphs, automatically update the personalized knowledge graph, and intelligently screen data; S7. Select templates according to the interaction scenario, and use reinforcement learning technology to dynamically adjust the digital human behavior strategy; perceive users' intentions through semantic communication technology, and generate personalized conversations in different scenarios; S8. Collect user feedback in real time, optimize system performance through reinforcement learning and swarm intelligence, predict and adjust users' needs by combining sentiment analysis technology, and use the FFMPEG and RTSP protocols to ensure audio-visual synchronization and smooth transmission.

10. The application of the interaction system described in any one of claims 1-8, or the interaction system described in claim 9, in intelligent digital virtual human generation, online education interaction, medical consultation assistance, and intelligent marketing recommendation.

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