Intelligent digital human system based on multi-modal deep learning and automatic content generation

Abstract

The invention relates to the technical field of digital people, in particular to an intelligent digital people system based on multi-modal deep learning and automatic content generation, which comprises a front-end interaction module, a rear-end processing module, a multi-modal data management module, an AI synthesis engine and a distributed storage module. The system realizes full-process intelligent operation from model training to content publishing through dynamic voiceprint extraction, audio-driven video generation and multi-modal data fusion. According to the method, the defects in personalized training, automatic content generation and multi-user cooperation management in the prior art can be overcome, the naturalness, flexibility and user experience of voice synthesis and video driving are remarkably improved, and an efficient and intelligent solution is provided for digital human content production.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically a smart digital human system based on multimodal deep learning and automated content generation. Background Technology

[0002] With the rapid development of artificial intelligence technology, digital humans (or virtual humans) have been widely used in news broadcasting, government affairs interpretation, online education, and digital marketing. These digital human systems provide users with a highly realistic interactive experience by simulating human voice, facial expressions, and body movements, thereby significantly improving the efficiency and appeal of information delivery. However, current digital human solutions on the market generally rely heavily on human customization, making it difficult to achieve large-scale and automated production processes. Specifically, existing technologies exhibit significant limitations in the following aspects: First, existing digital human systems have relatively weak model management capabilities, lacking flexible multi-model personalized training and unified management mechanisms. This presents enterprises with high technical barriers and operational costs when building diverse digital human image libraries. Second, the content production chain has a low degree of automation; from manuscript writing and review to final publication, each stage still requires significant manual intervention, making it difficult to meet the demands for efficient and rapid content production. Furthermore, the speech synthesis and video generation capabilities of existing systems are relatively isolated, lacking deeply integrated multimodal processing capabilities, resulting in difficulties in guaranteeing the quality and consistency of generated content. Finally, existing systems are inadequate in enterprise-level collaboration and access control, making it difficult to support the data security management needs of multi-user collaboration and complex business scenarios.

[0003] To address the aforementioned issues, there is an urgent need for an intelligent digital human content generation system capable of achieving full-process automation, thereby resolving pain points in existing technologies such as high costs of manual intervention, poor scalability, low production efficiency, and unsatisfactory user experience. This invention aims to provide an end-to-end solution through deep integration of multimodal model training, automated content generation, and collaborative management, significantly improving the intelligence level and practical application value of digital human systems. Summary of the Invention

[0004] This invention aims to address the technical bottlenecks of existing digital human systems in personalized training, automated content generation, and multi-user collaborative management, particularly in achieving highly natural speech synthesis and video-driven processing, flexible model management, and an efficient content production chain. To achieve these goals, this invention provides an intelligent digital human system based on multimodal deep learning and automated content generation. By combining multimodal data fusion, dynamic voiceprint feature extraction, and audio-driven video generation, it achieves intelligent operation throughout the entire process from model training to content publishing, thereby significantly improving the flexibility, scalability, and user experience of the digital human system.

[0005] This invention discloses a smart digital human system based on multimodal deep learning and automated content generation, comprising a front-end interaction module, a back-end processing module, a multimodal data management module, an AI synthesis engine, and a distributed storage module. The front-end interaction module provides a user-friendly graphical interface, supporting functions such as manuscript writing, review and publication, model training management, and task progress query. This module embeds a real-time preview function, allowing users to perform preliminary checks on the generated audio and video content before submission, thereby improving the efficiency and accuracy of content production. The back-end processing module, as the core logic layer of the system, is responsible for task scheduling, permission verification, data flow control, and asynchronous task queue management. The multimodal data management module stores and manages user-uploaded audio and video materials, model training data, and synthesis results, and enables rapid retrieval and access through intelligent indexing technology. The AI ​​synthesis engine integrates multiple deep learning algorithms, enabling fully automated operations from voiceprint feature extraction to audio and video synthesis. The distributed storage module adopts an object storage architecture to ensure high availability and scalability of large-scale data, while supporting cross-regional access and backup.

[0006] The front-end interaction module further includes a dynamic manuscript editor, a review status tracker, and a model library management tool. The dynamic manuscript editor allows users to write content online and uses semantic analysis algorithms to detect grammatical errors or sensitive words in the manuscripts in real time, thereby reducing the workload in subsequent review stages. The review status tracker adopts a hierarchical state machine model, supporting flexible configuration of various review processes, such as single-level review, multi-level review, and cross-review modes. The model library management tool allows users to register new digital human models by uploading reference audio or video, and supports adding, deleting, modifying, and querying existing models. In addition, the tool has a built-in intelligent recommendation system that can recommend the most suitable model images based on the user's historical selections, thereby improving the user experience.

[0007] The backend processing module further includes a task scheduling unit, a permission management unit, and a data flow control unit. The task scheduling unit employs a priority-based dynamic scheduling algorithm, which can rationally allocate system resources according to the urgency of tasks and resource consumption. Its scheduling strategy can be expressed by the following formula: Where is the task priority weight, is the estimated execution time of the task, is the resource requirement of the task, and is the total available resources of the current system. This algorithm ensures the stability and fairness of the system under high concurrency scenarios. The permission management unit adopts a role-based access control (RBAC) model, supporting fine-grained permission division and dynamic adjustment. The data flow control unit is responsible for coordinating data transmission between the front-end and back-end, and uses an improved sliding window protocol to optimize the efficiency of chunked transmission of large files. Its transmission rate can be estimated by the following formula: Where is the window size, is the network bandwidth, and is the latency for a single transmission. This mechanism significantly improves the upload and download speeds of audio and video files.

[0008] The multimodal data management module further includes a data preprocessing unit, a feature extraction unit, and an intelligent indexing unit. The data preprocessing unit performs standardization processing on user-uploaded audio and video materials, such as sampling rate conversion, format unification, and noise filtering. The feature extraction unit employs an improved convolutional neural network (CNN) architecture to extract deep acoustic features from the audio signal. Specifically, for the input audio signal, it is first converted into a spectrogram using a short-time Fourier transform (STFT). Then, the spectrogram is input into a 3D convolutional layer to extract multi-scale feature vectors: These feature vectors are further fed into a bidirectional long short-term memory (BiLSTM) network to capture temporal dependencies in the audio signal: The final output feature vector is used for subsequent speaker recognition modeling and speech synthesis. The intelligent indexing unit encodes the extracted feature vector based on a hash algorithm to generate a unique identifier, thereby enabling fast retrieval and matching.

[0009] The AI ​​synthesis engine further includes a speech synthesis unit and a video generation unit. The speech synthesis unit employs a diffusion-based TTS algorithm, capable of generating high-quality, customized speech waveforms. Its core idea is to transform random noise into the target speech signal through progressive denoising. Where is the initial noise, is the number of denoising steps, is the denoising function, and is the target distribution. This algorithm can not only replicate the original timbre of the model but also adjust the speech rate, tone, and emotional expression according to user needs. The video generation unit utilizes a novel audio-driven GAN to map the generated speech signal to the facial expressions and lip movements of the digital human. Its key lies in learning the non-linear relationship between audio features and facial landmarks: Where is the audio feature vector, is the facial identity feature vector, and are the parameters of the generator. This model can generate highly realistic broadcast videos and supports fine-grained control over micro-expressions.

[0010] The distributed storage module further includes an object storage unit and a data synchronization unit. The object storage unit employs sharding technology, dividing large files into multiple smaller blocks and distributing them across different physical nodes, thereby improving storage efficiency and disaster recovery capabilities. The data synchronization unit uses a consistent hashing algorithm to achieve cross-region data synchronization, ensuring users receive a consistent experience when accessing data from different locations.

[0011] The workflow of the intelligent digital human system disclosed in this invention is as follows: Users write manuscripts through the front-end interactive module and submit them for review. After approval, the system calls the AI ​​synthesis engine to perform speech synthesis and video generation based on the model selected by the user. The task scheduling unit dynamically allocates resources according to the current system load and stores the synthesis results in a distributed storage module. Users can view the task progress in real time and download the final generated audio and video files through the front-end interface. The entire system executes the above task scheduling, data processing, and content generation processes in a high-frequency loop, ensuring millisecond-level response speed and a high level of automation.

[0012] Furthermore, the system of this invention also includes a data analysis and optimization module, which interacts with the backend processing module to record, analyze, and optimize the system's operational performance. This module can monitor the execution time, resource consumption, and user feedback of each task, and automatically generate optimization suggestions through machine learning algorithms. For example, when frequent failures of certain tasks are detected, the system will automatically adjust the scheduling strategy or prompt the user to change the model image, thereby improving overall operational efficiency and user experience.

[0013] The intelligent digital human system disclosed in this invention effectively addresses the pain points of existing technologies in personalized training and automated content generation. The diffusion-based speech synthesis algorithm proposed in this invention overcomes the limitations of traditional TTS models in timbre replication and emotional expression at the fundamental level, while the audio-driven adversarial network significantly enhances the realism and naturalness of video generation. Through the integration of multimodal data fusion, dynamic scheduling algorithms, and a distributed storage architecture, this system ensures high availability and scalability, thus providing users with an efficient, flexible, and intelligent digital human content production solution. Attached Figure Description

[0014] Figure 1 The system architecture diagram shows the overall structure of the intelligent digital human system, including the composition and interrelationships of the front-end interaction module, back-end processing module, multimodal data management module, AI synthesis engine, and distributed storage module.

[0015] Figure 2The front-end interaction module functional diagram details the functional division and operation process of the dynamic manuscript editor, the review status tracker, and the model library management tool.

[0016] Figure 3 The AI ​​synthesis engine workflow diagram describes the core algorithms and data flow processes of the speech synthesis unit and video generation unit, including the specific steps of audio feature extraction, voiceprint modeling, speech synthesis, and video driving.

[0017] Figure 4 The distributed storage module structure diagram shows the technical implementation details of the object storage unit and the data synchronization unit, including the application of the sharded storage mechanism and the consistent hash algorithm for cross-region data synchronization. Detailed Implementation

[0018] This invention discloses an intelligent digital human system based on multimodal deep learning and automated content generation, the specific implementation of which is described in conjunction with the accompanying drawings. Figures 1 to 4 A detailed description is provided. The system includes a front-end interaction module, a back-end processing module, a multimodal data management module, an AI synthesis engine, and a distributed storage module. Through the collaborative work of these modules, it achieves intelligent operation throughout the entire process from manuscript writing to audio and video content generation.

[0019] like Figure 1 As shown, the system's overall architecture consists of multiple functional modules. The front-end interaction module serves as the primary entry point for user interaction, providing a user-friendly graphical interface that supports functions such as manuscript writing, review and publication, model training management, and task progress tracking. In practical applications, users first write content online through the dynamic manuscript editor in the front-end interaction module. The editor incorporates a semantic analysis algorithm that can detect grammatical errors or sensitive words in the manuscript in real time. For example, when a user enters text containing sensitive words, the editor immediately prompts the user to modify the relevant content, thereby reducing the workload in subsequent review stages. The review status tracker adopts a hierarchical state machine model, supporting flexible configuration of various review processes, such as single-level or multi-level review modes. In a specific application scenario, enterprise users may need to conduct multi-level reviews of important manuscripts. In this case, the system can automatically allocate review tasks according to preset rules and update the review status in real time for users to view. Furthermore, the model library management tool allows users to upload reference audio or video to register new digital human models. The system uses an intelligent recommendation algorithm to recommend the most suitable model image based on the user's historical selections, thereby improving the user experience.

[0020] The backend processing module is the core logic layer of the system, responsible for task scheduling, permission verification, data flow control, and asynchronous task queue management. The task scheduling unit employs a priority-based dynamic scheduling algorithm to ensure system stability and fairness under high concurrency scenarios. Specifically, the formula for calculating task priority weights is: in As the priority weight of the task, The estimated execution time of the task. The resource requirements for the task. This represents the total available resources of the current system. For example, when the system receives multiple task requests, the scheduling unit will rationally allocate system resources based on the urgency of the tasks and resource usage, thereby ensuring efficient task execution. The permission management unit adopts a role-based access control (RBAC) model, supporting fine-grained permission division and dynamic adjustment. For example, within an enterprise, users in different departments may require different operation permissions; the system administrator can flexibly configure the permission scope for each user through the permission management unit. The data flow control unit is responsible for coordinating data transmission between the front-end and back-end, employing an improved sliding window protocol to optimize the efficiency of chunked transmission of large files. The formula for estimating the transmission rate is: in For transmission rate, For window size, For network bandwidth, This represents the latency for a single transmission. This mechanism significantly improves the upload and download speeds of audio and video files, especially when processing large-scale data.

[0021] The multimodal data management module stores and manages user-uploaded audio and video materials, model training data, and synthesis results, and enables rapid retrieval and access through intelligent indexing technology. The data preprocessing unit standardizes user-uploaded audio and video materials, performing operations such as sampling rate conversion, format unification, and noise filtering. For example, when a user uploads an audio file with a sampling rate of 48kHz, the system automatically converts it to a uniform 16kHz sampling rate for subsequent processing. The feature extraction unit employs an improved convolutional neural network (CNN) architecture to extract deep acoustic features from the audio signal. Specifically, for the input audio signal x(t), it is first converted into a spectrogram X(f,t) using a short-time Fourier transform (STFT): X(f,t) = STFT[x(t)]. Then, the spectrogram is input into a three-dimensional convolutional layer to extract multi-scale feature vectors F: F = Conv3D[X(f,t)]. These feature vectors are further fed into a bidirectional long short-term memory (BiLSTM) network to capture the temporal dependencies in the audio signal: H = BiLSTM(F). The final output feature vector H is used for subsequent voiceprint modeling and speech synthesis. The intelligent indexing unit encodes the extracted feature vector based on a hash algorithm to generate a unique identifier, thereby enabling fast retrieval and matching. For example, when a user needs to find training data for a specific model, the system can quickly locate relevant data through the intelligent indexing unit, significantly improving retrieval efficiency.

[0022] The AI ​​synthesis engine integrates multiple deep learning algorithms, enabling fully automated operation from voiceprint feature extraction to audio-video synthesis. The speech synthesis unit employs a diffusion-based TTS algorithm to generate high-quality, customized speech waveforms. Its core idea is to transform random noise into the target speech signal through progressive denoising, specifically using the formula: y_t=D(y_{t-1},z_t,θ), where y_0 is the initial noise, t is the number of denoising steps, D is the denoising function, and θ is the target distribution. For example, when a user needs to generate speech with specific emotional expression, the system adjusts the speech rate, tone, and emotional parameters according to the user's needs to generate a suitable speech waveform. The video generation unit utilizes a novel audio-driven GAN to map the generated speech signal to the digital human's facial expressions and lip movements. The key lies in learning the non-linear relationship between audio features and facial key points, specifically the formula: G(A,I;Φ)=Y, where A is the audio feature vector, I is the facial identity feature vector, Φ is the generator parameters, and Y is the generated video frame. For example, when the system receives a voice signal, the video generation unit generates corresponding facial expressions and lip movements based on the audio features, thereby generating a highly realistic broadcast video.

[0023] The distributed storage module adopts an object storage architecture to ensure high availability and scalability for large-scale data, while supporting cross-regional access and backup. The object storage unit uses sharding technology to divide large files into multiple smaller blocks and distribute them across different physical nodes, thereby improving storage efficiency and disaster recovery capabilities. For example, when a user uploads a one-hour high-definition video, the system will divide it into multiple smaller blocks and store them on different physical nodes; even if one node fails, the integrity of the overall data will not be affected. The data synchronization unit uses a consistent hashing algorithm to achieve cross-regional data synchronization, ensuring a consistent user experience when accessing data from different locations. For example, when a user uploads a file in Beijing, the system will use a consistent hashing algorithm to synchronize the file to a storage node in Shanghai, ensuring that the user in Shanghai will receive the same file content.

[0024] The system's specific operation flow is as follows: Users write scripts through the front-end interactive module and submit them for review. After approval, the system uses the AI ​​synthesis engine to perform speech synthesis and video generation based on the user-selected model. For example, if a company needs to create a product promotional video, the user first writes promotional text in the dynamic script editor and submits it for review through the review status tracker. After approval, the user selects a registered digital human model and submits a task request. The task scheduling unit dynamically allocates resources based on the current system load and assigns the task to the AI ​​synthesis engine for processing. The speech synthesis unit generates customized speech waveforms according to user needs, and the video generation unit generates corresponding facial expressions and lip movements based on the generated speech signals. After the synthesis results are stored in the distributed storage module, users can view the task progress in real time and download the final generated audio and video files through the front-end interface. The entire system executes the above task scheduling, data processing, and content generation processes in a high-frequency loop, ensuring millisecond-level response speed and a high level of automation.

[0025] Furthermore, the system includes a data analysis and optimization module that interacts with the backend processing module to record, analyze, and optimize system performance. This module monitors the execution time, resource consumption, and user feedback for each task and automatically generates optimization suggestions using machine learning algorithms. For example, when frequent task failures are detected, the system will automatically adjust its scheduling strategy or prompt the user to change the model's appearance, thereby improving overall efficiency and user experience. For instance, in a specific application scenario, if the system finds that the speech synthesis of a certain digital human model is poor, potentially increasing the task failure rate, the data analysis and optimization module will prompt the user to change to another model's appearance, thus improving the task success rate.

[0026] In summary, the intelligent digital human system disclosed in this invention ensures the high availability and scalability of the entire system through the integration of multimodal data fusion, dynamic scheduling algorithms, and distributed storage architecture, thereby providing users with an efficient, flexible, and intelligent digital human content production solution.

Claims

1. A smart digital human system based on multimodal deep learning and automated content generation, characterized in that, include: The front-end interaction module is designed to provide a user-friendly graphical interface and support functions such as manuscript writing, review and publication, model training management, and task progress query. The backend processing module is configured to be responsible for task scheduling, permission verification, data flow control, and asynchronous task queue management. The multimodal data management module is set up to store and manage user-uploaded audio and video materials, model training data, and synthesis results, and enables fast retrieval and access through intelligent indexing technology; The AI ​​synthesis engine is designed to integrate multiple deep learning algorithms to automate the entire process from voiceprint feature extraction to audio-visual synthesis. The distributed storage module is configured to use an object storage architecture to ensure high availability and scalability of large-scale data and support cross-regional access and backup.

2. The intelligent digital human system according to claim 1, characterized in that, The front-end interaction module further includes: The dynamic manuscript editor is designed to allow users to write content online and detect grammatical errors or sensitive words in the manuscript in real time through semantic analysis algorithms. The audit status tracker is configured to use a hierarchical state machine model to support flexible configuration of various audit processes. The model library management tool is designed to allow users to register new digital human models by uploading reference audio or video, and supports adding, deleting, modifying, and querying existing models.

3. The intelligent digital human system according to claim 1, characterized in that, The backend processing module further includes: The task scheduling unit is configured to use a priority-based dynamic scheduling algorithm to rationally allocate system resources according to the urgency of tasks and resource consumption. The access control unit is configured to use a role-based access control model to support fine-grained permission division and dynamic adjustment; The data flow control unit is configured to optimize the efficiency of chunked transmission of large files using an improved sliding window protocol.

4. The intelligent digital human system according to claim 1, characterized in that, The multimodal data management module further includes: The data preprocessing unit is configured to perform standardized processing on user-uploaded audio and video materials, including sampling rate conversion, format unification, and noise filtering. The feature extraction unit is configured to extract deep acoustic features from audio signals using an improved convolutional neural network architecture and capture temporal dependencies through a bidirectional long short-term memory network. The intelligent indexing unit is configured to encode the extracted feature vectors using a hash algorithm to generate unique identifiers for fast retrieval and matching.

5. The intelligent digital human system according to claim 1, characterized in that, The AI ​​synthesis engine further includes: The speech synthesis unit is configured to generate high-quality, customized speech waveforms using a diffusion-based TTS algorithm. The video generation unit is configured to use an audio-driven adversarial network to map the generated speech signals onto the facial expressions and lip movements of the digital human.

6. The intelligent digital human system according to claim 1, characterized in that, The distributed storage module further includes: The object storage unit is configured to use sharding technology to divide large files into multiple small blocks and distribute them across different physical nodes; The data synchronization unit is configured to achieve cross-region data synchronization using a consistent hashing algorithm.

7. The intelligent digital human system according to claim 1, characterized in that, The front-end interaction module has an embedded real-time preview function that allows users to perform a preliminary check on the generated audio and video content before submitting their work.

8. The intelligent digital human system according to claim 1, characterized in that, The task scheduling unit of the backend processing module uses the following formula: Calculate task priority weights, where Task priority weights Estimated execution time for the task Task resource requirements This represents the total amount of available resources in the current system.

9. The intelligent digital human system according to claim 1, characterized in that, The feature extraction unit of the multimodal data management module converts the input audio signal into a spectrogram through short-time Fourier transform and inputs the spectrogram into a three-dimensional convolutional layer to extract multi-scale feature vectors.

10. The intelligent digital human system according to claim 1, characterized in that, It also includes a data analysis and optimization module, which is set to record, analyze, and optimize the system's operating performance and automatically generate optimization suggestions through machine learning algorithms.

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