A film and television content generation system based on multi-agent collaboration and autonomous evolution

Abstract

This invention discloses a multi-agent collaborative autonomous evolution film and television content generation system and method, involving artificial intelligence, self-developed neural rendering, and multimodal fusion. It addresses pain points in existing technologies such as broken links, lack of self-developed rendering, weak cross-frame consistency, poor audio-visual lip-sync, rudimentary agent collaboration, and lack of autonomous evolution capabilities. This invention achieves text standardization through dedicated word segmentation and cross-modal feature fusion using a hybrid expert master model. It establishes a film and television professional agent cluster, relying on memory, communication, and decision-making mechanisms for efficient collaboration. A self-developed spatiotemporal rendering engine supports 2K / 4K cinematic-level image quality, ensuring stable long video footage and accurate lip-sync across multiple languages. A self-evolutionary engine enables agent iteration and core module optimization, achieving standardized output through feature fusion. This invention can generate finished films end-to-end, adapting to various film and television industrial production processes, effectively reducing costs and improving quality, demonstrating significant technological innovation and commercial value.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a film and television content generation system and method based on multi-agent collaboration and autonomous evolution. More particularly, it relates to a multimodal film and television content generation technology involving a cluster of 20 film and television professional agents, encompassing full-process collaboration, agent short-term + long-term memory mechanisms, dynamic task allocation and bidirectional communication, an agent self-evolution engine, self-developed heavyweight spatiotemporal rendering, cross-frame feature memory, and native audio-visual lip-sync. This technology is applicable to the entire process of film and television scriptwriting, storyboard design, content editing, visual rendering, audio-visual synchronization, and long-form video production, enabling industrialized, cinematic-level intelligent production of film and television content. It can seamlessly connect with the generation needs of various types of film and television content, including short videos, short dramas, and theatrical films. Background Technology

[0002] Current film and television content generation technologies mostly employ single models to complete single film and television production tasks. Some platform-level tools only achieve process-oriented assistance by integrating external models, and a few multi-agent technologies only achieve simple division of labor and collaboration, failing to form a professional and complete agent cluster system. This results in the following core technical defects: 1. A complete disconnect in the film and television production chain, lacking self-developed underlying rendering capabilities and relying on third-party models for visual generation, making it impossible to achieve end-to-end autonomous generation from natural language requirements to high-definition finished products; 2. Coarse-grained division of labor among agents, failing to cover all professional aspects of film and television production, lacking independent memory systems and communication mechanisms, resulting in low information exchange efficiency between agents, poor task matching accuracy, and an inability to form professional collaborative creation capabilities; 3. Models lack cross-frame memory capabilities, leading to industry-wide problems in long video generation such as character drift, scene collapse, and discontinuous timing. Audio-visual and lip-sync synchronization is mostly achieved through post-production stitching, resulting in low synchronization accuracy and a lack of naturalness; 4. The intelligent agent lacks autonomous evolution capabilities, and optimization operations rely entirely on human intervention. Moreover, the optimization scope only covers a single module, making it impossible to achieve coordinated iteration of parameters across the entire chain, such as intelligent agents, text processing, and visual rendering. The model's capabilities cannot be continuously improved with use. 5. It has poor adaptability to professional film and television scenes, only supporting the generation of short videos / short dramas of 10-30 seconds, with a maximum image quality of 1080p, which cannot meet the industrial production needs of minute-long videos and 2K / 4K cinematic image quality.

[0003] The aforementioned deficiencies prevent existing technologies from achieving fully autonomous and intelligent generation of film and television content from natural language creation needs to cinematic finished products. This makes it difficult to meet the high efficiency, professionalism, and continuity requirements of industrialized film and television production, thus hindering the intelligent upgrading process of film and television content production. Furthermore, existing technologies have not proposed an effective integrated solution for a cluster of 20 film and television professionals with full division of labor, integrated intelligent agent memory-communication-evolution, cross-frame memory constraints, and self-developed spatiotemporal rendering. Consequently, they cannot fundamentally address the core pain points of intelligent film and television generation. Summary of the Invention

[0004] Purpose of the invention Addressing the core shortcomings of existing technologies, such as fragmented end-to-end processes, lack of self-developed rendering capabilities, poor cross-frame consistency, low audio-visual lip-sync accuracy, coarse-grained agent division of labor, and lack of autonomous memory and evolution capabilities, this invention provides a film and television content generation system and method based on multi-agent collaboration and autonomous evolution. It aims to achieve end-to-end autonomous generation from natural language requirements to cinematic-quality finished products, precise collaboration across the entire process involving 20 professional film and television agents, deep integration of short-term and long-term agent memories, efficient interaction through dynamic task allocation and bidirectional communication, autonomous evolution of agents throughout the entire process, deep integration of a self-developed rendering engine and cross-frame memory, and native audio-visual lip-sync. This solves the technical pain points of existing technologies, including fragmented processes, poor collaboration, limited agent capabilities, inability to autonomously optimize, poor consistency in long videos, and reliance on third-party rendering capabilities. It improves the efficiency, professional quality, and image quality of film and television content generation, constructing a complete, cinematic, industrialized, and fully closed-loop intelligent film and television production system. Technical solution

[0005] This invention achieves autonomous and intelligent generation of film and television content across the entire chain through six core technological innovations, requiring no human intervention and relying on any third-party models: A dedicated word segmentation module for film and television is built, breaking through the limitations of traditional text processing. It supports dual standardization processing of natural language creation needs and standard film and television script texts, generating input sequences with unified dimensions that are compatible with deep learning models. A deep learning master model with a hybrid expert architecture (MoE) is constructed, embedding a grouped query attention (GQA) mechanism and rotation position encoding, integrating an upgraded FilmBlock module, and fusing attention branches, MoE branches and agent collaboration branches to achieve efficient extraction and temporal preservation of text features. At the same time, the master model connects to the feature layer of the self-developed rendering engine to complete cross-modal fusion, and adopts a sparse activation mechanism to activate only the expert network of the matching task, which greatly reduces the computational power consumption. A cluster of 20 film and television professional-specific intelligent agents is initialized, covering the entire film and television production process according to five major categories: narrative, directing, art direction, editing, and rendering. Each agent is equipped with an independent core reasoning layer, communication and interaction layer, and task-specific output head. At the same time, a short-term + long-term memory system, a dynamic task allocation mechanism, a fully connected bidirectional communication mechanism, and a weighted voting decision-making mechanism are built to realize the agent's memory storage, accurate task matching, efficient information interaction, and consensus decision-making. Each agent has built-in capability scores and activity marker parameters to lay the foundation for self-evolution. The self-developed heavyweight spatiotemporal rendering engine adopts a layered temporal coding and spatiotemporal integrated GodDiT architecture, natively supports the generation of 2K / 4K cinematic-quality visual frames, and combines audio-visual lip-sync unit to complete the native linkage of audio features, lip-sync dynamics features and visual frames, achieving accurate lip-sync alignment for 8+ languages. The cross-frame memory constraint unit globally constrains the core visual features generated by the agent, ensuring consistency in long videos. A self-evolution engine for the entire intelligent agent chain is built. The sliding average algorithm is combined with the task reward value to update the intelligent agent's ability score in real time. Three-level thresholds of strengthening, reorganizing and eliminating are set. The engine periodically performs parameter strengthening of high-quality intelligent agents, parameter reorganization of medium-quality intelligent agents, and marking and eliminating of inefficient intelligent agents. The self-evolution engine synchronously iterates and optimizes the core parameters of the self-developed rendering engine and cross-frame memory unit. A multi-feature fusion mechanism is constructed to combine the output features of the voting consensus of 20 intelligent agents with the features of the deep learning master model to generate the final film and television content features. This mechanism supports multi-format output of scripts, storyboards, high-definition videos, and synchronized audio-visual lip-syncing, thus realizing a complete closed loop in film and television production. Beneficial effects

[0006] It achieves end-to-end integrated autonomous generation from natural language requirements to cinematic finished products, connecting the entire process of text processing, feature extraction, 20 intelligent agents collaboration, cross-frame constraints, audio-visual synchronization, self-developed rendering, and content output. It breaks down the barriers between each link, without relying on any third-party models / tools, and truly realizes the intelligent closed-loop of film and television production. It pioneered a cluster of 20 film and television professional intelligent agents, which are precisely divided into five categories and twenty professional intelligent agents according to the film and television production process, covering the entire process from plot planning to 3D rendering. Each intelligent agent has independent reasoning, communication and task output capabilities, with strong professional targeting, which greatly improves the professionalism and accuracy of film and television content generation and matches the industrialized film and television production standards. An integrated system of agent memory, communication, and decision-making is established. The short-term and long-term memory system enables agents to store and integrate historical features. The dynamic task allocation mechanism enables precise matching of tasks and agents. The fully connected bidirectional communication mechanism supports agent-directed / broadcast interaction. The weighted voting decision-making mechanism ensures the consensus and rationality of the output results, greatly improving the collaborative efficiency of agents. Equipped with a full-link self-evolution engine for intelligent agents, it enables autonomous iteration of parameter enhancement, parameter reorganization, and inefficient elimination for intelligent agents. The model's capabilities continue to improve with use, and the evolution engine covers core modules such as a self-developed rendering engine and cross-frame memory units, achieving collaborative optimization of parameters across the entire link without the need for manual intervention. Equipped with a cross-frame memory constraint unit, it performs global memory and real-time deviation correction on the core features of film and television content such as characters, scenes, lighting, and composition, completely solving the industry pain points of character drift, scene collapse, and discontinuous timing in long video generation, and stably supports the continuous generation of minute-level long videos. The self-developed heavyweight spatiotemporal rendering engine adopts a layered temporal coding and spatiotemporal integrated GodDiT architecture. It natively supports the generation of visual frames at a resolution of 2048×2048 and can be seamlessly expanded to 4K. Combined with the audio-visual lip-sync unit, it achieves native linkage between audio and lip movements. The accuracy and naturalness of lip-sync for 8+ languages ​​far exceed existing technologies, reaching the film-level rendering standard. The deep learning main model adopts a sparse activation mechanism. The upgraded FilmBlock module integrates multi-branch features, which significantly reduces computing power consumption while ensuring the generation effect, achieving lightweight and efficient feature extraction and reducing the model deployment and running costs. The final generated film and television content supports standardized output in multiple formats, including TXT / Word script text format, PNG / JPG storyboard image format, MP4 / AVI high-definition video format, and audio-visual lip-synced final film format. It is adapted to the needs of multiple scenarios in industrialized film and television production, such as script creation, post-production, and platform release, and significantly reduces the manpower and time costs of film and television production.

[0007] Brief description of the illustrations in the instruction manual Appendix Figure 1 The diagram illustrates the entire process of film and television content generation based on multi-agent collaboration and autonomous evolution. It is a vertical text arrow flowchart that clearly shows the complete execution process from the input of natural language creation needs / original text of film and television scripts, through the processing of each core module, to the final product of film-level film and television content. It clarifies the sequence of each step and the flow of data, highlighting the core link of 20-agent collaboration. Appendix Figure 2 The overall system architecture diagram of this invention is a vertical text arrow flowchart, which clearly shows the connection relationship of each functional unit such as the data loading unit, the film and television-specific word segmentation unit, the deep learning main model unit, and the 20 film and television professional division of labor intelligent agent cluster unit, and clarifies the positive data transmission path and the feedback path of the intelligent agent self-evolution engine. Appendix Figure 3 The diagram shows the internal architecture of the self-developed heavyweight spatiotemporal rendering engine. It is a vertical text arrow flowchart that clearly shows the execution order of internal modules such as the patch embedding module, the layered temporal coding module, and the cinematic attention module, and clarifies the injection method of external features and the output path of the rendering results. Appendix Figure 4The diagram illustrates the collaborative process of a cluster of 20 film and television professional intelligent agents. It is a vertical text arrow flowchart that clearly shows the complete collaborative process of 20 intelligent agents from initialization, memory retrieval, task allocation, reasoning, communication, decision-making to memory update and self-evolution, and clarifies the internal execution logic of the intelligent agent cluster. Detailed Implementation

[0008] The invention will now be described in further detail with reference to the complete process. Module connection methods and algorithm execution logic not described in detail in this invention are all implemented using conventional techniques in the field and do not affect the completeness and innovativeness of the core technical solution of this invention. 1. Data Preprocessing Stage The original text of a natural language creation request or film and television script is input into a film and television-specific word segmentation module. First, redundant spaces, special symbols, and other invalid content in the text are removed using regular expressions, and professional vocabulary formats are standardized according to film and television industry standards. Then, a film and television-specific vocabulary list is called to complete accurate word segmentation, avoiding missegmentation of film and television professional vocabulary by general word segmentation. Finally, the sequence is encoded by a Transformer encoder to generate a standardized vector sequence with uniform dimensions. This sequence is transmitted to the deep learning main model, and data verification is performed at the same time to avoid invalid data affecting the model's running efficiency and generation effect.

[0009] 2. Feature Extraction Stage The standardized encoding sequence is input into the deep learning main model. The efficiency and accuracy of feature extraction are improved through the grouped query attention (GQA) mechanism. Combined with rotation position encoding, the temporal features and semantic logic of the text are preserved, avoiding feature loss. The hybrid expert architecture (MoE) of the main model adopts a sparse activation mechanism, which only activates the matching expert network according to the current film and television task type, while the unmatched expert network is in a dormant state, which greatly reduces the computational power consumption. The upgraded FilmBlock module first fuses the features of the attention branch and the MoE branch, and then transmits the fused features to the 20 film and television professional intelligent agent cluster unit. The main model also connects to the feature layer of the self-developed heavyweight spatiotemporal rendering engine to realize cross-modal fusion of text features and visual rendering features, laying the foundation for subsequent rendering stages.

[0010] 3. 20 Film and Television Professional Intelligent Agent Cluster Collaboration Phase This stage is the core of the invention, realizing the full-process collaboration of the intelligent agent's memory retrieval, task allocation, reasoning, communication, and decision-making. The specific steps are as follows: 3.1 Agent Initialization and Memory Retrieval

[0011] The 20 film and television professional intelligent agent cluster is initialized according to five categories: narrative, directing, art, editing, and rendering. Each intelligent agent reads its own short-term and long-term memory features. The short-term memory is the N most recent reasoning features stored in the queue structure, and the long-term memory is the high-quality output features selected by the scoring threshold. The memory fusion subunit transmits the weighted fusion of short / long-term memory features to the core reasoning layer of each intelligent agent.

[0012] 3.2 Dynamic Task Allocation The agent dynamic task allocation unit processes the input features through a task-agent matching gating network to generate allocation weights for 20 agents. Based on the current film and television task type (scriptwriting / director design / art production / editing rhythm / 3D rendering), the weights of the corresponding category agents are strengthened. The execution range of the agents is limited through a masking mechanism, and high-weight agents are activated to achieve accurate matching between tasks and agents.

[0013] 3.3 Initial Inference of the Agent Activated agents input the features output by the FilmBlock module and their own fused memory features into the core inference layer to complete the initial inference, generating task output features and communication messages. Inactive agents output zero features and do not participate in subsequent steps.

[0014] 3.4 Fully Connected Bidirectional Communication The agent bidirectional communication unit stacks all communication messages generated by agents, controls the directional / broadcast communication of agents through communication masks, weights and aggregates the communication messages that each agent can receive, generates communication input features, and transmits them to the communication interaction layer of the corresponding agent.

[0015] 3.5 Secondary Inference by Intelligent Agents Each agent fuses the input features of the initial reasoning with the communication input features, and then inputs them again into the core reasoning layer to complete the secondary reasoning, generating the final professional task output features. The secondary reasoning incorporates the communication information of other agents, improving the professionalism and collaboration of the output features.

[0016] 3.6 Weighted Voting Decision

[0017] The agent voting decision unit obtains real-time capability scores of all agents, dynamically adjusts the voting weight of each agent based on the scores, performs weighted fusion of the final task output features of the 20 agents, eliminates abnormal output data, and generates unified consensus output features to ensure the rationality and professionalism of the decision results.

[0018] 3.7 Memory System Update The agent memory system unit stores the final task output features of each agent in the short-term memory queue. If the agent's ability score reaches a preset threshold, the feature is filtered and stored in the long-term memory to provide historical feature support for subsequent reasoning.

[0019] 4. Rendering and Audio-Visual Synchronization Stage The cross-frame memory constraint unit globally memorizes and constrains the core visual features such as characters, scenes, lighting, and composition generated by 20 agents in real time. The consistency evaluation module detects inter-frame feature deviations, and if the deviation exceeds the threshold, it is transmitted to the agent cluster unit for dynamic correction through the feature feedback module. The audio-visual lip-sync unit converts the externally input Mel spectrum audio into audio feature vectors through the audio encoding subunit, and then maps it into lip-sync dynamic features through the lip-sync dynamics mapping subunit. After completing the precise temporal alignment through 3D rotation position encoding, it is injected into the self-developed heavyweight spatiotemporal rendering engine. The rendering engine completes feature mapping through the patch embedding module, the hierarchical temporal encoding module captures short-term action temporal features and long-term narrative features simultaneously, the cinematic attention module enhances core visual features, and finally completes the generation of 2K / 4K cinematic visual frames through the pixel restoration module.

[0020] 5. Final Feature Fusion and Content Output Stage The final feature fusion unit concatenates the consensus output features generated by the voting decisions of 20 agents with the features output by the deep learning main model. The final feature fusion is completed through a linear layer, a GELU activation layer, an RMSnorm normalization layer, and a dropout layer. The fused features are then transmitted to the content output unit or the self-developed heavyweight spatiotemporal rendering engine, depending on the film and television task type. The content output unit converts different types of film and television content into standardized formats according to the needs of industrial film and television production. The script text supports TXT / Word format, the storyboard images support PNG / JPG format, and the high-definition video and the finished film support MP4 / AVI format, thus completing the standardized output of film and television content.

[0021] 6. The full-chain self-evolution stage of intelligent agents The agent's self-evolution engine executes evolutionary operations at preset intervals. The specific steps are as follows: Ability rating update The moving average algorithm is used, combined with the agent's task reward value (calculated by the average weight of dynamically assigned tasks) to update the ability score of each agent in real time. The score ranges from 0 to 1, and the higher the score, the better the agent's task performance. Enhancement of high-quality intelligent agents For agents with a capability score ≥ 0.8, perform a parameter enhancement operation on the core inference layer, multiplying the parameter data by 1.05 to improve their task execution efficiency and accuracy, and record the evolution log. Medium-level agent reorganization For agents with ability scores between 0.6 and 0.8, randomly select one from the currently active high-quality agents, and fuse its parameters with those of the agent in a 6:4 ratio to complete parameter reorganization and optimize the agent's reasoning ability. Inefficient intelligent agents to be phased out For agents with a capability score ≤0.3, their activity flag parameter is set to an inactive state, so that they are filtered out by the masking mechanism in subsequent task allocation and no longer participate in the reasoning and communication process, thus realizing the autonomous elimination of inefficient agents; End-to-end parameter feedback The self-evolutionary engine feeds back the optimized agent parameters to the 20 film and television professional agent cluster units. At the same time, it adaptively adjusts the allocation of the number of heads of the movie-level attention module, the parameters of the hierarchical temporal coding convolution kernel, and the mapping weights of the pixel restoration module of the self-developed heavyweight spatiotemporal rendering engine. It also optimizes the feature memory and consistency evaluation threshold of the cross-frame memory constraint unit. All optimized parameters are fed back to the deep learning main model unit throughout the entire process, realizing autonomous iterative optimization of the model throughout the entire process and ensuring the stability and effectiveness of the evolution process.

Claims

1. A method for generating film and television content based on multi-agent cooperation and autonomous evolution, characterized in that, Includes the following steps: Step 1: Construct a word segmentation module specifically for the film and television industry to perform standardized cleaning, word segmentation, and sequence encoding on the original text of film and television scripts or natural language creation requirements, and generate input sequences that are compatible with deep learning models; Step 2: Build a deep learning master model with a hybrid expert architecture (MoE), embed Group Query Attention (GQA) mechanism and rotation position encoding to complete the extraction and fusion of film and television text features. The master model is connected to the feature layer of the self-developed spatiotemporal rendering engine to realize cross-modal fusion of text features and visual rendering features. Step 3: Initialize a cluster of 20 film and television professional division-of-labor intelligent agents. The intelligent agent cluster is divided into five categories according to the film and television production process: narrative, directing, art direction, editing, and rendering. It covers the entire process of plot planning, character creation, storyboard design, and 3D rendering. Each intelligent agent is equipped with an independent inference layer, communication interaction layer, and task-specific output head. At the same time, a short-term + long-term memory system, dynamic task allocation mechanism, fully connected bidirectional communication mechanism, and weighted voting decision-making mechanism are built to realize information interaction, memory storage, accurate task matching, and consensus decision-making among intelligent agents. Step 4: The encoded input sequence is fed into the main model. After feature extraction, it is transmitted to the 20-agent collaborative control module. Each professional agent integrates its own memory features and communication information to complete secondary inference. The visual rendering agent calls the self-developed heavyweight spatiotemporal rendering engine to generate 2K / 4K level visual frames. The audio-visual synchronization and lip-shape generation agents work together to achieve native alignment of audio features and visual frames. Step 5: Build a full-link self-evolution engine for intelligent agents. Based on the ability score and task reward value of the intelligent agents, execute the parameter enhancement of high-quality intelligent agents, the marking and elimination of inefficient intelligent agents, and the parameter reorganization of medium-quality intelligent agents at preset thresholds. At the same time, the self-evolution engine synchronously iterates and optimizes the core parameters of cross-frame memory units and self-developed rendering engines to achieve autonomous iterative optimization of the model across the entire link. Step Six: Integrate the voting consensus output of 20 intelligent agents with the features of the deep learning master model to generate the final film and television content, which includes script, storyboard, high-definition video, and synchronized audio-visual lip-sync, thus completing the intelligent generation of film and television content throughout the entire process.

2. A film and television content generation system based on multi-agent collaboration and autonomous evolution, characterized in that, include: Film and television-specific word segmentation unit, deep learning main model unit, 20 film and television professional division of labor intelligent agent cluster unit, intelligent agent memory system unit, intelligent agent dynamic task allocation unit, intelligent agent bidirectional communication unit, intelligent agent voting decision unit, intelligent agent self-evolution unit, self-developed heavyweight spatiotemporal rendering unit, cross-frame memory constraint unit, audio-visual lip-sync unit, and content output unit. The output of the film and television-specific word segmentation unit is connected to the input of the deep learning main model unit. The output of the deep learning main model unit is connected to the input of the 20 film and television professional division-based intelligent agent cluster unit and the feature input of the self-developed heavyweight spatiotemporal rendering unit. The 20 film and television professional division-based intelligent agent cluster unit interacts bidirectionally with the intelligent agent memory system unit, the intelligent agent dynamic task allocation unit, the intelligent agent bidirectional communication unit, and the intelligent agent voting decision unit. The output of the 20 film and television professional division-based intelligent agent cluster unit is connected to the input of the intelligent agent self-evolution unit. The outputs of the cross-frame memory constraint unit and the audio-visual lip-sync unit are connected to the execution input of the self-developed heavyweight spatiotemporal rendering unit. The output of the intelligent agent self-evolution unit provides feedback to the deep learning main model unit, the self-developed heavyweight spatiotemporal rendering unit, and the 20 film and television professional division-based intelligent agent cluster unit. All units cooperate to realize the film and television content generation method described in claim 1.

3. The method according to claim 1, characterized in that, The 20 film and television professional division of labor intelligent agents in step three are divided into five categories: narrative (plot planner, character creator, scriptwriter, emotional rhythm master, theme depth master), directing (general director, director of photography, storyboard artist, camera movement designer, composer), art (lighting artist, color director, sound designer, special effects director, scene artist), editing (editor, rhythm controller, transition designer, narrative flow master), and rendering (3D rendering chief director). Each intelligent agent is equipped with an independent core reasoning layer, communication interaction layer, and task-specific output head, and has built-in capability scoring and activity marking parameters.

4. The method according to claim 1, characterized in that, In step three, the agent's short-term + long-term memory system uses a queue structure to store the agent's most recent N-step reasoning features as short-term memory, and selects high-quality output features based on the ability scoring threshold to store them as long-term memory. After the short-term memory and long-term memory are fused, they participate in each step of the agent's reasoning process.

5. The method according to claim 1, characterized in that, The dynamic task allocation mechanism in step three generates allocation weights through a task-agent matching gating network. Based on the film and television task type (scriptwriting / director design / art production / editing rhythm / 3D rendering), the corresponding category of agents is weighted and strengthened. The execution range of the agents is limited through a masking mechanism to achieve precise matching between tasks and agents.

6. The method according to claim 1, characterized in that, The fully connected bidirectional communication mechanism in step three sets a communication mask to control the agent's directional / broadcast communication. The received multi-agent communication features are weighted and aggregated through the message fusion layer, and the aggregated communication features are injected into the agent inference layer to complete the secondary feature inference.

7. The method according to claim 1, characterized in that, The weighted voting decision-making mechanism in step three dynamically adjusts the voting weights based on the real-time capability scores of the agents, weights and fuses the output features of all agents to generate consensus results, eliminates abnormal output data, and ensures the rationality and professionalism of the decision results.

8. The method according to claim 1, characterized in that, The agent-wide self-evolution engine in step five uses a moving average algorithm combined with task reward values ​​to update the agent's ability score. It sets reinforcement thresholds, reorganization thresholds, and elimination thresholds. It strengthens the core layer parameters of high-scoring agents, reorganizes medium-scoring agents by fusing high-quality agent parameters, and eliminates low-scoring agents by marking them as inactive.

9. The system according to claim 2, characterized in that, The intelligent agent memory system unit includes a short-term memory subunit, a long-term memory subunit, and a memory fusion subunit. The short-term memory subunit uses a queue structure to implement feature caching, the long-term memory subunit stores high-quality features according to the ability scoring threshold, and the memory fusion subunit performs weighted fusion of short / long-term memory features and transmits them to each intelligent agent.

10. The system according to claim 2, characterized in that, The deep learning main model unit integrates a Grouped Query Attention (GQA) module, a Hybrid Expert Architecture (MoE) layer, and a FilmBlock upgrade module. The FilmBlock upgrade module merges the attention branch, the MoE branch, and the 20-agent collaborative branch. Achieve multi-level feature extraction and fusion.

11. The method according to claim 1, characterized in that, The agent's secondary reasoning process in step four is as follows: The agent first fuses the input features with its own memory features to complete the initial reasoning and generate a communication message. After obtaining the aggregated communication features through a two-way communication mechanism, it inputs them again into the core reasoning layer to complete the secondary reasoning and outputs the final task features.

12. The system according to claim 2, characterized in that, In the 20 film and television professional division of labor intelligent agent cluster unit, each agent is configured with independent ability score parameters and activity label parameters. The ability score parameters are updated in real time through a moving average algorithm, and the activity label parameters are dynamically modified by the agent's self-evolution unit according to the score threshold.

13. The method according to claim 1, characterized in that, The feature fusion process in step six is ​​as follows: the output features of the 20 agents' voting consensus are concatenated with the output features of the deep learning main model, and the final feature fusion is completed through a linear layer, activation layer, normalization layer and dropout layer. The fused features are then transmitted to the content output unit or the self-developed heavyweight spatiotemporal rendering unit.

14. The method according to claim 1, characterized in that, The self-developed heavyweight spatiotemporal rendering engine in step four adopts a layered temporal coding architecture, which simultaneously captures the short-term action temporal characteristics and long-term plot narrative characteristics of film and television content, and realizes the coherent generation of minute-level long videos.

15. The method according to claim 1, characterized in that, The audio-visual lip-syncing process in step four is as follows: first, the Mel spectrum is converted into audio feature vectors through the audio encoding module, then mapped into lip dynamics features by the lip-shape generation agent, and finally injected into the spatiotemporal rendering engine to complete the native linkage between visual frame lip shape and audio, supporting accurate lip alignment for 8+ languages.

16. The method according to claim 1, characterized in that, The cross-frame memory constraint unit in step three performs global memory and real-time constraint on the character, scene, lighting, and composition features generated by the 20 agents, achieving character consistency and scene coherence throughout the entire sequence of film and television content without the need for external reference graph intervention.

17. The system according to claim 2, characterized in that, The self-developed heavyweight spatiotemporal rendering unit adopts the spatiotemporal integrated GodDiT architecture, which includes a patch embedding module, a cinematic attention module, and a pixel restoration module. It natively supports the generation of visual frames at a resolution of 2048×2048 and supports seamless expansion of resolution to 4K.

18. The system according to claim 2, characterized in that, The audio-visual lip-sync unit includes an audio coding subunit, a lip-shape dynamics mapping subunit, and a timing alignment subunit. The timing alignment subunit achieves precise matching between audio features and visual frame timing through 3D rotational position coding.

19. The system according to claim 2, characterized in that, It also includes a data loading unit, which reads and preprocesses film and television script datasets and transmits the processed data to a film and television-specific word segmentation unit. The deep learning main model unit includes a feature normalization module and a linear mapping module to achieve feature dimension unification and data normalization processing.

20. The method according to claim 1, characterized in that, The final generated film and television content supports multiple output formats, including script text format, storyboard image format, MP4 / AVI high-definition video format, and audio-visual synchronized final film format, adapting to the multi-scenario needs of industrialized film and television production.

Images