Ten-modal fusion infinite agent collaborative AGI large model system and reasoning method

Abstract

The application discloses a ten-modal fusion infinite intelligent agent collaborative AGI large model system and a reasoning method, and belongs to the technical field of artificial intelligence large models. The application constructs a ten-modal unified input system containing text, image, video, audio, depth map, infrared image, laser radar point cloud, industrial sensor time series data, structured table data and three-dimensional Gaussian scene data, and configures ten kinds of modal exclusive encoders to realize high-precision parallel feature extraction. The application integrates field automatic recognition, intelligent task splitting, dynamic infinite intelligent agent generation, multi-expert adaptive parallel reasoning, model memory scheduling and multi-agent consensus fusion core mechanism, and constructs an end-to-end full-automatic AGI reasoning closed loop. The application solves the technical shortcomings of traditional large models, such as single mode, low feature extraction precision, fixed intelligent agent, poor complex task adaptation capability and weak industrial scene landing, and can be widely applied to general intelligent question and answer, industrial intelligent perception, automatic driving analysis, three-dimensional scene understanding, intelligent detection, government affair analysis and other full-scene fields, and has strong technical innovation and industrial application prospects.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence large model, multimodal perception fusion, adaptive expert model scheduling, dynamic multi-agent collaborative reasoning, and industrial 3D perception technology. Specifically, it relates to a ten-modal fusion infinite agent collaborative AGI large model system and reasoning method. Background Technology

[0002] Existing general-purpose AI models typically only support four basic modalities: text, image, audio, and video. This limited modal coverage makes them unsuitable for high-end and complex scenarios such as industrial inspection, autonomous driving, 3D reconstruction, infrared night vision, LiDAR sensing, and industrial sensor time-series analysis. Existing multimodal fusion technologies mostly employ simple feature splicing methods without dedicated custom encoder structures, resulting in low accuracy in feature extraction for various modalities and poor fusion correlation. Existing agent architectures are mostly fixed structures with a fixed number of single agents or finite agents. They cannot automatically split tasks or generate the corresponding number of agents based on task complexity, and they lack dynamic adaptive reasoning capabilities. Existing large-scale model inference architectures lack dynamic expert scheduling, model memory reuse, and multi-agent consensus fusion mechanisms. They have significant shortcomings in inference stability, scenario adaptability, and industrial applicability, making it impossible to build a truly universal, all-scenario, and industrial-grade AGI inference system. Summary of the Invention

[0003] 3.1 Purpose of the Invention To address the shortcomings of existing technologies, such as single modality, universal encoding, fixed agents, rigid reasoning, and weak adaptability to industrial scenarios, this invention provides a ten-modal fusion infinite agent collaborative AGI large model system and reasoning method, realizing a general AGI system with full-modal native perception, dynamic task decomposition, infinite agent generation, multi-expert adaptive reasoning, and multi-result consensus fusion. 3.2 Technical Solution The system of this invention is divided into five layers: input selection layer, modality coding layer, backbone feature layer, agent scheduling layer, and fusion output layer. The input selection layer supports ten modal inputs, including text, images, videos, audio, depth maps, infrared images, LiDAR point clouds, industrial sensor time series, structured tables, and 3D Gaussian scenes. Each modality is configured with an independent dynamic switch, which can be enabled, calculated, and encoded as needed. The modality coding layer is equipped with ten completely independent dedicated encoders, and the feature extraction logic is customized for the data characteristics of each modality to achieve high-precision and high-specificity feature coding for each modality. The backbone feature layer completes multimodal unified semantic representation, feature alignment and normalization processing through a deep backbone network, realizing a global unified semantic space mapping of ten modal features. The agent scheduling layer includes three core capabilities: domain recognition, intelligent task decomposition, and dynamic agent generation. It automatically identifies the task domain, automatically decomposes complex tasks, and automatically generates a matching number of dedicated sub-agent clusters. The fusion output layer achieves unified verification and semantic integration of multi-way inference results through dynamic multi-expert parallel reasoning, model memory scheduling optimization, and multi-agent consensus fusion, and finally decodes and outputs stable, accurate, and complete intelligent response results. 3.3 Beneficial Effects This invention natively supports ten modal inputs, covering civilian scenarios, industrial sensing scenarios, autonomous driving scenarios, 3D reconstruction scenarios, and intelligent detection scenarios. The modal coverage far exceeds that of existing general-purpose large models. Each modality is equipped with an independent dedicated encoder, avoiding feature extraction distortion caused by a general encoder and significantly improving the accuracy of multimodal understanding. It has the ability to automatically identify the domain and break down tasks, and can adaptively handle simple tasks as well as extremely complex combined tasks. It has the ability to generate an unlimited number of dynamic intelligent agents, breaking through the traditional limitation of a fixed number of intelligent agents and achieving dynamic matching between task volume and the number of intelligent agents. By introducing a dynamic multi-expert scheduling and memory reuse mechanism, the inference speed is faster, the computing power utilization is higher, and the stability is stronger. By adopting a multi-agent consensus fusion mechanism, the bias of single-path inference is avoided, and the output results are more rigorous and reliable. The overall architecture is highly engineered, can be privately deployed, can be customized for specific industries, and can be commercially deployed at scale, possessing extremely high industrial value. Detailed Implementation The system of this invention is deployed on general computing devices or GPU inference devices, and completes the initial loading of the backbone network, ten modal encoders, domain recognition module, task splitting module, intelligent agent factory, dynamic expert module, memory scheduling module, and consensus fusion module. During system operation, the system receives user-input text and optional multimodal data, and filters valid input data based on the modality on / off status. It performs standardized preprocessing operations on various modal data, including size normalization, numerical normalization, and format unification. Ten modal encoders work in parallel to extract deep features of their respective modalities and complete single-modal feature encoding. All effective modal features are dimensionally aligned and concatenated, then fed into the backbone network to complete global semantic encoding and feature normalization, thus constructing a unified multimodal semantic representation. The domain identification module classifies the current overall input content into domains to determine the industry and scenario type to which the task belongs. The task splitting module decomposes complex user instructions hierarchically, generating multiple independent, parallel, and independently reasonable subtask queues. The intelligent agent factory dynamically generates a corresponding number of sub-intelligent agents with corresponding functions based on the number and type of sub-tasks, forming a dynamic intelligent agent cluster. The dynamic multi-expert module adaptively activates the corresponding expert sub-models, and works with the model memory scheduling module to achieve parameter reuse and inference acceleration, realizing parallel inference computation of multiple agents. The consensus fusion module fuses, aligns, verifies, and filters the output features of all sub-agents to obtain the globally optimal unified feature representation. Finally, the global features are converted into natural language text through the decoding layer, and the final AGI intelligent reasoning result is output, completing a complete reasoning loop. Attached image description: Figure 1 is a schematic diagram of the ten-modal fusion infinite agent collaborative AGI large model system and inference method of the present invention. This flowchart fully illustrates the overall operation process of the present invention, which includes: receiving user multimodal input requests; filtering valid input data through a ten-modal dynamic switch; performing standardized preprocessing on multimodal data; performing parallel feature extraction using a ten-modal dedicated production-grade encoder; achieving unified fusion and dimensional alignment of multimodal features; completing overall feature encoding and normalization processing through a backbone base; automatically completing AI business domain identification; intelligently and automatically splitting complex instructions into tasks; dynamically generating infinite sub-agents according to task requirements; calling multiple expert models to complete parallel inference calculations; cooperating with model memory scheduling to complete inference optimization; performing consensus fusion on the inference results of multiple agents; and finally generating the final AI response through semantic decoding. The present invention constructs a fully automatic end-to-end AGI inference closed-loop system with ten-modal native fusion and dynamic agent collaborative inference, effectively solving the technical defects of traditional large models, such as a single number of modalities, fixed agent structure, insufficient adaptability to complex industrial scenarios, and poor multimodal fusion accuracy.

Claims

1. A ten-modal fusion infinite intelligent agent collaborative AGI large-scale model system, characterized in that, It includes: a text input module, a ten-modal selectable input module, ten dedicated modal encoder modules, a backbone module, a dynamic multi-expert generation module, a model memory scheduling module, an agent factory module, a domain recognition module, a task decomposition module, an agent consensus fusion module, and an inference output module; The ten modalities include text, images, videos, audio, depth maps, infrared images, LiDAR point clouds, industrial sensor time-series data, structured tabular data, and 3D Gaussian scene data; Each modality is configured with an independent enable switch, dynamically selecting whether to activate the encoding and inference process of the corresponding modality based on user input.

2. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The ten dedicated modal encoders correspond to the dedicated feature extraction structures of ten modal data, and perform customized encoding for the data dimensions, data structures and distribution characteristics of different modalities to achieve high-precision independent feature extraction for each modality.

3. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The domain identification module is used to automatically identify the business domain type corresponding to the user input, output the domain classification result, and provide a priori basis for subsequent task splitting and agent generation.

4. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The task splitting module is used to automatically break down a complex overall task input by the user into multiple sub-task units that can be executed in parallel and have independent reasoning capabilities.

5. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The intelligent agent factory module dynamically generates a corresponding number of dedicated sub-intelligent agents with corresponding functions based on the domain identification results and the split sub-tasks, thereby realizing dynamic matching between tasks and intelligent agents.

6. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The dynamic multi-expert generation module dynamically activates the corresponding expert sub-models to participate in inference based on the number of currently enabled modalities and the task type, thereby achieving dynamic routing and adaptive computation.

7. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The model memory scheduling module is used to store, reuse, and update the feature parameters and inference states of sub-models, thereby improving the stability and speed of inference in continuous inference scenarios.

8. The ten-modal fusion infinite agent collaborative AGI large model system according to claim 1, characterized in that, The agent consensus fusion module performs unified fusion, consensus verification, and semantic alignment on the reasoning features and output results of multiple sub-agents, and outputs unique, stable, and unified final reasoning features.

9. A ten-modal fusion infinite agent collaborative AGI large model inference method, characterized in that, It includes the following steps: Step 1: Receive text input from the user and optional ten-modal sensing input data; Step 2: Filter the currently valid input modalities based on the modality enable switch, and perform standardized preprocessing on various types of data; Step 3: Extract deep features of each modality in parallel using ten dedicated modal encoders; Step 4: Perform dimensional alignment and unified fusion on the multimodal features, and input them into the backbone base to complete global semantic encoding and normalization processing; Step 5: Automatically determine the business domain to which the current task belongs through the domain identification module; Step Six: Decompose complex tasks into multiple independent executable subtasks using the task splitting module; Step 7: The agent factory dynamically generates the corresponding sub-agent clusters based on the sub-tasks; Step 8: Complete multi-agent parallel inference computation through the dynamic multi-expert module and memory scheduling module; Step 9: Achieve fusion and semantic unification of multi-way inference results through the consensus fusion module; Step 10: Decode and generate natural language results to complete AGI intelligent reasoning output.

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