Progressive visual content generation system based on thinking chain reasoning

Abstract

The invention discloses a progressive visual content generation system based on thinking chain reasoning, which belongs to the technical field of visual content generation, and comprises a thinking chain reasoning module for constructing a semantic-driven logical reasoning framework and converting an abstract concept input by a user into an executable visual generation instruction; the progressive generation module executes a staged content generation strategy, adopts a'global-local-optimization 'three-order generation normal form, and improves the efficiency on the premise of ensuring the generation quality through a dynamic resolution switching and resource self-adaptive allocation mechanism; and a multi-modal data processing module. Through the synergistic effect of the semantic-driven thinking chain reasoning framework and the knowledge graph, it is ensured that generated content strictly follows physical rules and professional specifications, logic contradictions and common learning errors are effectively avoided, a global-to-local progressive generation strategy is adopted, a dynamic resource allocation mechanism is combined, and the generation efficiency is improved. And the generation efficiency is remarkably improved while the visual quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of visual content generation, and in particular to a progressive visual content generation system based on thought chain reasoning. Background Art

[0002] Current visual content generation technologies mostly employ an end-to-end, single-stage generation model, which suffers from a lack of interpretability in generation logic and a significant loss of detail. Traditional methods struggle to effectively align the semantics of multimodal inputs, leading to mismatches between text descriptions and visual elements, often producing erroneous content that violates physical laws or spatial relationships. Existing systems generally neglect the systematic integration of domain knowledge, failing to verify the logical rationality of generated results. They also employ fixed computational strategies for resource allocation, making it prone to memory overflow or a sharp drop in generation efficiency when faced with complex scenarios, making it difficult to balance quality and performance requirements.

[0003] In existing technologies, most generative models lack a closed-loop feedback mechanism for user interaction and are unable to dynamically adjust outputs based on real-time correction instructions, resulting in a rigid creative process. Knowledge base updates rely on manual maintenance, making it difficult to respond to emerging field needs in a timely manner. In addition, cross-modal alignment methods mostly remain at the static feature matching level and are unable to handle the spatiotemporal consistency constraints in video generation. At the same time, traditional distributed computing architectures have obvious shortcomings in task scheduling and fault-tolerant recovery. Large-scale generation tasks are often interrupted by node failures, seriously affecting system availability and stability. These technical bottlenecks have severely restricted the practical application of visual generation systems in the field of professional creation; therefore, we propose a progressive visual content generation system based on thought chain reasoning to solve this problem. Summary of the Invention

[0004] The purpose of the present invention is to provide a progressive visual content generation system based on thought chain reasoning to solve the problems raised in the above background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A progressive visual content generation system based on thought chain reasoning, comprising: The Thinking Chain Reasoning Module builds a semantically driven logical reasoning framework, transforming abstract concepts input by users into executable visual generation instructions. Through a three-level processing flow, it achieves digital simulation of the human thinking process, ensuring that the generated content conforms to real-world logic and domain knowledge rules. The progressive generation module implements a phased content generation strategy, adopting a three-stage generation paradigm of "global → local → optimization". Through dynamic resolution switching and adaptive resource allocation, it improves efficiency while ensuring generation quality, solving the problems of detail loss and resource waste in traditional single-stage generation. The multimodal data processing module achieves unified representation and alignment of multi-source data, constructs a cross-modal semantic space, and fuses visual / textual information of different granularities through a feature pyramid architecture. This provides standardized data input for downstream modules and eliminates information loss caused by modality differences. The user interaction module provides a natural human-computer collaborative creation interface, supports multi-channel input and real-time feedback, integrates a personalized preference learning system, and dynamically optimizes the generated style based on the user's historical behavior, achieving intelligent evolution of "the more you use, the better you understand the user"; A dynamic evaluation and feedback module builds a dual evaluation system for generation quality. Through an iterative optimization mechanism driven by reinforcement learning, it implements an autonomous evolutionary cycle of "generation → evaluation → correction". A specially designed logical consistency verification unit is used to prevent the generation of counterintuitive content. The knowledge graph module stores and manages the domain knowledge system required for visual generation, including object attribute libraries, spatial relationship rule sets, and artistic creation guidelines. It continuously expands the knowledge boundaries through online learning mechanisms, provides explainable decision-making basis for thought chain reasoning, and ensures that the generated content complies with physical laws and industry standards. A multi-stage generation controller coordinates system-level task scheduling and resource management, adopts a hierarchical decision-making architecture, and dynamically balances generation quality, efficiency, and resource consumption through reinforcement learning strategies to support the stable generation of complex scenarios. The cross-modal alignment module builds a multimodal semantic mapping bridge to achieve accurate correspondence between text descriptions, sketches, and generated results. It also innovatively introduces a spatiotemporal consistency constraint mechanism to ensure that the motion trajectory of objects in video generation scenarios is consistent with physical laws. The distributed computing module provides an optimization solution for underlying computing resources. It supports the real-time generation of large-scale visual content through heterogeneous computing acceleration and intelligent memory scheduling strategies. It also designs a fault-tolerant recovery mechanism to ensure the system's continuous service capabilities and automatically switches to a backup computing unit in the event of a single node failure.

[0006] Preferably, the thought chain reasoning module includes: The semantic parser converts user input text / speech into a computable semantic structure tree. It uses dependency parsing and entity relationship extraction technology, supports nested semantic parsing, and introduces a dynamic pruning algorithm to automatically filter redundant modifiers and retain the core semantic framework. Logical relationship modeler, which builds a spatiotemporal constraint network between elements, models the proportion, perspective, and occlusion relationships between objects through graph neural networks, and verifies logical rationality using causal reasoning algorithms; The dynamic knowledge base integrates ConceptNet general knowledge graph and professional domain knowledge base, supports real-time update mechanism, and dynamically corrects knowledge weights through user feedback data.

[0007] Preferably, the progressive generation module includes: The contour generation submodule generates 256×256 low-resolution sketches based on an improved diffusion model and enhances global consistency through a multi-scale training strategy; The local refinement submodule divides the image into superpixel regions and gradually fills in details using a region-level Transformer structure, allowing users to specify regions for focused optimization. The adaptive optimization submodule dynamically adjusts the generation parameters through adversarial training and uses a dual discriminator architecture to guide the optimization direction.

[0008] Preferably, the multimodal data processing module includes: The visual feature extractor, based on the ViT-Large model, extracts hierarchical image features, including low-level texture, mid-level shape, and high-level semantics. It also incorporates deformable convolution to improve the ability to capture features of objects from unconventional perspectives. The text semantic encoder encodes the text description into a 768-dimensional semantic vector and uses a hierarchical attention mechanism to distinguish the subject, predicate, and object weights; The cross-modal alignment network achieves image-text alignment through the improved CLIP model, and jointly trains with contrast loss and reconstruction loss.

[0009] Preferably, the user interaction module includes: Intent understanding interface, which supports mixed input of text, sketches, and voice, and realizes multimodal intent fusion through joint embedding space; A real-time feedback system, based on the CycleGAN architecture, dynamically corrects the generated results. After the user selects an area, the system completes the local regeneration. A personalized preference library records user historical operation data and achieves rapid style transfer through a meta-learning framework.

[0010] Preferably, the dynamic evaluation and feedback module includes: Semantic consistency detector, which verifies the matching degree between the generated results and the semantic tree based on the graph attention network and detects logical conflicts; Aesthetic score predictor, which integrates the NIMA model with manually annotated data to evaluate dimensions such as compositional balance and color harmony; The iterative optimization controller uses Bayesian optimization and reinforcement learning to dynamically adjust the generation parameters. After each round of generation, the policy network is updated according to the evaluation results. The optimization goal is the weighted sum of multiple indicators.

[0011] Preferably, the knowledge graph module includes: Visual concept ontology library, which stores basic object attributes and modeling tools to define class-instance hierarchical relationships; A set of spatial relationship rules that define topological constraints, perspective rules, and physical laws between objects; Dynamic update engine, updates the knowledge base based on incremental learning, and designs a conflict detection mechanism to prevent erroneous knowledge injection.

[0012] Preferably, the multi-stage generation controller includes: The task decomposer breaks down complex generation goals into ordered subtask chains and uses hierarchical reinforcement learning to plan the optimal execution path; Resource allocator, dynamically schedules GPU memory based on priority queues and designs preemptive resource allocation algorithms; The exception handling unit detects logical conflicts and resource overruns during the generation process and triggers automatic repair strategies.

[0013] Preferably, the cross-modal alignment module includes: Semantic Attention Network, which achieves soft alignment of text words and image regions through an interpretable attention mechanism; Spatiotemporal synchronizer, which predicts object motion trajectories in video generation, integrating optical flow estimation and LSTM time series modeling techniques; Style transfer bridge, establishes a mapping matrix between text sentiment words and visual tones, and supports fine-grained style control based on StyleGAN.

[0014] Preferably, the distributed computing module Heterogeneous computing scheduler coordinates CPU preprocessing, GPU model inference, and TPU batch training tasks, and designs DAG-based task dependency management; Memory optimization pool, which enables dynamic reuse of video memory and eliminates low-priority data through an improved LRU algorithm; Parallel generation pipeline, supporting collaborative generation of multi-resolution images.

[0015] The beneficial effects of the present invention are: 1. In the present invention, the described progressive visual content generation system based on thought chain reasoning constructs a semantically driven logical framework through the thought chain reasoning module, combines the domain knowledge rules of the knowledge graph module with the consistency check of the dynamic evaluation module, and ensures that the visual content strictly adheres to physical laws and industry standards. The progressive generation strategy refines the global structure and local details in stages, and cooperates with cross-modal alignment technology to accurately match text descriptions and visual elements, effectively avoiding logical contradictions and counterintuitive errors in traditional generation. The dynamic update mechanism of the knowledge graph continuously injects new knowledge, enabling the generation system to have the reasoning ability to adapt to complex scenarios and produce high-quality content that conforms to both objective reality and professional requirements. 2、The progressive visual content generation system based on thought chain reasoning provided by the application, based on the global-local optimization generation paradigm, adopts dynamic resolution switching and resource adaptive allocation strategy, concentrates computing resources in key stages to improve detail accuracy, intelligently reduces processing intensity in non-key areas, greatly reduces redundant calculation, and through heterogeneous resource scheduling and memory optimization strategy, supports parallel processing of large-scale data, ensures real-time generation of high-quality content, and dynamically adjusts task priority through multi-stage controller, maximizes resource utilization rate under the premise of ensuring visual effect, solves the contradiction between quality and efficiency of traditional single-stage model; 3、The progressive visual content generation system based on thought chain reasoning provided by the application provides a multi-modal mixed input interface, supports flexible combination of text, sketch and voice, accurately captures user creation intention through cross-modal semantic alignment technology, and allows users to make local corrections to generated content through real-time feedback mechanism, and a personalized preference learning system continuously analyzes user behavior data to automatically optimize generation style and detail processing method, which greatly reduces the creation threshold and enables non-professional users to efficiently express creativity through natural interaction, while meeting the fine adjustment needs of professional users, and realizes intelligent collaborative creation in a true sense; 4、The progressive visual content generation system based on thought chain reasoning provided by the application establishes a closed loop link of generation-evaluation-correction through an iterative optimization mechanism driven by reinforcement learning, continuously optimizes the generation strategy and corrects logical bias, and the online learning capability of the knowledge graph module supports incremental knowledge expansion, so that the system can quickly adapt to emerging fields and special scene needs, and an exception handling unit monitors the generation process in real time and combines a fault tolerance recovery mechanism to ensure stable execution of complex tasks, which makes the system continuously improve the generation quality and scene adaptability in long-term use, forming a positive development cycle of becoming more intelligent; 5、The progressive visual content generation system based on thought chain reasoning provided by the application ensures that the generated content strictly follows physical laws and professional specifications through the synergistic effect of the semantic-driven thought chain reasoning framework and the knowledge graph, effectively avoids logical contradictions and common sense errors, adopts a global-to-local progressive generation strategy combined with a dynamic resource allocation mechanism to significantly improve generation efficiency while ensuring visual quality, and a multi-modal interactive interface supports multi-channel input such as natural language and sketch, and cooperates with real-time feedback and personalized learning functions to reduce the creation threshold and realize accurate intention understanding, and the built-in closed-loop optimization mechanism of the system has self-evolution ability through continuous evaluation and knowledge base expansion, can dynamically adapt to complex scene needs, and continuously improves the generation quality and scene adaptability in long-term use, forming an intelligent positive development cycle. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1This is a system block diagram of a progressive visual content generation system based on thought chain reasoning proposed by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Reference Figure 1 , a progressive visual content generation system based on thought chain reasoning, including: The Thinking Chain Reasoning Module builds a semantically driven logical reasoning framework, transforming abstract concepts input by users into executable visual generation instructions. Through a three-level processing flow, it achieves digital simulation of the human thinking process, ensuring that the generated content conforms to real-world logic and domain knowledge rules. The progressive generation module implements a phased content generation strategy, adopting a three-stage generation paradigm of "global → local → optimization". Through dynamic resolution switching and adaptive resource allocation, it improves efficiency while ensuring generation quality, solving the problems of detail loss and resource waste in traditional single-stage generation. The multimodal data processing module achieves unified representation and alignment of multi-source data, constructs a cross-modal semantic space, and fuses visual / textual information of different granularities through a feature pyramid architecture. This provides standardized data input for downstream modules and eliminates information loss caused by modality differences. The user interaction module provides a natural human-computer collaborative creation interface, supports multi-channel input and real-time feedback, integrates a personalized preference learning system, and dynamically optimizes the generated style based on the user's historical behavior, achieving intelligent evolution of "the more you use, the better you understand the user"; A dynamic evaluation and feedback module builds a dual evaluation system for generation quality. Through an iterative optimization mechanism driven by reinforcement learning, it implements an autonomous evolutionary cycle of "generation → evaluation → correction". A specially designed logical consistency verification unit is used to prevent the generation of counterintuitive content. The knowledge graph module stores and manages the domain knowledge system required for visual generation, including object attribute libraries, spatial relationship rule sets, and artistic creation guidelines. It continuously expands the knowledge boundaries through online learning mechanisms, provides explainable decision-making basis for thought chain reasoning, and ensures that the generated content complies with physical laws and industry standards. A multi-stage generation controller coordinates system-level task scheduling and resource management, adopts a hierarchical decision-making architecture, and dynamically balances generation quality, efficiency, and resource consumption through reinforcement learning strategies to support the stable generation of complex scenarios. The cross-modal alignment module builds a multimodal semantic mapping bridge to achieve accurate correspondence between text descriptions, sketches, and generated results. It also innovatively introduces a spatiotemporal consistency constraint mechanism to ensure that the motion trajectory of objects in video generation scenarios is consistent with physical laws. The distributed computing module provides an optimization solution for underlying computing resources. It supports the real-time generation of large-scale visual content through heterogeneous computing acceleration and intelligent memory scheduling strategies. It also designs a fault-tolerant recovery mechanism to ensure the system's continuous service capabilities and automatically switches to a backup computing unit in the event of a single node failure.

[0019] Preferably, the thought chain reasoning module includes: The semantic parser converts user input text / speech into a computable semantic structure tree. It uses dependency parsing and entity relationship extraction technology, supports nested semantic parsing, and introduces a dynamic pruning algorithm to automatically filter redundant modifiers and retain the core semantic framework. Logical relationship modeler, which builds a spatiotemporal constraint network between elements, models the proportion, perspective, and occlusion relationships between objects through graph neural networks, and verifies logical rationality using causal reasoning algorithms; The dynamic knowledge base integrates ConceptNet general knowledge graph and professional domain knowledge base, supports real-time update mechanism, and dynamically corrects knowledge weights through user feedback data.

[0020] Preferably, the progressive generation module includes: The contour generation submodule generates 256×256 low-resolution sketches based on an improved diffusion model and enhances global consistency through a multi-scale training strategy; The local refinement submodule divides the image into superpixel regions and gradually fills in details using a region-level Transformer structure, allowing users to specify regions for focused optimization. The adaptive optimization submodule dynamically adjusts the generation parameters through adversarial training and uses a dual discriminator architecture to guide the optimization direction.

[0021] Preferably, the multimodal data processing module includes: The visual feature extractor, based on the ViT-Large model, extracts hierarchical image features, including low-level texture, mid-level shape, and high-level semantics. It also incorporates deformable convolution to improve the ability to capture features of objects from unconventional perspectives. The text semantic encoder encodes the text description into a 768-dimensional semantic vector and uses a hierarchical attention mechanism to distinguish the subject, predicate, and object weights; The cross-modal alignment network achieves image-text alignment through the improved CLIP model, and jointly trains with contrast loss and reconstruction loss.

[0022] Preferably, the user interaction module includes: An intent understanding interface supports mixed input of text, sketch, and voice, and multi-modal intent fusion is achieved through a joint embedding space. A real-time feedback system realizes dynamic correction of generated results based on a CycleGAN architecture, and the system completes local regeneration after the user circles an area. A personalized preference library records user historical operation data and realizes fast style transfer through a meta-learning framework.

[0023] Preferably, the dynamic evaluation and feedback module comprises: A semantic consistency detector verifies the matching degree of the generated result and the semantic tree based on a graph attention network, and detects logical conflicts. An aesthetic score predictor integrates an NIMA model and artificial annotation data to evaluate dimensions such as composition balance and color harmony. An iterative optimization controller dynamically adjusts generation parameters using Bayesian optimization and reinforcement learning, updates the policy network after each generation according to the evaluation results, and optimizes the target as a weighted sum of multiple indicators.

[0024] Preferably, the knowledge graph module comprises: A visual concept ontology library stores basic object attributes and models tool-defined class-instance hierarchical relationships. A set of spatial relationship rules define topological constraints, perspective rules, and physical laws between objects. A dynamic update engine updates the knowledge base based on incremental learning and designs a conflict detection mechanism to prevent the injection of incorrect knowledge.

[0025] Preferably, the multi-stage generation controller comprises: A task decomposer decomposes complex generation targets into an ordered task chain and plans an optimal execution path using hierarchical reinforcement learning. A resource allocator dynamically schedules GPU memory based on a priority queue and designs a preemptive resource allocation algorithm. An exception handling unit detects logical conflicts and resource overruns during generation and triggers automatic repair strategies.

[0026] Preferably, the cross-modal alignment module comprises: A semantic attention network realizes soft alignment between text words and image regions through an interpretable attention mechanism. A space-time synchronizer predicts object motion trajectories in video generation and integrates optical flow estimation and LSTM time series modeling techniques. A style transfer bridge establishes a mapping matrix between text sentiment words and visual color tones to support fine-grained style control based on StyleGAN.

[0027] Preferably, the distributed computing module A heterogeneous computing scheduler coordinates CPU pre-processing, GPU model inference and TPU batch training tasks, and designs a task dependency management based on DAG; A memory optimization pool realizes dynamic reuse of GPU memory, and eliminates low-priority data through an improved LRU algorithm. A parallel generation pipeline supports collaborative generation of multi-resolution images.

[0028] The above describes in detail the progressive visual content generation system based on thought chain reasoning provided by the present application. The principles and implementation modes of the present application are described by using specific embodiments, and the above embodiment description is only used to help understand the method and core idea of the present application. It should be pointed out that, for those skilled in the art, without departing from the principles of the present application, the present application can be improved and modified in several ways, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. A progressive visual content generation system based on thought chain reasoning, characterized by: include: The Thinking Chain Reasoning Module builds a semantically driven logical reasoning framework, transforming abstract concepts input by users into executable visual generation instructions. Through a three-level processing flow, it achieves digital simulation of the human thinking process, ensuring that the generated content conforms to real-world logic and domain knowledge rules. The progressive generation module implements a phased content generation strategy, adopting a three-stage generation paradigm of "global → local → optimization". Through dynamic resolution switching and adaptive resource allocation, it improves efficiency while ensuring generation quality, solving the problems of detail loss and resource waste in traditional single-stage generation. The multimodal data processing module achieves unified representation and alignment of multi-source data, constructs a cross-modal semantic space, and fuses visual / textual information of different granularities through a feature pyramid architecture. This provides standardized data input for downstream modules and eliminates information loss caused by modality differences. The user interaction module provides a natural human-computer collaborative creation interface, supports multi-channel input and real-time feedback, integrates a personalized preference learning system, and dynamically optimizes the generated style based on the user's historical behavior, achieving intelligent evolution of "the more you use, the better you understand the user"; A dynamic evaluation and feedback module builds a dual evaluation system for generation quality. Through an iterative optimization mechanism driven by reinforcement learning, it implements an autonomous evolutionary cycle of "generation → evaluation → correction". A specially designed logical consistency verification unit is used to prevent the generation of counterintuitive content. The knowledge graph module stores and manages the domain knowledge system required for visual generation, including object attribute libraries, spatial relationship rule sets, and artistic creation guidelines. It continuously expands the knowledge boundaries through online learning mechanisms, provides explainable decision-making basis for thought chain reasoning, and ensures that the generated content complies with physical laws and industry standards. A multi-stage generation controller coordinates system-level task scheduling and resource management, adopts a hierarchical decision-making architecture, and dynamically balances generation quality, efficiency, and resource consumption through reinforcement learning strategies to support the stable generation of complex scenarios. The cross-modal alignment module builds a multimodal semantic mapping bridge to achieve accurate correspondence between text descriptions, sketches, and generated results. It also innovatively introduces a spatiotemporal consistency constraint mechanism to ensure that the motion trajectory of objects in video generation scenarios is consistent with physical laws. The distributed computing module provides an optimization solution for underlying computing resources. It supports the real-time generation of large-scale visual content through heterogeneous computing acceleration and intelligent memory scheduling strategies. It also designs a fault-tolerant recovery mechanism to ensure the system's continuous service capabilities and automatically switches to a backup computing unit in the event of a single node failure.

2. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The thought chain reasoning module includes: The semantic parser converts user input text / speech into a computable semantic structure tree, using dependency parsing and entity relationship extraction technology to support nested semantic parsing; Logical relationship modeler, which builds a spatiotemporal constraint network between elements, models the proportion, perspective, and occlusion relationships between objects through graph neural networks, and verifies logical rationality using causal reasoning algorithms; The dynamic knowledge base integrates ConceptNet general knowledge graph and professional domain knowledge base, supports real-time update mechanism, and dynamically corrects knowledge weights through user feedback data.

3. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The progressive generation module includes: The contour generation submodule generates 256×256 low-resolution sketches based on an improved diffusion model and enhances global consistency through a multi-scale training strategy; The local refinement submodule divides the image into superpixel regions and gradually fills in details using a region-level Transformer structure, allowing users to specify regions for focused optimization. The adaptive optimization submodule dynamically adjusts the generation parameters through adversarial training and uses a dual discriminator architecture to guide the optimization direction.

4. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The multimodal data processing module includes: Visual feature extractor, which extracts hierarchical image features based on the ViT-Large model, including low-level texture, mid-level shape, and high-level semantics; The text semantic encoder encodes the text description into a 768-dimensional semantic vector and uses a hierarchical attention mechanism to distinguish the subject, predicate, and object weights; The cross-modal alignment network achieves image-text alignment through the improved CLIP model, and jointly trains with contrast loss and reconstruction loss.

5. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The user interaction module includes: Intent understanding interface, which supports mixed input of text, sketches, and voice, and realizes multimodal intent fusion through joint embedding space; A real-time feedback system, based on the CycleGAN architecture, dynamically corrects the generated results. After the user selects an area, the system completes the local regeneration. A personalized preference library records user historical operation data and achieves rapid style transfer through a meta-learning framework.

6. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The dynamic evaluation and feedback module includes: Semantic consistency detector, which verifies the matching degree between the generated results and the semantic tree based on the graph attention network and detects logical conflicts; Aesthetic score predictor, which integrates the NIMA model with manually annotated data to evaluate dimensions such as compositional balance and color harmony; Iteratively optimize the controller and dynamically adjust the generation parameters using Bayesian optimization and reinforcement learning.

7. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The knowledge graph module includes: Visual concept ontology library, which stores basic object attributes and modeling tools to define class-instance hierarchical relationships; A set of spatial relationship rules that define topological constraints, perspective rules, and physical laws between objects; Dynamic update engine, updates the knowledge base based on incremental learning, and designs a conflict detection mechanism to prevent erroneous knowledge injection.

8. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The multi-stage generation controller includes: The task decomposer breaks down complex generation goals into ordered subtask chains and uses hierarchical reinforcement learning to plan the optimal execution path; Resource allocator, dynamically schedules GPU memory based on priority queues and designs preemptive resource allocation algorithms; The exception handling unit detects logical conflicts and resource overruns during the generation process and triggers automatic repair strategies.

9. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The cross-modal alignment module includes: Semantic Attention Network, which achieves soft alignment of text words and image regions through an interpretable attention mechanism; Spatiotemporal synchronizer, which predicts object motion trajectories in video generation, integrating optical flow estimation and LSTM time series modeling techniques; Style transfer bridge, establishes a mapping matrix between text sentiment words and visual tones, and supports fine-grained style control based on StyleGAN.

10. The progressive visual content generation system based on thought chain reasoning according to claim 1 is characterized in that: The distributed computing module Heterogeneous computing scheduler coordinates CPU preprocessing, GPU model inference, and TPU batch training tasks, and designs DAG-based task dependency management; Memory optimization pool, which enables dynamic reuse of video memory and eliminates low-priority data through an improved LRU algorithm; Parallel generation pipeline, supporting collaborative generation of multi-resolution images.

Images