Super body system based on AI underlying architecture

Abstract

The invention discloses a super body system based on AI underlying architecture, which comprises a planning stage module, a system development module, a capability introduction module, an operation engine module, a research and development cycle module, a continuous optimization module and a visualization task module, and is characterized in that the planning stage module is in control connection with the system development module; according to the method, by collecting and sorting internal documents of an enterprise and integrating private database resources, the dimension of available data is greatly improved, and the personalized requirements of complex tasks can be met; a security event response mechanism is established, security audit and evaluation are performed regularly, protection measures are updated according to new threats, strategy formulation can be improved according to user feedback data, and the sustainable evolution efficiency of the system is greatly improved; an optimization strategy is continuously adjusted according to feedback, a long-acting monitoring mechanism is established to prevent performance rollback, the system has an adaptive learning framework and a dynamic iteration capability, model parameters can be automatically adjusted or reasoning logic can be optimized according to real-time feedback, and knowledge system updating is larger than actual requirements.

Description

Technical Field

[0001] This invention relates to the field of superbody system technology, specifically to a superbody system based on an AI underlying architecture. Background Technology

[0002] The core advantages of the "AI Underlying Architecture Super System" are: through high integration, intelligent management, and open design, it greatly improves the efficiency, performance, reliability, and security of AI research and development and applications, laying a solid foundation for building the next generation of artificial general intelligence (AGI); it can quickly adapt to different deployment scenarios from edge computing to hyperscale data centers; it significantly reduces training and inference costs and shortens model iteration cycles; it reduces the burden of manual parameter tuning and continuously maintains high performance of the system in dynamic environments; it enhances user trust, meets increasingly stringent compliance requirements (such as GDPR), and reduces the risk of abuse.

[0003] However, current super-body systems suffer from several significant drawbacks: First, they rely solely on publicly available datasets for knowledge fusion, failing to integrate private database resources. This results in limited data dimensions and insufficient scenario coverage, making it difficult to meet the personalized needs of complex tasks. Second, the systems lack adaptive learning frameworks and dynamic iteration capabilities, hindering the automatic adjustment of model parameters or optimization of inference logic based on real-time feedback, leading to knowledge system updates lagging behind actual requirements. Finally, the testing and optimization phases suffer from missing key capabilities. The systems lack comprehensive monitoring data collection modules and effective channels for accessing user feedback data, resulting in passive problem identification and improvement strategy formulation, severely restricting the system's continuous evolution efficiency. Therefore, designing a super-body system based on an AI-based underlying architecture is essential. Summary of the Invention

[0004] The purpose of this invention is to provide a super-body system based on an AI underlying architecture to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a super system based on an AI underlying architecture, comprising a planning phase module, a system development module, a model training module, a testing and optimization module, a technical implementation module, a knowledge introduction module, a capability introduction module, a runtime engine module, a research and development cycle module, a continuous optimization module, and a visualization task module. The planning phase module controls and connects to the system development module, the system development module controls and connects to the model training module and the research and development cycle module, the model training module controls and connects to the testing and optimization module, the testing and optimization module controls and connects to the technical implementation module and the continuous optimization module, and the technical implementation module controls and connects to the runtime engine module, the knowledge introduction module, the capability introduction module, and the visualization task module, respectively.

[0006] As a further technical solution of the present invention, the planning stage module consists of a requirements definition module, a technology selection module, and a prototype design module. The requirements definition module controls and connects to the technology selection module, and the technology selection module controls and connects to the prototype design module.

[0007] As a further technical solution of the present invention, the system development module consists of a design architecture module, an interaction layer module, a coordination layer module, and an intelligent decision-making layer module, and the design architecture module controls and connects the interaction layer module, the coordination layer module, and the intelligent decision-making layer module respectively.

[0008] As a further technical solution of the present invention, the model training module consists of a training structure configuration module, a core training stage module, and a model evaluation module. The training structure configuration module controls and connects to the core training stage module, and the core training stage module controls and connects to the model evaluation module.

[0009] As a further technical solution of the present invention, the test optimization module consists of a performance optimization module, a multi-environment testing module, a setup and preparation module, a test case design module, a test execution monitoring module, a performance tuning module, a performance baseline evaluation module, a location and analysis module, a security verification module, a standards and inspection module, a security assessment implementation module, a remediation and verification module, a continuous monitoring module, a content security assessment module, a data privacy assessment module, an algorithm bias assessment module, and an adversarial attack testing module. The performance optimization module controls and connects to the multi-environment testing module, the performance tuning module, and the security verification module, respectively. The multi-environment testing module controls and connects to the setup and preparation module, the setup and preparation module controls and connects to the test case design module, the test case design module controls and connects to the test execution monitoring module, the performance tuning module controls and connects to the performance baseline evaluation module, and the performance baseline evaluation module controls and connects to the location and analysis module.

[0010] As a further technical solution of the present invention, the security verification module controls the connection to the standard and inspection module, the standard and inspection module controls the connection to the security assessment implementation module, the security assessment implementation module controls the connection to the rectification and verification module, the rectification and verification module controls the connection to the continuous monitoring module, and the security assessment implementation module controls the connection to the content security assessment module, the data privacy assessment module, the algorithm bias assessment module, and the adversarial attack testing module, respectively.

[0011] As a further technical solution of the present invention, the knowledge introduction module comprises a knowledge acquisition module, a knowledge graph integration module, a database connection module, a private library access module, a knowledge processing module, a knowledge fusion module, a knowledge enhancement module, a knowledge service module, a knowledge application module, a knowledge retrieval module, a knowledge reasoning module, and a knowledge feedback module. The knowledge acquisition module controls and connects to the knowledge graph integration module, the database connection module, and the private library access module, respectively. The knowledge graph integration module, the database connection module, and the private library access module control and connect to the knowledge processing module, respectively. The knowledge processing module controls and connects to the knowledge fusion module, the knowledge enhancement module, and the knowledge service module, respectively. The knowledge fusion module, the knowledge enhancement module, and the knowledge service module control and connect to the knowledge application module, respectively. The knowledge application module controls and connects to the knowledge retrieval module, the knowledge reasoning module, and the knowledge feedback module, respectively.

[0012] As a further technical solution of the present invention, the capability introduction module consists of a selection and preparation module, a capability selection source module, an interface standardization module, a development environment preparation module, an integration and implementation module, a capability module integration module, a testing and verification module, and a deployment and launch module. The selection and preparation module controls the connection of the capability selection source module, the interface standardization module, and the development environment preparation module. The capability selection source module, the interface standardization module, and the development environment preparation module control the connection of the integration and implementation module. The integration and implementation module controls the connection of the capability module integration module, the testing and verification module, and the deployment and launch module.

[0013] As a further technical solution of the present invention, the continuous optimization module consists of a targeted optimization module, a model optimization module, an algorithm optimization module, a system optimization module, a resource optimization module, and a verification and iteration module. The targeted optimization module controls and connects the model optimization module, the algorithm optimization module, the system optimization module, and the resource optimization module. The model optimization module, the algorithm optimization module, the system optimization module, and the resource optimization module control and connect the verification and iteration module.

[0014] As a further technical solution of the present invention, the visualization task module consists of an architecture design module, an orchestration mode selection module, a core component design module, a technology framework selection module, a development and implementation module, an orchestrator development module, an execution engine implementation module, and a monitoring and debugging module. The architecture design module controls and connects the orchestration mode selection module, the core component design module, and the technology framework selection module. The orchestration mode selection module, the core component design module, and the technology framework selection module control and connect the development and implementation module. The development and implementation module controls and connects the orchestrator development module, the execution engine implementation module, and the monitoring and debugging module.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By collecting and organizing internal enterprise documents, designing a knowledge classification and tagging system, developing a document parsing and indexing system, realizing knowledge vectorization and semantic representation, designing a knowledge quality assessment process, constructing knowledge retrieval and question-answering interfaces to realize knowledge recommendation and association analysis, designing a knowledge update and feedback mechanism, and integrating private database resources, the available data dimensions are significantly improved, enabling it to meet the personalized needs of complex tasks; a security incident response mechanism is established, and security audits and assessments are conducted regularly. Protection measures are updated based on new threats, and strategies can be improved based on user feedback data, greatly enhancing the system's continuous evolution efficiency; strategies are continuously adjusted and optimized based on feedback, and a long-term monitoring mechanism is established to prevent performance regression. The system possesses an adaptive learning framework and dynamic iteration capabilities, enabling it to automatically adjust model parameters or optimize inference logic based on real-time feedback, with the knowledge system being updated beyond actual needs. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the architecture of the planning phase module in this invention; Figure 3 This is a schematic diagram of the system development module architecture in this invention; Figure 4 This is a schematic diagram of the architecture of the model training module in this invention; Figure 5 This is a schematic diagram of the architecture of the test optimization module in this invention; Figure 6 This is a schematic diagram of the architecture of the knowledge introduction module in this invention; Figure 7 This is a schematic diagram of the architecture of the capability introduction module in this invention; Figure 8 This is a schematic diagram of the architecture of the continuous optimization module in this invention; Figure 9 This is a schematic diagram of the architecture of the visualization task module in this invention.

[0017] In the diagram: 1. Planning Phase Module; 2. System Development Module; 3. Model Training Module; 4. Testing and Optimization Module; 5. Technical Implementation Module; 6. Knowledge Introduction Module; 7. Capability Introduction Module; 8. Runtime Engine Module; 9. R&D Cycle Module; 10. Continuous Optimization Module; 11. Visualization Task Module; 101. Requirements Definition Module; 102. Technology Selection Module; 103. Prototype Design Module; 201. Design Architecture Module; 202. Interaction Layer Module; 203. Coordination Layer Module; 204. Intelligent Decision-Making Layer Module; 301. Training Structure Configuration Module; 302. Core Training Phase Module; 303 401 Model Evaluation Module; 402 Performance Optimization Module; 403 Multi-Environment Testing Module; 404 Setup and Preparation Module; 405 Test Case Design Module; 406 Test Execution Monitoring Module; 407 Performance Tuning Module; 408 Performance Baseline Evaluation Module; 409 Location and Analysis Module; 410 Security Verification Module; 411 Standards and Inspection Module; 412 Security Assessment Implementation Module; 413 Rectification and Verification Module; 414 Continuous Monitoring Module; 415 Content Security Assessment Module; 416 Data Privacy Assessment Module; 417 Algorithm Bias Assessment Module; 418 Adversarial Attack Module The module includes: 601 Knowledge Acquisition Module; 602 Knowledge Graph Integration Module; 603 Database Connection Module; 604 Private Library Access Module; 605 Knowledge Processing Module; 606 Knowledge Fusion Module; 607 Knowledge Enhancement Module; 608 Knowledge Service Module; 609 Knowledge Application Module; 610 Knowledge Retrieval Module; 611 Knowledge Reasoning Module; 612 Knowledge Feedback Module; 701 Selection and Preparation Module; 702 Capability Selection Source Module; 703 Interface Standardization Module; 704 Development Environment Preparation Module; 705 Integration and Implementation Module; 706 Capability... The module is divided into the following sections: Module Integration Module; 707 Test and Verification Module; 708 Deployment and Launch Module; 1001 Targeted Optimization Module; 1002 Model Optimization Module; 1003 Algorithm Optimization Module; 1004 System Optimization Module; 1005 Resource Optimization Module; 1006 Verification and Iteration Module; 1101 Architecture Design Module; 1102 Orchestration Mode Selection Module; 1103 Core Component Design Module; 1104 Technology Framework Selection Module; 1105 Development and Implementation Module; 1106 Orchestrator Development Module; 1107 Execution Engine Implementation Module; 1108 Monitoring and Debugging Module. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see the appendix Figure 1-9This invention provides an embodiment of an AI-based super-system, comprising a planning phase module 1, a system development module 2, a model training module 3, a testing and optimization module 4, a technical implementation module 5, a knowledge introduction module 6, a capability introduction module 7, a runtime engine module 8, a research and development cycle module 9, a continuous optimization module 10, and a visualization task module 11. The planning phase module 1 controls and connects to the system development module 2; the system development module 2 controls and connects to the model training module 3 and the research and development cycle module 9; the model training module 3 controls and connects to the testing and optimization module 4; the testing and optimization module 4 controls and connects to the technical implementation module 5 and the continuous optimization module 10; and the technical implementation module 5 controls and connects to... The system comprises an engine module 8, a knowledge introduction module 6, a capability introduction module 7, and a visualization task module 11; the planning phase module 1 consists of a requirements definition module 101, a technology selection module 102, and a prototype design module 103, with the requirements definition module 101 controlling and connecting to the technology selection module 102, and the technology selection module 102 controlling and connecting to the prototype design module 103; the system development module 2 consists of a design architecture module 201, an interaction layer module 202, a coordination layer module 203, and an intelligent decision-making layer module 204, with the design architecture module 201 controlling and connecting to the interaction layer module 202, the coordination layer module 203, and the intelligent decision-making layer module 204 respectively; the model training module 3 consists of a training structure configuration module... The module consists of module 301, core training phase module 302, and model evaluation module 303. The training structure configuration module 301 controls and connects to the core training phase module 302, and the core training phase module 302 controls and connects to the model evaluation module 303. The test optimization module 4 comprises a performance optimization module 401, a multi-environment testing module 402, a setup and preparation module 403, a test case design module 404, a test execution monitoring module 405, a performance tuning module 406, a performance baseline evaluation module 407, a location and analysis module 408, a security verification module 409, a standards and inspection module 410, a security assessment implementation module 411, a rectification and verification module 412, and a continuous monitoring module. The system comprises a content security assessment module, a data privacy assessment module, an algorithm bias assessment module, an adversarial attack testing module, and an adversarial attack testing module. The performance optimization module 401 controls and connects to the multi-environment testing module 402, the performance tuning module 406, and the security verification module 409. The multi-environment testing module 402 controls and connects to the setup and preparation module 403. The setup and preparation module 403 controls and connects to the test case design module 404. The test case design module 404 controls and connects to the test execution monitoring module 405. The performance tuning module 406 controls and connects to the performance baseline assessment module 407. The performance baseline assessment module 407 controls and connects to the localization and analysis module 408.The security verification module 409 controls the connection to the standards and inspection module 410. The standards and inspection module 410 controls the connection to the security assessment implementation module 411. The security assessment implementation module 411 controls the connection to the rectification and verification module 412. The rectification and verification module 412 controls the connection to the continuous monitoring module 413. The security assessment implementation module 411 controls the connection to the content security assessment module 414, the data privacy assessment module 415, the algorithm bias assessment module 416, and the adversarial attack testing module 417, respectively. The knowledge introduction module 6 consists of the knowledge acquisition module 601, the knowledge graph integration module 602, the database connection module 603, the private library access module 604, the knowledge processing module 605, the knowledge fusion module 606, and the knowledge enhancement module. The module comprises a knowledge acquisition module 607, a knowledge service module 608, a knowledge application module 609, a knowledge retrieval module 610, a knowledge reasoning module 611, and a knowledge feedback module 612. The knowledge acquisition module 601 controls and connects to the knowledge graph integration module 602, the database connection module 603, and the private library access module 604. The knowledge graph integration module 602, the database connection module 603, and the private library access module 604 control and connect to the knowledge processing module 605. The knowledge processing module 605 controls and connects to the knowledge fusion module 606, the knowledge enhancement module 607, and the knowledge service module 608. The knowledge fusion module 606, the knowledge enhancement module 607, and the knowledge service module 608 control and connect to the knowledge application module 609. The knowledge application module 609 controls and connects to the knowledge retrieval module 610, the knowledge reasoning module 611, and the knowledge feedback module 612, respectively. The capability introduction module 7 consists of a selection and preparation module 701, a capability selection source module 702, an interface standardization module 703, a development environment preparation module 704, an integration and implementation module 705, a capability module integration module 706, a testing and verification module 707, and a deployment and launch module 708. The selection and preparation module 701 controls and connects to the capability selection source module 702, the interface standardization module 703, and the development environment preparation module 704. The capability selection source module 702, the interface standardization module 703, and the development environment preparation module 704 control and connect to the integration and implementation module 705. The integration and implementation module 705 controls the connection capability module, integration module 706, testing and verification module 707, and deployment and online module 708; the continuous optimization module 10 consists of a targeted optimization module 1001, a model optimization module 1002, an algorithm optimization module 1003, a system optimization module 1004, a resource optimization module 1005, and a verification and iteration module 1006. The targeted optimization module 1001 controls and connects the model optimization module 1002, algorithm optimization module 1003, system optimization module 1004, and resource optimization module 1005. The model optimization module 1002, algorithm optimization module 1003, system optimization module 1004, and resource optimization module 1005 control and connect the verification and iteration module 1006.The visualization task module 11 consists of an architecture design module 1101, an orchestration mode selection module 1102, a core component design module 1103, a technology framework selection module 1104, a development and implementation module 1105, an orchestrator development module 1106, an execution engine implementation module 1107, and a monitoring and debugging module 1108. The architecture design module 1101 controls and connects to the orchestration mode selection module 1102, the core component design module 1103, and the technology framework selection module 1104. The orchestration mode selection module 1102, the core component design module 1103, and the technology framework selection module 1104 control and connect to the development and implementation module 1105. The development and implementation module 1105 controls and connects to the orchestrator development module 1106, the execution engine implementation module 1107, and the monitoring and debugging module 1108.

[0020] Working Principle: In the planning phase, module 1 (requirements definition module 101) conducts market research and competitor analysis, collects feedback from potential users, and determines the system's core value proposition and differentiating advantages. In the technology selection module 102, a development toolchain suitable for the project scale is selected, employing a "low-code + lightweight code" combination to lower the development threshold. Data storage and processing schemes are planned. In the prototype design module 103, a hierarchical skills learning system is built. The execution skill layer provides specific operational capabilities, the abstract skill layer provides task understanding and planning capabilities, and the meta-skill layer provides self-learning and optimization capabilities. Algorithms used in the training phase are determined, such as pipeline parallelism and model splitting techniques. Multi-node collaborative computing accelerates large-scale model training, mathematically employing delayed updates. The update mechanism balances computational efficiency and accuracy, including gradient descent variants such as Adam and SGD, and regularization methods L1 / L2, used to adjust model parameters. The weight update formula is w=w−α×∂MSE / ∂w, based on a dynamic allocation algorithm for heterogeneous computing resources (CPU / GPU / TPU), mathematically expressed as a resource cost minimization problem: min∑cjuij+λbalance. System development module 2's architecture module 201 is divided into an interaction layer module 202, a coordination layer module 203, and an intelligent decision-making layer module 204. The interaction layer module 202 supports multimodal input and output such as text, voice, and images, enabling natural language understanding and generation, and designing user interfaces and interaction flows. The coordination layer module 203 is responsible for task decomposition and priority management, implementing a multi-agent collaboration mechanism, and handling system resource scheduling and load balancing. The intelligent decision-making layer module 204 includes memory, planning, reasoning, and tool invocation modules, achieving a balance between long-term and short-term memory, providing an interpretable decision-making process, and contributing to the development cycle. Block 9 is divided into the basic research and development stage, the optimization and iteration stage, and the industry application stage. In the training structure configuration module 301 of the model training module, the hardware platform is selected as a GPU / TPU cluster, and the software framework is deployed as a training framework such as TensorFlow / PyTorch. The parameter settings determine hyperparameters such as learning rate, batch size, and number of iterations. In the core training stage module 302, the model learns feature representations from the input data, and backpropagation adjusts the parameters using algorithms such as δL=∇aC⊙σ′(zL). Parameter updates are performed by updating the model weights using an optimizer. In the model evaluation module 303, performance metrics are tested using an independent validation set, and the model's capabilities are evaluated through benchmark testing. Based on the evaluation results, it is decided whether to continue training or adjust the parameters. In the test optimization module 4, the performance optimization module 401 is divided into a multi-environment testing module 402, a performance tuning module 406, and a security verification module 409. The multi-environment testing module 402 is further divided into a setup and preparation module 403, a test case design module 404, and a test execution monitoring module 405.In the setup and preparation module 403, an offline training environment is built to simulate the data distribution and load characteristics of a real production environment. Hardware and network configurations consistent with the production environment are configured. In the test case design module 404, test cases covering core functions such as text understanding, text generation, and multi-turn dialogue are designed, including scenarios such as short text instant response, long text streaming response, and extreme stress testing. Adversarial input and privacy data leakage test scenarios are also designed. In the test execution monitoring module 405, an intelligent test case generator and adaptive execution engine are used to record test logs and results. In the performance baseline evaluation module 407, key indicators are quantified, including model performance metrics such as accuracy, precision, recall, F1 score, and AUC score, and a performance benchmark recording system is established. Performance on the standard test set is assessed. In the localization and analysis module 408, profiling tools are used to identify performance hotspots, analyze CPU / GPU / memory resource usage, and check for model inference latency and throughput bottlenecks. In the standards and inspection module 410, compliance with AI safety management standards such as ISO 42001 is ensured, and data privacy protection measures are checked to ensure compliance with regulations such as GDPR, verifying algorithm transparency and interpretability. In the content security assessment module 414 of the security assessment implementation module 411, the output is checked for illegal, violent, pornographic, or other harmful content. In the data privacy assessment module 415, it is verified whether training data can be inferred from the output. In the algorithm bias assessment module 416, discrimination based on gender, etc., is detected. The system's ability to resist malicious input is tested in the anti-attack testing module 417. In the rectification and verification module 412, rectification plans are formulated for discovered security vulnerabilities, content review strategies and privacy protection measures are optimized, and the rectification effect is re-verified until it meets the standards. In the continuous monitoring module 413, a security incident response mechanism is established, regular security audits and assessments are conducted, and protection measures are updated based on new threats. The system can also improve strategy formulation based on user feedback data, greatly enhancing its continuous evolution efficiency. The technical implementation module 5 is divided into a knowledge introduction module 6, a capability introduction module 7, and a runtime engine module 8. The knowledge acquisition module 601 is divided into a knowledge graph integration module 602, a database connection module 603, and a private library access module 604. Knowledge graph integration... Module 602 employs an "entity-relationship-attribute" triple model to construct a knowledge network, designs a domain ontology and knowledge representation framework, defines knowledge update and version control mechanisms, establishes knowledge quality assessment standards, designs a knowledge conflict detection mechanism, and implements knowledge credibility scoring. Module 603, the database connection module, unifies the data access layer, designs fine-grained access control, and implements data anonymization and encrypted transmission. Module 604, the private database access module, collects and organizes internal enterprise documents, designs a knowledge classification and tagging system, develops a document parsing and indexing system, implements knowledge vectorization and semantic representation, designs a knowledge quality assessment process, constructs knowledge retrieval and question-answering interfaces, implements knowledge recommendation and association analysis, designs a knowledge update and feedback mechanism, and integrates private database resources.Significantly increasing the available data dimensions to meet the personalized needs of complex tasks, the knowledge processing module 605 is divided into a knowledge fusion module 606, a knowledge enhancement module 607, and a knowledge service module 608. In the knowledge fusion module 606, identical entities from different sources are identified and merged, relationship conflicts between different knowledge sources are resolved, and a large model is used to predict missing knowledge relationships. In the knowledge enhancement module 607, new knowledge is derived based on rules and statistical methods, and cross-validation is used to ensure knowledge accuracy. A knowledge version management and iteration mechanism is established. In the knowledge service module 608, standardized knowledge access interfaces are provided, an efficient knowledge caching strategy is designed, and knowledge usage analysis and optimization are implemented. The knowledge application module 609 is divided into a knowledge retrieval module 606, a knowledge retrieval module 607, and a knowledge service module 608. 10. The knowledge reasoning module 611 and the knowledge feedback module 612, in the knowledge retrieval module 610, understand the user's query intent and return relevant knowledge, supporting multiple query methods such as text and images, and providing customized knowledge based on user history. The knowledge reasoning module 611 performs rule-based knowledge deduction and uses probability models for knowledge prediction. The knowledge feedback module 612 collects knowledge usage and user evaluations, identifies knowledge errors and inconsistencies, and triggers knowledge update and optimization processes. The selection and preparation module 701 is divided into a capability selection source module 702, an interface standardization module 703, and a development environment preparation module 704. The capability selection source module 702 selects stable and reliable third-party API services, and the interface standardization module... Module 703 establishes standardized data formats and communication protocols to achieve authentication, access control, and data encryption. Module 704, the development environment preparation module, simulates a real-world environment for integration testing, preparing necessary SDKs and development toolkits. Module 705, the integration and implementation module, is divided into three parts: capability module integration (Module 706), testing and verification (Module 707), and deployment (Module 708). In capability module integration (Module 706), functional integration is achieved through API calls or SDK embedding, and task scheduling and flow control logic is developed. In testing and verification (Module 707), the basic functionality of external capabilities is verified, the performance indicators of the integrated system are evaluated, and potential security vulnerabilities and risks are checked. In deployment (Module 708), stability is first verified in a small-scale environment. The system establishes performance metrics and error log monitoring. Within the continuous optimization module 10, the targeted optimization module 1001 is divided into a model optimization module 1002, an algorithm optimization module 1003, a system optimization module 1004, and a resource optimization module 1005. In the model optimization module 1002, techniques such as model pruning, quantization, and distillation are used to simplify the model. In the algorithm optimization module 1003, training strategies and inference algorithms are improved. In the system optimization module 1004, parameters such as batch size and concurrency control are adjusted. In the resource optimization module 1005, computing resources are rationally allocated to improve utilization. In the verification and iteration module 1006, the optimization effect is verified through small-scale testing. Based on feedback, the optimization strategy is continuously adjusted, and a long-term monitoring mechanism is established to prevent performance regression.The system possesses an adaptive learning framework and dynamic iteration capabilities, enabling it to automatically adjust model parameters or optimize inference logic based on real-time feedback. The knowledge system updates exceed actual needs. The architecture design module 1101 within the visualization task module 11 is divided into an orchestration mode selection module 1102, a core component design module 1103, and a technology framework selection module 1104. In the orchestration mode selection module 1102, a suitable orchestration mode is chosen. In the core component design module 1103, basic node types are defined, and the data flow and control flow transmission methods between nodes are designed. In the technology framework selection module 1104, a framework supporting graphical editing is selected, and task scheduling and execution are determined. The underlying technology of the workflow is designed, including workflow status tracking and recovery mechanisms. Module 1105 is divided into an orchestrator development module 1106, an execution engine implementation module 1107, and a monitoring and debugging module 1108. The orchestrator development module 1106 uses a WYSIWYG drag-and-drop interface, encapsulates AI capabilities and business functions as reusable nodes, and designs the workflow's storage and loading format. The execution engine implementation module 1107 develops a priority-based task scheduling algorithm to parse the logical relationships between tasks. The monitoring and debugging module 1108 displays the workflow execution status and data flow, supporting pauses and checks during workflow execution.

[0021] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A super system based on an AI underlying architecture, comprising a planning phase module (1), a system development module (2), a model training module (3), a testing and optimization module (4), a technical implementation module (5), a knowledge introduction module (6), a capability introduction module (7), a runtime engine module (8), a research and development cycle module (9), a continuous optimization module (10), and a visualization task module (11), characterized in that: The planning stage module (1) controls the connection system development module (2), the system development module (2) controls the connection model training module (3) and the research and development cycle module (9), the model training module (3) controls the connection test optimization module (4), the test optimization module (4) controls the connection technology implementation module (5) and the continuous optimization module (10), and the technology implementation module (5) controls the connection running engine module (8), the knowledge introduction module (6), the capability introduction module (7) and the visualization task module (11) respectively.

2. The AI-based underlying architecture super body system of claim 1, wherein: The planning stage module (1) is composed of a requirement definition module (101), a technology selection module (102) and a prototype design module (103), and the requirement definition module (101) controls the connection technology selection module (102), and the technology selection module (102) controls the connection prototype design module (103).

3. The AI-based underlying architecture super body system of claim 1, wherein: The system development module (2) is composed of a design architecture module (201), an interaction layer module (202), a coordination layer module (203) and an intelligent decision-making layer module (204), and the design architecture module (201) controls the connection of the interaction layer module (202), the coordination layer module (203) and the intelligent decision-making layer module (204) respectively.

4. The AI-based underlying architecture super body system of claim 1, wherein: The model training module (3) is composed of a training structure configuration module (301), a core training stage module (302) and a model evaluation module (303), and the training structure configuration module (301) controls the connection of the core training stage module (302), and the core training stage module (302) controls the connection of the model evaluation module (303).

5. The AI-based underlying architecture super body system of claim 1, wherein: The test optimization module (4) is composed of a performance optimization module (401), a multi-environment test module (402), a building and preparation module (403), a test case design module (404), a test execution monitoring module (405), a performance tuning module (406), a performance baseline evaluation module (407), a positioning and analysis module (408), a security verification module (409), a standard and inspection module (410), a security evaluation implementation module (411), a rectification and verification module (412), a continuous monitoring module (413), a content security evaluation module (414), a data privacy evaluation module (415), an algorithm bias evaluation module (416) and an adversarial attack test module (417), and the performance optimization module (401) controls the connection of the multi-environment test module (402), the performance tuning module (406) and the security verification module (409) respectively, the multi-environment test module (402) controls the connection of the building and preparation module (403), the building and preparation module (403) controls the connection of the test case design module (404), the test case design module (404) controls the connection of the test execution monitoring module (405), the performance tuning module (406) controls the connection of the performance baseline evaluation module (407), and the performance baseline evaluation module (407) controls the connection of the positioning and analysis module (408).

6. The AI-based underlying architecture super body system of claim 5, wherein: The security verification module (409) controls the connection of the standard and check module (410), the standard and check module (410) controls the connection of the security evaluation implementation module (411), the security evaluation implementation module (411) controls the connection of the rectification and verification module (412), the rectification and verification module (412) controls the connection of the continuous monitoring module (413), and the security evaluation implementation module (411) controls the connection of the content security evaluation module (414), the data privacy evaluation module (415), the algorithm bias evaluation module (416) and the adversarial attack test module (417) respectively.

7. The AI-based underlying architecture super body system of claim 1, wherein: The knowledge introduction module (6) is composed of a knowledge acquisition module (601), a knowledge graph integration module (602), a database connection module (603), a private library access module (604), a knowledge processing module (605), a knowledge fusion module (606), a knowledge enhancement module (607), a knowledge service module (608), a knowledge application module (609), a knowledge retrieval module (610), a knowledge reasoning module (611) and a knowledge feedback module (612). The knowledge acquisition module (601) is connected to the knowledge graph integration module (602), the database connection module (603) and the private library access module (604) respectively, and the knowledge graph integration module (602), the database connection module (603) and the private library access module (604) are connected to the knowledge processing module (605). The knowledge processing module (605) is connected to the knowledge fusion module (606), the knowledge enhancement module (607) and the knowledge service module (608) respectively, and the knowledge fusion module (606), the knowledge enhancement module (607) and the knowledge service module (608) are connected to the knowledge application module (609). The knowledge application module (609) is connected to the knowledge retrieval module (610), the knowledge reasoning module (611) and the knowledge feedback module (612) respectively. 8.The AI-based underlying architecture super body system of claim 1, wherein: The capability introduction module (7) is composed of a selection and preparation module (701), a capability selection source module (702), an interface standardization module (703), a development environment preparation module (704), an integration and implementation module (705), a capability module integration module (706), a test verification module (707) and a deployment online module (708). The selection and preparation module (701) controls the connection of the capability selection source module (702), the interface standardization module (703) and the development environment preparation module (704), and the capability selection source module (702), the interface standardization module (703) and the development environment preparation module (704) control the connection of the integration and implementation module (705). The integration and implementation module (705) controls the connection of the capability module integration module (706), the test verification module (707) and the deployment online module (708). 9.The AI-based underlying architecture super body system of claim 1, wherein: The continuous optimization module (10) is composed of a targeted optimization module (1001), a model optimization module (1002), an algorithm optimization module (1003), a system optimization module (1004), a resource optimization module (1005), and a verification and iteration module (1006). The targeted optimization module (1001) is connected to the model optimization module (1002), the algorithm optimization module (1003), the system optimization module (1004), and the resource optimization module (1005). The model optimization module (1002), the algorithm optimization module (1003), the system optimization module (1004), and the resource optimization module (1005) are connected to the verification and iteration module (1006). 10.The AI-based bottom-layer architecture super body system of claim 1, wherein: The visualization task module (11) is composed of an architecture design module (1101), an arrangement mode selection module (1102), a core component design module (1103), a technology framework selection module (1104), a development and implementation module (1105), an arranger development module (1106), an execution engine implementation module (1107), and a monitoring and debugging module (1108). The architecture design module (1101) is connected to the arrangement mode selection module (1102), the core component design module (1103), and the technology framework selection module (1104). The arrangement mode selection module (1102), the core component design module (1103), and the technology framework selection module (1104) are connected to the development and implementation module (1105). The development and implementation module (1105) is connected to the arranger development module (1106), the execution engine implementation module (1107), and the monitoring and debugging module (1108).

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