Self-evolution artificial intelligence system based on distributed operator and implementation method

Abstract

The application discloses a self-evolution artificial intelligence system based on a distributed operator and an implementation method, and the system comprises a digital life core module, an operator library module, a distributed scheduling module, an evolution engine module, a quantum computing module, a holographic interaction module, a federal learning module, a monitoring visualization module and a security audit module. The application realizes algorithm power integration and parallel acceleration through a unified distributed operator architecture, continuously improves system performance through an autonomous evolution mechanism, realizes privacy protection collaborative computing through federal learning, guarantees compliance traceability through security audit, and improves display and experience through multi-modal interaction. The application is suitable for large-scale intelligent processing, distributed algorithm power scheduling, privacy computing, complex task optimization, visual interaction and the like, solves the problems of traditional system algorithm power dispersion, difficulty in evolution, unsafe collaboration and unreliable audit, and has the advantages of high availability, scalability, autonomy and stable deployment.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, distributed computing, quantum computing, data processing, holographic rendering, federated learning, system security, and autonomous evolution, specifically to a self-evolving artificial intelligence system based on distributed operators and its implementation method. Background Technology

[0002] With the widespread application of artificial intelligence technology in industries such as industry, finance, healthcare, and government, existing AI systems are gradually revealing various technical shortcomings: Computing resources are scattered across different nodes and devices, making it impossible to achieve unified scheduling and decentralized self-healing, resulting in low overall system utilization and insufficient stability. Various algorithms, models, and reasoning capabilities exist as independent modules, lacking a unified operator execution framework, making it difficult to efficiently integrate and extend multimodal capabilities; The system relies on manual updates and parameter tuning, making it unable to achieve autonomous evolution and adaptive optimization, and thus its intelligence level and processing efficiency are difficult to continuously improve. It does not natively integrate quantum computing and distributed parallel capabilities, resulting in limited efficiency when facing complex optimization problems and large-scale data processing. The demand for cross-institutional and cross-industry data collaboration is increasing, and traditional centralized data processing poses a risk of privacy leakage and lacks a secure and reliable federated learning mechanism. The system operation, task execution, and data access logs can be tampered with and deleted, failing to meet compliance review and security traceability requirements; The interaction and display formats are limited, mostly text or two-dimensional interfaces, lacking three-dimensional and multimodal interaction capabilities, resulting in limited user experience and visualization.

[0003] The aforementioned problems make it difficult for existing technologies to build high-performance, highly reliable, and autonomous artificial intelligence systems that integrate distributed scheduling, unified operators, autonomous evolution, privacy computing, security auditing, and multimodal interaction. Summary of the Invention

[0004] Technical problems to be solved The purpose of this invention is to overcome the shortcomings of the prior art and solve the technical problems of existing artificial intelligence systems, such as dispersed computing power, inconsistent operator capabilities, inability to evolve autonomously, insecurity in cross-institutional collaboration, unreliable auditing, and limited interaction methods. The invention provides a self-evolving artificial intelligence system based on distributed operators and its implementation method. Technical solution

[0005] A self-evolving artificial intelligence system based on distributed operators includes: a digital life core module, an operator library module, a distributed scheduling module, an evolutionary engine module, a quantum computing module, a holographic interaction module, a federated learning module, a monitoring and visualization module, and a security audit module. The digital life core module is used for system state management, memory storage, unique identifier generation, and backup and recovery; the operator library module provides a unified set of operators for mathematics, multimodal computing, quantum computing, data processing, and security auditing; the distributed scheduling module enables data sharding, parallel execution, computing power scheduling, and decentralized self-healing; the evolutionary engine module achieves autonomous evolution and performance improvement through reinforcement learning, architecture search, and automatic parameter tuning; the quantum computing module performs quantum-related calculations and complex optimization tasks; the holographic interaction module enables model creation, rendering generation, and multimodal visualization output; the federated learning module enables privacy-preserving collaborative computing and secure aggregation among multiple clients; the monitoring and visualization module provides system status, performance data, alarms, and dashboard services; and the security audit module ensures that operation logs are written once, cannot be deleted, cannot be tampered with, and are subject to compliance traceability.

[0006] A self-evolving artificial intelligence implementation method based on distributed operators includes: initializing a digital life core, operator library, distributed scheduling, evolution engine, quantum computing, holographic interaction, federated learning, monitoring visualization, and security audit module; starting the digital life core module to complete unique identifier generation, state startup, and backup; starting monitoring and alarm services; executing business tasks: data input, sharding, distributed parallel computing, federated collaboration, quantum computing, and security review; driving the evolution engine to perform autonomous optimization based on task performance scoring and feedback; writing operation records to the security audit module; and entering a continuous running loop to achieve autonomous operation, self-optimization, state backup, and distributed expansion. Beneficial effects

[0007] Compared with the prior art, the present invention has the following beneficial effects: By using a distributed operator architecture, unified scheduling of computing power, parallel execution of tasks, and decentralized self-healing are achieved, significantly improving system utilization and stability. It has the ability to evolve autonomously and can continuously optimize operator weights and system structure through reinforcement learning, architecture search and automatic parameter tuning, thereby continuously improving its intelligence level and processing efficiency. It natively supports the integration of quantum computing and multimodal capabilities, can handle high-intensity tasks such as complex optimization and large-scale inference, and has strong scalability; Federated learning enables privacy-preserving collaborative computation across institutions and nodes, allowing joint modeling to be completed without data leaving the domain, thus ensuring high security. The security audit module is used to ensure that logs are written once, cannot be deleted, and cannot be tampered with, thus meeting the requirements of compliance review and security traceability. It supports three-dimensional interaction and visualization, providing a multimodal and immersive interactive experience; The system can operate autonomously, is scalable, and can be stably deployed in production environments, making it suitable for a wide range of scenarios. Attached Figure Description

[0008] Figure 1 System Overall Architecture Diagram: This diagram shows the core components of the system of the present invention, as well as the core sub-functions and relationships of each module. The digital life core module is the core, which links the other eight functional modules, clearly presenting the overall system architecture hierarchy and the collaboration logic between modules. Figure 2. Operator Library and Distributed Scheduling Flowchart: This shows the entire process logic of business data from input to output, presenting the execution order and inter-module collaboration methods of key steps such as data sharding, load detection, parallel execution of operators, performance statistics, and result aggregation. Figure 3 System Status Management and Backup Recovery Flowchart: This shows the system's full lifecycle status management process from initialization to continuous operation, including the triggering conditions and execution steps for unique identifier generation, running status recording, scheduled backup, anomaly recovery, and distributed expansion. Figure 4. Flowchart of Autonomous Evolution and Optimization: This shows the autonomous evolution cycle of the system with the feedback of task execution effect, and presents the logical order and optimization path of key evolutionary links such as effect scoring, reinforcement learning optimization, architecture search, automatic parameter tuning, and intelligence level improvement. Figure 5 Multimodal Interaction and Visualization Flowchart: Shows the closed-loop process of multimodal interaction between users and the system, presenting the execution logic and information flow direction of user interaction input, core module processing decision, model creation and rendering, visualization output, status result feedback, etc. Detailed Implementation

[0009] The present invention will be further described in detail below with reference to specific embodiments.

[0010] 6.1 System Initialization The initialization module registers unified operators for mathematical operations, tensor operations, multimodal reasoning, quantum computing, data processing, and security auditing, enabling unified operator registration, invocation, statistics, and anomaly capture. The distributed scheduling module establishes node pools, process pools, and thread pools to achieve task sharding, load balancing, parallel execution, decentralized self-healing, traffic management, and multi-node expansion. The digital life core module generates a globally unique identifier and implements state control, short-term and long-term memory management, state persistence, backup, and recovery mechanisms. The evolutionary engine module sets the initial intelligence level, operator weights, optimization goals, search space, and evolutionary strategies, supporting reinforcement learning, architecture search, and automatic hyperparameter tuning. The monitoring and visualization module starts panel services, data interfaces, performance statistics, and alarm push notifications, enabling full-process state visibility. The security audit module establishes a write-once, non-deletable, and tamper-proof storage structure to record, hash-verify, and trace critical operations. The federated learning module connects to multiple clients, enabling local model updates, secure aggregation, and privacy-preserving computation. Initialize the holographic interaction module, completing model creation, image rendering, visualization output, and multimodal interaction logic. Initialize the quantum computing module, supporting quantum simulators and hardware scheduling, capable of performing tasks such as combinatorial optimization, energy calculation, and quantum classification.

[0011] 6.2 System Startup The core module of the digital life system executes the initialization process, generates a globally unique identifier, loads the running status, and completes the startup self-check. The system outputs basic operational information, including the identifier, status, and module loading status. It starts the monitoring visualization service, opening interfaces for querying system status, performance data, evolution history, and alarm information. It performs the initial state backup, writing the initial information to persistent storage and a security audit database. It initializes holographic interaction and visualization output, preparing the user interaction channel.

[0012] 6.3 Task Execution Process The system acquires input data from the business data channel and completes data parsing, validation, and preprocessing. The distributed scheduling module shards the data and allocates computation tasks based on node load. Tasks are distributed to the process pool or thread pool, where the operator library module executes unified operator logic. The system executes federated learning tasks, with multiple clients performing local updates and a global model obtained through secure aggregation, without leaking the original data. The system executes quantum computing tasks to optimize and accelerate computation for complex problems. Security audits and content verification are performed to ensure task compliance. All critical operations throughout the process are written to the security audit module, forming an immutable record. Scores are generated based on task execution results, time consumption, and accuracy, serving as feedback signals for the evolutionary engine. The evolutionary engine adjusts operator weights, system structure, and operating parameters based on the scores to improve the performance of subsequent tasks. The system performs periodic state backups and can automatically recover after anomalies, power outages, or restarts. If a task times out, fails, or encounters an anomaly, the monitoring module automatically triggers an alarm.

[0013] 6.4 Cyclic Operation and Evolution The system enters a continuous running loop, executing the complete task process according to a fixed cycle. Each loop completes status self-checks, resource reclamation, memory organization, performance statistics, and backups. The evolution engine continuously optimizes the system based on task feedback, achieving continuous improvement in intelligence level and processing power. The system supports replica generation and distributed expansion, enabling state synchronization and computing power expansion across multiple nodes. Through a self-healing mechanism, it automatically switches and recovers when some nodes fail, ensuring uninterrupted overall service. The system achieves long-term stable, autonomous, self-optimizing, and continuously evolving operational results.

Claims

1. A self-evolving artificial intelligence system based on distributed operators, characterized in that, include: The core module of digital life is used to realize system status management, memory storage, identifier generation and backup and recovery; The operator library module provides a unified set of operators for mathematics, multimodal computing, quantum computing, data processing, and security auditing. The distributed scheduling module is used to implement data sharding, parallel execution, computing power scheduling, and decentralized self-healing; the evolutionary engine module is used to achieve autonomous evolution and performance improvement through reinforcement learning, architecture search, and automatic parameter tuning. The quantum computing module is used to perform quantum-related computations and complex optimization tasks; The holographic interaction module is used to realize model creation, rendering generation, and multimodal visualization output; The federated learning module is used to enable privacy-preserving collaborative computing and secure aggregation among multiple clients; The monitoring visualization module is used to provide system status, performance data, alarms, and dashboard services; The security audit module is used to ensure that operation logs are written once, cannot be deleted, cannot be tampered with, and are subject to compliance traceability.

2. The system according to claim 1, characterized in that, The core module of the digital life system includes a unique identifier, status control, short-term memory, long-term memory, and backup and recovery units.

3. The system according to claim 1, characterized in that, The distributed scheduling module supports process pools, thread pools, task sharding, load balancing, traffic management, and multi-node expansion.

4. The system according to claim 1, characterized in that, The evolutionary engine module supports reinforcement learning optimization, automatic architecture search, automatic hyperparameter tuning, and adaptive performance level improvement.

5. The system according to claim 1, characterized in that, The quantum computing module supports quantum task execution, simulator and hardware scheduling, complex optimization and classification tasks.

6. The system according to claim 1, characterized in that, The holographic interaction module supports model creation, real-time rendering, image output, and three-dimensional interactive display.

7. The system according to claim 1, characterized in that, The federated learning module supports multi-party collaborative computing, local updates, secure aggregation, and privacy data protection.

8. The system according to claim 1, characterized in that, The security audit module adopts an immutable storage mechanism, and all operations are traceable, auditable, and meet compliance requirements.

9. A method for implementing self-evolving artificial intelligence based on distributed operators, applied to the system described in any one of claims 1-8, characterized in that, include: Initialize the digital life core, operator library, distributed scheduling, evolution engine, quantum computing, holographic interaction, federated learning, monitoring visualization, and security audit modules; Start the core module of digital life, complete the generation of unique identifiers, status startup and backup; start monitoring and alarm services; execute business tasks: data input, sharding, distributed parallel computing, federated collaboration, quantum computing, and security review; Based on task performance scores and feedback, the evolution engine is driven to perform autonomous optimization; operation records are written to the security audit module. It enters a continuous running cycle, enabling autonomous operation, self-optimization, state backup, and distributed expansion.

10. The method according to claim 9, characterized in that, The continuous running cycle is an autonomous running process that supports anomaly recovery, distributed expansion, replica generation, and long-term stable operation.

Images