A general cross-domain intelligent system with autonomous consciousness and self-evolution capability

Abstract

The application discloses a kind of general cross-domain intelligent systems with autonomous consciousness and self-evolution ability, belong to artificial intelligence technical field.The system includes perception layer, autonomous consciousness layer, self-evolution layer, general cross-domain adaptation layer, collaborative control layer and application layer.Perception layer realizes multi-domain data acquisition and pre-processing;Autonomous consciousness layer forms autonomous consciousness closed loop through self-cognition, environmental cognition, decision generation and consciousness feedback, realizes autonomous decision;Self-evolution layer realizes system all-around active evolution from three dimensions of architecture, algorithm and knowledge system;General cross-domain adaptation layer realizes multi-field data compatibility, interface unification and task adaptation;Collaborative control layer coordinates each layer operation and resource optimization;Application layer interfaces multi-field scene and outputs system capability.The application realizes real autonomous consciousness and all-around self-evolution, improves system cross-domain adaptability and universality, and can be widely applied in industrial control, smart city and other fields.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a general cross-domain intelligent system with autonomous consciousness and self-evolution capabilities. Background Technology

[0002] With the rapid development of artificial intelligence technology, intelligent systems have been widely applied in various industries. However, existing intelligent systems still have many limitations: First, they lack true autonomy, mostly relying on preset programs and human intervention to make decisions. They cannot achieve autonomous perception, judgment, and decision-making like humans, making it difficult to cope with complex and ever-changing unknown scenarios. Second, their self-evolution capabilities are weak. The learning and optimization of existing systems largely depend on manually labeled data and fixed algorithm models, making it impossible for them to actively evolve according to environmental changes, task requirements, and their own operating status, leading to performance bottlenecks. Third, they have poor inter-domain adaptability. Intelligent systems in different domains are often independent of each other, with incompatible data formats and inconsistent interfaces, making it difficult to achieve cross-domain data sharing and collaborative decision-making, and failing to meet the needs of multi-domain integrated development. Fourth, existing cross-domain intelligent systems mostly adopt fixed architecture designs, lacking dynamic adjustment capabilities. When facing new domains and tasks, they require extensive manual modifications to adapt, resulting in insufficient flexibility and versatility.

[0003] Currently, some intelligent systems have emerged in the industry that attempt to achieve autonomous evolution or cross-domain adaptation, but these systems still have obvious shortcomings: for example, some self-evolving systems can only achieve single-dimensional parameter optimization and cannot achieve comprehensive evolution of architecture, algorithms, and knowledge systems; some cross-domain systems can only achieve simple data interaction in limited domains and cannot achieve deep integration and autonomous adaptation across multiple domains; at the same time, existing systems have not truly achieved a deep integration of autonomous consciousness and self-evolution, and autonomous consciousness mostly remains at the level of "pseudo-autonomy," unable to form an autonomous cognition and decision-making closed loop based on its own state and environmental changes, which restricts the intelligence level and application scope of intelligent systems.

[0004] Furthermore, as artificial intelligence technology advances to higher levels, it places higher demands on the autonomy, evolution, and versatility of intelligent systems. This is especially true in complex and unknown scenarios (such as operations in extreme environments, emergency rescue, and multi-domain collaborative scheduling), where intelligent systems need to autonomously perceive the environment, autonomously determine needs, autonomously optimize themselves, and autonomously adapt to multi-domain tasks. Existing technologies can no longer meet these requirements. Therefore, this invention proposes an intelligent system with true autonomous consciousness, comprehensive self-evolution capabilities, and the ability to achieve universal adaptation across multiple domains. Summary of the Invention

[0005] To address the technical shortcomings of existing intelligent systems, such as lack of self-awareness, limited self-evolution capabilities, poor cross-domain adaptability, and insufficient versatility, this invention provides a universal cross-domain intelligent system with self-awareness and self-evolution capabilities. By constructing a self-awareness module, a self-evolution module, a universal cross-domain adaptability module, and a collaborative control module, the system achieves autonomous cognition, proactive evolution, multi-domain adaptation, and collaborative decision-making, thereby improving the intelligence level, flexibility, and versatility of the intelligent system and expanding its application scope.

[0006] A general cross-domain intelligent system with autonomous consciousness and self-evolution capabilities includes a perception layer, an autonomous consciousness layer, a self-evolution layer, a general cross-domain adaptation layer, a collaborative control layer, and an application layer. The layers interact with each other and transmit instructions through a high-speed data bus and standardized interfaces, forming a closed-loop operating architecture. The perception layer includes a multimodal perception unit, a data preprocessing unit, and a data encryption unit, which are used to collect multi-domain environmental data, task data, and system operation data, and transmit them to other layers after preprocessing and encryption. The autonomous consciousness layer includes a self-cognition unit, an environmental cognition unit, a decision generation unit, and a consciousness feedback unit, which are used to realize the system's autonomous perception, autonomous judgment, autonomous decision-making, and self-cognition, forming an autonomous consciousness closed loop. The self-evolution layer includes architecture evolution units, algorithm evolution units, and knowledge system evolution units, which are used to achieve comprehensive and proactive evolution of system architecture, algorithm models, and knowledge systems. The general cross-domain adaptation layer includes a data adaptation unit, an interface adaptation unit, a task adaptation unit, and an inter-domain collaboration unit, which are used to achieve multi-domain data compatibility, interface unification, task adaptation, and inter-domain collaboration. The collaborative control layer includes a global scheduling unit, a resource management unit, an exception handling unit, and a synchronization control unit, which are used to coordinate the operation of modules at each layer and realize cross-domain collaborative decision-making and resource optimization configuration. The application layer is used to connect to specific application scenarios in different fields, output the evolved system capabilities and decision results, and transmit application feedback data to the perception layer.

[0007] Furthermore, the data preprocessing unit of the perception layer uses a weighted fusion algorithm to fuse the multimodal perception data. The fusion formula is as follows: in, For the merged data, To perceive the types and quantities of data, For the first The weighting coefficients of class-aware data satisfy , For the first The normalized values ​​of the class-aware data; the weighting coefficients It is dynamically optimized and adjusted by the self-evolution layer.

[0008] Furthermore, the self-awareness unit of the autonomous consciousness layer realizes autonomous cognition of the system's own state by constructing a self-awareness model. The expression of the self-awareness model is: in, The result represents self-awareness, and its value ranges from [0,1]. For system architecture state parameters, For the algorithm model running parameters, Configure parameters for resources. These are system performance parameters. For system evolution history parameters, This is a self-cognition mapping function.

[0009] Furthermore, the architecture evolution unit of the self-evolution layer adopts reconfigurable hardware technology and microservice architecture to achieve architecture evolution, and the objective function of architecture evolution is: in, For the target value of architectural evolution, For architectural adaptability, For the sake of architectural operating efficiency, For architectural reliability, , , Let be the weighting coefficient, satisfying .

[0010] Furthermore, the algorithm evolution unit of the self-evolution layer uses reinforcement learning and genetic algorithms to achieve autonomous optimization of the algorithm model, and the algorithm parameter adjustment formula is as follows: in, For the adjustment amount of algorithm parameters, The learning rate has a value range of [0.001, 0.1]. Let be the algorithm error loss function. These are the algorithm parameters.

[0011] Furthermore, the knowledge system evolution unit of the self-evolution layer uses a knowledge graph update algorithm to achieve dynamic updates of the knowledge system, and the formula for calculating the confidence of knowledge nodes is: in, The confidence level of a knowledge node. This represents the number of times the knowledge node has been correctly applied. This represents the total number of times the knowledge node has been applied. This is the time decay coefficient, with a value range of [0.8, 1].

[0012] Furthermore, the data adaptation unit of the general cross-domain adaptation layer constructs a general data model to achieve unified conversion of heterogeneous data from different domains; the interface adaptation unit integrates multiple standardized interfaces and supports the autonomous expansion of interfaces; the task adaptation unit is based on a general cross-domain task model to achieve autonomous adaptation and conversion of tasks from different domains.

[0013] Furthermore, the global scheduling unit of the collaborative control layer adopts a priority scheduling algorithm to sort and schedule tasks of different priorities; the exception handling unit can autonomously identify system operation exceptions and generate exception handling solutions; the synchronization control unit adopts a time synchronization protocol to realize data synchronization and instruction synchronization of each layer module.

[0014] Furthermore, the workflow of the system includes the following steps: S1: System initialization, the collaborative control layer completes the startup and parameter configuration of each module, and the perception layer begins to collect multi-domain data; S2: The perception layer preprocesses and encrypts the collected raw data before transmitting it to the self-awareness layer and the self-evolution layer. S3: The autonomous consciousness layer generates self-awareness reports and environmental awareness reports, and the decision-making generation unit autonomously generates task execution plans and resource allocation plans; S4: The general cross-domain adaptation layer adapts tasks across domains, while the application layer connects to specific application scenarios and outputs task execution results. S5: The consciousness feedback unit feeds back relevant data to the autonomous consciousness layer and the self-evolution layer; S6: The self-evolution layer determines whether evolution is triggered. If triggered, it executes the evolution of architecture, algorithm, and knowledge system. S7: The collaborative control layer updates system parameters and resource configurations, and the system enters the next cycle.

[0015] Furthermore, the application layer includes an industrial control module, a healthcare module, a smart city module, an autonomous driving module, and a smart terminal module. It can call upon the core capabilities of the system to realize personalized applications according to the needs of the application scenario.

[0016] The beneficial effects of this invention are as follows: 1. Achieving true self-awareness: By constructing a self-awareness layer, integrating self-cognition, environmental cognition, decision generation, and awareness feedback units, a closed loop of self-awareness is formed, enabling the system to autonomously perceive its own state, autonomously judge environmental changes, and autonomously generate decision-making solutions without human intervention. This breaks through the limitations of the existing system's "pseudo-autonomy" and enables it to cope with complex and ever-changing unknown scenarios.

[0017] 2. Possesses comprehensive self-evolution capabilities: Constructs a multi-dimensional self-evolution system to achieve autonomous evolution of architecture, algorithms, and knowledge systems. It can proactively optimize its own performance based on system operating status, task requirements, and environmental changes, continuously improve the system's adaptability and processing capabilities, avoid system performance bottlenecks, and extend the system's life cycle.

[0018] 3. Strong general cross-domain adaptability: Through the general cross-domain adaptation layer, it realizes the unification of data formats, interfaces and tasks in multiple fields, supports seamless connection in multiple fields such as industrial control, medical health, and smart cities, and can adapt to application scenarios in different fields without manual modification, thereby improving the versatility and flexibility of the system and reducing the cost of cross-domain applications.

[0019] 4. High coordination and reliability: The collaborative control layer enables the collaborative work and resource optimization of each module, and has a complete anomaly handling mechanism that can autonomously identify and handle operational anomalies to ensure stable system operation. At the same time, through a closed-loop feedback mechanism, the system’s cognitive, decision-making and evolutionary capabilities are continuously optimized to improve the system’s reliability and adaptability.

[0020] 5. Wide range of applications: It can be widely used in industrial control, medical and health care, smart cities, autonomous driving, smart terminals and other fields. It can meet the personalized task requirements of different fields, and is especially suitable for complex and unknown scenarios and multi-domain collaborative scenarios. It has extremely high practical value and promotion prospects. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a diagram showing the overall architecture of the system of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] A general cross-domain intelligent system with autonomous awareness and self-evolution capabilities comprises: a perception layer, an autonomous awareness layer, a self-evolution layer, a general cross-domain adaptation layer, a collaborative control layer, and an application layer. These layers interact and transmit commands via a high-speed data bus and standardized interfaces, forming a closed-loop operational architecture. Specifically, the perception layer collects multi-domain environmental data, task data, and the system's own operational data; the autonomous awareness layer enables the system to autonomously perceive, judge, decide, and recognize itself; the self-evolution layer enables comprehensive and proactive evolution of the system architecture, algorithm model, and knowledge system; the general cross-domain adaptation layer achieves multi-domain data compatibility, interface unification, and task adaptation; the collaborative control layer coordinates the operation of modules across layers, enabling cross-domain collaborative decision-making and resource optimization; and the application layer interfaces with specific application scenarios in different domains, outputting the evolved system capabilities and decision results.

[0025] 1. Perception layer The perception layer includes a multimodal perception unit, a data preprocessing unit, and a data encryption unit, which are used to realize the comprehensive collection, preprocessing, and secure transmission of multi-domain data, providing data support for subsequent autonomous consciousness judgment and self-evolution.

[0026] The multimodal sensing unit adopts a distributed deployment approach, integrating various sensing devices such as visual sensors, auditory sensors, tactile sensors, environmental sensors, industrial sensors, and network sensors. The types of data that can be collected include: environmental data (temperature, humidity, air pressure, light intensity, noise, etc.), task data (task type, task objectives, task constraints, etc.), system operation data (operating status of each module, resource utilization, algorithm execution efficiency, error rate, etc.), and cross-domain interaction data (device data, business data, user demand data, etc. from different fields). Among these, the deployment location and sensing frequency of the sensing devices can be autonomously adjusted according to the application scenario, and the collaborative control layer issues adjustment instructions based on task requirements and environmental changes.

[0027] The data preprocessing unit is used to clean, denoise, normalize, extract features, and fuse the collected raw data, removing invalid and interfering data to improve data quality. The data fusion process employs a weighted fusion algorithm, calculated using the following formula: In the formula, For the merged data, To perceive the types and quantities of data, For the first The weighting coefficients of class-aware data (satisfying) ), For the first The normalized values ​​of class-aware data; weighting coefficients The self-evolutionary layer dynamically optimizes and adjusts based on data reliability and task requirements to ensure the accuracy and effectiveness of the fused data.

[0028] The data encryption unit uses the national standard SM4 encryption algorithm to encrypt the pre-processed fused data to prevent it from being stolen or tampered with during data transmission, thus ensuring data security. At the same time, it uses data desensitization technology to process sensitive data, protecting user privacy and business security.

[0029] 2. Level of Self-Awareness The autonomous consciousness layer is the core module of this invention, used to realize the system's autonomous cognition and autonomous decision-making, breaking through the limitations of the existing system's "pseudo-autonomy". It includes a self-cognition unit, an environmental cognition unit, a decision generation unit, and a consciousness feedback unit. Each unit works together to form an autonomous consciousness closed loop.

[0030] The self-awareness unit is used to enable the system to autonomously perceive and recognize its own state, including real-time monitoring and analysis of information such as its own architecture, algorithm model, resource configuration, operating performance, and evolutionary history. By constructing a self-awareness model, it autonomously judges the system's own strengths, weaknesses, and operational anomalies, and generates a self-awareness report. The expression of the self-awareness model is as follows: In the formula, The result represents the self-awareness outcome (with a value range of [0,1], where the closer to 1, the clearer the self-awareness and the more stable the system operation). For system architecture state parameters, For the algorithm model running parameters, Configure parameters for resources. These are system performance parameters. For system evolution history parameters, The self-cognition mapping function is obtained by training using a deep learning algorithm.

[0031] The environmental cognition unit is used to enable the system to autonomously perceive and cognize the external environment and tasks, including environmental change trends, task requirement analysis, and identification of interference factors. By integrating environmental data and task data collected by the perception layer, an environmental cognition model is constructed to autonomously judge the complexity and uncertainty of the environment and the priority and constraints of the tasks, and generate an environmental cognition report. The environmental cognition model works in conjunction with the self-cognition model to provide support for decision generation.

[0032] The decision generation unit is used to make autonomous decisions based on self-awareness reports and environmental awareness reports, without human intervention. The decision-making process includes: task prioritization, resource allocation, algorithm selection, and action execution plan generation. The decision generation adopts reinforcement learning algorithms, combined with the system's evolutionary history and task execution experience, to generate the optimal decision plan. At the same time, the decision generation unit has the ability to adjust itself. When the environment or task changes, it can adjust the decision plan in real time to ensure the adaptability and effectiveness of the decision.

[0033] The awareness feedback unit is used to feed back the decision execution results, system operation status, and cognitive biases to the self-awareness unit and the environmental awareness unit, forming an autonomous awareness closed loop. Through the feedback mechanism, the self-awareness model and the environmental awareness model are continuously corrected, improving the system's autonomous cognitive ability and decision accuracy. For example, when errors occur in decision execution, the awareness feedback unit feeds back the error information to the self-awareness unit, which adjusts its own cognition, the environmental awareness unit re-analyzes environmental factors, and the decision generation unit optimizes subsequent decision-making schemes.

[0034] 3. Self-evolution level The self-evolution layer is used to achieve the comprehensive and proactive evolution of the system, including three dimensions: architectural evolution, algorithm evolution, and knowledge system evolution. Its core is to build an evolution-driven model, which, based on the cognitive results of the autonomous consciousness layer and the system operation data, enables the autonomous generation and execution of evolution strategies without human intervention.

[0035] 3.1 Architectural Evolution Architecture evolution is used to dynamically adjust the system's hardware and software architecture to adapt to the task requirements and environmental changes in different fields. Hardware architecture evolution adopts reconfigurable hardware technology to optimize hardware performance by autonomously adjusting the connection methods and resource allocation of hardware modules. Software architecture evolution adopts a microservice architecture, which divides the system into multiple independent microservice modules. Each module can be autonomously upgraded, replaced, and extended to achieve flexible adjustment of the software architecture.

[0036] The triggering conditions for architectural evolution are determined by the autonomous consciousness layer, when the system's self-awareness results... Architecture evolution is triggered when environmental perception results indicate that the current architecture cannot meet task requirements; the objective function for architecture evolution is as follows: In the formula, For the target value of architectural evolution, For architecture adaptability (value range is [0,1]), For architecture operating efficiency (value range is [0,1]), For architecture reliability (value range is [0,1]), , , Weighting coefficients (satisfying) The self-evolution layer dynamically adjusts according to task requirements.

[0037] 3.2 Algorithm Evolution Algorithm evolution is used to achieve autonomous optimization and upgrading of the system's core algorithm model, including three aspects: algorithm parameter adjustment, algorithm structure optimization, and new algorithm generation. Based on reinforcement learning and genetic algorithms, algorithm evolution combines system operation data and task execution experience to autonomously find the optimal algorithm model. For example, for deep learning models, the algorithm evolution unit can autonomously adjust parameters such as the number of network layers, the number of neurons, and activation functions to optimize the model's training efficiency and prediction accuracy. For complex tasks, it can autonomously integrate multiple algorithms to generate new hybrid algorithm models, thereby improving task processing capabilities.

[0038] The error correction formula for algorithm evolution is as follows: In the formula, For the adjustment amount of algorithm parameters, The learning rate (ranging from [0.001, 0.1]). Let be the algorithm error loss function. These are the algorithm parameters; by continuously adjusting the algorithm parameters, error loss is reduced, and the algorithm model is optimized and evolved.

[0039] 3.3 Evolution of Knowledge Systems Knowledge system evolution is used to enable the autonomous accumulation, updating, and optimization of system knowledge, and to construct a dynamically updated knowledge graph. The knowledge system includes domain knowledge, task knowledge, operational knowledge, and evolutionary knowledge. Through cross-domain data and task execution experience collected by the perception layer, it autonomously extracts knowledge nodes and updates the knowledge graph. At the same time, through knowledge reasoning algorithms, it autonomously discovers the relationships between knowledge and improves the knowledge system. When the system encounters new domains or new tasks, it can autonomously learn new knowledge and quickly adapt to new scenarios without the need for manual input of domain knowledge.

[0040] The knowledge system evolution employs a knowledge graph update algorithm, the core of which is calculating the confidence score of knowledge nodes. When the confidence score is below a threshold (default 0.5), the knowledge node is deleted. When new knowledge data is collected, the correlation between the new node and existing knowledge nodes is calculated. If the correlation is above a threshold (default 0.7), the new node is added to the knowledge graph, and the correlation is updated. The formula for calculating the confidence score of a knowledge node is as follows: In the formula, The confidence level of a knowledge node. This represents the number of times the knowledge node has been correctly applied. This represents the total number of times the knowledge node has been applied. This is a time decay coefficient (with a value range of [0.8,1]), used to reduce the confidence of old knowledge and ensure the timeliness of the knowledge system.

[0041] 4. General Cross-Domain Adaptation Layer The general cross-domain adaptation layer is used to achieve compatibility and adaptation across multiple domains, and to solve problems such as data incompatibility, inconsistent interfaces, and poor task adaptability between existing system domains. It includes data adaptation units, interface adaptation units, task adaptation units, and inter-domain collaboration units.

[0042] The data adaptation unit is used to achieve unified conversion of data formats in different fields, supporting data format compatibility in multiple fields such as industrial control, healthcare, smart cities, and autonomous driving. By constructing a general data model, it converts heterogeneous data from different fields into a unified data format, enabling cross-domain data sharing. At the same time, it adopts a data mapping algorithm to establish the correspondence between data in different fields, ensuring data consistency and availability.

[0043] The interface adaptation unit is used to unify the interfaces of devices and systems in different fields. It integrates a variety of standardized interfaces (such as API interfaces, Modbus interfaces, CAN bus interfaces, etc.) and supports the independent expansion of interfaces. When connecting to devices or systems in new fields, the interface adaptation unit can automatically identify the interface type and generate an adaptation interface without manual development, thus improving the efficiency of cross-domain adaptation.

[0044] The task adaptation unit is used to enable the system to autonomously adapt to tasks in different domains. Based on a general cross-domain task model, it parses and transforms tasks from different domains into task instructions that the system can execute. At the same time, it adjusts the task execution strategy by combining the evolution results of the self-evolution layer to ensure efficient execution of tasks in different domains. For example, when the system switches from the industrial control domain to the medical and health domain, the task adaptation unit converts the medical monitoring task into the system's executable perception, decision-making, and execution instructions, while calling the evolved algorithm model and knowledge system to improve task processing capabilities.

[0045] Inter-domain collaboration units are used to enable collaborative work between multiple domains, coordinate task execution and resource allocation in different domains, and achieve cross-domain collaborative decision-making. For example, in smart city scenarios, they coordinate tasks in the transportation, environmental protection, and security fields to achieve optimized allocation and collaborative management of urban resources. Inter-domain collaboration units adopt distributed collaboration algorithms to ensure the real-time performance and reliability of multi-domain collaboration.

[0046] 5. Collaborative Control Layer The collaborative control layer is used to coordinate the operation of the perception layer, autonomous awareness layer, self-evolution layer, general cross-domain adaptation layer and application layer, and realize the collaborative work and resource optimization of each layer module. It includes a global scheduling unit, a resource management unit, an exception handling unit and a synchronization control unit.

[0047] The global scheduling unit is used to perform global scheduling of tasks in each module according to task requirements and system operating status to ensure the orderly execution of tasks. It adopts a priority scheduling algorithm to sort tasks of different priorities, execute high-priority tasks first, and at the same time take into account the real-time performance of tasks and the load balancing of the system.

[0048] The resource management unit is used to uniformly manage and optimize the allocation of the system's hardware resources (CPU, memory, storage, sensing devices, etc.) and software resources (algorithm models, knowledge systems, interfaces, etc.). Based on task requirements and system evolution results, it dynamically adjusts the resource allocation scheme to improve resource utilization. For example, when the system is undergoing self-evolution, the resource management unit prioritizes allocating computing resources to the self-evolution layer to ensure the efficient execution of the evolution process.

[0049] The anomaly handling unit is used to monitor the operating status of each module in the system in real time, identify operational anomalies (such as equipment failure, data anomalies, excessive algorithm errors, evolution failures, etc.), and autonomously generate anomaly handling solutions. For example, when a sensing device fails, the anomaly handling unit autonomously switches to a backup sensing device to ensure the continuity of data acquisition. When the algorithm evolution deviates, it autonomously adjusts the evolution strategy and re-executes the evolution process. The anomaly handling unit feeds back the anomaly information to the autonomous awareness layer to optimize self-cognition and decision generation.

[0050] The synchronization control unit is used to realize data and instruction synchronization between modules at each layer, ensuring the closed-loop operation of the system; it adopts a time synchronization protocol to unify the timestamps of modules at each layer, avoid delays in data transmission and instruction execution, and improve the system's coordination and reliability.

[0051] 6. Application Layer The application layer is used to connect to specific application scenarios in different fields, outputting the system's autonomous awareness, self-evolution, and cross-domain adaptation capabilities to practical applications, including industrial control modules, healthcare modules, smart city modules, autonomous driving modules, and smart terminal modules. Each application module uses a standardized interface to connect with the general cross-domain adaptation layer, and can call the system's core capabilities to realize personalized application requirements according to the needs of the application scenario. At the same time, the application layer transmits feedback data from the application scenario (such as task execution results, user needs, etc.) to the perception layer to drive the system's self-evolution and autonomous awareness optimization.

[0052] The general cross-domain intelligent system of the present invention, which possesses autonomous consciousness and self-evolution capabilities, includes the following steps in its workflow: Step 1: System initialization. The collaborative control layer completes the startup and parameter configuration of each module, and the multimodal sensing devices deployed in the perception layer begin to collect multi-domain environmental data, task data, and system operation data. Step 2: The perception layer cleans, denoises, normalizes, extracts features, and fuses the collected raw data. After being encrypted by the data encryption unit, the data is transmitted to the autonomous consciousness layer and the self-evolution layer. Step 3: The autonomous awareness layer, through the self-awareness unit and the environmental awareness unit, autonomously recognizes the system's own state and the external environment and tasks, respectively, and generates a self-awareness report and an environmental awareness report; based on the above two reports, the decision generation unit autonomously generates task execution plans and resource allocation plans, which are then distributed to each module through the collaborative control layer. Step 4: The general cross-domain adaptation layer adapts the tasks across domains according to the task execution plan issued by the decision generation unit, converts tasks from different domains into executable instructions of the system, coordinates multi-domain resources, and executes specific tasks; the application layer connects to specific application scenarios and outputs task execution results. Step 5: The consciousness feedback unit feeds back the task execution results, system operation status and cognitive biases to the self-cognition unit and the environmental cognition unit, and transmits relevant data to the self-evolution layer at the same time; Step 6: The self-evolution layer, based on the cognitive feedback from the self-awareness layer and system operation data, determines whether evolution needs to be triggered (if the self-awareness result...). If the task execution error exceeds a preset threshold, or if there is a significant change in the environment, evolution will be triggered. If evolution is triggered, the evolution strategy will be executed from three dimensions: architecture, algorithm, and knowledge system, to generate the evolved system architecture, algorithm model, and knowledge system. Step 7: The self-evolution layer feeds back the evolution results to the collaborative control layer, which updates the system parameters and resource configurations and synchronizes the evolved capabilities to each module. Step 8: The system enters the next cycle. The perception layer continues to collect data, the self-awareness layer updates the cognitive results, the self-evolution layer continuously optimizes the system's capabilities, and the general cross-domain adaptation layer and application layer adjust the task execution strategy according to the evolution results, so as to realize the continuous autonomous operation and optimization evolution of the system.

[0053] Example 1: Application in Industrial Control In this embodiment, a general cross-domain intelligent system with autonomous awareness and self-evolution capabilities is applied to the intelligent management and control scenario of an industrial production workshop to realize workshop equipment scheduling, production process optimization, anomaly warning and autonomous operation and maintenance.

[0054] 1. Sensing Layer Deployment: Multimodal sensing devices such as temperature sensors, humidity sensors, vibration sensors, current sensors, and cameras are deployed in the industrial production workshop to collect workshop environmental data (temperature, humidity, dust concentration, etc.), equipment operation data (speed, current, vibration frequency, energy consumption, etc.), production task data (production plans, output targets, product quality standards, etc.), and cross-domain data (supply chain data, inventory data, etc.). The data preprocessing unit cleans and denoises the collected raw data and uses a weighted fusion algorithm for data fusion, with fusion weights... (Equipment data) (Environmental data) (Task data), generate fused data and transmit it in encrypted form.

[0055] 2. Autonomous Awareness Layer Operation: The self-awareness unit constructs a self-awareness model under the workshop management scenario, monitors the system's own algorithm running efficiency, resource utilization, task execution error and other parameters in real time, and generates a self-awareness report; the environmental awareness unit analyzes changes in the workshop environment (such as abnormal temperature, abnormal equipment vibration) and production task requirements (such as output increase, quality optimization), and generates an environmental awareness report; based on the two reports, the decision generation unit autonomously generates equipment scheduling plans (such as adjusting equipment operating parameters, optimizing equipment start-up and shutdown sequence), production process optimization plans (such as adjusting production cycle, optimizing process connection), and abnormal early warning plans (such as setting equipment vibration thresholds and temperature thresholds, and triggering an early warning when the thresholds are exceeded).

[0056] 3. Self-Evolution Layer Operation: During system operation, when equipment operating errors exceed a preset threshold (5%), or production efficiency falls below the target value (80%), self-evolution is triggered. The architecture evolution unit adjusts the software architecture, adds a microservice module for equipment fault diagnosis, optimizes hardware resource allocation, and prioritizes CPU resources for the equipment monitoring module. The algorithm evolution unit optimizes the equipment fault diagnosis algorithm and adjusts the parameters of the deep learning model (learning rate). This reduces fault diagnosis errors; the knowledge system evolution unit extracts equipment fault data and production optimization experience, updates the knowledge graph, and adds new fault types and optimization strategies.

[0057] 4. General Cross-Domain Adaptation Layer Operation: The data adaptation unit converts workshop equipment data and production data into a unified format and interfaces with the data of the supply chain system and inventory system; the interface adaptation unit interfaces with workshop equipment through the Modbus interface and with the supply chain system through the API interface to achieve cross-domain data sharing; the task adaptation unit converts the supply chain's inventory data and production plan data into workshop control task instructions to achieve collaboration between production and the supply chain.

[0058] 5. Collaborative Control Layer Operation: The global scheduling unit prioritizes scheduling high-priority tasks (such as equipment fault handling and emergency production tasks); the resource management unit prioritizes allocating memory resources to the fault diagnosis module and the production optimization module; the anomaly handling unit monitors the equipment operating status in real time, and when abnormal equipment vibration is detected, it autonomously switches to backup equipment and triggers the fault diagnosis algorithm to generate a fault handling plan; the synchronization control unit realizes data synchronization and instruction synchronization of each layer of modules to ensure the real-time nature of production control.

[0059] 6. Application Layer Operation: The industrial control module interfaces with the workshop management system, outputting equipment scheduling instructions, production optimization instructions, and anomaly warning information to achieve autonomous management of the workshop; at the same time, it feeds back the production execution results (such as output, quality, and equipment operating status) to the perception layer, driving the system to further evolve.

[0060] In this embodiment, after one month of operation and evolution, the system improved the accuracy of equipment fault diagnosis from 88% to 98%, increased production efficiency by 15%, and shortened the abnormal warning response time to less than 10 seconds, realizing autonomous control and continuous optimization of the industrial workshop and reducing the cost of manual intervention.

[0061] Example 2: Applications in the field of smart cities In this embodiment, a general cross-domain intelligent system with autonomous consciousness and self-evolution capabilities is applied to the collaborative management and control scenario of smart cities to achieve collaborative work in traffic control, environmental monitoring, security early warning, and resource scheduling.

[0062] 1. Perception Layer Deployment: Distribute traffic cameras, traffic radars, air quality sensors, noise sensors, security cameras, water resource sensors, and other sensing devices throughout the city to collect traffic data (vehicle flow, speed, congestion, etc.), environmental data (PM2.5 concentration, noise levels, water quality, etc.), security data (personnel movement, abnormal behavior, etc.), and resource data (water resources, electricity resources, etc.). The data preprocessing unit normalizes and merges the collected heterogeneous data, with the fusion weight dynamically adjusted according to task priority to generate unified urban management data.

[0063] 2. Autonomous Awareness Layer Operation: The self-awareness unit monitors the operational status of each module of the system, including data acquisition efficiency, decision execution effectiveness, and cross-domain collaboration capabilities, and generates a self-awareness report; the environmental awareness unit analyzes urban traffic conditions, environmental quality, security situation, and resource supply and demand, identifies weak links in urban management (such as traffic congestion sections and areas with excessive air quality), and generates an environmental awareness report; the decision generation unit autonomously generates collaborative management and control plans, such as traffic congestion mitigation plans (adjusting traffic light durations and guiding vehicles to detour), environmental governance plans (activating dust control equipment and adjusting sewage treatment processes), and security early warning plans (issuing early warnings for abnormal personnel movement and dispatching security personnel).

[0064] 3. Self-evolution layer operation: When the urban traffic congestion relief rate is below 60%, or the environmental governance effect fails to meet the standard, self-evolution is triggered; the architecture evolution unit optimizes the system software architecture, adds cross-domain collaborative microservice modules, and improves the collaborative efficiency in the fields of transportation, environmental protection, and security; the algorithm evolution unit optimizes the traffic flow prediction algorithm and the environmental quality prediction algorithm, and uses genetic algorithms to optimize algorithm parameters and improve prediction accuracy; the knowledge system evolution unit accumulates urban management experience, updates the knowledge graph, and adds new congestion mitigation strategies, environmental governance methods, and security early warning modes.

[0065] 4. General Cross-Domain Adaptation Layer Operation: The data adaptation unit converts data from different fields such as transportation, environmental protection, security, and resources into a unified format to achieve cross-domain data sharing; the interface adaptation unit integrates API interfaces, CAN bus interfaces, etc., and interfaces with urban traffic control systems, environmental monitoring systems, security systems, and resource management systems to achieve interface unification; the task adaptation unit converts tasks from various fields into executable instructions for the system to achieve collaborative management and control of transportation, environmental protection, security, and resources.

[0066] 5. Collaborative Control Layer Operations: The global scheduling unit coordinates task execution across various domains, prioritizing urgent tasks (such as sudden security incidents and environmental quality exceeding standards); the resource management unit optimizes urban resource allocation, prioritizing power resources for environmental governance equipment and traffic control equipment; the anomaly handling unit identifies system malfunctions (such as sensor equipment failures and data transmission interruptions), autonomously switching to backup equipment and transmission channels to ensure the continuity of urban control; and the synchronization control unit synchronizes data and instructions across various domains, improving collaborative control efficiency.

[0067] 6. Application Layer Operations: The smart city module connects with various urban management and control systems, outputs collaborative management and control commands, and realizes integrated management and control of transportation, environmental protection, security, and resources. At the same time, it feeds back feedback data from urban management and control (such as the effect of congestion relief and the improvement of environmental quality) to the perception layer, driving the continuous evolution of the system.

[0068] In this embodiment, after the system has been running for 3 months, the duration of urban traffic congestion has been reduced by 30%, the PM2.5 concentration has decreased by 25%, and the response time for security incidents has been shortened to less than 5 minutes. This has enabled the autonomous and collaborative management and control of the smart city, and improved the level of intelligence and efficiency of urban management.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A general-purpose cross-domain intelligent system with autonomous consciousness and self-evolution capabilities, characterized in that, It includes a perception layer, an autonomous consciousness layer, a self-evolution layer, a general cross-domain adaptation layer, a collaborative control layer, and an application layer. The layers interact with each other and transmit commands through a high-speed data bus and standardized interfaces, forming a closed-loop operating architecture. The perception layer includes a multimodal perception unit, a data preprocessing unit, and a data encryption unit, which are used to collect multi-domain environmental data, task data, and system operation data, and transmit them to other layers after preprocessing and encryption. The autonomous consciousness layer includes a self-cognition unit, an environmental cognition unit, a decision generation unit, and a consciousness feedback unit, which are used to realize the system's autonomous perception, autonomous judgment, autonomous decision-making, and self-cognition, forming an autonomous consciousness closed loop. The self-evolution layer includes architecture evolution units, algorithm evolution units, and knowledge system evolution units, which are used to achieve comprehensive and proactive evolution of system architecture, algorithm models, and knowledge systems. The general cross-domain adaptation layer includes a data adaptation unit, an interface adaptation unit, a task adaptation unit, and an inter-domain collaboration unit, which are used to achieve multi-domain data compatibility, interface unification, task adaptation, and inter-domain collaboration. The collaborative control layer includes a global scheduling unit, a resource management unit, an exception handling unit, and a synchronization control unit, which are used to coordinate the operation of modules at each layer and realize cross-domain collaborative decision-making and resource optimization configuration. The application layer is used to connect to specific application scenarios in different fields, output the evolved system capabilities and decision results, and transmit application feedback data to the perception layer.

2. The general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The data preprocessing unit of the perception layer uses a weighted fusion algorithm to fuse multimodal perception data. The fusion formula is as follows: ； in, For the merged data, To perceive the types and quantities of data, For the first The weighting coefficients of class-aware data satisfy , For the first The normalized values ​​of the class-aware data; the weighting coefficients It is dynamically optimized and adjusted by the self-evolution layer.

3. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The self-awareness unit of the autonomous consciousness layer realizes autonomous cognition of the system's own state by constructing a self-awareness model. The expression of the self-awareness model is: ； in, The result represents self-awareness, and its value ranges from [0,1]. For system architecture state parameters, For the algorithm model running parameters, Configure parameters for resources. These are system performance parameters. For system evolution history parameters, This is a self-cognition mapping function.

4. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The self-evolutionary layer's architectural evolution unit employs reconfigurable hardware technology and a microservices architecture to achieve architectural evolution. The objective function for architectural evolution is: ； in, For the target value of architectural evolution, For architectural adaptability, For the sake of architectural operating efficiency, For architectural reliability, , , Let be the weighting coefficient, satisfying .

5. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The algorithm evolution unit of the self-evolution layer uses reinforcement learning and genetic algorithm to achieve autonomous optimization of the algorithm model. The algorithm parameter adjustment formula is as follows: ； in, For the adjustment amount of algorithm parameters, The learning rate has a value range of [0.001, 0.1]. Let be the algorithm error loss function. These are the algorithm parameters.

6. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The knowledge system evolution unit of the self-evolution layer uses a knowledge graph update algorithm to achieve dynamic updates of the knowledge system. The formula for calculating the confidence of knowledge nodes is as follows: ； in, The confidence level of a knowledge node. This represents the number of times the knowledge node has been correctly applied. This represents the total number of times the knowledge node has been applied. This is the time decay coefficient, with a value range of [0.8, 1].

7. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The data adaptation unit of the general cross-domain adaptation layer constructs a general data model to achieve unified conversion of heterogeneous data from different domains; the interface adaptation unit integrates multiple standardized interfaces and supports the independent expansion of interfaces; the task adaptation unit is based on a general cross-domain task model to achieve independent adaptation and conversion of tasks from different domains.

8. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The global scheduling unit of the collaborative control layer adopts a priority scheduling algorithm to sort and schedule tasks of different priorities; the exception handling unit can autonomously identify system operation exceptions and generate exception handling solutions; the synchronization control unit adopts a time synchronization protocol to realize data synchronization and instruction synchronization of each layer module.

9. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capabilities as described in claim 1, characterized in that, The system's workflow includes the following steps: S1: System initialization, the collaborative control layer completes the startup and parameter configuration of each module, and the perception layer begins to collect multi-domain data; S2: The perception layer preprocesses and encrypts the collected raw data before transmitting it to the self-awareness layer and the self-evolution layer. S3: The autonomous consciousness layer generates self-awareness reports and environmental awareness reports, and the decision-making generation unit autonomously generates task execution plans and resource allocation plans; S4: The general cross-domain adaptation layer adapts tasks across domains, while the application layer connects to specific application scenarios and outputs task execution results. S5: The consciousness feedback unit feeds back relevant data to the autonomous consciousness layer and the self-evolution layer; S6: The self-evolution layer determines whether evolution is triggered. If triggered, it executes the evolution of architecture, algorithm, and knowledge system. S7: The collaborative control layer updates system parameters and resource configurations, and the system enters the next cycle.

10. A general cross-domain intelligent system with autonomous consciousness and self-evolutionary capability according to any one of claims 1-9, characterized in that, The application layer includes industrial control modules, healthcare modules, smart city modules, autonomous driving modules, and smart terminal modules. It can call upon the core capabilities of the system to realize personalized applications according to the needs of the application scenario.

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