Immersive artificial intelligence operation and maintenance operation method and system

Abstract

The invention discloses an immersive artificial intelligence operation and maintenance operation method and system. The system comprises a user interaction layer, a cloud management layer, an edge acquisition layer and a learning optimization layer. According to the system, voice, gesture and eye movement control is achieved through multi-mode interaction, semantic analysis, risk assessment and strategy generation are conducted through an AI decision engine, operation and maintenance state three-dimensional visual display is achieved in combination with a digital twin rendering module, and strategy self-optimization is achieved through a knowledge graph and reinforcement learning. The system has the characteristics of natural interaction, safety, traceability and intelligent self-learning, and is suitable for multi-scene intelligent operation and maintenance management.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence and intelligent operation and maintenance management technology, and more specifically to an immersive artificial intelligence operation and maintenance method and system. Background Technology

[0002] With the continuous advancement of smart hospital and medical informatization construction, the number of devices, types of information systems, and complexity of data interaction within hospitals have increased dramatically. Traditional manual operation and maintenance and decentralized monitoring methods are no longer sufficient to meet the demands for safe, efficient, and intelligent management. Existing hospital operation and maintenance systems typically rely on manual on-duty personnel and regular inspections. When system failures, equipment malfunctions, or network outages occur, the response speed is slow, risk warnings are delayed, and data is fragmented between different systems, making unified monitoring and collaborative decision-making impossible.

[0003] Furthermore, hospital IT infrastructure encompasses various systems, including server clusters, medical terminals, energy-consuming equipment, and network security modules. The operational tasks are complex and involve numerous nodes, making it difficult to fully grasp the operational status using traditional two-dimensional interface management methods. While existing intelligent operation and maintenance systems incorporate some AI algorithms, they remain limited to static alarms and rule matching, lacking a deep understanding of complex environments and the ability to generate dynamic strategies. This prevents them from achieving global visualization, risk prediction, and self-learning optimization. Simultaneously, medical data is highly sensitive and requires confidentiality. Traditional operation and maintenance platforms have significant shortcomings in security isolation and log traceability, making it difficult to promptly trace and recover from misoperations or network attacks.

[0004] Therefore, there is an urgent need for an immersive operation and maintenance operating system that combines artificial intelligence decision-making, digital twin simulation, and multimodal interaction, which can realize three-dimensional visualization management, intelligent decision control, and security traceability of hospital information equipment, and improve the automation level and security assurance capabilities of medical institutions in the field of information operation and maintenance. Summary of the Invention

[0005] The purpose of this invention is to address the problems of existing intelligent operation and maintenance systems, such as limited interaction, delayed response, insufficient security, and lack of self-learning optimization, by providing an immersive artificial intelligence operation and maintenance method and system that realizes intelligent control of the entire process from data collection and AI decision-making to immersive execution feedback.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An immersive AI-powered operation and maintenance operating system includes: a user interaction layer, a cloud management layer, an edge acquisition layer, and a learning and optimization layer. The layers communicate with each other via a system bus to achieve encrypted communication and data synchronization.

[0008] The user interaction layer includes a user terminal and a multimodal interaction module. The user terminal is a virtual reality or augmented reality operating interface, and the multimodal interaction module is used to realize speech recognition, gesture capture, eye tracking and context awareness.

[0009] The cloud management layer includes an AI decision engine module, a policy learning module, and a security isolation module. The AI ​​decision engine module is used for semantic analysis, risk assessment, and policy generation of operation and maintenance data. The policy learning module is used for log recording, behavior modeling, and policy updates. The security isolation module is used for anomaly detection, data isolation, and log chain backtracking.

[0010] The edge acquisition layer includes a data acquisition module and a digital twin rendering module. The data acquisition module is used to collect equipment operating status, log information and external environmental parameters. The digital twin rendering module is used to generate a three-dimensional simulation model based on the collected data and realize the visualization display of the operation and maintenance status.

[0011] The learning optimization layer includes a policy optimization unit and a system self-learning unit, which are used to optimize parameters and adaptively update the model based on historical logs and knowledge graphs.

[0012] Preferably, the AI ​​decision engine module includes a semantic parsing unit, a state perception unit, a risk assessment unit, and a strategy generation unit. The semantic parsing unit is used to perform intent recognition on multimodal inputs, the state perception unit is used to detect the system operating status, the risk assessment unit is used to perform security judgment on the executed task, and the strategy generation unit is used to output control commands or protection schemes.

[0013] Preferably, the digital twin rendering module includes a data preprocessing unit, a semantic mapping unit, a 3D modeling engine, a dynamic simulation engine, and a visualization unit. The 3D modeling engine generates virtual scenes based on device instances and network topology. The dynamic simulation engine is used to update energy consumption curves and alarm status in real time. The visualization unit realizes 3D rendering and immersive interactive operation.

[0014] Preferably, the multimodal interaction module includes a speech recognition submodule, a gesture recognition submodule, an eye tracking submodule, and a context awareness submodule, which are used to convert voice commands, action signals, and gaze focus information into system-parseable operation instructions, and output feedback information in the form of voice broadcast, visual prompts, or vibration signals.

[0015] Preferably, the security isolation module includes an anomaly monitoring unit, a risk protection unit, and a log chain generation unit. The anomaly monitoring unit is used to identify unauthorized access and illegal commands. The risk protection unit executes isolation policies. The log chain generation unit uses hash algorithms and timestamp mechanisms to perform chained storage and traceable management of operation records.

[0016] Preferably, the strategy learning module includes a behavior modeling unit and a model optimization unit. The behavior modeling unit is used to analyze the execution characteristics of operation and maintenance tasks and form a behavior model. The model optimization unit realizes the dynamic updating and adaptive adjustment of the strategy through reinforcement learning algorithms.

[0017] Preferably, the system bus supports a bidirectional communication encryption mechanism, including a data encryption transmission unit and a synchronization control unit. The data encryption transmission unit realizes cross-layer communication based on a secure channel, and the synchronization control unit is used to maintain time consistency and data integrity between the edge and the cloud.

[0018] Preferably, the system further includes a visual operation and maintenance console, which is used to display the device operating status, energy consumption distribution and alarm information in a three-dimensional scene, and supports the verification and adjustment of policy execution results through a virtual operation terminal.

[0019] Preferably, the immersive AI-based operation and maintenance method of the system includes the following steps:

[0020] S1. Collect system operating status, log information and environmental parameters through the edge acquisition layer, and perform data preprocessing;

[0021] S2. Input the cleaned data into the AI ​​decision engine module for semantic parsing, risk assessment and strategy generation;

[0022] S3. The generated control commands are sent to the immersive execution module via the system bus to perform virtual scene operations and provide real-time feedback.

[0023] S4. Send the execution results and status information back to the strategy learning module for logging and model optimization.

[0024] S5. The strategy optimization unit based on the learning optimization layer updates the model parameters, forming a closed-loop self-learning control mechanism.

[0025] Preferably, in step S3, the immersive execution module performs remote operation and maintenance tasks through the VR / AR interactive environment, and outputs the feedback signals of the executed actions synchronously with the visual interface, voice prompts and tactile responses to achieve real-time interaction and traceable operation.

[0026] Compared with existing technologies, this invention has significant advantages. By combining artificial intelligence decision-making, digital twin simulation, and multimodal interaction technology, an immersive intelligent operation and maintenance system for hospital scenarios is constructed, realizing visualized, intelligent, and secure management of medical information equipment, energy consumption systems, and network security modules. The system utilizes an AI decision engine to perform semantic analysis and risk prediction on multi-source data, proactively identifying potential faults and automatically generating optimization strategies, effectively improving the accuracy and timeliness of hospital operation and maintenance responses. The digital twin module maps key hospital equipment and environmental data into a three-dimensional simulation model, enabling operation and maintenance personnel to monitor operational status in real time and perform remote diagnosis in a virtual space, reducing the frequency of on-site maintenance and improving safety. The multimodal interaction module achieves natural operation through voice, gestures, and eye tracking, reducing the operational burden on medical engineers and IT personnel and significantly improving work efficiency. The system also features log chain traceability and security isolation mechanisms to ensure the verifiability of operation and maintenance activities and data security. Compared with traditional hospital information operation and maintenance platforms, this invention realizes the transformation from passive management to proactive intelligent decision-making, significantly improving the overall level of hospital equipment management, system security and energy consumption optimization, and providing technical support for the safe, efficient and sustainable operation of smart hospitals. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the overall structure of the immersive artificial intelligence operation and maintenance operating system of the present invention;

[0029] Figure 2 This is a functional structure diagram of the digital twin rendering module of the present invention;

[0030] Figure 3 This is a logical framework diagram of the multimodal interaction and AI decision-making linkage of the present invention;

[0031] Figure 4 This is a flowchart of the system operation of the present invention. Detailed Implementation

[0032] 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.

[0033] Example 1

[0034] like Figures 1 to 4 As shown, the immersive AI-powered operation and maintenance method and system proposed in this invention constructs an operation and maintenance system with intelligent decision-making, immersive interaction, virtual simulation, and self-learning optimization functions through multi-layered collaborative work. This system comprehensively utilizes AI analysis algorithms, digital twin modeling engines, and multimodal perception and interaction technologies to achieve closed-loop control throughout the entire process, from data collection and intelligent judgment to virtual execution and feedback optimization. This significantly improves the accuracy, security, and interactive experience of decision-making in complex operation and maintenance scenarios.

[0035] The overall system structure is as follows Figure 1 As shown, the system mainly consists of a user interaction layer, a cloud management layer, an edge acquisition layer, and a learning and optimization layer. The user interaction layer includes a VR / AR display terminal and a multimodal interaction module, supporting speech recognition, gesture capture, eye tracking, and context awareness, capable of converting natural language or action signals into system-understandable operation commands. The cloud management layer is the intelligent core of the system, including an AI decision engine module, a strategy learning module, and a security isolation module. The AI ​​decision engine module intelligently generates strategies through semantic analysis, state awareness, and risk assessment; the strategy learning module records operation logs and optimizes the decision model based on reinforcement learning algorithms; the security isolation module monitors abnormal behavior in real time and ensures the security and traceability of operations through a log chain backtracking mechanism. The edge acquisition layer includes a data acquisition module and a digital twin rendering module. The former is responsible for collecting equipment operating status, energy consumption information, and external environmental data, while the latter inputs the collected results into the digital twin simulation engine to generate a virtual 3D operation and maintenance scene synchronized with the real environment for monitoring, analysis, and operation verification. The learning and optimization layer continuously updates strategy parameters through knowledge graphs and model self-learning mechanisms, enabling the system to have long-term adaptive capabilities.

[0036] like Figure 2As shown, the digital twin rendering module consists of a data preprocessing unit, a semantic mapping unit, a 3D modeling engine, a dynamic simulation engine, and a visualization unit. This module receives multi-source information from the data acquisition layer, and after cleaning, alignment, and semantic mapping, generates a virtual device topology in the 3D modeling engine. The dynamic simulation engine dynamically refreshes the virtual scene based on real-time acquired operating status and energy consumption curves, allowing maintenance personnel to view system status changes, fault distribution, and energy consumption trends in an immersive interface. The visualization unit displays the simulation results in 3D on a VR / AR terminal, achieving real-time visualization operations that integrate virtual and real worlds, providing a verification environment and operational feedback for the AI ​​decision-making module.

[0037] like Figure 3 As shown, the multimodal interaction and AI decision-making linkage logic framework embodies the system's intelligent interaction mechanism. The multimodal perception layer acquires user input through voice, gestures, and eye tracking, while the context perception unit identifies environmental parameters and user status. The instruction parsing layer uses a semantic fusion engine to convert multimodal input into operational intentions, the AI ​​decision-making layer generates strategies based on these intentions, and the execution layer achieves virtual control through immersive interaction. The execution results are transmitted to the learning module via the feedback layer for model updates and strategy optimization, thus forming a continuous intelligent loop. The system can self-adjust control parameters under different task scenarios, achieving dynamic strategy optimization and self-learning capabilities.

[0038] like Figure 4 As shown, the system operation process consists of five stages: data acquisition and input, modeling and analysis, decision-making and analysis, execution and feedback, and learning and optimization. First, the system collects device operation data and environmental parameters from the edge layer, models them using the digital twin module, and then inputs them into the AI ​​decision engine. Second, the AI ​​module generates optimization schemes based on state information and historical strategies. Then, the immersive execution module completes virtual operations and outputs visual feedback. Subsequently, the strategy learning module analyzes and models the execution data. Finally, the system uses the optimization module to update the model parameters, achieving dynamic self-evolution of intelligent decision-making.

[0039] In specific application scenarios, this invention can be widely applied to data center management, industrial equipment operation and maintenance, energy dispatching systems, and smart city infrastructure control. Maintenance personnel enter a virtual operation and maintenance space by wearing VR / AR terminals, using voice, gestures, or eye commands to monitor and operate equipment in real time. The AI ​​decision engine automatically generates operation strategies based on the operation and maintenance environment and risk level. The digital twin module reflects changes in equipment status in real time and executes simulated operations in the virtual scenario, achieving strategy verification and security testing. The system records the entire operation process through a log chain, automatically isolating and tracing anomalies when detected, effectively ensuring operation and maintenance security. The learning and optimization layer continuously learns through knowledge graphs and historical data, enabling the system to gradually improve judgment accuracy and strategy reliability over long-term operation.

[0040] In summary, the immersive AI-powered O&M operating system of this invention achieves a closed-loop intelligent O&M model of "perception-analysis-decision-execution-learning" through the deep integration of artificial intelligence, virtual reality, and digital twin technologies. The system not only performs real-time analysis and visualization of multi-source data but also achieves adaptive strategy optimization through a self-learning mechanism. This significantly improves work efficiency and response speed while ensuring O&M security, providing an innovative overall solution for future intelligent O&M systems.

[0041] Example 2

[0042] like Figures 1 to 4 As shown, the immersive AI-powered operation and maintenance method and system provided by this invention further optimizes the linkage mechanism of the digital twin simulation engine, AI decision-making algorithm, and security isolation module in this embodiment, focusing on improving the system's adaptive operation and maintenance capabilities and security response capabilities in complex multi-node environments. This embodiment uses a large data center as an application scenario, achieving visualized management and intelligent closed-loop control of equipment operating status through multimodal human-computer interaction and virtual simulation.

[0043] like Figure 1 As shown, the overall system architecture still consists of a user interaction layer, a cloud management layer, an edge acquisition layer, and a learning and optimization layer. The user interaction layer supports multi-terminal collaborative operation, allowing maintenance personnel to simultaneously enter an immersive maintenance scenario through VR headsets, desktop terminals, or mobile terminals. The multimodal interaction module can automatically identify the priority of different input channels. If voice commands and gesture operations are triggered simultaneously, the system will perform fusion judgment based on semantic weights to ensure the consistency and reliability of command execution.

[0044] In the cloud management layer, the AI ​​decision engine module includes a multi-model decision architecture, such as... Figure 3As shown, it integrates a semantic parsing submodule, a state awareness submodule, a risk assessment submodule, and a policy generation submodule. The semantic parsing submodule uses a multi-task deep learning model to convert voice and text input into standard operational intent labels; the state awareness submodule assesses system stability by analyzing device operating parameters, energy consumption data, and log chain status; the risk assessment submodule quantifies the risk score of each node's operational behavior and predicts execution consequences based on historical policy models; the policy generation submodule combines knowledge graphs and reinforcement learning algorithms to form the optimal instruction sequence. Execution results are pre-verified through a virtual simulation module to ensure the feasibility and security of the policy.

[0045] like Figure 2 As shown, the digital twin rendering module in this embodiment boasts higher real-time performance and rendering accuracy. The data acquisition module collects multi-dimensional data such as CPU temperature, energy consumption, current, voltage, wind speed, and equipment vibration through IoT sensors deployed at edge nodes. After preprocessing, this data is input into the 3D modeling engine via the semantic mapping module to generate a virtual device topology. The dynamic simulation engine synchronously adjusts the virtual model based on the strategy commands output by the AI ​​decision-making module, displaying the execution effect in real time. Maintenance personnel can observe performance changes, system latency, and risk indices before and after operations in the virtual space. This module supports bidirectional synchronization, meaning that changes in the simulation state can be fed back to the AI ​​model to correct strategy parameters, thus forming a "virtual-driven—decision-corrected" interactive loop.

[0046] like Figure 4 As shown, the system operation flow in this embodiment forms a complete cycle of "collection—modeling—analysis—execution—backtracking—optimization". First, the data acquisition module collects real-time parameters at millisecond intervals at the edge; second, the digital twin module completes simulation mapping; the AI ​​decision-making module generates the optimal operation strategy based on multi-source data; the immersive execution module executes in the virtual environment and outputs real-time feedback; the security isolation module records each operation through a log chain structure and uses a hash verification mechanism to ensure the authenticity of the records. If the system detects a risk event, the security module immediately triggers the isolation protocol, separating the abnormal node from the main network and generating a traceable report, ensuring the security and controllability of the overall operation and maintenance process.

[0047] In the learning and optimization layer, the policy learning module automatically updates the policy model based on reinforcement learning algorithms and knowledge graph analysis through the collaborative operation of behavior modeling and model optimization units. After each task execution, the system records information such as execution status, risk level, time consumption, and resource utilization, forming a historical dataset. The AI ​​decision-making module uses this dataset to perform reverse optimization of the policy generation algorithm, enabling the system to have higher response speed and decision accuracy in subsequent tasks. The self-learning unit achieves "experience transfer" through dynamic weight adjustment, which can transfer experienced policies from specific scenarios to similar environments, thereby reducing the cost of repeated training.

[0048] Furthermore, this embodiment introduces a log chain tracing system into the security isolation mechanism. The log chain generation unit uses hash functions and timestamp technology to form an immutable chain data structure for all operation records and stores it in a distributed ledger, enabling operation traceability and result verification. When the system detects unauthorized instructions or abnormal access behavior, it can trace its source, scope of impact, and responsible party through the log chain, thereby achieving accurate tracing and post-event verification.

[0049] In a typical application scenario, when the system detects an abnormally high temperature on a server node, the AI ​​decision-making module uses a digital twin model to simulate and calculate airflow distribution and power load, displaying the risk area through an immersive interface. Maintenance personnel can then use gestures to select virtual device nodes and issue cooling commands. The AI ​​module automatically adjusts the airflow control strategy based on model predictions and verifies the cooling effect, completing the risk response within seconds. All operations and feedback processes are logged to ensure system recoverability and verifiable behavior.

[0050] Through this embodiment, the present invention not only realizes intelligent operation and maintenance and virtual risk prediction at the data center level, but also realizes self-learning, self-diagnosis and self-repair operation and maintenance logic through the integration of multimodal interaction and AI decision-making algorithms. This significantly improves the system's response efficiency and operational security in complex network environments, and provides a standardized and scalable technical foundation for future intelligent operation and maintenance systems.

[0051] Example 3

[0052] like Figures 1 to 4 As shown, the immersive AI-powered operation and maintenance method and system of this invention are applied to smart energy management and remote equipment maintenance scenarios in this embodiment. By integrating multimodal perception, AI decision engine, digital twin simulation, and security log chain traceability, it achieves intelligent collaborative operation and maintenance and visualized decision control across regions, devices, and platforms. This embodiment further enhances the multimodal interactive response capability, the dynamic adaptability of the decision algorithm, and the data collaboration efficiency of virtual-real fusion based on the aforementioned system structure, enabling the operation and maintenance system to possess high-precision prediction, highly robust control, and multi-terminal collaborative functions.

[0053] like Figure 1As shown, the overall system architecture still consists of a user interaction layer, a cloud management layer, an edge acquisition layer, and a learning and optimization layer. The user interaction layer enables natural communication between operations and maintenance personnel and the system through a multimodal interaction module and an AI semantic interface. Voice commands and gestures can simultaneously trigger task execution, which is then parsed by the AI ​​decision-making module to generate operations and maintenance strategies. The system supports multi-user collaborative operation. When multiple operations and maintenance personnel issue task commands through different terminals, the system automatically identifies task priorities and device status, and performs task scheduling and strategy merging based on a conflict detection algorithm to ensure consistency and security of multi-node operations.

[0054] like Figure 2 As shown, the digital twin rendering module in this embodiment is applied to the 3D simulation control of energy transmission and distribution equipment. This module collects data on voltage, current, energy consumption, temperature, humidity, and equipment load, which is then converted into a recognizable parameter model by a semantic mapping unit. A 3D modeling engine then generates a virtual energy distribution network. The dynamic simulation engine calculates the system's energy consumption fluctuation trend in real time and performs power regulation simulation. The visualization unit displays the operating status, load changes, and energy efficiency indicators in a 3D format. Maintenance personnel can view energy flow, equipment status, and alarm nodes in the virtual scene and execute strategy verification operations via voice commands. The system first simulates the strategy results in the virtual environment before sending the decision instructions to the real equipment layer, achieving a dual-layer safety control mechanism of "virtual verification—real execution."

[0055] like Figure 3 As shown, the multimodal interaction and AI decision-making linkage logic is further extended to the environmental perception and dynamic strategy evaluation module in this embodiment. In addition to voice, gesture, eye tracking, and contextual perception, the multimodal perception layer also includes an environmental semantic acquisition unit, which can identify environmental noise levels, personnel density, and lighting conditions. The instruction parsing layer combines environmental features with user operation intentions. Figure 1 The data is then input into the AI ​​decision engine, where the AI ​​module uses multi-dimensional input vectors for decision modeling. The risk assessment module predicts potential anomalies based on energy consumption curves and historical operation results, while the strategy generation module dynamically adjusts the control logic. The execution feedback layer provides real-time prompts on the execution status through visual highlighting and voice broadcasting, while the self-learning layer records the entire operation process and generates feedback logs for continuous model optimization.

[0056] like Figure 4As shown, the system operation process still follows the five-stage logic of "collection—modeling—decision-execution—optimization," but this embodiment incorporates a multi-source data fusion and feedback self-calibration mechanism. First, the edge acquisition layer collects operational data from different devices and unifies the format; second, the digital twin module completes energy consumption modeling and operational status mapping; subsequently, the AI ​​decision-making module jointly analyzes the current system state and historical data to generate the optimal strategy; the immersive execution module completes operation rehearsal in a virtual environment and then sends instructions to the device for execution; finally, the learning optimization layer evaluates the execution results and updates the AI ​​model in reverse through reinforcement learning and knowledge graphs, enabling the strategy to have long-term evolution capabilities.

[0057] In smart energy scenarios, when the system detects abnormal fluctuations in power load, the AI ​​decision engine first invokes the digital twin module to predict the energy balance state and simulates the execution results of the scheduling strategy in a virtual environment. Maintenance personnel select load distribution paths via gesture commands, and the AI ​​module automatically generates the optimal power allocation scheme based on the simulation results and executes the adjustments. The entire process is presented in a dynamic energy flow format within a 3D virtual interface. The system uses a log chain tracing module to encrypt and record all operations, strategy parameters, and feedback results for subsequent security audits and behavioral analysis. If abnormal fluctuations occur during execution, the security isolation module immediately triggers a risk protection protocol, isolating the affected nodes and restoring them to a safe state.

[0058] In the learning and optimization layer, the policy learning module continuously records input commands, device responses, and system outputs during the operation and maintenance process, and uses reinforcement learning algorithms and Bayesian adaptive models to continuously optimize policy selection. The self-learning unit combines knowledge graphs to analyze the load characteristics of different energy nodes, forming an adaptive policy weight adjustment mechanism. When the system runs under different seasons, different energy consumption scenarios, or different device configurations, the AI ​​module can automatically switch to the optimal model version, achieving cross-scenario transfer learning.

[0059] Through this embodiment, the present invention achieves integrated full-domain immersive operation and maintenance with virtual simulation decision-making for smart energy systems. The system not only reduces the risk of human intervention through virtual operation but also achieves self-diagnosis and predictive maintenance based on AI algorithms, ensuring high security and efficiency in energy system operation. The deep integration of digital twin simulation and AI decision-making modules enables the system to achieve real-time optimization, risk prediction, and safe scheduling of complex energy consumption networks, providing a scalable intelligent operation and maintenance solution for future smart city energy management, industrial automation maintenance, and distributed energy control.

[0060] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An immersive AI-powered operation and maintenance operating system, characterized in that, include: The system comprises a user interaction layer, a cloud management layer, an edge acquisition layer, and a learning and optimization layer, with each layer communicating and synchronizing data via a system bus. The user interaction layer includes a user terminal and a multimodal interaction module. The user terminal is a virtual reality or augmented reality operating interface, and the multimodal interaction module is used to realize speech recognition, gesture capture, eye tracking and context awareness. The cloud management layer includes an AI decision engine module, a policy learning module, and a security isolation module. The AI ​​decision engine module is used for semantic analysis, risk assessment, and policy generation of operation and maintenance data. The policy learning module is used for log recording, behavior modeling, and policy updates. The security isolation module is used for anomaly detection, data isolation, and log chain backtracking. The edge acquisition layer includes a data acquisition module and a digital twin rendering module. The data acquisition module is used to collect equipment operating status, log information and external environmental parameters. The digital twin rendering module is used to generate a three-dimensional simulation model based on the collected data and realize the visualization display of the operation and maintenance status. The learning optimization layer includes a policy optimization unit and a system self-learning unit, which are used to optimize parameters and adaptively update the model based on historical logs and knowledge graphs.

2. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The AI ​​decision engine module includes a semantic parsing unit, a state perception unit, a risk assessment unit, and a strategy generation unit. The semantic parsing unit is used to identify the intent of multimodal inputs, the state perception unit is used to detect the system's operating status, the risk assessment unit is used to make security judgments on the executed tasks, and the strategy generation unit is used to output control commands or protection schemes.

3. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The digital twin rendering module includes a data preprocessing unit, a semantic mapping unit, a 3D modeling engine, a dynamic simulation engine, and a visualization unit. The 3D modeling engine generates virtual scenes based on device instances and network topology. The dynamic simulation engine is used to update energy consumption curves and alarm status in real time. The visualization unit realizes 3D rendering and immersive interactive operation.

4. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The multimodal interaction module includes a speech recognition submodule, a gesture recognition submodule, an eye tracking submodule, and a context awareness submodule, which are used to convert voice commands, action signals, and gaze focus information into operation instructions that the system can parse, and output feedback information in the form of voice broadcast, visual prompts, or vibration signals.

5. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The security isolation module includes an anomaly monitoring unit, a risk protection unit, and a log chain generation unit. The anomaly monitoring unit is used to identify unauthorized access and illegal commands. The risk protection unit executes isolation policies. The log chain generation unit uses hash algorithms and timestamp mechanisms to perform chained storage and traceable management of operation records.

6. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The strategy learning module includes a behavior modeling unit and a model optimization unit. The behavior modeling unit is used to analyze the execution characteristics of operation and maintenance tasks and form a behavior model. The model optimization unit realizes the dynamic updating and adaptive adjustment of the strategy through reinforcement learning algorithms.

7. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The system bus supports a bidirectional communication encryption mechanism, including a data encryption transmission unit and a synchronization control unit. The data encryption transmission unit enables cross-layer communication based on a secure channel, and the synchronization control unit is used to maintain time consistency and data integrity between the edge and the cloud.

8. The immersive AI-powered operation and maintenance operating system according to claim 1, characterized in that, The system further includes a visual operation and maintenance console, which displays the device operating status, energy consumption distribution and alarm information in a three-dimensional scene, and supports the verification and adjustment of policy execution results through a virtual operation terminal.

9. An immersive AI-based operation and maintenance method based on the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: S1. Collect system operating status, log information and environmental parameters through the edge acquisition layer, and perform data preprocessing; S2. Input the cleaned data into the AI ​​decision engine module for semantic parsing, risk assessment and strategy generation; S3. The generated control commands are sent to the immersive execution module via the system bus to perform virtual scene operations and provide real-time feedback. S4. Send the execution results and status information back to the strategy learning module for logging and model optimization. S5. The strategy optimization unit based on the learning optimization layer updates the model parameters, forming a closed-loop self-learning control mechanism.

10. The immersive AI-powered operation and maintenance method according to claim 9, characterized in that, In step S3, the immersive execution module performs remote operation and maintenance tasks through the VR / AR interactive environment, and outputs the feedback signals of the executed actions synchronously with the visual interface, voice prompts and tactile responses to achieve real-time interaction and traceable operation.

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