Remote intelligent control system based on universe

Abstract

The invention discloses a remote intelligent control system based on meta universe, which relates to the technical field of remote intelligent control and comprises a sensing modeling module, a verification optimization module, a user interaction module, an intelligent control module, a remote execution module, a storage management module and a learning optimization module. According to the remote intelligent control system based on the meta-universe, the meta-universe technology and the remote intelligent control technology are combined, the virtual environment of the meta-universe is utilized, the remote control technology is combined, intelligent control and management of remote equipment or systems are achieved, the information display and interaction experience is improved, and the user experience is improved. Meanwhile, the meta-universe technology provides a simulation training environment for operators, training of response and event processing can be carried out, and therefore the actual operation capacity is improved, deep analysis is carried out on data in cooperation with artificial intelligence and the cloud computing technology, and optimal control strategies and instructions are generated according to user instructions and information provided by the perception modeling module.

Description

Technical Field

[0001] The present invention relates to the field of remote intelligent control technology, and in particular to a remote intelligent control system based on a metaverse. Background Art

[0002] The metaverse is a virtual collection of time and space, composed of a series of augmented reality AR, virtual reality VR and the Internet. It is a virtual world concept parallel to the real world, with the characteristics of virtual space and real time. It contains digital replicas of the real world and virtual creations, and exists as a parallel space that is both real and independent.

[0003] With the evolution and maturity of science and technology, people's imaginations about the metaverse have been realized in technology. A series of technological innovations and breakthroughs, such as AR, VR, MR, cloud computing, big data, blockchain, artificial intelligence, and digital twins, have provided a solid foundation for the development of the metaverse. These technologies not only make the construction and realization of the metaverse possible, but also promote the evolution of the metaverse in multiple directions, such as the deep integration of the physical world and the digital world, and the exploration of the human spiritual world.

[0004] In the existing technology, since traditional remote intelligent control systems often rely on two-dimensional interfaces for interaction, although this method can realize basic remote control functions, it is relatively limited in information display and interactive experience; secondly, traditional remote intelligent control systems can usually only provide limited perspectives or scene information, resulting in incomplete user perception of the environment and insufficient spatial perception; traditional control methods rely on the experience of designers and lack adaptive capabilities, resulting in low model accuracy, slow response, and weak anti-interference capabilities. In addition, in remote intelligent control systems that require collaborative work by multiple people, traditional interactive methods have problems such as poor communication and low collaboration efficiency.

[0005] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technical deficiencies and provides a remote intelligent control system based on the metaverse. Summary of the invention

[0006] The purpose of the present invention is to provide a remote intelligent control system based on the metaverse to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a remote intelligent control system based on the metaverse, comprising:

[0008] A perception modeling module, which is responsible for collecting and analyzing information in the virtual environment of the Metaverse, including but not limited to virtual objects, spatial layout, user behavior, environmental data, object status, and interaction data, and performing real-time modeling to construct an accurate virtual world model;

[0009] A verification and optimization module, which is responsible for verifying and evaluating the constructed model, and optimizing and adjusting the model according to the verification results to improve its performance and stability;

[0010] A user interaction module, which provides an interactive interface between the user and the system, is responsible for receiving the user's control instructions, and feeding back the control results to the user, including command input, information feedback, etc., so that the user can operate and control the objects or environment in the metaverse;

[0011] An intelligent control module, which is responsible for making intelligent decisions and calculating control algorithms according to user instructions and information provided by the perception modeling module, and generating optimal control strategies and instructions;

[0012] A remote execution module, which is responsible for converting control instructions into actual actions and executing specific control actions, such as moving virtual objects, adjusting environmental parameters, etc., to control remote devices or systems;

[0013] A storage management module, which is responsible for storing and managing system-related data, including environmental models, user data, control strategies, etc., and provides data support;

[0014] A learning and optimization module, which is responsible for learning and optimizing according to the system's operating data and user feedback, improving the performance and intelligence level of the remote intelligent control system, enabling the system to continuously adapt to new environments and user needs, and providing a better control experience;

[0015] The output end of the perception modeling module is connected to the input end of the verification and optimization module, and the output end of the verification and optimization module is connected to the input end of the user interaction module, and the user interaction module receives user instructions and feedback from remote devices or systems through an interface, one input end of the intelligent control module is connected to the user interaction module, and the other input end of the intelligent control module is connected to the learning optimization module, and the output end of the intelligent control module is connected to the input end of the remote execution module, the output end of the remote execution module is connected to the input end of the storage management module, and the output end of the storage management module is connected to the input end of the learning optimization module.

[0016] Furthermore, the information collection method of the perception modeling module specifically includes:

[0017] Sensor data: For the interaction between the metaverse environment and the real world, physical sensors such as cameras, radars, and sonars are used to capture data about the real environment, which is then converted and incorporated into the virtual model.

[0018] User input: includes user operations, instructions and feedback, such as user movements, gestures or voice commands, which can be captured and converted into actions or commands in the virtual world;

[0019] Network data: In the Metaverse, users and other entities interact through the network, and the data generated by the interaction, such as chat information, transaction records, etc.

[0020] Virtual environment simulation: Generate data of the virtual environment by simulating the physical and logical rules contained in the metaverse itself.

[0021] Furthermore, the real-time modeling is a dynamic process of continuously updating and optimizing the virtual world model based on the collected information, which specifically includes the following processes:

[0022] Data preprocessing: Perform preprocessing operations such as cleaning, denoising and format conversion on the collected raw data to improve data quality and usability;

[0023] Feature extraction: extract key features that can reflect the essential attributes and change laws of the virtual environment from the preprocessed data;

[0024] Model building: Based on the extracted features, select appropriate algorithms and tools to build a virtual world model, including space modeling, object modeling, and behavior modeling;

[0025] Model update: As new data is continuously collected and processed, the virtual world model is updated in real time to reflect the latest changes in the virtual environment.

[0026] Furthermore, the verification optimization module specifically includes:

[0027] Data preparation submodule: responsible for preparing data sets for model validation, including data collected from the actual operating environment, data generated by simulation or historical data sets, and performing data processing operations such as data cleaning, format conversion, and annotation to ensure that the data quality meets the validation requirements;

[0028] Performance evaluation submodule: responsible for performance evaluation of the constructed model, selecting appropriate evaluation indicators, including calculation of precision, recall rate, accuracy, F1 score and other indicators, designing evaluation experiments, and conducting performance tests;

[0029] Optimization judgment submodule: responsible for combining the performance evaluation results to determine whether the model needs to be optimized;

[0030] Analysis and positioning submodule: Based on performance evaluation, it is responsible for in-depth analysis and positioning of model errors to identify scenarios where the model performs poorly and find out the reasons, such as improper feature selection, unreasonable algorithm parameter settings, etc., and propose targeted improvement measures;

[0031] Model update submodule: responsible for formulating optimization strategies for the model based on the results of error analysis and positioning, involving aspects such as improving algorithms, adjusting model parameters, optimizing feature selection, introducing new data sources, and updating model parameters and structures according to the optimization strategies.

[0032] Furthermore, one output end of the optimization determination submodule is connected to the analysis and positioning submodule for outputting the determination results that require model optimization, and another output end of the optimization determination submodule is connected to the user interaction module for outputting the determination results that do not require model optimization.

[0033] Furthermore, the operation process of the intelligent control module is as follows:

[0034] Information reception: receiving instructions or goals from the user, and obtaining environmental information and system status from the perception modeling module;

[0035] Data processing and feature extraction: pre-process the acquired information, such as filtering and denoising, and extract key features or parameters for subsequent decision-making and control;

[0036] Intelligent decision-making: making decisions based on rules and knowledge bases, using machine learning methods for pattern recognition or prediction;

[0037] Control algorithm calculation: select or adjust the control algorithm according to the decision results, and calculate the control parameters;

[0038] Generate control strategies and instructions: Combine the decision-making and control algorithm results to generate specific control strategies and instructions, and send the control instructions to the actuator.

[0039] Furthermore, the intelligent decision-making involves the following machine learning method:

[0040] Decision tree: A tree structure is used to represent a series of decision nodes and result nodes in the decision-making process. Specifically, a decision tree model is constructed to decompose complex decision problems into a series of simpler sub-problems, and decision rules are formulated for each node based on historical data or expert knowledge. This helps to clarify the decision-making process and select paths in intelligent decision-making.

[0041] Support vector machine: It can classify or predict new data by finding a hyperplane to separate different types of data. It is suitable for classification and regression problems and performs well in dealing with high-dimensional data and nonlinear problems. It is often used in pattern recognition and classification tasks in intelligent decision-making.

[0042] Neural network: By simulating the connection mode of human brain neurons, it can learn and identify complex patterns and associations in data. It is used to process large-scale data sets and automatically extract useful features to provide strong support for decision-making.

[0043] Ensemble learning: A machine learning method that improves overall performance by combining the prediction results of several learners. Ensemble learning techniques include but are not limited to random forests, gradient boosting machines, etc.

[0044] Reinforcement learning: learning to make decisions by interacting with the environment. Based on a trial-and-error mechanism, it optimizes long-term returns by constantly trying and adjusting strategies. In intelligent decision-making, it is used to deal with complex and uncertain decision-making problems, such as robot control and autonomous driving.

[0045] Deep learning: By designing and establishing an appropriate amount of neural computing nodes and a multi-layer deep computing structure, selecting appropriate input and output layers, and establishing a functional relationship from input to output through network learning and tuning, the successfully trained network model is used to achieve the automation requirements for complex transaction processing.

[0046] Furthermore, the control algorithm includes PID control, fuzzy control, neural network control, and optimal control. The PID controller forms a control signal by combining three items: proportional P, integral I, and differential D to achieve precise control of the controlled object. The algorithm formula is as follows:

[0047] [u(t)=K_p\cdote(t)+K_i\cdot\inte(t),dt+K_d\cdot\frac{de(t)}{dt}]

[0048] Where u(t) is the output of the controller at time t, e(t) is the error between the target value r(t) and the actual output value y(t), that is, e(t) = r(t) - y(t), K_p, K_i, K_d are the proportional coefficient, integral coefficient, and differential coefficient respectively;

[0049] The proportional term is used to respond to errors immediately. When an error occurs, the proportional term will immediately produce a corrective action proportional to the control error; the integral term is used to eliminate the steady-state error of the system by integrating the cumulative effect of the error; the differential term is used to predict the changing trend of the error and adjust the control output according to the rate of change of the error, thereby speeding up the response speed of the system and reducing overshoot.

[0050] Furthermore, the intelligent control module includes:

[0051] Microcontroller or DSP: used to execute control algorithms and decision logic;

[0052] Sensors and actuators: used to obtain environmental information and execute control instructions;

[0053] Programming languages ​​and frameworks: such as C / C++, Python, etc., used to write control logic and algorithms;

[0054] Machine learning libraries and tools: such as TensorFlow, PyTorch, etc., used to implement machine learning algorithms;

[0055] Communication protocols and networks: used for information exchange and coordination between modules.

[0056] Furthermore, the operation process of the learning optimization module is as follows:

[0057] S1. Collect operational data, including user behavior, system performance, error logs, etc., and receive user feedback, which directly reflects the user's views and suggestions on system functions and performance;

[0058] S2. Use data mining and machine learning techniques to identify patterns and trends in data, and then find problems and room for improvement in the system. For example, by analyzing user behavior, find out the pain points and inconveniences that users encounter during use, and by analyzing system performance data, find performance bottlenecks and optimization points.

[0059] S3. Use the results of the analysis to train and optimize models, including machine learning models, predictive models, and other types of models. By continuously adjusting the parameters and structure of the model, it can better fit the data and improve performance.

[0060] The present invention provides a remote intelligent control system based on the metaverse, which has the following beneficial effects:

[0061] The present invention combines Metaverse technology with remote intelligent control technology, utilizes the virtual environment of Metaverse, and combines remote control technology to realize intelligent control and management of remote devices or systems, and improves information display and interactive experience. At the same time, Metaverse technology provides operators with a simulated training environment, which can be trained in response and event processing, thereby improving practical operation capabilities, and cooperates with artificial intelligence and cloud computing technology to conduct in-depth analysis of data, so as to generate optimal control strategies and instructions according to user instructions and information provided by the perception modeling module, and uses a new dual closed-loop correction model based on deep reinforcement learning adaptive PID control algorithm. By using the perception ability of deep learning and the decision-making ability of reinforcement learning, the optimal control strategy can be learned in the continuous interaction with the controlled object, and the model has low requirements for model accuracy, fast response, good dynamic performance and tracking performance, and strong anti-interference ability, which solves the problem that the performance of traditional PID control methods depends on the experience of designers and lacks adaptive ability. In addition, through the remote intelligent control system of Metaverse, team members in different locations can collaborate in a virtual environment, break geographical restrictions, and improve team collaboration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the operation flow of a remote intelligent control system based on the metaverse of the present invention;

[0063] Figure 2 This is a logic block diagram of the operation of a perception modeling module of a remote intelligent control system based on the metaverse of the present invention;

[0064] Figure 3 This is a logic block diagram of the operation of a verification and optimization module of a remote intelligent control system based on the metaverse of the present invention;

[0065] Figure 4 This is a logic block diagram of the operation of an intelligent control module of a remote intelligent control system based on the metaverse of the present invention. DETAILED DESCRIPTION

[0066] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0067] like Figure 1-Figure 4 As shown, a remote intelligent control system based on the metaverse includes:

[0068] The perception modeling module is responsible for collecting and analyzing information in the virtual environment of the Metaverse, including but not limited to virtual objects, spatial layout, user behavior, environmental data, object status, and interaction data, and performing real-time modeling to build an accurate virtual world model; the information collection methods of the perception modeling module include:

[0069] Sensor data: For the interaction between the metaverse environment and the real world, physical sensors such as cameras, radars, and sonars are used to capture data about the real environment, which is then converted and incorporated into the virtual model.

[0070] User input: includes user operations, instructions and feedback, such as user movements, gestures or voice commands, which can be captured and converted into actions or commands in the virtual world;

[0071] Network data: In the Metaverse, users and other entities interact through the network, and the data generated by the interaction, such as chat information, transaction records, etc.

[0072] Virtual environment simulation: Generate virtual environment data by simulating the physical and logical rules contained in the metaverse itself;

[0073] Real-time modeling is a dynamic process of continuously updating and optimizing the virtual world model based on the collected information. It includes the following processes:

[0074] Data preprocessing: Perform preprocessing operations such as cleaning, denoising and format conversion on the collected raw data to improve data quality and usability;

[0075] Feature extraction: extract key features that can reflect the essential attributes and change laws of the virtual environment from the preprocessed data;

[0076] Model building: Based on the extracted features, select appropriate algorithms and tools to build a virtual world model, including space modeling, object modeling, and behavior modeling;

[0077] Model update: As new data is continuously collected and processed, the virtual world model is updated in real time to reflect the latest changes in the virtual environment;

[0078] The verification and optimization module is responsible for verifying and evaluating the constructed model, and optimizing and adjusting the model according to the verification results to improve its performance and stability; the verification and optimization module specifically includes:

[0079] Data preparation submodule: responsible for preparing data sets for model validation, including data collected from the actual operating environment, data generated by simulation or historical data sets, and performing data processing operations such as data cleaning, format conversion, and annotation to ensure that the data quality meets the validation requirements;

[0080] Performance evaluation submodule: responsible for performance evaluation of the constructed model, selecting appropriate evaluation indicators, including calculation of precision, recall rate, accuracy, F1 score and other indicators, designing evaluation experiments, and conducting performance tests;

[0081] Optimization judgment submodule: responsible for combining the performance evaluation results to determine whether the model needs to be optimized;

[0082] Analysis and positioning submodule: Based on performance evaluation, it is responsible for in-depth analysis and positioning of model errors to identify scenarios where the model performs poorly and find out the reasons, such as improper feature selection, unreasonable algorithm parameter settings, etc., and propose targeted improvement measures;

[0083] Model update submodule: responsible for formulating optimization strategies for the model based on the results of error analysis and location, involving aspects such as improving algorithms, adjusting model parameters, optimizing feature selection, introducing new data sources, and updating model parameters and structures according to the optimization strategies;

[0084] One output end of the optimization determination submodule is connected to the analysis and positioning submodule for outputting the determination result that requires model optimization, and the other output end of the optimization determination submodule is connected to the user interaction module for outputting the determination result that does not require model optimization;

[0085] The user interaction module provides an interactive interface between the user and the system, is responsible for receiving the user's control instructions, and feeding back the control results to the user, including command input, information feedback, etc., so that the user can operate and control the objects or environment in the metaverse;

[0086] The intelligent control module is responsible for making intelligent decisions and calculating control algorithms based on user instructions and information provided by the perception modeling module, and generating optimal control strategies and instructions. The operation process of the intelligent control module is as follows:

[0087] Information reception: receiving instructions or goals from the user, and obtaining environmental information and system status from the perception modeling module;

[0088] Data processing and feature extraction: pre-process the acquired information, such as filtering and denoising, and extract key features or parameters for subsequent decision-making and control;

[0089] Intelligent decision-making: making decisions based on rules and knowledge bases, using machine learning methods for pattern recognition or prediction;

[0090] Control algorithm calculation: select or adjust the control algorithm according to the decision results, and calculate the control parameters;

[0091] Generate control strategies and instructions: Combine the decision-making and control algorithm results to generate specific control strategies and instructions, and send the control instructions to the actuator;

[0092] Intelligent decision making involves the following machine learning methods:

[0093] Decision tree: A tree structure is used to represent a series of decision nodes and result nodes in the decision-making process. Specifically, a decision tree model is constructed to decompose complex decision problems into a series of simpler sub-problems, and decision rules are formulated for each node based on historical data or expert knowledge. This helps to clarify the decision-making process and select paths in intelligent decision-making.

[0094] Support vector machine: It can classify or predict new data by finding a hyperplane to separate different types of data. It is suitable for classification and regression problems and performs well in dealing with high-dimensional data and nonlinear problems. It is often used in pattern recognition and classification tasks in intelligent decision-making.

[0095] Neural network: By simulating the connection mode of human brain neurons, it can learn and identify complex patterns and associations in data. It is used to process large-scale data sets and automatically extract useful features to provide strong support for decision-making.

[0096] Ensemble learning: A machine learning method that improves overall performance by combining the prediction results of several learners. Ensemble learning techniques include but are not limited to random forests, gradient boosting machines, etc.

[0097] Reinforcement learning: learning to make decisions by interacting with the environment. Based on a trial-and-error mechanism, it optimizes long-term returns by constantly trying and adjusting strategies. In intelligent decision-making, it is used to deal with complex and uncertain decision-making problems, such as robot control and autonomous driving.

[0098] The control algorithms include PID control, fuzzy control, neural network control, and optimal control. The PID controller combines the three terms of proportion P, integral I, and differential D to form a control signal to achieve precise control of the controlled object. The algorithm formula is as follows:

[0099] [u(t)=K_p\cdote(t)+K_i\cdot\inte(t),dt+K_d\cdot\frac{de(t)}{dt}]

[0100] Where u(t) is the output of the controller at time t, e(t) is the error between the target value r(t) and the actual output value y(t), that is, e(t) = r(t) - y(t), K_p, K_i, K_d are the proportional coefficient, integral coefficient, and differential coefficient respectively;

[0101] The proportional term is used to respond to errors immediately. When an error occurs, the proportional term will immediately produce a corrective action proportional to the control error. The integral term is used to eliminate the steady-state error of the system by integrating the cumulative effect of the error. The differential term is used to predict the trend of the error change and adjust the control output according to the rate of change of the error, thereby speeding up the system's response speed and reducing overshoot.

[0102] The above reinforcement control algorithm can also be combined with a deep learning neural network Q, which is trained using a strategy (π) to select the action (At) that optimizes the strategy (π), that is, the expected value accumulated at each training stage, and the function is defined as:

[0103]

[0104] Here, in state (st), training is performed to derive the optimal Q function Q* for action (at). At this point, the Q function is essentially in a tabular form, so it can be functionalized into a similar function with new parameters using Function Approximator:

[0105] Q(s,a:0)≈Q*(s,a)

[0106] The reinforcement learning model trained in this way uses the state information (St) and feedback (Rt) as an action (At) to determine the next instruction, and the determined next instruction is reflected on the instruction (S). The above process is repeated to optimize the strategy of the reinforcement learning model.

[0107] The intelligent control module includes:

[0108] Microcontroller or DSP: used to execute control algorithms and decision logic;

[0109] Sensors and actuators: used to obtain environmental information and execute control instructions;

[0110] Programming languages ​​and frameworks: such as C / C++, Python, etc., used to write control logic and algorithms;

[0111] Machine learning libraries and tools: such as TensorFlow, PyTorch, etc., used to implement machine learning algorithms;

[0112] Communication protocols and networks: used for information exchange and coordination between modules;

[0113] The remote execution module is responsible for converting control instructions into actual actions and executing specific control actions, such as moving virtual objects, adjusting environmental parameters, etc., to control remote devices or systems;

[0114] The storage management module is responsible for storing and managing system-related data, including environmental models, user data, control strategies, etc., and provides data support;

[0115] The learning and optimization module is responsible for learning and optimizing based on the system's operating data and user feedback, improving the performance and intelligence level of the remote intelligent control system, enabling the system to continuously adapt to new environments and user needs, and providing a better control experience; the operation process of the learning and optimization module is as follows:

[0116] S1. Collect operational data, including user behavior, system performance, error logs, etc., and receive user feedback, which directly reflects the user's views and suggestions on system functions and performance;

[0117] S2. Use data mining and machine learning techniques to identify patterns and trends in data, and then find problems and room for improvement in the system. For example, by analyzing user behavior, find out the pain points and inconveniences that users encounter during use, and by analyzing system performance data, find performance bottlenecks and optimization points.

[0118] S3. Use the results of the analysis to train and optimize models, including machine learning models, predictive models, and other types of models. By continuously adjusting the parameters and structure of the model, it can better fit the data and improve performance.

[0119] In summary, combined Figure 1-Figure 4 As shown, the use process of the remote intelligent control system based on the metaverse is as follows:

[0120] Step 1: The perception modeling module collects and analyzes information in the virtual environment of the Metaverse through four channels: sensor data, user input, network data, and virtual environment simulation. It also performs real-time modeling through data preprocessing, feature extraction, model construction, and model updating to build an accurate virtual world model.

[0121] Step 2: The verification and optimization module prepares a data set for model verification through the data preparation submodule and performs data processing operations. Then, the performance evaluation submodule evaluates the performance of the constructed model, selects appropriate evaluation indicators, designs evaluation experiments, and performs performance tests. Then, the optimization judgment submodule combines the performance evaluation results to determine whether the model needs to be optimized. If optimization is required, the analysis and positioning submodule is triggered to conduct in-depth analysis and positioning of the errors in the model, find out the causes, propose targeted improvement measures, and send them to the model update submodule, so that it updates the model parameters and structure according to the optimization strategy, and feeds the new model back to the performance evaluation submodule, and re-executes the above operations to realize the cycle until the optimization judgment submodule outputs a judgment result that does not need to be optimized, then the user interaction module is triggered;

[0122] Step 3: The user inputs control instructions to the system through the user interaction module, providing an interactive interface between the user and the system, and operates and controls the objects or environment in the metaverse. The intelligent control module receives information provided by the user instructions and the perception modeling module, performs intelligent decision-making and control algorithm calculations, generates optimal control strategies and instructions, and sends the control instructions to the remote execution module, which converts the control instructions into actual actions, executes specific control actions, and controls the remote device or system.

[0123] Step 4: The storage management module stores and manages system-related data and provides data support. In addition, the learning optimization module learns and optimizes according to the system's operating data and user feedback to improve the performance and intelligence level of the remote intelligent control system.

[0124] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A remote intelligent control system based on the metaverse, characterized in that: include: A perception modeling module, which is responsible for collecting and analyzing information in the metaverse virtual environment, and performing real-time modeling to construct an accurate virtual world model; A verification and optimization module, which is responsible for verifying and evaluating the constructed model, and optimizing and adjusting the model according to the verification results to improve its performance and stability; A user interaction module, which provides an interactive interface between the user and the system, is responsible for receiving the user's control instructions, and feeds back the control results to the user, so that the user can operate and control the objects or environment in the metaverse; An intelligent control module, which is responsible for making intelligent decisions and calculating control algorithms according to user instructions and information provided by the perception modeling module, and generating optimal control strategies and instructions; A remote execution module, which is responsible for converting control instructions into actual actions and executing specific control actions to control remote devices or systems; A storage management module, which is responsible for storing and managing system-related data and providing data support; A learning and optimization module, which is responsible for learning and optimizing according to the system's operating data and user feedback, improving the performance and intelligence level of the remote intelligent control system, enabling the system to continuously adapt to new environments and user needs, and providing a better control experience; The output end of the perception modeling module is connected to the input end of the verification and optimization module, and the output end of the verification and optimization module is connected to the input end of the user interaction module, and the user interaction module receives user instructions and feedback from remote devices or systems through an interface, one input end of the intelligent control module is connected to the user interaction module, and the other input end of the intelligent control module is connected to the learning optimization module, and the output end of the intelligent control module is connected to the input end of the remote execution module, the output end of the remote execution module is connected to the input end of the storage management module, and the output end of the storage management module is connected to the input end of the learning optimization module.

2. According to claim 1, a remote intelligent control system based on the metaverse is characterized in that: The information collection methods of the perception modeling module specifically include: Sensor data: For the interaction between the metaverse environment and the real world, the data of the real environment is captured through physical sensors and then converted into the virtual model; User input: includes user operations, instructions and feedback; Network data: data generated by the interactions between users and other entities in the Metaverse through the network; Virtual environment simulation: Generate data of the virtual environment by simulating the physical and logical rules contained in the metaverse itself.

3. According to the metaverse-based remote intelligent control system of claim 1, it is characterized in that: The real-time modeling is a dynamic process of continuously updating and optimizing the virtual world model based on the collected information, which specifically includes the following processes: Data preprocessing: preprocessing the collected raw data; Feature extraction: extract key features that can reflect the essential attributes and change laws of the virtual environment from the preprocessed data; Model building: Based on the extracted features, select appropriate algorithms and tools to build a virtual world model, including space modeling, object modeling, and behavior modeling; Model update: As new data is continuously collected and processed, the virtual world model is updated in real time to reflect the latest changes in the virtual environment.

4. According to claim 1, a remote intelligent control system based on the metaverse is characterized in that: The verification optimization module specifically includes: Data preparation submodule: responsible for preparing the data set for model validation and performing data processing operations to ensure that the data quality meets the validation requirements; Performance evaluation submodule: responsible for performance evaluation of the constructed model, selecting appropriate evaluation indicators, designing evaluation experiments, and conducting performance tests; Optimization judgment submodule: responsible for combining the performance evaluation results to determine whether the model needs to be optimized; Analysis and positioning submodule: Responsible for in-depth analysis and positioning of model errors based on performance evaluation, to identify scenarios where the model performs poorly, find out the reasons, and propose targeted improvement measures; Model update submodule: responsible for formulating optimization strategies for the model based on the results of error analysis and positioning, and updating model parameters and structures based on the optimization strategies.

5. A remote intelligent control system based on the metaverse according to claim 4, characterized in that: One output end of the optimization determination submodule is connected to the analysis and positioning submodule for outputting the determination result that requires model optimization, and the other output end of the optimization determination submodule is connected to the user interaction module for outputting the determination result that does not require model optimization.

6. The remote intelligent control system based on the metaverse according to claim 1, characterized in that: The operation process of the intelligent control module is as follows: Information reception: receiving instructions or goals from the user, and obtaining environmental information and system status from the perception modeling module; Data processing and feature extraction: pre-process the acquired information and extract key features or parameters for subsequent decision-making and control; Intelligent decision-making: making decisions based on rules and knowledge bases, using machine learning methods for pattern recognition or prediction; Control algorithm calculation: select or adjust the control algorithm according to the decision results, and calculate the control parameters; Generate control strategies and instructions: Combine the decision-making and control algorithm results to generate specific control strategies and instructions, and send the control instructions to the actuator.

7. A remote intelligent control system based on the metaverse according to claim 6, characterized in that: The intelligent decision-making involves the following machine learning methods: Decision tree: A tree structure is used to represent a series of decision nodes and result nodes in the decision-making process. Specifically, a decision tree model is constructed to decompose complex decision problems into a series of simpler sub-problems, and decision rules are formulated for each node based on historical data or expert knowledge. This helps to clarify the decision-making process and select paths in intelligent decision-making. Support vector machine: It can classify or predict new data by finding a hyperplane to separate different types of data. It is suitable for classification and regression problems and performs well in dealing with high-dimensional data and nonlinear problems. It is often used in pattern recognition and classification tasks in intelligent decision-making. Neural network: By simulating the connection mode of human brain neurons, it can learn and identify complex patterns and associations in data. It is used to process large-scale data sets and automatically extract useful features to provide strong support for decision-making. Ensemble learning: A machine learning method that improves overall performance by combining the prediction results of several learners. Ensemble learning techniques include but are not limited to random forests, gradient boosting machines, etc. Reinforcement learning: learning to make decisions by interacting with the environment. Based on a trial-and-error mechanism, it optimizes long-term returns by constantly trying and adjusting strategies. In intelligent decision-making, it is used to deal with complex and uncertain decision-making problems. Deep learning: By designing and establishing an appropriate amount of neural computing nodes and a multi-layer deep computing structure, selecting appropriate input and output layers, and establishing a functional relationship from input to output through network learning and tuning, the successfully trained network model is used to achieve the automation requirements for complex transaction processing.

8. The remote intelligent control system based on the metaverse according to claim 6, characterized in that: The control algorithm includes PID control, fuzzy control, neural network control, and optimal control. The PID controller combines the three terms of proportion P, integral I, and differential D to form a control signal to achieve precise control of the controlled object. The algorithm formula is as follows: [u(t)=K_p\cdote(t)+K_i\cdot\inte(t),dt+K_d\cdot\frac{de(t)}{dt}]Where u(t) is the output of the controller at time t, e(t) is the error between the target value r(t) and the actual output value y(t), that is, e(t)=r(t)-y(t), K_p, K_i, K_d are the proportional coefficient, integral coefficient and differential coefficient respectively. The above-mentioned enhanced control algorithm can also be combined with the deep learning neural network Q, and the strategy (π) is used for training to select the action (At) of the optimization strategy (π), that is, the expected value accumulated in each training stage, and the definition function is: Here, in state (st), training is performed to derive the optimal Q function Q* for action (at). At this point, the Q function is essentially in a tabular form, so it can be functionalized into a similar function with new parameters using Function Approximator: Q(s,a:θ)≈Q * (s,a) The reinforcement learning model trained in this way uses the state information (St) and feedback (Rt) as an action (At) to determine the next instruction, and the determined next instruction is reflected on the instruction (S). The above process is repeated to optimize the strategy of the reinforcement learning model.

9. The remote intelligent control system based on the metaverse according to claim 1, characterized in that: The intelligent control module comprises: Microcontroller or DSP: used to execute control algorithms and decision logic; Sensors and actuators: used to obtain environmental information and execute control instructions; Programming languages ​​and frameworks: used to write control logic and algorithms; Machine learning libraries and tools: used to implement machine learning algorithms; Communication protocols and networks: used for information exchange and coordination between modules.

10. The remote intelligent control system based on the metaverse according to claim 1, characterized in that: The operation process of the learning optimization module is as follows: S1. Collect operation data and receive user feedback; S2. Use data mining and machine learning techniques to identify patterns and trends in the data, and then find problems and room for improvement in the system; S3. Use the results of the analysis to train and optimize the model, and continuously adjust the parameters and structure of the model to better fit the data and improve performance.

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