Intelligent analysis and remote control algorithm based on digital twinning

Abstract

The invention discloses an intelligent analysis and remote control algorithm based on digital twinning. The intelligent analysis and remote control algorithm comprises a digital twinning model building and dynamic updating module, an intelligent analysis algorithm module based on digital twinning, a self-adaptive remote control algorithm module and an algorithm process and implementation module. Through multi-dimensional modeling and data fusion, the precision of the digital twin model is improved, and the system state can be predicted more accurately. In combination with deep learning and a multi-objective optimization algorithm, intelligence and optimization of a control strategy are realized, and the operation efficiency and safety of the system are improved. Based on model predictive control and a distributed architecture, real-time and accurate remote control of a physical system is realized, and the dynamic adaptability of the system is enhanced. The algorithm framework has good generalization ability, can be suitable for different types of complex systems, and supports to adapt to new application scenarios through modular extension.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis and remote control algorithms, and particularly to an intelligent analysis and remote control algorithm based on digital twin. Background Art

[0002] Traditional control methods rely on experience or simple mathematical models and are difficult to cope with the dynamic changes and uncertainties of the system. Digital twin technology can construct a virtual model of a physical entity and can map and predict the system state in real time. However, how to deeply integrate digital twin with intelligent analysis and remote control to achieve efficient decision-making and control is still a key problem faced currently. In the prior art, problems such as insufficient accuracy of digital twin models, low data fusion efficiency, and lack of adaptability of control strategies limit their application effects in complex systems. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent analysis and remote control algorithm based on digital twin to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An intelligent analysis and remote control algorithm based on digital twin, including a digital twin model construction and dynamic update module, an intelligent analysis algorithm module based on digital twin, an adaptive remote control algorithm module, and an algorithm process and implementation module;

[0005] The digital twin model construction and dynamic update module includes:

[0006] The multi-dimensional modeling sub-module uses a method combining mechanism modeling and data-driven to construct a multi-dimensional digital twin model including the geometric structure, dynamic characteristics, and thermodynamic characteristics of the physical system;

[0007] The real-time data fusion sub-module designs a multi-source data fusion module, integrates Internet of Things sensor data, historical data, and external environment data, and performs noise reduction, calibration, and spatio-temporal alignment on the data through Kalman filtering and Bayesian network algorithms to achieve the dynamic update of the digital twin model and ensure a high degree of consistency between the virtual model and the physical system;

[0008] The intelligent analysis algorithm module based on digital twin includes:

[0009] The state prediction and health assessment sub-module uses the digital twin model and combines deep learning algorithms of long short-term memory network (LSTM) and graph neural network (GNN) to predict the future state of the system and evaluate the health state of the device. For example, by analyzing the vibration data and temperature changes of the device, the probability of a fault occurrence is predicted;

[0010] The optimized decision-making support sub-module constructs a multi-objective optimization model with system efficiency, energy consumption, and safety as the optimization objectives. Using the non-dominated sorting genetic algorithm (NSGA-II) and reinforcement learning algorithm, virtual simulation and optimization are carried out in the digital twin environment to generate the optimal control strategy;

[0011] The adaptive remote control algorithm module includes:

[0012] The real-time control instruction generation sub-module dynamically adjusts the control parameters according to the prediction results and optimization decisions of the digital twin model, combined with the adaptive control theory; designs the model predictive control (MPC) algorithm, and realizes the real-time and precise control of the physical system through rolling optimization and feedback correction;

[0013] The remote collaborative control sub-module establishes a distributed control architecture to support the collaborative control of multiple digital twin models and physical systems; through 5G and edge computing technologies, realizes the low-latency transmission of remote control instructions to ensure the real-time and reliability of control;

[0014] The algorithm process and implementation module includes:

[0015] The data acquisition and preprocessing sub-module collects the real-time data of the physical system through the sensor network and performs filtering and normalization preprocessing;

[0016] The digital twin model update sub-module uses the preprocessed data to update the parameters of the digital twin model through the data fusion algorithm to ensure the accuracy of the model;

[0017] The intelligent analysis and prediction sub-module uses deep learning algorithms to analyze the digital twin model and predict the future state and potential faults of the system;

[0018] The control strategy optimization sub-module generates the optimal control strategy through the multi-objective optimization algorithm in the digital twin environment;

[0019] The remote control execution sub-module converts the optimized control strategy into control instructions and transmits them to the actuator of the physical system through the communication network to achieve remote control;

[0020] The feedback and iteration sub-module iteratively optimizes the digital twin model and control strategy according to the actual response of the physical system to form a closed-loop control.

[0021] Preferably, the multi-dimensional modeling sub-module adopts a method that combines mechanism modeling and data-driven approach. Specifically, through in-depth understanding of the physical system, a mechanism model based on physical laws is established, which can describe the basic operating laws and characteristics of the system. Machine learning algorithms are used to optimize and supplement the parameters of the mechanism model to improve the accuracy and adaptability of the model. Mechanism modeling provides the basic framework of the system, while the data-driven method uses actual operating data to finely adjust the model. The combination of the two can construct a digital twin model that not only conforms to physical laws but also adapts to the actual operating conditions.

[0022] Preferably, the Kalman filter algorithm formula is:

[0023] X k|k-1 = F k X k-1|k-1 + B k U k + W k

[0024] where X k|k-1 is the predicted value of the state at time k based on the state estimate at time k-1; F k is the state transition matrix, which describes how the system state transfers from one time to the next; X k-1|k-1 is the optimal state estimate value at time k-1; B k is the control input matrix, U k is the control input at time k; W k is the process noise, which is usually assumed to follow a Gaussian distribution with a mean of 0 and a covariance of Q k , that is, W k ~ N(0, Q k );

[0025] The Bayesian network algorithm formula is:

[0026] Assume that there are n variables X1, X2,..., X n in the Bayesian network, then its joint probability distribution is expressed as:

[0027]

[0028] where Parents(X i ) represents the set of parent nodes of variable X i , and P(X i |Parents(X i )) is the conditional probability distribution of X i given the state of the parent nodes.

[0029] Preferably, the combined long short-term memory network algorithm formula is:

[0030] i t = σ(W xi x t + W hi h t-1 + b i )

[0031] where i t is the output of the input gate at time t; σ is the Sigmoid activation function; W xi and W hi are the weight matrices from the input and hidden states to the input gate respectively; b i is the bias of the input gate; x t is the input at time t; h t-1 is the hidden state at time t-1;

[0032] The deep learning algorithm formula of the graph neural network is:

[0033]

[0034] where H (l) is the node feature matrix of the l-th layer, H (0) = X; W (l) is the weight matrix of the l-th layer; σ is the activation function; is the adjacency matrix with self-loops added, and I is the identity matrix; is 's degree matrix, that is is the symmetrically normalized adjacency matrix, used to balance the degree differences of nodes; is the symmetrically normalized adjacency matrix, used to balance the degree differences of nodes.

[0035] Preferably, the formula of the non-dominated sorting genetic algorithm is:

[0036]

[0037] where f i max and f i min are the maximum and minimum values of the i-th objective respectively; and are two solutions adjacent to the i-th objective value and X in the current layer;

[0038] The reinforcement learning algorithm formula is:

[0039]

[0040] where s t and a tThey are the state and action at time t respectively; α is the learning rate (0 < α ≤ 1), which controls the influence degree of new experience on the Q value; r t+1 is the immediate reward obtained after executing the action a t ; γ is the discount factor 0 ≤ γ ≤ 1, indicating the degree of emphasis on future rewards; is the next state s t+1 and is the maximum Q value of all possible actions under s

[0041] Preferably, the formula of the designed model predictive control algorithm is:

[0042]

[0043] wherein, is the state vector of the system at time k; is the control input vector of the system at time k; is the output vector of the system at time k; are the state transition matrix, input matrix and output matrix respectively; is the process noise, is the measurement noise.

[0044] Preferably, the data acquisition and preprocessing sub-module is specifically responsible for collecting real-time data from sensors, devices or other data sources, and performing preprocessing operations such as cleaning, denoising and normalization to ensure the accuracy, integrity and consistency of the data; by preprocessing the data, the accuracy and efficiency of subsequent analysis and control are improved; in addition, the data acquisition and preprocessing sub-module also has the functions of data compression and storage, so that historical data can be quickly accessed and used when needed;

[0045] The digital twin model update sub-module is specifically to use the preprocessed data, through the integrated learning algorithm and online learning mechanism, to continuously update and optimize the parameters and structure of the digital twin model to adapt to the dynamic changes and uncertainties of the physical system; it also has the functions of model verification and calibration to ensure the accuracy and reliability of the digital twin model, providing a solid foundation for subsequent intelligent analysis and remote control.

[0046] Preferably, the intelligent analysis and prediction sub-module is specifically; applying deep learning algorithms and big data analysis techniques to mine and analyze the massive data in the digital twin model, identifying the operation patterns and potential laws of the system; constructing an anomaly detection model to monitor the operation state of the physical system in real time, and timely discover and warn potential faults or abnormal situations; through the comprehensive analysis of historical data and real-time data, predicting the future development trend of the system, providing scientific basis and forward-looking suggestions for decision-makers; the intelligent analysis and prediction sub-module can also combine the knowledge and experience of experts to automatically extract and integrate knowledge to form an intelligent knowledge base to support the optimization and upgrade of the system;

[0047] The control strategy optimization sub-module specifically uses genetic algorithm and particle swarm optimization algorithm evolution calculation technology in the digital twin environment, combines expert system and heuristic rules, conducts virtual simulation and evaluation on various control strategies, and screens out the optimal control strategy that can not only meet the system performance requirements but also reduce energy consumption and costs. The control strategy optimization sub-module also has an adaptive adjustment function, which can dynamically adjust the parameters and rules of the control strategy according to the real-time feedback of the physical system and the changes in the external environment to ensure the optimization of the control effect. In addition, the control strategy optimization sub-module can also visually display and generate reports on the optimized control strategy for the convenience of users to understand and apply.

[0048] Preferably, the remote control execution sub-module specifically transmits the optimized control strategy to the actuator of the physical system in real time through the communication network to achieve precise remote control of the physical system. The remote control execution sub-module also has security verification and permission management functions to ensure the security and reliability of control instructions and prevent illegal access and malicious operations. In addition, the remote control execution sub-module supports multiple communication protocols and interfaces to adapt to the requirements and compatibility of different physical systems.

[0049] The feedback and iteration sub-module specifically iteratively optimizes the digital twin model and control strategy according to the difference between the actual response data of the physical system and the prediction results of the digital twin model. Through continuous learning and adjustment, the accuracy of the digital twin model and the effectiveness of the control strategy are improved to form a closed-loop control system. The feedback and iteration sub-module can also record the historical data and optimization process of each iteration to provide reference for subsequent algorithm improvement and performance evaluation. Through continuous iterative optimization, it is ensured that the digital twin model always maintains a high degree of consistency with the physical system, improving the accuracy and reliability of remote control.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] Through multi-dimensional modeling and data fusion, the accuracy of the digital twin model is improved, and the system state can be predicted more accurately. Combining deep learning and multi-objective optimization algorithms realizes the intelligence and optimization of the control strategy, improving the operation efficiency and safety of the system. Based on model predictive control and distributed architecture, real-time and precise remote control of the physical system is achieved, enhancing the dynamic adaptability of the system. The algorithm framework has good generalization ability, can be applied to different types of complex systems, and supports modular expansion to adapt to new application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is the system schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0054] Please refer to Figure 1 , the present invention provides an intelligent analysis and remote control algorithm based on digital twin, including a digital twin model construction and dynamic update module, an intelligent analysis algorithm module based on digital twin, an adaptive remote control algorithm module, and an algorithm process and implementation module;

[0055] The digital twin model construction and dynamic update module includes:

[0056] The multi-dimensional modeling sub-module uses a method combining mechanism modeling and data-driven to construct a multi-dimensional digital twin model including the geometric structure, dynamic characteristics, and thermodynamic characteristics of the physical system;

[0057] The real-time data fusion sub-module designs a multi-source data fusion module, integrates Internet of Things sensor data, historical data, and external environment data, and performs noise reduction, calibration, and spatio-temporal alignment on the data through Kalman filtering and Bayesian network algorithms to achieve dynamic update of the digital twin model and ensure a high degree of consistency between the virtual model and the physical system;

[0058] The intelligent analysis algorithm module based on digital twin includes:

[0059] The state prediction and health assessment sub-module uses the digital twin model and combines deep learning algorithms such as long short-term memory network (LSTM) and graph neural network (GNN) to predict the future state of the system and evaluate the health state of the device. For example, by analyzing the vibration data and temperature changes of the device, the probability of failure occurrence is predicted;

[0060] The optimization decision support sub-module constructs a multi-objective optimization model with system efficiency, energy consumption, and safety as optimization goals, and uses non-dominated sorting genetic algorithm (NSGA-II) and reinforcement learning algorithm to perform virtual simulation and optimization in the digital twin environment to generate the optimal control strategy;

[0061] The adaptive remote control algorithm module includes:

[0062] The real-time control instruction generation sub-module dynamically adjusts the control parameters according to the prediction results and optimization decisions of the digital twin model, combines with the adaptive control theory, and designs a model predictive control (MPC) algorithm to achieve real-time and precise control of the physical system through rolling optimization and feedback correction;

[0063] The remote collaborative control sub-module establishes a distributed control architecture, supporting the collaborative control of multiple digital twin models and physical systems; through 5G and edge computing technologies, it realizes the low-latency transmission of remote control instructions, ensuring the real-time and reliability of control;

[0064] The algorithm process and implementation module includes:

[0065] The data acquisition and preprocessing sub-module collects the real-time data of the physical system through the sensor network and performs preprocessing such as filtering and normalization;

[0066] The digital twin model update sub-module uses the preprocessed data to update the parameters of the digital twin model through data fusion algorithms, ensuring the accuracy of the model;

[0067] The intelligent analysis and prediction sub-module analyzes the digital twin model using deep learning algorithms to predict the future state and potential faults of the system;

[0068] The control strategy optimization sub-module generates the optimal control strategy through multi-objective optimization algorithms in the digital twin environment;

[0069] The remote control execution sub-module converts the optimized control strategy into control instructions and transmits them to the actuators of the physical system through the communication network to achieve remote control;

[0070] The feedback and iteration sub-module iteratively optimizes the digital twin model and control strategy based on the actual response of the physical system to form a closed-loop control.

[0071] The multi-dimensional modeling sub-module adopts a method combining mechanism modeling and data-driven approach. Specifically, through in-depth understanding of the physical system, it establishes a mechanism model based on physical laws, which can describe the basic operating rules and characteristics of the system; uses machine learning algorithms to optimize and supplement the parameters of the mechanism model to improve the accuracy and adaptability of the model; mechanism modeling provides the basic framework of the system, while the data-driven method uses actual operation data to finely adjust the model. The combination of the two can construct a digital twin model that not only conforms to physical laws but also adapts to the actual operation situation.

[0072] The formula of the Kalman filter algorithm is:

[0073] X k|k-1 =F k X k-1|k-1 +B k U k +W k

[0074] where X k|k-1 is the predicted value of the state at time k based on the state estimate at time k-1; Fk is the state transition matrix, which describes how the system state transfers from one moment to the next; X k-1|k-1 is the optimal state estimate at time k-1; B k is the control input matrix, U k is the control input at time k; W k is the process noise, which is usually assumed to follow a Gaussian distribution with mean 0 and covariance Q k , that is, W k ~N(0,Q k );

[0075] The formula of the Bayesian network algorithm is:

[0076] Assume that there are n variables X1, X2, …, X n in the Bayesian network, then its joint probability distribution is expressed as:

[0077]

[0078] where Parents(X i ) represents the set of parent nodes of variable X i , and P(X i |Parents(X i )) is the conditional probability distribution of X i given the states of its parent nodes.

[0079] The formula combined with the long short-term memory network algorithm is:

[0080] i t =σ(W xi x t +W hi h t-1 +b i )

[0081] where i t is the output of the input gate at time t; σ is the Sigmoid activation function; W xi and W hi are the weight matrices from the input and hidden states to the input gate respectively; b i is the bias of the input gate; x t is the input at time t; h t-1 is the hidden state at time t-1;

[0082] The deep learning algorithm formula of the graph neural network is:

[0083]

[0084] where H (l) is the node feature matrix of the l-th layer, H (0)= X; W (l) is the weight matrix of the l-th layer; σ is the activation function; is the adjacency matrix with self-loops added, and I is the identity matrix; is the degree matrix of, that is is the symmetrically normalized adjacency matrix, which is used to balance the degree differences of nodes; is the symmetrically normalized adjacency matrix, which is used to balance the degree differences of nodes.

[0085] The formula for the non-dominated sorting genetic algorithm is:

[0086]

[0087] where, f i max and f i min are the maximum and minimum values of the i-th objective respectively; and are two solutions adjacent to the i-th objective value in the current layer and X;

[0088] The formula for the reinforcement learning algorithm is:

[0089]

[0090] where, s t and a t are the state and action at time t respectively; α is the learning rate (0 < α ≤ 1), which controls the influence degree of new experience on the Q value; r t+1 is the immediate reward obtained after executing the action a t ; γ is the discount factor 0 ≤ γ ≤ 1, which represents the degree of emphasis on future rewards; is the maximum Q value of all possible actions in the next state s t+1 .

[0091] The formula for the design model predictive control algorithm is:

[0092]

[0093] where, is the state vector of the system at time k; is the control input vector of the system at time k; is the output vector of the system at time k; are the state transition matrix, input matrix and output matrix respectively; is the process noise, is the measurement noise.

[0094] The data acquisition and preprocessing sub-module is specifically responsible for collecting real-time data from sensors, devices or other data sources, and performing preprocessing operations such as cleaning, denoising and normalization to ensure the accuracy, integrity and consistency of the data; by preprocessing the data, the accuracy and efficiency of subsequent analysis and control are improved; in addition, the data acquisition and preprocessing sub-module also has the functions of data compression and storage, so that historical data can be quickly accessed and used when needed;

[0095] The digital twin model update sub-module is specifically responsible for using the preprocessed data, through integrated learning algorithms and online learning mechanisms, to continuously update and optimize the parameters and structure of the digital twin model to adapt to the dynamic changes and uncertainties of the physical system; it also has the functions of model verification and validation to ensure the accuracy and reliability of the digital twin model, providing a solid foundation for subsequent intelligent analysis and remote control.

[0096] The intelligent analysis and prediction sub-module is specifically responsible for; using deep learning algorithms and big data analysis techniques to mine and analyze the massive data in the digital twin model, identifying the operating patterns and potential laws of the system; building an anomaly detection model to monitor the operating state of the physical system in real time, and promptly detecting and warning potential faults or abnormal situations; through the comprehensive analysis of historical data and real-time data, predicting the future development trend of the system, providing scientific basis and forward-looking suggestions for decision-makers; the intelligent analysis and prediction sub-module can also combine the knowledge and experience of experts to automatically extract and integrate knowledge, forming an intelligent knowledge base to support the optimization and upgrade of the system;

[0097] The control strategy optimization sub-module is specifically responsible for, in the digital twin environment, using genetic algorithms and particle swarm optimization algorithm evolution calculation techniques, combined with expert systems and heuristic rules, to perform virtual simulation and evaluation on multiple control strategies, and screening out the optimal control strategy that can not only meet the system performance requirements but also reduce energy consumption and costs; the control strategy optimization sub-module also has an adaptive adjustment function, which can dynamically adjust the parameters and rules of the control strategy according to the real-time feedback of the physical system and the changes in the external environment to ensure the optimization of the control effect; in addition, the control strategy optimization sub-module can also visually display and generate reports on the optimized control strategy for the convenience of users to understand and apply.

[0098] The remote control execution sub-module is specifically responsible for transmitting the optimized control strategy to the actuator of the physical system in real time through the communication network to achieve remote and precise control of the physical system; the remote control execution sub-module also has the functions of security verification and permission management to ensure the security and reliability of the control instructions and prevent illegal access and malicious operations; in addition, the remote control execution sub-module also supports a variety of communication protocols and interfaces to adapt to the needs and compatibility of different physical systems;

[0099] The feedback and iteration sub-module specifically iteratively optimizes the digital twin model and control strategy according to the difference between the actual response data of the physical system and the prediction results of the digital twin model; through continuous learning and adjustment, it improves the accuracy of the digital twin model and the effectiveness of the control strategy, forming a closed-loop control system; the feedback and iteration sub-module can also record the historical data and optimization process of each iteration, providing a reference for subsequent algorithm improvement and performance evaluation; through continuous iterative optimization, it ensures that the digital twin model is always highly consistent with the physical system, improving the accuracy and reliability of remote control.

[0100] In specific use, the operator can input control parameters and target requirements through the user interface. The algorithm first collects the real-time data of the physical system through the data acquisition and preprocessing sub-module and performs necessary preprocessing. Then, the digital twin model update sub-module uses this data to update the digital twin model to ensure the consistency of the model with the physical system state. The intelligent analysis and prediction sub-module uses deep learning algorithms to analyze the updated digital twin model, predict the future state and potential faults of the system, and provide a basis for the optimization of the control strategy. The control strategy optimization sub-module conducts virtual simulation and optimization in the digital twin environment to generate the optimal control strategy. The remote control execution sub-module converts these control strategies into specific control instructions and transmits them to the actuator of the physical system through the communication network to achieve remote control of the physical system. At the same time, the feedback and iteration sub-module continuously adjusts and optimizes the digital twin model and control strategy according to the actual response of the physical system, forming a closed-loop control to ensure the continuous high accuracy and reliability of remote control.

[0101] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent substitution on some of the technical features. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent analysis and remote control algorithm based on digital twin, characterized in that: It includes a digital twin model construction and dynamic update module, an intelligent analysis algorithm module based on digital twin, an adaptive remote control algorithm module, and an algorithm process and implementation module; The digital twin model construction and dynamic update module includes: The multi-dimensional modeling sub-module uses a method combining mechanism modeling and data-driven to construct a multi-dimensional digital twin model including the geometric structure, dynamic characteristics, and thermodynamic characteristics of the physical system; The real-time data fusion sub-module designs a multi-source data fusion module, integrates Internet of Things sensor data, historical data, and external environment data, and performs noise reduction, calibration, and spatio-temporal alignment on the data through Kalman filtering and Bayesian network algorithms to achieve the dynamic update of the digital twin model and ensure a high degree of consistency between the virtual model and the physical system; The intelligent analysis algorithm module based on digital twin includes: The state prediction and health assessment sub-module uses the digital twin model and combines deep learning algorithms such as long short-term memory network and graph neural network to predict the future state of the system and evaluate the health state of the equipment; The optimization decision support sub-module constructs a multi-objective optimization model with system efficiency, energy consumption, and safety as the optimization objectives, and uses non-dominated sorting genetic algorithm and reinforcement learning algorithm to perform virtual simulation and optimization in the digital twin environment to generate the optimal control strategy; The adaptive remote control algorithm module includes: The real-time control instruction generation sub-module dynamically adjusts the control parameters according to the prediction results and optimization decisions of the digital twin model, combines with the adaptive control theory, and designs a model predictive control algorithm to achieve real-time and precise control of the physical system through rolling optimization and feedback correction; The remote collaborative control sub-module establishes a distributed control architecture to support the collaborative control of multiple digital twin models and physical systems; through 5G and edge computing technologies, it realizes the low-latency transmission of remote control instructions to ensure the real-time and reliability of control; The algorithm process and implementation module includes: The data acquisition and preprocessing sub-module collects the real-time data of the physical system through the sensor network and performs filtering and normalization preprocessing; The digital twin model update sub-module uses the preprocessed data to update the parameters of the digital twin model through the data fusion algorithm to ensure the accuracy of the model; The intelligent analysis and prediction sub-module uses deep learning algorithms to analyze the digital twin model and predict the future state and potential faults of the system; The control strategy optimization sub-module generates the optimal control strategy through the multi-objective optimization algorithm in the digital twin environment; The remote control execution sub-module converts the optimized control strategy into control instructions and transmits them to the actuator of the physical system through the communication network to achieve remote control; The feedback and iteration sub-module iteratively optimizes the digital twin model and the control strategy according to the actual response of the physical system to form a closed-loop control.

2. The intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The multi-dimensional modeling sub-module adopts a method that combines mechanism modeling and data-driven approach. Specifically, through in-depth understanding of the physical system, a mechanism model based on physical laws is established, which can describe the basic operation laws and characteristics of the system. Machine learning algorithms are used to optimize and supplement the parameters of the mechanism model to improve the accuracy and adaptability of the model. Mechanism modeling provides the basic framework of the system, while the data-driven method uses actual operation data to finely adjust the model. The combination of the two can construct a digital twin model that not only conforms to physical laws but also adapts to the actual operation situation.

3. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The formula of the Kalman filter algorithm is as follows: X k|k-1 = F k X k-1|k-1 + B k U k + W k where, X k|k-1 is the predicted value of the state at time k based on the state estimate at time k-1; F k is the state transition matrix, which describes how the system state transfers from one time step to the next; X k-1|k-1 is the optimal state estimate value at time k-1; B k is the control input matrix, U k is the control input at time k; W k is the process noise, which is usually assumed to follow a Gaussian distribution with mean 0 and covariance Q k , that is, W k ~N(0, Q k ); The formula of the Bayesian network algorithm is as follows: Suppose there are n variables \(X_1, X_2, \ldots, X\) in a Bayesian network n , then its joint probability distribution is expressed as: Among them, Parents(X i ) represents the set of parent nodes of variable X i . P(X i |Parents(X i )) is the conditional probability distribution of X i given the states of its parent nodes.

4. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The formula of the combined long short-term memory network algorithm is as follows: i t = σ(W xi x t + W hi h t-1 + b i ) where, i t is the output of the input gate at time t; σ is the Sigmoid activation function; W xi and W hi are the weight matrices from the input and the hidden state to the input gate respectively; b i is the bias of the input gate; x t is the input at time t; h t-1 is the hidden state at time t-1; The formula of the deep learning algorithm of the graph neural network is as follows: Among them, H (l) is the node feature matrix of the l-th layer, and H (0) = X; W (l) is the weight matrix of the l-th layer; σ is the activation function; is the adjacency matrix with self-loops added, and I is the identity matrix; is 's degree matrix, that is is the symmetrically normalized adjacency matrix, which is used to balance the degree differences of nodes; is the symmetrically normalized adjacency matrix, which is used to balance the degree differences of nodes.

5. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The formula of the non-dominated sorting genetic algorithm is as follows: Among them, and are the maximum value and the minimum value of the i-th target respectively; and are two solutions adjacent to the i-th target value and X in the current layer; The formula of the reinforcement learning algorithm is as follows: Among them, s t and a t are the state and action at time t respectively; α is the learning rate (0 < α ≤ 1), which controls the influence degree of new experience on the Q value; r t+1 is the immediate reward obtained after executing the action a t ; γ is the discount factor 0 ≤ γ ≤ 1, indicating the degree of emphasis on future rewards; is the maximum Q value of all possible actions under the next state s t+1 .

6. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The formula of the designed model predictive control algorithm is as follows: wherein, is the state vector of the system at time k; is the control input vector of the system at time k; is the output vector of the system at time k; are the state transition matrix, the input matrix, and the output matrix, respectively; is the process noise, is the measurement noise.

7. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The data acquisition and preprocessing sub-module is specifically responsible for collecting real-time data from sensors, devices or other data sources, and performing preprocessing operations such as cleaning, denoising and normalization to ensure the accuracy, integrity and consistency of the data. By preprocessing the data, the accuracy and efficiency of subsequent analysis and control are improved. In addition, the data acquisition and preprocessing sub-module also has the functions of data compression and storage, so that historical data can be quickly accessed and used when needed. The digital twin model update sub-module is specifically to use the preprocessed data, through the ensemble learning algorithm and the online learning mechanism, to continuously update and optimize the parameters and structure of the digital twin model to adapt to the dynamic changes and uncertainties of the physical system. It also has the functions of model verification and validation to ensure the accuracy and reliability of the digital twin model, providing a solid foundation for subsequent intelligent analysis and remote control.

8. The intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The intelligent analysis and prediction sub-module is specifically as follows: Using deep learning algorithms and big data analysis techniques, mine and analyze the massive data in the digital twin model to identify the operation patterns and potential laws of the system. Build an anomaly detection model to monitor the operation status of the physical system in real time, and timely discover and warn of potential faults or abnormal situations. Through the comprehensive analysis of historical data and real-time data, predict the future development trend of the system, providing scientific basis and forward-looking suggestions for decision-makers. The intelligent analysis and prediction sub-module can also combine the knowledge and experience of experts to automatically extract and integrate knowledge to form an intelligent knowledge base to support the optimization and upgrade of the system. The control strategy optimization sub-module is specifically in the digital twin environment, using genetic algorithm and particle swarm optimization algorithm evolutionary computing techniques, combined with expert systems and heuristic rules, to perform virtual simulation and evaluation on multiple control strategies, and screen out the optimal control strategy that can not only meet the system performance requirements but also reduce energy consumption and costs. The control strategy optimization sub-module also has an adaptive adjustment function, which can dynamically adjust the parameters and rules of the control strategy according to the real-time feedback of the physical system and the changes in the external environment to ensure the optimization of the control effect. In addition, the control strategy optimization sub-module can also visually display the optimized control strategy and generate reports, which is convenient for users to understand and apply.

9. An intelligent analysis and remote control algorithm based on digital twin according to claim 1, characterized in that: The remote control execution sub-module specifically transmits the optimized control strategy to the actuator of the physical system in real time through the communication network to achieve precise remote control of the physical system; the remote control execution sub-module also has security verification and permission management functions to ensure the security and reliability of control instructions and prevent illegal access and malicious operations; in addition, the remote control execution sub-module also supports multiple communication protocols and interfaces to adapt to the requirements and compatibility of different physical systems; The feedback and iteration sub-module specifically iteratively optimizes the digital twin model and the control strategy according to the differences between the actual response data of the physical system and the prediction results of the digital twin model; through continuous learning and adjustment, it improves the accuracy of the digital twin model and the effectiveness of the control strategy to form a closed-loop control system; the feedback and iteration sub-module can also record the historical data and optimization process of each iteration to provide reference for subsequent algorithm improvement and performance evaluation; through continuous iterative optimization, it ensures that the digital twin model always maintains a high degree of consistency with the physical system and improves the accuracy and reliability of remote control.

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