145 results about "Real systems" patented technology

The invention provides a power distribution network symbiotic simulation system and method based on digital twinning. The system comprises a power distribution network digital twinning simulation platform, a cloud edge hierarchical deployment collaborative architecture and a power distribution network terminal. The digital twinborn simulation platform for the power distribution network comprises a data layer, a model layer, a service layer and an application layer which are covered in sequence by taking a cloud platform as a bottom, and the cloud edge hierarchical deployment collaborative architecture performs hierarchical deployment according to different service functions of the power distribution network. According to the invention, through the constructed full-attribute isomorphic power distribution network digital twinborn simulation platform and the cloud edge hierarchical deployment collaborative architecture, the fusion demand of multi-scene multi-service flow is fully considered, through the symbiotic simulation technology, dynamic feedback and collaboration between the simulation system and a real system are realized, and the highly integrated and collaborative simulation system has the advantages of high simulation efficiency and high reliability. Various changes in the power distribution network can be quickly responded, and a solid guarantee is provided for safe and efficient operation of the power distribution network.

The invention discloses an attack trapping method and system based on network attack surface adaptive conversion, and the method comprises the steps: dynamically converting vulnerabilities and weaknesses in a system, so that an attacker is difficult to determine an entrance of a real system; threat intelligence and a network environment are monitored and analyzed in real time, and abnormal traffic and potential attack behaviors are identified; constructing a false target and monitoring attack behaviors, and obtaining behavior characteristics and attack means of an attacker; attack behavior data are analyzed by using artificial intelligence and a machine learning algorithm, potential threats are quickly identified and responded, a threat intelligence library and a defense strategy are updated, and the method better adapts to constantly changing attack means and threat forms. The method not only can trap attack behaviors, but also can collect and analyze behavior characteristics of attackers, and provides more accurate data support for network security decision and threat prediction.

A state-space model which includes one or more neural networks. The state-space model is configured to stochastically model a technical system by modelling uncertainties both in latent states of the technical system and in weights of the one or more neural networks. Thereby, the state-space model may be able to capture both aleatoric uncertainty (inherent unpredictability in observations) and epistemic uncertainty (uncertainty in the model's parameters or weights. During the training and during subsequent use for model-predictive control, moment matching across neural network layers is used, which may ensure that the model's predictions are consistent and close to real system behavior.

The invention discloses a masonry robot virtual-real cooperative control method and system based on digital twinning, and aims to realize high-precision and high-safety automatic masonry of a coke oven by a robot. The method comprises the following steps: firstly, constructing a digital twin system of a masonry robot, and setting a masonry robot entity industrial control process: firstly, conveying refractory bricks to a specified photographing position by a brick conveying platform, and then photographing and identifying by a truss clamping jaw; the truss clamping jaw moves to a refractory brick grabbing position for grabbing, and meanwhile, the mechanical arm with the slurry pipe is used for spraying slurry; and after the movable truss moves to the refractory brick placement position, accurate placement is identified. In the virtual-real cooperative control process, the virtual entity model is updated in real time through state information acquired by a sensor of a real system; making and optimizing a control strategy in the virtual environment; and real-time decision making is carried out by utilizing the virtual entity model, so that the operation of the physical entity is adjusted.

The invention provides a four-honey threat exploration method for an enterprise intranet. The four-honey threat exploration method comprises the following steps: constructing a honey court, a honey point, a honey hole and a honey array in the enterprise intranet; the honey court is used for monitoring network traffic and carrying out black, white and grey traffic identification on the network traffic; the honey point is used for carrying out trapping detection on the network flow passing through the honey point; the honey hole is used for collecting attacker information and performing traceability countering according to the attacker information; the honey array is in communication connection with the honey court, the honey point and the honey hole, obtains the safety condition of the enterprise intranet and the deployment information of the honey court, the honey point and the honey hole, and carries out deployment scheduling according to the safety condition of the enterprise intranet. The four-honey system constructed by applying the method fits the enterprise intranet environment and has dynamic adaptive capacity. A real system can be effectively isolated, and attacks are prevented from reaching an actual network. Global linkage disposal can be realized for security threats, security intelligence and attack information can be comprehensively shared, deployment points can be coordinated and adjusted, source tracing and countering can be quickly carried out, and a secure enterprise-level network environment can be constructed.

The invention discloses a building air conditioning system control optimization method based on mechanism-reinforcement learning and an intelligent agent, and relates to the technical field of building energy saving and intelligent control. Comprising the steps of constructing a multi-region thermodynamic mechanism model, carrying out mixed training on a strategy, carrying out online dynamic control and deploying a lightweight agent, and generating a preliminary strategy model by constructing the multi-region thermodynamic mechanism model and combining a digital twinborn simulation environment and real system transfer learning. The model can dynamically control a building air conditioning system on line, and environment data are collected in real time and control parameters are dynamically adjusted by designing a reinforcement learning agent. The problems that a traditional building air conditioning system control method is low in energy efficiency and fluctuates in comfort degree are solved, and meanwhile the physical interpretability and the training efficiency of a pure data driven reinforcement learning method are improved.

The invention discloses a suspension control fault detection and recovery method and device for a maglev train and a medium, and relates to the field of maglev trains, and the method comprises the following steps: S1, constructing a linearized suspension system model; s2, reconstructing a state vector, and generating a suspension gap estimation value; s3, constructing a health probe set, and executing a signal selection strategy; s4, detecting a slow change fault based on the statistical accumulation characteristic of the residual error, and outputting a slow change fault detection result; s5, monitoring the variation trend of the residual error, and triggering a reset mechanism to clear historical accumulated data when a reset condition is met; and S6, generating a final fault mark, and forcibly updating the fault mark to a normal state if a reset mechanism is triggered. According to the hierarchical collaborative decision-making architecture based on the maglev train lap joint structure dynamic model, the condition of performance degradation or failure does not occur in a real system by the algorithm, so that the engineering applicability and reliability of fault diagnosis are improved.

The invention relates to a fault-tolerant control technology, and aims to provide an AUV fault-tolerant control method based on near-end strategy optimization and tubular model prediction. Comprising the following steps: establishing an underwater robot mathematical model, and discretizing into a nominal system and a real system; nonlinear model predictive control is achieved for a nominal system, and a nominal control law is obtained through solution and rolling optimization; introducing a near-end strategy optimization algorithm based on tubular model predictive control, outputting an auxiliary feedback control law based on an actor and commentator network, and adding the auxiliary feedback control law with a nominal control law to obtain a fault-tolerant control law for the underwater robot propeller; keeping the parameters of the nominal control law unchanged; meanwhile, an auxiliary feedback control law is adaptively updated based on a strategy obtained through near-end strategy optimization training, and online fine adjustment of fault-tolerant control of the propeller is achieved. Influences of faults and delay of the propeller are passively processed by means of inherent robustness of the controller, the design of a fault device control law is simplified while the stability is guaranteed, and the conservative property of a classical tubular model is reduced.

The present disclosure provides a threat intelligence detection method, device, equipment and medium based on honeypot trapping, which relates to the field of network security technology and aims to improve the threat detection and response capabilities of network security. The method includes: obtaining network topology data of an IPv6 network; generating an undirected graph based on the network topology data, wherein the nodes of the undirected graph represent devices in the IPv6 network, and the edges of the undirected graph represent the connection relationship between the devices; based on the undirected graph, determining the target node based on the first parameter and the second parameter of each node, wherein the first parameter represents the importance characteristics of the node, and the second parameter represents the vulnerability characteristics of the node; based on the target node, deploying a honeypot trapping strategy, wherein the honeypot trapping strategy is used to simulate the vulnerabilities of the real system to attract attackers to actively attack; detecting threat intelligence according to the honeypot trapping strategy.

The invention discloses an air conditioning system control method and system based on reinforcement learning and a twinborn model strategy. The method comprises the steps that S1, a data-driven twinborn model of a central air conditioning system is constructed; s2, performing offline pre-training based on a virtual environment formed by a twin model; s3, performing online integration on a plurality of pre-trained reinforcement learning algorithms and the twin model to form a reinforcement learning strategy integration framework; and S4, the final control action is issued to execution equipment of the central air conditioning system. According to the invention, by constructing the serial data-driven twinborn model, the running state, energy consumption and cooling and heating load supply-demand relationship of the central air-conditioning system are accurately simulated, a high-fidelity virtual environment is provided for various reinforcement learning algorithms, offline pre-training is realized, an initial control strategy is generated, and risks caused by blind exploration in a real system are avoided. Continuous self-adaptive optimization is achieved, and the stability and the energy efficiency level of intelligent control of the air conditioning system are remarkably improved.

The invention discloses a complex network key node identification method in the field of network science and computer science, which comprises the following steps: firstly, adding hyperedges for a network model through a spectral clustering algorithm to construct a hyperedge model, and then extracting local and global features for nodes based on the hyperedge model to construct feature vectors; and correcting a regression mean square error loss function by using a sorting loss function to obtain a more accurate sorting sequence, inputting an adjacent matrix of the hypernetwork model and a feature matrix of nodes into a graph convolutional neural network for training, and obtaining a calculation result of a key score as a sorting standard to obtain a key node. According to the method, deeper-level representation of mutual relation and effects among real system nodes is realized, the focus of attention is transferred from node scores to sorting, and effective identification of key nodes is realized.

The invention discloses a spacecraft electric propulsion system-level simulation model, which comprises an industrial personal computer (including test equipment), an EPCU simulator, a ground remote control and telemetering system, a storage and supply subsystem and a PPUamp, a TSU subsystem; the system comprises a ground remote control and telemetering system, a storage and supply subsystem and a PPUamp. The TSU subsystems are all real systems; the EPCU simulator comprises a ground remote control and telemetering system data interaction module, a storage and supply subsystem data communication module and a PPUamp; the TSU subsystem comprises a data communication module and a satellite-borne EPCU application module; the industrial personal computer is used for realizing task scheduling of the satellite-borne EPCU application module, calling of the ground remote control and telemetering system data interaction module, calling of the storage and supply subsystem data communication module and PPUamp; and calling a data communication module of the TSU subsystem. According to the method, the design scheme of the satellite electric propulsion system can be quickly verified on the premise that no satellite-borne electric propulsion control unit (EPCU) exists, and the method is suitable for verifying the feasibility of scheme design and the conformity condition of functional performance indexes of products selected by the system in the early stage of system design.

The invention provides a system operation lagging construction method and device, equipment, a medium and a program product. Relates to the technical field of system performance testing. The method comprises the steps that the CPU thread number and a watchdog threshold value of a current system are obtained; on the basis of the CPU thread number and the watchdog threshold value, the duration of the target thread executing busy waiting operation is calculated through a preset formula; initializing a spin lock of a kernel mode, and creating target threads of which the number is the same as that of the CPU threads, so that the target threads execute preset processing logic to construct system operation jamming; the preset processing logic comprises the following steps: periodically obtaining a spin lock; responding to the situation that the spin lock is not obtained, and continuing to execute the spin lock obtaining operation; and in response to obtaining the spin lock, executing a busy waiting operation. According to the method, CPU resources are continuously occupied through kernel spin lock competition, and a real system jamming scene is simulated. The busy waiting operation duration is dynamically calculated, it is ensured that the single-thread lock holding time is slightly lower than a watchdog threshold value, and crash of a system triggering unrecoverable is avoided.

The invention discloses a resource scheduling optimization method based on digital twinning, and relates to the technical field of data processing, and the method comprises the following steps: S1, constructing a three-dimensional model, collecting hardware information and software information, and obtaining a visual three-dimensional model according to the hardware information and the software information; s2, collecting basic data and historical data, and obtaining task demand information and resource information of server nodes according to the software information; s3, establishing a scheduling mechanism according to the historical data and the visual three-dimensional model, and selecting a scheduling strategy; s4, acquiring an optimal scheduling strategy and data information according to the scheduling strategy, and storing the data information to a knowledge base; and S5, according to the visual three-dimensional model, calling the optimal scheduling strategy, obtaining the task demand information, and updating the knowledge base. According to the method, a virtual simulation environment is created, various scheduling strategies are allowed, and risks possibly caused in a real system due to improper scheduling strategies are avoided.

The invention discloses a network attack protection method integrating a large model and a honeypot technology, and belongs to the field of information security. The problems that an existing network attack protection method lacks dynamic response capability and is poor in network attack protection performance when facing complex attacks are solved. The method comprises the following steps: collecting interaction data generated in an operation process of a to-be-protected system; performing data desensitization to generate a desensitized data set; based on the desensitization data set, utilizing a LoRA fine tuning method to perform fine tuning on a pre-trained large model to obtain a generative honeypot large model; the method comprises the following steps: deploying a honeypot environment in a Docker container, and constructing a honeypot system based on large model service in combination with an Ollan platform; creating a honeypot system configuration file according to the configuration information of the real system, and simulating system services and ports; attacks are attracted, when an attack instruction is received, the honeypot system based on the large model service generates a false response, and one-time response to an attack request of an attacker is completed. The method is suitable for network protection.

The presently disclosed subject matter provides a state-space model composed of one or more neural networks. The state-space model is configured to randomly model the technical system by modeling uncertainty in both hidden states of the technical system and weights of the one or more neural networks. Thus, the state-space model may be capable of capturing both accidental uncertainty (inherent unpredictability in observations) and cognitive uncertainty (uncertainty in parameters or weights of the model). During training and subsequent use for model predictive control, moment matching across neural network layers is used, which can ensure that the predictions of the model are consistent and close to real system behavior. The above measures can improve the control and security of many technical systems.

The invention discloses a simulation program tuning acceleration method and system, and the method comprises the steps: extracting function operation information of target software during operation under real system configuration by using a performance analysis tool, screening out hotspot functions, and obtaining upstream and downstream hierarchies and a calling relation between the hotspot functions; identifying a load behavior type of the hotspot function, selecting a simulation program function module from the simulation function template according to the load behavior type, and constructing a function module of a simulation program in combination with upstream and downstream hierarchies and a calling relationship between the hotspot functions; and combining the function modules to generate a simulation program of the target software, and adjusting the total running time of the simulation program according to preset global control time. The invention aims to greatly shorten the time consumption of single-round operation of large-scale software under different system configurations under the condition of not changing performance behavior characteristics, so that the efficiency of performance testing in the configuration optimization process of an operating system is improved.

The invention relates to the technical field of electric power training, and discloses an electric power training mirror image simulation practical training system based on digital twinning, which comprises a data acquisition and preprocessing module, a high-fidelity mirror image modeling module, a practical training scene generation module, a multi-modal interaction module, an intelligent evaluation and feedback module, a system management module and a virtual-real linkage control module, the data acquisition and preprocessing module is used for acquiring multi-source operation data of an electric power physical entity in real time and performing noise filtering, space-time alignment and standardization processing, the system constructs a 1: 1 precision electric power equipment digital twin body through laser point cloud scanning and multi-physical field coupling modeling, and combines real-time data acquisition and hybrid drive correction technologies to realize real-time data acquisition and hybrid drive correction of the electric power equipment digital twin body. The simulation precision is remarkably improved, students can perform operation training in a highly real virtual environment, and the problems that a traditional simulation model is insufficient in precision and disjointed with a real system are effectively solved.

The invention discloses a crane position control method and system based on MPC and a control obstacle function. The method comprises the steps that S101, all to-be-controlled parameter values are calculated according to given positions; s102, solving an optimal control sequence u at the moment according to a predictive control algorithm; s103, designing a state observer according to the output y, the input u and the state at the moment, observing the state at the next moment, designing by adopting a full-dimensional linear state observer method, and adjusting parameters by adopting a DLQR method to obtain state information at the next moment for the step S102 at the next moment; and S104, the output of the real system is measured and sent to the step S103 at the next moment, and the predictive control algorithm is a model predictive control algorithm fused with the control barrier function. According to the crane position control method and system based on the MPC and the control barrier function provided by the invention, the model prediction control is combined with the control barrier function, so that the crane can reach a specified position.

The invention provides a self-evolution control method and device for an optimal heat storage and release strategy of an electric boiler, and the method comprises the steps: constructing a digital twin environment of an electric boiler system, and enabling the digital twin environment to comprise an equipment model library, a historical database and a prediction interface; defining a reinforcement learning framework based on the digital twin environment, wherein the framework comprises a state space, an action space and a multi-target reward function; performing off-line pre-training on a reinforcement learning agent by using the digital twin environment to generate an initial control strategy, and optimizing strategy parameters through historical data or closed-loop interaction of a simulation scene in the pre-training process; after the initial control strategy is deployed to a real system, safety verification is executed, operation feedback data is collected, online fine adjustment is carried out based on the feedback data, meanwhile, the strategy performance is evaluated regularly through a digital twin environment, and the strategy is rolled back to a stable version when the performance is degraded.

The invention belongs to the technical field of computers, and particularly relates to a system fault response method, a system fault response device and electronic equipment. The method comprises the following steps: acquiring a fault injection event; mapping the fault injection event according to the fault injection model to obtain a fault score corresponding to the fault injection event; mapping the fault injection event according to a strategy matching model to obtain a target response strategy and a strategy matching score corresponding to the target response strategy; updating model parameters of the fault injection model or the strategy matching model according to a numerical relationship between the fault score and the strategy matching score; and generating a response strategy for a real system fault detected by the transaction management system according to the strategy matching model after the model parameters are updated. According to the invention, system fault response intelligence and automation can be realized.

The invention relates to the technical field of digital twinning, in particular to a digital twinning dynamic updating and virtual-real mapping optimization method for a complex scene, which comprises the following steps of: establishing a bidirectional value function of virtual and physical spaces, searching a cooperative Nash equilibrium solution through multi-round game theory iteration, and realizing cooperative Pareto optimization of a virtual-real system under constraints; an anomaly detection incremental learning and elastic weight consolidation mechanism is introduced, and causal discovery and anti-factual reasoning are combined to realize root cause diagnosis and interpretable decision intervention. According to the complex scene-oriented digital twinborn dynamic updating and virtual-real mapping optimization method, an isolation forest is adopted to isolate abnormal samples through random segmentation, and a variational auto-encoder is combined to learn low-dimensional latent space representation, so that the problem of novel abnormal missing detection is solved, and an elastic weight consolidation mechanism is introduced to avoid disastrous forgetting; a variable causal relationship is mined from observation data based on a PC algorithm, and anti-fact root cause diagnosis is realized by taking a structural causal model and do-calculation as a framework.

The invention relates to a profile drifting buoy heterogeneous digital twinborn model construction method, which belongs to the technical field of model construction, and comprises the following steps: constructing a mechanical motion model of a profile drifting buoy, acquiring actual operation data, defining an error function according to the mechanical motion model, and calculating an error value between the mechanical motion model and the actual operation data according to the error function. Calculating a mean value and a standard deviation of the error values through a neural network, and performing error correction on the error values according to the mean value to obtain a heterogeneous digital twinborn model; a model error result is calculated according to the predicted operation state and actual operation data; an error threshold value is defined according to the standard deviation; a loss function is calculated according to the error threshold value and the model error result; and parameters of the heterogeneous digital twin model are updated according to the loss function. A deviation quantification mechanism between a physical model and a real system can be established, and high-precision modeling and prediction of the running state of the profile drifting buoy are achieved.

The invention discloses an automatic driving behavior simulation method and device, and the method achieves a high-precision simulation model which is consistent with the real world in a driving behavior distribution level through the unified vectorization representation of a driving environment, the behavior learning with a vehicle as a center, the introduction of routing guidance, and the construction of a trajectory prediction model. And it is ensured that large-area long-time simulation such as city-level simulation has high stability. Furthermore, illegal and unrealistic trajectory behaviors are avoided through a rule and security post-processing compensation mechanism, and a stable guarantee is provided for subsequent closed-loop simulation and real system deployment. Furthermore, through a closed-loop calling mechanism, the problem that the traditional single-step reasoning method cannot stably simulate for a long time is avoided, meanwhile, the complex behavior mode experienced by the vehicle in the urban road network is covered, and the authenticity and continuity of simulation are greatly improved.

This invention provides a method and system for optimizing energy management in the integration of intelligent IoT and the metaverse, involving the field of metaverse technology. The system collects IoT device energy consumption parameters and user interaction data, calculates energy utilization and resource occupancy, determines efficiency coefficients and redundancy coefficients, generates device operating mode adjustment plans and virtual scene resource configuration plans, and implements device state adjustment and resource reallocation. This invention can effectively reduce IoT device energy consumption, improve metaverse scene resource utilization efficiency, and achieve energy optimization management in the integration of virtual and real systems.

The invention discloses a hardening prediction and dynamic regulation and control method for a digital twin mill based on LSTM (Long Short Term Memory), and relates to the technical field of industrial intelligent manufacturing and equipment predictive maintenance. The method comprises the following steps: constructing a mill digital twinning model, and realizing virtual-real synchronous mapping; the method comprises the steps of collecting multi-sensor operation data, performing data cleaning and standardization processing, and combining a sliding window to extract time sequence features; constructing a double-layer LSTM network model fused with a self-attention mechanism, performing training by taking Focal Loss as a loss function, and outputting a hardening probability; verifying and optimizing the model through simulation of a digital twin system; and finally, accessing an actual mill system to realize visual monitoring and dynamic regulation and control based on probability early warning. According to the method, the problems of prediction lag and insufficient visualization of a traditional method are solved, high-precision hardening prediction and closed-loop active intervention are realized, and the operation reliability and production efficiency of the mill are remarkably improved.

The invention relates to the technical field of network security, and discloses an attack analysis method based on a mysqldump connection characteristic honeypot, and the method comprises the steps: obtaining a simulation tool of a MySQL server; embedding an attack vector in the version information of the simulation tool, and constructing a honeypot server based on the simulation tool; in response to the fact that an attacker is connected with the honeypot server through mysqldump, obtaining version information returned by the honeypot server; obtaining multi-dimensional attack behavior data of the attacker based on the returned version information; and based on the multi-dimensional attack behavior data, analyzing the attack behavior of the attacker. According to the method, the attack vector is embedded in the version information by using the connection characteristic to attract an attacker, prevent the attacker from attacking a real system and strive for time for defense measures, and when the attacker attacks, multi-dimensional attack behavior data is obtained and analyzed, so that the attack analysis accuracy is improved, powerful support is provided for formulating an effective defense strategy, and the method is suitable for popularization and application. And defense capability is enhanced.

The invention relates to the technical field of electric vehicle charging facility intelligent control, and discloses a charging pile load balancing method and system based on reinforcement learning. The system comprises a digital twinborn simulation module, a data sensing and processing module, a state characterization module, a double-track decision module, a baseline strategy unit, a shadow learning agent, a safety verification and execution module, a double-path reward calculation module, a shadow experience learning and verification module and an online deployment and continuous learning module. Through the technical path of simulation pre-training, shadow evaluation, safety verification and continuous learning, the problem of high deployment risk of reinforcement learning in a real system is solved, safe and smooth transition from simulation to reality is realized, absolute safety and continuous evolution of system operation are ensured, and the method is suitable for popularization and application. A prospective technical architecture is constructed to cover future scenes, and dynamic collaborative optimization of multiple targets such as charging efficiency, fairness, economy and power grid safety is realized.

Methods and systems for securing deployments are disclosed. The deployments may be secured by generating and deploying security models to components of the deployment. The security models may be obtained through simulation of the operation of the deployment. During the simulation, predictions of different types of attacks and the potential defenses to the attacks on its operation may be evaluated. Further, limits may be imposed on the different attacks and potential defenses to simulate various scenarios that may be encountered in real systems.

The present application relates to a kind of profile drift buoy isomerization digital twin model construction method, belong to model construction technical field, including the mechanical motion model of construction profile drift buoy, obtain actual operation data, according to mechanical motion model definition error function, according to error function the error value of mechanical motion model and actual operation data;The mean and standard deviation of error value are calculated by neural network, the error value is corrected according to mean, and isomerization digital twin model is obtained;According to isomerization digital twin model, the predicted running state is obtained, and the model error result is calculated according to the predicted running state and actual operation data, according to standard deviation definition error threshold, according to error threshold and model error result, loss function is calculated, according to loss function, the parameter of isomerization digital twin model is updated, the deviation quantification mechanism between physical model and real system can be established, high-precision modeling and prediction to the running state of profile drift buoy are realized.