99 results about "Real systems" patented technology

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 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 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 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 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.

The invention provides a self-adaptive physical data hybrid-driven vehicle transverse dynamics modeling and state estimation method, which can improve the modeling precision under limiting conditions, enhance the self-adaptive capability of an unstructured environment and improve the real-time calculation efficiency, and comprises the following steps of: S1, constructing a nominal physical model of vehicle transverse dynamics, obtaining prediction output of the nominal physical model; s2, determining a total model residual error based on the predicted output of the nominal physical model and the state of the real system of the vehicle at the next moment; explicitly decomposing the total model residual error into a static structure residual error representing time-invariant system deviation and a dynamic random residual error representing time-varying random deviation; and S3, fusing prediction output of the nominal physical model, a static structure residual error estimation value output by the deep neural network model and a dynamic random residual error estimation value output by the sparse Gaussian process model to obtain a vehicle state estimation value.

The invention discloses a rack testing device of a vehicle intelligent system and an acceleration safety evaluation method thereof, and belongs to the technical field of automobile safety, and the device comprises a vehicle testing cabinet, a wire control movable rack, a wire control steering rack, and a self-driving scene display system. According to the method, a tested system is iteratively fitted through a Gaussian process and a logistic function, and an agent model efficiently approaching the real tested system is generated to predict a security test result of the system; three acquisition functions are introduced to respectively explore the whole multi-dimensional continuous scene parameter space, namely, the safety performance of the system is represented by a small number of test scenes; accurately positioning a system security boundary; unsafe areas can be quickly identified, and dangerous scenes can be quickly found; and finally, giving the unsafe probability of the tested system in the test scene. The device and the method provided by the invention have relatively high test efficiency on the premise of ensuring the reliability and the accuracy of the vehicle intelligent system bench test.

The application discloses a controller driver semi-physical simulation verification system for a space manipulator system, comprising: a real system and a simulation system; the real system comprises a space manipulator controller, joint drivers and joint units; the simulation system comprises a controller simulation module, joint driver simulation modules and a Vrep manipulator simulation module, which are used for simulating the related functions of the space manipulator controller, joint drivers and joint units respectively; the joint driver simulation modules are used for simulating the related functions of the joint drivers; and the Vrep manipulator simulation module is used for simulating the related functions of the joint units. The application aims at solving the difficulty in function verification caused by the high coupling degree of each subsystem of the space manipulator system, and designs simulation software of the subsystems and corresponding hardware and simulation models to build multiple semi-physical simulation subsystems, so that the function decoupling of the complete control system is completed, and each subsystem has the ability of independent debugging and joint debugging.

The invention relates to the technical field of vehicle electronics, in particular to a power line anti-interference method and system adopting a whole vehicle test speed regulation module, which completely reproduce the real wire harness impedance, grounding characteristics and system coupling environment through direct test on a whole vehicle, greatly improve the accuracy and reliability of the test result, and improve the reliability of the test result. The problem that the whole vehicle fails when the rack test is passed is effectively avoided; meanwhile, by synchronously monitoring multiple paths of signals, systematic cascading failures caused by power supply interference can be found and positioned, and real system-level electromagnetic compatibility evaluation is realized.

The invention relates to the technical field of artificial intelligence and software engineering crossing, and particularly provides a heterogeneous business system integration method and device based on a large model, a terminal and a medium, and the method comprises the steps: constructing an interface semantic index fusing a code structure and natural language description; receiving a natural language instruction and generating a task execution graph with a dependency relationship based on the semantic index; the task graph is rehearsed in the isolation sandbox, the read operation calls a real system to obtain data, and the write operation generates simulation data through a large model; performing logic and compliance dual verification on the rehearsal logs by using the large model; and after verification is passed, real calling is executed, compensation operation is generated for each step, and automatic rollback is carried out through a rollback stack mechanism during failure so as to guarantee transaction consistency. According to the method, the tolerance of interface change is improved, the data security risk is remarkably reduced through rehearsal and verification mechanisms, and the transaction-level guarantee of cross-system operation is realized.

This invention discloses a digital twin method and system for autonomous driving that integrates virtual and real environments based on a full-link interface. The invention includes: converting multi-source perception data from autonomous vehicles in a real physical environment into a real object state dataset; establishing a standardized state mapping between the real system state space and the virtual simulation system state space; converting the real object state dataset into virtual environment state data executable by the simulation engine; constructing a virtual environment; performing virtual perception based on the constructed virtual environment: performing sensor-level sampling on the virtual environment state data to generate virtual raw sensor data, forming a virtual perception result data stream; and verifying the virtual perception result data stream through the twin verification loop. This invention solves the problem of simulation engines being unable to dynamically generate objects during runtime, achieving high-fidelity, low-latency reproduction of real-world traffic flow in a simulation environment.

The present invention provides a method and system for fault detection of a motorhome energy storage system, belonging to the technical fields of new energy vehicles and intelligent detection. The method captures load start / stop operation instructions with timestamps in real time, and synchronously collects voltage and current signals at the output end of the energy storage system, strictly aligning the operation behavior and electrical dynamics on the time axis. Based on the load identifier in the operation instruction, a pre-stored feature template is retrieved, and the transient electrical features extracted in real time are subjected to spatio-temporal consistency verification with it, effectively distinguishing legal load startup from real system faults. When the verification fails, the similarity with the fault feature library is further calculated for risk assessment and hierarchical protection is executed. The corresponding system includes an operation instruction capture module, an electrical signal acquisition module, a central processing and synchronization module, a load fingerprint matching module, a fault risk assessment module, and a hierarchical protection execution module. The present invention significantly reduces the false alarm rate and improves the accuracy of fault detection and the continuity of system power supply.

This invention provides a universal spaceborne computer simulator, comprising: a universal simulator motherboard and a dedicated simulator daughterboard. The universal simulator motherboard provides common hardware for different spaceborne computers; the dedicated simulator daughterboard provides customized hardware for different spaceborne computers. The universal simulator motherboard includes a storage resource expansion system, an I / O interface simulation system, a power conversion module, and a motherboard debugging interface. The dedicated simulator daughterboard includes a target processor and memory components, providing a hardware platform for the operation of onboard software, and configuring the storage components according to the actual situation of the target processor. The universal simulator motherboard adopts a general-purpose hardware implementation for efficient, fast, and low-cost design, while the dedicated simulator daughterboard adopts a personalized design based on the actual state of the simulated object, meeting the application requirements of different spaceborne computers. By simulating the physical interfaces, logical relationships, and timing characteristics of real systems, it provides users with a rapid prototyping and verification platform.

The present application relates to the technical field of energy storage system, and particularly to a BMS simulation verification method and platform based on fault injection closed-loop joint debugging.A BMS simulation verification method and platform based on fault injection closed-loop joint debugging, the platform comprises the following: a model management layer; a scene generation and scheduling layer; a fault injection layer; a closed-loop joint debugging layer; a result collection and evaluation layer; a report generation and backfilling layer.Compared with the prior art, through the combination of the model library, the scene library and the fault injection library, the virtual BMS is quickly verified under different objects, different working conditions and different fault types; through the closed-loop joint debugging between the battery model, the thermal management model, the actuator model and the communication model, the closeness of the verification result to the real system is improved; through a unified coverage, response quality and protection effect evaluation mechanism, the comparability and screening efficiency between different verification tasks are improved.

The invention discloses a crankshaft defect coping method and system based on digital twinning. The method is characterized by comprising the following steps: constructing a digital twin virtual environment consistent with a real environment, wherein virtual camera parameters are consistent with a real multi-view vision system; a crankshaft defect image is collected through a real multi-view vision system; the defect pixel positions are mapped to the surface of the virtual crankshaft to form virtual defects; in the virtual environment, the virtual robot plans a grinding path and generates an instruction; and the instruction is sent to the real robot to execute grinding. The system comprises a real physical system, a digital twin virtual system and a control communication system. According to the method, path planning and verification are completed in the digital twin virtual environment, and then the instruction is issued to the real system for execution, so that the precision, the safety and the automation degree of crankshaft defect grinding are effectively improved, and the collision risk and the debugging cost are reduced.

Provided is a simulation device capable of presenting, to a user, an item of a parameter that may contribute to improving a simulation result. The simulation device causes a virtual system in a virtual space, the virtual system having virtual components corresponding to components included in a system in a real space, to reproduce an operation of the system in the real space by simulation using real data acquired from the system in the real space. The simulation device comprises a parameter setting processing unit and a simulation unit. The parameter setting processing unit reads data item management information including a value of a parameter to be set for the virtual component, and outputs a parameter setting screen that allows the value of the parameter to be changed. The simulation unit simulates an operation of the virtual system according to content set on the parameter setting screen. The parameter setting screen includes a parameter value type corresponding to the parameter value.