94 results about "Physical system" patented technology

A hardware-in-the-loop simulation system for liquid hydrogen-superconducting hybrid energy storage, capable of improving the accuracy and reliability of a simulation result. The system comprises: a liquid hydrogen-superconducting hybrid energy storage physical system (7), a real-time simulator, and a superordinate computer. The liquid hydrogen-superconducting hybrid energy storage physical system (7) comprises: a fuel cell device (1), a water-electrolysis-based hydrogen production device (2), and a liquid hydrogen-cooled superconducting magnetic energy storage device (6). The fuel cell device (1) is configured to convert hydrogen into electric energy and provide power supply for a power system; the water-electrolysis-based hydrogen production device (2) is configured to convert electric energy into chemical energy to be stored in hydrogen; the liquid hydrogen-cooled superconducting magnetic energy storage device (6) is configured to perform liquid hydrogen energy storage and superconducting magnetic energy storage; the real-time simulator is configured to generate control signals of the liquid hydrogen-superconducting hybrid energy storage physical system (7) on the basis of operation parameters of the liquid hydrogen-superconducting hybrid energy storage physical system (7) and an operating condition of a simulated renewable energy power generation system; and the superordinate computer is configured to monitor and manage operating conditions of the real-time simulator and the liquid hydrogen-superconducting hybrid energy storage physical system (7).

This invention relates to the field of industrial equipment diagnostic technology and discloses a method for fault diagnosis and tracing of industrial equipment based on multi-source heterogeneous parameter fusion and deep learning networks. The method includes: acquiring a multi-dimensional heterogeneous signal sequence representing the operating state of the controlled object; mapping the signal sequence into a two-dimensional spatial image tensor using the Gram angle field algorithm; constructing a dual-stream feature extraction network containing graph convolution branches and cyclic unit branches to extract spatial topological features and temporal evolution features respectively; determining the logical offset matrix based on the spatial flow feature gradient divergence, and resampling the temporal flow features accordingly to achieve spatiotemporal causal phase alignment, and outputting diagnostic analysis results. This invention compensates for the physical system response hysteresis through asynchronous resampling operators in the computational domain, eliminates feature phase contamination caused by conventional rigid alignment, enhances the model's ability to capture weak abnormal signals, and achieves causal consistency in fault root cause localization.

The present application relates to the field of sewage treatment, and more particularly to an AOA sewage treatment intelligent control system based on digital twinning. It comprises a physical perception layer, a data transmission layer, a digital twinning layer, an intelligent control layer and an execution layer. The physical perception layer collects AOA process physical data throughout the whole process and preprocesses them into standardized data; the data transmission layer realizes the bidirectional transmission of data and control instructions between each level; the digital twinning layer, based on the denitrification and phosphorus removal microbial metabolism mechanism of the AOA process, constructs a whole-process twinning model synchronized with the physical system in real time and generates simulation prediction data; the intelligent control layer generates control instructions according to the data; the execution layer executes the instructions and collects feedback data, which is used to update the twinning model and correct the control instructions. Through the five-layer series closed-loop structure, the present application realizes the intercommunication of data, the interaction of instructions and the closed-loop feedback of each level, and constructs a whole-process closed-loop intelligent control system of "perception-transmission-twinning-decision-execution".

Methods and apparatuses are provided for performing at least one of optimizing, monitoring, and controlling an actual physical system. Data representing the physical system is acquired and used to generate one or more Hamiltonians. These Hamiltonians are used in the quantum signal processing circuits of a quantum computer to simulate the imaginary time evolution of a Thermal Pure Quantum (TPQ) state of a set of qubits representing the Gibbs state of the system. Optionally, a Bayesian machine can be trained based on the evolution of the TPQ state to predict the operation of the physical system over time. Classical shadow tomography is then used to provide a classical representation of the TPQ state to a classical computer to facilitate classical optimization or control of the actual physical system.

The application discloses a kind of FFC self-learning assembly methods based on parallel reinforcement learning, establishes including real physical assembly system and simulation system parallel learning system, and physical assembly system and simulation system are run in parallel, experience data in assembly process is gathered to experience pool of parallel learning system, simulation system is trained by experience data provided by physical assembly system in experience pool, and feedback guides real physical system to execute assembly task;Wherein, the physical information of physical assembly system is stored into parameter server for simulation system to train after being handled by Softmax classifier of parallel learning system.The self-learning assembly method of the application not only has the advantages of high assembly efficiency and learning efficiency, but also has the advantage of high assembly success rate.

The utility model discloses a kind of for simulating semi-physical system of stability of moving between turret, belong to weapon testing equipment technical field.The system includes six degrees of freedom swing platform, real object turret, sensor group and control terminal, six degrees of freedom swing platform is connected with real object turret by flange plate;Sensor group is integrated in the pitch axis, gyration axis and cannon tube end of the real object turret, for collecting angular velocity and acceleration data;Control terminal is electrically connected with the six degrees of freedom swing platform and sensor group, for realizing the control of electric cylinder in six degrees of freedom swing platform.The utility model constructs the closed-loop simulation system of moving between turret by the mechanical coupling of six degrees of freedom swing platform and real object turret, compared with real vehicle test, the system saves site and energy consumption, shortens test cycle.

This invention discloses an AI inference resource decoupling scheduling method and system based on a two-dimensional orthogonal state space. User AI service rights are decomposed into storage rights dimension σ and computing power rights dimension φ, constructing an orthogonal state space S=(σ,φ), where σ and φ change independently and have no causal dependency. Four service modes are derived from the state space: full service, read-only memory, temporary session, and guided recovery. State transitions in the computing power rights dimension are triggered by critical conditions of discrete quota counting: the cost r for a single token generation is defined as the lower limit of physical resources required for a single forward propagation of the model decoder. When the remaining quota Q(t) is less than r, the physical system cannot complete the next calculation, and the state will inevitably transition and terminate the computation flow. State switching is achieved through metadata-level atomic operations, and the execution time is independent of the user data scale. This invention clarifies the inevitability of the technical effect through logical deduction, is applicable to software or hardware-software co-implementation, and solves the problems of resource deadlock, switching latency, and service interruption under subscription systems.

The application discloses an energy-saving scheduling method for self-hanging real-time tasks, and belongs to the field of real-time system scheduling and energy efficiency management. The method gives a task division strategy by considering the load difference of tasks and processors on a multi-core CPU platform. On a GPU platform, the real-time performance and energy consumption are balanced by selecting a suitable execution core. Through the combination of the two, energy-saving scheduling and task division for a multi-core CPU and GPU heterogeneous computing platform are realized. The energy consumption of the CPU and the GPU can be optimized synergistically on the premise of guaranteeing the real-time task deadline. A system-level energy efficiency perception scheduling framework is constructed, which is suitable for real-time cyber-physical systems, automatic driving, embedded vision and other scenes with strict requirements for energy efficiency and timing.

The present application relates to the field of aero-engine simulation technology, more particularly to a semi-physical simulation method and system based on a prediction model.The method comprises: collecting engine test data; identifying a transfer matrix of a state space of the prediction model according to the engine test data, the transfer matrix being used to describe the relationship between an input sequence and an output sequence; calculating matrix coefficients of the prediction model based on the transfer matrix; calculating a predicted value of the output sequence corresponding to the input sequence based on the matrix coefficients and current engine state quantities; and adjusting and updating the operating state of an engine physical system in real time based on the predicted value of the output sequence, so that the engine meets the expected performance.The present application provides high-precision prediction and control for the engine control system by constructing a real-time updated prediction model in combination with actual engine test data.The robustness and adaptability of the control system under fault conditions are enhanced by a fault injection simulation method which simulates various fault types.

Quantum computing systems and methods for determining a state of a physical system are provided. In some examples, a method can include obtaining a defined function associated with the physical system, the defined function encoding an estimated value of at least one property to be simulated by a quantum computing system as a gradient of the defined function. The method can include implementing a quantum circuit on a plurality of qubits in a quantum computing system to perform a quantum operation (e.g., the Gilyén quantum gradient algorithm) on the plurality of qubits. The quantum operation is operable to determine the gradient of the defined function. The method can include determining the estimated value for the at least one property based at least on at least one of the plurality of qubits after implementation of the quantum operation.

The present invention relates to a video monitoring method in an area where a plurality of CCTV cameras is installed, such as an urban area. As a digital twin Cyber Physical System (CPS) is constructed, and a control server connected to a camera and a computer connected to the control server are configured, and a model object generated from a CPS model and video information transmitted from the camera are collected in the control server and transmitted to the computer, users may confirm a digital twin-based model object together with actually captured video information. Through the present invention, it is possible to immediately and clearly confirm surrounding spaces, as well as the location of a camera transmitting specific video information in video monitoring of a wide area such as urban areas, and efficiency and accuracy of viewing the video information of a plurality of cameras can be dramatically improved.

Method for providing measurement points (u) for measuring a physical system (2), comprising the following steps: - Providing (S1) several initial measurement points (ustart), each determined by an input value of an input quantity (E) or input values ​​of several input quantities (E) and each exhibiting a gradient with respect to one or more input quantities (E), wherein the measurement points are applied cyclically with a cycle time Δt, such that gradients of one or more input quantities result from the sequence of input values ​​of the input quantities at the successively applied measurement points, wherein differences between the input values ​​of an input quantity from one measurement point to the next determine the gradient of the input quantity E in question; - Successively selecting (S2) one of the initial measurement points (ustart) from a subset or all of the initial measurement points (ustart);- Generating (S3) a set of measurement points (u) depending on the selected initial measurement point (ustart), wherein the generated measurement points (u) are generated with increasing dynamic component, the dynamic component of the measurement point being determined by its gradient(t) = u(t) − u(t − 1) − t; - Applying the generated measurement points (u) to the physical system (2) and acquiring (S4) a resulting output quantity (y), wherein the generation and application of the measurement points (u) based on a selected initial measurement point (ustart) is carried out until the resulting output quantity (y) violates a predefined limit condition.

An adaptive control method for cyber-physical systems (CPS) subjected to injection and deception attacks includes the following steps: S1: Constructing a control system model under a cyber-physical framework; S2: Defining actuator attacks when the system's sensors and actuators are subjected to adversary injection and deception attacks; S3: Defining sensor attacks when the system is subjected to sensor attacks; S4: Rewriting the system model under cyberattacks; S5: Based on the rewritten system model, obtaining the adaptive control law u using the BackStepping method; S6: Further deriving that all signals in the closed-loop system are globally bounded based on the adaptive control law u in S5, and designing a controller by introducing Nussbaum even functions and their variable derivatives. When the system is subjected to injection and deception attacks, the control parameters are adjusted to make the adjustment error arbitrarily small. This invention can ensure that the adjustment error can be arbitrarily small under injection and deception attacks by adjusting the control parameters.

This invention relates to the field of computing resource scheduling technology, proposing a task scheduling method and system for computing centers based on carbon-computing power coupling optimization. The method includes the following steps: acquiring real-time status data of each node in the computing center, and a queue of tasks to be processed; calculating the carbon efficiency value of each node according to the carbon efficiency dynamic equilibrium equation, and allocating any task in the task queue based on the carbon efficiency value of each node to obtain a first scheduling scheme; inputting the first scheduling scheme into a carbon-aware scheduling model based on Markov decision method and dual deep Q-network algorithm, wherein the carbon-aware scheduling model generates a second scheduling scheme based on the current status of each node; and deploying the second scheduling scheme to the physical system of the computing center. This invention integrates the spatial coupling characteristics of carbon footprint and computing tasks, effectively improving resource utilization while ensuring the reliability of the task scheduling scheme.

This invention discloses a multimodal integrated security control method for industrial cyber-physical systems (ICPS), comprising: constructing an adaptive discrete memory event-triggered communication mechanism to process the output data of the sensing side of the ICPS to obtain system transmission values; after the values ​​are subjected to a false data injection attack, they form downstream data on the sensing side; using a trained attack reconstruction model to repair the downstream data on the sensing side; designing a robust observer based on the repaired downstream data on the sensing side; and generating initial control quantities through a multimodal integrated security controller based on the state estimates and fault estimates obtained by the observer; processing the initial control quantities using the aforementioned trigger communication mechanism to obtain control data; after the data is subjected to a false data injection attack, it forms downstream data on the execution side; and using the trained attack reconstruction model to reconstruct and compensate the downstream data on the execution side before applying it to the controlled object, thereby achieving integrated security control of the ICPS.

This invention discloses a quality traceability management system for the entire aluminum profile production process, specifically relating to the field of fault prediction and health management in aluminum profile production. It includes a data construction module, a feature fusion module, a risk identification module, an anomaly backtracking module, and a closed-loop optimization module. The data construction module collects process parameters and equipment parameters at each stage of aluminum profile production, aligns them with the material flow cycle time, and extracts key indicators reflecting the trend of state changes. These indicators are then fed into the equipment analysis path and the process analysis path, forming a first dataset and a second dataset. This invention links the dynamic changes in equipment health status with batch quality judgment in real time and combines risk assessment and traceability mechanisms at the cyber-physical system level to identify chronic deviations, limit their impact scope, and dynamically update batch records. This achieves a deep integration of the entire aluminum profile production process quality traceability system with fault prediction and health management.

Method for designing hardware blocks and associated devices The present invention relates to a method for designing the hardware blocks of a physical system comprising a prediction device that predicts an output for given inputs, the method comprising a step of: - obtaining datasets corresponding to the outputs given by the device in the presence of the inputs of the dataset, - receiving the probability that a dataset is observed during a predefined use, - collecting the predicted outputs, - determining the distribution of the prediction accuracy of the predicted output for each dataset, - aggregating the determined distributions, - applying a risk metric to the time evolution of the aggregated prediction accuracy distribution to obtain a quantity representative of the performance domain of the device, and - modifying the blocks according to the quantity obtained.Figure for the abbreviation: figure 2.

This invention provides a digital twin control method for a magnetically driven rotor system oriented towards fault scenarios. First, based on the dynamic mechanism of the magnetically driven rotor system, a static digital twin model is established in digital space to characterize the system's dynamic response behavior when the physical system parameters remain constant. Then, key parameters of the magnetically driven rotor in the actual physical system are identified in real time and injected into the constructed static digital twin model, transforming it into a dynamic digital twin model capable of reflecting changes in the physical system's state in real time. Finally, for situations involving sensor and controller failures in the physical system, the digital twin model ensures that the magnetically driven rotor system can still operate stably under fault conditions. This invention effectively solves the problems of control lag and loss of control under faults caused by the disconnect between the model and the physical system state, significantly improving the system's fault-tolerant control capability and operational stability when sensors and controllers fail.

The application belongs to the technical field of power dispatching management, and provides a power dispatching system based on a cloud platform, in order to solve the problem of low technical accuracy of power dispatching in traditional technologies, the system provides global and high-credibility decision basis for abnormal identification by fusing five layers of data of physical entities, control execution, model algorithms, business management and system operation and maintenance based on the cloud platform; through the layered diagnosis logic of 'wisdom first, then physics', the reliability of data and models is verified first, ensuring the accuracy of subsequent decision input; after confirming the abnormality of the physical system, the digital twin model is used for hypothesis simulation and abnormal propagation chain construction, realizing accurate positioning from abnormal appearance to physical root cause equipment; finally, alarm information is generated based on the traceability result, which can significantly improve the accuracy and reliability of abnormal monitoring of the power dispatching system, and ensure the accuracy and efficiency, intelligence and automation of power supply dispatching and control.

This application relates to a network security intelligent protection system and method for novel power cyber-physical systems, belonging to the field of power system network security technology. The system consists of a data acquisition and management module, a scenario modeling and topology identification module, a security detection module, a fault simulation and propagation analysis module, an event response module, a system recovery module, and a visualization module. Each module relies on unified data and a coupled scenario model for closed-loop collaboration. The method forms a closed-loop protection process through data standardization, cross-layer coupled scenario modeling, abnormal threat detection, fault simulation, strategy handling, recovery verification, and visualization. This invention achieves multi-source heterogeneous data fusion, network-physical cross-layer modeling, and a closed-loop security protection process, adapting to multiple scenarios such as dispatching master stations, substations / converter stations, and new energy power plants, thereby improving the network security protection, engineering implementation, and operational support capabilities of novel power cyber-physical systems.

This invention discloses a proactive defense method and system for a power cyber-physical system, comprising: acquiring the current state information of each node in the power cyber-physical system; determining a target defense node set based on the current state information of each node under defense resource constraints, and deploying corresponding types of decoys for the nodes in the target defense node set; determining a target diversified node set from real nodes that have not deployed decoys and are in a safe state, and implementing operating system diversity configuration for the nodes in the target diversified node set; updating the security state and vulnerability of each node according to the attack results of the nodes to obtain the system state at the next moment; and repeatedly executing decoy deployment and operating system diversity configuration based on the updated system state to achieve dynamic proactive defense of the power cyber-physical system. This invention improves the attack capture rate, reduces the risk of critical nodes being compromised, and takes into account defense costs under the condition of limited defense resources.

Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and / or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.

According to one aspect, a computer-implemented method for generating an anomaly detection model in a system is proposed. The method comprises: - obtaining (10) a training data matrix corresponding to the normal operation of said system, - decomposing said training data matrix into singular values ​​(11), - calculating (12) a new basis (V'), - defining (16) a maximum Mahalanobis distance threshold representing a limit of the normal operation of said system, - defining an anomaly detection model (MDL) from said new basis (V') and said maximum Mahalanobis distance threshold (MTS). Figure for the abstract: Figure 1

A CPS security assessment and scheduling method based on event-triggered reinforcement learning under time-varying channels is proposed. The method comprises the following steps: (1) establishing a cyber-physical system model and assessment benchmark environment based on time-varying channels; (2) constructing a multi-source assessment utility index and an event-triggered mechanism with hysteresis and refractory period; (3) establishing a partially observable Markov decision process model from the perspective of the assessment end; (4) learning and training assessment and scheduling strategies based on the near-end policy optimization algorithm; and (5) executing event-triggered adaptive security assessment decisions and closed-loop state updates. This invention aims to provide a CPS security assessment and scheduling method based on event-triggered reinforcement learning under time-varying channels that can realize on-demand triggering, adaptive scheduling, and closed-loop assessment of abnormal communication intervention behaviors, and solve the problems of insufficient adaptability to time-varying channels, insufficient characterization of scheduling utility, and weak multi-mode collaborative decision-making capabilities in existing technologies.

The present disclosure provides a computer-implemented method for training a PINN to predict a physical quantity of an object, the method comprising: discretizing a PDE on a grid comprising a plurality of points into a linear equation, the PDE being formulated to characterize a physical system related to the object with a structural quantity and the physical quantity of the object, the linear equation being formulated to characterize the physical system with a first discretization matrix and the physical quantity at the plurality of points of the grid; determining a preconditioning matrix based on the first discretization matrix; predicting, by the PINN, values of the physical quantity of the object at the plurality of points of the grid; obtaining a loss based on the predicted values of the physical quantity of the object and the preconditioning matrix; updating learnable parameters of the PINN based on the loss.

A system and method are provided for determining an impedance of a physical system based on a ratio of a perturbation to the physical system and a response of the physical system. The determining of the impedance comprises converting a response signal into the frequency domain to identify a magnitude and phase of a first harmonic of the response signal and a magnitude and phase of a third harmonic of the response signal, expressing the magnitude and phase of the first harmonic as a first complex term and expressing the magnitude and phase of the third harmonic as a third complex term, and in determining the impedance, using as the response a sum of at least the first complex term and three times the third complex term. Thereby, a correction may be applied to the impedance measurement to improve accuracy and thereby to allow larger perturbation amplitudes to be used.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

This invention belongs to the technical field of power system information security, specifically relating to a security risk assessment system and method for power cyber-physical systems. The system includes: edge nodes for preprocessing collected target risk element data from the power cyber-physical system to obtain preprocessed target risk element data; and a cloud platform for using the preprocessed target risk element data as input to a pre-established risk assessment model to predict the risk level of the power cyber-physical system. The risk assessment model is established using historical risk element data from the power cyber-physical system. The technical solution provided by this invention combines edge computing and cloud-based distributed training, with both cloud and edge nodes participating in the training of the risk assessment model. This reduces the complexity of model training, improves the accuracy and efficiency of risk assessment, effectively protects data privacy, and reduces computational and storage costs.