848 results about "Physical system" patented technology

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

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

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention discloses an energy management three-dimensional visualization method and system based on digital twin and Internet of Things, and relates to the technical field of energy management visualization, and the method comprises the steps: obtaining multi-source heterogeneous energy data of an energy management object; constructing a virtual mirror image model corresponding to the physical energy system based on the multi-source heterogeneous energy data and the digital twin model; generating a three-dimensional visual scene based on the virtual mirror image model so as to map the geographic position distribution of the physical energy system, and presenting the real-time energy consumption state, the energy flow transmission path and the environmental parameter change of the equipment; receiving a user interaction instruction, carrying out interaction operation on the target area or equipment, calling the associated multi-source heterogeneous energy data, and carrying out superposition display on the associated multi-source heterogeneous energy data; energy consumption abnormity early warning information or an equipment regulation and control strategy is generated in combination with the energy management optimization rule, and the equipment regulation and control strategy is input into the physical energy system to be executed. The invention provides an energy management method capable of realizing multi-source data fusion and accurate mapping of a physical system.

The invention discloses a physics-based method for discovering a control equation from scarce and noise data. According to the method, a physical information neural network and a sparse regression method are combined, and a partial differential control equation of a dynamic system is found from scarce and noisy data. Firstly, the size of a candidate function library is effectively reduced through a dimension verification method. And then modeling a physical system and calculating a candidate function by using the strong nonlinear fitting capability and automatic differential characteristic of the deep neural network. And finally, the form of a control equation and the coefficient of the equation can be obtained through sparse regression, and the coefficient is finely adjusted through DNN. The method not only can discover the control equation from the data, but also can obtain the network model to realize the response prediction of the dynamic system. The method is easy to implement, efficient, high in precision and high in universality. The method can be widely applied to physical knowledge mining, modeling and reasoning of a complex dynamic system.

The invention belongs to the technical field of digital twinning, and discloses a digital twinning rehabilitation robot state monitoring and control system based on the Internet of Things. Multi-dimensional state data such as joint angles, torque output, electromyographic signals and environment temperature of the rehabilitation robot are obtained in real time through an Internet of Things sensor network, a digital twin model is constructed, and hierarchical updating is carried out based on a self-adaptive updating frequency adjustment mechanism. And the system dynamically adjusts the updating periods of the high-frequency updating layer and the low-frequency updating layer according to the change rate of the state parameters and the deviation trend of the prediction response vector, so that accurate synchronization of the digital twin model and the physical system is realized. The state prediction module predicts a future response behavior, the error compensation module generates an error compensation vector adjustment model update and control instruction, and the control module generates an optimization control instruction meeting multiple constraints. The control precision and safety of the rehabilitation robot are remarkably improved, personalized precise rehabilitation is supported, and rapid changes and irregular motions can be effectively coped with.

The invention relates to the technical field of UV printing, and discloses an intelligent scheduling method and system for UV printing nozzles based on multi-objective optimization, and the method comprises the steps: collecting the real-time state data of a physical UV printing system, and forming a state data set; establishing a digital twinborn model at the edge computing node; scheduling simulation is carried out by adopting an improved ant colony algorithm, and a scheduling scheme is generated through path search of virtual ants; the scheduling scheme is applied to a physical system, feedback data are collected, a physical mathematical bidirectional feedback system is constructed, and hierarchical pheromones of edge nodes and a cloud end are cooperatively updated; each UV printing nozzle autonomously calculates a multi-objective optimization function value, and an optimal working strategy is selected; according to the method, a completely distributed self-organizing scheduling architecture is adopted, so that the single-point fault problem and the expansibility limitation of traditional centralized scheduling are effectively solved.

The invention discloses a virtual power plant green energy consumption cooperation method and system fusing digital twinning and reinforcement learning, and the method comprises the steps: building a green energy power plant digital twinning model through a simulation tool, collecting data in real time, and carrying out the normalization and abnormal value cleaning; inputting the data into the digital twinborn model, mapping the operation state of a physical system, and rehearsing the influence of different energy scheduling strategies on the green energy consumption rate and the power grid frequency in a virtual environment; optimizing and updating the energy scheduling strategy based on a near-end strategy optimization PPO algorithm; the optimized and updated energy scheduling strategy is fed back to a physical system to be executed, the strategy execution effect is monitored in real time, the digital twin model parameters are updated, and closed-loop control is formed; the method can solve the problem that the intermittency of renewable energy sources is not matched with the dynamic demand of the load.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention discloses a calculation and measurement fused high-fidelity digital twinning dynamic monitoring method for a cantilever beam structure. Firstly, a real-time updated database is constructed based on a physical entity system; secondly, constructing a three-dimensional digital model by using SolidWorks, and performing multi-physics field simulation analysis in combination with Ansys to obtain high-precision mechanism data of a structural key area; thirdly, performing reduced-order calculation on a simulation result by using MWorks, training and optimizing a reduced-order model through a feedforward neural network, and reducing an error between simulation data and reduced-order data; fusing the one-dimensional system data order reduction model and the three-dimensional field data order reduction model based on a Modelica language; and finally, integrating a mechanism-data hybrid model, constructing a dynamic response characteristic and spatial distribution characteristic integrated high-fidelity digital twinborn body, comparing twinborn body calculation data with sensor actual measurement data in real time, and accurately representing geometric morphology, mechanical properties and dynamic behaviors of a physical system. The method is suitable for high-precision monitoring, real-time state evaluation and performance optimization of the cantilever beam structure.

A system to protect an industrial asset includes a plurality of monitoring nodes each generating a data stream of current monitoring node values in time-domain, and a virtual agent associated with each of the plurality of monitoring nodes. the virtual agent being configured to detect anomalous performance of the corresponding monitoring node and configured to communicate with one or more other virtual agents via a network.

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

The invention discloses an armored vehicle permanent magnet synchronous motor health monitoring model construction method and system based on a reduced-order digital twin model, and the method comprises the steps: building an electromagnetic-temperature coupling field finite element model based on the parameters of a permanent magnet synchronous motor, and simulating the operation parameters of the permanent magnet synchronous motor at a preset rotating speed; constructing an original feature set based on the operation parameters of the permanent magnet synchronous motor at the preset rotating speed; performing dimensionality reduction and cumulative variance interpretation on the original feature set by using a principal component analysis method to obtain a matrix formed by principal component feature vectors; an ordinary differential neural network is adopted to continuously solve an ordinary differential equation fitting historical state through dimension reduction time sequence data obtained through simulation, and an adjoint state of network parameters is reversely solved to form a permanent magnet synchronous motor digital twin reduced-order model; the coupling relation between the physical system and the preset initial value is relieved, the initial value problem is prevented from affecting the prediction effect, and reliable model and experience support is provided for health management operation and maintenance of the permanent magnet synchronous motor.

The invention discloses an AI-based multimodal transport resource collaborative dynamic configuration method, which comprises the following steps of: constructing a real-time data layer for acquiring multi-dimensional data; preprocessing the data of the real-time data layer; dynamically constructing a digital twin platform based on the preprocessed data information; training a plurality of agents; carrying out cooperative training on a plurality of agents under a federated learning cluster framework; jointly training a global AI model; the AI decision center generates an optimal or nearly optimal dynamic resource configuration decision; the dynamic configuration engine dynamically schedules resources and generates an instruction; issuing the generated detailed instruction to a physical system of an execution layer; and the IoT equipment continuously monitors the execution state and the physical environment change, and feeds back new data to the real-time data layer. According to the invention, the bottleneck of data islands and response delay is broken through, and a cost-aging-carbon emission multi-target balanced intelligent decision-making system is constructed; and performing multi-agent collaborative training under a federated learning framework to realize cross-domain collaborative optimization.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention discloses a fast training type unmanned aerial vehicle reinforcement learning strategy method suitable for a microcontroller, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: constructing a standard four-rotor dynamic model containing the first-order delay characteristic of a motor, and simulating a real physical system; an asymmetric actor-commentator architecture is adopted to ensure the robustness and mobility of the strategy; a reward function coefficient is dynamically adjusted based on a course learning mechanism, and network parameters are optimized by adopting an offline strategy algorithm; performing network training and process optimization; and carrying out lightweight processing and deployment on the trained strategy network, landing the strategy in simulation to a real hardware platform with limited resources, jointly realizing rapid training, reliable migration and lightweight deployment, and realizing high-frequency autonomous flight control of the unmanned aerial vehicle on an embedded platform. According to the method, the high-performance reinforcement learning control strategy can be stably and efficiently operated on the microcontroller with limited resources, and the hardware cost and the power consumption of the intelligent unmanned aerial vehicle are greatly reduced.

The invention discloses an ocean wind power underwater noise monitoring method and system, and belongs to the technical field of ocean environment monitoring, artificial intelligence and autonomous systems.The ocean wind power underwater noise monitoring method comprises the steps that a digital twinborn model fusing multi-mode perception is constructed; deducing future states of the system and the environment based on a digital twinborn model to generate a predictive state sequence; solving and generating a cooperative control strategy according to a joint optimization target of data quality and energy consumption; and analyzing the cooperative control strategy to synchronously regulate and control the physical system. According to the method, a digital twinborn driven cognitive decision and autonomous control architecture is adopted, a high-fidelity digital twinborn body is constructed to carry out prospective and uncertainty deduction on a physical system and a marine environment, and a closed-loop decision is carried out by taking risk-income optimization as a principle; the monitoring system can have long-term, autonomous and online self-improvement and risk avoidance capabilities, and the reliability of monitoring data in a complex marine environment and the full-life-cycle autonomous operation level of the system are remarkably improved.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention discloses a zero-carbon park inter-industry information fusion operation and maintenance system, particularly relates to the technical field of carbon emission dynamic management and control, and is used for solving the problem of carbon accounting distortion caused by state deviation of an existing static carbon emission model and a physical system. The method comprises the following steps: establishing an initial carbon emission model containing energy efficiency reference parameters in a design stage, adopting multi-working-condition dynamic calibration in a construction stage to obtain an operation boundary characteristic spectrum and synchronously acquiring a thermodynamic trace, and identifying topological fragile nodes based on Lyapunov exponential stability analysis and optical flow method thermal gradient analysis; quantizing a carbon emission cascade conduction effect by combining an equipment energy topology network, generating a dynamic carbon emission model correction instruction to update an energy efficiency parameter, and finally executing double-instruction collaborative management and control in an operation and maintenance stage; a complete-cycle carbon data flow closed loop is realized, and through multi-source information fusion and conduction chain modeling, the effect of continuously reducing the carbon emission intensity of the park is achieved.

The invention relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligent AI-driven digital twinning low-carbon scheduling system, which comprises a physical sensing layer, a digital twinning engine layer, a group intelligent decision-making layer, a block chain evidence storage layer and a dynamic scheduling execution layer, and forms a'sensing-modeling-decision-evidence storage-execution 'closed loop; the physical sensing layer collects equipment and luggage state data; the digital twinborn engine layer constructs a total-factor twinborn body to realize synchronous mapping and carbon accounting of a physical system; the group intelligent decision-making layer adopts an improved carbon sensitive ant colony algorithm to generate a multi-objective optimization strategy of total energy consumption, residence time and load balance; the block chain evidence storage layer ensures credibility and traceability of the carbon data; and the dynamic scheduling execution layer converts the decision into a control signal and corrects simulation and actual deviation. According to the method, low-carbon, high-efficiency and reliable luggage scheduling is realized, and the method is suitable for green operation of a smart airport.

The invention discloses an electric locomotive three-dimensional digital-analog simulation teaching system constructed based on an unreal engine technology, and relates to the technical field of electric locomotive teaching. Comprising a three-dimensional model construction module which is used for generating a hierarchical three-dimensional model of electric locomotive equipment through combination of polygon modeling and parametric modeling; and the physical engine interaction module is integrated with an unreal engine physical system and is configured to process the real-time solution of the locomotive kinetic parameter equation set. According to the method, a highly accurate three-dimensional model is created by combining polygon modeling and parametric modeling on the complex equipment of the electric locomotive, the model can support detailed level-of-detail rendering so as to adapt to different observation distances, and in the physical engine interaction module, the model can be applied to the physical engine interaction module. Through the train longitudinal dynamics calculation unit, the traction transmission simulation unit and the braking system modeling unit, the system can simulate non-linear changes in the wheel-rail contact force, traction motor torque transmission and tread braking process in real time, so that various dynamic effects can be visually displayed in the teaching process.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

The invention aims to solve the problem of insufficient robustness of a traditional power grid state estimation method in post-disaster recovery and emergency communication scenes, and provides a power system emergency state estimation method based on an unmanned aerial vehicle collaborative graph neural network. According to the method, unmanned aerial vehicle dynamic communication scheduling and a space-time diagram neural network are fused, and the problems of data missing, topology time varying, communication interruption and the like are solved. A node voltage estimation and confidence thermodynamic diagram is generated by constructing a simulation data set, designing a space-time diagram convolutional network and combining a diagram feature reconstruction module, and a priority recovery strategy is formulated. Meanwhile, an unmanned aerial vehicle emergency communication optimization model is established, and efficient distribution of communication resources is realized. The method has the advantages that an information-physical system collaborative optimization method is provided, a rapid and reliable communication alternative scheme is provided, and the problems of measurement missing and topology time varying are solved. By establishing a closed-loop feedback model, dynamic optimization of communication resource allocation scheduling is realized, and the method adapts to a real-time dynamic mode of a power system.

A system to protect an asset includes a plurality of monitoring nodes each generating a data stream of current monitoring node values in time-domain. An anomalous space data source stores sets of anomalous feature vectors for each of the plurality of monitoring nodes generated by an anomaly detection model. A current system function processor, coupled to the plurality of monitoring nodes over a network, receives receive data streams from each of the plurality of monitoring nodes, and generates a set of current feature vectors. An anomaly detection computer coupled to the plurality of monitoring nodes receives the data streams from the plurality of monitoring nodes and the set of anomalous feature vectors, and outputs at least one decision boundary based on processing, using the anomaly detection model, of the current feature vectors relative to the sets of anomalous feature vectors.

The invention relates to the technical field of computational fluid mechanics and system control, and discloses a multiphase flow flow control and heat exchange simulation system, which comprises a multi-physical field tensor modeling module for reconstructing a control equation into a uniform high-order tensor and extracting a curvature tensor; the dynamic tensor dimension reduction and implicit coupling solving module is used for performing dynamic rank reduction on the tensor equation and constructing a tensor product Jacobian matrix; the physical constraint agent modeling and migration optimization module is used for constructing an agent objective function embedded with a physical conservation law; the asynchronous time stepping and multi-scale coordination module is used for realizing asynchronous time propulsion and strong coupling solution based on curvature tensor; and the multi-objective optimization and decision mapping module is used for performing multi-objective optimization based on the proxy objective function. According to the method, a multi-physical field control equation is reconstructed into a unified high-order tensor, and a tensor decomposition strategy for dynamic rank adaptive adjustment based on a phase interface curvature tensor and a Jacobian condition number is combined, so that efficient and stable solution of the high-dimensional coupling physical system is realized.

The invention provides a construction method of a digital twin system, the method is applied to at least one agent, and the method comprises the following steps: obtaining mapping information associated with a physical system; analyzing the mapping information, and generating a resource configuration strategy meeting twin requirements; and generating the digital twin system in response to the resource configuration strategy. The mapping information of the physical system is analyzed through the intelligent agent, the resource configuration strategy adaptive to the twinborn demand is automatically generated, and the consistency of the digital twinborn system and the physical system is further ensured. The invention further provides electronic equipment, a computer program product and a construction device of the digital twin system.

The invention provides a weak supervision video anomaly detection method and system based on potential energy field damping dynamics. The method comprises the following steps: inputting a video into an anomaly detection model to obtain an original video feature sequence; calculating fluctuation potential energy by using the original video feature sequence and further obtaining a global inertia proxy vector; calculating interaction potential energy through the global inertia proxy vector and the original video feature vector; nonlinear self-adaptive damping force is generated through interaction potential energy; applying a nonlinear adaptive damping force to the original video feature vector to obtain a purified dynamic feature vector; constructing a loss function by using the purified dynamic feature vector, and training the model to obtain an optimized model; and inputting the video into the optimized model to obtain a final frame-level anomaly detection result. According to the method, anomaly detection is reconstructed from a classification problem to a signal decoupling and energy dissipation problem in a physical system, and the physical nature of a depolarization mechanism is explained from the theoretical level.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.