46 results about "Dynamic system model" patented technology

The invention provides a carbon emission calculation method based on adaptive Kalman filtering. According to the carbon emission calculation method based on adaptive Kalman filtering, a data matrix is generated by acquiring multi-source data, and a unified data basis is provided for subsequent calculation; a dynamic system model and an error measurement matrix are initialized, a state transition model indicates a carbon emission evolution law, an observation model establishes a mapping relation between multi-source data and carbon emission, and the error measurement matrix dynamically indicates a model effect and adaptively adjusts calculation parameters to ensure the adaptability of the model to the dynamic change of the system; a carbon emission prediction parameter and an error measurement matrix are calculated in real time through adaptive Kalman filtering, a prediction value is corrected through an observation parameter, an optimal estimation parameter and an observation residual error are obtained, meanwhile, the error measurement matrix is updated in real time, and dynamic fusion and noise suppression of multi-source data are achieved through the closed-loop process. And the precision and the real-time performance of carbon emission calculation are improved.

The invention discloses a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method and a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control system, and the permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method is constructed by taking a permanent magnet synchronous motor nonlinear dynamic system model as a starting point. A hybrid control method combining high-frequency pulsating square wave signal injection under a linear dynamic system model, a rotor polarity judgment and rotor initial position estimation algorithm and an unscented Kalman filtering extension algorithm in an additive noise form under a nonlinear discrete dynamic system model is adopted; and obtaining the optimal estimation observation value of the system state variable in the static, low-speed, medium-speed and high-speed full-speed domain range under the condition that the permanent magnet synchronous motor is not provided with a mechanical position sensor. The optimal estimation observation value is used for compensating and feeding back a permanent magnet synchronous motor drive control system adjusting strategy, disturbance of load torque is improved, the noise amount and disturbance amount introduced by three-phase current measurement are eliminated, and efficient and accurate control operation of the permanent magnet synchronous motor is achieved.

The invention discloses a dynamic load response adjustment method and system for a hot water boiler, and relates to the field of boiler load adjustment, and the method comprises the steps: carrying out the deep learning of boiler and weather data through LSTM, and precisely predicting the thermal load change trend in a period of time in the future; on the basis, the current system state of the boiler is estimated in real time in combination with a Kalman filter, and the predicted load, the real-time state and a dynamic system model of the boiler are incorporated into a model predictive control (MPC) framework. And the MPC calculates an optimal gas valve opening sequence considering the physical time lag of the boiler through rolling optimization in a prediction time domain. The prospective prediction is combined with optimization control based on model dynamic characteristics, so that the boiler can adjust fuel supply in advance, response delay caused by system thermal inertia is effectively overcome, rapid and accurate response to dynamic loads is achieved, and the defect that traditional control is always slow by half beat is overcome.

The invention relates to the technical field of medical image processing, in particular to a cardiovascular surgery patient circulation state image real-time monitoring and early warning system. Comprising a video acquisition module, an image preprocessing module, a microcirculation evaluation module, a multi-part perfusion fusion module based on differential manifold, a trend prediction and early warning module and a clinical interaction and data integration module. A local perfusion index and a pulsation stability index are extracted after preprocessing, multi-part microcirculation signals are creatively mapped to a differential manifold for characterization, time-frequency dual-channel fusion driven by Riemannian geometry and an attention weighting mechanism are integrated into a whole-body perfusion comprehensive index, modeling is carried out based on a dynamic system on the manifold, and a whole-body perfusion model is established. Early warning can be provided before the conventional blood pressure is obviously reduced, precious time is gained for clinical intervention, invasive complications are avoided through a non-invasive monitoring mode, and whole-course continuous monitoring can be achieved.

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

The application provides a response lag compensation method of a urea liquid injection system, which solves the problem of response lag between the urea liquid rate supplied by a metering pump and the urea liquid rate sprayed by a gas-assisted nozzle in the gas-assisted urea injection system, and comprises the following steps: a first-order dynamic system model about the residual urea liquid amount in a urea pipe is constructed to associate the urea liquid rate sprayed by the gas-assisted atomizing nozzle with the urea liquid rate pumped by the metering pump; an imbalance difference between the urea liquid rate pumped by the metering pump and the urea liquid rate sprayed by the gas-assisted atomizing nozzle is obtained, the urea liquid rate sprayed by the gas-assisted atomizing nozzle at t2 is calculated as X2, the required urea liquid injection rate at the next moment t2 is given as Xneed according to the actual working condition of the engine, the urea liquid rate pumped by the metering pump at the next moment t2 is calculated as Y1, the urea liquid rate pumped by the metering pump at the next moment t2 is corrected as Y2 which is calculated to compensate, and in addition, the application also provides a gas-assisted urea liquid injection system applying the method.

The present application relates to the technical field of medical image processing, in particular to a real-time monitoring and early warning system for the circulation state image of a cardiovascular surgery patient, comprising a video acquisition module, an image preprocessing module, a microcirculation evaluation module, a multi-site perfusion fusion module based on differential manifold, a trend prediction and early warning module, and a clinical interaction and data integration module; the video of the patient's fingernail bed and lips is simultaneously acquired by a high-frame-rate camera, and after preprocessing, the local perfusion index and pulsatility stability index are extracted; innovatively, the multi-site microcirculation signals are mapped onto the differential manifold for representation; through the time-frequency dual-channel fusion driven by Riemann geometry and the attention weighting mechanism, the whole-body perfusion comprehensive index is integrated; and based on the dynamic system modeling on the manifold, early warning can be provided before the conventional blood pressure significantly decreases, valuable time is won for clinical intervention, the non-invasive monitoring method avoids invasive complications, and whole-process continuous monitoring can be realized.

The application relates to a multi-agent cooperative control method and device for transmission data loss, and belongs to the technical field of multi-agent control. In the method, a directed network topology thought is introduced into information interaction of a multi-agent system, a Bernoulli distribution is introduced to express packet loss in a data transmission process between agents, a second-order dynamic system model of the agent is constructed to model the state, a following error equation of the multi-agent system is established, Liapunov stability theory and robust control theory are used to analyze the tracking error equation, a stability control condition is obtained, and thus a control gain coefficient capable of realizing consistent stability of the multi-agent system can be obtained by solving the stability control condition.

This invention belongs to the field of measurement and control, and relates to a fixed-time preset performance control method, device, computer equipment, and storage medium for a macro-micro composite motion platform. The method includes: constructing a dynamic system model of the macro-micro composite motion platform; transforming the dynamic system model into a state-space equation suitable for a backstepping recursive control framework; designing a fixed-time preset performance function to constrain the tracking error signal within a preset range within a fixed time; constructing a nonlinear filter to simplify the control design process and establishing a switching function to resolve coupling terms in the system; designing an adaptive fixed-time preset performance tracking control algorithm and proving the stability of the closed-loop system based on Lyapunov stability theory. By designing the controller using an adaptive backstepping recursive control framework, the control design process is simplified, and the computational load is reduced; the tracking error signal of the closed-loop system converges to the preset range within a fixed time, improving the transient performance and control accuracy of the system.

A method of monitoring an elevator car in an elevator shaft includes acquiring position data indicative of a position of the elevator car, acquiring motion data indicative of a motion of the elevator car, and determining, from a dynamical system model, an estimated position of the elevator car. The dynamical system model describes a motion of the elevator car based on input variables that include the position data and the motion data. The method further includes determining an offset value indicative of a motion data offset, wherein the offset value is generated such that the dynamical system model fits the position of the elevator car indicated by the position data, determining a sensor reliability parameter based on the offset value, and providing output data including the sensor reliability parameter.

This invention belongs to the technical field of fuel cell gas supply systems and provides a control method and system for PEMFC injection gas supply systems based on deep reinforcement learning. Addressing the difficulty in establishing accurate control models for PEMFC injection gas supply systems, this invention proposes a control method for PEMFC injection gas supply systems based on deep reinforcement learning. First, a dynamic system model of the PEMFC injection gas supply system is established using a deep neural network. Second, an actor-critic framework is used to interact with the learned dynamic system model of the PEMFC injection gas supply system and maximize the cumulative reward within the prediction interval, thus learning a neural network strategy based on model predictive control. Finally, by fixing the parameters of the actor network model and deploying the actor network in the controller of the PEMFC injection gas supply system, real-time optimal control of the PEMFC injection gas supply system can be achieved.

The application discloses a random complex network state estimation method and system, and belongs to the technical field of estimators. When the measurement signal transmission requirement of a target node obtained in real time meets a preset dynamic event triggering condition, an augmented error system model is constructed based on a constructed dynamic system model, a missing measurement model and the dynamic event triggering condition, and a gain matrix of a constructed state estimator is obtained by solving a linear matrix inequality. The state estimator is updated according to the gain matrix, and the measurement signal of the target node at a triggering moment is input into the updated state estimator to obtain an updated state estimation signal at a current moment. The triggering condition includes a preset waiting time interval and a forced triggering time constraint. The internal dynamic variable of the dynamic event triggering condition is updated according to the updated state estimation signal at the current moment. The method balances the state estimation precision and the communication frequency by introducing a dynamic event triggering mechanism.

Methods, systems, and apparatuses for combining pressure measurement with a dynamic system model to estimate the air mass within an actuator, and then estimate the applied contact force which may be based on a quasi-static deformation model. An example method includes, in response to a request to apply a contact force by an actuator at a first actuator pressure, determining a second actuator pressure for the actuator to apply the contact force. The contact force is generated using one or more components of a haptic assembly, and the second actuator pressure is determined based on one or more of properties of the components of the haptic assembly and pressure data obtained via one or more sensors. The example method further includes causing the haptic assembly to adjust the first actuator pressure to the second actuator pressure such that the actuator applies the contact force.

This invention discloses a robot navigation method suitable for large sparse environments, comprising the following steps: Step 1, establishing a system evolution equation based on the robot's state vector and kinematic equations, incorporating an environmental sparsity factor into the system evolution equations, and then introducing trajectory tracking weights and obstacle avoidance weights to construct a dynamic system model; Step 2, determining whether the dynamic system model is in a critical state between stability and chaos by solving the Jacobian matrix and Lyapunov exponent of the dynamic system model. When the maximum Lyapunov exponent is approximately equal to 0, the critical stability condition is reached; Step 3, based on the critical state, designing a path evaluation function to screen stable path points, and dynamically adjusting the obstacle avoidance weights through bifurcation parameter adjustment control equations to achieve adaptive switching between ordered tracking and chaotic obstacle avoidance, thus completing robot navigation in large sparse environments. This invention can ensure that robots can efficiently and stably complete navigation in large sparse environments.

The invention provides a multi-unmanned aerial vehicle wireless energy transmission and wireless data collection collaborative optimization method based on adaptive hierarchical deep reinforcement learning, comprising: constructing a dynamic system model composed of a plurality of unmanned aerial vehicles, a plurality of information devices and a plurality of energy devices, the information devices uploading data to the unmanned aerial vehicles, and the energy devices uploading data to the unmanned aerial vehicles; the energy equipment receives wireless energy from the unmanned aerial vehicle; minimizing the total information age of all the information devices and minimizing the total energy hunger degree of all the energy devices are set as common optimization objectives, and the double-objective optimization problems are fused into a single-objective optimization function according to weights; and constructing and operating a three-layer multi-agent deep reinforcement learning framework, solving the single-target optimization function, and outputting an optimal cooperative control strategy. According to the method, real-time balance, autonomous decision making and efficient cooperation of the multiple unmanned aerial vehicles for heterogeneous Internet of Things equipment requirements are realized.

A horticulture growing system where a growing regimen is prescribed to achieve desired growing results. The system has self-learning mechanisms where the prescribed growing regimens are continually optimized to achieve the desired results through machine learning and deep learning. The system uses both a cloud based dynamic system model for growing and a local grow model. Various techniques are utilized to improve data collection and labeling. The results (the system's ability to accurately create growing regimens which produce the desired grow objectives) are improved using the dynamic system model and the local grow model. The models are trained and adjusted using datasets from multiple growing operations to increase the efficacy of the self-learning mechanisms. This system may also include a mechanism for in-harvest re-optimization to improve grow results in real-time.

The invention belongs to the technical field of parameter identification, and provides a parameter identification method and system for a hybrid linear dynamic system, and the method comprises the steps: obtaining a fitting high-dimensional time sequence track of the hybrid linear dynamic system; constructing a hybrid linear dynamic system model based on the obtained fitting high-dimensional time sequence track to obtain a high-dimensional state transition matrix; disassembling a parameter estimation task based on a hierarchical idea and a row decomposition strategy, and disassembling the obtained high-dimensional state transition matrix into a low-dimensional row vector estimation task; updating parameters of the disassembled low-dimensional row vector by adopting a mobile data window increment stochastic gradient algorithm; a self-adaptive step length mechanism is introduced, the step length is dynamically adjusted and updated based on the parameter estimation residual error, and a prediction residual error is obtained; and estimating a transfer matrix and a noise covariance of the hybrid linear dynamic subsystem according to the obtained prediction residual error, and completing parameter identification of the hybrid linear dynamic system.

The invention provides a multi-agent formation method, system and device based on a virtual structure and an improved potential function, and the method comprises the steps: building a nonlinear multi-agent dynamic system model with N same agents, and making an obstacle avoidance strategy; designing a controller based on the nonlinear multi-agent dynamic system model; adding the controller into the nonlinear multi-agent dynamic system model to obtain a second-order multi-agent system; and according to a second-order multi-agent system stability method, controller parameters and system parameters of the multi-agent system meet discrimination conditions, and system errors of the multi-agent system are gradually converged to zero by using an obstacle avoidance strategy, so that the multi-agent formation is ensured to reach a stable expected trajectory. According to the invention, the multi-agent formation can avoid the obstacle and then reconstruct the formation, and the situation that the unmanned vehicle falls into a local extreme value and cannot continue to move is effectively avoided.

The application relates to a multi-agent cooperative control method and device for transmission time lag, and belongs to the technical field of multi-agent control. In the method, a directed network topology thought is introduced into information interaction among agents in a multi-agent system, a second-order dynamic system model of the agent is constructed to model the internal state of the agent, a tracking error equation of the multi-agent system containing the transmission time lag is established, the tracking error equation is analyzed by using Lyapunov stability theory and finite time stability theory, and a stability control condition is obtained, so that a control gain coefficient capable of realizing consistency stability of the multi-agent system in a finite time can be obtained by solving the stability control condition.

The application provides a digital exhibition intelligent control method and system based on the Internet of Things, obtains a dynamic system model and a current state deviation of a target controlled object in a digital exhibition environment; based on a system topological structure and historical state data of the digital exhibition environment, a graph convolution gating recurrent unit network is used to predict a state deviation of the target controlled object at a certain time in the future, and a predicted deviation vector is obtained; a nonlinear integral sliding surface of fusion prediction information is obtained by using the current state deviation and the predicted deviation vector; an estimated value of lumped uncertainty is obtained by using a nonlinear disturbance observer to estimate the lumped uncertainty received during system operation; a total control amount is composed of an equivalent control term and a robust approaching term; and the target controlled object is controlled by using the total control amount.

The invention provides a digital exhibition intelligent control method and system based on the Internet of Things. The method comprises the following steps: acquiring a dynamic system model and a current state deviation of a target controlled object in a digital exhibition environment; predicting the state deviation of the target controlled object at a certain moment in the future by using a graph convolution gating cycle unit network based on the system topology structure and historical state data of the digital exhibition and display environment to obtain a predicted deviation vector; obtaining a nonlinear integral sliding mode surface fused with prediction information by using the current state deviation and the measured deviation vector; a nonlinear disturbance observer is adopted to estimate the lumped uncertainty borne by the system in the operation process on line, and an estimated value of the lumped uncertainty is obtained; forming a total control quantity by an equivalent control item and a robust approaching item; and controlling the target controlled object by using the total control quantity.

The invention discloses a system simulation debugging method and device and electronic equipment. The method comprises the following steps: acquiring module information of a dynamic system model; an execution script is generated according to the module information, the dynamic system model is simulated according to the execution script, and the execution script comprises execution logic and a debugging function corresponding to the dynamic system model; target module information and a step-back step number when the dynamic system model performs simulation execution to a preset breakpoint are obtained, the target module information is module information adjusted according to an update demand of a target object, and the step-back step number is a time step number of the target object expecting to return in a simulation process; and debugging the dynamic system model according to the target module information and the step number. According to the method and the device, the technical problems that a dynamic system cannot flexibly return to any time point and module parameters are difficult to adjust in real time during simulation debugging in the prior art are solved.

The invention discloses a road accident prediction method, system and device under an air-ground network based on RBF-DNN approximation. The method comprises the following steps: step 1, obtaining road data through a mobile sensor and a fixed sensor of the network and processing the road data; step 2, according to the air-ground networking system model and a possible unknown fault type model, in combination with a tracking error function and a robust adaptive fault-tolerant sliding mode function, obtaining a dynamic system model with an internal actuator unknown fault; step 3, according to the obtained dynamic system model, designing an adaptive law with projection and sigma-correction and a non-Zeno event triggering mechanism, and constructing a DNN closed-loop control air-ground networking prediction control model based on RBF; 4, after the obtained prediction control model is trained, road accident prediction is carried out on the obtained road real-time data; the problem that communication link defects still exist in an existing prediction technology in a highway sudden dangerous case scene is solved.

The invention discloses a tumor treatment strategy decision-making method based on threshold guidance and a related device, and aims to solve the problems of drug resistance, lack of adaptability, difficulty in clinical implementation and the like of an existing tumor treatment strategy, and the method comprises the following steps: defining a threshold-guided treatment period and a threshold-guided non-treatment period, and constructing a threshold-guided dynamic system model, carrying out theoretical analysis and numerical simulation on dynamic behaviors, carrying out comparative analysis with other treatment strategies, carrying out theoretical parameter sensitivity analysis, and optimizing an adaptive treatment strategy by utilizing reinforcement learning. According to the method, treatment decisions are dynamically adjusted through a single-threshold strategy, high individuation and precise treatment are achieved, heterogeneity of tumor dynamics among patients can be effectively handled, tumor development time is remarkably prolonged, the dosage is reduced, toxic and side effects are reduced, and the living quality and treatment compliance of the patients are improved; and a novel effective method is provided for tumor treatment.

The invention discloses an isoplane paving digital art pattern generation method and device based on a partial differential equation. The method comprises the following steps: taking an isoplane body shape after interactive curve editing as a paving unit basic domain; a Laplacian equation is introduced into a basic domain, geometric constraints of paving units are converted into Dirichlet boundary conditions of a Laplacian operator, then smooth invariant mapping meeting isohedron symmetric constraints is constructed, and seamless transition of textures at joints of the paving units is guaranteed; and finally, finishing texture coloring in combination with the power system model and the quasi-regular pattern model. The method has the core advantages that the method can adapt to any isohedron type, the symmetry, the texture details and the visual effect of the pattern can be flexibly regulated and controlled by adjusting the constraint vertex, the power system function and the quasi-regular pattern function, a brand new solution is provided for dynamic real-time generation and personalized design of the digital art pattern, and the method can be applied to the field of digital art design. And abundant material sources are provided for the fields of costume design, textile design and the like.

The invention relates to the technical field of agricultural information, in particular to a vegetable pest prevention and control decision-making method and system based on multi-source data. The method comprises the following steps: acquiring data of sound signals, electrochemical signals, spectral characteristics and microenvironment parameters related to vegetable diseases and insect pests; the data is preprocessed; extracting acoustic characteristic parameters and electrochemical characteristic parameters; obtaining a comprehensive feature vector; constructing a dynamic system model; acquiring time-space infection density of diseases and insect pests; constructing a topology perception prevention and control decision model; and generating a prevention and control decision in combination with an improved topological optimization algorithm. According to the method, acoustic and electrochemical data are considered, and an improved signal noise reduction and feature extraction algorithm is combined, so that a disease and pest dynamic propagation model is constructed; through introduction of topological persistence analysis and multi-scale optimization decision, accurate prediction and adaptive prevention and control of time-space infection density of diseases and insect pests are realized, the identification precision is significantly improved, the pesticide application amount is reduced by 28% or more, and perception-decision closed-loop intelligent prevention and control is achieved.

The invention discloses a distributed multi-trolley optimal hunting control method based on a switching observer, and belongs to the technical field of multi-agent cooperative control. The method comprises the following steps: constructing a trolley dynamic system model; according to the denial of service attack state variable, whether the denial of service attack is eliminated is judged; constructing a surrounding object estimator to obtain a surrounding object estimation position of the neighbor trolley; constructing a distributed adaptive optimal estimator to obtain an optimal position local estimation value of the surrounding object; and constructing a trolley controller. According to the invention, through the switching type neighbor information state observer, the advantage of information compensation is provided in a denial of service attack environment; a distributed self-adaptive optimal estimator of a cost function is introduced, so that each trolley can cooperatively track a global optimal solution changing along with time; when the communication fails, each trolley performs open-loop prediction by depending on the historical state and the own model, and continuously estimates the information state of the neighbor trolley, thereby improving the robustness and stability of the multi-trolley hunting system.

The invention provides a cooperative control system based on digital twinning in spandex fiber production, and the system comprises a global optimization layer which comprises a global digital twinning model, and the global digital twinning model comprises a hybrid dynamic system model and a multi-target optimizer; the edge collaboration layer is deployed at an edge computing node, the edge collaboration layer comprises a function element decomposition engine, a space-time planner and a terminal execution layer, the terminal execution layer is provided with an adaptive controller, and the adaptive controller is used for generating an optimal control parameter according to a function element with a space-time voucher; and the feedback self-evolution module comprises a product compliance verification unit and a targeted model correction unit. The method and the device are used for solving the technical problems of low dynamic task response efficiency and insufficient global optimization capability.

The invention provides a method and system for controlling charging time based on a load end capacitance value, and relates to the technical field of new energy. The method comprises the following steps: establishing a corresponding simulation model in a dynamic system modeling tool according to a solid-state power control unit circuit, and determining a fitting curve through the simulation model; controlling a current limiting branch in the solid state power control unit circuit to be switched on and switched off so as to generate a current response signal and a voltage signal at a load end, and processing the current response signal and the voltage signal in sequence by using a sampling circuit, Fourier transform, an FPGA and a fitting curve so as to obtain a capacitance value of the load end; and according to the maximum value of the plurality of preset capacitance value ranges, the resistance of the current limiting branch and the capacitance value of the load end, determining the turn-on time of the current limiting branch, and according to the turn-on time, controlling the current limiting branch to be turned on so as to enable the capacitor of the load end to complete charging. Therefore, the switching-on time of the current limiting branch is dynamically adjusted by utilizing the capacitance value of the load end, and the efficiency and the reliability of the system can be improved.