318 results about "Linear system" patented technology

The invention relates to the technical field of mechanical transmission, in particular to a reinforcement learning compensation method and system for gear multi-body dynamics constraint, and the method comprises the steps: building a multi-body dynamics digital twinborn model containing bearing rigidity and gear meshing force; constructing an adaptive reward function based on nonlinear manifold mapping, and dividing a system state space into a small deviation domain and a large deviation domain according to an angle deviation threshold value; training the multi-body dynamic model based on a depth deterministic strategy gradient algorithm to obtain a compensation strategy; the obtained compensation strategy is applied to a multi-body dynamic model, and an optimal compensation signal is obtained through self-adaptive fusion of static compensation and dynamic compensation, so that the training efficiency of a reinforcement learning algorithm in a complex nonlinear system is improved, the adaptability of the system to working condition changes is enhanced, the vibration of a gear system is remarkably reduced, and the reliability of the system is improved. The angle control precision is improved, and the service life of equipment is prolonged.

The invention discloses a novel model-free super-spiral sliding mode control method based on a sliding mode disturbance observer, and the method comprises the steps: designing a novel model-free super-spiral sliding mode controller of a rotating speed ring based on the combination of a novel super-local model and a second-order super-spiral law; meanwhile, a sliding mode disturbance observer is designed to estimate uncertain and unknown disturbance parts of system parameters in real time and feed back to a novel model-free super-spiral sliding mode controller of a rotating speed ring, so that the rotating speed of the motor is controlled; the controller introduces a linear system state item and a nonlinear disturbance item according to the novel super-local model, establishes a novel super-local model of the system, combines the novel super-local model of the system with a rotating speed ring state equation of the motor, establishes a novel super-local model of a rotating speed ring, and performs the super-local model of the rotating speed ring; and establishing a novel model-free super-spiral sliding mode control law of the rotating speed ring based on the novel super-local model of the rotating speed ring and an observation result of the sliding mode disturbance observer. The influence of external disturbance on the motor is effectively solved, buffeting is weakened, and effective tracking of the given rotating speed can be achieved.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention discloses an armored vehicle diesel engine digital twin model reduction method and system, and the method comprises the steps: building a diesel engine twin model which is a high-dimensional nonlinear system and comprises a thermodynamic equation of a diesel engine; operating the diesel engine twin model under different working conditions, collecting input and output data of variables, and performing standardization processing on the input and output data of the variables to obtain simulation data; a neural network is used for constructing a nerve differential equation model, simulation data is used for training the nerve differential equation model to obtain a reduced-order model, and the output of the diesel engine is predicted based on the obtained reduced-order model. Through Simulink modeling, neural network training, incremental learning and real-time data updating, a dynamic model of the diesel engine is effectively simplified, and it is ensured that high-precision prediction capacity is kept under different working conditions and operation environments. The system architecture supports real-time monitoring, online learning and self-adaptive adjustment, and high efficiency and stability of the diesel engine control system in practical application are guaranteed.

The invention discloses a fault-tolerant control method and device for a faulty unmanned aerial vehicle based on incremental nonlinear dynamic inverse terminal sliding-mode control, and the method comprises the steps: determining an incremental expression based on a terminal sliding-mode control law, determining a sliding-mode control law of the faulty unmanned aerial vehicle based on an attitude control algorithm of incremental nonlinear dynamic inverse nonsingular terminal sliding-mode control, and carrying out the fault-tolerant control of the faulty unmanned aerial vehicle. The method comprises the steps of calculating the rotating speed of a rotor wing based on a sliding-mode control law and incremental expression, calculating the acceleration required to be applied by a faulted unmanned aerial vehicle based on the rotating speed of the rotor wing, and controlling the attitude and position of the faulted unmanned aerial vehicle based on the acceleration, and comprises the steps of converting a nonlinear system of the faulted unmanned aerial vehicle into a linear system, and designing a terminal sliding-mode control law; and determining an incremental expression according to the sliding-mode control law to calculate the acceleration required to be applied by the faulted unmanned aerial vehicle, thereby solving the problems that the fault-tolerant control cost is high and an expected index is not easy to reach under a relatively serious fault condition that the faulted unmanned aerial vehicle fails in a single-rotor or double-rotor mode.

The invention belongs to the technical field of airplane nonlinear system control, and discloses a deicing vehicle hierarchical control method and system under an airplane idling condition. According to the method, a deicing vehicle overall dynamic model under incomplete constraint is constructed based on a Lagrange equation, a vehicle-arm combined trajectory tracking error state model is constructed, a cost function of a model prediction controller is designed according to a control target of deicing vehicle combined trajectory tracking, a quadratic programming problem is constructed on an upper layer to solve an optimal virtual control quantity, and the optimal virtual control quantity is calculated. And the lower layer analyzes the virtual control quantity into control input which can actually act on an execution mechanism by designing a self-adaptive fuzzy logic strategy so as to realize trajectory tracking control. According to the invention, a hierarchical control strategy is provided by combining model predictive control and an adaptive fuzzy logic system (FLS), the stability and high efficiency of system operation are ensured, and the deicing vehicle is ensured to always maintain a stable operation state in a complex and changeable operation environment.

The invention discloses an EMB system modeling and estimation integration method and device based on a Koopman operator, and relates to the technical field of automatic driving and intelligent chassis, and the method comprises the steps: obtaining an EMB system input and output offline data set, constructing a clamping force estimation high-dimensional linearization model through the Koopman operator, and carrying out the offline training of a clamping force estimation-oriented high-dimensional linear system matrix set; inputting the matrix set into a clamping force estimator based on a Koopman operator to obtain a real-time clamping force; the real-time clamping force is combined with a pre-established EMB system dynamic model to obtain a unified linearization model of the EMB system, and an adaptive estimation algorithm based on minimum recursion is adopted to obtain an estimation result of time-varying parameters of the EMB system. And dynamic online unified linear modeling and self-adaptive parameter updating of the electro-mechanical braking EMB system are realized.

The invention relates to the technical field of wireless communication, in particular to a wireless communication system signal sending and receiving method based on an AI model. The system comprises a delay coordinate embedding unit, a neural flow tracking unit, a continuous evolution unit, a Riemann decoding unit and a closed-loop control unit. The system maps the pilot frequency to a high-dimensional phase space by using a Takens principle, and reconstructs a channel dynamic topology; the core of the method is that a manifold derivative network is utilized to calculate a flow field vector, continuous time deduction is carried out through a Shenchang differential equation, evolution deviation is eliminated in combination with a geometric projection correction term, and thus the future channel state is accurately predicted. Calculating a CSI matrix based on the prediction result to optimize beam transmission; according to the method, the limitation of single-variable observation is broken through, and reconstruction and perception from discrete data to a full view of a high-dimensional nonlinear system are realized.

The invention provides a flexible connecting rod robot nonlinear system control method and system based on an observer, and belongs to the technical field of robot control, and the method comprises the steps: building a flexible connecting rod robot quasi unilateral Lipschitz nonlinear system dynamics model; creating a state observer based on the state vector of the nonlinear system; constructing an error system and an error expansion system containing predictable target signal information; designing a state feedback controller, and substituting the state feedback controller into the error expansion system to obtain a closed-loop system; analyzing stability and H-infinity performance conditions of the closed-loop system to obtain a state feedback controller gain matrix; and returning the gain matrix of the state feedback controller to the quasi unilateral Lipschitz nonlinear system to obtain a predictive controller based on the observer, and tracking and controlling the target signal. According to the method, the control performance of a closed-loop system is improved by combining a quasi single-side Lipschitz condition and utilizing predictable information of a target signal.

The invention provides an unmanned excavator track planning and control method, and relates to the field of artificial intelligence vehicle control. According to the system, aiming at engineering requirements of linear excavation and the like, an excavator working device is simplified into a four-degree-of-freedom mechanical arm, and a target excavation track is planned through an RRT-Connect algorithm and an S-type speed interpolation function; constructing the nonlinear system into a second-order linear system, and identifying model parameters on line by adopting a recursive least square method; and then an augmentation system containing error integration is constructed, model prediction control is combined, and high-precision trajectory tracking is realized through discretization processing, objective function optimization and a QP solver. The problem that a traditional control strategy is insufficient in precision is solved, the operation precision and adaptability of the unmanned excavator under complex working conditions are improved, the method is suitable for scenes such as groove excavation and slope leveling, and intelligent development of the excavator is promoted.

The invention discloses a dynamic load identification method and device based on a physical guidance convolution long-short-term memory network, and relates to the field of data processing, and the method comprises the steps: constructing a dynamic load identification model based on the convolution long-short-term memory network, and training the dynamic load identification model, and obtaining a trained dynamic load identification model; loss functions used in the training process of the dynamic load recognition model comprise a data driving loss function and a physical guidance loss function, the physical guidance loss function is constructed based on a kinetic equation converted from a motion equation of a linear system under the action of power, and an overall transfer matrix is used; acquiring the acceleration and / or strain response time domain signal of each test point acquired in each time window when the to-be-identified dynamic load is applied to one or more action points on the structure, and inputting the acceleration and / or strain response time domain signal into the trained dynamic load identification model to obtain the predicted dynamic load corresponding to the one or more action points. The problem that current load identification is high in dependence on data is solved.

The invention discloses a sparse triangular linear system solving accelerator based on an FPGA (Field Programmable Gate Array), and belongs to the technical field of calculation, reckoning or counting. The accelerator comprises an HBM, an instruction prefetching and decoding module, an input cache module, an FIFO module, a processing unit array, a partial sum register file module and a data decoding register file module. A super-long instruction word architecture instruction calculation scheduling and data information generation are adopted, FIFO and a register file are used as cache modules for node conversion and node multiplexing, periodic automatic alignment is realized without synchronization cost, a full interconnection structure realizes temporary storage of multiplexing data and intermediate results in the register file, and non-reused calculation results are directly written back. According to the method, a double-floating-point calculation operation granularity capable of fully reusing nodes and parallelism is adopted, a multi-PE parallel high-reusability operation mode is fully exerted, calculation control is simplified through scheduling of software and hardware, and an implementation scheme for efficient parallelism of SpTRSV on an FPGA is provided.

The invention discloses a switching system dynamic quantization control method and device based on event-triggered communication, and the method comprises the steps: building a continuous time switching linear system model for a switching system, and giving a system stability guarantee mechanism; designing a dynamic quantizer for the continuous time switching linear system model, giving a dynamic quantization parameter and a dynamic quantization updating strategy, and cooperatively designing an event triggering mechanism according to a state variable of the system and the dynamic quantization parameter; and designing a switching law according to an event triggering mechanism, and adjusting dynamic quantization parameters of the system to realize stable control of the system. On the premise of ensuring the system performance, the communication frequency and calculation burden of the system can be reduced, the resource utilization efficiency of the system can be improved, the method can be widely applied to the fields of network control systems, industrial automation systems and the like, and an effective solution is provided for solving the control problem of a complex system.

The invention discloses a transfer learning-based aero-engine full envelope transition state optimization method, which comprises the following steps of: training a deep reinforcement learning agent in a source task environment to obtain an optimal transition state control strategy under the working condition, and applying the strategy of the source task agent to a target task environment in a parameter transfer manner, and further training and optimizing the strategy in the target task environment until the optimal control strategy of the target working condition is obtained. According to the method, autonomous optimization and expansion of the control strategy can be realized according to different operation conditions of a complex nonlinear system, and the method is of great significance to intelligent control of the propulsion aero-engine and improvement of the operation performance and reliability of the engine.

The invention relates to the technical field of flow metering, in particular to a temperature return control method of an intelligent control valve. The method comprises the following steps: receiving a temperature return control strategy sent by a user and a set value of specified temperature return, and judging an initial control parameter set according to the temperature return control strategy; real-time operation data are collected from the temperature sensing module and the flowmeter according to the sampling period, the return water temperature and flow at the current moment are obtained, and the return temperature deviation is calculated; calculating a control target value of the intelligent control valve by utilizing a model fuzzy logic predictive control algorithm based on the temperature return deviation; and the valve opening degree is adjusted based on the control target value, and the control parameters are dynamically corrected through the temperature return control strategy for self-adaptive temperature return control. According to the method, through a composite algorithm of fusing fuzzy logic control and model predictive control (MPC), on the basis that preliminary control signals are generated through fuzzy reasoning, a nonlinear system model is combined to carry out multi-step prediction on the future return water temperature change trend.

The invention discloses a safety control method of a high-order chain type multi-agent system under spoofing attack, and belongs to the technical field of safety control of multi-agent systems. The method comprises the following steps: establishing a dynamic model of a high-order chain type multi-agent system; constructing an extreme attack scene in which all communication links are subjected to bounded spoofing attacks; based on a backstepping control method, a distributed fixed time security control protocol is designed for a system, and the protocol counteracts attack influence by designing a virtual control strategy and a Lyapunov function; a Lyapunov stability theory is utilized to prove that under the action of the protocol, a system tracking error can be converged to zero within fixed time, so that safe and consistent tracking is realized. The defect that the prior art mainly aims at a linear system or only can realize asymptotic convergence is overcome, the method is particularly suitable for scenes such as non-complete constraint wheeled mobile robots, and the robustness and the rapid recovery capability of the system under extreme spoofing attacks are effectively improved.

The invention provides an intelligent modeling method and system for a self-adaptive regulation and control water-turbine generator set. Multi-source data in the operation process of a water-turbine generator set speed regulation system are collected, a bidirectional sparse attention residual network BiKAN is used for conducting preliminary prediction on the operation state of the system, and deviation signals are formed by the predicted data and actually-measured data. A local sparse variational mode decomposition LSVMD method is introduced to carry out multi-scale mode decomposition on prediction errors, an online sparse gated cycle unit network OSBiGRU is constructed, and dynamic capture and online correction of non-stationary errors are realized by taking decomposition errors and multi-source state features as input. A hierarchical progressive adaptive regulation and control mechanism is constructed, different regulation and control levels are dynamically divided according to a unified error evaluation criterion, and corresponding model updating strategies are driven. And dynamic optimal adjustment of model structure parameters is realized by using a triangular topology aggregation algorithm in which a Newton Raphson search rule strategy is introduced. Compared with the prior art, the modeling precision and the prediction stability of a complex nonlinear system are remarkably improved.

The invention relates to a PID-DOB composite driving track control method for a hydraulic mechanical arm, and belongs to the field of track gauges of hydraulic mechanical arms. The method comprises the steps that firstly, modeling is conducted on a hydraulic system of the hydraulic mechanical arm; the saturation characteristic and the dead zone characteristic of the hydraulic system are analyzed; secondly, all shafts of the mechanical arm are identified and modeled by analyzing a signal spectrogram of normal work of the hydraulic mechanical arm, second-order linear system models of all the shafts are obtained, and a segmented PID control method is designed on the basis of the identified second-order linear system models; composite PID-DOB control rate design is carried out on the basis of a PID control method; the DOB estimates and compensates external disturbance and modeling errors of the system in real time; according to the segmented PID control method, the control process is divided into a starting segment, a main body segment and a braking segment according to time, speed and position information. The control precision and stability of the hydraulic mechanical arm are improved.

The invention relates to a belt weigher weighing precision compensation method based on multi-sensor data fusion. According to the method, an original weighing signal and an electromagnetic interference signal of the belt weigher in a running state and a baseline noise signal in a no-load state are synchronously acquired, a feature vector is extracted based on the electromagnetic interference signal, and an intermodulation noise component in the weighing signal is predicted by using a nonlinear system identification model in combination with the baseline noise signal. Then, the noise component is subtracted from the original weighing signal through an adaptive cancellation algorithm to obtain a local compensation weighing signal, and finally, a final weighing signal is generated based on a plurality of local compensation weighing signals through a signal-to-noise ratio weighted fusion algorithm, so that intermodulation noise caused by electromagnetic interference generated by the variable frequency driver is effectively suppressed. The weighing precision and the anti-interference capability of the belt weigher are improved.

The invention discloses a multi-spacecraft attitude and orbit coupling all-drive convergence time controller generation method, which is applied to the technical field of aerospace and comprises the following steps: establishing a multi-spacecraft attitude and orbit coupling error system all-drive model based on all-drive characteristics of a tracking error model; wherein the multi-spacecraft attitude and orbit coupling error system all-drive model is a pseudo-linear system model aiming at the stability problem of a multi-spacecraft attitude and orbit coupling error system; aiming at the multi-spacecraft attitude and orbit coupling error system all-drive model, constructing an all-drive convergence time controller based on an all-drive system method, a sliding mode control method and a preset time control method, and adjusting the convergence time of the system based on the all-drive convergence time controller; the all-drive convergence time controller is a controller designed based on a preset time parameter and an adjustable time parameter. According to the method for adjusting the preset time through attitude and orbit coupling, the multiple spacecrafts can maneuver to the expected attitude and orbit within the preset time, formation is completed, and the formation efficiency is improved.

The invention discloses a PID (Proportion Integration Differentiation) tracking control method based on error perception nonlinear gain, which is used for high-precision tracking control of an MIMO (Multiple Input Multiple Output) nonlinear system. Comprising the following steps: 1) constructing an adaptive gain by using current error information, historical error information and prediction error information; 2) realizing intrinsic integral saturation resistance through nonlinear integral design; and 3) complete autonomous operation can be realized without accurate system dynamics priori knowledge or manual adjustment. According to the method, the nonlinear compensation capability and the robust adaptive performance are enhanced while the structural simplicity of the PID controller is kept. Through rigorous Lyapunov analysis, the stability of the method to a non-linear system in a general form is verified, and the method shows excellent performance in both adjustment and time-varying reference tracking scenes. The method can be widely applied to the fields of linear motor servo systems, sea surface ship control, unmanned vehicle dynamics control and the like needing high-precision tracking control.

The invention discloses a three-axis modulation inertial navigation decoupling control method which comprises the following steps: establishing a three-axis modulation inertial navigation dynamic model which comprises inter-axis coupling torque and equivalent rotational inertia disturbance torque; obtaining the rotational inertia of the inner ring, the middle ring and the outer ring of the three-axis modulation inertial navigation along each axis; the rotation angles of the inner ring, the middle ring and the outer ring are obtained in real time, and angular acceleration is obtained through differential; according to the dynamic model, the rotational inertia, the rotation angle and the angular acceleration, inter-axis coupling torque and equivalent rotational inertia disturbance torque are obtained through calculation; compensating the calculated inter-axis coupling torque and equivalent rotational inertia disturbance torque, and changing the dynamic model into a first-order linear model, so that the three-axis modulation inertial navigation system is changed into a decoupling linear system; and finally, carrying out closed-loop control on the three-axis inertial navigation through a position loop-speed loop-current loop three closed-loop control structure. The control complexity can be reduced, and the system stability can be improved.

The invention discloses a heavy-load mechanical arm obstacle function adaptive sliding mode control method and device based on a disturbance observer, and the method comprises the steps: building a dynamic model which considers the model error, an unmodeled part, friction and lumped disturbance caused by an external load; converting a lumped disturbance estimation problem into a linear system state estimation problem of defined auxiliary state variables and unknown input driving, and designing a sliding mode disturbance observer used for estimating and compensating lumped disturbance; for a disturbance estimation error, a bounded adaptive control gain of a controller is designed by adopting a barrier function, and the error is kept within a preset range while control jitter is avoided. Through systematic control architecture design, the key problem that high-precision tracking and smoothness control of the excavator in a complex working environment are difficult to consider at the same time is effectively solved.

The embodiment of the invention provides a robot driving control method and device, electronic equipment and a storage medium, and belongs to the technical field of nonlinear system control. The method comprises the following steps: constructing a state prediction model according to a first observation state sequence, a second observation state sequence and a historical control input sequence, and calling the state prediction model to perform state error prediction based on the first observation state sequence, the second observation state sequence and the historical control input sequence, performing parameter adjustment on the state prediction model according to the predicted observation state error sequence and a preset dimension reduced-order matrix to obtain an error compensation state space model; based on the first given state data and the first control input data of the current time, an error compensation state space model is called to conduct state prediction, second given state data corresponding to the to-be-predicted time is obtained, and the target robot is driven and controlled according to the second given state data. According to the embodiment of the invention, the driving control accuracy of the robot can be improved.

The present application relates to a kind of based on position dynamics adaptive anisotropic cloth simulation method, system, electronic equipment and storage medium, belong to computer graphics physics simulation field.The method includes setting cloth grid;Topology relationship of grid is calculated, and the set of point edge surface is formed;According to Newton's law, the linear system of external force is constructed, and change coordinate is solved using conjugate gradient method;Using graph coloring method, data dependence is decoupled to grid, using the method of position dynamics, strain constraint iteration parallel computing is carried out to change coordinate, and the coordinate correction amount after cloth motion deformation is obtained;According to the revised coordinate, grid tensor field is calculated, grid dynamic refinement and coarsening are carried out, and non-uniform anisotropic cloth grid is formed.The present application uses position dynamics method to calculate dynamic non-uniform grid, simulates the anisotropy of cloth deformation, retains the dynamic behavior of cloth wrinkle under fine scale, is quickly solved on the basis of not losing simulation details, satisfies real-time.

The application discloses a macro modeling method for ensuring direct current precision, and the method comprises the following steps: reading n-port network parameters; initializing sampling points for constructing a macro model; initializing the order of the macro model; initializing the pole of the macro model; establishing a weighted linear system according to the current order, the sampling points and the pole; solving a linear equation set, judging convergence according to an error, and obtaining the macro model; and outputting the optimized macro model by applying a residue matrix disturbance to optimize the macro model. The macro modeling method for ensuring direct current precision provided by the application controls the direct current precision in two stages. In the first stage, a weighted least square method is adopted on the basis of a linear system established by a traditional vector fitting algorithm to preliminarily control the direct current precision. In the second stage, a small disturbance is applied to the residue matrix of the macro model, so that the disturbed macro model has accurate direct current characteristics.

The invention relates to a model predictive control algorithm applied to a control valve to adjust chamber pressure, and relates to the technical field of semiconductor manufacturing equipment.The model predictive control algorithm comprises a modeling module configured to abstract a chamber pressure dynamic process into a three-order linear system model; the control algorithm module is configured to establish a state space expression based on the three-order model, and adopts a model prediction control method to perform rolling optimization on a future valve opening sequence by taking a minimum pressure set point tracking error and a valve action penalty as objective functions in a prediction time domain; the pressure sensor is configured to combine real-time pressure sensor feedback. According to the invention, the opening degree of the vacuum control valve is dynamically optimized to realize high-precision adjustment of the pressure of the chamber.

The invention discloses a rigid-flexible coupling system modeling and self-adaptive control method based on data driving, and belongs to the field of space tethered robots. The method comprises the following steps: constructing a physical simulation model of the space tethered robot, and collecting state variables and control input data of system operation to construct a training data set; on the basis of the Kupman operator theory, constructing a mapping model from a nonlinear system to a dimension raising linear system with a deep neural network as an embedded function, and training the mapping model on the basis of the training data set; designing a linear quadratic optimal controller; under a reinforcement learning framework, a strategy optimization target taking controller parameters and model parameters as optimization objects is constructed, and collaborative adaptive adjustment of the model parameters and the controller parameters is realized through iterative updating. According to the invention, the optimal control of the space tethered robot is realized.

The invention discloses a self-triggering pulse T-S fuzzy nonlinear system synchronization control method, and relates to the technical field of synchronization control, and the method comprises the following steps: collecting communication parameter information among multiple nodes, and constructing an incidence matrix used for representing communication characteristics among the nodes based on the communication parameter information; extracting a coupling strength index and a node weight index which are used for guiding system synchronous control; based on the current system state, local synchronization errors of all nodes are monitored in real time, and whether pulse control operation is triggered or not is judged according to a preset multi-channel triggering mechanism; and if the trigger condition is met, applying a pulse control signal to the target node. According to the method, the influence of different network environments on system synchronization can be accurately modeled by constructing the three-dimensional heterogeneous incidence matrix integrating the time delay, the packet loss rate and the protocol weight, and the control signal can be flexibly applied according to the real-time synchronization error and the node state of the system by designing the dual-channel trigger condition and the self-adaptive pulse control signal.

The invention relates to a mark point hole filling method, device and equipment. The method comprises the steps of obtaining boundary point data of a target hole in the surface of a three-dimensional model, wherein the target hole is a hole formed by scanning mark points; filling the target hole according to the boundary point data of the target hole, and determining a triangular mesh and / or a triangular filling vertex used for filling; according to the triangular mesh and / or the triangular filling vertex, updating the initial topological structure of the three-dimensional model to obtain the topological structure of the updated mesh data corresponding to the three-dimensional model; and if the target hole is filled by using the triangular filling vertex, optimizing and adjusting the position of the triangular filling vertex in the topological structure of the updated grid data by using a Laplace operator linear system so as to obtain the grid data of the three-dimensional model after hole filling. By adopting the method, the hole filling effect of the marked points can be achieved.