1280 results about "Dynamic equation" patented technology

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

Provided is a self-adaptive identification method for nonlinear dynamic parameters of a reducer, which belongs to the design field of a reducer. The method includes: modeling a harmonic reducer corresponding to a flexible joint as a concatemer of a rigid reducer and an elastic torsion spring, and carrying out dynamic theoretical modeling and parameter variable independence processing on the concatemer to form a dynamic equation for parameter identification; giving an optimized motion trajectory to each joint of a robot and controlling the robot to act accordingly, acquiring relevant data needed for parameter identification based on a built-in torque sensor and double encoders inside the joint; using an offline identification algorithm to accurately identify a plurality of dynamic parameters of a collaborative robot considering joint flexibility and friction, and obtaining a minimum parameter set.

The invention discloses a supply chain financial risk management system based on credit assessment, and relates to the field of financial science and technology. A five-dimensional data model is constructed through a dynamic data modeling unit, and transaction subject levels and legal potency dimensions are integrated; the causal analysis engine adopts a dynamic updating algorithm of time sequence feature coding and compliance score constraint, and introduces a legal potency score constraint mechanism to strengthen compliance verification. The cross-domain risk conduction model combines physical space voxel grid monitoring with a digital space fluid dynamic equation, and the risk conduction velocity is quantified through a risk diffusion coefficient and a partial differential equation; and the self-evolution defense module integrates a confrontation sample generator and an incorruption degree feedback mechanism, simulates logistics track abnormity through Levy distribution, and improves the defense capability of the system in combination with a dynamic weight adjustment strategy of approval skipping times. According to the system, the deep supply chain risk conduction path identification precision is improved, and the problems of a multi-level network risk monitoring blind area and cross-domain risk tracking lag are effectively solved.

The invention relates to a physical information constraint embedded non-stationary industrial process anomaly detection method, and belongs to the technical field of industrial process time sequence anomaly detection. According to the method, a dynamic coupling relation between key variables is extracted through frequency domain lagging correlation, original time sequence data is decomposed into two subspaces, namely a slowly-varying trend subspace and a quickly-varying disturbance subspace, through slow feature analysis, and long-term stable change and transient disturbance change are modeled respectively. Furthermore, a high-dimensional linear evolution model is constructed in a fast space and a slow space by adopting a Kupman operator, so that the multi-scale dynamic modeling precision is effectively improved. In addition, two types of physical information supervision mechanisms are introduced in the model training process: based on a water pump flow-pressure second-order dynamic equation and a motor electric power conservation law, the physical consistency and engineering interpretability of the prediction process are significantly enhanced. According to the invention, abnormal working condition identification and early warning under variable working conditions in a non-stationary industrial process can be realized.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention discloses a virtual marshalling multi-train cooperative adaptive sliding mode control method based on an extended state observer, and the method comprises the steps: firstly constructing an urban rail multi-train tracking error dynamic model based on a unit train dynamic equation; secondly, establishing a nonsingular terminal sliding mode surface according to the urban rail multi-train tracking error dynamical model to obtain an equivalent control law, combining the equivalent control law with a switching control law to obtain a nonsingular terminal sliding mode control law, and introducing a gain adaptive law and a saturation function into the nonsingular terminal sliding mode control law to obtain an adaptive nonsingular terminal sliding mode control law; and finally, an extended state observer is adopted to estimate external interference, an observation value of the interference is added to the output of the adaptive nonsingular terminal sliding mode controller to serve as feed-forward compensation, and the adaptive nonsingular terminal sliding mode control method based on the extended state observer is constructed. According to the method, the multi-train cooperative operation tracking control precision and stability can be remarkably improved under the external interference condition.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The invention discloses a humanoid robot step terrain walking method based on a whole-body kinetic model, and the method comprises the steps: designing a nonlinear model prediction control problem oriented to the walking of a humanoid robot, and reasonably planning a system state, input and a reference track of a foot end; equality constraints and inequality constraints which are necessary for walking of the robot are designed, and the constraint that a landing point of the robot needs to fall in a landing area is taken as one of the inequality constraints, so that the humanoid robot is capable of walking on a step terrain; and finally, solving a nonlinear model predictive control problem in real time by utilizing a solver, and issuing a robot joint torque according to an inverse kinetic equation of the whole-body kinetic model and PD in combination with a feedforward controller, so that the robot is better in control effect and more stable in walking.

The invention belongs to the technical field of spinning tension control, particularly relates to an intelligent self-adaptive spinning tension control method and system, and aims to solve the problems that an existing traditional tension control method often depends on simple PID (Proportion Integration Differentiation) control and is difficult to deal with complex dynamic changes, and the control precision is high. In order to solve the problems of low tension control precision, low response speed and incapability of adapting to various textile materials and process requirements in the prior art, the invention provides the following scheme: the method comprises the following steps of: 1, establishing a detailed tension dynamic model based on the Newton's second law and the quality, acceleration, friction force, gravitational acceleration and external disturbance factors of a fabric in a textile process; 2, converting a kinetic equation of a time domain into a complex frequency domain, and establishing a transfer function of a Laplacian domain to describe the response of the system to input and disturbance; the response speed and stability of the control system are improved, the controller further has the adaptive capacity, and control parameters can be adjusted according to real-time feedback so as to meet control requirements under different working conditions.

The invention provides a wind power blade aeroelastic coupling modeling method considering a pre-bending shape, which comprises the following steps of: establishing a finite element kinetic equation of a geometric precise beam structure of a pre-bending blade and solving to obtain the section speed of the blade; calculating the inflow velocity according to the section velocity of the blade; calculating a lift coefficient after dynamic stall of the blade; establishing an unsteady aerodynamic load calculation equation according to the inflow velocity and the lift coefficient after dynamic stall of the blade; and constructing a pre-bent blade aeroelastic coupling model according to the unsteady aerodynamic load calculation equation. The method can solve the technical problems that a modeling method in the prior art cannot accurately describe nonlinear deformation of the blade, the continuity of a blade model is poor, the number of calculation units is too large, the calculation efficiency is low, and the modeling method is not suitable for the long flexible blade.

A balance control method for a wheel-legged robot is provided. The robot includes a moving wheel, n links, and n rotating joints, the moving wheel being connected to a first link through a first rotating joint of the n rotating joints, and the n links being connected in series through n−1 rotating joints other than the first rotating joint, n being a positive integer greater than 1. The method includes: acquiring a state quantity of the wheel-legged robot at a first moment; determining dynamics model parameters according to a dynamics equation of the wheel-legged robot and the state quantity at the first moment; establishing a sliding surface according to the state quantity at the first moment; calculating rotation torques of the n rotating joints according to the sliding surface and the dynamics model parameters; and controlling the rotating joints according to the rotation torques of the n rotating joints.

According to the spatial configuration bidirectional fluid-solid coupling analysis method for the deep-sea mining hose, an internal flow coupling equation is constructed based on internal fluid parameters and hose physical property parameters, an external flow coupling equation is constructed based on external fluid parameters and hose physical property parameters, boundary conditions are obtained based on near-wall fluid parameters and hose physical property parameters, and the deep-sea mining hose is obtained. Coupling the external flow coupling equation and the internal flow coupling equation through boundary conditions to construct a bidirectional coupling dynamic equation set; an undamped free vibration equation and a hose balance equation are constructed based on hose physical property parameters, the undamped free vibration equation and the hose balance equation are combined, and an additional fluid mass matrix is introduced to establish a wet modal analysis model; and high-precision simulation and efficient optimization of the deep-sea mining hose spatial configuration are realized.

The invention discloses a finite iteration error tracking learning control method for robot accurate trajectory tracking. The method comprises the steps of establishing a kinetic equation of a repeated operation robot system, determining a discrete dynamic model of the robot system, designing a finite iteration error tracking learning controller and carrying out simulation verification. According to the method, under the conditions that the initial position of the robot system is arbitrary and the expected trajectory changes, the number of iterations required for achieving arbitrary precision tracking of the given expected error trajectory by the output error of the robot system can be calculated by designing an error tracking learning controller and only utilizing error information obtained through first iteration. According to the method, the limitation that zero error convergence needs to depend on infinite iterations in a traditional iterative learning control method is effectively overcome; meanwhile, it is ensured that the robot system can still achieve the preset tracking precision with the rapid convergence capacity under the disturbance conditions that the initial position is arbitrary and the expected trajectory changes, and the practicability and the popularization value of iterative learning control in actual engineering are improved.

The invention relates to an optimization design method for a multi-fulcrum elastic ring supporting structure of an aero-engine rotor system, and belongs to the technical field of aero-engine vibration control and rotor dynamics simulation. Firstly, a finite element or lumped unit model is adopted to establish a coupling kinetic equation of a rotor and an elastic ring, and fluid-solid coupling factors such as inner and outer oil films of the elastic ring and orifice flow are considered; then, through critical rotating speed and vibration harmonic response analysis, sensitivity evaluation is carried out on rigidity and damping characteristics of different supporting positions, and target vibration reduction modes corresponding to all fulcrums are identified; then, coupling simulation and multi-objective optimization are carried out around the number of bosses of the elastic ring, the height of the bosses, the damping aperture, the thickness of a thin wall and other key structure parameters, and the optimal design meeting the vibration reduction requirement of a rotor system and the strength reliability requirement of the elastic ring is obtained through iteration; and finally, an optimization result is applied to structure manufacturing and assembling. The method can effectively reduce the vibration amplitude of the rotor when the rotor crosses the multi-order critical rotating speed.

The invention relates to the technical field of belt conveyor dynamic characteristic research, in particular to a trailer type belt conveyor dynamic characteristic simulation method, which comprises the following steps: respectively carrying out stress analysis on a trailer frame, each wheel and a conveying belt unit in a trailer unit, and sorting the degree of freedom; establishing an overall coordinate system, a vehicle body coordinate system, a contact coordinate system and a conversion relation; respectively establishing kinetic equations of the wheel pair and the vehicle supporting frame; kinetic parameters of the trailer unit are determined; according to the Hamiltonian principle, a vehicle supporting frame vertical motion equation and a vehicle supporting frame nodding motion equation are established, and then a linear second-order differential equation of a vehicle supporting unit is obtained; and compiling a calculation program by using numerical calculation software, and simulating the wheel-rail vibration characteristics of the trailer unit in the straight-line-segment advancing process. On the basis of the multi-degree-of-freedom multi-body dynamics theory, a trailer type belt conveyor system nonlinear dynamics equation is constructed, and a wheel pair, a trailer frame and a six-degree-of-freedom motion equation for coupling the wheel pair and the trailer frame are analyzed.

The invention relates to the technical field of unmanned ship trajectory tracking, in particular to an unmanned ship trajectory tracking control method based on self-adaptive dynamic event triggering, and establishes a kinematic model, an expected trajectory and a position error dynamic equation of an unmanned ship. A dynamic event triggering mechanism and self-adaptive dynamic planning are combined, a cost function containing a dynamic event triggering control law and a Hamilton function are given, and resource consumption of an unmanned ship controller is reduced. And an unknown cost function and an optimal control law are approximately obtained by using the good nonlinear approximation capability of the neural network. And applying the calculated optimal control law to a control system of the unmanned ship, and driving the unmanned ship to perform accurate tracking according to a predetermined trajectory. The method provided by the invention can effectively improve the precision of trajectory tracking of the unmanned ship, and saves system calculation and communication resources.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.

The invention discloses a power system inertia estimation method based on multi-mode decomposition and reconstruction, and belongs to the technical field of power system inertia identification control, and the method comprises the steps: obtaining a system frequency deviation signal and three-phase voltage and current data in real time; inputting the frequency deviation signal into a signal processing algorithm, extracting a multi-mode oscillation signal, and reconstructing the multi-mode oscillation signal into an equivalent single oscillation mode signal; the frequency change rate of the reconstructed signal is calculated, and the frequency change rate is obtained and analyzed; after a fault occurs, analyzing the moment corresponding to the maximum value of the frequency change rate, and combining power deviation to trigger inertia constant calculation; according to the frequency change rate and the power deviation, calculating a system inertia constant through a dynamic equation at a triggering moment, and converting the system inertia constant into a per unit value; and reconstructing a frequency signal of a non-measurement node by adopting a weighted least square method to finish inertial estimation. Through a synchronous phasor data dynamic decomposition and reconstruction technology, high-precision online sensing of inertial parameters under complex working conditions is realized.

The invention provides a neural network prediction method and device for two-dimensional quantization vortex dynamics, and the method comprises the following steps: S1, determining a two-dimensional vortex dynamics equation for a to-be-predicted flow field, and converting the two-dimensional vortex dynamics equation into a Schrodinger equation of a quantum system; s2, obtaining wave function evolution data of the quantum system according to a Schrodinger equation, and generating a wave function data set; s3, constructing a full-connection layer neural network model, and performing phase integration on the full-connection layer neural network model; s4, introducing a normalization factor into the full-connection neural network model; s5, training the full connection layer neural network model by using the wave function data set; s6, performing wave function prediction according to a given initial value by using the trained full-connection layer neural network model; and S7, converting the predicted wave function into flow field evolution. According to the method provided by the invention, the conservation law of the system can be ensured in the prediction of the Schrodinger system, so that the flow field prediction precision can be ensured in a long time period.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle cluster distributed optimal formation control method considering multiplicative noise and a computer readable medium, and the method comprises the following steps: constructing a communication topological graph of an unmanned aerial vehicle cluster, each edge represents a communication relationship between two adjacent unmanned aerial vehicles; constructing a Laplacian matrix of the communication topological graph; establishing an unmanned aerial vehicle kinetic equation considering the multiplicative noise influence; defining an objective function needing to be optimized and constraint conditions; constructing a Hamilton function of the target function; and designing a distributed optimal control strategy by applying a random optimal control theory, setting operation time, system parameters and initial state information of the unmanned aerial vehicle cluster, and obtaining a formation operation track and a final formation state of the unmanned aerial vehicle cluster under optimal control. According to the invention, the global optimality and robustness of the unmanned aerial vehicle cluster control algorithm are ensured.

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention discloses a simulation method, device and equipment for predicting flow and deformation of a porous medium and the medium. The method comprises the steps that a computational domain is selected, a corresponding computational grid is generated, and porosity initialization is conducted on the computational domain; determining a time step length and a discrete format, and setting a non-uniform factor to solve a control equation set which comprises a continuity equation, a fluid dynamics equation, a skeleton mechanics equation and a porosity updating equation; and analyzing and displaying a solving result. According to the porous medium flow and deformation simulation method independent of hypothesis and empirical parameters, the method is based on the basic principle of the microcosmic fluid-solid coupling effect, a porous medium fluid-solid coupling body kinetic equation is adopted, the situation that a traditional modeling method depends on volume averaging hypothesis and depends on a multi-phase interaction model is avoided, and the modeling efficiency is improved. The method has cross-scale applicability, and provides a new theoretical basis and prediction means for the development of technologies related to porous medium multiphase dynamics and multi-field coupling problems.

The invention discloses a dynamic response analysis method of a floating breakwater anchor chain system. The dynamic response analysis method comprises the following steps: determining parameters of a floating box and an anchor chain system in the floating breakwater system; a sea condition environment where the system is located is given; motion of the anchor chain unit is described, and a global position vector of any point P on the axis of the anchor chain unit is given; constructing an expression of an anchor chain unit kinetic energy and mass matrix; calculating strain energy and elastic force of the anchor chain unit; calculating the environmental load borne by the floating breakwater anchor chain system; assembling a system mass matrix and a constraint equation of the system; establishing a dynamic equation set of the system, and correcting to obtain a first-order differential equation set; performing simulation to extract displacement, speed and acceleration data of the top end of the anchor chain rope, and substituting the data as external constraint conditions of the top end of the anchor chain into the dynamic equation set; and performing dynamic solution on the first-order differential equation set. According to the invention, the accuracy of dynamic response calculation is improved.

The invention discloses a high-speed train multi-mass-point cooperative control method, system, equipment and medium, and relates to the field of high-speed train operation control, the method comprises the following steps: dividing carriages of a high-speed train into multiple groups, and constructing a discretization kinetic equation of the high-speed train; performing multi-objective optimization on the control gain and the sliding mode surface parameters according to the displacement tracking error and the coupler force of the high-speed train in the set time period; based on the optimal control gain, the inter-group equivalent coupler force and composite resistance of the high-speed train are estimated in real time; determining a nonsingular fast terminal sliding mode surface based on the optimal sliding mode surface parameter and a displacement tracking error of the high-speed train; and according to the nonsingular fast terminal sliding mode surface, the inter-group equivalent coupler force estimated value, the composite resistance estimated value and the discretization kinetic equation, solving control input in real time so as to control the traction braking force of the front drive group and the traction braking force of the rear drive group in real time. The control precision, the anti-interference capability and the safety of the coupler force of the high-speed train are improved.

The invention provides a storm surge single-point conventional forecasting method, medium and system, and belongs to the technical field of ocean storm surge prediction.The method comprises the steps that historical water level data, historical flow speed data and historical meteorological data are collected at a forecasting target point to serve as training data; performing time sequence decomposition on the training data to obtain a fundamental component and a harmonic component, constructing a characteristic matrix, calculating a water level contribution coefficient, a flow velocity contribution coefficient and a meteorological contribution coefficient, and constructing a storm surge water level forecasting equation set comprising a storm surge dynamic equation and a storm surge continuous equation according to the contribution coefficients, inputting real-time water level data, real-time flow velocity data and real-time meteorological data into the storm surge water level forecasting equation set to obtain a storm surge forecasting result, and outputting an early warning signal when the storm surge forecasting result exceeds an early warning threshold value; the physical basis of forecasting is guaranteed, the calculation efficiency is improved, and the technical problem that in the prior art, forecasting precision and timeliness of the sudden strong storm surge are not enough is solved.

The invention provides a remote monitoring method and system applied to children's watches, and relates to the technical field of children's watches, time axis synchronous alignment is carried out on collected multi-source heterogeneous data through an interpolation method, data time sequence chaos caused by sampling rate differences is prevented, and then the data time sequence chaos caused by sampling rate differences is prevented. Through dynamic coupling calculation of a light intensity mutation gradient and a heart rate stress coefficient, the cooperative influence of environment change on the physiological state of the child is quantified in real time, and the physiological state of the child is determined by further combining power spectrum centroid frequency and physiological phase difference analysis of the motion rhythm and logarithmic correlation of acoustic envelope frequency and GPS speed. In addition, an acoustic propagation delay compensation mechanism is introduced, a two-factor dynamic equation model is constructed through a time delay cross term, and a dynamic risk threshold generation mechanism based on a noise energy ratio is combined, so that the dynamic risk threshold of the motion intensity, the rhythm characteristics and the acoustic scene is effectively distinguished. Effective remote monitoring on child safety in different scenes is realized.

A balance control method and apparatus (1600) for a wheel-legged robot (200), a device, and a storage medium. The method is executed by a computer device (101). An n-order inverted pendulum model as which the wheel-legged robot (200) is equivalently represented comprises: wheels (1016), n connecting rods and n rotating joints; first connecting rods (1026, 1110) are equivalently derived from at least two leg mechanisms (1022, 1024) of the wheel-legged robot (200), and the wheels (1016) are equivalently derived from moving wheels (1012, 1014) respectively connected to the at least two leg mechanisms (1022, 1024). The method comprises: step 1310, acquiring an actual state quantity of the wheel-legged robot (200) at a first moment; step 1320, calculating an equivalent state quantity at the first moment on the basis of the actual state quantity at the first moment; step 1330, establishing a sliding mode surface on the basis of the equivalent state quantity at the first moment; step 1340, determining a force and torque instruction of the whole body joint on the basis of a dynamic equation of the sliding mode surface and the equivalent state quantity at the first moment; and step 1350, at a second moment, respectively controlling the n rotating joints on the basis of the force and torque instruction. The method is adapted to a wheel-legged robot (200) in movement, facilitating improvement of the robustness of balance control.