309 results about "System dynamics model" patented technology

The invention discloses a source network load storage system cooperative regulation and control method and system, and the method comprises the steps: employing a high-precision sensor and multi-protocol communication to obtain system multi-dimensional data through global data collection and preprocessing, and carrying out the noise reduction; constructing a dynamic association model based on a graph neural network, and accurately capturing a system node relationship in combination with a multi-head attention mechanism and topological constraints; layered multi-objective decision, reinforcement learning real-time regulation and control, layered control architecture and closed-loop feedback correction are adopted, and economic optimization, safety guarantee and strategy iteration are considered. The system is composed of a global data sensing unit, a system dynamic modeling unit and the like, and all the units are in close cooperation. According to the method and the system, the defects of insufficient data processing, low model precision, single regulation and control and the like in the prior art are effectively overcome, the system fault prediction accuracy can be improved, the carbon emission is reduced, the power fluctuation coping capacity is enhanced, and the operation efficiency and the stability of the source network load storage system are remarkably improved.

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention discloses a multi-unmanned-aerial-vehicle cooperative hoisting method and system, and the method comprises the steps: firstly, enabling each unmanned aerial vehicle to operate an extended state observer, regarding internal and external disturbances (such as airflow, unknown load parameters, internal coupling force and the like) as a lumped disturbance, and carrying out the real-time estimation; and then, through a communication network operation consensus algorithm between the unmanned aerial vehicles, fusing the local disturbance estimation values of the unmanned aerial vehicles into a global consistent system-level disturbance estimation. And finally, each unmanned aerial vehicle actively counteracts the influence of disturbance by using the accurate disturbance estimation value in a feedforward compensation controller, and accurately tracks the respective target trajectory in combination with feedback control. According to the method, active observation of disturbance, distributed fusion and feed-forward compensation are combined, dependence on an accurate system dynamic model is eliminated, and trajectory tracking accuracy, stability and stability of the system in a complex environment are remarkably improved.

The invention provides a vehicle dynamics performance analysis method and system considering suspension parameter performance degradation, and relates to the technical field of railway vehicle dynamics, and the method comprises the steps: building a nonlinear Maxwell hydraulic damper model; establishing a vehicle system dynamics model in simulation software based on a vehicle dynamics theory; integrating the nonlinear Maxwell hydraulic damper model into a vehicle system dynamic model, and establishing a vehicle system nonlinear dynamic model considering suspension parameter performance degradation; setting model parameters of the nonlinear Maxwell hydraulic damper to obtain a simulation result; and analyzing the dynamic performance evaluation index of the vehicle so as to complete dynamic performance evaluation of the vehicle under the conditions of actual service mileage and environment temperature. The method effectively reflects the influence of suspension parameter degradation on the dynamic performance of the vehicle, improves the precision of a dynamic simulation model of the railway vehicle and the reasonability of an analysis method, and has important theoretical research significance for innovative design and intelligent operation and maintenance of the railway vehicle.

The invention discloses a constraint following control method of a two-arm collaborative robot based on differential homeomorphic mapping, and relates to the field of robot control. According to the method, a nominal dynamical model of a robot is established, and a geometric constraint equation is defined according to a cooperative task; establishing global differential homeomorphic mapping by constructing constraint tracking variables and complementary coordinates, and decoupling a system dynamics model into a constraint tracking subsystem and a complementary subsystem; designing a robust adaptive controller fusing sliding mode control and an adaptive law, and estimating and compensating the uncertainty of the system on line; according to the method, the constraint processing complexity is effectively simplified through differential homeomorphic mapping, the constraint tracking precision and robustness of the system under model uncertainty and external interference are remarkably improved, and high-precision and high-stability cooperative task execution of the two-arm robot in the dynamic environment is achieved.

The invention discloses a cross-domain cluster system distributed safety formation control method in an urban interference environment, and the method comprises the steps: constructing a distributed elastic observer to estimate the state of a virtual leader, and providing a formation output reference for cooperative formation tracking control; the augmentation system of the air-ground unmanned cluster system is constructed by combining the state data of the distributed elastic observer and the state data of the air-ground unmanned cluster system, and a control gain matrix is constructed by using a feedforward-feedback control method. Initial excitation is applied to an augmentation system of the air-ground unmanned cluster system, when operation time meets an initial excitation condition, state data of operation of the air-ground unmanned cluster system is collected, optimal formation tracking control is determined by using an online double-integrator reinforcement learning method, and optimal formation tracking control of the air-ground unmanned cluster system is realized. According to the method, the optimal safe formation tracking control of the cross-domain air-ground unmanned cluster system can be realized under the conditions that the dynamic model of the system is unknown and the distributed denial of service is attacked.

The invention discloses a lower limb exoskeleton control system and method based on phase recognition and model prediction control. The system comprises a kinematics modeling module, a gait database module, a data processing and feature extraction module, a gait phase recognition module, a dynamics modeling module, a model prediction controller and an actuator control unit. By recognizing the gait phase and dynamically adjusting and controlling the target, precise assistance of the natural gait of the wearer is achieved. The system calculates the trajectory of the ankle joint relative to the hip joint in real time based on a kinematic model, and constructs a database in combination with gait data collected under multiple working conditions to provide an expected trajectory and reference parameters for control. In the dynamic modeling process, motor driving torque and man-machine interaction torque are introduced, a system dynamic model is established in a unified mode, and control characteristics under different gait phases are reflected.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention provides a suspension optimal control method and system based on feedforward and feedback, and the method comprises the steps: constructing a suspension system dynamic model, and constructing a suspension control force model according to the suspension system dynamic model; a state observer is constructed, then the current suspension state of the vehicle is estimated by using the state observer, then a feedback controller is constructed, and the suspension state is imported into the feedback controller to obtain feedback control force; constructing a feed-forward controller, then obtaining road surface unevenness information, and importing the road surface unevenness information into the feed-forward controller to obtain feed-forward control force; and importing the feedforward control force and the feedback control force into a suspension control force model to obtain the optimal control force of the system. According to the vehicle suspension control method and device, the problems that in the prior art, the vehicle suspension is controlled according to the running state of the vehicle affected by vibration, if sudden large-amplitude vibration influence occurs, operation among parts of the suspension exceeds the normal range, and the vehicle cannot run stably are solved.

The invention discloses an ice region ship shock absorber matching optimization method based on NSGA-III, which comprises the following steps: establishing a motor-silicone oil shock absorber-shafting-propeller coupling system kinetic model, calculating transient torsional vibration response under ice load impact by adopting a Newmark-beta method, and constructing a multi-target optimization model by taking the outer diameter, thickness, side clearance and top clearance of a shock absorber as design variables; an initial population is generated in combination with Latin hypercube sampling, and an optimal parameter combination is obtained after iterative optimization is conducted through an NSGA-III algorithm; experimental data show that the torsional vibration stresses of the motor shaft, the 1 # intermediate shaft, the 2 # intermediate shaft and the propeller shaft of the optimized shock absorber at the rated rotating speed are respectively reduced by 18.0%, 11.1%, 11.8% and 17.7%, and the effectiveness of the method is verified.

The invention provides a control method and system for a quad-rotor unmanned aerial vehicle for random pulse spoofing attacks, and relates to the technical field of unmanned aerial vehicle control. Establishing inner and outer ring error equations under the random pulse spoofing attack based on the inner and outer ring system dynamics models and random pulse injection error data; a continuous outer ring controller and a continuous inner ring controller are constructed, controller gains are designed according to the intensity and frequency of random pulse spoofing attacks, the outer ring controller performs continuous control on a leading-following task of the quad-rotor unmanned aerial vehicle, and an expected control quantity is issued to the inner ring controller; and the inner ring controller performs actual control on the quadrotor unmanned aerial vehicle according to the expected control quantity, and when the error between the expected control quantity and the actual control quantity of the inner ring controller tends to 0, the leader-following task of the quadrotor unmanned aerial vehicle is controlled to be realized. According to the invention, the quad-rotor unmanned aerial vehicle can resist the random pulse spoofing attack when carrying out the leader-following task.

The invention relates to the technical field of robot flexible operation and intelligent control, and discloses a modeling uncertainty-oriented dexterous hand collaborative grabbing control method and system, and the method comprises the following steps: S1, building a dexterous hand system dynamics model; s2, virtual coupling is introduced among multiple fingers of the dexterous hand system to construct a communication topological structure; a collaborative grabbing error is defined, and a corresponding potential energy function is constructed; s3, designing a gradient descent distributed control law based on time-varying gain; s4, motion gravity change is processed through a gravity item integral compensator, and kinematics parameter uncertainty is compensated based on a time-varying gain Jacobian matrix adaptive law; and S5, integrating the distributed control law in the step S3 and the adaptive processing mechanism in the step S4, so that the dexterous hand system realizes collaborative grabbing control within the preset time of the user. The method is accurate in control and flexible in response, and can realize efficient, robust and quick response control of the grabbing task in collaborative operation of small-batch customized products.

The invention belongs to the technical field of EL system control, and discloses a preset performance control method considering dynamic and constraint of an execution mechanism. The method comprises the following steps: firstly, establishing a system dynamics model considering dynamic and input saturation constraints of an executing mechanism; then, designing an asymmetric tubular performance boundary to restrain a system tracking error, and defining a conversion error according to the system tracking error; meanwhile, a disturbance observer based on immersion and invariance is designed, and composite disturbance is estimated. Furthermore, a tubular preset performance controller is designed by adopting a backstepping method, a virtual control law and an expected torque are constructed, and a multi-state auxiliary system is introduced to compensate the input saturation influence. And finally, a control instruction is generated by integrating the auxiliary system state, the virtual control law, the expected torque and the interference estimation value, and the control instruction acts on the system after being processed by a saturation function. According to the method, actuator dynamics, input saturation and external disturbance can be processed at the same time, and tracking errors are ensured to strictly follow a preset performance boundary.

The invention provides a satellite disc rotating speed detection method and system of MOCVD equipment, computer equipment and a storage medium, and relates to the technical field of semiconductor detection. Weak rotating speed characteristic signals are accurately extracted from noise through time domain and frequency domain fusion and a state estimation algorithm; the detection capability and the peak position stability of the rotating speed characteristic under the strong noise background are obviously improved; by taking a fusion result as an initial value and introducing state estimation of system dynamics, millisecond-level real-time updating and high-precision rotating speed analysis can be realized; the preset system dynamics model can dynamically fuse the current observation value and the historical state, the random error caused by airflow disturbance, mechanical vibration and electromagnetic interference is effectively inhibited, the output result is smooth and stable, and the reliability is greatly enhanced.

The invention relates to the technical field of industrial robot control, and discloses a single-connecting-rod mechanical arm system fault-tolerant asynchronous control method based on double-variable decision, which comprises the following steps: establishing a semi-Markov jump system dynamic model for describing the operating characteristics of a single-connecting-rod mechanical arm; based on the dynamic model, designing a fault-tolerant asynchronous controller structure based on a double-variable decision and an experience-machine cooperative operation strategy; establishing sufficient conditions which enable the closed-loop system to be randomly stable and meet H infinity performance indexes; and the numerical value of the gain of the machine controller is obtained by solving the linear matrix inequality set, and a final control law is generated to conduct fault-tolerant asynchronous control on the single-connecting-rod mechanical arm. According to the fault-tolerant asynchronous control method for the mechanical arm, fault-tolerant asynchronous control of the mechanical arm is achieved through a double-variable decision and an experience-machine cooperation strategy in combination with modal transfer rate boundary processing, and the random stability and the H-infinity performance of the system are guaranteed under the modal mismatching and fault working conditions.

The invention discloses a face gear dynamics analysis method considering hybrid thermal elastohydrodynamic lubrication and microtexture coupling, which comprises the following steps: step 1, based on a gear dynamics theory, constructing an eight-degree-of-freedom face gear-rotor system dynamics model; 2, establishing a finite element analysis model of the micro-texture face gear transmission pair, solving time-varying meshing stiffness and coupling the time-varying meshing stiffness to a system kinetic equation; 3, coupling a micro-texture item representing the film thickness equation with the film thickness equation of mixed thermal elastohydrodynamic lubrication; 4, coupling the mixing and thermal influence with the elastohydrodynamic lubrication basic equation, and establishing a mixed thermal elastohydrodynamic lubrication model; 5, carrying out iterative solution on the mixed thermal elastohydrodynamic lubrication and micro-texture coupling equation, outputting the tooth surface friction force and the friction coefficient of a solution domain, and inputting the tooth surface friction force and the friction coefficient as parameters into a system kinetic equation; and 6, solving the coupled system kinetic equation by using a Runge-Kutta numerical integration method to obtain the kinetic response of the face gear transmission system.

The invention relates to the technical field of floating fans, in particular to a parameter tuning method suitable for a multi-tuned mass damper of a floating fan, which sequentially comprises the following steps of: establishing a system dynamics model; defining an optimization variable and a constraint range; constructing a comprehensive fitness function; initializing a grey wolf population and algorithm parameters; executing a grey wolf position optimization updating mechanism; setting convergence judgment conditions; and outputting the optimal TMD parameter combination. According to the method, structural dynamic characteristics and an intelligent algorithm strategy are fully fused, particularly, spatial arrangement coordinates of the TMD are brought into an optimization variable domain, collaborative design of tuning parameters and arrangement space is achieved, the method is different from a single-parameter or fixed-point optimization mode, the limitation of artificial experience layout is avoided, overall suppression of structural multi-order modal response is achieved, and the method is suitable for large-scale popularization and application. The damping capacity and the stability margin of the fan structure under wind wave flow coupling excitation are improved, cross-modal and multi-source vibration cooperative treatment is achieved on the system response coordination and control strategy level, and the method has the adaptability to offshore wind power scenes.

The invention discloses a GIS in-place robot coordination control method with a posture correction function, and belongs to the technical field of robot coordination control. The method comprises the following steps: establishing a 2D-3D coordinate mapping corresponding relation; fusing the corresponding relation with an operation state predicted by an IMU-based inertial measurement and system dynamics model; performing error calculation on the obtained 6D pose information and target pose information; constructing a nonlinear kinetic equation of the robot, and converting a control target into an optimization problem of various constraints; and converting the obtained control instruction into a torque signal for actually driving the robot to move. According to the method, the influence of mismatching on pose estimation is effectively inhibited by establishing the 2D-3D coordinate mapping relation; through a joint torque PID control algorithm, the torque required by each joint is calculated in real time, and high-precision tracking of an expected trajectory is realized; an MPC optimization control framework with multiple constraints is constructed, and precise, smooth and safe coordination control over the GIS in-place process is achieved.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a large transport track planning method considering internal and external comprehensive safety. Comprising the following steps: acquiring road information, and constructing a three-dimensional safety corridor; obtaining a dynamic model of the large transportation system with cargo connection characteristics; setting road trafficability safety boundary conditions, namely external constraint conditions; planning a driving track of the large transportation system, and optimizing a driving track set by adopting a multi-objective optimization algorithm; and an evaluation index is selected to design a trafficability efficiency comprehensive evaluation system of the large transport system, the comprehensive trafficability efficiency of each node on different transport routes is evaluated, and an optimal track is selected. According to the invention, the problems of passing safety and passing efficiency of trajectory planning of a large-piece transportation system in a complex environment are solved.

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 system dynamics, and discloses an electricity certificate carbon market price analysis method and system based on system dynamics, and the method comprises the steps: S1, determining the main body of a green certificate transaction market and the main body of a carbon emission permit transaction market, and determining a renewable energy power generation mode; s2, constructing a TCC cost control mechanism; s3, TCC cost is calculated according to a TCC cost control mechanism; s4, establishing stock flow dynamic model frames of the green certificate market and the electricity market and the carbon emission permit trading market and the electricity market, and combining the two dynamic model frames to generate an electricity certificate carbon market stock flow dynamic model frame; and S5, analyzing the electricity evidence carbon market price. The system comprises an information input unit, a mechanism construction unit, a calculation and analysis unit and a system dynamics model framework construction unit. According to the method, the system dynamics and the method for controlling and optimizing the TCC cost are combined, and the analysis accuracy and reliability are improved.

The invention provides an optimal primary frequency control method based on reinforcement learning. The method comprises the following steps: establishing a dynamic model of a power system; designing a frequency control strategy based on a deep reinforcement learning algorithm; the system frequency deviation is used as a reward signal, and a control strategy is trained through interaction with a power system; lyapunov function stability constraints are introduced into the reinforcement learning algorithm; a neural network is adopted to carry out parameterization design on the controller; discretizing the frequency dynamic equation, and proposing a multi-node collaborative optimization target; and deploying the trained control strategy to an actual power system, and adjusting control parameters in real time through an online optimization technology to adapt to the dynamic change of the system operation state. By means of the method, the frequency control accuracy and stability of the power system are improved, and the global optimal performance of frequency control of the whole power grid is achieved in actual deployment.

The invention discloses a wave energy prediction method based on optimized empirical mode decomposition and deep learning, and the method comprises the steps: optimizing an empirical mode decomposition process through an improved starfish algorithm, obtaining a limited number of intrinsic mode functions through the decomposition of wave data, and predicting each intrinsic mode function obtained through the decomposition through employing an improved model, according to the model, two layers of CNN are fused to enhance extraction of spatial features, three layers of LSTM network are fused to extract time features, a sparse probe and distillation operation are used to optimize a Transform decoding layer, each predicted intrinsic mode function is subjected to superposition reconstruction, and a final wave data prediction result is obtained. And inputting the predicted data into a system dynamics model of the wave energy device, and solving an optimal damping control sequence by combining real-time data and utilizing rolling optimization calculation so as to realize maximum power control.

The invention discloses a self-adaptive control method and system for a two-stage swing type rotor aircraft hoisting system, and the method comprises the steps: building a two-stage swing type rotor aircraft hoisting system dynamics model, taking the rotor aircraft hoisting system to simultaneously drive four rotors to reach an expected position and inhibit the swing of a lifting hook and a load as a control target, and carrying out the control of an outer ring, generalized lifting hook and load position signals are introduced, an energy storage function is constructed, and the energy storage function is dissipated through the generalized lifting hook and load position signals; the outer loop tracking error is converted into a generalized load position error, and a shaped total storage function is obtained; and designing an adaptive law to estimate the load mass online, obtaining a reconstruction energy function of outer ring control, further obtaining an outer ring controller, and realizing control of system displacement and a load swing angle. By adopting a generalized displacement method of the lifting hook and the load and reconstructing an energy storage function, the dynamic coupling characteristic among the four rotors, the lifting hook and the load is remarkably enhanced, and therefore the transient response characteristic of the system is effectively improved.

The invention discloses a wide-body vehicle batch automatic simulation method which comprises the following steps: establishing a hydraulic system dynamic model of a wide-body vehicle, and exporting an FMU model file; establishing a whole vehicle dynamic model of the wide-body vehicle; importing the FMU model file into the whole vehicle dynamic model to form a joint simulation model; combining dynamic response behaviors of the wide-body vehicle under different working conditions with actual engineering experience, and determining a combination of discrete parameters and continuous parameters based on sensitivity analysis; sampling the discrete parameters and the continuous parameters to obtain a parameter combination sample; automatically writing each group of parameter combination samples into an ESL script configuration file to replace simulation input parameters, generating a corresponding simulation task configuration file, and running a Python batch simulation script to perform simulation solution on the whole vehicle model under different parameter combinations; and performing unified archiving and processing on simulation results. According to the wide-body vehicle batch automatic simulation method, the wide-body vehicle simulation analysis efficiency and the data consistency are improved.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention discloses a cooperative control method for controlling a suspension load by multiple unmanned aerial vehicles, and the method comprises the steps: firstly constructing a frame based on a trajectory, combining an online kinematics and dynamics motion planner with an airborne trajectory tracking controller, and generating a feasible trajectory considering dynamics coupling and constraint through a finite time optimal control problem (OCP); then, an extended Kalman filter (EKF) estimator is used for fusing data of multiple unmanned aerial vehicle inertial measurement units (IMU) and a system dynamics model, and the load attitude, torsion and the cable state are estimated in real time; and finally compensating the tension interference of the cable in real time through an INDI (Incremental Nonlinear Dynamic Inversion) controller. According to the invention, the agility and robustness of the suspension load system are cooperatively improved through online motion planning and the trajectory tracking controller, the low-speed limitation of traditional cascade control is broken through, an additional sensor does not need to be deployed at the load end, and the practicability and reliability of the system under the scenes of high-speed obstacle avoidance, complex trajectory tracking and the like are remarkably improved.

The invention provides a cooperative heave-rolling compensation method and system for a double-ship lifting arm system, and the method comprises the steps: building a double-ship lifting arm system dynamic model considering heave-rolling coupling motion, and enabling the model to comprise disturbance terms caused by ship motion; based on the kinetic model, generating a nominal trajectory under input saturation constraint and state constraint, and generating an expected trajectory through prediction control of a tightening constraint nonlinear model; for an actual disturbed system, designing a nonlinear robust model prediction controller, and driving an actual state to track the expected trajectory; and the stability of a closed-loop system is guaranteed through a terminal cost function and a terminal constraint set, and cooperative heaving-rolling compensation of the double-ship lifting arm is achieved. According to the method, through dynamic modeling, nominal track generation, robust control and stability guarantee, the double-ship lifting arm cooperative compensation heaving-rolling coupling motion is achieved, multi-degree-of-freedom oscillation can be effectively restrained under the five-level sea condition, and the operation stability and precision under the complex sea condition are improved.

The invention discloses a system-level coupling modeling method, system, equipment and medium for a multi-wind-wheel multi-tower-drum floating type wind turbine generator set, and relates to the field of simulation modeling, and the method comprises the steps that parameter information of the multi-wind-wheel multi-tower-drum floating type wind turbine generator set is obtained, and a multi-system structural dynamics model is obtained based on the parameter information of the multi-wind-wheel multi-tower-drum floating type wind turbine generator set; the unsteady aerodynamic model, the hydrodynamic model and the mooring structure dynamic model are used for outputting aerodynamic loads, hydrodynamic loads and mooring cable tension, the aerodynamic loads, the hydrodynamic loads and the mooring cable tension are fed back to the multi-system structure dynamic model, and based on a Lagrange framework, the models are subjected to joint simulation operation through a joint simulation interface, so that the mooring cable tension is obtained. And the multi-wind-wheel and multi-tower-drum floating type wind turbine generator is designed. According to the method, a complete system dynamics model can be established, and complete machine level rapid evaluation and engineering iteration under multiple working conditions can be supported.

The invention provides an urban flood disaster dynamic risk assessment method under multi-scale model coupling, and relates to the technical field of urban flood disaster risk prevention and control and emergency risk avoidance. The method comprises the following steps: evaluating the urban flood disaster risk according to urban underlying surface type data, vertical elevation data, drainage pipe network data, hydrogeological data, actually measured rainfall data, hydrological monitoring data and disaster-bearing system instability failure threshold data; constructing a multi-agent system model covering a disaster-inducing factor agent, a disaster-pregnant environment agent and a disaster-bearing body agent, and outputting a dynamic risk value of a disaster-bearing body; constructing a dynamic model of a disaster-affected body disaster-affected system by using a system dynamic modeling technology, outputting a risk change process of a disaster-affected body and quantifying a risk value; the flood disaster emergency prevention and control effect is evaluated by combining engineering and non-engineering measures, and emergency risk avoiding measures are optimized. According to the flood disaster assessment method and device, the flood disaster assessment can be more accurate through the combined action of the macroscale, the mesoscale and the microscale.