1872 results about "Control objective" patented technology

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention provides an unmanned aerial vehicle consistency control method based on a Stackelberg-Nash game, and relates to the technical field of multi-agent consistency and game optimization control. The method comprises the following steps: establishing a motion attitude nonlinear dynamic model of each unmanned aerial vehicle in a multi-unmanned aerial vehicle system, and defining a communication network topological relation; constructing a consistency error and a performance index function; the method comprises the following steps: establishing a layered Stackelberg-Nash game mechanism with a plurality of participants; utilizing a Bellman optimality principle to construct a coupling HJB equation to solve an optimization control strategy of the leader and the follower; and constructing a single evaluation network to estimate the optimization control strategy of the leader and the follower in each execution so as to realize an optimization control target. A favorable tool is provided for analyzing a series of control problems of master-slave consistency of multiple unmanned aerial vehicles in the control field, and the reliability of a control system can be enhanced to a certain extent.

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention discloses a hydroelectric generating set speed regulation control method considering excitation coordination. The method comprises the steps that 1, system state information is collected and preprocessed; step 2, on-line identification of excitation speed regulation coupling characteristics; 3, decomposing a multi-time-scale control target; step 4, designing a self-adaptive multivariable controller; 5, working condition self-adaptive parameter optimization; step 6, intelligently inhibiting the water attack effect; 7, multi-machine cooperative reactive power optimization control is carried out; 8, optimizing and executing a control strategy in real time; the technical problems that excitation speed regulation dynamic coupling, multi-time scale response mismatch, multi-machine cooperative control and the like in a hydroelectric generating set control system cannot be comprehensively solved in the prior art are solved, and the regulation response speed and the system operation safety are improved while the frequency and voltage stability is ensured.

The invention discloses a modeling and optimization control method for a gas supply system of a proton exchange membrane fuel cell, and belongs to the technical field of hydrogen energy power. The method comprises the following steps: establishing a nonlinear state space model of a proton exchange membrane fuel cell gas supply system; determining the optimal oxygen excess ratio of the system under different load currents through experiments, and fitting the optimal oxygen excess ratio into a reference function about the load currents; based on the nonlinear state space model, a model prediction control problem with tracking of the optimal oxygen excess ratio and minimization of the cathode and anode pressure difference as control targets is constructed and expressed as a constrained quadratic programming problem; and decomposing and iteratively solving the quadratic programming problem by adopting an alternating direction multiplier method to obtain the optimal control input of the current control period and act on the system. The method effectively solves the problem that traditional model predictive control is difficult to deploy in real time in a vehicle-mounted controller due to large calculated amount, so that efficient and accurate cooperative control of the proton exchange membrane fuel cell gas supply system is realized.

The invention belongs to a food and beverage sterile filling technology, and discloses a sterile nitrogen filling pressure-flow cooperative control system for particle-containing high-viscosity orange juice. Comprising a multi-source signal processing module used for synchronously obtaining a pressure numerical sequence, a temperature numerical sequence and a voiceprint spectrum vector of orange juice in a pipeline; the rheological state identification module is used for determining a particle density index and a particle size distribution probability of the orange juice flowing through the valve port area at present, and inverting an apparent viscosity value and a thixotropic state parameter of the orange juice so as to form a comprehensive state signature; the prediction collaborative decision module is used for generating a control target instruction set according to the maximum shear stress and further generating a pressure setting curve instruction and a displacement travel curve instruction; and the execution closed-loop control module is used for responding to the pressure setting curve instruction to adjust the pressure of the sterile nitrogen and driving the liquid filling valve to execute opening operation based on the displacement stroke curve instruction, so that dual-objective optimization of high-efficiency circulation and low-damage filling of orange juice is realized.

The invention discloses a weight coefficient-free model prediction control system and method for a permanent magnet synchronous motor, and particularly belongs to the field of permanent magnet synchronous motor control. The control system comprises a direct current voltage source, a three-phase converter, a permanent magnet synchronous motor, a rotating speed outer ring PI control module, an MTPA flux linkage calculation module and a model prediction control module. The control method comprises the following implementation steps: firstly, establishing a torque and flux linkage prediction model of the permanent magnet synchronous motor; then, a reference torque amplitude value and a reference flux linkage amplitude value are obtained through a rotating speed outer ring PI controller and a maximum torque per ampere (MTPA) calculation module respectively; secondly, constructing an independent torque cost function and a flux linkage cost function according to the torque error and the flux linkage error, and forming a cascade form; evaluating the switching states by using a cost function, selecting two vectors from the eight switching states of the converter by using the independent cost function of the torque (or flux linkage), and selecting an optimal vector from the two vectors by using the independent cost function of the flux linkage (or torque); and finally, the execution sequence of the torque and flux linkage cost function is alternated in each subsequent period. Compared with the prior art, the method avoids complex weight coefficient design through a cost function cascading mode, alternates the execution sequence of the cost functions in each period, dynamically balances the weights of the two control targets, and reduces the number of calculation times.

The invention provides a micro-grid control method based on edge side AI control equipment and a program product, and relates to the field of power supply and consumption system control. The micro-grid control method comprises the following steps: deploying a lightweight large model on edge side control equipment, wherein the lightweight large model is obtained through compression based on a pre-selected universal large model; micro-grid operation demand information input in a natural language form is acquired by the lightweight large model, and a control target is analyzed from the micro-grid operation demand information; generating a control strategy according to the control target; and controlling the operation state of the micro-grid according to the control strategy. According to the scheme of the invention, the lightweight large model is deployed at the edge side control equipment, the microgrid operation demand information is obtained through a natural language, the convenience and flexibility of control are improved, and the control demand is better met. And a light-weight large model accurately generates a control target and a strategy, so that more accurate operation control is realized, the adaptive capability is enhanced, and intelligent autonomous control is realized.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses an adhesive tape binding self-adaptive control method based on harness size real-time feedback, relates to the technical field of automatic control management, and is used for solving the problem of insufficient stability in an adhesive tape binding process. According to the method, a closed-loop control flow of multichannel data acquisition, state identification and control adjustment is constructed, accurate identification and response of size sudden change, edge offset and tension abnormity in the binding process are realized, a control target is generated through gray box prediction and scene identification, and time delay compensation and phase alignment are completed by adopting equivalent advance, so that the accuracy of the binding process is improved. Under the condition that the bandwidth is limited or the time delay fluctuates, stable parameter issuing is ensured through event-driven communication and instruction beat calibration, meanwhile, hysteresis and minimum retention time mechanisms are introduced, so that a control strategy has the anti-disturbance capability, and finally, a control closed-loop result is used for dynamic optimization of a material and environment parameter library and strategy rule entries. And a continuous self-learning mechanism is constructed, so that the binding quality stability and the equipment adjustment accuracy are improved.

The invention discloses a vehicle control method and device based on multi-source information fusion and actuator health state monitoring. The vehicle control method comprises the steps that driver state data, vehicle dynamics data and environment information data are obtained; sensing and fusing the multi-source data to generate comprehensive sensing data; performing scene classification according to the comprehensive perception data, identifying and judging a current driving scene type, and determining a control demand of current driving; health state monitoring is conducted on vehicle actuators, and the working performance and reliability of all the actuators are evaluated in real time; according to a scene judgment result and an actuator health state monitoring result, dynamically adjusting weight distribution of each control target in a decision making process; and based on the adjusted weight distribution, executing multi-target collaborative optimization, generating an optimal control strategy, converting the optimal control strategy into a control instruction, and issuing the control instruction to a vehicle execution mechanism. And the technical bottlenecks of the existing system in complex scene adaptability, actuator collaborative optimization, fault handling capacity and the like can be solved.

The invention discloses an equipment control method and device based on multivariate data and a storage medium, and relates to the technical field of smart home / smart home, and the equipment control method based on multivariate data comprises the steps: obtaining historical application data collected in a target environment; continuous device operations of the user are extracted from the historical application data, a device operation sequence is obtained, and the continuous device operations are device operations of which the time interval between the operation time and the operation time of adjacent device operations is smaller than a time threshold value; generating an equipment operation scene corresponding to the equipment operation sequence according to the environment characteristics of the target environment when the equipment operation sequence is executed in the historical application data; according to the technical scheme, the equipment operation scene is pushed to the user, the intelligent home equipment in the target environment is controlled to execute the execution command under the condition that the user executes the selection operation on the equipment operation scene and the trigger condition is detected, and by adopting the technical scheme, the problems that in the related technology, the control efficiency of the intelligent home equipment is low and the like are solved.

The invention discloses a storage tank pressurization control method and system based on LNG gas station fuzzy self-adaptive PID control, and aims to solve the technical problems that a traditional pressurization control method of an LNG storage tank is low in control precision, poor in stability and rapid in equipment abrasion. According to the control method, dynamic compensation for the target pressure is introduced, so that the system can aim at a more accurate control target in the initial stage, the steady-state control deviation is reduced, and the control precision is improved. And static pressure head change disturbance caused by liquid level change is overcome through dynamic compensation, the adjusting burden of the controller is reduced, the controller can focus on processing other dynamic disturbance, and therefore the control process is more stable, and overshoot is smaller. Through the design of a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex working conditions, and fast, stable and overshoot-free pressure control is always kept. Therefore, frequent and large-amplitude action and surge phenomena of the valve are avoided, and the energy consumption of an executing mechanism is reduced.

The invention discloses an intelligent layering and guiding device for highway tunnel fire smoke and a control method, and relates to the technical field of tunnel fire safety, a smoke flow Froude number is introduced as a direct control target, the parameter represents the ratio of smoke flow inertia force to buoyancy force, the numerical value directly determines the stability of a layering interface, and the layering interface can be directly controlled. The Froude number of each subarea of the tunnel is calculated in real time through a sensing network and is compared with a critical value calibrated through an experiment, when a real-time value is lower than the critical value, it is indicated that inertia force is relatively insufficient, the buoyancy effect is dominant, and instability and entrainment are likely to happen to an interface between a smoke layer and lower cold air, and then according to a control algorithm, the smoke layer and the lower cold air are controlled. The micro jet fan applies directional inertia force to a smoke layer by adjusting the angle and the speed, the intelligent induction smoke outlet optimizes local pressure difference and flow by adjusting the opening degree and assists in maintaining flowing, the smoke flowing state is actively locked in a stable layered flow interval, and mixing and sedimentation caused by flow state instability are prevented.

The invention discloses a motor coil press fitting process parameter self-adaptive regulation and control method and device, and relates to the technical field of motor vehicle motor manufacturing. The method comprises the steps that press fitting target parameters including a target termination position and target pressing force are obtained, press fitting is started, actual pressure and displacement values are collected in real time, a process state deduction model is constructed, whether double targets can be met at the same time when press fitting is finished in a current control mode or not is predicted, and an analysis result is output. If the conditions cannot be met at the same time, the predicted pressure deviation value and the displacement deviation value are taken as optimization targets, a process state compensation value is introduced, a multi-target optimization function is constructed to solve a control target weighting coefficient, a press fitting tail-section hybrid controller is reconstructed, output instructions of a position ring and a force ring are fused, and press fitting is executed. According to the invention, synchronous high-precision control of the pressing force and the termination position under material fluctuation and environmental interference is realized, and the adaptability and stability of coil press-fitting processes of various motors such as a new energy automobile driving motor and a household appliance motor are improved.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.

The invention discloses a power system demand control method and device, equipment and a medium, and relates to the technical field of power control. The method comprises the following steps: collecting multi-dimensional sensing data and generating a power system state vector through denoising and filtering; inputting the power system state vector into a BP neural network, and outputting a PID initial control parameter; adjusting the PID initial control parameter based on the load change rate to obtain a PID control parameter; according to the target power demand and the actual power demand, demand deviation, a demand deviation grade coefficient and demand deviation acceleration are determined; according to the PID control parameters, the demand deviation, the demand deviation grade coefficient and the demand deviation acceleration, determining energy storage charging and discharging target power; adjusting the energy storage charging and discharging actual power through a sliding mode control algorithm; pID control parameters are adjusted according to the energy storage charging and discharging actual power so as to achieve the demand control target. By adopting the demand control method of the power system, the demand control precision can be improved.

The invention discloses a micro-grid off-grid operation distributed control method based on consistency cooperation, belongs to the field of new energy micro-grid control, and aims to solve the problems of high single-point fault risk and single control target of existing centralized control. The method is operated in a three-layer architecture formed by a distribution network master station control layer, a micro-grid coordination control layer and an equipment distributed coordination control layer, a distributed communication network is constructed through edge agents, and weight coefficients and security constraints are configured; the edge agent receives a master station total power target, collects and interacts equipment data, calculates and limits amplitude to obtain a final power instruction based on a consistency updating law fusing global power difference distribution, energy storage SOC balance and system loss minimization, and then decomposes the final power instruction into equipment-level instructions to be issued and executed. According to the method, a single-point fault is eliminated, multi-target collaborative optimization is realized, control is simplified, expansibility is high, and power balance and stable and efficient operation when the micro-grid is off-grid are effectively guaranteed.

The invention relates to the technical field of laser processing process control, and discloses an intelligent control method and system for a laser oscillator, and the method comprises the steps: collecting an optical signal, determining the current spectrum ratio measurement, and adjusting the laser oscillator according to the current spectrum ratio measurement; according to the method and the system, the technical problem that a static control target is easy to lose efficacy when facing working condition drift is solved, the control precision is improved by monitoring the adjusting quantity of the controller, and the control precision is improved. A fast and slow loop self-adaptive control logic is constructed, so that the system obtains the capability of self-calibrating a control target value, thereby being capable of self-adapting to working condition drift caused by material batches or equipment aging, and ensuring long-period process consistency.

The invention relates to a mechanical arm control method and device, computer equipment and a readable storage medium. The method comprises the steps of generating a plurality of candidate trajectories for a target task through a target diffusion strategy model; the target diffusion strategy model is obtained by training an original diffusion strategy model based on collected example data; evaluating and screening the plurality of candidate trajectories according to a single-step value discriminator to obtain an optimal trajectory in the plurality of candidate trajectories; the single-step value discriminator is a multi-modal feature regression model constructed based on a target diffusion strategy model; and the target mechanical arm is controlled to execute the target task according to the optimal track. By the adoption of the control method and device, the control accuracy and safety of the mechanical arm can be improved.

The invention provides an automobile driving safety simulation test method and device and electronic equipment, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining an anthropomorphic driving model at the beginning of a test, controlling a target background vehicle through the anthropomorphic driving model, and testing a tested vehicle based on the target background vehicle. According to the method, the interactivity between the target background vehicle and the tested vehicle and the randomness of the driving decision of the target background vehicle can be controlled in a personification manner, so that potential risk events can be triggered more effectively, and the tested vehicle can be tested better.

The invention relates to the technical field of automobiles, in particular to a four-wheel steering vehicle track control method based on model predictive control. Firstly, a rear wheel proportionality coefficient changing along with the longitudinal speed of the vehicle is constructed; expressing a rear wheel steering angle as a function of a front wheel steering angle based on the proportionality coefficient, and substituting the function into a traditional three-degree-of-freedom four-wheel steering model to construct a simplified three-degree-of-freedom dynamic model with lower control dimension; performing linearization and discretization processing on the simplified model, and constructing an MPC prediction model; constructing a control objective function and related constraint conditions; and performing rolling optimization solution on the control target function to obtain an output rotation angle control instruction. According to the method, the complexity of a control problem is effectively reduced through model simplification, and the real-time performance and stability of a control system can be remarkably improved while the vehicle trajectory tracking precision is ensured.

The invention discloses a U-turn control method and device of an automatic driving vehicle and the automatic driving vehicle. The method comprises the steps that a first prediction track of a traffic participation object is acquired, and meanwhile, a second prediction track of a target automatic driving vehicle is acquired; when it is determined that a coincident trajectory exists between the first prediction trajectory and the second prediction trajectory based on a comparison result of the first prediction trajectory and the second prediction trajectory, obtaining first arrival time when the traffic participant arrives at each prediction coincident point on the coincident trajectory; evaluating the first arrival time to obtain the influence degree of the traffic participant on the turning-around operation; adjusting the original turning-around strategy according to the influence degree to obtain a target turning-around strategy; and controlling the target autonomous vehicle to execute a turning-around operation according to the target turning-around strategy. According to the invention, the technical problem that the traffic capacity of the autonomous vehicle is limited due to the obvious insufficiency of the autonomous vehicle turn-round path planning in the aspects of dynamic traffic flow influence processing and adaptive adjustment in the related technology is solved.

The invention relates to the field of oil field heating furnace control, and discloses an oil field heating furnace intelligent digital implementation method and control method.The control method comprises the following steps that S1, a temperature sensor is used for monitoring the temperature of an oil field heating furnace in real time; s2, calculating a temperature error between the upper limit and the lower limit of the real-time temperature and the set temperature according to the monitoring data of the temperature sensor; s3, a self-adaptive PID control algorithm is applied, and heating power is calculated based on the temperature error; and S4, dynamically adjusting the heating power to control the temperature of the oil field heating furnace. The precision and stability of temperature control can be improved, the oil field heating furnace can control the target temperature more accurately, and the production efficiency is improved. Meanwhile, the self-adaptive PID control algorithm can automatically adjust parameters according to actual working conditions and environment changes, the adaptability and robustness of the system are improved, and the requirements for manual intervention and adjustment are reduced. In addition, the stable and efficient heating process can be achieved by dynamically adjusting the heating power, and energy and cost are saved.

The invention relates to a ship navigation method and device for resisting wind wave influence, and belongs to the technical field of ships, and the method comprises the steps: carrying out the coupling of first data based on an MMG model, and obtaining a meteorological constraint condition; the ship state quantity change rate is obtained based on the state quantity and the control quantity of the target ship, the state quantity is obtained based on the first data, and the control quantity is obtained based on the propulsive force; constructing a target function based on the error of the state quantity and the error of the control quantity of the target ship according to a model predictive control theory; solving the target function by taking the ship state quantity change rate, the initial state quantity of the target ship, the value range of the control quantity and the meteorological constraint condition as constraint conditions to obtain a control quantity set; obtaining a propeller rotating speed and a rudder angle of the target ship at the next moment based on the control quantity set; and controlling the target ship to sail based on the propeller rotating speed and the rudder angle at the next moment. According to the invention, the accuracy of the ship navigation trajectory is improved.

The invention relates to the technical field of intelligent vehicles, in particular to an intelligent vehicle automatic driving formation obstacle avoidance control method and device, and the method comprises the steps: constructing an intelligent vehicle formation controller according to the destination data and obstacle data of a target intelligent vehicle formation, so as to control the target intelligent vehicle formation to move towards the destination; establishing an intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient according to the queue expansion coefficient of the target intelligent vehicle formation and the driving safety boundary; performing dynamic optimization on the intelligent vehicle formation controller by adopting a Levy flight random search logic based on a cuckoo algorithm to obtain an optimized intelligent vehicle formation controller; and controlling the target intelligent vehicle formation to avoid obstacles by using the optimized intelligent vehicle formation controller and the intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient. Therefore, the problems that when intelligent vehicle formation obstacle avoidance is carried out through a traditional artificial potential field method, local minimum value and obstacle avoidance track jittering are likely to happen, and complex dynamic environments cannot be rapidly adapted are solved.