4638 results about "Control models" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention discloses a target recognition and grabbing posture planning system for a collaborative robot, and belongs to the technical field of mechanical arms. According to material posture information provided by a sensing module, the optimal grabbing posture is intelligently matched from a preset grabbing strategy library by fusing mechanical arm movement track parameters and target material physical characteristics; the initial accuracy of the grabbing scheme is ensured, in the grabbing process, the camera continuously collects the material poses in real time, and the offset is accurately calculated by comparing the real-time poses with the expected poses. Once material deviation is detected, a visual and force closed-loop adjusting mechanism is triggered immediately, a force-position hybrid control model is utilized, position deviation is synchronously corrected, and grabbing force parameters are dynamically adjusted, so that the grabbing stability and precision are remarkably improved, the grabbing failure rate is effectively reduced, manual intervention and reset time are greatly shortened, and the grabbing efficiency is improved. Therefore, the production efficiency is comprehensively improved.

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The disclosed technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

The invention provides an electromagnetic-assisted nickel-based alloy laser melting deposition crack inhibition method and system, relates to the technical field of automatic control, constructs an intelligent control system, carries out deep fusion and real-time prediction on multi-source heterogeneous sensing data, generates a prediction result, sets a reward function and an action-value network, and improves the performance of the nickel-based alloy laser melting deposition crack inhibition system. Solving to obtain an optimization control strategy and analyzing and outputting the optimization control strategy; and constructing a three-dimensional electromagnetic field distribution matrix. In combination with real-time feedback of the shape of the molten pool, the molten pool is stimulated to flow directionally and oscillate non-linearly according to the resonance effect, oscillation parameters are obtained, thermodynamic behaviors corresponding to the molten pool are predicted according to the oscillation parameters, and a thermodynamic prediction result is fed back to the central control unit; the method comprises the following steps: detecting the surface appearance of a part, extracting defect characteristic parameter vectors, inverting defect influence factors, generating a dynamic regulation and control curve, dispersing the dynamic regulation and control curve into multi-dimensional control instruction vectors, updating a preset regulation and control model, outputting optimized regulation and control model parameters, and carrying out crack suppression operation again.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

The invention provides an air conditioner energy-saving control method and system based on a virtual power plant, and the method comprises the steps: determining an air conditioner energy-saving control model corresponding to a plurality of air conditioner devices through obtaining an operation parameter set and real-time power grid load data of the plurality of air conditioner devices in a target region, and carrying out the energy-saving control of the plurality of air conditioner devices according to the real-time power grid load data and load prediction data; and determining a power grid load fluctuation threshold value of the target area, dynamically optimizing the operation parameter set of the plurality of air conditioning equipment through a parameter optimization model, generating an optimized equipment operation parameter subset, and adjusting temperature set values and operation modes of the plurality of air conditioning equipment in a future time period. And generating an air conditioner group control strategy corresponding to a future time period, obtaining operation feedback data, updating weight parameters of the load prediction model according to a deviation value between actual power grid load data and load prediction data, calibrating an optimization rule of the parameter optimization model, and generating a calibrated model. According to the invention, the fine regulation and control capability and the energy coordination efficiency of the air conditioner group can be improved.

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

The invention provides an operation control model training method and a robot operation control method and system, and the method comprises the steps: carrying out the reinforcement learning training of an initial strategy network according to the first sample state information of a simulation robot and a reward function corresponding to each action task, and obtaining a target strategy network corresponding to each action task; according to second sample state information of the simulation robot, a task label corresponding to a preset training task and expected action information corresponding to the second sample state information output by a target strategy network corresponding to each action task, carrying out joint training of transfer learning, reinforcement learning training and imitation learning on the initial operation control model; and constructing an operation control model according to the trained initial operation control model. Through joint training of multiple training tasks, smooth switching among different action tasks of the robot can be ensured while multi-task compatible processing of the robot can be realized, and the operation control performance of the robot is effectively improved.

The invention provides an AR-based intelligent integrated production line special circuit control method and system, and relates to the technical field of production line control, and the method comprises the steps: collecting a real-time point cloud model of production line equipment through AR glasses, and carrying out the registration of the real-time point cloud model and a preset three-dimensional model, and obtaining the state deviation data of the equipment; and analyzing an equipment operation trend by using a time sequence convolutional network, and generating an operation state evaluation index and a control parameter correction amount. A to-be-adjusted circuit control node is projected and displayed in the AR glasses, a control instruction is generated according to eye movement tracking and gesture interaction operation of an operator, and a control model is formed by recording a corresponding relation between the instruction and an operation state evaluation index. And based on the model, an AR auxiliary control system is constructed by an edge calculation unit, a control instruction is generated through graph neural network reasoning according to a new operation state evaluation index, and operation guide information is displayed on an AR interface. And after the operator confirms, an instruction is issued to a production line controller, and an execution result is fed back to update the control model, so that closed-loop control is realized.

The invention relates to the technical field of new energy grid-connected converter control, in particular to a grid-connected converter control method based on grid-following and grid-construction hybrid control, which flexibly adjusts the characteristic proportion of grid-following and grid-construction by constructing a self-adaptive hybrid synchronous control model, ensures the smooth and efficient grid-connected process, and improves the grid-connected efficiency. A voltage and current double-loop control model is constructed to accurately regulate and control output voltage and current, the high-standard electric energy quality requirement of a power grid for new energy power generation is met, meanwhile, power grid impedance estimation is introduced for virtual impedance control, and the anti-interference capability and the response speed of the system are further enhanced. In conclusion, according to the grid-connected converter control method based on grid following and grid construction hybrid control, the adaptability and stability of the system to the complex power grid environment are remarkably improved, and the overall operation efficiency and reliability of the system are improved.

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention discloses a highway situation awareness and accident risk early warning method fusing multi-source data. The method comprises the following steps: S1, constructing multi-source data fusion and multi-scale traffic flow data; s2, studying and judging an accident influence factor mechanism, and excavating factors influencing the occurrence of a traffic accident; s3, early warning of accident risks is carried out, and the safety risk level of the road section is evaluated; and S4, researching the beforehand management and control system, and constructing a beforehand management and control strategy system based on deep mining analysis of traffic accidents and actual operation experience. According to the invention, on the basis of the high-speed portal data and the charging flow data, the detection data of the thunder-vision integrated roadside equipment are fused for fusion processing, so that fine traffic operation parameters such as traffic flow and vehicle speed before and after a historical accident are obtained; traffic flow characteristics causing accidents are mined, and a traffic accident risk dynamic early warning management and control model and a post-accident evaluation mechanism are established based on the traffic flow characteristics, so that a management department is helped to recognize potential risks in advance.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention discloses an intersection entrance lane mixed vehicle team cooperative guidance control method, which overcomes the problems of large calculation amount and difficulty in achieving a global optimal state in the prior art, and comprises the following steps: 1) obtaining road information; 2) dividing intelligent agents; and 3) establishing a multi-agent ecological cooperative control model of the intersection entrance lane mixed vehicle fleet: firstly, establishing a single-agent speed trajectory planning model, calculating an optimal passing trajectory of a single agent, and then establishing a multi-agent cooperative control model by taking the overall operation efficiency of the vehicle fleet as an optimization target on the basis of the optimal passing trajectory of the single agent, so as to realize the multi-agent ecological cooperative control of the intersection entrance lane mixed vehicle fleet. Cooperative control among a plurality of agents is realized, and a model is trained through a reinforcement learning method after model construction is completed; and 4) the controllable agents execute the optimized trajectory: issuing the established and trained model to each controllable agent, adopting a rolling time domain updating strategy, calculating the speed trajectory of each agent passing through the intersection by using road traffic information, and inputting the speed trajectory to a CAV controller for execution.

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

The invention discloses a semiconductor packaging device electromagnetic compatibility comprehensive test method and system, and belongs to the technical field of electromagnetic compatibility testing. The method comprises the following steps: collecting structure parameters, packaging topology and predefined function states of a to-be-tested packaging device, and constructing a polymorphic working model; establishing a disturbance injection control model according to each state and configuring disturbance source parameters; implementing dynamic disturbance injection and acquiring response data in a real working state of the device; performing time domain and frequency domain conjoint analysis on the response data, constructing an electromagnetic response dynamic feature sequence, inputting the electromagnetic response dynamic feature sequence into a machine learning model, extracting multi-dimensional coupling features and predicting tolerance; calculating performance indexes such as an interference tolerance score and a coupling strength index based on model output indexes, comparing the performance indexes with a standard, and evaluating a compatible risk level in a full state; the method realizes quantitative evaluation of the EMC performance of the packaging device with high reduction degree and multi-state coverage, and has the advantages of comprehensive test, accurate prediction, explainable attribution and the like.

The invention discloses a heating control system based on solar energy-heat pump-building collaborative optimization and a construction method, and belongs to the technical field of renewable energy source heating. Comprising a parameter acquisition module, a dynamic prediction module and an optimization control module. The method comprises the following steps: constructing a building heat transfer dynamic prediction model, a solar energy performance prediction physical model, constructing a ground source heat pump performance prediction model and a solar energy-ground source heat pump system collaborative optimization control model, and predicting and optimizing system operation parameters in the next day. Through cooperative work of the parameter acquisition module, the dynamic prediction module and the optimization control module, intelligentization and high efficiency of building heating are achieved, solar energy and the heat pump efficiency can be utilized to the maximum extent, the ground source heat pump efficiency is improved, and energy consumption is reduced while the indoor comfort degree is guaranteed. In addition, a time-of-use electricity price mechanism is utilized to further optimize system operation parameters, the system operation cost is reduced, and the energy efficiency and the response capability of the system are improved.

The invention relates to the technical field of supply chain management, and discloses an intelligent supply chain optimization management and control method. The method comprises the following steps: acquiring full-link real-time operation data of a supply chain through a distributed data acquisition module; extracting topological feature data by using a graph neural network; inputting the multi-target collaborative optimization model, and generating supply chain strategy optimization parameters by adopting a mixed integer linear programming framework and a dynamic constraint relaxation mechanism; constructing an adaptive resource scheduling model, realizing node dynamic scheduling by using an improved particle swarm algorithm, and outputting optimal configuration data; and establishing a layered supply chain management and control model which comprises a strategic planning layer, a dynamic coordination layer and an execution control layer, and realizing full-link intelligent optimization management and control. The invention also relates to risk event processing, strategic planning layer resource network planning and the like. The system can comprehensively collect data, optimize resource configuration, improve response speed, reduce cost, effectively control risks and improve the overall competitiveness of a supply chain.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

An energy storage control method and apparatus for a micro-grid system and a micro-grid system are provided. The method includes: for a target period within a first operation period, based on first predicted operation parameters of the micro-grid system in the target period, obtaining, via a first control model, a first control parameter of the energy storage unit in the target period; and for a target period within a second operation period, based on first historical state parameters of the micro-grid system before the target period, obtaining, via a second control model, a second control parameter of the energy storage unit in the target period. In this way, the energy storage regulation of the entire life cycle of the micro-grid system can be realized, effectively reducing the user electricity cost.

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The invention discloses a self-adaptive photovoltaic tracking control method and system, and the method comprises the steps: collecting and fusing multi-source data, detecting the operation environment of a photovoltaic system in real time, and carrying out the real-time processing of the collected environment data; constructing a self-adaptive photovoltaic tracking control model, training the self-adaptive photovoltaic tracking control model, and dynamically adjusting a tracking strategy of the photovoltaic panel according to historical data and real-time environment change; a dynamic error compensation mechanism is introduced, errors of a tracking system are corrected in real time, a multi-objective optimization strategy is designed, and tracking modes are automatically switched according to different environmental conditions. The system comprises a multi-source data acquisition module, a photovoltaic tracking control module and a tracking mode switching module. According to the invention, the precision and efficiency of operation monitoring of the photovoltaic system are improved; high-quality data input is provided for a photovoltaic tracking control model, and the decision-making ability of the model is improved; the intelligent and automatic photovoltaic panel tracking strategy is realized, and the power generation efficiency of a photovoltaic system is improved.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention relates to the technical field of brain-computer interfaces, and discloses a self-adaptive dynamic feedback brain-computer interface training method and system based on virtual reality. The training method comprises the steps that a user performs motor imagery according to a training scene presented by the VR display device; the electroencephalogram acquisition equipment acquires electroencephalogram signals; configuring a data processing module, and operating a motor imagery continuous control model to calculate characteristic parameters by taking the electroencephalogram signals as input; under the condition that the task exists, the characteristic parameters are used as input, and a dynamic self-adaptive characteristic-feedback mapping model is operated to calculate control parameters; sending the control parameters to a functional electrical stimulation device to load electrical stimulation of corresponding intensity to the muscle to obtain tactile feedback; meanwhile, the dynamic adaptive feature-feedback mapping model is dynamically updated according to latest input feature parameters; the control parameters are sent to the VR display device to present visual feedback, and the user adjusts the motor imagery based on the visual feedback and the haptic feedback. The participation sense of the user and the training efficiency can be improved.

The invention provides a self-adaptive control method and system for an automatic driving truck, and the method comprises the steps: carrying out the filtering and structural processing of multi-dimensional data collected by a vehicle-mounted sensor network, and generating a standardized data package containing vehicle state parameters and environment information; and according to the standardized data packet, retrieving a deep learning neural network model library, selecting a currently applicable basic model, and dynamically adjusting parameters of the basic model to generate an optimized adaptive control model. And inputting the standardized data packet into an adaptive control model for operation, generating a candidate control instruction, and generating an optimal control strategy through rapid simulation and multi-objective optimization calculation. And finally, the optimal control strategy is executed by the vehicle controller, vehicle response is monitored in real time, and controller parameters are adaptively adjusted based on deviation. According to the method, the dynamic adaptability and strategy optimization of the control model are realized, and the safety and efficiency of the automatic driving truck in a variable environment are improved.

The embodiment of the invention provides an aircraft attitude control model generation method and device, equipment and a medium, and is applied to the technical field of aircraft control. The method comprises the following steps: acquiring a flight task and a flight environment; according to a pre-constructed simulation model, carrying out analogue simulation on the flight task and the flight environment, and outputting simulated flight data; training a pre-constructed reinforcement learning model by taking a flight strategy as an output and the simulation flight data as an input; and when a preset stop condition is met, stopping training, and taking the trained reinforcement learning model as an aircraft attitude control model. In this way, the training period and training cost of the controller can be reduced, and the aircraft attitude control model with a better control effect can be trained.

The invention relates to the technical field of pressure sensing, and discloses a pressure distribution data acquisition method and device, and the method comprises the steps: carrying out the calibration and signal preprocessing of a pressure sensor array, and obtaining an initial state matrix, a preprocessing parameter set, and a response curve database; calculating a pressure change rate to obtain a pressure sampling dynamic trigger threshold value and a pressure sampling control matrix; performing partition control on the pressure data acquisition process to obtain a pressure acquisition partition control model; collecting target pressure data and inputting the target pressure data into the time weighted processing network model for feature extraction to obtain multi-modal pressure distribution feature data; the multi-modal pressure distribution characteristic data is input into a closed-loop control system, and the pressure sampling parameters and the pressure processing parameters are updated to obtain a pressure acquisition control parameter set, so that high-precision pressure distribution acquisition is realized, and the limitation of a traditional method in processing non-linear and time-varying pressure signals is solved.