953 results about "Fuzzy control system" patented technology

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention discloses an intelligent energy scheduling method and system for a flow battery, and relates to the technical field of flow batteries, and the method comprises the following steps: collecting energy operation parameters of the flow battery in real time, and dynamically estimating an electrolyte state to obtain multiple pieces of energy state information; a target to be dispatched is set, and a fuzzy control strategy is formulated through retrograde multi-target optimization; executing a fuzzy control strategy to generate a regulation and control signal set and scheduling; and finally, according to a scheduling result, performing reverse tracing to optimize a fuzzy control strategy, and updating a regulation signal set to obtain a multi-stage regulation instruction to perform intelligent scheduling on the energy of the flow battery. The method solves the technical problems of low matching degree between a scheduling strategy and an actual demand, large power loss and fast battery life attenuation caused by difficulty in accurately capturing an energy state in real time in a traditional flow battery scheduling method, and achieves the purposes of intelligent flow battery energy scheduling, power loss reduction, battery life prolonging and energy conservation. And the matching degree between the scheduling and the actual demand is improved.

The embodiment of the invention relates to the technical field of robot control, in particular to a robot control method and system combining fuzzy control and a neural network. The method comprises the steps of obtaining a multi-dimensional state data set of a robot in a target operation scene, performing fuzzy control feature extraction processing on the multi-dimensional state data set, and calling a pre-trained neural network control model to perform hierarchical decision processing on the fuzzy control feature set, and generating a real-time control instruction set of the robot, adjusting the operation state of the robot in the target operation scene based on the real-time control instruction set, and feeding back the adjusted operation state to the multi-dimensional state data set to execute loop control processing. In this way, the robot can be controlled more accurately, intelligently and dynamically, and the problems that in the prior art, control is not accurate under a complex scene, and adaptability is poor are effectively solved.

The invention relates to the technical field of asphalt pavement paving, and discloses an asphalt pavement 3D intelligent paving cooperative control system and method based on BIM + GIS, and the system comprises a data fusion layer, a real-time control layer, a multi-machine cooperation and task distribution layer, and a quality closed-loop layer. The real-time control layer comprises multi-source data acquisition (positioning / temperature / compaction degree and the like) and three types of core decisions carried out by the cloud center, and the three types of core decisions carried out by the cloud center comprise feeding demand prediction (based on paving speed + thickness) of a dumper, thickness / speed adaptive adjustment of a paver and dynamic planning of a rolling path of a road roller. According to the method, real-time correction of transverse deviation and elevation deviation is achieved through combination of PID control and fuzzy control, the expansion amount of a hydraulic cylinder of the ironing plate is dynamically adjusted by associating the paving speed, the multi-source sensing technology of Beidou, IMU, laser ranging and infrared imaging is adopted, time sequence consistency data synchronization is achieved through 5G communication, and the real-time correction of the transverse deviation and the elevation deviation is achieved. And the precision and the closed-loop optimization effect can be controlled in real time.

The invention discloses a laser cutting method for cutting complex shapes, and particularly relates to the field of laser cutting. A three-dimensional curved surface model of a to-be-cut workpiece is obtained, and a surface normal tensor field is constructed; extracting a driving point set based on the curvature derivative and generating a sliding path network; constructing a laser power density mapping model in combination with the thermal diffusion response model and the thermophysical parameters of the material; thermal feedback images are collected in the cutting process, an energy offset function is constructed, laser focus displacement and the scanning rate are dynamically adjusted through fuzzy control, and closed-loop control over power, paths and thermal feedback is achieved; according to the method, self-adaptive adjustment of laser power distribution, curved surface adaptation of a cutting path and thermal field real-time compensation in the machining process can be achieved under a complex geometric structure, the cutting precision, stability and energy efficiency are effectively improved, and the method is suitable for laser machining application of high-requirement complex structures such as aerospace, mold manufacturing and medical instruments.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

The invention discloses a wire protection plastic sleeve production method based on an optimization algorithm. The method comprises the following steps: S1, collecting production process data through a sensor network; s2, preprocessing the collected production process data; s3, designing a nonlinear feedback control system and establishing a parameter adjustment mechanism; s4, performing staged feature learning by adopting an improved neighbor algorithm, and adjusting and optimizing process parameters in real time; s5, adjusting equipment and material parameters in real time by adopting a self-adaptive fuzzy control technology, and optimizing the production process through periodic evaluation and feedback; and S6, monitoring a production state in real time, optimizing a parameter adjustment strategy by using a weighted response function, and controlling an error range. According to the invention, through combination of a sensor network technology, an optimization algorithm, a nonlinear feedback control system and an adaptive fuzzy control technology, intelligent adjustment and optimization of a wire protection plastic sleeve production process are realized, production efficiency and product quality stability are improved, and energy consumption and operation errors are reduced.

The invention discloses a management method for prolonging the cycle life of a carbon-lead battery. The core of the management method is a carbon-lead battery temperature control method. A multi-dimensional temperature-life loss model is constructed, battery state parameters are collected in real time to pre-evaluate a future loss predicted value, a temperature control strategy is generated according to the future loss predicted value, and temperature control and closed-loop feedback correction are executed. Multi-dimensional model construction covers collection of cycle data of different conditions of a new battery and extraction of characteristic parameters in stages, dynamic loss function modeling is performed based on an Arrhenius equation, and a fuzzy controller is established in combination with factors such as battery aging and the like to output a model parameter correction coefficient. The generation of the temperature control strategy comprises the steps of defining a temperature control target layer, constructing a strategy parameter space based on particle swarm optimization, and performing predictive control and rolling optimization. The method effectively solves the problems of poor dynamic adaptability and inaccurate temperature control strategy of a temperature-life loss model in the prior art, and prolongs the cycle life of the carbon-lead battery.

The invention discloses a belt type roasting machine operation control method and system. The method comprises the steps that roasting machine operation control parameters including the material level, the fan rotating speed and the air bellow temperature target value are set; collecting the blanking amount, the machine speed, the material level and the temperature measurement values of the roasting section air bellow and the soaking section air bellow in real time; calculating a speed target value based on the blanking amount change rate and the material level error, and dynamically adjusting the speed of the trolley; and a fuzzy control strategy or a regular control strategy is selected according to the blanking fluctuation state, the historical control mode and the fan-speed matching degree. In the fuzzy control mode, slope characteristics are extracted through temperature curve fitting, fuzzy decision is carried out in combination with temperature errors, and the fan rotating speed adjustment amount is output; and in the rule control mode, the fan rotating speed adjustment amount is determined according to a predefined rule table. And finally, the fan target rotating speed is updated according to the fan rotating speed adjustment amount, and the rotating speeds of the main induced draft fan and the backheating fan are adjusted according to the fan target rotating speed. The stability of thermal parameters of the belt type roasting machine can be improved.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.

The invention provides an underwater propeller control method and system based on tensor recognition and fuzzy control. The underwater propeller control method and system are suitable for improving the propelling efficiency and adjustment intelligence in a complex flow field. According to the method, a wake flow simulation model is constructed based on geometric parameters and boundary conditions of a propeller, a rotation tensor and a strain tensor are derived after flow field data are obtained, and a tensor field index is extracted through function space mapping so as to determine a target grid region. A high-rotation candidate area is screened through a spectral clustering algorithm, a vortex structure is identified, and multi-dimensional features such as the scale, the strength, the axial direction and the vortex core position of the vortex structure are extracted. And constructing a state vector by combining the current propulsive efficiency and flow field disturbance parameters, inputting an adaptive fuzzy neural network model, reasoning a relationship between a vortex and an operation state, outputting a propeller rotating speed and an attack angle adjusting quantity, and realizing intelligent response and energy efficiency optimization of wake flow disturbance.

The invention discloses a military battery operation adjusting system based on extreme environment identification and detection, and relates to the technical field of military power management, the system comprises an environment sensing module, an environment identification and decision module, a thermal management execution module and a battery management module; the environment sensing module is used for collecting environment data such as temperature, humidity, wind speed, dust concentration and solar radiation intensity in real time; the environment recognition and decision module adopts a BP neural network and fuzzy control logic to recognize and classify working condition states, and outputs a control strategy in combination with a PID adjustment algorithm; the heat management execution module realizes heating, heat dissipation and sand prevention operation according to the instruction; and the battery management module dynamically adjusts a charging and discharging strategy according to the identification state. Self-adaptive adjustment of battery temperature and energy management can be realized under extreme working conditions, the system stability is improved, the service life is prolonged, and the system is suitable for battery management in severe environments such as desert, high temperature, low temperature, strong wind and sand, high radiation and the like.

The invention relates to a motor energy-saving control method for adaptive parameter adjustment. The method comprises the following steps: constructing an operation parameter sequence and comparing the operation parameter sequence with a preset reference model by collecting the rotating speed, the torque and the current signal of a motor in real time to generate a parameter deviation vector; based on a deviation threshold comparison result, carrying out noise processing on the overrun deviation to obtain a filtered sequence, and calculating a time delay degree in combination with historical parameters; calculating a compensation gain according to the time delay, fusing the compensation gain with the filtered deviation to generate a control instruction sequence, correcting the compensation gain through fuzzy control to obtain a stabilization parameter, and further generating a correction control instruction sequence; and finally, acquiring load data, fusing the load data with the correction instruction to obtain a system operation characteristic value, and generating an optimization state identifier when a judgment standard is met. By adopting the method, adaptive adjustment of the control parameters can be realized, the dynamic working condition is accurately responded, and the energy-saving efficiency of the motor under the variable-load working condition is improved.

The invention relates to the technical field of intelligent agriculture and Internet of Things control, and discloses an intelligent greenhouse control method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-factor environment state vector; strawberry image features are extracted based on YOLOv5; inputting a strawberry growth stage prediction model in combination with the time sequence to determine target environment parameters; constructing an environmental parameter prediction model; constructing a dual error term, and inputting the dual error term into a fuzzy controller to generate a preliminary control instruction; optimizing and outputting an optimization control instruction by the Q learning network; compared with a greenhouse control method which mostly depends on a fixed threshold value or a static rule in the prior art, the technical problem that dynamic target setting and self-adaptive adjustment of environmental parameters are difficult to realize especially under the condition that demands of strawberry crops in different growth stages change rapidly is solved. Intelligent greenhouse environment control in combination with the growth stage of strawberry crops is realized, and the adaptability of the greenhouse to crop growth conditions is improved.

The invention provides a pipeline welding all-position welding seam identifying and tracking system and method, and belongs to the technical field of pipeline welding automation. In order to solve the problems of strong arc light interference, low tracking precision, slow response speed and insufficient robustness in the existing pipeline all-position welding tracking technology, the system comprises a sensing layer, a processing layer, a decision-making layer and an execution layer. The welding seam identifying and tracking method comprises the following steps: S1, calibrating the welding seam identifying and tracking system; s2, all-position welding tracking is carried out; and S3, performing adaptive adjustment on different scenes. According to the method, dynamic weighted fusion and fuzzy PID control are adopted, multi-posture welding such as flat welding, vertical position welding, horizontal position welding and overhead position welding can be automatically adapted, parameters do not need to be manually adjusted, and the operation threshold is lowered.

The invention provides an autonomous adjustment method and system for a thermal power plant desulfurization wastewater dosing device, and the method comprises the steps: collecting multiple parameters such as flow, pH value, heavy metal ion and suspended matter concentration in real time through on-line monitoring instruments arranged at a water inlet end, a reaction unit and a water outlet end; constructing and continuously updating a dynamic mathematical model based on historical data and a reaction mechanism; a control system integrating intelligent algorithms such as a neural network and fuzzy control is utilized, and optimal dosing parameters are calculated in combination with the model and real-time data; a dosing pump or a valve is driven to execute precise dosing; and finally, closed-loop optimization is formed through effluent quality feedback. According to the invention, automatic, accurate and self-adaptive adjustment of the dosing device is realized, the defects that a traditional mode depends on manpower and response lags behind are overcome, stable and standard discharge of desulfurization wastewater is effectively guaranteed, meanwhile, medicament consumption and operation cost are remarkably reduced, and the automation level and the capability of coping with working condition fluctuation of the system are improved.

The invention relates to a puncture deviation correction method, device and equipment based on dynamic vision and a storage medium, and the method comprises the steps: collecting image data and spatial position data of a to-be-punctured object, extracting target positioning information from the image data, and constructing a corresponding three-dimensional deformation model; determining corresponding offset data according to the three-dimensional deformation model, and fusing the target positioning information with the offset data to generate a deviation vector; on the basis of the deviation vector, fuzzy control rule calculation is executed, and a control output quantity is generated in combination with historical control data; the deviation direction and amplitude of a preset time period are predicted by analyzing the change trend of the deviation vector at multiple time points, a feed-forward compensation amount is generated, and then a compensation control instruction is generated; and third-order track information of the puncture path is generated according to the compensation control instruction, and dynamic deviation correction control is completed according to the third-order track information. The method has the effect of improving the accuracy of the automatic puncture system.

The invention provides a hybrid energy storage optimization method and system considering fluctuation stabilization and energy storage protection, and relates to the technical field of hybrid energy storage optimization. Comprising the steps of calculating hybrid energy storage total power; the total power of the hybrid energy storage is decomposed, a low-frequency component and a high-frequency component are distributed to a lithium battery and a super capacitor in the hybrid energy storage respectively, and primary power distribution is completed; on the basis of primary power distribution, the power of the lithium battery and the power of the super capacitor are adjusted on the basis of a low-amplitude component in low-frequency components, the charging and discharging states of the lithium battery and the super capacitor and the charging and discharging margin of the super capacitor, and the power of the lithium battery is optimized on the basis of fuzzy control; and the power of the lithium battery is corrected based on the grid-connected power fluctuation rate condition, and secondary power distribution of the lithium battery and the super capacitor is completed. According to the invention, the energy storage charging and discharging conversion frequency is reduced, the SOC of the super capacitor and the lithium battery is ensured not to be out of limit, the problem generated by adopting traditional fuzzy control on the super capacitor is avoided, and the grid-connected fluctuation ratio is prevented from being out of limit.

The invention relates to the technical field of electric vehicle data analysis, and discloses an electric vehicle charging demand prediction and distribution network optimization method based on data fusion. According to the method, related data of the electric vehicle are collected through a multi-source data acquisition module, feature fusion is performed by using a convolutional neural network, and charging demand parameters are predicted through a long-short-term memory network model. And constructing a multi-target distribution network optimization model, performing global optimization by adopting a genetic algorithm and introducing adaptive crossover and mutation probability, and outputting an optimal distribution network scheme. A layered charging control model is established, a strategy layer performs global planning, a scheduling layer performs local scheduling, and an execution layer realizes charging control based on fuzzy control. In addition, a differential privacy technology is adopted to protect user data privacy, a path is planned based on an A * algorithm of real-time road resistance, a prediction result is corrected in combination with weather factors, accurate prediction of the charging demand of the electric vehicle and distribution network optimization are achieved, and the resource utilization efficiency and the distribution network stability are improved.

The invention discloses a fuzzy-control-based autonomous obstacle avoidance system for an underwater robot, and relates to the technical field of fuzzy control, which comprises the steps of realizing accurate perception and boundary correction of obstacles by analyzing sonar data, optical images and inertial navigation information, extracting a target position in combination with echo signal features, enhancing the accuracy of environmental data, and obtaining an autonomous obstacle avoidance result. A fuzzy K-nearest neighbor algorithm is adopted, target clustering, moving target classification and environmental impact factor calculation are carried out based on dynamic information of obstacles, so that obstacle avoidance path planning can adapt to different topographic conditions and obstacle distribution, the applicability of path calculation is improved, a Dikstra algorithm is adopted to demarcate a traffic area, and the obstacle avoidance path planning efficiency is improved. And combining path interference calculation and risk assessment, optimizing a path weight matrix, and ensuring that an obstacle avoidance path realizes an optimal decision at a balance point of safety and trafficability. The path optimization link is based on energy consumption calculation and propulsion power optimization, the navigation stability is improved, and the burden of a propulsion system is reduced.

The invention relates to a temperature control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring the real-time temperature of a controlled object, determining a temperature deviation value and a temperature deviation change rate according to the real-time temperature and a preset reference temperature, determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the real-time temperature and a preset temperature interval, and determining a fuzzy control weight coefficient and a proportional-differential-integral control weight coefficient according to the fuzzy control weight coefficient and the proportional-differential-integral control weight coefficient; the preset temperature interval is determined based on a preset reference temperature, a preset dual-mode switching temperature threshold value and a preset temperature buffer area, and the temperature of the controlled object is adjusted based on the temperature deviation value, the temperature deviation change rate, the fuzzy control weight coefficient and the proportion-differential-integral control weight coefficient. And the temperature deviation value between the real-time temperature and the preset reference temperature is within the preset temperature error range. By adopting the method, the precision of temperature control can be improved.

The invention relates to the technical field of beacon light control, in particular to a beacon light anti-interference control method and system based on environmental perception and dynamic modulation. The method comprises the following steps: extracting an ambient light reference based on adaptive weight sliding filtering; in combination with target detection and trajectory prediction, interference pre-judgment features are obtained; on the basis of an evidence theory, analyzing comprehensive interference evaluation characteristics through the ambient light reference, the interference pre-judgment characteristics and the electromagnetic interference characteristics; fusing meteorological environment perception and influence features and self health state features, and evaluating equipment operation risks; and based on modulation signal optimization characteristics, the comprehensive interference evaluation characteristics and the equipment operation risk, fuzzy control and rule reasoning are utilized to realize dynamic output of anti-interference control parameters. In order to solve the problem that the signal recognition rate is reduced due to the fact that a beacon light is easily affected by multiple factors in a complex ocean navigation channel environment, data are obtained through multi-dimensional environment perception, and dynamic modulation control is achieved after feature extraction and fusion.

The invention relates to an electric drive semitrailer grading AEB control decision-making method considering road surface factors, and the method comprises the following steps: S1, enabling a vehicle to obtain front vehicle information and road surface information through a sensor, a high-precision map and other ways, calculating a correction adhesion coefficient through combining with the state of the vehicle, and determining the maximum gradient limit; s2, outputting a maximum braking deceleration correction factor through fuzzy control by taking the correction adhesion coefficient and the gradient as input; s3, according to the advantages and disadvantages of the road surface working conditions and the maximum braking deceleration correction factor, the maximum braking deceleration is calculated according to the working conditions; s4, calculating a safe braking distance and collision time according to the current state of the vehicle and the speed and distance of the front vehicle; and S5, the collision time is used for early warning decision making, the safety braking distance is used for emergency braking decision making, the corrected maximum braking deceleration serves as a full-force braking target value, and graded AEB control is achieved. According to the method, road surface factors are fully considered, vehicle safety can be guaranteed under different attachment conditions and slope road surfaces, and the braking stability is remarkably improved.