137 results about "Fuzzy logic controller" patented technology

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention provides a distributed BMS battery active equalization type safety management method and system, and the method comprises the steps: obtaining the state information and performance change trend corresponding to each single battery through real-time monitoring, constructing a target battery pack topological structure model, combining a reinforcement learning algorithm, generating an intelligent agent model, and employing the intelligent agent model and a fuzzy logic controller to achieve the active equalization type safety management of a battery pack. Determining an energy flow management scheme; based on the feedback data and historical data of the energy flow management scheme, using a deep belief network to construct an anomaly detection model, based on the anomaly detection model, generating a risk assessment report, and constructing a security record platform based on the block chain technology to obtain security record information; optimizing each model parameter based on the risk assessment report and the security record information, and generating a security management system in combination with a transfer learning algorithm and a quantum computing method; and high-precision monitoring, intelligent electric quantity balance regulation and control, anomaly detection, risk management and continuous optimization of the distributed BMS battery system are realized.

The invention provides a photovoltaic cleaning robot sensor data fusion and obstacle avoidance method and system, and relates to the technical field of photovoltaic cleaning robots, and the method comprises the steps: obtaining multi-source sensor data, carrying out the probability distribution modeling through employing a Gaussian mixture model, and achieving the data fusion through employing a Kalman filtering algorithm of an adaptive covariance matrix, and constructing a three-dimensional semantic map, marking obstacle information, and dynamically adjusting a motion track in combination with global path planning and a fuzzy logic controller of a local obstacle avoidance layer to realize smooth obstacle avoidance of the photovoltaic cleaning robot. The environment perception capability and the obstacle avoidance efficiency of the photovoltaic cleaning robot are improved, the collision risk is reduced, and the safety and the efficiency of photovoltaic panel cleaning operation are guaranteed.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention discloses a power system frequency modulation method and system based on flywheel energy storage auxiliary wind power, a flywheel energy storage system is equivalent to a first-order inertia link, an inertia time constant of the flywheel energy storage system is dynamically adjusted, a virtual damping coefficient is optimized in real time in combination with a fuzzy logic controller, and the stability of power grid frequency is remarkably improved. When the frequency of the power grid fluctuates, the flywheel energy storage system can respond quickly, the frequency modulation power close to the rated frequency can be output in an extremely short time, and the steady-state frequency deviation is strictly controlled in an extremely small range. Meanwhile, the fuzzy logic is used for dynamically correcting the power instruction, and the problem of frequency modulation instruction tracking errors caused by wind power output randomness is effectively solved. Besides, after frequency modulation is finished, the damping coefficient is dynamically adjusted through a fuzzy decision mechanism, the flywheel rotating speed recovery time is greatly shortened, secondary frequency disturbance caused by rotating speed lag is avoided, and the recycling efficiency of flywheel energy storage in a continuous frequency modulation scene is ensured.

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

A waypoint, navigation controller and corresponding controlling methods are described, where the controller functions as a multiple input-multiple output, e.g., nonlinear angular velocity and linear speed controller for a land vessel such as a skid-steer vehicle. The controller and the controlling methods may be based on a fuzzy logic controller (alternatively referred to as “fuzzy controller”). The membership functions of the fuzzy controller may employ a trapezoidal structure with a symmetric rule-base. In addition, a Hierarchical Rule-Base Reduction (HRBR) is incorporated into the controller so as to select only the rules most influential on state errors by selecting inputs / outputs, determining the most globally influential inputs, and generating a hierarchy relating inputs via a Fuzzy Relations Control Strategy (FRCS). This disclosure is further directed to a fuzzy logic controller with Hierarchical Rule-Base Reduction (HRBR) and implemented as neural network and training of such a fuzzy logic controller via reinforcement learning based on an ANFIS actor.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system and method based on fuzzy logic control. Comprising a renewable energy power generation unit, an electrolytic cell array, a multi-type energy storage device, a power divider and a fuzzy logic controller, the renewable energy power generation unit is connected with the electrolytic cell array and the power divider through a direct-current bus, and the output end of the power divider is connected with the storage battery pack and the super-capacitor bank. The fuzzy logic controller collects the bus voltage fluctuation ratio, the electrolytic cell temperature gradient and the energy storage charge state in real time to serve as input variables, and outputs a multi-target optimization instruction to the power divider. Wherein the system dynamically adjusts the start-stop combination and the energy storage charge-discharge priority of the electrolytic cell array. According to the scheme, the mindset that fluctuation suppression and life management are separated in the prior art can be broken through, and a new paradigm of multi-parameter cooperative control is established.

The invention discloses a frequency stability control method for accessing new energy to an offshore energy platform, and the method comprises the steps: firstly carrying out the state recognition and parameter extraction of frequency modulation resources, building a frequency-power response model of the frequency modulation resources, carrying out the superposition construction of a system frequency response total model, and carrying out the packaging of a dictionary structure; based on a frequency safety index, disturbance occurrence and grade are judged in real time, and a frequency change trend is dynamically estimated; according to the disturbance level and the response characteristics of various resources, a fast-medium-slow layered frequency modulation control strategy is constructed, different types of resources are scheduled in stages for cooperative adjustment, the frequency state is monitored, and control parameters are dynamically corrected; constructing a fuzzy logic controller to process nonlinear disturbance, and introducing a whale optimization algorithm to set and optimize parameters of the fuzzy controller; and finally, dynamically correcting the control strategy according to the frequency control result and the resource utilization rate index. The method can guarantee the frequency stability, reduces the frequency modulation cost, and improves the frequency control precision, the response speed and the overall operation toughness.

The invention discloses an ultrahigh-pressure jet cleaning system with an intelligent adjusting function. According to the system, the control algorithm sub-module is integrated with the PID controller, the fuzzy logic controller and the machine learning model, so that the control algorithm sub-module can realize accurate monitoring and intelligent adjustment of the system operation state, and the efficiency and the stability of the cleaning process are ensured. The PID controller quickly responds to errors, eliminates steady-state errors, reduces system overshoot and oscillation, and improves real-time performance and accuracy of control; the fuzzy logic controller is used for processing nonlinearity and uncertainty of the system, so that the adaptability of the system to complex working conditions is enhanced; and the fault rate and the maintenance cost of the equipment are reduced through predictive maintenance, so that the operation convenience, the operation reliability and the economical efficiency of the system are integrally improved, and efficient and intelligent cleaning experience is brought to users.

The invention relates to the technical field of automatic feed distribution, in particular to an automatic feed production regulation and control method and system.The method comprises the following steps that healthy and growth rate data are collected and input into a data stream, the number of dynamic Bayesian network nodes and a connection mode are adjusted, real-time data are analyzed, and the learning rate and the transition probability are adjusted; and a prediction error matching feedback mechanism is established, key indexes are extracted, a distribution instruction is generated through a fuzzy logic controller, the feed ratio is adjusted to balance cost nutrition and environmental influence, and an optimal configuration result is obtained. According to the method, health and growth data are comprehensively utilized, feed feeding is more personalized and accurate, a feedback mechanism is analyzed and optimized in real time, it is ensured that the feed ratio responds to specific nutritional requirements of animals in real time, accordingly, resource waste is reduced, healthy growth of the animals is enhanced, feeding instructions are finely regulated and controlled through fuzzy logic, and the feeding efficiency is improved. The method adapts to different stages of animal growth, and a particle swarm optimization algorithm is adopted to balance the cost, nutrition and environmental influence.

The invention relates to the technical field of agricultural environmental protection, and discloses a farmland emission reduction and carbon sequestration collaborative optimization system, which is characterized in that a data processing module is used for collecting farmland multi-source data of main grain products and preprocessing the collected data to obtain preprocessed multi-source data; the model building module is used for screening key factors which have obvious influence on farmland carbon reserves on the basis of the preprocessed multi-source data, and building a farmland emission reduction and carbon sequestration model; the collaborative optimization module is used for searching a Pareto optimal solution set in a parameter space by adopting a non-dominated sorting genetic algorithm, and introducing a fuzzy logic controller to process target conflicts to obtain a collaborative optimization scheme of farmland emission reduction and carbon sequestration; the real-time monitoring module is used for monitoring farmland environment change and emission reduction and carbon sequestration effects in real time and regularly adjusting and optimizing a collaborative optimization scheme; the synergism and effectiveness of the technology are improved, collaborative improvement of farmland yield increase and emission reduction carbon sequestration is achieved, and the problem that yield increase and emission reduction carbon sequestration are not collaborative in the prior art is solved.

The invention discloses a fuzzy self-adaptive PID (Proportion Integration Differentiation) control method for a high-temperature environment of a small closed space. According to the method, a fuzzy logic controller and PID controller cooperative control architecture based on the variable universe theory is constructed, and PID parameters are adjusted on line by utilizing a fuzzy inference rule by collecting the temperature deviation and the change rate of the closed space in real time. The fuzzy rule base design adopts a variable universe theory, a membership function and a universe range are dynamically adjusted according to a system state, and the adjustment direction and amplitude of PID parameters are accurately mapped. A real-time monitoring mechanism based on feedback of a temperature sensor is introduced, high-frequency sampling is carried out on the temperature of the space, and timeliness and accuracy of control signals are ensured. According to the method, by setting a temperature threshold value and an overshoot limiting mechanism, the phenomena of temperature overshoot and oscillation are effectively avoided, and the stability and reliability of the system are improved. According to the invention, the temperature can be quickly and accurately controlled in a high-temperature environment of a complex small closed space, and the temperature fluctuation range is obviously reduced.

The invention relates to the technical field of electric tricycle safety, and discloses an electric tricycle safety collision early warning system and method, and the system comprises a multi-source sensor module, a feature extraction and fusion module, a collision risk assessment module, a path optimization module and a grading early warning control module. The method comprises the steps that the multi-source sensor module collects real-time environment and vehicle state data; the convolutional neural network extracts fusion environment data features to generate vectors, and the vectors are input into a Bayesian network model to output a collision risk probability value; constructing a multi-target path optimization model according to the probability value, and searching an optimal obstacle avoidance path by using an improved particle swarm optimization algorithm; and establishing a grading early warning control model according to the path data, and outputting brake pressure and sound-light alarm signals through a fuzzy logic controller. The system can accurately sense the environment, accurately evaluate the risk, optimize the obstacle avoidance path and perform graded early warning control, improves the driving safety of the electro-tricycle, also has sensor self-inspection and hot backup functions, and enhances the system reliability.

The present invention relates to the technical field of data processing, and is a sewage treatment intelligent dosing adjustment system based on fuzzy logic, comprising: a data acquisition module, which collects water quality data of sewage and optimizes the data acquisition process, including chemical oxygen demand, ammonia nitrogen content, total phosphorus content and sewage flow; a data analysis module, which is used to receive real-time data and perform small batch gradient descent analysis on the data to obtain analysis results; an optimization module, which is used to optimize the parameters and rules of the fuzzy logic controller through differential evolution according to the analysis results to obtain optimized parameters and rules; a fuzzy logic control module, which performs fuzzy logic calculation of dosing amount through simulated annealing according to the received water quality parameters and optimized rules, and converts it into a dosing control signal. The present invention realizes comprehensive intelligent management from data acquisition to dosing adjustment by integrating advanced data acquisition, analysis, optimization and control technologies.

According to the laser radar complex working condition recognition-based load reduction control method for the wind turbine generator, a high-precision laser radar system is installed on the wind turbine generator, three-dimensional wind speed distribution in a wind field is monitored in real time, data are transmitted to a control system, and the real-time performance and accuracy of the data are ensured. Collected original data are preprocessed, a time sequence analysis method is adopted, the rules of wind speed and wind direction changing along with time are mined, and key features of sudden wind speed change and rapid wind direction change are captured. According to historical data, a fuzzy logic rule is defined, and various complex working conditions are accurately described. And designing an adaptive fuzzy logic controller (AFLC), and when the AFLC recognizes a complex working condition, immediately starting load reduction control logic to adjust operation parameters of the wind turbine generator. Meanwhile, the AFLC dynamically adjusts the load reduction threshold value according to the wind field condition, and it is ensured that complex working condition changes are responded in time. In the load reduction control process, the control effect is evaluated, and a fuzzy rule and a membership function in the AFLC are optimized according to the control effect.

The invention discloses a hydrogen fuel hybrid power unmanned aerial vehicle energy management method considering rapidity and energy saving, and relates to the technical field of hydrogen fuel hybrid power unmanned aerial vehicle energy management. The method comprises the following steps: establishing an unmanned aerial vehicle demand power and battery SOC model; generating an unmanned aerial vehicle control parameter fuzzy logic rule; and solving the newest position and speed through a particle swarm optimization algorithm to improve the energy efficiency and balance electric quantity of the unmanned aerial vehicle. The unmanned aerial vehicle demand power model and the battery SOC model are constructed, and the membership function and the fuzzy rule of the fuzzy controller are updated according to the parameters optimized by the PSO algorithm, so that the response of the system to the input is adjusted, the control output is optimized, and the effective control and management of the dynamic system are realized. The fuzzy logic controller can better adapt to system changes, and control precision and system performance are improved.

The invention discloses sealing ring trimming equipment and a self-adaptive control system thereof. The sealing ring trimming equipment comprises a combined cutting device, a detection device and the self-adaptive control system. The combined type cutting device integrates a rough cutting assembly and a mechanical fine cutting assembly, and rough cutting and fine trimming of a sealing ring blank can be completed in sequence. The detection device adopts a multispectral confocal sensor, and the self-adaptive control system comprises a rough cutting module which controls a rough cutting assembly to reserve a set flash; the fine cutting control module dynamically generates a tool path correction parameter and a cutting depth compensation value according to the real-time scanning data; the edge feature analysis module extracts key quality indexes such as residual flash contours and surface roughness; and the correction module dynamically adjusts the reserved flash amount and the fine cutting parameters through the fuzzy logic controller. The equipment is further provided with a cutter resistance monitoring module and an abrasion prediction compensation module, closed-loop control from rough cutting to fine cutting is achieved, the edge machining precision and production efficiency of the sealing ring are remarkably improved, and the equipment is suitable for the field of high-precision sealing element manufacturing.

The invention relates to the technical field of direct-current power transmission and transformation, in particular to a flexible direct-current system voltage active support control method and system and a medium, and the method comprises the steps: setting a reactive support reference value and an alternating-current voltage reference value as control targets; obtaining a reactive power error and an AC voltage error according to the actual measured value of the grid-connected point of the AC power grid and the control target, and processing the reactive power error and the AC voltage error to obtain a reactive support control signal and an AC voltage support control signal; a fuzzy logic controller is used to optimize and adjust the reactive power support control signal and the AC voltage support control signal, and a proportional-integral controller is used to obtain a control result; and superposing the control result and the virtual excitation end voltage target value of the virtual synchronous machine to obtain a target control quantity actively supported by the voltage of the flexible direct current system. According to the invention, active support accurate regulation and control of the AC power grid voltage are realized by combining the AC voltage fluctuation condition and the fuzzy membership coefficient, and the stability of a power system is improved.

The invention discloses a volume correction instrument error analysis method and system based on an improved PE equation, and aims to improve the accuracy of fuel gas and biogas metering. According to the equipment, performance testing can be carried out under the conditions of different temperatures (20 DEG C to 40 DEG C) and pressures (101.3 kPa to 104.0 kPa) through an integrated fuzzy logic algorithm, and the average correction error is only 0.22%. A high-precision sensor and a data processing unit are used for monitoring and calibrating the gas volume in real time and compensating the influence of temperature and pressure changes. The fuzzy logic controller adjusts the reading of the gas meter according to real-time data, the accuracy of a metering result is ensured, algorithm optimization and intelligent equipment integration are focused, and the energy management efficiency is improved. The volume correction instrument provides help for a modern energy metering system and optimization of energy efficiency and cost.

The invention discloses an energy fine management method and device for an optical storage and charging integrated power station. The method comprises the following steps: determining the charging power difference between the power generation power of new energy of the power station and the power required by charging of an electric device; establishing a relation function between the unit electricity price and the charging power difference of the power station, and representing the charging power difference through the unit electricity price; the unit electricity price is used as an input variable of the fuzzy logic controller, and the output current of the energy storage battery is used as an output variable of the fuzzy logic controller; and the magnitude and the flow direction of the output current of the energy storage battery are controlled by using the output result of the fuzzy logic controller. By adopting the scheme, the expected charging and discharging state of the battery is controlled, the service life of the battery is prolonged, the transmission power between the power station and the power grid is stable, no large instantaneous fluctuation exists, stable control over the light storage and charging integrated power station is achieved, meanwhile, the dependence of the power station on the external power grid is reduced, and the economic benefits of power consumers are improved.

The invention relates to the technical field of medical cosmetology, discloses a laser hair removal method based on multi-parameter feedback, and aims to solve the problems of unstable curative effect and insufficient safety caused by single parameter of traditional laser hair removal. The method comprises the following steps: synchronously acquiring multi-dimensional physiological parameters such as skin type, hair follicle depth and epidermis temperature through multispectral imaging, high-frequency ultrasound and temperature sensing; constructing a personalized treatment parameter model based on a deep neural network, and generating initial energy density, pulse width and cooling parameters; laser output is regulated and controlled in real time by using a fuzzy logic controller in the treatment process, and it is ensured that thermal injury accurately acts on the hair papilla; performing adaptive optimization on the treatment strategy in combination with reinforcement learning, and continuously updating the model through a closed-loop learning mechanism; meanwhile, a treatment history database is established, curative effect prediction and parameter backtracking are supported, precision, individuation and high safety of laser depilation are achieved, depilation efficiency is remarkably improved, and the risk of skin adverse reaction is reduced.

The invention provides a wind storage combined system frequency modulation control method and device, and the method comprises the steps: carrying out the fuzzy calculation through a first fuzzy logic controller according to the frequency offset and the frequency change rate, and generating the total active power increment of a wind storage combined system; performing fuzzy calculation through a second fuzzy logic controller according to the operation parameters of the wind turbine generator and the current charge state of the energy storage system, and generating a frequency modulation participation coefficient of the wind turbine generator; performing frequency modulation control on the wind storage combined system according to the frequency modulation participation coefficient and the total active power increment of the wind turbine generator, and formulating a wind storage combined frequency modulation control strategy by analyzing the controllable potential of the wind storage combined system and describing a calculation method of a total output power and a power distribution principle of the wind storage combined system based on a fuzzy control theory. And the frequency modulation capability and stability are improved, so that the wind turbine generator can provide active standby support, thereby ensuring stable operation of the system.

The invention relates to an infusion speed alarm method and system. The infusion speed alarm method comprises the steps that a multi-mode sensing data set is formed; according to the multi-mode sensing data set, whether the conditions of dripping speed abnormity, pressure sudden increase, too large bubbles, pipeline blockage or backflow exist or not is judged, and corresponding abnormal feature information is generated; inputting the abnormal feature information into a prediction model and a fuzzy logic controller of a long short-term memory network, outputting a prediction result of a future infusion speed in an infusion process, and dynamically adjusting an alarm threshold according to the prediction result; executing graded early warning based on the prediction result and the dynamic adjustment alarm threshold value to obtain a graded early warning result of infusion speed alarm; uploading the grading early warning result to a centralized monitoring end of a nurse station through wireless communication to obtain a nursing response result; and performing iterative optimization on the prediction model and the fuzzy logic controller based on reinforcement learning. The accuracy of infusion speed alarm can be improved.

The invention discloses a fuzzy control-based multi-stack fuel cell energy management method and a fuzzy control-based multi-stack fuel cell energy management system. The method comprises the following steps of constructing an energy system comprising a multi-stack fuel cell and a lithium battery; constructing a fuzzy logic controller by taking the required power of the energy system and the charge state of the lithium battery as input variables and taking the output power of the multi-stack fuel battery as an output variable; establishing a hydrogen consumption function of the energy system according to the relationship between the output power of the multi-stack fuel cells and the lithium batteries and the hydrogen consumption, and establishing an optimization function by taking the value of the minimum hydrogen consumption function as a target; and solving the optimization function to obtain an optimal parameter of the fuzzy logic controller, and controlling the output power of the multi-stack fuel cell according to the optimal parameter. According to the method, the energy consumption of the fuel cell is optimized, the use of hydrogen fuel is reduced, and the energy utilization efficiency of the system is improved.

The invention provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.