109 results about "Fuzzy logic controller" patented technology

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 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.

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 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.

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 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 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 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.

The invention relates to the technical field of electric vehicle charging control, and discloses a charging pile power regulation and control method and device, electronic equipment and a storage medium. The method comprises the following steps of: monitoring multi-dimensional time sequence data such as charging voltage, current and battery temperature in real time, performing state segmentation, and calculating the similarity between a data sequence of each stage and a standard charging template so as to accurately screen out a stable charging state stage. And extracting a power adjustment amount based on the time sequence characteristic and the fluctuation mode of the stage, fusing the adjustment amount of the environment temperature, the equipment load parameter and the charging power, and dynamically generating a power switching rule by using a fuzzy logic controller. And finally, smooth switching and cyclic adjustment of the power levels of the charging piles are realized according to the rule. According to the method, the accuracy and the anti-interference capability of charging state identification are improved, and the adaptability and the collaborative optimization capability of power regulation to a complex environment and a power grid condition are enhanced, so that the charging efficiency and the power grid stability are both considered on the premise of ensuring the safety of the battery.

The invention belongs to the technical field of industrial measurement and control, and discloses a multi-case cascade vibration noise data acquisition clock synchronization method. A clock drift prediction model based on deep learning is constructed by acquiring local clock signals and environment parameters of all cases in real time, and accurate prediction of clock behaviors is achieved. A multi-mode communication strategy combining optical fibers and wireless radio frequency is innovatively adopted, reliable transmission mechanisms which are redundant to each other are formed, and the anti-interference capability is remarkably enhanced. A dynamic compensation mechanism based on the fuzzy logic controller can intelligently adjust compensation parameters according to clock states and environment changes, and accurate correction is achieved. A multi-level verification mechanism including local self-inspection, mutual inspection between cases and global optimization is established, a graph neural network technology is introduced to carry out system-level optimization, and the system has self-learning and self-optimization capabilities. According to the method, the time consistency of the vibration noise data is remarkably improved.

The invention discloses a tippler dynamic unhooking robot control method based on visual identification, and belongs to the field of industrial robots, and the method comprises the steps: deploying a visual camera array in a tippler operation area, collecting tippler-carriage image data, and carrying out the dynamic target identification of the tippler-carriage image data; constructing a compartment claw instance segmentation network to perform segmentation positioning on the dynamic instance target set; acquiring claw relative motion state data to predict claw motion, compensating a claw motion deviation prediction parameter to a vehicle claw connection position, and determining an unhooking operation position parameter; and performing smooth unhooking control on an execution mechanism of the dynamic unhooking robot of the car dumper through the fuzzy logic controller based on the unhooking operation position parameters. The technical problems that in the prior art, unhooking of a car dumper depends on manual assistance or a fixed program, the adaptability to dynamic conditions such as claw vibration and image interference is poor, and unhooking stability and accuracy are caused are solved.

Embodiments herein generally relate to a system and method for model risk management (MRM) of an artificial intelligence (AI) or machine learning (ML) model. In at least one example, the system comprises: an AI validation system (AIVS) comprising validation processing subsystems, which comprise a fuzzy logic controller (FLC) to implement a fuzzy logic MRM program associated with the AI / ML model. Validation devices are communicatively coupled to the validation processing subsystems and FLC. The FLC receives metadata related to risk management inputs and outputs for the fuzzy logic MRM program. The metadata is received, and a rule base is created. The MRM program receives the inputs from the validation devices, pre-processes the inputs, and fuzzifies the pre-processed inputs. Rules in the rule base are executed using the fuzzified inputs to calculate rule consequent values, which are aggregated. An output fuzzy state is assigned, and actions are performed based on the assigning.

The invention relates to the technical field of vehicle safety, and discloses an integrated motorcycle auxiliary driving system and method. The method comprises the following steps: synchronously acquiring vehicle kinematics parameters and environment three-dimensional point cloud data through a multi-source sensing system of a motorcycle; executing a dynamic risk mapping operation by using the collected data to generate a risk probability distribution diagram; acquiring a color image and a depth image of a road scene through a stereoscopic vision camera, inputting the color image and the depth image into the multi-scale feature extraction network for analysis in combination with the risk probability distribution map, and outputting a comprehensive risk score; performing obstacle trajectory prediction according to the comprehensive risk score to obtain an obstacle prediction trajectory, scanning a target area by using an infrared sensor, processing point cloud data by using a point cloud segmentation algorithm based on the prediction trajectory, and extracting actual obstacle attributes; and inputting the comprehensive risk score and the actual obstacle attribute into a fuzzy logic controller for data fusion, and finally generating an integrated motorcycle aided driving control instruction.

The invention belongs to the technical field of wind power plants, and discloses a wind power plant AGC and primary frequency modulation system coordination control method and device, and the method comprises the steps: S1, system initialization and parameter configuration; s2, real-time data monitoring and preprocessing; s3, dynamic priority allocation execution; s4, power prediction and instruction correction; and S5, switching and recovering the control right. Through multi-dimensional innovation, the stability and economical efficiency of grid-connected operation of the wind power plant are remarkably improved; according to the technology, AGC and primary frequency modulation functions are integrated, parallel control is achieved through a hardware isolation channel, the frequency modulation response speed is smaller than or equal to 20 ms, and the AGC instruction correction period is smaller than or equal to 50 ms. The pitch angle subsection control and the virtual inertia control are cooperated, so that the frequency drop is effectively inhibited; a fuzzy logic controller is adopted to dynamically allocate response weights, the LSTM neural network is combined to predict a power value in the next five seconds, and an AGC instruction is optimized through a PID controller to adjust the step length; the particle swarm algorithm optimizes a frequency modulation dead zone threshold value, the genetic algorithm sets PID parameters, and the frequency recovery time is shortened.

The invention discloses a multi-fuel combustion boiler SCR (Selective Catalytic Reduction) ammonia injection fuzzy PID (Proportion Integration Differentiation) parallel superposition mixing control system and method, and belongs to the field of boiler flue gas pollutant control. The system adopts a hybrid control architecture in which a fixed parameter PID controller and a fuzzy logic controller (FLC) work in parallel. The system calculates the deviation and the change rate of the NOx concentration of an outlet in real time, on one hand, the fixed parameter PID controller outputs the reference ammonia spraying amount, and accurate control under the stable working condition is achieved; on the other hand, the fuzzy logic controller outputs an intelligent compensation control quantity according to the deviation and the change rate, and the intelligent compensation control quantity is specially used for coping with severe working condition changes. And finally, the two outputs are superposed as a total ammonia spraying instruction. According to the method, the adaptive capacity and the control robustness to multi-fuel complex working conditions are improved. The fuzzy controller can perform coarse adjustment quickly when the working condition changes drastically, and the deviation is restrained quickly; and the fixed PID is responsible for fine adjustment after the system is stable, so that ammonia escape is effectively reduced, and the denitration efficiency is ensured.

The invention is suitable for the energy management technology of an extended-range electric vehicle, and provides a range extender fuzzy adaptive multi-point operation mode control method, which comprises the following steps: optimizing an APU working point offline based on a dynamic programming algorithm, and defining a comprehensive performance evaluation index; dividing an APU working area based on a demand power probability density function, and calculating a power and torque interval; designing a mixed point-linear energy management strategy, and dynamically adjusting a threshold parameter in combination with a fuzzy logic controller; and performing multi-objective optimization on the parameters through a multi-objective backbone particle swarm optimization method, and realizing quantitative decision based on a Pareto solution set. The problems that in the prior art, the APU is low in high-load working condition efficiency, complex in control logic, insufficient in consideration of battery life factors and the like are solved, the energy consumption economy and emission performance of the whole vehicle are remarkably improved, and the service life of the battery is remarkably prolonged.

The invention provides an off-grid optical storage inverter system cooperative control method and system based on a multi-mode intelligent algorithm, and relates to the field of optical storage inversion, and the method comprises the steps: obtaining a hardware configuration demand, collecting multi-source data in real time, and carrying out the precise tracking of a phase through a phase locking algorithm, and obtaining a standardized control signal; photovoltaic voltage and current data, a battery state and illumination abrupt change environment parameters are obtained, photovoltaic tracking is carried out, battery management is carried out in combination with a fuzzy logic controller, and a cooperative control instruction is output; historical load data and a real-time state flag are obtained, a load demand prediction curve is generated through cloud model processing, a working mode program is driven to make a decision, and a seamless switching instruction is output; real-time monitoring data is processed through multi-algorithm fusion, and a closed-loop protection instruction is obtained in combination with convolutional neural network fault diagnosis and voice feedback. The method is used for solving the problems of low energy conversion efficiency, poor dynamic response speed and poor all-working-condition adaptability of off-grid optical storage.

The invention discloses a source, network, load and storage distributed cooperative control method, and relates to the technical field of power grid regulation and control, and the method comprises the steps: collecting the real-time operation data of a source, a network, a load and storage, and carrying out the data preprocessing, and obtaining the standardized multi-source data; carrying out system state modeling based on the standardized multi-source data, constructing a distributed cooperative control model, adjusting a data source weight coefficient through a fuzzy logic controller, carrying out cooperative control strategy prediction, and outputting a cooperative control strategy prediction result; the cooperative control strategy prediction result is converted into a control instruction, the control instruction is distributed to each distributed unit, a distributed optimization algorithm is adopted to adjust the data source weight coefficient again, an optimization control instruction is generated, and each distributed unit executes the optimization control instruction. According to the method, the reliability and the integration of state recognition can be improved, and invalid scheduling triggered by misjudgment is reduced; global coordination of source network load storage multiple subjects is realized, and the stability of a power system is enhanced.