61 results about "Fuzzy logic controller" patented technology

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 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 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 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 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 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.

The invention relates to the technical field of digital data processing, in particular to a water gate remote control system based on the internet of things, which comprises a basic parameter initialization module, an initial discrete action sequence construction module, a fluid pressure gradient calculation module, a real-time pressure gradient numerical value calculation module and a gate action self-adaptive shaping module. A fuzzy logic controller is used for calculating an action correction coefficient, an initial discrete action sequence is adjusted to generate a shaped drive control instruction, and a discrete stepping drive execution module is used for sending a control signal and generating a brake locking instruction when the positioning deviation reaches the standard. According to the invention, real-time fluid pressure is collected and gradient is calculated, a fuzzy logic controller is used to calculate a correction coefficient according to pressure gradient deviation, an initial discrete action sequence is subjected to adaptive shaping, and a driving strategy is adjusted during pressure fluctuation so as to smooth mechanical stress, ensure opening and closing stability and realize high-precision position locking. And a water hammer effect and mechanical damage are avoided.

A control method for a turbine speed regulation optimization system based on hybrid intelligent feedforward compensation belongs to the field of turbine speed regulation system control technology. This method simultaneously triggers two feedforward channels—data-driven and knowledge-driven—when a change in head or load is detected: the data-driven channel outputs a first opening compensation amount using a neural network model trained on historical operating data, while the knowledge-driven channel outputs a second opening compensation amount using a fuzzy logic controller built based on the turbine's physical characteristics and expert experience. The severity index of the operating condition change is calculated based on the changes in head and load, and the two compensation amounts are adaptively weighted and fused to generate the final feedforward opening compensation amount. This compensation amount is then superimposed on the PID controller output command to form the main guide vane opening control signal. This invention overcomes the lag of traditional feedback control through a hybrid feedforward mechanism, improving the dynamic response speed and regulation stability of the unit under conditions of large head fluctuations and frequent load changes.

The invention discloses an improved DWA local path planning method based on a guide field and self-adaption. Relates to the technical field of autonomous mobile platform path planning, in particular to an improved DWA local path planning method based on a guide field and self-adaption. A global path guide field is constructed, a multi-dimensional evaluation function is improved, constraints are added into a DWA model, and a fuzzy logic controller is fused to adaptively adjust the weight of the evaluation function. The method comprises the following steps: acquiring a global path, and constructing a global path guide field; constructing a multi-dimensional evaluation function; constructing and improving a DWA model, generating a candidate trajectory set, generating a weight coefficient of each evaluation item of each candidate trajectory, and obtaining a score of each candidate trajectory; judging whether the current position of the autonomous mobile platform is in a circular area which takes the target point as the center and has the radius of 0.5 m or not; if yes, local path planning is stopped, and a smooth path is obtained; and judging whether collision occurs on the smooth path or not.

The invention discloses a mobile robot control method and system based on multi-source fusion dynamic trajectory re-planning. The method comprises the steps that vehicle kinematics data and environment characteristic observation data are acquired in real time; state prediction is carried out based on a kinematic model; performing multi-dimensional feature extraction and confidence evaluation on the environment features; carrying out measurement updating by utilizing extended Kalman filtering; performing time domain differential analysis on the posterior state; when the state jump is detected, generating a smooth dynamic transition track by taking the current posterior state as a starting point; acquiring predicted curvature information of the front path from the global map; inputting the confidence score and the predictive curvature into a fuzzy logic controller, and calculating the adjustment amount of PID control gain; and calculating a motor control instruction based on the dynamic transition trajectory by using an adaptive PID algorithm, and outputting a final control signal. According to the method, high-precision positioning and stable control can be realized only by depending on a low-cost vehicle-mounted sensor, the cost is low, the universality is high, and the method is suitable for various complex indoor scenes.

The invention relates to the technical field of underground structure displacement measurement, and discloses a structure displacement monitoring method and system based on an intelligent sensor. The method comprises the following steps: acquiring an initial inclination angle signal, an acceleration signal and a gyroscope static signal; generating a zero initial depth reference point; constructing a depth time sequence; the fuzzy logic controller FLC executes real-time compensation adjustment; constructing an equal-interval sampling point sequence; and calculating accumulative displacement based on a weight curvature corrected dual-channel displacement fusion mechanism. The technical problem that in the prior art, a displacement estimation method depending on tilt angle sensor integration or acceleration sensor accumulation is difficult to realize high-precision structural displacement measurement especially in a soft soil layer scene with multi-source signal drift and probe speed fluctuation is solved. Due to the fact that a speed increment adjusting mechanism driven by the fuzzy logic controller is introduced, dynamic deviation correction and depth displacement decoupling modeling of multi-sensor data are achieved, and the spatial analysis precision of a displacement monitoring result is improved.

The invention discloses an energy storage system virtual impedance control parameter optimization method, which combines a fuzzy logic controller and a genetic algorithm to carry out control parameter optimization, takes power grid operation data as the input of the fuzzy logic controller, and utilizes the fuzzy logic controller to calculate the fuzzy adjustment amount of PID control parameters; constructing a fitness function by taking virtual impedance adjustment comprehensive error minimization as an optimization target, generating an optimization rule base by utilizing a genetic algorithm, and extracting association rules of PID control parameters according to the optimization rule base to obtain a parameter relational expression; the PID control parameters are updated by using the parameter relational expression and the fuzzy adjustment amount, the updated PID control parameters are used as the basis for adjusting the virtual impedance, the PID control parameters are adjusted on line in real time by using the fuzzy logic controller according to the state of the power grid, and meanwhile, the rule base and the membership function of the fuzzy logic controller are fuzzy offline by using the genetic algorithm. Therefore, a parameter relational expression is established, and oscillation or overshoot caused by single parameter adjustment is avoided.

The invention relates to the technical field of equipment control, and provides a thermal control method and device for a vehicle air conditioner, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring a reference temperature set by a user and an actual temperature in a compartment; according to the reference temperature and the actual temperature, a temperature difference value is calculated, and before the actual temperature reaches the reference temperature, the temperature variation of the temperature difference value in each sampling period is calculated; the temperature difference value and the temperature variable quantity are input into a preset fuzzy logic controller, dynamic reasoning is conducted by calling a plurality of pre-stored fuzzy control rules, and the target rotating speed of the compressor is obtained; and controlling the compressor to operate according to the target rotating speed to adjust the temperature in the compartment.

The invention discloses a ship cabin healthy environment dynamic regulation and control method and system based on multi-modal data fusion and deep learning. The method comprises the steps that multi-modal environment data and user physiological data (such as heart rate, body movement and skin conductance) are collected through a multi-sensor network; preprocessing the data; extracting space and time features by using a hybrid model of a convolutional neural network and a long-short-term memory network, and generating a fusion feature vector; generating a user health state score based on a deep reinforcement learning framework; environment parameter adjustment amounts (such as temperature, humidity, air purification intensity and illumination adjustment amount) are calculated through a fuzzy logic controller; and realizing closed-loop regulation and control by controlling the environment equipment through the actuator network. The problems of inaccurate health risk assessment and regulation response lag caused by single data and simple processing of an existing environment regulation system are solved, real-time, accurate and adaptive dynamic regulation is realized, and the safety and comfort of the cabin health environment are improved.

This application relates to the field of artificial intelligence and discloses a spatiotemporal graph convolutional method for order demand prediction that integrates functional semantic similarity. The method includes: collecting multi-source heterogeneous data streams containing orders, delivery personnel poses, road conditions, and the environment; constructing a unified grid feature vector representation through spatiotemporal gridding processing; performing order demand prediction using a spatiotemporal graph convolutional network that integrates spatial adjacency edges and functional semantic similarity edges; capturing spatiotemporal correlation features and generating prediction results through a multi-head attention mechanism and gated recurrent units; encoding the prediction results as state vectors and inputting them into a reinforcement learning policy network to output optimal allocation instructions; triggering a pre-scheduling mechanism based on the prediction results to guide idle delivery personnel to move to high-demand grids before orders are generated; performing distributed computing using an edge-cloud collaborative architecture; and dynamically adjusting and optimizing weights and performing anomaly detection and emergency recovery through a fuzzy logic controller.

This application relates to the field of multi-terminal flexible DC transmission system equipment, specifically to a method and system for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The method includes: real-time monitoring and acquisition of local electrical quantities; inputting the electrical quantities as input variables to a main fuzzy logic controller; outputting an initial adjustment amount for the droop coefficient; inputting the absolute values ​​of the real-time power margin deviation and DC voltage deviation to the corresponding auxiliary fuzzy logic controllers to obtain a power margin weighting factor and a DC voltage weighting factor, which are then used to weight and correct the initial adjustment amount to obtain the droop coefficient adjustment amount; adding the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and performing amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient; applying the method to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics, thus forming a hierarchical intelligent decision-making system.

The invention relates to the technical field of wireless communication secure transmission, and provides a dynamic secure transmission strategy optimization method and device based on RIS-UAV. According to the method, the gain change caused by the directivity of the RIS antenna is dynamically compensated by introducing the three-dimensional attitude angle of the RIS, and accurate control on a wireless channel is realized, so that the leakage of privacy signals to an eavesdropper is inhibited while the shielding obstacle is overcome. A constrained optimization problem is converted into an unconstrained double-layer optimization problem through a Lagrange duality method, so that the learning complexity of the intelligent agent is reduced; a post-decision state mechanism is introduced into problem solving, so that the intelligent agent can quickly learn and adapt to the change of user channel state information in the dynamic wireless communication system; the fuzzy logic controller can increase exploration when the environment uncertainty is high and enhance the utilization when the strategy is stable, so that more intelligent and efficient exploration-utilization balance is realized, the convergence speed is remarkably accelerated, and the final performance is improved.

The application discloses an improved DWA local path planning method based on a guide field and self-adaption, relates to the technical field of autonomous mobile platform path planning, and particularly relates to an improved DWA local path planning method based on a guide field and self-adaption. A global path guide field is constructed, a multi-dimensional evaluation function is improved, a constraint is added to a DWA model, and a fuzzy logic controller is integrated to adaptively adjust the weight of the evaluation function. The method comprises the following steps: obtaining a global path, constructing a global path guide field; constructing a multi-dimensional evaluation function; constructing and improving a DWA model, generating a candidate trajectory set, generating the weight coefficient of each evaluation item of each candidate trajectory, and obtaining the score of each candidate trajectory; judging whether the current position of the autonomous mobile platform is in a circular region with the target point as the center and a radius of 0.5 m; if yes, stopping local path planning to obtain a smooth path; and judging whether a collision occurs on the smooth path.

The present application relates to the technical field of Internet of Vehicles edge computing, and particularly relates to a sensing context task offloading method for Internet of Vehicles edge computing, comprising: constructing an upper fuzzy logic controller and a lower reinforcement learning decision model; acquiring a current time and an urgency of a to-be-processed task and inputting the upper fuzzy logic controller, reasoning through a predefined fuzzy rule base, and outputting dynamic reward weights of time delay, energy consumption and economic cost; constructing a state space and an action space of the reinforcement learning decision model; generating a binary action mask according to a link connection time between a source vehicle and a candidate task offloading node, eliminating unreachable actions in the action space, and remodeling an action probability distribution; executing an action based on the remodeled action probability distribution, calculating a reward value according to a reward function containing the dynamic reward weights, and iteratively executing until convergence to obtain an optimal task offloading strategy; the present application is significantly superior to a benchmark algorithm in comprehensive utility, time delay guarantee and convergence efficiency.

This invention discloses a multi-dimensional monitoring system and method for engineering project progress based on information technology, applicable to the monitoring field. The system includes: an initial construction plan construction module, a data acquisition and environmental monitoring module, a data analysis and plan optimization module, a construction progress prediction module, and a risk management module. By collecting multi-dimensional data from the construction site in real time and applying an intuitionistic fuzzy inference algorithm, this invention can dynamically adjust the construction plan to cope with constantly changing site conditions and external environmental factors. Utilizing the intuitionistic fuzzy inference algorithm to analyze the needs and bottlenecks of the construction project, combined with construction logic and physical constraints, allows for more precise resource allocation. A fuzzy logic controller handles fuzzy and uncertain situations, ensuring the project complies with all relevant building and safety standards. Through continuous monitoring and timely adjustments, project quality and safety are guaranteed.

The invention relates to the technical field of artificial intelligence and logistics distribution, and discloses an intelligent logistics distribution scheduling system and method based on deep learning prediction.The system adopts a cloud-side-end collaborative architecture, a cloud server is responsible for deep learning model training and global optimization, and the cloud server is responsible for deep learning model training and global optimization; and loading the compressed lightweight model by the edge computing node to execute region real-time scheduling. The method comprises the following steps: constructing grid spatial-temporal characteristics by collecting multi-modal data; order demand prediction is carried out by using a space-time diagram convolutional network which introduces a space adjacency and function similarity dual relationship and a multi-head attention mechanism; triggering a transport capacity pre-scheduling mechanism based on the prediction result; and the edge end generates a real-time distribution instruction by using a reinforcement learning strategy network, and dynamically adjusts a weight coefficient of a multi-target reward function according to a real-time service scene in combination with a fuzzy logic controller. According to the method, the implicit association between the regions is effectively mined, the prediction precision is remarkably improved, and active management and multi-target adaptive optimization of the transport capacity resources are realized.

The application discloses a cab cleanliness determination method, device, equipment and storage medium, and belongs to the technical field of automobiles. The method specifically comprises the following steps: acquiring environment image information of a cab and air sensing information of the cab; based on the environment image information, a preset visual cleanliness evaluation strategy is used to obtain visual cleanliness; based on the air sensing information, a preset air cleanliness evaluation strategy is used to obtain air cleanliness; based on the visual cleanliness and the air cleanliness, a fuzzy logic controller is used to determine the cab cleanliness and a processing suggestion corresponding to the cab cleanliness; and the cab cleanliness and the processing suggestion corresponding to the cab cleanliness are output.

The invention discloses a crawler belt self-propelled intelligent traditional Chinese medicinal material harvester and a control method thereof, and belongs to the technical field of traditional Chinese medicinal material harvesting machinery. The harvester comprises a gantry type crawler chassis, an operation part and an intelligent measurement and control system, and the gantry type crawler chassis forms a channel between two crawler walking assemblies so as to adapt to a fritillary-corn interplanting mode; the intelligent measurement and control system collects multi-source data such as excavation resistance and soil humidity in real time through a sensor group, fuses the data by adopting a Kalman filtering algorithm, generates a soil resistance index representing a comprehensive working condition, and calculates an optimal operation parameter set in real time through a fuzzy logic controller; and the control module drives each operation part to accurately execute the instruction in a closed-loop manner. Through closed-loop self-adaptive adjustment of sensing, decision making and control, soil and environment changes can be responded in real time, the damage rate and the loss rate of traditional Chinese medicinal materials are remarkably reduced, and the working efficiency and the automation level are improved.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

A prosthetic limb for replacing a portion of a leg of a subject includes a prosthetic limb body, a prosthetic limb knee, a prosthetic limb ankle and a multimodal sensor array to obtain sensor data used to derive parameters indicative of a physiological state of the subject and an environment in a proximity of the prosthetic limb. The prosthetic limb includes a data processing unit to process the sensor data to generate a processed sensor data generated using a machine learning (ML) model. The ML model outputs the processed sensor data as a predicted subject intent and predicted prosthetic limb parameters. The prosthetic limb includes a control system to generate a control signal having control parameters based on the processed sensor data using a fuzzy logic controller and a proportional-integral-derivative (PID) controller of the control system for adjusting a movement of the prosthetic limb.

The present application relates to the field of physical therapy equipment, especially to a kind of spinal step quantitative traction physiotherapy control system based on closed-loop force feedback, the system includes: data acquisition module, real-time traction force value, electromyographic signal data, displacement rate of change between traction bed movable bed plate and traction bed fixed bed body are collected;Traction bed drive module, according to the traction force control instruction output by self-learning fuzzy logic controller, execute traction action to traction bed body;Self-learning fuzzy logic controller, according to real-time traction force value, electromyographic signal data, displacement rate of change and the pain level signal feedback by the object to be traction, using step traction strategy, generates traction force control instruction, traction force control instruction is set to use the fuzzy parameter adjustment model pre-constructed based on neural network and fuzzy inference, dynamically generated by updating the shape parameter of membership function of first fuzzy logic rule.The present application effectively improves the self-adaptability and accuracy of traction force regulation.

This invention discloses a fuzzy adaptive intelligent wiper motor control method and system based on noise evaluation. It includes an initial speed curve design module for generating an initial angular velocity curve based on the wiper operating frequency and wiping angle; an adaptive algorithm optimization module that processes real-time wiper operating data through a fuzzy logic controller, outputting speed and acceleration adjustment values; a neural network optimization module that uses a multi-layer feedforward neural network to optimize the fuzzy logic controller parameters to minimize the objective function; and a motor control module that adjusts the motor setpoint in real time based on the optimized adjustment values. This invention solves the problem of abnormal noise from automotive wipers, which is caused by excessive noise or insufficient wiping efficiency due to improper speed control. It significantly reduces operating noise, ensures that the wiping frequency meets regulatory requirements, and improves the adaptability and stability of the control system.

The application provides a method and system for evaluating animal welfare level of a farm and a storage medium. The method comprises: collecting to-be-processed data, wherein the to-be-processed data comprises environmental feature data, feeding feature data and behavior feature data of an animal to be evaluated; inputting the to-be-processed data into a back propagation neural network model for processing to obtain to-be-processed level data; and inputting the to-be-processed level data into a fuzzy logic controller model for processing to obtain animal welfare level data. According to the embodiment of the application, the environmental feature data, the feeding feature data and the behavior feature data of the animal to be evaluated are organically classified and fused, so that the redundancy and mutual exclusivity among the data can be eliminated, the difficulty of weight distribution of the data can be effectively avoided, the various types of data can be mutually complementary and cooperative, and the reliability of the animal welfare evaluation and monitoring data can be effectively improved.

The invention discloses a self-adaptive virtual inertia VSG grid-connected control method and system, and relates to the technical field of new energy grid-connected control, and the method comprises the steps: detecting the frequency change rate, frequency deviation and power vacancy degree of a power grid in real time; a virtual inertia parameter and a damping coefficient are dynamically adjusted through a fuzzy logic controller, and a quantitative relation model of the virtual inertia and the stability margin is established; the power outer ring adopts improved droop control to realize power decoupling; an inner ring adopts a predictive control algorithm to select an optimal on-off state sequence; the power grid parameters are identified on line through small signal injection and frequency domain analysis, the problem that in an existing virtual synchronous generator control technology, the virtual inertia self-adaptive adjustment precision is insufficient is solved, and the frequency stability of a new energy grid-connected system is effectively improved.