18 results about "Fuzzy subset" patented technology

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

According to the method, a fuzzy subset is set according to the required torque, the current SOC value and the difference value between the engine rotating speed and the motor rotating speed, and the torque of an ISG motor is dynamically adjusted through a fuzzy control algorithm. The fuzzy control adopts a fixed fuzzy rule, so that a Q table is established through a Q learning algorithm, the compensation torque of the ISG motor is obtained, the final output torque of the ISG motor is determined to be the sum of the output torque of the fuzzy controller and the compensation torque output by the Q learning algorithm, and the torque output by the ISG motor is determined. According to the method, the advantage of high response speed of the torque of the motor is utilized, the difference value between the required torque and the actual torque is effectively compensated, the rotating speed fluctuation of the engine caused by overlarge change of the torque of the engine is avoided, and the fuel economy and the driving operation stability are improved.

The application provides a kind of hybrid model fault early warning method and system based on timing prediction and fuzzy logic, device state feature set is constructed by collecting the operation and maintenance data of equipment in continuous operation cycle, feature correlation analysis is carried out on device state feature set, device state potential vector reflecting the dynamic coupling relationship between parameters is generated, fuzzy rule matching is carried out by calling pre-trained fuzzy logic inference model, and fuzzy state set containing multi-dimensional fuzzy subset is generated;Fuzzy state set is processed by space-time correlation, and multi-dimensional decision cloud chart is generated, which is reconstructed in space by quantization weight distribution mechanism, and the optimized decision matrix is obtained;Based on the confidence distribution characteristics corresponding to each fault type, the fault early warning signal containing fault early warning level and fault location positioning information is generated.The application can improve the accuracy and timeliness of fault early warning, and provides reliable decision support for equipment operation and maintenance management.

The application discloses a control method for switching of an excitation power supply of a superconducting phase modifier. The method includes sampling actual current and reference current of an excitation winding of the superconducting phase modifier, obtaining a current error through difference, determining an output mode through a fuzzy controller, and switching a circuit. The fuzzy controller includes a fuzzy data set and a fuzzy rule set. The fuzzy data set includes a plurality of range intervals, and a partially overlapping interval is arranged between adjacent range intervals. The union of each fuzzy subset is a maximum error limit of the current error. The fuzzy rule set includes a plurality of judgment rules. The judgment rules determine the output mode according to whether the value of the current error falls within the detection value of the corresponding range interval. Through the "soft" decision of the fuzzy logic, the mode is prevented from being frequently switched due to small error fluctuations, the switching loss of the power device is significantly reduced, and the system reliability and service life are improved.

The application belongs to the technical field and discloses a control method and equipment of a high-speed motor and an electromagnetic bearing system, the method comprising the following steps: selecting input variables and output variables of a fuzzy controller; fuzzifying the input variables and the output variables, determining a quantization factor, a basic domain and a fuzzy domain; defining fuzzy subsets and membership functions in the fuzzy domain of the input variables and the output variables; calculating the membership degrees through the membership functions; substituting the membership degrees into a fuzzy value formula to obtain fuzzy values of the output variables, obtaining accurate control amounts of the output variables through defuzzification; and outputting the accurate control amounts of the output variables to an electromagnetic bearing model for control. The method can effectively reduce the oscillation and vibration of the system, improve the stability of the overall system, and adapt to large disturbances or complex environments.

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.

The invention discloses a sludge incineration ash bin discharging system and a control method, and belongs to the field of sludge incineration equipment. The discharging system comprises a bin body, a weighing device, a pneumatic gate valve, a variable-frequency discharger, an expansion joint and a PLC; the control method comprises the following steps: S1, setting sampling periods and the ideal unloading amount of each sampling period, and starting unloading according to the upper limit value of the unloading frequency; s2, constructing an input fuzzy membership function and an associated fuzzy rule base, and defining input variables of the input fuzzy membership function and the associated fuzzy rule base; constructing an associated output fuzzy subset; s3, in each sampling period, whether the current discharging frequency needs to be corrected or not is judged again; and S4, when correction is needed, the corrected discharging frequency is obtained through a fuzzy algorithm, and discharging operation is executed according to the frequency. And S5, when the discharging amount reaches the standard, discharging is stopped. According to the method, the mathematical modeling difficulty and the control complexity can be reduced, and flying dust is inhibited while the unloading uniformity and stability are guaranteed.

This invention provides a torque control method, system, medium, and device for hybrid electric vehicles, comprising: creating a fuzzy control toolbox, taking the total vehicle torque, battery SOC value, and motor speed as inputs, and engine output torque as output; setting fuzzy subsets for the total vehicle torque, battery SOC value, motor speed, and engine output torque, and constructing membership functions; establishing fuzzy rules for the total vehicle torque, battery SOC value, motor speed, and engine output torque; constructing a fuzzy rule library, performing fuzzy inference based on the membership functions, fuzzy inference algorithm, and fuzzy rule library to obtain fuzzy quantities; converting the fuzzy quantities into clear values ​​through a defuzzification algorithm; and calculating the motor output torque based on the clear values. This invention dynamically adjusts the torque distribution rules according to the relationship between motor speed and efficiency. At high speeds, the motor output torque is reduced, and at low speeds, the torque is increased, ensuring that the motor always operates within its high-efficiency range.

The invention belongs to the technical field of hard disk tuning, and particularly relates to an HDD hard disk automatic tuning method based on a fuzzy algorithm, which comprises the following steps of: mapping core parameters into low, medium and high fuzzy subsets by monitoring and preprocessing multi-dimensional operation data such as I / O load, disk rotating speed, temperature and the like of an HDD hard disk in real time; a load type-tuning parameter fuzzy rule base is called, a tuning parameter fuzzy output set is generated through Mamdani fuzzy reasoning, a gravity center method is adopted for defuzzification, and specific tuning decisions such as the pre-reading size and the I / O scheduler type are obtained and automatically executed. And meanwhile, wear state parameters such as redistributed sector counting are collected in real time, and after fuzzy risk assessment, parameters of the medium / high wear risk hard disk are dynamically corrected and optimized. The limitation of traditional fixed threshold adjustment and optimization is broken through, self-adaptive matching of a load scene is achieved, the performance and the service life of the hard disk are balanced, whole-process automatic execution is achieved, and adjustment and optimization accuracy and stability 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.

The application provides a new energy station oscillation suppression method and system, comprising: when an oscillation component is greater than an oscillation component threshold, determining a wind turbine generator set grid-connected electrical distance fuzzy subset and a wind turbine generator set operating power relative value fuzzy subset based on acquired parameter information; adopting a fuzzy logic strategy to determine a power instruction distribution coefficient based on the wind turbine generator set grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset; and lowering the wind turbine generator set operating power based on the power instruction distribution coefficient. The application adopts a fuzzy logic strategy to determine a power instruction distribution coefficient based on a wind turbine generator set grid-connected electrical distance fuzzy subset and an operating power relative value fuzzy subset, and lowers the wind turbine generator set operating power, thereby minimizing the amount of abandoned wind and light, and improving the safety and stability margin of a new energy grid-connected system.

The present application relates to the field of quadruped robot, in particular to a kind of quadruped robot joint motor parameter self-adaptive adjustment method based on fuzzy control.The present application includes the following steps:S1: current error and error rate of change are calculated according to current reference instruction and feedback current;S2: current error and error rate of change are divided into minimum, small, medium and large, fuzzy by triangular membership function and form fuzzy set, and the membership of fuzzy set is calculated;S3: according to fuzzy rule, obtain proportional coefficient and integral coefficient fuzzy subset and membership;S4: by inverse triangular membership function, clear value of proportional coefficient and integral coefficient is obtained, and clear value is transmitted to current loop proportional integral parameter.The present application can improve control accuracy, enhance adaptability, reduce electromagnetic noise influence and simplify parameter adjustment process.

The invention relates to the technical field of laser gyroscopes, and discloses a laser gyroscope threshold processing method and system based on a fuzzy control theory, so as to improve efficiency and accuracy. The method comprises the following steps: controlling a rotary table to rotate 0.03 degree towards a gyro pulse reduction direction every time, sequentially obtaining a point set P of laser gyro static pulse output in a symmetrical starting and ending range, and fitting a standard straight line; the slope between every two adjacent elements in the set P is calculated, and a set Q of the slopes between two adjacent points is obtained; comparing whether the absolute value of the slope between any two adjacent points in the Q set and the absolute value of the difference between the slope and the standard slope meet a preset threshold judgment rule of the fuzzy subset where the corresponding test point is located or not, and then judging whether the test point is located in a nonlinear region or not; subtracting the minimum value from the maximum value of the sequence of the test points in all the non-linear regions and adding 1 to obtain the number w of the test points in the non-linear regions; calculating the threshold value H of the laser gyroscope as H = C * cosA * sin (w * 0.03 degrees); wherein A is the local latitude, and C is the earth rotation angular velocity.

The invention provides an information flow-based early warning signal generation method and equipment for a manufacturing industry chain. The method comprises the steps of obtaining an information data set to determine a target early warning index system comprising a plurality of target early warning indexes; determining an influence relationship among the plurality of target early warning indexes to construct a network analytic hierarchy structure model, and obtaining weight quantification information of each target early warning index through the network analytic hierarchy structure model; determining a fuzzy evaluation matrix of the target early warning index, obtaining a fuzzy subset based on the fuzzy evaluation matrix, obtaining a weight vector and the fuzzy subset corresponding to the target early warning index according to the weight quantization information, and obtaining a main factor evaluation matrix; according to the main factor evaluation matrix and a pre-configured first formula, determining an early warning grade of a manufacturing industry chain and a comprehensive risk score of a target early warning index located at a bottom layer, and generating a risk sequence according to the comprehensive risk score; and outputting early warning information, wherein the early warning information comprises an early warning level, a comprehensive risk score and a risk sequence.

The application provides a pump station water level dynamic boundary adjustment method based on a fuzzy firefly algorithm, comprising the following steps: presetting a target water level in a water level intelligent monitoring system, calculating a water level deviation and a water level change rate, and determining a sensitivity parameter; presetting a language variable of a fuzzy variable to obtain a fuzzy subset of a high sensitivity parameter, a fuzzy subset of a low sensitivity parameter, a fuzzy subset of a dynamic boundary adjustment value and a fuzzy subset of a flow adjustment value; determining corresponding triangular membership functions according to input variables and output variables of different sensitivity parameters; setting fuzzy rules and calculating the membership degrees of the input variables; determining fuzzy output of the dynamic boundary adjustment value according to the activation condition of the fuzzy rules and the membership function values; and using a barycenter method to accurately calculate the fuzzy output of the dynamic boundary adjustment value to obtain an accurate dynamic boundary adjustment value. The application can more accurately control the pump station under various dynamic hydrological conditions.

This invention discloses an intelligent diagnostic expert system for underground drainage pipe networks based on fuzzy rules for water flow velocity, comprising external factors, internal pipe network processes, water flow velocity data, and a working condition analysis and diagnostic system. The working condition analysis and diagnostic system in this expert system is implemented using fuzzy rules for water flow velocity, including two parts: establishing a fuzzy rule base and fuzzy inference. Establishing the fuzzy rule base includes the steps of: determining the fuzzy inference engine structure, determining the fuzzy subset membership functions, establishing fuzzy control rules, and identifying water flow velocity parameters. Fuzzy inference includes the steps of: fuzzifying water flow velocity data, establishing a fuzzy inference engine, and defuzzifying the data. This invention, using fuzzy rules for water flow velocity to implement an intelligent diagnostic expert system for underground pipe networks, has the characteristic of requiring no manual operation. It relies on water flow velocity data to analyze and diagnose the status of underground drainage pipes and can be applied to the complex working conditions of underground pipe networks.

The invention provides air conditioning equipment, prediction and prediction model generation methods and devices and a storage medium, and relates to the technical field of air conditioning equipment. The prediction model generation method for the air conditioner comprises the steps that the interval of each kind of air conditioner data associated with a prediction model is subjected to fuzzy segmentation, a fuzzy subset of each kind of air conditioner data is obtained, and a membership function of each fuzzy subset is determined; associating the collected different types of air conditioner data to obtain sample data; a model rule is generated according to the fuzzy subsets to which the air conditioner data in the sample data belong, and the model rule comprises the incidence relation between the fuzzy subsets of different types of air conditioner data; and determining a prediction model according to the model rule and the membership function corresponding to the sample data.

The invention belongs to the technical field of electrolytic hydrogen production, and provides a voltage-power dynamic mapping-based control method for direct-current bus voltage in a hydrogen production system. The method comprises the following steps of: selecting charge state deviation of an energy storage unit and a power demand of a PEM electrolytic bath as input variables of a fuzzy controller, and taking correction of a droop coefficient as an output variable; dividing the domain of discourse of each variable into five fuzzy subsets; dividing each variable into five fuzzy interval grades, and constructing a multivariable partition dynamic mapping relation, so that each input combination is uniquely mapped to a specific output decision; constructing a voltage-power dynamic mapping relation, and stipulating the correction of the droop coefficient under different input combinations; and the charge state deviation of the energy storage unit and the power requirement of the PEM electrolytic cell are collected in real time, the droop coefficient is updated based on the correction amount of the droop coefficient, and the updated droop coefficient is converted into a direct current bus to provide an optimized voltage reference instruction for the energy storage unit.