22 results about "Gaussian membership function" patented technology

The invention discloses an alternating-current and direct-current hybrid power transmission network rack optimization evaluation method and system, which are used for solving the technical problems that a traditional alternating-current and direct-current hybrid power transmission network rack evaluation method depends on static parameters or empirical data, cannot dynamically respond to cross-regional load fluctuation and the like, is difficult to cope with risks, and causes low quality of a rack scheme. The method comprises the following steps: generating comprehensive evaluation indexes of a plurality of grid schemes according to acquired operation data of an AC / DC hybrid power transmission network by adopting power system simulation software; constructing a fuzzy evaluation matrix of each grid scheme according to the comprehensive evaluation index of each grid scheme by adopting an improved descending semi-trapezoidal membership function and a Gaussian membership function; calculating a target weight corresponding to the comprehensive evaluation index of each grid scheme; and according to the target weight corresponding to the comprehensive evaluation index of each grid scheme and the fuzzy evaluation matrix, calculating the comprehensive score of each grid scheme, and selecting the grid scheme with the maximum comprehensive score as the target grid scheme of the AC / DC hybrid power transmission network.

The invention relates to the technical field of process optimization, in particular to a fertilizer production process optimization method and system. The method comprises the following steps: obtaining original data composed of process parameters and evaluation indexes of a plurality of production batches, carrying out standardization processing, processing interval type evaluation indexes by using a Gaussian membership function, calculating an initial weight through an entropy weight method in combination with a standardization value and a time sequence fluctuation coefficient, carrying out correlation correction on the initial weight, and carrying out correlation correction on the initial weight; and when the absolute value of the correlation coefficient of any two evaluation indexes is greater than the corresponding correlation threshold value, reducing the initial weight according to the variance contribution degree, redistributing other indexes by using the reduced weight to obtain a final weight, constructing a comprehensive evaluation model based on the final weight, and determining an optimization scheme. According to the scheme, the interval type evaluation indexes can be standardized, the problems of information redundancy and weight distribution distortion can be eliminated, then the accuracy of the evaluation result is improved, and a more reliable optimization scheme is provided for the fertilizer production process.

The invention relates to the technical field of slope health degree evaluation, in particular to a slope health degree intelligent evaluation system and method based on multi-source data fusion, and constructs a multi-dimensional feature matrix such as fusion change rate by receiving displacement time sequence and rainfall environment data of a plurality of monitoring terminals of a slope. According to the core technology, all feature values are mapped through a Gaussian membership function and converted into initial reliability distribution under different risk levels, and a risk reliability vector is formed. Differentiated weighting is carried out on the reliability vectors, and evidence items representing local risk contributions are generated. Finally, all weighted evidence items are input into an improved evidence reasoning model, multi-source information contradiction is effectively solved, the global membership probability of the whole side slope on each risk level is solved, a continuous overall health degree score capable of dynamically reflecting a stable state is generated through fusion, the crossing from local heterogeneous perception to overall intelligent research and judgment is realized, and the overall health degree score is improved. And a core basis is provided for slope safety early warning and accurate management and control.

The invention relates to the technical field of milling process risk assessment and parameter optimization, in particular to a deep neural fuzzy reasoning system which comprises a semantic layer, a rule layer and a reasoning layer which are in signal connection in sequence and cooperatively achieve tool risk and quality risk assessment of a milling process. The method specifically comprises the steps that a semantic layer receives milling parameters, and numerical milling parameters are converted into a fuzzy feature matrix through an interval Type-2 Gaussian membership function; according to the method, expert knowledge is embedded through interval Type-2 fuzzy logic to adapt to a small sample milling scene, an IF-THEN activation rule is output to enable a risk decision basis to be traceable, reasoning real-time performance is guaranteed by means of Top-k sparse aggregation, meanwhile, closed-loop self-adaptive adjustment of milling process parameters is supported, the method is suitable for milling scenes of complex parts and novel materials, and the method is suitable for large-scale popularization and application. And the production efficiency can be improved while the machining quality is guaranteed.

The invention provides a steam turbine fault symptom identification method based on fuzzy vector mapping and independent element space reconstruction, relates to the field of steam turbine fault diagnosis, and aims to solve the problems that normal working conditions and fault features are difficult to distinguish and the non-linear and non-stationary signal processing capacity is weak in a traditional vibration analysis method, an oil analysis method and an independent element analysis method. The method comprises the following steps: collecting multivariable sensor data of a steam turbine in a normal state for preprocessing; establishing an initial independent element analysis model; a fuzzy observation vector is constructed by using a Gaussian membership function, and the influence of large-amplitude normal fluctuation is weakened; performing dynamic reconstruction in an independent element space through the hybrid matrix, and calculating a residual vector; and constructing a square prediction error statistic based on the residual vector to carry out fault judgment. According to the method, the problem of order and amplitude uncertainty in independent component analysis is effectively solved, the recognition accuracy of the early weak fault of the steam turbine is remarkably improved, and reliable technical guarantee is provided for safe and stable operation of a power system.

A joint angle prediction method and system based on rule dimension reduction and a medium are related to the technical field of human-computer interaction. The method comprises the following steps: acquiring signals related to joint movement, extracting time domain features, frequency domain features and nonlinear features from the signals to form a multi-dimensional input feature vector; clustering the multi-dimensional input feature vector to generate a plurality of cluster centers, configuring a Gaussian membership function for the cluster centers, and constructing an initial fuzzy system; calculating the membership degrees of the current input multi-dimensional feature vector to each original fuzzy rule to form an original membership degree vector; mapping the original membership degree vector to a low-dimensional membership degree vector by using a learnable dimension reduction matrix; performing fuzzy reasoning by using the low-dimensional membership degree vector and corresponding consequent function parameters to output a joint angle prediction value; and determining the dimension reduction matrix and the consequent function parameters through joint optimization. The application can realize accurate prediction of joint angle.

The application relates to the technical field of electric control systems, in particular to a production control method for an antistatic and conductive geomembrane, which comprises the following steps: collecting temperature data in a geomembrane production process, denoising data segments in the temperature data, obtaining reconstruction data, obtaining a first fluctuation coefficient of the reconstruction data according to the difference values of various data points in the reconstruction data, obtaining a fluctuation difference of the reconstruction data according to the difference between second fluctuation coefficients of adjacent data points in the reconstruction data, obtaining a to-be-stabilized time interval according to the second fluctuation coefficients of all data points in the reconstruction data, obtaining a response coefficient of a Gaussian membership function according to the fluctuation difference of the reconstruction data and the to-be-stabilized time interval, obtaining an adjustment coefficient of the Gaussian membership function in combination with the first fluctuation coefficient of the reconstruction data, and then obtaining a variance value of the Gaussian membership function, and performing temperature control in the geomembrane production process. The temperature control is more robust, and the production yield of the geomembrane is ensured.

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 relates to the technical field of electrical control system monitoring, and discloses a real-time fault detection method and system for a new energy automobile. The method comprises the steps of collecting voltage and current control signals of a battery management system and a motor control system to calculate a working condition feature vector, establishing a fast and slow dual-time scale state observer, calculating an adaptive gain matrix through a mahalanobis distance and a Gaussian membership function to correct the slow observer to obtain a health parameter estimated value, and judging a sudden fault by using a fast observer residual error and analyzing a health parameter trend to judge a gradual change fault. The method solves the problems that the estimation precision is reduced and the false alarm rate is increased due to the fact that the observer gain cannot adapt to multi-working-condition changes in the prior art.

The invention relates to the technical field of renewable energy power generation, and particularly provides a multi-type renewable energy integrated power generation control method and system, and the method comprises the steps: obtaining the current power generation data and corresponding environment data of different types of renewable energy power generation equipment, and the current state data of a power generation system; inputting the current power generation data and the environment data into a prediction model established based on a Gaussian membership function, a T-S fuzzy reasoning model and a BP neural network model to obtain a plurality of prediction sequences in a preset time step length; and determining an optimal control sequence based on the prediction sequence, the current state data and a model prediction control algorithm to schedule the current power supply power of the power generation equipment, and updating model parameters of the prediction model in combination with the state data of the power generation system after scheduling. The problems that in the prior art, a multi-type renewable energy power generation control method is difficult to cope with rapid changes of environmental parameters and instantaneous changes of system loads, and the self-adaptive adjusting capacity is insufficient are solved.

The invention discloses an underground water level early warning method and system. The method comprises the following steps: acquiring an underground water level time sequence data set of all stations in a target area; a hierarchical clustering algorithm based on dynamic time warping is adopted to recognize spatial heterogeneity of underground water level changes, and all stations are classified; for each type of stations, acquiring the groundwater level mean value and the water level change rate of each type of stations as input variables, and standardizing the input variables; performing fuzzification processing on the standardized input variables through a Gaussian membership function to generate multiple pieces of input variable fuzzy data; performing fuzzy reasoning on the multiple pieces of input variable fuzzy data according to a preset fuzzy reasoning rule to obtain multiple pieces of output variable fuzzy data; the multiple pieces of output variable fuzzy data are converted into underground water level risk coefficients through a gravity center method; and determining the underground water level early warning level of the corresponding type of station according to the underground water level risk coefficient of each type of station.

The invention provides a color box printing self-adaptive drying method based on multi-mode perception, and belongs to the technical field of intelligent manufacturing and printing process control. Multi-modal data in the printing process are collected through a multi-modal sensor; preprocessing the multi-modal feature sequence into a multi-modal feature sequence with consistent space; and inputting the multi-modal feature sequence into the lightweight cross-modal adaptive coding network, obtaining spatial features of each modal through the shallow feature extraction network and performing weighted optimization, capturing a data time evolution rule of each modal through the time dynamic modeling network, and realizing heterogeneous modal fusion and consistency calibration through the modal consistency correction network. And outputting a comprehensive representation vector of the printing ink drying state, inputting the comprehensive representation vector into an adaptive controller, completing feature fuzzification mapping through a Gaussian membership function, and realizing neural fuzzy reasoning based on a fuzzy rule to generate an optimal drying parameter. According to the method, intelligent upgrading is realized on a closed-loop control link of perception, analysis, decision making and correction, and the energy consumption and the rejection rate are remarkably reduced.

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 provides a composite error-based fuzzy fast terminal sliding mode robot control method and system, and the method comprises the steps: constructing a robot dynamic model, decomposing a nominal part and a lumped uncertainty part, enabling a controller to achieve the processing of known dynamic and unknown disturbances, and improving the adaptability of a system; a tracking error and a fast terminal sliding mode surface model are defined, and correction processing is combined, so that the smoothness and the stability of the control process are ensured; a fuzzy basis function is calculated through a fuzzy rule base and a Gaussian membership function, and a composite error driven adaptive law is coupled, so that the system can still maintain high-precision tracking in a steady-state stage; and finally, by integrating the nominal part torque, the self-adaptive compensation torque and the robust switching torque and carrying out amplitude limiting processing on the total torque, high-frequency buffeting of the control torque is restrained, actuator abrasion is reduced, it is guaranteed that the output torque is within a safe range, and the method is suitable for high-dynamic scenes such as precise assembly or medical operation.

The present application relates to the technical field of data classification, and particularly relates to a performance testing method and system of special cable for petrochemical system, comprising: performing quantile transformation on cable performance testing data and dividing the cable performance testing data into multiple feature subsets according to attribute domains, presetting multiple groups of candidate kernel parameters for each feature subset and generating parallel candidate feature representations through kernel principal component analysis, constructing a probability shadow set model for the candidate feature representations, transforming the probability shadow set model to obtain membership degrees by using a Gaussian membership function, calculating local outlier factors and converting the local outlier factors into local uncertainty weights to adjust the membership degree threshold, and then dividing positive domains, negative domains and boundary domains, generating basic probability assignment functions according to the division results, selecting a feature representation combination with the minimum total evidence conflict coefficient as an optimal model, and outputting a cable performance analysis conclusion. The present application can adjust the domain division boundary according to the local distribution characteristics of data points, and reduce the evidence conflict in the multi-granularity fusion process.

The application relates to the technical field of electrical control system monitoring, and discloses a new energy automobile real-time fault detection method and system. The method comprises the following steps: collecting voltage and current control signals of a battery management system and a motor control system to calculate a working condition characteristic vector; a fast-slow double-time-scale state observer is established; an adaptive gain matrix is calculated through Mahalanobis distance and a Gaussian membership function to correct the slow observer and obtain a health parameter estimation value; a fast observer residual error is used to determine a sudden fault and analyze a health parameter trend to determine a gradual change fault. The application solves the problem that, in the prior art, the observer gain cannot adapt to changes in multiple working conditions, resulting in a decrease in estimation accuracy and an increase in false alarm rate.

This invention discloses a dynamic optimization control method and system for elevator maintenance cycles based on an energy consumption model. The method determines the energy consumption state model of the target elevator using a Gaussian membership function. Based on the energy consumption state model, it performs time value of money discounting and couples it with a trapezoidal numerical integration method to obtain a fuzzy energy consumption cost model for the target elevator. By constructing a variable cost tanh saturation model and combining it with fixed cost parameters, it generates a full-cycle maintenance cost model for the target elevator. Using a standard normal cumulative distribution function and combining it with the currently predicted remaining lifespan of the target elevator, it constructs a fuzzy operational reliability model for the target elevator. Based on the fuzzy energy consumption cost model, the full-cycle maintenance cost model, and the fuzzy operational reliability model, and with the elevator's nonlinear dynamic safety state as a constraint, it constructs a maintenance cycle optimization model and solves the optimal maintenance cycle strategy for the target elevator online, achieving real-time optimization of elevator dynamic operation and maintenance.

This invention discloses a dynamic identification method for the rain enhancement potential of stratified mixed clouds in complex terrain areas, relating to the field of electronic digital data processing technology. The method includes: determining whether high-rain enhancement rate cumulus cloud cells possess macroscopic rain enhancement conditions based on satellite data and preset cloud top brightness temperature and cloud top height thresholds, obtaining a judgment result; classifying the rain enhancement potential of stratified mixed cloud systems in the target area into levels based on microscopic parameter characteristics and the judgment result; calculating the comprehensive membership score of four radar parameters in the range database at each rain enhancement potential level based on the microscopic parameter characteristics and the level classification result, using a composite Gaussian membership function; selecting the level with the highest comprehensive membership score as the rain enhancement potential level of the range database; if multiple rain enhancement potential levels have the same comprehensive membership score, selecting the higher level as the rain enhancement potential level of the range database. This invention improves data reliability, making the judgment of rain enhancement potential more systematic and scientific.

The application provides a power system network risk defense method based on Markov rule reasoning, and realizes dynamic identification and defense of power system network risk. First, a fuzzy reasoning model for multi-source power system operation data is established, a rule capable of reflecting differences in distribution of different data characteristics is constructed in combination with triangular, trapezoidal and Gaussian membership functions, and accurate description of input state is realized; second, a rule updating mechanism based on Markov decision process is designed, state reasoning, action selection and score evaluation are performed, it is ensured that the rule can continuously adjust with the change of system state, and dynamic optimization of the number and parameters of the rule is realized; finally, a decision method for generating risk levels and defense strategies is constructed in combination with comprehensive membership output and loss function feedback, so that the model can maintain accuracy and stability when facing power grid system risk situations, and the reliability of power system network risk defense is improved.

The application discloses an egg target detection method and system based on fuzzy theory and time sequence correction, and the method constructs an end-to-end detection model comprising a YOLOv8 backbone network module, a fuzzy feature extraction module composed of a global pooling enhancement module GPBlock and a time sequence correction module Bi-TCN, a multi-scale fuzzy fusion module FFM based on a Gaussian membership function, a fuzzy attention enhancement module FA based on a Gaussian membership function and a detection classification module; a dataset containing egg bounding box labels is constructed, and the model is trained by using a joint loss function of fuzzy feature loss and detection loss; during inference, the egg video is input into the model frame by frame, and detection results are output. The application significantly improves the accuracy and robustness of egg target detection.

This invention discloses a method for assessing and warning of collision risks in ship berthing and outgoing operations based on fuzzy logic, comprising: S1, acquiring motion state parameters, dynamic positioning system status data, and current environmental data of the ship and the target ship or offshore platform; S2, calculating collision hazard assessment factors; S3, performing fuzzy reasoning on each collision hazard assessment factor using a five-level Gaussian membership function system, and outputting a risk score through fuzzification, rule-based reasoning, implication truncation, and aggregation, and finally defuzzifying using the centroid method; S4, assigning differentiated weight coefficients to the risk scores and calculating a comprehensive collision risk score; S5, determining the risk level of the comprehensive collision risk score based on a preset grading threshold, and dynamically generating warning information and operational suggestions based on the comparison results of the real-time values ​​of each collision hazard assessment factor and the corresponding warning threshold. This invention integrates multiple factors such as kinematic parameters, dynamic positioning system status, and environmental conditions for ship collision risk assessment and warning.

The invention relates to a fuzzy fault detection method and device for a pitching motion system of a large amphibious aircraft and a storage medium, and the method comprises the steps: analyzing the time-varying characteristics of the pitching motion of the large amphibious aircraft, carrying out the preprocessing of data through Kalman smoothing, estimating the state of the system, and reducing the error caused by the time-varying uncertainty; a dynamic model of a time-varying system is constructed based on T-S fuzzy logic, nonlinear mapping is carried out on system parameters through a Gaussian membership function and a fuzzy rule, an optimization objective function containing a state error and an output error is designed, fuzzy system parameters are trained through a gradient descent algorithm, and therefore accurate modeling of the time-varying system is achieved. Through a residual error generation algorithm and a fault detection mechanism, real-time monitoring and alarm of faults are realized in combination with threshold judgment. According to the method, the time-varying characteristic and noise interference of the pitching motion system of the large amphibious aircraft are fully considered, the accuracy and robustness of fault detection are improved, and the method is suitable for fault detection requirements in a complex dynamic environment.