9 results about "Fuzzy uncertainty" patented technology

A multi-objective optimization method for crop planting considering fuzzy uncertainty includes the following steps: Step 1: Obtain historical data; Step 2: Construct a corresponding fuzzy parameter set based on the historical data; Step 3: Describe the planting area of ​​various crops on different plots as variables, and construct the expression relationship between their total planting area and total yield; Step 4: Construct a fuzzy multi-objective optimization model for crop planting based on the fuzzy parameter set; Step 5: Introduce plot area constraints, crop suitability constraints, crop rotation constraints, and minimum planting area constraints into the fuzzy multi-objective optimization model; Step 6: Obtain the clarified multi-objective optimization model; Step 7: Obtain a Pareto optimal planting scheme set; Step 8: Select the target planting scheme from the Pareto optimal planting scheme set and output the planting area configuration results of various crops on different plots. This invention can obtain a planting method that better conforms to actual production conditions.

This invention belongs to the technical field of Safety Integrity Level (SIL) verification for Safety Instrumented Systems (SIS), specifically disclosing a dynamic verification method and system for SIL considering uncertainty. Addressing the uncertainty issues in SIL verification, this invention introduces stochastic uncertainty theory and fuzzy uncertainty theory, combining stochastic uncertainty theory with LSTM networks and fuzzy uncertainty theory with fuzzy logic to propose a novel dynamic SIL verification method. It also performs source tracing analysis on the SIL verification results and uses a random forest method for feature importance analysis. The SIL level is dynamically updated as data is updated. This invention solves the problems of uncertainty and static lag in traditional SIL verification, eliminates the fuzzy uncertainty of expert judgment and the static nature of field data, and enables the final verification results to be dynamic and up-to-date.

The invention belongs to the technical field of cloud computing networks, and discloses a computing power network workload spatio-temporal dynamic prediction method and device, equipment and a medium, and the method comprises the steps: determining a fuzzy set for identifying a workload mode through employing a clustering algorithm based on dynamic time warping according to a performance index during operation; determining a state transition conditional probability matrix of the computing power node, the heterogeneous hypergraph and the working load mode; adopting hypergraph convolution kernel learning to obtain a heterogeneous feature fusion model of workload and computing power nodes; processing the fuzzy set by adopting a fuzzy membership covariance, and further training by adopting a time convolution network of a dynamic receptive field to obtain a time sequence feature of a workload; and according to the heterogeneous feature fusion model and the time sequence features of the workload, carrying out space-time fusion training of cross entropy measurement probability and fuzzy uncertainty by adopting a probability intuitionistic fuzzy relation matrix to obtain a workload space-time dynamic prediction model. The method has the beneficial effect that the error rate of resource scheduling decision is reduced.

The invention discloses a possibility-based optimization design method for an aero-engine thrust augmentation cylinder, which comprises the following steps of: firstly, analyzing and identifying an important region of interest of the thrust augmentation cylinder, cutting the region by utilizing a sub-model technology, and realizing parametric modeling and finite element modeling; calculating the low-cycle fatigue life of the position with the maximum stress by adopting a local stress-strain method; the method comprises the following steps: representing geometric configuration, material attributes and load environment parameters of a stress application cylinder as fuzzy variables, and carrying out reliability analysis through a method of combining fuzzy simulation and adaptive Kriging to obtain a failure possibility degree; and finally, by taking the minimum volume of the stress application cylinder as a target and the design value of the failure possibility degree as a constraint, solving an optimization design model based on the failure possibility degree by adopting a method based on an enhanced expectation improvement learning function and combining an active constraint criterion and an adaptive Kriging model, so as to obtain design parameters of the stress application cylinder. According to the method, the problem that a traditional optimization algorithm is poor in adaptability under multi-constraint and fuzzy uncertainty is solved.

The application discloses a water surface environment perception method based on Bayesian-fuzzy fusion, which comprises the following steps: (1) collecting multi-modal environment perception information, performing time alignment and space alignment on the collected modal environment perception information, and identifying obstacles in the current environment based on the multi-modal environment perception information; (2) predicting the probability P that a target obstacle in the current environment belongs to a real obstacle based on a Bayesian inference model and a fuzzy inference model, and identifying the target obstacle with a large probability P as an obstacle. Through the hierarchical coupling of Bayesian inference and fuzzy discrimination, the probability uncertainty and fuzzy uncertainty problems in the complex water environment are simultaneously solved, so that the accuracy and stability of the environment perception result are improved, and the perception robustness in the complex scene is also improved.

The invention discloses a multi-scale fuzzy uncertainty perception width learning method for ECG classification, and belongs to the technical field of computer-aided medical diagnosis. The method comprises the following steps: carrying out data acquisition and preprocessing on an original electrocardiogram (ECG) signal; constructing a multi-scale feature mapping layer which is used for mapping the preprocessed ECG signals to a plurality of time scales in parallel to obtain high-dimensional feature representation; uncertainty perception enhancement nodes are introduced into the width learning enhancement layer, combined modeling is carried out on input disturbance, structural disturbance and random projection, and enhancement mapping sensitive to uncertainty is constructed; and solving an output weight at an output layer by adopting a fuzzy weighting pseudo-inverse solving mechanism to obtain a discrimination result of the ECG signal category. On the basis of keeping efficient training of a width learning system, the classification robustness, generalization ability and interpretability of electrocardiosignals in a noise environment, form variation and cross-patient difference are remarkably improved, and the method is suitable for real-time diagnosis scenes such as dynamic electrocardiogram monitoring.

The invention discloses a pre-disaster difference planning method and system for power distribution network toughness improvement, and belongs to the technical field of planning of power distribution network and traffic network toughness improved.The method comprises the steps that lines and roads are reinforced at different grades, the position of a standby energy storage power source is planned, and a multi-grade difference planning model is constructed; in combination with historical data, constructing a fuzzy uncertainty set of faults of partial components under the conditions of different reinforcement levels and disaster intensities; carrying out constraint modeling by adopting a chance constraint model to form fault probability condition data; after-disaster severe scenes are generated, repair team repair and generator tripping and load shedding scheduling are carried out, the total cost function is evaluated, and whether the minimum total cost target is achieved or not is judged. According to the method, difference planning is carried out based on the multi-level fault probability uncertainty and the deep coupling characteristic of the power grid and the traffic network, the pre-disaster investment cost is reduced while the network toughness is improved, the relation between toughness improvement and economy is effectively balanced, and the method has good applicability.

The invention discloses an area measurement method for model confirmation of a fuzzy uncertainty structure system, which relates to the technical field of model verification and confirmation, and comprises the following steps of: introducing fuzzy uncertainty of model parameters and measurement errors into a calculation model according to a fuzzy set theory in a practical model confirmation problem of engineering; calculating upper and lower boundary values of an output response under a given membership level through an optimization method, and obtaining a membership function of a calculation model; an empirical membership function based on experimental measurement under fuzzy uncertainty adopts a stepped linear form; according to the model confirmation method based on fuzzy uncertainty area measurement, the difference between a calculation model and experimental data is quantitatively evaluated by calculating the area difference between corresponding curves of a membership function of the model and an empirical membership function of experimental measurement. Therefore, by the adoption of the area measurement method for model confirmation of the fuzzy uncertainty structure system, the difference between the calculation model and the true value can be quantitatively measured, and the precision and reliability of model confirmation are improved.

The application discloses a kind of based on two-layer decision system's watershed water resource utilization efficiency optimization method, first, construct the watershed water resource optimal allocation model based on two-layer fractional fuzzy programming, the multi-level nature of watershed water resource management system, multi-objective and the fuzzy uncertainty characteristic of agricultural land policy is characterized, realize water resource utilization efficiency optimum;Then, obtain input data by historical database, and obtain the fuzzy distribution of agricultural cultivated land area in combination with cultivated land expansion plan and expected grain yield;Introduce credibility parameter, calculate the corresponding fuzzy variable value under different credibility level, as the input parameter of the watershed water resource optimal allocation model is brought into calculation;Then, according to fractional programming algorithm, introduce new decision variable, and convert the planned model into linear programming model;Finally, coupled with multi-layer iteration and fuzzy satisfaction degree algorithm, solve the overall satisfaction degree of upper and lower layer decision to obtain global optimal solution.