12 results about "Hybrid kernel" patented technology

The embodiment of the invention discloses an insulator pollution flashover dynamic monitoring and early warning method, device and equipment and a storage medium, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: collecting the multi-source monitoring data of the operation state of an insulator in real time, the multi-source monitoring data comprising an electrical parameter, an environmental parameter and a pollution parameter; performing feature fusion processing on the multi-source monitoring data to construct a feature matrix, and obtaining an environment-pollution dynamic coupling factor; a hybrid kernel function support vector machine model is constructed, the hybrid kernel function support vector machine model adopts a hybrid kernel function of a Gaussian kernel and a polynomial kernel, and the kernel function proportion is dynamically optimized through an adaptive weight adjustment mechanism; based on the feature matrix and the environment-pollution dynamic coupling factor, using the mixed kernel function support vector machine model to realize pollution flashover early warning; the problems that a traditional monitoring method is poor in adaptability to a complex environment and low in early warning accuracy can be solved.

The present application relates to the technical field of complex equipment component repair, in particular to a device component performance and unit body maintenance level relationship mining method capable of effectively improving the use efficiency of an aero-engine, which first carries out expansion processing of repair samples, and then selects a support vector machine regression method which is better in the condition of small sample problems to solve the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair. Since a component is generally composed of multiple unit bodies, each component has multiple maintenance levels, and the mapping relationship between the component performance before repair, unit body maintenance level and component performance after repair is a many-to-one mapping relationship. In order to improve the accuracy of support vector machine regression, a hybrid kernel function method is used to optimize it, and a particle swarm algorithm is used to optimize the related parameters.

The present application relates to a kind of short-term load prediction method based on ISSA-HKELM, the method includes: first, in view of the defect of kernel extreme learning machine KELM, combined with Gaussian kernel function and polynomial kernel function, construct the hybrid kernel extreme learning machine HKELM with stronger generalization ability;Second, in view of the problem that sparrow search algorithm is easy to fall into local extremum, adaptive t distribution strategy and dynamic adaptive weight are introduced to improve sparrow search algorithm;Third, the improved sparrow search algorithm ISSA is used to optimize the parameter of hybrid kernel extreme learning machine HKELM and establish ISSA-HKELM prediction model;Finally, short-term load prediction is carried out using the established ISSA-HKELM model.Compared with prior art, the present application has good prediction accuracy and robustness and the like advantages.

The present application belongs to the field of building, especially a kind of rigid-flexible combination bagged pile vertical bearing capacity calculation method and system, the method specifically includes: obtaining the initial parameter set that influences pile vertical bearing capacity, including pile geometry, bag performance, soil mechanical parameters, and the one-dimensional control point vector of bottom expansion form extracted based on point cloud data reconstruction by non-uniform rational B spline, divided into side resistance parameter subset and end resistance parameter subset;Using historical sample training set, construct the hybrid kernel matrix of polynomial kernel and radial basis kernel combination for two subsets respectively, and weight according to the sensitivity difference of parameter to resistance;Mean centering and feature decomposition are carried out to the hybrid kernel matrix, and the principal component is selected according to the cumulative variance contribution rate and parameter correlation coefficient absolute value and threshold value rule, and the core factor of side resistance and end resistance is obtained by decoupling;Gaussian process regression model is established respectively with the two core factors, the side friction resistance and end resistance are predicted, and the sum is obtained to obtain the vertical bearing capacity of pile.

This invention discloses a waveform sampling lidar ground feature classification method based on a hybrid kernel SVM and OvA strategy. The method includes the following steps: Step 1, preprocessing of the original stripe image; Step 2, multi-domain physical feature extraction; Step 3, feature selection based on the RFE algorithm; Step 4, construction of the hybrid kernel SVM model; Step 5, training and classification of the multi-classification model based on the OvA strategy. This method eliminates the need for point cloud inversion, significantly shortening the data processing cycle. The average recognition time per sample is only 0.34ms. It can achieve rapid and accurate identification of four typical ground features: buildings, slopes, flat land, and vegetation, with an overall classification accuracy of 95.71%. It can meet the requirements of airborne real-time processing and is suitable for scenarios requiring real-time imaging and target recognition, such as geographic surveying, military reconnaissance, and environmental monitoring.

This invention discloses a method and device for monitoring the gate voltage of an IGBT module during high-speed switching transients. By constructing a physically guided hybrid kernel function and embedding a Gaussian process-constrained variational inference framework, it achieves real-time and accurate reconstruction of low-sampling-rate gate voltages into extremely high-sampling-rate waveforms without pre-training. Addressing the complex physical characteristics exhibited by the gate voltage during high-speed switching, such as the quasi-steady-state plateau, rapid jumps caused by large switching transient gate voltage change rates, and accompanying damped oscillations, the designed physically guided hybrid kernel function explicitly encodes these multimodal dynamic characteristics. Gaussian process modeling is employed to capture smooth transient dynamic features and provide post-calibration uncertainty assessment. This invention enables accurate IGBT module status monitoring information at extremely low sampling rates, improving signal transmission and storage efficiency by more than a hundred times and providing a reliable technical means for remote online status monitoring systems.

The embodiment of the invention discloses a hybrid kernel function support vector machine model construction method, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: constructing a hybrid kernel function support vector machine model, the model adopts a mixed kernel function of a Gaussian kernel function and a polynomial kernel function, the Gaussian kernel function is used for capturing local features of data, and the polynomial kernel function is used for capturing global features of the data; dynamically optimizing a kernel function proportion through an adaptive weight adjustment mechanism; training the mixed kernel function support vector machine model by using a historical data set, and optimizing model parameters including penalty factors and kernel function parameters: processing the collected multi-source monitoring data of the marginal operation state based on the trained mixed kernel function support vector machine model to realize pollution flashover early warning; a model suitable for classification and prediction of a complex data set can be constructed, such as dynamic monitoring and early warning of pollution flashover of an insulator of a power system, so as to improve prediction accuracy and adaptability.

This invention relates to the field of fault detection technology and discloses a substation fault detection method and system. The method includes: windowing the collected substation time-series data to extract a first feature vector and calculate a second feature vector; constructing a hybrid kernel function composed of a weighted combination of radial basis function (RBF) kernel functions and polynomial kernel functions, and calculating the weight coefficients of the RBF kernel function and polynomial kernel function; training a single-class support vector machine (SVM) using training samples under normal operating conditions based on preset model parameters and the hybrid kernel function to obtain the fault decision boundary; calculating an early warning threshold and setting an early warning boundary within the fault decision boundary; and performing the same feature extraction on the test data as on the training samples and inputting it into the trained SVM to determine the state of the test data. This invention improves the accuracy of fault detection, can identify early signs of equipment deterioration, and provides early warning information for operation and maintenance personnel.

The application provides a coal and gas outburst danger grade prediction method, which adopts an improved golden sine algorithm (Improved Golden Sine Algorithm, IGSA) to combine ten-fold cross-validation to optimize parameters of a hybrid kernel function support vector machine (Hybrid Kernel Support Vector Machine, HKSVM) composed of a Laplace kernel and a linear kernel, and then establishes a coal and gas outburst danger grade prediction model according to the optimization result. The method effectively prevents the support vector machine parameters from falling into a local optimal solution when being combined and optimized, and makes the prediction model have better generalization performance.

According to the enterprise energy consumption prediction method, the variational mode decomposition (VMD) method is adopted to decompose energy consumption data, so that the randomness of the data is reduced; then constructing a hybrid kernel extreme learning machine (HKELM) model optimized by adopting a grey wolf algorithm (GWO); and finally, inputting the decomposed subsequences into a hybrid kernel extreme learning machine (HKELM) model optimized by a grey wolf algorithm (GWO) for energy consumption prediction. According to the method, the problem of blindness of parameter selection of the HKELM model can be effectively solved, and the prediction effect of enterprise energy consumption data is improved.

This invention proposes a priori-enhanced multi-kernel canonical variable analysis method and device for monitoring blast furnace ironmaking. First, historical multivariate data is collected, standardized, and past and future Hankel matrices are constructed. Then, a CSI-MKCVA model is constructed, and an enhanced hybrid kernel function is built to simultaneously capture local, global, and time-related nonlinear features of the blast furnace ironmaking process, and the covariance matrix and Laplace matrix are reconstructed using weighted averages. Next, the projection matrices of past and future data are iteratively trained to extract priori spatiotemporal nonlinear features (CSNF) with conformal and discriminative capabilities. Finally, based on the extracted CSNF, the kernel density estimation method is used to calculate control limits, enabling real-time monitoring. This invention enhances the spatial correlation modeling and fault category identification capabilities in the blast furnace ironmaking process, significantly improving the anomaly monitoring accuracy under conditions of data nonlinearity, dynamism, and class imbalance, ensuring the safe and stable operation of blast furnace production.

The invention relates to a hybrid kernel search optimization reactive power scheduling method based on a crown porcupine algorithm, and the method comprises the following steps: S1, converting active loss and voltage deviation into a single objective function through employing a weighted summation method, and building a reactive power scheduling model in combination with constraint conditions; s2, randomly initializing an active power scheduling matrix of the generator set; s3, randomly initializing another scheduling scheme, and sequentially taking out the output of a certain generator to replace the active power of the corresponding generator set in the scheduling matrix to form a new scheduling matrix; s4, according to Newton-Rafson power flow, solving the active power of the last group of generators by a power flow equation; s5, calculating the penalty function value of the last group of generator active power exceeding the constraint; s6, calculating the sum of the actual power loss and the voltage deviation of the generator set in the scheduling scheme, and adding a penalty function value for common normalization; s7, executing a hybrid kernel search optimization algorithm based on a crown porcupine algorithm to update the active power of the generator set; s8, if the maximum number of iterations is reached, outputting the optimal generator set output; otherwise, turning to S3.