13 results about "Adaboost algorithm" patented technology

The application discloses a GIS partial discharge pattern recognition method based on polar coordinate distribution entropy optimization, and belongs to the field of large power grid on-site power equipment defect diagnosis and recognition. Firstly, aiming at the signal missing phenomenon of photoelectric partial discharge signals, a non-subsampled contourlet transform is used to fuse and process photoelectric PRPD graphs, so that photoelectric fusion graphs are obtained. Then, feature points and scale vectors of the fusion PRPD graphs are extracted based on a KAZE algorithm. Next, the feature points are dispersed to polar coordinate expressions according to coordinates and scale vectors. Distribution entropy of each region divided on the polar coordinates is calculated to form a feature vector. Finally, the feature vector is loaded into a long short-term memory network optimized by an Adaboost algorithm to verify the accuracy of partial discharge pattern recognition. Compared with a statistical parameter method and a KAZE feature extraction method, the algorithm can better extract a PRPD graph feature vector, and the accuracy of partial discharge defect pattern recognition is significantly improved.

The application provides a kind of multi-model fusion inverse calculation pumping unit well dynamic liquid level method, system, electronic equipment and storage medium, the method comprises: based on oilfield data lake and oil and gas production internet of things database, obtains dynamic liquid level inverse calculation parameter data and is stored in dynamic liquid level inverse calculation system standard library;With the data in the dynamic liquid level inverse calculation system standard library, respectively through mechanism analysis method model, AdaBoost algorithm and MLP&RNN algorithm, inverse calculation obtains dynamic liquid level depth H1, H2 and H3;Adopt multivariate linear regression method, combine the three inverse calculation results, construct dynamic liquid level fusion refined calculation model, to realize the accurate prediction of actual dynamic liquid level depth H.The application can accurately obtain the dynamic liquid level of pumping unit well, improve the accuracy and adaptability of dynamic liquid level parameter, provide accurate data basis for dynamic analysis to take measures and determine reasonable working system.

The application discloses a short-term photovoltaic power prediction method suitable for multi-county geographical heterogeneity, and relates to the technical field of photovoltaic power prediction; the method comprises the following steps: a data preprocessing step, collecting multi-county meteorological and power generation data, performing time alignment, missing value filling, abnormal value elimination and feature engineering processing; a county heterogeneity quantification step, performing K-means++ clustering based on geographical features and meteorological statistical features, and quantifying the power contribution degree of each factor by adopting a SHAP value analysis method; an adaptive feature screening step, screening core features based on a factor weight vector, and generating a county exclusive feature subset; a mixed model integration step, constructing an LSTM, XGBoost and GPR basic model library, and integrating by dynamically distributing model weights by adopting an AdaBoost algorithm; and a summer and autumn scene optimization step, applying special correction to different county scenes. The application fully considers the influence of geographical heterogeneity on photovoltaic power generation, realizes accurate prediction of multi-county photovoltaic power, and improves the prediction accuracy.

The invention particularly relates to a radar target detection method based on an Adaboost algorithm and constant false alarm detection, and the method comprises the steps: firstly carrying out the preliminary screening of a distance Doppler image through a sorting constant false alarm detection algorithm, and extracting a candidate target unit, so as to reduce the data processing scale; and then, extracting amplitude features, statistical features and other auxiliary features from the candidate target units, and inputting the features into a pre-constructed strong classifier model for accurate classification. Through a grading detection framework of coarse screening of a sorting constant false alarm detection algorithm and fine classification of a strong classifier model, the detection probability of weak and small targets can be effectively improved, the false alarm rate is reduced, the algorithm calculation amount can be controlled by adjusting model parameters, and the method is suitable for radar target detection application scenes.

The application discloses a dynamic local sensitive discriminant analysis fault diagnosis method based on Mahalanobis distance, which is used to establish an accurate process fault diagnosis model in a complex chemical field. First, the original data is expanded by using a dynamic data expansion technology, and then a local sensitive discriminant analysis method combined with Mahalanobis distance is used to extract features of fault data, so that the behavior of the system is accurately described. The data after feature extraction is used to train a weak classifier, and the AdaBoost algorithm is used to integrate the weak classifier into a strong classifier, so as to improve the accuracy of the fault diagnosis model. The method is used to establish a fault diagnosis model for a Tennessee-Eastman process fault case, and the accuracy of the model is significantly improved, so that the method has certain superiority and application prospect.

This invention discloses a ventilation hole identification and localization method based on the AdaBoost algorithm, comprising: deploying a detection robot with visual detection capabilities into the generator chamber, and acquiring video images of elliptical ventilation holes, single-row circular ventilation holes, and double-row circular ventilation holes through two cameras on its abdomen; processing the acquired images; performing Brenner gradient calculation, Tenengrad gradient calculation, Laplacian gradient calculation, and variance calculation on each frame of the acquired video images, and performing normalization processing; performing image recognition; image labeling; for images where the ventilation hole is about to enter or exit the field of view or where the image edge is only 0.3-0.7mm, the camera continuously acquires five images, and then locates the ventilation hole according to the label; establishing an SVM-AdaBoost model, importing the training set data into the model for training, testing the generated model using the test set data, and visualizing the training accuracy of the model; calculating the training error of the weak classifier to obtain the final classification judgment criterion.

The present application discloses a method for early warning brandish of a transmission wire based on an improved Bayes-Adaboost algorithm, including: forming a classifier by training a historical brandish fault training set, and by using an Adaboost ensemble learning method, and obtaining an early warning result of the brandish of the transmission wire via the classifier according to real-time forecast meteorological information and information of different parameters of the transmission wire. The present invention can realize calculation and processing of forecast information of meteorological characteristic factors of the brandish of the transmission wire, structural parameters of the transmission wire and other related data, and obtain an early warning analysis result of a brandish disaster of the transmission wire in a region.

The invention relates to the technical field of power systems, and discloses a multi-energy-consumption carbon emission prediction method, system, device, medium and product, and the method comprises the steps: obtaining power operation sample data and carbon emission sample data of a plurality of energy consumption types of a power enterprise, and carrying out the feature extraction of the power operation sample data; using the operation sample feature data and the carbon emission sample data as mapping samples, constructing a training data set, constructing an extreme learning machine based on an AdaBoost algorithm, and training the extreme learning machine by using the training data set to obtain a trained carbon emission prediction model; and the carbon emission prediction value of the current calculation time period is predicted through the trained carbon emission prediction model, so that the accuracy and reliability of carbon emission prediction are effectively improved.

The embodiment of the invention provides a novel energy storage intelligent management method and system based on big data, and the method comprises the steps: collecting the operation data of an energy storage system in real time, and carrying out the preprocessing of the operation data; constructing a data set according to the preprocessed operation data, performing clustering analysis on the data in the data set, and screening out a training sample set according to a clustering result; based on the screened training sample set, an improved Adaboost algorithm is adopted to carry out iterative training so as to construct a strong classifier; and inputting energy storage system operation data collected in real time into the strong classifier, outputting a classification prediction result of the energy storage state, and performing intelligent scheduling management on the energy storage system according to the classification prediction result. According to the technical scheme provided by the invention, comprehensive monitoring, intelligent scheduling and optimal management of the energy storage system can be realized, so that the operation efficiency and the energy utilization rate of the energy storage system are improved, and optimization and sustainable development of an energy structure are promoted.

The present application relates to the technical field of air temperature risk assessment, and particularly relates to a city air temperature inference and heat exposure risk assessment method based on AdaBoost. The method comprises the following steps: obtaining original city form data and city meteorological data of the city; performing data preprocessing on the original city form data and the city meteorological data of the city to generate processed original city form data and city meteorological data; integrating the processed original city form data and the city meteorological data into a model training set and a model test set; designing an artificial neural network architecture by using an AdaBoost algorithm, and performing model training on the model training set by using the artificial neural network architecture to generate a city air temperature inference pre-model. The present application improves the accuracy and reliability of city air temperature inference and heat exposure risk assessment by fusing multi-source data, applying advanced machine learning algorithms, and strengthening sensitivity analysis and Monte Carlo simulation.

The invention discloses a landslide disaster susceptibility intelligent evaluation system and method, and relates to the technical field of geological disaster prediction and evaluation. The method comprises the following steps: preprocessing multi-source geographic spatial data by utilizing a geographic information system (GIS), and extracting a plurality of factor layers influencing landslide occurrence; original geological factor data are directly used, weighting processing is not carried out, and geological factor features are fused and learned through a deep learning network; constructing a base model comprising GBDT, a support vector machine (SVM) and an Adaboost algorithm, and inputting a prediction result of the base model as a feature into a deep learning fusion model for training; and carrying out landslide susceptibility analysis on a target area by utilizing the trained model, dividing susceptibility results into five grades (extremely low, low, medium, high and extremely high) through a natural breakpoint method, and generating a landslide susceptibility prediction map.

The application belongs to the technical field of luminescent material emission wavelength prediction, and discloses a method for predicting the emission wavelength of a doped Pr 3+ The application first collects descriptors of luminescent materials from multiple channels as input data of the model; then, the data is screened and converted; then, the data is divided into input variables and target variables, and divided into a training set and a test set; finally, the model is established based on the AdaBoost algorithm, the combination of multiple decision tree regressors is learned through iteration, the model performance is optimized by adjusting the parameters of the decision tree regressors, and the fitting degree of the model is quantified by calculating the determination coefficient R 2 , which effectively reduces the experimental time and cost, and improves the prediction accuracy and reliability.

The invention discloses a metallogenic prediction method based on a three-dimensional structure-attribute model and a Geo-AdaBoost algorithm. The method comprises the following steps: collecting and preprocessing multi-source heterogeneous geological data; constructing a high-precision three-dimensional geologic structure model based on the volcanic rock semi-automatic modeling function module; carrying out attribute interpolation under the spatial constraint of the three-dimensional structure model, and constructing a three-dimensional geological attribute model; extracting features based on the attribute model to construct a data set, and performing iterative training and prediction by adopting a Geo-AdaBoost algorithm with an integrated convolutional neural network as a weak classifier to obtain a three-dimensional mineralization probability; and finally, carrying out three-dimensional visualization and dynamically delineating a metallogenic favorable area. According to the method, high-precision three-dimensional geological modeling and adaptive enhanced machine learning (AdaBoost) are systematically fused, a complete technical chain from data to decision is formed, metallogenic prediction of a machine learning algorithm under three-dimensional geological space constraints is realized, and the precision, objectivity and spatial directivity of a prediction result are remarkably improved.