22 results about "Support vector machine algorithm" patented technology

Provided are a method for regulating a wind-photovoltaic-storage power station based on electricity, green certificate, and carbon price prediction, a device, a medium, and a product. The method includes: inputting acquired historical price data into a price prediction model, and outputting a predicted price; determining a deviation vector of price data based on the historical price data and the predicted price, and generating an uncertainty set of the predicted price by using a multi-kernel-based one-class support vector machine algorithm; classifying the uncertainty set of the predicted price by using a neural network classifier, to obtain multiple types of price scenarios; solving, based on predicted prices under the multiple types of price scenarios, a joint clearing model by using a Pied Kingfisher Optimization (PKO) algorithm, to obtain an operation strategy for the wind-photovoltaic-storage power station; and regulating the wind-photovoltaic-storage power station based on the operation strategy for the wind-photovoltaic-storage power station.

The application discloses a coal mine slope slip identification method and system based on radar monitoring, and belongs to the field of coal mine slope slip identification. The method comprises the following steps: obtaining phase difference information in a denoising radar signal data set, calculating a millimeter-level displacement vector of a slope surface through interference measurement, and determining a micro-deformation information distribution map; fusing geological complex parameters for the micro-deformation information distribution map, classifying deformation feature types by using a support vector machine algorithm, and judging which deformation features belong to abnormal inclination changes; if the abnormal inclination changes exceed a preset threshold, extracting time sequence data from the micro-deformation information distribution map to obtain an inclination change trend sequence; comparing the inclination change trend sequence with historical slip hidden danger data, predicting a dynamic evaluation index by using a support vector machine algorithm, and determining an early signal strength level; integrating a stability analysis model according to the early signal strength level, and generating a slip hidden danger probability distribution map and a risk identification report.

This application relates to a method, system, equipment, and medium for assessing the ESG resilience of heating facilities. The method includes: first, acquiring facility type data and dynamic environmental indicators; then, filtering and extracting environmental performance parameters and social demand indicators for heating scenarios to form a preliminary screening dataset; using a random forest algorithm to calculate the impact degree of dynamic environmental factors, obtaining a subset of high-risk environmental performance; extracting governance factor data and training a risk prediction model using a support vector machine algorithm; based on the equilibrium point output by the model, iteratively optimizing to obtain factor balance index data; simultaneously acquiring emission relationship index data and using a random forest algorithm for regression analysis to construct an emission relationship model; finally, extracting allocation fairness features, matching social demand to form an assessment framework, and integrating data to determine the adjustment results for the heating facility resilience assessment. This method can accurately reflect the actual resilience level of heating facilities and improve the scenario adaptability and generalization ability of the assessment method.

The application discloses a method for calculating DC bias intrusion current based on even harmonic absolute content, which comprises the following steps: establishing a magnetic flux-current relationship curve according to no-load test data; establishing an excitation current even harmonic and a reference data group based on the magnetic flux-current relationship curve; performing linear fitting on the excitation current even harmonic and the reference data group by using a support vector machine algorithm to form a DC bias intrusion current-even harmonic sum relationship function; analyzing actual recording data collected on site to obtain actual even harmonic sum of the recording data; and bringing the actual even harmonic sum into the DC bias intrusion current-even harmonic sum relationship function to obtain a quantitative result of the DC bias intrusion current of the recording data.

The application provides a power plant electric power equipment overheating fault detection method based on visual recognition technology, which can accurately detect the overheating fault of the power plant electric power equipment and improves the efficiency and accuracy of fault detection. The method comprises the following steps: S100, collecting image data of the power plant electric power equipment by using an infrared thermal imager; S200, pre-processing the collected image data, including denoising, enhancement and other operations; S300, segmenting the target region of the pre-processed overheating image by using an improved VLSBC level set contour model with a fusion bias field; S400, identifying the fault image region by comparing the support vector machine (SVM) algorithm; and S500, calculating the similarity value between the two by using the cosine similarity method, comparing the characteristic of the known fault mode, judging whether the equipment has a fault and the type and degree of the fault, and triggering the corresponding alarm mechanism. The application automatically learns and extracts the fault characteristics in the image by using the deep learning technology, thereby improving the efficiency and accuracy of feature extraction.

The present specification relates to the technical field of safety management, and particularly relates to a natural gas pipeline failure prediction method and device, the method comprising: collecting natural gas pipeline monitoring data and natural gas pipeline detection data; inputting the natural gas pipeline monitoring data and the natural gas pipeline detection data into a pipeline failure probability prediction model to obtain a pipeline failure probability output by the pipeline failure probability prediction model, wherein the pipeline failure probability prediction model comprises a pipeline corrosion prediction model and a pipeline model, the pipeline corrosion prediction model is obtained by training pipeline sample monitoring and detection data based on fuzzy support and a support vector machine, and the pipeline model is obtained by training a finite element model according to random variables. The present specification realizes the mapping between the pipeline monitoring data, the detection data and the fusion output result through the fuzzy support and the support vector machine algorithm, realizes the prediction of the future corrosion rate, and establishes the burst failure pressure of the natural gas pipeline containing defects under various conditions.

The application belongs to the technical field of agricultural product detection, and discloses a method for evaluating quality differences of different varieties of Hainan fragrant rice based on multiomics. The method comprises the following steps: selecting Hainan tropical fragrant rice samples, vacuum packaging after shelling, fine polishing, crushing and impurity removal, and low-temperature refrigeration for standby; carrying out flavoromics, metabolomics and lipidomics detection; normalizing the multiomics data and constructing a characteristic matrix, screening cross-omics markers with VIP>1, FC>2 or <0.5, and P<0.05; constructing an evaluation model based on the analytic hierarchy process, random forest or support vector machine algorithm to realize fragrant rice quality grading and variety identification. The application also provides applications such as variety identification, germplasm screening and process optimization based on the method, and provides technical support for the industrialization control of Hainan fragrant rice.

The present application relates to the technical field of wet desulfurization, and relates to a method and apparatus for predicting the density of a limestone slurry, a storage medium, and an electronic device. The method comprises: first acquiring feature data related to a limestone slurry, wherein the feature data comprises a limestone slurry tank level, a limestone slurry pump current value, an instantaneous limestone slurry flow rate, and an instantaneous limestone powder flow rate; and then, on the basis of the feature data, using a particle swarm optimized support vector machine model to predict the density of the limestone slurry. By means of the technical solution of the present application, on the basis of feature data related to a limestone slurry, a particle swarm optimized support vector machine algorithm is used to predict the density of the limestone slurry in real time, thereby improving the accuracy of measuring the density of the limestone slurry, providing a basis for production, and further improving the desulfurization efficiency of a unit and the economy in a desulfurization process by controlling the density of the limestone slurry.

This invention discloses a transformer intelligent monitoring method and system based on acoustic vibration, comprising: acquiring the transformer's vibration signal and high-frequency acoustic signal to construct two-dimensional raw data; using wavelet transform to extract vibration harmonic distortion and acoustic spectrum abrupt change features to determine weak anomaly indicators; using a support vector machine algorithm to assign indicator weights and determine potential anomaly types; using a deep neural network to fuse the two-dimensional fundamental frequency and high-frequency details to obtain a comprehensive diagnostic model output; using a multi-channel extension unit to synchronously acquire data from multiple measurement points to ensure signal capture integrity; using a fusion algorithm to process multi-channel data and correlate it with weak anomaly indicators to obtain precise fault location coordinates; adjusting the sampling rate to adapt the weak signal amplitude to determine the priority of the warning level; obtaining an overall assessment of the operating status based on the priority, determining potential fault trends, and obtaining transformer fault monitoring results. This invention significantly improves the early detection capability and maintenance accuracy of latent transformer faults.

This invention discloses an intelligent detection method and system for energy pipeline network leakage. It includes: (1) acquiring historical time-series data of instantaneous and cumulative flow of network sources and users over a time period T, forming an original data sample D; (2) preprocessing the original data sample D to obtain a processed sample CD; (3) calculating the working fluid imbalance CD1 based on the processed sample CD, and statistically analyzing its mean µ and variance σ²; (4) determining the significance level α, and using the µ test to determine the mean µ1 and µ2 of the working fluid imbalance, respectively; (5) calculating the probability density CP1 of pipeline network leakage and the probability density CP2 of pipeline network non-leakage; (6) combining CP1 and CP2 as a sample, including time-series correlation features, inputting it into a support vector machine model to obtain the pipeline network leakage status judgment result. This invention combines statistical probability with the support vector machine algorithm to eliminate fluctuation errors in flow modeling, achieving high accuracy and a low misjudgment rate.

The application provides an air conditioner terminal air outlet noise suppression adjustment method, comprising: obtaining transmission sound pressure data and sealing member compression force distribution data from the center area of the cover plate and the gaps around the cover plate, obtaining acoustic consistency indexes of the cover plate area and frame pressure uniformity indexes; according to the acoustic consistency indexes and the frame pressure uniformity indexes, using a support vector machine algorithm to analyze the correlation between the transmission sound pressure data and the sealing member compression force distribution data, determining the potential position distribution of the gap leakage channel; after obtaining the optimized compression force distribution parameters, re-collecting the leakage sound pressure data at the gaps around the cover plate, determining the noise radiation attenuation increment; according to a further lock buckle pre-tightening reference adjustment scheme, obtaining compression force distribution update data, judging the overall consistency improvement level of the sound wave transmission coefficient of the cover plate area; extracting a noise attenuation index from the judged overall consistency improvement level, and determining an air conditioner terminal air outlet noise suppression adjustment scheme.

This application provides a method for coordinated scheduling of multiple home energy devices, including: comparing the expected dwell time with a preset duration threshold; if the dwell time is lower than the threshold, assessing the power supply gap risk level after the mobile energy storage leaves using a support vector machine algorithm; determining the adjusted lower limit of discharge depth based on the difference between the power supply gap risk level and the current state of charge, combined with the remaining power of the electric vehicle; using a linear regression algorithm to process the lower limit of discharge depth and the system's dispatchable capacity to determine the distribution of the number of charge and discharge cycles in each time period of the day; integrating the optimized discharge depth value and the supplementary adjustment scheme with the initial scheduling basic information to generate the final fixed energy storage discharge strategy and deploy and execute it, thereby completing the optimized scheduling of the fixed energy storage's daily charge and discharge cycles.

This invention belongs to the field of heating control technology, and particularly relates to a heating control method for a solar-thermal coupled air source heat pump. The method includes: acquiring real-time temperature data and solar radiation intensity data from environmental sensors; analyzing these data to determine the current low-temperature environment level and the fluctuation trend of solar energy collection, thus obtaining environmental change parameters; based on equipment power adjustment requirements, acquiring the current output data of the solar energy collection module and the real-time heat distribution status of the main heating equipment, determining whether the two are in an unbalanced state, and obtaining a dynamic coordination index; based on the dynamic coordination index, using a support vector machine algorithm to process historical energy consumption records and operating load data, determining an optimized heat distribution scheme, and obtaining a path to improve energy efficiency; based on the heating efficiency correction value, using a random forest algorithm to analyze the risk of insufficient heat in a low-temperature environment, determining the final energy consumption reduction configuration, and obtaining the overall system operating status.

This invention relates to the field of wind-storage coordinated control technology, specifically to a method and related equipment for the coordinated operation of a single offshore wind turbine with grid connection. The method involves: acquiring real-time operating data of the wind turbine generator, status data of the energy storage system, and grid load data as input data; using a support vector machine algorithm to predict wind power and grid load within a set future time period, and formulating a power allocation plan based on real-time grid operating data; dynamically adjusting the power allocation plan based on a droop control strategy, and optimizing grid reactive power compensation through a PQ control strategy; introducing an energy storage lifetime loss model and lifetime loss penalty factor to optimize the charging and discharging behavior of the energy storage system; and the synergistic effect of each step to achieve comprehensive performance improvement in efficient power allocation, stable operation, and extended equipment lifespan of the wind-storage coordinated system, ultimately achieving optimal economy and reliability under balanced operating conditions.

This invention provides a method for igneous rock facies classification and prediction based on multi-physics discriminant factors, relating to the field of oil and gas field exploration and development technology. The method includes: S1: Constructing a multi-dimensional facies classification model based on diagenesis, integrating lithology index and structural maturity index to quantitatively classify geological facies; S2: Performing principal component analysis on well logging curves to select sensitive well logging curves and construct a sensitive well logging dataset; S3: Constructing rock physical facies factors through Fisher discriminant analysis; S4: Projecting known facies samples onto the rock physical facies factor space, training a classification model using a support vector machine algorithm, and generating a facies identification map; S5: Substituting target well data into the model to complete single-well facies identification; S6: Establishing the correlation between rock physical facies factors and seismic elastic parameters, inverting the seismic data volume into a three-dimensional attribute volume of rock physical facies factors based on a Bayesian inversion framework, fusing them to generate a three-dimensional facies model, thus realizing facies prediction from well point to three-dimensional space.

This invention discloses a contamination judgment method, a contamination judgment system, and a cleaning method for a vehicle-mounted camera. The contamination judgment method includes the following steps: allowing a vehicle-mounted camera to capture a poster containing scene information through contaminated specimens of different gray levels to obtain a series of specimen images; converting the original image data of the series of specimen images into HSV format image data; extracting the mean and variance of the HSV color space of the HSV format image data of the series of specimen images as features and inputting them into a support vector machine algorithm model to train a classification algorithm model; inputting the image data of a detection image captured by the vehicle-mounted camera as input features into the classification algorithm model to allow the classification algorithm model to classify the detection image captured by the vehicle-mounted camera; and determining whether the lens surface of the vehicle-mounted camera is contaminated based on the classification result of the classification algorithm model.

This invention belongs to the field of fiber optic sensing and recognition technology, specifically an object shape recognition system based on machine learning and fiber optic microphone arrays. The system includes a measurement optical path based on a single-core feedback Sagnac fiber optic interferometer, a microphone array sensor composed of multiple fiber optic coils (a component of the optical path), an obstacle movement device, a sound source, and a signal processing unit. By sensing sound signals through the fiber optic microphone array and extracting time-frequency features using a short-time Fourier transform algorithm, combined with a ResNet50 convolutional neural network or a support vector machine (SVM) algorithm, it achieves the recognition of obstacles of different shapes and types in the sound field. This system possesses advantages such as strong anti-electromagnetic interference capability, high sensitivity, low distortion over long distances, small size, and high recognition accuracy.

This invention relates to a Raman spectroscopy decoupling method for liquid packaging, belonging to the field of hazardous liquid safety inspection technology, and solves the problem of severe interference caused by existing liquid packaging to the Raman spectra of liquids. The decoupling method includes: collecting Raman spectral samples and performing standardized preprocessing on each sample; clustering all collected Raman spectral samples using a fuzzy clustering algorithm to obtain a predetermined number of clusters; filtering out Raman spectral samples within each cluster using a support vector machine algorithm to obtain pure Raman spectral samples for each cluster; determining the liquid type to which each cluster belongs based on the pure Raman spectral samples, thereby obtaining all pure Raman spectral samples corresponding to each liquid. This reduces the influence of liquid packaging on the Raman spectra of the liquid.

This application discloses a method and device for evaluating vehicle longitudinal comfort based on occupant electroencephalogram (EEG) signals, relating to the field of vehicle comfort evaluation technology. The method includes: acquiring multi-channel EEG signals from occupants and obtaining subjective comfort scores for the current operating conditions; preprocessing the EEG signals to obtain clean EEG signals; extracting features from the clean EEG signals; constructing a training dataset using the extracted features and subjective comfort scores; training a model based on a support vector machine (SVM) algorithm to obtain an initial longitudinal comfort evaluation model; using a recursive feature elimination algorithm to select and optimize features in the initial model to obtain a final longitudinal comfort evaluation model; acquiring the EEG signals of the occupant to be evaluated, and using the same preprocessing and feature extraction methods to obtain a feature set to be evaluated, which is then input into the final model, and the evaluation result is output. This application achieves accurate evaluation of vehicle longitudinal comfort and solves the problem of insufficient objectivity in subjective evaluation.

The application discloses an inland lake water depth inversion method based on lake bottom classification, comprising the following steps: S1, acquiring data required for lake water depth inversion and processing; S2, classifying lake bottom topography based on a support vector machine classification algorithm, and dividing shallow water areas and deep water areas; S3, constructing a lake shallow water area water depth inversion model by using a deep neural network algorithm; S4, inversing lake deep water area water depth based on the support vector machine algorithm; and S5, combining the shallow water area and deep water area water depths obtained in S3 and S4 to obtain inland lake water depth information. The method constructs different models for different lake bottom topographies, and inverses water depths in different regions, so that the lake water depth inversion precision is greatly improved.

The application discloses a wideband Doherty power amplifier classification optimization method based on a support vector machine, and builds a simulation environment for the Doherty power amplifier; source traction and load traction simulation is respectively performed on each transistor of the Doherty power amplifier, and training data required for establishing a proxy model is acquired; a classification proxy model based on a support vector machine algorithm is established and trained by using the simulation data; design variables of the Doherty power amplifier are sampled, the sampled design variables are substituted into the Doherty power amplifier topological structure for simulation; simulation data is input into the classification proxy model for classification discrimination, and the optimal solution of each iteration is selected according to a discrimination result, and iteration is repeated until an error tolerance is satisfied or a maximum iteration number is reached, and the optimal design variable of the Doherty power amplifier is obtained. The application can obtain an accurate proxy model with low resource consumption, has high modeling precision, and is suitable for power amplifier design of high-dimensional design variables.