554 results about "Bayesian optimization algorithm" patented technology

A method and a system for reconstructing aerosol chemical components based on CNN-BiLSTM-BO, including collecting multi-source environmental observation data through observation equipment, preprocessing and extracting key characteristic variables. The pre-treated multi-source environmental observation data are input into the CNN-BILSTM model for feature analysis, and the CNN-BiLSTM hyperparameters are adjusted by Bayesian optimization algorithm to generate a reconstructed model of aerosol chemical components. After verifying the performance and stability of the reconstructed model, the predicted results of the chemical components of the aerosol are output. On the basis of not relying on traditional chemical analysis technology, the invention can accurately reconstruct various aerosol chemical components, greatly reduce the cost and time of chemical analysis, effectively solve the problems of variable inconsistency, data missing, and spatio-temporal mismatch in multi-source observation data, and automatically adjust hyperparameters through Bayesian optimization algorithm to ensure that the output prediction results are more accurate.

The invention relates to the technical field of cultural heritage digital protection, in particular to a super-volume historic building three-dimensional simulation modeling method based on multi-source heterogeneous data, and the method comprises the steps: firstly collecting node multi-source heterogeneous data such as laser point cloud, images, structural mechanical parameters and historical repair records, and then carrying out node feature enhancement through a node feature enhancement module; using an improved generative adversarial network to strengthen node edge features, adopting an adaptive threshold segmentation algorithm to extract surface texture features, converting mechanics and size data into a three-dimensional constraint condition parameter matrix, then using a topological relation verification algorithm, using a graph neural network to traverse and verify a component connection relation, and obtaining a three-dimensional confrontation model; and a re-calibration mechanism is triggered when the deviation exceeds the limit, the weight is adjusted based on a Bayesian optimization algorithm, fusion verification is carried out again, finally, hierarchical grid division is adopted to construct high-precision sub-models, and the sub-models are spliced into an integral three-dimensional model, so that the model precision and reliability are improved, and reliable digital support is provided for ancient building protection.

The invention discloses a long downhill section truck brake pad high temperature detection and cooling method and system, and the method comprises the steps: collecting the surface temperature data of a truck brake pad in real time according to high-precision infrared temperature sensor arrays and thermal imagers disposed at the two sides of a long downhill section, and generating a time-space correlation temperature field distribution diagram; based on the temperature field distribution diagram, dynamically adjusting a temperature safety threshold curve through reinforcement learning, and outputting a dynamic early warning trigger signal; according to the dynamic early warning trigger signal, a corresponding spraying instruction is generated so as to drive a piezoelectric ceramic microvalve array of a roadside spraying device to conduct spraying cooling on the brake pad; and according to the temperature attenuation rate and infrared thermal image feedback data of the brake pad after spraying, the temperature safety threshold curve and the spraying instruction are corrected through a Bayesian optimization algorithm, and closed-loop safety regulation and control are formed. According to the embodiment of the invention, the temperature of the brake pad can be monitored in real time, accurate spraying cooling control is realized, and safe and stable braking performance of a truck is ensured.

The invention discloses a cascade reservoir optimization regulation and control method based on nitrogen and phosphorus circulation of a water body, and relates to the technical field of water resource management optimization. Multi-dimensional data of a gradient reservoir is collected, and a time-space aligned multi-modal data set is constructed; according to a biogeochemical process of nitrogen and phosphorus circulation, a physical equation of the nitrogen and phosphorus circulation is deduced, and a physical constraint loss item is designed and is subjected to weighted combination with data driving loss to form a mixed loss function. According to the method, the physical constraint neural network model is constructed, and the physical equation of nitrogen and phosphorus circulation is embedded into the neural network loss function, so that the strong fitting capability of the data driving model is utilized, and the model output is ensured to accord with the biogeochemical law through the physical constraint; the key parameters of the physical equation are calibrated through the Bayesian optimization algorithm, the prediction precision of the model on the nitrogen and phosphorus migration and transformation process is further improved, the dependence on big data is effectively reduced, and a reliable prediction result can still be provided especially under the condition that the temporal-spatial resolution of data is low.

The invention discloses a multivariable time series prediction method based on an NMF multi-scale lightweight space-time convolutional neural network, and belongs to the technical field of machine learning and deep learning application. The method specifically comprises the following steps: (1) collecting and processing multivariable time sequence data, and unifying the scale of the data; (2) decomposing the time sequence data by using NMF, extracting basis matrixes and coefficient matrixes of different scales, and constructing a multi-scale feature pyramid; (3) applying a space-time convolution layer on the basis of the output of the feature pyramid, introducing a Bayesian optimization algorithm, and adopting a feature fusion layer fused with a multi-scale attention mechanism; (4) dividing a training data set and a test data set, and carrying out model training; and (5) predicting future time sequence data by using the trained model and outputting an expected value. According to the method, a more powerful and more efficient multivariable time sequence prediction model is effectively constructed, and the method is suitable for analysis and prediction of multivariable time sequence data.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

The invention discloses a dynamic optimization method and device for an automatic fiber placement path of a composite material and a medium, and belongs to the technical field of automatic fiber placement of composite materials. The method comprises the steps that a three-dimensional model of a component to be laid is obtained, and curved surface curvature distribution characteristics and fiber laying angle constraints are obtained based on the three-dimensional model; processing the curved surface curvature distribution characteristics based on a path planning algorithm to generate an initial fiber placement path; on the basis of the tension mapping table and the temperature compensation coefficient, the fiber placement head is driven to execute reverse pre-tension control; acquiring actual fiber trend data according to a preset sampling period based on a polarization laser polarization instrument, and extracting a fiber angle deviation of the actual fiber trend data; processing the fiber angle deviation and the resin viscoelasticity data based on a Bayesian optimization algorithm so as to dynamically correct the laying path and generate an updating instruction; and adjusting the spatial pose and tension of the fiber placement head according to the updating instruction. Based on the method, dynamic multi-target fiber placement path optimization and real-time feedback control are realized.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle shooting system control method based on adaptive optimization. A multi-modal semantic map fusing task description, equipment types, geographic positions, historical data, illumination and weather information and image optical flow features is constructed, and an equipment space distribution probability, a shooting difficulty level and key route nodes are obtained by adopting graph neural network reasoning. On the basis, a control strategy candidate set is generated, and optimal shooting parameter configuration is screened out in combination with a Bayesian optimization algorithm. The unmanned aerial vehicle collects multi-source information in real time in the flight process, dynamic fusion is conducted through an attention mechanism, the combined control module is driven to synchronously adjust the attitude of a holder and camera parameters, and accurate imaging control in a complex scene is achieved. And when the recognition confidence is low, triggering a supplementary shooting control mechanism based on the semantic map, and performing local fine tuning to improve the image quality. And the system also continuously updates the control strategy through transfer learning, so that the adaptability to a new task environment is enhanced.

The invention relates to a spinel multi-objective reverse design method based on machine learning and a Bayesian optimization algorithm, electronic equipment and a storage medium, and the design method comprises the steps: firstly extracting related data of a spinel material from a database, and constructing a balanced data set through preprocessing; feature engineering is carried out, a comprehensive feature set is constructed, and key features are reserved; then training a multi-target prediction model through hyper-parameter optimization by using a multi-task gradient elevator model; and finally, integrating to a Bayesian reverse design framework, expanding a design space through a specific encoder, and combining a Gaussian process proxy function and an expected hyper-volume improvement criterion to screen candidate materials meeting conditions and verify performance, thereby completing reverse design optimization. Compared with the prior art, the intelligent and efficient spinel novel multi-target reverse design method can be used for solving the problem of data scarcity, and development of high-performance spinel solar cell materials is accelerated.

The invention relates to the technical field of hydroelectric generating set parameter optimization, in particular to an intelligent hydroelectric generating set parameter optimization method and system based on multi-modal data fusion, and the method integrates multi-modal data, a graph neural network and Bayesian optimization to realize efficient and intelligent hydroelectric generating set parameter optimization. Time domain, frequency domain, acoustics, temperature and hydraulic multi-modal data are integrated, and feature vectors are generated through intra-modal feature extraction and hierarchical attention mechanism fusion; thirdly, constructing a unit knowledge graph, and extracting priori knowledge by using a graph attention network to realize cross-domain knowledge migration; and finally, on the basis of a multi-objective constrained Bayesian optimization algorithm, in combination with a Gaussian process agent model, determining optimal PID parameter configuration, and performing incremental optimization through closed-loop verification. According to the method, the optimization time is shortened from several days to 15 minutes or less, the efficiency is improved by 95% or above, and the parameter optimization efficiency and precision of the hydroelectric generating set are remarkably improved.

The invention discloses a battery thermal runaway and safety threshold dynamic adjustment system, which comprises a data acquisition module used for acquiring battery state parameters, environmental parameters and vehicle use conditions; the data preprocessing module is used for preprocessing the collected data; the initial safety threshold setting module is used for calculating an initial risk threshold of the thermal runaway of the battery by utilizing a statistical rule in combination with a large amount of collected historical sample data, and taking the initial risk threshold as a judgment standard of an initial deployment stage; the dynamic threshold adjustment module is used for performing dynamic personalized adjustment on the initial risk threshold according to a Bayesian optimization algorithm; and the thermal runaway prediction model module is used for predicting the battery risk level in the future 24 hours and the battery temperature in the future 1 hour in combination with a traditional machine learning and deep learning model. The invention also discloses a battery thermal runaway and safety threshold dynamic adjustment method. According to the invention, an early warning mechanism with high accuracy and high recall rate is realized.

The invention discloses a multi-stage task processing method and system based on an intelligent Agent model, and relates to the technical field of task processing, and the method comprises the steps: collecting heterogeneous data streams through a distributed sensor network, and generating a dynamic feature vector through a feature encoder inspired by quantum annealing; calculating a path expected utility value by using a Bayesian optimization algorithm, and projecting a high-order task space to a Kupman space; starting multi-thread asynchronous calculation, collecting execution state data in real time and constructing a causal graph model; the short-term execution logs are integrated through a neural Turing machine, edge computing nodes are called for distributed knowledge extraction, a multi-mode interpretable report is generated, and a long-term memory library is updated. According to the method, by starting multi-thread asynchronous calculation, the execution state data are collected in real time, the causal graph model is constructed, the execution result matrix with the confidence score is output, and the stability and reliability of task execution are improved.

The invention discloses a medical image intelligent diagnosis method and system based on deep learning, and the method comprises the steps: obtaining multi-modal medical image data through a medical image collection device, carrying out the data preprocessing, and forming a standardized image data set; constructing a deep learning diagnosis model based on multi-scale feature fusion, inputting the standardized image data set into the deep learning diagnosis model for transfer learning training, and optimizing model parameters by adopting a dynamic weight adjustment strategy; verifying the trained deep learning diagnosis model through an integrated learning framework, generating a diagnosis confidence score, and performing probability calibration on a diagnosis result in combination with a Bayesian optimization algorithm to obtain an optimized diagnosis model; and inputting medical image data to be diagnosed, and outputting a pathological classification result. The problems that in the prior art, multi-modal medical image data processing is insufficient, model optimization strategies are insufficient, and diagnosis confidence coefficient calibration methods are insufficient are solved.

The invention discloses an axial flux motor temperature prediction method based on equivalent thermal circuit method calculation, and the method comprises the following steps: 1, motor thermal circuit unit division: decomposing a motor into a stator unit, a rotor unit, an air gap unit and a housing unit, and constructing an equivalent thermal network through a node-branch method; 2, calculating a multi-physics coupling heat source; 3, thermal resistance parameter dynamic calculation and CFD coupling optimization: calculating conduction thermal resistance, convection thermal resistance and contact thermal resistance; and 4, parameter self-calibration based on machine learning: constructing a PINN model according to a physical law and known physical parameters, introducing a Bayesian optimization algorithm on the basis of the PINN model, adjusting and optimizing the parameters of the PINN model by using Bayesian optimization, designing a reinforcement learning algorithm, and carrying out self-calibration on the parameters of the PINN model. Enabling the reinforcement learning algorithm to dynamically adjust parameters according to the real-time operation data of the system and the prediction result of the PINN model; 5, constructing and solving a heat balance equation; and step 6, visualizing and verifying the temperature field.

The invention provides a single pile bearing capacity prediction method based on an XGBoost machine learning algorithm, comprising the following steps: (1) acquiring and preprocessing test data including soil layer input parameters, pile parameters and construction parameters, and dividing the preprocessed test data into a test set and a training set; (2) constructing a machine learning model in a pile foundation design stage according to the soil layer input parameters and the pile parameters, constructing a machine learning model in a pile foundation construction stage according to the soil layer input parameters, the pile parameters and the construction parameters, and respectively optimizing the two machine learning models by adopting a Bayesian optimization algorithm; and (3) the two optimized machine learning models are used for calculating the single-pile bearing capacity in the pile foundation design stage and the single-pile bearing capacity in the construction stage according to needs. According to the method, influence factors of all stages are comprehensively considered, algorithm learning is carried out on the influence factors, the bearing capacity of the reinforced concrete prefabricated pipe pile can be rapidly and effectively predicted, and the method can be used for optimizing the pile length design, reducing the pile material cost and improving the construction quality.

The invention relates to the technical field of vocal music training evaluation, and discloses a vocal music training intelligent evaluation method and system based on voice analysis. The method comprises the following steps: firstly, acquiring vocal music training original data, and processing and aligning multi-modal data by using a transfer learning small sample completion method when the data is insufficient; constructing a pronunciation stability evaluation model and a comprehensive pronunciation performance evaluation model; constructing a pronunciation scene simulator for different vocal music styles; the evaluation parameters are analyzed through a Shapley value decomposition algorithm, and a lightweight index system is constructed; and establishing a training scheme optimization agent model by using Gaussian process regression and a Bayesian optimization algorithm. The system comprises a data acquisition and completion module, an evaluation module, a simulation module, an analysis module, an optimization module and the like. The vocal music training system can comprehensively and accurately evaluate vocal music training, adapts to various styles, optimizes a training scheme, and assists a singer to improve the singing level through real-time feedback and visual display.

The invention relates to the technical field of automobile electronic testing, and discloses an integrated ECU power-on burning test monitoring system and method. The system comprises a power supply intelligent regulation and control module, a burning verification module, a test diagnosis module, an abnormity monitoring module and a feedback optimization module. The power supply intelligent regulation and control module realizes stable power supply output through a dynamic voltage frequency regulation algorithm and a temperature self-adaptive algorithm; the burning verification module is used for ensuring the burning integrity by using a block redundancy check and increment verification algorithm; the test diagnosis module diagnoses faults based on the multi-dimensional test data and the decision tree model; the abnormity monitoring module monitors abnormity in real time by using real-time streaming data processing and an isolated forest algorithm; and the feedback optimization module optimizes test diagnosis through incremental learning and a Bayesian optimization algorithm. According to the system and the method, comprehensive and accurate monitoring and optimization of the ECU are realized, and the performance and the reliability of the ECU are effectively improved.

The invention relates to the technical field of intelligent traffic infrastructure engineering, in particular to a roadbed settlement high polymer grouting repair grouting parameter optimization method, which comprises the following steps of: firstly, constructing a multi-source fusion training data set, and learning a prediction model based on a training machine; establishing a driving mapping relation between the grouting parameters and the road lifting effect and the maximum stress data of the repair area; then, based on the mapping relation, a multi-objective optimization model including pavement settlement repair precision, road stress, economic cost control and environmental friendliness evaluation is constructed; solving the model by adopting a Bayesian optimization algorithm to obtain an optimized grouting parameter combination; a marginal contribution of each parameter to a prediction result is calculated in combination with an SHAP interpretability analysis method, and an engineering decision basis is provided for parameter selection; constructing a transfer learning adaptation framework to adapt to different disease, geology and road types; and finally, establishing a real-time monitoring system, and realizing optimal control by combining sensor data and model feedback.

The invention belongs to the technical field of sewage treatment and artificial intelligence, and particularly relates to an effluent ammonia nitrogen concentration measurement method based on a neural network and Bayesian optimization, which comprises the following steps: step 1, constructing a basic model, collecting a characteristic variable x, preprocessing x, and normalizing x; 2, inputting x into a basic model for feature extraction and reinforcement; 3, extracting long-term time sequence features of a feature sequence from the x after feature extraction and enhancement through a bidirectional long-short-term memory neural network, dynamically adjusting an input step length, capturing a bidirectional time dependency relationship in the sequence, and outputting a predicted value; and step 4, performing parallel Bayesian optimization algorithm, dynamically adjusting the acquisition function, simultaneously evaluating a plurality of candidate points, performing parallel adaptive acquisition function, and outputting a measurement value. According to the scheme, the acquisition function can be dynamically adjusted according to different stages of the optimization process, the parameter space coverage rate is improved, multiple candidate points are evaluated at the same time, the exploration speed of the parameter space is increased, and the generalization ability of the model is enhanced.

The invention discloses an urban green land carbon sink metering method and system based on multi-source data fusion, and relates to the technical field of environmental monitoring, and the method comprises the steps: collecting urban green land multi-source data, and carrying out the preprocessing; performing parameter completion on vegetation in the building shielding blind area by adopting a dual-channel generative adversarial network to obtain complete green land leaf area index distribution data; simulating a reflected light path of a building glass curtain wall by using a ray tracing algorithm, obtaining a photosynthetically active radiation correction coefficient received by a vegetation canopy, and identifying urban green land carbon sink distribution data through multi-scale data fusion; and obtaining a carbon sink amount error based on the urban green land carbon sink amount distribution data, performing dynamic correction through a Bayesian optimization algorithm, and generating an urban green land carbon sink amount measurement report. According to the method, the adversarial network is generated through two channels, and the cooperative training mechanism of the U-Net generator and the PatchGAN discriminator is utilized, so that the high-precision spatial continuity reconstruction of the leaf area index of the building shielding blind area is realized.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention discloses a coal seam passive source seismic survey system and method based on deep learning, and relates to the technical field of coal mining safety monitoring and early warning, and the method comprises the following steps: S1, collecting the passive source seismic data of a coal seam area; s2, deep fusion features are extracted; s3, constructing a semi-supervised self-attention prediction network model; s4, extracting coal seam structure prediction parameters; s5, the corrected coal seam structure parameters are extracted; s6, constructing a fast seismic wave forward model, and calculating and outputting an initial model of the coal seam underground structure; s7, constructing a Transform optimization network, and extracting a depth feature vector; and S8, constructing a coal seam structure inversion model, and training iteration by using the improved batch depth Bayesian optimization algorithm. The method overcomes the limitation of manual analysis and slow response in a traditional coal seam seismic survey method, and provides an efficient and accurate solution for safe production of a coal mine.

The invention discloses a cutting fluid cooling effect optimization method and system based on an intelligent algorithm, and the method comprises the steps: constructing an intelligent prediction model through fusing a convolutional neural network and a gating circulation unit, dynamically adjusting hyper-parameters in combination with a Bayesian optimization algorithm, and achieving the high-precision prediction and optimization of the cutting fluid cooling effect. The prediction efficiency is remarkably improved, the calculation cost is reduced, the method can adapt to complex nonlinear machining environments and different working condition requirements, the problems that a traditional method depends on empirical formulas, data are difficult to obtain and the model universality is poor are effectively solved, meanwhile, cutting fluid formula optimization is guided through an accurate prediction result, and the prediction efficiency is improved. The machining efficiency, the workpiece quality and the resource utilization rate are remarkably improved, and reliable technical support can be provided for intelligent upgrading of an industrial cutting fluid system.

The invention discloses a park heterogeneous load prediction method based on EEMD and Kmeans adaptive optimization. The method comprises the following steps: carrying out missing value filling, abnormal value elimination and feature coding on park historical load data and external influence factors; dividing the park load into a plurality of typical scenes through a Kmeans clustering algorithm based on the similarity of load fluctuation characteristics; eEMD decomposition is carried out on the load sequence corresponding to each typical scene, Gaussian white noise is added to suppress modal aliasing, multi-frequency components are extracted and fused with external variables, and a prediction input data set is constructed; a prediction model is selected for different scenes, and adaptive adjustment and optimization are carried out through a Bayesian optimization algorithm; and carrying out dynamic weight fusion on the prediction result of each scene, dynamically adjusting the weight according to the historical load proportion of the scene, and outputting an overall load prediction value of the park.

The invention discloses a brain health monitoring and cerebral infarction early warning method and system for old people. The method comprises the steps that electroencephalogram, electrocardio, blood oxygen and cerebral blood flow velocity multi-mode physiological signal data are collected; adopting a characteristic mode decomposition method to decompose the multi-mode physiological signal data; the method comprises the following steps: introducing a KAN network into an MATE structure, carrying out nonlinear mapping and feature reconstruction, and constructing a KAMATE model; an improved Bayesian optimization algorithm is obtained based on the Bayesian optimization algorithm in combination with a dynamic balance strategy; the improved Bayesian optimization algorithm is combined with the characteristic mode decomposition method to achieve characteristic decomposition of the multi-mode physiological signals, and parameters of the KAMATE model are optimized; dividing the brain health state into four grades according to a prediction result output by the KAMATE model; according to the invention, real-time early warning of abnormal cerebral blood supply, cognitive decline and cerebral infarction high-risk states of the elderly can be realized, intelligent support is provided for health management of the elderly, and the method has important application value.

The invention belongs to the field of material performance optimization, and particularly discloses a carbon ceramic resistor formula optimization method based on a genetic algorithm and Bayesian optimization, and the method comprises the steps: receiving formula parameter combinations and corresponding performance parameters of a plurality of groups of carbon ceramic resistors; a Gaussian process regression model based on a radial basis kernel function is established to construct a mapping relation between formula parameters and performance parameters, and a performance prediction model of the carbon ceramic resistor is obtained through training by maximizing marginal likelihood optimization model hyper-parameters; and based on the performance prediction model, performing joint optimization by using a genetic algorithm and a Bayesian optimization algorithm, and determining an optimal formula combination. According to the method, global exploration and local fine convergence can be considered, the prediction efficiency can be improved, and the accuracy, comprehensiveness and reliability of a prediction result can be improved.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction method based on Bayesian optimization XGboost-LSTM, and the method comprises the following specific steps: 1, inputting wind power data collected by a wind power plant and numerical weather prediction data of a corresponding time sequence; 2, data preprocessing, wherein missing value interpolation and data deduplication are carried out on input data; according to the method, an XGBoost feature optimization method is used for selecting key features influencing the wind power, the influence of irrelevant feature noise on the model prediction precision and accuracy is eliminated, then a Bayesian optimization algorithm is used for carrying out hyper-parameter tuning on an LSTM model, and finally an XGBoost-LSTM-BO model is constructed. XGboost feature optimization and Bayesian hyper-parameter optimization can obviously improve the prediction effect of the LSTM on future data and improve the model prediction precision, and compared with a traditional prediction model, the wind power data generalization ability of the model can be improved while the high prediction precision is kept, and higher prediction performance is achieved.

The invention provides a lithium ion battery health state estimation method and system based on a dual-drive interpretable integrated model, and belongs to the field of lithium ion battery health state estimation. The problems that an existing lithium battery health state monitoring method is single in feature source, insufficient in model generalization ability and poor in interpretability are solved. According to the method, based on an incremental capacity curve and a first-order RC equivalent circuit model, IC peak value features and ohmic internal resistance features are extracted, and a multi-source health feature space is constructed in combination with voltage statistical features; an integrated learning model based on Stacking is established, three basic models of a random forest, kernel ridge regression and an interpretable enhancement machine are integrated, and collaborative optimization of model hyper-parameters is realized by adopting a tree structure-based Bayesian optimization algorithm.

The invention discloses a thyroid cancer pathological image classification method and system based on multi-modal feature fusion and Bayesian optimization, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a thyroid ultrasound image data set, and carrying out the preprocessing operation; extracting an improved local binary pattern feature, a Haralick texture feature and a VGG16 depth feature, and constructing a mixed feature space; and splicing the features in the mixed feature space into a 4119-dimensional mixed feature vector, and carrying out feature importance screening by utilizing ExtraTres. Improved local binary pattern features, Haralick texture features and VGG16 depth features are fused, a mixed feature space is constructed, feature importance screening and PCA dimension reduction are performed by using ExtraTrees, multi-level features of the image are effectively extracted, the accuracy of benign and malignant thyroid nodule classification is remarkably improved, meanwhile, network hyper-parameters are dynamically adjusted through a Bayesian optimization algorithm, and the classification accuracy of benign and malignant thyroid nodules is improved. And model convergence is accelerated in combination with a cosine annealing strategy, so that the generalization ability of the model is enhanced, and the model can be excellently expressed on different data sets.