353 results about "Hyperparameter optimization" patented technology

The invention discloses a multi-target intelligent optimization method and system for blasting parameters of a strip mine in a high-altitude cold region. The method comprises the following steps: carrying out data acquisition to obtain a parameter data set; performing data preprocessing on the parameter data set to obtain a feature sample set; constructing an initial blasting parameter model based on a machine learning algorithm, and performing hyper-parameter optimization on the model to obtain a blasting parameter model; a multi-objective optimization function is constructed: based on the multi-objective optimization function and the blasting parameter model, solving is carried out in combination with environmental condition constraints, and a pareto optimal solution set is obtained; according to the pareto optimal solution set, a representative solution is selected, a visual scheme is generated, and blasting parameter optimization of the strip mine in the high-altitude cold region is completed. According to the method, temperature, oxygen and frozen soil constraint conditions of the high-cold and high-altitude environment are introduced, blasting safety, lumpiness uniformity and the explosive utilization rate are considered at the same time through multi-target collaborative optimization, the method can adapt to the extreme environment, meanwhile, the one-sidedness of single-target optimization is avoided, and the intelligent level of blasting design and implementation is greatly improved.

The invention provides a comprehensive energy consumption prediction method for a tilting type fuel gas aluminum melting furnace based on CPO-ITCN-GRU. The method comprises the steps that original energy consumption data generated in the running process of the tilting type fuel gas aluminum melting furnace and auxiliary characteristic data related to energy consumption are collected; comprehensive energy consumption conversion is carried out on the original energy consumption data, and denoising smoothing is carried out on the comprehensive energy consumption data obtained through conversion and auxiliary feature data related to energy consumption; dividing the denoised and smoothed data into a training set and a test set according to a preset proportion; constructing an ITCN-GRU comprehensive energy consumption prediction model, training the ITCN-GRU comprehensive energy consumption prediction model by using the training set, and performing hyper-parameter optimization by using a crown porcupine optimization algorithm in the training process; and performing performance evaluation on the trained ITCN-GRU comprehensive energy consumption prediction model by using the test set. According to the method, the capturing capability of the model on the local space-time characteristics and accurate hyper-parameter optimization are improved, so that the comprehensive energy consumption prediction accuracy of the tilting type fuel gas aluminum melting furnace is improved.

The invention provides a lithium ion battery health state lightweight detection method based on a physical information neural network, and the method comprises the steps: collecting the time, voltage, current, temperature and state-of-charge data of a battery in a takeoff and landing stage discharge process, processing the data into takeoff and landing stage discharge time sequence data, and carrying out the detection of the lithium ion battery health state based on the takeoff and landing stage discharge time sequence data. The method comprises the following steps: designing characteristic factors related to battery aging, screening the characteristic factors by utilizing a Pearson's correlation coefficient and a grey relational degree algorithm to obtain optimal characteristic sequence data, inputting the optimal characteristic sequence data into a physical information neural network model constructed by two serially connected neural networks for training, and in the training process, obtaining the optimal characteristic sequence data. And performing hyper-parameter tuning on the two neural networks by adopting a Bayesian optimization algorithm, then performing fine tuning on the second neural network by adopting a hierarchical transfer learning strategy, and finally applying the trained physical information neural network model to battery health state detection. The method improves the quality of feature data, reduces the calculation complexity of features and models, and achieves the accuracy and reliability of the detection of the health state of the battery under the airborne condition.

The invention relates to the technical field of deep learning hyper-parameter optimization, in particular to a multi-target hyper-parameter joint optimization method and system for a deep learning model. The specific implementation process comprises the steps of obtaining a current hyper-parameter of a deep learning model, generating a learning track vector, and performing utility prediction to generate a learning utility projection; on the basis of the dominating relationship between the learning utility projection and the current Pareto optimal leading edge, pruning the disadvantage training task, updating the Pareto optimal leading edge by using non-dominating sorting, and constructing a Pareto strategy network; and carrying out topology congestion degree analysis on the updated Pareto optimal leading edge, generating a leading edge exploration bias vector, inputting the leading edge exploration bias vector into the Pareto strategy network to update hyper-parameter codes, and entering the next round of iteration. According to the method, through the multi-objective optimization algorithm and in combination with the dynamically updated Pareto strategy network, the problems that the hyper-parameter optimization process is long in time consumption, low in efficiency and prone to falling into local optimum are effectively solved, and the optimization efficiency and the solving quality are improved.

The invention discloses a corroded oil and gas pipeline residual strength prediction method considering a physical constraint loss function, and the method comprises the steps: collecting multi-source feature data of a corroded oil and gas pipeline, obtaining a residual strength measured value as a label, and constructing a training data set; an XGBoost regression model is combined with an SHAP interpretability analysis technology, and the influence degree and the influence direction of each feature on the residual intensity are quantified; constructing a neural network model, and determining an optimal architecture of a neural network by adopting a hyper-parameter optimization method; constructing a physical constraint term based on the influence degree and the influence direction of each feature, introducing the physical constraint term into a loss function of a neural network model, and forming a comprehensive loss function together with a data-driven loss term; and training the optimized neural network model by using a comprehensive loss function to obtain a final residual intensity prediction model. The method has the advantages that the prediction precision is improved, the model interpretability is enhanced, overfitting is prevented, and multi-source feature data are effectively integrated.

The embodiment of the invention discloses an air conditioner maintenance data classification method and system based on machine learning, and the method comprises the steps: integrating multi-source heterogeneous maintenance records generated in the maintenance process of air conditioner equipment, building a correlation index through a common identification field, and fusing dispersed data into a maintenance data set in a uniform format; performing hierarchical semantic analysis on unstructured texts in the set to generate structured semantic features, and performing time sequence feature extraction on structured data; then constructing a hybrid classification model training framework fusing semantic and time sequence features, and generating a maintenance data classification model through feature space alignment, dynamic weight distribution, hyper-parameter optimization and an early stop strategy; and finally, classifying newly-added maintenance records by applying the model, checking by combining an expert knowledge base, manually rechecking conflict results, and returning corrected data as an incremental sample back to the model to realize continuous optimization.

The invention relates to the technical field of shield tunnel construction intelligent control, in particular to a shield tunneling machine tunneling speed intelligent prediction method based on multi-algorithm collaborative optimization. The method comprises the following steps: firstly, acquiring operation parameters and geological and environmental parameters of the shield tunneling machine in real time through a multi-source sensor and an industrial bus; carrying out localized cleaning, normalization and feature extraction on the data by utilizing an edge computing device; further, integrating particle swarm optimization, a genetic algorithm, a sparrow search algorithm and a starvation game search algorithm, and realizing global automatic optimization of the hyper-parameters of the bidirectional long-short-term memory network through a parallel independent optimization and result aggregation strategy; and finally, predicting the tunneling speed in real time by using the optimized model. According to the method, the problems of low prediction precision, weak model generalization capability, dependence on manpower on hyper-parameter optimization and the like caused by insufficient multi-source heterogeneous data processing capability are effectively solved, the prediction accuracy, the adaptability and the engineering practical value are improved, and reliable support is provided for safe and efficient propulsion of shield construction.

The invention provides an LSTM underwater robot modeling method based on TPE hyper-parameter optimization, and relates to the technical field of underwater robot modeling, and the method comprises the steps: carrying out the time sequence feature extraction through a memory unit comprising a forgetting gate, an input gate and an output gate, and predicting the state change amount delta Yt'at a t + 1 moment through an output layer; performing iterative optimization on the number of layers and the number of units of the LSTM network and training the model to obtain an optimized LSTM model; evaluating the optimized LSTM model through a multi-step cumulative prediction error, inputting an initial real state into the model to carry out T-step recursive prediction, updating a current state by utilizing a prediction state increment in each step, and finally calculating an average position error and an attitude error in a T-step window on a verification set; and selecting the hyper-parameter combination with the minimum comprehensive error of the verification set as a final model parameter, and completing the dynamic modeling of the underwater robot. According to the method, the problem that the model is inaccurate due to excessive parameterization in a nonlinear dynamic model can be solved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention discloses a fault detection model training method, a fault detection method, a fault detection device and fault detection equipment, which are applied to the technical field of computers, and comprise the following steps: obtaining an improved butterfly optimization algorithm; the improved butterfly optimization algorithm is an algorithm for guiding the population to move towards the direction of the optimal solution in the optimization process to realize convergence; hyper-parameters needing to be optimized of the fault detection model are determined, the fault detection model is trained based on an improved butterfly optimization algorithm, and target optimal hyper-parameters are obtained; and constructing a fault detection model based on the target optimal hyper-parameter to obtain a target fault detection model. Compared with the low training efficiency of the current fault detection model, the improved butterfly optimization algorithm is used for guiding the population to move towards the direction of the optimal solution in the optimization process to realize convergence, and the target optimal hyper-parameter is determined, so that the randomness of the search process is kept, the controllability of the direction is enhanced, and the fault detection efficiency is improved. The population position is continuously close to the region where the optimal solution is located, so that the efficiency of hyper-parameter optimization is improved.

The invention belongs to the technical field of underground structure prediction, and particularly discloses a tunnel settlement prediction method and system based on experience information and data driving, and the method comprises the steps: receiving influence factor data of multiple aspects of tunnel settlement, and carrying out the sampling based on the influence factor data, and obtaining a tunnel settlement data set; constructing a neural network model, and fusing the settlement development rule as a constraint condition into the loss function to obtain a total loss function fusing empirical information loss and data loss; performing hyper-parameter optimization on the neural network model by adopting a Bayesian optimization algorithm to determine an optimal hyper-parameter; training the neural network model, and updating the neural network model by using the total loss function to obtain a trained prediction model; and performing tunnel settlement prediction performance evaluation on the prediction model, and performing interpretability evaluation on the prediction model by adopting an SHAP interpretation method. According to the invention, the accuracy and efficiency of the model prediction result can be improved.

The invention discloses a hydrological forecasting method based on multi-feature combination and a Transform model. The method comprises the following steps: acquiring measured data of a target watershed hydrological station, building a physical hydrological model, acquiring output data and derivative feature data of the physical hydrological model, and integrating to obtain basic hydrological data; creating enhanced hydrological physical features, and forming a multi-dimensional original feature pool; constructing a plurality of combination strategies based on the basic hydrological data and the multi-dimensional original feature pool; capturing a long-term dependency relationship of the hydrological time sequence by using an improved Transform model; and the model performance is improved through automatic hyper-parameter optimization. According to the method, the influence of different input feature combinations on the flood forecasting precision is highlighted, and effective technical support is provided for water resource management and flood control and disaster reduction.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention discloses a lithium battery health state prediction method and system based on an improved artificial travel mouse optimization algorithm, and relates to the technical field of lithium ion battery health state prediction. The historical operation data is preprocessed; a CNN-LSTM-Self-Attention hybrid prediction model is constructed, and the CNN-LSTM-Self-Attention Performing hyper-parameter optimization on the CNN-LSTM-Self-Attention hybrid prediction model by adopting an improved artificial travel mouse algorithm to obtain an optimization model; and deploying the optimization model to a battery management system, inputting operation data, outputting a health state prediction value, and triggering a maintenance instruction when the health state prediction value is lower than a threshold value. The invention provides a more efficient and more robust automatic hyper-parameter optimization framework so as to fully release the potential of the deep learning model in battery SOH prediction.

The invention relates to a Parkinson's disease walking state recognition method based on tensor singular value decomposition and automatic hyper-parameter optimization. The Parkinson's disease walking state recognition method comprises the following steps: acquiring a sensor signal of a target person; constructing a tensor according to the sensor signal; performing t-SVD decomposition processing on the tensor to obtain a frequency domain core tensor; the frequency domain core tensor and the sensor signal are subjected to feature extraction, the extracted features are input into a trained classification model, the binary classification recognition result of the Parkinson's disease patient and the healthy person is obtained, and the trained classification model is obtained through training of a training set marked with a patient and health contrast label. According to the method, efficient dimension reduction and noise suppression are realized by using t-SVD, and adaptive optimization is performed on the key hyper-parameters of the classification model in combination with an automatic machine learning technology, so that the dichotomy recognition accuracy and robustness of the Parkinson's disease patient and the health control are improved.

The invention discloses a two-stage algorithm selection and hyper-parameter joint optimization method, which comprises the following steps of: in the first stage, processing a training set and a test set through row sampling operation and column dimension reduction operation to form a reduced data set; randomly sampling a certain number of configurations in the hyper-parameter space of each candidate algorithm, evaluating the performance of each candidate algorithm by using the reduced data set, and extracting an optimal performance score; in the second stage, a previous algorithm is screened according to the optimal performance score to form a candidate set, and a pruned hyper-parameter search space is formed so as to reduce the calculation complexity of processor hyper-parameter search; and performing hyper-parameter optimization on the pruned hyper-parameter search space by using the original data set, and outputting an optimal algorithm adaptive to the target technical task and hyper-parameter configuration thereof. Algorithm screening and hyper-parameter tuning adaptive to a specific scene are realized through a two-stage optimization strategy, and meanwhile, the method is suitable for a traditional table type dichotomy task and aims at improving the deployment efficiency and performance of a machine learning model.

The invention relates to the technical field of oil and gas field development, in particular to a gas drive front description method based on a multi-modal fusion deep learning model. The method comprises the steps of collecting multi-modal data, and constructing a multi-modal comprehensive database; performing data preprocessing and cleaning on the multi-modal comprehensive database; constructing a multi-modal fusion deep learning model; carrying out training and hyper-parameter optimization on the multi-modal fusion deep learning model; inputting real-time data into the trained model, and outputting a leading edge prediction result; and according to an output prediction result, new data meeting requirements are added into a multi-modal comprehensive database, model incremental training is triggered, and parameters are updated to adapt to dynamic changes of the oil reservoir. And rapid, accurate and global dynamic prediction of the gas-driven front is realized.

The invention discloses a carbon emission model prediction cross validation and fusion optimization improvement system. The system comprises a model construction module which constructs a plurality of prediction models according to preprocessed historical energy consumption data and corresponding historical carbon emission data; the cross validation optimization module divides a training set and performs hyper-parameter optimization by applying a cross validation technology to obtain an optimal hyper-parameter set of each prediction model and each optimized prediction model; the model fusion module adopts Stacking ensemble learning to divide all the optimized prediction models into a base learner and a meta learner, the base learner trains a training set to obtain preliminary prediction carbon emission and combines the preliminary prediction carbon emission into a feature matrix, and the meta learner trains through the feature matrix; and dynamically adjusting the parameter configuration of the base learner and the meta learner by using a particle swarm optimization algorithm, and finally obtaining a prediction model after fusion optimization. According to the method, the carbon emission prediction precision is improved, and the carbon emission prediction accuracy and stability are improved.

The invention discloses a grinding wheel state recognition method and system based on a multi-strategy improved DBO algorithm. The method comprises the steps that multi-sensor data of a numerical control gear grinding machine tool are collected, preprocessed and marked with abrasion labels; performing efficient global optimization on hyper-parameters of the CNN-BiLSTM-Attention model by adopting a multi-strategy improved dung beetle optimization algorithm fusing Logistic chaotic mapping initialization, a spiral search strategy optimization breeding stage and a Levy flight strategy optimization stealing stage; and finally, precise prediction and evaluation of the abrasion state of the grinding wheel are achieved through the optimized model. The system comprises a multi-source data preprocessing module, an intelligent hyper-parameter optimization module, a depth prediction model training module and a state prediction and performance evaluation module. According to the method, the problems that a traditional hyper-parameter optimization method is low in efficiency and prone to falling into local optimum are effectively solved, and the precision, convergence speed and working condition adaptability of a state recognition model are remarkably improved.

The invention discloses a PINN-based numerical control machine tool spindle Z-direction thermal error compensation method, and belongs to the technical field of numerical control machine tool thermal error compensation. S2, performing hyper-parameter optimization; and S3, model verification and error compensation: applying the established PINN thermal error prediction model to a machine tool of the same type as the tested machine tool for thermal error prediction. According to the numerical control machine tool spindle Z-direction thermal error compensation method based on the PINN, the core is that a spindle axial thermal deformation elongation physical formula is converted into a physical loss item through a PINN model, the physical loss item and a data loss item jointly form a total loss function, and compared with the problem that a traditional pure data driving model (such as a BP neural network) is prone to being limited by sample distribution, the numerical control machine tool spindle Z-direction thermal error compensation method based on the PINN is more accurate. According to the method, model output is restrained through a physical mechanism, even under the unseen rotating speed and temperature working conditions, stable thermal error prediction precision can be kept, model failure caused by working condition changes is avoided, and the generalization ability of the model in different machining scenes is greatly improved.

The invention provides a lithium battery residual life prediction method based on multi-frequency decomposition and a hybrid neural network, and relates to the technical field of lithium battery residual life prediction. According to the lithium battery residual life prediction method, a component decomposition mechanism is introduced, and proper prediction models are designed for different frequency components, so that local fluctuation and long-term trend in a battery degradation process are effectively captured, and the accuracy and robustness of residual life prediction are remarkably improved; proper prediction models are respectively designed according to complexity differences of different components, so that redundant calculation caused by uniformly using complex models on all components is avoided, and the balance between prediction precision and calculation efficiency is realized; based on a hyper-parameter optimization strategy of hierarchical reinforcement learning, an optimal parameter combination can be automatically and efficiently searched according to different battery data characteristics, and the defects that a traditional optimization method is high in calculation cost and poor in adaptability are overcome.

According to a method to aid installation of large-scale BESS in a power network, a hyperparameter optimization engine generates a feasible configuration of BESS defined by location and sizing parameters subject to an installation constraint. A power system simulation engine conducts an energy market simulation for the power network with the generated configuration over a defined simulation horizon to determine a configuration value. The power system simulation engine comprises one or more subroutines characterizing the impact of the BESS on the market clearing mechanism, to compute an expected generation cost associated with the BESS. The configuration value is determined based on the expected generation cost and a total installation cost of the BESS. The hyperparameter optimization engine is iteratively executed to generate an updated configuration based on the configuration values of previous configurations, to determine a final configuration of BESS defined by a set of optimal location and sizing parameters.

The invention discloses an open-web gravity dam deformation prediction and mechanism interpretation method, system, equipment and medium, and belongs to the technical field of dam and hydraulic structure health monitoring and state evaluation. Constructing the preprocessed monitoring data into corresponding feature vectors, dividing a time sequence data set, and performing XGBoost model training and hyper-parameter optimization to obtain a prediction model; calculating a corresponding SHAP value, and screening a key factor as an independent variable for model analysis; and selecting a control index to evaluate the performance of the model, and deploying the model to a dam safety monitoring system after verification so as to carry out real-time prediction and early warning. According to the method, while high-precision deformation prediction of the open-web gravity dam is realized, quantifiable mechanism explanation of the prediction result is provided, so that transparent and reliable decision support is provided for safety monitoring and early warning of the dam.

The invention discloses a cable elbow-shaped head ponding recognition method based on knocking peak alignment and wavelet scattering, and belongs to the technical field of voiceprint recognition. Comprising the following steps: receiving a cable elbow type terminal knocking sound signal, realizing a knocking signal peak alignment algorithm through knocking peak value detection, windowing processing and signal mirror image filling, and outputting an alignment sound signal; wavelet scattering coefficients are extracted from the aligned sound signals to serve as characteristic parameters, and a voiceprint characteristic parameter set under the corresponding ponding degree is obtained; inputting the voiceprint characteristic parameter set into an LSTM model for training, outputting a water level discrimination label, and obtaining a water level state recognition model through hyper-parameter optimization; and identifying the water accumulation state of the cable elbow-shaped head by using a water level state identification model. Safety risks caused by water inflow of the elbow-shaped connector can be avoided, manpower and material resource cost consumption caused by a traditional maintenance mode is remarkably reduced, and the elbow-shaped connector is of great significance in improving operation safety of an electric power system.

The invention discloses a patient rehabilitation effect evaluation system based on nursing information processing. The system comprises an evaluation data acquisition module, a data preprocessing module, an effect evaluation model construction module, a model hyper-parameter optimization module and a rehabilitation effect evaluation module. The invention relates to the technical field of intelligent patient rehabilitation effect evaluation, in particular to a patient rehabilitation effect evaluation system based on nursing information processing, and the system comprises the steps: obtaining an effect evaluation original data set through data collection; a data preprocessing method of data cleaning, feature extraction, time sequence alignment, data standardization and data set segmentation is adopted; a double-flow deep network model is adopted as an effect evaluation model, and it is ensured that an evaluation result conforms to clinical logic while feature collaboration of heterogeneous medical data is achieved; an improved leapfrog algorithm is adopted to optimize model hyper-parameters, dynamic balance exploration and development are achieved, meanwhile, the stability of the algorithm is enhanced through population division, and stable performance of an evaluation system in different clinical scenes is ensured.

The invention discloses a multi-source data driven irrigation district resource regulation and control method and system fused with a neural network, and relates to the technical field of irrigation district resource regulation and control, and the method comprises the steps: collecting irrigation district multi-source data, carrying out the cleaning, standardization and time-space dimension reconstruction processing, constructing a hybrid neural network regulation and control model fused with CNN spatial feature extraction and LSTM time sequence modeling, and carrying out the calculation of the hybrid neural network regulation and control model. A weighted mean square error is used as a loss function, model training is completed in combination with an Adam optimizer and hyper-parameter optimization, and finally optimal regulation and control parameters meeting resource constraints and crop requirements are output; according to the method, the defects that traditional regulation depends on artificial experience and the multi-source data fusion capability is weak are effectively overcome, data space-time correlation information is fully mined, scientificity and accuracy of irrigation area resource regulation are improved, water resource waste and fertilizer loss are reduced, accurate matching of crop supply and demand is achieved, the crop yield is guaranteed, the ecological environment is improved, and the economic benefit is increased. The method is suitable for multi-resource cooperative regulation and control of complex irrigation areas.

The invention discloses an oil and gas pipeline corrosion prediction method and platform based on deep reinforcement learning, and relates to the technical field of oil and gas pipeline corrosion prediction. Comprising the following steps: collecting time sequence data related to pipeline operation through a multi-dimensional sensor, realizing unequal interval data reconstruction through an improved signal processing technology, constructing a depth Q network prediction framework fused with a time sequence residual error corrosion state prediction algorithm, determining an optimal model through multi-group hyper-parameter combination iteration training optimization, and predicting the corrosion state of the pipeline. Combining a residual error correction mechanism to output a normalized corrosion rate estimation value, and performing inverse transformation to obtain an actual corrosion rate prediction result; and specific processes of prediction framework modeling, network structure construction, residual algorithm embedding, hyper-parameter optimization, test data processing, prediction result correction and the like are carried out, and the platform realizes full-process closed-loop operation of data acquisition, processing, modeling, prediction and analysis by virtue of each functional unit. According to the method, the corrosion prediction accuracy and the time sequence adaptability are remarkably improved, and technical support is provided for safe operation of the oil and gas pipeline.

The invention discloses an ice water remote sensing classification method based on an LAU-Net model, and belongs to the technical field of remote sensing image intelligent processing. The method comprises the following steps: constructing an ice water classification sample set; based on a standard U-Net network architecture, constructing an improved model LAU-Net fused with a local attention module; training the LAU-Net model by using the ice water classification sample set, and dynamically adjusting a learning rate, a batch size, an optimizer weight attenuation factor and regularization strength through an automatic hyper-parameter optimization module; and adopting the trained LAU-Net model to carry out ice water classification on the input image. According to the method, the local attention mechanism and network structure optimization are introduced, so that the classification precision of the ice water boundary in the remote sensing image and the model operation efficiency are improved, and the technical requirements of dynamic sea ice monitoring are met.

The invention discloses a metallocene polypropylene synthesis catalytic performance prediction method, which comprises the following steps: data collection: collecting experimental data of metallocene catalyst synthesis polypropylene, and establishing a data set containing input variables and output variables; data preprocessing: cleaning, standardizing and coding experimental data; feature engineering: carrying out feature correlation analysis on the preprocessed data; model construction: constructing independent prediction models for predicting catalyst activity, number-average molecular weight and molecular weight distribution by using a plurality of machine learning algorithms respectively; performing hyper-parameter optimization: performing hyper-parameter optimization on the prediction model by adopting a Bayesian optimization algorithm to obtain an optimal model parameter; model evaluation: evaluating the performance of the prediction model by adopting a cross validation method; and predicting: inputting process parameters and catalyst ligand parameters input by a user into the optimized and evaluated optimal prediction model, and outputting predicted values of catalyst activity, number-average molecular weight and molecular weight distribution.

The invention relates to the technical field of rock mechanics and intelligent detection, in particular to a rock strength and failure mode prediction method and system. The method comprises the following steps: acquiring data of a rock sample to be detected to obtain a multi-source feature set; constructing a feature coefficient prediction model, and inputting the multi-source feature set into the feature coefficient prediction model to obtain a feature coefficient; screening the multi-source feature set based on the feature coefficient to obtain an optimal feature subset; constructing a rock strength and failure mode prediction model, and optimizing hyper-parameters of the rock strength and failure mode prediction model by adopting an automatic hyper-parameter optimization framework; and inputting the optimal feature subset into the optimized rock strength and failure mode prediction model to obtain a rock strength prediction value and a failure mode. The problem that rock strength and failure mode prediction precision is not accurate enough and cannot be predicted at the same time in the prior art is solved, and the prediction precision is greatly improved through multi-source data collaborative prediction.