798 results about "Hyperparameter optimization" patented technology

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

The invention relates to an automatic driving large model training optimization method based on multi-scene data balance. Comprising the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, training by adopting an iterative training framework based on a real vehicle high-speed driving scene library, designing a multi-task joint loss function and a weight adaptive adjustment strategy, realizing multi-task target balance, and obtaining a trained visual language automatic driving large model; (3) dynamic simulation is carried out for the automatic driving working condition, and a high-fidelity simulation data test set is constructed based on simulation data; and (4) performing hyper-parameter optimization and lightweight processing on the trained visual language automatic driving large model according to the high-fidelity simulation data test set to obtain a scene data balanced automatic driving large model. According to the method, the large model reasoning speed is increased, and resource occupation is reduced, so that the judgment capability of the large model on dynamic working conditions and high-risk scenes is remarkably improved.

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 an adaptive feature weighted fusion fault diagnosis method for multi-source heterogeneous data of a motor, and the method comprises the steps: constructing a multi-head deep convolutional neural network, designing a plurality of parallel convolutional channels in the neural network, deeply mining the abnormal features of original signals of current and vibration, and focusing on the signal features in different dimensions, thereby achieving the fault diagnosis of the multi-source heterogeneous data of the motor. And comprehensive capture of key feature information is ensured. And adaptive weighted fusion is performed on the extracted multi-dimensional features by adopting a correlation analysis method to highlight abnormal key features, so that the feature fusion effect of the multi-source heterogeneous data and the accuracy of anomaly detection are improved. An SELU activation function is adopted and a hyper-parameter optimization strategy of a dynamic attenuation learning rate is combined so that an overfitting problem which is easy to occur in a model training process can be effectively solved and convergence stability of the model and high precision of fault identification can be enhanced. Through adaptive fusion of multi-source heterogeneous data features, key fault features are effectively extracted, and the diagnosis accuracy in an electromechanical coupling fault scene is improved.

Techniques for increasing the quality of output from large language models using reinforcement learning to select inference-time hyperparameters are disclosed. The large language model is configured with a set of values corresponding to a set of inference-time hyperparameters that are used to influence the output of the machine learning model after the model has been frozen. After obtaining a set of performance metrics that indicate the quality of the output, a reinforcement learning agent computes an adjustment for one or more of the hyperparameters, resulting in a modification of the hyperparameter values. Applying the new hyperparameter values, the large language model is then applied to a new set of input to generate a second output. The process iterates until the performance metrics associated with the output are satisfactory.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

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 discloses an explainable method of a cardiovascular disease prediction model based on SHAP, and aims to construct an accurate cardiovascular disease prediction model and provide explainable analysis by combining multi-source medical data. The method comprises the following steps: acquiring medical data related to cardiovascular diseases, performing data preprocessing and feature engineering coding, and establishing a cardiovascular disease data set; carrying out importance evaluation on the features by using a Boruta algorithm, screening out features which play an important role in cardiovascular disease prediction in combination with a random forest, and completing the division of a training set and a test set; adopting a grid search algorithm to adjust and optimize hyper-parameters, and optimizing the performance of the model; training the training set data by using an XGBoost algorithm, establishing a cardiovascular disease prediction model, and outputting feature importance; and inputting the data set into the trained model, outputting a prediction result of each patient, visualizing the prediction results by using an SHAP interpretation tool, and analyzing the interaction effect of characteristic variables and the contribution of a single characteristic. According to the technical method, accurate prediction and analysis support can be provided for clinic, doctors are helped to formulate effective prevention measures, and therefore the early diagnosis and treatment effect of cardiovascular diseases is improved.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction precision improvement method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a plurality of initial weather prediction models based on a Transform structure in combination with a PIDL (Precision Independent Design Language) algorithm; taking the power curve of the wind turbine generator and the performance characteristic curve of the photovoltaic module as constraint conditions, and integrating the constraint conditions into each initial meteorological prediction model to obtain a plurality of basic meteorological prediction models; based on a preset automatic hyper-parameter tuning tool, performing hyper-parameter optimization on each basic meteorological prediction model in combination with a cross validation algorithm; fusing the plurality of basic meteorological prediction models after hyper-parameter optimization by adopting an integrated learning algorithm to obtain a target meteorological prediction model; and according to the meteorological characteristic data and the target meteorological prediction model, the target meteorological prediction result is determined, and the meteorological prediction precision is improved.

The invention discloses a risk prediction and adaptive budget allocation system and method based on construction settlement, and the method comprises the following steps: S1, collecting historical settlement and initial budget data of a construction project, carrying out the multi-dimensional marking, and generating a structured data set; s2, processing the data by applying multi-level time sequence analysis, and identifying key levels; s3, generating an optimal quantity-price coupling relationship based on a multi-party game model, and performing preliminary tuning; s4, performing hyper-parameter optimization through a dual-domain heterogeneous population tuning mechanism; s5, analyzing the influence of external factors on the cost by using a structural transition model, and carrying out deviation analysis; s6, risk scenes under different construction situations are generated through risk simulation, and risk factors are identified and adjusted; s7, adjusting budget allocation in real time based on an adaptive complex network mechanism; and S8, a review report is generated, and multi-party collaborative review is supported. According to the method, the dynamic adjustment capability of budget allocation is improved through various optimization mechanisms, and audit transparency is improved.

The invention discloses a cable state evaluation method based on Bayesian optimization and XGBoost, and the method employs a cross entropy loss function on the basis of an XGBoost algorithm, and can effectively reduce the loss. And hyper-parameters in the XGBoost algorithm are adjusted and optimized by using a Bayesian parameter optimization method, so that key features in the operation process of the cable are extracted more effectively, and finally, the state of the cable is evaluated. By using the cross entropy loss function, the complexity of the cable state can be better adapted, and the robustness of the model is improved. Meanwhile, the Bayesian hyper-parameter optimization is beneficial to finding the optimal model parameter configuration, so that the model can capture the cable state characteristics more accurately.

A distributed optical fiber sensing perimeter intrusion event open set identification method based on self-supervised comparative learning comprises the following steps: step 1, an experiment scene: establishing a DAS system, and collecting data samples of various intrusion events; 2, data preprocessing: carrying out noise reduction processing on the experimental data sample, and then converting the experimental data sample into a GASF image; step 3, the processed data is divided into two parts during training, one part is data with labels and known categories, and the other part is data without labels and containing all types of events; step 4, constructing a self-supervised contrast open set class learning (SCOC) algorithm model; step 5, training the constructed SCOC algorithm model, performing hyper-parameter optimization by using a BoHo optimization algorithm, and obtaining an SCOC algorithm training model under the optimal hyper-parameter combination; and step 6, open set event classification testing. According to the invention, the problems of identification and alarm of unknown intrusion events in an open set scene and the like can be solved.

The invention discloses a laser welding spatter real-time recognition method based on OCT keyhole depth measurement. The method comprises the steps of S1, keyhole depth data collection based on OCT measurement and data set construction and S2, construction training of a deep learning model and spatter defect recognition. According to the method, an SD-OCT measurement technology is combined with a composite deep learning model based on 1DCNN-BiLSTM splash defect recognition, a Dropout technology and adaptive moment estimation are adopted to prevent model overfitting, and a TPE algorithm is adopted to carry out hyper-parameter optimization on the model, so that the trained 1DCNN-BiLSTM splash defect recognition model can accurately predict the splash state in the dynamic laser welding process, and the accuracy of the dynamic laser welding process is improved. Manual intervention is not needed, and the laser welding spatter defect can be rapidly and accurately recognized.

The invention relates to a target trajectory prediction method and system based on a bidirectional long-short-term memory network, and the method mainly comprises the following steps: firstly, carrying out the spatial-temporal feature extraction of target trajectory data through a bidirectional long-short-term memory network layer, obtaining the spatial-temporal feature vector of target data, capturing a long-term dependency relationship and a short-term dependency relationship, and carrying out the spatial-temporal feature extraction of the target trajectory data; wherein the long-term dependency describes the time sequence and continuity of motion, and the short-term dependency describes the maneuvering characteristics of the motion; secondly, a prediction model is constructed, and hyper-parameters of the prediction model are optimized, so that the prediction performance and generalization ability of the model are improved; and finally, predicting a future trajectory of the target by using the trained optimization model, outputting a trajectory coordinate sequence of a plurality of time points in the future, and realizing accurate prediction of the target in a period of time in the future. According to the method, the bidirectional long-short-term memory network and hyper-parameter optimization are combined, so that high-precision trajectory prediction of the target is realized, and the adaptability of the prediction model to complex motion characteristics is enhanced.

The invention relates to the technical field of power system load prediction, and discloses a load characteristic prediction method based on an Informer hyper-parameter optimization model, which comprises the following steps of: firstly, acquiring long-time weather and power grid data of a power supply company region and preprocessing to obtain a characteristic data set corresponding to load data; and carrying out normalization processing on the feature data set corresponding to the load data. Then, inputting the processed data into an Informer model for training, fully exploring the time correlation among the data, and utilizing a multi-head sparse self-attention mechanism to mine the correlation between the states before and after the feature information; and then determining hyper-parameters needing to be optimized and a value range, and performing hyper-parameter optimization on the Informer model by adopting a TPE algorithm to obtain an optimal combination of the hyper-parameters. And finally, obtaining a prediction result through the optimized model, then establishing a model comparison scheme, and carrying out prediction result comparative analysis on the Informer parameter optimization model, the LSTM and the GRU to embody the feasibility of the method.

The invention discloses a settlement amount time sequence prediction method and system for deep foundation pit excavation adjacent buildings. The method comprises the steps that original monitoring data of on-site building settlement are acquired; performing data preprocessing on the obtained original monitoring data; constructing a recurrent neural network; model input and output parameters are determined through principal component analysis; optimizing the recurrent neural network based on an optimizer; performing hyper-parameter optimization based on an optimization result; performing settlement time sequence prediction by using the optimized recurrent neural network; the system comprises a data acquisition module, a preprocessing module, a model construction module, an analysis module, an optimization module, a parameter optimization module and a prediction module. By constructing the settlement prediction model, dynamic modeling and accurate prediction of the settlement trend of the building in the excavation process of each stage of the foundation pit are realized; historical settlement monitoring data and multi-layer soil body excavation information are combined, and multi-source input parameters are introduced, so that the adaptability of the model to complex working conditions is enhanced.

The invention discloses a construction parameter optimization method and device based on dessert prediction, a medium and equipment, and the method comprises the steps: obtaining a data set of a shale gas well production process, and determining a dessert indication factor; training a network model by taking the data set as input and the dessert indication factor as output to obtain a dessert indication factor prediction model; optimizing the dessert indicator factor prediction model by using a Bayesian hyper-parameter optimization algorithm, and inputting a data set obtained in real time into the optimized dessert indicator factor prediction model to obtain a prediction result; inverting a prediction result into three-dimensional space distribution characteristics of desserts in the shale reservoir; and determining contribution values and partial dependency relationships of the plurality of geological parameters and the plurality of engineering parameters to a prediction result based on the three-dimensional space distribution characteristics, deploying construction parameters according to the contribution values and the partial dependency relationships, and synchronously updating a sweet spot indication factor prediction result to realize spatial dynamic optimization and adjustment of the construction scheme. Through the scheme, the construction parameters can be scientifically optimized.

The invention discloses a space-time carbon emission prediction method, system and device and a medium. The method comprises the steps of obtaining load historical sequence data; carrying out load flow calculation by adopting a first load flow analysis algorithm to obtain a load flow calculation result; based on the load flow calculation result, carbon flow analysis is carried out through power transmission between the nodes, and the carbon emission coefficient of each node is obtained; constructing a time sequence diagram neural network model, and performing preliminary training on the time sequence diagram neural network model; performing hyper-parameter optimization on the preliminarily trained time sequence diagram neural network model by adopting a first hyper-parameter optimization technology to obtain an optimized time sequence diagram neural network model; and inputting load sequence data and unit power output data at the current moment into the optimized sequence diagram neural network model, and carrying out online prediction on a carbon emission coefficient. According to the method, the real-time prediction of the carbon emission coefficient is realized based on the characteristic learning of the historical operation and monitoring data of the power system.

The invention provides a short-term load prediction method, which comprises the following steps: collecting multivariable data of a power system, and preprocessing the collected multivariable data; wherein the multivariable data at least comprises power load data, weather data, holiday and festival information and date data; inputting the preprocessed multivariable data into a pre-trained prediction model to obtain a corresponding prediction result; wherein the prediction model is a model combining a time convolution network and a long short-term memory network, hyper-parameter optimization is carried out through a preset search algorithm, training is carried out through historical multivariable data, and a plurality of intrinsic mode component prediction results are combined to be output as the model; and determining the short-term load corresponding to the power system according to the prediction result. According to the method, the nonlinear features in the power load data are effectively extracted, more accurate load prediction is realized through advantage complementation of multiple models, and a solid guarantee is provided for stable operation of a power system.

The invention discloses a coal gangue concrete stress response prediction method based on a physical information neural network. The method comprises the following steps: constructing a stress-strain curve database of coal gangue concrete; acquiring damage variables of the coal gangue concrete in a compaction stage, a yield stage and a fracture stage under uniaxial compression; deducing a mesoscopic statistical damage constitutive model of the coal gangue concrete according to the damage variables: constructing a physical information neural network, and embedding the mesoscopic statistical damage constitutive model and boundary conditions into a loss function of the neural network; performing normalization processing on the coal gangue concrete stress-strain curve database to obtain a data set; training a physical information neural network model according to the training set, and performing hyper-parameter optimization according to the verification set to obtain a trained model; and predicting the stress of the coal gangue concrete by using the trained model according to the test set. According to the method, the damage constitutive equation and the deep learning algorithm are fused, so that high-precision and low-data-demand mechanical property prediction is realized.

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 discloses a hyper-parameter optimization method and system of an icing galloping prediction model based on meta-learning. The method comprises the following steps: collecting original data from meteorological monitoring equipment, a line state sensor and historical icing galloping records; uniformly aligning the collected original data according to time granularity to obtain multi-modal time sequence data; performing internal and external double-loop training on hyper-parameters of the constructed icing galloping time sequence prediction model by adopting the obtained multi-modal time sequence data, performing iterative training on internal loops of the internal and external double-loop training under the given hyper-parameters, and after model parameters converge, obtaining the icing galloping time sequence prediction model. And performing meta-learning-level hyper-parameter optimization on the basis of converged model parameters in the outer loop, and performing adaptive search on hyper-parameters on the premise of fixing an inner loop training normal form to obtain optimal hyper-parameters. According to the method, the key hyper-parameters of the model can be quickly and adaptively optimized under different meteorological scenes and line working conditions, the prediction precision and the convergence speed are improved, and the icing galloping risk of the power transmission line is warned in real time.

Swarm learning systems and methods can perform hyperparameter tuning. Network nodes train a common machine learning (ML) algorithm using data local to each of the network nodes. Hyperparameter optimization is also performed at each of the network nodes. Model parameters and optimized hyperparameters are shared by the network nodes, and are subsequently merged. An updated version of the common ML algorithm based on the merged model parameters and the merged optimized hyperparameters is generated. That updated version of the common ML algorithm is re-trained at each of the network nodes.

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 discloses a short-circuit current zero crossing point multi-step prediction method based on a double-breakpoint circuit breaker, and belongs to the technical field of power system phase selection breaking. The method comprises the following steps: S1, data preprocessing: carrying out normalization processing on original short-circuit current data; s2, hybrid model construction: constructing a CNN-SE-GRU hybrid model based on the current data processed in the S1 in combination with a convolutional neural network, an attention mechanism and a GRU network; s3, performing CEO algorithm optimization: performing hyper-parameter optimization on the constructed hybrid model by using a CEO algorithm to obtain a CEO-CNN-SE-GRU hybrid prediction model; s4, model training: training the hybrid prediction model to obtain a stable model; and S5, performing prediction execution: calling the trained hybrid prediction model to realize multi-step prediction of the short-circuit current, and predicting a zero crossing point of the short-circuit current. Compared with the prior art, the short-circuit current zero crossing point multi-step prediction speed and precision are improved, and meanwhile better generalization is achieved.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.

A system for classifying apple leaf diseases using deep learning and feature fusion, consisting of: A data input module, which includes a data storage module, is configured to store a data set created using various data sources, with the data set containing apple leaf images being derived from the data sets "Apple Leaf 9", "Kashmiri Apple Plant Disease" and "Plant Village Apple Leaf"; a data preprocessing module configured to perform preprocessing of the newly prepared dataset of apple leaves, wherein the data preprocessing module is configured to perform data decoding, expansion, resizing, segmentation, scaling and color conversion of the input image data; a feature extraction module that is operationally connected to the data processing module and is configured to receive preprocessed data and transfer the preprocessed data to one or more convolutional neural network models for feature extraction; a feature fusion module configured to combine the extracted features from the Convolutional Neural Networks to develop a fused feature vector; a hyperparameter optimization module configured to optimize the hyperparameters of the feature extraction models by implementing a particle swarm optimization algorithm; a classification module configured to classify the fused trait vector into 13 apple leaf disease classes using a random forest classifier; and a user interface connected to the classification module, configured to display the classification results.

Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. For example, the system may apply a profiling model using a time-series embedding of the dataset combined with the aggregate statistical profile. In either case, the profiling model may be trained on the scoring policy and / or a time-series embedding of the dataset combined with the aggregate statistical profile to determine a likelihood of the effectiveness of a given model on the given dataset and / or likely hyperparameters for the given model.

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 discloses a Yungang grotto environmental parameter prediction method and system based on an artificial neural network, and relates to the field of neural network prediction algorithms, and the method comprises the steps: collecting and preprocessing external and internal environmental parameters of a grotto through a sensor, and constructing a data sample set through the preprocessed data; constructing a CNN-LSTM network prediction model according to the sample set, establishing hyper-parameters of the prediction model, setting a combination interval of the hyper-parameters to be optimized, and executing a BO algorithm on the hyper-parameters; and selecting a next group of hyper-parameters to carry out a new round of training according to a probability model acquisition function until the number of iterations is reached, and outputting a predicted value. According to the method, the BO-CNN-LSTM model is combined with the Bayesian optimization algorithm, hyper-parameter optimization is remarkably improved, more excellent performance indexes are achieved, the BO-CNN-LSTM model has remarkable advantages in prediction of the internal environment parameters of the Yungang grotto, microclimate changes in the grotto are effectively captured and predicted, an actual environment parameter sample is accurately predicted, and the prediction efficiency is improved. And a comprehensive and accurate prediction result is provided for the internal environment parameters of the Yungang grotto.