944 results about "Bayesian optimization" patented technology

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention discloses a viscous coarse-grained soil permeability coefficient prediction method based on a physical constraint neural network, and the method comprises the following steps: carrying out an indoor viscous coarse-grained soil seepage test, and establishing a viscous coarse-grained soil permeability coefficient formula considering porosity and grain composition characteristics, further constructing a mixed model containing a physical driving item and a neural network data driving item, forming a complete data set through a numerical simulation technology and literature investigation on the basis of a seepage test, complementarily collecting porosity, grain composition characteristics and corresponding permeability coefficient data of the viscous coarse-grained soil sample, and dividing the complete data set into a training set and a test set; according to the method, optimal hyper-parameters are dynamically searched in combination with Bayesian optimization for model training, a loss function curve and permeability coefficients of the viscous coarse-grained soil under different porosity and grading characteristics are obtained, tests show that high-precision prediction of the permeability coefficients of the viscous coarse-grained soil is achieved, and the problems that a traditional method is insufficient in physical constraint and low in prediction precision are solved.

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

The invention relates to the technical field of intelligent fire safety linkage control, in particular to an intelligent locking linkage control system and method based on fire monitoring, and the system comprises an acquisition module, a modeling module, a simulation engine module, a decision center module and an execution module. The acquisition module acquires physical quantity data of a key area of a building, wherein the physical quantity data comprises high-temperature radiation spectrum offset, aerosol particle swarm distribution characteristics and local heat convection intensity. The modeling module generates an environmental interference confidence index through convolutional neural network fusion features, and starts an incremental clustering algorithm to update an interference knowledge base when the confidence is insufficient. And the execution module dynamically compresses the delay window according to the risk map, and triggers a cross-level linkage mechanism through a people flow density threshold. And the environment sampling frequency adjustment coefficient is fed back to the acquisition module, the execution state data reverse drive modeling module iteratively updates the knowledge base, a Bayesian optimizer is combined to screen high-value characteristics to reconstruct simulation parameters, and full-link closed-loop learning and continuous evolution of the false alarm suppression capability are realized.

The invention relates to the technical field of flow measurement, and discloses a multiphase flow flowmeter dynamic compensation system, which comprises a multi-modal data acquisition module, a dynamic compensation module and a dynamic compensation module, the feature extraction and standardization module performs frequency domain conversion and feature extraction on the data to generate a fusion feature matrix; the graph structure modeling module constructs a graph model based on the fusion features and extracts graph feature vectors; the compensation optimization module obtains an optimal compensation parameter through Bayesian optimization; the flow estimation module generates a compensated flow value according to the optimal compensation parameter; and the adaptive feedback module calculates a flow residual error and updates the graph structure modeling module and the compensation optimization module. By introducing a dynamic compensation and self-adaptive feedback mechanism, the method can adapt to the change of the flowing state of the multiphase flow in real time, the precision and stability of flow estimation are effectively improved, the problem that a traditional method is difficult to quickly respond to flow pattern change and working condition fluctuation is solved, and the real-time performance and reliability of flow measurement are remarkably improved.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention relates to the technical field of cue word optimization, and discloses a Bayesian automatic cue word optimization method based on meta cue words, which comprises the following steps: constructing a challenging data set pool aiming at cognitive weaknesses of a target large language model, obtaining initial cue words, and constructing a layered optimization target system in combination with the data set pool; based on the hierarchical optimization target system and the data set pool, constructing an optimization target, a task example and a historical learning module of the meta cue word, and assembling the optimization target, the task example and the historical learning module into a complete meta cue word according to a predetermined format; using the meta cue word to drive the optimizer large language model to generate new cue word candidates, and outputting the optimal cue word after the new cue word candidates are improved to convergence through data set pool performance evaluation and Bayesian optimization strategy iteration; according to the method, accurate and automatic optimization of the cue words of the target large language model is realized, and the suitability of the cue words and the model requirements and the stability of the optimization effect are guaranteed.

The invention discloses a customized production-oriented edge node lightweight AI model adaptive compression method, which belongs to the technical field of intelligent manufacturing and edge computing, and comprises the following steps of: dynamically integrating compression strategies such as pruning, quantification and knowledge distillation by analyzing demand constraints and edge node hardware resources of customized production tasks; constructing an adaptive decision engine by utilizing reinforcement learning and Bayesian optimization, and generating an optimal compression scheme; in the deployment stage, compression parameters are dynamically adjusted through real-time monitoring and a closed-loop feedback mechanism, and the balance of model precision, reasoning efficiency and resource occupation is achieved. According to the method, the adaptability of the model in a heterogeneous edge environment can be remarkably improved, the deployment cost is reduced, and the small-batch and multi-task quick response requirement in a customized production scene is met.

The invention discloses a wind and light output scene generation method based on depth feature mining and adaptive clustering, and the method comprises the steps: cleaning wind and light output data, carrying out the normalization processing of the data, and enabling the data to be mapped to a preset interval, so as to eliminate the dimension influence; constructing a deep convolutional feature extraction network to extract a corresponding feature map from the normalized wind and light output data; a K-Means + + algorithm is adopted to initialize a clustering center, an improved ISODATA clustering algorithm is executed based on a density threshold dynamic splitting mechanism, clustering parameters are optimized through Bayesian optimization, and a typical scene is generated. The accuracy of the wind and light output scene is remarkably improved.

The invention discloses an electric vehicle vehicle-mounted battery health degree prediction method and system based on a neural network, and relates to the technical field of battery health degree prediction, and the method comprises the steps: collecting battery multi-source heterogeneous data, extracting key health factors through preprocessing, room temperature correction and staged feature engineering, and introducing a time window embedding strategy to construct a time sequence sample; a multi-module U-BiLSTM hybrid network is adopted as a core prediction model, and Bayesian optimization and an Adam optimizer are combined to complete hyper-parameter optimization and model training; and a prediction result is optimized through digital twinning correction, a physical compensation mechanism and residual service life label normalization processing. The method solves the problems that a traditional method is insufficient in deep feature extraction, low in time sequence information utilization rate and poor in prediction precision under complex working conditions, the aging state and the remaining service life of the battery can be accurately reflected, and reliable technical support is provided for safety management, operation and maintenance optimization and service life evaluation of the battery of the electric vehicle.

The invention discloses a space-air-ground integrated ecological environment monitoring data fusion method and system, and the method comprises the steps: obtaining the original data of heterogeneous multi-source environment monitoring in real time, carrying out the preprocessing, and carrying out the time-space data synchronization of the preprocessed data; performing data fusion on the synchronized data; the data fusion comprises the following steps: calculating initial fusion data by adopting a weighted average fusion method based on space-time weight to obtain an initial fusion result; acquiring a data quality factor, a space-time synchronization factor and a sensor stability factor, and determining an initial weight of each data source according to the data quality factor, the space-time synchronization factor and the sensor stability factor; dynamically adjusting the weight in real time through a Bayesian optimization strategy; and carrying out dynamic optimization on the data after real-time fusion based on an adaptive Kalman filtering algorithm. According to the method, the problem that a traditional data processing method is insufficient in multi-source heterogeneous data fusion capability is solved, and the utilization efficiency and value of the data are improved.

The invention relates to the technical field of artificial intelligence, and discloses an internet television regulation and control method and device, a storage medium and electronic device.The method comprises the steps that network topology data, real-time performance parameters, user behavior data and environment data of nodes of an internet television are collected in real time through a space-time hypergraph convolutional network, a dynamic hypergraph model is constructed, and the network topology data of the nodes of the internet television, the real-time performance parameters of the nodes of the internet television and the environment data of the nodes of the internet television are obtained; extracting a spatial feature matrix and a time feature matrix of the nodes of the Internet television; performing feature fusion on the spatial feature matrix and the time feature matrix to generate a congestion probability prediction result of each node of the Internet television; and based on a congestion probability prediction result, combining historical flow data, generating a resource allocation optimal strategy by using multi-target Bayesian optimization, and training and updating a congestion prediction model by using a lightweight federated incremental learning framework. The Internet television regulation and control method provided by the invention is further improved in the aspects of network congestion prediction precision, real-time performance, energy consumption and the like.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

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 relates to the technical field of parameter optimization, in particular to an automatic kernel network parameter optimization method, which comprises the steps of constructing an enhanced deep Q network model, and integrating the enhanced deep Q network model with a priority playback buffer area, a meta learning module, a Bayesian optimizer and a neural architecture search module; using performance index data to train an enhanced deep Q network model, the training process including using a priority playback buffer to store and sample empirical data, using a meta-learning module to perform task adaptation, and monitoring training indexes of multiple dimensions to evaluate the convergence state of the model; selecting a kernel parameter adjustment action according to the current state through the trained enhanced deep Q network model; executing the selected kernel parameter adjustment action, and evaluating a parameter adjustment effect based on the multi-target reward function; and updating the enhanced deep Q network model according to an evaluation result, wherein the priority playback buffer area and the Bayesian optimizer are utilized in the updating process.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

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.

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an intelligent recommendation method and system based on the plasticizing industry, and the method comprises the steps: obtaining multi-source heterogeneous data from a plurality of third-party data sources of the plasticizing industry; constructing the multi-source heterogeneous data into a dynamic knowledge graph in combination with a plasticizing industry knowledge base; performing demand prediction on the dynamic knowledge graph based on a knowledge-guided hybrid particle swarm algorithm to obtain a first plasticizing recommendation strategy; enhancing a recommendation model through causal reasoning, and performing anti-fact prediction on the dynamic knowledge graph in combination with industry real-time change data to obtain a second plasticizing recommendation strategy; the first plasticizing recommendation strategy and the second plasticizing recommendation strategy are dynamically fused based on Bayesian optimization, a plasticizing industry recommendation report is generated, the plasticizing industry recommendation report comprises the industry flow relation change trend and the corresponding industry hotspot recommendation, and a user is assisted in marketing decision making. The method can shorten the time consumption of the whole supply-demand docking process, effectively alleviates the information asymmetry problem, and remarkably improves the industry operation efficiency.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention provides an integrated feature selection method and product based on quantum computing and Bayesian optimization, and relates to the technical field of data processing. According to the embodiment of the invention, an original mathematical integration model is converted into a QUBO model which can be solved by quantum calculation, and the QUBO model is decomposed into a joint optimization discretization step length optimization sub-problem and a parameterization QUBO sub-problem. In a mixed quantum classical optimization algorithm framework, a self-adaptive Q learning model is designed on the upper layer, and proper sub-problems can be dynamically selected in the search process. In the lower layer, a dropout Bayesian optimization algorithm is provided for effectively optimizing the high-dimensional discretization step length in each iteration. A CIM-based quantum computing method is adopted, and a parameterized QUBO sub-problem under the given discretization step length is efficiently solved. According to the method provided by the embodiment of the invention, the feature selection problem can be successfully and efficiently solved, and the selected features of the credit classification problem and the credit classification model for classification based on the selected features can be obtained.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention provides a battery electrochemical parameter identification method, system and device and a program product, and the method comprises the steps: carrying out the discharge test of a plurality of discharge rates on a battery, and obtaining the experimental data of the discharge test; constructing a battery electrochemical model based on experimental data; performing cross-working-condition sensitivity analysis on the model input parameters of the battery electrochemical model by adopting a global sensitivity analysis method to obtain global sensitivity parameters; constructing a target function based on key region constraint based on experimental data; and alternately adopting a constraint Bayesian optimization method based on a trust domain and a granular self-adaptive local search method to explore the optimized target function, and iteratively optimizing the target function to obtain an optimal solution of the global sensitivity parameter. According to the method, a set of parameter identification system with high precision, cross-working-condition robustness and calculation efficiency is constructed, and cross-working-condition high-precision identification of the electrochemical parameters of the high-capacity lithium ion battery is realized.