395 results about "Bayesian optimization algorithm" patented technology

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

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

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

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

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

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

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

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

The invention provides a radio frequency circuit schematic diagram-to-layout collaborative optimization method and system based on an AI model, and the method comprises the steps: building a mapping database from the geometric parameters of a passive device to corresponding electromagnetic characteristics through obtaining the geometric parameters of the passive device; based on a database, a corresponding AI agent model is trained for each passive device, through the AI agent models, millisecond-level model prediction is used for replacing time-consuming electromagnetic simulation to serve as design guidance, the calling frequency of calculation-intensive electromagnetic simulation is remarkably reduced, the layout parasitic effect is further considered in the schematic diagram design stage, and the design efficiency is improved. And a performance gap between a schematic diagram and a layout is effectively bridged. Then a target schematic diagram is obtained, passive devices in the target schematic diagram are automatically replaced based on a Bayesian optimization algorithm and an AI agent model, and the design period from the schematic diagram to a qualified layout is greatly shortened; by optimizing the layout effect in the schematic diagram design stage, it can be ensured that the simulation performance of the finally generated physical layout is consistent with a schematic diagram simulation result.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

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 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 belongs to the technical field of carbonate rock oil-gas exploration, and particularly discloses a complex carbonate rock logging lithology identification method based on a diffusion model, and the method comprises the following steps: determining the lithology types of a plurality of observation wells based on the on-site rock core observation and slice analysis, and synchronously obtaining the logging data of the corresponding observation wells, constructing a lithology training data set in combination with a depth corresponding relationship between the lithology category and the logging data; a diffusion model is adopted to generate and supplement lithology categories with insufficient samples; logging data is adopted as an input feature, the lithology category is adopted as an output label, and a convolutional neural network fused with a Bayesian optimization algorithm and a Transformer hybrid model are utilized to train a lithology identification model; and inputting to-be-identified logging data into the trained lithology identification model, and outputting a lithology identification result. According to the method, high-precision lithology identification can be realized under the conditions of deep layers and complex stratums.

The invention belongs to the technical field of underground engineering intelligent construction, and particularly discloses a shield tunneling performance prediction method and system based on an interpretable BO-LGBM model, and the method comprises the steps: receiving detection data in a shield tunneling process, and determining tunneling parameters, geological parameters and karst parameters based on the detection data, so as to construct an input feature set; constructing an LGBM model, performing multi-stage screening on the input feature set to obtain a comprehensive feature importance sequence, and determining key target features corresponding to model prediction influence according to the comprehensive feature importance sequence; on the basis of key target features, a Bayesian optimization algorithm is used to optimize hyper-parameters of the LGBM model, and a trained BO-LGBM prediction model is obtained; the BO-LGBM prediction model is used for predicting ground surface settlement, tunneling efficiency, specific energy and overexcavation rate of the detection data; and obtaining a performance index based on the trained prediction model so as to evaluate model performance and prediction precision. According to the invention, the model prediction accuracy of the shield performance can be improved.

The invention discloses a battery life prediction method based on Bayesian optimization and a physical information neural network, and belongs to the field of battery health monitoring. The method comprises the following steps: collecting and preprocessing lithium battery charging and discharging data, and then dividing the data into a training set and a test set; then, extracting a dQ / dV curve and other related features, constructing a physical information neural network integrated with physical constraints, and optimizing hyper-parameters of the physical information neural network by using a Bayesian optimization algorithm; and training the model by using a training set, updating parameters through back propagation in the process, evaluating precision by using a test set, and adjusting a strategy. And finally, inputting the characteristics of the lithium battery to be predicted into the trained model, outputting a residual service life prediction value, and comparing with actual data evaluation. According to the invention, reliable lithium battery life prediction can be provided for the photovoltaic energy storage system, and the system operation and maintenance efficiency can be significantly improved, the maintenance cost can be reduced, and the safe and stable operation of the energy storage system can be ensured by early warning the health state of the battery in advance.

The invention discloses a weld defect identification method based on a dense connection convolutional network model, and the method specifically comprises the following steps: S1, constructing a dense connection convolutional network model, and embedding a coordinate attention module behind a transition layer of a convolutional network; s2, data acquisition and processing: acquiring an RGB image of the welding seam through an industrial camera, constructing a data set of the image, and performing image enhancement and standardization processing; s3, performing hyper-parameter optimization, and performing global optimization on the constructed model by adopting a Bayesian optimization algorithm; s4, performing model training and verification, and training a dense connection convolutional network model by using the optimized hyper-parameter combination; and S5, defect identification: inputting a to-be-detected welding seam image into the trained dense connection convolutional network model, and outputting a defect category and a positioning result. According to the method, the transition layer of the convolutional network is embedded into the coordinate attention module, so that the convolutional network model more accurately positions the welding seam position, and the detail features of the welding seam are extracted.

The invention discloses a partial discharge signal denoising method based on STFT-SVD and Bayesian kurtosis threshold adaptive optimization, and the method comprises the steps: collecting an analog signal outputted by a high-frequency current transformer, carrying out the analog-to-digital conversion, obtaining a one-dimensional time domain signal sequence, carrying out the DC component removal and amplitude normalization of the signal, and obtaining a preprocessing time domain signal; performing short-time Fourier transform on the preprocessed time-domain signal to obtain a time-frequency spectrum, suppressing low-amplitude noise by adopting a soft mask method, and retaining main characteristics of partial discharge pulses; performing singular value decomposition on the time-frequency spectrum after soft masking, automatically selecting a principal component number according to a principal component, and adaptively reserving a main signal component to obtain a principal component spectrum; and performing inverse short-time Fourier transform on the principal component atlas to reconstruct a time domain signal, adaptively selecting a kurtosis threshold in combination with a Bayesian optimization algorithm, and outputting a denoised time domain signal.

The invention discloses a river and lake backflow recognition and driving mechanism analysis method based on interpretable machine learning, and relates to the technical field of hydrology and water resource analysis and artificial intelligence application, and the method comprises the steps: collecting long-sequence hydrology data of a river and lake system; taking operation nodes of the large hydro-junction project as boundaries, and dividing the time sequence into different characteristic periods; constructing a multi-dimensional input feature set; a class weight balance strategy and a Bayesian optimization algorithm are adopted to train a backward flow recognition model based on a gradient lifting decision tree; a marginal contribution value of each hydrological driving factor is calculated by using an SHAP interpretability method, a physical threshold for inducing backward flow is identified by combining an SHAP dependency graph with a binary box plot, analysis results in different periods are compared, and an evolution rule of a backward flow driving mechanism is revealed. The method effectively solves the problems that a traditional method is difficult to capture nonlinear hydrological response and the black box model lacks physical mechanism explanation, and can accurately recognize the backward flow event.

The invention relates to the technical field of safety monitoring, in particular to an elevator safety monitoring system and method fused with computer vision, and the system comprises a data collection module which is used for synchronously collecting video data, sensor data and environment parameter data of an elevator; the data processing module is used for carrying out preprocessing and feature fusion on the collected data and outputting a fusion feature vector F; the risk assessment module is used for calculating a dynamic risk score according to the fusion feature vector; and the alarm module is used for triggering an alarm when the dynamic risk score exceeds a preset threshold value. According to the method, the feature weight is dynamically adjusted through the Bayesian optimization algorithm, so that important risk features obtain higher weight distribution; and meanwhile, the LSTM network is utilized to predict the dynamic threshold value based on the historical risk sequence, safety references of the elevator in different time periods and under different working conditions are adapted in real time, the problems of false report and missing report of a traditional fixed threshold value when the working conditions change are solved, and the dynamic adaptability and accuracy of risk assessment are greatly improved.

The invention discloses a telescope parameter efficient optimization method and system based on a Bayesian algorithm and a fluctuation optical TTL coupling noise calculation model, and aims to solve the problem that a fluctuation optical high-precision calculation module is used for improving TTL coupling noise calculation precision in an existing telescope parameter optimization method. And due to frequent calling of a high-time-consumption module, the optimization time is sharply increased, and the efficient design requirement of a space gravitational wave detection task is difficult to meet. According to the method, on the basis of the principle of a Bayesian optimization algorithm, potential optimal to-be-evaluated telescope parameters are searched through an agent model, a collection function and the optimization function of Zermarkes software; and calculating TTL coupling noise of the telescope to be evaluated by combining a light field propagation calculation method based on a wave optics theory with a light field-phase resolving method, carrying out iterative optimization on telescope parameters, and outputting optimal TTL coupling noise and optimal telescope optical parameters.

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.

According to the new energy Internet of Things multi-level analysis sharing platform based on digital twinning, the intelligent perception and edge access module collects data through a multi-mode sensor, and reliable data is provided for subsequent analysis through edge calculation preprocessing and hierarchical transmission. And the digital twinning construction and dynamic updating module constructs equipment-level, station-level and regional-level digital twinning models based on a Bayesian optimization algorithm to realize dynamic calibration of the models. And the multi-level analysis decision module realizes equipment fault diagnosis, generation power prediction, energy storage scheduling and power grid optimization through a four-level architecture. The digital twin mirror image regulation and control module establishes a bidirectional control channel and supports remote configuration of equipment parameters and automatic triggering of fault plans. The cross-domain data security sharing module adopts a three-layer security protection system and a federal learning mechanism to guarantee data security and sharing. According to the platform, full-process intelligent management of the new energy system is realized, and the operation efficiency and safety of the system are improved.

The invention discloses a rapid early warning method and system for water quality of a drinking water source. The system integrates a multi-source monitoring module, a data fusion processing module, a water quality prediction analysis module, a dynamic early warning decision module and a digital twinborn simulation platform, and acquires multi-dimensional data such as physical and chemical parameters, biological behavior indexes and spectral characteristics; a self-adaptive weighted deep belief network (AW-DBN) model is used for pollutant identification, diffusion simulation and overproof time prediction, an early warning threshold value is dynamically adjusted by combining a Bayesian optimization algorithm so as to reduce the false alarm rate and the missing report rate, and visual simulation and emergency disposal scheme evaluation of the pollution diffusion process are realized based on the digital twinborn technology. Rapid and accurate early warning and scientific emergency decision making of the water quality risk are realized, and the safety guarantee capability of the drinking water source is remarkably improved.

The invention discloses an asphalt material anti-rutting performance prediction and design optimization method based on data driving, which comprises the following steps: sorting and preprocessing data obtained by a dynamic shear rheometer test of a rubber powder / SBS composite modified asphalt material with various contents in different aging states, and calculating the rutting resistance of the asphalt material by adopting a grey correlation analysis method; screening the data set based on a gray analysis result, and determining input and output of a prediction model; three machine learning models of GPR, LSBboost integration and ANN are established, the deformation resistance of the asphalt material is predicted, a better model is obtained through multi-index comparison, and the prediction result of the optimal model is explained based on SHAP; and based on the optimal prediction model, performing targeted design optimization on the formula of the asphalt material by using a Bayesian optimization algorithm, and simulating application requirements under different actual working conditions by limiting part of parameters in the formula. According to the invention, the test cost of the asphalt material in deformation resistance test and formula design is reduced.

The invention belongs to the technical field of metal-based composite material additive manufacturing, and particularly relates to a graphene titanium-based composite material additive manufacturing method based on machine learning, and the graphene titanium-based composite material additive manufacturing method based on machine learning comprises the following steps: collecting and preprocessing multi-modal data; constructing a multi-target prediction GNN model and carrying out model training and evaluation to obtain an optimal GNN model; the optimal GNN model is optimized through a second-generation non-dominated sorting genetic algorithm, and a multi-objective optimization result is obtained; a qEHVI Bayesian optimization algorithm is adopted to screen out an optimal parameter group from the multi-target optimization results; and 3D printing verification and closed-loop iteration are carried out. According to the method, machine learning prediction, 3D printing verification and data feedback are integrated into a closed-loop iteration system, dynamic optimization of'parameter recommendation-> 3D printing-> performance testing-> model updating 'and autonomous optimization of material performance are achieved, and research and development efficiency is remarkably improved.

The invention discloses an urban atmospheric pollution real-time monitoring and tracing method based on reinforcement learning, and relates to the technical field of atmospheric environment monitoring, and the method comprises the following steps: S1, collecting a pollution source data set; s2, constructing a Gaussian plume fidelity simulation model; s3, constructing a CFD fidelity simulation model; s4, constructing a multi-fidelity simulation model based on a Gaussian process proxy model; s5, constructing a DPPO reinforcement learning model; s6, using an improved multi-fidelity Bayesian optimization algorithm to continuously train and iterate the DPPO reinforcement learning model; and S7, generating a standardized pollution source real-time traceability analysis report. According to the method, the limitations of much manual intervention, low efficiency and poor real-time performance in a traditional urban pollution monitoring and tracing method are overcome, and an efficient and accurate solution is provided for intelligent real-time monitoring of urban environmental pollution and accurate and rapid tracing of pollution sources.

The invention discloses an automatic target tracking method and system based on satellite laser ranging echo signals, and the method comprises the steps: S1, carrying out the space scanning of an uncertain domain of a ranging target through employing a regular hexagon dense scanning strategy, and completing the searching and capturing of the target; s2, after the target is captured, correcting the direction of the telescope by dynamically switching a global optimization mode and a local optimization mode by adopting a miss distance optimization method combining a Bayesian optimization algorithm and a local optimization algorithm, so as to complete the optimization of the miss distance; s3, target tracking is carried out based on the optimized miss distance, and echo signal intensity is monitored in real time; and S4, judging whether the signal is attenuated or not, re-triggering the miss distance optimization process when detecting that the signal is attenuated, and otherwise, maintaining the current tracking state.

The invention belongs to the technical field of hydraulic engineering emergency rescue, and discloses a rapid decision-making method and system for a dike breach blocking scheme and electronic equipment. A data set is obtained based on a breach-pile body-material system analysis model established by a finite element model and is used for training a Gaussian process regression model, an optimal hyper-parameter combination is searched by adopting a Bayesian optimization algorithm, and screening is performed through physical constraint to obtain a breach plugging scheme rapid decision model; multi-source three-dimensional breach feature parameters monitored in real time are input into a breach plugging scheme rapid decision model, and an optimal breach plugging scheme is rapidly generated; and along with updating of real-time multi-source three-dimensional breach characteristic parameters in plugging emergency rescue construction, a corresponding optimal breach plugging scheme is continuously and efficiently generated, and adjustment of plugging scheme design is facilitated. According to the method, the Gaussian process regression model of physical constraint is innovatively introduced, the time cost of calculation is reduced, and a breach plugging scheme conforming to the engineering mechanics principle is rapidly provided.