1105 results about "Gaussian process" patented technology

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

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 an AI-driven intelligent design and preparation method of an inorganic hydrated salt phase change material, and solves the problem that the traditional technology is mainly based on experience trial and error and single performance optimization and cannot give consideration to multi-performance balance and multi-scene efficient adaptation development requirements of the inorganic hydrated salt phase change material. The method comprises the following steps: acquiring multi-dimensional performance requirements (including phase change temperature, latent heat value and the like) of a material, generating a candidate formula and a prediction result by using a trained Gaussian process regression model, and performing multi-objective optimization to screen out a Pareto optimal formula; and carrying out experimental verification and calculating deviation, retraining the model by complementary data exceeding a threshold value, and determining a final formula after reaching the standard so as to be matched with continuous process large-scale preparation. The method has the advantages that the AI replaces experience trial and error, multi-performance cooperation of materials is achieved, the research and development period is greatly shortened, the cost is reduced, and the method is suitable for multiple energy storage scenes.

The invention discloses an automobile front subframe lightweight design method considering modal constraint. The method comprises the following steps: (1) constructing a front subframe parameterized model and a weight-modal simulation model by adopting CATIA (Computer-graphics Aided Three-dimensional Interactive Application) and Altar Optistruct; (2) establishing a population in a design space based on a space filling Latin hypercube sampling method; (3) constructing a classification cooperation speed updating formula based on a particle swarm algorithm to generate offspring particle individuals; (4) constructing a Gaussian process machine learning model and a fitness evaluation function, and screening an optimal offspring particle individual based on a feasibility rule; and (5) constructing a comprehensive score calculation formula according to a statistical ranking method to determine the updating condition of the population individuals, updating the database and skipping to the step (3) until the optimized structure meets the requirement. According to the method, the coverage capability of the filial generation in the target space is enhanced through the classification collaborative speed updating formula, and the prediction precision of the Gaussian process machine learning model is continuously improved, so that the optimization performance of the lightweight of the front subframe of the automobile is improved.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

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

The invention relates to the technical field of 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 an unmanned aerial vehicle transmission, transformation and distribution integrated intelligent inspection method and system based on an image processing algorithm, and a medium. The method comprises the following steps: constructing a gridding intelligent inspection network, and configuring an unmanned aerial vehicle nest integrating charging, meteorological monitoring and 5G communication; the central control platform integrates transmission, transformation and distribution inspection requirements and dynamically optimizes a task sequence; the unmanned aerial vehicle collects an equipment image, and an optimized image is generated through multi-dimensional feature fusion; an improved MFB-Otsu algorithm is combined with a Gaussian process to model and position an optimal segmentation threshold; intelligent defect identification is realized by using a multi-scale deep convolutional neural network and a cross-professional attention mechanism, and a three-dimensional evaluation result including types, positions and severity is output; and edge real-time processing and central platform closed-loop optimization are realized through a two-stage architecture. According to the method, efficient collaborative inspection of the transmission, transformation and distribution equipment is realized, the defect identification precision and efficiency are improved, and the method has the characteristics of adaptive optimization and cross-professional fusion.

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

The invention relates to a multi-modal image threshold segmentation preprocessing method based on a convolutional neural network, and the method comprises the steps: unifying an image into a standard space, carrying out the pixel value mapping, carrying out the resampling, generating high and low frequency sub-bands, carrying out the soft threshold denoising of the high frequency sub-bands, enhancing the contrast of the low frequency sub-bands, and carrying out the fusion; an optimized VGGnet framework is constructed; a noise adversarial network is generated to carry out active learning loop training on a convolutional neural network model; the image is input into the model for prediction; a local entropy and a gradient magnitude are calculated based on a prediction result; optimal segmentation is realized by setting a double-layer matrix of a feature tag-segmentation method; grey matter Dice calculation is carried out on the segmented images, and preprocessing parameters of unqualified images are optimized through a dynamic parameter adjusting module based on a Gaussian process regression model. The segmentation precision and the processing efficiency of the multi-modal image are effectively improved, and the adaptability of the model to a complex image is enhanced.

The invention discloses a mobile robot path planning method and system based on a circular trajectory control obstacle function, and aims to realize trajectory smooth control in an obstacle avoidance process. Firstly, an envelope circle is used for modeling an obstacle, an actual boundary is approached in a continuous and simple geometric form, and the calculation complexity of collision detection is reduced. Secondly, designing a control obstacle function based on a circular trajectory, constructing a derivable constraint in combination with a geometrical relationship between a tangent line and an obstacle boundary, and achieving trajectory smoothing, reducing speed fluctuation and prolonging the service life of an execution mechanism while guaranteeing a safe distance. For disturbance caused by factors such as ground unevenness, historical data are learned by using a Gaussian process, the disturbance trend is predicted in real time, and the motion stability is improved. And finally, through nonlinear optimization solution, generating control input with safety, continuity and disturbance adaptive capability. According to the method, geometric modeling, safety constraint, disturbance prediction and a dynamic adjustment mechanism are fused, and the safety and adaptability of robot path planning are remarkably enhanced.

The invention discloses a wind field data modeling method and system for a small and medium-sized unmanned aerial vehicle wind resistance test, and the method comprises the steps: collecting the topographic data, wind speed data and fan data of a test wind field, and determining a preset region and an unmeasured region according to the preset trajectory of an unmanned aerial vehicle based on the test wind field; the method comprises the following steps: acquiring boundary conditions of an unmeasured area and disturbance grid wake flow through fluid simulation based on topographic data and a preset area, performing wind speed vector decomposition on a grid according to a preset wind field boundary condition, and acquiring a preset wind field model according to a disturbance time sequence, an unmanned aerial vehicle maneuvering weight and the disturbance grid wake flow, and obtaining a predicted wind field of the unmeasured area through Gaussian process regression according to the boundary condition of the unmeasured area based on a preset wind field model, and coupling the predicted wind field through a grid time sequence by using different unmanned aerial vehicle preset trajectories to obtain a target wind field model. According to the method, through space-time collaborative interpolation and fluid simulation of Gaussian process regression, the inlet boundary condition better conforms to the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved.

The invention relates to the technical field of soil monitoring and data analysis, and discloses a soil carbon and nitrogen content dynamic prediction method based on deep learning. The method comprises the following steps: acquiring soil monitoring data from an environment monitoring platform, performing dimension reduction by using a multi-layer perceptron model to obtain core features, and dividing a dynamic monitoring data set according to the core features; taking the data set as input, and constructing an initial prediction model by using a time convolutional network; and constructing a meteorological factor library and an influence map, replacing an initial model time node, and obtaining a climatic factor node prediction model through cross validation. And performing regression fitting and cross validation verification by using a Gaussian process, and constructing a soil dynamic prediction model. According to the method, through multi-step data processing and model construction, the influence of soil data characteristics and meteorological factors is effectively mined, the dynamic change of the soil carbon and nitrogen content can be accurately predicted, and powerful support is provided for the fields of precision agriculture, environmental protection and the like.

The invention discloses a process deviation-oriented analog circuit parameter optimization method, which comprises the following steps of: S1, carrying out multi-objective optimization on a given analog circuit under a nominal process condition, identifying and eliminating a design region with poor performance according to an optimization result, and carrying out clustering analysis to obtain an optimal design region; dividing the remaining high-quality areas into a plurality of initial trust areas; s2, modeling the finite Monte Carlo simulation data in the initial trust area by using a heterovariance Gaussian process model, capturing an average level and a change trend of circuit performance, and predicting behaviors of the circuit under process change; and S3, the required yield is kept while the performance is optimized, probability assurance is provided for the performance value by adopting the risk value, and a design point optimization set and a corresponding Pareto optimal solution set are finally obtained through multiple iterations in the optimization stage of considering the process change. According to the method, the high-quality Pareto optimal solution set meeting the yield constraint is found under the process change of unknown distribution, and the size of an analog circuit is optimized.

The invention discloses an unmanned aerial vehicle autonomous target search method, system and device based on hierarchical decision, which combine global exploration and local search to improve the search efficiency of an unmanned aerial vehicle in a complex unknown environment. The method comprises the following steps: in a global exploration layer, constructing an incremental environmental map, and modeling an access sequence of to-be-searched and explored areas as an asymmetric traveling salesman problem; a Bi-RRT algorithm is utilized to generate a connection path in a free space, the visibility cost of the path is evaluated, and an access sequence with the minimum total cost is selected; in the local search layer, a hierarchical Gaussian process is used for fitting and evaluating the search value of the target area, and a priority target area is determined; generating a plurality of candidate viewpoints for the local target area, and selecting an optimal viewpoint as a local target search point; and navigating to the target search point and identifying the specific target article in combination with the positioning information and the target detection model. The method has efficient search and environment adaptability.

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

Techniques for concept shift detection and correction using probabilistic models and learned feature representations are described. A gaussian process model is trained using representations generated by a primary machine learning (ML) model for existing training data elements in a training memory. For a new batch of data elements, representations again generated by the primary ML model can be used as input for the gaussian process model to generate predictive distributions. When the true targets for the new data elements are not sufficiently likely according to the corresponding predictive distributions, concept shift is likely and the training memory can be purged of the existing data elements before further retraining of the primary ML model.

The invention relates to the technical field of computer information processing, and discloses a high-fidelity terrain surface interpolation method and system for large-scale point cloud data, and the method comprises the steps: carrying out the multi-level cleaning of original point cloud data, and generating a cleaned point cloud data set; performing adaptive tile grid division based on the cleaned data to generate a tile set covering the target area; in each tile, taking the to-be-interpolated grid point as a query point, constructing and optimizing a local Gaussian process model, and obtaining a real elevation predicted value of the query point; processing all query points in the tiles in parallel to obtain a tile elevation interpolation result set; performing weighted average on each tile result to generate a global seamless terrain surface; according to the method, the problems of poor expandability and surface discontinuity caused by local interpolation in large-scale point cloud data processing are effectively solved, and high-precision and high-fidelity terrain surface construction is realized.

Disclosed in the present invention is an aircraft engine maintenance policy optimization method based on nonparametric reinforcement learning. Firstly, for sparse aircraft engine operation data, constructing an aircraft engine model by means of a Bayesian network and a Gaussian process; then, establishing a policy network and a value network, interacting with the aircraft engine model to form a state / action value group, storing same into a replay buffer, and performing random sampling for use in a training set; updating the policy network and the value network, and updating the training set; and finally, performing aircraft engine maintenance optimization policy decision-making. Provided in the present invention is, for the first time, a Gaussian process-based nonparametric reinforcement learning method for an aircraft engine, which improves the degree of fit between overall training data and a model by means of dynamic data updating while integrating system uncertainty, thereby improving the sampling efficiency of an algorithm. Action selection is performed on the basis of prior maintenance experience data fused with uncertainty, improving the safety and sampling efficiency of a system and thereby solving the problem of predictive maintenance of an aircraft engine system.

The invention discloses a self-adaptive thermal management system for an energy storage container, and particularly relates to the technical field of thermal management of the energy storage container, which is characterized in that a joint probability model fusing multi-source data is constructed, Gaussian process regression and multi-precision CFD simulation fusion are introduced, and a chaotic feature extraction and anomaly recognition mechanism is combined, so that the self-adaptive thermal management of the energy storage container is realized. High-confidence dynamic modeling of complex airflow and temperature fields in the energy storage container and accurate recognition of abnormal areas are achieved, and the self-adaptive regulation and control capacity of a heat management system is improved; by collecting temperature and humidity data, constructing a condensation early warning mechanism and a micro-airflow intervention strategy and combining edge calculation and reinforcement learning, condensation risk real-time identification and control strategy optimization are achieved, the defects that in a traditional scheme, response to the problems of thermal runaway and condensation water accumulation is slow, and control lags are effectively overcome, and the method is suitable for large-scale popularization and application. And the safety and the reliability of the self-adaptive thermal management system of the energy storage container under the dynamic working condition are 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 discloses a lithium battery SOH-SOC intelligent joint prediction method and device, and the method comprises the steps: obtaining the voltage, current, temperature and capacity data of a lithium battery, extracting related health features based on an IC curve, and screening out the features with higher relevancy through a ChiMIC algorithm; constructing a second-order RC equivalent circuit model, identifying equivalent circuit parameters in combination with an improved attraction and rejection algorithm, and constructing an SOC estimation model based on a multi-kernel Gaussian process regression model to perform SOC estimation; and based on the health feature data and the SOC estimation result, performing SOH prediction by using a Mama model optimized by an attraction and rejection algorithm. The SOH predicted value is converted into a predicted capacity value of the battery to serve as input of SOC estimation of the next cycle, and joint estimation of the SOC and the SOH under cyclic charging and discharging is achieved. By constructing multi-model joint prediction, the accuracy and reliability of lithium battery state prediction are improved, and effective support is provided for health management of the lithium battery.

The embodiment of the invention discloses a hydrology-landslide coupling forecast and parameter optimization method, and the method comprises the steps: constructing a coupling model, the coupling model comprises a hydrology model and a landslide model, the hydrology model is used for simulating the spatial and temporal changes of water volume and energy flux in a watershed, and the landslide model is used for representing the stability of a slope; establishing an intelligent forecasting model of the coupling model through a machine learning algorithm; performing parameter optimization on the intelligent forecasting model by using an intelligent substitution model based on Gaussian process regression, and outputting an optimized parameter sample and a target function value thereof; the objective of simultaneously forecasting flood and landslide disasters in a research area can be achieved, and the precision and efficiency of model calculation are improved.

The invention provides a cloud edge-end collaborative multi-modal data distributed governance method, which comprises the following steps that: an edge node obtains multi-modal time sequence data, adopts Gaussian process regression to carry out probability interpolation, and obtains a multi-modal time sequence based on a heterogeneous space association graph which is pre-constructed at a cloud end and contains geomechanical parameters; the method comprises the following steps: extracting a multi-modal space fusion feature sequence through a space geological attention map neural network, inputting the multi-modal space fusion feature sequence into a hierarchical cross attention Transform network, and generating time sequence features at edge nodes by an intra-modal coding layer; the cloud decides a calculation position of an inter-modal fusion layer according to a real-time load, network time delay and an early warning level: if edge execution is carried out, a full-modal fusion feature is generated and uploaded; if the cloud executes the operation, the data is transmitted to the cloud for fusion, the cloud decodes the full-modal representation prediction multi-measuring-point state, the uncertainty function of the graph structure is optimized through the residual covariance, and the association graph weight and the geomechanical parameters are iteratively updated.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

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 belongs to the technical field of power distribution networks, and discloses a load prediction and early warning method and system for a transformer area containing distributed resources, and the method comprises the steps: combining a two-dimensional time sequence data sample set of a high-risk transformer area with a transformer area feature operation data set according to the transformer area and a timestamp, and generating a prediction model training sample data set; building a lightweight gradient boosting tree as a main prediction model, inputting a training sample data set for training, and optimizing hyper-parameters of the main prediction model by adopting a Bayesian optimization algorithm; and establishing a residual error correction model based on local weighted Gaussian process regression, superposing a load prediction result of a prediction day of the main prediction model of the to-be-predicted transformer area with a residual error correction value of a prediction day of the residual error correction model to obtain a final load prediction result, and outputting transformer area weight / overload early warning information. According to the method, the LGBM is adopted as the main prediction model for load prediction, the residual error correction model is adopted for residual error correction, and the robustness and adaptability of the model are improved.

The invention provides a distributed optical fiber-based vibration source transverse distance determination method and system, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting distributed optical fiber vibration signals, extracting features, constructing a multi-resolution scanning region, carrying out the traversal through employing a global optimization algorithm, and calculating the propagation speed of the vibration signals. According to the method, velocity field distribution characteristics based on Gaussian process regression are constructed, velocity field parameters are optimized in combination with a probability sampling optimization method, the transverse distance of a vibration source is accurately determined through discretization space traversal and anisotropy correction, and the precision and reliability of vibration source positioning are improved.

The invention provides a water-based adhesive coating control method and system based on artificial intelligence optimization, and the method comprises the steps: obtaining a historical data set composed of process parameters of a coating process and coating quality parameters, carrying out the clustering, and obtaining a global induction point set according to a clustering result; determining a stage induction point subset according to the target parameter value of the current coating stage and the boundary of the target parameter, and updating the sparse Gaussian process regression model by using the global induction point set and the stage induction point subset; calculating a quality fluctuation index, determining the length of a prediction time domain based on the quality fluctuation index, constructing an optimization problem in the prediction time domain by adopting a multi-level opportunity constraint mode, and when the deviation value between the actual value of any key process parameter and the prediction trajectory based on the sparse Gaussian process regression model exceeds a deviation threshold value, determining that the prediction trajectory does not exceed the deviation threshold value. And solving the optimization problem to obtain an optimal control action sequence in the prediction time domain, and determining a final control action from the optimal control action sequence and sending the final control action to an execution mechanism.