595 results about "Gaussian process regression gpr" 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 relates to a regulating valve opening / flow linear correction system and a control method, and the system comprises a flow deviation unit which is used for calculating a real-time deviation value between an actual control flow and a dynamic ideal flow of a regulating valve and comprises a flow prediction module based on an LSTM network, and the flow prediction module outputs a working condition self-adaptive ideal flow reference value; the manual / automatic undisturbed switching unit is used for tracking an ideal flow reference value in a manual mode, keeping a deviation value at the switching moment in an automatic mode and pre-judging the switching opportunity; the deviation correction unit is used for dynamically generating a nonlinear compensation amount by adopting Gaussian process regression, and solving an optimal opening correction value through an online convex optimization algorithm; the correction condition limiting unit is internally provided with a digital twinborn simulation module and a safety boundary prediction model, virtual verification is carried out before opening correction is executed, and the maximum opening limit value is dynamically adjusted; and the digital valve fingerprint database is used for storing valve mechanical wear characteristics, seasonal characteristic drift data and a medium influence matrix.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

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 relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

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 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 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.

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 production motor operation frequency prediction model training method, which comprises the following steps: acquiring a tobacco image group, extracting tobacco image features by using a three-dimensional point cloud model, converting the tobacco image features into a tobacco cross section area change sequence, dividing production stages, and screening out pre-selected stage information of which the fluctuation ratio meets requirements; synchronously acquiring motor operation information and matching to obtain pre-selected motor information and operation frequency thereof; a Gaussian process regression algorithm is adopted to construct a prediction model, trend features and local fluctuation features are obtained through multi-scale feature extraction to serve as input, the preselected operation frequency serves as an output label for training, and hyper-parameters are optimized to obtain a final prediction model; the established dynamic confidence interval evaluation mechanism can realize model increment training. The operation frequency of the motor is dynamically adjusted according to the actual state of the tobacco leaves, and the drying quality and the batch stability are remarkably improved.

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 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 photovoltaic module residual life prediction method and system by fusing Gaussian process regression and a dynamic Gamma process. The prediction method comprises the steps of determining a residual life distribution function of a photovoltaic module at a to-be-measured moment based on a dynamic Gamma process degradation model of the photovoltaic module; acquiring degradation data of the photovoltaic module, and performing interpolation processing by using a GPR (General Purpose Register); performing parameter real-time updating on the residual life distribution function of the photovoltaic module at the to-be-measured moment in combination with a sliding window and a maximum likelihood estimation method according to the degradation data of the photovoltaic module after interpolation; and carrying out life prediction on the photovoltaic module by using the residual life distribution model after parameter updating. According to the method, the degradation characteristic data of the target photovoltaic module can be fully utilized, the uncertainty and nonlinearity presented in the degradation process are considered, and more accurate life prediction is realized in combination with the interpolated data, so that the precision and reliability of the residual life prediction of the photovoltaic module 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.

The invention discloses an industrial multi-source heterogeneous data feature fusion and dynamic modeling method and system, and relates to the technical field of industrial multi-source heterogeneous data processing, and the system comprises a data collection module, an event triggering type local space-time alignment module, a time sequence data set generation module, a feature fusion module and an industrial equipment state model generation module. According to the method, an event-triggered local space-time alignment module is arranged, an event-driven dynamic space-time anchoring mechanism is adopted, and in a preset tolerant time window, an image feature time sequence is constructed through industrial camera superframe sampling to be matched with sensor time sequence data, so that feature dislocation caused by sampling frequency difference is avoided; compared with an existing interpolation method, the non-linear coupling relation between the process parameter data and the sensor time sequence data is obtained, and the problem of feature fusion distortion caused by the time granularity difference is solved by performing non-linear interpolation on the process parameter data through Gaussian process regression and generating a continuous proxy curve synchronous with the sensor time sequence data.

The invention relates to the technical field of power supply thermal management control, and discloses a high-voltage variable-frequency power supply thermal management control method for improving energy efficiency, and the method comprises the following steps: carrying out the order reduction processing in combination with a solar high-temperature thermal power generation system, and obtaining a low-dimensional model; carrying out data fusion by using Gaussian process regression and a Kalman filtering algorithm, and reconstructing temperature field information of the system; establishing a multi-objective optimization function, and dynamically adjusting a weight optimization objective; generating a control parameter sequence by using an improved particle swarm optimization algorithm, and adjusting a control decision in real time through iterative optimization; the optimized control parameters are input into a multi-rate hierarchical control system for division execution; and detecting a fault type and switching to a corresponding fault-tolerant control mode. According to the method, a multi-physical field coupling model and an intrinsic orthogonal decomposition technology are combined, an accurate coupling model of an electromagnetic field, a temperature field and a flow field in the high-voltage variable-frequency power generation system is established, through order reduction processing, the dimension of the model is reduced, the calculation complexity is remarkably reduced, and the calculation efficiency is improved.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

The invention relates to a microwave wireless energy transmission reflector antenna fast multi-objective optimization method comprising the following steps: establishing an MWPT system simulation model, adopting a Bernstein polynomial basis function to carry out dimension reduction processing on a decision space of an antenna, and reducing the number of decision variables; expressing the constructed MWPT system transmitting antenna optimization model as a multi-objective optimization model through a multi-objective optimization function; constructing an agent model based on Gaussian process regression GPR; a proxy model and an evolutionary strategy are integrated through a Gaussian process regression proxy model and a covariance matrix adaptive evolutionary algorithm, a non-dominated solution set is obtained through iterative optimization, and then the multi-objective design of reflector antenna aperture illumination is optimized. According to the method, three performance indexes of BCE, PRL and APC are comprehensively considered, the Bernstein polynomial basis function is adopted to carry out dimension reduction processing on the antenna design decision space, the number of antenna design decision variables is effectively reduced, the proxy model is introduced into the antenna optimization design method, and the optimization design efficiency is improved.

The invention discloses a real-time emergency prevention and control system and method for an electrochemical energy storage system based on digital twinning, and belongs to the field of energy storage safety. The system comprises a multi-source sensing unit, a digital twin deduction unit and a visual emergency decision unit. The sensing unit collects multi-source data to form a state vector; the deduction unit outputs a future thermal runaway evolution path containing a temperature field, a gas concentration field and a fire blast risk probability and uncertainty measurement thereof in real time through a pre-trained Gaussian process regression agent model; and the decision-making unit performs three-dimensional visual rendering, jointly determines a high-confidence risk area based on the risk level and the uncertainty level, and generates an emergency prevention and control instruction with accurate spatial positioning. The problems that in the prior art, disaster evolution paths cannot be predicted in real time, and prevention and control measures are extensive are solved, millisecond-level online deduction and accurate active prevention and control of the thermal runaway process are achieved, and the safety of an energy storage system is remarkably improved.

The invention discloses a filter tip modeling parameter visual identification and adjustment method, and relates to the technical field of filter tip production, a laser diameter measuring instrument and a linear array camera are used for synchronously collecting peripheral images of a filter tip, and high-precision and consistent size and gray scale double baselines are generated; a texture segmentation technology is adopted to identify a suspected recess area and generate a recess position vector, a confocal displacement sensor is utilized to carry out local depth walk-up to obtain depth distribution data, and a size compensation coefficient is generated and a size matrix is corrected through Gaussian process regression; statistical threshold analysis is conducted on the corrected size matrix, when parameters exceed the limit, the gap of a forming nozzle is adjusted, the size stability of the filter tip is ensured, the adjustment result and related data are written into a manufacturing knowledge base, follow-up batch optimization is supported, and the measurement reliability and the production line efficiency are improved; tiny recesses on the surface of the filter tip can be effectively identified and compensated, and the measurement accuracy and the assembly stability of modeling parameters are ensured.

The invention relates to the technical field of wind field construction, and provides a low-altitude three-dimensional real-time wind field construction method and system based on unmanned aerial vehicle data inversion, and the method comprises the steps: collecting the multi-dimensional data of a flight attitude, a power system, navigation positioning and the like in real time through an unmanned aerial vehicle group in the process of executing a distribution task; and extracting key indexes such as attitude change features, control response deviation features, energy consumption features and the like from the data, inputting the features into a deep neural network with a physical model as a constraint to carry out wind field inversion training, and finally carrying out multi-scale spatio-temporal interpolation on discrete observation points through a Gaussian process regression algorithm fused with the physical constraint. And generating continuous three-dimensional wind field distribution meeting the law of conservation of mass. Dynamic wind field monitoring under urban complex terrains is realized by utilizing the characteristics of high-frequency and wide-coverage operation of the unmanned aerial vehicle, and the problem of insufficient observation data of a traditional weather station in a building dense area is effectively solved.

The invention discloses a multi-unmanned-aerial-vehicle intelligent safety communication method and system based on digital twinning. The method comprises the following steps: constructing a multi-unmanned-aerial-vehicle auxiliary communication system; fitting is carried out through Gaussian process regression, and the optimal value of a kernel function hyper-parameter of the prediction model is determined; based on the optimal value of the kernel function hyper-parameter of the prediction model, performing optimization training through digital twinning driving in combination with a deep reinforcement learning algorithm, and constructing a trained digital twinning environment; the unmanned aerial vehicles are guided to generate differentiated flight paths and data acquisition actions in a physical environment through a fusion-elimination updating method, and intelligent safety communication of the multiple unmanned aerial vehicles is achieved. According to the method, the dynamic change rule of the position of the eavesdropper is modeled, and the multi-unmanned aerial vehicle system is guided to adopt a differentiation strategy according to the dynamic change rule, so that active avoidance and secure data transmission under the threat of mobile eavesdropping are effectively realized. The multi-unmanned aerial vehicle intelligent security communication method and system based on digital twinning can be widely applied to the technical field of unmanned aerial vehicle communication.

The invention belongs to the technical field of crossing of high polymer materials and artificial intelligence, and discloses a method for optimizing the proportion of a nano-composite flame-retardant master batch in engineering plastic based on machine learning. The method is used for solving the problems that a traditional experience trial and error method is low in efficiency in multi-target performance collaborative optimization, high-dimensional nonlinear parameter space search is difficult, and microscopic coupling effect modeling is missing. According to the method, a multi-dimensional input feature set containing material composition and process parameters is constructed, six key performance indexes are combined to form training data, a mixed model formed by cascading a multi-layer perceptron and Gaussian process regression is trained, an improved non-dominated sorting genetic algorithm (NSGA-II) is adopted for multi-target global optimization, and an optimal ratio meeting preset constraints is obtained. The method is an industrial internet technology system service, and solves the technical problem that multi-target performance is difficult to collaboratively optimize due to the fact that a traditional experience trial-and-error method cannot perform modeling and searching on a high-dimensional nonlinear parameter space of a nano-composite flame-retardant system.

The invention relates to a vibration suppression-oriented electromagnetic actuator data driving collaborative optimization design method, and belongs to the technical field of electromechanical equipment optimization design. Sample data are collected through Box-Behnken experimental design, a Gaussian process regression agent model is constructed based on the data, and a nonlinear complex mapping relation between each objective function and a design parameter is accurately represented. The influence degree of the design parameters is quantitatively evaluated through sensitivity analysis, the main design variables and the secondary design variables are distinguished accordingly, and the optimization efficiency is improved. Multi-objective optimization is carried out for the main design variables, and a design scheme with the optimal comprehensive performance is selected from a Pareto solution set through an objective decision-making mechanism based on an ideal point method in combination with the optimization result of the secondary design variables. According to the method, limitation of a traditional physical model is broken through through data-driven modeling, global optimization balance of electromagnetic performance, loss and volume is realized, and an efficient and quantifiable evaluation method is provided for design of the high-performance electromagnetic actuator.

The invention provides a magnetic positioning error compensation method and system based on Gaussian process regression, and a medium. The method comprises the following steps: extracting a feature vector reflecting magnetic field environment distortion in real time; inputting the feature vector into a pre-trained Gaussian process regression model to obtain a predicted mean value and a predicted variance of the positioning error; dynamically adjusting a measurement noise covariance matrix in the Kalman filter based on the prediction variance so as to adaptively change a filtering weight; and executing state updating by using the adjusted covariance matrix, and performing deviation compensation on the output pose in combination with the predicted mean value. According to the method, the observation uncertainty is quantified through Gaussian process regression, the filter parameters are adaptively adjusted according to the observation uncertainty, and in a complex metal interference environment, the positioning precision and dynamic response can be intelligently balanced, the trajectory jump can be effectively inhibited, and the robustness and reliability of a magnetic positioning system can be remarkably improved.