410 results about "Gaussian process regression gpr" patented technology

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 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 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 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 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 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 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 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 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 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 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.

The invention relates to the field of mechanical engineering, in particular to a hydraulic-structure collaborative optimization design method for a shield pump, and aims to solve the problems of load distortion and structure matching imbalance caused by traditional staged optimization. The method comprises the following steps: constructing a fluid-solid bidirectional coupling simulation model, and extracting transient hydraulic loads under multiple working conditions; establishing a structure dynamic response agent model based on modal superposition and Gaussian process regression; key coupling parameters such as the impeller outlet width and the volute base circle diameter are recognized through a Sobol method; and the efficiency, the lift fluctuation rate, the stress and the vibration are taken as multiple targets, and a Pareto optimal solution is searched through an improved genetic algorithm. Manufacturing constraints are embedded in the optimization process, the single period is shorter than 48 hours, and fatigue life evaluation and knowledge base intelligent recommendation are supported. According to the method, closed-loop cooperation of hydraulic excitation and structural response is achieved, the vibration prediction error is lower than 10%, the optimization dimension is compressed to be within 5 dimensions, the efficiency is improved by 20 times or above, and the performance reliability and the design intelligence level of the shield pump are remarkably improved.

The invention relates to a metal additive manufacturing three-dimensional temperature field Gaussian process prediction method, belongs to the technical field of material science, and particularly relates to a metal additive manufacturing three-dimensional temperature field prediction method. Logarithmic transformation is adopted to preprocess a temperature field, and prediction difficulty caused by extreme gradient near a molten pool is avoided; dividing the overall computational domain into a plurality of sub-domains by adopting a domain decomposition strategy to reduce the problem dimension; for each sub-domain, further combining singular value decomposition to extract a temperature field reduced-order base; establishing a local Gaussian process regression model based on a Maren kernel function and carrying out parallel training so as to establish rapid mapping from process parameters to reduced-order output; during online prediction, efficient and accurate prediction of a complete temperature field is realized through parallel calculation and full-field assembly of each local model.

The invention discloses a ship port entering-berthing autonomous navigation control method based on LBMPC, and the method comprises the steps: building a port environment model, and selecting an optimal berth from a candidate berth set according to the port information and the ship navigation state; dividing the whole ship entering-berthing process into a ship entering stage, a berthing area stage and a low-speed berthing stage, and calculating track nodes of all stages based on a port environment model; an improved fast exploration random tree star algorithm is adopted to plan a path of a port entering stage, a Durbin curve is adopted to plan a path of a berthing area stage, and a nonlinear attenuation model is combined to carry out speed distribution to obtain a global trajectory; in the actual sailing process, learning type model predictive control based on Gaussian process regression compensation is adopted for local dynamic pose planning and trajectory tracking control, and in the low-speed berthing stage, motion constraint is tightened, so that the berthing task of the ship is completed. According to the method, the control precision and safety of navigation of the ship in the whole process of entering the port can be improved.

The invention provides a characterization method and system for soil fertility of a citrus plantation, and relates to the technical field of characterization of soil fertility, and the characterization method comprises the following steps: collecting deep soil samples according to uniform grids in the citrus plantation, arranging three-parameter sensors, and obtaining a multispectral remote sensing image at the same time; secondly, respectively constructing a space continuous function, a time continuous function and a spectrum continuous function by using common Kriging interpolation, Gaussian process regression and spectral index inversion; then selecting high-yield area samples with the first 10% of yield in continuous three years, generating high fertility reference distribution based on kernel density estimation, and fusing the three types of functions by using an entropy evaluation method to obtain a comprehensive fertility function; finally, principal components are extracted through function principal component analysis, the Tukey depth of the principal components relative to reference distribution is calculated, a soil fertility index is generated, and five-level fertility areas are divided through an improved natural fracture method.

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.

The invention relates to a slope instability probability evaluation method based on burial depth constraint space random field simulation, and belongs to the technical field of slope engineering. The method comprises the following steps: acquiring shear strength parameters c and phi at different buried depths of a slope soil body in a research area; the obtained shear strength parameters are expanded according to Gaussian process regression in combination with a radial basis function, the reliability of the expanded parameters is determined through K-S inspection, and cross correlation between c and phi is obtained; determining statistical characteristics of the c and phi expansion data; under the burial depth constraint condition, slope model determinacy research is carried out, a safety coefficient is calculated, the model is compared with a traditional model, and the difference between the potential slip plane of the slope under the burial depth constraint and the potential slip plane of the traditional slope is obtained; the traditional Monte Carlo simulation MCS is optimized; according to deterministic analysis and a random field theory, carrying out uncertainty research on the side slope under the burial depth constraint, and calculating the instability probability of the depth evolution random field side slope model.

The invention discloses a vehicle trajectory tracking control method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a residual model of a pre-constructed initial prediction model of a vehicle through Gaussian process regression, and determining a target prediction model according to the initial prediction model and the residual model; wherein the target prediction model is used for predicting the state quantity and the control quantity of the next moment according to the state quantity and the control quantity of the vehicle at the current moment; determining a target function according to a preset expected condition and the target prediction model; wherein the preset expected conditions comprise trajectory difference minimization, control quantity minimization and control quantity change rate minimization; and determining a constraint condition of the target function according to the working condition of the vehicle, and solving the target function under the constraint condition to obtain a target control quantity to control the vehicle. According to the method, the adaptability of the vehicle to different working conditions can be improved, and a good trajectory tracking effect can be achieved while the safety of the vehicle is ensured even under extremely dangerous working conditions.

The invention belongs to the technical field of magnetic field measurement and instruments, and relates to a magnetic gradient tensor field interpolation method under physical potential field constraint, which comprises the steps of establishing a Gaussian process model of a magnetic gradient tensor field, acquiring a magnetic potential core and performing hyper-parameter optimization. According to the method, multi-component interpolation and curving are fused into a unified Gaussian process regression framework, and the multi-component interpolation and the curving are subjected to potential field physical constraints in a unified manner; a complete potential field physical constraint is used, meanwhile, no-rotation and no-divergence constraints are met, and magnetic gradient tensor measurement data interpolation with measurement errors is more accurate and better in anti-interference capability; by introducing a complete physical constraint fusion Gaussian process regression framework, the magnetic gradient tensor interpolation precision and stability are improved. Based on the magnetic potential core, all magnetic gradient tensor components can be modeled at the same time in the joint probability model, physical constraints among the components are guaranteed, non-planar data which are distributed at will can be processed naturally based on a Gaussian process regression framework, and an additional potential field continuation step is not needed.

The invention discloses a numerical control machine tool thermal error prediction method based on dynamic physical information fusion, and belongs to the technical field of intelligent manufacturing and precision machining. The method creatively introduces a Bayesian dynamic weight adjustment mechanism and a multi-stage joint optimization strategy through hierarchical fusion of a physical mechanism and a data driving model, and specifically comprises the following steps: establishing a lightweight analysis model based on a thermal deformation mechanism; key temperature and displacement data are collected through a thermal characteristic test, and model parameters are fitted; constructing a fusion prediction model based on Gaussian process regression, taking a mechanism model as a mean value function, and combining global and local kernel function combinations to fit the time-varying characteristics of thermal errors; based on the distribution consistency of a KL divergence dynamic evaluation mechanism and data prediction, generating an adaptive weight factor through a Sigmoid function; a double-stage training strategy is adopted, kernel parameters are optimized through pre-training, dynamic weight adjustment is gradually introduced, and collaborative optimization of mechanisms and data is achieved. According to the method, the thermal error prediction precision (the root mean square error is less than or equal to 0.6 mu m) is remarkably improved while the physical interpretability is ensured, the adaptability of the model to multiple working conditions is enhanced through a dynamic weight mechanism, and the method is suitable for real-time monitoring and prediction of thermal deformation of a high-precision numerical control machine tool.

The invention provides a multi-parameter water quality data fusion analysis method and system. The method comprises the steps that water quality parameters are collected to form a three-dimensional data cube; constructing a space-time tensor model by using Tucker decomposition and a graph convolution network, and generating a core tensor matrix; constructing a dynamic constraint library and embedding the generative adversarial network; training a generative adversarial network by using Transform and physical constraint loss, generating synthetic data and verifying the synthetic data; performing space-time fusion by using meta learning weight distribution and Bayesian deep learning to generate a weight matrix and a confidence interval; missing data are restored through physical constraint interpolation and Gaussian process regression, and SHAP and LIME interpretation and path diagrams are generated; and performing real-time analysis by using an edge-cloud collaborative architecture to generate an intelligent report. Through physical constraint modeling, dynamic weight distribution and edge-cloud collaborative architecture, the problems that synthetic data violates physical laws, weight staticization, response lag and insufficient interpretability are solved.

Disclosed herein is a method and system for analyzing wellbore fluid samples using impedance spectroscopy to identify chemical constituents and detect contaminants. The approach involves collecting fluid samples from a wellbore and performing chemical impedance spectroscopy by applying multiple discrete frequencies of alternating current, generating impedance spectra that are transformed into Nyquist and Bode plots for analysis. A data processing workflow handles raw impedance data through steps of labeling, cleaning incomplete sweeps, temperature thresholding, and temperature compensation to a reference standard. A hybrid modeling approach that integrates physics-based modeling (using a modified Randles circuit) with advanced data analytics techniques (utilizing Gaussian Process Regression) is utilized. Derived electrical properties, particularly susceptance and permittivity, are used to enhance the predictive capabilities of the model. This enables quantitative measurement of contaminants including mud filtrate and various salt types in wellbore fluids.

The invention discloses a community equivalent rainfall flood model construction and real-time correction method and system, and belongs to the technical field of urban hydrology, rainfall flood management and digital twinning. Aiming at the problem that the traditional model application is limited due to incomplete community pipe network data and lack of long-sequence runoff monitoring data, constructing a community digital twinborn observation system to obtain rainfall process-runoff process data; generalizing a community into an equivalent hydraulic response unit (EHRU) 'ash bin 'model, and differentially processing permeable / impermeable underlying surface runoff; extracting multi-dimensional rain pattern features, and constructing a mapping model of rain patterns and EHRU optimal parameters through Gaussian process regression (GPR); the model is corrected online in real time, runoff is predicted in a rolling mode in combination with rainfall forecast, and a closed loop is formed through event post-evaluation and model retraining. The system modularizes and integrates the functions of data acquisition, offline training, online prediction and the like, the runoff prediction precision and the model robustness are improved, and the method is suitable for rainfall flood management of communities with incomplete basic data.

The invention relates to the technical field of bearing detection, and discloses a bearing degradation starting point detection method based on an unsupervised streaming update threshold, which comprises the following steps: acquiring a real-time vibration signal of a bearing; inputting the collected real-time vibration signal into a pre-trained Gaussian process regression model for prediction to obtain a predicted vibration signal; calculating a mahalanobis distance between the predicted vibration signal and the real-time vibration signal, wherein the mahalanobis distance is used for representing the performance state of the bearing; and inputting the calculated mahalanobis distance into a pre-trained DSPOT model, updating a threshold value, and comparing a relative value of the input mahalanobis distance with the updated threshold value and the threshold point exceeding the threshold value to obtain a degradation starting point of the bearing. The detection method has no requirement for input signal feature distribution, only needs to determine the initial threshold value according to the signals in the normal operation stage, and then dynamically updates the threshold value according to the actual operation condition, so that the degradation trend starting point is accurately determined, and the method does not depend on specific working conditions and is wide in application.

The invention discloses a battery pack health state prediction method and system driven by a data fusion model, and the method comprises the steps: extracting aging features according to battery test data, and obtaining feature data; according to the feature data, constructing a state space model based on Gaussian process regression, and obtaining an initial prediction model; carrying out transfer learning on the initial prediction model according to the pre-training data set to obtain a transfer model adaptive to the target battery pack; constructing a particle filtering framework according to the migration model, inputting real-time characteristic data acquired on line into the particle filtering framework for state updating and parameter correction, and obtaining a closed-loop estimation result; and outputting a health state prediction value of the battery pack according to a closed-loop estimation result. According to the method, high-precision and high-robustness online prediction of the full-life-cycle health state of the power battery pack is realized, and the generalization ability of the model in a data scarcity scene and the long-term estimation stability in a dynamic environment are remarkably improved.

The invention relates to the technical field of model simulation, and discloses a precise traceability method and system for underground water organic pollutants, and the method comprises the steps: building a space-time feature set based on monitoring well concentration data and geological parameters, and generating a preliminary continuous concentration field through Gaussian process regression; secondly, extracting geometric features of a concentration field, establishing a flow field model in combination with an underground water level gradient, simulating a pollutant transport process, screening a potential source position set according with a time deviation threshold value, and constructing a source strong release model to invert optimal release parameters; a migration path is generated, and an effective path is screened according to the cumulative flux coverage degree to reconstruct a global concentration field; and finally, determining a final pollution source coordinate through concentration field gradient rotation analysis and isoline geometric continuity evaluation in combination with Bayesian probabilistic reasoning. According to the method, the traceability accuracy can be improved.

The invention discloses a numerical control machine tool cutting parameter optimization method and system based on multi-source data fusion. The method comprises the following steps: synchronously collecting machine tool state, dynamic response and machining result multi-source data, and carrying out preprocessing and feature association; based on historical data, deriving a material removal rate, a flutter risk, an acoustic emission energy ratio and a surface quality implementation coefficient feature, and constructing an enhanced feature data set; by taking the cutting parameters as input and the features as output, constructing and incrementally updating a machining process response surface agent model by adopting Gaussian process regression; and defining a search space by combining a mechanism hard constraint based on a tool capability, machine tool performance and a stability lobe graph, calling an agent model, and performing optimization by adopting a multi-objective evolutionary algorithm to obtain a Pareto optimal parameter solution set. According to the method, deep coupling of data and a mechanism is achieved, the efficiency can be improved through dynamic optimization on the premise that the machining stability and quality are guaranteed, and high adaptability and reliability are achieved.