692 results about "Gaussian process regression gpr" patented technology

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention discloses a multi-terminal vehicle management system based on an intelligent algorithm, relates to the technical field of vehicle management, and is used for solving the problem of insufficient real-time performance and cooperative efficiency of vehicle management. According to the method, a multi-modal sensor fusion technology is combined with Gaussian process regression, complex environment data is modeled in real time, dynamic target and static background features are accurately extracted, environment changes are analyzed and predicted in combination with a time sequence, accurate input is provided for path planning and task allocation, a path is dynamically adjusted through an incremental optimization algorithm, emergencies are coped, and the path planning and task allocation efficiency is improved. According to a distributed path optimization mechanism, multi-vehicle task path conflicts are eliminated, the cooperation efficiency is improved, a priority model is constructed based on the task emergency degree, the time constraint and the resource matching degree, the weight is dynamically adjusted, vehicle and task matching is optimized in combination with reinforcement learning, the task execution efficiency is ensured, resource competition and path conflicts are solved, and the task execution efficiency is improved. Efficient cooperation in a complex dynamic environment is realized, and the real-time performance and management efficiency of vehicle management are improved.

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 relates to the technical field of high-voltage connectors, and discloses a high-voltage connector fault prediction method and system based on thermal management, and the method comprises the steps: collecting the temperature distribution data and electric signal transmission characteristic data of a high-voltage connector, and building a thermal-electric coupling network model comprising a temperature node and a signal transmission node; separating and optimizing the transmission characteristic function to obtain a temperature-transmission characteristic mapping relation; gaussian process regression calculation is carried out on the temperature time sequence data to obtain temperature dynamic characteristic statistical characteristics; inputting the statistical characteristics of the dynamic temperature characteristics into a specified time delay thermal management neural network for signal transmission delay prediction to obtain delay prediction characteristic parameters; according to the method, the weighted error value in the transmission feature space is calculated, and the signal transmission delay is dynamically adjusted to obtain the compensated signal transmission delay parameter, so that the accurate prediction of the signal transmission delay is realized, and the accuracy and robustness of the compensation effect are improved.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

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 provides a Gaussian modeling method combined with laser scanning point cloud data processing, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the registration of a plurality of point cloud data sets through employing a point cloud fusion algorithm, and obtaining a target point cloud data set; performing data precision, position deviation and noise influence analysis on the plurality of point cloud data sets, determining point cloud data precision, a position deviation coefficient and a noise influence coefficient, and comprehensively evaluating and determining point cloud acquisition precision; setting a data dimension reduction proportion according to the point cloud acquisition precision, and performing covariance matrix dimension reduction on the target point cloud data set; and selecting a predetermined kernel function, performing data fitting and Gaussian process regression modeling on the dimension reduction point cloud data set, and outputting a surface fitting model. According to the method, the technical problems that the requirements for storage and computing resources are too high and efficient three-dimensional modeling cannot be realized due to the fact that a large number of covariance matrixes need to be calculated in a traditional Gaussian modeling method can be solved; the dimensionality of data can be effectively reduced, and the efficiency of three-dimensional modeling is remarkably improved.

The invention discloses a cumulative environmental risk early warning analysis method and system for soil heavy metal pollution, particularly relates to the technical field of soil environment monitoring, and is used for solving the problems that the existing risk assessment of leakage from heavy metal pollution to underground water is incomplete and insufficient in accuracy. Heavy metal concentration depth distribution data are obtained by measuring heavy metal concentrations in soil at different depths; judging whether a heavy metal pollution risk influencing underground water exists or not according to the data; when the risk exists, analyzing the dynamic influence of surface charge change of soil particles on heavy metal adsorption and desorption behaviors by adopting Gaussian process regression, analyzing the retention effect of the heavy metal in a fracture network through a random walk model, and evaluating the delay influence of the retention effect on the migration rate and path of the heavy metal; and finally, according to the generated heavy metal underground leakage risk assessment result, the groundwater pollution risk is graded, and the accuracy of groundwater pollution risk assessment can be effectively improved.

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 provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention relates to the technical field of illumination control, in particular to an LED illumination efficiency optimization method and system, and the method comprises the following steps: recognizing an illumination use mode and a change trend of an environment condition based on real-time external environment data and historical LED illumination operation data, and obtaining an environment and operation data analysis result. According to the invention, prediction analysis is carried out on the LED illumination demand through the Gaussian process regression model, accurate prediction of the illumination demand under differentiated environmental conditions and time periods is realized, the responsiveness and adaptability of the illumination equipment are improved, the illumination control decision process is optimized by comprehensively considering the real-time external environment data and the historical illumination operation data, and the accuracy of the illumination control decision process is improved. The introduction of the hybrid dynamic Bayesian network enhances the real-time evaluation capability of the energy efficiency state of the LED lighting equipment, so that the energy efficiency management is more precise and efficient, and the use of a multi-objective optimization strategy provides a more comprehensive solution for balancing the energy efficiency, the illumination quality and the user comfort.

The invention discloses a GA-LSTM-GPR-based photovoltaic inverter fault diagnosis method, and the method comprises the steps: collecting the fault data of a photovoltaic inverter, and carrying out the preprocessing of the fault data through the filling of missing values, normalization and standardization; a Pearson correlation coefficient and a Spearman correlation coefficient are adopted to analyze correlation between features in the fault data, and a recursive feature elimination (RFE) method is established based on a random forest RF to extract important features; the traditional LSTM network is improved by adding an attention layer, and the attention weight is optimized by adopting a genetic algorithm GA, so that the overall performance and prediction accuracy of the model are improved; and on the basis of the first prediction result of the GA-LSTM model, a fault diagnosis model of the photovoltaic inverter is constructed by combining Gaussian process regression GPR. According to the photovoltaic inverter fault diagnosis method provided by the invention, not only can the complex nonlinear dynamic relationship among the feature data be captured, but also the distribution condition of prediction results can be known through comprehensive point prediction, interval prediction, probability prediction and quantification uncertainty, so that more comprehensive and reliable fault diagnosis information can be provided.

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 racing car racing control method based on Gaussian process regression enhancement model predictive control, and belongs to the field of unmanned driving system trajectory planning and tracking control. The method mainly comprises the following steps: firstly, constructing a racing car racing random optimal control problem model comprising a state equation, a measurement equation and probability constraints, and secondly, respectively modeling system uncertainty and measurement links by adopting a Gaussian process regression model; the method comprises the following steps: firstly, designing prudent model predictive control of racing car racing and an extended Kalman filter GP-EKF based on Gaussian process regression to realize high-precision state estimation, finally, fusing the GP-EKF and the prudent model predictive control, constructing learning model predictive control GP-EKF-LMPC based on GP-EKF enhancement, and feeding back posterior state estimation to an optimization problem, so as to realize high-precision state estimation. And solving an optimal control instruction of racing car racing in real time. Experiments show that the method can significantly improve the control precision and robustness in a complex scene.

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 discloses an intelligent fault diagnosis and alarm system for a high-temperature and high-pressure dyeing machine, and particularly relates to the technical field of intelligent fault diagnosis of industrial equipment, which comprises a data acquisition module, a data preprocessing module, a data processing module, a data processing module, a data processing module and an alarm module, and is characterized in that the data acquisition module is used for acquiring operation data of the high-temperature and high-pressure dyeing machine in real time; the system comprises a data acquisition module used for acquiring data, a clustering analysis module used for identifying similar equipment groups, an analysis module used for carrying out feature deviation analysis and judging whether the current dyeing equipment has potential fault deviation, an adjustment module used for constructing a dynamic threshold value model, and a diagnosis alarm module used for carrying out fault diagnosis alarm operation. According to the method, the actual operation state of the equipment is continuously monitored, dynamic adjustment is carried out in combination with historical data, the appropriate Gaussian process regression kernel function type is automatically selected, the subtle change of the equipment state can be accurately captured, and the problem of false alarm or missing alarm caused by the fact that a threshold value is not suitable for the current equipment state change in a traditional method is solved.

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 relates to the technical field of marine science, in particular to a method for acquiring data of different depths of seawater based on a salinity sensor, and the method comprises the following steps: S1, collecting salinity data of seawater at different depths in real time; s2, preprocessing the collected salinity data; s3, constructing a depth-salinity mathematical model by adopting a Gaussian process regression algorithm; s4, acquiring environmental data in real time, and dynamically correcting parameters of the depth-salinity model according to the environmental data; and S5, inputting the preprocessed salinity data in the S2 into the dynamically corrected depth-salinity mathematical model, and calculating corresponding seawater depth information. According to the method, a nonlinear mathematical model of the depth and the salinity is constructed through a Gaussian process regression algorithm and a dynamic correction technology, and accurate prediction and dynamic adaptation of the relationship between the salinity and the depth in a complex marine environment are realized in combination with data preprocessing and inverse calculation.