558 results about "Root-mean-square deviation" patented technology

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to the technical field of trajectory tracking control of piezoelectric motion tables, and discloses a trajectory tracking control method and device for a piezoelectric console, and the method comprises the steps: constructing an initial dynamic model of a piezoelectric motion table based on an asymmetric Bouc-Wen hysteresis model, and obtaining a to-be-identified parameter set; based on the root-mean-square error between the actual output displacement of the piezoelectric motion platform at the plurality of sampling time points and the predicted output displacement of the initial dynamical model, constructing a self-adaptation function of the parameter set to be identified; identifying and acquiring a target model parameter set by using a multi-modal Bayesian gradient optimization algorithm and taking the value convergence of the self-adaption function as an optimization target to obtain a target dynamic model; based on the target dynamic model, constructing a hysteresis state observer, and obtaining feed-forward compensation voltage; constructing a self-adaptive fuzzy PID feedback controller, and obtaining a feedback control voltage; and summing the feedforward compensation voltage and the feedback control voltage to generate a driving voltage so as to drive the piezoelectric motion table to perform trajectory tracking.

The invention belongs to the field of cable eccentricity detection, and particularly relates to a cable eccentricity detection method and system based on X rays. The method comprises the following steps: calculating a gradient magnitude diagram and a gradient direction diagram through an X-ray image of a cable, screening a point with the local maximum gradient magnitude and low neighborhood divergence as a contour starting point, performing contour tracking to generate a contour point set, and selecting a next contour point based on a tangential prediction direction; performing ellipse fitting on the contour point set to obtain a geometric center, long and short axis parameters and a root-mean-square error of a fitting ellipse, dividing the fitting ellipse into an inner candidate ellipse and an outer candidate ellipse according to a long axis, and matching the inner candidate ellipse meeting the condition for each outer candidate ellipse; and screening out an outer candidate ellipse and an inner candidate ellipse which meet conditions from the candidate pairs, and calculating the eccentricity of the cable to be measured based on geometric center coordinates of the outer candidate ellipse and the inner candidate ellipse. According to the invention, the accuracy and reliability of cable eccentricity measurement results can be improved.

The invention relates to a rainfall nowcasting method based on U-KAN grading loss weighting and frequency self-adaption, which comprises the following steps: (1) carrying out quality control and screening on a radar puzzle, and establishing a data set; (2) dividing a training set, a verification set and a test set, and standardizing; (3) constructing a U-KAN model, selecting training parameters and inputting data: combining a traditional Unet structure with a KAN network to construct the U-KAN model; then performing model training to obtain a prediction result; the prediction result is restored to the original magnitude through destandardization; (4) introducing a loss function based on a root-mean-square error and grade weighting in a model training stage, and performing post-processing on model output by adopting a frequency deviation correction method; (5) integrating and averaging the forecast products processed by the two complementary strategies, and recording the forecast products as U-KANE; and (6) predicting a rainfall result in the next three hours by using radar echo data in the past one hour, and outputting a rainfall short-term and imminent forecast result by the U-KANE.

The invention provides a slope displacement monitoring method and system based on reinforcement learning and enhanced Kalman filtering, and the method comprises the steps: carrying out the preprocessing of displacement data collected by Beidou, and carrying out the abnormal value elimination, missing value interpolation and time consistency inspection; establishing a Kalman filtering model containing displacement and speed state vectors, and initializing a process noise covariance matrix Q and an observation noise covariance matrix R as initial filtering parameters; q and R matrixes are dynamically optimized through a PPO reinforcement learning algorithm, and parameter self-adaptive adjustment is achieved; carrying out displacement trend analysis on the filtered output data, marking abnormal trend data by adopting a statistical test and trend inflection point recognition algorithm, and feeding back a root-mean-square error of the abnormal trend data to a PPO algorithm to carry out parameter readjustment; data stage changes are analyzed based on a sliding window technology, independent experience playback buffer areas are set for data in different stages in PPO, and associated updating of filtering parameters is achieved. According to the invention, the precision and reliability of slope displacement monitoring are improved.

The invention discloses an improved vibration compensation method suitable for an atom interference gravimeter. The method comprises the following steps: acquiring an atom interference signal and a vibration voltage signal output by a seismometer; initializing population parameters of the improved whale optimization algorithm, wherein the population parameters comprise a search space of a population size, a maximum number of iterations, a gain coefficient K and a delay coefficient tau; the method comprises the following steps: generating an initial whale population by adopting a method of combining Logistic chaotic mapping and Latin hypercube sampling LHS; calculating a phase shift caused by vibration noise and an atomic interference phase delta phi, and calculating the fitness of the whale individual by taking a root-mean-square error of compensated interference fringes as a target function; iteratively updating the population position through an improved whale optimization algorithm, outputting an optimal gain coefficient and an optimal delay coefficient, and obtaining an atomic interference signal after vibration compensation; according to the atomic interference gravimeter vibration compensation method, the global search capability, the local development precision and the stability of a vibration compensation algorithm are improved.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention discloses a determination method, an application method and a related system of a grain yield prediction model, and relates to the field of grain yield prediction, and the determination method comprises the steps: obtaining time-space dynamic data; preprocessing and normalizing climate data, management data and soil data in the spatial-temporal dynamic data to obtain a spatial-temporal data set; respectively inputting the spatio-temporal data set into a plurality of machine learning models to obtain outputs of the plurality of machine learning models; respectively calculating a root-mean-square error and a decision coefficient of each machine learning model based on grain annual output data corresponding to the spatio-temporal data set and the output of each machine learning model; abandoning the machine learning model with the determination coefficient smaller than a preset value to obtain a screened machine learning model; and based on the screened machine learning model and the corresponding root-mean-square error, constructing a weighted average grain yield prediction model. According to the invention, a scientific basis can be provided for grain production under different climate scenes and management strategies.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

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

The invention relates to a hydraulic pump motor degradation evaluation method and system based on an LSTM neural network and similarity calculation. The hydraulic pump motor degradation evaluation method comprises the steps that key indexes representing hydraulic pump motor degradation and input parameters influencing hydraulic pump motor working conditions are extracted from test data in different working conditions; selecting and dividing the obtained original test data into a training data set and a test data set; performing normalization and data screening on samples in the training data set to obtain data enhancement samples for model training; samples in the test data set are only subjected to normalization processing and are used for model testing; setting model parameters based on data features, obtaining a volumetric efficiency calculation result, and calculating a root-mean-square error; determining operation conditions of the hydraulic pump motor in the test data, and analyzing change trends of degradation index parameters of the hydraulic pump motor in different time periods; the similarity of the degradation index parameters of the hydraulic pump motor in different time periods is calculated, and the degradation degree is obtained; and respectively multiplying a prediction result of the neural network and a similarity calculation result by a weight coefficient of 0.5 to realize degradation evaluation of the hydraulic pump motor.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention discloses a forest vegetation carbon sink potential prediction system and method based on a forest growth rule, and belongs to the technical field of carbon sink potential prediction. The invention aims to solve the problem of dynamic response to forest carbon sink potential and long-term environmental climate change. The method comprises the steps of collecting data, establishing model construction, verifying a data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data; performing data processing; constructing a forest vegetation biomass model for predicting biomass growth of forest vegetation; evaluating the fitting result by adopting a root mean square error RMSE and a determination coefficient R2 to obtain a verified forest vegetation biomass model; and performing forest vegetation biomass growth prediction by using the verified forest vegetation biomass model, and performing forest vegetation carbon sink potential prediction according to an obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result.

The invention relates to the technical field of intelligent agriculture and computer vision, in particular to a small-sample high-density chicken counting framework based on a deep learning Mamba structure, which comprises a feature extraction network, a support-query enhancement module and a decoder. According to the method, a multi-scale feature extraction network based on a residual block (ResNet Block) is introduced, so that local detail information is effectively reserved; and then, a support-query enhancement module is constructed by introducing a Mamba structure, and context interaction between support features and query features is effectively enhanced by utilizing the long sequence modeling capability of linear complexity of the support-query enhancement module, so that the problems of individual overlapping and boundary fuzziness in a high-density chicken flock scene are solved. Experimental results on a PoultryCount real breeding data set show that the mean absolute error (MAE) and the root-mean-square error (RMSE) of the method are reduced compared with those of an existing method, and the chicken counting precision and generalization ability under the condition of a small number of labeled samples are remarkably improved.

The invention belongs to the technical field of photoelectric stabilized platform intelligent control, and particularly relates to a photoelectric stabilized platform composite anti-interference method based on improved active-disturbance-rejection control, which realizes multi-band disturbance cooperative suppression through an improved extended state observer (ESO) and non-linear state error feedback (NLSEF) core architecture. For the problems of phase distortion and parameter sensitivity under traditional ADRC high-frequency disturbance, an ALMRBF-ESO observer is constructed by adopting a dynamic regularization constrained radial basis function neural network and an adaptive damping adjustment mechanism, gradient dispersion limitation of a traditional RBF in a high-frequency domain is broken through, and disturbance root-mean-square errors are reduced; by reconstructing a sliding mode gain equation of nonlinear tracking error feedback and implanting a parameter self-correction mechanism, dynamic response of a system is accelerated, overshoot is reduced at the same time, and the parameter drift rate of a controller is stabilized at 0.12. According to the method, on the basis of completely reserving ADRC robustness, the problem of collaborative optimization of ESO broadband disturbance observation precision and NLSEF dynamic tracking performance is solved.

The invention relates to an indoor cross-scene multi-band sub-terahertz channel prediction method, a storage medium and software, and belongs to the technical field of wireless communication. In order to solve the problems of poor adaptability to nonlinear features, high calculation complexity and insufficient generalization ability in traditional channel modeling, a channel prediction model is constructed based on a back propagation neural network, and a particle swarm optimization algorithm is utilized to optimize a network initial weight and a threshold value. According to the method, system parameters and environment characteristics are fused, an indoor sub-terahertz channel simulation data set is constructed, and the indoor sub-terahertz channel simulation data set is subjected to normalization processing and then used for model training and verification. And finally, evaluating the prediction performance according to indexes such as a root-mean-square error, a mean absolute error and a decision coefficient. The method can improve the accuracy and generalization ability of channel characteristic prediction, is suitable for various indoor scenes, and provides technical support for 6G communication system deployment.

Provided is a generalization generation method of industrial robot pose trajectories supporting changeable operation path points, which relates to the field of robot trajectory planning. The method includes: acquiring a plurality of groups of robot end pose trajectories; aligning time steps of the pose trajectories via a multi-dimensional dynamic time warping algorithm; constructing a Gaussian mixture model of robot pose trajectories in combination with a variational Bayesian method and an attitude quaternion tangent space mapping method; calculating reference pose trajectory distribution via a Gaussian mixture regression method; performing kernelized representation on pose trajectory distribution, and solving an optimal hyperparameter of a kernel function by minimizing a root mean square error of a reproduced reference trajectory; and generating the robot end pose trajectories adapted to the operation path points by updating the reference pose trajectory distribution.

The invention discloses a pressure vessel lower chamber model order reduction method combining sparse grids and POD, and the method comprises the steps: adding one to the grade of the sparse grids after iteration initialization, and obtaining test grid points according to important grid points; executing the lower chamber full-order computational fluid mechanics model corresponding to the test grid points to obtain a first snapshot; reconstructing a second snapshot by using the mapping relation and the POD modal matrix; calculating a root-mean-square error of the two snapshots, and if the root-mean-square error is smaller than a threshold value, completing construction of a reduced-order model Otherwise, the grid points with the errors meeting the requirements are stored in a non-important grid point set, the grid points which do not meet the requirements are marked as important grid points, and the snapshot matrix is updated. Performing singular value decomposition on the snapshot matrix, updating the mapping relation, judging whether the maximum sparse grid grade is reached or not, and continuing iteration if the maximum sparse grid grade is not reached. According to the method, the field distribution of the thermal hydraulic parameters in the lower chamber can be quickly obtained through matrix algebraic operation, and the method can be used for application scenes such as parameterization analysis, design optimization and digital twinning.

The invention discloses a self-piercing riveting simulation prediction and parameter optimization method and related equipment, and relates to the technical field of mechanical connection numerical simulation, and the method comprises the steps: building a material failure model according to material failure information; setting boundary conditions of the self-piercing riveting joint forming simulation model according to test working conditions to obtain section data and a joint model of the self-piercing riveting joint; establishing a mechanical property simulation model of self-piercing riveting according to the mechanical property sample based on the joint model, and setting boundary conditions of the mechanical property simulation model; calculating according to the mechanical property simulation model to obtain a load displacement curve; optimizing parameters of the material failure model by taking the undercut amount and the bottom thickness size error of the cross section and the root mean square error of the load displacement curve as optimization targets; the optimized parameters are substituted into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, corresponding boundary conditions are set for simulation, and a simulation result is obtained. According to the method, the simulation precision and the process optimization efficiency are improved.

The invention provides a quality control method and system for a small box girder, and the method comprises the following steps: constructing a small box girder design BIM model according to a bridge design drawing; converting the BIM design model of the small box girder into discrete point cloud; obtaining a prefabricated field template point cloud, and preprocessing the prefabricated field template point cloud; performing registration on the point cloud model and the BIM model; and selecting a control point on the registered point cloud model as a reference for external mold control lofting, and calculating and evaluating field lofting data. According to the quality control method and system for the small box girder provided by the invention, the point cloud is generated by adopting BIM model interpolation, high-precision registration is completed in combination with a point cloud-point cloud registration algorithm, and the registration effect is evaluated based on the root-mean-square error. And closed-loop quality control from design to construction reduces manual intervention and can realize full-process digitization. And through algorithm fusion and error registration, engineering requirements are met, and the precision is effectively improved. The problems that a traditional method is low in precision and poor in efficiency are solved.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention provides a surrounding vehicle track prediction method and system based on an attention mechanism in a network connection environment, and the method comprises the steps: constructing a vehicle network which comprises a road side unit and a vehicle; cooperative driving data of the target vehicle and surrounding vehicles are obtained through a road side unit and a sensor on the vehicle; the driving data is preprocessed; inputting the preprocessed driving data into the trained Transform trajectory prediction model based on the attention mechanism to obtain a trajectory prediction result of the surrounding vehicles; calculating a root-mean-square error between the trajectory prediction result and the actual running trajectory of the vehicle; constructing a vehicle collaborative decision according to the trajectory prediction result and the root-mean-square error, and controlling the operation of the vehicle based on the vehicle collaborative decision; according to the invention, an attention mechanism is adopted to effectively capture a complex dynamic interaction relationship between vehicles, and the precision of trajectory prediction is remarkably improved.

The invention relates to the field of artificial lateral line optimization layout, in particular to an artificial lateral line array optimization method for attitude estimation of an underwater vehicle, which comprises the following steps of: 1, forming a quaternion model, acquiring a vector parameter, and deducing a vector after rotation; 2, forming an artificial lateral line array, and constructing an underwater vehicle pressure field model; 3, deducing a relative root mean square error to obtain an effective estimation range of vector parameters; 4, determining a quaternion model vector parameter value range, and determining an artificial side line array layout parameter constraint condition; 5, determining an optimization target; and 6, verifying the reliability of the optimization method. By fusing an artificial lateral line array and a quaternion model and based on an array attitude estimation performance quantitative evaluation index system, the problem of accumulative errors existing in the integral operation process of an inertial sensing system is effectively avoided, and the problem of universal joint deadlock during Euler angle operation is solved.

The invention provides an SAR image vegetation coverage inversion method and system based on multi-dimensional feature optimization and model optimization, a storage medium and electronic equipment, and the method comprises the following steps: carrying out the preprocessing of a Sentinel-2 image, calculating a normalized difference vegetation index (NDVI) through a wave band calculation formula, and carrying out the calculation of the NDVI; a reference vegetation coverage (FVC) of a research area is obtained by combining a pixel bipartite model (DPM), and then an optimal SAR feature combination is selected by applying an improved genetic algorithm ET-GA, using XGBoost as an adaptability evaluation function and using a root-mean-square error (RMSE) as an evaluation index. According to the method, SAR features are extracted through multiple feature decomposition methods, multi-dimensional feature optimization, intelligent algorithm optimization and a deep learning model are combined, the core problems of feature redundancy, insufficient precision, low efficiency and the like of SAR data in vegetation parameter inversion are solved, and an innovative technical scheme is provided for the field of remote sensing quantitative inversion.

The invention discloses a complex nonlinear MIMU (Micro Inertial Measurement Unit) signal noise reduction method based on improved modal decomposition. Dynamic adaptive optimization of noise parameters is realized by introducing Hilbert spectrum analysis into a CEEMDAN (Complex Empirical Empirical Mode Decomposition Number) algorithm; an IMF component discrimination model is constructed based on multi-dimensional feature fusion, and accurate classification of IMF components is realized; and aiming at a discrimination result, adopting a hierarchical processing strategy of combining variational mode decomposition and empirical wavelet transform for different types of mode components to realize high-quality signal reconstruction. Compared with the prior art, the method has the advantages that the signal decomposition quality is remarkably improved, the multi-feature fusion discrimination model is excellent in performance when the boundary fuzzy region is processed, the problem of discontinuity of a traditional hard threshold method at the feature boundary is solved, the signal-to-noise ratio is remarkably improved, the root-mean-square error is greatly reduced, and the noise reduction effect is remarkably improved.