109 results about "Square error" patented technology

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 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 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 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 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.

The invention provides an abrasive flow polishing device and method for gear tooth surface homogenization. According to the method, tooth-shaped drainage pieces with the same shape as the gear are installed at the two ends of the upper portion and the lower portion of the gear, drainage section design is introduced, an abrasive flow medium path runner model is constructed, main parameters influencing an experiment are sampled and grouped through a Latin hypercube sampling method, abrasive flow polishing simulation is conducted in a grouped mode, and a Gaussian regression process is utilized to obtain an abrasive flow polishing simulation result. Selecting the minimum root-mean-square error of each group of PiVi data, determining the group of experimental parameters, obtaining a group of abrasive flow machining process parameters of optimal inlet pressure, back pressure and drainage section length, and finally guiding the experiment; according to the method, the influence of the inlet effect is relieved, the abrasive flow medium path runner model is constructed, a set of optimal abrasive flow machining process parameter combination is obtained, and the method is suitable for spur gears and bevel gears and high in universality.

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 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 high-voltage cable on-line monitoring method and device, and relates to the technical field of on-line monitoring. The method comprises the following steps: after a high-voltage cable insulating layer is extruded, acquiring and processing electric field intensity data to obtain processed data; constructing an ideal electric field distribution reference model, comparing the processed data to calculate an electric field matching root-mean-square error, and if the error exceeds a first threshold value, performing early warning and marking an insulation abnormal area; detecting after the shielding layer is formed to obtain excitation end input power and receiving end output power, calculating electromagnetic signal power attenuation, and marking a shielding layer defect area if the electromagnetic signal power attenuation exceeds a second threshold value; calculating a spatial cross validation index according to the error and the attenuation amount, and marking a cross defect area if the spatial cross validation index exceeds a third threshold value; and constructing a defect evolution trend probabilistic model, inputting a real-time error, an attenuation amount and a cross validation index, outputting a defect deterioration posterior probability, and carrying out graded early warning. According to the method, defects of all links of production are covered through full-process real-time monitoring, and the limitation that traditional sampling detection cannot cover production batches is changed.

Disclosed in the present invention are a fault diagnosis method and system for blade icing and blade mass imbalance of wind turbines. The method comprises the steps of: acquiring operation data and blade icing information of a plurality of wind turbines; labeling the operation data with an icing state label on the basis of the blade icing information, so as to obtain fault data; extracting fault features in the fault data, and ranking the fault features according to the degree of importance, so as to generate an optimal feature set; on the basis of a criterion of minimizing a squared error, selecting optimal features in the optimal feature set to generate an optimal decision tree; and performing classification on the basis of the optimal decision tree, so as to obtain a diagnosis test result including fault information. The present invention has the advantages of a high level of diagnostic accuracy, etc.

The invention discloses an underwater sound double-expansion channel parameter estimation method based on Newton orthogonal matching pursuit. The method comprises the following steps: firstly, realizing frame synchronization by using a cross-correlation peak of an LFM signal and a received signal; a coarse estimation value of a Doppler coefficient is obtained on a discrete time frequency grid through an ambiguity function method by utilizing the self-correlation characteristic of m-sequence pilot frequency, multipath time delay of a channel is detected by utilizing a matched filtering peak value of a signal after frequency offset compensation, and a coarse estimation result of the channel is obtained; executing Newton iteration optimization in a continuous parameter space by adopting a Newton orthogonal matching pursuit algorithm to realize super-resolution off-grid estimation of the Doppler coefficient of the first data block; and finally, proposing an iterative block tracking strategy, initializing the compensation of the rear block through the estimated value of the front block, and updating the time-varying Doppler coefficient by using the balanced signal feedback to realize the dynamic tracking of the time-varying Doppler coefficient. According to the invention, the Doppler estimation root-mean-square error is obviously reduced, and the calculation efficiency is high.

The invention belongs to the technical field of industrial automation and computer vision, and discloses a grabbing point pose calculation method based on a target detection model and application of the grabbing point pose calculation method. The grabbing point pose calculation method comprises the following steps: step 100, according to a target center area obtained by processing a grayscale image of a to-be-grabbed object through a target detection model, extracting a corresponding sub-area point cloud from a three-dimensional point cloud of the to-be-grabbed object; step 200, performing rough matching on the sub-region point cloud and the template point cloud through the FPFH feature vector and the RANSAC to obtain a rough matching result; step 300, performing fine matching on the subarea point cloud and the template point cloud through the rough matching result and the GICP to obtain a fine registration result; step 400, judging whether a precise registration result meets a preset requirement or not according to the interior point root-mean-square error and the overlap ratio, if so, entering the next step, and otherwise, terminating the process; and 500, according to the fine registration result and coordinate transformation, the grabbing point pose of the to-be-grabbed object is obtained through calculation. The process is simple in steps and easy to implement and control.

The invention discloses a geophysical prospecting signal denoising method based on VMD (variational mode decomposition) combined with an improved wavelet threshold function, and belongs to the technical field of mineral exploration geophysical prospecting signal process.The method includes the steps that firstly, a decomposition mode number K of a signal is determined through VMD in a self-adaptive mode, a plurality of IMFs (intrinsic mode components) are obtained, and then according to the frequency characteristic and noise distribution of each IMF component, a wavelet threshold function is obtained; and carrying out targeted noise suppression by adopting an improved wavelet threshold function containing an adjustment parameter alpha, and finally, carrying out linear superposition reconstruction on all the processed IMF components to obtain a de-noised geophysical prospecting signal. Experimental verification shows that compared with a traditional method, the method has the advantages that the signal-to-noise ratio and the correlation coefficient of the noisy geophysical prospecting signals can be effectively increased, the root-mean-square error can be effectively reduced, the inherent defects of the traditional method are overcome, the geologic features of the geophysical prospecting signals can be effectively reserved, the method is suitable for various mineral exploration scenes, and reliable data support is provided for anomaly recognition in mineral exploration.

The invention discloses an electricity price prediction method and system based on a converter and a bidirectional gating cycle network, and belongs to the technical field of power systems and artificial intelligence prediction.The method comprises the steps that a historical electricity price data set is acquired, the historical data set is constructed, and the historical data set is preprocessed; the preprocessed data is input into a Transform encoder layer in a hierarchical multi-head attention mechanism; a bidirectional gating recurrent neural network optimized through a global attention mechanism; a cross attention mechanism is combined with the output of a Transform encoder and the output of a BiGRU layer; and performing performance evaluation on a prediction result by adopting an absolute mean error, a mean square error and a root-mean-square error. According to the invention, while the time sequence dynamic modeling capability is maintained, the physical constraint information of the power system is effectively integrated, and the generalization capability of the electricity price prediction model for the multi-source uncertainty in the high-proportion renewable energy penetration scene is significantly improved.

The invention provides a multi-unmanned aerial vehicle optimal game limited accelerated reinforcement learning control method and device, and relates to the technical field of accelerated reinforcement learning. The method comprises the following steps: constructing an evaluation neural network, and approaching a performance index function of a hyperbolic tangent function and a game neighbor item, the optimal limited control input of the unmanned aerial vehicle and the limited control input of the unmanned aerial vehicle under the worst condition; based on the input parameters, constructing a Hamilton-Jacobian error equation; through an error equation, constructing a summation square error which contains current information and past information and is provided with adjusting parameters; designing a weight updating law for evaluating the neural network; calculating the weight of the next iteration according to a weight updating law; and solving an error between two adjacent iteration weights, comparing the error with a preset threshold value, if the error is smaller than the preset threshold value, stopping iteration, and outputting a solution approaching the optimal game consistency control problem of the distributed unmanned aerial vehicle system. According to the invention, the cooperation efficiency between the unmanned aerial vehicles can be improved.

The invention relates to the technical field of electromagnetic scattering characteristic analysis and modeling, in particular to a scattering center establishing and correcting method based on a geometric model and reference data. The method comprises the following steps: inputting a target geometric or grid file, and extracting geometric features of a plane, an edge and a curved surface to construct an initial scattering center model; inputting electromagnetic scattering reference data of the same target, solving a scattering center correction coefficient by adopting a pseudo-inverse or Tikhonov regularization method, and optimizing an initial model; and outputting an optimized scattering center model with angle and frequency expansibility. According to the method, the electromagnetic scattering characteristics of metal and coating type complex structure targets in single-station, double-station and full-polarization scenes can be uniformly represented, and the problems of insufficient model precision, incomplete types and high data dependence degree in the prior art are solved; the method achieves the excellent effects that the RCS root-mean-square error is smaller than 2dB and the high-resolution imaging structure similarity exceeds 90% in typical target modeling, and is suitable for the fields of target characteristic simulation and characteristic extraction and recognition.

The invention discloses a power distribution network operation state analysis method and system, and relates to the field of power distribution network operation and maintaining.The key points of the technical scheme are that a graph structure is constructed according to operation data, and a feature matrix of the graph structure is extracted; constructing a graph neural network, training the graph neural network in a two-stage training mode by taking the feature matrix as input and taking the topological connection state and the node electrical quantity predicted value as output, and when the maximum training frequency is reached, obtaining the topological connection state and the node electrical quantity predicted value. Outputting an operation state analysis model capable of predicting the communication state of a power distribution network branch and the node electrical quantity capability; wherein the first-stage training mode adopts self-supervised pre-training to train the graph neural network, and the second-stage training mode is training of a weighted loss function of mean square error loss and square error loss of a power flow equation residual error; and collecting current operation data of the target power distribution network, inputting the current operation data into the operation state analysis model, and analyzing an analysis result of the target power distribution network.

The invention discloses a strain sensor layout optimization method based on proxy model assistance, and the method comprises the steps: carrying out the finite element simulation of underwater equipment, and constructing a simulation strain field data set of the surface of the underwater equipment; dividing the underwater equipment into different candidate areas, wherein candidate measurement points are distributed in each candidate area; converting a strain sensor layout problem into an optimization problem of determining a candidate measurement point from each candidate region, thereby constructing a design space of the optimization problem; modeling is carried out on the optimization problem; in the optimization model, using a root-mean-square error between a simulation strain field and an interpolation strain field obtained by interpolation reconstruction as a real fitness function; and solving the optimization problem based on an SO-I algorithm to obtain an optimal strain sensor layout scheme. According to the method, the problem of layout optimization of the complex-structure strain sensors of underwater equipment is solved, and maximization of a coverage area with the minimum number of strain sensors is realized.

The invention discloses a digital surface model (DSM)-guided hyperspectral and LiDAR combined unmixing method, relates to the field of multi-modal image processing, and aims to solve the problems of end member confusion and insufficient space structure maintenance caused by spectrum similarity in hyperspectral unmixing. According to the method, a hyperspectral image and LiDAR data of the same area are obtained, a LiDAR elevation map is expanded into a multiband profile through an attribute configuration file method, and a digital surface model (DSM) is generated. Constructing a spectrum and space double-branch auto-encoder, respectively extracting spectrum and space features and carrying out fusion mapping, obtaining an abundance matrix by using a normalized exponential function, and reconstructing a hyperspectral image; a double-branch adaptive mixed channel attention mechanism is designed in a spectrum branch, and a space attention mechanism is introduced in a space branch. In the training process, a self-defined loss function is formed by combining DSM-guided structure entropy regularization, spectral angular distance and root-mean-square error, and the space continuity and boundary retention of the abundance graph are improved. The method is suitable for hyperspectral unmixing and multi-modal remote sensing fine identification under complex terrains.

The embodiment of the invention relates to a multi-target optimization method and a related device for a water-wind-light uncertainty multi-energy complementary system, and the method comprises the steps: inputting 24-hour water-wind-light output data corresponding to a target typical scene, intra-day state transition characteristics in the target typical scene, and a day-ahead power generation plan into a multi-target operation optimization model; a multi-objective evolutionary algorithm is used for solving, a non-inferior solution set uniform distribution Pareto leading edge is obtained and serves as the basis of hydropower output adjustment in the dispatching period, and the multi-objective operation optimization model aims at the maximum peak regulation capacity in the dispatching period and the minimum root-mean-square error of the output process and the load process of the water-wind-light multi-energy complementary system. According to the method, the influence caused by fluctuation in actual operation can be better handled, the risks of instability and wind and light abandoning of the water-wind-light multi-energy complementary system are reduced, the wind abandoning rate and the light abandoning rate are reduced, the wind power and photovoltaic absorption level is improved, and coordinated and efficient operation of the water-wind-light multi-energy complementary system is achieved.

The invention discloses a pressurized water reactor steam generator liquid level control method and system, and belongs to the technical field of nuclear power station control. The method comprises the steps that a liquid level set value and a measured value of the steam generator are obtained, and deviation is calculated; inputting the set value, the measured value, the deviation and the bias constant into a preset BP neural network; the network dynamically outputs three coefficients of proportion, integral and differential through forward propagation to serve as real-time parameters of a PID (Proportion Integration Differentiation) controller; the PID controller calculates and outputs a water supply flow demand value, and a water supply valve is adjusted after conversion; and calculating a root-mean-square error of liquid level control as a loss function, and updating the weight of the neural network on line by using the new liquid level deviation through a back propagation algorithm to realize adaptive optimization of PID parameters. According to the invention, the self-learning ability of the BP neural network is combined with the reliability of PID control, the control quality and stability of the liquid level of the steam generator under various power levels are significantly improved, the system can be deployed in a power plant DCS through modular packaging, and the engineering practicability is high.

The invention relates to the technical field of electric power systems, and discloses an electric power measurement data management system and method based on an intelligent terminal and an RPA, and the method comprises the steps: obtaining clock synchronization topological structure data, an original timestamp sequence and measurement data of intelligent terminals of a whole network; extracting a clock synchronization dependency relationship of the terminal based on a graph structure analysis algorithm, and generating a directed dependency graph; inputting the timestamp sequence and the dependency graph into a causal discovery algorithm, and identifying a clock error propagation path and causal intensity; constructing a graph neural network, taking an intervention effect prediction matrix as node feature input, and predicting a whole network cascade correction effect by minimizing a weighted square error between a corrected inter-node timestamp difference value and a reference difference value; according to the invention, systematic elimination of clock drift cascade influence in a power system is realized, and time sequence consistency of whole network measurement data is ensured.

The invention discloses a flood simulation verification method based on multi-source data fusion, and belongs to the technical field of flood disaster simulation and risk assessment. The method comprises the following steps of collecting at least two types of multi-source data in insurance claim settlement, Internet of Things monitoring, remote sensing images and social media, extracting real submerging information after preprocessing, and constructing a comprehensive verification data set; simulating a submerging range and water depth by using a flood model, calculating a hit rate, a fitting statistical value, a deviation score and a root-mean-square error index, allocating weights to each verification data source, performing weighted fusion to obtain a comprehensive verification index, and quantitatively evaluating simulation accuracy; model parameters are iteratively optimized based on comprehensive verification indexes, and simulation precision is improved; and finally outputting a visual result, and applying the visual result to insurance claim settlement and disaster emergency management. According to the method, the problems of single data, narrow coverage and poor timeliness of a traditional verification method are solved, high-efficiency and comprehensive verification is realized, and the reliability and the practical value of the flood model are remarkably improved.

The invention relates to a multivariate time sequence anomaly detection method. An anomaly detection model used in the method comprises a U-Net encoder, a U-Net decoder, a time sequence characteristic memory module and a space interaction perception module. Performing multi-scale time sequence feature extraction on the weight-standardized one-dimensional convolutional layer in the U-Net encoder to obtain multi-level encoding features; in a time sequence feature memory module, enhancing long-term dependence features for the multi-level coding features; inputting the memory features into a space interaction perception module, constructing a graph structure between variables through a graph attention network, and outputting space enhancement features; in a U-Net decoder, splicing the corresponding memory features with the up-sampling features of the upper layer, and obtaining reconstruction features through one-dimensional convolution fusion; and calculating a dimension-by-dimension square error sum between the original time sequence fragment and the reconstructed data to obtain an abnormal score. Compared with the prior art, the method has the advantages of high precision, strong robustness, high efficiency and the like.

The invention belongs to the technical field of image processing, and particularly relates to a real-time monitoring method for human body rehabilitation training of rehabilitation equipment, which comprises the following steps of: calculating a local motion energy factor according to the distribution condition of instantaneous speed and instantaneous acceleration in a local track section at each time point; calculating a root-mean-square error of a local ideal trajectory and a local trajectory segment to obtain local trajectory stability, combining the local ideal trajectory and the local trajectory segment with a local average speed to calculate a dynamic detail significance score for adjusting a global basic threshold value to obtain an adaptive distance threshold value, and processing an original motion trajectory sequence by applying a Douglas-Peucker algorithm to obtain an adaptive distance threshold value; and obtaining an optimized track sequence, and calculating the training stability of the patient according to a root-mean-square error between the ideal motion track sequence and the optimized track sequence to generate a visual rehabilitation report. According to the method, random noise is effectively filtered out, the motion trail is smoothed, and meanwhile pathological detail features influencing rehabilitation evaluation are reserved.

The embodiment of the invention discloses a sleeping posture recognition method and device, electronic equipment and a storage medium, and relates to the technical field of sleeping posture recognized.The method comprises the steps that a reset gate and an update gate in a gate control loop unit GRU are designed to share an activation function, the activation function is combined into a reset update gate, and a WCGRU model is obtained; combining a root-mean-square error, a mean absolute error, a recall rate and an accuracy rate to construct a comprehensive weighted quality assessment index WEM; constructing an optimization algorithm taking the WEM as a target function, and finding out optimal hyper-parameter configuration for the WCGRU model according to the target function; collecting a data set of BCG three-channel signals, inputting the data set into the WCGRU model to obtain model output, and training the WCGRU model by reducing an error between the model output and target output and updating the weight of the data set; when the error reaches a threshold value, a trained WCGRU model is obtained, and sleeping posture recognition is carried out on collected signals through the WCGRU model. The problems that in the prior art, model memory consumption is large, and high accuracy and low resource consumption efficiency cannot be considered at the same time are solved.

The invention relates to the technical field of artificial intelligence, and discloses a regional ocean current analysis method and device, and the method comprises the steps: obtaining the observation data of a regional ocean current, carrying out the standardization processing of the observation data, generating standardized ocean current data, and calculating the data point distribution density of the standardized ocean current data, so as to obtain the data point distribution density of the regional ocean current, the method comprises the steps of performing dynamic self-adaptive grid division on regional ocean current based on data point distribution density, generating a non-uniform grid structure, performing numerical interpolation calculation on the non-uniform grid structure based on standardized ocean current data, generating an initial interpolation result of the regional ocean current, performing algorithm correction on the initial interpolation result, generating a corrected interpolation result of the regional ocean current, and obtaining the corrected interpolation result of the regional ocean current. Performing root-mean-square error analysis on the corrected interpolation result to generate a final interpolation result of the regional ocean current; the method can solve the problem that the analysis conclusion deviates from reality due to the reduction capability of the limit interpolation result to the actual ocean current distribution condition and error accumulation.

The application discloses a kind of interframe image coding rate distortion optimization method and system for machine vision, it is related to image processing technical field, including steps: extracting the feature map of current video frame original image and corresponding reconstruction image under each scale;According to the feature map of original image and reconstruction image, obtain the distortion measure based on feature similarity;According to the point coordinate pixel value of original image and reconstruction image, obtain the distortion measure based on square error;By mixing respectively based on feature similarity, based on square error distortion measure, rate distortion optimization under the control of mixed distortion measure is carried out.The application can better meet the sensitivity of machine vision system to video content features by mapping video frames to feature space and extracting multi-scale features using residual deep neural network, which not only improves the coding efficiency, but also ensures that the key features of the encoded video content are preserved during machine processing, thereby improving the performance and task completion of the algorithm.

The invention provides a method for quantifying tidal non-stationarity, and belongs to the technical field of natural science research based on nonlinear data analysis. Firstly, hour-by-hour tide observation data with the observation duration not less than one month in a target sea area are obtained; separating non-tidal low-frequency and high-frequency tidal changes in the data by using an empirical mode decomposition method, and extracting a tidal component; returning the tide component through a classical harmonic analysis CHA model to obtain CHA return tide; then calculating a correlation coefficient, a mean absolute error and a root-mean-square error of the tidal component and the CHA, and respectively dividing the mean absolute error and the root-mean-square error by an average value of the observation absolute values to obtain a normalized index; finally, (1, 0, 0) is used as a reference point, (CC, NMAE, NRMSE) is used as an observation point, and the tidal non-stationarity index NS is calculated according to a two-point distance formula. The device can be widely applied to global sea areas and is suitable for sea areas of different tide types and sea areas of different tidal ranges.