353 results about "Root-mean-square deviation" 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 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 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 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 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 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 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 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 parameter identification, and provides a photovoltaic model parameter identification method and system based on an improved artificial bee colony algorithm, and the method specifically comprises the steps: obtaining a to-be-identified photovoltaic model, and constructing an optimization objective function which is based on a root-mean-square error; initializing an artificial bee colony algorithm and performing terrain complexity evaluation to obtain a terrain type, the terrain complexity evaluation being based on the population fitness difference matrix; and according to the terrain type, adaptive search is carried out to obtain a final identification result, and the adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism. According to the method, the defect of a traditional artificial bee colony algorithm facing a complex terrain area is avoided, and the accuracy and efficiency of photovoltaic model parameter identification are improved.

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.

A polarization hyperspectral target classification method, system and device based on pixel-level adaptive fusion and a medium, the method comprising: performing spectral angle, spectral information divergence, root-mean-square error and red-edge spectral difference index calculation on a non-polarization hyperspectrum of each pixel and each polarization hyperspectrum to obtain m classes of similarity indexes under k polarization channels; converting the pixel-level similarity images into pixel-level similarity images, and stacking the pixel-level similarity images according to feature dimensions to form a pixel-level multi-dimensional similarity feature stack; performing standardization processing on each pixel, selecting K components with the highest characteristic values by adopting a pixel-level Top-K adaptive weighting strategy, and calculating weights according to scale coefficients to generate a single-channel weight map; performing multiplicative modulation processing on the non-polarization hyperspectral data to obtain fused polarization hyperspectral data; inputting the data into a classification recognizer for classification, and outputting a classification result; the system, the equipment and the medium are used for implementing the method. According to the invention, the classification precision and the anti-interference capability are improved.

The invention relates to the technical field of photogrammetry, in particular to an earthwork measuring and calculating system based on an unmanned aerial vehicle surveying and mapping technology, which comprises a BIM (Building Information Modeling) route partitioning module, a point cloud quality evaluation module, a digital earth surface model generation module and an earth volume measuring and calculating module. According to the method, by analyzing geometric parameters such as gradient and curvature of a designed curved surface, intelligently partitioning and optimizing an unmanned aerial vehicle surveying and mapping route, and executing independent density clustering on point cloud data streams according to route partitions, dynamic evaluation and marking of spatial data quality are realized, and vegetation point clouds are removed in combination with image color information; the method comprises the following steps: correcting data by using an abnormal mark of quality evaluation, generating an accurate digital earth surface model, finally carrying out elevation deviation comparison on the accurate digital earth surface model and a designed curved surface, finely calculating the earth volume through a regular grid, and carrying out statistical analysis by using a root mean square error, thereby improving the accuracy and reliability of a measurement result.

The invention discloses a tunnel ventilation system and a control method thereof, and relates to the technical field of tunnel ventilation energy-saving control. According to the method, traffic data, environment data and equipment starting data in a tunnel are preprocessed and divided into data sets, a traffic prediction model based on LSTM and an environment prediction model based on full connection are constructed, and training is carried out through a root-mean-square error and an average absolute percentage error; and sequentially predicting traffic and environment data by using the model, and solving an optimal ventilation equipment combination through a sequential quadratic programming algorithm by taking energy consumption minimization as a target and environmental standard reaching as a constraint, and adjusting operation. The method has the advantages that advanced regulation and control are achieved through the two-stage prediction model, pollutants are prevented from exceeding the standard, and the air quality is guaranteed; dynamic training, multi-parameter optimization and a fault tolerance mechanism are combined, energy conservation and environment regulation and control are accurately balanced, meanwhile, the operation reliability and the intelligent level of the system are improved, and manual intervention is reduced.

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 a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

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

The invention 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 relates to the technical field of agricultural remote sensing and precise irrigation, in particular to a method and equipment for inverting leaf water potential of grapes before dawn based on multispectral remote sensing, and the method comprises the steps: measuring leaf water potential of representative grape plants in each planting test plot before dawn in different growth periods, and collecting canopy multispectral images in the noon period; preprocessing the remote sensing original image data; calculating a vegetation index; on the basis of the model fitting data set and the training feature set, constructing basic prediction models in different growth periods by adopting a unary linear regression algorithm, a multiple linear regression algorithm and a partial least square regression algorithm respectively; determining a PLSR inversion model according to the decision coefficient and the root-mean-square error; and carrying out pre-dawn leaf water potential prediction through the PLSR inversion model. Through the method, high-precision inversion of the water potential of the leaves before dawn based on the noon canopy multispectral data can be realized, and reliable technical support is provided for accurate management of vineyard moisture and intelligent irrigation.

The invention provides a method for performing parameter fitting on spectroscopic measurement data, and a wafer data processing method and equipment. The method comprises the following steps: acquiring target spectroscopic measurement data; performing curve fitting based on the target spectroscopic measurement data to obtain a first fitting equation; calculating a residual error between the first fitting equation and the target spectroscopic measurement data; judging whether a trough exists in the residual error or not; if a trough exists in the residual error, adding a first preset peak to the first fitting equation to obtain a new first fitting equation, and returning to carry out iteration to obtain a candidate fitting equation; calculating a first root mean square error of the candidate fitting equation and the target spectroscopic measurement data; judging whether the first root mean square error meets a preset convergence condition or not; and if the first root mean square error does not meet the preset convergence condition, adding a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation, returning to carry out iteration, and taking the corresponding candidate fitting equation as a target fitting equation of the target spectroscopic measurement data based on the preset convergence condition.

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 lake water quality prediction method and system based on a hybrid neural network, and a computer readable storage medium, and belongs to the field of environmental science engineering and deep learning. The method comprises the following steps: screening original water quality data, removing abnormal values, performing linear interpolation, dividing a training set and a test set, decomposing a sequence by using VMD, optimizing VMD parameters by using PSO, reconstructing a new sequence with noise removed, and finally performing prediction by using LSTM-KAN. Through verification of total phosphorus concentration data of four sections of the Dian Lake, comparison with LSTM, VMD-LSTM, VMD-LSTM-KAN and LSTM-KAN models is carried out, and a correlation coefficient (), a mean absolute error (MAE) and a root-mean-square error (RMSE) are selected to evaluate precision. The result shows that the PVLK model has the best performance in single-step and multi-step prediction, the total phosphorus concentration prediction of each section can be kept at 0.75 in 10-step prediction with the step length of 4 hours, the applicability to time sequence data containing abnormal values and high sampling frequency is good, and efficient prediction of lake water quality is effectively promoted.

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 grasp point pose calculation method based on a geometric clustering algorithm without deep learning and a GPU (Graphics Processing Unit) and application of the grasp point pose calculation method. The grabbing point pose calculation method comprises the steps that 100, three-dimensional point cloud data of a to-be-grabbed object are obtained through an industrial-grade structured light three-dimensional camera, a geometric clustering algorithm is adopted for screening the three-dimensional point cloud data, and a unique target point cloud cluster conforming to the target form and size is extracted; step 200, performing rough matching on the target point cloud cluster and the template point cloud through the FPFH feature vector and RANSAC to obtain a rough matching result; step 300, performing fine matching on the target point cloud cluster 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 invention provides a flood inundation situation intelligent perception and lightweight analysis model construction method, and relates to the technical field of hydrological monitoring and flood control and disaster mitigation. Unmanned aerial vehicle LiDAR point cloud data and satellite multispectral image data are acquired, a differential manifold topographic representation model is constructed, the earth surface is regarded as a Riemannian manifold, and complex topographic features are accurately described; a lightweight submerging calculation model is constructed based on a manifold diffusion theory, and a Laplace-Beltrami operator and a multi-scale solving strategy are adopted to realize rapid and accurate prediction of a flood submerging range and water depth; through a multi-terminal early warning information pushing mechanism, full-process intelligent support is provided, the simulation time is shortened to be within 2 hours from traditional 24 hours, the submerging range prediction goodness of fit reaches 89.7%, the water depth prediction root-mean-square error is controlled to be 0.35 m, and the flow velocity prediction error in a gradient sudden change area is reduced by 12%.

The invention belongs to the field of detection of release substances in tea leaves, and particularly relates to a method for detecting release kinetics of tea polyphenol in tea soup based on a near infrared spectrum. The method comprises the following steps: preparing tea soup samples at different time points through a gradient sampling method, determining the content of tea polyphenol as a reference value by adopting a national standard ferrous tartrate colorimetric method, and collecting spectral transmission data by utilizing a near infrared spectrometer. Preprocessing the spectrum data, constructing a machine learning model, and establishing a mapping relation between the spectrum variable and the tea polyphenol content; and evaluating model performance through a decision coefficient and a root-mean-square error, and screening an optimal prediction model. And based on a high-precision result predicted by the optimized model SVM, carrying out dynamic fitting analysis, and determining that the optimal dynamic model of tea polyphenol release is a Higuchi model. According to the method, non-invasive real-time monitoring of the tea polyphenol content is achieved, the accuracy rate of the prediction model reaches 0.99 or above, and rapid and accurate technical support is provided for tea quality evaluation and brewing process optimization.

The invention relates to the field of ecological hydrology, and discloses an arid and semi-arid region plant ecological water consumption calculation method, which comprises the following steps: acquiring meteorological, soil and vegetation phenological data; constructing a root system depth dynamic evolution model, and updating the maximum depth of the root system day by day; determining the depth weight of the effective moisture extraction layer based on the dynamic root system distribution; calculating the weighted average effective soil water content; and outputting daily-scale ecological water consumption in combination with the corrected transpiration scale model. By coupling meteorological driving, soil moisture stress and a dynamic regulation and control mechanism of a plant growth stage on a root system, the water consumption inversion precision is remarkably improved, the root-mean-square error is reduced by more than 25% through actual measurement verification, and a high-precision quantification tool is provided for ecological water demand evaluation and water resource management.