780 results about "Curve fitting" patented technology

Disclosed is a complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, including: generating a deformation interferogram by using an InSAR extraction technology combining persistent scatterer (PS) points and distributed scatter (DS) points and relying on a shuttle radar topography mission digital elevation model (SRTM DEM) image; constructing a Delaunay triangulation network and an annular zone, obtaining a time series deformation data of PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge; decomposing a time series by an SAR-self-attention model, decomposing InSAR time series data into a trend component and a seasonal component, and interpreting and analyzing deformation of the sea-crossing bridge; and describing time series dynamics and a seasonal pattern by comparing a curve fitting method with a seasonal and trend decomposition method.

The present invention relates to the technical field of power transmission conductor galloping, and in particular to a method for solving a conductor galloping trajectory equation of a high-voltage transmission line. The method comprises the following steps: establishing a coordinate system; analyzing a nonlinear motion of any point on a conductor; solving a conductor galloping trajectory equation; solving a torsion angle and an acceleration under an arc length constraint; and carrying out convex optimization on an analytic expression of the torsion angle. According to the present invention, an inter-station RTK differential mode is used, and Beidou observation data analyzed by a monitoring station and Beidou observation data analyzed by a reference station are combined, thereby improving the accuracy of a monitoring result; a finite element analysis method is used to carry out discretization processing on the problem of conductor motion integral, so that conductor displacement can be effectively restored to obtain a good curve fitting effect; and a calculation method for the torsion angle under the limitation of the arc length of the conductor is provided, overcoming the error of calculating a trajectory integral by using the acceleration, and an optimized calculation method is provided.

The invention relates to the technical field of gas detection, and discloses a gas detection method and device based on an MEMS gas sensor array. The method comprises the following steps: performing grouping and detection data acquisition on a plurality of MEMS gas sensors according to target gas types to obtain an original detection data set; performing curve fitting and feature extraction to obtain a feature parameter set, and constructing a sensor array feature model; correcting the original detection data set to obtain standardized response data; extracting a plurality of response characteristic quantities and inputting the response characteristic quantities into an unknown input observer for residual calculation and signal abnormal point identification to obtain abnormal response data; performing piecewise linear regression and dynamic calibration to obtain a target detection data set; and inputting the target detection data set into the sensor network model for array deployment optimization to obtain an array deployment optimization scheme. The adaptive deployment optimization of the sensor array is realized, and the space coverage efficiency of the detection network is improved.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention relates to the technical field of network communication, and discloses a network communication fault early warning system based on machine learning, which comprises a machine learning module, and the module uses historical operation data to train a model so as to predict the change trend of future communication parameters. The data monitoring module is responsible for monitoring communication parameters of network equipment in real time and performing data cleaning. And the curve fitting module is used for drawing the real-time data and the predicted data into a curve and carrying out visual comparison. And the residual analysis module calculates the residual of the two curves and marks abnormal points. And finally, the early warning notification module judges potential fault risks according to the abnormal points and the evaluation rules and sends early warning information. The system does not depend on a fixed threshold value, can adapt to dynamic changes of a network environment, accurately captures subtle changes and trends of communication parameters, and effectively avoids false alarms and missing alarms. Through the application of the system, the accuracy and timeliness of network communication fault early warning can be remarkably improved, and economic loss and inconvenience are reduced.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention relates to the technical field of construction monitoring, in particular to a tunnel construction real-time monitoring system based on big data. Comprising a server, a real-time monitoring module and a safety early warning module, comprehensive analysis is carried out by fusing multiple parameters such as stress, temperature, tunnel depth and accumulation of micro-seismic events instead of single-dimensional data, and the accuracy of rockburst risk prediction is improved through a multi-dimensional fusion analysis mode; meanwhile, the observation time window length of multi-dimensional data analysis is dynamically adjusted according to the rockburst risk of each area, and data support is provided for safety in the tunneling process; by performing curve fitting on the rockburst risk index change in the observation time window, the risk change trend of each region can be dynamically monitored, the region with the rising risk can be accurately identified, early warning can be given in advance, and refuge route planning can be performed, so that rapid refuge under the rockburst risk is realized, and the safety of tunnel constructors is guaranteed to the greatest extent.

The invention relates to the field of industrial vision, in particular to an operator behavior analysis method and system based on trajectory analysis. The method comprises the following steps: acquiring an action video of an operator, performing action feature extraction on a picture of the action video, performing fitting processing on an action feature point set, and setting a paragraph step length, the action track curve is divided into paragraphs according to the paragraph step length, the space distance between action track points in the action track paragraphs and standard track points in the standard operation track is calculated, the dynamic parameter difference is analyzed according to the action track paragraphs and the standard track paragraphs matched with the action track paragraphs, and a behavior monitoring result is obtained according to the dynamic parameter difference. The system comprises a video acquisition device, a video identification model, a curve fitting module, a paragraph matching module and an anomaly detection module. The method is lower in cost, cannot be interfered by an industrial field environment, and is wider in applicability. And the track is dynamically divided into track paragraphs, so that the applicability of the method is improved.

The invention provides a light stripe center extraction method and system and a medium. The method comprises the following steps: judging the pixel state of each pixel point column by column according to gray values of pixel points in an image containing light strips, and determining an inflection point of a current column and a first end point of a peak interval where the inflection point is located; based on the gray value of the first end point of the peak interval, calculating the absolute value of the difference between the gray value of each pixel point of the inflection point of the current column in the rear row and the gray value of the first end point, and judging a second end point of the peak interval; and constructing a curve fitting model based on the gray values and the row coordinates of the pixel points in the peak interval, obtaining stagnation points, which are greater than zero, of a fitting curve, and selecting the stagnation point closest to the inflection point as a light stripe center pixel point in the peak interval. The dynamic peak interval and cubic curve fitting are combined for use, the accuracy of the finally extracted light stripe center is higher than the calculation results of a gray centroid algorithm, Gaussian fitting and Rayleigh fitting, and the method has good accuracy and robustness.

The invention belongs to the technical field of ocean temperature data identification, and discloses a thermocline identification method and system based on hyperbolic tangent function fitting. The method comprises the steps of data preparation and preprocessing; selecting a section range; fitting a curve; and determining a thermocline upper bound and a thermocline lower bound through a rolling function. According to the method, the hyperbolic tangent function is used for fitting the S-shaped characteristic curve of the temperature-depth profile, and the structure of the ocean thermocline is accurately described. Compared with a traditional Sigmoid function, the hyperbolic tangent function is better in performance when fitting a steep change area, and rapid temperature change of a thermocline central area can be caught more finely. According to the method, fitting parameters (amplitude, steep degree, central position and vertical offset) are optimized by adopting a trust region reflection algorithm, so that a fitting result is ensured to have higher precision and robustness, and the method is suitable for complex ocean profile data.

The invention relates to the technical field of data statistics, in particular to an intelligent water meter missing data complementation method and system.The intelligent water meter missing data complementation method includes the steps that initial segmentation points of a water consumption data curve are determined by analyzing the slope change condition of the water consumption data curve, and the variation index of the initial segmentation points is further analyzed to determine segmentation points of the water consumption data curve; performing clustering analysis on all segmentation points in the water consumption data curve of the user in all days, and constructing a standard water consumption data curve of the user based on a clustering result; and respectively using a Bezier curve fitting method and a regression analysis method, and combining the change trend of the standard water consumption data curve to carry out data completion on the missing data segment. Through the method and the system provided by the invention, complementation data which conform to the overall trend and are in smooth transition can be obtained, the integrity and the accuracy of the data are remarkably improved, and a more reliable data basis is provided for subsequent water resource management and decision support.

The invention relates to the technical field of cable layout design, in particular to a branch cable automatic layout method of a given topological structure based on a dynamic programming algorithm. Comprising the following steps: determining an optimization target of automatic cable layout design according to engineering requirements of wiring, giving a three-dimensional model and a wiring harness electrical definition of a product to be wired, constructing a path search map on sampling points on the surface of the model, and executing an automatic cable layout algorithm based on dynamic planning in the path search map, solving a cable path and the position of a branch point which meet the constraint condition and enable the wiring objective function to be optimal, and finally obtaining a geometric model of the cable through curve fitting and profile endowing. The method has the advantages of being simple in algorithm and high in solving efficiency, the accuracy of wiring and optimal path selection can be improved, complex connection requirements can be met, the automatic layout efficiency and quality of branch cables can be effectively improved, and the method has high practical application potential in cable layout design of complex mechanical and electrical products.

The invention discloses a machine vision-based wood surface defect online detection method and system. The method comprises the following steps of: acquiring surface image data of wood, performing noise reduction on the data by utilizing adaptive median filtering, eliminating surface texture interference based on curve fitting, and performing multi-scale feature extraction on initial wood surface image data by utilizing an improved ResNet-18 network; and through weighted fusion of multispectral channels, extracting difference features of the wood surface, calculating an adaptive threshold segmentation defect area based on a gray average and a standard deviation of initial wood surface image data, performing optimization by using a Canny operator and morphological operation, and identifying segmented image data and a three-dimensional feature matrix by using a YOLOv8s model. And classifying and positioning the defects. According to the method, rapid and accurate classification and positioning of various wood defects are achieved, and compared with a traditional detection method, the detection efficiency and the detection accuracy are improved.

The invention relates to the technical field of data processing, in particular to a circuit breaker operation state monitoring method and system based on big data, and the method comprises the steps: obtaining an operation state vector; obtaining a gain influence at each acquisition moment by using a filtering algorithm, and determining a fluctuation characteristic value based on a curve fitting result in a data space constructed by the gain influence; determining the fluctuation degree of the monitoring data at the acquisition moment according to the influence degree of the data fluctuation at the data abnormal moment on the monitoring data at the acquisition moment and the fluctuation characteristic value, and completing the updating of the Kalman gain matrix at the acquisition moment; and obtaining a prediction result of the operation state vector based on the updated Kalman gain matrix by using an extended Kalman filtering algorithm, and obtaining a monitoring result of the operation state of the circuit breaker based on the prediction result. According to the invention, through more accurately predicting the operation state monitoring data of the circuit breaker, the accuracy of monitoring the operation state change of the circuit breaker is improved.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

The invention relates to the technical field of building information modeling, in particular to a historical building model construction method and system based on BIM, and the method comprises the following steps: obtaining building component surface spectral reflectivity data, setting a detection range, extracting spectral features, integrating to form time sequence data, and obtaining component spectral feature parameters. According to the invention, through the detailed analysis of the spectral reflectivity, the scheme accurately evaluates the aging of the component, avoids subjective judgment errors, improves the accuracy, finely quantifies the aging process by using the mean square error and curve fitting technology, reduces the evaluation errors, comprehensively evaluates the stability of the building and optimizes the safety prediction of the structure by combining dynamic mechanical analysis and incorporating environmental factors. The component matching precision is improved through geometric adaptation analysis, errors are reduced, the reconstruction effect is optimized, the geometric and mechanical accuracy of a building model is enhanced, and the practicability and reliability of the model are improved through systematic data integration.

The invention belongs to the field of distributed optical fiber sensing, and provides a Brillouin temperature extraction method and system based on a self-attention recurrent neural network, and the method comprises the steps: collecting real Brillouin gain spectrum data through a Brillouin optical time domain analysis system which is built in advance, and carrying out the preprocessing of the real Brillouin gain spectrum data; based on the preprocessed Brillouin gain spectrum data, utilizing the trained self-attention recurrent neural network model to obtain a corresponding Brillouin temperature; a meta-learning algorithm is introduced in the process of a self-attention recurrent neural network model, so that the network model can share initialization parameters among a plurality of tasks, the parameters are optimized through meta-training, the network model can quickly adapt to a new task, training from zero is not needed in the face of multi-task learning or the new task, and the training efficiency is improved. The model can be quickly adjusted through a small amount of gradient updating, the training time is shortened, the performance is improved, and the problems of long time, poor real-time performance, poor adaptability and the like of curve fitting are solved.

The invention discloses a low-temperature environment LNG (Liquefied Natural Gas) metering sensor drift compensation method and system, and particularly relates to the technical field of sensor drift compensation. Output data of a sensor are collected and processed, and a temperature compensation model of the sensor is established; in the working temperature range, digital filtering processing is carried out on data of displacement change of each measuring range point, and then analysis and mining are carried out; a data longitudinal interval in a temperature range is refined by using a table look-up method, so that the data interval is reduced. And calculating the G value according to the residual error obtained by the table look-up method and the numerical value change condition in each temperature section. If the G value is greater than a preset threshold value of the system, selecting a proper fitting method, such as a least square method or Legendre orthogonal polynomial fitting, according to the distribution of the G value and the data interval when curve fitting is used, so as to further optimize the temperature compensation model; and if the G value is smaller than the system preset threshold value, no subsequent operation is carried out.

The invention belongs to the technical field of path planning, and particularly relates to a three-dimensional dynamic path planning method based on high-density radar point clouds, which comprises the following steps: collecting three-dimensional point cloud data streams of a continuous time sequence, removing ground points after noise filtering, and then dividing the space in a scanning range into voxels with unequal side lengths to form a map set; establishing a multi-dimensional evaluation function about the trafficability of each voxel, screening passable areas, and forming a three-dimensional passing voxel map; setting a cost function, carrying out spatial path search, extracting path key points, and carrying out optimization and curve fitting on a path obtained by search to form a trajectory curve; selecting control input, issuing the control input to an execution unit of the intelligent agent, and performing dynamic tracking control on the trajectory curve; and when an abnormal state occurs, performing path reconstruction by adopting an incremental optimization strategy, and generating a new path until the intelligent agent arrives at the target point. The autonomous navigation task can be efficiently, safely and stably completed in a complex dynamic environment.

The invention relates to the technical field of pressure measurement, in particular to a sensor intelligent compensation method, which comprises the following steps of: firstly, acquiring pressure values of a pressure sensor at different temperatures, and obtaining a pressure curve at each temperature through curve fitting; and then determining local similarity according to the area difference enclosed by the pressure curve at each temperature and the adjacent pressure curve, thereby calculating an initial penalty parameter of the SVR model, and then correcting the initial penalty parameter by analyzing the fluctuation degree of the pressure value at each temperature to obtain a final penalty parameter. And constructing and training an SVR model by using the corrected parameters, and placing the trained model in a compensation module to realize real-time compensation of a pressure value acquired by the pressure sensor. According to the method, the accuracy, the stability and the strain capacity of the SVR model are improved, the local optimal defect is avoided, the measurement error caused by the temperature is accurately compensated, and the measurement precision of the pressure sensor in different temperature scenes is enhanced.

The invention discloses an unmanned aerial vehicle transformer substation inspection path planning method and device, equipment and a medium. The method comprises the following steps: firstly, generating a grid map; dividing the substation inspection space into a plurality of sub-regions; establishing an electromagnetic exposure intensity model, and setting an electromagnetic exposure intensity safety threshold; randomly sampling a plurality of nodes in each divided sub-region, constructing a random geometric graph, and generating an initial inspection path by adopting an RRT algorithm; optimizing the generated initial inspection path by using a path pruning optimization algorithm and a gradient descent convex optimization algorithm, and smoothing the path into a continuous curve by using a B-spline curve fitting technology to obtain a final optimized inspection path; according to the invention, through an overall strategy of first region division and then path planning, random geometric graph tree building, path pruning optimization, gradient curve smoothing and key region guiding sampling mechanisms are fused, and the efficient, stable and high-environment-adaptability unmanned aerial vehicle three-dimensional path planning method for the substation complex scene is realized.

The invention relates to a part outer contour-oriented abnormal bulge detection method and system. The method comprises the following steps of: acquiring a part point cloud, acquiring upper and lower surface point clouds of a part from the part point cloud, and extracting boundary points from the upper and lower surface point clouds by using a tracking boundary method to obtain a boundary point cloud; performing quadratic curve fitting on the boundary point cloud to obtain a segmentation result of the boundary point cloud; using a ray method for the boundary point cloud and the part point cloud to obtain an outer contour point cloud and a convex point set, and segmenting the outer contour point set and the convex point set according to the fitted quadratic curve parameter and the segmentation result of the boundary point cloud to obtain a point cloud set; projecting the point cloud set to a plane, preliminarily screening salient points according to the area and density, and expanding the salient points based on an entropy function to obtain all the salient points; according to the method, boundary tracking and quadratic curve fitting are combined, so that the problem that an abnormal bulge and a normal curvature area in a complex contour are difficult to distinguish is effectively solved, and the detection accuracy is improved.

The invention relates to the technical field of bridge monitoring, and discloses a real-time bridge monitoring and evaluating method based on beam end displacement and rotation angle, which comprises the following steps: acquiring beam end displacement and rotation angle data acquired by a displacement sensor and a rotation angle sensor in a continuous monitoring period, and filtering and normalizing the acquired data to obtain processed data; constructing a discrete curve and performing polynomial curve fitting by using a settlement trajectory multi-cycle residual error clustering identification method, calculating a single-cycle residual error sequence, determining an edge fluctuation trend point by comparing adjacent cycle residual error values, and performing anomaly marking; inputting the marked abnormal data into an abnormal recognition model, predicting the risk level of the bridge structure through the abnormal recognition model, and outputting an abnormal recognition result; outputting a comprehensive structure response diagnosis report according to the abnormity identification result; the precision of monitoring and evaluation is improved, and safe operation of the bridge is guaranteed.

The invention relates to the field of data processing, in particular to a spacer vibration endurance capacity detection method, which comprises the following steps of: acquiring a historical amplitude value of a wire and a stress value of a connecting part of the wire corresponding to the spacer based on a time sequence, and acquiring the change credibility between the amplitude value and the stress value at each moment in the history; performing curve fitting on the data points of the stress values at all the moments according to a least square method so as to calculate the deviation degree of the stress values at all the moments; an isolated forest algorithm is adopted, the change credibility and the deviation degree are combined, the priority of break points is obtained, the optimal break point is determined, an isolated tree is efficiently constructed, abnormal stress data are accurately recognized, the vibration endurance capacity of the spacer is calculated according to the abnormal stress data, and the endurance capacity is compared with a preset threshold value to evaluate the state of the spacer. By improving the segmentation efficiency and accuracy of the model, safe and stable operation of the power system is ensured.

The invention discloses a welding seam self-adaption B spline curve fitting method for a 3D dense out-of-order point set, and aims at solving the problem of complex curved surface welding seam track generation in robot welding and breaking through the limitation of the prior art through a three-stage self-adaption fitting mechanism. The method comprises the following steps: firstly, self-adaptive screening and projection sorting are carried out by utilizing a straight line fitting error, so that the disordering problem of discrete points of a quadratic curved surface intersection line is effectively solved, and the strong dependence of a traditional method on a CAD model is overcome; secondly, in combination with a region growing method guided by an end tangent vector, robust conversion from an out-of-order point set to an ordered control point is realized through dynamic neighborhood search and projection value sorting, and path continuity of an open / closed curve is guaranteed; and finally, mean sampling quadratic fitting is adopted, control point distribution is optimized while noise is restrained, and a smooth B-spline track meeting the movement requirement of the welding gun is generated. According to the method, direct fitting of the 3D dense out-of-order point set under the model-free condition is achieved for the first time, and the generation efficiency and adaptability of the irregular welding seam track are remarkably improved.

The invention relates to the technical field of hydrological data analysis, in particular to a similar flood-based water level flow relationship intelligent extension method. The method comprises the following steps: acquiring actually measured water level and flow data and performing curve fitting; carrying out three-property test on the fitting curve, and if the fitting curve does not meet the requirement, adopting multi-point sectional type fitting to obtain an output curve; screening a similar curve based on a historical flood graphic library, and carrying out extension processing to form an extended water level flow relation curve; and the real-time water level is subjected to flow pushing by utilizing the extension curve, so that accurate prediction and dynamic extension of the water level and flow relationship are realized. According to the method, the reliability of the curve is guaranteed through actually measured water level flow data fitting and segmented optimization in combination with three-property test, and intelligent complementation and real-time correction under the condition of insufficient data are realized by utilizing a historical flood similar curve extension and dynamic matching mechanism, so that the precision, continuity and adaptability of the water level flow relation curve are remarkably improved; and the accuracy and practicability of the hydrological plug flow process are ensured.

The invention discloses a detection method, device and equipment for wafer edge polishing. The detection method comprises the following steps: positioning a wafer to be detected; obtaining plane part morphology data and curved surface morphology data of the positioned wafer to be detected; performing curve fitting on the plane part morphology data and the curved surface part morphology data to obtain a first curve and a second curve; performing feature extraction and feature point matching on the curve; calculating a homography transformation matrix between the two curves based on the matched feature point pairs; determining an overlapping area of the first curve and the second curve; determining an optimal stitching point; and butting the first curve and the second curve according to the optimal stitching point, and taking the butted curve as an edge detection curve of the wafer to be detected. Through the integrated design of the functions of centering positioning, height compensation, curve fitting, morphology observation and the like of the wafer, the data synchronism of multi-parameter measurement is ensured, and the accuracy and reliability of a measurement result are enhanced.

The invention discloses an industrial silicon smelting process prediction method and system based on data analysis, and relates to the technical field of industrial silicon smelting management and control. The physical structure of the smelting furnace is analyzed, thermocouple nodes are positioned in the virtual three-dimensional model to serve as temperature mapping points, and a spatial topological relation is established; historical smelting data are collected, temperature monitoring parameters and process operation parameters are aligned according to time nodes, and a time-space associated smelting parameter set is constructed; generating a three-dimensional temperature performance state based on the temperature mapping point data, and classifying the temperature performance state through temperature change curve fitting and heat conduction correlation analysis; the current process parameters are matched with the reference historical process operation parameter sequence library in real time, and a three-dimensional temperature field prediction result corresponding to the optimal historical parameter group is output; according to the method, the problems of low efficiency and large error of traditional manual analysis are effectively solved, and reliable technical support is provided for intelligent control of industrial silicon smelting.

The invention relates to the field of NURBS curve fitting airfoil profile data, and particularly discloses a node vector dynamic distribution strategy based on curvature self-adaption, which comprises the following steps: firstly preprocessing airfoil profile data, calculating distance accumulation between data points through a chord length accumulation method to generate a parameter t, estimating a parameter # imgabs1 # on an airfoil profile curve C (t) corresponding to a data point # imgabs0 #, then determining a node vector structure, and finally determining the node vector structure. Repeating the head and tail node vectors for p + 1 times for starting and ending, and dynamically allocating middle internal nodes according to curvature; thirdly, calculating the curvature of each data point, and dynamically adjusting node distribution; and finally, detecting a high-curvature area, performing quantile division to merge node values of the high-curvature area and the low-curvature area, and inserting a middle section in an ascending order to form a non-decreasing node vector U. According to the method, a curvature-adaptive dynamic allocation strategy is introduced into node vector generation, so that the fitting precision is remarkably improved.

The invention discloses an accurate monitoring system for linear deformation of a loess tunnel portal side upward slope, and relates to the technical field of geotechnical engineering and surveying and mapping. The system comprises a sensing network layer, a data acquisition and transmission layer, a cloud platform and data analysis layer and an application display layer, displacement data of discrete monitoring points are reconstructed into continuous full-field displacement and strain distribution through Kriging interpolation, then a displacement vector field is regarded as a whole to be analyzed, the maximum shear strain field of the displacement vector field is calculated, an abnormal area with the deformation gradient abrupt change is accurately positioned, and the deformation gradient abrupt change accuracy is improved. The abnormal point sets are merged into independent clusters according to spatial proximity and deformation consistency, and the geometric trajectory of a potential slip crack surface or crack is accurately outlined through curve fitting, so that the technical path fundamentally changes the limitation of point-mode monitoring, and can automatically identify a hidden linear failure trend which is difficult to find by naked eyes in a non-contact manner, so as to improve the detection accuracy of the hidden linear failure trend. And refined data support is provided for slope stability evaluation.