651 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 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 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 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 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 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 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 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 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.

The invention discloses a curve fitting method and device and a computer readable storage medium, and the method comprises the steps: segmenting a to-be-fitted curve into a plurality of curve segments in a staggered manner according to a preset window length, and enabling the first points of two adjacent curve segments to be adjacent; fitting each curve segment, and calculating to obtain a functional equation and an error value of each curve segment; and according to the functional equation and the error value, selecting a set composed of a group of curve segments from the plurality of curve segments to combine and generate a final curve. According to the curve fitting method based on segmented quadratic superposition correction, any curve is fitted, noise in a machining track can be effectively eliminated, interpolation can be carried out, the machining yield is increased, and the sampling cost is reduced.

The invention discloses a containment failure probability curve fitting method and system based on a random forest, and relates to the technical field of nuclear power safety. Comprising the following steps: acquiring key parameters influencing ultimate bearing capacity of a containment, determining a distribution interval of the key parameters, and establishing a key parameter table; performing finite element calculation on the basis of the key parameter table, obtaining ultimate bearing capacity of the containment vessel under different parameter combinations and temperature loads, and establishing a database; performing normalization preprocessing on the database to form a training data set; establishing a random forest model and performing regression model training by using the training data set; inputting actual design parameters of the containment and the temperature required to be evaluated into the trained model to obtain an ultimate bearing capacity predicted value of the containment; and fitting of a failure probability curve is carried out based on the central limit theorem. According to the method, complex probabilistic safety analysis is converted into a standardized calculation task, and the implementation threshold of nuclear power plant design verification and safety review is greatly reduced.

The present application relates to a blood glucose concentration analysis method, apparatus, device, storage medium, and computer program product. The method comprises: obtaining intensity sets corresponding to multiple photoplethysmographic wave sets of a target subject within a target time period; the intensity sets include average AC intensity and average DC intensity; performing curve fitting on the multiple intensity sets to obtain fitting coefficients and fitting bias; comparing the fitting bias with the bias range corresponding to the target subject, and if it is determined that the fitting bias is not within the bias range, adjusting the fitting bias to obtain an adjusted fitting bias; based on the adjusted fitting bias, performing curve fitting on the multiple intensity sets to obtain adjusted fitting coefficients, and using the adjusted fitting coefficients as the target subject's blood glucose concentration during the target time period. This method can improve the accuracy of blood glucose concentration analysis.

The invention relates to the technical field of path planning, and discloses a multi-sensor dynamic path planning method and system for a teaching-free welding robot of a large iron tower component, and the method comprises the steps: collecting multi-sensor synchronous data through a laser sensor, a visual sensor and an arc sensor, and solving segmented calibration parameters; calculating three-dimensional position data of the welding seam by combining the segmented calibration parameters and the synchronous data of the multiple sensors; performing error compensation on the three-dimensional position data of the welding seam at the segmented switching point to obtain a welding seam track position; according to the method, error prediction and real-time correction are carried out at the segmented switching points, accumulated positioning deviation is effectively restrained, high-precision teaching-free welding of the long-distance welding seam of the large iron tower component is achieved, and the welding quality of the long-distance welding seam of the large iron tower component is improved. The technical problems that a traditional method is low in precision and poor in efficiency are solved.

According to the method for designing the reasonable arch axis of the large-span arch bridge based on the hybrid calculation algorithm and the self-adaptive load distribution, an analytic equation method, a curve fitting method and an intelligent optimization algorithm are combined, and by selecting the initial arch axis, introducing a dynamic load distribution model and applying a genetic algorithm and a particle swarm optimization algorithm, the reasonable arch axis of the large-span arch bridge is designed. Global and local optimization is carried out, and the optimal shape and load distribution of the arch axis are ensured. And finally outputting design parameters, such as a control point, a bending moment, thrust and the like. According to the method, the design precision, flexibility and safety can be effectively improved, the material utilization rate is optimized, the cost is reduced, and the method is particularly suitable for design of large-span bridges and has high practical value.

The embodiment of the invention discloses a voice coil motor fast reflector generalized internal model control method and device and a medium, belongs to the technical field of voice coil motor fast reflectors, and solves the problem that the performance of a controller is reduced due to perturbation of system parameters. Comprising the following steps: analyzing input and output data of the voice coil motor fast reflector system to obtain a corresponding nominal model; and performing loop shaping on the nominal model through a pre-compensator, and performing loop shaping controller calculation based on a standard H-infinity control algorithm to obtain a performance controller. The additive uncertainty of the nominal model and the model under the real working condition is determined, and an upper bound function corresponding to the additive uncertainty is obtained through curve fitting. And performing iterative processing on the preset controller to obtain a robust controller. And based on the performance controller and the robust controller, solving to obtain an inner ring controller so as to realize generalized internal model control of the voice coil motor fast reflector.

The invention discloses a flexible scoliosis brace personalized adaptation system based on artificial intelligence analysis. The flexible scoliosis brace personalized adaptation system comprises a dimensional scanning module, a feature extraction and curve fitting module, a machine learning prediction module and an adaptation verification and closed loop module. The three-dimensional scanning module is used for adopting multi-frequency phase structured light projection and dual-band laser to assist calibration; and the feature extraction and curve fitting module is used for accurately matching the three-dimensional image of the spine of the patient with the reference anatomical model Mref through an improved I-ICP algorithm. The invention belongs to the technical field of medical auxiliary data processing, and aims to solve the problems that in the prior art, the measurement precision is limited, quantitative evaluation standards are lacked, individual differences are difficult to adapt, and a dynamic tracking and rapid iteration mechanism is lacked. The method has the technical effects that the problems that the measurement precision is limited, a quantitative evaluation standard is lacked, individual differences are difficult to adapt, and a dynamic tracking and rapid iteration mechanism is lacked are effectively solved.

The invention relates to the technical field of grinding machining, in particular to a complex curve indexable blade grinding method based on NURBS curve fitting, and the method comprises the following steps: S1, collecting and processing data, extracting coordinates, denoising and homogenizing the data; s2, performing NURBS curve fitting, and solving an equation to fit a curve; s3, back calculation of the adaptive interpolation curve is carried out, and curvature smoothness is verified; s4, curve machining acceleration and deceleration control, segmented acceleration and deceleration and dynamic parameter adjustment are carried out; and S5, the numerical control grinding machine executes machining, coordinate conversion is conducted, and real-time error monitoring is passed. Complex curve machining is achieved based on URBS curve fitting, a seven-section S-shaped curve acceleration and deceleration control algorithm is adopted, it is guaranteed that the machining speed reaches the planned maximum speed and cannot reach the planned maximum acceleration, acceleration sudden change is eliminated, mechanical shock and vibration are effectively reduced, and the machining precision is improved. And the requirements of different working conditions can be met by adjusting parameters such as the maximum speed, the maximum acceleration and the jerk.

The invention relates to the technical field of ocean remote sensing monitoring, in particular to a remote sensing image-based warm discharge water reference temperature self-adaptive extraction method, which specifically comprises the steps of S01, data acquisition and preprocessing, S02, cumulative histogram generation, S03, curve fitting and smoothing, S04, second derivative calculation and S05, reference temperature determination. According to the method, the reference temperature is determined by analyzing the statistical distribution characteristics of the sea surface temperature data field, namely the form of the accumulative histogram, the physical demarcation point of the background water body and the warm drainage area on the temperature distribution is automatically positioned by using the second derivative, the process is completely driven by data, the interference of subjective factors is eliminated, and the accuracy of temperature distribution is improved. The method can automatically adapt to water areas with different geographical forms and different warm drainage diffusion modes, and objectivity and universality of reference temperature extraction are achieved.

The invention discloses a welding seam track identification method for impeller wrap angle welding based on point cloud data, and belongs to the field of machine welding seam identification, and the method comprises the following steps: carrying out filtering, down-sampling and clustering on original scanning point cloud; classifying the point cloud data through a region growing clustering algorithm; determining an optimal interception plane based on principal component analysis, and performing curve fitting on section data to obtain a preliminary main welding seam track; the main welding seam track is projected to a local approximate plane in a segmented mode, and accurate extraction of the main welding seam track is completed; and performing plane fitting and thickness calculation on the wrap angle surface, solving an intersection line in combination with an adjacent local approximate plane, calculating an intersection point of the intersection line and the main welding seam track, and finally generating a wrap angle welding seam track in combination with the direction of the intersection line and the wrap angle thickness. Through cooperative calculation of local plane projection and spatial intersection line solving, the whole process operation from recognition, planning to welding can be automatically completed, and unmanned intelligent welding in the true sense is achieved.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and / or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

The invention relates to the technical field of semi-trailer safety monitoring, and discloses a semi-trailer reversing folding real-time monitoring system based on multi-sensor fusion. The data acquisition module acquires sensor data such as position, angle and speed, and divides normal and potential folding areas through time synchronization processing; the data fusion module generates a fusion state vector and calibrates a space coordinate system; the folding state identification module judges the folding risk according to the path trajectory density, the motion feature distribution pattern and the span of the key monitoring point; and the real-time monitoring module expands the data area to generate a continuous track, and evaluates the folding probability in real time by using a curve fitting slope change rate and a convolutional neural network model. The system integrates multiple types of sensors, and adopts dynamic filtering, multi-anchor-point reference and intelligent algorithms, so that accurate real-time monitoring of the backing folding risk of the semi-trailer is realized, and the driving safety is improved.

The invention relates to the technical field of roll grinders, and discloses a grinding control method and system for a roll grinder, and the method comprises the steps: deploying a pressure sensor, collecting pressure data and position coordinates, and building an initial data set. Abnormal data are identified and removed by detecting a time-pressure curve, missing values are complemented by utilizing curve fitting, and a corrected data set is formed. Workpiece boundaries are recognized based on pressure distribution, and after effective data are screened, a weighted average pressure value is calculated in the vertical direction of the grinding wheel. And dynamically adjusting the normal force of the grinding wheel by adopting a proportional control algorithm to enable the pressure to approach a target value. Abnormal data can be monitored, equipment health state evaluation is supported, the workpiece surface burning risk is reduced through pressure feedback compensation, and the size precision is improved.

The embodiment of the invention provides a method for determining initial parameters of a transient absorption spectrum kinetics curve, and the method comprises the steps: obtaining an initial absorption point and an absorption attenuation termination point of the transient absorption spectrum kinetics curve, and determining a curve segment between the initial absorption point and the absorption attenuation termination point as a total absorption attenuation interval; carrying out weighting processing on a first-order derivative and a second-order derivative of the transient absorption spectrum dynamic curve to obtain a weighted curvature value sequence; the first N-1 weighted curvature values are selected from the weighted curvature value sequence from large to small, and the total absorption attenuation interval is divided into N absorption attenuation intervals corresponding to curve fitting orders according to sampling points corresponding to the first N-1 weighted curvature values; and determining an initial parameter based on a quantitative relationship of 1 / e of the absorption intensity attenuated to the corresponding interval starting point in each absorption attenuation interval in the exponential fitting model, wherein the quantitative relationship is that the absorption intensity at the interval starting point in each absorption attenuation interval is attenuated to the absorption intensity at the corresponding interval starting point. And the fitting efficiency and accuracy of the dynamic curve are improved.