149 results about "Error function" patented technology

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

A quality prediction and adaptive compensation method and apparatus for curved surface assembly are provided. The method includes: inputting a geometric error function and a thermal error function into a spatial error model, to obtain a machining error prediction model; superimposing obtained machining errors on a theoretical surface of an assembly surface, to obtain a predicted machining surface; calculating, according to an assembly median plane determined based on the assembly surface, shape errors and assembly gap errors, and predicting curved surface assembly quality of a part by using the shape errors and the assembly gap errors; calculating an adaptive compensation amount of each assembly plane of the assembly surface based on the shape errors, the assembly gap errors, and the machining errors, when the curved surface assembly quality does not meet a preset assembly quality requirement, and compensating the corresponding assembly plane by using the adaptive compensation amount.

The invention discloses a fractal mask bilinear interpolation-based checkerboard angular point positioning method, which comprises the following steps of S1, acquiring a checkerboard image by using image acquisition equipment, and extracting candidate angular points in the checkerboard image; s2, determining a direction angle of the edge of the local area by using an edge detection algorithm and a polar coordinate transformation and clustering algorithm; s3, based on the direction angle of the edge of the local area, using a gray integral method and a least square method to preliminarily position the coordinates of candidate angular points; s4, in the neighborhood of the preliminarily positioned candidate angular point coordinates, obtaining a sub-pixel-level gray value by using fractal mask bilinear interpolation, and calculating local gradient distribution; s5, dynamically generating a weight mask based on a fractal theory, constructing a central point minimum error function according to a corner local gradient consistency principle, iteratively optimizing candidate corner coordinates, and outputting a sub-pixel level position; according to the method, the sub-pixel-level accurate positioning of the checkerboard angular points is realized by fusing the edge detection, the gray integration and the iterative optimization of the fractal theory.

The invention discloses a three-dimensional structure modeling method based on zero sample semantic segmentation and physical attribute estimation, which belongs to the technical field of image data processing, and comprises the following steps: constructing an image set D1; generating an initial three-dimensional point cloud model M0 based on D1; the M0 is divided into S sub-point clouds; generating a physical attribute graph of the s sub-point cloud Ms under each visible view angle, and constructing a multi-view-angle physical attribute consistency error function Ls; adjusting the Ms by minimizing the Ls to obtain an optimized sub-model Ms' and an optimized attribute atlas Cs; and constructing an optimized point cloud set G by using the sum Cs of all the sub-point clouds, and training a three-dimensional conditional diffusion model for three-dimensional reconstruction. According to the method, fine modeling of a target structure and details can be realized, the reconstruction efficiency and authenticity are improved, controllable material and style reconstruction is supported, and the method has the advantages of weakening supervision dependence and enhancing interpretability and adjustability of the model. The method is suitable for the fields of virtual reality, industrial digital modeling, element universe scene generation and the like.

The invention relates to the technical field of deformation detection, in particular to a method for measuring the deformation of a rotary body part, which comprises the following steps of: arranging measuring points on a target rotary body part, and performing dotting measurement by using a three-coordinate measuring instrument to obtain dotting data which are three-dimensional coordinates; based on the dotting data, calculation is carried out, and deformation information of the target rotary body part is obtained, the calculation comprises the steps of determining a data source, normalizing coordinates, converting a coordinate form, sequencing path measuring points, compensating a measuring head, calculating a position-error function, calculating all-directional axial deformation and radial deformation, speculating all-position deformation, and performing any combination calculation in multi-coordinate system data integration. According to the invention, the deformation of the rotary part can be measured conveniently, quickly and efficiently.

The invention belongs to the technical field of robot safety control, and discloses a safe motion planning method for a dual-redundancy mechanical arm. And a linear mapping relation between the joint space speed of the redundant mechanical arm and the speed of an end effector is established. And establishing an end effector speed constraint, a safety distance constraint and a joint amplitude limiting constraint, and fusing a differential kinematics model and the three constraints to establish a multi-target combined constraint model. The optimal decision variable meets the Carlo demand-Kuhn-Tuck condition, the original expression form of the Carlo demand-Kuhn-Tuck condition is defined, and a nonlinear complementary function is introduced to reconstruct the Carlo demand-Kuhn-Tuck condition. Based on an improved Carlo demand-Kuhn-Tuck condition, a vector error function is defined, and an adaptive return-to-zero neural network is designed; and performing iterative solution on the vector error function through an adaptive return-to-zero neural network to obtain an optimal decision variable of the multi-target joint constraint model, taking the optimal decision variable as a mechanical arm joint motion control instruction, and outputting the mechanical arm joint motion control instruction to complete mechanical arm motion planning.

The invention provides an APU characteristic correction method and device, and belongs to the field of aviation electromechanics. The method provided by the invention comprises the following steps: establishing an APU system simulation model; determining a plurality of correction parameters and correction targets; calculating a calculation value corresponding to the correction target based on the APU system simulation model and the correction parameter; constructing a correction error function based on the calculated value of the correction target and a test value of the correction target obtained through a test; determining an updated component in the APU, and determining an optimization strategy based on the identity of the updated component; based on the optimization strategy, executing a particle swarm algorithm to obtain an optimal correction parameter corresponding to the correction error function; and updating the APU system simulation model based on the optimal correction parameter to obtain a corrected APU characteristic model. According to the APU characteristic correction method and device provided by the invention, the accuracy of the model correction result can be improved after the local parts of the APU are updated.

The invention provides an SLAM (Simultaneous Localization and Mapping) optimization method and equipment based on point cloud registration and adaptive resolution, and a medium, and relates to the technical field of AGV (Automatic Guided Vehicle) positioning and mapping, and the method comprises the following steps: (1) inputting and preprocessing sensor data; (2) front-end scanning and matching: based on the preprocessed data, carrying out point cloud registration through a GICP algorithm, updating the pose of the current frame and constructing a local subgraph; (3) global SLAM: receiving sensor data, poses and subgraph data from the front-end scanning and matching step, and carrying out loopback detection and global optimization; wherein a self-adaptive resolution switching strategy based on environment complexity is adopted in the loopback detection process; according to the GICP algorithm, the surface of the point cloud is modeled as Gaussian distribution, local features are described through a covariance matrix, and a target error function constructed based on the mahalanobis distance is optimized to estimate pose transformation. According to the method, the local matching precision and robustness are improved by replacing ICP with GICP, and the global calculation efficiency is adjusted and optimized in combination with the dynamic resolution.

The invention discloses a multi-joint torque control method of a mechanical arm for cleaning foreign matters on a power distribution line. The multi-joint torque control method comprises the following steps: S1, setting an initial value and an expected track of a joint in the mechanical arm; s2, a mechanical arm multi-joint torque control model is constructed based on a fuzzy neural network; s3, performing initialization processing on the fuzzy rule, and determining an initial value of a fuzzy neural network parameter; s4, constructing an error function to calculate an error, and performing adjustment and iterative correction on each parameter; s5, the error and the error change rate are calculated to serve as input of the fuzzy neural network, and the control torque parameter of the mechanical arm serves as output; s6, performing iterative optimization processing on the output parameters of the fuzzy neural network based on a particle swarm and grey wolf hybrid optimization algorithm to obtain a global optimal solution of the output parameters of the fuzzy neural network, and obtaining the joint torque of the mechanical arm; according to the method, the joint output torque can be accurately controlled, the joint position tracking error is reduced, and the overall robustness and stability of a mechanical arm control system are improved.

A multi-modal tight coupling SLAM method based on gradient descent optimization comprises the following steps: acquiring environment characteristic data in a movement process of intelligent mobile equipment in real time, and determining a spatial position of the intelligent mobile equipment by detecting a reflective mark so as to construct a time-synchronized multi-modal observation set. And identifying and extracting multi-modal features from the multi-modal observation set, so as to carry out feature fusion on the multi-modal features to obtain a united-coded joint feature set. And based on the joint feature set, constructing an initial pose map of the multi-modal feature constraint and the marked anchor point constraint. And calling an error function to model the multi-modal observation residual error and the marking residual error, and optimizing the initial pose image by iteratively adjusting the pose and the error of modeling estimation. And performing global consistency verification on the optimized initial pose map, and outputting an environment modeling result and the pose state of the intelligent mobile device, thereby solving the problem of difficult SLAM convergence caused by narrow space and metal interference in a complex scene.

The invention provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

The invention provides a control method and system for a five-degree-of-freedom marine crane under a non-inertial system, and relates to the technical field of automatic control of cranes. The method comprises the following steps: acquiring a real-time state variable of the crane under a non-inertial system; the state variables are input into a self-adaptive radial basis function neural network, a lumped disturbance estimated value is obtained, and the neural network is designed based on a five-degree-of-freedom dynamical model established by utilizing a Lagrange equation and a virtual work principle under a ship coordinate system and considering ship six-degree-of-freedom motion and installation position offset; calculating a tracking error function according to the state variable and the expected trajectory; and inputting the disturbance estimation value and the error function into a feedback controller, calculating to generate a control torque, and outputting the control torque to an execution mechanism to drive the crane to track an expected trajectory and suppress load swing. According to the method, direct modeling and control under a non-inertial system are realized, the problems of coordinate transformation disturbance and singularity caused by inertial system modeling are solved, and the trajectory tracking precision and the anti-interference robustness are improved.

The invention discloses a transformer partial discharge positioning method and system based on digital twinning and simulation, and the method comprises the steps: obtaining an initial discharge source position based on a conventional time difference of arrival method, constructing a transformer simulation model, setting a simulation discharge source at the initial discharge source position, and updating a reference sensor, calculating the time difference of arrival of each sensor relative to the updated reference sensor to obtain a simulation time difference of arrival; constructing an error function according to a difference value between the simulation time difference of arrival and the actually measured time difference of arrival, judging whether the error function meets a convergence condition or not, and if so, outputting a current discharge source position; and if not, updating the position of the analog discharge source and the error function until the error function converges. According to the method, the partial discharge ultrasonic positioning precision can be improved, the robustness of the algorithm is improved, the method is adaptive to complex medium characteristics, and the requirement of high-precision positioning is met.

The invention belongs to the technical field of control theories, and provides a ZNN model design method for solving a time-varying Sylvester equation based on a filter, and the method comprises the steps: building an error function based on the basic knowledge of the time-varying Sylvester equation; designing a predefined time function based on a high-gain method, introducing the predefined time function into a ZNN model, and considering unknown noise interference in a ZNN model solving process; for unknown noise interference, a filter containing a predefined time function is introduced, and a subsystem of a neural network model is constructed; a Lyapunov function is constructed, a ZNN is obtained through stability analysis, noise can be effectively suppressed, and a time-varying Sylvester equation is solved at predefined time; and solving a time-varying Sylvester equation by using the predefined time ZNN model based on the filter, and verifying the performance of the predefined time ZNN model based on the filter. The method is used for solving a time-varying Sylvester equation, and the influence of noise can be effectively suppressed.

This invention provides a method for calculating the minimum delay time between two seismic traces: Calculating a first error value between the two seismic traces under at least three first delay times (either preset or obtained after the last iteration update); fitting an error function between the delay time and the error value; calculating a second delay time and a second error value at the extreme point of the error function; if the first difference between the second delay time and a threshold delay time of any of the first delay times is greater than or equal to a first threshold, or the second difference between the second error value and the first error value is greater than or equal to a second threshold, then the second delay time is used to replace one of the edge delay times in the current first delay time. All the above steps are repeated until the first difference is less than the first threshold and the second difference is less than the second threshold, and this second delay time is determined as the minimum delay time between the two seismic traces. This invention improves the calculation speed for calculating the minimum delay time between two seismic traces.

The application belongs to the technical field of load identification of structural health monitoring, and provides a concentrated load positioning method based on principal stress constraint and strain gradient trajectory identification, which comprises the following steps: arranging strain sensors in a monitoring area of a planar structure plate and collecting strain data of measuring points, constructing a strain field inversion function based on a radial basis function and establishing an error optimization function containing a fitting error term and a smooth constraint, introducing a principal stress direction constraint and a gradient projection smooth constraint to form a comprehensive error function and solving to obtain an optimal strain field distribution, then calculating strain gradient vectors of each grid node and tracking a gradient trajectory through a gradient descent method, and finally performing an iterative clustering analysis based on distance statistics on a trajectory end point to output a cluster center as a concentrated load application position identification result. The application can improve the concentrated load positioning accuracy, enhance the anti-noise capability, and realize fast, stable and large-sample-free load position identification.

The invention provides an underwater high-pressure bubble initial radius physical information neural network inversion method fused with a bubble kinetic equation. According to the method, a bubble kinetic equation is fused into a physical error function to establish a physical information neural network model, initial conditions of calculation are provided according to a pulsating bubble radius or a low-frequency bubble load and the like, and then the accurate analysis capability of an underwater high-pressure bubble experiment is improved. According to the method, accurate calibration of the initial radius of the underwater high-pressure bubble is achieved according to the pulsating bubble radius or load measured through an experiment, the requirement for a precise measuring instrument is lowered, and the method can assist numerical simulation, theoretical analysis and other modes to carry out comparative research with higher precision.

The application provides a UWB and PDR fusion positioning method and system in an indoor shielding environment and a medium, and belongs to the technical field of indoor positioning and multi-source information fusion. The method comprises the following steps: calculating a UWB positioning track and a PDR positioning track. Taking translation, rotation, step scaling and scale change as combined parameters, a track alignment error function is constructed by using the Euclidean distance of the two tracks at each timestamp. An improved vole optimization algorithm is used to solve the optimal combined parameters. An initial population with uniform distribution is generated by mapping a Sobol difference sequence. In the iteration, the UWB track is used as the reference, the PDR track is temporarily transformed by using the combined parameters, the Euclidean distance is calculated, and the vole with the smallest distance is selected to breed the next generation. After reaching the maximum number of iterations, the PDR track is corrected by using the optimal combined parameters at one time, and the UWB track is fused and output. The application does not require a priori model, can simultaneously suppress UWB non-line-of-sight drift and PDR cumulative error, and has high positioning accuracy.

The invention discloses a temperature estimation model construction method and device, computer equipment and a storage medium, relates to the field of motor control, and is used for solving the problem of low temperature estimation precision in related technologies. The method comprises the following steps: constructing an initial temperature estimation model based on a heat transfer network of to-be-tested equipment; in the operation state of the to-be-tested equipment, acquiring actual temperatures of the to-be-tested equipment in a plurality of preset temperature nodes in the heat transfer network, and estimating predicted temperatures of the preset temperature nodes based on the initial temperature estimation model; and adjusting the initial temperature estimation model through a preset error function based on the actual temperature and the predicted temperature of each preset temperature node to obtain a target temperature estimation model.

The present application relates to a kind of profile drift buoy isomerization digital twin model construction method, belong to model construction technical field, including the mechanical motion model of construction profile drift buoy, obtain actual operation data, according to mechanical motion model definition error function, according to error function the error value of mechanical motion model and actual operation data;The mean and standard deviation of error value are calculated by neural network, the error value is corrected according to mean, and isomerization digital twin model is obtained;According to isomerization digital twin model, the predicted running state is obtained, and the model error result is calculated according to the predicted running state and actual operation data, according to standard deviation definition error threshold, according to error threshold and model error result, loss function is calculated, according to loss function, the parameter of isomerization digital twin model is updated, the deviation quantification mechanism between physical model and real system can be established, high-precision modeling and prediction to the running state of profile drift buoy are realized.

The application discloses a kind of SA-CNN measurement model construction methods, steps are as follows: S1, determine network initial structure, including convolution layer, pooling layer, fully connected layer and activation layer, and preliminary test model effect;S2, add corresponding self-attention layer and discard layer hierarchy structure, improve network model training effect;S3, according to the network structure determined in S2, train picture sample, the error function of the predicted value obtained and actual value is used as model evaluation standard, adjust each network layer hyperparameter until error function reaches model measurement minimum error standard;S4, output trained model.This method constructs SA-CNN model, by adding network layer, it improves the phenomenon that exists in convolutional neural network training, such as slow training speed, overfitting appears, training result error is big, can be quickly and accurately measured under the condition of given measurement sample Workpiece surface roughness.

A method for determining a set of physical parameters of a sample, comprising the following steps: - A acquisition of a measured sample time trace Es(t), - B acquisition of a measured reference time trace Eref(t), - C determination of a broadened reference time trace, called Eref0(t), and determination of the discrete Fourier transform of the broadened reference time trace - D determination of a modeling of the impulse response of the sample in the frequency domain, called sample frequency model, from the Fourier transform of the broadened reference time trace and a physical behavior model of the sample, according to a set of physical parameters (pi), - E application of an optimization algorithm to the set of physical parameters (pi), comprising the following sub-steps: - El initialization of the physical parameters (pi), - iteratively implementing the following sub-steps: - E2 computation of the inverse discrete Fourier transform of the sample frequency model, called estimated sample time trace E est {p i}(t), - E3 computation of an error function (ε er {pi}) until obtaining a set of values of the physical parameters (pi opt ) that minimize the error function.

The invention relates to the technical field of highway bridge safety monitoring, and discloses a bridge dynamic weighing algorithm based on a regularization algorithm. According to the method, a penalty term is added into an error function to obtain an axle load calculation formula with a regularization matrix, and the regularization matrix is determined by an axle load covariance matrix and a load response covariance matrix. The method specifically comprises the steps of obtaining influence line distribution through a calibration test; giving an initial regularization matrix to obtain an initial axle weight; calculating a load response covariance matrix based on the influence line distribution, the initial axle load and the measurement noise, and iterating a stable axle load covariance matrix in combination with a preset initial axle load covariance matrix; generating a regularization matrix according to the load response covariance matrix and the updated axle load covariance matrix, and calculating a new axle load; and repeating the iteration process until the axle load update difference value is smaller than a preset value, and finally outputting an axle load result. According to the invention, the axle load identification precision of the bridge dynamic weighing system is effectively improved.

The invention relates to a coordinate processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a first coordinate point set corresponding to a target point in a programming coordinate system, and obtaining a second coordinate point set corresponding to the target point in a preset actual coordinate system; performing transformation parameter calculation on the first coordinate point set and the second coordinate point set based on a least square method to obtain an initial transformation matrix; calculating the initial transformation matrix based on an error function to obtain an error value; if the error value is greater than an error threshold value, optimizing the initial transformation matrix by using a random sampling consensus algorithm based on the first coordinate point set and the second coordinate point set to obtain a first transformation matrix; performing iterative optimization on the first transformation matrix based on an iterative nearest point algorithm until an iteration termination condition is met, and obtaining a target transformation matrix; and performing coordinate transformation on the first coordinate point set based on the target transformation matrix to obtain a corresponding target coordinate point set. According to the invention, the precision of coordinate conversion can be effectively improved.

The application belongs to the field of grating measurement, and provides an error compensation method for grating interferometer measurement, which comprises the following steps: constructing an angle error function based on the angle error of grating interferometer measurement; constructing a relative displacement error function based on the relative displacement error of grating interferometer measurement; constructing an explicit compensation model based on the angle error function and the relative displacement error function; constructing an implicit compensation model based on the data set formed by the angle error and the relative displacement error; obtaining an explicit accuracy mean value based on the explicit compensation model, and obtaining an implicit accuracy mean value based on the implicit compensation model; when the implicit accuracy mean value is not less than the explicit accuracy mean value, the explicit compensation model is used to compensate the error of the grating interferometer measurement value. The method guarantees the reliability of the selected model by analyzing the consistency of the internal mapping of the model and the physical law, so as to realize high-precision, high-robustness and strong-explainability displacement measurement compensation.

A system, method and computer program product for training a deep neural network model using a pre-trained teacher model. Student training data samples are input to the deep neural network model and teacher training data samples are input to the pre-trained teacher model. The trained deep neural network model is generated using the training data samples to optimize an error function that is evaluated using a plurality of student label prediction outputs and a plurality of Markov transformed teacher label prediction outputs. Each Markov transformed teacher label prediction output is generated based on a teacher label prediction output by the pre-trained teacher model in response to receiving one of the teacher training data samples as an input. Each Markov transformed teacher label prediction output is generated through a Markov transform involving matrix multiplication using a Markov matrix.

A predefined time ZNN model design method for solving pseudo-inverse of a time-varying matrix comprises the steps that a pseudo-inverse matrix is solved based on the time-varying matrix, an error function is established, the time-varying matrix is known, and the pseudo-inverse matrix to be solved is unknown; designing a predefined time function based on a high-gain method, and introducing the predefined time function into the ZNN model; aiming at bounded noise interference in a ZNN model solving process, introducing a disturbance estimator comprising a predefined time function, and constructing a subsystem of a neural network model; a Lyapunov function is constructed for stability analysis, and a ZNN model obtained through analysis can effectively suppress noise and solve a pseudo-inverse matrix of a time-varying matrix at predefined time. And applying the predefined time ZNN model to control of the permanent magnet synchronous motor chaotic system. According to the designed predefined time ZNN model, a complex parameter adjustment process is avoided in parameter adjustment. The method has the advantages of high robustness, high calculation precision and high solving speed.

The invention discloses a space mapping optimization method for an SISL filter. The space mapping optimization method comprises the following steps of: endowing an initial value to a space mapping model variable x; simulating in electromagnetic simulation software by using the value of the variable x, and calculating to obtain an error function; judging whether the error function is less than or equal to 0, if so, stopping the process, and taking the current variable x as an optimal solution; if not, optimization is carried out; the mapping relation is optimized, so that a matching result between the space mapping model and data generated by the electromagnetic simulation software conforms to expectation, and a new mapping relation is obtained; on the basis of the new mapping relation, the variable x value is adjusted to be consistent with the design index; and using the updated variable x to re-calculate the error function in the electromagnetic simulation software, and repeating the subsequent steps. According to the method, the equivalent rough model is constructed, and the accurate mapping relation is established, so that the optimization efficiency of the SISL device can be remarkably improved while the precision is ensured.

The application discloses a spacecraft magnetic moment attitude control method based on a non-inverse iteration algorithm, and the method establishes a linear periodic model for a spacecraft attitude control system which only relies on a magnetic moment; on the basis, according to a linear quadratic optimal control theory, an optimal controller design problem is converted into a solution problem of a discrete periodic Riccati equation associated with the optimal controller design problem; on the basis of a gradient descent principle, an error function is constructed, and a non-inverse iteration algorithm based on the gradient descent principle is proposed to solve the discrete periodic Riccati equation, so that a matrix inversion operation in a traditional solution method is avoided, and the calculation efficiency and the numerical stability are effectively improved; the obtained Riccati equation solution is used to obtain an optimal state feedback control law, so that the spacecraft attitude optimal control only using the magnetic moment is realized, and a new solution scheme is provided for an attitude control problem of a limited actuator in a space task.

The embodiment of the invention provides an extrapolation method and device for the reflectivity of a colorimeter, the ratio of the reference light intensity to the measurement light intensity can be calculated to obtain the reflectivity, window data collection is carried out on the reflectivity obtained through calculation, and therefore a fitting error function can be created according to model parameters, and the reflectivity of the colorimeter can be obtained. And screening the plurality of candidate models according to the fitting error function to obtain a plurality of target models, adaptively adjusting the size of a preset window according to the plurality of target models, and finally calculating to obtain the tristimulus value of the color, thereby improving the consistency of measurement through a local adaptive mechanism while maintaining the smoothness of the spectrum curve, and improving the accuracy of the measurement. And the physical rationality of data is ensured.