186 results about "Error function" patented technology

The invention discloses a three-level inverter dual-tracking model prediction control method and an inverter, and the method comprises the steps: constructing an integral sliding mode observer, and obtaining a current prediction value and a lumped disturbance estimation value through the integral sliding mode observer; calculating a voltage error of the inverter, and substituting the voltage error into the PI controller to obtain a voltage tracking control item; based on the voltage tracking control term, a switching state that minimizes the cost function is selected to control the inverter. The integral sliding mode observer is configured to calculate a current prediction error between a current prediction value of a current control period and a system output current, introduce an integrator to track the current prediction error, and generate an error function based on an output result of the integrator and the current prediction error. And feeding back a current predicted value and a lumped disturbance estimated value of the next control period through an error function. According to the invention, the method can achieve the quick estimation of the operation state of the inverter and the robust compensation of disturbance, reduces the steady-state tracking error, and reduces the total harmonic distortion of the inverter.

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.

The invention provides a motor parameter online identification method based on an improved golden section method, and belongs to the technical field of motor parameter identification, and the method comprises the steps: S1, obtaining a residual function of a recursive least square method with a forgetting factor when parameter identification is not carried out on a motor model for the first time; s2, detecting whether the residual function exceeds a preset threshold value or not, and executing a step S3 when the residual function exceeds the preset threshold value; s3, taking the residual function as a target function, and adjusting the forgetting factor by using a golden section method to obtain an optimized forgetting factor; and S4, carrying out motor parameter online identification by using a recursive least square method with the optimized forgetting factor. The method has the advantages that the forgetting factor is dynamically adjusted through the golden section method, the residual function serves as the target function, the optimal solution of the forgetting factor under the corresponding working condition can be found when the system changes rapidly, the tracking speed is increased, the steady-state precision is further improved, and therefore the overall performance of parameter identification is improved.

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 belongs to the technical field of flow instruments, and provides a dynamic compensation and error correction system of a high-precision flow instrument, which comprises a multi-dimensional data acquisition and preprocessing module, a module capable of being additionally provided with a sensor, a module for establishing a data stream with a timestamp and processing data, a module for fluid network modeling and priori knowledge construction, and a data processing module. A distributed collaborative optimization and parameter calibration module capable of constructing a directed graph model and generating initial compensation parameters; a network association reasoning and dynamic weight distribution module capable of adding a global penalty term to optimize parameters on the basis of a traditional error function; a working condition adaptation calibration and cross-domain parameter migration module capable of constructing an association graph, detecting abnormity and distributing weights; real-object-free calibration and parameter migration can be realized; according to the system, through multi-module cooperation, the problem that traditional single-table compensation is local and is not global is solved, and high precision and real-time performance of industrial-grade cooperative metering are supported.

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 present invention belongs to the field of lifting equipment control technology, and specifically relates to a dynamic modeling method and control method for double-bridge lifting equipment. The dynamic equations of a five-degree-of-freedom double-bridge crane are constructed using the Lagrangian method, and the geometric constraint relationships between trolley I and trolley II in the direction parallel to the bridge and the vertical direction are constructed. The dynamic model of the double-bridge crane is constructed by combining the dynamic equations and the geometric constraint relationships, and the dynamic model is transformed into a general model. Based on the general model, a first error function is constructed based on the difference between the actual position and the expected position, and a virtual controller is constructed based on the first error function. A second error function is constructed based on the virtual controller, and an actual controller is constructed based on the first error function, the virtual controller, and the second error function. This technical solution models the system as a whole through the constraint relationship between the two trolleys, taking the mass of the hook into consideration during the modeling process, and solving the singularity problem.

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

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 relates to a method for eliminating a double-planetary-calendar forecast error based on post-event precise ephemeris. The method comprises the following steps: forecasting a satellite position of the day at an ephemeris reference moment based on double planetary calendars and an SDP4 model; calculating the satellite position of the ephemeris reference moment on the day based on the post-event precise ephemeris of the double planetary calendars on the ephemeris reference moment on the day, and taking the satellite position as a reference satellite position; acquiring a satellite position forecast error by comparing the difference between the forecast satellite position and the reference satellite position of the day at the ephemeris reference moment, and fitting the satellite position forecast error of the satellite in one orbit period to obtain an error function of the double-planetary-calendar forecast satellite position; and performing error elimination on the forecast satellite position of the target date according to the error function to obtain the forecast satellite position after error elimination. By adopting the method, the satellite position forecasting error of the double planetary calendars can be eliminated, and the precision of the double planetary calendars for medium and long term forecasting is improved.

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 discloses an improved UWB error compensation positioning method based on a VEGA + algorithm, and relates to the technical field of indoor accurate positioning, and the method mainly comprises the steps: carrying out the preprocessing of original collection data, obtaining a preliminary sample set, carrying out the secondary sampling through a VEGAS + algorithm, obtaining a complete sample set, and carrying out the calculation of the complete sample set; and constructing a space error function by using a sparse polynomial to obtain an error model, and performing error prediction and compensation on a UWB real-time positioning result to obtain a corrected position signal. By implementing the improved UWB error compensation positioning method based on the Vegas + algorithm provided by the invention, delay and positioning errors can be reduced, and the long-term stability and environmental adaptability of the system can be improved.

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.

The invention discloses a hydrogen trap characteristic parameter determination method based on a genetic algorithm, and relates to the technical field of hydrogen trap characteristic calculation. Placing the sample in a hydrogen filling device for hydrogen filling treatment, wherein the hydrogen concentration in the sample is saturated; transferring the sample into a heating thermal desorption device for hydrogen desorption treatment, collecting overflowed hydrogen through a mass spectrometer to obtain an actually measured TDS curve, and analyzing the category number of hydrogen traps in the material; according to the McNabb and Foster theories, in combination with an Oriani hypothesis, a calculated TDS curve is obtained; establishing an error function between the actually measured TDS curve and the calculated TDS curve; and according to the error function, a genetic algorithm is adopted to invert and solve the concentration and binding energy of various hydrogen traps. The method can accurately predict the concentration of various hydrogen traps in the material and the characteristic parameters of binding energy, and provides technical support for hydrogen embrittlement prevention design, safety evaluation and the like of steel for hydrogen energy storage and transportation equipment.

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 relates to a profile drifting buoy heterogeneous digital twinborn model construction method, which belongs to the technical field of model construction, and comprises the following steps: constructing a mechanical motion model of a profile drifting buoy, acquiring actual operation data, defining an error function according to the mechanical motion model, and calculating an error value between the mechanical motion model and the actual operation data according to the error function. Calculating a mean value and a standard deviation of the error values through a neural network, and performing error correction on the error values according to the mean value to obtain a heterogeneous digital twinborn model; a model error result is calculated according to the predicted operation state and actual operation data; an error threshold value is defined according to the standard deviation; a loss function is calculated according to the error threshold value and the model error result; and parameters of the heterogeneous digital twin model are updated according to the loss function. A deviation quantification mechanism between a physical model and a real system can be established, and high-precision modeling and prediction of the running state of the profile drifting buoy are achieved.

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.