55 results about "Sum squared error" patented technology

The invention discloses a wind power output clustering method and system based on a DTW-Kmedoids algorithm, and relates to the technical field of wind power output scene generation, and the method comprises the steps: carrying out the interpolation, dimension reduction, per-unit processing and smoothing processing of wind power historical data; carrying out distance measurement by adopting a dynamic time warping (DTW) algorithm, carrying out iteration by adopting a Kmedoids clustering algorithm to obtain a clustering result, and outputting a center curve of each cluster; for clustering results of different cluster numbers, calculating an error sum of squares of each cluster, and determining an optimal cluster number by adopting an elbow rule; and according to the clustering result under the optimal cluster number, generating a typical wind power output scene curve, and establishing a clustering effect evaluation index system. The method introduces the dynamic time bending distance algorithm, can adapt to the nonlinear change of the wind power output curve, improves the accuracy of similarity measurement through the dynamic matching time sequence, and enables the generated typical scene to be more representative. Therefore, the clustering center point is more representative, and the stability of the generated wind power output scene is better.

The invention discloses a small fault detection method based on a prediction error sum of squares and JS divergence, and relates to the technical field of engineering detection, the algorithm of the application replaces a traditional algorithm for calculating JS divergence based on projection data after PCA dimension reduction, and a probability density function of the prediction error sum of squares of projected data is fitted by using kernel density estimation, so that the detection accuracy is improved. And calculating the JS divergence between different data distributions. The sum of squares of prediction errors reflects parts, which cannot be well reconstructed in the PCA model, of original data points, and the parts may contain abnormal or fault information. According to the method, the fault extraction capability of the prediction error sum of squares in the JS divergence is utilized, so that tiny faults can be better detected. By introducing the time sliding window, the method can more sensitively detect the local abnormal condition causing the increase of the JS divergence value, thereby timely and accurately capturing the process change, providing the early warning about the fault occurrence, and ensuring the safety and reliability of industrial production.

The invention discloses a power distribution network traveling wave fault positioning method and a related device, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting a transient signal after a fault at a preset key monitoring point of a power distribution network, extracting an initial traveling wave head from the transient signal, and measuring the moment when the initial traveling wave head reaches other preset monitoring points in the power distribution network, obtaining traveling wave arrival time between the key monitoring point and each monitoring point; the time difference between the traveling wave arrival time is used as the actually measured traveling wave arrival time difference to construct a fault positioning optimization model with the purpose of minimizing the error sum of squares of the theoretical traveling wave arrival time difference and the actually measured traveling wave arrival time difference; and solving the fault positioning optimization model through a pre-constructed chaos particle swarm optimization algorithm to obtain a traveling wave fault positioning result of the power distribution network. The method solves the problems that the positioning effect is not ideal due to the fact that the prior art is prone to falling into local optimum and is poor in adaptability to a complex topological structure, and the convergence speed cannot meet the requirement for rapid power supply recovery.

The invention provides an evaluation index data processing and evaluation method, computer equipment, a medium and a product. In a specific embodiment, the evaluation index data processing method comprises the following steps: obtaining evaluation index data of a plurality of to-be-evaluated targets; the number of clustering centers of a K-means clustering algorithm is determined according to the sum of squares of errors corresponding to different numbers of clustering centers, the average contour coefficient corresponding to the number of the partial clustering centers and the number of preset evaluation grade values, and the number of the clustering centers of the K-means clustering algorithm is smaller than or equal to the number of the preset evaluation grade values; according to the number of clustering centers of the K-means clustering algorithm, grouping normalization based on the K-means clustering algorithm is carried out on the evaluation index data of the multiple to-be-evaluated targets, and evaluation grade values corresponding to the evaluation index data of the multiple to-be-evaluated targets are obtained. According to the embodiment, the objectivity of normalization of the evaluation index data and the rationality in physical significance can be improved, so that the reliability of an evaluation result is improved.

The invention relates to the technical field of fault diagnosis, and discloses a generator fault diagnosis method and system based on vibration trend prediction. The method comprises the steps that prediction model training is carried out on a regularized vibration sequence through an extreme learning machine algorithm, a vibration trend prediction model is obtained, and the model comprises an input layer, a hidden layer and an output layer; performing adaptive adjustment processing on the vibration trend prediction model through an error sum of squares evaluation function to obtain an optimized prediction model, and when a cycle index error ratio is greater than 2, setting a rule function value to zero; when the cycle index error ratio is less than or equal to 2, the law function value is equal to the product of the last law function value and the natural logarithm of the error ratio; and performing comparative analysis on an output result of the optimized prediction model and real-time monitoring data to obtain a generator fault diagnosis result. According to the invention, the efficiency and accuracy of generator fault diagnosis based on vibration trend prediction are improved.

The invention relates to the technical field of micro thrust measurement of spaceflight micro thrusters, and discloses a micro thrust dynamic identification method and system. The recognition method comprises the following steps: constructing a kinetic equation of a thrust measurement system, rewriting the kinetic equation into a state space form, and carrying out integration to obtain a theoretical system response model at any moment; rewriting the theoretical system response model into a numerical model in a step-by-step integral recursion format; constructing an actual system response sequence and constructing an empirical model in combination with the kinetic equation of the thrust measurement system, and rewriting the empirical model; configuring a micro-thrust increment expression, rewriting a numerical model and an empirical model by applying the expression, and further rewriting the numerical model and the empirical model into an augmented form; predicting the prediction output of the future # imgabs0 # moments in the time domain; and a cost function is constructed and solved, so that the sum of squares of errors of predicted output and expected output in an actual system response sequence is minimum, the smoothness of micro-thrust input is considered, and at the moment, predicted micro-thrust corresponding to the predicted output is equal to actually measured micro-thrust corresponding to the expected output.

The invention provides a magnetic element magnetic core loss prediction method and device, equipment and a storage medium. The method comprises the steps of obtaining a corresponding real magnetic core loss value when a magnetic element works in a working temperature range; the working temperature range of the magnetic core of the magnetic element is partitioned, and a piecewise function representing magnetic core loss in each temperature interval is constructed; constructing a magnetic core loss model based on the piecewise function; estimating parameters of the magnetic core loss model by taking the minimum error sum of squares between the predicted value of the magnetic core loss and the true value of the magnetic core loss as a target to obtain a prediction model of the magnetic core loss; and realizing prediction of the magnetic core loss of the magnetic element based on the magnetic core loss prediction model. By using a piecewise function, the temperature is converted into a qualitative variable, and the influence of the qualitative variable on the magnetic core loss is divided into different intervals. A magnetic core loss model is constructed based on a piecewise function, and the model can better capture local changes of experimental data in different temperature ranges, so that the fitting precision is improved.

The invention belongs to the technical field of unmanned aerial vehicle aircraft design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a closed-loop state and a large angle-of-attack range, and the method comprises the steps: building a flight simulation model; performing aerodynamic characteristic simulation based on a flight simulation model to obtain an aerodynamic load value, comparing the aerodynamic load value with data tested in an actual flight experiment, and calculating and minimizing an error sum of squares; dividing the whole angle-of-attack range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, and carrying out model accuracy verification; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, and verifying the applicability of the flight simulation model. The method has universality and is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention discloses a railway roadbed settlement prediction method based on an IGM-PSVR model, and the method comprises the steps: firstly collecting roadbed settlement data, and carrying out the preprocessing of the collected roadbed settlement data; performing level ratio verification on the data, establishing a corresponding differential equation, solving to obtain a time response function, further optimizing a background value of a gray model, constructing an improved gray model IGM (1, 1), and obtaining a model prediction result; then, a particle swarm optimization algorithm is adopted to optimize hyper-parameters of the support vector regression SVR model, a support vector machine model of particle swarm optimization is constructed, and a model prediction result is obtained; and finally, giving different weights to the prediction results of the two steps by using an error square and reciprocal method, and finally combining to obtain a combined prediction result. Experiments show that compared with a single model, the combined model is higher in prediction precision, can better express the roadbed settlement change trend, and plays an important role in guaranteeing the safety of railway transportation business.

The application discloses a lightweight indoor 3D laser ground extraction algorithm method, system and medium. The method comprises the following steps: acquiring indoor 3D laser radar data; obtaining coordinate values based on a laser radar coordinate system according to the indoor 3D laser radar data; sending the coordinate values to a preset plane equation to obtain a ground plane normal vector; sending the ground plane normal vector to a first preset coordinate system to establish a constraint to obtain a comprehensive error value; and accumulating squares of values in the comprehensive error value to obtain a comprehensive error sum of squares; extracting a minimum value in the comprehensive error sum of squares, and setting a coordinate point corresponding to the minimum value as an optimal current pose. The application is suitable for various lasers, and by giving a preset threshold, whether it is 16 lines, 32 lines or 128 lines, the algorithm accuracy and robustness are ensured, and the accuracy of recognized ground points is improved.

The application provides a ship type classification prediction method and system based on K-means and XG-Boost, acquires ship data and carries out pretreatment, then adopts a K-means clustering algorithm to respectively cluster each kind of data in the pretreated ship data to obtain multiple clusters, calculates error sum of squares of all data in each cluster, calculates the classification number of each kind of data according to the error sum of squares, selects a certain classification number by using an elbow method, marks the ship type of all clustered ships according to the classification number, then takes the ships with marked ship types as training set samples, adopts an XG-Boost classification algorithm to train the training set samples to obtain multiple classification prediction models, verifies the multiple classification prediction models to obtain an optimal classification prediction model, and predicts the ship types of all ships in the world according to the optimal classification prediction model and marks the ship types. The application can accurately classify all ships in the world, and can avoid overfitting and underfitting of the model while ensuring the accuracy.

This invention discloses a data visualization monitoring method and system for heating networks in heating station supply areas, specifically relating to the management field. The method includes synchronously collecting multi-dimensional data, establishing a heating network spatial coordinate system to locate the data collection nodes, and storing the coordinate data in a data storage center. It employs a K-means clustering algorithm to extract three typical heating patterns and simultaneously evaluates the matching degree of user heating patterns. An optimization cycle is set to extract optimized samples, and the gradient descent method is used to minimize the sum of squared errors between model predictions and actual values, updating relevant model parameters. Based on data analysis results, this invention generates three targeted decision-making suggestions: resource allocation, inspection priority, and heating guidance. Combined with historical data tracing and operational pattern mining, it provides comprehensive support for short-term operational optimization and long-term planning of the heating network, effectively improving the efficiency of heating resource utilization, reducing operational risks, and minimizing resource waste, thus exhibiting significant economic and management benefits.

The invention discloses a servo system reliability evaluation method based on K-S test. The method comprises the following steps: based on a fault data set of a servo system, performing K-S test by using an empirical distribution function and a fault probability distribution function of a Weibull distribution model; and if the K-S test is not passed or the end condition of the particle swarm algorithm is not satisfied, updating the position of each particle in the state search space through the particle swarm algorithm, outputting the current parameter of the Weibull distribution model, updating the Weibull distribution model by using the current parameter, and returning to the step of performing the K-S test. According to the method, the maximum difference of individuals is concerned in the K-S test, the particle swarm algorithm takes an error sum of squares function as a target function, the error of the whole is concerned to be minimum, the two functions are combined, the parameters of the solved Weibull distribution model are more accurate, and the convergence speed is higher through the particle swarm algorithm.

The invention belongs to the technical field of unmanned aerial vehicle design, and particularly relates to an open-loop state large-angle-of-attack range longitudinal aerodynamic coefficient identification calculation method, which comprises the following steps of: comparing simulation data with test data in an actual flight test, calculating an error sum of squares of the simulation data and the test data, and minimizing the error sum of squares; dividing the whole attack angle range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, comparing the optimal aerodynamic parameters with test data for verification, and executing the next step when the difference value between the optimal aerodynamic parameters and the test data is within a set range; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, verifying the applicability of the flight simulation model, and optimizing the robustness of the flight simulation model. The method is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention relates to a tunnel unmanned aerial vehicle cluster mobile ad hoc network and navigation method and system in a closed space. Determining the number of unmanned aerial vehicles most suitable for covering the current ground user group and the target position of each unmanned aerial vehicle by adopting a clustering algorithm based on error sum of squares and contour coefficient value optimization so as to realize an unmanned aerial vehicle cluster mobile ad hoc network; and a dynamic adaptive potential field algorithm based on multi-agent reinforcement learning is adopted to plan an air route for each unmanned aerial vehicle to arrive at the corresponding target position. According to the method, the coverage range and the service quality of the unmanned aerial vehicle system are effectively improved, and efficient path planning is realized in a dynamic and complex environment.

The present application relates to a cross-validation training method, apparatus, and equipment for a large-scale flow field proxy model. The method comprises: determining the range of design variables and performing normalization processing; performing a recursive permutation evolution experimental design on the design variables, generating sample points, and performing numerical simulation to obtain flow field data as the response values of each node in the flow field; constructing a proxy model for each node in the large-scale flow field, and calculating the corresponding basis function matrix of the proxy model based on the same shape parameters; calculating the sum of squared cross-validation errors for the entire flow field by constructing a second intermediate matrix related only to the shape parameters and design variables and a global matrix related only to the node response values, and optimizing the shape parameters with minimizing the sum of squared errors as the optimization goal, until convergence and output of an optimal proxy model based on the optimal shape parameters to perform the flow field prediction task. This method can achieve rapid and reliable verification of large-scale flow field proxy models and efficient and accurate flow field prediction.

The application relates to a privacy protection recommendation method and system based on matrix decomposition and belongs to the technical field of privacy protection recommendation. The method comprises the following steps: obtaining a historical rating matrix of a user; performing matrix decomposition on the historical rating matrix to obtain a shared rating matrix; sending the shared rating matrix and a scored item set to a recommendation server to generate public parameters, a decomposition matrix and a shared value of the decomposition matrix; generating inner product triplets and multiplication triplets by using a triplet server; calculating errors between real ratings and predicted ratings by using the recommendation server to generate an error shared matrix; optimizing the error shared matrix until the error squares of all the error shared matrices are less than a threshold value; calculating partial derivatives of a loss function on the decomposition matrix to obtain a gradient shared matrix; updating the decomposition matrix to obtain a user decomposition shared matrix; and generating a privacy protection recommendation result of the user by using the user decomposition shared matrix. The application provides strong protection in the aspect of protecting user privacy.

The invention belongs to the field of circuits and systems, and particularly relates to a two-dimensional recursive filter design based on an improved particle swarm optimization algorithm. Comprising the following steps: 1) performing mathematical analysis on a transfer function of a two-dimensional recursive filter to establish a model; 2) finding out an optimal parameter set by using an improved particle swarm optimization algorithm, and enabling the sum of squares of errors between the amplitude response of the designed filter and the amplitude response of an ideal filter to be minimum; 3) integrating a dynamic inertia weight strategy, a dynamic learning factor and a prying strategy into a particle swarm optimization algorithm; 4) setting algorithm operation parameters; and 5) by minimizing the objective function, obtaining a parameter set and combining the parameter set with a filter model structure to form an optimal two-dimensional recursive digital filter meeting conditions. According to the scheme, the stability of the filter can be ensured, and compared with the previous scheme, the amplitude response of the two-dimensional recursive digital filter is closer to that of an ideal two-dimensional recursive digital filter.

The invention relates to the technical field of power systems and renewable energy scheduling, and discloses a small hydropower station cluster planning and grouping method and device and a medium, and the method comprises the steps: obtaining multi-source data of each small hydropower station, and constructing a small hydropower station cluster feature space model; constructing a mathematical effectiveness index and a field rationality index, and establishing a k-means clustering algorithm multi-dimensional evaluation index system; executing an improved k-means + + clustering algorithm, and generating clustering schemes and evaluation results under different K values; constructing a derivative evaluation index based on an error sum of squares, distributing an index weight, calculating a comprehensive score corresponding to each K value, and determining an optimal clustering k value; and outputting a small hydropower station cluster grouping scheme corresponding to the optimal clustering number. According to the method, adaptive grouping of small hydropower station clusters is realized through a clustering algorithm driven by a small amount of data, and a small hydropower station cluster division scheme with reasonable spatial distribution, unified basin attributes and coordinated operation characteristics is provided for regional power grid dispatching.

The present invention discloses a method for restoring missing load based on similar block chimerism and first-order polynomial interpolation. First, the load data of power users is collected and a third-order load tensor is constructed. According to the position of the missing load, a matching similar block offset is constructed, and the matching similar block offset is specifically the offset between the position of the missing load and the position of the matching similar block. The matching similar block is selected by the spreading rule and irregular search optimization, and the sum of the squared errors between the matching similar block and the data block at the missing load position is used as the selection criterion, and the optimal similar block is used to generate preliminary restored data. First-order polynomial interpolation is performed on the load data at the missing position of the unfolded tensor to construct auxiliary restored data. The preliminary restored data and the auxiliary restored data are combined to obtain the final result of restoring the missing load. The method proposed by the present invention helps to fully explore the characteristics of the load data itself, and has certain practical significance for realizing the restoration of randomly missing user load data.

A deformable block registration improvement method based on historical information correction comprises the steps that an image is layered according to scenery information, and key areas are marked according to application requirements and a layering result; random block selection and deformation calculation in the key area: randomly selecting blocks in the key area, adjusting the size of the selected blocks, and calculating an initial deformation matrix of the selected blocks by adopting a normalized error sum of squares and an affine model; correcting a deformation result based on a historical deformation field: constructing historical deformation field data, extracting historical deformation data of the current block, and correcting an initial deformation matrix through a dynamic weight model; and performing new deformation field fitting: applying consistency constraint to the correction block, fitting a global deformable field by adopting thin-plate spline interpolation, outputting after filtering optimization, and updating a historical deformation field database. According to the method, the problems of low registration precision and error accumulation of deformable blocks in a multi-moving-target scene are solved through marking key areas by image layering, performing targeted random block selection, performing historical deformation field correction and performing consistent deformation field fitting.

The invention discloses a power system state estimation Hessian matrix method based on optimal control, and belongs to the technical field of power system state estimation. According to the method, a node power equation for state estimation of the alternating-current power distribution network is established, and a Jacobian matrix and a Hessian matrix of node power unbalance to node voltage and phase angle are deduced; a state estimation optimization model with the minimum sum of squares of node power measurement errors as a target is constructed, the power system state estimation Hessian matrix method based on optimal control is provided, the state estimation convergence is improved, meanwhile, the calculation speed is guaranteed, and compared with a traditional Newton-Raphson state estimation method, the method has the advantages that the calculation speed is improved, and the calculation efficiency is improved. The state estimation method is few in iteration times and good in convergence.

The present invention discloses a railway subgrade settlement prediction method based on the IGM-PSVR model. First, the subgrade settlement data is collected and preprocessed. Then, the data is subjected to a level ratio check, a corresponding differential equation is established, and the time response function is solved. The background value of the gray model is further optimized, and an improved gray model IGM(1,1) is constructed to obtain the prediction results of the model. Then, the particle swarm optimization algorithm is used to optimize the hyperparameters of the support vector regression (SVR) model, and a particle swarm optimized support vector machine model is constructed to obtain the prediction results of the model. Finally, the inverse sum of squared errors method is used to assign different weights to the two-step prediction results, and finally the combined prediction results are obtained. Experiments show that the combined model has higher prediction accuracy than the single model, can better reflect the trend of subgrade settlement changes, and plays an important role in ensuring the safety of railway transportation.

This invention relates to an automatic focusing method for laser galvanometers based on a polynomial regression algorithm, including variable definition, data collection of sample data within different power ranges from 10% to 80%, and data preprocessing; employing a piecewise polynomial regression model, and adding an L2 regularization term to the model to prevent overfitting; and minimizing the observed value D. i The model parameters are estimated using the sum of squared errors between the predicted and actual values. The model is used to predict values ​​in real time. When the absolute value of the deviation ∈ between the predicted and actual roundness values ​​D exceeds the threshold Th, the galvanometer height H or laser power P is dynamically adjusted to make D approach the target value D. target =0; the system is based on the optimal height H * The Z-axis position of the galvanometer is adjusted in real time to focus the laser beam at the optimal position; the deviation between the actual value D and the predicted value ∈ is periodically collected to update the regression model coefficients. This invention enables precise focusing of the laser beam, improving product consistency and yield.

The invention discloses a new energy automobile intelligent cabin fault mode identification method, and belongs to the field of machine learning model training, and the method comprises the steps: obtaining cabin data based on a plurality of sensor systems of a new energy automobile, marking the cabin data, and obtaining a marked data set; constructing a generative adversarial network for interactive exploration of quantum state information, and expanding the labeled data set based on the generative adversarial network to obtain a training data set; and performing feature extraction on the original data of the new sample by using the trained feature extraction model. And the obtained feature vectors are processed through the trained data dimension reduction model. And the feature vectors after dimension reduction are sent to a classifier for final fault type judgment. According to the method, the parameter selection of the support vector machine is optimized by adopting the Gaussian Newton method, the optimal solution is quickly converged through the minimization of the sum of squares of errors, and the classification performance is optimized.

The invention relates to a straight line and circular arc combined convex shape optimization method for a cylindrical roller, and aims to solve the problems that in existing logarithmic shape modification fitting, circular arc segmentation connection points depend on experience setting, and adaptability is poor. According to the method, a corresponding line-arc combination curve model is constructed based on a logarithmic modification curve, and the line-arc combination curve is iteratively optimized through an optimization method to obtain an optimal line-arc combination curve, so that the optimal line-arc combination curve approaches the logarithmic modification curve. The modeling process comprises segmentation point setting, connection point generation, arc parameter solving and curve construction, the optimization target is to minimize fitting error quadratic sum, and the geometric rationality and convexity of the curve are constrained and guaranteed. The adopted optimization algorithm is a sequence least square quadratic programming (SLSQP) algorithm. According to the method, fitting precision and engineering applicability are remarkably improved, and the method has good universality and expansibility, is suitable for modification design of various roller bearings and has engineering application value.

The invention discloses a dynamic injection posture adjustment method based on fry size information, and relates to the technical field of aquaculture, and the method comprises the steps: carrying out the image collection of a fry injection region, and carrying out the preprocessing of the collected image; identifying fry size information according to the preprocessed fry image; establishing a mathematical model between the injection parameter and the fry size according to the fry size information, and calculating to obtain a predicted fry injection parameter according to the mathematical model; calculating to obtain an injection parameter error sum-of-squares function according to the predicted fry injection parameters in combination with the injection angle under the optimal injection effect; planning a movement track of an injection needle by utilizing a path planning algorithm, and predicting a fry injection position by combining the movement speed of the fry; according to the predicted fry injection position and the actual fry position, fry injection accuracy is calculated, and the injection posture is dynamically adjusted in combination with the injection angle error. According to the invention, the injection accuracy and efficiency are improved, the damage to fish fries is reduced, and the automation level of the production process is improved.

The application discloses a kind of closed loop identification method, device, terminal equipment and storage medium based on nuclear power steam generator model, by adopting autoregressive model structure, according to several historical operation data, the initial system model of nuclear power steam generator is structured, then least square method and auxiliary variable method are used, to determine the observation variable in initial system model, to be identified parameter, residual error and the parameter initial value of to be identified parameter, therefore, the present application can overcome the nonlinear problem when the nuclear power steam generator is modeled at present, and the problem of low quality of closed loop operation sampling data of nuclear power steam generator.Finally, by minimizing the way of prediction error square sum, the iteration model parameter is updated using prediction error method, effectively improve parameter identification precision, finally obtain the target value of to be identified parameter, to build the system coupling model that can be used to carry out real-time monitoring and control to the operating state of steam generator.

The application provides a robot joint friction model parameter identification method, device and equipment. The method provided by the application comprises the following steps: a speed-related friction model, a temperature-related friction model and a load-related friction model are established to constitute a robot joint friction model; a plurality of groups of friction characteristic data of a robot joint in different working states are acquired, and initial value ranges of various parameters in the model are set based on expert knowledge and prior physical knowledge to constitute an initial parameter search space; an error square sum of model prediction is taken as a fitness function, the initial parameter search space is optimized based on the plurality of groups of friction characteristic data by using a genetic algorithm to obtain a candidate parameter search space; and an optimization function with the optimization objectives of minimizing a prediction error and minimizing a parameter physical deviation is constructed for the robot joint friction model.

The invention relates to a polynomial regression algorithm-based automatic focus searching method for a laser galvanometer, which comprises the following steps of: defining variables, acquiring sample data in different power sections within 10-80%, and preprocessing the data; a piecewise polynomial regression model is adopted, and an L2 regularization item is added into the model in order to prevent the model from being over-fitted; estimating model parameters by minimizing the sum of squares of errors between the observed value Di and the predicted value; when the absolute value of the deviation epsilon between the predicted value and the actual roundness value D exceeds a threshold value Th, dynamically adjusting the galvanometer height H or the laser power P to enable D to approach a target value Dtarget = 0; the system adjusts the Z-axis position of the galvanometer in real time according to the optimal height H *, so that the laser beam is focused at the optimal position; and periodically collecting a deviation epsilon between an actual value D and a predicted value, and updating a regression model coefficient. According to the invention, the laser beam can be accurately focused, and the consistency and the yield of products are improved.