113 results about "Polynomial regression" patented technology

The invention discloses an artificial intelligence inspection system and method based on an unmanned aerial vehicle, and particularly relates to the technical field of artificial intelligence of the unmanned aerial vehicle. The method comprises the following steps: acquiring magnetic field interference parameters and terrain complex parameters in pipeline inspection paths in real time, extracting magnetic field intensity fluctuation characteristics and obstacle density abnormal change characteristics, calculating navigation and positioning system accuracy weight assignment of each path in combination with machine learning methods such as polynomial regression, and generating a comprehensive inspection accuracy index; the comprehensive accuracy index is compared with a gradient standard threshold value, so that the accuracy division of the inspection path is realized: for incomplete accuracy inspection, the accuracy abnormal degree of a future navigation system is predicted, and if necessary, the flight height is adjusted and an obstacle dense area is avoided; the problems of false detection and missing detection caused by magnetic field interference and complex terrain in the prior art are effectively solved, the efficiency, accuracy and reliability of the unmanned aerial vehicle inspection task are remarkably improved, and major disasters caused by the fact that leakage is not detected in time are avoided.

The invention relates to the field of data processing, in particular to an intelligent deviation correction method and system for strip steel tailoring, and the method comprises the steps: firstly obtaining and smoothing deviation contour data of strip steel in real time; then, by calculating the local variation energy of the second derivative of the deviation profile, identifying the region with the form having significant change, and further calculating the feature scale of the region; then, dynamically generating a self-adaptive analysis window according to the feature scale of the macroscopic deviation; and finally, utilizing low-order polynomial regression in the window to accurately decouple the complex deviation form into basic parameters such as translation, inclination, C-shaped bending and S-shaped bending, and transmitting the basic parameters to a controller so as to realize rapid and accurate differential deviation correction. According to the method, the macroscopic deviation is identified by calculating the local variation energy and the feature scale, the adaptive analysis window is constructed, and the deviation form is decoupled by using local polynomial regression, so that accurate control is realized.

The invention relates to the technical field of precise injection molding, and discloses an injection process optimization method for a precise long and thin-wall product, computer equipment and a medium, the method comprises the following steps: S1, dynamically optimizing a screw position of a V / P switching point through a simulation experiment and regression analysis, and specifically comprises the following steps: S101, setting initial injection process parameters, carrying out an experiment by utilizing injection molding simulation software; s102, based on experimental data, adopting second-order polynomial regression analysis to construct a prediction model of the width w (x) and the thickness t (x) of the far gate region and the position x of the screw; and S2, on the basis of the solidification time, the pressure distribution in the mold cavity is adjusted by adopting stepped decreasing multi-section pressure maintaining control. According to the method, the problem of dimensional deviation caused by insufficient pressure of a far pouring gate area of the long thin-wall plastic part is solved by dynamically optimizing the position of the screw at the V / P switching point and performing stepped multi-section pressure maintaining control, so that the width and thickness tolerances of the near pouring gate area and the far pouring gate area meet the design requirements.

According to the dioxin closed-loop control method based on real-time detection and dynamic optimization, an online monitoring system is deployed, a tunable laser ionization time-of-flight mass spectrometer is arranged at an incineration flue gas discharge outlet to detect the concentration of trichlorobenzene serving as a dioxin indicator in real time, and a sensor is combined to detect working condition parameters; polynomial regression is used for real-time conversion of dioxin toxicity equivalent concentration, and an on-line monitoring-toxicity evaluation integrated system is formed. And a double-layer random forest-eagle mixed optimization model is constructed, the first layer is based on historical working condition data to train a random forest model to predict the dioxin emission trend and the potential standard exceeding risk, and the second layer is used for dynamically optimizing the incineration parameter combination through an improved eagle optimization algorithm and issuing the incineration parameter combination to an incineration system in real time. And meanwhile, a calcium-based retardant directional spraying device is deployed in a flue gas purification section, so that uniform covering and accurate dosage control of the retardant are realized, and the low-temperature resynthesis reaction of dioxin is inhibited. And finally, parameters of the double-layer optimization model are dynamically updated based on the error of an actual detection value and a predicted value, the adaptability of the model to working condition fluctuation is improved, therefore, dioxin emission is effectively reduced, and the environmental protection property and stability of the incineration process are improved.

The present invention discloses a real-time data collection and processing method and system for a smart park based on edge computing, which relates to the field of data collection technology and is used to improve the problem of different display effects caused by the use of a unified processing method for park images at different locations. The method comprises obtaining attribute data and point information of each collection device in the park, setting a collection force scoring mechanism to score the collection force of different collection devices using fuzzy reasoning, dividing the park into regions according to the collection force score and point information, performing real-time detection on the detection area and the number of faults of the collection equipment in each divided area and calculating the real-time coverage rate of each divided area using a polynomial regression algorithm, classifying each divided area, obtaining regional images of low real-time coverage areas for marking and collecting the amount of dust and humidity in the space, calculating the humidity transfer rate, and judging whether the corresponding area uses an enhanced edge computing algorithm to process the regional image by using a geometric mean method.

The invention discloses a metal dust explosion risk comprehensive index early warning method and system, and particularly relates to the technical field of metal dust. The method comprises the following steps: standardizing real-time dust environment data collected in a working site and standard dust data, generating a feature vector, calculating similarity, and identifying a potential abnormal state; processing temperature deviation characteristics and charge concentration fluctuation characteristics are further extracted, a multi-dimensional risk assessment model is constructed, a metal dust explosion risk comprehensive index is calculated by using a polynomial regression algorithm and is compared with a gradient risk threshold value, and multi-stage early warning signal triggering and linkage response control are realized; the method has a self-adaptive recognition capability on a novel or unknown dust state, the accuracy and response timeliness of risk early warning are remarkably improved, and the intrinsic safety guarantee capability of the system under a complex working condition is enhanced.

The invention relates to a response surface optimization method of a power device packaging thermal resistor, and aims to solve the problems of low efficiency and high cost of a traditional optimization method. According to the method, a three-level seven-factor orthogonal standard configuration table is configured, a packaging thermal resistance simulation model is combined, and an orthogonal experiment method is adopted to carry out combined simulation testing on parameters such as the chip area, the chip thickness, the welding layer thickness, the frame thickness, a plastic packaging material, a welding material and a lead frame material which affect thermal resistance. The deviation sum of squares, the degree of freedom and the F ratio of all the factors are calculated through variance analysis, and the factors remarkably influencing the thermal resistance are screened out. And further establishing a thermal resistance prediction model by using a response surface method, analyzing the interactive influence among significant factors in combination with a polynomial regression equation, finding out a parameter combination of a minimum thermal resistance value, and verifying an optimization effect through simulation. According to the method, the efficiency and the precision of packaging thermal resistance optimization are remarkably improved, the experiment cost is reduced, and the method is suitable for optimization design of a power device packaging process.

The invention provides a transformer anti-short circuit capability quantitative evaluation method and system based on multi-physics field simulation mapping, and belongs to the technical field of transformer anti-short circuit capability evaluation. Comprising the following steps: obtaining structure parameters of a to-be-tested type transformer, and constructing a multi-physics field coupling simulation model; running a preset defect working condition by using the multi-physics field coupling simulation model, and calibrating an anti-short-circuit capability limit value corresponding to the defect working condition according to the running data; applying a unit pulse voltage excitation signal, and obtaining a feature vector corresponding to the defect working condition based on the frequency domain response curve; analyzing the mapping relation by using a polynomial regression algorithm to obtain an anti-short-circuit capability quantitative evaluation function; and in a power failure off-line state of the transformer, injecting a unit pulse voltage excitation signal into the winding by using a pulse frequency response tester, calculating an anti-short-circuit capability limit value corresponding to an actually measured operating temperature value according to the anti-short-circuit capability quantitative evaluation function, and evaluating the anti-short-circuit capability of the transformer in combination with a rated thermally stable current.

The invention discloses a logging curve abnormal section reconstruction method based on an attribute co-occurrence relation. The logging curve abnormal section reconstruction method comprises the steps of research area data preprocessing, attribute correlation analysis, curve trend characteristic extraction, modeling of a nonlinear correlation relation between curves, generation of attribute co-occurrence relation characteristics of a missing section and construction of an ACF-Informer curve reconstruction model. According to the method, co-occurrence attributes are screened by using Spearman correlation coefficients, nonlinear equations among the attributes in all periods are modeled through polynomial regression, and ACR features are fused to an Informer encoder-decoder architecture in stages as priori knowledge; the multi-strategy ACR features are fused by adopting integrated learning, so that the model can adapt to the spatial variability of the high-heterogeneity geological environment; a sub-model is constructed through a plurality of feature fusion strategies, and final output is optimized by adopting an integrated learning technology, so that the robustness and prediction precision of the model in a complex geological environment are improved.

The invention provides a virtual marshalling train minimum tracking spacing generation method based on parameter identification. The method comprises the following steps: acquiring actual operation data of a virtual marshalling train, wherein the actual operation data comprises train operation time, train operation speed, train mass and train braking force; establishing a virtual marshalling train dynamic model by considering the braking force, the basic resistance and the random interference of the train, and performing parameter identification on the virtual marshalling train dynamic model by applying a polynomial regression algorithm based on the actual operation data of the train; and predicting a braking track of the virtual marshalling train based on the dynamic model of the virtual marshalling train after parameter identification, and obtaining the minimum tracking distance of the virtual marshalling train based on the braking tracks of the front train and the rear train in the virtual marshalling train. According to the method, the improvement of a virtual marshalling train control strategy is realized, and compared with a method for realizing a relative braking distance by adopting a fixed braking rate, the method has the advantages that the train tracking distance is shortened, and more efficient and safer train operation is realized.

The invention provides a method for predicting a memory effect of a natural gas hydrate, belongs to the technical field of intelligent modeling and prediction of the natural gas hydrate, and aims to realize accurate modeling and intelligent identification of a memory effect behavior in a hydrate formation and dissociation process. The method comprises the following steps: S1, collecting experimental data, including a plurality of variable characteristics influencing the memory effect, such as synthesis temperature, dissociation pressure, synthesis pressure, synthesis-decomposition cycle index, decomposition temperature and the like, and constructing a training data set by taking nucleation time as a dependent variable; s2, preprocessing the experimental data, completing missing value filling, abnormal value elimination and standardization processing, and obtaining a standardized sample; s3, constructing a prediction model based on support vector regression, random forest, XGBoost, polynomial regression and other algorithms, and improving prediction performance by adjusting model hyper-parameters; and S4, a graphic visual interface is constructed based on Python and PyQt5, a user can input experimental conditions and select a model, and a system automatically predicts a memory effect and judges whether a nucleation promoting behavior exists according to the memory effect. The method integrates key modules such as feature engineering, active learning, hyper-parameter optimization and visual interaction, has high precision, high adaptability and good expansibility, and is suitable for various thermodynamic application scenes such as natural gas hydrate phase change behavior prediction, energy storage and transportation optimization, carbon sequestration process regulation and control and the like.

The invention belongs to the technical field of power system and equipment management, and provides a system and a method for evaluating the life extension of an immediate electric energy meter based on anomaly evaluation, and the system comprises a data collection module which is used for collecting and obtaining the power utilization data of a transformer area group and the work operation data of the immediate electric energy meter; the batch division management module is used for carrying out batch division and management on the immediate electric energy meters based on the work operation data in combination with the immediate electric energy meter equipment parameter data; the anomaly analysis module is used for carrying out anomaly analysis on the electric energy meters which are divided in batches and are in time on the basis of an anomaly detection algorithm in combination with the working operation data to obtain an anomaly analysis result; and the life extension evaluation module is used for calculating a comprehensive life extension score by using a polynomial regression function according to the abnormal analysis result and the marketing data of the electric energy meter, and obtaining life extension evaluation results of the plurality of batches of immediate electric energy meters according to the comprehensive life extension score. According to the invention, online intelligent diagnosis, batch intelligent division, batch abnormity monitoring and batch quality evaluation can be realized.

The invention discloses a smart park real-time data acquisition processing method and system based on edge computing, relates to the technical field of data acquisition, and aims to solve the problem of different display effects caused by a unified processing method for park images at different positions. Setting an acquisition force scoring mechanism to carry out acquisition force scoring on different acquisition devices by utilizing fuzzy reasoning, and carrying out regional division on the park according to acquisition force scores and point location information; detecting the detection area and the fault number of the acquisition equipment in each divided region in real time, calculating the regional real-time coverage rate of each divided region by using a polynomial regression algorithm, classifying each divided region, obtaining a regional image of a low-real-time coverage region, marking the regional image, and acquiring the space dust amount and humidity; and calculating a humidity transfer rate, and judging whether a region image is processed by adopting an enhanced edge calculation algorithm in a corresponding region through a geometric averaging method.

A computer device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: a) receive, from the image capture device, a plurality of images for a continuous scan phase shift interferometry (PSI), wherein each image of the plurality of images includes a first plurality of pixels of a first item and a second plurality of pixels of a partial cavity; b) perform intensity scans of each pixel in the second plurality of pixels; c) identify zero transitions for each of the second plurality of pixels; and d) statistically evaluate the identified zero transitions by polynomial regression analysis.

The invention discloses an automatic raw material feeding control system for PVC plastic tile production, and relates to the technical field of building material manufacturing, and the system comprises a raw material identification module, a PLC central control unit configured on a production line, a bar code and RFID technology for identifying different types of raw materials and obtaining material type information, a preprocessing module, and a control module for controlling the automatic feeding of raw materials based on the material type information. And the weighing module is used for conveying the pretreated raw materials to a weighing assembly at the bottom of the feeding barrel through an automatic conveying belt and an elevator, weighing treatment is carried out, and the weight change trend of the raw materials is obtained. According to the invention, by integrating the dual identification technology of the bar code scanner and the RFID reader-writer, the identification precision is greatly improved, various types of raw materials can be effectively processed, and the adaptability and efficiency of production are improved. A nonlinear mathematical model constructed on the basis of polynomial regression in combination with a neural network is adopted, and a multi-target particle swarm algorithm is matched to perform mixing optimization, so that accurate control of the raw material flow velocity is realized.

In order to explore the effectiveness of the functional data analysis method in the hyperspectral image processing, the application proposes a classification method combining the Gaussian regression mixture model and the MRF hyperspectral function data; first, the polynomial regression is used to fit the hyperspectral image pixel spectrum curve, so as to express the pixel spectrum information in the form of function; then, the neighborhood relationship is introduced to establish the Markov random field model, and the neighborhood Gaussian regression mixture model is established in combination with the Gaussian regression mixture model; finally, according to the maximum posterior probability criterion, the final hyperspectral image classification result is obtained. Since the spatial-spectral information of the hyperspectral image is fully combined, the algorithm has high-precision classification result, and effectively improves the classification performance of the hyperspectral image.

The invention relates to the technical field of power system dynamic safety analysis, and discloses a mechanism-data fusion new energy power system minimum inertia demand rapid evaluation method, which comprises the following steps: in an offline stage, establishing an expansion system frequency response model containing new energy; generating a plurality of sample scenes in a given disturbance power range and a new energy permeability range; a model minimum inertia demand and a simulation minimum inertia demand are obtained through expansion of a system frequency response model and stepping type time domain simulation calculation, a polynomial regression method and a polynomial regression equation are utilized, and the coefficient and order of the polynomial regression equation are determined by adopting an optimization algorithm; during online evaluation, real-time disturbance power and new energy permeability of a power grid are obtained; and obtaining a model minimum inertia demand and a corresponding minimum inertia demand deviation compensation value, and outputting a final system minimum inertia demand evaluation result. According to the method, the final evaluation result has the calculation efficiency of the extended SFR model and the evaluation precision of stepping simulation.

The present invention provides a resin bed life assessment method based on a multiple regression model, comprising the following steps: 1) resin bed operation monitoring test: test equipment, test parameters and test results; 2) prediction model: mathematical modeling, numerical analysis, least squares method, polynomial regression and multiple regression analysis; 3) prediction model construction and verification: model establishment, prediction model verification and prediction model application; mathematical modeling plays a good role in solving practical problems and involves a wide range of application fields; it requires the integration and application of various knowledge; it requires the cooperation of various technical means, etc., and a method of using multiple nonlinear regression to solve the model generally uses variable interchange to convert the nonlinear model into a linear model. This method improves the efficiency of calculation, increases the accuracy of calculation results, and realizes the life construction model processing and evaluation of the resin bed during its operation.

The invention provides an azimuth observation data real-time processing method and system with a backtracking and repairing function, and belongs to the field of data processing. The problems that phase delay exists during real-time smoothing and outlier processing of underwater target observation data, and spot type outlier detection and correction processing are improper are solved. According to azimuth and course observation data received in real time, the azimuth observation data are smoothed in real time by adopting a local polynomial regression method based on a Gaussian kernel, and outliers existing in the azimuth data and the course data are identified in real time by adopting an innovation chi-square detection method based on Kalman filtering and are eliminated. And according to the existing non-outlier effective data prediction, correcting the azimuth data at the outlier, and carrying out backtracking restoration on the interval of the outlier data. The method is small in phase delay and good in data real-time performance; the Gaussian kernel property is combined with the past and future data secondary correction of the outlier interval, the accuracy of target position calculation can be improved, the accumulative error of the calculation algorithm is reduced, and the method has good theoretical and engineering application values.

The application relates to the technical field of fire rescue equipment monitoring, and discloses a firefighter air respirator residual amount prediction method and system based on dynamic pressure compensation. The method comprises the following steps: acquiring historical consumption time data of firefighters, using polynomial regression fitting to establish a theoretical pressure consumption rate function corresponding to an individual and establishing a theoretical prediction logic; receiving a combat organization instruction to obtain an initial full load pressure, starting timing and calculating and displaying a current theoretical prediction residual pressure; capturing an artificial interactive correction instruction, extracting a correction time node and an actual residual pressure, and solving to generate a dynamic compensation coefficient; applying the dynamic compensation coefficient to reconstruct and solve an actual prediction residual pressure and control and update display; comparing the actual prediction residual pressure with an alarm critical pressure value to trigger an alarm, and extracting actual combat data to iteratively update the theoretical function after the task is completed. The application realizes individualized benchmark prediction of air consumption and dynamic adaptive error correction of the working environment, and improves residual amount early warning accuracy.

The invention relates to the technical field of battery monitoring, in particular to a battery SOC (state of charge) estimation method and system based on whole-process monitoring, and the method comprises the steps: collecting terminal voltage, temperature, current and timestamp under constant-current discharge, and reading a relation curve of nominal capacity and open-circuit voltage state of charge; interpolating the discrete curve generation voltage to SOC, and obtaining an initial SOC according to the voltage at the first moment; calculating the sampling interval hour and the interval charge quantity, subtracting the percentage of the SOC accounting for the nominal capacity at the previous moment to obtain coulomb metering SOC, averaging the coulomb metering SOC and the interpolation SOC to obtain a reference SOC, and outputting a label; carrying out db1 wavelet decomposition and hard threshold processing on the voltage and the temperature, then carrying out inverse transformation to obtain a de-noising sequence, and calculating a signal-to-noise ratio according to a power ratio; polynomial regression or tree integration regression is trained through the four-dimensional features; and the evaluation index is combined with a signal-to-noise ratio comparison threshold value and a model combination, and the output of the target SOC estimator is solidified to estimate the SOC. The method can solve the problems that existing SOC estimation is highly dependent on standing conditions, coulomb metering errors are accumulated, noise interference is large and the like.

The application discloses an online roll temperature and thermal crown prediction method based on finite difference and polynomial regression, aiming at the technical pain point that the accuracy of the traditional analytical model is insufficient and numerical simulation cannot meet the online real-time calculation demand, a plurality of groups of working conditions are designed through an orthogonal experiment method, and a finite difference method is used to calculate roll steady-state temperature field data to construct a training data set, based on the data set, a polynomial regression algorithm with elastic network regularization is used to fit an axial temperature distribution general model with the roll temperature field as an input variable, after obtaining the online surface axial temperature distribution of the roll, the general model is substituted into the internal temperature field to restore the internal temperature field and calculate the radial thermal expansion amount at each position, so that compensation information is provided for a shape control system of a rolling mill. The application simplifies complex physical field simulation into lightweight algebraic formula operation, retains the high analytical accuracy of the finite difference method, realizes real-time prediction, and can significantly improve the rolling quality of silicon steel products.

The present invention discloses an unmanned aerial vehicle (UAV) artificial intelligence inspection system and method, which specifically relates to the field of UAV artificial intelligence technology. By acquiring magnetic field interference parameters and terrain complexity parameters in the pipeline inspection path in real time, and extracting magnetic field intensity fluctuation characteristics and obstacle density abnormal change characteristics, the navigation and positioning system accuracy weight assignment of each path is calculated in combination with machine learning methods such as polynomial regression to generate a comprehensive inspection accuracy index. By comparing the comprehensive accuracy index with the gradient standard threshold, the accuracy division of the inspection path is achieved: for incomplete accuracy inspections, the accuracy abnormality of the future navigation system is predicted, and the flight altitude is adjusted when necessary to avoid areas with dense obstacles. This effectively solves the problems of false detection and missed detection caused by magnetic field interference and complex terrain in the existing technology, significantly improves the efficiency, accuracy and reliability of UAV inspection tasks, and avoids major disasters caused by failure to detect leaks in a timely manner.

The invention relates to the field of strain detection, in particular to a self-adaptive strain detection method and system and a medium. The method comprises the following steps: inputting grid point coordinates, noisy displacement data and a maximum smooth half window; calculating the overall noise level sigma through local linear regression analysis; extracting local strain curvature characteristic quantity through cubic polynomial regression; establishing a noise and curvature balance relation based on the sigma and the curvature characteristic quantity, and calculating an adaptive smooth half window; obtaining the strain estimation value of each grid point through linear polynomial regression, and finally obtaining the strain distribution of the whole field. According to the method, the optimal smooth window can be automatically adjusted without manual intervention, balance is achieved between noise suppression and detail reservation, the problem that a traditional fixed window method is insufficient in precision in a non-uniform deformation area is solved, high-precision full-field strain detection of a noise-containing displacement field is achieved, and the detection precision is improved. The method is suitable for derivative calculation of digital image related systems and various noisy continuous signals.

The application relates to the technical field of high-performance computing, and particularly discloses a method for predicting the performance of an application program and a computing device. The method comprises the following steps: constructing a performance characteristic data set based on the running parameters of an application program in each job of a cluster system; determining similar characteristic data from the performance characteristic data set based on the running parameters of the application program on a single computing node of the cluster system; classifying the similar characteristic data according to the number of computing nodes, generating simulation characteristic data by using an application performance prediction model based on the number of similar characteristic data in each classification; generating an application data set by using the similar characteristic data and the simulation characteristic data; fitting the relationship between the number of computing nodes and the running time by using a polynomial regression algorithm based on the application data set; and predicting the running time of the application program on each computing node of the cluster system by using the fitted relationship. According to the application, accurate computing power use selection suggestions can be provided for the application program.

The invention discloses a multi-objective optimization method, device and system for an impeller centrifugal pump and a storage medium, and the method comprises the steps: building a parameterized model of the centrifugal pump based on geometric parameters of the centrifugal pump; grid division is conducted on the parameterized model, and the centrifugal pump performance corresponding to each set of design parameters is calculated through numerical simulation; a response surface method is adopted, polynomial regression is conducted on numerical simulation data, and a response surface model between the centrifugal pump performance and design parameters is established; a natural evolution process is simulated by introducing a genetic algorithm, and the response surface model is globally searched and optimized. By the adoption of the technical scheme, comprehensive optimization of multiple performance parameters of the centrifugal pump is achieved, and the overall performance and adaptability of the centrifugal pump are improved.

The present application relates to the field of strain detection, and particularly to a self-adaptive strain detection method, system and medium. The method comprises: inputting grid point coordinates, noisy displacement data and a maximum smoothing half window; calculating an overall noise level sigma through local linear regression analysis; extracting a local strain curvature characteristic quantity through cubic polynomial regression; establishing a noise and curvature balance relationship based on sigma and the curvature characteristic quantity, and calculating an adaptive smoothing half window; obtaining strain estimation values of each grid point through linear polynomial regression, and finally obtaining overall strain distribution. The method can automatically adjust the optimal smoothing window without manual intervention, balances between noise suppression and detail preservation, solves the problem of insufficient accuracy of traditional fixed window methods in non-uniform deformation areas, realizes high-precision full-field strain detection of noisy displacement fields, and is suitable for derivative calculation of digital image correlation systems and various noisy continuous signals.

The application discloses a digital elevation model elevation error correction method based on a particle swarm optimization random forest, and belongs to the technical field of remote sensing. The particle swarm optimization random forest method is used for correction, so that the precision of the corrected digital elevation model is further improved. SRTM is selected as the digital elevation model used in the test, ICESat-2 strong light beam ground surface photon data is used as the reference elevation control point used in the test, Globeland30 is used as the global ground surface cover data used in the test, airborne LIDAR DTM published by NEON is used as the verification data used in the test. The method proposed in the application and the correction method based on polynomial regression are tested, the root mean square error is used as the verification index, the elevation error of SRTM can be effectively reduced, the elevation error of the corrected SRTM is reduced by 42% to 46% compared with the elevation error before correction, and the correction precision is better than that of the correction method based on polynomial regression.

The invention belongs to the technical field of simulation optimization, and particularly relates to a centrifugal pump impeller multi-physics field coupling simulation optimization method which comprises the following steps: constructing a geometric model of a centrifugal pump by adopting three-dimensional drawing software according to an actual structure of the centrifugal pump; finite element modeling is conducted, and a finite element analysis model of the centrifugal pump is obtained; hexahedral grids are divided for the impeller, a shape variable space of the impeller is built, and the impeller deformation serves as a design variable; solving the finite element analysis model after grid division through a solver; an experimental design sample is generated by adopting a Latin hypercube sampling method, a global response surface model is constructed through quadratic polynomial regression fitting, an optimal impeller deformation amount is iteratively solved by adopting a global response surface optimization algorithm, a finite element analysis model is adjusted, and an optimization model is formed. According to the method, fluid-solid coupling simulation, a global response surface method and an automatic iteration process are fused, and the efficiency of the centrifugal pump impeller is maximized and optimized on the basis of accurately matching actual working conditions.

The present invention discloses an online wrist torque estimation method based on neural features and LSTM, comprising the following steps: (1) the experimenter keeps the arm still and applies torque to the torque sensor through the wrist; (2) synchronously collects the torque sensor data and high-density surface electromyography signals; (3) uses a blind source separation algorithm to decompose the high-density surface electromyography signals to obtain a motor unit spike train MUST; (4) constructs input and output vectors based on the original HD-sEMG signal and the decomposed MUST, trains the LSTM, and simultaneously performs polynomial regression on the discharge rate of the neural features and the torque; (5) calculates the real-time discharge rate DR of the CST through a sliding window for real-time torque estimation. The present invention is used for natural and real-time control of a robotic prosthesis, can provide a better interactive experience for people with disabilities, and can also be widely used in rehabilitation robots, human-computer interaction and other fields.