141 results about "Polynomial regression" patented technology

The invention discloses an intelligent smelting process parameter optimization management method and system, and relates to the technical field of smelting process data processing, and the method comprises the steps: determining the initial time lag of a process parameter according to the input of the process parameter and the output of a smelting raw material, and considering the time lag characteristic of the process parameter during the adjustment. And the smelting state of the smelting equipment is defined, and the working state of the smelting equipment is defined, so that a reliable basis is provided for optimization of subsequent process parameters. The nonlinear relation between process parameter input and smelting raw material output and the coupling relation between process parameters are determined, the nonlinear relation is analyzed by considering the process parameter input, the smelting state and the smelting raw material output, and description is carried out through a polynomial regression mode. And the initial time lag is adjusted in combination with the nonlinear relation and the coupling relation. The process parameters are stably, rapidly and reasonably adjusted, the accuracy and adaptability of optimization and adjustment of the process parameters are improved, and it is guaranteed that the smelting process is efficiently and safely carried out.

The invention discloses a light quantum technology system for sewage treatment, relates to the technical field of sewage treatment, and is used for solving the problem that the sewage treatment efficiency of a light quantum technology is reduced due to the fact that suspended solids shield the illumination intensity and range of sewage. Calculating an ammonia nitrogen weight by using a polynomial regression algorithm, carrying out weighted calculation on the ammonia nitrogen weight and the ammonia nitrogen content in the sample to obtain an ammonia nitrogen coverage potential coefficient, and obtaining a phosphate coverage potential coefficient by using the same method. Calculating an equilibrium potential coefficient according to the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, formulating an equilibrium restriction rule to obtain a restriction difference value, randomly selecting two time points within a period of time through a recursive acquisition method to acquire mark parameter data, calculating an increase rate, and synthesizing the increase rate and the restriction difference value to obtain the content of a treated substance. Suspended matter treatment is perfected, and the sewage treatment efficiency of the light quantum technology is improved.

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 discloses a dynamic AI index system index prediction fitting method and device based on machine learning. According to the method, through parallel training of multiple machine learning and deep learning models such as linear regression, polynomial regression, SVM, XGBoost, GBRT, DNN and Transformer, the relation between the index value of the index system and the comprehensive index value can be captured more comprehensively, and therefore the prediction accuracy is improved. The invention innovatively proposes a triple condition ranking screening method based on RMSE, R2 and time cost, can effectively screen out an optimal prediction model, and ensures the accuracy and reliability of a prediction result. The system can automatically detect updating of an index system or an actual data set and automatically execute a new round of model training, testing and prediction, and timeliness and accuracy of a prediction result are ensured. When the index system or the actual data set is updated each time, each model is retrained to ensure that the model can adapt to a new data environment and avoid outdated time.

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 discloses a ventilation parameter optimization method and a dust concentration control method for a tunneling roadway. The method comprises the following steps: acquiring roadway airflow-dust data information of a target tunneling roadway under different ventilation parameters; fitting the acquired data information based on a polynomial regression algorithm; performing data cleaning based on a difference value between a fitting result and actual data; the ventilation parameters are used as optimization objects, the dust concentration is used as an optimization target, and a multi-target particle swarm optimization algorithm, a multi-target genetic algorithm and a polynomial regression algorithm are adopted to optimize the ventilation parameters of the target tunneling roadway; and according to the obtained optimal solution set, obtaining an optimal ventilation parameter of the target tunneling roadway based on the Pareto leading edge solution set, and completing ventilation parameter optimization of the target tunneling roadway. According to the method, ventilation parameter optimization and dust concentration control of the target tunneling roadway are achieved, the reliability is higher, and the accuracy is better.

The invention discloses a virtual scene immersion prediction method based on electroencephalogram signals. The method comprises the following steps: firstly, based on a binocular imaging principle, constructing a space distortion model of different distortion effects caused by mismatching of a camera shooting field angle and a human eye field angle, and quantifying a distortion degree by using an index based on a field angle difference; secondly, a man-machine interaction experiment involving virtual reality space distortion is designed, and subjective I PQ scoring data and objective EEG signal data under different distortion conditions are collected; preprocessing such as filtering, artifact removal and spectral analysis is carried out on the collected EEG original signal so as to calculate the absolute power value of a specified brain wave frequency band. And finally, combining the calculated electroencephalogram absolute power value with the distortion index, constructing an immersion prediction model based on a polynomial regression algorithm, and realizing objective prediction of subjective immersion. According to the method, the influence mechanism of virtual space distortion on immersion can be effectively revealed, and a solid theoretical support is provided for optimizing virtual reality space design.

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.

The invention relates to the field of sensor data processing, and discloses a sensor response time synchronous matching method in UCTD in a complex turbulence environment, which comprises the following steps: generating different flow rates and turbulence intensities by using a temperature control water flow tank, and placing a UCTD probe in the temperature control water flow tank to measure temperatures and conductivities under different flow rates and turbulence intensities; recording the response time of the temperature and conductivity sensor in the UCTD under different flow velocities and turbulence intensities; performing polynomial regression analysis on the recorded experimental data, and establishing a temperature and conductivity sensor response time model; minimizing an error between a predicted value and an actual value of the model by using a gradient descent method, and optimizing parameters in the model; and establishing a synchronous response time model by using the optimized model, and performing synchronous matching on the response time difference of the temperature sensor and the conductivity sensor. According to the method disclosed by the invention, the data accuracy and reliability of the UCTD in marine environment monitoring are greatly improved.

The invention provides a method and a system for predicting the service life of a pipe under the corrosion action of environmental factors, and relates to the technical field of corrosion and protection of pipeline materials. Environmental factors such as water flow velocity, pH value and temperature are acquired, interaction of the factors is analyzed, a standard corrosion rate is generated, and corrosion rate data of each experimental point are collected; comparing with a standard corrosion rate, recording data of experimental points exceeding the standard corrosion rate, constructing a quadratic polynomial regression equation related to the corrosion rate, calculating a statistic value F and a probability value P in the equation, judging whether the regression equation is significant or not, predicting the corrosion rate under the current environmental condition, and determining the corrosion rate of the pipe according to the predicted corrosion rate and the initial thickness of the pipe. And calculating the remaining service life of the pipe. According to the method, the service life of the pipe under the corrosion of the environmental factors is predicted by acquiring the environmental factors, analyzing the interaction, collecting the experimental data and constructing the regression equation.

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 fluororubber static sealing material service life evaluation method based on an accelerated aging test, and belongs to the technical field of fluororubber static sealing materials. The method comprises the following steps: designing a reasonable accelerated aging temperature, carrying out a constant-temperature accelerated aging test on four temperature points, regularly sampling, measuring compression set, and carrying out polynomial regression analysis by taking the logarithm of aging time as an independent variable and the compression set as a dependent variable to obtain a regression equation at each temperature point; calculating to obtain failure time under each temperature point; a function of test temperature and failure time of the fluororubber static sealing material is established on the basis of a WLF (Williams-Landel-Ferry) equation to evaluate the service life of the fluororubber static sealing material, and compared with other accelerated aging methods based on an Arrhenius equation describing a chemical reaction, the method comprehensively considers physical aging and chemical aging, so that the service life of the fluororubber static sealing material is evaluated. And the service life of the fluororubber static sealing material can be evaluated more scientifically.

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.

Disclosed is an online estimation method for wrist torque based on neural features and LSTM, including following steps: (1) an experimenter keeps his / her arms stationary and applies torque to a torque sensor through his / her wrist; (2) acquiring data from the torque sensor and high-density surface EMG ((HD-sEMG) synchronously; (3) decomposing the HD-sEMG using a blind source separation algorithm to obtain a motor unit spike train (MUST); (4) constructing input and output vectors on the basis of the original HD-sEMG and the decomposed MUST, performing training of the LSTM, and performing polynomial regression of a discharge rate and torque of a neural feature; and (5) calculating a real-time discharge rate (DR) of the CST using a sliding window approach for real-time estimation of the torque.

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 discloses an air preheater control system and method based on big data model analysis, and belongs to the technical field of air preheater control. The system comprises a data acquisition module, a data preprocessing module, a big data analysis module, an optimization control module, a fault early warning module and a man-machine interaction module. The big data analysis module extracts a performance trend by using polynomial regression fitting, and determines a causal relationship through a Bayesian network; the optimization control module generates an optimization operation scheme by adopting a graph optimization algorithm and a dynamic adjustment strategy based on a causal relationship; and the fault early warning module constructs a prediction network according to the causal relationship and the performance trend to realize fault early warning. The method comprises the steps of collecting and preprocessing operation data, analyzing and determining a causal relationship, dynamically adjusting an operation scheme, predicting faults, performing early warning and performing visual display. Accurate monitoring, dynamic optimization and fault early warning of the operation state of the air preheater are achieved, and the operation efficiency and reliability are improved.

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.

The present invention relates to the field of sensor data processing, and discloses a method for synchronous matching of sensor response time in UCTD under complex turbulent environment, comprising the following steps: using a temperature-controlled water flow tank to generate different flow velocities and turbulence intensities, placing a UCTD probe in the temperature-controlled water flow tank to measure the temperature and conductivity under different flow velocities and turbulence intensities, and recording the response time of the temperature and conductivity sensors in UCTD under different flow velocities and turbulence intensities; establishing a temperature and conductivity sensor response time model by performing polynomial regression analysis on the recorded experimental data; using a gradient descent method to minimize the error between the model prediction value and the actual value, and optimizing the parameters in the model; using the optimized model to establish a synchronous response time model, and synchronously matching the response time difference of the temperature sensor and the conductivity sensor. The method disclosed by the present invention greatly improves the accuracy and reliability of UCTD data in marine environment monitoring.

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 provides a wafer positioning device, which is characterized in that a controller receives image data of an outer edge of a wafer, calculates an accurate position of a positioning characteristic of the outer edge of the wafer by matching a first-order derivative and a second-order derivative of the image data with polynomial regression operation, eliminates the positioning characteristic in the image data, and outputs the positioning characteristic of the outer edge of the wafer. The center offset of the wafer is calculated according to the image data excluding the positioning features, and then the correction angle and the X-Y correction amount are calculated. The controller controls the driving device to control the rotating platform module to rotate according to the correction angle so as to enable the positioning feature to rotate to a specified angle; and controlling a driving device of the wafer positioning device according to the X-Y correction amount to enable the wafer to move on a horizontal plane of a rotating platform module of the wafer positioning device so as to enable the circle center of the wafer to be aligned with a specified position of the rotating platform module.

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.

Provided is a computer implemented method (100) for representing at least part of a real-world environment based on measurements performed by a measurement device in said real-world environment. The method (100) comprises transforming (110) a ray, represented by a line (R1) in a first coordinate system of said real-world environment, from the first coordinate system to a second coordinate system used for representation of said measurements, wherein the ray (R1) is emitted by a light-emitting source of the measurement device or received at the measurement device (200), sampling (120) the transformed ray (R2) to be represented by a plurality of synthetic points (P1, P2), wherein the synthetic points are separated by a specific distance (L1), determining (130) a discrepancy (D1) between the transformed ray (R2) and a straight line (S1) connecting the synthetic points (P1, P2), if the discrepancy (D1) is larger than a specific threshold value, reducing (140) the specific distance (L1) between the synthetic points (P1, P2), and / or adding (140) a new synthetic point (P3) between the synthetic points (P1, P2), performing (150) a polynomial regression based on at least a subset of the synthetic points (P1, P2, P3) to determine a polynomial function representing the synthetic points (P1, P2, P3), and thereby providing a correlation between the first coordinate system and the second coordinate system, representing (160) said at least part of the real-world environment using said correlation for transforming, to the second coordinate system, the coordinates of at least some of said measurements performed by the measurement device in said first coordinate system. An apparatus on which the method (100) is implemented is also provided.