38results about How to "High precision prediction" patented technology

The invention relates to a position control method for a sensorless steering wheel. According to the position control method, continuous, smooth and stepless change of a steering ratio at different car speeds is realized by a regulation motor for controlling an ECU (Electronic Control Unit) gear ratio; meanwhile, the change rule is calculated by utilizing a correlation formula, and rotation angle compensation is carried out according to the calculated change rule; reverse steering adjustment is actively carried out during car oversteer, and the regulation motor and an assisting motor are flexibly controlled and mutually compensated so as to perfectly combine the flexibility, the portability and the control stability of the steering, so that the control performance and the safety are improved.

The invention provides a method for predicting the tail gas pollution distribution in a city road network. The method comprises the following steps: acquiring multi-source heterogeneous data; carryingout stack-type self-encode features dimension reduction, and constructing a multi-layer sparse self-encoder network structure to extract the features of the multi-source heterogeneous data; generating sequential data based on spatio-temporal semi-supervised learning; pre-training a deep spatio-temporal network model replacing the corrected model data with the telemetry data of the real monitoringpoints, and re-training the corrected regional tail gas emission prediction model; determining the weighted parameters of the model to obtain a deep spatio-temporal network model, and inputting the multi-source heterogeneous data t to obtain a predicted regional tail gas pollution emission result. The invention is based on a stack-type self-encoder dimension reduction feature extraction method, which can learn essential feature mapping between road network information, meteorological information, traffic flow information, POIs information and regional tail gas emission, and can realize higherprecision regional tail gas prediction on real telemetry data.

The present invention provides a railroad train operation management system and program for automatically giving various types of operation guidance such as the guidance on the stop station, the variation from the scheduled time, warning of the train speed limit to the motorman at an adequate timing at each check point arbitrary set on a predetermined line to support the motorman and realizing operation management with high precision by predicting the position of the train with high precision at low cost even in a region where the GPS signal is hard to receive. The system comprises line data storage means (2) for storing line specific position data and check point position data, operation schedule data storage means (3) for storing operation schedule data, GPS signal receiving means (6) for receiving the GPS signal, operation management means (9) for performing operation management of trains according to the GPS data and operation schedule data on the trains computed from the GPS signal, and operation guidance output means (7) for giving the motorman the information acquired by the operation management means.

The invention belongs to the technical field of thermal error predicating methods, and provides a thermal error predicating method for a rolling ball screw feeding system of a numerical-control machine tool. Thermal errors of the rolling ball screw feeding system are predicated through a self-adaptive real-time model (ARTM). The thermal error predicating method comprises the following steps of: firstly, establishing experiments for measuring temperature changes, along with time, of the surface of a workbench; secondly, establishing the self-adaptive real-time model for predicating transient temperature distribution and thermal error distribution of rolling ball screws; thirdly, determining a heating rate of two bearings, a movable nut and a slide rail of the rolling ball screw feeding system through a finite element combined monte carlo method (MC); fourthly, establishing an exponential equation of feeding speed and time according to finite element calculating data for describing and measuring temperature difference changes, along with time, between surface points and kinematic pair centers; and finally, establishing a numerical value predicating algorithm for predicating thermal errors of the rolling ball screw feeding system. Corresponding thermal errors are quickly predicated with high precision by the numerical value predicating algorithm in a mode of monitoring surface temperatures of two bearing bases and a motion nut side surface.

The invention discloses a multi-classification deep learning short-term wind power prediction method based on pitch angle classification. The method comprises the following steps: equally dividing original fan data into four data sets according to pitch angle intervals; respectively carrying out Pearson correlation analysis on the four data sets to determine the correlation degree between each variable in each data set and the wind power; according to the relevancy of the variables in each data set, selecting several variables with high relevancy with the wind power as the input of the deep neural network model, and taking the wind power as the output; and dividing each data set into a training set and a test set in proportion, and training and testing the deep neural network model by using the training set and the test set to obtain a final deep neural network model. According to the prediction method, the problem of low wind power prediction accuracy in the prior art is solved.

With conventional methods, there is a reduction in the prediction accuracy of arriving wind information, such as arriving wind speed or wind direction. This wind measurement LIDAR device 1B: is mounted on a windmill 2; transmits, through the atmosphere, transmission light, which is pulsed laser light, in a plurality of beam directions determined with respect to the front direction of the windmill 2; and measures the wind speed in the beam directions at a plurality of distances from the windmill, from the Doppler frequency deviation with respect to transmission light of reflected light, which is the transmission light reflected by particles that move together with the atmosphere. This invention comprises: spectrum integration units 12c, 12e that obtain an integrated spectrum, which is an integration, for each wind speed measurement section consisting of a combination of a beam direction and a time section, of a spectrum obtained from split reception signals of a plurality of pulses transmitted after the wind speed was calculated previously; a wind speed calculation unit 12h that calculates a wind speed for each wind speed measurement section, for an integrated spectrum with an SN ratio that is at least a first threshold value; and an arriving wind information prediction unit 16 that, on the basis of the wind speed for each wind speed measurement section, predicts arriving wind information, namely information of arriving wind, which is wind that will arrive at the windmill 2.

The invention provides a meteorological AI platform based on big data, which comprises a hardware layer, a system layer, a software interface layer and a meteorological service layer. The meteorological service layer comprises an intelligent client and an intelligent weather model; the intelligent weather model is used for analyzing a field sequence based on weather in a latest set time length ofa forecast area in the meteorological and geographic information data, inputting to a depth neural network model based on training, and obtaining a weather forecast field sequence of the forecast areawithin the forecast duration from the current moment; the field sequence analysis based on weather within the latest set time length is as follows: setting a field sequence analysis based on weatherwithin a time length from the current moment to the front; and the intelligent client is used for realizing data interaction between the weather AI platform and external intelligent equipment. The prediction result is more accurate, and the prediction result can be used as a reference basis for the forecast of a forecaster together with the result of the mode forecast, so that the forecaster can provide more accurate weather forecast.

The invention discloses a region tail gas migration prediction method and system based on domain adaptation and a storage medium. The method comprises the steps: obtaining and processing historical tail gas data and external factor data of a source region and a target region, taking monitoring points as nodes for the source region data and the target region data, and enabling the source region data and the target region data to be connected pairwise; constructing graph structure data by taking the weight as the reciprocal of the monitoring point distance, and dividing a time sequence set according to the tail gas concentration change characteristics of the source region and the target region; constructing a tail gas spatio-temporal feature extraction module, and performing shallow feature extraction and fusion on the time sequence data of the source region and the target region; constructing an automatic encoder, and utilizing the encoder to non-linearly map shallow spatio-temporal features of the source domain and the target domain belonging to different feature spaces to the same feature space; performing depth extraction on the shallow layer features, and outputting a prediction result. According to the method, efficient utilization of the source domain data is realized by utilizing the domain adaptation method, so that higher-precision regional tail gas prediction of the target domain lacking data is realized.

The invention discloses a high-precision weld joint morphology prediction method suitable for myriawatt-level laser welding, and aims to solve the problem that the weld joint morphology prediction precision of myriawatt-level laser welding is low due to the fact that a weld pool and plume coupling behavior cannot be considered in an existing weld joint morphology prediction method. According to the method, a compressible two-phase flow numerical calculation method based on pressure is adopted to solve the coupling behavior of the molten pool and plume, and therefore high-precision prediction of the myriawatt-level laser welding seam morphology is achieved. Firstly, a welding seam morphology function and welding parameters at the initial moment are input; secondly, a compressible two-phase flow numerical calculation method based on pressure is adopted to obtain a welding seam morphology function at the next moment; and drawing a welding seam morphology function at the next moment, and extracting welding seam morphology and welding seam morphology characteristics. Compared with an existing weld joint morphology prediction method, the weld pool and plume coupling behavior in myriawatt-level laser welding can be accurately calculated, the algorithm is simple and easy to implement, the calculation efficiency is high, the physical conservation is good, and high-precision prediction of the myriawatt-level laser welding weld joint morphology can be achieved.

The invention discloses an ultrasonic synthetic aperture imaging method and device introducing machine learning, and the method comprises the steps: collecting a synthetic aperture echo signal in a sparse scanning mode through a one-dimensional linear ultrasonic transducer array; then, machine learning is adopted to predict related signals to solve the problem of sparse spatial arrangement of echo signals caused by a sparse scanning mode; and finally, realizing ultrasonic synthetic aperture focusing imaging based on a delay superposition algorithm (DAS). After optimization processing of machine learning, compared with a traditional synthetic aperture focusing technology (SAFT), the efficiency and the imaging quality of synthetic aperture focusing imaging are greatly improved, and meanwhile, the influence of side lobes and the like on an imaging result is reduced. The method has the characteristics of high scanning speed and high image reconstruction precision, and is beneficial to popularization and application of the ultrasonic synthetic aperture imaging technology in nondestructive testing and medical diagnosis.