94results about How to "Accurate estimate" patented technology

The invention provides a rainfall monitoring method and system based on unmanned aerial vehicle multi-mode noise reduction in the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: S1, collecting a visible light image and a long-wave infrared image through a multispectral vision module after an unmanned aerial vehicle takes off, and collecting a rainfall audio from an acoustic cabin through a microphone; s2, carrying out noise reduction processing on the visible light image, the long-wave infrared image and the rainfall audio; s3, extracting a raindrop size distribution histogram and a spatial density thermodynamic diagram based on the visible light image and the long-wave infrared image, and extracting acoustic MFCC features based on rainfall audio; s4, performing feature fusion operation based on the raindrop size distribution histogram, the visual density thermodynamic diagram and the acoustic MFCC features to obtain rainfall intensity; and S5, carrying out cumulant dynamic compensation based on the rainfall intensity to obtain the cumulative rainfall. The rainfall monitoring method has the advantages that the precision, robustness and practical level of rainfall monitoring are greatly improved.

The invention discloses a robot anti-interference control method based on a neural network interference observer, and the method specifically comprises the following steps: 1, building a robot dynamics model containing external interference, and constructing a state space model; step 2, establishing a fixed time interference observer based on a radial basis function neural network, and performing external interference estimation on the robot kinetic model; and step 3, a fixed time sliding mode controller is designed based on the interference estimation result, and fixed time control under the condition of external interference is realized. According to the robot anti-interference control method based on the neural network interference observer, the interference is accurately and rapidly estimated by using the fixed time interference observer, the convergence of the system state in the fixed time is realized by combining the fixed time sliding mode controller, and the feedforward compensation and feedback suppression of the interference are combined, so that the system stability is improved. The method has the advantages of simple implementation mode, high interference estimation precision and high response speed.

This invention discloses a method, apparatus, and electronic device for estimating the state of charge (SOC) of a vehicle battery. The method includes: acquiring target discharge parameters corresponding to a target battery, wherein the target discharge parameters represent the discharge state of the target battery at a first time step; predicting the target discharge parameters using a target neural network to obtain a target prediction result corresponding to the target battery, wherein the target prediction result represents the SOC of the target battery at a second time step. The target neural network includes multiple base learners, which are trained using multiple sets of historical discharge parameters. Each set of historical discharge parameters includes: discharge voltage, discharge current, a first measurement temperature, and a second measurement temperature. This invention solves the technical problem of low accuracy in battery SOC estimation caused by insufficient nonlinearity in the battery SOC estimation model in related technologies.

The application provides a radar precipitation measurement method and device based on a 45-degree slant depolarization ratio, which uses a radar with or modified to realize 45-degree slant polarization to collect single-shot double-receiving two-channel complex voltage data, calculates a slant baseline linear depolarization ratio and a reflectivity factor Z; identifies a melting layer according to a vertical pointing profile observation collected by the radar in a vertical pointing mode, and applies upper and lower boundaries of the melting layer to a near-horizontal scanning mode to realize liquid rainfall identification; performs rain attenuation correction on and Z, and according to the rain attenuation corrected and Z, looks up a table or a neural network to inverse precipitation parameters such as rainfall intensity, mass-weighted mean diameter, normalized intercept, number concentration and water content. The application improves the precision and robustness of precipitation measurement.

The application discloses a kind of emergency closing gate front backwater peak estimation method of gated channel, comprising the following steps: S1, one-dimensional unsteady flow simulation model of channel is constructed;S2, determine typical working condition of emergency control;S3, according to the simulation model in step S1, the typical working condition in step S2 is simulated, and the gate front backwater peak value of typical working condition is determined;S4, according to the gate front backwater peak value of typical working condition in step S3, draw gate front backwater peak value curve cluster;S5, according to the gate front backwater peak value curve cluster in step S4, determine the range of gate front backwater peak value when emergency disposal;S6, according to the gate front backwater peak value curve cluster in step S4, determine the accurate gate front backwater peak value when emergency disposal.The application has high estimation accuracy, is convenient and fast to use, can quickly and accurately estimate the gate front backwater peak value under different gate initial flow and gate flow amplitude, can not only simply and quickly estimate the range of gate front backwater peak value, but also can more accurately determine the gate front backwater peak value.

The application discloses an OTFS system channel estimation method, device, storage medium and equipment, belongs to the wireless communication technical field, and the method comprises the following steps: acquiring a sending pilot signal, a receiving pilot signal and a noise variance; preprocessing the sending pilot signal and the receiving pilot signal to obtain sending pilot data and receiving pilot data; inputting the sending pilot data, the receiving pilot data and the noise variance into a trained channel sparsity estimation model to obtain a channel sparsity estimation value, wherein the channel sparsity estimation model is a deep neural network; and reconstructing the channel based on the channel sparsity estimation value by using an OMP algorithm to obtain a channel estimation value; the application estimates the channel sparsity through the deep neural network, overcomes the selective bias problem in the traditional OMP algorithm, improves the accuracy of channel estimation by combining offline training and data driving, and can also reduce the overhead of the pilot signal.

The invention discloses a vehicle fault-tolerant control method under the abnormal condition of a multivariable measurement sensor, and belongs to the crossing field of electric digital data processing and intelligent driving vehicle control technologies. The method comprises the following steps: firstly, based on spatial-temporal correlation and physical consistency constraints of multi-source sensor data, carrying out redundancy modeling and credibility evaluation on a key state of a vehicle; then, when it is detected that the sensor is abnormal, real-time compensation and correction of failure observation are achieved through a state reconstruction model driven by redundant data; and further combining a rapid fault identification and virtual sensor switching mechanism to construct a fault-tolerant control strategy for the minimum function demand of the vehicle so as to ensure that the vehicle can still maintain basic driving and safety control capability under the condition that the functions of part of sensors are damaged. According to the invention, the real-time performance of the system is ensured, the safety and robustness of the intelligent driving vehicle in a complex environment and an abnormal condition are obviously improved, and the method is suitable for a multi-sensor fusion intelligent vehicle control system.

This invention discloses a method, apparatus, equipment, and program product for calculating the volume of a coal pile, relating to the field of three-dimensional reconstruction technology. The method includes: acquiring global three-dimensional point cloud data of a target coal yard area, and projecting the global three-dimensional point cloud data onto the bottom plane of the coal pile to obtain each two-dimensional projection point and the target height corresponding to each two-dimensional projection point; triangulating each two-dimensional projection point to obtain a target three-network structure; back-projecting the target three-network structure into three-dimensional space to obtain each unit projected triangular prism; calculating the unit prism volume of a single unit projected triangular prism according to the target calculation method corresponding to the unit projected triangular prism; and calculating the target coal pile volume of the target coal yard area based on the unit prism volumes of each unit projected triangular prism. The technical solution of this invention reduces the error of the unit prism volume and improves the calculation accuracy of the coal pile volume.

The invention discloses an observer-based satellite attitude system active fault-tolerant control method under dynamic interference, which comprises the following steps of: firstly, establishing a mathematical model of a satellite attitude system by considering external interference, actuator fault and model uncertainty, and further designing a nonlinear interference observer to estimate the external interference in real time; meanwhile, an actuator fault is dynamically estimated through a fault estimator, and an adaptive law is constructed to carry out approximation on uncertainty of a system model; and designing an anti-interference fault-tolerant controller according to a satellite attitude system model in combination with an observer, an estimator, an adaptive law and a system state to realize attitude control of the satellite. According to the method, under the action of various factors, the stability of the system of the satellite can be kept, interference factors can be accurately compensated, good robustness and practicability are achieved, and the anti-interference capability of the satellite is effectively improved.

The application provides a PIV-oriented multi-scale pyramid optical flow estimation method, comprising: performing adaptive image preprocessing on an image pair; inputting the processed image into a lightweight feature extraction network to obtain multi-scale feature maps and context feature maps of two frames of images; using a traditional dense optical flow algorithm to calculate an initial optical flow and down-sample the initial optical flow to the deepest layer resolution; using the initial optical flow to locally sample the second frame feature map, and calculating a dot product of the first frame feature map and the second frame feature map to obtain a correlation body; fusing the correlation body and the optical flow information through a multi-scale motion encoder to regress a residual optical flow; performing weighted up-sampling on the residual optical flow based on neighboring pixels; iteratively performing the above steps layer by layer until a full-resolution optical flow estimation is output. Through the fusion of a traditional optical flow prior and a multi-scale pyramid iterative network, the application solves the problem of low optical flow estimation accuracy caused by large changes in PIV environment illumination, similar foreground and background, and transparent objects, reduces the calculation complexity, and meets the real-time requirement.

The invention relates to a ball head rod disordered grabbing method based on 3D vision, belongs to the technical field of robot operation and automatic manufacturing, and particularly relates to a workpiece pose estimation method based on combination of YOLOv8 and Triplet Attention modules, which is used for disordered grabbing tasks in complex scenes. According to the method, the color and depth images of the workpiece are acquired through the RGB-D camera, and the efficient lightweight Triplet Attention module is introduced, so that the processing speed and precision of the model on the target detection and segmentation task on the mobile terminal equipment are remarkably improved. And the Triplet Attention module enhances the space attention mechanism of the model through three-way rotation convolution operation on the features, so that the detection and segmentation precision is remarkably improved. The segmented mask region is applied to the depth image, corresponding point cloud data are extracted, and six-degree-of-freedom pose estimation is carried out by using a normal consistency optimization (NDT) algorithm. The NDT algorithm is excellent in processing noise and partially shielded scenes. Experimental results show that compared with an original YOLOv8 model, the model added with the Triplet Attention module is improved in segmentation precision and recognition accuracy. The method has high accuracy and robustness in the aspects of workpiece recognition and pose estimation, and is suitable for real-time industrial application scenes.

The application discloses a method for suppressing main lobe interference of a multi-station radar system based on oblique projection. The method aims to solve the problem that the estimation of noise subspace is not accurate due to the energy leakage of interference, so that the noise subspace is no longer orthogonal to the interference subspace, and the orthogonal projection anti-interference performance is invalid. The implementation steps of the application include: directing the receiving antenna beams of each radar station in the multi-station radar system to the target and the area where the jammer is located; aligning the echo baseband signal vectors of each radar station in the multi-station radar system in the time domain to obtain a receiving signal matrix of the radar system; generating a covariance matrix; estimating a signal subspace; constructing an oblique projection operator; and multiplying the receiving signal matrix of the radar system by the oblique projection operator to obtain a signal after interference suppression. The application has the advantage that the suppression type interference signal entering from the main lobe of the radar station antenna can be effectively suppressed in a low signal-to-noise ratio scene.

A method and apparatus for super-resolution localization of multiple targets using frequency-modulated continuous wave radar includes: determining the number K of targets based on the number of eigenvalues ​​satisfying a condition in the covariance matrix of the acquired radar array signal; finding the frequency values ​​corresponding to the K largest spectral peaks based on the acquired number of targets K, using these frequency values ​​as coarsely estimated intermediate frequency (IF) values, and determining the final IF value using the coarsely estimated IF values; calculating the distance of the corresponding target relative to the center O of the antenna array based on the final IF value; obtaining the noise subspace of the array signal and the steering vector related to the target distance and angle based on the eigenvalues ​​of the covariance matrix of the array signal, constructing a MUSIC spectral function, and determining the estimated value of the target echo signal direction; and then determining the position of the corresponding target. This invention has higher range and angle resolution and lower algorithm complexity, enabling super-resolution localization of multiple targets.

The application provides a sparse auxiliary variational nonlinear signal decomposition method and system, comprising: determining the number of sub-signals of an original signal and initial instantaneous frequencies of each sub-signal, calculating a preliminary demodulation signal of each sub-signal, calculating an optimized demodulation signal of each sub-signal, calculating an initial instantaneous frequency increment of each sub-signal, calculating an optimized instantaneous frequency increment of each sub-signal, calculating an initial optimized instantaneous frequency of each sub-signal, calculating a final optimized instantaneous frequency of each sub-signal, reconstructing each sub-signal x according to the optimized demodulation signal and the final optimized instantaneous frequency of each sub-signal i ; setting a termination iteration threshold value ε, and judging whether the reconstructed sub-signal satisfies the set termination iteration threshold value; if not, repeating the execution, and if yes, completing the decomposition of the original signal. The application realizes the decomposition of a complex multi-component signal, accurately estimates the instantaneous frequency of each sub-signal and reconstructs each sub-signal.

The application discloses an antenna scanning period estimation method and device based on amplitude feature analysis. Firstly, the sliding window mean amplitude peak value is calculated, and the corresponding peak time is obtained. Then, the peak duration containing the peak time is obtained. Whether the pulse amplitude difference and the pulse number meet the first preset threshold is judged. If yes, the amplitudes corresponding to the pulses in the peak duration are set to zero. The above steps are executed. Through the main lobe query condition combined with the initial signal main lobe, multiple signal main lobes are determined. If the number of the signal main lobes is not less than the second preset threshold, the peak times of the signal main lobes are incrementally sorted to obtain a main lobe peak time sequence. The antenna scanning period is determined according to the main lobe peak time sequence. The problems that the antenna scanning period is not accurate in the prior art and the time consumption is too long in the related method are overcome, and the accurate estimation of the signal antenna scanning period is realized.

The present application belongs to the field of lithium battery SOC calculation, and particularly relates to a lithium battery SOC estimation method based on high-precision ampere-hour integral algorithm. The method comprises establishing an ampere-hour integral mathematical model and solving, and performing Taylor expansion on the solution; the average slope is obtained by means of linear combination of three-point slope by investigating the slope of two points in the interval, so as to ensure that the precision of the third-order Taylor expansion formula and the equivalent formula is the same. When the algorithm is executed, the charge state and the capacity value are initialized first, then the sampling time is obtained according to the sampling frequency, and then the relevant parameters are calculated according to the current sampling value, and finally the current SOC is calculated according to the specific value and the three slopes and is iterated in a loop. This method can improve the ampere-hour integral precision, and weaken the cumulative error and SOC jump.

The invention discloses a step frequency imaging method based on speed matched filtering and false scattering point elimination, and the method comprises the steps: firstly, obtaining a distance unit corresponding to the maximum amplitude in a non-coherent processing channel and a speed matched filtering result, and calculating a target compensation speed range; traversing each speed value in the compensation speed range, and generating a target one-dimensional range profile corresponding to different speeds according to the sub-pulse data; taking the ratio of the kurtosis of the range profile to the envelope entropy as a cost function, and taking the range profile corresponding to the maximum cost function as a range profile after speed compensation; and finally, calculating a scattering point correlation coefficient by using the distance image after speed compensation and the scattering point sample, performing false scattering point judgment according to the scattering point correlation coefficient and an amplitude ratio, and obtaining a high-resolution one-dimensional distance image of the target through false scattering point elimination. According to the invention, accurate estimation of the target speed is realized, the imaging accuracy of the step frequency system is improved, and the measurement and calculation precision of detection target parameters is improved.

The invention discloses an intelligent piezoelectric beam vibration suppression control method based on a fuzzy improved active disturbance rejection controller. Firstly, a kinetic model and a state-space equation of the intelligent piezoelectric beam are established; thirdly, according to the state-space equation of the intelligent piezoelectric beam, constructing a fuzzy improved active-disturbance-rejection controller, including designing a nonlinear Arcfu function based on an arctan function, and constructing an extended state observer and a nonlinear state error feedback control law based on the nonlinear Arcfu function; the fuzzy controller generates a deviation increment of the vibration displacement tracking error and the vibration speed tracking error according to the deviation of the vibration displacement tracking error and the vibration speed tracking error, and dynamically adjusts a control law parameter by using the deviation increment; finally, the vibration displacement of the intelligent piezoelectric beam is obtained in real time, the fuzzy improved active disturbance rejection controller generates system control quantity according to the vibration displacement, the system control quantity acts on the piezoelectric ceramic piece, and active vibration suppression control is conducted on the intelligent piezoelectric beam. According to the method, high-frequency oscillation is avoided, and the vibration suppression control effect is enhanced.

The invention relates to a polycrystalline silicon reduction furnace soft measurement model construction method, a polycrystalline silicon reduction furnace control method and computer equipment. The method comprises the following steps: determining an input variable set and an output variable set; the output variable set comprises in-furnace definition, and the input variable set comprises hydrogen flow, silicon rod diameter, heating power and cooling water flow; determining a lag time constant set; constructing an in-furnace atmosphere temperature estimation sub-model; constructing a to-be-trained model structure based on the input variable set, the output variable set, the lag time constant set and the in-furnace atmosphere temperature estimation sub-model; training the to-be-trained model structure based on historical operation data of the polycrystalline silicon reduction furnace to obtain a corresponding soft measurement model; the soft measurement model is configured to receive real-time data of the input variable set and output at least an in-furnace sharpness estimate. By adopting the method, the real-time and accurate estimation of the definition in the furnace can be realized, and an accurate state feedback basis is provided for the multivariable cooperative control of the polycrystalline silicon reduction furnace.

The invention discloses a heart CT imaging method and device, electronic equipment and a storage medium. The heart CT imaging method comprises the steps of obtaining a plurality of reconstructed images corresponding to a plurality of periods based on original heart scanning data; determining an initial motion field of scanning angles corresponding to the plurality of reconstructed images based on a motion field estimation model; fitting the initial motion field to obtain a target motion field of all scanning angles; and performing motion compensation on the heart images corresponding to the plurality of scanning angles based on the target motion field to obtain a plurality of compensation images, and fusing the plurality of compensation images to obtain a target image. According to the heart CT imaging method, the deep learning network is combined with the multi-phase image to estimate the operation field, the heart motion field can be estimated more accurately, and the imaging effect is better.

The invention relates to the technical field of signal processing, and discloses a simultaneous same-frequency full-duplex self-interference elimination method and system, and the method comprises the steps: carrying out the data preprocessing of a generated self-interference signal, generating a self-interference signal containing a nonlinear factor, and constructing a training data set according to the self-interference signal; establishing a plurality of CNN-LSTM attention models for extracting nonlinear features and time sequence features of the self-interference signals; an auxiliary sub-module is introduced in the training stage of the model, so that the model has internal knowledge migration, and knowledge distillation of a feature level is realized; training the model, and performing parameter optimization by adopting a loss function which considers the amplitude and the phase of the complex signal at the same time; based on the trained model, linear and nonlinear two-stage self-interference elimination is carried out on the received signal, self-interference suppression in a full duplex system is realized, and the problems that the existing full duplex nonlinear self-interference elimination method based on deep learning is insufficient in feature extraction capability, limited in supervision information and low in training speed are effectively solved through the method.

The invention discloses a railway hopper car remote state estimation method, device and equipment, and the method comprises the steps: modeling a railway hopper car sensor network into a discrete time random system comprising a plurality of nodes, and obtaining a state space model corresponding to the railway hopper car sensor network; establishing a packet loss and spoofing attack model of a sensor measurement signal under an amplification forwarding relay strategy; wherein a measurement signal of each sensor is transmitted to the remote estimator through the amplification forwarding relay; determining a remote estimation model according to the state space model and the packet loss and spoofing attack model; and calculating an estimator gain matrix by using the remote estimation model, and outputting a railway hopper car operation state estimation value according to the estimator gain matrix. According to the method, the running state of the railway hopper car is accurately and reliably estimated, and the safety and the operation efficiency of railway freight are improved.

The application discloses a kind of inhibiting ICI radar communication integrated system signal processing method, compensates ICI in echo signal using SIDFnT algorithm, removes the influence of random communication information loaded in echo signal using Hadamard division, the phase matrix of Hadamard quotient is sought, remove the influence of excessive amplitude produced by division, finally using the characteristics of two-dimensional FFT energy accumulation, target energy is concentrated, so as to estimate target distance and speed by the position of maximum amplitude in matrix, and the estimated result is used to complete the compensation of interference caused by speed distance. The application uses the energy focusing characteristics of SIDFnT, suppresses echo ICI, can effectively improve radar target estimation accuracy, and directly uses radar estimation result to complete communication end compensation, greatly reduces the complexity of communication end processing method.

The application discloses a method for estimating regional road motor vehicle emission based on typical traffic flow investigation, and relates to the technical field of motor vehicle emission estimation, and comprises the following steps: constructing a test road, obtaining experimental emission based on the test road; obtaining a first emission threshold and a second emission threshold based on the experimental emission; obtaining a first parameter value and a second parameter value based on the first emission threshold, the second emission threshold and the experimental emission; obtaining a parameter fitting function based on the first parameter value and the second parameter value; obtaining a guessed path based on the information of the investigation-estimated regional road and a traffic checkpoint; obtaining the total emission of the regional road based on the guessed path and the parameter fitting function; the application aims to solve the problem that the prior art fails to fully consider the actual influence of the regional road environment on motor vehicle emission, and it is difficult to accurately obtain the driving path and speed of a target vehicle, thereby leading to inaccurate emission estimation results.

The invention provides a solid-state battery peak power estimation method and device based on a PMC-GPR method, which are applied to a solid-state battery and comprise the following steps: constructing an electromechanical coupling model comprising an equivalent stress model and an equivalent circuit model; inputting the acquired battery operation data into an electromechanical coupling model to obtain a current state including SOC, branch voltage and expansion stress; under the first time scale, on the basis of the current state, estimating the battery capacity on line by adopting an unscented Kalman filtering algorithm; under the second time scale, based on the battery capacity and the current state, an unscented Kalman filtering algorithm is adopted for online estimation, and an estimated SOC is obtained; wherein the first time scale is larger than the second time scale; and constructing feature data based on the estimated SOC, and inputting the feature data into the trained Gaussian process regression model to obtain predicted peak power. According to the scheme, the mechanical effect of the solid-state battery is fully considered, and the precision, robustness and reliability of peak power estimation of the solid-state battery are effectively improved.

This invention provides a method, apparatus, equipment, and medium for predicting oil and gas field flow data. The oil and gas field flow correction method includes the following steps: S1, acquiring and preprocessing wellhead data of oil and gas wells; S2, identifying influencing factors of wellhead flow data by analyzing the correlation between the wellhead data and wellhead flow data; S3, identifying influencing factors of wellhead flow data by analyzing the correlation between the wellhead data and wellhead flow data; S4, establishing a hybrid virtual metering model of oil and gas wells based on the aforementioned mechanism model, and labeling the characteristic variables of the hybrid virtual metering model according to the influencing factors; S5, predicting wellhead flow data based on the hybrid virtual metering model. This invention, by using existing data for mining and analysis, can achieve accurate measurement of wellhead flow, replacing the current complex back-matching mechanism, and can enhance the core competitiveness in the field of efficient oil and gas field development.

The invention relates to an optical parameter detection device and method, in particular to an optical system wavefront detection device and method based on phase modulation. In order to solve the problems in the prior art that a multi-frame acquisition strategy adopted by a phase recovery method based on a point spread function is difficult to adapt to a dynamic test scene, a single-frame acquisition strategy is difficult to stably invert wavefront with conjugate symmetry, and iterative inversion operation time is relatively long and may fall into a local optimal solution, the invention provides a phase recovery method based on a point spread function. The invention discloses an optical system wavefront detection device based on phase modulation. The optical system wavefront detection device comprises a module, a first imaging lens, a first plane mirror, a beam splitter, a microscope objective, a second plane mirror, a partition phase plate, a second imaging lens and a photoelectric detector, and asymmetric phase coding is achieved through the partition phase plate. Based on the device, according to the method, the unsupervised deep neural network is combined with the physical driving layer, so that accurate inversion of different optical systems to be detected is realized.

The invention discloses a lithium battery charge state estimation method based on a self-adaptive gray system model, which comprises the following steps: collecting limited data in a battery discharge process, and establishing a self-adaptive real number field gray system model; solving parameters and a recursive formula of the gray system model, and capturing a nonlinear mapping relation between the state of charge and the open-circuit voltage by combining an accumulative effect of the discharge capacity; nonlinear parameters of the model are optimized through a simulated annealing intelligent algorithm; and in combination with an equivalent circuit model and an extended Kalman filtering algorithm, the state-of-charge estimation of the power battery under different driving cycle tests is realized. According to the method, a mining thought without using high-order polynomial fitting is introduced, so that a relatively high lithium battery charge state estimation result can be obtained, and a battery manufacturer and a user can accurately know the residual electric quantity condition of the battery so as to perform reasonable charging plan arrangement.

The present application relates to a kind of electric vehicle charging power calculation method, the method includes the following steps: using the database data of Internet of Vehicles to the space-time characteristic parameter in trip chain is statistically analyzed and is synthesized and establishes mathematical model to simulate user travel regularity;Different probability distribution functions are constructed to simulate the single charging behavior characteristics of electric vehicle user in real time;Determine the power size of charging and the duration length of charging;The charging power of single electric vehicle in daily is estimated and calculated;Obtain the space-time distribution curve of electric vehicle charging power in single day.The present application considers the charging behavior habit of user, the state of electric vehicle battery and the anxiety psychology of user to potential trip, establishes more real user charging decision model, can more practically and accurately estimate the space-time distribution of electric vehicle charging power in single day, has strong practicability.