458results about How to "Improve estimation accuracy" patented technology

The invention discloses a multimode GNSS / inertia tight combination factor graph optimization method based on variance component estimation, and belongs to the technical field of satellite / inertia combination navigation. The implementation method comprises the following steps: fusing observed quantities of a multimode GNSS and an inertial navigation system on the basis of a factor graph optimization method under the condition of distributing prior weights among multiple GNSS systems; on the basis of a posteriori state estimation result obtained after factor graph optimization, combining a multi-mode GNSS observation equation, calculating a posteriori residual error, and utilizing a variance component estimation method to adaptively adjust weights among the multiple GNSS systems in real time according to the signal quality of satellites of the satellite navigation systems; on the basis of gross error detection and a weight reduction algorithm, weight reduction processing is carried out on the satellite observed quantity containing gross errors before the weight between the systems is adjusted in a self-adaptive mode. According to the invention, the robust variance component estimation is used to determine the weight among multiple GNSS systems, the reliability of satellite observation information of different satellite navigation systems is adjusted in real time, and the precision and robustness of the multimode GNSS / inertial tight integration navigation system are improved.

The invention discloses a lithium battery SOC (State of Charge) estimation method based on constrained optimization Kalman filtering. The method comprises the following steps: building a battery RC (Resistance-Capacitance) model; establishing a Kalman filtering model and initializing parameters; processing the collected battery data; iteratively updating Kalman filtering parameters continuously through constraint optimization to obtain more accurate values; mSE and RMSE are used for a Kalman filter to evaluate the performance of the filter. In order to overcome the limitation of the Kalman filter on parameter setting and the sensitivity of the Kalman filter on model change, the parameter adjustment process of the Kalman filter is optimized by introducing constraint conditions, and the parameters of the filter are dynamically adjusted in combination with an advanced optimization algorithm, so that the filter can keep the optimal estimation performance under different working conditions. Meanwhile, through limitation of constraint conditions, it is ensured that the estimation result conforms to the physical characteristics and actual working conditions of the battery, and the accuracy and reliability of SOC estimation are further improved.

The invention discloses a three-phase permanent magnet synchronous motor full-speed-domain driving control system and a control method thereof. The system is composed of a torque limiting module, a full-speed-domain control neural network module, a current control module, a coordinate transformation module-I, a coordinate transformation module-II, a coordinate inverse transformation module, an SVPWM module, a SiC MOSFET driving module, a full-speed-domain position rotating speed observation module, a weak magnetic feedback module and a three-phase permanent magnet synchronous motor. The control method is used for driving the control system. According to the method, accurate tracing of the optimal dq-axis reference current working point is realized based on deep learning, and accurate control of three operation modes of MTPA, field weakening control and MTPV is realized; an improved mobile vision field estimation algorithm is adopted, and the rotor position and the rotor rotating speed are accurately estimated within the full-speed domain range; the SiC MOSFET is used as an inverter switching tube, and a brand new drive circuit is designed to adapt to the operation condition of the electric vehicle.

The invention discloses a positioning processing method suitable for observation data under a GNSS complex condition. The positioning processing method comprises the following steps: step 1, setting residual check thresholds of a pseudo-range positioning observation value and a Doppler velocity observation value; step 2, on the basis of a residual check threshold of a pseudo-range positioning observation value, calculating an optimal estimated value of pseudo-range positioning observation and a corresponding variance-covariance matrix according to residual satellites for satellites with abnormal pseudo-range positioning observation values; step 3, based on a residual error test threshold value of the Doppler velocity observation value and a detection result in the step 2, eliminating satellites with abnormal Doppler velocity observation values, and calculating an optimal estimated value of Doppler velocity observation and a corresponding variance-covariance matrix according to the residual satellites; and step 4, updating the Kalman filtering state vector of the GNSS based on the results of the step 2 and the step 3. The method can improve the estimation precision of the initial values of the position and the speed, and further improves the positioning precision of the GNSS.

The invention discloses a perception fusion method of an unmanned driving system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining a laser point cloud, image data and millimeter wave radar data; a Zhang's calibration method is adopted to obtain internal parameters of a camera, and a first pose transformation relation of a camera coordinate system relative to a laser radar coordinate system is solved; a second pose transformation relation of the millimeter wave radar relative to the laser radar is obtained by using the millimeter wave reflector; a Point RCNN method is adopted to obtain first target information; obtaining target information II by adopting a YOLOv8 method; obtaining target information III by adopting a clustering algorithm; and carrying out coordinate unification on the target information by utilizing a pose transformation relation, fusing the multi-sensor target information under a unified coordinate system by utilizing an unscented Kalman filtering algorithm to obtain target state estimation, and generating an unmanned driving control instruction. The target sensing precision is improved aiming at the low target sensing precision of multiple sensors under the unmanned driving scene of the well industry and mining in the prior art.

The invention relates to a lithium ion battery health state estimation method based on an MSCNN, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing an aging cycle test on a lithium ion battery monomer, and establishing a working condition test experiment database; collecting voltage, current and temperature data of the battery in each aging cycle, respectively calculating average values, standard deviations, skewness and kurtosis of the voltage, the current and the temperature, taking the average values, the standard deviations, the skewness and the kurtosis as health factors, and screening out the health factors which are highly related to the health state of the battery; constructing an SOH estimation model, and training the model by taking the screened health factors as input; and the model is used for predicting the health state of the lithium ion battery after being trained. The method can effectively extract features from data of different scales, has higher SOH estimation precision compared with a traditional CNN, can perform accurate SOH estimation on data sets of different working conditions, and has good generalization ability.

The invention relates to a three-dimensional human body posture estimation method based on space-time Transform, which belongs to the technical field of human body three-dimensional posture estimation, and comprises the following steps: S1, carrying out two-dimensional posture estimation on an input human body posture image by using a cascaded pyramid network (CPN); s2, utilizing a space transformer block based on a Transform attention mechanism to obtain space information between joint points; s3, inputting the spatial information into a global time sequence feature learning module based on a Transform attention mechanism, and outputting global time sequence feature information; s4, different joints are separated in the time dimension, parallel modeling is conducted on the different joints, and local time sequence feature information is output through a local time sequence feature learning module based on a Transform attention mechanism; and S5, carrying out fusion and regression on the global time sequence feature information and the local time sequence feature information to obtain a three-dimensional attitude coordinate.

The invention discloses an off-network block sparse orthogonal matching pursuit underwater acoustic time delay Doppler domain channel estimation method, and belongs to the technical field of underwater acoustic channel estimation. The method solves the problems that a channel estimation method based on traditional orthogonal matching pursuit cannot estimate frequency shift parameters on non-grid points and is high in calculation complexity. According to the method, channel parameters of integer grid points are obtained based on a BOMP algorithm, and then residual Doppler factors on non-grid points are subjected to further fine estimation based on maximum likelihood criterion fitting. The problem that a traditional OMP channel estimation method in underwater OTFS communication can only estimate channel parameters on grid points is solved, and the estimation precision of channel frequency shift parameters is further improved by estimating frequency shift parameters on non-grid points; and meanwhile, a fractional order dictionary is prevented from being established in a channel parameter process, so that the system complexity is reduced to a certain extent. The method can be applied to underwater sound time delay Doppler domain channel estimation.

The invention discloses an unmanned aerial vehicle risk assessment method and system based on a behavior recognition mode. The method comprises the steps of obtaining a target acoustic signal and a target image sequence of an enemy unmanned aerial vehicle; analyzing and processing the target acoustic signal to obtain sound source positioning information; analyzing and processing the target image sequence to obtain optical flow data; fusing the sound source positioning information and the optical flow data to obtain target fusion data; inputting the target fusion data into a target neural network for identification to obtain a target behavior mode of the hostile unmanned aerial vehicle; calculating the target behavior mode and the current sensor data to obtain probability distribution data of candidate behavior modes corresponding to the target behavior mode; and inputting the probability distribution data into a target risk assessment model for assessment to obtain a target risk level. The method can improve the risk assessment accuracy of the hostile unmanned aerial vehicle, facilitates the formulation of targeted coping strategies and defense measures, and can be widely applied to the technical field of security defense.

The invention relates to the field of seismology and geology, in particular to a G-R relation earthquake maximum magnitude estimation method based on fault feature weighting, which comprises the steps of extracting geometric features and structural features of a target fracture, fitting a region Gutenberg-Richter relation, calculating a comprehensive weight factor of the fracture, and adjusting a value a of the region based on the weight to obtain a specific value a of a single fracture; estimating the maximum magnitude of a single fracture by adopting an empirical relationship through combining the adjusted specific value a and the adjusted region value b; according to the method, the accuracy and reliability of earthquake maximum magnitude estimation are improved, and the method has superiority especially in areas with insufficient earthquake catalog data.

The invention provides a multi-base satellite-borne SAR (Synthetic Aperture Radar) three-dimensional imaging method and device, and belongs to the technical field of SAR image three-dimensional imaging. The method comprises the steps of image preprocessing; geocoding and registration are carried out; removing a flat ground effect and a terrain phase; compensating an atmospheric phase error; the invention relates to high-precision tomography SAR three-dimensional imaging. The device comprises an external DEM data acquisition module, a registration module, a simulation module, a compensation module and an imaging module. According to the invention, a high-precision three-dimensional imaging result can be obtained.

The invention relates to a lithium ion battery charge state estimation method and system. The method comprises the following steps: acquiring initial current, initial voltage and temperature data of a battery under different working conditions; inputting the initial voltage and the initial current data into a mechanism model to obtain voltage simulation data and current simulation data; inputting the voltage simulation data, the current simulation data and the temperature data into a neural network for training, constructing an SOC estimation model, and obtaining an SOC output result; and according to the SOC output result and the SOC true value, respectively calculating a root mean square error RMSE and a mean absolute error MAE for verification and testing so as to evaluate the estimation performance of the SOC estimation model. According to the method, the calculation speed of the SOC estimation process of the battery is high, the relative precision is high, a solid foundation is provided for a battery management system to guarantee safe and reliable operation of a battery power supply system, and the applicability of the SOC estimation model under different operation conditions and temperatures is improved.

The invention discloses a mixed field channel estimation method for a super-large-scale MIMO (Multiple Input Multiple Output) system. Combining the sparse transformation matrixes of the two channels into one sparse transformation matrix, and carrying out sparse representation on the mixed field channel; a mixed field channel estimation problem is converted into a sparse estimation problem, and the problem that two channels interfere with each other in the two sparse estimation processes is avoided. In the estimation process, the far-field sparse channel and the near-field sparse channel are jointly estimated according to the current support set every time the support set of the mixed field sparse channel is updated, and the residual error is calculated according to the current estimation value and used for updating the support set next time. The problem that mutual interference of a far-field sparse channel and a near-field sparse channel in the support set selection process cannot be solved in an existing scheme is solved. A simulation result shows that the scheme has higher estimation precision.

The invention discloses an object recognition and pose estimation method for a grabbing robot, and relates to the technical field of robot automatic grabbing, and the method comprises the following steps: obtaining multi-modal data through simulating a visual array of insect compound eyes and an auditory sensor for bat echo positioning, and obtaining a fusion model through deep learning feature fusion network processing; an object recognition and grabbing strategy model is constructed based on reinforcement learning, the former combines multi-factor training and optimal selection, and the latter determines a strategy according to a large number of tests and can be dynamically adjusted. A specific algorithm is combined with SIFT and deep learning to calculate a pose, and fine tuning is carried out by means of physical simulation, particle swarm optimization and a force sensor; according to the method, the accuracy, stability and intelligence of recognition, pose estimation and grabbing of the robot in a complex environment are remarkably improved, automatic operation in the fields of industrial production, logistics storage and the like is facilitated, and the method has wide application prospects.

The invention relates to the technical field of motor control, and discloses a bus capacitor temperature online estimation method and device of a motor controller, and the method comprises the steps: building a thermal network model, and determining a temperature collection point and a current collection point according to the thermal network model; capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are calculated according to the current acquisition data, and a rotating speed-torque-voltage three-dimensional loss table is made; calculating a thermal resistance parameter and a thermal capacity parameter in the thermal network model; according to the current rotating speed, torque and voltage, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are inquired from a three-dimensional loss table; and establishing a thermal network state equation, and calculating the current capacitance temperature. Thermal resistance parameters and thermal capacitance parameters are obtained through experimental test calculation, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are obtained through off-line calculation, the precision of thermal network model parameters and loss is improved, and then the estimation precision of the capacitor temperature is improved.

The invention discloses an indoor temperature and humidity spatial data prediction method fusing spatial interpolation and a graph convolutional network, and relates to the field of smart city and environment monitoring, and the method comprises the steps: obtaining indoor multi-point temperature and humidity data actually measured by a sensor, and the spatial position information of a monitoring mark point corresponding to the indoor multi-point temperature and humidity data; the temperature and humidity data, the spatial position information and the time dimension features of the marked points are used as input of Kriging interpolation, and temperature and humidity linear predicted values of part of unmarked points are generated; constructing a multiple linear regression model taking the Kriging prediction result, the measured data of the mark points, the time dimension characteristics, the spatial characteristics and the external meteorological data as independent variables and the target temperature and humidity values as dependent variables, and performing multi-dimensional fitting on the Kriging interpolation result; and constructing a feature matrix by using a linear predicted value output by the regression model and other features, and taking the feature matrix and an adjacent matrix as input of a graph convolutional network to generate indoor temperature and humidity three-dimensional space distribution. According to the invention, high-precision indoor space data prediction can be realized.

The invention discloses a lithium battery SOH (state of health) estimation method based on pre-training fused multi-source data. The method comprises the following steps: acquiring constant-voltage charging current data and electrochemical impedance spectroscopy data; preprocessing the data and forming a data set; constructing a pre-training task 1, outputting two features through an encoder 1 and an encoder 2, and constructing a loss function loss1 of the task 1 according to cosine similarity between feature fragments; constructing a pre-training task 2, randomly masking segments of two features, outputting fusion features through an encoder 3, constructing two decoder reconstruction mask segments to obtain reconstruction loss loss2 and loss3, and weighting based on correlation to obtain a loss function loss4 of the task 2; the encoder is frozen to form an SOH estimation model with an estimator; and estimating the SOH on line. According to the invention, the encoder can fully fuse two kinds of data aging information through the two pre-training tasks, and the SOH of the lithium battery is accurately estimated.

The invention discloses a phase interferometer DOA and polarization parameter joint estimation method based on multiple linearly polarized antennas, and belongs to the technical field of radar signal processing. According to the method, the problems of low ambiguity resolution probability and low signal DOA and polarization parameter estimation precision of an existing method are solved. Noise variance and signal power estimation is carried out by adopting a method of receiving data covariance matrix high-order power, and the complexity is reduced. Aiming at the problem that the defuzzification probability is limited due to polarization mismatch, the method firstly adopts a method of solving a short base line by a long base line to reduce a phase difference error, then utilizes a multi-base-line phase weighted defuzzification method, and utilizes phase differences and amplitude information of all base lines to perform polarization domain phase difference and spatial domain phase difference decoupling, so that the defuzzification probability is limited. And the power estimation error is reduced through the LMMSE method, and the precision of signal DOA and signal polarization parameter estimation is improved. The method can be applied to joint estimation of DOA and polarization parameters of the phase interferometer.

The invention discloses an end-to-end intelligent direction of arrival estimation method based on subarray sampling, and belongs to the technical field of wireless communication. A sub-array sampling technology is adopted to replace traditional full-array sampling, and signal processing is completed by using fewer radio frequency links, so that the hardware cost is reduced; an end-to-end learning framework is adopted, so that a network can automatically capture features and rules according to a data set, and excessive dependence on a model is avoided; data features are preliminarily captured through a deep neural network, a pre-estimated value is given, then an iteration process of a maximum likelihood estimation method is converted into a deep expansion network, initialization is performed by using output of the deep neural network, and the effect of the method is improved while convergence is accelerated; a large number of data sets are generated through a sub-array sampling signal mathematical model, the network is trained, and the problem that overfitting is prone to occurring under a small data set is avoided. The method is suitable for the technical field of wireless communication, and realizes direction of arrival estimation with lower complexity and higher accuracy in a low-snapshot scene.

The invention belongs to the technical field of battery parameter estimation, and particularly relates to a battery SOC estimation method and device based on Kalman filtering, equipment and a medium. According to the scheme provided by the invention, aiming at the problems of small error and large influence caused by the voltage plateau period of the battery when SOC estimation is carried out on the lithium iron phosphate battery by a traditional Kalman filtering method, on the basis of a Kalman filtering algorithm, the improved formula is introduced, and the value of the gain G in the voltage plateau period in the improved formula is determined, so that the SOC estimation accuracy of the lithium iron phosphate battery is improved. The SOC estimation precision of the lithium iron phosphate battery is effectively improved, the maximum effectiveness of the battery can be ensured through accurate SOC estimation, the cost is reduced, and resource waste is prevented.

The invention relates to the field of automatic driving, and provides an out-of-distribution point cloud object detection method based on fuzzy membership supervision and evidence deep learning. Comprising the following steps: obtaining a candidate foreground object by using a 3D target detection model; foreground object point cloud feature extraction; calculating a fuzzy membership degree based on fuzzy C-mean clustering; the fuzzy membership degree value is used as mark information, and evidence deep learning model training is carried out; and inferring an uncertain value of a foreground object by using the trained evidence deep learning model, and judging whether the object belongs to an out-of-distribution sample or not. According to the method, the fuzzy membership degree obtained by fuzzy clustering is used as supervision information, evidence deep learning is used for training on the point cloud target detection data set, and the uncertain value predicted by the evidence deep learning is used for point cloud open set target detection, so that robustness and interpretability are high; and the open set detection effect can be ensured in a large-scale sparse point cloud scene.

The invention provides a lithium ion battery consistency rapid screening method based on an ultrasonic technology, and belongs to the technical field of battery management and echelon utilization. The method comprises the following steps: testing acoustic parameters of the lithium ion battery by using an ultrasonic technology, wherein the acoustic parameters comprise longitudinal wave velocity and ultrasonic flight time; obtaining the electrical characteristic parameter charge and discharge capacity of the battery through a charge and discharge cycle test; based on a machine learning model, a relationship between acoustic characteristic parameters and electrical characteristic parameters of the battery is established, and accurate estimation of the health state of the lithium ion battery is realized by using an ultrasonic technology, so that rapid screening of battery consistency is realized based on clustering analysis. According to the lithium ion battery consistency rapid screening method based on the ultrasonic technology, the problems that an existing lithium ion battery screening method is long in period and low in efficiency are solved.

The invention relates to the technical field of remote sensing image processing, in particular to a residual feature fusion inversion-based accumulated snow coverage estimation method, which comprises the following steps of: acquiring terrestrial satellite data and performing image data screening according to a screening condition; acquiring meteorological satellite data, preprocessing the meteorological satellite data and splicing the meteorological satellite data with the geographic elevation data; generating a binary accumulated snow map, calculating the accumulated snow coverage according to a definition method and a meteorological satellite resolution, and obtaining an accumulated snow coverage label; cutting the meteorological satellite image into a rectangle according to the accumulated snow coverage label; establishing an accumulated snow coverage estimation model based on deep learning; optimal experiment parameters are found through repeated experiment debugging and comparison; after the prediction is completed, obtaining respective accumulated snow coverage estimation results and splicing the accumulated snow coverage estimation results into a complete accumulated snow coverage estimation result; and the cloud shielding interference is effectively filtered by combining the generated snow coverage estimation result with the satellite data of the front and back time phases, so that the method has higher time resolution and reliability.

The invention discloses a method for monitoring a packaging aging mode of a power module in real time and a related device, and belongs to the technical field of power electronic devices. According to the method, thermal network models corresponding to different packaging aging modes are established according to the structure of the power module, and frequency separation points corresponding to the different packaging aging modes are found; setting a test frequency, and collecting the conduction voltage drop of the power module and the load current of the inverter circuit; and analyzing the difference of the conduction voltage drop hysteretic curve according to the deviation or rotation of the conduction voltage drop hysteretic curve caused by different aging modes such as bonding wire falling, welding layer fatigue and heat dissipation system aging, and determining the packaging aging mode of the power module. By adopting the method, the packaging aging mode of the power module can be monitored in real time, aging reasons can be effectively distinguished, maintenance measures can be accurately taken, serious faults can be avoided, and the maintenance cost can be reduced; and meanwhile, the reliability and the stability of the whole system can be improved by monitoring and repairing the packaging aging problem in time.

The invention discloses a missing data estimation method and system for UPFC sub-module state parameter monitoring, and the method specifically comprises the steps: obtaining the operation data and historical data of a UPFC sub-module, converting the data from different sources into a unified format, and further carrying out the preprocessing of the data, and forming an operation data set and a historical data set; the operation data set and the historical data set are detected, missing data points are searched and positioned, data are perfected and supplemented, and a repaired operation data set is formed; a support vector machine method is further combined to calibrate and update data in the repaired operation data set to form an operation data set with higher estimation precision; and integrating the operation data set with higher estimation precision with the historical data set, and updating the historical data set. The method can form operation data with higher estimation precision, and has the advantages of high data recovery reliability, high data integrity and the like.

The invention belongs to the technical field of signal processing, and particularly relates to an under-snapshot 2D-MUSIC direction finding method based on spatial translation invariance. According to the method, the space translation invariant characteristic of a uniform linear array or a uniform planar array is utilized, a calculation method of a forward and backward space-time cross-correlation function is provided, a pseudo covariance matrix is constructed by using the space-time cross-correlation function based on the Hermitian-block Toeplitz characteristic of a signal ideal covariance matrix, then a MUSIC spatial spectrum is constructed, and the wave direction is extracted. Compared with a traditional 2D-MUSIC algorithm, the method still can maintain the high-precision angle estimation capability under the condition that the number of snapshots is limited, and does not greatly improve the calculation amount. Compared with a spatial smoothing MUSIC algorithm, the method does not need to divide sub-arrays, fully utilizes the full array aperture, and avoids the loss of resolution.

The invention discloses a robust incoherent distribution source two-dimensional arrival angle estimation method under an L-shaped array, which comprises the following steps of: establishing a generalized array flow pattern receiving model under the L-shaped array, and the L-shaped array consists of two uniform linear arrays which are respectively positioned on an x axis and a z axis of an xoz plane; sampling data output by the uniform linear arrays on the x axis and the z axis at N moments; constructing aperture expansion array cross covariance vectors corresponding to the uniform linear arrays on the x axis and the z axis by using statistical average operation and a conjugate symmetry strategy; according to the cross covariance vector of the two aperture expansion arrays, all direction angle estimated values taking the x axis as the reference and all direction angle estimated values taking the z axis as the reference are obtained, and all two-dimensional arrival angle estimated values are obtained through pairing; the method has the advantages that the method is not only suitable for uniform and non-uniform noise scenes, but also does not depend on prior information of the number of signal sources, and the practicability and flexibility of the algorithm are improved.

The invention provides a lithium battery energy state and core temperature estimation method and a related device, and relates to the technical field of lithium battery state estimation. The method comprises the following steps: establishing a lithium battery electrothermal coupling model, including establishing a battery electrical characteristic and thermal characteristic coupling model, and identifying electrothermal model parameters and an open-circuit voltage-residual available energy curve; the method comprises the following steps: firstly, carrying out battery electrothermal model parameter identification through a particle swarm optimization algorithm according to lithium battery voltage, current and temperature data measured offline; and secondly, designing an active-disturbance-rejection battery power optimization controller for electrothermal coupling calibration, regulating and controlling the voltage and the surface temperature of the lithium battery electrothermal coupling model on line through the active-disturbance-rejection battery power optimization controller to always track the actual battery state, and finally obtaining the battery energy state and the core temperature. Accurate estimation of the battery energy state and the core temperature is achieved by continuously calibrating the battery model state tracking actual measurement value, the hardware cost is reduced, and the method has the advantages of being high in estimation precision and easy to achieve.

The invention discloses an SAR sea surface flow field inversion method and system based on Doppler frequency shift correction, and belongs to the technical field of synthetic aperture radars. The method comprises the following steps: dividing an SAR single-view complex image into # imgabs0 # sub-image blocks; estimating the Doppler center frequency shift of each sub-image block; estimating distance deviation according to the polarization mode and the beam center visual angle; calculating a direct current offset according to the adjacent land area data; removing a non-geophysical item Doppler frequency shift error by combining the distance direction deviation and the direct current offset; doppler frequency shift caused by sea surface wind waves is calculated and removed, and a sea surface flow field is obtained. According to the method, the estimation precision of the Doppler frequency shift error of the non-geophysical item is improved, and the error of SAR sea surface flow field inversion is reduced. And meanwhile, the requirement of fitting a non-geophysical item Doppler frequency shift error on land area data is reduced, and the application scene of SAR sea surface flow field inversion is expanded.

The invention discloses a charging duration prediction method based on multi-dimensional spatial-temporal characteristics and battery aging physical constraints, and belongs to the field of new energy automobile battery management. The method comprises the following steps: firstly, reading operation discharge data of a vehicle from a vehicle-mounted OBD device, and obtaining a battery capacity label based on a fixed SOC interval; then, constructing a multi-dimensional data sample by utilizing a voltage interval, and converting traditional time sequence data into graph structure data based on correlation among different data; and then, based on the random discharge data and the space-time model, automatically extracting multi-dimensional space-time characteristics representing battery aging, and taking the aging rate of the battery as a physical constraint to accurately predict the battery capacity. And finally, constructing a battery charging duration empirical formula by using a dynamic particle swarm algorithm, and ensuring that the residual charging duration of the battery is accurately predicted under different charging conditions.