180results about How to "Estimates are accurate" patented technology

The invention discloses a method for estimating lithium ion battery remaining available capacity. The method comprises the steps of acquiring standard temperature, a standard discharging current and battery internal resistance from a battery instruction book, acquiring battery nominal capacity based on battery bench testing data, drawing a temperature-current-SOC three-dimensional surface, estimating the remaining available capacity of a battery under a battery future current according to the current battery temperature, the current battery SOC, the battery standard nominal capacity, the battery internal resistance, the battery future current and the temperature-current-SOC three-dimensional surface, and calculating the battery remaining battery capacity according to the remaining available capacity, a battery current end voltage, the battery internal resistance and the battery future current. According to the method for estimating the lithium ion battery remaining available capacity, influences on the lithium ion battery remaining available capacity by the battery discharging current, the battery temperature and the battery internal resistance are taken into solid consideration, an accurate estimation value of the lithium ion battery remaining available capacity can be offered, and the estimation value of the lithium ion battery remaining available capacity is further used for estimation of remaining mileage of an electric automobile and automobile overall energy management.

The invention relates to an estimating method of the charge state of a power battery. The estimating method is characterized by including the following steps: estimating system on chip (SOC) initial value of the battery according to last charge-discharge state of the battery and SOC value and standing time of the battery since last-time use comprehensively, calculating battery inner resistance and polarization voltage of the battery, collecting working current and voltage at two ends of the battery in real time, estimating SOC of the battery according to the SOC comprehensive estimation algorithm based on an expansion Kalman filtering method and an ampere-hour metering method, storing the SOC of the battery and the corresponding working current and voltage at two ends of the battery, storing current time, battery SOC and battery charge-discharge state before the system is closed down and closing the system down. The estimating method has the advantages of being small in SOC initial value, simple in algorithm, small in metering quantity and high in accuracy.

The invention discloses a method and device for estimating a power battery charge state on line. The method comprises the steps that a capacity correction factor of a power battery is calculated according to the accumulated released energy of the power battery, the internal resistance of the power battery is calculated, a state space model of the power battery is established, and the state space model of the power battery is substituted into an unscented Kalman filter to obtain the estimated value of the battery charge state. According to the method and device for estimating the power battery charge state on line, the capacity correction factor of the power battery is calculated according to the accumulated released energy of the power battery, the accumulated released energy of the power battery is the sum of energy consumed by load acting and energy consumed by the internal resistance from the first time up to now, the accumulated released energy of the power battery can reflect the using history of the battery like the cycle index and is more beneficial to accurate measuring in numerical value, and therefore the method of correcting the related parameters of the battery through the accumulated released energy of the power battery is better in operability and is more accurate in estimated value.

The invention discloses a battery charge state detecting method. The method comprises the steps that the initial SOC of a battery is determined; the self-discharge effect of the battery is considered; a table look-up method is used to accurately estimate the SOC of the battery; an ampere-hour integral method is used to acquire the SOC estimation value of the battery of a next moment; a battery Thevenin model is combined, and an extended Kalman filter algorithm is used to correct the SOC of the battery; and finally, low power, over-current, over-temperature and the like of the battery are judged to carry out early warning and protection. According to the method provided by the invention, the influence of self-discharge of the battery on the battery capacity and the influence of temperature, the number of times of cycle charge and discharge and charge and discharge current on the rated capacity of battery are comprehensively considered, wherein self-discharge of the battery is caused by first time of running and downtime of the battery; error correcting is carried out on the SOC of the battery; and the accuracy is improved.

The invention relates to the multi-sensor time calibration algorithm field, especially relates to a visual inertial range measurement method and equipment using online time calibration and aims at increasing camera pose calculation precision. The visual inertial range measurement method comprises the following steps of roughly aligning the timestamp of a camera image with the time system of an IMUand taking the data of a camera and the IMU as input; taking the time delay closest to a camera pose calculated by the camera data and the IMU data as the preliminary estimation value of camera timedelay; and using an IMU error item expression method with a time delay parameter to add a correction term related to the time delay into the pre-integration result of the IMU, taking as the parameterof nonlinear optimization and realizing the accurate calibration of the time delay. Compared with an existing method, by using the method of the invention, time delay calibration is accurate, and forequipment which does not carry out hardware synchronization, better performance can be achieved when the equipment rapidly makes a turn and suddenly stops.

The invention relates to the technical field of mobile communication and provides a self-adaption radio frequency interference offsetting device, method and receiver and a wireless full-duplex communication system. The device comprises an amplitude and phase adjusting module, a subtractor and a baseband extracting and filtering module. The amplitude and phase adjusting module is used for adjusting the amplitude and the phase of radio frequency reference signals and outputting radio frequency adjusting signals to enable the radio frequency adjusting signals to be collected in self-interference signals of radio frequency receiving signals. The subtractor is used for outputting radio frequency residue signals which are the difference value signals between the radio frequency receiving signals and the radio frequency reference signals. The baseband extracting and filtering module is used for receiving the radio frequency reference signals and radio frequency residue signals outputted from the subtractor and extracting the baseband signals. The baseband signals are processed in a minimum mean square self-adaption filtering mode to obtain amplitude phase control signals which are outputted to the amplitude and phase adjusting module. The self-adaption radio frequency interference offsetting device, method and receiver adopt an LMS self-adaption filtering algorithm, so that the convergence rate is higher and the estimation results are more accurate.

The invention discloses a water loss and soil erosion remote-sensing dynamic monitoring method based on a modified universal soil loss equation, which includes the implementation steps: 1) acquiring a rainfall erosivity factor, a soil erodibility factor and a terrain factor; 2) acquiring remote sensing images of a to-be-monitored area, performing land use/coverage category division according to the remote sensing images to obtain multiple land categories, and respectively estimating a vegetation coverage-management factor of each land category so as to obtain a comprehensive vegetation coverage-management factor; 3) extracting characteristic information affecting water and soil conservation from the remote sensing images, and estimating a water and soil conservation measure factor according to the characteristic information affecting water and soil conservation; and 4) calculating an average annual soil loss amount estimated value of the to-be-monitored area according to the modified universal soil loss equation, and outputting the value. The water loss and soil erosion remote-sensing dynamic monitoring method based on the modified universal soil loss equation has the advantages of reasonable estimated result, high universality, suitability for long-term dynamic monitoring of water loss and soil erosion in the area and wide application range.

The invention relates to a raininess estimation method based on dual polarization Doppler weather radar detection. The method comprises the steps that when echo intensity ZH of rainfall is small, the rainfall is estimated through the echo intensity ZH; when the echo intensity ZH of the rainfall is increased, if a value of ratio differential phase shift KDP and a value of a differential reflectivity factor ZDR are small, the rainfall is estimated through the echo intensity ZH and the differential reflectivity factor ZDR, if the value of the ratio differential phase shift KDP is small and the value of the differential reflectivity factor ZDR is large, the rainfall is estimated through the ratio differential phase shift KDP and the differential reflectivity factor ZDR, and if the value of the ratio differential phase shift KDP and the value of the differential reflectivity factor ZDR are large, the rainfall is estimated through the ratio differential phase shift KDP. According to the meteorology feature of precipitation particles, the raindrop spectrum feature of the precipitation particles of different sizes and the influence of rain belt attenuation are considered, a combined rainfall algorithm is adopted, the estimation result is accurate, precision is improved, and flash flood monitoring capacity is improved.

The invention relates to a docking ring circle center extraction method based on a close-range short-arc image. The docking ring circle center extraction method comprises the following steps: carrying out epipolar rectification on a binocular image containing a short-arc docking ring, dividing a threshold by using a standard deviation method, extracting a ring surface by means of regional attributes, and then extracting inner and outer edges; carrying out dense reconstruction on the extracted ring surface region by using a semi-global matching method to obtain three-dimensional point cloud in a camera coordinate system; calculating a ring surface normal vector of the point cloud, and projecting a coordinate in a plane vertical to the normal vector; distinguishing the inner and outer edges in a new plane, and respectively carrying out perfect circle fitting on the inner and outer edges; and finally, obtaining an accurate circle center coordinate of the docking ring by use of concentric ring constraint according to obtained centers and radiuses of the inner and outer edges. According to the docking ring circle center extraction method provided by the invention, from the characteristics of the docking ring, three-dimensional point cloud data are directly projected into a normal incidence spatial plan, ellipse fitting is simplified to circle fitting, and meanwhile, error points are eliminated by constraint conditions of inner and outer rings, and the docking ring circle center extraction method has the advantages of accurate result and small calculated amount.

The invention discloses a time bias estimation device and a time bias estimation method for an orthogonal frequency division multiplexing (OFDM) system. The time bias estimation method comprises the following steps of: acquiring a pilot-bit frequency domain channel estimation value of a target user according to a received frequency domain demodulation reference mark and a local frequency domain demodulation reference mark; transforming the acquired pilot-bit frequency domain channel estimation value to a time domain by using inverse Fourier transform; realizing pilot-bit channel estimation multiuser separation by computing the length of an effective channel shock response window of the target user, and performing time domain noise reduction treatment on each user on an antenna of the user; searching for a peak value of each user in the effective channel shock response window, and finding a main path position of a signal; and computing a time bias estimation value of the user accordingto the peak value corresponding to a main path and generating a time adjustment command word. The time bias estimation device and the time bias estimation method of the invention can effectively estimate the relative time bias between a base station and a terminal, simplify the computation method of the time bias, reduce device complexity and obtain an accurate estimation value even on a working site with a low signal-to-noise ratio.

A method for extracting the fault feature of an antifriction bearing based on a sliding entropy-ICA algorithm is provided. The method comprises main steps of: (1) subjecting a single-channel actually measured signal to EMD to obtain respective IMF components; (2) screening out effective IMF components by using a sliding entropy cross correlation coefficient to form a virtual channel signal; (3) integrating the single-channel actually measured signal with the effective IMF components to form a composite signal matrix, separating the composite signal matrix by using a FastICA algorithm to obtain respective source signal estimated values; (4) retaining a source signal containing a bearing fault feature, and extracting a plurality of time-domain feature parameters and frequency-domain feature parameters to form a feature parameter set; and (5) subjecting a high-dimensional feature set to data fusion by using an LLE algorithm to obtain an accurate low-dimensional feature parameter. The method uses the sliding entropy-ICA algorithm in combination with the LLE algorithm, is suitable for extracting the fault feature of rotating machines including the antifriction bearing, and can extract the source signal containing fault information just by using the single-channel signal. The low-dimensional feature parameter obtained by the method can describe bearing fault information.

The invention discloses a method for detecting coated ice and estimating the thickness of the coated ice on the basis of an image. The effect of detecting the edge of an ice-coated transmission line with a complex and dark background is optimized by using the priori knowledge of color and position, and the actual ice-coated transmission line thickness is estimated by the thickness estimating algorithm of using a camera to calibrate parameters and the moving target tracking algorithm under the condition that the ice-coated transmission line is displaced. By the method, the problem on detection of the edge in a complex background can be effectively solved, the defect that most algorithms cannot be utilized to estimate the thickness when the ice-coated transmission line is displaced is overcome, and the method for estimating the thickness is more precise than the non-calibration algorithm.

The invention belongs to the technical field of near-field measurement of radars, and particularly relates to a locomotive radar speed measuring method based on adaptive Kalman filter. The locomotive radar speed measuring method comprises the specific steps: (1) calculating an echo power spectrum; (2) restraining low-frequency interference; (3) predicting locomotive doppler frequency by utilizing a spectrum center method; (4) performing radar position self calibration and speed calculation; (5) filtering by utilizing an adaptive Kalman filter; (6) outputting locomotive speed. According to the locomotive radar speed measuring method, real-time measurement of the locomotive speed can be realized, the speed is measured without lag, and the measurement accuracy is high.

The invention discloses a power load modeling method based on an interactive multi-model algorithm. The method includes the steps that model state variable and covariance matrix interaction is performed on the state variable and state covariance matrix, at the time k-1, of each model in a first model set, and interacted state variables and covariance matrixes of the models are obtained; the corresponding state variables and covariance matrixes of the models are input into corresponding extended Kalman filters for model state estimation, and estimated state variables and covariance matrixes, at the time k, of the models are obtained; the weights of the modes are calculated according to likelihood function values and information covariance matrixes in model state estimation, and weighted summation is performed on the weights of all submodels to obtain the parameters of the corresponding models, wherein the weights of the models are used for state interaction at the next time and model power output fusion at the current time. The load modeling process is recursively achieved, the covariance matrixes do not need to be stored, and therefore memory space is saved.

The invention discloses a preparation method of a C/C composite tube of an optical telescope. The method comprises the following steps: 1) carbon fiber fabrics and carbon fiber felts are spread layer by layer, needle-punching is performed on each layer to prepare a preformed reinforcement body, the density of the preformed reinforcement body is estimated before layer spreading and the radial density of the preformed reinforcement body is calculated according to the estimation result; 2) composite densifying is performed; 3) high temperature treatment is performed; and 4) the subsequent treatment is performed. The densifying process comprises the following steps: 201) performing gas-phase carbon-settling densifying treatment, namely using a gas-phase carbon-settling device to perform gas-phase carbon-settling treatment; and 202) performing resin impregnation, curing and carbonization treatment, namely performing further densifying treatment on the preformed reinforcement body after thegas-phase carbon-settling densifying treatment. The method has reasonable design, simple processing steps, low input cost, simpleness in operation and convenience in quality control; and the processed C/C composite tube has good performance, higher overall strength and stability and an ultralow thermal expansion coefficient.

The invention discloses a method for estimating identification error intervals of flight vehicle pneumatic parameters, and is used for estimating the error intervals of the pneumatic parameters identified from flight test measuring data. On the basis of the common method for estimating a standard C-R (Cramer - Rao) bound of the parameter identification error intervals, aiming at solving the problem of too small estimating value of the error intervals when the standard C-R bound is applied to the flight test measuring data, a C-R bound correction method is derived by a principle, and the correction method can be used for accurately estimating the identification error intervals of the flight vehicle pneumatic parameters. The method for estimating the identification error intervals has the advantages that the convenience and the practicality are realized, and the error interval of each pneumatic parameter identification result is given by only needing the samples of the single flight test measuring data; and compared with the prior art, more accuracy and more reliability are realized, and a foundation is laid for applying the pneumatic parameters identified from the flight test measuring data into the design and improvement of the flight vehicle.

The disclosure relates to methods and systems for automatically focusing multiple images of one or more objects on a substrate. The methods include obtaining, by a processor, a representative focal distance for a first location on the substrate based on a set of focal distances at known locations on the substrate. The methods also include acquiring, by an image acquisition device, a set of at least two images of the first location. The images are each acquired using a different focal distance at an offset from the representative focal distance. The methods further include estimating, by a processor, an ideal focal distance corresponding to the first location based on comparing a quality of focus for each of the images, and storing the estimated ideal focal distance and the first location in the set of focal distances at known locations.

The invention discloses a permanent magnet synchronous motor sensorless control method based on high-frequency signal injection. Due to the existence of the magnetic resistance torque of an interior permanent magnet synchronous motor, when a sensorless algorithm based on traditional high-frequency pulse vibration voltage signal injection is adopted, electromagnetic torque high-frequency pulsationis generated, especially in a control method when the non-id is equal to 0, the high-frequency pulsation of the electromagnetic torque is more obvious. When the injected d-axis and q-axis high-frequency voltage components are distributed, the minimum electromagnetic torque high-frequency pulsation is taken as the principle, the optimal high-frequency current angle and the optimal high-frequency voltage angle are obtained according to a torque formula, and the most appropriate high-frequency voltage injection direction is selected; when a rotor position estimation value is calculated, the responded high-frequency current component is controlled to be zero at the orthogonal position of the optimal high-frequency current angle, so that the error of the rotor position estimation value is zero,and an accurate rotor position estimation value is obtained. According to the method, the electromagnetic torque high-frequency pulsation caused by high-frequency signal injection can be effectivelyreduced, so that the performance of a sensorless control system is improved.

The invention provides a system for estimating a carrier frequency deviation, comprising the following modules: a correlator module, a peak value search engine, an averaging device module, a phase angle difference calculator module and a carrier frequency deviation calculator module, wherein the correlator module is used for carrying out correlation calculation on a baseband signal and a locally-generated PN (Pseudo-Noise) sequence; the peak value search engine is used for carrying out peak value searching after receiving a parallel correlated value output by the correlator module, further determining a starting position of a PN section in the input baseband signal and feeding the position information back to the correlator module; the averaging device module is used for averaging results of the multiple times of correlated calculation of front and back sections, which are output by a correlator; the phase angle difference calculator module is used for calculating a phase angle difference of the correlated calculation and outputting the result; and the carrier frequency deviation calculator module is used for calculating an estimation value of the carrier frequency deviation and taking the estimation value of the carrier frequency deviation as an estimation value of PN rough estimation. The system has the beneficial effects that the problem that PN rough capturing is inaccurate when stronger multi-path interference exists can be effectively solved, and the robustness of the system under an actual wireless transmission channel can be improved.

The invention discloses a moving target positioning method based on a TDOA (Time Difference of Arrival) and an FDOA (Frequency Difference of Arrival). The moving target positioning method comprises the steps of: setting that one moving target for transmitting a signal, one mobile sensor used for receiving the signal and used as a reference sensor and a plurality of mobile sensors only used for receiving the signal exist; then calculating a distance difference between a transmission distance from the moving target to each mobile sensor only used for receiving the signal and a transmission distance from the moving target to the reference sensor and a distance difference change rate; next, calculating initial values of a position and a speed of the moving target; then determining a positioning problem of the moving target so as to solve to obtain an accurate estimated value of the position of the moving target; and finally, optimizing an estimated value of the speed of the moving target by utilizing the accurate estimated value of the position so as to obtain an accurate estimated value of the speed. The moving target positioning method has the advantages that the position and the speed of the moving target can be accurately estimated; calculation complexity is low; and running time is short.

The invention discloses a lithium battery SOC online estimation method based on a backward smooth filtering framework, and the method comprises the following steps: 1, testing a lithium battery so asto obtain a function of an open-circuit voltage with respect to a state of charge, and obtaining an initial SOC of the lithium battery through the open-circuit voltage; 2, establishing a lithium battery equivalent circuit model, and determining a discrete state equation and an observation equation of the lithium battery; 3, carrying out model parameter identification, and identifying the parameters of the equivalent circuit model of the lithium battery; 4, establishing a backward smooth square root cubature Kalman filter; and 5, acquiring real-time voltage and current data of the lithium battery, and estimating the SOC of the battery. According to the algorithm, on the basis of a traditional square root cubature Kalman algorithm, a backward smooth filtering framework is combined, backwardsmooth recursion operation is carried out by utilizing latest measurement information, the influence of factors such as observation noise and observation errors is reduced, and the convergence speed is increased while the estimation precision is improved.

The invention discloses a non-local wavelet coefficient contraction-based image denoising method, which mainly solves the problem that image details are lost when denoising is performed by adopting a traditional image denoising method. The non-local wavelet coefficient contraction-based image denoising method comprises the following implementation steps: (1) establishing noise image-contained similarity groups, performing two-dimension wavelet transformation on similarity blocks in the similarity groups, and calculating non-local means of wavelet coefficients of the similarity groups; (2) contracting the wavelet coefficients by using a double-L1 norm model, then, performing wavelet inverse transformation to obtain estimated values of the similarity blocks, and performing integration on the estimated values to obtain primary estimation images; (3) performing residual cover on the primary estimation image, and executing the step (1) and the step (2) to obtain base estimation images; (4) establishing similarity groups of the base estimation images, and further obtaining the noise image-contained similarity groups; (5) performing Wiener collaborative filtering on the noise image-contained similarity groups to obtain denoised images. According to the non-local wavelet coefficient contraction-based image denoising method disclosed by the invention, while noise is smoothened, edge textures of images can be kept better. The non-local wavelet coefficient contraction-based image denoising method can be used for denoising processing of natural images.

The invention discloses an iterative updating-based UKFNN aluminum electrolysis power consumption model construction method. The method comprises the following steps of initializing structural parameters of an aluminum electrolysis power consumption model; calculating a group of sampling points of the power consumption model; predicting a state and a covariance of the power consumption model at next moment by a state equation F according to a state estimation value of the power consumption model at previous moment; re-sampling sampling points generated by taking a predicted state value as a center and a predicted variance as a covariance, updating the sampling points and the covariance by using an observation function, and performing covariance updating of a state variable and an observation value; and re-performing state estimation updating by using the updated state at the next moment and covariance matrix at the next moment. According to the method, an iterative method is combined with UKFNN so as to obtain a more accurate estimation value.