17 results about "Kalman algorithm" patented technology

The invention relates to the technical field of auxiliary driving, and discloses a deep learning-based road signboard recognition method, which comprises the following steps of: acquiring a road traffic video sequence stream in a vehicle driving process; inputting the road traffic video sequence stream into a deep learning detection framework based on improved YOLOv11 for initial positioning to obtain an initial positioning result of the road signboard; performing cross-frame target tracking on the initial positioning result of the road signboard through an improved Kalman algorithm to obtain a road signboard identification result with a motion track; and displaying the identification result of the road signboard on a vehicle-mounted head-up display of the vehicle. According to the road signboard identification method and system based on deep learning provided by the invention, a scheme based on deep learning and optimized Kalman filtering is adopted, traffic signboards in a complex road traffic environment are identified, and a perfect auxiliary driving system is established, so that key signboard information can be quickly and accurately found in the complex road environment; and a driver is assisted to make correct judgment.

The application provides a conveyor speed detection method, device and equipment based on a dual-antenna RFID. The method comprises: tracking and processing an RFID tag by using a first antenna, and recording a first phase sequence of the RFID tag; tracking and processing the RFID tag by using a second antenna, and recording a second phase sequence of the RFID tag; determining a phase difference between the first phase sequence and the second phase sequence, and determining a phase difference speed according to the phase difference; determining a Doppler speed according to movement data of the RFID tag; fusing the phase difference speed and the Doppler speed by using a Kalman algorithm, determining a final conveyor belt speed, and outputting the final conveyor belt speed. The phase difference speed is determined according to the phase difference between the phase sequences of the two antennas. The Doppler speed is also determined. The phase difference speed and the Doppler speed are fused by using the Kalman algorithm, and the final conveyor belt speed is determined, so that the conveyor belt speed is accurately detected with high precision.

The invention provides a three-axis coil error parameter identification method and device based on Kalman filtering, a medium and electronic equipment, and belongs to the technical field of motor control. According to the method, an error parameter identification model of a three-axis Helmholtz coil under the condition that a single-group coil offset error and a three-axis non-orthogonal error are superposed is constructed, and interference parameters in the error parameter identification model are identified by adopting a Kalman algorithm; according to identification interference parameters obtained through identification, a central uniform region vector magnetic field of the same three-axis Helmholtz coil under all current conditions is obtained through calculation; the method can effectively solve the problem that a traditional least square method or a neural network method is low in reliability of magnetic field simulation of the three-axis Helmholtz coil, magnetic field calculation errors caused by interference coefficient errors of the three-axis Helmholtz coil are remarkably reduced, and the identification accuracy is greatly improved.

The invention relates to a permanent magnet synchronous motor parameter identification method based on a multi-information least two and multi-information extended Kalman algorithm, and the method specifically comprises the steps: S1, modeling a permanent magnet synchronous motor digital model, and simplifying the digital model through a coordinate transformation method; s2, performing online parameter identification on the digital model by using a multi-information least square method; and S3, expanding the nonlinear function into Taylor series around the filtering value by using an extended Kalman filtering algorithm, and removing second-order and above terms to obtain an approximate linearization model, and finally completing parameter estimation of the permanent magnet synchronous motor. Through the method, the parameter acquisition accuracy of the permanent magnet synchronous motor is improved.

The invention relates to the technical field of medical equipment and signal processing, in particular to a cerebral infarction thrombectomy postoperative intracranial pressure management method and system based on fNIRS-TCD combined monitoring, and the method comprises the steps: firstly, synchronously collecting fNIRS cerebral blood oxygen concentration change data and TCD cerebral blood flow velocity data through a head-mounted device, and calculating a pulsation index; then, on the basis of a Bayesian data assimilation framework, constructing the data into a real observation vector, combining a state vector predicted by an intracranial physiological model and an observation vector predicted by an observation model, calculating a Kalman gain through an iterative integrated Kalman algorithm, and further correcting the state vector in real time; and obtaining state estimation containing precise intracranial pressure. And finally, the system carries out three-level early warning according to the real-time intracranial pressure data in combination with brain blood oxygen and pulsation index changes, and carries out stepped individual management from observation, drug intervention to emergency rescue according to the three-level early warning. According to the invention, the intracranial pressure management accuracy is improved.

The invention provides a system-level power station SOH estimation method and device, a terminal and a storage medium, and the method comprises the steps: inputting the charging and discharging data of a power station system into a trained LSTM model, and obtaining an SOH1 prediction value outputted by the trained LSTM model; calculating an equivalent internal resistance value in real-time operation of the power station system based on a power station voltage equivalent value and a power station current RMS value in the charging and discharging data of the power station system, and calculating an SOH2 predicted value by using the equivalent internal resistance value; and fusing the SOH1 predicted value and the SOH2 predicted value based on a Kalman algorithm to obtain an SOH value of the power station. According to the system-level power station SOH estimation method and device, the terminal and the storage medium, the equivalent value of a power station system can be efficiently and rapidly extracted, the overall calculation amount can be reduced, subsequent rapid analysis and processing of data can be achieved, and the input requirement needed by an LSTM model is met.

This invention discloses a deep learning-assisted extended Kalman dual-band infrared radiation attitude estimation method. The method analyzes the impact of geometric and random errors in dual-band infrared radiation measurement signals on attitude estimation reliability. A CBGTE model is constructed to eliminate geometric and random errors in the dual-band infrared radiation signal. First, a dual-band infrared radiation signal dataset is collected and cleaned using a hardware-in-the-loop simulation platform. The dataset is then input into a BGT neural network for feature learning and optimized weights are saved. Data acquired by the dual-band infrared radiation sensor is then input into the BGT neural network with optimized weights to obtain infrared radiation data after random error compensation. This data, after random error filtering, is then input into an extended Kalman algorithm to filter out geometric errors, thereby obtaining reliable attitude angle information. This invention provides a high-precision and robust method for acquiring and processing attitude information for a spinning drone using infrared radiation as its attitude measurement method.

The present invention discloses a method for estimating the key parameters of a lithium battery used for braking energy recovery in an electric-driven dump truck for mining. The estimation method uses a fractional-order equivalent circuit model (FOM) and integrates a dual adaptive unscented Kalman algorithm (DAUKF) to achieve multi-time-scale cyclic estimation of the key characteristic parameters of the lithium battery in the braking energy recovery system of the electric-driven dump truck for mining, providing an accurate data basis for the efficient and safe recovery of braking energy. The present invention improves the defects of existing methods for estimating characteristic parameters of lithium batteries, thereby achieving rapid and accurate estimation of characteristic parameters of lithium batteries under extreme working conditions such as frequent charging and discharging, and large current shocks. The safety and stability of the braking energy recovery system of electric-driven dump trucks for mining are improved. The present invention can solve the technical problem that existing methods for estimating key characteristic parameters of lithium batteries are mainly aimed at lithium batteries in ordinary working environments, but are not suitable for estimating key parameters of lithium batteries for braking energy recovery in electric-driven dump trucks for mining.

The invention relates to the technical field of automobile control, and discloses a four-wheel steering vehicle fault-tolerant control method, which comprises the following steps: constructing a vehicle two-degree-of-freedom dynamic model; real-time data in the vehicle running process are collected, and a vehicle state estimation value is obtained through an unscented Kalman algorithm; constructing a residual reference model to obtain a residual sequence, and determining a fault sensor by using the residual sequence; and the vehicle state estimation value is used as the input of a reinforcement learning model-free control algorithm, and tire rotation angle control in the vehicle operation process is adjusted in real time. According to the method, the vehicle kinetic equation is used as a network regular constraint term, the accuracy and reliability of residual calculation are remarkably improved, rapid and accurate fault positioning is achieved through matching analysis of the residual sequence and the preset fault vector table, meanwhile, rapid self-adaption and control law optimization of a dynamic environment are achieved through reinforcement learning without a model, and the method is suitable for large-scale popularization and application. And the fault-tolerant performance and the control stability of the system are improved.

This invention belongs to the field of state control and control optimization application technology for complex systems, and particularly relates to a SOC calibration algorithm based on Kalman filter multi-sensor fusion. The algorithm includes constructing a lithium battery circuit model, lithium battery model identification, Kalman filter multi-sensor fusion SOC calibration, and improving the accuracy of multi-sensor fusion Kalman filtering. This invention considers the charging and discharging characteristics of the lithium battery model, the complexity of battery internal parameter identification, and the complexity of the Kalman algorithm. It proposes a Kalman filter multi-sensor fusion SOC calibration that takes into account process noise and measurement noise, ensuring accurate SOC. This can improve user experience, extend battery life, ensure safety, and optimize energy management. This invention utilizes the cloud platform of the BMS to define and analyze data, adjust the parameter matrix of the battery model and the covariance of the sensors, and still maintain SOC accuracy even under sensor aging conditions.

The invention provides an improved unscented Kalman lithium battery state joint estimation method based on IPOA optimization. The improved unscented Kalman lithium battery state joint estimation method comprises the following steps: S1, firstly, constructing a fractional-order second-order RC equivalent circuit model number; according to the method, a battery fractional-order second-order equivalent circuit model is constructed, model parameters are identified by adopting an adaptive genetic algorithm (AGA), the influence of historical data is considered on the basis of UKF, a multi-information theory is combined, an adaptive attenuation factor is introduced to overcome the influence of historical measured values on an estimation result, initial value deviation is inhibited, and the estimation accuracy is improved. A noise adaptive link is introduced to carry out adaptive updating on system noise covariance, an improved pelican algorithm (IPOA) is adopted to optimize distribution adjustment parameters of a UKF during UT transformation, the improved pelican algorithm is adopted to optimize a fractional order multi-innovation adaptive unscented Kalman algorithm (IPOA-FOMIAUKF) to estimate SOC, and finally, a multi-time scale theory is combined, so that the system noise covariance is optimized. The SOC of the battery is estimated by adopting the IPOA-FOMIAUKF under the micro-scale, the SOH is estimated through the UKF under the macro-scale, and the state joint estimation based on the IPOA-FOMIAUKF-UKF is realized through iterative updating.

The application provides a crane outrigger dynamic electronic fence construction method and device and electronic equipment, and relates to the technical field of crane safety monitoring. The method comprises the following steps: acquiring historical key point information in the continuous previous N frames of history images of a target crane outrigger and a current frame image; performing key point extraction on the current frame image to obtain real-time key point information of a plurality of key points of the current frame image; if the number of key points corresponding to the real-time key point information of the current frame image is less than the number of preset position points of the target crane outrigger, calculating the missing information of the key points of the current frame image according to the historical key point information by using a Kalman algorithm, and obtaining effective point information of the preset position points by comprehensively combining the missing information of the key points and the real-time key point information; and calculating a minimum convex hull region by using a convex hull algorithm according to the effective point information, and taking the minimum convex hull region as an electronic fence. The application can accurately construct an electronic fence when the key points of the crane outrigger are blocked.

The invention provides a conveyor speed detection method, device and equipment based on double-antenna RFID. The method comprises the following steps: performing tracking processing on an RFID tag by using a first antenna, and recording a first phase sequence of the RFID tag; performing tracking processing on the RFID tag by using a second antenna, and recording a second phase sequence of the RFID tag; determining a phase difference between the first phase sequence and the second phase sequence, and determining a phase differential velocity according to the phase difference; the Doppler velocity is determined according to the movement data of the RFID tag; and fusing the phase differential velocity and the Doppler velocity by using a Kalman algorithm, determining a final conveyor belt velocity, and outputting the final conveyor belt velocity. The accurate phase differential velocity is determined according to the phase difference of the phase sequences of the two antennas; the Doppler velocity is determined, and the phase difference velocity and the Doppler velocity are fused by using a Kalman algorithm, so that the accurate final conveyor belt velocity is determined, and the high-precision accurate detection of the conveyor belt velocity is realized.

The method for grabbing dynamic objects by the mechanical arm based on visual detection and deep reinforcement learning comprises the following steps: step 1, a visual servo control system of the mechanical arm is built in a simulation scene; step 2, in the visual servo control system of the mechanical arm, a target object is identified based on an improved YOLOv8-pose network to obtain position information of object key points in a two-dimensional image; step 3, the position information of the object key points in the two-dimensional image is combined with a three-dimensional coordinate system of the object itself, a PNP algorithm is used to restore a 6D pose of the object, and a Kalman algorithm is used to smooth a motion trajectory of the moving object and estimate a pose at a next moment; and step 4, a deep reinforcement learning DDPG algorithm model is constructed according to the estimation result to drive the mechanical arm to complete tracking and grabbing of the dynamic object. The efficiency and stability of pose estimation are improved, and the tracking and grabbing performance of the mechanical arm on the dynamic target is improved as a whole.

The invention discloses a battery circuit monomer short-circuit resistance fault identification method and a terminal, and the method comprises the steps: building an equivalent circuit model containing short-circuit equivalent resistance for each battery monomer of a series lithium battery system; according to circuit parameters of the equivalent circuit model, in each detection period, SOC estimation is carried out on each single battery through a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is carried out through an ampere-hour integral method to obtain a second SOC data set; calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; according to the short-circuit resistance data set, calculating the deviation between the short-circuit resistance of each single battery and a short-circuit resistance mean value, and judging whether the single short-circuit resistance has a fault or not; and short circuit identification without complex electrochemical principle modeling is realized.

The invention discloses a system for improving the data acquisition precision of a force sensor, and the system employs a force sensor AD chip CS1237 to collect AD values, stores the collected AD values in a buffer area, stores the collected AD values in a data flow manner, removes possible interference values through an amplitude limiting filtering algorithm, obtains stable data through an average value filtering algorithm, and carries out the data collection through a data flow. Whether a fast Kalman algorithm or a slow Kalman algorithm is used is finally judged through noise judgment of the data, and finally accurate data are obtained; compared with the prior art, the method has the advantages that compared with a simple average filtering method and a first-order filtering method in the prior art, data values become more accurate and stable, the stability and accuracy of data acquisition of the force sensor are improved, regular floating data and burst peak data can be recognized and filtered out, and the accuracy of data acquisition is improved. The problem that data collected by the force sensor is inaccurate due to signal interference is solved.

The application discloses a multi-channel dereverberation method, comprising inputting multi-channel audio data into a diffuse noise field model and calculating an MVDR superdirectivity beam output; taking the superdirectivity beam output as an expected signal of a Kalman algorithm and predicting a dereverberation output signal according to the Kalman algorithm; iteratively updating the dereverberation output signal of a current frame according to a convergence strategy until a preset condition is reached, stopping the iterative update to obtain a dereverberation signal; wherein the convergence strategy comprises a correction coefficient and a convergence number. Since the Kalman algorithm has direction interference suppression under the superdirectivity beam forming addition effect, the mid-high frequency signal-to-noise ratio is improved and low-frequency reverberation is suppressed, so that the multi-channel linear prediction dereverberation output signal is more accurate, thereby improving the final dereverberation sound quality effect; and the convergence strategy is used to update the dereverberation output signal, so that the performance loss caused by complete diagonalization is overcome and the dereverberation sound quality is improved.