21results about How to "Reduce estimation error" patented technology

The application discloses a metabolic equivalent driven endurance training intensity gradient generation system and method thereof, and belongs to the technical field of intelligent sports training. The system comprises a non-contact physiological signal acquisition module, a multi-dimensional metabolic state modeling module, a floating threshold generation module, a multi-channel feedback regulation module and an edge abnormality filtering module. Respiratory and heart rate and other multi-dimensional physiological parameters are collected through the fusion of a millimeter wave radar and a PPG sensor. A three-dimensional metabolic state dynamic evaluation model is constructed based on a GRU network, and real-time MET values are output. Intensity adjustment threshold values are adaptively generated according to MET deviation. The exercise intensity is cooperatively regulated through the tactile feedback of intelligent running shoes and the slope adjustment of a treadmill. Abnormal data are filtered at the edge by using a LOF algorithm. The application solves the problems of single physiological signal dimension, static metabolic state evaluation and abnormal data interference in the prior art, and realizes the precise real-time regulation of endurance training intensity.

The driving range estimation method based on unit charge range and battery aging correction mainly includes four steps: online battery SOC estimation, periodic battery SOH update, unit SOC range estimation, and driving range estimation. This invention uses periodic battery SOH estimation to correct the battery SOC estimation, and combines unit SOC range estimation to achieve accurate driving range estimation. It has the advantages of simple model structure, low computational load, high estimation accuracy, and easy implementation in vehicle BMS. Under normal driving conditions, the maximum driving range estimation error of this invention is within 3km, which meets the operating requirements of electric vehicles.

A colon endoscope camera pose estimation method based on an iterative refinement strategy relates to the technical field of medical image processing and computer vision, and comprises the steps of construction and preprocessing of an image data set, construction of an overall framework of a network model, design and implementation of a feature extraction network, and design and implementation of a camera motion classification network. Designing and realizing an iterative relative pose refining network; training and using a network model; and the like. According to the method, two camera motion modes of insertion and withdrawal are distinguished by using the camera motion classification network, the pose estimation precision is improved by combining a coarse-to-fine iteration refining strategy of the iteration relative pose refining network, the learning ability and generalization performance of the whole method are effectively improved, accurate estimation of the colon endoscope camera pose is realized, and the method is suitable for popularization and application. The method can be widely applied to the camera pose estimation task of the colon endoscope, and provides accurate camera pose support for clinical application scenes such as three-dimensional reconstruction and polyp screening of the colon endoscope.

The application provides a high-efficiency network traffic measurement architecture based on timing filtering and dynamic migration. When a data packet arrives, the flow activity is evaluated by timing filtering to filter out low-frequency transient flows, and the continuously active flow is connected to the main measurement structure. In combination with the data flow characteristics and the dynamic migration model, the history Sketch area and the active queue area are dynamically scheduled: the Sketch relies on compact hashing and probability counting to depict the global traffic profile; the active area is configured with independent counters to accurately track the active flow without loss. The frequency is updated according to the flow type difference: the continuously active flow is accurately counted in the active queue, the inactive flow is accumulated in the history Sketch area, and the low-frequency transient flow is directly discarded. If the promotion threshold is triggered, the hierarchical dynamic migration is completed through cyclic iteration until the measurement period ends. The architecture can adaptively allocate storage space according to the flow timing characteristics, reduce memory occupation and hash collision, and effectively improve the accuracy and running efficiency of large-scale network traffic measurement.

This invention belongs to the field of data processing technology and relates to a method and system for error correction in the joint evaluation of battery SOC and SOH. The method includes: S1, preprocessing the acquired multi-source data; S2, constructing a joint SOH-SOC estimation model; S3, segmenting the preprocessed data from different time points to obtain first window data and second window data; S4, obtaining a fused SOC estimate based on the first and second window data using an ampere-hour integral algorithm and a quartile distance group detection algorithm; and S5, dynamically correcting the joint SOH-SOC estimation model using a gradient backpropagation mechanism based on the fused SOC estimate and the SOC value at the end of the second window data to obtain a corrected joint SOH-SOC estimation model. This invention solves the problems of low estimation accuracy and low reliability in existing methods by dynamically correcting the joint SOH-SOC estimation model.

The application relates to the technical field of vehicles, in particular to a vehicle speed estimation method and device, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring the state of each wheel at a current moment, wherein the state comprises a first state and a second state; if the state of all the wheels is the first state, acquiring the vehicle speed of the vehicle at a historical moment before all the wheels enter the second state, and estimating the vehicle speed of the vehicle at the current moment according to the vehicle speed of the vehicle at the historical moment; otherwise, estimating the vehicle speed of the vehicle at the current moment according to the wheel speed of the wheel in the second state. Thus, the problems of low accuracy of vehicle speed estimation and large estimation error in the related art are solved.

The invention provides a parameter identification method for a proton exchange membrane fuel cell system, belongs to the technical field of fuel cell identification, and solves the technical problem of low parameter identification precision caused by the fact that an internal electrochemical reaction of the proton exchange membrane fuel cell system cannot be accurately described by a traditional modeling method. According to the technical scheme, the method comprises the following steps: 1) establishing a fractional order multiple-input single-output state space model of the proton exchange membrane fuel cell system; and step 2) constructing an identification process of the hierarchical multi-information recursive minimum error entropy algorithm. The hierarchical multi-information recursive minimum error entropy algorithm provided by the invention has relatively high convergence speed and relatively high convergence precision, and can be well suitable for parameter identification of the proton exchange membrane fuel cell system.

The invention relates to the technical field of metallurgical process control, and discloses a molten pool smelting intelligent dynamic control system based on multi-parameter coupling, which comprises a global perception sensor network, a data fusion and state evaluation module and a precise execution control system. According to the system, heat-fluidization multivariable variables of a molten pool are obtained at high frequency through a global perception sensor network, a data fusion and state evaluation module completes prediction-updating through Kalman filtering, abnormal value elimination is carried out through a Mahalanobis distance threshold, working condition drifting is restrained in combination with self-adaptive R / Q, and the state of the molten pool is evaluated. State evaluation is carried out, the middle temperature, the copper matte grade, the liquid level, the change rate and the circulation strength are generated, an LSTM model is adopted for short-time prediction, control suggestions are generated, an amplitude / slope limiting function and a safety interlocking mechanism are matched, a closed-loop execution process is formed, the accurate execution control system converts the control suggestions into actual control actions, and the actual control actions are controlled. And the state of the molten pool is accurately adapted, operation feedback and state monitoring data are transmitted back, and the intelligent control operation efficiency is high.

This invention discloses a signal processing, motion state estimation method, apparatus, vehicle-mounted equipment, and medium to solve the problem of the inevitable compromise between hysteresis and root mean square noise values ​​in the prior art. The method includes: acquiring the raw signals detected by each body sensor on the vehicle at the previous time step; the body sensors include different types of sensors, with multiple sensors of each type; estimating the signal increment value of another body sensor of the same type per unit time based on the raw signals detected by at least three of the multiple body sensors of the same type at the previous time step; calculating the signal estimate value of the body sensor at the current time step based on the raw signals and corresponding signal increment values ​​of the body sensors at the previous time step; and fusing the raw signals of the body sensors at the previous time step and the signal estimate value at the current time step to obtain the fused signal value of the body sensors at the current time step.

This invention discloses a network resilience evaluation method based on data fusion and adaptive weight update in the field of data processing technology. The method includes acquiring headquarters-branch office data of industries in a target region, classifying the headquarters-branch office data, fusing the classified data at the city level, preprocessing the fused data to obtain preprocessed data, setting corresponding KPIs based on the headquarters-branch office data, calculating the KPI change rate based on historical and newly added headquarters-branch office data, and calculating the updated coupling strength between headquarters-branch office data based on the KPI change rate. This invention constructs a hierarchical, multi-source spatiotemporal data industry network model, and combines it with an adaptive association strength update method and a spatiotemporal hierarchical weight adjustment mechanism to achieve real-time updates of headquarters-branch office coupling strength and node / edge capacity, thereby improving the accuracy of industry network resilience assessment.

This invention discloses a method for estimating voltage and frequency deviations in microgrids for different scenarios, comprising: collecting voltage, current, power, and frequency data; performing coordinate transformation and filtering to obtain system time-series characteristic data; extracting power injection load response and frequency-voltage coupling state, constructing a disturbance-free reference evolution process, and generating a counterfactual reference trajectory; constructing a synchronization envelope, identifying deviance, crossing, and regression segments, and generating the original frequency and voltage deviation; inputting the improved AGCRN network to form a disturbance backtracking chain and extracting structural deviation components; determining the operational deviation based on the original deviation and structural deviation, constructing a recovery evolution fingerprint, and generating a deviation correction; fusing the original deviation, structural deviation, operational deviation, and correction, performing continuity correction, and outputting the final frequency and voltage deviation. This invention achieves accurate estimation of microgrid voltage and frequency deviations by constructing a counterfactual reference trajectory, improving AGCRN topology modeling, and correcting with a recovery evolution fingerprint.

The invention provides a power management system and a power management method for a handheld terminal, and belongs to the technical field of power management. The system comprises a battery management terminal, a power management module and a battery pack, wherein the power management module is electrically connected with the battery management terminal, the battery pack and a controller. The power management module obtains battery state parameters from the battery pack and reports the battery state parameters, and the battery management terminal generates and issues a battery parameter configuration file and a current limit configuration file. The power management module updates battery pack configuration parameters according to the battery parameter configuration file, compensates the current charge state, and displays the compensated charge state on the terminal; and according to the current limiting configuration file, the working parameters of the power utilization module of the handheld terminal are limited through the controller. According to the method, the electric quantity estimation and current limiting strategy can be dynamically optimized according to the battery state, the accuracy and smoothness of electric quantity display are improved, and the abnormal shutdown risk of the handheld terminal under the low-temperature, high-load and aging working conditions is reduced.

This application provides a channel estimation method, apparatus, storage medium, and program product. The method includes: estimating the angle of a base station and a first scattering object surrounding the base station using a discrete Fourier transform algorithm to determine first angle information; designing a codebook-based transmit beamforming matrix based on the first angle information; and transmitting an uplink pilot signal to the base station based on the codebook-based transmit beamforming matrix. The solution of this application, based on the determined angle information, designs a codebook-based transmit beamforming matrix, improving the accuracy of uplink channel estimation and overcoming the limitations of channel estimation in traditional MIMO-OFDM systems.

This invention belongs to the field of integrated sensing and communication technology, specifically relating to a high-precision ranging method for OFDM-ISAC based on adaptive CZT. First, the communication information of the received signal from the OFDM-ISAC system is extracted to obtain a two-dimensional matrix containing target distance and velocity information. Then, a two-dimensional fast Fourier transform is performed on the two-dimensional matrix to obtain a complex-valued periodic graph. The amplitude features of spectral peaks and their neighboring spectral lines are extracted from the distance slices. Next, the analysis window for CZT spectrum refinement is adaptively determined based on the amplitude difference relationship between adjacent spectral lines to the left and right of the spectral peak. CZT refinement is then performed on the local spectrum within the window to obtain high-resolution spectral information. Finally, based on the refined spectral features, an adaptive peak extraction strategy is used to accurately estimate the spectral peak position, and then the target distance is calculated through the mapping relationship between the peak index and the distance. This invention improves ranging accuracy without introducing significant computational overhead and ensures the robustness of the system.

The present disclosure provides a sound source positioning method, related device and medium, the method comprising: obtaining time domain frame data by performing frame windowing processing on time domain data of an audio signal received by a microphone array; obtaining frequency domain frame data by performing short-time Fourier transform on the time domain frame data; reconstructing enhanced frequency domain frame data of the frequency domain frame data by performing noise suppression on the frequency domain frame data frame by frame; and estimating the direction of arrival of a sound source frame by frame based on the enhanced frequency domain frame data and using an adaptive beamforming algorithm to perform spectral peak search. The present disclosure reduces the interference of noise on sound source positioning and improves the accuracy of sound source positioning.

The invention discloses an energy storage system SOC dynamic correction method and system based on three-dimensional fusion. The method comprises the steps that a battery dynamic model, a temperature-internal resistance-SOC correlation model and a power grid frequency interaction model of an energy storage system are established respectively; establishing a first objective function, a second objective function and a third objective function corresponding to the battery dynamic model, the temperature-internal resistance-SOC correlation model and the power grid frequency interaction model respectively; unifying the first target function, the second target function and the third target function into a total optimization target based on a weighted fusion function; and performing minimization calculation processing on the total optimization target according to the real-time working condition characteristics of the energy storage system and the weight parameter of the total optimization target based on an adaptive switching strategy, and performing dynamic correction processing on the SOC of the energy storage system according to the minimization total optimization target. In the embodiment of the invention, full-link accurate mapping is realized, and the SOC estimation error is reduced to be within + / -1.5% from the original + / -5%.

The invention discloses a lithium battery BMS (Battery Management System) device combined with impedance test and a health monitoring method, and the device not only has the function of a conventional BMS, but also has the function of realizing the acquisition and analysis of electrochemical impedance information of each battery of a battery pack by a front-end AFE module through the control of a BMS core processor on a data selector. The system can carry out impedance sweep frequency test on the battery in a certain frequency range, and can also carry out key test aiming at a specific frequency point, the collected impedance characteristics of the battery can be used for health state estimation, battery aging prediction and temperature state analysis, and if an abnormal battery occurs, an early warning execution device is triggered to avoid accidents. According to the invention, the state of the battery can be monitored in real time through impedance testing, and impedance data and battery state prediction are combined, so that rapid safety early warning and active management are realized.

The invention relates to the technical field of human-computer interaction, in particular to a human-computer interaction sight line estimation system fusing eye movement and head posture, which comprises the following steps: acquiring an eye image sequence and head posture sensor data, detecting image coordinates of canthus points and pupil center points, and analyzing to obtain a head rotation angle. Performing three-dimensional reconstruction by using the eye corner point image coordinates and the internal reference of the camera to obtain eye corner point three-dimensional coordinates under the head coordinate system and fitting the initial position of the eyeball center. And converting to a camera and a screen coordinate system according to the attitude data to obtain an eyeball center estimation position under the screen coordinate system. Combining the pupil center point image coordinate, the camera internal reference and the position to calculate a sight direction vector, and intersecting with a screen plane to obtain a drop point coordinate; three-dimensional modeling of a head coordinate system and conversion of multiple coordinate systems along with postures are achieved, and head movement and individual anatomical differences are adapted. According to the invention, the spatial precision and dynamic adaptability of sight line estimation are improved.

This application discloses an identification method for a turbine-controlled system with time-delay elements, comprising: establishing an identification model of a fractional-order time-delay state-space model of the turbine-controlled system based on the system matrix; and constructing an identification process based on the identification model using the Adam algorithm based on Kalman filtering (KF). This application establishes a fractional-order time-delay state-space model for turbine-controlled systems with time-delay elements, using turbine torque as the input data and turbine flow rate as the output data. Unlike common integer-order models, this invention establishes a fractional-order model to describe the system, which can more accurately describe the actual system.

This invention relates to a method, apparatus, and device for anti-angle misjudgment angle measurement based on vortex electromagnetic waves, and relates to the field of radar signal processing technology. The method includes: using a circular array as a transmitter, generating and transmitting multiple modes of vortex electromagnetic waves to illuminate the target, obtaining echo signals; selecting a dual-mode combination, processing the echo signals corresponding to the combination to obtain angle measurement values ​​corresponding to each combination; calculating the error between any two sets of angle measurement values, comparing each error with a preset threshold to determine if angle misjudgment exists; if all errors are within the preset threshold, it is determined that there is no angle misjudgment, and the preset angle measurement value is output as the target angle measurement result; if an error exceeds the preset threshold, it is determined that there is angle misjudgment, and the unaffected measurement values ​​are filtered by the consistency and difference of each set of angle measurement values, and the target angle measurement result is output. This invention can achieve robust, continuous, and accurate measurement of target angles in complex interference environments.