145 results about "Drift error" patented technology

The invention relates to the technical field of industrial automation control, in particular to a container liquid level measurement method, system and equipment of a pressure sensor and a medium, and provides a method for eliminating gas phase pressure influence based on a double-sensor differential measurement mechanism and dynamically correcting liquid density and sensor drift errors in combination with a temperature compensation model. The temperature data are linked in real time through a three-dimensional compensation function, density calculation and drift correction are synchronously adjusted, and multi-parameter dynamic decoupling and self-adaptive compensation are achieved; a collaborative measurement mechanism is adopted to integrate bottom and top sensor data, and a built-in temperature probe and a preset threshold are combined to complete multi-source signal fusion and compensation calculation; a dynamic calibration method is designed, and system errors are eliminated through reference pressure point calibration and structure coefficient correction; constructing a closed-loop signal processing flow, and integrating high-precision analog-to-digital conversion, digital filtering and real-time algorithm to output a compensated liquid level value; the method improves the measurement precision and stability, and is suitable for temperature change, gas phase pressure fluctuation and multi-medium scenes.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention discloses a method and a system for measuring an error caused by wavelength drift of an optical fiber current transformer. The method comprises the following steps: constructing a first mathematical model of a half-wave voltage size and a wavelength size, and a second mathematical model of a Verdet constant and the wavelength size; according to the first mathematical model and the second mathematical model, constructing a third mathematical model of the Verdet constant and the half-wave voltage; and constructing a fourth mathematical model of the output optical signal and the half-wave voltage, and determining the total influence of the wavelength drift on the output optical signal according to the fourth mathematical model. The error of wavelength drift can be effectively compensated, and the measurement performance of the optical fiber current transformer is improved.

The invention discloses a displacement auxiliary measuring mechanism and a reservoir dam monitoring and early warning system thereof, and belongs to the technical field of dam monitoring. The reservoir dam monitoring and early warning system comprises a data acquisition module, an attitude calibration module, a data fusion judgment module, a risk grading analysis module and a remote early warning trigger module. The distance measurement precision under the complex field condition is remarkably improved, the current mechanical direction and the reference direction are fused, a correction vector pointing to a target reflection point is generated, the direction drift error is effectively eliminated, and the distance measurement accuracy is improved by constructing a double-path model of main path fusion and auxiliary path comparison. According to the method, non-structural anomalies caused by sensor body interference or environmental disturbance are eliminated, data offset is corrected in real time, a stable trend curve is output, when a main path has a drift or error trend, the main path is automatically guided to converge to an auxiliary path, and the abnormal data recognition and restoration capability is enhanced.

The invention discloses an automatic driving control method and system based on multi-source data aggregation, and particularly relates to the technical field of automatic driving. The method comprises the following steps: acquiring sensor data, extracting an instantaneous relative frequency drift feature and a sensor data fusion drift error feature, evaluating the accuracy of sensor data time synchronization based on an adaptive weighted fusion algorithm, and dynamically adjusting the confidence coefficient weight of each piece of sensor data. The recurrent neural network is used for predicting the future movement track of the obstacle on the data with high time synchronization accuracy, in addition, through a confidence feedback mechanism, the sensor time synchronization accuracy and the environment perception data quality are combined, the data fusion error is dynamically corrected, the control precision and the driving stability are improved, and the driving stability is improved. According to the method, the problem of data mismatching caused by time synchronization errors can be effectively reduced, the sensing accuracy, trajectory prediction capability and safety of an automatic driving system are improved, and the vehicle has more stable and intelligent decision-making capability in a complex traffic environment.

The invention provides an IGBT junction temperature prediction method and system based on dynamic thermal impedance correction. The method comprises the following steps: acquiring temperature data in real time, reversely deducing IGBT loss in combination with a pre-constructed thermal network model, and storing the IGBT loss in historical data; obtaining an IGBT loss average value under the same working condition, and calculating a difference value between the IGBT loss average value and the IGBT loss and a specific value of the IGBT loss average value to obtain a loss offset coefficient; judging whether the loss deviation coefficient is continuously greater than a preset value in a continuous period, if so, judging that the heat-conducting silicone grease is aged, calculating an aging coefficient through loss data and temperature data in the continuous period, and correcting the thermal network model by adopting the aging coefficient; and obtaining the corrected internal transient thermal impedance of the IGBT and the loss of the IGBT, and further predicting the junction temperature of the IGBT. According to the method, the thermal network model is adopted to reversely deduce the real-time loss power of the IGBT, and electric parameter drift errors are avoided; and a dynamic thermal impedance correction mechanism is introduced to ensure long-term accuracy of junction temperature prediction.

The invention provides an inertial navigation method and device, electronic equipment and a robot, and belongs to the technical field of navigation.The method comprises the steps that a current IMU set collected by an IMU sensor and an environment image which is collected by a camera and has a timestamp matching relation with the current IMU set are obtained; based on the time sequence relationship of the IMU data, performing estimation and error correction on the current IMU group to obtain a speed estimation value at the next moment; extracting image features of the environment image, fusing the image feature speed estimation values, and obtaining a prediction track of the target equipment at the next moment according to a fusion result; and controlling the motion of the target equipment according to the predicted trajectory. Therefore, through the time sequence relationship of the IMU data, the current IMU group is estimated and subjected to error correction, and the trajectory prediction is performed in combination with the environment image, the drift error is effectively reduced and even eliminated, and the accuracy of the predicted trajectory is improved, so that the navigation precision and stability of the target equipment in a complex environment are greatly improved.

The invention discloses a hemispherical resonator gyroscope attitude drift error compensation method, and relates to the technical field of resonator gyroscopes, and the method comprises the following steps: S1, system modeling: firstly, establishing a mathematical model of a hemispherical resonator gyroscope, including dynamic characteristics and a noise model, for designing a PID controller; s2, a PID controller is designed, the proportion P, integral I and differential D parameters of the PID controller are designed according to the system model, and the parameters determine the response speed and stability of the controller to error signals; s3, error signal calculation: calculating an error signal between the actual output and the expected output of the system in real time, and taking the error signal as the input of a PID (Proportion Integration Differentiation) controller; by comprehensively applying the steps, the robustness of the HRG can be remarkably enhanced, the HRG can still keep high precision and stability when facing environment change, system aging or non-ideal working conditions, and a closed-loop control mechanism is combined to ensure that the system can maintain good performance under various conditions.

The invention provides an ultrasonic-based surface density measurement method and system and electronic equipment, and relates to the technical field of signal processing. According to the method, an integral value of voltage and time of an ultrasonic pulse signal is monitored in real time to serve as a reference value of the ultrasonic pulse signal; and then the duty ratio of the ultrasonic pulse signal is adjusted by using the adjustment strategy determined by the reference value, so that the output voltage of the ultrasonic pulse signal is adjusted by using the integral value, the stable output of the ultrasonic pulse signal is realized, and a data drift error caused by external factors can be effectively inhibited.

The invention discloses a high-precision Beidou satellite navigation and positioning data acquisition method and system, particularly relates to the technical field of satellite navigation and positioning processing, and is used for solving the problem of positioning error increase caused by receiver hardware delay drift in the prior art. Inverting the first dynamic reference value by using carrier phase integral displacement, and extracting a third-order intermodulation component through coherent demodulation to generate a second dynamic reference value; adopting mutual information entropy analysis to select an effective reference value to calculate a residual error; fitting a theoretical value according to the carrier phase change rate to calculate deviation; marking a hardware drift error when the time domain correlation continuously exceeds a threshold value; finally, the ionosphere residual error is dynamically compensated to generate a corrected observation value, effective separation of the hardware drift error and the ionosphere residual error is realized, and the precision and reliability of long-time observation in a dynamic environment are improved.

The invention discloses a depth calibration method and device for a laser radar, a storage medium and a program product, and relates to the technical field of laser radars, and the method comprises the steps: obtaining calibration field place cloud data generated by the laser radar through the scanning of a calibration field; target laser point cloud data corresponding to each target are extracted from the point cloud data of the calibration field; for each target, extracting point cloud depth information and pulse width of the laser point cloud data of the corresponding target, and comparing the extracted point cloud depth information with the target depth truth value corresponding to the target to determine a corresponding drift error; and according to each extracted pulse width and the corresponding drift error, constructing a drift error-pulse width correction table for performing depth calibration on the laser radar. Therefore, the drift error is compensated according to the pulse width characteristic of the echo signal, and the depth measurement precision of the laser radar is improved.

The invention discloses an area array Gm-APD detection laser radar distance drift error correction method and system, and belongs to the field of data processing. The method comprises the steps of building a single-photon laser ranging system, adjusting the width of a laser pulse, detecting a target distance by using an area array Gm-APD detection laser radar, carrying out repeated measurement on a certain plane for multiple times, obtaining arrival time distribution of a target single-photon event, carrying out weighted average after carrying out filtering and denoising on ranging signals, and obtaining a signal region ranging value. And estimating the average detection photon number of the received monopulse by using the statistical detection probability, and correcting the drift error. According to the method, the drift error caused by echo signal photon accumulation in the area array Gm-APD detection laser radar can be corrected, and the ranging error of the area array Gm-APD detection laser radar is reduced.

The invention relates to the technical field of drainage control, in particular to a liquid level sensing drainage control method and system based on electronic and floating ball cooperative detection, and the method comprises the steps: fusing the angular displacement, capacitance value and acceleration disturbance data of a floating ball, and constructing a liquid level state observer; a dynamic compensation factor is introduced to strip liquid shaking interference, and an accurate liquid level reference value is obtained; a composite control vector is constructed based on the liquid level deviation and the change rate, and a drainage instruction is generated through analysis to control a valve to act; meanwhile, a self-adaptive feedback adjustment mechanism is established, observer parameters are dynamically calibrated through liquid level response residual errors, and the drifting error of an actuator is offset; according to the technical scheme, the robustness of drainage control can be remarkably improved.

The invention discloses a pressure gauge temperature data processing method based on temperature calibration drift error compensation, and relates to the field of data processing, and the method comprises the steps of dynamic temperature drift compensation preprocessing, adaptive noise filtering and feature enhancement, pressure gauge temperature data modeling and the like. Dynamic temperature drift compensation is achieved, and the problem that temperature change and fault mutation cannot be distinguished through traditional static linear transformation is solved; noise interference is suppressed, potential fault features are reserved, and expression of key abnormal points is enhanced. The response capability to sudden faults is effectively improved; temperature drift and fault features are effectively decoupled, it is ensured that the network does not depend on specific temperature conditions, and the adaptability of the model to different temperature scenes is enhanced; the accuracy of fault prediction is improved, and prediction errors caused by temperature changes are reduced.

The invention provides an anti-cracking performance evaluation method and system based on a concrete shrinkage rate. The method comprises the following steps: firstly, collecting shrinkage rate real-time sequence data and an acoustic emission signal flow of a concrete sample in a curing process, separating a high-frequency elastic wave energy attenuation characteristic value from the acoustic emission signal flow, and carrying out space-time coupling on the high-frequency elastic wave energy attenuation characteristic value and a shrinkage rate jump point in the shrinkage rate real-time sequence data to generate a crack initiation tendency index; then obtaining a calibrated crack initiation tendency index based on an ambient temperature hysteresis effect and a temperature drift error of the calibrated crack initiation tendency index, and then outputting an anti-crack performance grade according to a grading deviation degree between a pre-designed grading parameter and a fluctuation range of the calibrated crack initiation tendency index; according to the technical scheme provided by the invention, the essential defect that a traditional method cannot correlate material characteristics with environmental response is overcome, direct quantitative mapping of material structure performance and damage evolution is also realized, and a traceable material-level basis is provided for engineering anti-crack design.

The invention discloses a wire state monitoring method and system based on adaptive Kalman filtering. The method comprises the following steps: acquiring first parameter information, and performing first prediction according to the first parameter information; acquiring second parameter information, and calculating a first target coefficient according to the second parameter information; and updating the result of the first prediction according to the first target coefficient, and monitoring the wire state according to the updated result. By adopting the adaptive Kalman filtering technology, the method can effectively combine the data of the GNSS sensor and the IMU sensor so as to improve the precision and reliability of wire state monitoring. In a dynamically changing environment, a GNSS signal may be interfered, and an IMU sensor may generate a drift error. According to the invention, the filtering parameters can be dynamically adjusted through the adaptive Kalman filtering algorithm, so that accurate lead state estimation can be provided in different environments.

The invention relates to the technical field of ultra-precision intelligent manufacturing, in particular to an ultra-precision machining error compensation method based on multi-modal information fusion, a medium and equipment. The method comprises the following steps: acquiring three-dimensional shape data of a workpiece through an in-situ measurement device, synchronously acquiring temperature field and vibration data, constructing a multi-modal space-time tensor, inputting a physical information neural network to decouple a static geometric error, a time-varying thermal drift error and a dynamic vibration error, and generating a four-dimensional correction tool path to realize reverse compensation. According to the method, physical constraints are embedded through a PINN model, the problem of false thermal drift misjudgment in a traditional method is solved, experiments show that the RMS error of free-form surface machining is reduced to 5 nm or below, and the precision is improved by 70% or above; and meanwhile, error decoupling is endowed with physical interpretability, the small sample generalization ability is enhanced, and a technical basis is provided for process optimization.

The invention provides an intelligent swing arm adjusting and detecting system for automobile driving. The intelligent swing arm adjusting and detecting system comprises a multi-source sensing module used for collecting vibration signals, stress distribution and displacement data of a swing arm in real time; the attitude resolving module receives the sensing data and constructs a three-dimensional dynamic swing arm model; the dynamic compensation module calculates attitude deviation based on the model and generates compensation parameters; the execution mechanism module drives the swing arm to adjust according to the compensation parameters; the abnormity diagnosis module analyzes the dynamic response spectrum to determine the fault type; the self-calibration module corrects the drift error of the multi-source sensing data; the control output module integrates the compensation parameter and the diagnosis result to generate a control signal. The stability and reliability of the automobile driving system can be improved, the maintenance cost is reduced, and the breakdown time is shortened.

The invention discloses an MEMS three-axis gyroscope thermal drift error calibration method based on segmented calibration, which comprises the following steps: forming an inertial system by using a three-axis accelerometer and a gyroscope, rotating an inertial unit according to a designed rotation scheme by using a low-cost indexing mechanism, and collecting data of the three-axis gyroscope and the accelerometer in a temperature rising process; and carrying out segmentation processing on rotation data in the temperature rise process, calibrating each piece of segmented data in a proposed 24-dimensional system state, and finally obtaining a zero-offset temperature drift curve of the MEMS gyroscope and a temperature drift curve of a non-orthogonal transformation matrix component. According to the method, the application precision of the MEMS three-axis gyroscope is improved, the calibration cost is reduced, and the positioning precision of intelligent mobile terminals such as an unmanned aerial vehicle and a mobile robot can be improved.

The invention discloses a differential processing algorithm for laser pulse ranging echoes, which is characterized in that the echoes of laser pulse signals are differentiated through a mathematical method, so that an extreme point, namely the peak moment of the echoes, is solved. According to the differential processing algorithm of the laser pulse ranging echo, the extreme point is obtained by differentiating the Gaussian signal of the time t. The change of the intensity of the echo does not cause the change of the extreme point moment, i.e., the t0 moment is not changed, so that the method fundamentally eliminates the moment discrimination error delta t of the leading edge discrimination method, naturally does not need to be optimized to obtain the dual-threshold leading edge discrimination method, and also avoids the problems of other methods in use. When the echo size changes, the peak moment can still be accurately identified, the drift error is greatly eliminated, in the pulse laser ranging echo moment identification method, the accuracy is far higher than that of other methods, linear fitting and other secondary error compensation are not needed, the engineering complexity is reduced, the workload is reduced, and the adaptability is improved.

The invention discloses an inertial navigation system point position correction method and device based on a transverse geography system, and the method comprises the steps: obtaining transverse longitude and latitude calculated by inertial navigation updating and angle measurement information measured by a gyroscope at the current navigation time, building a transverse geography system navigation drift error model, in combination with a conversion relation between navigation parameters of a transverse geographic coordinate system and a traditional geographic coordinate system, a navigation drift error compensation parameter is calculated through a transverse longitude error and a transverse latitude error, and the transverse longitude of inertial navigation update calculation and the transverse latitude of inertial navigation update calculation are corrected; the corrected transverse longitude and the corrected transverse latitude are obtained, navigation drift error compensation parameters are projected to a transverse geographic coordinate system, angle measurement information measured by a gyroscope is corrected, and corrected angle measurement information is obtained. According to the method, the inertial navigation high-latitude long-endurance navigation precision can be improved based on position correction and drift error suppression of high-latitude transverse geographical system long-endurance navigation.

The invention discloses a virtual angle sensor online measurement method based on a graph neural network. The method comprises the following steps: acquiring an environment magnetic field signal of a magnetometer and a motion attitude signal of an inertial sensor; training to obtain a graph neural network model capable of predicting the incidence relation between the environment magnetic field signal and the motion posture signal; when a drill bit is at a low speed in a drilling detection interval, inputting a collected motion attitude signal of an inertial sensor into the graph neural network model for reverse prediction, and performing compensation calibration on an environment magnetic field signal detected by a magnetometer; and when the drill bit is at a high speed in the drilling detection interval, measuring the drilling environment magnetic field of the drill bit by using the compensated and calibrated magnetometer as the input of the corrected and trained graph neural network model so as to predict the motion attitude of the output drill bit, and correcting the drift error of the inertial sensor. According to the invention, mutual correction of the magnetometer and the inertial sensor is realized, and the problem that errors exist in the magnetometer and the inertial sensor during measurement while drilling is solved.

The invention discloses an intelligent oven temperature regulation and control system and method based on a self-adaptive control method, and the method comprises the steps: collecting first temperature data in an oven based on sensors, and obtaining a redundancy check temperature value through a redundancy check method based on multiple temperature sensors; calculating a current temperature drift error value according to the redundancy check temperature value, inputting the temperature drift error value and historical drift trend data into a temperature drift prediction model, and outputting a predicted temperature drift compensation value; fusing the redundancy check temperature value with the predicted temperature drift compensation value to generate a first fused temperature value; calculating a temperature stability index in the oven based on the first fusion temperature value; based on the temperature stability index and a preset temperature fluctuation target range, the heating power of the oven is adjusted through an adaptive frequency heating control algorithm; and second temperature data in the oven are collected again, and cross validation is carried out on the second temperature data and the first fusion temperature value so as to correct the historical drift trend data.

According to the flow control system and method for the filter element flow resistance performance test provided by the invention, the self-adaptive PID control closed loop taking the real-time flow resistance coefficient as the core feedback quantity is constructed, and the high-precision sensing network and the intelligent decision algorithm are integrated; the defect that traditional manual or simple PID control is difficult to adapt to filter element characteristic change and environmental interference is successfully overcome; specifically, the system dynamically collects flow, pressure difference and temperature signals and calculates the flow resistance coefficient of a filter element in real time, PID control parameters (proportion, integration and differential terms) are adjusted in a self-adaptive mode according to the flow resistance coefficient, meanwhile, the reference value of a sensor is periodically calibrated so as to eliminate drift errors, and an electric control valve is driven to rapidly and accurately respond; according to the method, the dynamic stability and static precision of flow control are remarkably improved, multi-source interference such as pipeline pressure pulsation and fluid temperature change is effectively compensated, the steady-state error of the test flow is greatly reduced, and finally the efficient and high-precision automatic test of the flow resistance performance of the filter element is achieved.

The invention relates to a multi-fault real-time early warning system for a power distribution cabinet, and relates to the technical field of power distribution cabinet detection. Partial discharge, mechanical vibration, insulation dielectric loss and environmental parameters are synchronously acquired through a distributed sensor array; performing electromagnetic interference filtering and wavelet noise reduction preprocessing on the original data, and extracting the characteristics of pulse frequency, vibration kurtosis, dielectric loss change rate and the like; a'rule base + random forest 'mixed model is adopted to identify fault types and confidence; fault positioning with the precision of + / -3cm is realized based on sensor delay inequality and a triangulation positioning algorithm, and fault components are determined in combination with a component topology mapping table; early warning levels are divided according to fault types, confidence coefficients and component importance, and customized reports are dynamically pushed; a standard signal is injected every 24 hours to generate a compensation coefficient, and the drift error of the sensor is corrected in real time.

The invention discloses a hydraulic support attitude sensing method, device and equipment and a medium, and relates to the technical field of hydraulic support regulation, and the method comprises the steps: obtaining the relative attitude angles of a top beam and a shield beam relative to a base under a target coordinate system, and obtaining the absolute attitude angles of the top beam, the shield beam and the base under a geographic coordinate system; fusing the relative attitude angle and the absolute attitude angle to obtain attitude angles of the top beam and the shield beam; according to the method, the absolute attitude angles of the top beam, the shield beam and the base are obtained by using a complementary filtering algorithm under the condition of reducing noise interference, and the relative attitude angles and the absolute attitude angles are fused by using extended Kalman filter (EKF), so that the drift error of the IMU technology during attitude sensing of the hydraulic support is offset, and the attitude sensing accuracy of the hydraulic support is improved. And accurate attitude angles of the top beam and the shield beam of the hydraulic support are obtained.

The invention discloses a charging pile remote monitoring method and system based on an error model, and the method employs multi-axis dynamic bifurcation mapping and rotation residual aggregation to construct an error model based on multi-dimensional operation parameters, and can effectively capture the coupling characteristics of a drift error and a transient disturbance error through multi-axis disturbance bifurcation mapping. The method is more sensitive to complex fault response in actual operation, has higher multi-dimensional fitting capability and error generalization capability compared with a traditional method, can simulate periodic response offset of various operating parameters under different disturbance frequencies through rotation residual aggregation, generates stable error signals with definite directivity, and has the advantages of being high in accuracy and high in reliability. According to the method, dynamic prediction and guiding correction of the error trend can be achieved through the built error model, overfitting and error overcompensation are effectively avoided, accurate correction of the multi-dimensional operation parameters of the charging pile is achieved, and the judgment accuracy and response efficiency of remote monitoring of the charging pile are greatly improved.

The invention discloses a low-voltage alternating-current power distribution network electric leakage fault detection method and system, relates to the technical field of power grid fault detection, and is used for solving the problem of detection lag caused by incapability of timely detection when electric leakage current is relatively small but continuously exists. For each pre-selected node, acquiring and synthesizing a safety distance coefficient and a sensor compatibility ratio of each pre-selected node to obtain a key point location, acquiring the key point location and accessing a sensor of the corresponding point location, performing power-on detection to obtain a drift error and a direct current component, substituting the drift error and the direct current component into a logistic regression algorithm to obtain a sensor screening coefficient, and performing sensor screening; a sensor screening threshold is obtained through binary recursion, reserved sensors and deleted sensors are obtained, the temperature difference between the environment of each reserved sensor and the surface of a line and the total harmonic distortion ratio are detected, a set of fuzzy rules are formulated for fuzzy reasoning, the electric leakage fault detection result is determined, and the accuracy of electric leakage fault detection is improved.

The invention provides a temperature drift compensation method, device and equipment for a Hall current sensor, and relates to the technical field of Hall sensors. The method is applied to a temperature excursion compensation system, the temperature excursion compensation system comprises a temperature sensor, a microprocessor, a temperature excursion prediction model, a compensation signal generation module and a differential amplification circuit, and the temperature excursion compensation method comprises the following steps: acquiring environment temperature data of a Hall current sensor by using the temperature sensor; the microprocessor calculates a temperature drift error parameter corresponding to the current environment temperature data based on the environment temperature data and a temperature drift prediction model; the compensation signal generation module generates a corresponding compensation signal based on the temperature drift error parameter, and the compensation signal and the temperature drift error represented by the temperature drift error parameter are equal in size and opposite in direction; and superposing the compensation signal and an original sampling signal of the Hall current sensor through a differential amplification circuit to obtain a final current signal. The measurement accuracy of the Hall current sensor can be improved.

The invention relates to the technical field of unmanned aerial vehicles, provides an unmanned aerial vehicle fusion height estimation method, an unmanned aerial vehicle and a medium, and aims to solve the problem that a stable and credible height estimation value cannot be provided due to IMU measurement errors when both RTK and a barometer fail. The method comprises the following steps: performing high-frequency noise filtering on the original acceleration of the inertial measurement unit to obtain a first acceleration value; performing first-order difference on the speed of the global positioning system to obtain an acceleration compensation value; constructing a state space model containing a vertical speed, an acceleration and an accelerometer zero offset drift error; the first acceleration and the acceleration compensation value are fused through Kalman filtering, and drifting is estimated and stripped in real time; and outputting the smooth height by using trapezoidal integral. The device can output a whole-course continuous height estimation value with the drift distance smaller than 0.3 m / min and the bandwidth larger than or equal to 2 Hz, and is suitable for stable flight and uninterrupted operation when both the RTK and the barometer fail in complex scenes such as agricultural plant protection.