34 results about "Observational error" patented technology

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses an oil reservoir history fitting method based on graph lasso and set smooth multi-data assimilation, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, acquiring to-be-optimized model parameters and observation data of a target oil reservoir, constructing an observation error covariance matrix and initializing a model parameter set, performing numerical simulation by utilizing an oil reservoir numerical simulator to obtain prediction data, constructing a joint state matrix, performing dimensionless standardization processing on the joint state matrix to obtain an empirical correlation coefficient matrix, and then, performing optimization on the joint state matrix. And executing a graph lasso algorithm combined with an extended Bayesian information criterion to obtain an optimal dimensionless sparse precision matrix, extracting correlation coefficient sub-blocks required by Kalman updating from the optimal dimensionless sparse precision matrix based on a Scherr's theorem, obtaining a robust data auto-covariance matrix through a reverse reduction physical quantity outline, calculating Kalman gain in combination with an observation error covariance matrix, and calculating a Kalman filter. And the model parameter set is updated until the preset condition is met, the reservoir history fitting model parameter set is output, and the stability and precision of reservoir automatic history fitting are improved.

The invention discloses an atmospheric ocean multi-source data high-precision assimilation method and device, and relates to the technical field of ocean multi-source data assimilation, and the assimilation method comprises the steps: collecting multi-source observation data of a typhoon region of the South China Sea; preprocessing the data; decomposing the observation field into different scales by using wavelet transform; acquiring a background field from the numerical model, interpolating the background field to a grid consistent with observation, projecting the background field to an observation space, and performing multi-scale decomposition to enable the background field to correspond to an observation scale; calculating a difference vector of observation and background fields on each scale, and dynamically calculating an adaptive weight of each point on each scale according to an observation error, a background error and a current deviation; and constructing a cost function, solving through an optimization algorithm to obtain an analysis field, and outputting a final analysis field. According to the method, a multi-scale decomposition and self-adaptive regularization cooperation mechanism is introduced, so that high-precision dynamic fusion of the multi-source observation data and the numerical model can be realized.

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The invention relates to a cycle slip processing method, device, equipment and medium, and the method comprises the steps: obtaining the observation error data of each satellite according to the difference between the double-difference carrier observation data of each satellite in a plurality of satellites of a target carrier frequency and the double-difference satellite-to-earth distance data of each satellite based on a current epoch and a previous epoch for a reference station and a monitoring station; the receiver clock drift deviation between the base station and the monitoring station is estimated based on the observation error data of the two first satellites, clock drift deviation data is obtained, and the two first satellites are the two satellites with the minimum observation error data difference value in the multiple satellites; and on the basis of the double-difference carrier observation data of a second satellite in the plurality of satellites, subtracting the double-difference satellite-ground distance data and the clock drift deviation data of the second satellite, and determining single-difference ambiguity incremental data so as to perform single-frequency cycle slip processing on the target carrier frequency of the current epoch on the base station and the monitoring station. According to the invention, single-frequency cycle slip processing can be efficiently and jointly carried out on double stations without the help of an external sensor.

This invention relates to the field of power electronics technology, specifically to a method and system for monitoring the insulation of DC charging piles. The method includes the following steps: real-time monitoring of branch voltage deviation from the busbar; adjusting measurement compensation parameters; correcting the measured value of branch insulation resistance; identifying leakage current drift; locating the leakage branch and triggering relay control; sending alarm information; and generating a charging pile monitoring record. In this invention, the consistency of branch voltage measurement is improved by combining branch voltage deviation trends with voltage compensation parameters. Observational errors are corrected by comparing insulation resistance fluctuations, optimizing measurement accuracy. Current deviation compensation under no-load conditions eliminates sensor zero-point drift interference. Leakage branch identification is achieved by combining current jump amplitude and voltage direction, enhancing location accuracy. Relay control and event information linkage are used to form a closed loop between alarm response and equipment status recording, improving the real-time performance and completeness of system monitoring.

This application discloses a one-dimensional variational inversion method and apparatus for GNSS occultation data. The method includes background field preparation: interpolating temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data; simultaneously, interpolating the liquid water content and ice water content in the clouds to the latitude and longitude of the occultation data to obtain the background field profile required for inversion; and inputting observation data and background field: inputting the occultation data, background field profile, observation error covariance matrix, and background field error covariance matrix into the one-dimensional variational inversion system. By adding liquid water content and ice water content terms to the observation operator, the deviation between the observation data and the background field data is calculated, and the distribution characteristics of the deviation are statistically analyzed. After adding liquid water and ice water, the average refractive index deviation between the two types of data is significantly reduced.

The application discloses a TLE target library matching method and system for radar observation data, and belongs to the field of space target monitoring; the method comprises the following specific steps: S1, using an SGP4 model to derive and store position information of all targets in a TLE library; S2, converting radar measurement data into position coordinates of targets in an inertial system; S3, using an extended Laplace algorithm to perform initial orbit determination on a measurement arc segment; S4, screening targets in the TLE library according to the orbit determination result; S5, performing coarse matching of an observation arc segment and a space target by taking position mean square error as a criterion, and obtaining candidate targets; and S6, considering observation error and TLE orbit error, and determining a matching result according to covariance analysis; the application reduces the calculation amount of orbit prediction by using Lagrange interpolation technology, and screens TLE by using initial orbit determination results, so that unnecessary matching calculation can be avoided, coarse matching and fine matching are combined, the target matching speed can be ensured, and the matching precision can also be ensured.

The application provides a diversion tunnel surrounding rock mechanics parameter inversion method and device based on multi-target learning, and the method comprises the following steps: a sampling method containing directional number disturbance low difference sampling sequence is adopted to construct a parameter-response sample data set of the diversion tunnel surrounding rock; a multi-penalty item loss function is used for model training on a multi-target machine learning algorithm to construct a diversion tunnel surrounding rock response multi-output proxy model; the multi-penalty item loss function comprises an observation error item, a time sequence monotonic-stable penalty item and a cross-measurement point collaborative penalty item; the proxy model is used for parameter inversion to generate an optimal mechanics parameter solution; the precision and generalization ability of the surrounding rock mechanics parameter inversion can be significantly improved under the condition of a small sample; the physical consistency of the inversion result is ensured through time sequence monotonic and cross-measurement point collaborative constraints; the method has good noise resistance and asynchronous data robustness, can realize optimal parameter output under multi-target weighting, and forms a closed-loop verification from inversion to numerical review.

The invention provides a high-speed wind tunnel free flow correction method based on total temperature regular data assimilation, and the method comprises the steps: (1) determining a wind tunnel design working condition, and determining a test model and an observation physical quantity; obtaining observation data on the surface of the test model, and giving noise of the observation data; (2) constructing an incoming flow working condition sample set; (3) constructing a prior sample set; (4) determining a total temperature regular value and a weight matrix; (5) carrying out TEnKF optimization, constructing an observation error covariance matrix according to observation data noise, constructing a sample covariance matrix according to a prior sample, calculating a Kalman gain matrix, and updating to obtain a posterior state vector; (6) calculating the difference between the priori state vector and the posteriori state vector; and (7) obtaining corrected wind tunnel incoming flow parameters. According to the method, the assimilation algorithm is ensured to correct the incoming flow correction result in a reasonable physical range, and the incoming flow working condition of the hypersonic wind tunnel can be effectively corrected.

The application discloses a cross-dimension multi-source satellite altimeter sea level anomaly joint estimation method and system, and relates to the field of ocean remote sensing.The joint estimation method comprises the following steps: obtaining multi-source observation data of a target sea area about a target period, forming a corrected joint observation data set through cross-dimension data processing and reconstruction operation and bias correction operation, and mapping the corrected joint observation data set to a corresponding regular space-time grid of the target sea area; based on a preset space-time search radius, constructing an observation operator, a background covariance matrix and an observation error covariance matrix corresponding to each grid point in the regular space-time grid, and generating a regular gridded sea level anomaly product of the target sea area about the target period through an optimal interpolation method without performing explicit system error correction.The application can consider large-scale background consistency and small and medium-scale structure resolution capability on multiple spatial scales, and improves the spatial continuity, statistical consistency and overall stability of the sea level anomaly estimation result.

The invention discloses a proportional-integral state estimation method of a nonlinear wind turbine system based on dynamic summation type event triggering. The method comprises the steps of 1, describing nonlinear dynamic characteristics of a wind turbine system based on a T-S fuzzy model method, and establishing a state space model of the wind turbine system; 2, in order to save network resources, an event triggering mechanism based on dynamic summation is established, and redundant triggering is reduced; 3, designing a proportional-integral estimator to estimate the system state due to all the system states which are difficult to measure in actual engineering; and 4, in combination with the T-S fuzzy wind turbine system and the proportional-integral estimator, establishing a closed-loop estimation error system, and performing state estimation on the wind turbine system by designing a state estimator gain matrix. Compared with a traditional event triggering mechanism based on current information, the event triggering mechanism based on historical sampling data is provided, internal variables are introduced into triggering conditions, the triggering interval is further expanded, and more network resources are saved; compared with an event triggering mechanism which only adopts a static threshold, a dynamic triggering threshold is designed, the threshold can be adaptively adjusted along with the evolution of the system, and the flexibility of the triggering mechanism is improved. In addition, compared with a traditional proportional estimator, the method introduces an integral term of a state observation error to improve the precision of system state estimation, and has a certain engineering application value.

The invention relates to the technical field of electric power, and discloses a synchronous positioning and map construction method suitable for high-altitude electric power inspection, which comprises the following steps: constructing the position attitude and environmental map characteristics of an inspection robot into a joint state vector, and performing recursive estimation based on extended Kalman filtering; obtaining the observed quantity of each sensor and carrying out weighted fusion on the observed quantity to obtain a fusion observation result; introducing an adaptive noise estimation model, wherein the estimation model adjusts a covariance matrix of state noise and observation noise of extended Kalman filtering according to the observation error and the prediction error; evaluating an uncertain area in the map according to the updated environment map features, and performing local optimization on the uncertain area according to the observed covariance matrix and the error between the observed value and the predicted value of the map features. The improved SLAM algorithm provided by the invention can adapt to a complex high-altitude environment by optimizing sensor data fusion, enhancing robustness and improving a map updating strategy.

The application relates to a method for constructing an inertial navigation system parameter error model based on gravity disturbance. The method comprises the following steps: constructing an accelerometer error model and a gyro observation error model by using random constant zero bias of an accelerometer and random constant drift of a gyro and zero mean random white noise; decomposing measured gravity into gravity disturbance and gravity disturbance measurement error, and constructing velocity error by using the accelerometer error model, the gravity disturbance and the gravity disturbance measurement error; expanding the velocity error to obtain eastward velocity error and northward velocity error; equivalently setting the carrier height of the inertial navigation system to zero, and calculating position error by using the eastward velocity error and the northward velocity error; calculating attitude error by using the gyro observation error model and misalignment angle; and constructing a navigation system parameter error model according to the velocity error, the position error and the attitude error. The method can improve error compensation precision in the navigation process.

The application discloses a laser wind measurement radar and a data processing and assimilation method thereof, and relates to the field of meteorological radar data processing and assimilation.The method comprises the following steps: acquiring laser wind measurement radar observation data, atmospheric stability parameters and terrain undulation characteristic data; constructing a three-dimensional space weight distribution function based on the above parameters, which represents the initial influence weight of an observation point on surrounding grids; dynamically adjusting the horizontal influence range and vertical attenuation characteristics of the function according to the local terrain complexity and atmospheric vertical stratification change information, and generating a dynamic weight distribution matched with local atmospheric physics and terrain characteristics; introducing the distribution as a spatial adjustment factor of an observation error covariance matrix into a cost function, and simultaneously optimizing a wind field analysis field and the dynamic weight distribution by minimizing the cost function; and outputting the optimized wind field analysis field for numerical weather prediction.The application realizes accurate and efficient fusion of observation information in three-dimensional variational assimilation, and significantly improves the authenticity of wind field analysis and the reliability of prediction under complex conditions.

The application provides a line feature observation error determination method and device and electronic equipment, and relates to the technical field of instant positioning and map construction, and comprises the following steps: determining a visual observation error of a line feature by comparing the difference between the back projection plane of an observed line segment on an acquired image and the Plucker coordinate description of the line feature in a space corresponding to the observed line segment; establishing an observation information matrix of the line feature based on the observation noise of the observed line segment on the acquired image in terms of angle and position; and combining the visual observation error and the observation information matrix to determine the observation error of the line feature, so as to solve the technical problem of improving the reliability of SLAM application under the influence of noise and other performances of the line feature.

The invention discloses a method and a system for automatically tracking a personnel track in a dangerous area, and relates to the technical field of navigation. Satellite data and inertial measurement data are synchronously collected, and based on inertial data, a Kalman filter is used for state prediction; carrying out availability evaluation on the satellite data, if the satellite data are available, carrying out multi-system fusion solution and calculating an observation error, and if the satellite data are not available, generating a pseudo observation error by utilizing historical inertial data and a pre-training error prediction model; the observation error or the pseudo observation error is used for Kalman filtering correction state prediction, optimal position information is output, and a track is visually generated on an offline map in real time. According to the method, inertial navigation drifting can still be continuously corrected when a satellite is unavailable, and continuous and reliable personnel trajectory tracking is achieved.

This invention discloses a real-time probabilistic inversion method for TBM (Tunnel Boring Machine) extrusion load based on a surrogate model. First, the extrusion load vector and its prior value range are defined for the TBM shield tunneling environment. A surrogate model is constructed and iteratively updated based on a small number of high-fidelity numerical simulation samples to establish a rapid mapping relationship between the extrusion load and the shield structure response. Then, on-site measured shield monitoring data are acquired, and a likelihood function is constructed by combining sensor observation errors and surrogate model prediction errors. Finally, within a Bayesian probabilistic inversion framework, a Markov chain-Monte Carlo algorithm is used to call the surrogate model for sampling, obtaining the posterior probability density distribution, optimal estimate, and confidence interval of the extrusion load, and calculating the shield structure failure probability accordingly. This invention can meet the real-time requirements of inversion calculations and provide a quantitative assessment of the uncertainty of the inversion results, offering a scientific basis for safety decisions when TBMs traverse strata with large extrusion deformation.

The application discloses a CO2 geological storage reservoir parameter inversion method combined with well-seismic multi-source data, relates to the technical field of CO2 geological storage, and obtains multi-source observation data in the CO2 geological storage process; a priori geological model set for representing reservoir heterogeneity is established; an observation error covariance matrix for comprehensively reflecting multi-source observation uncertainty is constructed; forward calculation is performed on the priori geological model to obtain predicted observation data; high-dimensional observation information is processed in a dimension reduction manner; and reservoir model parameters are iteratively updated. The application implements reservoir parameter inversion by combining well time-series data and seismic spatial data, significantly reduces the posterior uncertainty range of key parameters such as reservoir permeability, improves the precision of CO2 plume distribution and storage amount prediction, and enhances the numerical stability and physical rationality of the assimilation process. The TSVD dimension reduction strategy is introduced to effectively solve the ill-conditioned problem and set degeneration phenomenon in high-dimensional data assimilation, and the robustness of the algorithm under the condition of high-dimensional observation data is ensured.

This invention discloses a method, device, and medium for precision testing of linear guideways, relating to the field of guideway precision testing technology. The method includes: constructing a separable multi-condition detection input; acquiring five-component observation errors along the travel path under each condition; defining different travel positions of the linear guideway; constructing an event type library, including zero-crossing events and straightness extreme value events; defining corresponding event functions and event conditions for each event; and performing deduplication processing on selected event state sequences. The method of this invention maps the raw output of multi-channel sensors to straightness and attitude error expressions with clear engineering semantics based on a unified measurement model, enabling comparability and composability of observations with different dimensions and installation positions within the same error space.

The invention relates to the technical field of ocean remote sensing and numerical weather forecast, and discloses a satellite-borne GNSS-R sea surface wind speed fusion method based on three-dimensional variational assimilation, which comprises the following steps: performing format conversion on variable information of an obtained GNSS-R inversion secondary sea surface wind speed product, and generating a BUFR file as assimilation observation data; acquiring ground and high-altitude meteorological observation data in a PREPBUFR format; geographic data and a GFS meteorological element field are processed through WPS, and a background field file is generated through WRF initialization and mode integration; setting a background error and observation error covariance matrix by adopting a 3DVar method, designing an observation operator to realize observation and mode space mapping, and minimizing a cost function to obtain an optimal analysis field; and running a GSI program to complete assimilation fusion, and outputting a lattice sea surface wind speed analysis field. Through error modeling and observation operator construction for GNSS-R data characteristics, the GNSS-R data characteristics and a numerical mode background field are fused under a GSI three-dimensional variational assimilation framework, and a sea surface wind speed analysis product with high precision and continuous space is generated.

A rapid method for measuring ship heel and draft based on a laser level is characterized by comprising a reference establishment end, a distance acquisition end, an inclination calculation end, a draft calculation end, and a correction and optimization end. Specifically, it includes the following steps: S1. Installing a laser level on the centerline of the ship's deck to construct a three-dimensional reference plane; S2. The laser level laterally scans multiple symmetrical points on the port and starboard sides of the ship, simultaneously measuring the distance from each point to the laser and calculating the actual water level height data; S3. Fitting a straight line of water level inclination based on the actual water level height data to calculate the ship's heel angle and heel difference reference value; S4. Calculating the actual draft depth on the port and starboard sides based on the ship's heel angle and heel difference reference value to obtain the heel difference draft value; S5. Performing environmental factor correction and averaging of multiple measurements on the measurement results, outputting the final corrected heel difference draft measurement value. This method effectively reduces human observation errors, significantly improves measurement efficiency, and achieves rapid and accurate measurement of ship heel and draft.

This invention discloses a model structure error correction method that couples data-driven approaches with physical constraints. The invention constructs a corrected groundwater model, comprising a groundwater numerical model, a structure error correction model, and observation errors. Markov chain Monte Carlo simulation is used to identify the posterior distributions of the parameters of the groundwater numerical model and the hyperparameters of the structure error correction model. Based on these posterior distributions, the prediction results of the corrected groundwater model are obtained. The prediction performance of the corrected groundwater model is then evaluated. This invention constructs a novel combinatorial likelihood function, explicitly integrating physical mechanism constraints into the construction process of the error correction model. This effectively constrains the structure error model while correcting structural deviations in the groundwater model, thus significantly improving the performance of the corrected prediction results in conforming to physical mechanisms.

The invention provides a sand and dust weather assimilation data generation method and device fused with AI algorithm re-optimization, and relates to the technical field of fusion of computer science and atmospheric science. The method comprises the following steps: acquiring multi-channel observation data provided by a satellite; introducing a Bayesian optimization process to enable background errors and observation errors to be mutually independent, and obtaining assimilated analysis field data; a joint optimization framework is constructed, the framework responds to input analysis field data, a preliminary post-optimization data field is generated through CNN to serve as CNN global features, SVR local correction features are obtained through SVR, and balance between the CNN global features and the SVR local correction features is achieved through dynamic weighted fusion. According to the method, through the cooperative processing of the multi-satellite channel data and the combination and comparison of the actual sand and dust weather process, the reconstruction precision of the large-range sand and dust data is remarkably improved, and more accurate support is provided for the overall construction of the data after current sand and dust assimilation.

The invention relates to the technical field of occultation observation, and discloses an occultation atmospheric bending angle observation error confirmation method. The confirmation method comprises the following steps: obtaining an occultation atmospheric bending angle in occultation data, and a first meteorological data set and a second meteorological data set which correspond to an occultation profile in the occultation data and are not derived from the current occultation; determining a first atmospheric bending angle according to the first meteorological data set, and determining a second atmospheric bending angle according to the second meteorological data set; obtaining a first bending angle error between the occultation atmospheric bending angle and the first atmospheric bending angle, a second bending angle error between the occultation atmospheric bending angle and the second atmospheric bending angle, and a third bending angle error between the first atmospheric bending angle and the second atmospheric bending angle; and determining obscuration atmospheric bending angle observation errors through a formula. By adopting the confirmation method, the objectivity of the observation error can be improved. The invention further discloses a device for confirming the obscuration atmospheric bending angle observation error and a using method.

The application discloses an improved nudging method for ocean tide assimilation research. In view of the defects of the traditional nudging method, the application combines the updating idea of the sequential assimilation method, calculates the model and observation errors first, then uses the model and observation errors to calculate the nudging relaxation coefficient by weighting, and further uses the observation data to update the model state, so that the observation data and the ocean model are combined scientifically, and a new tide assimilation method is formed. The application overcomes the defect that the given relaxation coefficient in the traditional nudging method has no reasonable basis, scientifically gives a new nudging relaxation coefficient based on the updating idea in the sequential assimilation method, overcomes the problem that the tidal amplitude is small at the coast in the global ocean model assimilation research of the traditional nudging method, and causes the large deviation of the split tide angle after the assimilation, and provides a new assimilation method for accurate ocean tide simulation.

The application relates to the technical field of occultation observation, and discloses a method for confirming an observation error of an occultation atmospheric bending angle. The method comprises the following steps: obtaining an occultation atmospheric bending angle in occultation data, and first and second meteorological data sets corresponding to an occultation profile in the occultation data and not derived from a current occultation; determining a first atmospheric bending angle according to the first meteorological data set, and determining a second atmospheric bending angle according to the second meteorological data set; obtaining a first bending angle error of the occultation atmospheric bending angle and the first atmospheric bending angle, a second bending angle error of the occultation atmospheric bending angle and the second atmospheric bending angle, and a third bending angle error of the first atmospheric bending angle and the second atmospheric bending angle; and determining the observation error of the occultation atmospheric bending angle through a formula. The method can improve the objectivity of the observation error. The application also discloses a device for confirming the observation error of the occultation atmospheric bending angle and a use method.

This invention discloses an automatic personnel trajectory tracking method and system in hazardous areas, relating to the field of navigation technology. It involves synchronously acquiring satellite data and inertial measurement data, and using a Kalman filter for state prediction based on the inertial data. The availability of satellite data is evaluated; if available, multi-system fusion calculations are performed to calculate the observation error; if unavailable, pseudo-observation errors are generated using historical inertial data and a pre-trained error prediction model. The observation error or pseudo-observation error is then used to correct the state prediction using a Kalman filter, outputting optimal position information, and the generated trajectory is visualized in real-time on an offline map. This method can continuously correct inertial navigation drift even when satellites are unavailable, achieving continuous and reliable personnel trajectory tracking.

The application discloses a flow measurement correction method and device in a strong wind environment, wherein the method firstly constructs a multi-source time sequence flow measurement data set and carries out pretreatment; a composite feature vector is constructed based on the same, input into a multi-source flow speed adaptive fusion model based on a dynamic weighted fusion network, and a predicted surface flow speed is output, then a composite loss function containing an observation error and a hydrodynamic physical constraint is constructed, and end-to-end training is carried out on the model; finally, real-time collected flow measurement data and environmental parameters are input into the trained multi-source flow speed adaptive fusion model, a corrected stable surface flow speed in the strong wind environment is obtained, and combined with real-time measured cross-section water level and pre-acquired river channel geometric parameters, final cross-section flow monitoring values are calculated and output based on a hydraulics flow speed area method, and the application improves the accuracy of flow estimation, and can provide stable data support for long-term, continuous and reliable flow monitoring in the strong wind environment.