276 results about "Troposphere" patented technology

Disclosed is a time series InSAR tropospheric delay correction method for relieving atmospheric seasonal oscillation, so as to relieve the seasonal oscillation deviation caused by tropospheric delay. This method is based on the temporal and spatial characteristics of atmospheric delay. Firstly, based on the ERA-5 atmospheric grid data covering the study area, the ZTD negative exponential vertical profile function model is established. Fourier series function is introduced to establish the time series model for β and ZTDr, Finally, according to the radar incidence angle parameters, the delay is changed from zenith direction to radar line of sight direction.

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

Disclosed are an improved GNSS tropospheric delay (ZTD) real-time grid model construction method and system. Firstly, a global tropospheric delay elevation normalization grid model is constructed using global ERA5 reanalysis data from a plurality of years. Then, elevation normalization and grid horizontal interpolation are performed on GNSS real-time ZTDs to obtain a global tropospheric delay real-time grid model. A user only needs to provide position and time information and use the global tropospheric delay elevation normalization grid model and the global tropospheric delay real-time grid model to be able to obtain a ZTD at a target point. According to the present invention, the high spatial resolution of a numerical weather model (NWM) and the high precision of the GNSS tropospheric product are comprehensively used, and an elevation normalization function form is optimized, so that the construction of a tropospheric delay real-time grid model covering a larger height range is achieved, and high-precision tropospheric delay correction information can be provided for GNSS real-time high-precision data processing, thereby improving the data processing performance.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention provides a GNSS virtual observation value generation method and system based on real-time precise point positioning, and belongs to the technical field of GNSS positioning, and the method comprises the steps: obtaining ephemeris clock error data and observation data received by a reference station in real time, filling a UCUD PPP observation equation, and solving a non-difference floating point solution of a to-be-estimated parameter of each parameter station; through UPD estimation, non-difference floating point solution ambiguity parameters recover the whole cycle characteristic, and a fixed solution STEC and a fixed solution ZTD are obtained; the STEC is locally fitted into a function about the position, and a regional non-difference ionosphere model is established; the ZTD is locally fitted into a function about the position, and a regional non-difference troposphere model is established; and generating a double-difference atmospheric delay between the main reference station and the virtual reference station based on the non-difference ionosphere model and the regional non-difference troposphere model to obtain a virtual observation value. According to the method, the reliability of GNSS signal atmospheric delay is effectively improved, so that the quality of a virtual observation value is improved.

A user equipment (UE) may receive a set of reference signals (RSs) from a set of non-terrestrial network (NTN) nodes. The set of RSs may travel from the set of NTN nodes to the UE through a first portion of an atmosphere and a second portion of the atmosphere. The UE may measure the set of RSs. The UE may calculate, based on the measured set of RSs, a first set of atmospheric delays associated with the first portion of the atmosphere and a second set of atmospheric delays associated with the second portion of the atmosphere. The UE may calculate a location of the UE based on the measured set of RSs, the first set of atmospheric delays, and the second set of atmospheric delays. The UE may transmit the calculated location of the UE. The first portion may include an ionosphere. The second portion may include a troposphere.

The invention discloses a troposphere delay model construction method based on a numerical meteorological model and an unmanned aerial vehicle, and the method comprises the steps: obtaining meteorological parameter values in the numerical meteorological model for a target region; a troposphere observation value obtained by the target unmanned aerial vehicle through the airborne GNSS receiver and the airborne multi-source meteorological equipment in the process of moving along the preset path in the target area is obtained; determining first troposphere delay data according to the meteorological parameter values and a corresponding preset oblique troposphere delay model; determining second troposphere delay data by adopting a preset ambiguity fixing algorithm based on the satellite observation value; and according to the first troposphere delay data and the second troposphere delay data, determining troposphere delay data of the target area. According to the method, accurate estimation of troposphere delay in a complex region and a meteorological condition rapid change region is realized, and a reliable troposphere delay model is provided for realizing high-precision monitoring of InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring.

The invention relates to the technical field of satellite navigation and positioning, in particular to a zenith troposphere wet delay calculation method based on deep learning, and the basic steps of segmented ZWD multi-source data fusion based on an improved Transform model are as follows: establishing a ZTD model, establishing a TCA-Transform model, and fusing data. The task of establishing the ZTD model is to calculate the atmospheric refractive index according to meteorological data so as to obtain ZTDPP; the TCA-Transform model establishment comprises the following steps of: improving a Transform model by using a domain self-adaptive method; according to the data fusion, the ZWDPP and the ZWDMODIS are fused by using a TCA-Transform model. And finally, a ZWD product with high temporal-spatial resolution and high precision can be obtained. The generalization ability of an original Transform model is improved, so that the troposphere delay correction ability in a specific area is improved, and a zenith troposphere delay (ZWD) product with high resolution and high precision is obtained.

The invention relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention discloses a single base station PPP-RTK (point-to-point protocol-real-time kinematic) positioning method and equipment. The method comprises the following steps: firstly, according to acquired single base station observation data, single base station accurate coordinates and ephemeris data, satellite end error corrections and atmospheric delay corrections are respectively obtained based on different resolving models, and the satellite end error corrections comprise satellite clock error corrections and satellite orbit error corrections; the atmospheric delay correction number comprises an ionosphere delay correction number and a troposphere delay correction number of the first frequency; calculating an atmospheric delay difference between the single base station and the user based on an empirical model, and substituting the atmospheric delay difference into the atmospheric delay correction number to obtain an atmospheric delay correction number which can be used for the user; obtaining observation values of users to construct a PPP-RTK observation equation so as to eliminate error terms related to a single base station; and the satellite end error correction number and the atmospheric delay correction number are substituted into a user end PPP-RTK observation equation, so that single base station PPP-RTK positioning can be realized.

The invention belongs to the crossing field of space geodetics, satellite navigation positioning technology and atmospheric science, and discloses a real-time troposphere delay modeling method for enhancing a global forecasting system. The method comprises the following specific steps: firstly, acquiring zenith fluid static delay and zenith wet delay predicted by earth surface GFS, and constructing a regional grid difference time sequence by taking a fusion result of ERA5 data and a GNSS troposphere delay product as a reference; secondly, fitting the difference to form a GFS enhanced core model; meanwhile, a troposphere delay correction model from the grid height to the target site height is constructed based on ERA5 stratified atmosphere data, time sequence modeling is carried out on model coefficients, and finally refined troposphere delay output at different heights is achieved. The method has the advantages that (1) the real-time troposphere model is high in precision; (2) real-time application can keep robust performance without depending on a GNSS network; (3) expansion and deployment are facilitated; and (4) the real-time performance is strong, and seamless integration precision positioning application can be realized.

The invention belongs to the technical field of aerosol extinction, and relates to a CALIOP and Mie scattering laser radar combined aerosol extinction coefficient inversion method, which comprises the following steps of: calculating a first-order gradient according to a Mie scattering laser radar echo signal, and judging whether a strong positive and negative gradient pair layer exists or not according to whether a strong gradient exists in the first-order gradient; calculating the relative increase amplitude of the strong gradient, comparing the relative increase amplitude with a threshold value to judge the type of a strong positive and negative gradient pair layer, and setting an atmospheric boundary layer height range; a discrete wavelet covariance transformation function is adopted to invert the height of the atmospheric boundary layer, and in the Gaussian process machine learning network training process, scale factor parameters are selected for the discrete wavelet covariance transformation function so as to accurately invert the height of the atmospheric boundary layer; dividing the atmospheric layer into an atmospheric boundary layer and an atmospheric boundary layer top to a troposphere top according to the inversion atmospheric boundary layer height; according to the invention, the inversion precision of the aerosol extinction coefficient can be improved.

The invention discloses a troposphere BrO vertical profile inversion method, medium and equipment based on machine learning, and the method comprises the steps: obtaining BrO vertical profile data, inputting the profile data, aerosol characteristic parameters, geometric angle parameters and earth surface characteristic parameters into an atmospheric radiation transmission model, and obtaining BrO DSCD data; the obtained DSCD and the profile data set are preprocessed; training a troposphere BrO vertical distribution inversion model combining a convolutional neural network CNN and a long short-term memory network LSTM by using the preprocessed DSCD and profile data set; and performing QDOAS processing on MAX-DOAS spectral data to be inverted to obtain DSCD data, inputting the preprocessed DSCD data, aerosol parameters and geometric angle parameters into the trained CNN-LSTM troposphere BrO vertical distribution inversion model to perform data inversion, and finally obtaining an inversion result of troposphere BrO vertical distribution. According to the invention, the accuracy and stability of an inversion result can be significantly improved.

The invention relates to a troposphere over-the-horizon communication channel path loss prediction method based on a Transform network, and belongs to the field of communication technologies and deep learning. The method comprises the following steps: firstly, constructing a Transform network path loss prediction model; secondly, the method constructs data sets of a path loss prediction model, including a training data set and a test data set. The construction process of the data set comprises the following steps: firstly, based on a WRF mesoscale numerical meteorological mode, inverting marine troposphere beyond-visual-range environmental meteorological parameters, and performing large-area high-resolution long-time-efficiency evaporation waveguide height forecast by using an NPS model to obtain an evaporation waveguide height forecast value; secondly, predicting channel path loss by using a two-dimensional parabolic equation method, combining meteorological parameters, evaporation waveguide height and working parameters of communication equipment to form an X input feature, and taking a path loss predicted value as a Y input feature; and finally, model training optimization and performance evaluation are carried out by the method.

The invention discloses an unmanned aerial vehicle real-time tracking and positioning system and method based on a Beidou satellite, and relates to the technical field of high-precision navigation and unmanned aerial vehicle real-time tracking and positioning, and the system comprises a receiving module which receives signals of the Beidou satellite and a global navigation satellite system in real time through an antenna, and converts and demodulates the signal frequency to obtain a pseudo range; the processing module is used for performing multi-path effect detection and preliminary correction on the pseudo-range, and further correcting and optimizing the pseudo-range after preliminary correction according to the delay effect of the ionosphere and the troposphere; according to the method, multi-path effect correction is carried out on the pseudo-range, the influence of reflection and refraction on GNSS signals is reduced, the positioning precision is remarkably improved, the error of the pseudo-range is further reduced through correction of the ionosphere and the troposphere, and therefore the precision of position calculation is improved, and the positioning real-time performance of the unmanned aerial vehicle is remarkably improved.

The invention relates to the field of marine meteorological monitoring, and discloses a GNSS-based marine troposphere atmosphere rainfall real-time monitoring method, which comprises the following steps: estimating total troposphere zenith delay of an observation station position by using GNSS precise point positioning, and calculating troposphere zenith dry delay by using meteorological elements so as to obtain troposphere zenith wet delay; obtaining ERA5 reanalysis data, and constructing an atmospheric weighted average temperature model; calculating an atmospheric weighted average temperature and a water vapor conversion coefficient by using an atmospheric weighted average temperature model and combining with meteorological elements of the observation station position; and calculating atmospheric rainfall of the troposphere by using the troposphere zenith wet delay and the water-vapor conversion coefficient at the observation station position. According to the method disclosed by the invention, the GNSS signal which is high in space-time coverage and passively received is taken as a signal source, real-time precise single-point positioning is carried out, zenith troposphere wet delay is extracted, and atmospheric rainfall can be monitored in real time in a high-precision manner under the condition of lacking historical sounding data.

The invention discloses a troposphere delay mapping model construction method and system based on machine learning. The method comprises the following steps: acquiring meteorological reanalysis data; calculating the ZTD at the global site or grid and the STD at different azimuth angles and elevation angles based on the meteorological reanalysis data; based on the ZTD and the STD, constructing an asymmetric mapping relation from the ZTD to the oblique path delay; constructing a data set based on meteorological reanalysis data, the ZTD and the asymmetric mapping relation, inputting the data set into a troposphere delay mapping model to train the troposphere delay mapping model, and generating prior troposphere delay for GNSS high-precision positioning through the trained troposphere delay mapping model; wherein the troposphere delay mapping model is a machine learning model. According to the method, the empirical model of the ZTD and the unified asymmetric mapping relation from the ZTD to the STD can be constructed, and the precision and the applicability of the model are remarkably improved.

The invention relates to the technical field of engineering monitoring, in particular to an unmanned aerial vehicle dynamic monitoring and early warning system and method for the whole subway tunnel excavation process, and the system comprises a virtual base station differential positioning module which is used for carrying out the unmanned aerial vehicle dynamic monitoring and early warning according to a carrier phase observation value transmitted by a ground active control target and a satellite signal observation value obtained by an airborne receiving end, and calculating ionosphere residual values and troposphere residual values between the current position of the unmanned aerial vehicle and the three adjacent ground active control targets. According to the method, the pixel gradient vector of the thermal inertia matrix is further calculated, the edge candidate points exceeding the threshold value are extracted, and the edge candidate points are closed in the gradient vertical direction to form the geometric region, so that the fuzzy thermal field anomaly is converted into the quantized graph with the definite physical boundary, and the hidden danger area and the internal gradient mean value are combined for calculation; the risk early warning level can be dynamically generated according to the tunnel design tolerance, so that recognition and hierarchical management and control of hidden geological disasters are realized on the premise of not contacting the ground.

The embodiment of the invention provides a Beidou water vapor inversion method and system based on a buoy platform. The method is applied to the technical field of data processing, and comprises the following steps: receiving a Beidou dual-frequency signal through a direct antenna to obtain original observation data; the multi-path effect and the platform shake are inhibited by combining buoy motion characteristics obtained by the MEMS sensor to obtain pre-processed data; calculating a troposphere delay estimated value; establishing a delay-water vapor conversion model; suppressing short message interference to obtain effective observation data; and inputting the data into a model to calculate the amount of precipitable water and the relative humidity. According to the method, the key technical problems of sea surface multi-path effect suppression, platform shake compensation, marine atmosphere modeling, short message interference elimination and the like are solved, so that reliable water vapor parameter inversion on the buoy platform is realized.

The invention provides an inter-station time synchronization constrained VLBI world time measurement method, which comprises the following steps: determining at least two observation stations for observing a radio power source, and carrying out precise clock synchronization to obtain a precise inter-station clock difference; using the at least two observation stations to simultaneously observe the same radio power source to obtain observation time delays from the same radio power source to the at least two observation stations; separating the clock error in the observation time delay by using the precise inter-station clock error to obtain the observation time delay of the separated clock error; and removing ionosphere time delay, troposphere time delay and equipment time delay in the observation time delay of the separated clock error to obtain final observation time delay, and resolving a residual error between the final observation time delay and theoretical time delay to obtain world time. According to the invention, precise time synchronization between observation stations is carried out in advance, and inter-station errors are effectively separated. The problem that VLBI observation time delay and inter-station clock correction are difficult to separate is solved, the UT1 resolving precision can be effectively improved, and the requirement for high-precision UT1 in the fields of deep space exploration and the like is expected to be met.

The provided is a modeling method of tropospheric delay for arid-areas including sandy, gobi and desert regions based on GNSS. The modeling method incudes: obtaining a troposphere zenith hydrostatic delay ZHD and a troposphere zenith wet delay ZWD by utilizing a GPT-3 model, and summing to obtain a troposphere zenith total delay ZTD; inversely calculating the ZTD by utilizing a ground-based GNSS, obtaining a high-precision ZWD inversed by the GNSS by calculating a difference between the ZTD and the troposphere zenith hydrostatic delay ZHD obtained by a Saastamonien model, inversely calculating an atmospheric weighted average temperature T′m by utilizing an ERA5 data set, performing linear correction to obtain a corrected temperature Tmr, and calculating a priori value ZWD0 of the high-precision ZWD; and correcting the ZWD0, and adding the corrected ZWD0 with the ZHD to obtain a corrected value of the high-precision ZTD.

The invention discloses an ionosphere activeness rapid determination method and device based on a double-difference observation model, and belongs to the technical field of navigation positioning and ionosphere monitoring. The method comprises the following steps: acquiring observation station GNSS satellite observation data, and constructing a double-difference carrier phase observation equation; performing data quality screening on the received observation station GNSS satellite observation data; estimating an integer ambiguity parameter and a troposphere parameter according to the screened GNSS satellite observation data and the constructed double-difference carrier observation equation; according to the estimation results of the integer ambiguity parameter and the troposphere parameter, resolving to obtain an ionosphere residual error; and carrying out compression quantization on the ionized layer residual error information obtained by calculation to obtain the active degree of the ionized layer in the region. According to the invention, the feasibility and reliability of high-precision positioning of the user through the CORS service during the active period of the ionosphere are guaranteed.

The invention relates to the technical field of satellite navigation and ionosphere detection, in particular to a low-latitude ionosphere disturbance and non-uniform body multi-means collaborative detection and classification method. A multi-network cooperation-multi-source fusion observation mode is adopted, full space-time coverage of low-latitude ionosphere disturbance is achieved, in addition, a data fusion method based on physical constraints is applied, an ionosphere-middle atmosphere-troposphere multi-physics field coupling constraint mechanism is constructed, space-time consistency of data obtained through different observation means is optimized, and the low-latitude ionosphere disturbance is obtained. And finally, key characteristic parameters are extracted by using a multi-scale signal processing method, a disturbance characteristic matrix is constructed in combination with radar observation data (such as drift speed and spectral width), a'three-axis nine-element 'disturbance / non-uniform body classification standard is proposed firstly, then deep learning classification is used, accurate classification of low-latitude ionosphere disturbance is realized, and GNSS signal anomaly recognition capability is improved.

The invention provides a Beidou geological disaster displacement and image video monitoring fusion method and system based on a high-reliability communication link, and the method comprises the steps: collecting displacement, inclination angle and five-way high-definition video in real time, accessing microclimate and wire clamp temperature data, and carrying out the unified packaging through a data gateway; aPN special line transmission is adopted, and centimeter-level RTK differential service is provided for the unmanned aerial vehicle; based on a Beidou VRS virtual reference station and a long baseline monitoring resolving technology, a Beidou ground-based enhanced station network is multiplexed, and a virtual observation value is generated for real-time dynamic RTK resolving; inverting a three-dimensional water vapor field based on oblique path troposphere delay PWV, realizing tower-level rainfall prediction, and generating a risk assessment report by fusing an A I algorithm; superposing a displacement thermodynamic diagram, a real-time video and an early warning label in a BIM + GI digital twinning scene, and supporting 360-degree panoramic stitching and alarm trigger snapshot; and carrying out intelligent early warning decision. According to the invention, through high-precision Beidou monitoring, multi-modal data fusion and intelligent early warning, the real-time performance, precision and automation level of geological disaster monitoring are significantly improved.

The invention belongs to the technical field of navigation positioning, and discloses an offshore positioning method based on fusion of a numerical meteorological model and a Beidou short message, so as to realize offshore second-level continuous high-precision positioning by using single Beidou short message equipment. According to the offshore positioning method based on fusion of the numerical meteorological model and the Beidou short message, a CNES real-time satellite orbit and clock error data are adopted, NWP model meteorological data are utilized to invert zenith troposphere delay, and a time baseline method considering NWP improvement is utilized to weaken time accumulation errors and improve time baseline positioning performance; and then, based on the improved time baseline offshore high-precision positioning method, the PPP and Beidou short message communication technologies are combined, so that the BDS / GNSS offshore high-precision positioning navigation target can be realized by utilizing a single Beidou short message device, and the high-precision positioning cost is saved.

The invention relates to the technical field of positioning. The deep learning assisted satellite positioning rapid resolving method comprises the following steps: acquiring and preprocessing satellite observation data, local environment characteristic data and receiver state data to generate multi-source fusion input data; performing dynamic error prediction processing through a locally deployed deep learning model to generate a positioning error correction amount; the positioning error correction is fused to a precise single-point positioning calculation process for iterative optimization processing, and positioning coordinates meeting the requirement for regional surveying and mapping precision are generated; a local historical observation data set is combined to trigger an online learning mechanism to perform model parameter updating processing, and a self-adaptive optimization model is generated so as to dynamically adapt to ionosphere / troposphere disturbance in a local complex environment, improve the resolving convergence efficiency in a multi-path effect scene and improve the real-time performance of the multi-path effect scene. And through multi-source data coupling modeling, the technical effects of error correction and accurate adaptation of a real scene are realized.

The invention discloses a foundation space ionosphere and troposphere integrated joint detection method and device, and belongs to the technical field of satellite navigation, and the method comprises the steps: detecting the vertical total electron content of an ionosphere: extracting the inclined total electron content of the ionosphere at an ionosphere puncture point, and converting the inclined total electron content into the vertical total electron content of the ionosphere; detecting a foundation Beidou ionosphere disturbance index; and detecting an ionosphere phase scintillation index, a troposphere zenith total delay, a troposphere precipitable amount and an ionosphere amplitude scintillation index, and outputting ground-based Beidou space ionosphere and troposphere integrated joint detection parameters. The method effectively improves the reliability, robustness and meteorological perception capability of a navigation positioning system in a complex atmospheric environment, and is especially suitable for special environmental conditions such as severe convection weather, typhoon landing, violent disturbance of an ionized layer and the like.

The invention belongs to the technical field of flight test, and discloses a cockpit vision field evaluation test flight method suitable for a troposphere manned airship, which comprises the following steps: selecting an airship test condition; the airship climbs to a preset field height and keeps level flight; a preset flight test action is completed; and commenting by a flight tester, and filling in a cockpit vision field commenting card. According to the method, the blank of the flight test method for evaluating the visual field of the cockpit of the troposphere manned airship with the vector propulsion system is filled, and the flight test efficiency and the flight test quality of the manned airship are improved.

The invention relates to the field of communication signal processing, in particular to an air defense and disaster prevention early warning alarm real-time control system based on Beidou third-generation communication, which adopts a Daubechies wavelet family to perform five-layer discrete wavelet decomposition and decomposes the total delay of satellite signals into different scale coefficients. By accurately controlling the boundary scale, the system can effectively separate the ionosphere error from the troposphere error. And by inserting a zero value between filter coefficients, information loss caused by down-sampling operation is avoided. And aiming at different atmospheric error characteristics, the system adaptively selects an optimal wavelet basis function. A stable correction effect is kept under extreme weather conditions, and an efficient and practical air defense and disaster prevention real-time early warning control method is constructed.

The invention provides a power transmission line tower monitoring method and system based on Beidou positioning, and relates to the technical field of displacement measurement, and the method comprises the steps: determining a to-be-monitored power transmission line tower; determining a target area to which a power transmission line tower belongs; inputting the reference station network data and the Beidou ground-based augmentation station data of the target area into a correction model corresponding to the target area to obtain an ionosphere correction number and a troposphere correction number of the power transmission line tower; sending the ionosphere correction number and the troposphere correction number to a Beidou positioning module of the power transmission line tower to obtain a Beidou positioning coordinate of the power transmission line tower; displacement information is obtained according to the Beidou positioning coordinates and the initial positioning coordinates of the power transmission line tower; obtaining a monitoring result of the power transmission line tower according to the displacement information, the inclination angle, the wind speed, the vibration spectrum and the tower structure parameters of the power transmission line tower; and sending the monitoring result of the power transmission line tower to a monitoring center. And an accurate power transmission line tower monitoring result can be obtained.