569 results about "Satellite observation" patented technology

The invention relates to the field of landfill methane emission remote sensing monitoring, and discloses a landfill methane emission flux measuring and calculating method and device and a medium, and the method comprises the following steps: based on regional satellite TROPOMI observation data, carrying out qualitative analysis on annual average methane emission signals of a landfill, and determining a target landfill according to a qualitative analysis result; based on data of a satellite-borne hyperspectral imager, the methane emission flux of the target landfill is quantitatively measured and calculated, and the position of a methane emission hot spot area in the target landfill is determined; and positioning the position of an emission source in the target landfill through an active methane laser detection lens carried by an unmanned aerial vehicle platform, and independently calculating the methane emission flux of the target landfill by adopting an upper and lower section method. Based on the method, the annual average methane emission condition of the landfill can be rapidly and accurately evaluated, the methane emission flux of the whole landfill is obtained, and the internal emission hot spot area of the landfill is positioned.

The embodiment of the invention discloses an agricultural vehicle positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the posture, position and speed of an agricultural vehicle under a geographic coordinate system according to inertial navigation data obtained by an inertial navigation system; establishing an initial observation equation according to a satellite observation equation corresponding to a global satellite positioning system and a visual observation equation corresponding to a visual sensor; when it is detected that satellite data collected by a global satellite positioning system is valid, the initial observation equation serves as a target observation equation; when it is detected that the satellite data collected by the global satellite positioning system is invalid, the initial observation equation with the satellite observation equations removed serves as a target observation equation; the current error state quantity of the inertial navigation system is corrected according to the target observation equation, and the attitude, position and speed of the agricultural vehicle are corrected according to the updated error state quantity, so that the actual positioning information of the agricultural vehicle is obtained, and the robustness of the positioning result of the agricultural vehicle is improved.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention discloses an earth surface CO2 concentration space-time prediction method fused with satellite-earth observation data, and is suitable for the field of atmosphere and space data monitoring. Preprocessing the acquired XCO2 observation data of the multi-source satellite, the CO2 observation data of the ground station and the auxiliary variable data, and fusing the XCO2 data observed by the multi-source satellite based on an uncertainty weighting method; quantifying the correlation between each auxiliary variable data and the CO2 concentration of the ground station based on a Pearson's correlation coefficient, carrying out significance test, and screening auxiliary variable data with significant correlation; a CNN-GRU-Attention deep fusion model is constructed, spatial-temporal correlation modeling is carried out on the CO2 concentration of a ground station, multi-source satellite observation XCO2 fusion data around the ground station and remarkably related auxiliary variables, and a spatial-temporal characteristic relation is mined, so that spatial-temporal prediction is carried out on the CO2 concentration of the earth surface. The method is strong in generalization ability and high in prediction precision, and can be suitable for space-time prediction of the surface CO2 concentration in different regions.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

The invention relates to an optical satellite absolute radiometric calibration method, device and equipment under ocean low brightness based on an offshore pseudo-invariant field and a medium, and belongs to the technical field of in-orbit absolute radiometric calibration. According to the method, a marine pseudo-invariant field with stable water and atmospheric optical characteristics is determined according to marine observation data of a calibrated optical satellite, the off-water radiance of the marine pseudo-invariant field is calculated according to the chlorophyll a concentration of the marine pseudo-invariant field, the zenith radiance is theoretically calculated, a data source is provided for absolute radiometric calibration of a satellite to be calibrated, and calibration is completed. The method explores the potential of the marine pseudo-invariant field in supplementing the traditional radiometric calibration method, gets rid of the limitation of a fixed and single marine optical buoy MOBY site data source, improves the data volume, the coverage area and the acquisition frequency of effective calibration data, provides guarantee for the continuity of an optical satellite observation task, and has a wide application prospect. And a plurality of calibration sources can be utilized to realize rapid and accurate calibration of the optical satellite, so that the calibration efficiency of the offshore optical satellite is remarkably improved.

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

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 provides a positioning method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of positioning. According to the method, satellite observation data, SLAM positioning data and satellite shielding data are fused, the high-precision absolute positioning capability of an RTK algorithm and the high-precision relative positioning advantage of an SLAM algorithm are fully utilized, and meanwhile, the influence of satellite shielding on RTK positioning precision is considered, so that more accurate and reliable positioning is realized in a complex environment. According to the method, the positioning precision can be maintained by means of the relative positioning information of the SLAM algorithm under the condition that satellite signals are shielded or interfered, the SLAM result is corrected by means of the absolute positioning data of the RTK algorithm, and accumulated errors are effectively reduced. Besides, by considering satellite shielding data, the accuracy and reliability of positioning are further improved, so that the positioning system can stably output high-precision positioning results in various complex scenes.

The invention provides a polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device, and belongs to the field of remote sensing, and the method comprises the steps: carrying out the preprocessing of collected sea ice marginal region and open sea surface data; north-pole geometric adaptation correction is carried out, system deviation of multiple satellite sensors is eliminated, and consistency and accuracy of data are ensured. Real-time wave height forecast is used as a background field, a weighting function is constructed in combination with a satellite observation error covariance matrix, dynamic background field fusion is carried out, and the precision and reliability of a data sparse region are improved; according to different characteristics of a sea ice marginal region and an open sea surface, different weights are set, a transition weight function is introduced, partition fusion is carried out, boundary discontinuity is avoided, and therefore the final fusion wave height is obtained; the robustness of the fusion method is verified through K-fold cross validation, and meanwhile precision verification is conducted in combination with multi-dimensional data. According to the method, the problem of insufficient accuracy verification spatial representativeness caused by rare north pole in-situ observation quantity and concentrated distribution is solved.

The invention provides an improved robust Kalman filtering integrated navigation method and system based on chi-square detection, and belongs to the technical field of navigation, and the method comprises the following steps: constructing a GNSS / INS loose integrated navigation Kalman filtering basic framework; before combined navigation Kalman filtering measurement updating, calculating an innovation chi-square test amount, recording the number of available satellites of each epoch, and calculating a satellite observation quality index according to the number of the available satellites; sage-Husa robust Kalman filtering based on IAE windowing is improved, a weighting factor is determined through an innovation chi-square test amount and is used for adjusting the weight of current epoch innovation in a window, and the window length is dynamically adjusted according to the change of a satellite observation quality index; a robust factor obtained through calculation of improved Sage-Husa robust Kalman filtering based on IAE windowing acts on an observation noise covariance matrix, so that the observation noise covariance matrix is expanded, and the weight of observation information with gross error in data fusion is reduced.

The invention provides a wind cloud satellite observation data assimilation method, a wind cloud satellite observation data assimilation device, electronic equipment and a storage medium, and relates to the technical field of meteorological data processing. Real-time land surface temperature and space lattice points of a background field are subjected to temporal-spatial resolution alignment; if so, replacing the land surface temperature in the background field at the corresponding moment with the real-time land surface temperature corresponding to the non-missing measurement area; if the actual measurement area is a missing measurement area, correcting the land surface temperature at the corresponding moment in the background field based on a land surface temperature correction model to obtain an optimized background field; combining the observed radiance with the optimized background field, constructing an objective function, and calculating to obtain an analysis field; and carrying out iterative optimization on the analysis field to minimize the target function, and outputting an optimal analysis field. According to the method, background field errors can be reduced, so that the system has better satellite data assimilation capability, and meanwhile, the assimilation effect of the satellite data under the high-altitude complex terrain is improved.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention discloses a multispectral satellite cloud picture prediction method based on motion stripe decoupling. The method comprises the following steps: carrying out normalization preprocessing on multi-channel satellite observation data; utilizing a motion branch model to extract motion features based on a displacement field, iteratively updating a prediction frame in an autoregressive distortion-correction pipeline, and keeping physical consistency in combination with atmospheric dynamics and smoothness constraint; a texture branch model is utilized to sequentially pass through a high-fidelity encoder, long memory state space modeling and a high-fidelity decoder, time sequence texture features are extracted, and cloud picture details are kept; the motion features output by the motion branches and the texture features output by the texture branches are input into a gating fusion module, adaptive weighting of the features is achieved through convolution and a gating mechanism, and fusion features are output; and carrying out reverse normalization processing on the fusion features to obtain satellite cloud picture prediction results at a plurality of moments in the future. According to the method, the spatial texture fidelity of cloud picture prediction can be improved while the physical interpretability is ensured, and high-precision satellite cloud picture prediction is realized.

The invention provides a dike group deformation monitoring method and system based on Beidou multi-source fusion, and the method comprises the steps: receiving a satellite observation signal and sensing data of a dike group monitoring region, obtaining multi-source input data, inputting the multi-source input data into a preset dike group digital twin model, carrying out the physical behavior simulation of a dike group structure, and generating a global deformation field. Inputting the initial boundary condition into a deformation conduction dynamical model module in the digital twinborn model of the reservoir dam group, identifying a deformation key conduction path in the reservoir dam group, and executing stress-deformation forward calculation and reverse traceability analysis in the digital twinborn model of the reservoir dam group on the basis of the deformation key conduction path; and calculating the influence weight of the core risk source on the deformation of the reservoir dam group, constructing simulation scenes of various regulation and control strategies in the digital twin model of the reservoir dam group, and performing simulation deduction on the deformation trend of the reservoir dam group in each simulation scene to obtain deformation early warning information. According to the invention, the reliability of early warning response is effectively improved.

The invention discloses a satellite navigation positioning method and device based on scene recognition, electronic equipment and a storage medium, and belongs to the technical field of satellite navigation positioning, and the method comprises the steps: determining the geometric space characteristics of each satellite according to the observation time, the satellite number of each satellite and preset satellite ephemeris information; according to the observation time, the satellite pseudo-range of each satellite and the satellite distance change rate of each satellite, determining the signal quality characteristics of each satellite; determining a satellite observation feature set based on the geometric space features of the satellites and the signal quality features of the satellites; inputting the satellite observation feature set and the satellite signal-to-noise ratio of each satellite into a preset scene recognition model to generate a scene category; and determining the positioning coordinate of the satellite navigation receiver according to the scene category in combination with the satellite signal-to-noise ratio of each satellite and the satellite pseudo-range of each satellite. According to the invention, the problem that satellite navigation positioning is not accurate enough in the prior art can be solved.

The invention discloses a single-satellite target sight atmospheric refraction correction method and system based on multi-spectral range finding, and relates to the field of atmospheric refraction processing, and the method comprises the steps: determining a target radiation intensity ratio based on two radiation intensities on different wavebands at the same moment; obtaining observation sight lines of the satellite on the measured target on different wave bands at the same moment, and carrying out light trace tracking to obtain a light ray tracking trajectory; determining a target grid through which each ray tracing trajectory passes from the atmosphere gridding model, determining an extinction coefficient of each target grid, and constructing a target function of a radiation intensity ratio with respect to the height of the target from the ground in combination with a radiation intensity calculation formula; determining a target height from the measured target to the ground based on a target function and a target radiation intensity ratio, determining the position of the measured target according to a track point matched with the target height in each light ray tracing track, and determining a sight line vector after atmospheric refraction correction by combining the position of a satellite, therefore, high-precision atmospheric refraction correction under a single-star observation condition is realized.

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention discloses a working system of self-moving equipment and a control method of the self-moving equipment. The working system of the self-moving equipment comprises the self-moving equipment which is configured to walk autonomously and complete a working task; the base station is configured to obtain satellite observation data of the base station, calculate and generate differential data of the base station according to the satellite observation data and transmit the differential data to the self-moving equipment; wherein the self-moving device is further configured to obtain differential data of a network RTK service provider under the condition that the communication parameters of the self-moving device and the base station do not meet the corresponding parameter requirements.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention provides an earth observation task planning method, system and device, and relates to the technical field of satellite observation task planning, and the earth observation task planning method comprises the steps: obtaining user input; analyzing the user input to obtain a user task; generating a shooting plan according to the user task; establishing a corresponding satellite agent and a corresponding ground station agent according to a target shooting plan in the shooting plan; respectively sending bid invitation notifications to the corresponding satellite agents and the corresponding ground station agents so as to obtain task planning bids generated by the satellite agents and the ground station agents; evaluating the task planning bidding to obtain a corresponding bid-winning bidding document; and according to the winning bidding document, performing conflict detection on the user task to obtain a conflict detection result. According to the method, the system and the equipment provided by the invention, in the face of a new scene, the adaptability of an algorithm to complex constraints can be improved by utilizing text understanding and reasoning capabilities of a large model.

The invention discloses a ground and satellite observation constraint combined GPP multi-task learning estimation method, system, medium and equipment, and belongs to the technical field of remote sensing, the method comprises the steps of obtaining OCO-2 SIF data and TROPOMI SIF data, outputting a global GPP product prediction value through an expert network, and obtaining a global GPP product prediction result; the processing flow comprises the following steps: extracting initial features based on an OCO-2 SIF data set and a TROPOMI SIF data set, inputting the initial features into a shared encoder module, learning cross-sensor spatial and temporal features through a feature alignment and fusion mechanism, outputting OCO-2 SIF time features and TROPOMI SIF spatial features, carrying out independent decoding and outputting predicted values, and outputting the predicted values. According to the global GPP high-precision estimation method, the problem of an error mode of high-value underestimation and low-value overestimation caused by insufficient space-time generalization due to single task model estimation is solved.

The invention discloses a distributed satellite observation data processing task progress index real-time method. The index real-time method comprises the following steps: performing target code instrumentation on a distributed satellite observation data processing task based on OpenTelemetry framework multi-language characteristics; real-time receiving and persistent storage of Trace type telemetry data sequences are realized at a service monitoring rear end; and performing task completion time consumption prediction based on a task data volume and task time consumption relation model, and performing dynamic estimation on a real-time progress index of the task after the task completion time consumption prediction is combined with the received telemetry data. Through task progress index estimation vividly expressed by percentage, an index value represents a deviation degree between actual task execution and a task time consumption estimation value, and through combination with a Trace telemetry data sequence receiving condition, a health state of a task can be judged, and operation and maintenance personnel can take corresponding measures conveniently.

The invention relates to the technical field of data processing, and particularly provides a satellite coverage capability evaluation method and device, a storage medium and electronic equipment, and the method can comprise the steps: obtaining a ground surface coverage area of a sub-satellite point position of each satellite in a plurality of satellites at any moment; rasterizing the earth surface area to obtain a coverage time matrix; calculating coverage parameters of the plurality of satellites in a preset time period based on the earth surface coverage area and the coverage time matrix; the coverage parameters are analyzed, an evaluation result is obtained, and the evaluation result is used for representing the coverage capability of the multiple satellites; the average coverage rate is used for determining whether the satellite observation data of the multiple satellites can meet the data assimilation requirement or not and / or providing a decision basis for applications such as satellite planning tasks and the like. According to some embodiments of the invention, comprehensive and accurate evaluation of the satellite coverage condition can be realized.

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 discloses a multi-interference-source synchronous positioning method based on a single LEO satellite, and relates to the technical field of single-satellite multi-GNSS interference source positioning. The method comprises the following steps: firstly, establishing a scene in which a single satellite observes Doppler signals of multiple interference sources, modeling an interference source problem into an integer programming optimization problem, grouping Doppler observed quantities through equally-spaced sampling, and resolving frequency deviations and positions of the multiple interference sources by using a least square method; and further separating the homologous Doppler observed quantity by using a clustering method, and finally obtaining the accurate positions of a plurality of interference sources. According to the multi-GNSS interference source positioning method used in the invention, high-precision synchronous positioning of multiple ground interference sources by using a single low earth orbit satellite is realized, and the calculation complexity is reduced.

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 base station, a base station addressing device, a base station addressing method and an external device. The base station comprises: a base station body; the base station addressing device comprises an acquisition module which is configured to acquire satellite observation data of a current observation point; the calculation module is configured to evaluate the observation quality of the observation point according to the satellite observation data; the output module is configured to output a first prompt according to the observation quality, and the first prompt indicates the appropriate degree of the observation point for installing the base station; wherein the base station addressing device is detachably connected with the base station main body so as to execute an addressing task after being detached from the base station main body.