734 results about "Satellite data" patented technology

The invention discloses a lake terrain prediction method based on machine learning fused multi-source satellite data, and relates to the technical field of machine learning, and the method comprises the steps: extracting the optical features of a target lake from an optical satellite image; extracting water features of each water body pixel in the target lake from the SWOT satellite data; performing space-time alignment and fusion on the optical features and the water features to obtain a fusion feature vector; the actually measured water depth point of the real water depth is matched to the geographic position of the corresponding pixel, and then the real water depth and the fusion feature vector of the corresponding pixel form a training sample; selecting a machine learning model and initializing the structure or parameters of the machine learning model; training the initialized machine learning model by using the training sample; utilizing the trained machine learning model to predict the water depth value of the corresponding pixel according to the fusion characteristic vector of each water body pixel; and determining the terrain of the target lake according to each water depth value. According to the method, the terrain of a large-scale lake can be efficiently predicted at low cost.

The invention discloses an urban heat island effect decision-making method based on satellite data and artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining satellite remote sensing data and urban basic data of a target urban region, the urban basic data comprising population density distribution, historical intervention effects, infrastructure information and urban function partition data; preprocessing the satellite remote sensing data to generate a standardized surface temperature distribution image, and based on the surface temperature distribution image, identifying an urban heat island area and dividing the intensity grade of the urban heat island area; the method has the advantages that satellite remote sensing data and urban multi-dimensional basic data are fused, dynamic scoring and optimization algorithms are combined, the heat island area is accurately recognized, the optimal intervention strategy is generated, the problems that a traditional method is insufficient in data utilization and poor in decision-making scientificity are solved, and the method has the advantages of improving treatment accuracy and resource utilization efficiency.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention relates to a lightning potential forecasting method and system based on a random forest. The method comprises the following steps: (1) collecting and preprocessing multi-source meteorological data; (2) feature extraction: cloud picture texture and shape features are extracted from satellite data, and echo intensity, top height and vertical development speed features are extracted from radar data; (3) constructing and optimizing a model; and (4) early warning thunder and lightning. The method has the advantages that the random forest serves as a typical representation of the ensemble learning algorithm, and the core of the random forest is to integrate prediction results of a plurality of decision trees. According to the integration mode based on Bagging, multi-dimensional feature information can be fully integrated, and the overfitting problem which is likely to happen to a single decision tree is effectively restrained. In a thunder and lightning potential forecasting scene, the algorithm can accurately capture a complex nonlinear relationship between meteorological elements and thunder and lightning potential by adaptively learning association between a large amount of historical meteorological data and thunder and lightning events, so that the forecasting accuracy is remarkably improved.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention discloses a double-layer ionosphere modeling method fusing low-orbit satellite data, which comprises the following steps of: preprocessing ground-based GNSS (Global Navigation Satellite System) data to obtain an STEC expression of a ground-based GNSS; preprocessing the precise orbit determination data of the low-orbit satellite to obtain a VTEC expression of the precise orbit determination data; obtaining an electron density profile product of the radio occultation of the low-orbit satellite, and obtaining VTEC of the radio occultation through profile integration; using two groups of spherical harmonic coefficients to express global VTEC distribution, and constructing an observation equation; and determining weights of different types of data by using Helmert variance component estimation, and finally solving to obtain two groups of spherical harmonic coefficients to generate an ionized layer grid product. Projection errors caused by vertical layering in a traditional single-layer model are weakened, meanwhile, precise orbit determination data and radio occultation data of a low-orbit satellite are combined, the problem that ionosphere TEC cannot be correctly layered in a double-layer model is solved, data of areas where GNSS tracking stations are sparsely distributed are supplemented, and the reliability of the GNSS tracking stations is improved. Therefore, a global ionosphere model with higher precision is obtained.

The invention relates to the technical field of artificial intelligence and remote sensing information processing, in particular to an unmanned aerial vehicle-satellite remote sensing data fusion method based on dynamic weight distribution, which comprises the following steps: step 1, dynamic feature extraction and environment perception; 2, dynamic weight distribution and fusion execution: forming a feature vector by the feature value and an environment interference coefficient, inputting the feature vector into a pre-training weight distribution model, and outputting real-time weights of the unmanned aerial vehicle and the satellite; generating an interpolation scaling factor according to the resolution ratio, and performing up-sampling on the low-resolution data by adopting edge sensing interpolation; pixel-level weighted fusion is carried out on the unmanned aerial vehicle data and the up-sampling satellite data according to the real-time weight; and step 3, performing closed-loop feedback optimization. According to the method, the accuracy, timeliness and quality of remote sensing data fusion are effectively improved in a dynamic weight distribution and environment perception mode, and the method has wide application value.

The invention relates to the technical field of geological disaster recognition, in particular to a power transmission line geological disaster recognition method, system and device and a medium, and the method comprises the steps: regularly obtaining satellite InSAR data and unmanned aerial vehicle LiDAR point cloud data of a target area; performing first preprocessing on the satellite InSAR data, and extracting earth surface deformation information; performing second preprocessing on the unmanned aerial vehicle LiDAR point cloud data to generate a digital elevation model; carrying out geological disaster feature extraction based on a digital elevation model to obtain a disaster type classification result; and analyzing the surface deformation information and the disaster type classification result, and outputting geological disaster state information. The beneficial effects of the invention are that data support and technical guarantee are provided for safe and stable operation of the power transmission line, operation and maintenance cost is reduced, monitoring efficiency is improved, timely prevention measures are ensured, and influence of geological disasters on the power transmission line is reduced.

The invention discloses a space-ground collaborative substation geological hazard risk assessment model construction method, system, equipment and medium, and belongs to the technical field of geological hazard risk assessment, and the method comprises the steps: obtaining satellite remote sensing data and ground sensor data, carrying out the time-space alignment, and unifying the data to the same time-space reference; performing hidden danger feature extraction on the satellite data and the ground data, and performing multi-source data fusion based on an attention mechanism; based on the fusion features, combining a geological disaster physical driving model and historical disaster data, constructing a risk index system and performing quantification; inputting the fusion features into a space-ground collaborative risk assessment model, and outputting risk levels and occurrence probabilities of hidden danger points; and generating a multi-modal visual map based on an evaluation result and triggering early warning information of a corresponding level. According to the invention, a substation geological disaster intelligent prevention and control system which is adaptive to multiple scenes and has high-precision prediction capability and rapid response capability is constructed, and hidden danger identification accuracy and emergency response efficiency are improved.

The invention discloses an infrared satellite data processing system suitable for infrared small target detection, and relates to the technical field of remote sensing satellite data processing and infrared image recognition, and the system comprises an image splicing module which is used for arranging L0-level original infrared images according to a shooting time sequence, and identifying overlapped region feature points of adjacent image particles through an SIFT algorithm, eliminating radiation distortion of an optical lens, and generating a seamlessly spliced comprehensive image by adopting a multi-scale fusion technology. According to the infrared satellite data processing system suitable for infrared small target detection, the quality and consistency of infrared satellite images are remarkably improved through a standardized data processing flow, the identifiability of target signals is optimized from a data source, the false alarm rate under the conditions of a complex background and a low signal-to-noise ratio is effectively reduced, and the detection accuracy is improved. And high-quality input is provided for a subsequent detection algorithm, so that the accuracy and reliability of infrared small target detection are improved.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention relates to an earth surface deformation detection and classification method and system fusing CNN and SBAS-InSAR technologies, and the method comprises the steps: obtaining high-precision deformation data through a small baseline subset and an interference synthesis radar technology (SBAS-InSAR), constructing a CNN classification frame through combining PCA-enhanced optical satellite images, elevation, soil physical parameters, geological landform maps and other multi-source geographic information, and carrying out the detection and classification of the earth surface deformation through the CNN classification frame and the SBAS-InSAR technology. And identification of deformation types such as landslide, settlement and lifting is realized. The process comprises multi-source satellite data acquisition and preprocessing (including InSAR sight velocity decomposition and optical / topographic feature extraction), generation of a preliminary deformation label based on a classification algorithm of a gradient and a deformation velocity threshold, and recognition of a deformation type through a multilayer CNN model. According to the method, the deformation mode is automatically clustered and analyzed in a large-range area, the accuracy and detail performance of deformation type recognition are remarkably improved, the accuracy rate reaches 93%, the method is superior to a traditional method, a stable and extensible tool platform is provided for geological disaster monitoring, and positive risk management of landslide or land subsidence prone areas is assisted.

A method for transferring seed during a planting operation includes receiving, by a theft detection system, a first equipment identity of first equipment, receiving the seed into a seed bin of second equipment from the first equipment, determining, using a sensing device disposed on the second equipment and communicatively coupled to the theft detection system, a first weight value indicative of weight of the seed received into the seed bin from the first equipment, and at least one of transmitting, by the theft detection system using a satellite data communication protocol, the first weight value and the first equipment identity to a tracking system, and determining, by the theft detection system, whether any of the seed was stolen based on the first weight value and the first equipment identity and selectively generating and transmitting a notification to the tracking system indicating that the seed was stolen.

A navigation and positioning device including an inertial measurement unit, a receiver for receiving GNSS satellite positioning measurements, a closed-loop primary Kalman filter configured to compute navigation data corrections by hybridizing satellite data and non-satellite data, as well as a bank of N closed-loop secondary Kalman filters, each configured to compute navigation data corrections based solely on the non-satellite positioning data delivered at least by the inertial measurement unit, each secondary Kalman filter of index i, where 1≤i≤N, being able to reconfigure itself to the primary Kalman filter at a time (i−1)T from the start of navigation of the vehicle, and then periodically at a period N×T, T being a predetermined duration.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention relates to the technical field of satellite communication, and provides a high-orbit and low-orbit fused satellite antenna tracking method and system. The method comprises the following steps: constructing a global satellite database and carrying out time-space coordinate unified coding; predicting visible time according to the satellite coordinates, and identifying a visible time window overlapping region and a corresponding tracking satellite pair; based on the types of the satellite pairs in the overlapping time, tracking path optimization under space-time unified coordinates is carried out, and optimization control parameters are output; switching points and scanning tracks are spliced according to the control parameters, and antenna tracking control parameters are obtained and used for adjusting an antenna. According to the invention, the technical problem that the stability of a satellite communication link is reduced due to switching delay and signal interruption risks when a traditional ground station system dynamically tracks high and low orbit satellites is solved, seamless switching and continuous high-gain tracking of the satellite communication link are realized through cooperative tracking and real-time optimization operation of the multi-orbit satellites, and the stability of the satellite communication link is improved. And the link stability and the resource utilization rate of satellite communication are improved.

The invention provides a multi-source rainfall prediction method and device fusing GNSS and communication satellites, and the method comprises the steps: building a plurality of satellite-ground microwave links which are used for transmitting GNSS data and communication satellite data; gNSS data and communication satellite data on the multiple satellite-ground microwave links are acquired; performing atmospheric rainfall inversion on the plurality of satellite-ground microwave links based on GNSS data, and constructing a two-dimensional water vapor field according to an inversion result; performing rainfall intensity inversion on the plurality of satellite-ground microwave links based on communication satellite data, and constructing a two-dimensional rainfall field according to an inversion result; and fusing the two-dimensional water vapor field and the two-dimensional rainfall field by adopting Bayesian-maximum posteriori estimation to obtain a fusion field, and converting the fusion field to obtain a rainfall prediction field. According to the method, the rainfall field and the water vapor field are fused for rainfall prediction, so that the whole rainfall process can be accurately predicted.

The invention relates to an unmanned aerial vehicle high-precision positioning method based on a satellite-based enhancement system and adaptive Kalman filtering. The method comprises the following steps: acquiring multimode satellite data; analyzing the satellite-based enhanced telegraph text data, and performing error correction on the original observation data; based on the corrected observation data, dynamically adjusting a process noise covariance matrix by using the motion state of the unmanned aerial vehicle, and constructing an adaptive Kalman filtering model; and based on an adaptive Kalman filtering model, fusing the corrected observation data and inertial measurement unit data to obtain unmanned aerial vehicle positioning. The method aims to solve the problems that unmanned aerial vehicle positioning in complex environments (such as urban canyons, electromagnetic interference areas and mountainous areas without ground base stations) is remarkable in multipath effect and has dynamic noise interference, and traditional differential positioning depends on ground base stations. The centimeter-level continuous positioning of the unmanned aerial vehicle in a complex scene is realized through triple technical breakthroughs of satellite-based differential correction, dynamic optimization of environmental perception filtering parameters and multi-source data tight coupling solution.

The invention discloses a power distribution network satellite meteorological data analysis method, system and device and a medium, and the method comprises the following steps: obtaining and preprocessing multi-source satellite meteorological data, and generating a time-space continuous meteorological element distribution diagram; extracting disaster associated features through the meteorological element distribution map; constructing a first calculation model to train the disaster associated features, and outputting a disaster distribution confidence interval; carrying out risk space mapping on the disaster distribution confidence interval and the topological structure of the power distribution network to generate a tower overturning risk assessment result; and establishing a multi-level early warning mechanism according to a tower overturning risk assessment result, generating a three-dimensional risk map and deploying a mobile terminal application. Through a multi-source satellite data fusion preprocessing technology, the problem of a space-time blind area caused by a traditional single data source is effectively solved, collaborative acquisition and space-time registration of data of a static meteorological satellite, a polar orbit meteorological satellite and a radar are achieved, and the problems of resolution difference and cloud noise interference of different satellite systems are solved.

The invention discloses a satellite-ground integrated cooperative efficient alignment method based on a large-view-field optical load, relates to the technical field of spacecraft systems, and solves the problems that in the prior art, precision is low, response is slow, the requirements of remote sensing satellite high-speed data transmission for pointing precision and link availability are met, and satellite data transmission efficiency is improved. Comprising the following steps that S1, when a satellite enters a data transmission mode, a laser ground station opens beacon light, and a satellite center computer executes an alignment program; s2, satellite alignment is completed, and a data transmission link between the satellite and the laser ground station is established; and S3, the satellite carries out a data transmission test, and when the data transmission test is completed, the satellite and laser ground station data transmission link is quitted, and the operation is ended.

The present invention proposes a flood monitoring and early warning method and system based on the fusion of multi-source remote sensing satellite data. The present invention collects various types of remote sensing satellite data of flood areas; pre-processes the collected remote sensing satellite data; integrates the pre-processed multiple remote sensing satellite data with features of different scales to obtain feature fusion; generalizes the feature fusion based on the revisited channel attention module from the same-layer scene to the cross-layer scene; utilizes the fused remote sensing images to identify floods and extract the boundaries and scope of the flood area; conducts flood warnings based on the identified flood areas and historical flood data, combined with meteorological and hydrological information, to obtain warning results; and conducts a comprehensive analysis of the causes, processes, and impacts of flood events. By integrating multi-source satellite data, the present invention aims to improve the accuracy of large-scale flood detection and enhance the ability to predict flood trends, thereby ensuring the accuracy and reliability of spatiotemporal flood warnings.

The invention relates to the technical field of thunder and lightning early warning, and discloses a thunderstorm early warning system and method applied to a new energy station, and the system comprises a data collection module, a data processing module and an early warning decision module which are arranged in a target new energy station. The multi-source thunderstorm early warning information is sent to a data processing module; wherein the multi-source thunderstorm early warning information comprises lightning data, radar data, satellite data, micrometeorological data and atmospheric electric field data, and the data processing module performs feature extraction on the multi-source thunderstorm early warning information to obtain a first thunderstorm early warning feature vector and a second thunderstorm early warning feature vector; and the early warning decision module performs thunderstorm early warning based on the first thunderstorm early warning feature vector and the second thunderstorm early warning feature vector to obtain thunderstorm early warning information of the new energy station. According to the invention, the accuracy of the thunderstorm early warning information of the new energy station is improved.

The invention relates to the technical field of mine environmental protection, and discloses a gravel aggregate mine carbon emission real-time monitoring and metering method, which comprises the following steps: S1, multi-source sensing, including mining blasting monitoring, ore transportation monitoring and aggregate processing monitoring; s2, edge calculation, including data preprocessing, dynamic compensation calculation and local storage and communication; in the dynamic compensation calculation, the CO2 concentration is output and corrected in real time through a dynamic compensation algorithm, and the dynamic compensation algorithm comprises particulate matter dust drop, temperature and humidity deviation and vibration compensation; and S3, cloud platform processing is carried out, block chain evidence storage, carbon sink deduction calculation and early warning control are carried out, and the carbon sink deduction calculation is vegetation carbon sink absorption amount calculation deduction and waste rock recycling carbon emission reduction calculation deduction according to satellite data. The edge calculation layer adopts a dynamic compensation algorithm, temperature and humidity, vibration and particulate matter compensation items are fused in real time, and the hysteresis bottleneck of accounting is solved; a satellite-ground fusion deduction mechanism is adopted to construct a judicial-level trusted data chain.

A solar radiation forecasting method based on satellite data and a multi-temporal-spatial-scale super-large model comprises the following steps: acquiring satellite remote sensing data, and cutting a satellite image into a prediction area size; constructing a daily variation feature set and a seasonal variation feature set, recombining the data into a four-dimensional tensor, and dividing the four-dimensional tensor into a training set, a verification set and a test set; performing linear mapping on the daily period and monthly period characteristics through a learnable parameter matrix to generate comprehensive time embedding; a layered parameter control strategy is adopted, and model parameters and unfreezing parameters are frozen; generating a low-resolution preliminary prediction result through a regression convolution layer; performing up-sampling by using a residual super-resolution module, reconstructing a high-resolution image in combination with jump connection and residual scaling, outputting single-channel solar radiation intensity data, and de-standardizing the data to a physical quantity line; and configuring an optimizer. The method can realize regional-level high-precision solar radiation prediction in 0-4 hours in the future, and solves the problems of spatio-temporal dependency splitting, poor adaptability to few samples and insufficient spatial resolution in solar radiation prediction.

The invention discloses a low-altitude aircraft accurate positioning method based on Beidou satellite data, and the method comprises the following steps: collecting the multi-source data of Beidou positioning, IMU, barometer and altimeter in the flight process, and generating an original positioning data set; after multi-source data are aligned, a fusion data sequence is constructed; carrying out error modeling by adopting zero offset correction, drift compensation and scale normalization, and outputting a calibrated data sequence; an improved FGO algorithm is input, factors such as Beidou satellite pseudo-range, IMU integration and process prior are processed in a combined mode, track combined optimization is executed, and a preliminary positioning result is output; performing error correction by combining a ground differential base station and satellite-based enhanced data, and outputting a high-precision positioning result sequence; and abnormal point elimination and trajectory smoothing are realized through consistency discrimination and confidence evaluation, and a final positioning result is obtained. The method improves the positioning precision and robustness of the low-altitude aircraft, and is suitable for high-precision positioning application in a complex environment.

The invention discloses a resource scheduling optimization method for a giant constellation, relates to the technical field of satellite resource allocation of the giant constellation, solves the problem that an existing satellite resource scheduling method is difficult to give consideration to global optimization of time and energy consumption, and aims to improve the execution efficiency of a multi-satellite cooperative task and reduce energy consumption. A real satellite scheduling task is simulated and arranged to obtain satellite data and task data, an evolutionary process of a reinforcement learning guide genetic algorithm is adopted to optimize a task sequence, satellite resource allocation is performed on the optimized task sequence, time and energy consumption of the task sequence are completed, and finally normalization is performed to obtain a comprehensive index. According to the method, a task execution sequence with the minimum time and energy consumption comprehensive index is obtained, so that an optimal scheduling scheme is obtained, and the requirement of satellite scheduling under a giant constellation is met.

The invention provides a satellite service data analysis method and system based on a dynamic protocol, belongs to the technical field of satellite real-time data processing, and is used for analyzing satellite data downloaded by a data transmission link so as to complete closed-loop verification on a satellite data transmission core data flow path. The method depends on the support capability of an on-satellite data transmission system for a multi-channel downloading mode, and the independent, lossless and time sequence fidelity binary disk falling capability of ground detection equipment for each channel data. The method comprises the following steps: acquiring original downlink data highly consistent with an on-orbit state; secondly, adopting a scheduler model to carry out structured segmentation and affiliation allocation on the AOS frame after the original data stream is restored; and finally, dynamically loading a corresponding protocol description file for each data unit, and performing field-by-field decoding according to a mapping rule of binary data and readable engineering quantity to generate structured table data. The method can be widely applied to whole-satellite integration test scenes of various remote sensing satellites such as optics, SAR and infrared.

The invention discloses a satellite data storage safety monitoring method, system and device and a medium, and belongs to the technical field of safety monitoring, and the method comprises the steps: collecting satellite data, preprocessing the satellite data, generating a chaotic mapping initial seed according to the preprocessed satellite data, and storing the chaotic mapping initial seed in a database; setting a mapping parameter based on the chaotic mapping initial seed and generating a key, and encrypting an access path by using the key; storing the satellite data and the encrypted access path to a database, calculating a stability index based on the encrypted access path, and performing abnormality judgment according to the stability index; feedback data in the safety monitoring process are collected, the mapping parameters are adjusted based on the feedback data, and the adjusted safety monitoring result is displayed through a visual interface. According to the method, the security of data encryption is improved, the capability of detecting and responding to abnormal behaviors in the storage process is enhanced, and the anti-cracking capability of the encryption method is improved.

The embodiment of the invention provides a navigation state synchronization method, device and equipment and a computer storage medium, and belongs to the technical field of positioning. Receiving first GNSS satellite data, including a first timestamp; searching first IMU data corresponding to the first timestamp from an IMU data queue; carrying out fusion calculation, and determining a real-time navigation state under the first timestamp; moving a queue head position of the IMU data queue to second IMU data, and initializing an increment integral state; performing incremental integral calculation based on the second IMU data and a target IMU data set in the IMU data queue to obtain a real-time incremental pre-integral; and performing navigation state calculation according to the real-time navigation state and the real-time increment pre-integration under the first timestamp, and determining the current navigation state. According to the method disclosed by the embodiment of the invention, the purposes of accurately fusing navigation combinations and improving the navigation accuracy are achieved.