145 results about "Earth observation" patented technology

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses a meta reinforcement learning scheduling method for earth observation satellite task planning, and the method comprises the steps: carrying out the prediction of a target visible window for all visible time windows of a task target, carrying out the preprocessing of a satellite visible window, designing a dynamic reward mechanism, building a self-adaptive dynamic reward task Markov decision environment, and carrying out the prediction of the target visible window. Building a reinforcement learning algorithm based on meta learning, and finally loading the algorithm to input test data and output an imaging task planning strategy. A task dynamic reward mechanism is introduced, task generality is captured by utilizing a meta-learning layer, and through multiple rounds of training, an intelligent agent can quickly adapt to and efficiently make a decision in a variable environment, so that a satellite task planning result is obtained. The method can be effectively suitable for remote sensing satellite task planning with a dynamic task scene.

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 provides a large-scale constellation earth observation task planning method and device, and belongs to the technical field of satellite task planning. According to the embodiment of the invention, a large-scale constellation earth observation task problem is divided into a multi-satellite-cluster cluster allocation problem and a single-satellite-cluster cluster autonomous task planning problem, and the improved adaptive large neighborhood search algorithm fused with tabu search is adopted to allocate the large-scale constellation earth observation tasks to different satellite clusters. And then, multi-satellite task planning in a satellite cluster is realized by adopting a satellite cluster autonomous task planning algorithm based on an extended contract network mechanism. Therefore, the dimension reduction of the problem scale can be realized, the search space can be greatly reduced, the solving efficiency can be improved, the cooperative capability between satellites in the satellite cluster can be improved, and especially in the scene of satellite damage and interference, the anti-interference capability and cooperative completion efficiency of the task can be improved, and the robustness and reliability of task planning can be ensured.

The invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, and belongs to the technical field of ground task management and control of spaceflight earth observation satellites. The method comprises the steps that historical data transmission resource use rules of all satellites are mined, and data transmission resource use rule models of all the satellites are established; in a data transmission resource scheduling process, each satellite generates a corresponding resource demand set according to a data transmission timeliness requirement, a priority degree and a data transmission resource use rule model of an important task, conflict resolution is carried out on a conflicting resource use demand set by utilizing a genetic algorithm, and a data transmission resource scheduling result is obtained through a multi-round parallel resource matching and conflict resolution process. The multi-satellite data transmission resource planning scheme is obtained, large-scale satellite data transmission resource scheduling is achieved, the large-scale remote sensing satellite group resource use efficiency is effectively improved, and the implementation rate of reconnaissance tasks is improved. According to the method, the data transmission resource distribution income among multiple satellites can be comprehensively planned, and the intelligent scheduling capability level of large-scale remote sensing satellite group resources can be effectively improved.

To provide a system for efficiently providing information which a user requires.SOLUTION: A system comprises: reception means for receiving earth observation data sent from an earth observation satellite; analysis means including a generative AI for analyzing the received earth observation data; classification means for classifying the data analyzed by the analysis means to a prescribed category; integration means for integrating the classified data; generation means for generating real time information on the basis of the integrated data; and providing means for providing the generated information according to a search word.SELECTED DRAWING: Figure 1

The invention discloses an airborne laser radar sounding data processing method and system, and relates to the technical field of marine surveying and mapping and earth observation, and the method comprises the following steps: collecting original full-waveform data of an airborne laser radar sounding system, the original full-waveform data comprising infrared laser channel data and blue-green laser multichannel data; joint denoising processing is carried out on the original full-waveform data to obtain denoised waveform data, and the joint denoising processing comprises background noise modeling and removing based on machine learning and random noise filtering based on frequency domain low-pass filtering; a collaborative joint denoising strategy is formed by fusing background noise modeling based on machine learning and frequency domain low-pass filtering based on signal-to-noise ratio optimization. According to the method, complex background noise can be accurately estimated and removed, random noise can be adaptively filtered out, weak underwater echo signals are reserved to the maximum extent, and the problem of water depth information loss caused by excessive smoothness is effectively avoided.

Provided herein are systems and methods for enhanced marine ship detection. A system includes a mission planning system for generating a set of tasking commands to send to a plurality of satellites in an intersatellite link (ISL) relay layer, a plurality of Earth observation satellites having overlapping accessible swaths, wherein a first satellite collects first sensor data at a first set of coordinates based on a first tasking command and sends the data to the ISL relay layer, and at least a second satellite trailing behind the first satellite collects second sensor data at a second set of coordinates based on the second tasking command and the first sensor data, and sends the second sensor data to the ISL relay layer, and a reception system to receive ship detection data including the first sensor data and the second sensor data and to generate at least one vessel detection report.

The invention provides a synthetic aperture radar imaging radio frequency interference detection method and system, and the method comprises the steps: S1, setting an SAR working time sequence design for radio frequency interference feature collection; s2, observing earth through an SAR satellite to obtain original echoes; s3, according to the working time sequence of the SAR, interference detection is carried out on echo data corresponding to SAR non-transmitted pulses, interference parameters are extracted at the same time, and a radio frequency interference signal model is established; s4, according to the radio frequency interference signal model parameters and original echoes, interference detection and suppression are carried out on echo data corresponding to each transmitted pulse again; and S5, outputting SAR echo data after interference suppression, and performing focusing imaging processing. The method can effectively solve the interference signal-to-noise ratio problem of SAR satellite interference detection, improves the interference detection and suppression capability, and can provide important technical support for the future SAR system design.

This disclosure relates generally to system and method for estimating crop water requirement using multi-sensor data fusion. Increasing global population is imparting pressure on both agriculture for food demand and limited freshwater resources for consumption. Estimating crop water requirement reduces water demand for crop production. The method divides soil and crop into multiple vertical and horizontal profiles to estimate water balance thereby reducing the errors in estimation of crop water requirement. Additionally, the method has capability to interlink the multiple data sets such as satellite based earth observations, weather observations from IoT sensors, Weather forecasts from global circulation models, and crop knowledge base for crop water requirement estimation. The method is based on spatio-temporal modeling for multi-layer crop and soil water balance and helps to generate the additional insights on crop water requirement like moisture at different levels in soil profile, crop canopy growth at different locations.

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 relates to the technical field of remote sensing earth observation, in particular to a satellite video multi-target tracking method and system based on space-time state propagation and progressive trajectory association, and the method comprises the steps: obtaining a target detection result and a state transition result of adjacent frames in a video sequence frame image through a target tracking model; the target detection result comprises a target detection frame position representation and a target category representation, the state transition result comprises a coordinate transition result and a foreground probability, calculating a spatial position matching degree by using an intersection-to-union ratio, and calculating an adjacent frame target appearance feature similarity by using a cosine distance; and combining the spatial position matching degree and the appearance feature similarity to calculate a target space-time matching affinity, associating each target tracking frame in each video sequence frame based on the target space-time matching affinity, and adding the tracking frames to the corresponding target trajectory fragments to generate each target tracking trajectory. The core intelligence information can be accurately and efficiently extracted from the satellite video data, and the utilization efficiency of the satellite video data is improved.

The invention provides a satellite earth observation path planning method and device, equipment and a storage medium, and the method comprises the steps: obtaining the earth-surrounding orbit and shooting breadth of a satellite to be subjected to a task, and a to-be-observed task, dividing a predicted observation ground region into a two-dimensional grid chart containing a plurality of uniform squares, carrying out the observation demand representation of the two-dimensional grid chart, and carrying out the observation demand representation of the two-dimensional grid chart; and according to the time sequence change of the around-the-ground orbit, stacking the two-dimensional grid charts represented by the observation demand according to a time sequence to obtain a three-dimensional space-time grid, searching path observation points based on the three-dimensional space-time grid to obtain a target path observation point set meeting the minimization of a pre-constructed optimization target function, and searching path observation points based on the target path observation point set. And performing path conversion on the target path observation point set to obtain an optimal earth observation path for the satellite to execute the to-be-observed task. According to the method, two-dimensional path planning is expanded into path planning on a three-dimensional space-time grid, the dynamic time-varying constraint processing process is effectively simplified, and the efficiency of satellite earth observation path planning is improved.

The invention discloses a large-scale complex correlation earth observation task planning method based on reinforcement learning, and relates to the technical field of task resource planning. The method comprises the following steps: acquiring historical data of earth observation task planning, and constructing a sample data set; constructing a graph neural network and a reinforcement learning network; using the sample data set to train the graph neural network and the reinforcement learning network at the same time; acquiring a demand information list, a satellite information list, a data transmission task information list and an observation information list, and processing the information lists into demand feature vectors, satellite feature vectors, data transmission task feature vectors and observation feature vectors; and inputting the four feature vectors into a trained graph neural network to obtain an environment feature vector, inputting the environment feature vector and the demand feature vector into a trained reinforcement learning network, and outputting an earth observation task planning result. The method can solve the scheduling optimization problem under the condition of multiple tasks and limited resources, improves the task planning efficiency, and can be widely applied to the fields of satellite task scheduling, intelligent manufacturing, automatic driving and the like.

The invention relates to a method of determining one or more conditions, of a geographical line, GL, or its surroundings. The method comprises: acquiring geo-referenced earth observation data representing one or more spatially overlapping imagery layers covering from an aerial or space perspective a specific region of interest, ROI, of the Earth's surface, the ROI comprising the GL; geometrically transforming the geo-referenced observation data, at least in parts, into a local internal frame of reference of the GL within the ROI to obtain a mapping of the geo-referenced observation data to respective corresponding coordinates within the local internal frame of reference of the GL; and evaluating the mapped earth observation data as represented in the local internal frame of reference of the GL according to a classification scheme to obtain therefrom evaluation data representing a classification of one or more properties of the GL or of its surroundings according to one or more conditions of the GL.

The invention discloses a method for monitoring same-scale time sequence deformation information of a complex deformation body under a multi-source earth observation technology fusion background. The same-scale deformation information is accurately extracted from the multi-source observation data based on the multi-scale and multi-dimensional deformation characteristics of a complex deformation body, and the method has important application value for building safety operation, smart city construction, geological disaster monitoring and early warning and the like. According to the method, a high-resolution earth observation system and a single Beidou navigation positioning system in China are taken as main surveying and mapping technical means, and an intelligent extraction method for same-scale time sequence deformation of massive, complex-scale and multi-dimensional monitoring data is established according to an Msplit classification estimation theory, a time sequence model and a median function.

An aspect of the disclosure provides An earth observation apparatus to be carried by a moving aerial platform or satellite for obtaining images of the surface of the earth, the apparatus comprising: an optical train having an optical field of view for imaging a region of the surface of the earth and being configured to form an image of the region at an image plane; an image sensor disposed at the image plane providing an imaging field of view; a view adjuster configured to control the optical train to: provide forward motion compensation for a stare time; and to displace the image, relative to the imaging field of view, in an across-track direction in a sequence of discrete displacement steps during each stare time; wherein the image sensor comprises a plurality of active areas, each comprising an area array detector and the active areas being spaced apart by inactive areas at the image plane wherein each active area captures a frame of image data for each discrete displacement step thereby to capture a plurality of frames for each discrete displacement step and the plurality of frames captured for each discrete displacement step are displaced in the across-track direction, relative to the imaging field of view, from the plurality of frames captured for the next discrete step.

The invention discloses a differential measurement-based satellite-borne synthetic aperture microwave radiometer imaging correction method, which comprises the following steps of: respectively selecting a reference area and a correction area in a global range, and respectively intercepting scientific data and auxiliary data in a specific time period in the reference area and the correction area based on load normal earth observation data; averaging the visibility functions intercepted in the reference area to obtain a reference visibility function; generating a reference brightness temperature and a correction brightness temperature through a simulation model by using the auxiliary data intercepted in the reference area and the correction area respectively; performing difference and inversion processing on the normal earth observation visibility function to be subjected to imaging processing by using the reference visibility function to obtain a difference inversion brightness temperature; calculating a correction coefficient by using the reference visibility function, the reference brightness temperature, the visibility function intercepted in the correction area and the correction brightness temperature; and correcting and compensating the differential inversion brightness temperature by using the correction coefficient and the reference brightness temperature to obtain the final inversion brightness temperature.

The invention discloses a remote sensing satellite area coverage method based on greedy search, and belongs to the technical field of remote sensing satellite earth observation. The method comprises the following steps: firstly, gridding an area target to be observed according to the imaging breadth of a remote sensing satellite; secondly, calculating a grid set of potential coverage of the satellite based on the breadth and the side-sway capability of the satellite within the top-crossing access time of the satellite to the regional target; and then local greedy search and multi-round iteration are carried out to determine a final imaging satellite set and an imaging sequence, and an optimized multi-remote sensing satellite collaborative observation region target scheme is obtained. The method has the advantages of being simple in processing logic, high in flexibility, high in resource utilization rate, high in optimization efficiency and the like, and is suitable for the problem that multiple remote sensing satellites cooperatively cover area targets.

The invention relates to the technical field of giant satellite group constellation remote sensing satellite operation control, and discloses a multi-frequency satellite earth observation task decomposition method based on a space-time grid. The method comprises the following steps: firstly, constructing a satellite earth observation task space-time grid, and filling access data of satellites; inputting observation frequency requirements including observation positions, time periods, times, resolution, loads and the like; obtaining a related space-time grid according to the position; obtaining sequence information of related satellite access in the space-time grid according to the requirements of time period, frequency, resolution, load and the like; establishing a multi-frequency satellite earth observation evaluation function, and integrating factors such as a time interval, satellite side sway and the number of satellites; a multi-frequency satellite earth observation evaluation function is utilized, satellite grid element tasks which meet requirements and are accessed for n times are optimized to serve as a task decomposition scheme, and n is the frequency number required by the decomposition. The method has the characteristics of comprehensive consideration factors, good expandability, good reliability and the like.

The invention relates to the technical field of satellite observation task scheduling, in particular to a low-orbit satellite large-scale earth observation task self-adaptive scheduling method and system. The method comprises the following steps: generating a task allocation result according to a task candidate resource mapping table, namely generating an initial allocation scheme by using a greedy strategy of double factors of time and load, and establishing an allocation frequency memory matrix; constructing an inverse correlation selection probability model by utilizing the distribution frequency memory matrix, and outputting a task distribution result considering global revenue and load balance; task scheduling optimization is carried out based on a task allocation result, wherein conflict resolution fine adjustment is carried out by utilizing a time elastic adjustment mechanism of conflict perception; inter-satellite link collaborative rescheduling based on task characteristics; and global optimization and real-time response capability are effectively considered. According to the method, a complex joint scheduling problem is decoupled into two sub-problems with clear logic and low coupling degree, and the calculation complexity of single scheduling is greatly reduced.

This application provides a method, device, and computer-readable storage medium for evaluating the on-orbit calibration error of a microwave radiometer calibration system used in geostationary orbit. The application evaluates the on-orbit calibration error of the microwave radiometer calibration system by determining the errors of the calibration cold source, calibration heat source, Earth observation radiation brightness temperature, and Earth target brightness temperature. By separately evaluating the errors of the calibration cold source, calibration heat source, Earth observation radiation brightness temperature, and Earth target brightness temperature in the microwave radiometer calibration system used in geostationary orbit, each error is quantified, thus quantifying the on-orbit calibration error of the microwave radiometer calibration system. Furthermore, the different radiation transmission paths are considered during the evaluation of each error, making the evaluation of the sources of on-orbit calibration error of the microwave radiometer calibration system used in geostationary orbit not only effective and accurate but also highly implementable.

This invention discloses a method for collaborative Earth observation and data transmission scheduling across heterogeneous space platforms. The method employs integer encoding to represent the execution sequence of observation tasks from multiple space platforms, proposes a three-stage decoding strategy to repair infeasible chromosomes, and designs feasible observation time window selection strategies, as well as observation task insertion strategies based on time-based benefits, task urgency, and attitude transition time, to construct an initial chromosome population. Crossover operations are performed on the current population, constructing three neighborhood search operators: inter-platform exchange, intra-platform exchange, and insertion based on relaxation time, to perform local searches on the chromosomes within the current population. The algorithm terminates if the maximum number of iterations or the maximum computation time limit is reached, generating a scheme for collaborative Earth observation and data transmission scheduling across heterogeneous space platforms, providing technical support for space-based Earth observation.

The invention is applicable to the technical field of satellite remote sensing earth observation, and provides a vegetation canopy vegetation parameter inversion method based on an MOBCSM (Multi-output Band-Correlative Convolution Mixture Kernel) kernel, a storage medium and a processor. By introducing the MOBCSM kernel, a complex nonlinear relation in the hyperspectral data can be better processed, a stronger modeling capability is provided in multi-task collaborative optimization, in addition, the MOBCSM kernel can effectively model interaction between wavebands, and the precision and reliability of hyperspectral data analysis and vegetation parameter inversion are further improved. The storage medium for storing the method and the processor for operating the method also have the same technical effects.

The application discloses a kind of multi-star on-orbit cooperation observation task planning method and system, by designing heuristic initial solution generation strategy, roulette selection algorithm based on Boltzmann selection probability, adaptive crossover operator and mutation operator based on population dispersion degree, stop condition based on population convergence coefficient, from initial solution, selection operator, crossover operator, mutation operator, stop condition etc. Various angles improve the genetic algorithm for multi-star cooperative observation task planning, compared with the genetic algorithm based on circle next cross, variation, significantly improve the execution benefit of observation task and the observation number of target point, construct the scheduling system of constellation cloud computing system, reach the purpose of on-orbit cooperation multi-star efficient execution observation task.

The invention provides a radar satellite three-dimensional attitude guiding method and system, and the method comprises the steps: calculating the geocentric distance and geocentric latitude and longitude of a satellite according to satellite orbit parameter information, calculating the height of the satellite and the geodetic latitude and longitude of a droop point through a WGS-84 reference ellipsoid model, and further obtaining the geocentric latitude and the earth radius of the droop point. The position vectors of the droop point and the satellite in a WGS-84 earth-fixed coordinate system are obtained respectively, the position vectors of the droop point and the satellite are compared to obtain the position vector from the satellite to the droop point, then the position vector is converted into a satellite orbit coordinate system, and rolling and pitching guide angles are calculated. Doppler frequency deviation caused by earth rotation is compensated by adjusting the yaw guide angle. According to the method, the problem that loads such as a spaceborne radar altimeter vertically point to the earth surface of the earth reference ellipsoid model is successfully solved, meanwhile, Doppler frequency deviation generated by earth rotation is compensated, and important technical support can be provided for a subsequent radar satellite earth observation task.

The present application provides an earth observation system based on a single measuring station, and the working modes of its earth observation payload include: record first and then replay mode, in which the imaging window is earlier than the data transmission window, and the imaging window and the data transmission window do not overlap; replay first and then record mode, in which the data transmission window is earlier than the imaging window, and the data transmission window and the imaging window do not overlap, and the record file and the replay file are not the same file; replay first and then record and play mode, in which the imaging window is located in the data transmission window, and the record file and the replay file are not the same file; replay first and then quasi-real transmission mode: the data transmission window includes two data transmission segments, the first data transmission segment is used to replay historical files, and the second data transmission segment is used for quasi-real transmission, the imaging window does not overlap with the first data transmission segment, and partially overlaps with the second data transmission segment. The present application ensures the flexibility of the user's observation area, the timeliness of data, and the observation efficiency by designing a new payload mode, and effectively improves the earth observation efficiency of a single measuring station.

The invention discloses an in-orbit calibration method for correcting self-radiation of a heat mirror of a microwave imager based on reference radiation. The method comprises the following steps: calculating an included angle between the heat mirror of the microwave imager and the sun; calculating the earth observation brightness temperature of the microwave imager; calculating the observed brightness temperature of the reference microwave radiometer; simulation brightness temperature of the reference microwave radiometer and the microwave imager is obtained based on radiation transmission model simulation; calculating an observation brightness temperature deviation and a simulation brightness temperature deviation, and calculating a system error between the reference microwave radiometer and the microwave imager; based on the system error, the included angle between the microwave imager heat mirror and the sun is adopted for function relation fitting, and the heat mirror self-radiation brightness temperature of the reference microwave radiometer is calculated; the included angle between the microwave imager heat mirror and the sun is calculated on orbit in real time, and the earth observation brightness temperature of the microwave imager is corrected based on the self-radiation brightness temperature of the heat mirror. The self-radiation of the heat mirror is solved in real time by taking double deviations of observation brightness temperature and simulation brightness temperature as constraints, and the problem that the temperature of the heat mirror of the microwave imager cannot be actually measured in orbit is solved.

The present application belongs to the technical field of space-to-earth observation (satellite remote sensing) and watershed hydrology, geographical environment disciplines, and discloses a watershed scale high spatio-temporal resolution river network remote sensing information extraction method and system. After cloud and cloud shadow processing of optical remote sensing images, a simple non-iterative clustering superpixel segmentation algorithm is used to segment homogeneous landscape objects, and a hierarchical decision tree is constructed using a water body index and a vegetation index. In the area where the optical remote sensing image is seriously covered by clouds or the image is missing, the SAR image is filtered, the dual polarization water body index is calculated using the VV and VH polarization images, the maximum inter-class variance method is executed, and the image is binarized. After obtaining the water body result extracted from the remote sensing image, the river cut-off is repaired in combination with the water system diagram generated by the DEM model. The present application can automatically extract accurate, continuous and high spatio-temporal resolution river network remote sensing information, and can be expanded to global scale river network feature extraction.

To provide a system for detecting a marine ship.SOLUTION: A system for detecting a marine ship comprises: a mission planning system for generating a set of tasking commands to send to a plurality of satellites in an intersatellite link (ISL) relay layer; a plurality of Earth observation satellites having overlapping accessible swaths, in which a first satellite collects first sensor data at a first set of coordinates based on a first tasking command and sends the data to the ISL relay layer, and at least a second satellite trailing behind the first satellite collects second sensor data at a second set of coordinates based on the second tasking command and the first sensor data, and sends the second sensor data to the ISL relay layer; and a reception system to receive ship detection data including the first sensor data and the second sensor data and to generate at least one vessel detection report.SELECTED DRAWING: Figure 5