204 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.

The invention belongs to the field of space earth observation technology and application, and discloses a remote sensing image stony desertification grading method, device and equipment based on a feature space, and the method comprises the steps: obtaining a remote sensing image, elevation data DEM and land utilization data of a research area, and carrying out the preprocessing; terrain correction is carried out on the area with the gradient larger than 25 degrees through an SCS + C terrain correction model; constructing a rock index RCRI and a vegetation index NDRE, and performing normalization; an RCRI-NDRE feature space is constructed; constructing a stony desertification index according to a distance formula from a point to a straight line; high-resolution remote sensing images are used for assisting visual interpretation of stony desertification sample points of different degrees, and grading thresholds between stony desertification of different degrees in the RCRI-NDRE feature space are calculated; and stony desertification grading is carried out based on the grading threshold value, and distribution conditions of stony desertification of different degrees are obtained. According to the method, remote sensing image data are extracted, and the RCRI-NDRE feature space of the rock index RCRI and the vegetation index NDRE is constructed, so that different degrees of stony desertification grading of the target area can be realized.

There is provided a sinkage prediction device including: a variation data acquisition unit 01 analyzing time-series observation data measured by using an earth observation technology and acquiring horizontal direction variation data which is data indicating a horizontal direction variation on a ground surface; and a sinkage prediction unit 20 determining, based on the acquired horizontal direction variation data, whether at least one of the variation in the time axis and the variation in the space axis on the ground surface is in a predetermined state and predicting a location on the ground surface indicating the variation determined to be in the predetermined state as a potential sinkage location where there is a possibility of occurrence of sinkage, wherein it is possible to predict the location where the sinkage may be likely to occur by determining, based on the horizontal direction variation data obtained by analyzing the observation data measured by using the earth observation technology, whether the variation in the time axis or the variation in the space axis on the ground surface is in the predetermined state, by using the predetermined state related to the variation in the horizontal direction viewed on the ground surface where there is a sign that the sinkage is likely to occur as a determination criterion.

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.

The invention relates to the technical field of unmanned aerial vehicle pavement settlement measurement, in particular to an unmanned aerial vehicle-mounted InSAR pavement settlement measurement method, system and device and a medium, and the method comprises the following steps: an unmanned aerial vehicle collects pavement image data; image registration is carried out on the road surface image data, and positions of corresponding pixels in the main image and the auxiliary image are accurately paired; a multi-view processing algorithm is called to process the road surface image data; performing differential interference processing on the road surface image data to generate a differential interference pattern; performing coherence estimation and baseline estimation on the differential interferogram; performing horizon correction processing, filtering processing and phase unwrapping on the differential interferogram in sequence; converting the phase into relative deformation of the pavement; and obtaining a pavement deformation result. According to the invention, high-efficiency and reliable pavement deformation measurement is realized by using the advantage of high spatial resolution of earth observation of the InSAR technology and based on high sensitivity of the X-band synthetic aperture radar full-polarization heavy rail interference measurement technology to fine deformation.

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.

A method of providing Earth observation data comprises: acquiring Earth observation echo data on a first satellite in a low Earth orbit, determining a time required to downlink predetermined data to a ground station on Earth from the first satellite at the current position of the first satellite. If the time required exceeds a predetermined threshold, the predetermined data may be transmitted to another satellite in orbit above Earth using space-to-space radio communication. Alternatively, the first satellite may receive data relating to the current position of another satellite in orbit above Earth, determine a time required to the predetermined data to a ground station via the other satellite at the current position of the at least one other satellite, and if the determined time for the other satellite is less than the time determined for the first satellite, transmit the predetermined data to the other satellite in orbit above Earth using space-to-space radio communication.In some possible implementations, the echo data may be processed on-board the first satellite to generate an image and analysed to determine a portion of interest. Then the predetermined data may comprise a data structure comprising a location of the portion of interest which may be transmitted using the space-to-space link.

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 relates to the technical field of earth observation, and provides an unmanned aerial vehicle hydrothermal carbon and radiation flux synchronous observation and real-time monitoring method which is applied to an unmanned aerial vehicle hydrothermal carbon and radiation flux synchronous observation and real-time monitoring system which comprises an airborne observation system and a ground real-time monitoring system. The ground real-time monitoring system is used for storing preset user parameters, receiving and processing various observation data acquired by the airborne observation system in real time, and displaying hydrothermal carbon and radiation flux observation results in real time; the method comprises the steps of obtaining corresponding observation data based on preset user parameters, and calculating water vapor flux, heat flux, carbon flux, earth surface radiation four-component and photosynthetically active radiation flux based on the observation data. Synchronous observation of four components of water vapor flux, heat flux, carbon flux and earth surface radiation and photosynthetically active radiation flux regional values of an ecological system can be achieved, and basic observation conditions are provided for research of the coupling process of water circulation, carbon circulation and energy circulation of the ecological system.

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 belongs to the field of satellite imaging algorithms, and particularly relates to a method for predicting an earth observation imaging window of an imaging satellite, which is used for imaging an imaging target and comprises the following steps of: 1, roughly searching; step 2, performing imaging estimation; and step 3, performing imaging correction. Compared with the prior art, the imaging satellite task planning method solves the problems that in the prior art, imaging satellite task planning takes little consideration to constraint conditions, calculation errors are large, algorithms are complex, and the imaging effect is poor, and perturbation and errors caused by the factors such as the earth oblateness, the orbit eccentricity rate and the atmospheric resistance are considered in the scheme. And a simple and rapid iterative algorithm is further applied to solve a satellite imaging window, so that the efficiency and the accuracy of an autonomous task are improved.

The invention discloses a remote sensing image semantic segmentation method and device based on a hyperbolic neural network, and the method comprises the steps: constructing a hyperbolic space neural network model based on an encoder-decoder structure, obtaining a plurality of earth observation remote sensing images as a training set, and carrying out the training of the neural network model, and the semantic segmentation model is used for carrying out accurate semantic segmentation prediction on the earth observation remote sensing image. According to the neural network, a convolutional layer, batch normalization, nonlinear activation, residual connection, a Softmax function and a deconvolutional layer are mapped to a hyperbolic space by adopting a Lorentz model so as to learn low-distortion remote sensing image representation; pairwise correlation in a hyperbolic space and a Euclidean space is simulated at the same time in a parallel mode, and hyperspherical context dependency and Euclidean affinity are effectively combined. According to the method, the hierarchical structure and the spatial relationship in the remote sensing image can be more effectively captured, so that more accurate semantic segmentation is realized.

The invention relates to a constellation design method and device based on evolutionary reinforcement learning, equipment and a memory. The method comprises the following steps: converting a constellation design task problem into a multi-satellite earth observation task planning problem; modeling a multi-satellite earth observation task planning problem into a multi-target combinatorial optimization problem; a double-layer circulation model of a double-layer architecture based on a multi-target genetic algorithm and a multi-agent deep reinforcement learning algorithm is adopted to solve a multi-target combinatorial optimization problem, and a constellation deployment design scheme is optimized; the double-layer circulation model is used for realizing task planning of dynamic target observation through inner-layer circulation based on multi-agent reinforcement learning and feeding back awards and loss of a mobile network to outer-layer circulation; through outer layer circulation based on a multi-target genetic algorithm, a group of satellite constellation parameters which are maximum in return and minimum in loss and meet constraint conditions are searched as a target, and a constellation deployment design scheme is optimized. According to the method, the scheme design efficiency and the representation capability of the time window data are improved.

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.

The invention provides a heterogeneous satellite group dynamic planning method for multi-stage tasks, and belongs to the technical field of satellite task planning. The method comprises the following steps: firstly, constructing a satellite group for collaborative observation based on priori knowledge; subpackaging the observation task according to the received observation point type and observation point latitude and longitude information in the standardized task demand; secondly, quickly establishing a mapping relation model between a satellite group and a task package according to task and resource constraints; and finally, by constructing a satellite remote sensing grading task planning model, receiving earth observation tasks, and outputting a satellite task planning scheme. Satellite task planning is decomposed into a service line and a calculation line, the service line carries out satellite group construction and task subpackage, the calculation line carries out earth observation task arrangement, the two lines operate at the same time, the task planning time is effectively shortened by reducing the solution space of the satellite task planning process, and the task planning efficiency is improved. Therefore, more sufficient reaction time is reserved for subsequent tasks.