720 results about "Remote sensing satellite" patented technology

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

A remote sensing sub-pixel runoff inversion method based on an improved vegetation index method includes: extracting an image of a target water from remote sensing satellite data; calculating a width of the target water using remote sensing data, inversing a width of the target water using remote sensing subpixels, and using one of the widths of the target water with a higher accuracy as a target width; remote sensing and estimating a flow velocity and a depth of the target water, and calculating a runoff of a small river. The improved vegetation index method can decompose pixels to extract a river width with a higher accuracy, which can meet accuracy requirements of some small rivers in mountainous areas, and then effectively applied in flow inversion of the small rivers with high accuracy requirements to improve runoff inversion effect, which has good economy, practicability, flexibility, and a certain physical mechanism.

The invention provides a wetland vegetation intelligent monitoring method and system based on multi-dimensional data fusion, and the method comprises the following steps: collecting a water area unmanned plane image of a wetland water area through a monitoring unmanned plane, and obtaining a water area remote sensing image of the wetland water area through a remote sensing satellite; the water area unmanned aerial vehicle image and the water area remote sensing image are fused into a water area fusion image, the submerged vegetation distribution condition is recognized, and a submerged vegetation monitoring route is planned; the underwater monitoring robot is controlled to continuously detect a wetland water area, and a submerged vegetation standard image and a submerged vegetation multispectral image are collected at the same time; performing image enhancement on the submerged vegetation standard image through a submerged vegetation identification model, and identifying and marking target submerged vegetation from the submerged vegetation enhanced image; selecting a target multispectral region containing target submerged vegetation from the submerged vegetation multispectral image; and vegetation feature data of the target submerged vegetation are calculated based on the target multispectral region. According to the invention, related data of submerged vegetation can be continuously and accurately monitored.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

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 present invention relates to the technical field of soil monitoring, and disclosed is an intelligent soil moisture content monitoring method based on remote sensing technology. The method comprises: a processing terminal dividing a monitoring area into multiple sub-areas, wherein the division of the sub-areas may be by means of equal parts by area, acquiring image data of each sub-area on the basis of a remote sensing satellite, acquiring, on the basis of a sensor device, multi-source data related to the monitoring area and soil moisture content, and performing pre-processing on the image data and multi-source data; comprehensively analyzing the image data and the multi-source data, and evaluating the soil moisture content conditions of each sub-area; and, by means of the soil moisture content conditions of each sub-area, comprehensively analyzing the overall soil moisture content conditions of the monitoring area. The present monitoring method comprehensively analyzes the multi-source data to then evaluate the soil moisture of the monitoring area, such that analysis is more comprehensive, and accuracy is higher.

The invention discloses a dynamic scheduling method and system for a satellite-ground cooperative computing task, and the method comprises the following steps: constructing a satellite-ground cooperative computing system comprising a remote sensing satellite cluster, a satellite server node, a ground server / data center and the like, and enabling the satellite cluster to generate an in-orbit task and to describe dependence through DAG; available computing power, task queues and other resource states of a satellite and a ground server are collected in real time, and global observation is formed; on the basis of an MADRL framework, each node is regarded as an independent agent, a task allocation action is generated through a strategy network, and a collaborative decision is modeled through POMDP; optimizing the task execution sequence of the same node by using an EFT algorithm; issuing a task and monitoring execution; task completion time and energy consumption are collected, and a network optimization strategy is updated through a reward function and experience playback; the scheduling strategy is adjusted through loop iteration, satellite offline and link fluctuation are adapted, and the resource utilization rate and the task processing efficiency are improved.

The invention discloses a priori image constraint-based self-supervised remote sensing image space-spectrum fusion method and device, and relates to the technical field of remote sensing satellites. The method comprises the following steps: acquiring a panchromatic image and a multispectral image; a prior image is generated based on the spatial information of the panchromatic image and the spectral information of the multispectral image, and the prior image is used for constraining the fusion process of the panchromatic image and the multispectral image by a remote sensing image spatial-spectral fusion network; inputting the panchromatic image, the multispectral image and the prior image into the remote sensing image spatial-spectral fusion network, and obtaining a target fusion image output by the remote sensing image spatial-spectral fusion network; by implementing the technical scheme provided by the invention, the generalization ability of the model in processing different sensor data is enhanced, and a more stable fusion effect is realized.

The invention provides a lightweight visible light ship target detection method based on edge feature guidance, and relates to the technical field of ship detection image data processing, and the method comprises the steps: collecting remote sensing satellite images, and carrying out the random distribution of the images after screening and marking, and obtaining a training set and a verification set; the backbone network module comprises a plurality of Conv modules and C3k2 modules which are mutually stacked; the neck module comprises a detail-enhanced convolution module and a hierarchical pyramid module based on dynamic feature aggregation; in the head module, after the features of all detection layers are subjected to independent convolution processing, feature transformation is carried out through a multi-branch detail enhancement convolution module; performing data enhancement on the training set; and obtaining a trained ship target detection model through a back propagation algorithm and a gradient descent optimization method. According to the invention, the lightweight and precision improvement of the detection head are realized, the robustness of the model to the illumination change is enhanced, and the global semantic information and the local detail features are fused to balance the detection of the small target and the large target.

The invention belongs to the field of crop growth monitoring, and particularly relates to a crop irrigation decision-making method and system based on crop growth period water demand characteristics, and the method comprises the steps: firstly obtaining a remote sensing satellite image of a to-be-decided crop soil region, and the segmented growth period and transition time period attributes of each crop; secondly, monitoring the soil water content of a crop soil area to be decided by using a temperature vegetation drought index according to a remote sensing satellite image; and finally, combining the soil water content with the related attributes of the crop growth period, and generating a variable irrigation prescription map by means of a preset variable precise irrigation decision model, so as to realize the irrigation decision of the crop soil area to be decided. By comprehensively utilizing the remote sensing data and the crop growth period information, the irrigation scheme can be reasonably planned according to the water demand characteristics of different crops in different growth stages, the water resource utilization efficiency is effectively improved, and the crop growth is guaranteed.

The invention relates to the technical field of remote sensing spectrums, in particular to an urban black and odorous water body recognition method and system based on remote sensing spectrums. The method comprises the following steps: acquiring infrared spectrum and visible spectrum data based on a remote sensing satellite or an unmanned aerial vehicle, processing the data, eliminating noise and abnormal values in the data, and extracting spectral features to obtain a spectral feature data set; according to the invention, through infrared and visible spectrum data collected by a remote sensing satellite or an unmanned aerial vehicle and through combination with urban water body data of a geographic information system, the dimension and quality of the data are improved, so that monitoring of the urban black and odorous water body is not limited to surface coverage, and sensitivity of data analysis can be accurately regulated and controlled by dynamically adjusting a spectrum band weight; the method has the advantages that the method adapts to variable environmental conditions, recognition precision of various polluted water bodies is improved, continuity and accuracy of long-term monitoring are guaranteed by continuously updating and optimizing the processing flow, and urban water quality management and decision-making processes are effectively supported.

The invention discloses a remote sensing satellite autonomous task planning method and system, and the method comprises the steps: constructing an orbit prediction model, an attitude maneuver model, a load model and other models, and calling the models in a planning process for task sorting, constraint checking and meta-task generation; only point / line / surface related information, load related parameters, task imaging intervals and other necessary task demand parameters need to be injected on the ground, path planning, task decomposition, task sorting, constraint check, autonomous instruction generation and other operations are carried out through an on-satellite autonomous task planning system, and an instruction sequence related to task execution is generated and distributed to a related subsystem. The load subsystem, the data transmission subsystem, the control subsystem, the satellite service subsystem and the like cooperate to complete tasks. After the autonomous task planning system receives task information injected on the ground, satellite pre-planning information can be fed back to the ground in time, and the satellite pre-planning information can be used for ground synchronous task situations.

The invention discloses an atmosphere and sea color parameter joint inversion method under sparse remote sensing satellite data. The method comprises the following steps: firstly, acquiring various satellite remote sensing and sea color data in a unified format; secondly, using the obtained satellite remote sensing and sea color data to construct a sparse atmosphere sea color data set through pixel mask and combining observation geometric information carried by the satellite remote sensing data; then, based on remote sensing waveband data processing, a sparse remote sensing depth inversion network comprising a sparse processing network, an external depth estimation network and a scale fusion network is constructed; and finally, training the sparse remote sensing depth inversion network through the sparse atmospheric sea color data set, outputting an inversion result image, and performing model evaluation. According to the method, joint estimation of atmosphere and sea color multiple parameters can be realized, the inversion precision in a complex ocean and atmosphere interaction scene is improved, and reliable technical support is provided for high-precision atmosphere correction and ocean carbon flux estimation.

The invention discloses a landslide identification and early warning system and method based on remote sensing satellite images, and belongs to the technical field of geological monitoring. The problem that the accuracy and reliability of data are easy to interfere due to the fact that the precision and stability of part of monitoring equipment need to be improved in the prior art is solved, the satellite image of the target mountain area is obtained through the remote sensing satellite, landslide feature extraction is conducted on the satellite image through the synthetic aperture radar interferometry technology, and the landslide feature extraction accuracy is improved. Identifying a potential landslide area in the target mountain area; a landslide risk assessment model is constructed in combination, the landslide risk assessment model is utilized to predict real-time mountain data, the degree of landslide risks existing in a potential landslide area is obtained, and a risk assessment report is generated and fed back to the man-machine interaction unit to give an early warning; through fusion of satellite remote sensing images and ground monitoring data, real-time monitoring and early warning of landslide are realized, accuracy and reliability of identification and early warning are improved, and loss caused by geological disasters is reduced.

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 discloses a vertical rail scanning remote sensing satellite natural multi-coverage image resolution improving method and system, and relates to the field of remote sensing image processing. The method solves the problems that due to the fact that the distance between an existing vertical rail scanning imaging sensor and a ground target is increased, image pixels cover a wider ground range, the actually-measured spatial resolution is reduced, and fine detection and recognition of ships, aircrafts and the ground target cannot be supported. SIFT feature point extraction is carried out, and feature points extracted from different images are matched by using a Euclidean nearest distance matching strategy; taking one registered image as a reference, and constructing an initial high-resolution image through up-sampling; establishing an imaging degradation model from a high-resolution image to a low-resolution observation image; calculating a residual image of the simulation image and the real observation image; and back-projecting the residual error back to the high-resolution image space, and updating high-resolution image estimation.

The invention provides a low-orbit satellite internet heterogeneous federated learning method based on dynamic aggregation, and relates to the technical field of federated learning, and the method comprises the following steps: S1, a central server distributes a local training task for each low-orbit remote sensing satellite through a data set sharing method; s2, the satellite participating in local training receives the shared data and the parameters of the current global model, and uploads the updated local model parameters to a central server; s3, the central server calculates a weighted value according to the frequency at which each satellite participates in aggregation and the time at which the model participates in aggregation last time; judging whether convergence occurs based on the weighted value; s4, outputting a current global model; and S5, replacing the initial global model in the step S1 with the current global model, executing the steps S1 to S3 again until convergence, and outputting the current global model. According to the method, the efficiency, precision and stability of model training can be improved, and the generalization ability of a global model is enhanced, so that cooperative work between different satellites and efficient convergence of the model are ensured.

The invention discloses a remote sensing image hue information keeping method based on a diffusion super-division model, and the method comprises the steps: constructing a data set, carrying out the image preprocessing of an original high-resolution image, obtaining a feature image after preprocessing, and dividing the feature image into a test set and a verification set; constructing a diffusion super-division model; designing a residual function of the diffusion super-division model; performing cross reference on the training set and the verification set, combining a residual function, and optimizing model parameters by continuously minimizing the residual function to train a diffusion super-division model to obtain a trained diffusion super-division model; deploying the trained diffusion super-division model on a remote sensing satellite to form a hue information keeping module based on the diffusion super-division model; a remote sensing satellite generates an original image needing to be processed, the original image is input into a hue information keeping module based on a diffusion super-division model, model reasoning is conducted through the trained diffusion super-division model, a high-resolution image is obtained from a low-resolution image in a super-division mode, and image details are restored on the basis of keeping hue information.

The invention relates to the technical field of image defogging, discloses a multi-spectral remote sensing satellite image defogging method and system, and particularly provides a multi-spectral remote sensing satellite image defogging scheme which combines an atmospheric scattering model and deep learning and is self-adaptively optimized for a specific application scene. The transmissivity and the atmospheric light value are estimated through an atmospheric scattering model, and physical constraints are provided for the deep learning network. In the scene recognition stage, the scene type of the image is accurately recognized through multi-scale feature extraction, adaptive feature aggregation and a self-supervised learning mechanism. In the defogging stage, the physical constraint weight and the multi-band fusion weight are dynamically adjusted according to the identified scene, and it is ensured that the defogging result conforms to the atmospheric physical law and scene features. Therefore, detail retention, color rendition and physical consistency of the defogged image are effectively improved, the adaptive capacity of the deep learning network to complex scenes and diversified remote sensing images is enhanced, scene recognition errors are effectively reduced, and the quality and consistency of defogging results are further improved.

The invention discloses a building height extraction method, device and equipment, a medium and a program product, and the method comprises the steps: carrying out the matching positioning of a remote sensing satellite image of a target region through a digital elevation model and a digital orthoimage of the target region; then obtaining the bottom elevation of a target building in the target area through the digital elevation model; according to the bottom elevation, the vector contour of a target building in a digital line map is registered with the digital orthoimage and the remote sensing satellite image after matching and positioning, and the absolute roof elevation of the target building is obtained; and finally, determining the building height of the target building according to the bottom elevation and the absolute roof elevation, thereby realizing accurate registration of the vector contour of the building and improving the automatic extraction accuracy of the building height data of the building.

The invention discloses an optical remote sensing satellite high and low frequency relative radiation error correction method and system, and the method comprises the steps: S1, carrying out the systematic high frequency radiation error calibration of a remote sensing image, so as to eliminate stripe noise; s2, performing frequency domain decomposition on the image after high-frequency correction, and separating high and low frequency components; s3, calculating a global statistical moment of the low-frequency component as a correction reference; s4, calculating a local statistical moment of the low-frequency component column by column; s5, calculating an independent correction coefficient for each column according to the global and local statistical moments; s6, correcting the low-frequency component by using the correction coefficient; and S7, recombining the corrected low-frequency component with the original high-frequency component to generate a final image. The method is suitable for improving the quality of the remote sensing image with multiple composite radiation errors, high-frequency stripes and low-frequency brightness gradient can be effectively eliminated, original texture details of ground features are reserved to the maximum extent in the correction process, and the radiation fidelity is guaranteed.

The invention discloses a multi-satellite cooperative continuous observation and real-time data unloading method for a dynamic target, relates to the technical field of satellite communication, and discloses the multi-satellite cooperative continuous observation and real-time data unloading method for the dynamic target, which comprises the following steps: constructing a spatial information network architecture based on the dynamic target, a remote sensing satellite and a communication satellite; establishing an observation constraint, a link constraint and a dependency relationship constraint between observation and links based on a spatial information network architecture; constructing a target integer programming problem according to the position change information of the dynamic target, the observation constraint, the link constraint and the dependency relationship constraint of the observation and the link, and solving the target integer programming problem to obtain a remote sensing satellite switching and data unloading scheme; and completing continuous observation and data unloading of the dynamic target according to the relay satellite switching condition and the remote sensing satellite switching and data unloading scheme. According to the scheme, the uninterrupted communication coverage of the remote sensing satellite can be ensured to support the real-time unloading of the data while the dynamic target is continuously observed.

The invention relates to a remote sensing satellite group adaptive task planning method and system based on a large language model, and the method comprises the steps: building an optimization model based on decision variables, constraint conditions and optimization targets of remote sensing satellite group task planning; generating an initial task planning strategy by adopting a greedy algorithm based on the optimization model; performing iterative optimization on the initial task planning strategy through a large language model, generating a new round of task planning strategy in each iteration, performing scoring by taking an optimization target as an evaluation function, stopping optimization until a preset number of iteration optimization times is reached, and outputting the task planning strategy with the highest score; and based on the task planning strategy with the highest score, performing remote sensing satellite group adaptive task planning. According to the method, multi-satellite distributed task planning can be effectively carried out on the dynamic task, the task income is improved, and the satellite imaging time consumption is reduced.

The invention provides a multi-satellite task scheduling method and system based on uniform revisit routine and emergency observation integration, and belongs to the technical field of remote sensing satellite task planning and autonomous scheduling. The method comprises the following steps: dividing a ground area into a plurality of observation grids based on orbit parameters and imaging parameters of a remote sensing satellite; traversing orbit time sequences of all satellites under a unified time axis, and dynamically planning conventional observation tasks of all satellites according to sub-satellite point positions of the satellites and grid observation marks; when an emergency observation request of a specific area is received, performing emergency observation task replanning according to a current satellite conventional observation task; and obtaining a satellite joint scheduling plan including a conventional observation task and an emergency observation task. The method has the advantages of high scheduling fairness, controllable attitude adjustment, high response speed and the like, and is suitable for an efficient task joint scheduling scene of a large-scale low-orbit remote sensing constellation in conventional and emergency states.

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 discloses a remote sensing image low-frequency noise correction method and system, relates to the field of remote sensing image processing, realizes accurate removal of stripe noise under the condition of reducing satellite image detail loss, and relieves the problems of unstable correction effect, easiness in losing image details, difficulty in accurately depicting prior characteristics and the like of the existing remote sensing image low-frequency noise correction technology. The remote sensing image low-frequency noise correction process takes a column mean value or a row mean value as a basic processing unit; fine noise intensity calibration is carried out; low-frequency noise of the image is corrected in a mean value compensation mode, and detail loss of the corrected image is avoided. The method is suitable for multispectral and panchromatic remote sensing images of a remote sensing satellite, and is not influenced by ground feature types.

The invention relates to a system digital three-dimensional situation display and rendering system comprising an environment visualization module used for collecting environment information data from various data sources and integrating the collected scene information data into the system; the scene visualization module is used for displaying the satellite space environment, the operation effect, the search action and the sensor load by using the scene information data; the spacecraft visualization module is used for checking detailed parameters and detailed information of the spacecraft; and the three-dimensional earth scene rendering module is used for digital earth rendering, and scheduling visual area data based on a terrain scheduling algorithm by acquiring image data and elevation data of a map. According to the method, the understanding and sensing ability of a user to the remote sensing satellite situation is improved through the three-dimensional display technology, the accuracy and scientificity of space situation analysis are enhanced, the limitation of a traditional simulation system in the aspect of space situation display is solved, and situation display is more visual.

The invention discloses a high-orbit remote sensing satellite moving target tracking task-oriented search tracking efficiency evaluation method, which comprises the following steps of: determining subtasks of an efficiency evaluation index system according to task composition of a high-orbit satellite moving target tracking task; aiming at the subtasks of the efficiency evaluation index system, respectively designing corresponding efficiency evaluation schemes; according to the established evaluation index system, a performance index and bottom layer index evaluation data acquisition experiment is designed, and a corresponding evaluation index calculation method is established; according to the evaluation index calculation method and the experimental data, evaluation indexes of all the bottom layer indexes are obtained through calculation, and the high-orbit satellite moving target tracking task is evaluated. Based on the design of task classification and performance indexes, the output of the working sequence of the satellite system is converted into evaluation of the performance indexes. Aiming at the specific task of searching and tracking of the moving target tracking task, the verification of the overall system efficiency of the satellite system can be realized, and a reference is provided for comparison and improvement of a high-orbit satellite moving target tracking method.