596 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 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 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 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 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 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 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 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 discloses a vertical rail scanning remote sensing satellite image splicing method and system, and belongs to the technical field of remote sensing image processing. The method is used for solving many challenge problems, including inter-strip positioning deviation, image distortion, scale difference and discontinuous brightness color, faced by an existing remote sensing image splicing method in fusion processing of vertical rail scanning multi-strip images. According to the method, seamless fusion is completed through feature extraction and matching, RANSAC matching optimization, optimal splicing line generation, seam area feathering processing and seamless image output, robust matching and homography transformation are obtained by extracting SIFT features and combining an RANSAC algorithm, and the image registration and splicing precision is remarkably improved; the splicing line is generated by adopting dynamic planning, and feathering processing is performed on the splicing edge, so that seam artifacts and brightness inconsistency caused by multi-strip splicing are effectively reduced, and the continuity and visual consistency of the spliced image are improved. The method is suitable for large-range remote sensing data fusion and application.

The invention discloses a remote sensing satellite network security situation awareness method and system with dual prevention mechanisms, and the method comprises the steps: obtaining multi-source heterogeneous data from a remote sensing satellite ground system network, and generating a structured data set through cleaning, denoising and standardization processing; and according to the structured data set, recording network assets by adopting an asset identification algorithm, and generating a risk thermodynamic diagram of the network assets through a Bayesian network model and an entropy weight method. And according to the risk thermodynamic diagram and the structured data set, adopting a deep learning model to detect abnormal traffic and behaviors, and generating a hidden danger list marked with priorities through a general vulnerability scoring standard and a threat intelligence library. And according to the risk thermodynamic diagram and the hidden danger list, constructing a dynamic security situation map by adopting a graph database and a community discovery algorithm. Therefore, the real-time fusion capability of multi-source heterogeneous data is improved, a dynamic attack chain reasoning mechanism is enhanced, and collaborative decision-making of risk early warning and hidden danger treatment is realized.

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

The invention discloses a typhoon intensity prediction method and device fused with a space-time deep network, and a medium, and the method comprises the following steps: 1), collecting typhoon historical observation data, and obtaining typhoon data; 2) data preprocessing; 3) extracting data features; 4) feature fusion; 5) establishing a typhoon intensity prediction model; 6) model training; and 7) performing prediction by using the trained model, outputting a final prediction value, and restoring a prediction result into an actual typhoon intensity value through reverse normalization processing. The influence of the remote sensing satellite image and the characteristics of the tropical cyclone on the tropical cyclone strength estimation accuracy is considered, the typhoon strength prediction integrated with the spatio-temporal information is carried out, the spatio-temporal representation of the typhoon focus area is enhanced through the integration of the spatial characteristics and the time sequence characteristics of the typhoon, and the accuracy of the strength prediction result is improved.

The invention provides a remote sensing image classification method based on a spatial spectrum attention network, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a hyperspectral remote sensing satellite image, carrying out the data preprocessing, and constructing a hyperspectral remote sensing image classification data set; constructing a classification model DSAFNet based on a double-branch spectrum-space attention fusion network, and taking the classification model DSAFNet as an initial hyperspectral classification model; training the initial hyperspectral classification model through the hyperspectral remote sensing image classification data set to obtain a trained classification network model; obtaining a to-be-classified hyperspectral remote sensing image; and inputting a to-be-classified hyperspectral remote sensing image into the trained classification network model to obtain an image classification result. According to the technical scheme, generation of the hyperspectral remote sensing image classification model with high recognition accuracy and efficiency is realized.

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

A method for automatically monitoring dynamic changes of water blooms based on remote sensing images comprises the following steps: step 1, acquiring remote sensing reflectivity data of a multi-source remote sensing satellite in a screened time sequence range, calculating a spectral index, automatically determining an extraction threshold through pixel gradient statistics and an Otsu algorithm, and accurately identifying a water bloom area; and 2, calculating the water bloom area based on the multi-temporal data, researching the seasonal change, the long-term trend and the spatial distribution rule of the water bloom area in combination with a time sequence analysis method, and evaluating the accuracy of water bloom inversion by comparing the actually measured chlorophyll-a concentration with a water bloom inversion result. The application can monitor the change of the algal bloom area in real time.

The invention relates to the technical field of offshore wind plant area marine environment monitoring, and discloses an offshore wind plant wide-area environment panoramic sensing method and system, and the method comprises the steps: S1, laying seabed bottom type comprehensive observation equipment and low-altitude meteorological observation equipment in a target area of an offshore wind plant; the seabed bottom type comprehensive observation equipment comprises an acoustic Doppler flow velocity profiler, a tide gauge and a temperature-salinity-depth instrument; the low-altitude meteorological observation equipment comprises a meteorological station and a wind measuring radar; s2, collecting field data through the seabed bottom type comprehensive observation equipment and the low-altitude meteorological observation equipment, collecting remote sensing data through the meteorological remote sensing satellite, performing inversion on the remote sensing data to obtain remote sensing inversion data, and transmitting the field data and the remote sensing inversion data to a software processing platform and the like; according to the method and the system, collaborative acquisition, precise processing and fusion prediction of air-sky-sea multi-dimensional data can be realized, and the comprehensiveness and the accuracy of offshore wind plant environment perception are improved.

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

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention discloses a neural network-based remote sensing image farmland plot extraction method, system and product, and the method comprises the steps: firstly obtaining a high-resolution remote sensing satellite image, and carrying out the data preprocessing of the image; inputting the preprocessed image into a high-resolution remote sensing image farmland plot extraction neural network SED-GCNet to obtain a semantic and edge prediction image of the farmland; the SED-GCNet uses a semantic branch encoder, an edge branch encoder and a graph convolution fusion encoder to form a three-encoder, and a semantic branch decoder and an edge branch decoder form a double-decoder; and finally, fusing the semantics of the farmland and the edge prediction image through post-processing to obtain a final farmland plot extraction result. According to the method, the farmland plot extraction precision is improved on the plot scale and the pixel scale.

The invention discloses a mining area surface environment monitoring method, aiming at the current situation of surface ecological environment damage and prevention of a plain mining area, and researching a mining area dynamic change monitoring benchmark; for surface movement and deformation caused by mining area disturbance, a D-InSAR technology is applied to perform surface subsidence monitoring on a deformation block. According to the invention, technical means such as InSAR, GPS, remote sensing satellite images and the like are comprehensively utilized to obtain multi-source data of the mining area surface environment, key technologies of integration and application of the multi-source monitoring data are researched, a mining area surface environment multi-source monitoring technical system is established, and on the basis of multi-source high-resolution remote sensing data, technical means such as image processing, mode recognition and the like are adopted, so that the mining area surface environment multi-source monitoring system is established. Land utilization changes and ecological influence conditions are studied, InSAR and GNSS technologies are applied to perform high-precision monitoring analysis research, the overall monitoring capability, reliability and precision are improved, the technical defects of single technologies for mining area geological environment monitoring are overcome, and the mining area geological environment monitoring system has wide popularization and application prospects.

The invention discloses a flood disaster loss real-time estimation method and system. Relates to the technical field of flood disaster calculation methods. According to the method, remote sensing satellite data is combined, a loss evaluation system of flood disaster-inducing factors and interaction of the flood disaster-inducing factors is constructed, cross-platform efficient processing can be carried out, precision is improved through multi-algorithm fusion, multi-factor dynamic modeling is established, real-time monitoring is carried out, and an open framework is extensible and has high universality; the method comprises the following steps: carrying out statistical analysis before a disaster by utilizing historical disaster situation data, carrying out a damage situation estimation experiment on the basis of a built flood disaster hydrodynamic model under the condition that disaster-affected units are coupled with disaster-affected body types, constructing a loss state vulnerability curve and a loss rate curve through interaction of a plurality of disaster-inducing factors, and calculating the damage situation of the flood disaster according to the loss state vulnerability curve and the loss rate curve. Regional loss rate calculation and loss amount estimation are carried out, the loss amount is rapidly estimated in real time when an actual disaster occurs, a scientific basis is provided for emergency response and risk management, and then decision makers are helped to formulate scientific disaster response measures.

The invention relates to an agile satellite visibility analysis method based on an along-longitude push-broom mode, relates to the technical field of satellite task planning and visibility analysis, and solves the technical problem that an existing target visibility analysis method is difficult to accurately reflect a real visibility state in a satellite continuous imaging process. The method comprises the following steps: acquiring an imaging center point coordinate, an imaging duration, satellite imaging parameter information, satellite orbit information and a target shooting period; the visibility of the imaging center point in the shooting period is calculated, division is carried out according to the number of orbital circles, and a visible information set of the center point is obtained. The agile satellite visibility analysis method based on the along-longitude push-broom mode is suitable for efficient visibility calculation of an agile optical remote sensing satellite and oriented to an along-longitude imaging scene, the visibility state of a target in the imaging process can be accurately evaluated on the basis of considering satellite attitude maneuver characteristics and orbit constraints, and the accuracy of the visibility analysis is improved. Therefore, accurate support is provided for task arrangement and resource scheduling.

The invention discloses a GIS-based arid and semi-arid region farmland irrigation priority dynamic evaluation and zoning method, system, device and medium, and belongs to the technical field of farmland irrigation priority evaluation and zoning, and the method comprises the steps: collecting various types of remote sensing satellite data of a to-be-irrigated region; performing pretreatment; identifying the boundary and range of the to-be-irrigated area, and extracting a remote sensing image of the to-be-irrigated area; based on the remote sensing image of the to-be-irrigated area and the basic geographic information, parameter calculation is carried out, and normalization processing is carried out; performing superposition calculation on each normalized parameter according to a set weight to obtain an irrigation priority index; partitioning the to-be-irrigated area based on the irrigation priority index to generate an irrigation priority map; and judging whether agricultural irrigation management requirements are met or not according to preset conditions. According to the method, temperature vegetation drought indexes, normalized moisture indexes, water resource accessibility and crop economic value parameters are coupled; the irrigation priority index weight can be automatically adjusted according to a season strategy.