An optical satellite emergency response decision support method and service platform suitable for flood disaster monitoring

Abstract

This invention discloses an optical satellite emergency response decision-making support method and service platform suitable for flood disaster monitoring. It relates to the field of satellite remote sensing technology and addresses the problems of slow response speed, low utilization efficiency of platform constellation resources, and low data quality of acquired satellite imagery in existing flood disaster monitoring platforms. The optical satellite emergency response decision-making support method involves: obtaining flood emergency mission instructions based on hardware infrastructure and user needs; selecting a data transmission method to execute satellite imaging tasks and obtain satellite image data; preprocessing the acquired satellite images to obtain digital orthophoto (DOM) results; conducting various thematic analyses to obtain corresponding analysis results; and delivering the mission according to requirements. An optical satellite emergency service platform includes a basic layer, a data layer, a service layer, and an application layer. The method described in this invention is applicable to the intelligent scheduling and tiered response to nationwide flood emergency needs.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing technology, specifically to the field of flood disaster detection. Background Technology

[0002] Floods, as an extremely severe natural disaster, are highly destructive and pose a serious threat to human life and property. Floods cause widespread damage and enormous losses, with incalculable harm to the human living environment and socio-economic development. Furthermore, flood-affected areas are often difficult to access, creating a real need for water resources and emergency management departments to obtain timely and safe imagery data of these areas. However, most current flood monitoring platforms, when utilizing satellite resources for observation and planning, suffer from a series of problems due to the shortage of satellite resources in terms of both quantity and quality, as well as the low efficiency of satellite data transmission and processing. These problems include slow response times, low utilization efficiency of platform constellation resources, and low quality of acquired satellite imagery data.

[0003] In the prior art, Chinese patent document CN103376450A discloses a "multi-satellite resource planning system and method," such as... Figure 1 As shown, a multi-satellite resource planning system was constructed to address various disasters. This system utilizes a mapping processor to link the payload database with the disaster type database, a planning processor to link the mapping processor with the satellite database, and an interactive interface, enabling interaction with the user side. However, this technical solution does not address the timeliness of emergency response; the resolution of multi-source satellite imagery is limited to below 30 meters; and its planning processing function is limited to extracting and prioritizing available satellite resources, without covering planning for multiple missions or post-processing of satellite imagery related to various disasters, especially floods.

[0004] In summary, existing flood disaster monitoring platform technologies suffer from slow response times, low efficiency in utilizing platform constellation resources, and low data quality in acquired satellite imagery. Summary of the Invention

[0005] This invention aims to address the problems of slow response speed, low utilization efficiency of platform constellation resources, and low data quality of acquired satellite imagery in existing flood disaster monitoring platform technologies. This invention provides the following solution: Option 1: A decision-making support method for emergency response plans using optical satellites, comprising the following steps: Step S1: Obtain flood emergency task instructions based on hardware infrastructure and user needs; Step S2: Based on the flood emergency mission instruction, select the data transmission mode to perform the satellite imaging mission and obtain satellite image data, which includes satellite images and multi-source data; Step S3: Preprocess the obtained satellite images to obtain the digital orthophoto (DOM) results. The preprocessing includes production and quality inspection; The production process includes radiation treatment, decryption and decompression, geometric positioning, image matching, geometric correction, and light and color homogenization. The quality inspection includes image mosaicking and quality check; Step S4: Perform various thematic analyses based on the multi-source data and historical data to obtain corresponding analysis results; The thematic analysis includes flood extent extraction, auxiliary forecasting and early warning, and disaster loss assessment. Step S5: Based on the obtained digital orthophoto DOM results and analysis results, deliver the task according to the task requirements.

[0006] Furthermore, in one embodiment of the present invention, the data transmission method in step S2 is real-time data transmission or in-cycle data transmission. The real-time data transmission is used to acquire image data within a 600 km range in the direction of the ground station's descent. The data transmission time is within 1 hour, and it can provide more than 60 seconds of data transmission. The current number of revolutions is used to image an area within a 600 km radius centered on the ground station.

[0007] Furthermore, in one embodiment of the present invention, the flood range extraction in step S4 is as follows: selecting a cloudless sub-meter high-resolution satellite remote sensing image before the disaster to obtain the water distribution before the flood, and a sub-meter high-resolution satellite remote sensing image with less cloud cover after the disaster to obtain the water distribution after the flood, and combining Deeplabv3 and a semantic segmentation framework to extract the flood inundation area.

[0008] Furthermore, in one embodiment of the present invention, the auxiliary forecasting and early warning in step S4 is to make short-term, medium-term and long-term forecasts of rainfall in the region based on GFS, GRAPES, SMS or NB hydrological forecasting models, and to display the surface rainfall and rainfall isosurface in real time.

[0009] Furthermore, in one embodiment of the present invention, the disaster loss assessment in step S4 is to establish a multimodal flood disaster analysis model based on multi-source data, quickly delineate key disaster-stricken areas based on knowledge graph technology, and automatically analyze the damage and disaster repair status based on artificial intelligence technology.

[0010] Option 2: An optical satellite emergency service platform, the optical satellite emergency service platform comprising: The base layer is used to produce data and also to interact with the data layer. The data layer is used to store and query historical data, connect with user needs, deliver data to users, and interact with the service layer. The service layer is used to interact with the application layer to obtain disaster analysis results and corresponding emergency tasks. It is also used to classify emergency tasks and to activate corresponding emergency service modes based on the classification results. The application layer is used to process real-time disaster satellite imagery and also to assist in the analysis to obtain disaster analysis results.

[0011] Furthermore, in one embodiment of the present invention, the original data source for the production data of the base layer is real-time satellite imagery data obtained by the "Jilin-1" satellite.

[0012] Furthermore, in one embodiment of the present invention, the classification of emergency tasks specifically involves dividing emergency tasks into five levels: 1A, 1B, 2, 3, and 4, based on user needs and available hardware. The 1A indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware foundation is an emergency platform, a production system and a mobile station or a dedicated data line, and the delivery time for user needs is within 1 hour; The 1B level indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware infrastructure is an emergency platform and a dedicated data line, and the delivery time for user needs is within 3.5 hours; Level 2 indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform, production system and mobile station or data line, and the delivery time for user needs is within 1.5 hours; The Level 3 designation indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform and dedicated data lines, and the delivery time for user requirements is within 4 hours. Level 4 indicates that the hardware foundation is an emergency platform, and the delivery time for user needs is within 18 hours.

[0013] Furthermore, in one embodiment of the present invention, the corresponding emergency service mode specifically refers to: The emergency service mode corresponding to Level 1A is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 1B is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 2 is as follows: the response time is within 1 hour, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 3 is as follows: the response time is within 2 hours, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 4 is as follows: response time is within 4 hours, shooting time is the next day, and data transmission method is conventional data transmission.

[0014] The optical satellite emergency response decision-making support method and service platform for flood disaster monitoring described in this invention is based on the Jilin-1 satellite constellation and is designed to meet the rapid response needs of flood disasters. It aims to address the problems of slow response speed, low utilization efficiency of platform constellation resources, and low data quality of acquired satellite imagery in existing flood disaster monitoring platforms. Specific beneficial effects include: 1. The optical satellite emergency service platform described in this invention uses high-revisit-cycle, high-precision sub-meter-level satellite remote sensing images from the Jilin-1 satellite constellation as the main data source to construct an emergency service platform for flood disasters. Compared with similar data sources, the sub-meter-level data accuracy, hourly revisit cycle, and 10 Gbps downlink rate have significant advantages in accuracy and speed.

[0015] 2. The optical satellite emergency service platform described in this invention, based on the urgency of flood disaster response needs, has established a flood disaster task management subsystem for flood emergency needs. Combined with the user's hardware foundation, it performs task classification and configuration, which can allocate satellite resources more efficiently and visually to meet flood emergency needs. Moreover, through the combination of software and hardware, the required tasks can be delivered in as little as one hour.

[0016] 3. The optical satellite emergency service platform described in this invention, targeting the formation mechanism, actual impact, and damage of floods, utilizes semantic segmentation models, hydrological forecasting models, and multimodal analysis models from deep learning methods to achieve thematic analysis of flood range, early warning and forecasting, and disaster situation auxiliary judgment, respectively, and can provide users with more diverse data products and clearer data results.

[0017] 4. The optical satellite emergency service platform described in this invention comprises seven subsystems: a historical archive data query subsystem, a flood disaster demand reporting subsystem, a flood disaster task management subsystem, a data reception task scheduling subsystem, a standard data processing subsystem, a disaster auxiliary analysis subsystem, and a data delivery subsystem. This enables rapid response to flood disaster events and facilitates data processing and transmission. Furthermore, a robust interactive interface has been developed to facilitate training, use, and task request submission. The platform also features open web-based access, allowing frontline disaster relief personnel to interact directly with the platform, reducing communication and usage barriers, and ensuring its practicality.

[0018] 5. The optical satellite emergency service platform described in this invention addresses the challenges of existing optical satellite emergency service platform technologies in obtaining information about disaster-stricken areas before and after floods, and the inability of existing satellite imagery platforms to provide convenient and rapid responses to flood emergency imaging needs. Therefore, this invention leverages the high-resolution, high-revisit-cycle imagery data provided by the "Jilin-1" satellite constellation to build an intelligent, tiered optical satellite service platform for responding to nationwide flood emergency needs. An automated standard data production system and various automated flood-related thematic analysis modules ensure the timeliness of data production and thematic analysis. Through the combination of the aforementioned hardware and software and intelligent automated processing technologies, the platform guarantees timely response, enabling the completion of image acquisition and delivery within hours, timely detection of flood-inundated areas, and comparative analysis with historical data to assess disaster development trends. This provides strong support for disaster prevention and mitigation decision-making, giving it an irreplaceable advantage in flood monitoring and effectively improving flood response capabilities.

[0019] This invention is applicable to the emergency needs of intelligent scheduling and tiered response to floods across the country. Attached Figure Description

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of the multi-satellite resource planning system and method described in the background section.

[0021] Figure 2 This is a flowchart of the optical satellite emergency response auxiliary decision-making method described in Implementation Method 1.

[0022] Figure 3 This is a schematic diagram of the flood range extraction described in Implementation Method 3.

[0023] Figure 4 This is a schematic diagram of the auxiliary forecast and early warning system described in Implementation Method 4.

[0024] Figure 5 This is a schematic diagram of the disaster loss assessment described in Implementation Method 5.

[0025] Figure 6 This is an architecture diagram of the optical satellite emergency service platform described in Implementation Method Six.

[0026] Figure 7 This is a schematic diagram illustrating the query of historical data as described in Implementation Method Six.

[0027] Figure 8 This is a schematic diagram of the visible satellite transit described in Implementation Method Six.

[0028] Figure 9This is a schematic diagram illustrating the activation of the corresponding emergency service mode based on the graded results described in Implementation Method Nine.

[0029] Figure 10 This is a schematic diagram of the number of revolutions transmitted as described in Implementation Method Nine.

[0030] Figure 11 This is a schematic diagram of the real-time data transmission described in Implementation Method Nine. Detailed Implementation

[0031] Various embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The embodiments described with reference to the drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0032] Implementation Method 1: The optical satellite emergency response plan auxiliary decision-making method described in this implementation method, such as... Figure 2 As shown, it includes the following steps: Step S1: Obtain flood emergency task instructions based on hardware infrastructure and user needs; Step S2: Based on the flood emergency mission instruction, select the data transmission mode to perform the satellite imaging mission and obtain satellite image data, which includes satellite images and multi-source data; Step S3: Preprocess the obtained satellite images to obtain the digital orthophoto (DOM) results. The preprocessing includes production and quality inspection; The production process includes radiation treatment, decryption and decompression, geometric positioning, image matching, geometric correction, and light and color homogenization. The quality inspection includes image mosaicking and quality check; Step S4: Perform various thematic analyses based on the multi-source data and historical data to obtain corresponding analysis results; The thematic analysis includes flood extent extraction, auxiliary forecasting and early warning, and disaster loss assessment. Step S5: Based on the obtained digital orthophoto DOM results and analysis results, deliver the task according to the task requirements.

[0033] In this embodiment, the satellite image data in step S2 also includes various types of satellite image parameter information, such as shooting time, orbit information, side swing angle, solar altitude angle, and cloud cover data.

[0034] In this embodiment, the multi-source data mentioned in step S2 includes digital elevation, meteorological rainfall, and land cover classification data.

[0035] In this embodiment, the quality inspection standard in step S3 is the absence of obvious noise, spots, bad lines, and spectral overflow.

[0036] In this embodiment, step S5, which describes the requirement of "according to task requirements," refers to the corresponding requirements of the emergency service mode activated after classifying emergency tasks in the service layer as described in embodiment six.

[0037] The satellite imagery should be clear, with moderate contrast, saturated and vivid colors, consistent hue, rich detail, and the ability to identify small ground features with a resolution appropriate to the ground surface, thus meeting the interpretation requirements.

[0038] The satellite imagery should ensure that key survey areas are cloud-free. This is because weather conditions are unpredictable during floods, and ensuring cloud-free coverage in key survey areas allows for priority data production and guarantees timely delivery.

[0039] The satellite imagery should be in the CGCS 2000 plane coordinate system, projected in Gaussian Skrig 3-degree zones, with the 1985 National Height Datum as the elevation datum, and delivered data in GeoTIFF format by default.

[0040] The optical satellite emergency response decision-making method described in this embodiment is a method for rapidly responding to and acquiring satellite images of flood-affected areas. It mainly includes five parts: command interaction, mission planning, data production, thematic analysis, and mission delivery. Based on the user's hardware equipment and submitted emergency requirements, an emergency imaging mission is planned. Then, the ground station receives the data and produces and transmits processed satellite images. With the aid of flood-related thematic analysis, a flood emergency response plan is provided to the user within a timely timeframe.

[0041] Implementation Method Two: This implementation method further defines the optical satellite emergency response decision support method described in Implementation Method One. In this implementation method, the data transmission method in step S2 is either real-time image data transmission or intra-cycle data transmission. The real-time data transmission is used to acquire image data within a 600 km range in the direction of the ground station's descent. The data transmission time is within 1 hour, and it can provide more than 60 seconds of data transmission. The current number of revolutions is used to image an area within a 600 km radius centered on the ground station.

[0042] In this embodiment, the real-time data transmission is as follows: Figure 11 As shown.

[0043] In this embodiment, the data transmission has a certain probability of imaging an area within a radius of 600 km-1000 km; and a small probability of imaging an area within a radius of 1000 km-1500 km. The data transmission time is within 0.5 hours, and the request needs to be submitted one hour before the measurement and control. The number of cycles is transmitted as follows Figure 10As shown, the blue line represents the satellite orbits that the ground station can plan, upload, transmit, process, and distribute; the green line represents the image range that can be captured under the corresponding orbit; the red, yellow, and black circles correspond to the 600, 1000, and 1500 km buffer zones of the ground station, respectively. The 600 km buffer zone corresponds to the farthest distance that the satellite can capture under maximum side-swing conditions, so the satellite can complete image capture within a minimum 600 km range in the descent direction when passing the ground station; the 1000 km buffer zone corresponds to the maximum range of satellite laser data transmission, and data transmission must be completed within this range during each orbit; the 1500 km buffer zone corresponds to the maximum range that the platform can plan for capture and upload data, thereby capturing the corresponding satellites in the responsive mission orbits.

[0044] This implementation further defines the data transmission method and provides examples to illustrate it. The data transmission method for emergency satellite imaging is subject to factors such as transmission bandwidth and response efficiency. In single-orbit data transmission, the main steps of command uploading, satellite imaging, and data transmission need to be completed within a single orbital cycle. This places high demands on factors such as the efficiency of platform command issuance, the speed of satellite imaging, the bandwidth of data transmission, and the reliability of the satellite itself. Based on the platform's intelligent task allocation and the 10Gbps-level satellite-to-ground laser communication efficiency of the Jilin-1 satellite constellation, the response efficiency can be effectively improved based on the single-orbit data transmission function. Real-time imaging and transmission, on the other hand, focuses more on rapidly transmitting onboard image data to ground stations for processing. This mainly relies on the constellation's 100Gbps-level inter-satellite laser communication capability and the construction of ground stations to achieve the fastest data transmission given that the captured data has already been acquired.

[0045] Implementation Method 3: This implementation method further defines the optical satellite emergency response decision-making aid method described in Implementation Method 1. In this implementation method, the flood range extraction in step S4 is performed by selecting a cloudless, sub-meter-level high-resolution satellite remote sensing image before the disaster to obtain the water distribution before the flood, and a sub-meter-level high-resolution satellite remote sensing image with less cloud cover after the disaster to obtain the water distribution after the flood. The flood inundation area is extracted by combining Deeplabv3 and a semantic segmentation framework.

[0046] This embodiment further defines step S4 and provides an example of it. The flood extent extraction method targets sub-meter resolution satellite remote sensing imagery and combines Deeplabv3 and a semantic segmentation framework to achieve accurate boundaries and high spatial resolution for flood-inundated areas. The Deeplabv3 network incorporates a feature fusion strategy, allowing it to retain more shallow information. It also incorporates depthwise separable convolutions to optimize the segmentation network's speed, ultimately obtaining accurate segmentation boundaries. This method utilizes a semantic segmentation model within deep learning algorithms to extract flood extent based on high-precision optical remote sensing data, and the results are as follows... Figure 3 As shown, (a) series represents the water body extraction results before the flood, where the water color is relatively clear, while (b) series represents the water body impact after the flood, where the water color is turbid. Therefore, two sets of water body extraction algorithms based on different scenarios are needed for extraction. After extraction, the results can be obtained by vector post-processing to obtain the flood extraction results shown in (c) series.

[0047] Implementation Method 4: This implementation method further defines the optical satellite emergency response auxiliary decision-making method described in Implementation Method 1. In this implementation method, the auxiliary forecasting and early warning in step S4 is to make short-term, medium-term and long-term forecasts of rainfall in the region based on GFS, GRAPES, SMS or NB hydrological forecasting models, and to display surface rainfall and rainfall isosurface in real time.

[0048] This embodiment further defines step S04 and provides an example of it. The auxiliary forecasting and early warning, based on the hydrological forecasting model, can effectively estimate the extent of flooding before it occurs, allowing for necessary disaster prevention measures to minimize losses. The result is as follows: Figure 4 As shown, the rainfall in different regions is distinguished by isometric rainfall and rainfall isosurface, providing a reference basis for flood prevention and control.

[0049] Implementation Method 5: This implementation method further defines the optical satellite emergency response decision-making aid method described in Implementation Method 1. In this implementation method, the disaster loss assessment in step S4 is to establish a multimodal flood disaster analysis model based on multi-source data, quickly delineate key disaster-stricken areas based on knowledge graph technology, and automatically analyze the damage and disaster repair situation based on artificial intelligence technology.

[0050] This implementation further defines step S4 and provides an example. The disaster loss assessment is based on a multi-modal flood disaster analysis model established from multiple sources of data, including high-resolution land cover classification data, housing, road and population data, DOM data, and planning data. It also uses knowledge graph technology to quickly delineate key disaster-stricken areas and artificial intelligence technology to automatically analyze the damage and disaster recovery status. Taking the Dongting Lake dam breach flood disaster as an example, the damage to different land types caused by the flood disaster is shown in the following results. Figure 5 As shown, the main land use types affected by flooding are farmland and buildings. The impact on different land use types depends on the inundation depth, duration, and specific features. For crops, inundation depth and duration determine whether there is a complete crop failure; for buildings, they determine the degree of safety degradation. The impact on existing water bodies depends on whether they are fishponds. In summary, the flooded area can visually demonstrate the main impacts, and combined with the receding time and inundation depth, the extent of flood damage can be quickly estimated.

[0051] In summary, implementation methods three to five achieve a series of technical objectives, including flood inundation analysis and early warning forecasting.

[0052] Implementation Method Six: An optical satellite emergency service platform as described in this implementation method, the optical satellite emergency service platform having interactive functions, response functions, analysis functions, and delivery functions, such as... Figure 6 As shown, it includes: The base layer is used to produce data and also to interact with the data layer. The data layer is used to store and query historical data, connect with user needs, deliver data, and interact with the service layer. The service layer is used to interact with the application layer to obtain disaster analysis results and corresponding emergency tasks. It is also used to classify emergency tasks and to activate corresponding emergency service modes based on the classification results. The application layer is used to process real-time disaster satellite imagery and also to assist in the analysis to obtain disaster analysis results.

[0053] In this embodiment, the data layer includes: Historical data query subsystem, such as Figure 7 As shown, it is used to save and query historical data; The historical data includes pre-disaster archived data, socio-economic data, basic geographic information data, and satellite orbit data; The flood disaster demand uploading subsystem is used to connect with user needs; The user requirements for integration include receiving the overall task list and detailed requirements indicators provided by the user; and providing the user with satellite resource plans and receiving site parameters. The data delivery subsystem is used for delivering data. The delivered data includes automated push notifications, visual satellite overpasses, early warnings of hydrological disasters, and a visual interface to display task progress and key milestones.

[0054] The visible satellite transit, such as Figure 8 As shown; The flood disaster demand uploading subsystem preferably utilizes a user-friendly and simple UI to help users quickly locate the spatiotemporal area they need to monitor.

[0055] The preferred approach is to utilize the satellite transit visualization and historical data query to facilitate the selection of satellites in suitable orbits and the screening of appropriate pre-disaster images for later analysis.

[0056] In this embodiment, the service layer includes: The flood disaster task management subsystem is used to classify emergency tasks. The data receiving task scheduling subsystem is used to activate the corresponding emergency service mode based on the classification results.

[0057] The flood disaster task management subsystem is the core function of the platform, enabling tiered control of emergency responses and effectively allocating satellite and platform resources to prioritize the capture and processing of more urgent disaster needs. The data reception task scheduling subsystem links the platform, satellites, and the data processing center. Through the reception of satellite data and the analysis of corresponding scheduling instructions from the platform, it allocates resources from the data processing center for the production of data and thematic results.

[0058] In this embodiment, the application layer includes: A standard data processing subsystem is used to process the satellite imagery data; The disaster-assisted analysis subsystem is used to obtain disaster analysis results based on the satellite imagery data.

[0059] The standard data processing subsystem is preferably used for basic data production, including preprocessing tasks such as radiometric correction, geometric correction, atmospheric correction and image fusion of received satellite image data, as well as the production and delivery of thematic maps.

[0060] The disaster-assisted analysis subsystem is used for in-depth mining of image data, including but not limited to functions such as extracting flood-covered areas, assessing disaster losses, and assisting in forecasting and early warning.

[0061] The optical satellite emergency service platform described in this embodiment uses a four-layer structure linked by three main functions to integrate multiple functions and ensure the speed and quality of response to flood disasters. The foundation layer consists of various hardware infrastructures and is the basis for the platform's operation and response; the data layer aggregates and delivers all data, and emergency response requirements for disasters are also uploaded here; the service layer is the key technology, which improves the platform's timeliness by planning emergency tasks; and the analysis layer provides specific services to the platform.

[0062] Implementation Method Seven: This implementation method further defines the optical satellite emergency service platform described in Implementation Method Six. In this implementation method, the raw data source for the basic layer used to produce data is real-time satellite imagery data obtained by the "Jilin-1" satellite.

[0063] In this embodiment, the hardware infrastructure also includes computing clusters, databases, the Internet, dedicated lines, and ground stations.

[0064] Based on the construction of various hardware infrastructures in the sky-ground environment and the platform's automated processing capabilities, the ultra-high speed and high resolution remote sensing image transmission between satellites and between satellites and ground can reach 100 and 10 Gbps respectively, ensuring the timeliness of data transmission and delivery.

[0065] This implementation further defines the basic layer and provides an example. The "Jilin-1" satellite constellation boasts high resolution, wide coverage, and strong timeliness, enabling 38-40 revisits per day to any location globally. The high-precision satellite remote sensing image platform, by acquiring real-time, large-scale, high-resolution remote sensing data, can quickly and accurately capture dynamic changes in flood-affected areas, providing a solid data foundation for flood monitoring. Furthermore, the provided image resolution is sub-meter level, achieving a response time of one hour, better meeting the practical needs of flood disaster emergency response in terms of data quality and timeliness.

[0066] Implementation Method Eight: This implementation method further defines the optical satellite emergency service platform described in Implementation Method Six. In this implementation method, the classification of emergency tasks specifically involves dividing emergency tasks into five levels: 1A, 1B, 2, 3, and 4, based on user needs and available hardware. The 1A indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware foundation is an emergency platform, a production system and a mobile station or a dedicated data line, and the delivery time for user needs is within 1 hour; The 1B level indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware infrastructure is an emergency platform and a dedicated data line, and the delivery time for user needs is within 3.5 hours; Level 2 indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform, production system and mobile station or data line, and the delivery time for user needs is within 1.5 hours; The Level 3 designation indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform and dedicated data lines, and the delivery time for user requirements is within 4 hours. Level 4 indicates that the hardware foundation is an emergency platform, and the delivery time for user needs is within 18 hours.

[0067] In this embodiment, users equipped with mobile stations can choose to quickly receive and generate data through the mobile station, while others can use the platform's built-in production system for production and transmission.

[0068] In this embodiment, the hardware infrastructure refers to all the external hardware devices used to provide emergency services for the emergency service platform, such as the emergency platform, production system, mobile station, and dedicated data line.

[0069] In this implementation, to address different emergency task requirements, it is preferable that more urgent Level 1A and Level 2 response tasks with local equipment can be delivered through standard data processing. For other tasks requiring disaster-aided analysis, such as flood area and disaster loss assessment, the data needs to be processed by the disaster-aided analysis subsystem before delivery to the user. Without specialized analysis and with local production capabilities, a Level 1A emergency response can achieve a response time of less than one hour. With specialized analysis, a Level 1B emergency response can achieve a response time of less than three and a half hours.

[0070] This implementation method further defines the classification of emergency tasks and provides examples to illustrate the classification of emergency tasks. By classifying and managing and responding to disaster emergency needs, the efficient utilization of platform constellation resources is achieved, ensuring a rapid response to urgent needs.

[0071] Implementation Method Nine: This implementation method further defines the optical satellite emergency service platform described in Implementation Method Six. In this implementation method, the activation of the corresponding emergency service mode based on the classification results is as follows: Figure 9 As shown, specifically: The emergency service mode corresponding to Level 1A is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 1B is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 2 is as follows: the response time is within 1 hour, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 3 is as follows: the response time is within 2 hours, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 4 is as follows: response time is within 4 hours, shooting time is the next day, and data transmission method is conventional data transmission.

[0072] In this embodiment, it is preferable to use an intelligent task management and scheduling module to schedule the nearest satellite for imaging in case of emergency, so as to ensure the timeliness of data response.

[0073] In this implementation, it is preferred that the platform administrator has the right to manually adjust and control the emergency response level.

[0074] In this implementation, preferably in emergency level 1A or 1B, the platform will cancel the satellite orbital imaging mission involving the region and regenerate the mission instructions based on the emergency needs; in emergency level 2 or 3, the platform will plan the imaging scheme for the next day in advance and increase the imaging priority of the required area; in emergency level 4, the satellite will conduct normal imaging as needed.

[0075] This implementation further defines the emergency service modes corresponding to different levels, and provides examples to illustrate these modes. The classification of emergency levels depends on flood information and the equipment support available to the requesting party. The smaller the number, the more urgent the monitoring requirement is in the platform's judgment. The platform will also allocate different response times and shooting methods to user needs under different equipment conditions according to the emergency level classification, and match the corresponding delivery time. For example, in emergency levels A or B, due to differences in equipment between mobile stations and production systems, the delivery time for level B will be longer due to differences in data transmission efficiency. By classifying and responding to disaster emergency needs in this way, the platform's constellation resources are utilized efficiently, ensuring a rapid response to urgent needs.

[0076] This implementation provides an example of a Level 1A mission. An emergency platform and production system service were purchased. An information center within the coverage area of ​​the Miyun dedicated line learns that flooding is occurring in location X and uploads the flood information to the platform. The platform classifies the mission as a Level 1A emergency mission and searches for N satellites passing overhead within one hour (the Jilin-1 satellite constellation can achieve nearly 40 revisits per day for a single point, so N is at least 1). Within half an hour, a mission confirmation form is submitted to the user, and a shooting mission instruction is sent to the satellite. After user confirmation, the satellite shoots according to the instruction and receives the shooting data through the Miyun station via intra-orbit data transmission. The data transmission time is within half an hour. The shooting data is then transmitted to the local production system via the Miyun dedicated line, and data generation is completed within half an hour, allowing the user to view the satellite image data.

Claims

1. An optical satellite emergency scheme aided decision-making method, characterized in that, Includes the following steps: Step S1: Obtain flood emergency task instructions based on hardware infrastructure and user needs; Step S2: Based on the flood emergency mission instruction, select the data transmission mode to perform the satellite imaging mission and obtain satellite image data, which includes satellite images and multi-source data; Step S3: Preprocess the obtained satellite images to obtain the digital orthophoto (DOM) results. The preprocessing includes production and quality inspection; The production process includes radiation treatment, decryption and decompression, geometric positioning, image matching, geometric correction, and light and color homogenization. The quality inspection includes image mosaicking and quality check; Step S4: Perform various thematic analyses based on the multi-source data and historical data to obtain corresponding analysis results; The thematic analysis includes flood extent extraction, auxiliary forecasting and early warning, and disaster loss assessment. Step S5: Based on the obtained digital orthophoto DOM results and analysis results, deliver the task according to the task requirements.

2. The optical satellite contingency solution aided decision-making method of claim 1, wherein, The data transmission method described in step S2 is either real-time data transmission or intra-lap data transmission. The real-time data transmission is used to acquire image data within a 600 km range in the direction of the ground station's descent. The data transmission time is within 1 hour, and it can provide more than 60 seconds of data transmission. The current number of revolutions is used to image an area within a 600 km radius centered on the ground station.

3. The optical satellite emergency response decision-making aid method according to claim 1, characterized in that, The flood range extraction in step S4 involves selecting a cloudless, sub-meter-level high-resolution satellite remote sensing image from before the disaster to obtain the water distribution before the flood, and a sub-meter-level high-resolution satellite remote sensing image with less cloud cover from after the disaster to obtain the water distribution after the flood. The flood-inundated area is then extracted by combining Deeplabv3 and a semantic segmentation framework.

4. The optical satellite emergency response decision-making aid method according to claim 1, characterized in that, The auxiliary forecasting and early warning mentioned in step S4 is to make short-term, medium-term and long-term forecasts of rainfall in the region based on GFS, GRAPES, SMS or NB hydrological forecasting models, and to display the surface rainfall and rainfall isosurface in real time.

5. The optical satellite emergency response decision-making aid method according to claim 1, characterized in that, The disaster loss assessment described in step S4 involves establishing a multimodal flood disaster analysis model based on multi-source data, quickly delineating key disaster-affected areas based on knowledge graph technology, and automatically analyzing the damage and disaster recovery status based on artificial intelligence technology.

6. An optical satellite emergency service platform, characterized in that, The optical satellite emergency service platform includes: The base layer is used to produce data and also to interact with the data layer. The data layer is used to store and query historical data, connect with user needs, deliver data to users, and interact with the service layer. The service layer is used to interact with the application layer to obtain disaster analysis results and corresponding emergency tasks. It is also used to classify emergency tasks and to activate corresponding emergency service modes based on the classification results. The application layer is used to process real-time disaster satellite imagery and also to assist in the analysis to obtain disaster analysis results.

7. The optical satellite emergency service platform according to claim 6, characterized in that, The basic layer is used as the source of raw data for production data, which is real-time satellite imagery data obtained by the "Jilin-1" satellite.

8. The optical satellite emergency service platform according to claim 6, characterized in that, The aforementioned classification of emergency tasks involves dividing them into five levels—1A, 1B, 2, 3, and 4—based on user needs and available hardware. The 1A indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware foundation is an emergency platform, a production system and a mobile station or a dedicated data line, and the delivery time for user needs is within 1 hour; The 1B level indicates that the emergency task is a high-level flood risk warning or an ongoing flood alarm, the hardware infrastructure is an emergency platform and a dedicated data line, and the delivery time for user needs is within 3.5 hours; Level 2 indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform, production system and mobile station or data line, and the delivery time for user needs is within 1.5 hours; The Level 3 designation indicates that the emergency task is a medium-level flood risk warning, the hardware infrastructure is an emergency platform and dedicated data lines, and the delivery time for user requirements is within 4 hours. Level 4 indicates that the hardware foundation is an emergency platform, and the delivery time for user needs is within 18 hours.

9. The optical satellite emergency service platform according to claim 6, characterized in that, The corresponding emergency service mode is as follows: The emergency service mode corresponding to Level 1A is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 1B is as follows: response time is within 30 minutes, shooting time is shooting on the same day, and data transmission method is data transmission within the same cycle. The emergency service mode corresponding to Level 2 is as follows: the response time is within 1 hour, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 3 is as follows: the response time is within 2 hours, the shooting time is the next day, and the data transmission method is real-time shooting and data transmission. The emergency service mode corresponding to Level 4 is as follows: response time is within 4 hours, shooting time is the next day, and data transmission method is conventional data transmission.

Images