Disaster warning method, device and storage medium based on low-orbit satellite

Abstract

The present application discloses a disaster warning method, device, and storage medium based on a low-orbit satellite, including: determining a first wave position coverage map corresponding to a first ground area, determining the first wave position of the first ground area; determining a first geographic traffic map and a first grid map corresponding to the first wave position, generating a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map; constructing a graph structure corresponding to the first wave position based on the first grid connectivity relationship graph; inputting the graph structure into a pre-trained graph neural network model, outputting a first influence coefficient corresponding to each first node; comparing the first influence coefficient of each first node with a pre-set influence coefficient threshold, and issuing a disaster warning to users in a second ground area corresponding to a second grid. This method achieves the technical effect of accurately notifying potentially affected users without consuming a large amount of resources, thereby ensuring the safety of users.

Description

Technical Field

[0001] The present application relates to the field of disaster warning technology, and in particular to a disaster warning method, device and storage medium based on low-orbit satellites. Background Art

[0002] With the continuous development of low-orbit satellite-related technologies, low-orbit satellites have demonstrated significant advantages in terms of high transmission rates and high signal quality. Furthermore, because low-orbit satellites are closer to the ground and provide higher signal quality, they are often used to capture high-resolution remote sensing images of specific areas. For disaster warning, low-orbit satellites can be used to capture high-resolution remote sensing images and capture surface deformation, building collapses, and large-scale congestion in areas surrounding disasters. Once low-orbit satellites capture changes in the area surrounding a disaster, they can further transmit warning information to users' mobile devices, ensuring their safety and the ability to conduct normal activities.

[0003] However, using low-orbit satellites to send warning information to all users in the disaster-prone area consumes a significant amount of resources due to the large number of mobile terminals they need to connect to. Furthermore, since some users may not live or work in the disaster-prone area, this approach is not particularly useful for users who have no direct contact with the disaster area. Therefore, the existing approach of using low-orbit satellites to send warning information to users consumes a significant amount of resources and is not particularly useful for users who do not have direct contact with the disaster area.

[0004] Publication number CN118470939A, titled "A Method and Apparatus for Generating Disaster Warning Information Based on Satellite Remote Sensing Data," includes: obtaining an electronic map of the observation area, multi-source satellite remote sensing data, and a geological hazard distribution map; mapping multiple transportation hubs to the geological hazard distribution map, and then dividing the resulting multiple mapped transportation hubs into a subset of risk-mapped transportation hubs and a subset of non-risk-mapped transportation hubs; determining the geological hazard risk probability and geological hazard level for risk-mapped transportation hubs based on optical remote sensing data and radar remote sensing data; and determining the geological hazard risk probability corresponding to the optical remote sensing data or radar remote sensing data for non-risk-mapped transportation hubs; and generating geological hazard risk warning information for the observation area.

[0005] Publication number CN118114992A, titled "A Remote Sensing Satellite Geological Hazard Early Warning Method," includes the following steps: using remote sensing satellite information, analyzing regional terrain data and soil types, combining rainfall and vegetation coverage, assessing changes in hydrological and geological conditions, and generating a geological risk dataset.

[0006] Regarding the method of sending early warning information to users in the above-mentioned existing technology, low-orbit satellites consume a lot of resources and are not very necessary for users who have no intersection with the disaster-stricken areas. No effective solution has been proposed yet. Summary of the Invention

[0007] The embodiments of the present disclosure provide a disaster warning method, device and storage medium based on low-orbit satellites, so as to at least solve the technical problem that the method of sending warning information to users in the prior art requires low-orbit satellites to consume more resources and is not very necessary for all users who have no intersection with the disaster-prone areas.

[0008] According to one aspect of an embodiment of the present disclosure, a disaster warning method based on a low-orbit satellite is provided, including: determining a first wave position coverage map corresponding to a first ground area, and determining the first wave position at which the first ground area is located; determining a first geographic traffic map and a first grid map corresponding to the first wave position, and generating a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position; constructing a graph structure corresponding to the first wave position based on the first grid connectivity relationship map, wherein a first node of the graph structure corresponds to each first grid, and an edge of the graph structure corresponds to a connectivity relationship of each first grid; inputting the graph structure into a pre-trained graph neural network model, and outputting a first influence coefficient corresponding to each first node; and comparing the first influence coefficient of each first node with a preset influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determining the corresponding first node and a second grid corresponding to the first node, and performing a disaster warning for users in a second ground area corresponding to the second grid.

[0009] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0010] According to another aspect of the embodiment of the present disclosure, a disaster warning device based on a low-orbit satellite is also provided, including: a wave position coverage map determination module, used to determine a first wave position coverage map corresponding to a first ground area, and determine the first wave position at which the first ground area is located; a grid connectivity relationship graph generation module, used to determine a first geographic traffic map and a first grid map corresponding to the first wave position, and based on the first geographic traffic map and the first grid map, generate a first grid connectivity relationship graph corresponding to the first wave position, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position; a graph structure construction module, used to generate a first grid connectivity relationship graph based on the first grid structure; A grid connectivity relationship graph constructs a graph structure corresponding to the first wave position, wherein the first node of the graph structure corresponds to each first grid, and the edge of the graph structure corresponds to the connectivity relationship of each first grid; an influence coefficient output module is used to input the graph structure into a pre-trained graph neural network model and output the first influence coefficient corresponding to each first node; and a disaster warning module is used to compare the first influence coefficient of each first node with a pre-set influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

[0011] According to another aspect of the embodiment of the present disclosure, a disaster warning device based on a low-orbit satellite is also provided, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: determining a first wave position coverage map corresponding to a first ground area, and determining a first wave position at which the first ground area is located; determining a first geographic traffic map and a first grid map corresponding to the first wave position, and generating a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of grids corresponding to the first wave position; first grids; based on the connectivity relationship graph of the first grids, construct a graph structure corresponding to the first wave position, wherein the first nodes of the graph structure correspond to each first grid, and the edges of the graph structure correspond to the connectivity relationship of each first grid; input the graph structure into a pre-trained graph neural network model, and output the first influence coefficient corresponding to each first node; and compare the first influence coefficient of each first node with a pre-set influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

[0012] The present application discloses a disaster warning method based on low-orbit satellites. First, the processor determines a first wave position coverage map corresponding to a first ground area, and determines the first wave position where the first ground area is located. Then, the processor determines a first geographic traffic map and a first grid map corresponding to the first wave position, and generates a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map. Furthermore, the processor constructs a graph structure corresponding to the first wave position based on the first grid connectivity relationship graph. The processor then inputs the graph structure into a pre-trained graph neural network model and outputs a first influence coefficient corresponding to each first node. Finally, the processor compares the first influence coefficient of each first node with a pre-set influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determines the corresponding first node and the second grid corresponding to the first node, and performs a disaster warning for users in the first ground area corresponding to the second grid.

[0013] From the above content, it can be seen that before using low-orbit satellites to provide disaster warnings to users, the present application further divides the first wave position corresponding to the first ground area in advance, so that relative to the first wave position corresponding to the first ground area, the first grid corresponding to the first ground area can more accurately limit the surrounding range corresponding to the first ground area.

[0014] Furthermore, since the present application regards each first grid as the first node of the graph structure and the connectivity relationship between each first grid as the edge, a graph structure is constructed, and the first influence coefficient corresponding to each first node is determined based on the graph structure and the pre-trained graph neural network model, the low-orbit satellite can determine which users in the second ground area corresponding to the second grid need to send warning information based on the first influence coefficient corresponding to each first node.

[0015] Unlike existing technologies where low-orbit satellites need to send warning information to all users within the waveband area corresponding to the target area, this application only needs to identify the ground area with the greatest impact when a disaster occurs in the target area, and then issue a disaster warning to users in the ground area. Therefore, low-orbit satellites can accurately notify potentially affected users without consuming a lot of resources, thereby achieving the technical effect of ensuring user safety.

[0016] This solves the technical problem that the existing method of sending early warning information to users requires low-orbit satellites, which consumes a lot of resources and is not very necessary for all users who have no intersection with the disaster-stricken areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of this application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the drawings:

[0018] Figure 1 is a schematic diagram of a disaster warning system based on a low-orbit satellite according to Example 1 of the present application;

[0019] Figure 2A 1 is a schematic diagram of the hardware architecture of the low-orbit satellite according to Example 1 of the present application;

[0020] Figure 2B 1 is a schematic diagram of the hardware architecture of a user's mobile terminal according to Example 1 of the present application;

[0021] Figure 3 This is a flow chart of the disaster warning method based on low-orbit satellites according to Example 1 of the present application;

[0022] Figure 4 The first wave position coverage map corresponding to the target area according to Example 1 of the present application;

[0023] Figure 5 The first geographical traffic map corresponding to the first wave position according to Example 1 of the present application;

[0024] Figure 6 The first grid diagram corresponding to the first wave position according to embodiment 1 of the present application;

[0025] Figure 7 is the first grid connectivity diagram according to embodiment 1 of the present application; and

[0026] Figure 8 is a schematic diagram of a disaster warning device based on a low-orbit satellite according to Example 2 of the present application; and

[0027] Figure 9 This is a schematic diagram of a disaster warning device based on a low-orbit satellite as described in Example 3 of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present disclosure.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Example 1

[0031] According to this embodiment, a method embodiment of disaster warning based on low-orbit satellites is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1 Schematic diagram of a disaster warning system based on a low-orbit satellite according to Example 1 of the present application. Figure 1 As shown, the low-orbit satellite 10 interacts with the mobile terminal 20 within the first communication coverage range 110. It is worth noting that the low-orbit satellite 10 generates multiple beams, and each beam has its own second communication coverage range. For example, beam Q1 corresponds to the second communication coverage range 120, and the low-orbit satellite 10 can interact with the mobile terminal 20 within the second communication coverage range 120 through beam Q1. Therefore, when the low-orbit satellite 10 generates multiple beams and the second communication coverage ranges of the beams do not overlap, the following can be formed. Figure 1 A first communication coverage area 110 is shown.

[0033] Furthermore, the second communication coverage of each beam transmitted by the low-orbit satellite 10 has a corresponding cluster, and each cluster includes multiple beam positions. Thus, each beam can communicate with the mobile terminals in each beam position in sequence according to the beam hopping pattern.

[0034] Figure 2A It further shows Figure 1 Schematic diagram of the hardware architecture of the medium and low orbit satellite 10. Figure 2AAs shown, the low-orbit satellite 10 includes an integrated electronic system, which includes: a processor, a memory, a bus management module and a communication interface. The memory is connected to the processor, so that the processor can access the memory, read the program instructions stored in the memory, read data from the memory or write data to the memory. The bus management module is connected to the processor and is also connected to a bus such as a CAN bus. The processor can communicate with the onboard peripherals connected to the bus through the bus managed by the bus management module. In addition, the processor is also connected to devices such as cameras, star sensors, measurement and control transponders, and data transmission equipment via the communication interface. It can be understood by those skilled in the art that Figure 2A The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2A More or fewer components than shown, or with Figure 2A Different configurations shown.

[0035] Figure 2B It further shows Figure 1 Schematic diagram of the hardware architecture of the mobile terminal 20. Figure 2B As shown, the mobile terminal 20 may include one or more processors (the processor may include, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA) or other processing device), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, transmission device, and input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. Those skilled in the art will understand that Figure 2B The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2B More or fewer components than shown, or with Figure 2B Different configurations shown.

[0036] It should be noted that Figure 2A and Figure 2B The one or more processors and / or other data processing circuits shown in the figure may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computing device. As discussed in the embodiments of the present disclosure, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0037] Figure 2A and Figure 2BThe memory shown in the figure can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the low-orbit satellite-based disaster warning method in the embodiments of the present disclosure. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the low-orbit satellite-based disaster warning method for the aforementioned application. The memory can include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0038] It should be noted that, in some optional embodiments, the above Figure 2A and Figure 2B The devices shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. Figure 2A and Figure 2B This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the apparatus described above.

[0039] Under the above operating environment, according to the first aspect of this embodiment, a disaster warning method based on a low-orbit satellite is provided. Figure 2A The low-orbit satellite 10 shown in FIG. Figure 3 A schematic diagram of the process is shown in FIG. Figure 3 As shown, the method includes:

[0040] S302: Determine a first wave position coverage map corresponding to a first ground area, and determine a first wave position at which the first ground area is located;

[0041] S304: Determine a first geographic traffic map and a first grid map corresponding to the first wave position, and generate a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position;

[0042] S306: Constructing a graph structure corresponding to the first wave position based on the first grid connectivity graph, wherein the first nodes of the graph structure correspond to the first grids, and the edges of the graph structure correspond to the connectivity relationships of the first grids.

[0043] S308: Inputting the graph structure into a pre-trained graph neural network model, and outputting a first influence coefficient corresponding to each first node; and

[0044] S310: Compare the first influence coefficient of each first node with a preset influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

[0045] Specifically, first, when a disaster occurs in a certain ground area, the processor (i.e., the processor of the low-orbit satellite 10, hereinafter the same) takes the ground area as the first ground area, and determines the first wave position coverage map corresponding to the first ground area, and the first wave position of the first ground area (S302). Figure 4 is a first wave position coverage map corresponding to the first ground area according to the present application. Figure 4 As shown, when determining the first ground area 30, the processor generates a first wave position coverage map corresponding to the first ground area 30 based on the geographical location of the first ground area 30. When generating the first wave position coverage map corresponding to the first ground area 30, the processor can determine the first wave position 40 at which the first ground area 30 is located.

[0046] The processor then collects a first geographical traffic map corresponding to the first wave position 40 . Figure 5 It is the first geographical traffic map corresponding to the first wave position according to the embodiment of the present application. Figure 5 As shown, after the processor determines the first wave position 40 at which the first ground area 30 is located, the processor further collects a first geographic traffic map corresponding to the first wave position 40. The first geographic traffic map is used to display the road connectivity of the ground area corresponding to the first wave position 40. Simultaneously, the processor determines a first grid map corresponding to the first wave position 40. Figure 6 It is the first grid diagram corresponding to the first wave position according to the embodiment of the present application. Figure 6 As shown, when the processor determines the first wave position 40 , it divides the first wave position coverage map corresponding to the first wave position 40 and divides the first wave position coverage map into multiple grids of the same size, thereby generating a first grid map corresponding to the first wave position 40 .

[0047] Furthermore, Figure 7 is a first grid connectivity diagram according to an embodiment of the present application. Figure 7 As shown, when the processor determines the first geographical traffic map and the first grid map, the first geographical traffic map and the first grid map are superimposed to generate the following Figure 7 The first grid connectivity relationship diagram corresponding to the first wave position (S304) is shown, wherein the first grid connectivity relationship diagram shows the road connection status in multiple first grids corresponding to the first wave position.

[0048] The processor then constructs a graph structure corresponding to the first wave position based on the determined first grid connectivity graph (S306). Figure 7 As shown, in this embodiment, the processor uses each first grid in the first grid connectivity graph as a first node and the connectivity relationship between each first grid as an edge to construct a graph structure. For example, the graph structure G includes multiple first nodes and multiple edges Wherein, i=1~n, j=1~m. In this embodiment, when the processor constructs a graph structure corresponding to the first wave position, the influence coefficient of the first node corresponding to the first ground area is 1, and the influence coefficients of other first nodes in the graph structure are 0.

[0049] Furthermore, the processor inputs the graph structure into a pre-trained graph neural network model and outputs a first influence coefficient corresponding to each first node (S308). For example, when the processor inputs the graph structure into a pre-trained graph neural network model, the graph neural network model outputs a first influence coefficient corresponding to each first node. The corresponding first influence coefficient , and the first node The corresponding first influence coefficient , ...., with the first node The corresponding first influence coefficient The first impact coefficient is used to indicate the degree to which each first grid is affected after a disaster occurs.

[0050] Finally, the processor compares the first influence coefficient of each first node with a preset influence coefficient threshold. If the first influence coefficient of each first node is greater than the influence coefficient threshold, the corresponding first node and the second grid corresponding to the first node are determined, and a disaster warning is issued to users in the second ground area corresponding to the second grid (S310). For example, the processor determines the first node corresponding to each first node. The corresponding first influence coefficient The processor then compares the first impact coefficients and impact coefficient threshold If the processor determines that 、 as well as The corresponding first influence coefficient 、 as well as Greater than the influence coefficient threshold , then when a disaster occurs at the first node corresponding to the first ground area, 、 as well as The first grid corresponding to each node is affected to a greater extent. 、 as well as and will be combined with the first node above 、 as well as The corresponding first grid is used as the second grid, and a disaster warning is issued to users in a second ground area corresponding to the second grid. That is, the processor of the low-orbit satellite sends disaster warning information to mobile terminals of users in the second ground area corresponding to the second grid.

[0051] As described in the background, using low-orbit satellites to send warning information to all users in the disaster-prone area consumes significant resources due to the large number of mobile terminals they need to connect to. Furthermore, since some users may not live or work in the disaster-prone area, this approach is not essential for users who have no direct contact with the disaster area. Therefore, the existing approach of using low-orbit satellites to send warning information to users consumes significant resources and is not essential for users who do not have direct contact with the disaster area.

[0052] In view of this, before using low-orbit satellites to provide disaster warnings to users, the present application further divides the first wave position corresponding to the first ground area in advance, so that compared with the first wave position corresponding to the first ground area, the first grid corresponding to the first ground area can more accurately limit the surrounding range corresponding to the first ground area.

[0053] Furthermore, since the present application regards each first grid as the first node of the graph structure and the connectivity relationship between each first grid as the edge, a graph structure is constructed, and the first influence coefficient corresponding to each first node is determined based on the graph structure and the pre-trained graph neural network model, the low-orbit satellite can determine which users in the ground area corresponding to the first grid need to send warning information based on the first influence coefficient corresponding to each first node.

[0054] Unlike the prior art, where low-orbit satellites need to send warning information to all users within the waveband area corresponding to the first ground area, the present application only needs to identify the most affected ground area upon determining a disaster has occurred in the first ground area and issue a disaster warning to users in that ground area. This allows low-orbit satellites to accurately notify potentially affected users without consuming significant resources, thereby achieving the technical effect of ensuring user safety.

[0055] This solves the technical problem that the existing method of sending early warning information to users requires low-orbit satellites, which consumes a lot of resources and is not very necessary for all users who have no intersection with the disaster-stricken areas.

[0056] Optionally, the operation of generating a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map includes: dividing the first wave position corresponding to the first ground area in the first wave position coverage map into multiple first grids, and generating a first grid map corresponding to the first wave position, wherein the sizes of the first grids are the same; and superimposing the first grid map with the first geographic traffic map to determine the first grid connectivity relationship graph corresponding to the first wave position.

[0057] Specifically, refer to Figure 6 As shown, when the processor determines the first wave position 40 , it divides the first wave position coverage map corresponding to the first wave position 40 and divides the first wave position coverage map into multiple grids of the same size, thereby generating a first grid map corresponding to the first wave position 40 .

[0058] Furthermore, Figure 7 is a first grid connectivity diagram according to an embodiment of the present application. Figure 7 As shown, when the processor determines the first geographical traffic map and the first grid map, the first geographical traffic map and the first grid map are superimposed to generate the following Figure 7 The first grid connectivity relationship diagram corresponding to the first wave position is shown. The first grid connectivity relationship diagram shows the road connection status in multiple first grids corresponding to the first wave position.

[0059] Optionally, it also includes: pre-constructing a graph neural network model and training the graph neural network model. Further optionally, the operations of pre-constructing a graph neural network model and training the graph neural network model include: determining the second wave position coverage maps corresponding to each third ground area, and determining the second wave position of each third ground area; determining the second geographic traffic map and the second raster map corresponding to each second wave position, and generating a second raster connectivity relationship graph corresponding to each second wave position based on the second geographic traffic map and the second raster map; constructing a plurality of first graph structure samples corresponding to the second wave position based on the plurality of second raster connectivity relationship graphs; labeling the plurality of first graph structure samples and generating a plurality of second graph structure samples; and using the plurality of first graph structure samples as input samples and the plurality of second graph structure samples as output samples to train the graph neural network model.

[0060] Specifically, when the processor uses a pre-trained graph neural network model to determine the first influence coefficient corresponding to each first node in the graph structure, it is also necessary to pre-build the graph neural network model and train the graph neural network model.

[0061] First, the processor determines the historical period The area where the disaster occurred is designated as the third ground area. Then, the processor determines the third ground area The corresponding second wave position coverage map and determine each third ground area The second wave position , as shown in 4. For example, the processor determines the historical period Corresponding third ground area , and determine the third ground area The second wave position ; Processor determination and historical period Corresponding third ground area , and determine the third ground area The second wave position ;...;Processor determination and historical period Corresponding third ground area , and determine the third ground area The second wave position .

[0062] Further, the processor determines the second wave position The corresponding second geographic traffic map and the second grid map are generated based on the second geographic traffic map and the second grid map. The corresponding second grid connectivity diagram is as follows Figure 7 shown.

[0063] Then the processor constructs the second wave position based on the multiple second grid connectivity graphs. Corresponding multiple first graph structure samples The second grids in each second grid connectivity graph are used as nodes, and the connectivity between each second grid is used as an edge to construct multiple first graph structure samples corresponding to each second wave position. In the example, the second grid corresponding to the third sample area is used as the target node, and the initial influence coefficient corresponding to the target node is 1, and the initial influence coefficients of the remaining second nodes are 0. In the third sample area The corresponding second grid As a target node , thus with the target node The corresponding initial influence coefficient is 1, which is consistent with the first graph structure sample Other second nodes in The corresponding initial influence coefficient is 0. For example, in the first graph structure sample In the third sample area The corresponding second grid As target node , thus with the target node The corresponding initial influence coefficient is 1, which is consistent with the first graph structure sample Other second nodes in 、 The corresponding initial influence coefficient is 0.

[0064] Furthermore, the processor processes a plurality of first graph structure samples Mark and generate multiple second graph structure samples The above content will be described in detail later, so it will not be repeated here.

[0065] Finally, the processor converts the first graph structure samples into As the input samples of the graph neural network model, multiple second graph structure samples As the output sample of the graph neural network model, and train the graph neural network model.

[0066] Further optionally, the operation of labeling multiple first graph structure samples and generating multiple second graph structure samples includes: respectively taking the second node corresponding to the third ground area in each first graph structure sample as the target node; determining the trajectory information of each user in the third ground area corresponding to the target node, and determining the proportional relationship of each user in the third ground area moving to the fourth ground area based on the trajectory information, wherein the fourth ground area corresponds to other second nodes except the target node; and determining the proportional relationship corresponding to each second node as the influence coefficient corresponding to each second node, and labeling each second node, thereby generating multiple second graph structure samples.

[0067] Specifically, first, the processor respectively converts each first graph structure sample In the example, the processor takes the first graph structure sample as the target node. Middle, and the third ground area The corresponding second node As the target node, the processor takes the first graph structure sample Middle, and the third ground area The corresponding second node As the target node, ..., the processor takes the first graph structure sample Middle, and the third ground area The corresponding second node as target nodes respectively.

[0068] Then, the processor determines the trajectory information of each user in the third ground area corresponding to the target node. The processor of the low-orbit satellite can, for example, retrieve the trajectory information of each user in the third ground area corresponding to the target node from a database, and the trajectory information can, for example, be pre-collected by the processor of the low-orbit satellite based on remote sensing images.

[0069] Furthermore, the processor determines the proportion of each user in the third ground area moving to the fourth ground area based on the trajectory information. The fourth ground area corresponds to the second nodes other than the target node. For example, when the processor determines the first graph structure sample Corresponding target node In the case of further determining the ratio of each user in the third ground area moving to the fourth ground area For another example, the processor determines that the first graph structure sample Corresponding target node In the case of further determining the ratio of each user in the third ground area moving to the fourth ground area 、 .

[0070] Finally, the processor determines the proportional relationship corresponding to each second node as an influence coefficient corresponding to each second node, and labels each second node, thereby generating a plurality of second graph structure samples.

[0071] Therefore, according to the first aspect of this embodiment, it is possible to accurately notify potentially affected users without consuming a lot of resources, thereby achieving the technical effect of ensuring the safety of users.

[0072] In addition, reference Figure 1 As shown, according to a second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0073] Therefore, according to this embodiment, it is possible to accurately notify potentially affected users without consuming a lot of resources, thereby achieving the technical effect of ensuring the safety of users.

[0074] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0075] Through the description of the above embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0076] Example 2

[0077] Figure 8 FIG. 8 shows a disaster warning device 800 based on a low-orbit satellite according to this embodiment, which corresponds to the method according to embodiment 1. Figure 8 As shown, the device 800 includes: a wave position coverage map determining module 810, which is used to determine a first wave position coverage map corresponding to a first ground area and determine the first wave position at which the first ground area is located; a grid connectivity relationship graph generating module 820, which is used to determine a first geographic traffic map and a first grid map corresponding to the first wave position, and generate a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position; a graph structure constructing module 830, which is used to construct a grid connectivity relationship graph corresponding to the first wave position based on the first grid connectivity relationship graph. A graph structure corresponding to a wave position, wherein the first node of the graph structure corresponds to each first grid, and the edge of the graph structure corresponds to the connectivity relationship of each first grid; an influence coefficient output module 840, used to input the graph structure into a pre-trained graph neural network model, and output the first influence coefficient corresponding to each first node; and a disaster warning module 850, used to compare the first influence coefficient of each first node with a pre-set influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

[0078] Optionally, the grid connectivity relationship diagram generation module 820 includes: a grid map generation module, which is used to divide the first wave position corresponding to the first ground area in the first wave position coverage map into multiple first grids, and generate a first grid map corresponding to the first wave position, wherein the sizes of the first grids are the same; and an overlay module, which is used to overlay the first grid map with the first geographic traffic map, thereby determining the first grid connectivity relationship diagram corresponding to the first wave position.

[0079] Optionally, the device 800 also includes: a training module, used to pre-build a graph neural network model and train the graph neural network model.

[0080] Optionally, the training module includes: a second wave position determination module, used to determine the second wave position coverage map corresponding to each third ground area respectively, and determine the second wave position at which each third ground area is located; a geographic traffic map determination module, used to determine the second geographic traffic map and the second grid map corresponding to each second wave position, and generate a second grid connectivity relationship graph corresponding to each second wave position based on the second geographic traffic map and the second grid map; a graph structure sample construction module, used to construct a plurality of first graph structure samples corresponding to the second wave position based on a plurality of second grid connectivity relationship graphs; a graph structure sample generation module, used to label a plurality of first graph structure samples, and generate a plurality of second graph structure samples; and an input sample determination module, used to train the graph neural network model using a plurality of first graph structure samples as input samples and a plurality of second graph structure samples as output samples.

[0081] Optionally, the graph structure sample generation module includes: a target node determination module, which is used to respectively take the second node corresponding to the third ground area in each first graph structure sample as the target node; a proportional relationship determination module, which is used to determine the trajectory information of each user in the third ground area corresponding to the target node, and determine the proportional relationship of each user in the third ground area moving to the fourth ground area based on the trajectory information, wherein the fourth ground area corresponds to other second nodes except the target node; and a labeling module, which is used to determine the proportional relationship corresponding to each second node as the influence coefficient corresponding to each second node, and label each second node, thereby generating multiple second graph structure samples.

[0082] Therefore, according to this embodiment, it is possible to accurately notify potentially affected users without consuming a lot of resources, thereby achieving the technical effect of ensuring the safety of users.

[0083] Example 3

[0084] Figure 9FIG. 9 shows a disaster warning device 900 based on a low-orbit satellite according to this embodiment, which corresponds to the method according to embodiment 1. Figure 9 As shown, the device 900 includes: a processor 910; and a memory 920, connected to the processor 910, for providing the processor 910 with instructions for processing the following processing steps: determining a first wave position coverage map corresponding to a first ground area, and determining a first wave position at which the first ground area is located; determining a first geographic traffic map and a first grid map corresponding to the first wave position, and generating a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes a road connection relationship within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position; based on In the first grid connectivity relationship graph, a graph structure corresponding to the first wave position is constructed, wherein the first nodes of the graph structure correspond to each first grid, and the edges of the graph structure correspond to the connectivity relationship of each first grid; the graph structure is input into a pre-trained graph neural network model, and the first influence coefficient corresponding to each first node is output; and the first influence coefficient of each first node is compared with a pre-set influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, the corresponding first node and the second grid corresponding to the first node are determined, and disaster warnings are issued to users in a second ground area corresponding to the second grid.

[0085] Optionally, the operation of generating a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map includes: dividing the first wave position corresponding to the first ground area in the first wave position coverage map into multiple first grids, and generating a first grid map corresponding to the first wave position, wherein the sizes of the first grids are the same; and superimposing the first grid map with the first geographic traffic map to determine the first grid connectivity relationship graph corresponding to the first wave position.

[0086] Optionally, it also includes: pre-building a graph neural network model and training the graph neural network model.

[0087] Optionally, the graph neural network model is pre-constructed and the graph neural network model is trained, including: determining the second wave position coverage maps corresponding to each third ground area respectively, and determining the second wave position at which each third ground area is located; determining the second geographic traffic map and the second raster map corresponding to each second wave position, and generating a second raster connectivity relationship graph corresponding to each second wave position based on the second geographic traffic map and the second raster map; constructing a plurality of first graph structure samples corresponding to the second wave position based on a plurality of second raster connectivity relationship graphs; labeling the plurality of first graph structure samples and generating a plurality of second graph structure samples; and training the graph neural network model using the plurality of first graph structure samples as input samples and the plurality of second graph structure samples as output samples.

[0088] Optionally, the operation of labeling multiple first graph structure samples and generating multiple second graph structure samples includes: respectively taking the second node corresponding to the third ground area in each first graph structure sample as the target node; determining the trajectory information of each user in the third ground area corresponding to the target node, and determining the proportional relationship of each user in the third ground area moving to the fourth ground area based on the trajectory information, wherein the fourth ground area corresponds to other second nodes except the target node; and determining the proportional relationship corresponding to each second node as the influence coefficient corresponding to each second node, and labeling each second node, thereby generating multiple second graph structure samples.

[0089] Therefore, according to this embodiment, it is possible to accurately notify potentially affected users without consuming a lot of resources, thereby achieving the technical effect of ensuring the safety of users.

[0090] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0091] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0093] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk.

[0096] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A disaster warning method based on low-orbit satellites, characterized in that: include: Determining a first wave position coverage map corresponding to a first ground area, and determining a first wave position at which the first ground area is located; Determining a first geographic traffic map and a first grid map corresponding to the first wave position, and generating a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes road connection relationships within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position, and wherein generating the first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map includes: Dividing the first wave position corresponding to the first ground area in the first wave position coverage map into a plurality of first grids, and generating a first grid map corresponding to the first wave position, wherein the first grids have the same size; and Overlaying the first grid map with the first geographic traffic map to determine a first grid connectivity relationship map corresponding to the first wave position; Based on the first grid connectivity graph, constructing a graph structure corresponding to the first wave position, wherein first nodes of the graph structure correspond to respective first grids, and edges of the graph structure correspond to connectivity relationships of the respective first grids; Inputting the graph structure into a pre-trained graph neural network model, and outputting a first influence coefficient corresponding to each first node; as well as Compare the first influence coefficient of each first node with a preset influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

2. The method according to claim 1, characterized in that Also includes: A graph neural network model is pre-built and trained.

3. The method according to claim 2, characterized in that The operations of pre-building a graph neural network model and training the graph neural network model include: Determining second wave position coverage maps corresponding to respective third ground areas, and determining the second wave position at which each third ground area is located; Determining a second geographic traffic map and a second grid map corresponding to each second wave position, and generating a second grid connectivity relationship map corresponding to each second wave position based on the second geographic traffic map and the second grid map; Based on the plurality of second grid connectivity relationship graphs, constructing a plurality of first graph structure samples corresponding to the second wave positions respectively; Annotating the plurality of first graph structure samples and generating a plurality of second graph structure samples; and The graph neural network model is trained by using the multiple first graph structure samples as input samples and the multiple second graph structure samples as output samples.

4. The method according to claim 3, characterized in that The operation of labeling the plurality of first graph structure samples and generating a plurality of second graph structure samples includes: taking the second node corresponding to the third ground area in each of the first graph structure samples as a target node; Determining trajectory information of each user in a third ground area corresponding to the target node, and determining a proportion of each user in the third ground area moving to a fourth ground area based on the trajectory information, wherein the fourth ground area corresponds to other second nodes except the target node; and The proportional relationship corresponding to each second node is determined as the influence coefficient corresponding to each second node, and each second node is labeled, thereby generating a plurality of second graph structure samples.

5. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 4.

6. A disaster warning device based on a low-orbit satellite, characterized in that: include: a wave position coverage map determining module, configured to determine a first wave position coverage map corresponding to a first ground area, and determine a first wave position at which the first ground area is located; A grid connectivity relationship graph generation module is configured to determine a first geographic traffic map and a first grid map corresponding to the first wave position, and generate a first grid connectivity relationship graph corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes road connection relationships within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position, wherein the grid connectivity relationship graph generation module includes: a grid map generating module, configured to divide the first wave position corresponding to the first ground area in the first wave position coverage map into a plurality of first grids, and generate a first grid map corresponding to the first wave position, wherein the first grids have the same size; and A superposition module is used to superimpose the first grid map with the first geographic traffic map to determine the first grid connectivity relationship map corresponding to the first wave position A graph structure construction module, configured to construct a graph structure corresponding to the first wave position based on the first grid connectivity relationship graph, wherein the first nodes of the graph structure correspond to the first grids, and the edges of the graph structure correspond to the connectivity relationships of the first grids; an influence coefficient output module, configured to input the graph structure into a pre-trained graph neural network model and output a first influence coefficient corresponding to each first node; and The disaster warning module is used to compare the first influence coefficient of each first node with a preset influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

7. The device according to claim 6, characterized in that The device also includes: a training module for pre-building a graph neural network model and training the graph neural network model.

8. A disaster warning device based on a low-orbit satellite, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Determining a first wave position coverage map corresponding to a first ground area, and determining a first wave position at which the first ground area is located; Determining a first geographic traffic map and a first grid map corresponding to the first wave position, and generating a first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map, wherein the first geographic traffic map includes road connection relationships within the first wave position, and the first grid map includes a plurality of first grids corresponding to the first wave position, and wherein generating the first grid connectivity relationship map corresponding to the first wave position based on the first geographic traffic map and the first grid map includes: Dividing the first wave position corresponding to the first ground area in the first wave position coverage map into a plurality of first grids, and generating a first grid map corresponding to the first wave position, wherein the first grids have the same size; and Overlaying the first grid map with the first geographic traffic map to determine a first grid connectivity relationship map corresponding to the first wave position; Based on the first grid connectivity graph, constructing a graph structure corresponding to the first wave position, wherein first nodes of the graph structure correspond to respective first grids, and edges of the graph structure correspond to connectivity relationships of the respective first grids; Inputting the graph structure into a pre-trained graph neural network model, and outputting a first influence coefficient corresponding to each first node; as well as Compare the first influence coefficient of each first node with a preset influence coefficient threshold, and when the first influence coefficient is greater than the influence coefficient threshold, determine the corresponding first node and the second grid corresponding to the first node, and issue a disaster warning to users in the second ground area corresponding to the second grid.

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