Satellite imaging planning method and device for regional mapping

Abstract

The embodiment of the invention provides a satellite imaging planning method and device for regional mapping. The method comprises the following steps: acquiring imaging demand information and satellite resource information, wherein the imaging demand information at least comprises a target imaging area, a resolution requirement, a time window, a meteorological condition, a task priority and an imaging mode; the satellite resource information comprises satellite hardware resources, satellite orbit and position information and satellite communication capability; inputting the imaging demand information into the task planning model to obtain a satellite imaging task planning scheme; the task planning model is obtained by training according to historical imaging demand information and is used for predicting spatial-temporal characteristics of imaging demands to generate a mathematical model of a satellite imaging task planning scheme; according to the satellite resource information, optimizing an execution sequence of satellite imaging tasks in the satellite imaging task planning scheme, and generating a satellite imaging task execution plan; and executing a satellite imaging task according to the satellite imaging task execution plan, and obtaining an image of the target imaging area.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of satellite imaging technology, and in particular to a satellite imaging planning method and device for regional mapping. Background Art

[0002] With the rapid development of social economy and scientific and technological progress, the demand for geographic information is growing. Especially in the fields of urban planning, resource management, environmental protection, disaster monitoring, etc., high-resolution and high-timeliness geographic information has become an important basis for decision support.

[0003] In traditional satellite imaging technology, the imaging target area, time, resolution and other parameters are usually determined based on the user's imaging needs. After that, the imaging task is assigned to the appropriate satellite and a detailed shooting plan is formulated. The optimal shooting time window for the satellite is calculated to ensure that the satellite can reach above the target area. After completing the satellite imaging task planning, the satellite's attitude is controlled so that its camera is aimed at the target area, and the image of the target area is captured within the predetermined time window. After that, the collected raw image data is transmitted back to the ground station through a communication link. After the ground station receives the raw image data sent back by the satellite, it pre-processes the raw image data, and then performs processing such as stitching, cropping, and enhancement to generate the final image data for display.

[0004] However, in the above-mentioned traditional satellite imaging technology, mission planning relies on complex calculations and judgments by professional technicians. The entire process from mission planning to image delivery takes a long time, which is difficult to meet the user's requirements for high effectiveness. At the same time, traditional satellite imaging technology has limitations in high-resolution imaging and is difficult to meet the needs of high-precision applications. Therefore, it is urgent to propose a new satellite imaging planning method to improve the overall performance of satellite imaging and user experience. Summary of the invention

[0005] The embodiments of the present invention provide a satellite imaging planning method and device for regional mapping, which realizes efficient and accurate planning of satellite imaging tasks by combining big data technology, artificial intelligence technology and cloud computing technology, and meets the high resolution and high timeliness requirements of regional mapping.

[0006] In a first aspect, an embodiment of the present invention provides a satellite imaging planning method for regional mapping, the method comprising:

[0007] Acquire imaging requirement information and satellite resource information, wherein the imaging requirement information includes at least target imaging area, resolution requirement, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capability;

[0008] The imaging requirement information is input into a task planning model to obtain a satellite imaging task planning scheme; the task planning model is a mathematical model trained based on historical imaging requirement information and used to predict the spatiotemporal characteristics of imaging requirements to generate the satellite imaging task planning scheme;

[0009] According to the satellite resource information, the execution order of the satellite imaging tasks in the satellite imaging task planning scheme is optimized to generate a satellite imaging task execution plan;

[0010] The satellite imaging mission is executed according to the satellite imaging mission execution plan to acquire an image of the target imaging area.

[0011] In a possible design of the first aspect, obtaining imaging requirement information includes:

[0012] receiving an imaging request from a user, wherein the imaging request includes the target imaging area, the resolution requirement, the time window, the user priority, and the imaging mode;

[0013] The meteorological conditions are obtained through a third-party platform.

[0014] In a possible design of the first aspect, the training process of the task planning model includes:

[0015] Acquire the historical imaging demand information, wherein the historical imaging demand information includes historical imaging demand, historical satellite orbit information, historical meteorological data, and corresponding historical satellite imaging mission planning;

[0016] Extracting spatial features from the historical imaging requirements, extracting temporal features from the historical satellite orbit parameters, and extracting meteorological features from the historical meteorological data;

[0017] Fusing the spatial feature, the temporal feature, and the meteorological feature to obtain a fused feature;

[0018] Inputting the fused features into the model to be trained to obtain a predicted satellite imaging mission plan;

[0019] According to the pre-constructed joint loss function, the predicted satellite imaging mission plan and the historical satellite imaging mission plan, the parameters of the model to be trained are updated until the parameters of the model to be trained converge, thereby obtaining the mission planning model.

[0020] In a possible design of the first aspect, the joint loss function is:

[0021] L total =λ reg L reg +λ cls Lcls

[0022] Among them, L total represents the joint loss function, L reg represents the loss function of the regression task, L cls represents the loss function of the classification task, λ reg represents the weight coefficient of the regression task, λ cls Represents the weight coefficient of the classification task.

[0023] In a possible design of the first aspect, optimizing the execution order of satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information to generate a satellite imaging task execution plan includes:

[0024] Allocating the satellite imaging task to a corresponding satellite according to the target imaging area, the time window information, the satellite hardware resources, the satellite orbit and position information, and the satellite communication capability in the satellite imaging task planning scheme;

[0025] The execution order of the satellite imaging tasks is adjusted according to the imaging task priority, real-time weather information, and the satellite orbit and position information to generate the satellite imaging task execution plan.

[0026] In a possible design of the first aspect, executing the satellite imaging mission according to the satellite imaging mission execution plan to acquire an image of the target imaging area includes:

[0027] According to the satellite imaging mission execution plan, imaging parameters are configured, and images of the target imaging area are acquired according to the imaging parameters; wherein the imaging parameters include at least resolution, lighting conditions, meteorological conditions, and satellite attitude.

[0028] In a possible design of the first aspect, after executing the satellite imaging mission according to the satellite imaging mission execution plan and acquiring an image of the target imaging area, the method further includes:

[0029] monitoring the execution of the satellite imaging mission in real time to ensure that the satellite imaging mission is completed on time; and,

[0030] The working status of the target satellite is monitored in real time to ensure that the target satellite is in the best state when performing the satellite imaging mission; wherein the target satellite is a satellite that is performing the satellite imaging mission.

[0031] In a second aspect, an embodiment of the present invention provides a satellite imaging planning device for regional mapping, the device comprising:

[0032] An acquisition module is used to acquire imaging requirement information and satellite resource information, wherein the imaging requirement information at least includes target imaging area, resolution requirement, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capability;

[0033] A prediction module, used to input the imaging demand information into a mission planning model to obtain a satellite imaging mission planning scheme; the mission planning model is a mathematical model trained based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging demand to generate the satellite imaging mission planning scheme;

[0034] An optimization module, used to optimize the execution order of the satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information, and generate a satellite imaging task execution plan;

[0035] An execution module is used to execute the satellite imaging mission according to the satellite imaging mission execution plan to obtain an image of the target imaging area.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor;

[0037] The memory is connected to the processor and is used to store programs;

[0038] The processor is used to implement the satellite imaging planning method for regional mapping as described in any one of the first aspects of the embodiments of the present application by running the program in the memory.

[0039] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising computer program instructions, which, when executed by a processor, causes the processor to execute a satellite imaging planning method for regional mapping as described in any one of the first aspects of the embodiments of the present application.

[0040] The satellite imaging planning method and device for regional mapping provided by the embodiment of the present invention obtains imaging demand information and satellite resource information, wherein the imaging demand information includes at least the target imaging area, resolution requirements, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capabilities; the imaging demand information is input into the task planning model to obtain a satellite imaging task planning scheme; the task planning model is a mathematical model obtained by training based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging demand to generate a satellite imaging task planning scheme; according to the satellite resource information, the execution order of satellite imaging tasks in the satellite imaging task planning scheme is optimized to generate a satellite imaging task execution plan; the satellite imaging task is executed according to the satellite imaging task execution plan to obtain images of the target imaging area. According to the technical solution of the embodiment of the present application, efficient and accurate planning of satellite imaging tasks can be achieved to meet the high resolution and high timeliness requirements of regional mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the description, serve to explain the principles of the embodiments of the present invention.

[0042] Figure 1 A flowchart of a satellite imaging planning method for regional mapping is provided;

[0043] Figure 2 A schematic diagram of the process of training a mission planning model in a satellite imaging planning method for regional mapping;

[0044] Figure 3 A schematic diagram of the structure of a satellite imaging planning device for regional mapping provided by an embodiment of the present invention is shown;

[0045] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present invention.

[0046] The above drawings have shown specific embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the embodiments of the present invention in any way, but to illustrate the concepts of the embodiments of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0049] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the case where there is no conflict between the embodiments, the following embodiments and the features in the embodiments can be combined with each other. In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0050] First, the terms involved in the embodiments of the present invention are explained:

[0051] Geographic Information System (GIS): A system that integrates hardware, software, and data to capture, store, examine, analyze, and display all forms of geographically referenced information.

[0052] Regional mapping: usually refers to the process of collecting, processing, analyzing and displaying data for a specific geographic area in a geographic information system (GIS). This process can include data collection, data processing, data analysis, map making, application and sharing.

[0053] Time Window: refers to the time period during which a satellite can effectively image a target area. The selection of this time period is crucial because it can directly affect the quality, success rate and subsequent application effects of imaging.

[0054] With the rapid development of social economy and scientific and technological progress, the demand for geographic information is growing. Especially in the fields of urban planning, resource management, environmental protection, disaster monitoring, etc., high-resolution and timely geographic information has become an important basis for decision support. As a key means to obtain large-scale and high-precision geographic information, the importance of satellite imaging technology is self-evident. However, traditional satellite imaging technology has many limitations in mission planning, data processing and imaging quality, and it is difficult to meet the requirements of modern applications for efficiency and high precision. Therefore, it is urgent to propose a new satellite imaging planning method to improve the overall performance and user experience of satellite imaging.

[0055] Traditional satellite imaging technology usually follows the following workflow: users submit imaging requests to satellite operators based on their own application requirements (such as urban planning, agricultural monitoring, disaster warning, etc.). These requests usually include parameters such as the geographic coordinates of the target area, the required resolution, and the time window. Satellite operators select appropriate satellites for task allocation based on the user's imaging requirements. This process needs to consider factors such as the satellite's orbital parameters, attitude control capabilities, and fuel consumption to ensure that the satellite can reach the target area within the predetermined time window. Based on the satellite's orbit and attitude information, technicians calculate the best shooting time window and develop a detailed shooting plan. This step involves complex orbital mechanics calculations and attitude adjustment strategies to ensure that the satellite can accurately aim at the target area and complete the shooting mission. During the execution of the shooting mission, the satellite needs to adjust its attitude through the attitude control system (such as reaction wheels, thrusters, etc.) to aim the camera at the target area. The accuracy of attitude adjustment directly affects the quality of imaging, especially in high-resolution imaging tasks, where slight attitude deviations may cause image blur or distortion. When the satellite enters the predetermined time window, the camera begins to capture images of the target area. Satellites are usually equipped with a variety of sensors (such as optical sensors, radar sensors, etc.) that can obtain image data in different bands. In order to improve imaging quality, the satellite will also dynamically adjust the exposure time and aperture size based on lighting conditions, weather conditions and other factors.

[0056] After the satellite completes image acquisition, it transmits the original image data back to the ground station through a communication link. Due to the long distance between the satellite and the ground station, the data transmission rate is limited by the bandwidth, resulting in a long transmission time. In addition, the data transmission process may also be affected by factors such as signal interference and noise, affecting the integrity and accuracy of the data.

[0057] After receiving the raw image data sent back by the satellite, the ground station first performs preprocessing, including noise removal, geometric distortion correction, radiation correction, etc. The preprocessed image data can be used for subsequent processing such as stitching, cropping, and enhancement to generate the final image product.

[0058] The preprocessed image data usually needs to be further processed to meet the user's specific needs. For example, for large-scale imaging tasks, it may be necessary to stitch multiple small area images into a complete image; for high-resolution imaging tasks, it may be necessary to crop and enhance the image to highlight the area of ​​interest. In addition, image recognition, classification and other technologies can also be used to extract useful information (such as building distribution, vegetation coverage, etc.).

[0059] The processed image data is finally delivered to the user. The user can further analyze and apply the image according to their needs. For example, in urban planning, users can use satellite images to evaluate the expansion of the city; in disaster monitoring, users can use satellite images to quickly understand the losses in the disaster-stricken area.

[0060] Although traditional satellite imaging technology has played an important role in obtaining geographic information, it still has some limitations. Specifically, traditional satellite imaging mission planning relies on complex calculations and judgments by professional technicians. Technicians need to comprehensively consider multiple factors such as the satellite's orbital parameters, attitude control capabilities, fuel consumption, and meteorological conditions in order to develop a reasonable shooting plan. This process is not only time-consuming, but also prone to human errors, resulting in inaccurate mission planning and affecting the imaging effect. The entire process from mission planning to image delivery takes a long time and is difficult to meet users' requirements for high effectiveness. Especially in scenarios such as disaster monitoring and emergency response, users often need to obtain the latest geographic information in a short time in order to take timely countermeasures. However, due to complex mission planning and slow data transmission, traditional satellite imaging technology cannot provide high-quality image data in a short time, affecting the timeliness and accuracy of decision-making. Traditional satellite imaging technology has limitations in high-resolution imaging and is difficult to meet the needs of high-precision applications. Although some high-resolution satellites can provide sub-meter image data, the number of these satellites is limited and the imaging frequency is low, which cannot meet the needs of large-scale and high-frequency imaging. In addition, high-resolution imaging missions place higher demands on the satellite's attitude control and attitude adjustment, increasing the complexity and risk of the mission. Traditional satellite imaging technology is inefficient in data processing, especially when processing large-scale image data, which requires high computing resources and time costs. For example, operations such as stitching, cropping, and enhancement require a lot of computing resources, resulting in a long image processing time. In addition, most traditional image processing methods are rule-based manual operations that lack intelligence and automation capabilities and are difficult to adapt to rapidly changing application requirements.

[0061] Therefore, in order to solve the above problems, it is necessary to propose an improved satellite imaging planning method and device for regional mapping, so as to achieve efficient and accurate planning of satellite imaging tasks by combining big data technology, artificial intelligence technology and cloud computing technology, and meet the high resolution and high timeliness requirements of regional mapping.

[0062] The structure of the data processing device involved in the embodiment of the present invention is briefly described below. Figure 1 The figure is a flowchart of the satellite imaging planning method for regional mapping. Figure 1 As shown, the method may include the following steps 1100 to 1400:

[0063] Step 1100, acquiring imaging requirement information and satellite resource information, wherein the imaging requirement information includes at least the target imaging area, resolution requirement, time window, weather conditions, task priority, and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information, and satellite communication capabilities.

[0064] Step 1200: Input the imaging requirement information into the mission planning model to obtain a satellite imaging mission planning scheme; the mission planning model is a mathematical model trained based on historical imaging requirement information and used to predict the spatiotemporal characteristics of imaging requirements to generate a satellite imaging mission planning scheme.

[0065] Step 1300: Optimize the execution order of satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information, and generate a satellite imaging task execution plan.

[0066] Step 1400: Execute the satellite imaging mission according to the satellite imaging mission execution plan to acquire images of the target imaging area.

[0067] Among them, the target imaging area refers to a clear specific imaging area, area size and special area marking. The specific imaging area is usually expressed in longitude and latitude or other geographic coordinate systems, which can be a single location or a wide geographic area. The area size usually refers to the area or range of the target imaging area. Special area markings are, for example, key monitoring needs for certain important target areas, such as cities, disaster areas, military bases, etc.

[0068] Resolution requirements can include spatial resolution, spectral resolution, and temporal resolution. Among them, spatial resolution refers to the clarity requirement of imaging, which is usually described in terms of the actual distance per pixel on the ground, such as meters or pixels. Spectral resolution means that if imaging of a specific band is required, such as infrared, visible light, radar, etc., the resolution requirement of a specific band can be specified. Temporal resolution refers to the total time required to specify an imaging task, especially for long-term continuous observation tasks.

[0069] The time window includes imaging time, imaging frequency and imaging duration. Among them, imaging time refers to the time period within which the satellite imaging task must be completed. It can be a specific date and time, or the optimal time within a certain range, such as "morning light", "noon" or "dusk". Imaging frequency means that in the case of continuous imaging, it is necessary to set the imaging frequency of the task, such as once a day, once an hour, etc. Imaging duration refers to the total duration required for the specified imaging task, especially for long-term continuous observation tasks.

[0070] Imaging modes include push-broom mode or spot-scan mode, tilt angle, and image shooting direction. Push-broom mode or spot-scan mode means that different imaging modes will affect the coverage of imaging, such as panoramic imaging or focused imaging. Tilt angle refers to specifying the shooting angle of the satellite according to mission requirements to obtain the most appropriate viewing angle and image quality. Image shooting direction means that for a specific mission, imaging may be required from a specific direction, such as longitudinal, lateral, or oblique.

[0071] Task priority includes task urgency and task priority classification. Task urgency refers to assigning priority based on the urgency of the task, which determines the allocation and scheduling order of resources. Task priority classification refers to setting the priority classification of tasks based on different usage scenarios, such as meteorological monitoring, post-disaster assessment, military reconnaissance, etc.

[0072] Image quality requirements include radiation correction requirements and noise suppression requirements. Radiation correction requirements refer to the radiation correction requirements for imaging results, such as brightness, contrast, color and other parameters. Noise suppression requirements refer to the requirements for noise tolerance or noise suppression technology in the image to ensure image clarity and accuracy.

[0073] In some embodiments, the imaging requirement information may also include data processing requirements, such as requirements for image processing level: whether image preprocessing is required, such as geometric correction, radiation correction, etc., or post-processing, such as image stitching, classification, target extraction, etc. Requirements for data format: data format requirements for output images, such as GeoTIFF, JPEG, PNG, etc. Requirements for accuracy: geometric accuracy requirements for imaging results, such as coordinate accuracy, projection accuracy, etc.

[0074] This imaging requirement information can help the satellite mission planning and execution system to formulate reasonable mission scheduling plans to ensure the achievement of mission objectives and maximize the efficiency of satellite resource utilization.

[0075] In order to ensure efficient and accurate planning of satellite imaging missions, data collection and preprocessing are the basis of the entire solution. In the data collection and preprocessing steps, it is necessary to collect imaging demand information from multiple channels, and clean, convert and integrate these data to provide a high-quality data source for subsequent mission planning.

[0076] Specifically, the process of obtaining imaging demand information includes: receiving imaging requests from users, which include target imaging area, resolution requirements, time window, user priority and imaging mode; and obtaining meteorological conditions through a third-party platform.

[0077] Among them, users can input and submit imaging requests through a special satellite task scheduling platform or user interface according to their needs. Exemplarily, the imaging request can be a task application form, in which specific requirements are listed. For example, the target imaging area can be defined by geographic coordinates (latitude and longitude) or regional boundary boxes, and the required image clarity, such as meter level, centimeter level, etc., can be filled in at the resolution requirement. The time window is the time period or optimal date in which imaging is desired. User priority indicates the priority of the imaging task, and some tasks may require urgent processing. Imaging mode refers to optical imaging mode, SAR imaging mode, etc. Technical requirements, for example, may refer to specific requirements for image quality, data storage, etc. Resource constraints may include limitations in budget, data storage, etc. User feedback is, for example, historical imaging task feedback, usage experience, imaging demand updates, etc.

[0078] It is understandable that the imaging request from the user can be filled in by the user in the satellite operating company or the task scheduling system, and collected and integrated by the satellite operating company or the task scheduling system through the task scheduling platform. These platforms usually support automatic filling and updating of task parameters, allowing users to modify imaging requirements in real time. For some large-scale commercial users, imaging requirements can be automatically submitted through the API interface to process requirements in batches.

[0079] Meteorological conditions are obtained through third-party platforms, such as meteorological satellites, meteorological service providers, or platforms that integrate weather data. Meteorological conditions in imaging requirements, such as clear skies and cloudless skies, are obtained through integrated meteorological monitoring data, satellite remote sensing data, etc.

[0080] Satellite resource information involves data such as satellite hardware resources, satellite orbit and position information, and satellite communication capabilities. Among them, the way to obtain satellite hardware resources can be provided by satellite operators or by satellite monitoring systems. That is to say, satellite operators or satellite service providers will regularly provide detailed information about satellite hardware (such as sensor type, resolution, observation mode, etc.). Usually, this information is included in the satellite's technical specifications. Satellite operators usually deploy special monitoring systems to track the status of satellite hardware in real time, such as the health status of sensors, payload performance, etc. This information is transmitted to the ground station in real time through telemetry data and can be used in the task scheduling system.

[0081] The satellite orbit and position information can be obtained through a satellite orbit simulation and prediction system: the satellite's orbital position and motion status are usually obtained through a satellite orbit prediction system (such as a TLE data set). These systems use satellite orbit models to calculate and predict the satellite's orbital position, transit time, etc. in the future. Commonly used orbital data sets include TLE (Two-Line Element Set), which can accurately describe the satellite's orbital parameters. Alternatively, the satellite orbit and position information can be obtained through an orbit control and monitoring system: the satellite operator monitors the satellite's orbital status in real time through a ground station to ensure that the satellite is operating on the predetermined orbit. If the satellite's orbit is adjusted or offset, the operator will update the orbital information in real time. Alternatively, the satellite orbit and position information can be obtained through an orbit simulation and scheduling tool: the satellite scheduling system uses a satellite orbit simulation tool to obtain the satellite's visibility and transit time over a target area within a specific time period, thereby providing accurate orbit data for task scheduling.

[0082] The ways to obtain satellite communication capabilities include obtaining through communication satellites or ground communication links, obtaining through the resource management system of satellite operators, and obtaining through task scheduling systems. Among them, when obtaining through communication satellites or ground communication links, the communication capability of the satellite mainly depends on the communication equipment it carries and the network configuration of the ground station. The satellite's communication bandwidth, delay and other information are usually provided by the satellite communication system and fed back in real time through the ground monitoring system. When obtaining through the resource management system of the satellite operator, the satellite operator will monitor the communication link status of each satellite, including bandwidth usage, data transmission rate, communication window, etc., to ensure the efficiency of data transmission. When obtaining through the task scheduling system, when scheduling tasks, the scheduling system will reasonably plan the timing and path of data transmission based on information such as the satellite's communication capability and the amount of mission data. For example, when the satellite's communication bandwidth reaches the limit, the system may optimize the transmission order of the task or schedule other satellites to upload data.

[0083] In addition, the task scheduling system can also integrate resource information, that is, all satellite hardware resources, orbit information and communication capabilities are integrated into the satellite task scheduling system. The scheduling system will intelligently optimize the task execution order based on the user's imaging requirements and satellite resource information. For example, select the appropriate satellite, adjust the task time window, calculate the satellite's orbit visibility, arrange data transmission, etc.

[0084] After obtaining the imaging requirement information, the imaging requirement information is input into the mission planning model to obtain the satellite imaging mission planning scheme. Then, according to the satellite resource information, the execution order of the satellite imaging tasks in the satellite imaging mission planning scheme is optimized to generate a satellite imaging mission execution plan.

[0085] Specifically, the satellite imaging tasks are assigned to the corresponding satellites according to the target imaging area, time window information, satellite hardware resources, satellite orbit and position information, and satellite communication capabilities in the satellite imaging task planning scheme; and the execution order of the satellite imaging tasks is adjusted according to the imaging task priority, real-time weather information, and satellite orbit and position information to generate a satellite imaging task execution plan.

[0086] In this embodiment, the most suitable satellite is selected to perform the task according to the requirements of the satellite imaging task and the orbital parameters of the satellite. For example, some satellite imaging tasks may require satellites with high-resolution imaging to complete, while some satellite imaging tasks can be completed by satellites with medium and low resolutions. In addition, the optimal attitude adjustment strategy can be calculated based on the geographic coordinates of the target imaging area and the orbital parameters of the satellite to reduce unnecessary attitude changes of the satellite. If the satellite imaging task requires the satellite to adjust its orbit to better cover the target imaging area, it is necessary to evaluate the cost of the orbit change, such as fuel consumption, and adjust the satellite orbit when necessary.

[0087] Afterwards, the satellite imaging mission is executed according to the satellite imaging mission execution plan to obtain images of the target imaging area. That is, according to the satellite imaging mission execution plan, imaging parameters are configured, and images of the target imaging area are obtained according to the imaging parameters; wherein the imaging parameters at least include resolution, lighting conditions, meteorological conditions, and satellite attitude.

[0088] Specifically, the configuration of resolution may include the configuration of spatial resolution and the configuration of spectral resolution, wherein the configuration of spatial resolution refers to selecting appropriate sensors and imaging modes according to the user's requirements for the spatial resolution of the image. For example, for high-resolution satellite imaging tasks, select optical sensors and set higher resolution parameters; for large-scale satellite imaging tasks, select lower-resolution sensors to cover a larger area. The configuration of spectral resolution refers to selecting appropriate band combinations according to the user's requirements for the spectral resolution of the image. For example, for multi-spectral satellite imaging tasks, select red, green, blue, near-infrared and other bands; for hyperspectral imaging tasks, select multiple narrow bands.

[0089] The configuration of lighting conditions refers to automatically adjusting the exposure time and aperture size to ensure image quality according to the lighting conditions of the target imaging area, such as the sun's altitude angle, cloud cover, etc. For example, in the daytime with strong light, shorten the exposure time; in the case of weak light, increase the exposure time or increase the aperture.

[0090] Meteorological conditions refer to adjusting imaging parameters to cope with weather changes based on weather forecasts and real-time monitoring data. For example, when there is cloud cover, choose a suitable time window for imaging to avoid the image being blocked by clouds.

[0091] Satellite attitude configuration means that for tasks that require multi-angle shooting, the system will accurately control the satellite's attitude to ensure high-quality images from multiple angles. For example, in 3D modeling or stereo measurement tasks, the system will adjust the satellite's attitude to capture images of the same area from different perspectives.

[0092] Furthermore, in practical applications, if it is found that there is a time window conflict between satellite imaging tasks, the execution order of satellite imaging tasks can be automatically adjusted or some satellite imaging tasks can be postponed to ensure that all satellite imaging tasks can be completed on time.

[0093] When resource conflicts occur, the generated task queue is ensured to be within the resource limit, taking into account resource constraints such as satellite fuel and attitude control capabilities. If a satellite imaging task exceeds the resource limit, the execution order of the satellite imaging task is automatically adjusted or the satellite imaging task is canceled. For another example, for a satellite imaging task chain with dependencies, the execution order of the satellite imaging tasks can be optimized to ensure that the subsequent satellite imaging tasks can proceed smoothly after the previous satellite imaging tasks are completed.

[0094] In some feasible implementations, the goal of optimizing the execution order of satellite imaging tasks is to find an optimal satellite imaging task execution order so that all satellite imaging tasks can be completed on time with limited resources and maximize the success rate and efficiency of satellite imaging tasks.

[0095] Specifically, assume that each task queue represents a satellite imaging task execution order, where each satellite imaging task consists of a task number, which represents the execution order of the satellite imaging tasks. For example, assuming there are 5 satellite imaging tasks, the task queue D can be expressed as: D = [T1, T2, T3, T4, T5]. Among them, T1 represents the first satellite imaging task, T2 represents the second satellite imaging task, T3 represents the third satellite imaging task, T4 represents the fourth satellite imaging task, and T5 represents the fifth satellite imaging task. The order in the task queue D determines the execution order of the satellite imaging tasks.

[0096] Next, considering the need to minimize the total completion time of all satellite imaging tasks, minimize the number of satellite attitude adjustments, orbit changes and fuel consumption, give priority to completing high-priority tasks, ensure that emergency satellite imaging tasks are completed on time, ensure that each satellite imaging task is completed within the specified time window, and avoid conflicts between multiple satellite imaging tasks in the same time period, such as not being able to simultaneously photograph two different areas in the same time period, set the fitness function Fitness(C):

[0097] Fitness(D)=ω1·f time (D)+ω2·f resource (D)+ω3·f priority (D)+ω4·f conflict (D), where D represents the task queue, f time (D) represents the total completion time of all satellite imaging tasks, f resource (D) represents the number of satellite attitude adjustments, orbit changes, and the inverse of fuel consumption, f priority (D) represents the completion score of high priority tasks, f conflict (D) represents the score of conflict avoidance, ω1, ω2, ω3, and ω4 are the weights corresponding to each factor, which can be adjusted according to actual needs.

[0098] Several task queues are randomly selected, their corresponding fitness values ​​are calculated for comparison, and the task queue with the highest fitness value is selected as the target task queue. In the present embodiment, two target task queues need to be selected by this random selection method, and then, the two target task queues are cross-operated to exchange the partial task orders of the two target task queues to generate a new task queue.

[0099] Among them, the commonly used methods of crossover operation include single-point crossover, that is, cutting at a certain position of the two target task queues, exchanging the task order of the two parts, and obtaining a new task queue. Multi-point crossover, that is, cutting at multiple positions of the two target task queues, exchanging the order of multiple segments of tasks, and obtaining a new task queue. Partial mapping crossover, that is, keeping the positions of some satellite imaging tasks in the two target task queues unchanged, exchanging the order of other satellite imaging tasks, ensuring that satellite imaging tasks are not repeated, and obtaining a new task queue.

[0100] After obtaining the new task queue, the satellite imaging tasks in the new task queue are randomly modified to introduce new changes, and the random modification operation is stopped when a preset termination condition is reached.

[0101] Assume there are 5 satellite imaging missions, each with the following information:

[0102]

[0103] Five initial task queues are randomly generated, representing five different satellite imaging task execution orders:

[0104] D1=[T1, T2, T3, T4, T5];

[0105] D2=[T3, T1, T4, T2, T5];

[0106] D3=[T5, T4, T1, T3, T2];

[0107] D4=[T2, T5, T3, T1, T4];

[0108] D5=[T4, T1, T5, T2, T3].

[0109] According to the above fitness function, the quality of each initial task queue is evaluated. Assume that the weight values ​​are ω1=0.4, ω2=0.3, ω3=0.2, and ω4=0.1. Based on this, the fitness value of each initial task queue is calculated. For example, for the initial task queue D1, all satellite imaging tasks are completed within their respective time windows, the total time is 5 hours, the total number of attitude adjustments is 3+2+1+4+2=123+2+1+4+2=12 times, and the high priority tasks T1T1 and T4T4 are completed on time, with a score of 2. There is no satellite imaging task conflict, with a score of 1. Therefore, according to the above fitness function, the fitness value of the initial task queue D1 is calculated to be 0.605. The fitness values ​​of other initial task queues are calculated in turn.

[0110] Assume that according to the calculated fitness values, the initial task queues D1 and D2 are determined as the target task queues, and a single-point crossover operation is performed on the target task queues D1 and D2 to generate new task queues D6 and D7. The new task queue D6 is exchanged and mutated to randomly exchange the positions of the two satellite imaging tasks.

[0111] After multiple generations of evolution, the optimal task execution order is finally found. Assume that after 100 generations of evolution, the optimal task queue D best For: D best =[T1, T4, T2, T5, T3]. In other words, the execution order of satellite imaging tasks in the task queue can minimize task completion time, resource consumption, and ensure that high-priority tasks are completed on time.

[0112] It can be seen that by optimizing the execution order of satellite imaging tasks and ensuring that as many satellite imaging tasks as possible are completed under limited resources, the success rate and efficiency of satellite imaging missions can be effectively improved.

[0113] Furthermore, after executing the satellite imaging mission according to the satellite imaging mission execution plan and acquiring images of the target imaging area, the execution status of the satellite imaging mission can be monitored in real time to ensure that the satellite imaging mission is completed on time; and the working status of the target satellite can be monitored in real time to ensure that the target satellite is in the best state when executing the satellite imaging mission; wherein the target satellite is a satellite that is executing a satellite imaging mission.

[0114] Specifically, the satellite communication system can be used to monitor the working device of the target satellite in real time, such as the current position, current attitude, and current remaining fuel amount of the satellite, to ensure that the satellite is in the best condition when performing imaging tasks. If an abnormal situation is found, an alarm will be issued immediately and corresponding measures will be taken.

[0115] When monitoring the execution of satellite imaging tasks in real time, the progress of each satellite imaging task is tracked in real time to ensure that the satellite imaging tasks are completed on time. If a satellite imaging task is delayed, the execution order of subsequent satellite imaging tasks is automatically adjusted to minimize the impact on the overall task.

[0116] In practical applications, the scheduling of satellite imaging tasks can be dynamically adjusted based on the results of real-time monitoring. For example, if a satellite imaging task cannot be completed on time due to weather reasons, the time window of the satellite imaging task can be automatically rescheduled, or the satellite imaging task can be assigned to other available satellites.

[0117] In some feasible implementation methods, for emergencies such as natural disasters, military operations, etc., an emergency response mechanism can be activated to give priority to emergency satellite imaging tasks and interrupt ongoing non-emergency satellite imaging tasks when necessary to ensure the most reasonable use of resources.

[0118] Furthermore, data storage and management are crucial links in satellite imaging missions. It is not only responsible for storing raw images, processed images, mission logs and other data, but also needs to provide efficient data query, retrieval and sharing functions to support users to obtain the required data on demand.

[0119] Specifically, in order to improve the efficiency of data management and retrieval, different types of data need to be classified and structured for storage. In practical applications, common satellite imaging data include unprocessed satellite images, usually in binary format (such as JPEG2000, TIFF, etc.), containing metadata such as geographic coordinates, timestamps, sensor parameters, etc.; images after preprocessing, splicing, cropping, enhancement, etc., which may also include classification, annotation and other information; task logs that record detailed information of each satellite imaging mission, including but not limited to mission ID, satellite number, shooting time, target area, attitude adjustment records, data transmission records, etc.; and metadata describing related information of images and satellite imaging missions, such as resolution, band, projection mode, shooting angle, weather conditions, etc.

[0120] It can be understood that for the convenience of management and query, these data can be stored in different databases or file systems, and indexes can be established to speed up retrieval. Due to the huge amount of data generated by satellite imaging tasks, traditional stand-alone storage systems are difficult to meet the needs. Therefore, in this embodiment, a distributed storage system is adopted. For different types of satellite imaging data, select a suitable data storage method. Specifically, for large-scale unstructured data, such as original images and processed images, object storage is adopted. The object storage system can provide efficient file upload, download and version management functions, which is suitable for storing large files and historical archive data. For structured data, such as task logs and metadata, block storage is adopted. The block storage system can provide low-latency, high-performance disk access capabilities, which is suitable for frequent read and write scenarios.

[0121] In addition, in order to optimize storage costs and performance, a hot and cold data tiered storage strategy can be adopted. That is, for recently generated and frequently accessed hot data, such as the latest imaging task results, it is stored on high-performance storage media, such as SSD hard drives, to ensure fast access. For historical archived and infrequently accessed cold data, such as early imaging task results, it is stored on low-cost storage media, such as tape libraries and cold storage services, to reduce storage costs.

[0122] At the same time, in order to improve the efficiency of data query and retrieval, a complete metadata management system must be established. Metadata is the key information that describes satellite imaging data, including the image's geographic location, shooting time, resolution, band, sensor type, etc. Through metadata management, the required images and mission logs can be quickly located and retrieved.

[0123] In order to meet the diverse query needs of users, the data management system also provides flexible query and retrieval functions. Users can query data in a variety of ways. For example, users can perform fuzzy searches by entering keywords such as task ID, shooting time, target area, etc. to quickly find relevant data. Alternatively, an advanced search function based on metadata is provided, allowing users to perform precise queries based on multiple conditions, such as geographic location, time range, resolution, band, etc. Alternatively, through the map interface, users can select an area of ​​interest on the map, and the system automatically returns all satellite imaging tasks and image data in the area. Alternatively, users can select a specific time period through the timeline to view all imaging tasks and image data completed within the time period. This embodiment does not specifically limit the specific implementation method of the query and retrieval functions.

[0124] In order to help users better understand and use satellite imaging data, the data management system also provides a wealth of visualization and analysis tools. These tools can help users intuitively display and analyze image data and extract valuable information.

[0125] In general, by building an efficient and intelligent data storage and management layer, the management and utilization efficiency of satellite imaging tasks can be significantly improved.

[0126] In an embodiment of the present invention, by acquiring imaging demand information and satellite resource information, the imaging demand information includes at least the target imaging area, resolution requirements, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capabilities; the imaging demand information is input into the task planning model to obtain a satellite imaging task planning scheme; the task planning model is a mathematical model trained based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging requirements to generate a satellite imaging task planning scheme; based on the satellite resource information, the execution order of satellite imaging tasks in the satellite imaging task planning scheme is optimized to generate a satellite imaging task execution plan; the satellite imaging task is executed according to the satellite imaging task execution plan to obtain images of the target imaging area. According to the technical solution of the embodiment of the present application, efficient and accurate planning of satellite imaging tasks can be achieved to meet the high resolution and high timeliness requirements of regional mapping.

[0127] In this embodiment, the mission planning model is trained based on historical imaging demand information. Figure 2The figure is a flowchart of training a task planning model in a satellite imaging planning method for regional mapping. Figure 2 As shown, the process of training the task planning model may include the following steps 2100 to 2500:

[0128] Step 2100: Acquire historical imaging demand information, where the historical imaging demand information includes historical imaging demand, historical satellite orbit information, historical meteorological data, and corresponding historical satellite imaging mission planning.

[0129] Step 2200: extract spatial features from historical imaging requirements, extract temporal features from historical satellite orbit parameters, and extract meteorological features from historical meteorological data.

[0130] Step 2300: Fuse the spatial features, temporal features and meteorological features to obtain fused features.

[0131] Step 2400: Input the fused features into the model to be trained to obtain a predicted satellite imaging mission plan.

[0132] Step 2500: Update the parameters of the model to be trained according to the pre-constructed joint loss function, the predicted satellite imaging mission plan and the historical satellite imaging mission plan until the parameters of the model to be trained converge to obtain the mission planning model.

[0133] Among them, historical imaging requirements include the imaging area (latitude and longitude range) required by the user, imaging resolution, expected imaging time window, weather requirements (cloud coverage), etc. Historical satellite orbit parameters include the current orbit of the satellite, sensor performance, possible imaging modes, etc. Historical meteorological data include cloud coverage, wind speed, precipitation, etc.

[0134] When preprocessing the historical imaging demand information, extracting spatial features from the historical imaging demand may, for example, convert the geographic location into a format that can be used for the model, such as a rasterized map, coordinate conversion, etc. Extracting time features from historical satellite orbit parameters may, for example, convert the time window into relative time, such as calibrating the moment based on the flight cycle of the satellite orbit, or dividing the time into several time periods. Extracting meteorological features from historical meteorological data may, for example, be weather information, which may be a continuous variable, such as cloud cover percentage, or a categorical variable, such as sunny, cloudy, rainy, etc., which should be characterized according to the mission requirements. And all the above features are normalized, for example, the longitude and latitude are converted into a standardized coordinate system. This embodiment does not limit the specific method of normalization.

[0135] Afterwards, the spatial features, temporal features and meteorological features are fused, for example, spatial features, temporal features and meteorological features are sequentially spliced ​​together to obtain fused features. After the fused features are passed to the model to be trained, the model to be trained predicts the fused features and outputs a predicted satellite imaging mission plan, wherein the predicted satellite imaging mission plan may include information such as satellite orbit adjustment instructions, target imaging area, time window and satellite imaging mission priority.

[0136] Specifically, the satellite orbit adjustment instruction may be, for example, the flight path of the satellite. Satellite imaging task priority means that when there are multiple satellite imaging tasks, the task planning model may assign a priority to each satellite imaging task to help determine the order in which the satellite imaging tasks are executed.

[0137] In some feasible implementations, the predictive satellite imaging mission planning may also include a mission completion prediction, that is, based on the current satellite status and weather conditions, the mission planning model may predict whether the imaging mission can be successfully completed.

[0138] In other feasible implementations, the predicted satellite imaging mission planning may also include possible adjustment plans, that is, if the satellite imaging mission cannot be completed on time, the mission planning model may also provide adjustment plans, such as adjusting the imaging time or adjusting the satellite orbit.

[0139] In an example, suppose there is a mission that requires a satellite to take ground images in target imaging area A, where the latitude and longitude range of the target imaging area A is [30.0°N, 35.0°N, 120.0°E, 125.0°E]. It is required to be completed within two days, and the image resolution needs to reach 2 meters. At the same time, the weather requirement is that the cloud cover does not exceed 20%.

[0140] At this time, the imaging requirement information obtained is the target imaging area A: [30.0°N, 35.0°N, 120.0°E, 125.0°E], imaging resolution: 2 meters, imaging time window: 08:00 on December 10, 2024-08:00 on December 12, 2024, cloud coverage requirement: no more than 20%.

[0141] After obtaining the above imaging requirements, the regional longitude and latitude are converted into satellite orbit coverage data. According to the satellite orbit and mission time window, it is determined whether the satellite can cover the area within the time period. For the processing of meteorological data, it is assumed that the current weather forecast shows that the cloud coverage of the area is 15%, which meets the requirements.

[0142] After the above imaging requirement information is input into the mission planning model, the output satellite imaging mission planning scheme is predicted by the mission planning model: the best imaging time is 10:00 on December 11, 2024, and the satellite will adjust its orbit to cover the area with an imaging resolution of 2 meters. And because the mission deadline is tight, the mission priority is high. The prediction of the satellite imaging mission execution result is: the mission will be completed on time and the cloud coverage is moderate.

[0143] It can be seen that this mission planning model can predict the spatiotemporal characteristics of future satellite imaging missions by training historical imaging demand information, and generate the optimal satellite imaging mission planning scheme based on the input satellite imaging demand information. During use, the input imaging demand information, such as imaging area, time window, resolution requirements, etc., will be preprocessed. The mission planning model outputs the optimal satellite orbit adjustment, imaging schedule, and satellite imaging mission priority information based on these characteristics, thereby achieving efficient satellite imaging mission planning.

[0144] In one example, the joint loss function is: L total =λ reg L reg +λ cls L cls Among them, L total represents the joint loss function, L reg represents the loss function of the regression task, L cls represents the loss function of the classification task, λ reg represents the weight coefficient of the regression task, λ cls Represents the weight coefficient of the classification task.

[0145] It should be noted that the weight coefficient λ reg and λ cls Used to balance the importance of the two parts of the loss, these two weight coefficients can be adjusted according to the relative importance of the task. In practical applications, choose a suitable weight coefficient λ reg and λ cls The performance of the task planning model is crucial. Usually, the weight coefficient can be manually set according to the actual needs of the task. For example, if the regression task is more important, you can set λ reg >λ cls If the classification task is more important, you can set λ reg <λ cls If the regression task and the classification task are equally important, you can set λ reg =λ clsAlternatively, the weight coefficients can be automatically adjusted during the training of the task planning model through cross-validation to find the best weight combination. Alternatively, an adaptive weight method can be used, that is, dynamically adjusted weight coefficients are used to automatically adjust the weights according to the performance of the task planning model as the training process progresses, so that the task planning model can better balance the losses of the two tasks.

[0146] It should be noted that the regression task is used to predict continuous variables such as imaging time and satellite orbital position, and the classification task is used to predict whether the satellite imaging task can be completed on time, that is, the task fails or the task succeeds. In this embodiment, by using a joint loss function, the task planning model can simultaneously consider the losses of these two tasks during the training process, thereby improving the overall prediction performance of the task planning model.

[0147] in, n represents the total number of samples, represents the true value of the regression task of the i-th sample, Represents the predicted value of the regression task of the i-th sample.

[0148] n represents the total number of samples, Represents the true label of the classification task of the i-th sample, and its value is 0 or 1, indicating whether the task is successful. Represents the predicted probability of the classification task of the i-th sample, between 0 and 1.

[0149] In an example, suppose the user wants to predict both regression tasks and classification tasks at the same time, where the regression task is, for example, the specific value of the satellite imaging time, which may be the precise time of a certain moment. The classification task is, for example, whether the satellite imaging task is successfully executed, for example, whether the satellite can complete the imaging task on time. The classification result can be represented by a binary classification label indicating success or failure.

[0150] In this case, the joint loss function can be expressed as:

[0151]

[0152] in, is the true value of the imaging time, is the predicted value of imaging time, is the true label of whether the satellite imaging mission is successful. 1 indicates that the satellite imaging mission is successful, and 0 indicates that the satellite imaging mission fails. is the predicted probability of whether the satellite imaging mission is successful. Weight coefficient λ reg and λ cls Used to balance the importance of the two parts of the loss, these two weight coefficients can be adjusted according to the relative importance of the tasks.

[0153] In an embodiment of the present invention, a task planning model is obtained by training historical imaging demand information, which can predict the spatiotemporal characteristics of future satellite imaging tasks, and generate an optimal satellite imaging task planning scheme based on the input satellite imaging demand information. During use, the input imaging demand information, such as imaging area, time window, resolution requirements, etc., will be preprocessed, and the task planning model outputs the optimal satellite orbit adjustment, imaging time arrangement, and satellite imaging task priority information based on these characteristics, thereby realizing efficient satellite imaging task planning.

[0154] The satellite imaging planning method for regional mapping provided by an embodiment of the present invention is described above. The satellite imaging planning device for regional mapping provided by an embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0155] Figure 3 FIG. 2 shows a schematic diagram of the structure of a satellite imaging planning device for regional mapping provided by an embodiment of the present invention. Figure 3 As shown, the satellite imaging planning device 300 for regional mapping may include: an acquisition module 310 , a prediction module 320 , an optimization module 330 and an execution module 340 .

[0156] Among them, the acquisition module 310 is used to obtain imaging requirement information and satellite resource information. The imaging requirement information at least includes the target imaging area, resolution requirements, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capabilities.

[0157] The prediction module 320 is used to input the imaging demand information into the mission planning model to obtain a satellite imaging mission planning plan; the mission planning model is a mathematical model trained based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging demand to generate a satellite imaging mission planning plan.

[0158] The optimization module 330 is used to optimize the execution order of the satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information, and generate a satellite imaging task execution plan.

[0159] The execution module 340 is used to execute the satellite imaging task according to the satellite imaging task execution plan to obtain images of the target imaging area.

[0160] In one embodiment, the acquisition module 310 can be specifically used to receive an imaging request from a user, where the imaging request includes a target imaging area, resolution requirements, a time window, user priority, and an imaging mode; and obtain meteorological conditions through a third-party platform.

[0161] In one embodiment, the satellite imaging planning device 300 for regional mapping may also include a training module for obtaining historical imaging demand information, which includes historical imaging demand, historical satellite orbit information, historical meteorological data, and corresponding historical satellite imaging mission planning. Spatial features are extracted from historical imaging demands, time features are extracted from historical satellite orbit parameters, and meteorological features are extracted from historical meteorological data. The spatial features, time features, and meteorological features are fused to obtain fused features. The fused features are input into the model to be trained to obtain a predicted satellite imaging mission planning. According to the pre-constructed joint loss function, the predicted satellite imaging mission planning, and the historical satellite imaging mission planning, the parameters of the model to be trained are updated until the parameters of the model to be trained converge to obtain a mission planning model.

[0162] In one embodiment, the joint loss function is: L total =λ reg L reg +λ cls L cls Among them, L total represents the joint loss function, L reg Represents the loss function of the regression task, Lcl s represents the loss function of the classification task, λ reg represents the weight coefficient of the regression task, λ cls Represents the weight coefficient of the classification task.

[0163] In one embodiment, the optimization module 330 can be specifically used to assign satellite imaging tasks to corresponding satellites according to the target imaging area, time window information, satellite hardware resources, satellite orbit and position information, and satellite communication capabilities in the satellite imaging task planning scheme. According to the imaging task priority, real-time weather information, and satellite orbit and position information, the execution order of the satellite imaging tasks is adjusted to generate a satellite imaging task execution plan.

[0164] In one embodiment, the execution module 340 can be specifically used to execute the plan according to the satellite imaging mission, configure imaging parameters, and obtain images of the target imaging area according to the imaging parameters; wherein the imaging parameters include at least resolution, lighting conditions, meteorological conditions and satellite attitude.

[0165] In one embodiment, the satellite imaging planning device 300 for regional mapping may also include a monitoring module for real-time monitoring of the execution of satellite imaging tasks to ensure that the satellite imaging tasks are completed on time; and real-time monitoring of the working status of the target satellite to ensure that the target satellite is in the best state when performing the satellite imaging task; wherein the target satellite is a satellite that is performing a satellite imaging task.

[0166] The satellite imaging planning device for regional mapping of the embodiment of the present invention belongs to the same application concept as the satellite imaging planning method for regional mapping provided in the above embodiment of the present application, and can execute the satellite imaging planning method for regional mapping provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects of executing the satellite imaging planning method for regional mapping. For technical details not fully described in this embodiment, please refer to the specific processing content of the satellite imaging planning method for regional mapping provided in the above embodiment of the present application, which will not be repeated here.

[0167] It should be understood that the modules in the above devices can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device, wherein the processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory in the device or a memory outside the device. Alternatively, the unit in the device can be implemented in the form of a hardware circuit, and the functions of some or all units can be realized by designing the hardware circuit. The hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are realized by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit can be implemented by PLD, taking FPGA as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of some or all of the above units. All units of the above devices can be implemented in the form of a processor calling software, or in the form of a hardware circuit, or in part by a processor calling software, and the remaining part is implemented in the form of a hardware circuit.

[0168] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and run instructions, such as a CPU, a microprocessor, a GPU, or a DSP; in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0169] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0170] In addition, all or part of the units in the above device can be integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a SOC. The SOC may include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The type of the at least one processor may be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0171] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present invention. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0172] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0173] Processor 401 is used to execute the execution instructions stored in the memory to implement any one of the satellite imaging planning for regional mapping in the above method embodiments, by obtaining imaging demand information and satellite resource information, the imaging demand information includes at least the target imaging area, resolution requirements, time window, meteorological conditions, task priority and imaging mode; satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capabilities; the imaging demand information is input into the task planning model to obtain a satellite imaging task planning scheme; the task planning model is a mathematical model obtained by training based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging requirements to generate a satellite imaging task planning scheme; according to the satellite resource information, the execution order of satellite imaging tasks in the satellite imaging task planning scheme is optimized to generate a satellite imaging task execution plan; according to the satellite imaging task execution plan, the satellite imaging task is executed to obtain the image of the target imaging area. According to the technical solution of the embodiment of the present application, efficient and accurate planning of satellite imaging tasks can be achieved to meet the high resolution and high timeliness requirements of regional mapping.

[0174] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0175] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0176] The bus 403 is used to connect the memory 402 and the processor 401 .

[0177] This embodiment further provides a computer-readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various embodiments.

[0178] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0179] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0180] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0181] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0182] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0183] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, 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 an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0184] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0186] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0187] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0188] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0189] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A satellite imaging planning method for regional mapping, characterized in that: The method comprises: Acquire imaging requirement information and satellite resource information, wherein the imaging requirement information at least includes target imaging area, resolution requirement, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capability; The imaging requirement information is input into a task planning model to obtain a satellite imaging task planning scheme; the task planning model is a mathematical model trained based on historical imaging requirement information and used to predict the spatiotemporal characteristics of imaging requirements to generate the satellite imaging task planning scheme; According to the satellite resource information, the execution order of the satellite imaging tasks in the satellite imaging task planning scheme is optimized to generate a satellite imaging task execution plan; The satellite imaging mission is executed according to the satellite imaging mission execution plan to acquire an image of the target imaging area.

2. The method according to claim 1, characterized in that: The obtaining of imaging requirement information includes: receiving an imaging request from a user, wherein the imaging request includes the target imaging area, the resolution requirement, the time window, the user priority, and the imaging mode; The meteorological conditions are obtained through a third-party platform.

3. The method according to claim 1, characterized in that The training process of the task planning model includes: Acquire the historical imaging demand information, wherein the historical imaging demand information includes historical imaging demand, historical satellite orbit information, historical meteorological data, and corresponding historical satellite imaging mission planning; Extracting spatial features from the historical imaging requirements, extracting temporal features from the historical satellite orbit parameters, and extracting meteorological features from the historical meteorological data; Fusing the spatial feature, the temporal feature, and the meteorological feature to obtain a fused feature; Inputting the fused features into the model to be trained to obtain a predicted satellite imaging mission plan; According to the pre-constructed joint loss function, the predicted satellite imaging mission plan and the historical satellite imaging mission plan, the parameters of the model to be trained are updated until the parameters of the model to be trained converge, thereby obtaining the mission planning model.

4. The method according to claim 3, characterized in that The joint loss function is: L total =λ reg L reg +λ cls L cls Among them, L tota l represents the joint loss function, L reg represents the loss function of the regression task, L cls represents the loss function of the classification task, λ reg represents the weight coefficient of the regression task, λ cls Represents the weight coefficient of the classification task.

5. The method according to claim 1, characterized in that The step of optimizing the execution order of satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information to generate a satellite imaging task execution plan includes: Allocating the satellite imaging task to a corresponding satellite according to the target imaging area, the time window information, the satellite hardware resources, the satellite orbit and position information, and the satellite communication capability in the satellite imaging task planning scheme; The execution order of the satellite imaging tasks is adjusted according to the imaging task priority, real-time weather information, and the satellite orbit and position information to generate the satellite imaging task execution plan.

6. The method according to claim 1, characterized in that The step of executing the satellite imaging mission according to the satellite imaging mission execution plan to obtain an image of the target imaging area includes: According to the satellite imaging mission execution plan, imaging parameters are configured, and images of the target imaging area are acquired according to the imaging parameters; wherein the imaging parameters include at least resolution, lighting conditions, meteorological conditions, and satellite attitude.

7. The method according to claim 1, characterized in that After executing the satellite imaging mission according to the satellite imaging mission execution plan and acquiring the image of the target imaging area, the method further includes: monitoring the execution of the satellite imaging mission in real time to ensure that the satellite imaging mission is completed on time; and, The working status of the target satellite is monitored in real time to ensure that the target satellite is in the best state when performing the satellite imaging mission; wherein the target satellite is a satellite that is performing the satellite imaging mission.

8. A satellite imaging planning device for regional mapping, characterized in that: The device comprises: An acquisition module is used to acquire imaging requirement information and satellite resource information, wherein the imaging requirement information at least includes target imaging area, resolution requirement, time window, meteorological conditions, task priority and imaging mode; the satellite resource information includes satellite hardware resources, satellite orbit and position information and satellite communication capability; A prediction module is used to input the imaging demand information into a mission planning model to obtain a satellite imaging mission planning scheme; the mission planning model is a mathematical model trained based on historical imaging demand information and used to predict the spatiotemporal characteristics of imaging demand to generate the satellite imaging mission planning scheme; An optimization module, used to optimize the execution order of the satellite imaging tasks in the satellite imaging task planning scheme according to the satellite resource information, and generate a satellite imaging task execution plan; An execution module is used to execute the satellite imaging mission according to the satellite imaging mission execution plan to obtain an image of the target imaging area.

9. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the satellite imaging planning method for regional mapping as described in any one of claims 1 to 7 by running the program in the memory.

10. A computer program product, characterized in that The method comprises computer program instructions, which, when executed by a processor, enable the processor to execute the satellite imaging planning method for regional mapping as claimed in any one of claims 1 to 7.

Images