Dynamic satellite capability view generation and interaction system

Abstract

The present invention belongs to the technical field of space-based intelligent information systems and resource organization methods, and in particular, to a dynamic satellite capability view generation and interaction system. It includes: a data preprocessing and fusion module, which is used to preprocess and fuse satellite data collected in real time, generate high-dimensional data through capability virtualization, a high-dimensional capability view generation module, which is used to process high-dimensional data using dimensionality reduction technology and generate satellite capability views; a capability view interactive module, which is used to provide an interactive interface with a satellite capability view, and is also used to receive satellite capabilities and subscription requests selected by users; an order processing and task planning module, which is used to use a capability release ordering mechanism based on user selection and current satellite status, and use an intelligent matching algorithm to obtain the optimal task planning solution, and issue task execution instructions.

Description

Technical Field

[0001] The present invention belongs to the technical field of space-based intelligent information systems and resource organization methods, and in particular relates to a dynamic satellite capability view generation and interaction system. Background Art

[0002] With the development of satellite technology, satellite networks have become a key global communication infrastructure, providing multiple functions including data collection, communication transmission and geographic information services. In this context, how to efficiently manage and utilize satellite resources, especially to respond to user needs in real time, has become an important technical challenge. Existing technologies include but are not limited to satellite resource dynamic allocation technology, satellite data real-time processing and analysis technology, and user interface design, aiming to improve the utilization of satellite resources and the quality of user services.

[0003] The development of satellite network infrastructure and satellite applications has made it possible for ordinary users to use satellites and directly obtain space-based resources. In 2024, SpaceX's Starlink has provided Internet access services to more than 2 million global users in more than 70 countries and regions, and ordinary smartphones can directly communicate with Beidou satellites through short messages. It is conceivable that in the future, ordinary people can use handheld terminals to allocate space-based resources at any time according to their needs and obtain the required information services. For example, when an earthquake disaster occurs, space-based resources can be called up immediately to determine the extent of the disaster and road damage; when an air crash or shipwreck occurs, search and rescue can be carried out immediately; when sudden space environmental events such as geomagnetic storms occur, real-time observation can be carried out. This section proposes a capability view designed for user terminals that can interactively view and order space-based resources.

[0004] Although existing technologies have improved the management efficiency of satellite resources and the response speed of user services to a certain extent, there are still some technical defects. For example:

[0005] Low resource utilization: Existing resource management systems often lack sufficient flexibility to fully utilize dynamically changing satellite resources, especially in the face of emergency response and large-scale data processing needs.

[0006] Low accuracy: Due to the lack of real-time resource status update and user demand matching mechanism, existing technologies cannot ensure the best match between resource allocation and actual user needs, affecting service quality and user experience.

[0007] Insufficient interactivity: Existing systems are often not intuitive and flexible enough in terms of user interaction design, which limits users’ ability to directly participate in resource ordering and management, and reduces the overall efficiency of the system and user satisfaction. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the prior art and propose a dynamic satellite capability view generation and interaction system.

[0009] In order to achieve the above object, the present invention proposes a dynamic satellite capability view generation and interaction system, the system comprising:

[0010] The data preprocessing and fusion module is used to preprocess and fuse the satellite data collected in real time, and generate high-dimensional data through capability virtualization.

[0011] A high-dimensional capability view generation module is used to process high-dimensional data using dimensionality reduction technology to generate satellite capability views;

[0012] A capability view interactive module, configured to provide an interactive interface with a satellite capability view and to receive satellite capabilities selected by a user and a subscription request; and

[0013] The order processing and mission planning module is used to obtain the optimal mission planning plan based on user selection and current satellite status, using the capability release ordering mechanism and the intelligent matching algorithm, and issue mission execution instructions.

[0014] Preferably, the satellite data includes satellite status data and resource data.

[0015] Preferably, the preprocessing includes: performing numerical standardization and noise filtering on the satellite data collected in real time.

[0016] Preferably, the capability virtualization is as follows: quantifying the satellite's acquisition, processing and communication resources, virtualizing the resource capabilities into three levels: device attribute layer, satellite capability layer and cluster capability layer, and integrating task association constraints, task load constraints, spatiotemporal association constraints and node attribute constraints, abstracting the fused satellite data, and constructing high-dimensional data representing the capability virtualization model.

[0017] Preferably, the high-dimensional capability view generation module includes:

[0018] A dimensionality reduction processing unit, for projecting high-dimensional data into a lower-dimensional space using a deep topology adaptive tensor network, a principal component analysis method, or a uniform manifold approximation and projection method;

[0019] The visualization unit is used to superimpose the nonlinear surfaces projected by each satellite in a lower-dimensional space to form a satellite capability view, and use different colors and surfaces to display different capability dimensions to achieve a visualization representation of the capability view.

[0020] Preferably, the processing process of the deep topology adaptive tensor network includes:

[0021] By combining graph convolutional networks with tensor decomposition, the topological structure of the data is expressed in the form of a graph, and tensor decomposition and feature extraction are performed on this basis; wherein the feature extraction is to perform adaptive feature learning in combination with variational autoencoders to dynamically adjust the feature extraction process to adapt to real-time changes in data;

[0022] Information entropy maximization is applied to the extracted features, combined with multi-scale feature aggregation, so as to select and fuse the most expressive features.

[0023] Preferably, the processing of the order processing and task planning module includes:

[0024] Provides synchronization of information between the ground and satellites, based on user selection, using a combination of processes and channels that utilize satellite and / or constellation capabilities;

[0025] According to the published capability view, the required satellite capabilities are selected to form user requirements. The capability release and ordering mechanism is adopted. Through demand submission and task conflict decision-making, the intelligent matching algorithm is used to calculate the optimal task planning plan, obtain the result of capability ordering, and feed it back to the corresponding user, and issue task execution instructions.

[0026] Compared with the prior art, the advantages of the present invention are:

[0027] 1. Significant improvement in resource utilization efficiency: Through dynamically updated capability views, this technical solution can reflect the latest status of satellite resources in real time, making resource allocation more flexible and accurate. This not only improves resource utilization efficiency, but also enables quick response to users’ urgent needs;

[0028] 2. Improvement of service quality and user experience: With the help of intelligent matching algorithm, the present invention can ensure a high degree of matching between resource allocation and user needs, greatly improving service quality. At the same time, the friendly user interaction interface and simplified operation process significantly improve the user experience;

[0029] 3. Simplification of operation and management: The interactive design adopted by the present invention reduces the operational complexity of users in the resource ordering and management process, so that even non-professional users can easily complete the ordering and management of satellite resources, thereby reducing the operational error rate and improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of the space-based resource virtualization publishing interface of this system;

[0031] Figure 2 This is a schematic diagram of the space-based resource virtualization ordering interface of this system;

[0032] FIG3 is a schematic diagram of the capability view of users of different priorities, wherein FIG3(a) is a high priority user and FIG3(b) is a low priority user;

[0033] Figure 4 This is a schematic diagram of the acquisition capability view based on the heat map;

[0034] Figure 5 It is a dimensionality reduction analysis of high-dimensional capability views;

[0035] Figure 6 It is the user terminal capability release process;

[0036] Figure 7 It is the user terminal capability ordering process;

[0037] Figure 8 It is a comparison between traditional planning and the publish-subscribe mechanism adopted by the present invention;

[0038] Fig. 9 It is a data topology diagram. DETAILED DESCRIPTION

[0039] Capability view for interactive ordering:

[0040] (1) The capability view provides a bridge between users and space-based resources and is a window for the capability pool to interact with the outside world. Based on the organization of capability data, the capability view displays the capability pool in an understandable and readable form. The capability view visualizes the structured data according to application requirements, that is, the visualization of the capability cluster. While displaying capability data, the capability view provides a user-oriented interaction method, collects user requirements, decomposes the requirements, and submits them to the planning module for capability subscription.

[0041] (2) The satellite cluster capability data displayed in the capability view is generated in real time on the user terminal based on the characteristics of the application requirements. After the user logs in to the satellite through the direct satellite terminal, the data between the user terminal and the portal satellite is synchronized, and the latest resource status data of the satellite cluster, on-board task list and other information are sent to the user terminal. The terminal generates capability cluster data through capability virtualization calculation. The capability view is dynamic, just like the satellite capability and capability cluster. As the specific conditions of time and space change, the content displayed in the capability view also changes dynamically.

[0042] (3) In the capability view scenario, visualization needs to solve two problems: one is to display the numerous and complex status information of the satellite Internet in a limited screen space; the other is to enable system users to quickly find the information they are interested in. From the perspective of interaction, the amount of information displayed can be reduced by limiting the conditions set by the user, thereby speeding up the response speed from data calculation, retrieval to actual display. From the perspective of display, the Overview / Detail paradigm can be used to organize and present layers according to specific scenarios. Under the Overview / Detail paradigm, two or more viewports are displayed at the same time, one of which is the Overview viewport, which gives an overall overview of the data, and the other viewports are Detail viewports, which can display the details of a certain area in the scene from multiple angles.

[0043] (4) The acquisition capability view is used to describe the satellite cluster’s acquisition capability for a specific area within a certain period of time, including telemetry frequency band, image resolution, etc. Through the acquisition capability view, users can understand the observation frequency, observation intensity, and observation accuracy of the satellite cluster over a specified area in a certain period of time in the future.

[0044] (5) Publishing and ordering acquisition capability views: First, the user interactively selects a space area and then specifies the time period of interest. The capability view can then display the acquisition capability status in the area. The information acquisition capability of space-based resources is displayed in the form of overlay strips, and the available capabilities and ordered capabilities are marked with different colors, such as Figure 1 As shown. The pink strip in the figure is the available capacity, which is the spatiotemporal coverage of the area by satellites in the resource cluster, that is, the acquisition capacity cluster; the blue part in the figure is the subscribed capacity, that is, this part of the acquisition capacity has been occupied ① Spatiotemporal range selection: the spatiotemporal range of the observation area that the user is concerned about; ② Satellite selection: select the required satellite, that is, the resource cluster selection; ③ Subscription plan selection: Customized plan: interactively select the area to be observed to subscribe to information acquisition capabilities, preset plan: pre-designed multi-satellite joint observation plan, including information acquisition, calculation and transmission capabilities.

[0045] The user interactively selects satellite capabilities on this capability view, and the capability virtualization algorithm on the terminal performs resource constraint checks and calculations of the ordering plan, and displays the corresponding results, including the selected capabilities, unavailable capabilities, and information transmission capabilities and information computing capabilities. Figure 2 The red area in the figure is the subscription area selected interactively by the user; the gray part in the figure is the user selecting the red area, that is, subscribing to a part of the capabilities, which becomes unsubscribeable after the capability virtualization constraint check; ④ Displays the time when the user can obtain information data, that is, the information processing and distribution capabilities.

[0046] (6) The capability view supports a multi-user and multi-priority user system. Different resources are available for subscription according to different user priorities, which actually reflects the division of a resource cluster. Therefore, different capability views are presented. The capability view of users with different priorities is shown in Figure 3. Figure 3(a) is a high-priority user, and Figure 3(b) is a low-priority user. As can be seen from the figure, the capability view of high-priority users has more selectivity than that of low-priority users.

[0047] (7) When the number of satellite clusters increases, the capability view will be confusing. A heat map can be used to display satellite capabilities. Figure 4 As shown in the figure, various heat maps are generated based on the different attributes of the acquisition capability. The depth of the color indicates the degree of acquisition capability. Users can use the grid to select the required capability.

[0048] Figure 4 In the left image, the increase in the number of satellites and time range will lead to a confusing display of observation coverage; the upper right image shows the acquisition capability view generated by the number of coverages; the lower right image shows the acquisition capability view generated by the image resolution.

[0049] (8) The transmission capacity view is used to describe the ability of two endpoints to transmit data through a satellite Internet link at a specific moment, including expected transmission bandwidth, transmission quality, etc. The end-to-end communication capacity view is based on the communication capacity view. Users can understand the transmission capacity status between two endpoints of interest at a certain time in the future, or reserve the bandwidth of a certain link at a certain time in the future. After the user determines the geographic information, the capacity view also displays the access status of ground equipment within the relevant range. The display process first requires the user to interactively specify the uplink starting point and downlink end point, and the time period of interest. The transmission capacity view then displays several transmission links with the highest matching degree based on the transmission capacity that the satellite cluster can provide. Different line widths on the communication link represent the expected available transmission bandwidth, and different colors represent the expected transmission quality. For example Figure 5 Shown is the dimensionality reduction analysis of the high-dimensional capability view.

[0050] The dynamic satellite capability view and interactive user interface introduced in the present invention provide a new solution for the management and utilization of satellite resources. This not only improves the flexibility and accuracy of resource utilization, but also greatly simplifies the user's operation process and improves the user experience. Through the real-time updated capability view and intelligent matching algorithm, the present invention effectively solves the technical defects of the prior art in terms of low resource utilization, insufficient matching accuracy, and weak interactivity, and realizes efficient management and utilization of satellite resources.

[0051] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0052] Example

[0053] An embodiment of the present invention provides a dynamic satellite capability view generation and interaction system, the system comprising:

[0054] The order processing and mission planning module is used to obtain the optimal mission planning solution based on user selection and current satellite status, using the capability release ordering mechanism and the intelligent matching algorithm, and issue mission execution instructions.

[0055] Data preprocessing and fusion module: First, the satellite data is preprocessed and fused to provide it for dimensionality reduction analysis.

[0056] High-dimensional capability view generation module: It uses dimensionality reduction technology to process pre-processed data and generate dynamic and intuitive satellite capability views.

[0057] Capability View Interactive Module: Users browse the satellite capability view through an interactive interface, select the required satellite capabilities, and submit an order request.

[0058] Order processing and mission planning module: Based on user selection and current satellite status, the module uses intelligent planning algorithms to calculate the optimal mission planning solution and issue mission execution instructions.

[0059] High-dimensional data fusion and dimensionality reduction analysis: In order to intuitively display and analyze the resource capabilities of complex giant constellation systems, a high-dimensional capability view dimensionality reduction analysis method is used. This method converts the high-dimensional feature vector table of the satellite into a format that can be visualized in two-dimensional or three-dimensional space through dimensionality reduction technology.

[0060] Capability Data preprocessing: This includes steps such as normalizing values ​​and filtering out noise to facilitate data analysis. After processing the raw data, high-dimensional data processing techniques such as principal component analysis (PCA) or uniform manifold approximation and projection (UMAP) are used to project the high-dimensional data into a lower-dimensional space.

[0061] Capability virtualization is to quantify the satellite's acquisition, processing, communication and other resources, and divide the space-based resource capability virtualization into three levels, namely the device attribute layer, satellite capability layer and cluster capability layer, and integrate task association constraints, task load constraints, spatiotemporal association constraints, node attribute constraints, etc., such as the fastest return and shooting coverage constraints in task load constraints and spatiotemporal association constraints. Abstract the fused satellite data to construct high-dimensional data representing the capability virtualization model.

[0062] High-dimensional capability view generation: Through dimensionality reduction processing, the high-dimensional capability feature vector of each satellite will be mapped to a nonlinear surface superposition in the reduced-dimensional space to form a capability view. In this view, different capability dimensions can be represented by different colors and nonlinear surfaces to increase the information richness of the view.

[0063] In order to further optimize the performance of the capability view, cluster analysis can also be applied to identify potential patterns in the data, such as forming resource clusters based on the similarity of satellite resources, so as to more accurately plan resources and allocate tasks. In addition, interactive elements can be integrated into the capability view, allowing users to further explore the data through interactive operations, such as zooming in on an area, clicking to view detailed information, or adjusting the angle and scale of the view.

[0064] Ultimately, dimensionality reduction analysis through this high-dimensional capability view can provide the operation control center and end users with an intuitive and easy-to-understand way to browse and analyze the resource capabilities of the giant star constellation, thereby better supporting decision-making and mission planning.

[0065] The capability publishing and subscription mechanism for terminals to directly connect to satellites.

[0066] The capability release and subscription mechanism is the process and channel for users to select and use satellite / constellation capabilities. The release and subscription mechanism provides information synchronization between the ground and orbit. The ground terminal selects the required satellite capabilities based on the published capability view, forms user needs, and finally obtains the result of capability subscription through process nodes such as demand submission, mission planning, and on-orbit detection, and feeds back to the corresponding user.

[0067] The process of user terminal interactively ordering space-based resources instant service:

[0068] ① Intra-cluster state synchronization process: The master star in the cluster initiates the state consistency process to synchronize the latest state information such as the location information, resource state information, and task list of all slave stars in the cluster to the master star;

[0069] ②Terminal capability view publishing process: User terminal logs in, the cluster master satellite transmits the stored information such as the location information, resource status information and task list of the satellites in the cluster to the user terminal; the terminal generates a capability view and presents it to the user; Figure 6 The figure shows the user terminal capability publishing process.

[0070] ③ User capability subscription process: Users interactively subscribe to capability views through the terminal capability view subsystem. After users select capabilities, the terminal capability view subsystem interactively detects the resources selected by users through the constraint algorithm model and cancels capabilities that are no longer available. When user requirements are confirmed, the requirements are uploaded to the capability virtualization subsystem; Figure 7 The figure shows the user terminal capability ordering process.

[0071] ④ Order confirmation / on-orbit intelligent planning process: After receiving the user's demand, the slave satellite will forward the demand to the master satellite. After receiving the user's demand, the master satellite will initiate a multi-task autonomous decision-making process through the on-orbit intelligent planning subsystem, generate a multi-satellite planning plan, and send the order confirmation message to the user terminal through the access satellite of the user terminal;

[0072] ⑤ Planning scheme distribution process: After the planning scheme is generated, the master star sends the task list to each slave star. Each slave star generates the task list of the master star and returns confirmation information.

[0073] Figure 8 It is a comparison between traditional planning and the publish-subscribe mechanism adopted by the present invention.

[0074] Key technical points:

[0075] 1. Dynamic satellite capability view generation technology

[0076] Real-time data processing: Using advanced data synchronization technology, satellite status and resource data are collected and processed in real time to ensure the real-time and accuracy of the capability view data.

[0077] Efficient capability virtualization: By abstracting actual satellite resources and capabilities and building a capability virtualization model, the transformation from physical resources to logical capabilities can be achieved, improving the flexibility and efficiency of resource management.

[0078] Intelligent view generation algorithm: Uses optimization algorithms to dynamically generate and update satellite capability views, allowing users to filter based on time, space and functional requirements to display the most suitable satellite resources and capabilities.

[0079] 2. Interactive user interface design

[0080] Intuitive Operation Process: Adopting the latest UI / UX design principles to create a concise, easy-to-operate user interface, allowing users to easily browse and select satellite capabilities.

[0081] Custom Filtering and Search: Provides powerful filtering and search tools that allow users to locate and select desired satellite resources and capabilities based on specific needs.

[0082] Real-time feedback and guidance: Design a real-time feedback mechanism to provide users with immediate operation results and guidance information to enhance user operation experience and satisfaction.

[0083] 3. High-dimensional data fusion and dimensionality reduction analysis

[0084] Self-implemented deep topology adaptive tensor network DTAT-Net:

[0085] DTAT-Net realizes a dynamic and efficient satellite data analysis and visualization technology by integrating tensor decomposition, deep learning topology adaptation and multi-scale feature aggregation. It uses a deep graph network to dynamically adjust the topology of the data, while using tensor decomposition to efficiently process high-dimensional data and multi-scale feature aggregation to optimize data expression.

[0086] 1) Integration of Deep Graph Networks and Tensor Decomposition

[0087] By combining graph convolutional networks (GCN) with tensor decomposition, the topological structure of the data is expressed in the form of a graph, and tensor decomposition is performed on this basis: Among them (A (1) (GCN)) represents the factor matrix on the pattern (1) learned by the graph convolutional network, which is used to adapt to the topological changes of the data.

[0088] 2) Information entropy and feature aggregation

[0089] Apply information entropy maximization at the feature level, combined with multi-scale feature aggregation, to select and fuse the most expressive features: where F i (entropy maximized) represents the (i)th scale feature after applying information entropy maximization, ω i are the weights obtained through learning.

[0090] 3) Adaptive re-parameterization technology

[0091] Combined with variational autoencoder (VAE) for adaptive feature learning, it can dynamically adjust the feature extraction process to adapt to real-time changes in data: where z is the latent feature representation suitable for dynamic view generation.

[0092] 4) Algorithm Process

[0093] Data preparation: Organize into tensor format suitable for deep network processing.

[0094] Initialize the graph structure: Build a graph based on the initial dataset and apply the graph convolutional network.

[0095] Tensor decomposition and feature extraction:

[0096] The improved graph convolutional network and tensor decomposition method are used to extract features.

[0097] Maximize information entropy and aggregate multi-scale features.

[0098] Adaptive feature learning:

[0099] Use VAE to dynamically adjust features.

[0100] Dynamic topology update:

[0101] Update the graph topology based on real-time data.

[0102] Retrain the model to adapt to the new data topology.

[0103] View generation: Use aggregated features to generate dynamically updated views of satellite capabilities in a low-dimensional space.

[0104] Interaction and Update: Adjust view and model parameters based on user input.

[0105] Data topology diagram, using the basic idea of ​​Graph Convolutional Network (GCN) to project satellite data into low-dimensional space and perform dimensionality reduction. In this diagram, each node represents a satellite, and the color of the node represents the value of the first principal component, which helps to observe the differences between different satellites in the main feature dimensions. Fig. 9 shown.

[0106] DTAT-Net is efficient and accurate in processing high-dimensional data, and can flexibly adjust according to changes in data, providing a highly adaptive and powerful tool for satellite data analysis.

[0107] In one embodiment, high-dimensional data processing techniques such as principal component analysis (PCA) or uniform manifold approximation and projection (UMAP) can also be used to convert the high-dimensional feature vector table of the satellite into a format that can be intuitively displayed in two-dimensional or three-dimensional space. However, DTAT-Net is more efficient and accurate.

[0108] The capability data are preprocessed, such as numerical normalization and noise filtering, to more accurately reflect the satellite capabilities.

[0109] The high-dimensional capability view generated by dimensionality reduction processing can display the capability characteristics of each satellite in the form of nonlinear surface superposition. Different capability dimensions can be represented by different colors and surfaces, which improves the information richness of the view.

[0110] The ultimate goal of this invention is to provide better user services and maximize the value of satellite clusters. This technology aims to make it easier for the general public and non-professionals to understand and use the capabilities brought by space-based resources, break away from the original management and control thinking of special satellites, and provide more diverse space-based resource information services, which are suitable for various satellite application products, including but not limited to satellite Internet access services, earth observation, remote sensing data analysis, and disaster emergency response.

[0111] The following are the advantages of the present invention compared with the prior art:

[0112] Improve resource utilization: Existing technologies cannot fully utilize dynamically changing satellite resources, especially when responding to emergencies or processing large-scale data needs. The present invention makes resource allocation more flexible and efficient through a dynamic satellite capability view updated in real time to adapt to changing needs and resource status.

[0113] Improve matching accuracy: Resource allocation in the background technology often cannot accurately match user needs, affecting service quality and user experience. The present invention introduces an efficient user interaction mechanism and intelligent matching algorithm to ensure the best match between satellite resource allocation and actual user needs.

[0114] Enhanced interactivity: The user interaction design of existing systems is usually not intuitive and flexible enough, which limits the ability of users to directly participate in resource management and ordering. The present invention improves user participation by designing a friendly interactive interface and simplified operation process, thereby improving the overall efficiency of the system and user satisfaction.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. A dynamic satellite capability view generation and interaction system, characterized in that: The system comprises: The data preprocessing and fusion module is used to preprocess and fuse the satellite data collected in real time, and generate high-dimensional data through capability virtualization. The satellite data includes satellite status data and resource data. The capability virtualization is to quantify the acquisition, processing and communication resources of the satellite, virtualize the resource capabilities into three levels: device attribute layer, satellite capability layer and cluster capability layer, and integrate the task association constraints, task load constraints, spatiotemporal association constraints and node attribute constraints to abstract the fused satellite data and construct high-dimensional data representing the capability virtualization model. A high-dimensional capability view generation module is used to process high-dimensional data using dimensionality reduction technology to generate satellite capability views; A capability view interactive module, configured to provide an interactive interface with a satellite capability view and to receive satellite capabilities selected by a user and a subscription request; and The order processing and mission planning module is used to provide information synchronization between the ground and the satellite based on the user's selection, combined with the process and channel of using satellite and / or constellation capabilities; select the required satellite capabilities according to the published capability view, form user requirements, adopt the capability publishing and ordering mechanism, and calculate the optimal mission planning scheme through demand submission and mission conflict decision-making using the intelligent matching algorithm to obtain the result of capability ordering, and feedback it to the corresponding user, and issue a task execution instruction; The high-dimensional capability view generation module includes: a dimensionality reduction processing unit for projecting high-dimensional data into a lower-dimensional space using a deep topology adaptive tensor network, a principal component analysis method, or a uniform manifold approximation and projection method; The processing process of the deep topology adaptive tensor network includes: By combining graph convolutional networks with tensor decomposition, the topological structure of the data is expressed in the form of a graph, and tensor decomposition and feature extraction are performed on this basis; wherein the feature extraction is to perform adaptive feature learning in combination with variational autoencoders to dynamically adjust the feature extraction process to adapt to real-time changes in data; Information entropy maximization is applied to the extracted features, combined with multi-scale feature aggregation, so as to select and fuse the most expressive features.

2. The dynamic satellite capability view generation and interaction system according to claim 1, characterized in that: The preprocessing includes: performing numerical standardization and noise filtering on the satellite data collected in real time.

3. The dynamic satellite capability view generation and interaction system according to claim 1, characterized in that: The high-dimensional capability view generation module also includes: The visualization unit is used to superimpose the nonlinear surfaces projected by each satellite in a lower-dimensional space to form a satellite capability view, and use different colors and surfaces to display different capability dimensions to achieve a visualization representation of the capability view.

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