Cloud side-end collaborative satellite data management method and system

Abstract

The invention discloses a cloud side-end collaborative satellite data management method and system, and belongs to the technical field of satellite-borne computing. The method comprises the following steps that: an end side acquires a physical parameter signal and converts the physical parameter signal into sensing data; the edge side processes the perception data in combination with auxiliary data to obtain semantic data, and the perception data, the auxiliary data and the semantic data are stored in a distributed mode; and the cloud side manages the space-time big data including the semantic data, and carries out unified fusion expression, query and analysis processing on the space-time big data. According to the invention, multi-satellite cloud-entering cooperative intelligent calculation can be supported, real-time, intelligent, networked and holographic satellite data processing of the earth space environment is finally realized, and basic support for data processing and calculation processing is provided for an intelligent satellite brain.

Description

Technical Field

[0001] This invention relates to the field of spaceborne computing technology, specifically to a cloud-edge-device collaborative satellite data management method and system. Background Technology

[0002] With the continuous integration and development of aerospace technology, the Internet of Things, big data, and artificial intelligence, intelligent satellite brain technology has become an important development direction. In the intelligent satellite real-time data processing mode, the system not only needs to complete the real-time acquisition of satellite data, but also needs to analyze and intelligently calculate the sampled data in real time, and achieve efficient distributed intelligent processing of data through the aggregation, calculation, and analysis of massive amounts of big data.

[0003] Current mainstream satellite-borne computing architectures are mostly dedicated architectures, primarily focusing on high-performance dedicated computing systems and edge computing platforms. A primary drawback of these approaches is their limited onboard data processing capabilities, making them ill-suited for the intelligent onboard processing of space data. Furthermore, they lack real-time interaction with the ground, hindering effective real-time management of satellite data from the ground.

[0004] Regarding the few existing works on cloud-edge-device collaborative satellite data processing, such as Chinese patent application 202211673001.3 which discloses a cloud-edge collaborative computing system and its management method for satellite constellations, although it can integrate the computing resources of satellite constellations, realize computing collaboration between satellites and between satellites and ground, and improve data processing efficiency and computing resource utilization, it has the following significant shortcomings in data management:

[0005] 1. Existing work only briefly mentions remote sensing data, but does not describe the structure of the data itself in detail, nor does it explicitly explain or design the data processing methods.

[0006] 2. Existing work has only briefly touched upon data transmission from the satellite edge to the ground cloud, but has not considered the significant characteristics of satellite-to-ground and satellite-to-satellite communication, such as intermittent connectivity, bandwidth limitations, high latency, and unstable links. These characteristics are inherent in current practical data links and are important considerations for the system's practicality.

[0007] 3. Existing work has not addressed the need for edge autonomy during cloud-edge collaboration when communication with the ground station is impossible due to satellite orbital limitations (or when full-time communication is not possible in satellite relay communication scenarios).

[0008] 4. Existing work has involved the control of onboard payloads. However, it has not considered the fine-grained management of control parameters for end-side payload equipment and the linkage with cloud-edge-end data management processes. For example, the specific closed-loop details of parameter persistence at the edge to cope with reconnection and recovery, and on-demand data transmission.

[0009] In summary, there is currently no systematic work on intelligent satellite computing and multi-satellite cloud collaborative data management, which makes it difficult to meet the requirements of real-time, intelligent, networked, and holographic processing of satellite data for the Earth's space environment. Summary of the Invention

[0010] This invention discloses a cloud-edge-device collaborative satellite data management method and system, aiming to propose and design a mechanism and system for cloud-edge-device collaborative data management. This cloud-edge-device collaborative data management can support multi-satellite cloud-based collaborative intelligent computing, ultimately realizing real-time, intelligent, networked, and holographic processing of Earth space environment satellite data, providing fundamental support for data processing and computation for intelligent satellite brains.

[0011] To achieve the above objectives, the technical solution of the present invention includes the following:

[0012] A cloud-edge-device collaborative satellite data management method, the method comprising:

[0013] The device acquires physical parameter signals at the edge and converts these signals into sensing data.

[0014] The edge side processes the perceived data in conjunction with auxiliary data to obtain semantic data, and then stores the perceived data, the auxiliary data, and the semantic data in a distributed manner; wherein, the auxiliary data includes: geospatial data, global ionospheric model data, and target recognition model library data;

[0015] The cloud-based management system includes spatiotemporal big data, including the semantic data, and performs unified fusion, expression, querying, and analysis processing on this spatiotemporal big data.

[0016] Furthermore, the perceived data includes: numerical perceived data and multimedia perceived data;

[0017] The edge side combines auxiliary data to process the perceived data to obtain semantic data, including:

[0018] Numerical sensing data within a time period is extracted in units of components to obtain a vector representation of the numerical sensing data; wherein, the components include: the pos component or the speed component of the spatial position sensor sample value;

[0019] Intelligent processing is performed on multimedia-type perceptual data to obtain a semantic description of the multimedia-type perceptual data; wherein, the intelligent processing includes: orthorectification of image data, georegistration, and target recognition;

[0020] Based on the sampling time and location of the sensing data, the vector representation of the numerical sensing data, the semantic description of the multimedia sensing data, and the identifier of the edge side, semantic data corresponding to the sensing data is generated.

[0021] Furthermore, the sensed data is stored in a distributed manner, including:

[0022] The entire set of sensing data records is distributed across various storage nodes according to a Hash, Round Robin, or regional distribution strategy; where sensing data containing file pointers is stored on the same storage node as the corresponding data file.

[0023] Furthermore, the geospatial data and the semantic data are distributed and stored, including:

[0024] For the geospatial and semantic data required for spatiotemporal computation, spatiotemporal query and spatiotemporal data access operations on the edge side, filter out geospatial and semantic data whose usage frequency is greater than the set value.

[0025] A spatial or spatiotemporal data distribution algorithm is used to distribute the geospatial data and semantic data, and the geospatial data and semantic data whose usage frequency is greater than a set value are concentrated on a specific storage node.

[0026] Furthermore, the cloud side includes: an interface service, a data cataloging management module, an edge management module, a device twin management module, a command and pattern persistence module, a cloud-side spatiotemporal big data storage module, a cloud-side computing resource pool, a real-time message engine, and a first cloud-edge-device collaborative management module;

[0027] The interface service is used to provide standardized API interfaces for edge-side or other authorized users to access cloud-side data resources and computing power, and to issue control commands.

[0028] The data cataloging management module is used for unified metadata management, cataloging and indexing of spatiotemporal big data stored in the cloud, as well as metadata management and cataloging of data accessible at the edge.

[0029] The edge-side management module is used for registering, monitoring, distributing configurations, and managing the accessed edge devices.

[0030] The device twin management module is used to maintain a digital twin for each edge-side load device in the cloud, and the digital twin synchronizes the status, attributes and configuration of the edge-side load device.

[0031] The command and pattern persistence module is used to store commands, task configurations, data patterns, and models issued to the edge and endpoint sides.

[0032] The cloud-side spatiotemporal big data storage module is used to store spatiotemporal big data and other business data from the edge and terminal sides.

[0033] A cloud-side computing resource pool is used to provide computing resources to support model training and data analysis.

[0034] The real-time messaging engine is used to process message queues for command issuance and data upload between the cloud and edge sides based on a message bus mechanism.

[0035] The first cloud-edge-device collaborative management module is used to coordinate task allocation, data flow, and status synchronization among the cloud side, edge side, and device side.

[0036] Furthermore, the edge side includes: data service, task manager, data catalog manager, device manager, data processor, message bus, model and pattern module, second cloud-edge collaborative management module, edge-side spatiotemporal underlying storage, and edge-side computing resource pool;

[0037] The data service is used for data query processing, data subscription, and data transmission;

[0038] The task manager is used to receive, schedule, and execute computing tasks and business logic defined from the cloud or locally;

[0039] The data catalog manager is used to manage and catalog metadata for data that can be accessed by the edge local storage, cloud, and edge.

[0040] The device manager is used to manage the end-side connections via the device exporter;

[0041] The data processor is used to perform preprocessing operations on raw data collected from the edge or data received from the cloud. The preprocessing operations include cleaning, transformation, aggregation, and feature extraction.

[0042] The message bus is used for asynchronous communication between the internal modules on the side and with the end side.

[0043] The model and pattern module is used to store model files, data processing patterns, and business rules locally on the edge.

[0044] The second cloud-edge-device collaborative management module is used for the collaborative management unit on the edge side, and cooperates with the corresponding module on the cloud side;

[0045] The edge-side spatiotemporal underlying storage is used to persistently store raw data, processed data, application status, and necessary configuration information locally on the edge.

[0046] The edge computing resource pool is used to provide local computing resources.

[0047] Furthermore, the endpoint includes: a device and an application;

[0048] The device is used for data acquisition or receiving instructions to execute actions, and the device communicates with the device manager and message bus on the side through the device exporter;

[0049] The application refers to the logic that runs on smart devices or is directly controlled by the edge.

[0050] Furthermore, the process of collaborative work between the endpoint, the edge, and the cloud includes:

[0051] The cloud side uses interface services and a real-time message engine to send relevant instructions to the edge side through the first cloud-edge collaborative management module; wherein, the relevant instructions include: control instructions, task configuration, and model updates;

[0052] After receiving the instruction, the second cloud-edge collaborative management module on the edge side schedules local resources to execute tasks through the task manager, and controls the devices on the edge side through the device manager and device exporter.

[0053] After the device on the edge collects the data, it uploads the data to the message bus on the side through the device exporter;

[0054] After the edge-side data processor processes the data, it is uploaded to the cloud side via the second cloud-edge-device collaborative management module and data service, and then via the cloud-side real-time message engine.

[0055] The cloud-side device twin management module, the edge-side device manager, and related storage modules work together to maintain the consistency of the device and application status on the edge side, so that when the network is interrupted, the edge side can operate autonomously based on the model and pattern module, the edge-side spatiotemporal underlying storage, and the data cataloging management module.

[0056] Furthermore, the control of the cloud-side load device is achieved through the following steps:

[0057] The user selects the target end-side load device and sets the control parameters to be adjusted and the target values ​​of those control parameters;

[0058] The cloud side encapsulates the target-side load device, the control parameters that need to be adjusted, and the target value of the control parameters into a declarative instruction that conforms to a predefined specification, and then transmits the declarative instruction to the target edge side.

[0059] The target edge parses the declarative instruction, identifies the target end-side load device, the control parameter that needs to be adjusted, and the target value of the control parameter, and encapsulates the control parameter that needs to be adjusted and the target value of the control parameter into a low-level instruction that can be recognized by the target end-side load device;

[0060] After addressing and locating the target-side load device using the device manager, the target edge side sends the underlying instruction to the target-side load device and persists the parsing result of the declarative instruction locally on the target edge side.

[0061] A cloud-edge-device collaborative satellite data management system, the system comprising:

[0062] On the end side, it is used to collect physical parameter signals and convert the physical parameter signals into sensing data;

[0063] On the edge side, the system is used to process the perceived data in conjunction with auxiliary data to obtain semantic data, and to perform distributed storage of the perceived data, the auxiliary data, and the semantic data; wherein, the auxiliary data includes: geospatial data, global ionospheric model data, and target recognition model library data;

[0064] On the cloud side, it is used to manage spatiotemporal big data, including the semantic data, and to perform unified fusion, expression, query and analysis processing of the spatiotemporal big data.

[0065] Compared with the prior art, the present invention has at least the following beneficial effects.

[0066] Compared with existing technologies (e.g., those that focus only on space-based edge computing or lack sophisticated data management and collaborative transmission mechanisms specific to the constraints of satellite communications), this invention offers significant advantages and benefits in terms of data processing efficiency, system reliability, edge intelligence, network adaptability, and resource utilization. These benefits stem from several innovative technical features of this invention in areas such as data abstraction, semantic extraction, communication mechanisms, edge autonomy, and system architecture.

[0067] 1. Significantly improves data processing and transmission efficiency, and reduces bandwidth pressure:

[0068] ●Technical Approach: This invention employs a hierarchical data abstraction mechanism from "end → edge → cloud," particularly its unique method for generating and updating semantic data records based on vectorized representation. At the edge, numerical sensing data records are vectorized, fitting multiple continuous sampled values ​​into a time function vector=(f(t),t start ,t end It employs an intelligent update strategy that combines active vector comparison and threshold triggering; for multimedia sampling data, it extracts key semantic descriptions (such as JSON documents).

[0069] ●Principles and theoretical effects:

[0070] ■ In theory, vectorization can compress a large number of redundant time-series data points into a small number of parameterized vector descriptions, with a data compression ratio of several times or even tens of times (depending on the data stationarity and the set threshold). For example, for sensor data with gradual changes, hundreds of sampling points may only require one or a few vectors to accurately represent them.

[0071] ■ The active vector update mechanism ensures that new vectors are generated and uploaded to the cloud only when the data actually changes significantly (exceeding the threshold), greatly reducing the frequency of data uploads.

[0072] ■ Semantic description extraction of multimedia data enables the cloud to directly obtain key information, avoiding the need to transmit large original multimedia files for preliminary analysis.

[0073] ●Beneficial effects: It directly solves the pain point of extremely precious satellite communication bandwidth, greatly reduces the transmission of invalid and redundant data, saves satellite-to-ground communication resources, and at the same time improves the value density and processing efficiency of cloud data.

[0074] 2. Enhance the reliability and resilience of the system under extreme network conditions:

[0075] ●Technical means: This invention designs a "cloud-edge collaborative data transmission and management network architecture and collaborative subsystem" adapted to the characteristics of satellite communication. Its core includes a message bus-based communication mechanism (cloud-side real-time message engine, edge-side message bus), standardized declarative interface services, and edge autonomy mechanisms for applications and data (edge ​​metadata persistence, local storage and models, etc.).

[0076] ●Principles and theoretical effects:

[0077] ■ The message bus mechanism ensures the reliable and orderly transmission of instructions and data in intermittent networks through message queues, acknowledgments, and retransmissions.

[0078] ■ Declarative interfaces define the target state of an operation rather than the process, enabling the system to better handle high latency and intermittent connections. Even if there is a delay in instruction execution, the consistency of the final state can be guaranteed.

[0079] ■ The edge autonomy mechanism enables edge nodes to independently execute pre-defined tasks, process local data, and server-side devices even when they are disconnected from the cloud, through locally persistent configuration, metadata, models and patterns, and data. After the network is restored, the edge nodes can synchronize their state and resume data transmission with the cloud.

[0080] ●Beneficial effects: Significantly improved the system's operational stability and business continuity in harsh satellite communication environments, ensured reliable interaction of core data and commands, and enhanced the overall system's survivability and mission execution resilience.

[0081] 3. Achieve refined closed-loop control and intelligent status monitoring of the load equipment on the opposite side:

[0082] ●Technical means: This invention enables users on the cloud side to initiate control, which is then transmitted to the edge-side "Device Manager" for precise execution via a declarative interface. Control parameters are recorded in the edge-side "Persistent Configuration Management". At the same time, the device status data on the edge side is semantically transformed and intelligently transmitted back on demand based on policies by the edge-side "Data Processor".

[0083] ●Principles and theoretical effects:

[0084] ■ The declarative delivery and edge persistence of control parameters ensure that control intentions can be accurately executed or restored even after network fluctuations or device restarts.

[0085] ■ Edge semantic processing and on-demand back transmission of state data avoid the blind uploading of large amounts of raw state data, and only transmit information that is valuable to cloud decision-making, thereby improving the signal-to-noise ratio.

[0086] ■ A complete, efficient, and intelligent closed loop has been formed, from "cloud intent -> edge execution -> edge response -> edge perception and processing -> cloud feedback".

[0087] ●Beneficial effects: Improved the accuracy and response speed of remote end-side load devices, optimized the efficiency and intelligence level of status monitoring, and made the entire business process more agile and efficient. Attached Figure Description

[0088] Figure 1 This is a schematic diagram of the overall mechanism for cloud-edge-device collaborative data management.

[0089] Figure 2 It is a cloud-edge-device collaborative data management system architecture.

[0090] Figure 3 It involves the extraction of semantic data records. Detailed Implementation

[0091] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate the understanding of the present invention and do not limit it in any way.

[0092] In the intelligent satellite real-time data processing mode, the system not only needs to complete the real-time acquisition of satellite data, but also needs to analyze and intelligently compute the sampled data in real time. Through the aggregation, computation, and analysis of massive amounts of data, it achieves efficient distributed intelligent data processing. Satellite payload equipment generates a large amount of sampled data, including multi-ionospheric detectors, spectrometers, quantum wave instruments, SAR radar, etc. Furthermore, satellite-to-ground and inter-satellite communications are unreliable, requiring minimizing data communication costs. In this application scenario, traditional centralized data processing models using cloud computing and purely edge computing models cannot effectively meet the functional or performance requirements; therefore, a new cloud-edge-device collaborative data management model is needed.

[0093] In intelligent satellite brain systems, on-orbit satellites are not merely data acquisition devices. Fleeting space weather phenomena and ground anomalies require satellites to possess autonomous computing, analysis, and response capabilities. Simultaneously, they must transmit critical information to ground-based data centers and achieve data and computing power sharing through cloud-edge-device collaborative mechanisms. In these applications, a centralized data management model using cloud computing would overwhelm the cloud computing center, while a purely edge computing model cannot effectively support the intelligent analysis of massive amounts of data. Therefore, cloud-edge-device collaborative data management is the core key to overcoming system performance bottlenecks and achieving rapid development of the satellite industry.

[0094] By leveraging a cloud-edge-device collaboration mechanism, efficient data sharing and computational collaboration can be achieved between multiple satellites and between satellites and the ground through service calls, enabling unified on-board representation and modeling of space science data.

[0095] Specifically, the overall process of cloud-edge-device collaboration is described as follows: Figure 1 As shown, during the data sampling process of the cloud-edge-device collaborative system, the sampled data undergoes continuous abstraction along the "device → edge → cloud" path. Different levels of computing platforms ("device → edge → cloud") store data at different levels of abstraction, ensuring that the data at different levels is independent and collectively characterizes the state of the objective world.

[0096] ● An edge computing platform is a dedicated computing platform on sensing devices (such as sensors, multimedia monitoring equipment, sampling devices, etc.). It collects physical parameter signals and converts them into meaningful sensing data records through dedicated processing modules. For example, on-board imaging equipment transmits and receives observation signals, and then obtains image data of the observed area through processing by the edge computing platform.

[0097] ● An edge computing platform is a general-purpose computing platform that sits between edge computing platforms and cloud computing platforms, acting as a bridge between them. Typically, an edge computing platform can connect to and manage multiple edge computing devices; for example, a satellite edge computing platform can manage various sensor devices and multimedia sensing sampling devices on a satellite. The edge computing platform stores sensing data records from edge devices and processes them to obtain semantic data records, such as IoT state vectors, target recognition information, and geographic modeling data.

[0098] ● The cloud data management platform is also a general platform that manages semantic sampling data and other related data from all edge computing platforms within the system (which together constitute spatiotemporal big data). The cloud computing platform performs unified fusion, expression, querying, and analysis processing on this data.

[0099] The cloud-edge-device collaborative satellite data management method and system mainly includes three sub-methods and sub-systems.

[0100] 1. The first step is the convergence and flow of data along the "device → edge → cloud" path. This requires addressing the management challenges arising from the massive volume and heterogeneous nature of data from satellite-based edge-side payload sensing devices.

[0101] 2. Secondly, given the intermittent connection and bandwidth-limited nature of the data link from the satellite side to the ground cloud side, a special design is required for the network architecture supporting data transmission and management.

[0102] 3. Furthermore, how to effectively coordinate the edge and end sides to better control and manage the status and data sampling parameters of the end-side load sensing devices is crucial for effectively managing the source of data generation.

[0103] Based on the aforementioned overall process of cloud-edge-device collaboration, the architecture diagram of the cloud-edge-device collaborative data management system is as follows: Figure 3 As shown.

[0104] 1. Satellite data collaboration method and subsystem based on cloud-edge-device three-level collaboration

[0105] During the data sampling process of space environment data, such as Figure 2 As shown, data is abstracted step by step along the "device → edge → cloud" path, with different levels of computing platforms storing data at different levels of abstraction. The edge computing platform is a dedicated computing platform on sensing devices (such as various space environment data sampling devices), which is responsible for collecting on-site data.

[0106] An edge computing platform is a general-purpose computing platform located between edge computing platforms and cloud computing platforms. An onboard data processing platform is one such edge computing platform. It manages various space environment data acquisition devices on the satellite, performs corresponding data processing and vector extraction on the acquired sensing data, and generates and outputs semantic data.

[0107] Since edge computing platforms typically perform periodic data collection, they may generate a large amount of redundant data. To reduce redundancy, this invention employs a vector-based semantic data recording and representation method. A vector corresponds to multiple consecutive sampled values. Through straight-line or curve fitting, it can be represented as a time function f(t), while adding the start time t. start With end time t end To reduce vector redundancy, the difference between the actual sampled value and the calculated vector value is calculated and compared with a specified threshold ε. Two vectors with a difference less than ε are merged, ultimately reducing the data update frequency and data volume overhead of the cloud computing platform.

[0108] In the intelligent satellite brain cloud-edge-device collaborative system, the cloud computing platform (usually located on the ground) is the computing hub, aggregating data from all satellites connected to the system and providing data and program access interfaces. The on-board computing platform achieves data sharing by retrieving global data through data access interfaces; and it utilizes the computing power of the ground-based cloud computing platform through corresponding interfaces, thereby achieving the sharing of intelligent algorithms and computing power.

[0109] Specifically:

[0110] The main data managed by edge computing platforms consists of sensory data records (such as raw SAR imaging images) and semantic data records (such as geographic model data obtained through orthorectification, geographic calibration, and 3D modeling). Due to storage limitations, edge computing platforms typically store only data for a specific period. To facilitate geospatial computing, edge computing platforms often need to store geospatial data records (such as map information). There is also a small amount of other types of auxiliary data necessary for edge computing, such as global ionospheric model data and target recognition model library data. This data can be stored on one or more fixed processor nodes in the form of files or relational tables. The following mainly describes the distributed management method for sensory data records, semantic data records, and geospatial data records.

[0111] For distributed storage of sensory data records, since they do not contain geospatial information, a data distribution method based on end device identifiers can be adopted. This involves distributing the entire set of sensory data records across various storage nodes using strategies such as hashing, round-robin, or regional distribution. (For sensory data records containing file pointers, the data file and the sensory data record should be stored on the same node so that the file can be accessed locally via the file pointer on the data record during data processing.) This method ensures that all sensory data records generated by the same end device are stored on the same processor node, which is beneficial for the extraction and computation of semantic data records.

[0112] For distributed storage of semantic data records and geospatial data records, due to their spatiotemporal characteristics, spatial or spatiotemporal data distribution methods can be adopted. Through a reasonable data allocation strategy, the data records required for frequent spatiotemporal computations, queries, and data access operations performed by the edge computing platform can be concentrated on a small number of storage nodes, thereby improving overall computing performance.

[0113] The data processing tasks of the edge computing platform mainly fall into two categories: query processing and analysis computing tasks. Query processing tasks (such as data queries based on constraints like identifiers, time, space, and state values) can be efficiently completed using the aforementioned distributed structure. Analysis tasks (such as statistical analysis of sensory sampling data, modeling and calculation of raw SAR images, and target recognition) can employ a spatiotemporal Map / Reduce approach. This involves segmenting the data objects to be processed (such as semantic data record sets and raw SAR images) according to spatial or spatiotemporal attributes, distributing them to various storage nodes for parallel processing, and finally summing the results through a Reduce operation.

[0114] In addition to parallel processing of massive amounts of data, edge computing platforms can also perform parallel intelligent analysis of individual multimedia data. For example, when processing target recognition in SAR images, the image can be segmented and distributed to various servers for parallel processing. Finally, the final result is obtained by summarizing the sub-results.

[0115] By leveraging a cloud-edge-device collaborative mechanism, efficient data sharing and transfer between multiple satellites and between satellites and the ground can be achieved, thereby improving the efficiency of space information dissemination and processing and solving the bottleneck problem of space information transmission.

[0116] To elaborate further:

[0117] Because the sampling data from sensing devices corresponding to edge computing platforms exhibits significant heterogeneity, a unified data representation method is needed. This invention designs a Schema+Value-based method for representing sensing data records. Sensing data can be broadly categorized into two types: numerical sampling values ​​(such as data sampled by atmospheric density load and electronic density load sensors) and multimedia sampling values ​​(such as video surveillance images, high-altitude and geological exploration image data, and audio monitoring signals). Sensing data records (SenseData) can be uniformly represented in the following format (in the following description, TimeInstant, Point, String, and LowAltitudeFile are defined as time, spatial coordinates, string, and low-altitude file data types, respectively):

[0118] SenseData=(t,schemaValue,termID)

[0119] Where t∈TimeInstant is the sampling time corresponding to the sampled data; schemaValue∈String is a structured string composed of two parts: schema∈String and value∈String. schema and value are the “type” and “value” of the sampled data, respectively. The “type” describes the format and data type of the sampled data, and the “value” is the specific sampled data value; termID∈String is the identifier of the terminal computing platform.

[0120] The perception data records generated by the edge computing platform are uploaded to the edge computing platform. The edge computing platform processes the data and extracts vectors to generate and output semantic data records, such as... Figure 3 As shown.

[0121] Edge computing platforms typically employ periodic data sampling methods, resulting in significant redundancy in perceptual data records. Furthermore, multimedia perceptual data records consist only of raw sampled information (e.g., raw images without orthorectification or geographic matching). To address this, this invention designs a vector-based semantic data record representation method to minimize redundancy and reduce the amount of data uploaded to the cloud computing platform.

[0122] The process of generating semantic data records on an edge computing platform is as follows: For numerical perception data records, vector extraction is performed on a component basis (such as the pos component or speed component of spatial position sensor sampling values). A vector corresponds to multiple consecutive sampling values ​​and to a curve segment l in the V×T plane (where V and T are the range of the sampled value and the range of the sampling time, respectively). Vector extraction can be obtained through straight line fitting or curve fitting methods and can be expressed as a time function f(t), plus the start time t. start With end time t end ,Right now:

[0123] vector = (f(t), t start ,t end )

[0124] For each component of each sensing device, its last vector is its current active vector (t of the current active vector). end (Attribute is undefined). When new sensing data records arrive, the edge computing platform compares the new sampled value with the current activity vector. If the difference between the actual sampled value and the calculated vector value is less than the specified threshold ε, no processing is required. Only when the actual sampled value deviates from the current activity vector (i.e., the deviation exceeds the specified threshold) is the fitting calculation of the new vector triggered, generating a new activity vector and uploading it to the cloud computing platform (the t of the original activity vector). end (Updated to the current time). Since the update rate of vectors is much lower than the sampling rate of perceptual data records, the above method effectively reduces the data update frequency and data volume overhead of cloud computing platforms.

[0125] For multimedia sampling data, the edge computing platform can derive relevant semantic descriptions (such as the location and area of ​​the fire area, and the type and location of suspicious and sensitive targets) through intelligent processing (such as orthophoto calculation, georegulation, and target recognition of image data). These semantic descriptions can be represented as a JSON document and also generate a corresponding semantic data record. The multimedia files (such as low-altitude image files and geographic model data files) processed by the edge computing platform and the JSON document are stored on the same storage node through file pointer association to facilitate further file access and processing.

[0126] Combining the two types of perceptual data records mentioned above, the corresponding semantic data records (SemanticData) can be uniformly represented in the following format:

[0127] SemanticData=(t,pos,vect,edgeID)

[0128] Where t∈TimeInstant is the sampling time corresponding to the data record; pos∈Point is the sampling location corresponding to the data record; vect∈Vector∪JSON is the generated vector; and edgeID∈String is the identifier of the edge computing platform.

[0129] In a cloud-edge-device collaborative system, the cloud computing platform (usually located on the ground) acts as the central hub, aggregating data from all satellites connected to the system and providing data access interfaces and program access interfaces. The edge computing platform can then use the data access interfaces to query, retrieve, and access the global data from the central hub, thereby achieving data sharing.

[0130] To achieve satellite data collaboration based on cloud-edge-device three-level coordination, two supporting subsystems are established: a cloud-edge collaborative data transmission and management network architecture and coordination subsystem, and an edge-device collaborative end-side payload equipment control parameter management and status monitoring subsystem. These will be described in detail below.

[0131] 2. Cloud-edge collaborative data transmission and management network architecture and collaborative subsystem

[0132] Data links between satellite edge and ground / cloud environments are characterized by intermittent connectivity and limited bandwidth. Specifically, the challenges in building efficient and reliable data links between satellite edge and ground / cloud environments include: ① First, the participants in the communication, especially the transmitting and receiving terminals located at the edge, cannot guarantee continuous online connectivity; they may enter sleep or low-power states within specific time windows, posing significant challenges to the continuity, immediacy, and sequence of communication. ② Second, the inherent network limitations of satellite communication, primarily manifested in limited available bandwidth and high latency due to signal propagation, directly affect the data transmission rate and stability. ③ Third, the stability of network connections is difficult to guarantee; connections may be unstable due to various factors, and offline status may occur at any time. Under these circumstances, edge applications still need to possess the ability to operate continuously, ensuring uninterrupted service or proper handling even in offline states.

[0133] To address the aforementioned challenges, this invention designs a cloud-edge collaborative data transmission and management network architecture and collaborative subsystem, aiming to build a reliable cloud-edge collaborative data link. ① A message bus-based mechanism is designed. Its core advantage lies in ensuring the sequentiality of information during transmission and effectively addressing communication interruptions caused by intermittent terminal online activity or unstable network connections, ensuring the final reliable delivery of messages. ② This service model is dedicated to handling critical operations such as model parameter updates accurately and stably, while providing good support for remote operations and effectively tolerating execution delays caused by network factors such as high latency. ③ An edge autonomy mechanism for applications and data is designed. Specifically, this includes supporting the autonomous operation of edge devices in offline network conditions. By introducing key technologies such as edge metadata persistence and task management, it ensures that even when disconnected from the central network, the various application loads on the edge side can continue to operate stably and have rapid fault recovery capabilities.

[0134] The cloud-edge-device collaborative data transmission and management subsystem proposed in this invention has the following core architecture: Figure 3 As shown, it mainly comprises three parts: cloud, edge, and device, operating through defined interfaces and coordination mechanisms. The subsystem is dedicated to providing stable, efficient, and edge-autonomous data interaction and management capabilities in satellite and terrestrial network communication environments.

[0135] Subsystem architecture and composition:

[0136] (1) Cloud Side

[0137] As the central control and data aggregation node, it is responsible for global task scheduling, large-scale data storage and analysis, model training, etc. Its main modules (based on...) Figure 2 The Query area and related modules include:

[0138] ● Interface Services (Query - Data, Computation, General Commands): Provides standardized API interfaces for edge users or other authorized users to access cloud-side data resources and computing capabilities, and to issue control commands. Specifically, it includes data services, computing services, and general API services. Data services include data query processing, data subscription, and data transmission functions.

[0139] ● Data Catalog Management: Provides unified metadata management, cataloging, and indexing for various types of data stored in the cloud, facilitating querying and traceability. It also manages and catalogs metadata for data accessible from the edge.

[0140] ●Edge Management: Responsible for registering, monitoring, distributing configurations, and managing accessed edge nodes.

[0141] ● Device Twin Management: Maintain a digital twin for each edge-side payload device in the cloud, synchronizing its status, attributes, and configuration, and supporting remote monitoring and pre-operation.

[0142] ●Command & Schema Persistence: Stores commands, task configurations, data schemas, and models issued to the edge and terminals.

[0143] ● Cloud-Side Spatiotemporal Big Data Storage: Used to store large-scale spatiotemporal data and other business data from the edge and terminals.

[0144] ● Cloud Computing Resource Pool: Provides elastic computing resources to support model training, complex data analysis, and more.

[0145] ● Real-time Message Engine: As a key component for asynchronous message communication between the cloud and the edge, it adopts a message bus mechanism to handle message queues for command issuance and data upload.

[0146] ● Cloud-Edge-Device Collaboration Management: The core logical control unit that coordinates task allocation, data flow, and state synchronization among the cloud, edge, and device. A declarative interface is used between the cloud, edge, and device, combined with persistence capabilities, to ensure secure and reliable transmission of interface commands.

[0147] (2) Edge Side

[0148] Edge computing nodes, deployed close to the data source or execution point, are responsible for initial data processing, real-time task execution, and local autonomous management. Their main modules include:

[0149] ● Data Service: Includes functions such as data query processing, data subscription, and data transmission.

[0150] ●Task Manager: Responsible for receiving, scheduling, and executing computing tasks and business logic defined from the cloud or locally.

[0151] ●Data Catalog Management: Perform metadata management and cataloging for data stored locally on the edge, cloud edge, and data accessible between edge devices.

[0152] ● Device Manager: Manages end-side devices connected through the Device Exporter, monitors their status, and forwards control commands to them.

[0153] ● Data Processor: Performs preprocessing operations such as cleaning, transformation, aggregation, and feature extraction on raw data collected from edge devices or data received from the cloud.

[0154] ● Message Bus: As the core component for asynchronous communication between various modules within the edge side and with end-side devices (through the device exporter), it ensures the orderly flow of data and instructions.

[0155] ● Models & Schemas: Model files, data processing schemas, business rules, etc., stored locally on the edge, supporting the operation of local applications.

[0156] ● Cloud-Edge-Device Collaboration Management: The edge-side collaboration management unit works with the corresponding cloud-side module to execute collaborative tasks.

[0157] ●Edge-Side Spatiotemporal Underlying Storage: Used for local persistent storage of raw data, processed data, application status, and necessary configuration information on the edge.

[0158] ● Edge Computing Resource Pool: Local computing resources provided by edge nodes.

[0159] (3) Device Side

[0160] This refers to various sensors, actuators, and smart devices connected to the side.

[0161] ● Devices: The actual physical devices responsible for data acquisition or receiving commands to execute actions. They communicate with the "Device Manager" and "Message Bus" on the side via the "Device Exporter".

[0162] ●Applications: Simple logic that may run on some smart devices or be directly controlled by the edge.

[0163] Brief description of collaborative workflow:

[0164] A) Command and task issuance: The cloud side issues control commands, task configurations, model updates, etc. to the edge side through the "cloud-edge-device collaborative management" module via the "interface service" and "real-time message engine".

[0165] B) Edge-side processing and execution: The "Cloud-Edge-Device Collaborative Management" module on the edge receives instructions and executes them through the "Task Manager".

[0166] Schedule local resources to execute tasks and control end-side devices through "Device Manager" and "Device Exporter".

[0167] Data processing is performed by a "data processor".

[0168] C) Data Acquisition and Upload: Data is collected by the edge device and uploaded to the edge-side "message bus" via the "device exporter." After processing by the "data processor," the data is then managed as needed through the edge-side "cloud-edge-device collaborative management."

[0169] The modules and "data services" are uploaded to the cloud via the "real-time message engine".

[0170] D) State Synchronization and Autonomy: The cloud-side "Device Twin Management" and the edge-side "Device Manager" and related storage modules work together to maintain the consistency of device and application states. In the event of a network outage, the edge utilizes local storage's "Model and Schema," "Edge-side Spatiotemporal Underlying Storage," and "Data Catalog Management" to achieve autonomous operation.

[0171] 3. End-side load equipment control parameter management and condition monitoring subsystem with edge-end coordination

[0172] This paper provides a subsystem and method for edge-side load device control parameter management and status monitoring, which combines a cloud-edge collaborative data transmission and management network architecture and collaborative subsystem, and utilizes its basic capabilities to achieve precise control and intelligent status monitoring of the edge-side load device.

[0173] The work process mainly includes the following aspects:

[0174] (1) Process for issuing and managing control parameters of end-side load equipment:

[0175] A) Cloud-side user initiates a control request:

[0176] ● Users can select the target-side load device and set the control parameters to be adjusted (e.g., sensor sampling frequency, camera exposure time, actuator action threshold, etc.) and their target values ​​through the cloud application interface and the "Device Control" module in the "Cloud-side Transceiver Guard Service".

[0177] B) Declarative encapsulation and issuance of control commands:

[0178] ●The cloud side encapsulates the user-defined control parameter targets into declarative instructions that conform to predefined specifications.

[0179] ● This declarative instruction is securely and reliably transmitted to the "Side-side Sending and Receiving Guard Service" on the target edge node through the "Declarative Interface" provided by the "Data Link and Management Subsystem".

[0180] C) Side-side reception, parsing, and addressing:

[0181] ● The edge node's "edge transceiver module" (or via the service bus) receives declarative control commands from the cloud.

[0182] ● The “Side-side transmission and reception guard service” parses the commands and identifies the target device identifier, as well as the specific control parameters and target values.

[0183] D) Control is exercised through the Device Manager module:

[0184] ● The "Device Manager" module of the edge node locates the specific end-side load device based on the resolved device identifier.

[0185] ● The “Device Manager” module sends the parsed control parameters (converted into low-level instructions that the device can recognize) to the load device on the end side, driving the device to perform parameter adjustments.

[0186] E) Local persistence of control parameters:

[0187] ● After a control command is successfully issued or the device confirms the parameter update, the new control parameter values ​​will be recorded and updated in the "Persistent Configuration Management" module of the edge node. This ensures that even if the connection to the cloud is lost, the edge node retains the latest configuration state of the device and can restore the configuration after the device restarts or reconnects.

[0188] (2) End-side load equipment status monitoring and data feedback process:

[0189] A) End-side device status and data generation:

[0190] ●The operating state or characteristics of the data generated by the end-side load device may change after new control parameters are applied (for example, the adjustment of the sampling rate directly affects the data generation rate and density).

[0191] ●The device continuously collects or generates sensing data.

[0192] B) Sensing data injection and real-time edge processing:

[0193] ● The raw sensing data (sensing data records) generated by the edge devices are injected into the "event bus" (or edge real-time message engine concept, which is understood here as an efficient data distribution mechanism within the edge) of the edge nodes through the underlying data collector.

[0194] ●Specific business applications deployed on edge nodes can subscribe to and obtain this raw data from the "event bus" in real time for immediate analysis, processing, or local decision-making.

[0195] C) Data semantic transformation:

[0196] ● Edge nodes possess the ability to process and transform raw sensor data. Through preset or dynamically loaded vector extraction methods (or other data processing algorithms), they convert raw, potentially redundant sensor data records into more structured, information-dense semantic data records. This process helps reduce the amount of data transmitted subsequently and enhances data value.

[0197] D) On-demand intelligent backhaul:

[0198] ● Not all of the converted semantic data records are unconditionally sent back to the cloud.

[0199] ● The return strategy is based on the configuration of the "Cloud-Edge Data Stream Rotary System" stored in the "Persistent Configuration Management" module (e.g., by time window, by event trigger, by data change magnitude, by network conditions, etc.).

[0200] ● The edge node's "edge-side transceiver module" selectively and on demand transmits important semantic data records back to the cloud's "cloud-side transceiver guard service" through the channels provided by the "data link and management subsystem" based on these strategies.

[0201] E) Cloud-side data reception and application:

[0202] ●After receiving the semantic data records sent back from the cloud side, it can be further stored, analyzed, displayed, or used for higher-level decision support and model training.

[0203] Through the coordinated operation of the aforementioned control parameter management process and status monitoring process, this subsystem achieves closed-loop control and intelligent management of the end-side payload equipment, effectively improving system response speed, data processing efficiency, and network resource utilization in complex satellite communication environments.

[0204] In summary, this invention, through the organic combination of the aforementioned key technical features, constructs a highly collaborative, efficient, reliable, intelligent, and autonomous cloud-edge-device integrated data transmission and management system. This system not only effectively solves the bottleneck problem of data transmission in satellite communication environments, improves the depth and efficiency of data processing, and enhances system stability and edge autonomy, but more importantly, it lays a solid technical foundation for realizing complex spatial information services and intelligent applications, demonstrating significant technological advancements and broad application prospects.

[0205] This paper proposes a unified data representation method for heterogeneous data (sensory data, semantic data, geospatial data) of various types of satellite data, and designs a data generation and update method that combines current activity vector comparison and threshold triggering. These methods solve the problem of the difficulty in uniformly utilizing heterogeneous data, and significantly reduce the data update frequency and data transmission overhead from end devices to edge storage and then to the ground cloud. At the same time, they take into account the semantic description of the data, and are particularly suitable for the limited bandwidth of satellite-to-ground transmission links.

[0206] This invention proposes a cloud-edge collaborative data transmission and management subsystem adapted to the characteristics of satellite data links and networks. It specifically designs methods and systems including message bus mechanisms and declarative interface services to address the aforementioned characteristics of satellite data links, significantly improving the practicality of the methods and systems.

[0207] This invention emphasizes an edge-autonomous mechanism for data and applications. Through edge metadata persistence (data cataloging management, persistent configuration management), edge-side spatiotemporal underlying storage, local model and schema storage, and task management technologies, it ensures load operation and fault recovery even when offline. This ensures that edge nodes can still operate efficiently and autonomously with rapid fault recovery capabilities even during prolonged disconnections or network deterioration. Furthermore, the edge-side "task manager" and "data processor" can support more complex autonomous decision-making based on local data and models.

[0208] This invention aims to solve the problem of achieving remote and precise closed-loop control of end-side load devices, intelligently acquiring their status, and optimizing response speed and data efficiency. The "Edge-Side Collaborative End-Side Load Device Control Parameter Management and Status Monitoring Subsystem" details the process of issuing control parameters through a declarative interface via "device twin management," executing them on the edge-side "device manager," and recording them in "persistent configuration management," ensuring the accuracy and recoverability of control. Status monitoring data undergoes semantic transformation on the edge side and is intelligently transmitted back on demand based on policies (stored in "persistent configuration management"), forming an efficient closed loop. The concept of cloud-side "device twin management" is introduced to collaborate with the edge-side "device manager" for more refined cloud mapping and management of end-side load devices.

[0209] This invention discloses a satellite data abstraction and data collaboration mechanism based on cloud-edge-device three-level collaboration:

[0210] ● A processing flow is proposed that abstracts data hierarchically in the "device → edge → cloud" direction (from perceptual data records to semantic data records, and then to a global view in the cloud). This hierarchical abstraction mechanism, combined with the vectorized semantic data extraction method at the edge, significantly reduces data redundancy, optimizes transmission efficiency on bandwidth-constrained links, and enhances the application value of the data.

[0211] ● A unified data representation method for heterogeneous data is proposed, namely a unified representation method for perceptual data records based on schema+value. Corresponding distributed storage and management strategies are designed for different types of data (perceptual data, semantic data, geospatial data) (such as distribution based on end device identifiers and distribution based on spatiotemporal attributes).

[0212] ● It solves the problems of storage, transmission and processing pressure caused by massive amounts of raw sensing data, as well as the difficulty in uniformly utilizing heterogeneous data.

[0213] This invention discloses a method for generating and updating semantic data records based on vectorized representation:

[0214] ● An original vector-based semantic data recording representation method was designed (vector = (f(t), t)). start ,t end This is combined with an update mechanism that uses current active vector comparison and threshold triggering. This ensures that new vectors are generated and uploaded only when the data actually deviates from the model prediction, rather than uploading the original data in full or periodically.

[0215] ● It greatly reduces the data update frequency and data volume overhead of the cloud platform, and is particularly suitable for the compression and efficient expression of numerical time series data, while also taking into account the semantic description of multimedia data.

[0216] This invention discloses a cloud-edge collaborative data transmission and management subsystem architecture adapted to the characteristics of satellite data links and networks:

[0217] ● To address the intermittent connectivity, bandwidth limitations, and high latency of satellite links, a collaborative subsystem was designed, incorporating a message bus mechanism, declarative interface services, and edge autonomy mechanisms. This architecture (such as...) Figure 3 In this system, the cloud-side "real-time message engine" and the edge-side "message bus" work together to ensure the reliability and order of asynchronous communication; standardized "interface services" and "declarative interfaces" simplify interaction and tolerate latency; and edge nodes are given a high degree of autonomous operation capabilities through edge "metadata manager", "persistent configuration management" and "local storage".

[0218] ● It overcomes the vulnerability of traditional tightly coupled or simple master-slave architectures under harsh network conditions, ensuring the robustness of data links and the continuity of edge services.

[0219] This invention enables edge-end collaborative control parameter management and status monitoring:

[0220] ● A control and monitoring subsystem was built, which initiates control from the cloud side, precisely delivers control to the end-side load devices via a declarative interface and the edge-side "device manager," and then forms a closed loop through status monitoring, data semantic conversion, and on-demand intelligent backhaul. This includes innovative features such as local persistence of control parameters and policy-based on-demand backhaul.

[0221] ● It enables remote and precise control and intelligent status feedback of end-side devices, optimizes the efficiency of issuing control commands and transmitting status data, and forms an effective business closed loop.

[0222] While specific implementation methods of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples. Various changes or modifications can be made to these implementation methods without departing from the principles and implementation of the present invention. Therefore, the scope of protection of the present invention is defined by the appended claims.

Claims

1. A cloud-edge-device collaborative satellite data management method, characterized in that, The method includes: The device acquires physical parameter signals at the edge and converts these signals into sensing data. The edge side processes the perceived data in conjunction with auxiliary data to obtain semantic data, and then stores the perceived data, the auxiliary data, and the semantic data in a distributed manner; wherein, the auxiliary data includes: geospatial data, global ionospheric model data, and target recognition model library data; The cloud-based management system includes spatiotemporal big data, including the semantic data, and performs unified fusion, expression, querying, and analysis processing on this spatiotemporal big data.

2. The method according to claim 1, characterized in that, The sensing data includes: numerical sensing data and multimedia sensing data; The edge side combines auxiliary data to process the perceived data to obtain semantic data, including: Numerical sensing data within a time period is extracted in units of components to obtain a vector representation of the numerical sensing data; wherein, the components include: the pos component or the speed component of the spatial position sensor sample value; Intelligent processing is performed on multimedia-type perceptual data to obtain a semantic description of the multimedia-type perceptual data; wherein, the intelligent processing includes: orthorectification of image data, georegistration, and target recognition; Based on the sampling time and location of the sensing data, the vector representation of the numerical sensing data, the semantic description of the multimedia sensing data, and the identifier of the edge side, semantic data corresponding to the sensing data is generated.

3. The method according to claim 1, characterized in that, Distributed storage of the sensed data includes: The entire set of sensing data records is distributed across various storage nodes according to a Hash, Round Robin, or regional distribution strategy; where sensing data containing file pointers is stored on the same storage node as the corresponding data file.

4. The method according to claim 1, characterized in that, Distributed storage of the geospatial data and the semantic data includes: For the geospatial and semantic data required for spatiotemporal computation, spatiotemporal query and spatiotemporal data access operations on the edge side, filter out geospatial and semantic data whose usage frequency is greater than the set value. A spatial or spatiotemporal data distribution algorithm is used to distribute the geospatial data and semantic data, and the geospatial data and semantic data whose usage frequency is greater than a set value are concentrated on a specific storage node.

5. The method according to claim 1, characterized in that, The cloud side includes: interface service, data cataloging management module, edge management module, device twin management module, command and pattern persistence module, cloud-side spatiotemporal big data storage module, cloud-side computing resource pool, real-time message engine and first cloud-edge-device collaborative management module; The interface service is used to provide standardized API interfaces for edge-side or other authorized users to access cloud-side data resources and computing power, and to issue control commands. The data cataloging management module is used for unified metadata management, cataloging and indexing of spatiotemporal big data stored in the cloud, as well as metadata management and cataloging of data accessible at the edge. The edge-side management module is used for registering, monitoring, distributing configurations, and managing the accessed edge devices. The device twin management module is used to maintain a digital twin for each edge-side load device in the cloud, and the digital twin synchronizes the status, attributes and configuration of the edge-side load device. The command and pattern persistence module is used to store commands, task configurations, data patterns, and models issued to the edge and endpoint sides. The cloud-side spatiotemporal big data storage module is used to store spatiotemporal big data and other business data from the edge and terminal sides. A cloud-side computing resource pool is used to provide computing resources to support model training and data analysis. The real-time messaging engine is used to process message queues for command issuance and data upload between the cloud and edge sides based on a message bus mechanism. The first cloud-edge-device collaborative management module is used to coordinate task allocation, data flow, and status synchronization among the cloud side, edge side, and device side.

6. The method according to claim 5, characterized in that, The edge side includes: data service, task manager, data catalog manager, device manager, data processor, message bus, model and pattern module, second cloud-edge-device collaborative management module, edge-side spatiotemporal underlying storage, and edge-side computing resource pool. The data service is used for data query processing, data subscription, and data transmission; The task manager is used to receive, schedule, and execute computing tasks and business logic defined from the cloud or locally; The data catalog manager is used to manage and catalog metadata for data that can be accessed by the edge local storage, cloud, and edge. The device manager is used to manage the end-side connections via the device exporter; The data processor is used to perform preprocessing operations on raw data collected from the edge or data received from the cloud. The preprocessing operations include cleaning, transformation, aggregation, and feature extraction. The message bus is used for asynchronous communication between the internal modules on the side and with the end side. The model and pattern module is used to store model files, data processing patterns, and business rules locally on the edge. The second cloud-edge-device collaborative management module is used for the collaborative management unit on the edge side, and cooperates with the corresponding module on the cloud side; The edge-side spatiotemporal underlying storage is used to persistently store raw data, processed data, application status, and necessary configuration information locally on the edge. The edge computing resource pool is used to provide local computing resources.

7. The method according to claim 6, characterized in that, The endpoint includes: devices and applications; The device is used for data acquisition or receiving instructions to execute actions, and the device communicates with the device manager and message bus on the side through the device exporter; The application refers to the logic that runs on smart devices or is directly controlled by the edge.

8. The method according to claim 7, characterized in that, The process by which the endpoint, the edge, and the cloud work together includes: The cloud side uses interface services and a real-time message engine to send relevant instructions to the edge side through the first cloud-edge collaborative management module; wherein, the relevant instructions include: control instructions, task configuration, and model updates; After receiving the instruction, the second cloud-edge collaborative management module on the edge side schedules local resources to execute tasks through the task manager, and controls the devices on the edge side through the device manager and device exporter. After the device on the edge collects the data, it uploads the data to the message bus on the side through the device exporter; After the edge-side data processor processes the data, it is uploaded to the cloud side via the second cloud-edge-device collaborative management module and data service, and then via the cloud-side real-time message engine. The cloud-side device twin management module, the edge-side device manager, and related storage modules work together to maintain the consistency of the device and application status on the edge side, so that when the network is interrupted, the edge side can operate autonomously based on the model and pattern module, the edge-side spatiotemporal underlying storage, and the data cataloging management module.

9. The method according to claim 7, characterized in that, The following steps are used to control the cloud-side load equipment on the receiving end: The user selects the target end-side load device and sets the control parameters to be adjusted and the target values ​​of those control parameters; The cloud side encapsulates the target-side load device, the control parameters that need to be adjusted, and the target value of the control parameters into a declarative instruction that conforms to a predefined specification, and then transmits the declarative instruction to the target edge side. The target edge parses the declarative instruction, identifies the target end-side load device, the control parameter that needs to be adjusted, and the target value of the control parameter, and encapsulates the control parameter that needs to be adjusted and the target value of the control parameter into a low-level instruction that can be recognized by the target end-side load device; After addressing and locating the target-side load device using the device manager, the target edge side sends the underlying instruction to the target-side load device and persists the parsing result of the declarative instruction locally on the target edge side.

10. A cloud-edge-device collaborative satellite data management system, characterized in that, The system includes: On the end side, it is used to collect physical parameter signals and convert the physical parameter signals into sensing data; On the edge side, the system is used to process the perceived data in conjunction with auxiliary data to obtain semantic data, and to perform distributed storage of the perceived data, the auxiliary data, and the semantic data; wherein, the auxiliary data includes: geospatial data, global ionospheric model data, and target recognition model library data; On the cloud side, it is used to manage spatiotemporal big data, including the semantic data, and to perform unified fusion, expression, query and analysis processing of the spatiotemporal big data.

Images