Satellite internet derivative generation method based on digital twinning

Abstract

The invention discloses a satellite internet derivative generation method based on digital twinning, which is characterized by comprising the following steps of: acquiring physical environment data and communication network data; modeling by utilizing the physical environment data and the communication network data to obtain a topological structure model, a resource distribution model, a satellite orbit model, a space environment model, a network security model and a user distribution model; inputting the topological structure model into an intelligent arrangement scheduler for operation to obtain network topological data; inputting the resource distribution model into an intelligent arrangement scheduler for operation to obtain network flow data; inputting the satellite orbit model into an intelligent arrangement scheduler for operation to obtain satellite orbit data; inputting the space environment model into an intelligent arrangement scheduler for operation to obtain gravitation data; and inputting the network security model into an intelligent arrangement scheduler for operation to obtain network security data.

Description

Technical Field

[0001] The present invention relates to the field of satellite internet system digital technology and data processing, and specifically, to a satellite internet derivative generation method based on digital twins. Background Art

[0002] Satellite Internet data models are divided into mechanism models and data models according to whether there is a physical relationship or mathematical expression. Satellite Internet satellite orbit models, space environment models, etc. belong to mechanism models, which can be described by mathematical theorems or physical formulas; network security models and user distribution models belong to data models, which are mainly based on statistical feedback from the perception system.

[0003] The satellite Internet space environment is complex, user services are unevenly distributed, and the system is time-varying, which ultimately leads to the formation of numerous standardized models. However, the use of a single mechanism model or data model for modeling may result in the inability to accurately predict the future state of the satellite Internet or the prediction may result in large errors. In view of this, this application is hereby filed. Summary of the invention

[0004] The technical problem to be solved by the present invention is that it is difficult to accurately predict the future state of satellite Internet by using only mechanism model or data model, and system delay will occur when using the existing satellite Internet system to process large amounts of data.

[0005] In order to solve the above calculation problems, the present invention proposes a satellite Internet derivative generation method based on digital twins, which is implemented by the following technical solution and includes the following steps: Collect physical environment data and communication network data; Modeling using the physical environment data and the communication network data to obtain a topology model, a resource distribution model, a satellite orbit model, a space environment model, a network security model, and a user distribution model; The topology structure model is input into the intelligent orchestration scheduler for calculation to obtain network topology data; the resource distribution model is input into the intelligent orchestration scheduler for calculation to obtain network traffic data; the satellite orbit model is input into the intelligent orchestration scheduler for calculation to obtain satellite orbit data; the space environment model is input into the intelligent orchestration scheduler for calculation to obtain gravity data; the network security model is input into the intelligent orchestration scheduler for calculation to obtain network security data; the user distribution model is input into the intelligent orchestration scheduler for calculation to obtain user distribution data; A derived satellite Internet is generated based on the physical environment data, the communication network data, the network topology data, the network traffic data, the satellite orbit data, the gravity data, the network security data and the user distribution data.

[0006] Furthermore, the physical environment data includes: the number of networking satellites, the radius of the earth, gravity parameters, satellite altitude, orbital period, satellite orbital speed and satellite mass; The communication network data include: the shortest communication times, the number of communication nodes, the network bandwidth, the total number of user services, the total number of completed services, the total number of unfinished services, the sum of the total number of completed services and the total number of unfinished services, protection time, detection time, response time, system exposure time, total population, regional area, number of network users and network user penetration rate.

[0007] Furthermore, the method for obtaining the topological structure model is: modeling using the number of networking satellites, the shortest communication times and the number of communication nodes; the expression of the topological structure model is: E=Ve-Vf+2; Among them, E represents the shortest communication times, Ve represents the number of networking satellites, and Vf represents the number of communication nodes.

[0008] Furthermore, the method for obtaining the resource distribution model is: modeling using the network bandwidth, the total number of user services, the total number of completed services, the total number of uncompleted services, and the sum of the total number of completed services and the total number of uncompleted services; the expression of the resource distribution model is: , ; Where N represents the total number of user services; N1 represents the total number of completed services; N−N1 represents the total number of uncompleted services; C is the sum of the total number of completed services and the total number of uncompleted services; e represents the network bandwidth; r i Indicates the resources obtained by each business; r0 represents the resource threshold, which is used to distinguish the priority of the total number of completed businesses and the total number of unfinished businesses in resource allocation; i represents the number of businesses, and i is a positive integer.

[0009] Furthermore, the method for obtaining the satellite orbit model is: modeling using the earth radius, the satellite altitude, the gravity parameter, the orbit period and the satellite orbit velocity; the expression of the satellite orbit model is: , Where: T is the orbital period; R is the radius of the earth; h is the satellite altitude; v is the satellite orbital velocity; u is the gravity parameter; a is the semi-major axis of the orbit.

[0010] Furthermore, the method for obtaining the space environment model is: modeling using the satellite mass and the satellite altitude; the expression of the space environment model is: , Among them, F is the gravitational force on the satellite; G is the gravitational constant; M is the mass of the Earth; m is the mass of the satellite; and h is the altitude of the satellite.

[0011] Furthermore, the method for obtaining the network security model is: modeling using the system exposure time, the protection time, the detection time and the response time; the expression of the network security model is: Et=Pt×(Dt+Rt), Among them, Et represents the system exposure time, that is, the time window in which the system may be attacked; Pt represents the protection time, that is, the time the system can resist the attack; Dt represents the detection time, that is, the time when the system detects the attack; Rt represents the response time, that is, the time when the system responds to the attack.

[0012] Furthermore, the method for obtaining the user distribution model is: modeling using a user density formula and a user penetration formula; the user density formula is: , Where, t is the regional time in hours; P it is the network user penetration rate in the region at time t; u it is the number of network users in the region at time t; M it is the total population of the region at time t.

[0013] The user penetration formula is: , Where: D is the network user density; O u is the number of network users; A is the area of ​​the region.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: In view of the fact that it is difficult to predict the future state of satellite Internet by using a single mechanism model or data model, the present invention uses multiple mechanism models or data models for modeling, including: a topology model, a resource distribution model, a satellite orbit model, a space environment model, a network security model and a user distribution model, wherein the topology model can determine the current network topology type, the resource distribution model can predict the utilization status of the network bandwidth, the satellite orbit model can predict the operation status of the satellite in orbit, the space environment model can predict the gravitational influence on the satellite, the network security model is used to predict the security level of the network, and the user distribution model can predict the user distribution situation. A variety of models are used in combination to achieve a more accurate prediction of the state of satellite Internet; BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flowchart of a satellite Internet derivative generation method based on digital twins provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is apparent to one of ordinary skill in the art that these specific details are not necessarily employed to practice the present invention. In other embodiments, in order to avoid obscuring the present invention, well-known structures, circuits, materials, or methods are not specifically described.

[0019] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment," "an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. In addition, it will be appreciated by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] In the description of the present invention, the directions or positional relationships indicated by terms such as “front”, “rear”, “left”, “right”, “up”, “down”, “vertical”, “horizontal”, “high”, “low”, “inside” and “outside” are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the scope of protection of the present invention.

[0021] Example: The first aspect of this embodiment proposes a satellite Internet derivative generation method based on digital twins, which is driven by the fusion of large-scale data and mechanism models. On the one hand, the perception data is corrected, and on the other hand, the existing mechanism models are iteratively evolved to form standardized models, such as user distribution models, business distribution models, resource allocation models, and network routing models. The twin system based on the standard model can not only restore the system state with high fidelity, but also predict and deduce the future system behavior.

[0022] Before explaining the specific implementation steps of this method, the digital twin satellite Internet system and standardized modeling are first explained.

[0023] 1. Twin satellite network Digital twins make full use of physical models, sensor updates, operation history and other data, integrate multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, complete mapping in virtual space, and thus reflect the entire life cycle of the corresponding physical equipment. The digital twin network is a mapping or simulation of the real physical environment and can be regarded as a digital mapping system of one or more important and interdependent equipment systems. Through digital twins, a twin satellite network of the physical satellite network is constructed, mirroring a low-cost trial-and-error simulation verification environment, which can reduce unnecessary operations of the physical network, broaden the practice space of network operation and maintenance personnel, realize the adjustment of the physical network structure and parameters, and directly or indirectly improve the availability and security of the physical network.

[0024] 2. Standardized modeling The standardized model formation process mainly includes data perception, data processing, mechanism and data fusion iteration.

[0025] (1) Data perception Data perception is to use collection equipment and Internet equipment to obtain information data about the physical environment and satellite communication network. The physical environment affects the network, and simulation modeling is conducive to the smooth operation of the network. The physical environment includes the location of each component element of the system, temperature and humidity, gravity environment, electromagnetic environment and other information, which can be collected by various sensors and then fed back to the ground station through telemetry and command subsystems. Network data includes network traffic, service distribution, routing topology, etc., which plays a key role in building intersatellite link models, network topology models and network protocol models. There are many ways to collect network data, such as Simple Network Management Protocol (SNMP), sFlow, NetFlow, network telemetry, etc. The data collection efficiency of the polling mode based on SNMP is low and can only achieve minute-level traffic collection. sFlow and NetFlow can collect original code streams and count their start and end time, service type, number of packets and bytes contained, and other traffic information. Network telemetry is customizable, and its push mode enables streaming data collection to reach millisecond levels, and can upload massive amounts of data per unit time. While occupying network resources, it places higher demands on the server's storage and data processing performance.

[0026] (2) Data processing Data processing includes preprocessing, classification, cleaning and storage of physical environment perception data and network data. Physical environment raw data and network raw data have the characteristics of fast speed, large capacity and high dimension. If they are directly transmitted without preprocessing, it is easy to cause network congestion and the storage requirements of the server will be very high. RICKS et al. proposed data reduction technology that can improve data processing efficiency. A model library is established during the transmission process to reduce image transmission redundancy and save bandwidth resources. In addition, how to use the periodicity and historical data of satellites to reduce the sending of redundant information is also a direction worthy of research. After preprocessing the data at each node and reducing the amount of data transmitted, the data processing station will classify and clean the received data of different structural types to remove errors and redundant data. Then the normal data is stored in the target database for call, the error and redundant data is stored in the temporary database, and fed back to the collector to form negative feedback to correct data collection.

[0027] (3) Mechanism and data fusion iteration Mechanism models can be divided into constant mechanism models and non-constant mechanism models according to whether the mechanism is constant. Satellite Internet involves many non-constant mechanism models, such as user distribution models, routing optimization models, resource allocation models, etc. Factors such as electromagnetic environment, multi-scale fading and user mobility will affect the accuracy of non-constant mechanism models. If non-constant mechanism models are directly used to model and predict satellite Internet, the theoretical results and actual results are often quite different. Fitting and training the original non-constant mechanism model with a large amount of historical data and real-time data, optimizing parameters, completing constraints and continuously iterating and updating are the key to improving the accuracy of satellite Internet behavior prediction and realizing satellite Internet twins with high fidelity.

[0028] Satellite Internet not only has a large network entity scale and a wide variety of user services, but also many parameters are time-varying, which ultimately leads to the formation of numerous standardized models. Different models have different characteristics. For example, the network topology optimization model is mainly based on logical relationships; the atmospheric model is mainly based on mathematical calculations; the user link access model is a mixture of logical and mathematical operations. Different processors are good at handling different tasks. For example, a single CPU is good at handling complex tasks of serial logic; GPU is good at processing a large number of tasks in parallel; DSP is good at tasks related to digital signal processing algorithms; FPGA is used to accelerate special communication algorithms.

[0029] Based on the above explanation of the digital twin satellite internet system and standardized modeling, the first aspect of this embodiment proposes a satellite internet derivative generation method based on digital twins. The execution process of this method is as follows: Figure 1 As shown, the following steps are included: Step 1: Collect physical environment data and communication network data.

[0030] Specifically, the physical environment data includes: the number of networking satellites, the radius of the earth, gravity parameters, satellite altitude, orbital period, satellite orbital speed and satellite mass; The communication network data include: the shortest communication times, the number of communication nodes, the network bandwidth, the total number of user services, the total number of completed services, the total number of unfinished services, the sum of the total number of completed services and the total number of unfinished services, protection time, detection time, response time, system exposure time, total population, regional area, number of network users and network user penetration rate.

[0031] Specifically, the total number of user services is the number of all Internet access demand services within the current area time (in hours); the total number of completed services is the number of completed Internet access demand services within the current area time; the total number of unfinished services is the number of unfinished Internet access demand services within the current area time.

[0032] Step 2: Use the physical environment data and the communication network data to build a model to obtain a topology model, a resource distribution model, a satellite orbit model, a space environment model, a network security model and a user distribution model.

[0033] Specifically, the method for obtaining the topological structure model is: modeling using the number of networking satellites, the shortest communication times and the number of communication nodes; the expression of the topological structure model is: E=Ve-Vf+2; Among them, E represents the shortest communication times, Ve represents the number of networking satellites, and Vf represents the number of communication nodes.

[0034] Specifically, the method for obtaining the resource distribution model is: modeling using the network bandwidth, the total number of user services, the total number of completed services, the total number of uncompleted services, and the sum of the total number of completed services and the total number of uncompleted services; the expression of the resource distribution model is: , ; Where N represents the total number of user services; N1 represents the total number of completed services; N−N1 represents the total number of uncompleted services; C is the sum of the total number of completed services and the total number of uncompleted services; e represents the network bandwidth; r i Indicates the resources obtained by each business; r0 represents the resource threshold, which is used to distinguish the priority of the total number of completed businesses and the total number of unfinished businesses in resource allocation; i represents the number of businesses, and i is a positive integer.

[0035] Specifically, the method for obtaining the satellite orbit model is: modeling using the earth radius, the satellite altitude, the gravity parameter, the orbit period and the satellite orbit velocity; the expression of the satellite orbit model is: ; Where: T is the orbital period; R is the radius of the earth; h is the satellite altitude; v is the satellite orbital velocity; u is the gravity parameter; a is the semi-major axis of the orbit.

[0036] Specifically, the method for obtaining the space environment model is: using the satellite mass and the satellite altitude to build a model; the expression of the space environment model is: ; Among them, F is the gravitational force on the satellite; G is the gravitational constant; M is the mass of the Earth; m is the mass of the satellite; and h is the altitude of the satellite.

[0037] Furthermore, the method for obtaining the network security model is: modeling using the system exposure time, the protection time, the detection time and the response time; the expression of the network security model is: Et=Pt×(Dt+Rt); Among them, Et represents the system exposure time, that is, the time window in which the system may be attacked; Pt represents the protection time, that is, the time the system can resist the attack; Dt represents the detection time, that is, the time when the system detects the attack; Rt represents the response time, that is, the time when the system responds to the attack.

[0038] Specifically, the method for obtaining the user distribution model is: modeling using a user density formula and a user penetration formula; the user density formula is: ; Where, t is the regional time in hours; P it is the network user penetration rate in the region at time t; u it is the number of network users in the region at time t; M it is the total population of the region at time t.

[0039] The user penetration formula is: ; Where: D is the network user density; O u is the number of network users; A is the area of ​​the region.

[0040] Step 3: Input the topology structure model into the intelligent orchestration scheduler for calculation to obtain network topology data; input the resource distribution model into the intelligent orchestration scheduler for calculation to obtain network traffic data; input the satellite orbit model into the intelligent orchestration scheduler for calculation to obtain satellite orbit data; input the space environment model into the intelligent orchestration scheduler for calculation to obtain gravity data; input the network security model into the intelligent orchestration scheduler for calculation to obtain network security data; input the user distribution model into the intelligent orchestration scheduler for calculation to obtain user distribution data.

[0041] Specifically, the intelligent orchestration scheduler is an efficient and flexible application deployment and management tool that combines container orchestration and scheduling technology, as well as intelligent algorithms and machine learning technology to achieve automated and intelligent resource allocation and task scheduling. The intelligent orchestration scheduler can automatically decompress and run application images, and register services on the service discovery platform to achieve automated deployment of applications. According to the needs of the application and the resource status in the cloud service, the intelligent orchestration scheduler can allocate containers to appropriate resources to achieve efficient resource utilization. Through intelligent algorithms, the intelligent orchestration scheduler can allocate containers to nodes with the lowest resource load to ensure the performance and reliability of cloud services. In addition to the above functions, the intelligent orchestration scheduler described in this solution can also obtain network topology data, network traffic data, satellite orbit data, gravity data, network security data and user distribution data results by pre-loading the algorithms required by various mechanism models or data models through calculation. Among them, the network topology data is obtained in the following way: the shortest communication time, the number of networking satellites, and the number of communication nodes in the current satellite network are input into the intelligent orchestration scheduler for calculation, and then brought into the network topology model to obtain the topology structure type. The node information is obtained through the location information, status information and node type. The link information is obtained through the link type, maximum transmission rate and the time required for link transmission. The routing and switching information is obtained through the routing table and LAN configuration information. The topology structure type, routing and switching information, link information and node information are brought into the pre-installed intelligent algorithm training to obtain network topology data. Network topology data is information used to describe each node in the network (such as computers, servers, routers, switches, etc.) and the connection relationship between them.

[0042] Step 4: Generate a derived satellite Internet using the physical environment data, the communication network data, the network topology data, the network traffic data, the satellite orbit data, the gravity data, the network security data and the user distribution data.

[0043] Specifically, the physical environment data, the communication network data, the network topology data, the network traffic data, the satellite orbit data, the gravity data, the network security data and the user distribution data are cleaned by computer software, and then the data from different sources are identified and associated through artificial intelligence algorithms to form a complete data set. According to the satellite Internet entity and data characteristics, a suitable data model is constructed, and the data is trained using a machine learning algorithm to generate physical mirror data or predicted data. The data analysis results are intuitively presented in the form of 3D images, stereo models, etc., to display the generated derivative satellite Internet.

[0044] Based on the method provided in the first aspect and the system provided in the second aspect, the third aspect of this embodiment provides a computer device for executing the method described in the first aspect or any method that may be involved in the method described in the first aspect, including a memory, a processor and a transceiver that are sequentially connected in communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the method described in the first aspect or any method that may be involved in the method described in the first aspect. For example, the memory may include, but is not limited to, a random access memory (Random-Access Memory, RAM), a read-only memory (Read-Only Memory, ROM), a flash memory (FlashMemory), a first-in-first-out memory (First Input First Output, FIFO) and / or a first-in-last-out memory (First Input Last Output, FILO), etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. In addition, the computer device may also include, but is not limited to, a power module, a display screen and other necessary components.

[0045] The working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment can be referred to the method described in the first aspect or any method that may be involved in the first aspect, and will not be described in detail here.

[0046] The fourth aspect of this embodiment provides a computer-readable storage medium that stores the method described in the first aspect or any method that may be related to the method described in the first aspect, that is, the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the method described in the first aspect or any method that may be related to the method described in the first aspect is executed. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to computer-readable storage media such as floppy disks, optical disks, hard disks, flash memories, USB flash drives, and / or memory sticks, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0047] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be referred to the method described in the first aspect or any method that may be related to the method described in the first aspect, and will not be repeated here.

[0048] In a fifth aspect, the present embodiment provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the method described in the first aspect or any method that may be related to the method described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0049] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A satellite Internet derivative generation method based on digital twins, characterized in that: The following steps are involved: Collect physical environment data and communication network data; Modeling using the physical environment data and the communication network data to obtain a topology model, a resource distribution model, a satellite orbit model, a space environment model, a network security model, and a user distribution model; The topology structure model is input into the intelligent scheduling scheduler for calculation to obtain network topology data; The resource distribution model is input into the intelligent scheduling scheduler for calculation to obtain network traffic data; the satellite orbit model is input into the intelligent scheduling scheduler for calculation to obtain satellite orbit data; Input the space environment model into the intelligent scheduling and dispatching device for calculation to obtain gravity data; Inputting the network security model into the intelligent orchestration scheduler for calculation to obtain network security data; Inputting the user distribution model into the intelligent scheduling scheduler for calculation to obtain user distribution data; A derived satellite Internet is generated based on the physical environment data, the communication network data, the network topology data, the network traffic data, the satellite orbit data, the gravity data, the network security data and the user distribution data.

2. According to a satellite Internet derivative generation method based on digital twins according to claim 1, it is characterized in that: The physical environment data includes: the number of networked satellites, the radius of the earth, gravity parameters, satellite altitude, orbital period, satellite orbital speed and satellite mass; The communication network data include: the shortest communication times, the number of communication nodes, the network bandwidth, the total number of user services, the total number of completed services, the total number of unfinished services, the sum of the total number of completed services and the total number of unfinished services, protection time, detection time, response time, system exposure time, total population, regional area, number of network users and network user penetration rate.

3. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the topological structure model is: modeling using the number of networking satellites, the shortest communication times and the number of communication nodes; the expression of the topological structure model is: E=V e -V f +2; Among them, E represents the shortest communication number, V e Indicates the number of networked satellites, V f Indicates the number of communication nodes.

4. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the resource distribution model is: modeling using the network bandwidth, the total number of user services, the total number of completed services, the total number of uncompleted services, and the sum of the total number of completed services and the total number of uncompleted services; the expression of the resource distribution model is: ； ； Where N represents the total number of user services; N1 represents the total number of completed services; N−N1 represents the total number of uncompleted services; C is the sum of the total number of completed services and the total number of uncompleted services; e represents the network bandwidth; r i Indicates the resources obtained by each business; r0 represents the resource threshold, which is used to distinguish the priority of the total number of completed businesses and the total number of unfinished businesses in resource allocation; i represents the number of businesses, and i is a positive integer.

5. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the satellite orbit model is: modeling using the earth radius, the satellite altitude, the gravity parameter, the orbit period and the satellite orbit velocity; the expression of the satellite orbit model is: ； Where: T is the orbital period; R is the radius of the earth; h is the satellite altitude; v is the satellite orbital velocity; u is the gravity parameter; a is the semi-major axis of the orbit.

6. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the space environment model is: modeling using the satellite mass and the satellite altitude; The expression of the space environment model is: ； Among them, F is the gravitational force on the satellite; G is the gravitational constant; M is the mass of the Earth; m is the mass of the satellite; and h is the altitude of the satellite.

7. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the network security model is: modeling using the system exposure time, the protection time, the detection time and the response time; the expression of the network security model is: Et=Pt×(Dt+Rt); Among them, Et represents the system exposure time, that is, the time window in which the system may be attacked; Pt represents the protection time, that is, the time the system can resist the attack; Dt represents the detection time, that is, the time when the system detects the attack; Rt represents the response time, that is, the time when the system responds to the attack.

8. The satellite Internet derivative generation method based on digital twin according to claim 2 is characterized in that: The method for obtaining the user distribution model is: modeling using a user density formula and a user penetration formula; the user density formula is: ； Where, t is the regional time in hours; P it is the network user penetration rate in the region at time t; u it is the number of network users in the region at time t; M it is the total population of the region at time t; The user penetration formula is: ； Where: D is the network user density; O u is the number of network users; A is the area of ​​the region.

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