A star-ground fusion end-edge cloud collaborative computing offloading method

Abstract

The application claims a kind of star-ground fusion end edge cloud cooperation's computing offloading method, belongs to wireless communication technical field.For the problem that ground processing delay is big, satellite processing capacity is limited and causes task completion delay is high, by the edge computing is proposed a kind of end edge cloud cooperation's computing offloading method to star.In task modeling stage, the dependency relationship of split computing subtask is modeled in combination with linked list and directed acyclic graph, and the task model generated can effectively reduce task processing delay.In computing offloading stage, according to node offloading strategy and link transmission rate, the task completion delay is defined, and the delay list based on task offloadable node is calculated by using complete offloading strategy;Under the constraints of satellite residual coverage time, node computing capacity and link offload bandwidth, a real number coding genetic algorithm based on task completion delay is proposed to find the best offload point of computing task, and the optimal computing offloading strategy formed can effectively reduce delay and improve task completion rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication. Specifically relates to a star-ground fusion end-edge cloud collaborative computing offloading method. BACKGROUND

[0002] With the rapid development of information technology, the star-ground fusion end-edge cloud collaborative network facing 6G is gradually attracting widespread attention as a new type of communication architecture. The star-ground fusion end-edge cloud collaborative network integrates satellite communication, ground network, and edge cloud collaboration organically to form an efficient, flexible, and intelligent communication system. Among them: end-edge cloud collaboration refers to the integration of communication, computing, and storage resources of terminals, edge computing nodes, and cloud data centers, as well as the reasonable allocation of tasks among terminals, edge devices, and the cloud. However, with the rise of the Internet of Things technology, the popularity of wireless networks, and the increasing number of user devices, the surge in real-time tasks will lead to increased system latency, so how to reasonably allocate processing tasks between edge computing nodes and cloud computing centers has become a difficult problem that needs to be solved.

[0003] According to existing research on computing offloading, according to the offloading scenario, it can be mainly divided into three categories: (1) single server single user scenario, (2) single server multi-user scenario, (3) multi-user multi-server scenario. The first two scenarios are only suitable for simple applications, high-security scenarios, or scenarios with relatively low resource requirements but require flexible expansion; multi-server multi-user is suitable for large-scale applications or scenarios that require high availability and performance optimization. Most of these studies offload tasks to specific edge servers for execution or only use the resources of the edge network, without fully utilizing the resources in the dense edge network and the remote cloud, resulting in low device resource utilization. Therefore, when researching task offloading, the computing scenario of cloud-edge-end collaboration needs to be considered.

[0004] In terms of task models, it can be mainly divided into the following two categories: one mainly considers the coarse-grained category of tasks and almost does not consider the dependency relationship between tasks, regarding the task as a whole and placing it locally or offloading it to the server for execution; the other considers the dependency relationship between tasks. However, both of the above do not consider the dependency relationship between sub-tasks when completely offloading a single task node. Therefore, the research on the task model offloading strategy needs to be further explored.

[0005] In order to improve resource utilization and reduce task completion delay, the application provides a star-ground fusion end-edge cloud collaborative computing offloading method. In the task modeling stage, the task is split by adopting a function module subdivision strategy, the precedence, selection and parallel dependency relationship is defined, the task dependency is modeled by combining a linked list and a directed acyclic graph to determine the subtask priority. In the task offloading stage, a complete offloading mode is adopted, the propagation, transmission and processing delay of different tasks on different offloading nodes is calculated according to the distance between different nodes, the transmission rate and the computing capacity of the edge computing, cloud computing and satellite offloading nodes, and a task completion delay list is generated. Under the multiple constraints of communication, computing resource and satellite coverage time, a real number coding genetic algorithm is used to solve the optimal offloading strategy. SUMMARY

[0006] The application aims to solve the problems of the prior art. A star-ground fusion end-edge cloud collaborative computing offloading method is provided, which considers the timing and data dependency relationship between tasks, considers the joint offloading of satellites, edge computing and cloud computing, and uses a real number coding genetic algorithm to obtain a computing offloading decision method. The technical scheme of the application is as follows:

[0007] A star-ground fusion end-edge cloud collaborative computing offloading method, comprising the following steps:

[0008] S1: constructing a star-ground fusion end-edge cloud collaborative network model and calculating the ground, satellite-ground link transmission rate;

[0009] S2: splitting the task into a plurality of subtasks by adopting a function module subdivision strategy;

[0010] S3: defining the precedence, parallel and selection dependency relationship, combining a linked list and a directed acyclic graph to construct a serial, parallel and mixed task dependency relationship model;

[0011] S4: defining the serial, parallel and mixed task completion delay according to the node offloading strategy and the link transmission rate;

[0012] S5: calculating the task completion delay of the edge computing, cloud computing and satellite nodes by adopting a complete offloading strategy, and generating a task completion delay list based on the task offloadable nodes;

[0013] S6: calculating the satellite coverage arc length and the remaining coverage time according to the distance and the pitch angle between the user and the satellite;

[0014] S7: under the multiple constraints of the satellite remaining coverage time, the node computing capacity and the link offloading bandwidth, a real number coding genetic algorithm based on the task completion delay is proposed to find the best offloading point of the computing task, and then a computing offloading strategy minimizing the task completion delay is formed.

[0015] Further, the ground and the satellite-to-ground link transmission rate calculated in step S1 is

[0016] (1) Ground network link

[0017] The link uses frequency division multiplexing, the channel is modeled as a Rayleigh channel, and the link transmission rate between the user terminal i and the ground edge computing j 1 and the cloud computing server j 2 is:

[0018]

[0019] wherein, are the channel bandwidths of different links, is the transmit power of the user equipment on different ground network links, and N0 is the power of the additive white Gaussian noise of the link, is the channel gain of different links, and the calculation formula is:

[0020]

[0021] wherein, is the channel coefficient, and the calculation formula is:

[0022]

[0023] wherein, is a Rayleigh random variable, and γ is the path loss exponent;

[0024] (2) Satellite-to-ground uplink

[0025] The satellite-to-ground uplink uses Ka band for communication, and line-of-sight transmission dominates in the uplink, the channel is modeled as a Rician channel, and the link transmission rate between the user terminal i and the satellite m is:

[0026]

[0027] wherein, B i,m is the channel bandwidth of the satellite-to-ground uplink, P i,m is the transmit power of the user terminal i in the uplink, W i is the antenna gain of the user terminal i, G m is the receive antenna gain of the satellite m, and the calculation formula is:

[0028] G m ≈(4D RA f u / c) 2 (5) wherein D RA is the diameter of the satellite receive antenna, f u is the uplink carrier frequency, and c is the speed of light;

[0029] Considering the impact of free path loss and rain attenuation on satellite signal transmission quality, the uplink channel coefficient is:

[0030]

[0031] Among them, ρ i,m is a Ricean random variable, As the rain fades, is the free path loss, which is calculated as:

[0032]

[0033] Furthermore, the sequential, parallel and selection dependency relationships in step S3 are as follows:

[0034] (1) Successive dependency: The next subtask can continue to execute only after the previous subtask completes and transfers data to the next subtask.

[0035] (2) Parallel dependency relationship: A task has multiple parallel subtasks, and the subtasks do not interfere with each other;

[0036] (3) Select dependency row relationship: During the execution of a task, after the previous subtask is completed, the subtask to be executed will have a two-choice or multiple-choice situation.

[0037] Furthermore, the task dependency model in step S3 is:

[0038] The tasks generated by the user terminal are represented by a set, namely u y is the number of tasks; the subtasks divided by the user terminal are represented by a set, namely u s Represents the number of task subtasks; the task model is modeled as a four-tuple to characterize the task requirements, that is, y k =(S k ,ξ k ,w k ,q k );q k represents the amount of data of the kth task itself, w k Indicates the amount of computation required to process the kth task; at the same time, Indicates the dependency between subtasks. , indicating a subtask and There is no dependency between , indicating a subtask and There is a time sequence, data or time sequence and data double dependence; according to whether there is time sequence, data or time sequence and data double dependence relationship of subtask, the combination mode of linked list and directed acyclic graph is adopted to construct serial, parallel and mixed task dependence relationship model.

[0039] Further, the serial, parallel and mixed task completion delay in step S4 is:

[0040] The propagation delay, transmission delay and processing delay are respectively:

[0041]

[0042] Wherein, f represents the computing power of edge computing, cloud computing and satellite server, that is, the number of CPU cycles that the server can provide, R represents the transmission rate in different links, d represents the distance from the user terminal to the server, q k The data amount of task y k Itself, w k The computing amount required for processing task y k ; c represents the propagation speed of signal in optical fiber; R represents the transmission rate in different links.

[0043] The single task completion delay is:

[0044] T=T tran +T prop +T proc (11)

[0045] The completion delay of the kth serial task is:

[0046]

[0047] The completion delay of the kth parallel task is

[0048]

[0049] Wherein, The maximum computing amount in task k, The maximum data amount in task k;

[0050] The completion delay of the kth mixed task should be:

[0051]

[0052] Wherein, The computing amount required for processing task y k The nth subtask, that is, the number of cycles required for computing task, ∩ represents intersection, The computing amount required for processing task y kThe subtask with the largest computational cost and dependency relationship among them, u s Represents task y k Number of neutron missions.

[0053] Furthermore, the completion delay of offloading serial, parallel and hybrid tasks to edge computing, cloud computing and satellite in step S5 is:

[0054] Task y k Uninstall strategy vector To express, Represents task y k Binary decision variables for offloading to ground edge computing servers, cloud computing servers, satellites, and on-board edge computing servers. Indicates that the user terminal offloads the task to the ground edge server; if Indicates that the user terminal offloads the task to the cloud computing server; if Indicates that the user terminal offloads the task to the satellite; if Indicates that the user terminal offloads the task to the onboard edge computing server;

[0055] (1) The completion delay of serial, parallel and hybrid tasks offloaded to the ground edge computing server is:

[0056]

[0057] in, The computing power of the ground edge server, is the distance between the user terminal and the ground edge computing server, The transmission rate between the user terminal and the ground edge computing server;

[0058] (2) The completion delay of serial, parallel and mixed tasks offloaded to the cloud computing server is:

[0059]

[0060] in, The computing power of cloud computing servers, is the distance between the user terminal and the cloud computing server, The transmission rate between the user terminal and the cloud computing server;

[0061] (3) The completion delay of serial, parallel and hybrid tasks unloaded to the satellite is:

[0062]

[0063] Among them, f m is the computing power of the satellite, d i,mR is the distance between the user terminal and the satellite i,m is the transmission rate between the user terminal and the satellite

[0064] (4) When the satellite cannot handle due to limited computing resources, part of the edge computing is uploaded to the satellite, so the completion delay of the serial, parallel and hybrid task offloading to the satellite edge server is:

[0065]

[0066] wherein, is the computing capability of the satellite edge computing server.

[0067] Further, the satellite remaining coverage time and coverage arc length in step S6 are:

[0068] (1) According to the relative position of the satellite and the user terminal, the elevation angle θ between the user terminal and the satellite i,m is expressed as:

[0069]

[0070] wherein, h m is the height between the user equipment and the satellite orbit, R e is the radius of the earth, d i,m is the distance between the user terminal and the satellite, is the angle between the satellite and the straight line passing through the center of the earth perpendicular to the tangent of the user terminal;

[0071] (2) The central angle ζ1 corresponding to the satellite coverage area is expressed as:

[0072]

[0073] (3) The longest communication time between the user terminal i and the satellite is expressed as:

[0074]

[0075] wherein, v i,m is the speed of LEO satellite, L is the coverage arc length of the satellite to the user equipment, and the calculation formula is:

[0076]

[0077] Further, the constraint of minimizing the task completion delay in step S7 should be:

[0078]

[0079] wherein, respectively represent the computing capability of the ground edge computing, cloud computing and satellite edge computing server, fm Indicates the computing power of the satellite, represents the offloading bandwidth between the user terminal and the ground edge computing, cloud computing and satellite edge computing server links, B max is the total offloading bandwidth, C1 is the satellite coverage time constraint, C2, C3, C4 and C5 are the node computing capacity constraints, C6, C7, C8, C9, C10 and C11 are the offloading bandwidth constraints, and C12 is the offloading decision variable constraint.

[0080] The advantages and beneficial effects of the present invention are as follows:

[0081] This solution addresses the high task completion latency caused by limited satellite processing capacity and large ground processing latency in satellite-ground converged communications. It proposes a computation offloading method for satellite-ground converged edge-cloud collaboration. The main innovations of this invention are: 1) It employs a functional module segmentation strategy to split tasks, defining sequential, selective, and parallel dependencies. Task dependency modeling combines linked lists with directed acyclic graphs to determine subtask priorities. 2) Based on the node offloading strategy and link transmission rate, it defines serial, parallel, and hybrid task completion latencies. A complete offloading strategy is used to calculate the task completion latency for edge computing, cloud computing, and satellite nodes, generating a task completion latency list based on nodes that can be offloaded. 3) Under the multiple constraints of remaining satellite coverage time, node computing power, and link offloading bandwidth, a real-coded genetic algorithm based on task completion latency is proposed to find the optimal offloading point for computational tasks, thereby forming a computation offloading strategy that minimizes task completion latency. Existing research often arbitrarily partitions tasks, resulting in overly idealized task models. Therefore, this invention offers a creative and easily implementable solution. Furthermore, the present invention analyzes whether the signal bypasses the obstacle through reflection, diffraction or scattering, models the channel as Rayleigh and Rice channels, and comprehensively considers the influencing factors of path loss and rain attenuation to calculate the transmission rate in different links. In existing studies, most of them consider the impact of a single factor on the transmission rate. Therefore, the present invention is unique and practical. Finally, the present invention also adopts a complete offloading method to offload tasks to edge computing, cloud computing and satellite nodes for processing. In existing studies, most of them consider a layered partial offloading method. Although this method does not require the computing power of edge computing, cloud computing and satellite nodes, the resulting data migration will increase the propagation and transmission delay. Therefore, the present invention is reasonable and creative. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a network model of satellite-ground fusion and end-edge-cloud collaboration constructed by the preferred embodiment provided by the present invention;

[0083] Figure 2 It is a task model diagram;

[0084] Figure 3 It is a diagram of the relative positions of the satellite and the user;

[0085] Figure 4 This is a schematic diagram of the computation offloading process of satellite-ground integrated end-edge-cloud collaboration described in the present invention. DETAILED DESCRIPTION

[0086] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0087] The technical solution of the present invention to solve the above technical problems is:

[0088] A satellite-ground integrated edge-cloud collaborative computation offloading method is proposed to reduce task completion latency. First, a functional module subdivision strategy is adopted to split tasks, defining sequential, parallel, and selective dependencies. Task dependency modeling is combined with a linked list and directed acyclic graph to determine subtask priorities. Second, based on the node offloading strategy and link transmission rate, serial, parallel, and hybrid task completion delays are defined. A complete offloading strategy is used to calculate the task completion delays of edge computing, cloud computing, and satellite nodes, generating a task completion delay list based on task-offloadable nodes. Finally, under the multiple constraints of the remaining satellite coverage time, node computing power, and link offloading bandwidth, a real-coded genetic algorithm based on task completion delay is proposed to find the optimal offloading point for the computation task, thereby forming a computation offloading strategy that minimizes task completion delay.

[0089] The specific process is as follows:

[0090] Step 1: Build a satellite-ground integrated end-edge-cloud collaborative network model and calculate the transmission rates of the ground and satellite-ground links.

[0091] Step 2: Use the functional module segmentation strategy to split the task into several subtasks.

[0092] Step 3: Define sequential, parallel, and selective dependencies, combine linked lists with directed acyclic graphs, and build serial, parallel, and hybrid task dependency models.

[0093] Step 4: Define the serial, parallel, and hybrid task completion delays based on the node offloading strategy and link transmission rate.

[0094] Step 5: Use the full offloading strategy to calculate the task completion delay of edge computing, cloud computing and satellite nodes, and generate a task completion delay list based on task offloading nodes.

[0095] Step 6: Calculate the arc length of the satellite's coverage of the user and the remaining coverage time based on the distance and elevation angle between the user and the satellite.

[0096] Step 7: Under the multiple constraints of satellite residual coverage time, node computing capacity and link offloading bandwidth, a real number coding genetic algorithm based on task completion delay is proposed to find the best offloading point of computing task, and then a computing offloading strategy is formed to minimize the task completion delay.

[0097] Preferably, the third step of defining the precedence, parallel and selection dependency relationship is as follows:

[0098] (1) Precedence dependency relationship: only when the current subtask is completed and the data is transmitted to the next subtask, the next subtask can continue to execute.

[0099] (2) Parallel dependency relationship: the task has multiple branches of parallel subtasks, and the subtasks execute without interfering with each other.

[0100] (3) Selection dependency relationship: during the execution of the task, after the execution of the previous subtask is completed, the executed subtask will have a one or more one selection situation.

[0101] Preferably, the fourth step of defining the serial, parallel and hybrid task completion delay according to the node offloading strategy and the link transmission rate is as follows:

[0102] When the user terminal generates a computing task, the user can choose to offload the task to different servers for processing. In this process, the propagation delay, transmission delay and processing delay are respectively:

[0103]

[0104] Where f represents the computing capacity of edge computing, cloud computing and satellite server, i.e. the number of CPU cycles that the server can provide, c represents the propagation speed of signals in optical fiber, R represents the transmission rate in different links, d represents the distance from the user terminal to the server, q k represents the data volume of task k itself, w k represents the computing amount required when processing task k.

[0105] Generally speaking, the computing result is smaller than the original task data volume, so the time of transmitting the computing result to the user is often ignored. In order to facilitate analysis, this paper also does not consider the queuing delay of the task in the processing process, so the single task completion delay is:

[0106] T=T tran +T prop +T proc (35)

[0107] According to the above task model, there may be sequential, parallel, or selective dependencies between subtasks. Different dependencies lead to different execution orders of subtasks, resulting in different task completion delays. In this case, the completion delay of the kth serial task should be:

[0108]

[0109] The completion delay of the kth parallel task should be

[0110]

[0111] in, Indicates the maximum amount of computation in task k, Indicates the maximum amount of data in task k.

[0112] The completion delay of the kth mixed task should be:

[0113]

[0114] in, It is expressed as the amount of computation required for the kth nth subtask in the task, that is, the number of cycles required to calculate the task, ∩ represents the intersection, Represents task y k The subtask with the largest computational cost and dependency relationship among them, u s Represents task y k Number of neutron missions.

[0115] The models involved in the present invention are as follows:

[0116] 1. Network Model

[0117] The network model of satellite-ground integration and end-edge-cloud collaboration is as follows Figure 1 As shown in Figure 1, the model consists of user terminals, ground, and satellite networks. Users primarily include various sensors and smartphones, responsible for data collection, task generation, task division, and task reconstruction. The ground network primarily consists of edge computing and cloud computing, which jointly handle a large number of ground tasks and provide users with low-latency and intelligent services. The satellite network primarily consists of satellites and onboard edge servers. Moving edge computing to the satellite expands onboard computing resources. When the computing power of ground infrastructure is insufficient, the satellite network, with its wide coverage, large capacity, and high flexibility, provides computing services to users, effectively supporting the computing needs of various user services.

[0118] 2. The technical solution of the present invention is as follows:

[0119] This paper proposes a satellite-ground integrated end-edge-cloud collaborative computing offloading method to reduce task completion delay. First, a functional module subdivision strategy is used to split tasks, defining sequential, parallel, and selective dependencies. A linked list is combined with a directed acyclic graph to model task dependencies and determine subtask priorities. Second, based on the node offloading strategy and link transmission rate, serial, parallel, and hybrid task completion delays are defined. A complete offloading strategy is used to calculate the task completion delays of edge computing, cloud computing, and satellite nodes, generating a task completion delay list based on task-offloadable nodes. Finally, under the multiple constraints of the remaining satellite coverage time, node computing power, and link offloading bandwidth, a real-coded genetic algorithm based on task completion delay is proposed to find the optimal offloading point for the computing task, thereby forming a computing offloading strategy that minimizes the task completion delay.

[0120] 3. Objective function and constraints:

[0121]

[0122] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0123] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0124] The above embodiments should be understood as merely illustrating the present invention and not as limiting the scope of protection of the present invention. After reading the contents of the present invention, technicians may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A satellite-ground integrated end-edge-cloud collaborative computing offloading method, characterized in that: The following steps are involved: S1: Build a satellite-ground integrated end-edge-cloud collaborative network model to calculate the transmission rates of ground and satellite-ground links; S2: Use functional module segmentation strategy to split the task into several subtasks; S3: Define sequential, parallel, and selective dependencies, combine linked lists with directed acyclic graphs, and build serial, parallel, and hybrid task dependency models; S4: Define serial, parallel, and hybrid task completion delays based on node offloading strategies and link transmission rates; S5: Calculate the task completion delays of edge computing, cloud computing, and satellite nodes using a complete offloading strategy, and generate a task completion delay list based on task offloadable nodes; S6: Calculate the satellite coverage arc length and remaining coverage time based on the distance and pitch angle between the user and the satellite; S7: Under the multiple constraints of satellite remaining coverage time, node computing power, and link offloading bandwidth, a real-coded genetic algorithm based on task completion delay is proposed to find the optimal offloading point of the computing task, thereby forming a computing offloading strategy that minimizes the task completion delay. The completion delay of the serial, parallel and mixed tasks in step S4 is: The propagation delay, transmission delay, and processing delay are: Among them, f represents the computing power of edge computing, cloud computing, and satellite servers, that is, the number of CPU cycles that the server can provide, R represents the transmission rate in different links, d represents the distance from the user terminal to the server, and q k Represents task y k The amount of data itself, w k Represents processing task y k The amount of calculation required; c represents the propagation speed of the signal in the optical fiber; R represents the transmission rate in different links; The completion delay of a single task is: T=T tran +T prop +T proc (4) The completion delay of the kth serial task is: The completion delay of the kth parallel task is in, Represents task y k The maximum amount of calculation, Represents task y k The maximum amount of data in the The completion delay of the kth mixed task should be: in, Represented as task y k The amount of computation required for the nth subtask is the number of cycles required to compute the task. ∩ represents the intersection. Represents task y k The subtask with the largest computational cost and dependency relationship among them, u s Represents task y k Number of neutron missions; The completion delay of the serial, parallel and hybrid tasks offloaded to edge computing, cloud computing and satellite in step S5 is: Task y k Uninstall strategy vector To express, Represents task y k Binary decision variables for offloading to ground edge computing servers, cloud computing servers, satellites, and on-board edge computing servers; if Indicates that the user terminal offloads the task to the ground edge server; if Indicates that the user terminal offloads the task to the cloud computing server; if Indicates that the user terminal offloads the task to the satellite; if Indicates that the user terminal offloads the task to the onboard edge computing server; (1) The completion delay of serial, parallel and hybrid tasks offloaded to the ground edge computing server is: in, The computing power of the ground edge server, is the distance between the user terminal and the ground edge computing server, The transmission rate between the user terminal and the ground edge computing server; (2) The completion delay of serial, parallel and mixed tasks offloaded to the cloud computing server is: in, The computing power of cloud computing servers, is the distance between the user terminal and the cloud computing server, The transmission rate between the user terminal and the cloud computing server; (3) The completion delay of serial, parallel and hybrid tasks unloaded to the satellite is: Among them, f m is the computing power of the satellite, d i,m is the distance between the user terminal and the satellite, R i,m is the transmission rate between the user terminal and the satellite; (4) When the satellite cannot process the task due to limited computing resources, some edge computing is carried out on-board. Therefore, the completion delay of serial, parallel and hybrid tasks offloaded to the on-board edge server is: in, The computing power of the onboard edge computing server; The remaining satellite coverage time and coverage arc length in step S6 are: (1) According to the relative position of the satellite and the user terminal, the pitch angle θ between the user terminal and the satellite i,m Expressed as: Among them, h m Indicates the height between the user equipment and the satellite orbit, R e represents the radius of the Earth, d i,m represents the distance between the user terminal and the satellite, It represents the angle between the satellite and the straight line passing through the center of the Earth and perpendicular to the tangent line of the user terminal; (2) The geocentric angle ζ1 corresponding to the satellite coverage area is expressed as: (3) The maximum communication time between user terminal i and the satellite is expressed as: Among them, v i,m is the speed of the LEO satellite, L is the arc length of the satellite's coverage of the user equipment, and the calculation formula is: The constraint for minimizing the task completion delay in step S7 should be: in, They represent the computing power of ground edge computing, cloud computing and satellite edge computing servers, respectively, and f m Indicates the computing power of the satellite, represents the offloading bandwidth between the user terminal and the ground edge computing, cloud computing and satellite edge computing server links, B max is the total offloading bandwidth, C1 is the satellite coverage time constraint, C2, C3, C4 and C5 are the node computing capacity constraints, C6, C7, C8, C9, C10 and C11 are the offloading bandwidth constraints, and C12 is the offloading decision variable constraint.

2. The method for offloading computing in satellite-ground integrated device-edge-cloud collaboration according to claim 1 is characterized in that: In step S1, the ground and satellite-to-ground link transmission rates are calculated as follows: (1) Ground network link The link uses frequency division multiplexing, and the channel is modeled as a Rayleigh channel. User terminal i connects to ground edge computing j 1 and cloud computing servers 2 The link transmission rate between them is: in, are different link channel bandwidths, is the transmission power of the user equipment in different ground network links, N0 is the power of the additive white Gaussian noise of the link, is the channel gain of different links, and its calculation formula is: in, is the channel coefficient, and its calculation formula is: in, is the Rayleigh random variable, γ is the path loss exponent; (2) Satellite-to-ground uplink The satellite-to-ground uplink uses the Ka-band for communication. Line-of-sight transmission dominates the uplink. The channel is modeled as a Ricean channel. The link transmission rate between user terminal i and satellite m is: Among them, B i,m is the channel bandwidth of the satellite-to-ground uplink, P i,m is the transmit power of user terminal i in the uplink, W i is the antenna gain of user terminal i, G m is the receiving antenna gain of satellite m, which is calculated as follows: G m ≈(4D RA f u / c) 2 (29) Among them, D RA is the diameter of the satellite receiving antenna, f u is the uplink carrier frequency, c is the speed of light; Considering the impact of free path loss and rain attenuation on satellite signal transmission quality, the uplink channel coefficient is: Among them, ρ i,m is a Ricean random variable, As the rain fades, is the free path loss, which is calculated as:

3. The method for offloading computing in satellite-ground integrated device-edge-cloud collaboration according to claim 1 is characterized in that: The sequence, parallelism and selection dependency relationships in step S3 are as follows: (1) Successive dependency: The next subtask can continue to execute only after the previous subtask completes and transfers data to the next subtask. (2) Parallel dependency relationship: A task has multiple parallel subtasks, and the subtasks do not interfere with each other; (3) Select dependency row relationship: During the execution of a task, after the previous subtask is completed, the subtask to be executed will have a two-choice or multiple-choice situation.

4. The method for offloading computing in satellite-ground integrated device-edge-cloud collaboration according to claim 1 is characterized in that: The task dependency model in step S3 is: The tasks generated by the user terminal are represented by a set, namely u y is the number of tasks; the subtasks divided by the user terminal are represented by a set, namely u s Represents the number of task subtasks; the task model is modeled as a four-tuple to characterize the task requirements, that is, y k =(S k ,ξ k ,w k ,q k );q k represents the amount of data of the kth task itself, w k Indicates the amount of computation required to process the kth task; at the same time, Indicates the dependency between subtasks. , indicating a subtask and There is no dependency between , indicating a subtask and There is a timing, data, or dual timing and data dependency between subtasks; based on whether there is a timing, data, or dual timing and data dependency between subtasks, a combination of linked lists and directed acyclic graphs is used to construct serial, parallel, and hybrid task dependency models.

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