Method and Program Product for Offloading Near-Earth Satellite Communication Tasks Based on Ground Collaborative Computing

Abstract

The present invention discloses a method and a program product for offloading near-earth satellite communication tasks based on ground collaborative computing. In the present invention, a satellite communication task offloading model is established. Among them, the satellite communication task offloading model aims to minimize the total task offloading delay, and takes the task offloading rate strategy of the satellite, the offloading path strategy of the task on the satellite, and the offloading path strategy of the task on the ground station as variables to be solved. The total task offloading delay is specifically the sum of the computing delay of the task on the satellite, the transmission delay of the task from the satellite to the ground station, the transmission delay between ground stations, and the computing delay of the task on the ground station. Then, the satellite communication task offloading model is solved to obtain the task offloading strategy. The offloading strategy of the present invention has a small delay, high robustness, effectively overcomes the disadvantage of limited computing power of LEO, greatly raises the upper limit of the task volume received by a single LEO, and at the same time solves the problem of uneven task distribution in ground station processing.

Description

Technical Field

[0001] The present invention relates to satellite task offloading technology, and in particular to a method and program product for offloading communication tasks of low-earth orbit satellites based on ground collaborative computing. Background Art

[0002] With the development of 5G and 6G communication technologies, the defects of traditional ground communication systems restricted by geographical factors have become increasingly obvious, and it has been difficult to meet people's communication needs. In contrast, satellite communication networks have a wider coverage range, and service access is simple and fast. Even in remote areas with scarce ground signal stations, communication efficiency can be significantly improved through satellites. Moreover, due to the special geographical location of satellites, it is difficult to be interfered by ground environmental factors. In the event of a serious natural disaster on the ground, reliable communication services can still be guaranteed. Therefore, satellite communication has become a hot topic in current communication technology research.

[0003] In terms of satellite use, satellites can be classified by orbit into: GEO geostationary orbit satellites (Geostationary Earth Orbit Satellite), MEO medium earth orbit satellites (Medium Earth Orbit Satellite), and LEO low earth orbit satellites (Low Earth Orbit Satellite). Although GEO satellites were widely used in the initial stage of satellite communication due to the large coverage range brought by their high orbits, with the increasing business demands, the data transmission delays caused by the relatively high satellite orbits of GEO and MEO are unable to meet the requirements for low-latency satellite communication. In contrast, the low orbits, low manufacturing costs, and large quantities of LEO are more in line with current needs.

[0004] However, in terms of satellite task processing, the low cost and small size of LEO result in insufficient computing power, and it is difficult to handle a large number of computing task requirements relying solely on its own computing ability. Currently, the several focused solutions include: ground collaborative computing, satellite constellation collaborative computing, satellite edge computing, optimizing satellite algorithms, etc. Among them, ground collaborative computing has strong computing power, low latency, and high bandwidth at ground stations, making ground collaborative computing have a higher upper limit in optimizing satellite task processing latency and increasing the satellite's task acceptance volume.

[0005] In terms of ground collaborative computing task downloading, various current solutions are adopted. For the corresponding relationship between satellites and ground stations, there are single satellite corresponding to a single ground station, multiple satellites corresponding to a single ground station, single satellite corresponding to multiple ground stations, and multiple satellites corresponding to multiple ground stations. In terms of the multiple access schemes adopted, there are multiple forms of multiple access schemes such as frequency division multiple access, time division multiple access, and code division multiple access. However, the latency of current task offloading still needs to be further optimized. Summary of the Invention

[0006] Aiming at the problems existing in the prior art, the purpose of the present invention is to provide a method and program product for offloading near-earth satellite communication tasks based on ground collaborative computing with lower time delay.

[0007] In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solutions:

[0008] A method for offloading near-earth satellite communication tasks based on ground collaborative computing includes the following steps:

[0009] (1) Establish a satellite communication task offloading model, wherein the satellite communication task offloading model aims to minimize the total time delay of task offloading, and uses the task offloading rate strategy of the satellite α , the offloading path strategy of the task on the satellite γ , the offloading path strategy of the task on the ground station β as variables to be solved. The total time delay of task offloading is specifically the sum of the computing time delay of the task on the satellite, the transmission time delay of the task from the satellite to the ground station, the transmission time delay between ground stations, and the computing time delay of the task on the ground station;

[0010] (2) Set the number of outer loop iterations a =0, and the number of inner loop iterations b = 0;

[0011] (3) Assign the task offloading rate strategy of the satellite a in the α (a)(b) th outer loop and the γ (a)(b) th inner loop, the offloading path strategy of the task on the satellite β (a)(b) , and the offloading path strategy of the task on the ground station a (a) α (a) γ (a) β (a) α (a) 、β (a) 、γ (a)(b)

[0012] (a) in the

[0012] (4) In the satellite communication task offloading model, use α as the variable to be solved, and assign β, γ to β (a)(b) , γ(a)(b) , thus optimizing the satellite communication task offloading model into a partial task offloading sub-model, solving the partial task offloading sub-model, and obtaining the a task offloading rate strategy of the satellite at the α (a)(b+1) -th outer loop and the (b + 1)-th inner loop

[0013] ; γ as the variable to be solved, and assigning α to α (a)(b+1) , thus optimizing the satellite communication task offloading model into a satellite offloading path selection sub-model;

[0014] (6) Solve the satellite offloading path selection sub-model to obtain the a offloading path strategy of the task on the satellite at the γ (a)(b+1) -th outer loop and the (b + 1)-th inner loop

[0015] (7) Judge whether α (a)(b+1) , γ (a)(b+1) meet the preset convergence condition. If so, execute step (8); otherwise, set b = b + 1 and return to execute step (4);

[0016] (8) Set α (a+1) = α (a)(b+1) , γ (a+1) = γ (a)(b+1) ;

[0017] (9) Take β in the satellite communication task offloading model as the variable to be solved, and assign α, γ to α (a+1) , γ (a+1) respectively, thus optimizing the satellite communication task offloading model into a ground station offloading path selection sub-model;

[0018] (10) Solve the ground station offloading path selection sub-model to obtain the a+ offloading path strategy of the task on the ground station at the β (a+1) -th outer loop

[0019] (11) Judge whether β (a+1) is equal to β (a), if not, then a = a +1, b = 0, and return to execute step (3). If so, then α (a+1) , β (a+1) , γ (a+1) Output as the optimal value.

[0020] A computer program product includes a computer program / instructions. When the computer program / instructions are executed by a processor, the above method is implemented.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention considers the computing delay of satellite tasks in a short time, regards the communication parameters in a short time as fixed values, and calculates the communication task volume and interference on the path in advance. Furthermore, it is optimized from multiple aspects such as task offloading, path selection, and ground station task balance, effectively reducing the total delay of task offloading. Considering the actual situation of satellites as much as possible, it effectively overcomes the disadvantage of limited computing power of LEO, greatly raises the upper limit of the task volume received by a single LEO, and at the same time solves the problem of uneven distribution of task processing among ground stations, avoiding the problem that some ground stations are overloaded while some ground stations have idle computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic flowchart of a method for offloading near-earth satellite communication tasks based on ground collaborative computing provided by an embodiment of the present invention;

[0023] Figure 2 is a communication model diagram of a satellite and a ground station applicable to the present invention;

[0024] Figure 3 is a modeling diagram of the distance between a satellite and a ground station provided by the present invention;

[0025] Figure 4 is a flowchart of a solution method for a satellite offloading path selection sub-model provided by the present invention;

[0026] Figure 5 is a flowchart of a solution method for a ground station task offloading path selection sub-model provided by the present invention;

[0027] Figure 6 is a verification diagram of matlab simulation results. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] Embodiment 1.

[0030] An embodiment of the present invention provides a method for offloading near-earth satellite communication tasks based on ground collaborative computing, as Figure 1 shown, which includes the following steps:

[0031] (1) Establish a satellite communication task offloading model.

[0032] The communication model applicable to the present invention is as Figure 2 shown. In this model, multiple satellites communicate with a single ground station, multiple satellites communicate with each other, and multiple ground stations communicate with each other. When establishing the satellite communication task offloading model, the computing time and task transmission time of satellites and ground stations are comprehensively considered, and the task volume processed by the ground station is balanced to avoid the problem that some ground stations are overloaded while some ground station computing resources are idle due to the change of satellite geographical location. Therefore, the satellite communication task offloading model established by the present invention aims to minimize the total task offloading delay, and takes the task offloading rate strategy α of the satellite, the offloading path strategy γ of the task on the satellite, the offloading path strategy β of the task on the ground station as variables to be solved. The total task offloading delay is specifically the sum of the computing delay of the task on the satellite, the transmission delay of the task from the satellite to the ground station, the transmission delay between ground stations, and the computing delay of the task on the ground station.

[0033] (1.1) Computing delay of the task on the satellite.

[0034] The computing delay of the task on the satellite is specifically:

[0035] ,

[0036] In the formula, represents the computing delay of the task on satellite s mn , s mn represents the m-th satellite communicating with ground station n, represents the satellite s mn 's task offloading rate, represents the satellite s mn 's received task volume, represents the satellite s mn 's received task computing volume, represents the satellite s mn 's received task complexity, represents the satellite s mn 's task arrival rate ， represents the satellite smn Computing resources. In the present invention, the value of

[0037] is set as a variable that can take any value from 0 to 1. Such a setting is more flexible and reasonable than the binary 0, 1 values, and can significantly reduce the total delay.

[0038] Since the scenario is satellite communication and the distance between the satellite and the ground station is large, the time loss on the signal transmission path cannot be ignored here. Introduce a task satellite offloading path indication variable , whose value is 0 or 1. When the task is offloaded from satellite s jn to satellite s mn , otherwise . s jn represents the jth satellite communicating with the nth ground station. Therefore, the transmission delay of the task from the satellite to the ground station can be expressed as:

[0039] .

[0040] In the formula, represents the transmission delay of the task from satellite s mn to ground station n, represents the distance between satellite s mn and ground station n, represents the speed of light, represents the task offloading rate of satellite s jn , represents the amount of computation of the task received by satellite s jn , represents the task arrival rate of satellite s jn , ， represents the transmission rate of the task offloaded from satellite s mn to the ground station, , respectively represent the transmission rate and distance between satellite s jn and satellite s mn , represents the set of satellites communicating with ground station n, represents the transfer indication variable. When there is a task transmitted to satellite s mn and then offloaded to ground station n , otherwise , that is, when is 0, otherwise it is 1.

[0041] The derivation process is as follows: The satellite - ground station position modeling is shown in Figure 3 , where represents the height of the satellite from the ground, represents the angle between the satellite - ground station connection line and the satellite - geocenter connection line, represents the radius of the earth, represents the elevation angle between the satellite and the ground station, represents the angle between the satellite - geocenter connection line and the ground - station - geocenter connection line. Then, according to Figure 3 Using the sine theorem, it can be derived that:

[0042] ,

[0043] ,

[0044] Combining the above two equations and deriving, we get:

[0045] ,

[0046] After arrangement, we get: .

[0047] Through the cosine theorem, the distance between the satellite and the ground station can be expressed as:

[0048] .

[0049] The derivation process of Figure 3 is as follows: Referring to , represents the azimuth angle of the spherical coordinate system. Then the straight - line distance between the satellite with position s jn and the satellite with position s mn can be expressed as:

[0050] ,

[0051] 、 The expressions are respectively:

[0052] ),

[0053] )

[0054] Wherein, B is the channel bandwidth, is the signal-to-noise ratio of inter-satellite transmission, is the signal-to-noise ratio of transmission between the satellite ground stations. The present invention considers the access technology of orthogonal frequency division multiple access (OFDMA) for satellite communication, that is, without considering the channel interference between satellites, then can be expressed as:

[0055] ,

[0056] Wherein, is the transmission power of the satellite transmitting to the satellite, respectively represent the efficiencies of the satellite transmission antenna and the satellite receiving antenna, and respectively represent the gains of the transmission antenna and the receiving antenna. k represents the Boltzmann constant, T 1 represents the temperature of the inter-satellite communication environment, represents the remaining noise interference between satellites, represents the free space loss between satellites. The expression of the antenna gain in the formula is:

[0057] ,

[0058] Wherein, represents the size of the antenna diameter, represents the wavelength of the transmitted signal.

[0059] The expression of

[0060] ,

[0061] is similar to the expression of the signal-to-noise ratio between satellites, is expressed as:

[0062] ,

[0063] Wherein, is the transmission power of the satellite transmitting to the ground station, represents the efficiency of the ground station receiving antenna, represents the gain of the ground station receiving antenna. T 2 represents the temperature of the communication environment between the satellite and the ground station, represents the remaining noise interference between the satellite and the ground station, represents the free space loss between the satellite and the ground station, and the specific expression is:

[0064] .

[0065] Due to rain attenuation caused by rain interference in the communication path of the satellite ground station, the following is considered here:

[0066] ,

[0067] In the expression, is the rain attenuation, where and are coefficients determined by the frequency and polarization mode, R is the precipitation rate, and L is the distance that the communication between the satellite and the ground station passes through the rain area, which can be expressed as:

[0068] .

[0069] (1.3) Transmission delay of the satellite's mission between ground stations.

[0070] Here, the communication network between ground stations is considered as a wide area network (WAN), and the mission transmission process is also modeled as a queuing problem. Let be the transmission rate within the WAN, be the transmission bandwidth between ground stations. At the same time, considering optical fiber as the transmission medium, let represent the distance between ground stations n and i. Then the transmission delay is considered to be:

[0071] ,

[0072] In the formula, represents the delay for the mission unloaded by satellite s mn to be transmitted from ground station n to ground station i. The first part is considered as the queuing delay, the second part is the transmission delay, and the third part is the transmission time on the path. N represents the set of ground stations, represents the set of satellites l communicating with ground station represents the set of the remaining ground stations in N except ground station l respectively represent the mission offloading rate, the computational amount of the received mission, and the mission arrival rate of satellite s kl 。

[0073] Then the transmission delay of the mission of satellite s mn between all ground stations is:

[0074] ,

[0075] In the formula, represents the transmission delay of the mission of satellite s mn between ground stations. , both represent the task ground station offloading path indication variable. When the task is offloaded from the satellite s mn to ground station n and then to ground station i , otherwise , similarly.

[0076] (1.4) Computational delay of the satellite's task at the ground station.

[0077] Due to the uneven distribution of satellites, the amount of tasks that different ground stations need to process is different, and there may be a situation where some ground stations have excessive computational load while some ground stations have idle computing resources. Therefore, the present invention also considers the offloading of tasks between satellites to balance the task computational load of the ground stations. Using the M / M / 1 queuing model, the time-consuming mn for the satellite s to calculate the task at ground station i

[0078] is:[[]]

[0079] where represents the computing resources of ground station i , and is also the task ground station offloading path indication variable. represents the satellite s kl the amount of tasks received,[[]] respectively represent the satellite s kl the complexity of the tasks received, then the computational delay s mn of the satellite for the task at the ground station

[0080] .

[0081] (1.5) Total offloading task delay.

[0082] The total offloading task delay is the sum of the above several delays. Therefore, the specific establishment of the satellite communication task offloading model is:[[]]

[0083] ,

[0084] ,

[0085] s.t. ,

[0086] ,

[0087] ,

[0088] ,

[0089] ,

[0090] ,

[0091] ,

[0092] ,

[0093] ,

[0094] .

[0095] Wherein, is the maximum delay of task processing acceptable to the satellite s mn ; is the time for the satellite s mn to stay within the transmission range of the ground station; represents the time taken for the total process of s mn offloading the satellite task to the ground station; α= { }, , .

[0096] (2) Set the number of outer loop iterations a = 0, and the number of inner loop iterations b = 0.

[0097] (3) Set the task offloading rate strategy a of the satellite at the α (a)(b) -th outer loop and the b-th inner loop, the offloading path strategy of the task on the satellite γ (a)(b) , and the offloading path strategy of the task on the ground station β (a)(b) , and assign them to the task offloading rate strategy a at the α (a) -th outer loop, the offloading path strategy of the task on the satellite γ (a) , and the offloading path strategy of the task on the ground station β (a) , respectively, where α (a)、β (a) 、γ (a) The initial value of α (0) 、β (0) 、γ (0) is randomly generated.

[0098] (4) In the satellite communication task offloading model α is used as the variable to be solved, and β, γ are respectively assigned as β (a)(b) , γ (a)(b) , so as to optimize the satellite communication task offloading model into a partial task offloading sub-model, solve the partial task offloading sub-model, and obtain the task offloading rate strategy of the satellite at the a -th outer loop and the (b + 1)-th inner loop α (a)(b+1) .

[0099] Among them, the partial task offloading sub-model regards β, γ as a fixed value and assigns it as β (a)(b) , γ (a)(b) , and takes as the only variable. The part in P1 that is not affected by is removed. The specific form of the partial task offloading sub-model is:

[0100] ,

[0101] s.t. C1, C4, C5, C8, C9, C10,

[0102] In the formula, represents the total delay when the strategy is adopted, respectively represent 's assignments, that is, β (a)(b) , γ (a)(b) in 's values.

[0103] Since: 1) Each item in the above model is a linear function or a convex function, so the whole model can be regarded as a convex function; 2) The constraints are also linear or convex. Therefore, the convex optimization interior point method can be used here to solve, and Values, all values form a set, as the a th outer loop, the task offloading rate strategy of the satellite at the (b + 1)-th inner loop α (a)(b+1) .

[0104] (5) In the satellite communication task offloading model γ as the variable to be solved, assign α to be α (a)(b+1) , thus optimizing the satellite communication task offloading model into a satellite offloading path selection sub-model.

[0105] The specific satellite offloading path selection sub-model is:

[0106] ,

[0107] ,

[0108] s.t. C6, C7,

[0109] In the formula, represents the delay when adopting strategy , represents the assignment of, that is α (a)(b+1) in the value of, the value of is affected by , and the value of needs to be determined according to the situation of at this time before calculating the delay . the value of.

[0110] (6) Solve the satellite offloading path selection sub-model to obtain the offloading path strategy of the task on the satellite at the a th outer loop and the (b + 1)-th inner loop γ (a)(b+1) .

[0111] As Figure 4 shown, step (6) specifically includes:

[0112] (6.1) Select any ground station n that has not been traversed from the ground station set N ;

[0113] (6.2) Update the current strategy to , where the current strategy represents the offloading path strategies of all satellites currently communicating with the ground station n, is the offloading path strategies of all satellites communicating with the ground station n at the end of the previous iteration, The initial value is randomly generated;

[0114] (6.3) Solve the following model to obtain the offloading path strategies of all satellites communicating with ground station n during the current iteration process:

[0115] ,

[0116] ,

[0117] s.t. ,

[0118] ,

[0119] In the formula, represents the value of that minimizes represents the total delay of the tasks received by ground station n when the strategy is adopted; represents the offloading path strategies of all satellites communicating with ground station n during the current iteration process;

[0120] (6.4) Calculate the total delay of the tasks received by ground station n when the strategy is adopted, and the total delay of the tasks received by ground station n when the strategy is adopted. Among them, represents the union of and

[0121] (6.5) Judge whether is satisfied. represents the time increment threshold. If it is satisfied, execute step (6.7); otherwise, execute step (6.6);

[0122] (6.6) Set = , and return to execute step (6.2);

[0123] (6.7) Set as the optimal value of the offloading path strategies of all satellites communicating with ground station n ;

[0124] (6.8) Judge whether the ground station set N has been traversed. If so, execute step (6.9); if not, return to execute step (6.1);

[0125] (6.9) Set as The optimal value of, and assign it to γ (a)(b+1) 。

[0126] (7) Judge α (a)(b+1) 、 γ (a)(b+1) Whether the preset convergence condition is satisfied. If so, execute step (8); otherwise, set b = b + 1 and return to execute step (4).

[0127] The specific preset convergence condition is:

[0128] ,

[0129] In the formula, Represents When taking values respectively as The total time delay, Represents When taking values respectively as The total time delay, Represents the time delay threshold.

[0130] (8) Set α (a+1) = α (a)(b+1) , γ (a+1) = γ (a)(b+1) 。

[0131] (9) Take β In the satellite communication task offloading model as the variable to be solved, and assign α, γ To be α (a+1) 、 γ (a+1) respectively, so as to optimize the satellite communication task offloading model into a ground station offloading path selection sub-model.

[0132] The specific ground station offloading path selection sub-model is:

[0133] ,

[0134] ,

[0135] In the formula, Represents the ground station offloading path selection sub-model, specifically a game model. V represents the set of ground stations participating in the game, Is the utility function of satellite s mn To offload tasks, 、 respectively represent 、 the assignment of, that is α (a+1) in 、 the value of.

[0136] By executing the proposed potential game, multi-ground station cooperation determines where the unloading tasks of each vehicle are processed. Each ground station is responsible for selecting the best strategy improvement for each satellite unloading task within its coverage area.

[0137] (10) Solve the ground station unloading path selection sub-model to obtain the unloading path strategy of the task on the ground station at the a+ 1st outer loop β (a+1) .

[0138] In this step, a multi-round game method is used to solve the ground station unloading path selection sub-model. As Figure 5 shown, each round of the game specifically includes:

[0139] (10.1) Update the current strategy to the strategy , where the initial value of the current strategy is randomly generated, and

[0140] is the unloading path strategy at the end of the previous iteration;

[0141] , ,

[0142] In the formula, represents the unloading path strategy of satellite s mn , represents the that makes the smallest value;

[0143] (10.3) Add the unloading path strategies of all satellites to the total strategy set Ω and solve the following model:

[0144] ,

[0145] ,

[0146] In the formula, represents the total utility, represents the The smallest value;

[0147] (10.4) Determine whether it satisfies , where represents adding to to replace the original The total utility after that, represents the total utility under the current policy ; if so, execute step (10.5), otherwise execute step (10.6);

[0148] (10.5) Update the policy to the solved in step (10.3), and add to Ω, and return to execute step (10.3);

[0149] (10.6) Determine whether the policy is equal to the current policy , if not, return to execute step (10.1), if equal, the game reaches the Nash equilibrium, and use the policy as the final policy, assign it to β (a+1) .

[0150] (11) Determine whether β (a+1) is equal to β (a) , if not, then set a = a +1, b = 0, and return to execute step (3), if so, then use α (a+1) , β (a+1) , γ (a+1) as the optimal values α* , β* , γ* output.

[0151] Next, MATLAB simulation is used to verify the invention.

[0152] To verify the feasibility and optimization performance of the present invention, Figure 6 the MATLAB simulation results are shown. The blue line is the delay brought by using the method of the present invention; the red line is the delay when the offloading task is offloaded from the local satellite to the ground station for calculation without considering inter-satellite communication. Observing the simulation results, it is found that when dealing with different satellite task volumes, the present invention significantly reduces the task processing delay compared with the scheme of offloading all to the ground station, thus proving that the present invention can bring lower satellite task processing delay.

[0153] An embodiment of the present invention aims to provide a method for reducing latency in near-earth satellite communication based on game theory for task offloading and resource allocation. Aiming at the problems of excessive satellite task volume, the influence of rain on satellite transmission paths, and high loads at some ground stations while some ground stations have idle computing resources, a two-stage iterative algorithm is designed, which significantly reduces the latency of task processing, balances the task volume of each ground station, optimizes the total latency of task processing for multiple satellites and multiple ground stations, and through simulation, the present invention is compared with other solutions to verify the better optimization performance of the present invention. At the same time, the present invention expects to be combined with the solutions that optimize the task offloading performance within the satellite cycle time, which is studied more at present, to achieve greater optimization in aspects such as the latency and task download rate of satellite communication.

[0154] Embodiment Two.

[0155] The embodiment of the present invention also provides a computer product, such as an app on a mobile phone or tablet, an installation program on a computer, etc. The product includes computer programs / instructions, and when the computer programs / instructions are executed by a processor, the method described in Embodiment One is implemented. The code of the computer executable program for performing the operations of the present invention can be written in one or more programming languages or a combination thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0156] It should be understood that the above embodiments and the descriptions in the specification only illustrate the principles, main features and advantages of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the protection scope of the present invention.

Claims

1. A method for offloading near-Earth satellite communication tasks based on ground collaborative computing, characterized in that: The steps include: (1) Establishing a satellite communication task offloading model, wherein the satellite communication task offloading model aims to minimize the total delay of task offloading and takes the task offloading rate strategy of the satellite as the α , Task offloading path strategy on satellite γ , Task offloading path strategy on the ground station β is the variable to be solved, and the total delay of task offloading is specifically the sum of the calculation delay of the task on the satellite, the transmission delay of the task offloading from the satellite to the ground station, the transmission delay of the task between the ground stations and the calculation delay of the task on the ground station; (2) Set the number of outer loop iterations a =0, the number of inner loop iterations b=0; (3) a The satellite's mission offloading rate strategy during the bth outer cycle and the bth inner cycle α (a)(b) , Task offloading path strategy on satellite γ (a)(b) , Task offloading path strategy on the ground station β (a)(b) , respectively assigned as a Task offloading rate strategy in the second outer loop α (a) , Task offloading path strategy on satellite γ (a) , Task offloading path strategy on the ground station β (a) ,in, α (a) 、β (a) , γ (a) The initial value of is randomly generated; (4) Offloading satellite communication tasks into the model α As the variable to be solved, β, γ Assign values ​​to β (a)(b) , γ (a)(b) , thereby optimizing the satellite communication task offloading model into a partial task offloading submodel, solving the partial task offloading submodel, and obtaining the first a The satellite task offloading rate strategy during the b+1th outer cycle and the b+1th inner cycle α (a)(b+1) ; (5) Offloading satellite communication tasks into the model γ As the variable to be solved, α Assigned to α (a)(b+1) , thereby optimizing the satellite communication task offloading model into a satellite offloading path selection sub-model; (6) Solve the satellite unloading path selection sub-model and obtain a The unloading path strategy of tasks on the satellite during the b+1th outer cycle and the b+1th inner cycle γ (a)(b+1) ; (7) Judgment α (a)(b+1) , γ (a)(b+1) Whether the preset convergence condition is met, if so, execute step (8), otherwise, set b=b+1 and return to execute step (4); (8) Settings α (a+1) = α (a)(b+1) , γ (a+1) = γ (a)(b+1) ; (9) Offloading satellite communication tasks into the model β As the variable to be solved, α, γ Assign values ​​to α (a+1) , γ (a+1) , thereby optimizing the satellite communication task offloading model into a ground station offloading path selection sub-model; (10) Solve the ground station unloading path selection submodel and obtain a+ Task unloading path strategy on the ground station during one outer cycle β (a+1) ; (11) Judgment β (a+1) Is it equal to β (a) If not, then a=a +1,b=0, and return to step (3). If so, α (a+1) , β (a+1) , γ (a+1) As α, β, γ The optimal value output.

2. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 1 is characterized in that: The satellite communication task offloading model is specifically as follows: ， ， ， ， ， ， s.t. , ， ， ， ， ， ， ， ， ， In the formula, t represents the total delay of task offloading, 、 Respectively represent the ground station n, l , i A collection of satellites for communications, N represents the set of ground stations, Represents ground station n and satellite s mn The unloading delay of the task between mn represents the mth satellite communicating with ground station n, and k represents The middle element, z represents N\l Medium element, Indicates that the mission is on satellite s mn The computational delay on Indicates that the mission is from the satellite s mn The transmission delay to the ground station n is: Indicates satellite s mn The transmission delay of the task between ground stations, Indicates satellite s mn The computational delay of the task at the ground station; , Satellite s mn 、s jn 、s kl The task offloading rate is s jn represents the jth satellite communicating with the nth ground station, s kl Indicates l The kth satellite that the ground station communicates with, Indicates satellite s mn The amount of tasks received, Indicates satellite s kl The amount of tasks received, Satellite s mn 、s jn 、s kl The computational amount of the received task, Satellite s mn 、s kl The complexity of the received task, Satellite s mn 、s jn 、s kl The task arrival rate ， Indicates satellite s mn of computing resources, Indicates satellite s mn The distance from the ground station n, represents the speed of light, Indicates the mission satellite unloading path indicator variable with a value of 0 or 1. s jn Unloaded to satellite s mn hour ,otherwise , Indicates the transfer indicator variable. When a task is transmitted to the satellite s mn Then unloaded to ground station n ,otherwise , Indicates satellite s mn The transmission rate of the task offloaded to the ground station, , Satellite s jn With satellite s mn The transmission rate and distance between , Both represent the mission ground station unloading path indicator variables. s mn Unloaded to ground station n Unloaded to ground station again i hour ,otherwise , represents the transmission rate between ground stations, represents the bandwidth of transmission between ground stations, represents ground station n and ground station i The distance between Indicates ground station i of computing resources, For satellite s mn The maximum acceptable delay in task processing, For satellite s mn The time spent within the transmission range of the ground station, Indicates satellite s mn The total time it takes to offload the task to the ground station. α= { }, , .

3. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 2 is characterized in that: The partial task offloading sub-model described in step (4) is specifically: ， st C1,C4,C5,C8,C9,C10, In the formula, Indicates that when adopting a strategy The total delay of Respectively The assignment of β (a)(b) middle The value of γ (a)(b) middle The value of .

4. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 2, characterized in that: The partial task offloading sub-model described in step (4) is solved using the convex optimization interior point method.

5. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 2, characterized in that: The satellite unloading path selection sub-model in step (5) is specifically: ， st C6,C7, In the formula, Indicates that when adopting a strategy The time delay, express The assignment of α (a)(b+1) middle The value of .

6. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 5, characterized in that: Step (6) specifically includes: (6.1) Collect from ground station N Select any ground station n that has not been traversed yet; (6.2) Change the current strategy Updated to , where the current strategy represents the offloading path strategy of all satellites currently communicating with ground station n, is the offloading path strategy of all satellites communicating with ground station n at the end of the previous iteration, The initial value of is randomly generated; (6.3) Solve the following model to obtain the unloading path strategy of all satellites communicating with ground station n in the current iteration: ， ， s.t. , ， In the formula, Indicates that Smallest The value of Indicates that when adopting a strategy The total delay of the task received by ground station n is, represents the offloading path strategy of all satellites communicating with ground station n during the current iteration; (6.4) Calculation strategy The total delay of the task received by ground station n is , and adopt strategies The total delay of the task received by ground station n is ,in, express and The union of; (6.5) Determine whether , represents the time increment threshold. If it is satisfied, execute step (6.7), otherwise execute step (6.6); (6.6) = , and return to execute step (6.2); (6.7) As the optimal value of the offloading path strategy for all satellites communicating with ground station n ; (6.8) Determine the ground station set N Whether the traversal has been completed, if yes, execute step (6.9), if not, return to execute step (6.1); (6.9) As The optimal value of γ (a)(b+1) .

7. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 2, characterized in that: The ground station unloading path selection sub-model in step (9) is specifically: ， ， In the formula, represents the ground station unloading path selection sub-model, specifically a game model, V represents the set of ground stations participating in the game, Satellites mn The utility function of the offloaded task, 、 Respectively 、 The assignment of α (a+1) middle 、 The value of .

8. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 7 is characterized in that: In step (10), a multi-round game method is used to solve the ground station unloading path selection sub-model, and each round of the game specifically includes: (10.1) Change the current strategy Update to policy , where the current strategy The initial value of is randomly generated. is the unloading path strategy at the end of the previous iteration; (10.2) For each ground station n, solve the following model to obtain the offloading path strategy for each satellite communicating with ground station n: , ， In the formula, Indicates satellites mn Uninstall path strategy, Indicates that Smallest The value of (10.3) Add the unloading path strategies of all satellites to the total strategy set Ω and solve the following model: ， ， In the formula, represents the total utility, Indicates that Smallest The value of (10.4) Determine whether ,in, Indicates that join in Replace the original The total utility after Indicates that in the current strategy The total utility under ; if yes, execute step (10.5), otherwise execute step (10.6); (10.5) Strategy Update to the solution obtained in step (10.3) , and Add to Ω and return to step (10.3); (10.6) Judgment strategy Is it equal to the current strategy? If not, return to step (10.1). If equal, the game has reached Nash equilibrium. As The final strategy is assigned to β (a+1) .

9. The method for offloading near-Earth satellite communication tasks based on ground collaborative computing according to claim 2, characterized in that: The preset convergence condition in step (7) is specifically: ， In the formula, express The values ​​are The total delay of express The values ​​are The total delay of Indicates the latency threshold.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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