Data relay satellite network access capacity improving method and system with strong guarantee of user satellite service quality

Abstract

The invention provides a method and a system for improving access capacity of a data relay satellite network with strong guarantee of user satellite service quality. The method and the system mainly solve the problem that the user satellite service quality and the access capacity of the data relay satellite network are difficult to consider at the same time in the prior art. According to the implementation scheme, under a shortest distance association strategy, a transmission capacity Markov chain is constructed from the perspective of a transmission distance; storage, calculation and transmission processes are depicted into basic elements of a queue, a cascade queue system is constructed, business arrival, calculation and transmission processes of the cascade queue system are mapped to a yoke domain, a business arrival yoke, a processing service yoke and a transmission service yoke are obtained, and then a user satellite service quality boundary is calculated according to the business arrival yoke, the processing service yoke and the transmission service yoke. And calculating the upper bound of the access capacity meeting the stable condition of the system by using the monotonicity of the boundary, and determining the access capacity under the guarantee of the user satellite service quality in the range of the upper bound. According to the method, the service quality can be strongly guaranteed by using the service quality boundary, the access capacity under the service quality guarantee of the user satellite can be effectively improved by improving the service quality boundary, and the method can be used for the storage, calculation and transmission fusion data relay satellite network.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication technology, specifically relating to a method for improving the access capacity of a data relay satellite network, which can be used in a storage-computing-transmission integrated data relay satellite network. Background Technology

[0002] With the miniaturization and mass production of satellites, the number of low-Earth orbit (LEO) satellites in orbit is rapidly increasing, and the amount of data requiring backhaul is also continuously growing. However, due to the short visibility time and long revisit cycle between LEO satellites and ground stations, it is difficult to meet the timeliness requirements of on-orbit data backhaul. Transmitting data from LEO user satellites to high-Earth orbit (HEO) relay satellites, and then having the HEO relay satellites forward the data to ground stations, thus utilizing HEO relay satellites (i.e., geostationary orbit) for data relay, has become an important and effective means to improve the quality of user satellite services. A satellite network composed of LEO user satellites and HEO relay satellites is called a data relay satellite network, and the access capacity of the data relay satellite network is the number of user satellites it can support. By using a user satellite storage-computation-transmission model, the amount of data transmitted can be effectively reduced, and the access capacity can be increased. However, the limited storage and computing resources of user satellites, as well as the dynamically changing inter-satellite transmission process, lead to a high probability of mission timeouts and data loss. Therefore, ensuring the quality of satellite services for users under the converged storage, computing, and transmission architecture, as well as meeting the access capacity under quality of service constraints, is an important prerequisite and an urgent problem to be solved for satellite deployment and resource allocation in data relay satellite networks.

[0003] The paper "Modeling and Performance Analysis for Satellite Data Relay Networks Using Two-Dimensional Markov-Modulated Process" by Zhu Yan et al. proposes a method for modeling and performance analysis of data relay satellite networks based on a two-dimensional Markov modulation process. It constructs a service process for intermittent transmission by analyzing the visibility duration between user satellites and relay satellites, and analyzes end-to-end delay by constructing a service-to-service two-dimensional Markov modulation process. However, because this method constructs the transmission service process from the perspective of the visibility duration between user satellites and relay satellites, it ignores the dynamic nature of transmission capacity within the visible time, and only considers a single user satellite and a single relay satellite under the storage-transmission architecture. Therefore, it cannot be used for access capacity analysis of data relay networks.

[0004] Patent document CN201610514042.6 discloses a "random network calculus method for evaluating satellite network performance," which analyzes statistical indicators such as link delay and backlog in inter-satellite channels. This method, by constructing the envelope of service arrival and transmission processes using a moment generator function, abandons the random structure of services and services. While it can strictly guarantee service quality, its overly conservative boundaries can significantly reduce access capacity.

[0005] Patent document CN202510242352.6 discloses "A method and model for communication between a LEO satellite onboard terminal and a GEO satellite." This method involves a user satellite sending a service request to a ground center, which then injects the generated worksheet into a relay satellite. This establishes a transmission path between the user satellite, relay satellite, and ground center within the allocated time slots, enabling multi-user satellite access planning. However, this method only addresses the backhaul of user satellite data from the perspective of time slot allocation, failing to guarantee strong quality of service for user satellites and unable to directly calculate access capacity. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the prior art by proposing a method and system for enhancing the access capacity of a data relay satellite network with strong user satellite service quality assurance. In a data relay satellite network that integrates storage, computing, and transmission, it utilizes the user satellite service quality boundary to achieve strong user satellite service quality assurance, and effectively enhances the access capacity under service quality assurance by improving the tightness of the user satellite service quality boundary.

[0007] To achieve the above objectives, the technical solution of the present invention includes the following steps:

[0008] 1. A method for enhancing the access capacity of a data relay satellite network with strong guarantee of user satellite service quality, characterized in that it includes:

[0009] (1) Based on parameters such as user satellite altitude, inclination angle, and number of relay satellites, calculate the upper and lower bounds of transmission distance and the upper bound of transmission distance variation, and construct a Markov chain with inter-satellite transmission capacity as the state based on the mathematical relationship between inter-satellite transmission capacity and transmission distance, i.e., transmission capacity Markov chain.

[0010] (2) Equivalent the computing resources and transmission capacity Markov chain to the service process of the queue, and equivalent the storage resources to the storage capacity of the queue, forming a cascaded queue system of processing queue-transmission queue, and mapping the random process of service arrival, computing and transmission of this system to the martingale domain, and calculating the boundary of user satellite service quality.

[0011] (3) The upper limit of the access capacity under the condition of system stability is obtained by using the monotonicity of the user satellite service quality boundary, and the access capacity under the user satellite service quality guarantee is determined by the bisection method within the upper limit range.

[0012] Furthermore, the upper bound of user satellite access capacity that satisfies the system stability condition is calculated by utilizing the monotonicity of the user satellite service quality boundary in step (3), and its implementation includes:

[0013] 3a) The upper bound of the access capacity is initialized to 0 under the condition of system stability;

[0014] 3b) Set the current number of access user satellites as the upper limit of access capacity, calculate the average equivalent transmission rate and the average task generation rate, and compare the two to determine whether the system is stable:

[0015] 3c) Iteratively update the upper bound of the access capacity when the system is stable; output the upper bound of the current access capacity when the system is unstable.

[0016] Furthermore, the determination of the access capacity under the user satellite service quality guarantee using the bisection method within the upper bound range in (3) includes:

[0017] 3d) Initialize the lower bound of the access capacity to 0;

[0018] 3e) Set the current access capacity as the midpoint between the upper and lower bounds, calculate the service quality parameters corresponding to the current access capacity, and calculate the task timeout probability and loss probability boundaries based on these parameters. Compare them with the required user satellite service quality thresholds:

[0019] 3f) Set the lower bound of the access capacity when the user's satellite service quality requirements are met to the current access capacity; set the upper bound of the access capacity when the user's satellite service quality requirements are not met to the current access capacity;

[0020] 3g) Iterate through 3e) and 3f) until the upper bound of the access capacity equals the lower bound, and output the upper or lower bound of the access capacity as the access capacity under the user's satellite service quality assurance.

[0021] 2. A data relay satellite network access capacity enhancement system with strong user satellite service quality assurance, characterized in that it comprises:

[0022] Transmission distance calculation module: used to calculate the upper and lower bounds of the transmission distance and the upper bound of the change in transmission distance based on parameters such as user satellite altitude, inclination angle, and number of high-orbit relay satellites;

[0023] Transmission process construction module: used to construct the transmission capacity Markov chain and calculate its length, state and transition probability based on the upper and lower bounds of the transmission distance and the upper bound of the transmission distance variation, as well as the user satellite transmit power, frequency, antenna gain, link margin and noise parameters.

[0024] Queue Representation Module: Used to represent storage, computing, and transmission resources as basic elements of queues, represent computing resources and transmission capacity as service processes of processing queues and transmission queues respectively using Markov chains, and represent storage resources as the storage capacity of queues;

[0025] Service Quality Boundary Calculation Module: This module maps the stochastic processes of service arrival, computation, and transmission to the martingale domain, and calculates the service quality boundary of the data relay satellite network based on the service arrival martingale, processing service martingale, and transmission service martingale.

[0026] Access capacity calculation module: used to calculate the access capacity under the user satellite service quality guarantee, calculate the upper limit of the access capacity under the system stability condition, and use the bisection method to determine the access capacity under the user satellite service quality guarantee within the upper limit range.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] First, this invention uses Markov chains to accurately characterize the inter-satellite dynamic transmission process, representing the computation process and the transmission process as service processes of processing queues and transmission queues respectively, and equating storage resources with the storage capacity of queues, thereby realizing multi-dimensional resource representation of the storage-computation-transmission integrated data relay satellite network.

[0029] Second, this invention transforms the problem of ensuring user satellite service quality into a problem of solving user satellite service quality boundaries. By mapping the stochastic processes of service arrival, computation, and transmission to the martingale domain, the random structure of service arrival and service processes is preserved, and the access capacity is significantly improved by enhancing the compactness of the service quality boundaries. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method for improving the access capacity of a data relay satellite network with strong user satellite service quality assurance according to the present invention;

[0031] Figure 2 This is a model diagram of the cascaded queue system constructed in the method of this invention;

[0032] Figure 3 This is a block diagram of the data relay satellite network access capacity enhancement system for ensuring strong user satellite service quality according to the present invention;

[0033] Figure 4 This is a simulation experiment result diagram of the service quality boundary of the present invention;

[0034] Figure 5 This is a simulation experiment result diagram of access capacity under the user satellite service quality assurance of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0036] Example 1: Method for Enhancing Access Capacity of Data Relay Satellite Network with Strong Guarantee of User Satellite Service Quality

[0037] Reference Figure 1 The implementation steps for this example are as follows:

[0038] Step 1: Calculate the upper and lower bounds of the transmission distance and the upper bound of the change in transmission distance based on the parameters of the user satellite.

[0039] The data relay satellite network comprises user satellites and relay satellites, operating in low Earth orbit and geostationary orbit, respectively. User satellite parameters include orbital altitude. and tilt angle The parameters of relay satellites include the number Using these parameters, the upper and lower bounds of the transmission distance and the upper bound of the change in transmission distance are calculated. The implementation includes:

[0040] (1.1) Based on snapshot Domestic user satellite With relay satellite Distance between Under the shortest distance association strategy, the transmission distance of the user satellite is determined: ;

[0041] (1.2) Calculate the upper bound of the transmission distance and the lower realm :

[0042] ,

[0043] in, and These represent the orbital radii of the relay satellite and the user satellite, respectively. Indicates the altitude of the relay satellite;

[0044] (1.3) Calculate the upper bound of the change in transmission distance :

[0045] ,

[0046] in, Indicates the duration of the snapshot. Represents the gravitational constant. That's the mass of the Earth.

[0047] Step 2: Construct a Markov chain with transmission capacity.

[0048] The transmission capacity Markov chain includes: length ,state and transition probability The calculation is as follows:

[0049] (2.1) Based on the upper bound of the transmission distance and the lower realm Calculate the length of the Markov chain. :

[0050] ,

[0051] in, The set transmission distance interval;

[0052] (2.2) Based on the mathematical relationship between inter-satellite transmission capacity and transmission distance, the state of the Markov chain is determined. Represented as :

[0053] ,

[0054] in Indicates transmission distance.

[0055] Indicates the user's satellite transmission power.

[0056] and These represent the antenna gains of the user satellite and the relay satellite, respectively.

[0057] Represents Boltzmann's constant. Indicates system noise temperature.

[0058] This represents the ratio of the required bit energy to the noise power spectral density.

[0059] Indicates link margin. Indicates the transmission frequency. Represents the speed of light;

[0060] (2.3) Calculate the transition probability :

[0061] (2.3.1) Based on the upper bound of the change in transmission distance Calculate the maximum interval between state transitions between adjacent snapshots. :

[0062] ;

[0063] (2.3.2) Calculate the transmission distance using simulation software The proportion of occurrence: ;

[0064] (2.3.3) Calculate the first and second proposing probability matrices respectively. Line number Column elements and the first acceptance probability matrix Line number Column elements :

[0065] ,

[0066] ;

[0067] (2.3.4) The transition probability is calculated based on the result of (2.3.3): .

[0068] Step 3: Build a cascading queue system.

[0069] The cascaded queue system includes a processing queue and a transmission queue, and each queue contains its own buffer space and service process.

[0070] (3.1) Storage capacity The buffer space is divided into processing queues and transmission queues, based on the proportion of the processing queue. Calculate the buffer space of the processing queues respectively and the buffer space of the transmission queue :

[0071] ,

[0072] in, , and The units are all bits;

[0073] (3.2) The constant computation process and the transmission capacity Markov chain are characterized as service processes of the processing queue and the transmission queue, respectively, to obtain the original cascaded queue system, such as Figure 2 As shown in (a);

[0074] (3.3) Based on the buffer space of the processing queue and the buffer space of the transmission queue Calculate the number of tasks that the processing queue can store. The number of tasks that the transmission queue can store. :

[0075] ,

[0076] in This indicates the task size, expressed in bits per task. This indicates the compression rate of the task during processing;

[0077] (3.4) Based on the processor's processing speed and the Transmission capacity status Calculate the number of tasks that a user satellite can handle within a snapshot. and in the Number of tasks that can be transmitted under a given transmission capacity state :

[0078] ,

[0079] in, The unit is cycles / s. Conversion efficiency, which indicates the processor's operating speed, is measured in bits per cycle.

[0080] (3.5) Set the buffer space of the processing queue and the transmission queue to respectively and The service processes for the processing queue and the transmission queue are set as follows: and The processing queue and the transmission queue are combined in a cascaded manner, and a coefficient is added. The multipliers form the converted cascaded queue system, such as Figure 2 As shown in (b).

[0081] Step 4: Map the random processes of service arrival, computation, and transmission to the martingale domain.

[0082] The martingale domain represents the transformation of a random process to obtain the corresponding martingale. Service arrival, computation, and transmission correspond to the service arrival martingale, processing service martingale, and transmission service martingale, respectively, and their calculation is as follows:

[0083] (4.1) Calculate the equivalent service arrival rate based on the service arrival model of the memoryless Markov modulation process. :

[0084] ,

[0085] in This indicates the number of tasks generated within any snapshot. It is a free variable;

[0086] (4.2) The service arrival martingale is calculated based on the equivalent service arrival rate. :

[0087] ,

[0088] in express The cumulative number of tasks generated within each snapshot. ,

[0089] Indicates snapshot The number of tasks generated internally. Indicates the number of tasks that can be generated within a single snapshot;

[0090] (4.3) Calculate the equivalent processing rate based on the constant computation process model. :

[0091] ;

[0092] (4.4) Based on the equivalent processing rate The processing service martingale is calculated. :

[0093] ,

[0094] in express The cumulative number of tasks processed within each snapshot;

[0095] (4.5) Calculate the exponential transformation matrix of the transition probability of the Markov chain based on its transmission capacity. , its first Line number Column elements for:

[0096] ;

[0097] (4.6) The exponential transformation matrix of the Markov chain transition probability based on transmission capacity Calculate its spectral radius :

[0098] By analyzing the equation Solving the problem yields the following results: All eigenvalues,

[0099] The spectral radius is the maximum absolute value of all eigenvalues. ;

[0100] (4.7) The exponential transformation matrix of the Markov chain transition probability based on transmission capacity spectral radius Calculate the corresponding right eigenvector That is, solving the equation get ;

[0101] (4.8) Transform the exponential column matrix of Markov chain transition probability according to transmission capacity. spectral radius Calculate the equivalent transmission rate :

[0102] ;

[0103] (4.9) The transmission service martingale is calculated based on the equivalent transmission rate. :

[0104] ,

[0105] in, express The cumulative number of tasks transmitted within each snapshot.

[0106] Indicates snapshot The number of tasks that can be transmitted within the device.

[0107] Indicates snapshot Internal transmission capacity.

[0108] Step 5: Calculate the boundary of user satellite service quality.

[0109] The boundary of the user satellite service quality includes the mission timeout probability boundary. And the task loss probability boundary, where the task loss probability boundary further includes the task loss probability boundary of the processing queue. and the overall task loss probability boundary The calculation is as follows:

[0110] (5.1) Based on the equivalent service arrival rate Equivalent processing rate and equivalent transmission rate Calculate the maximum free variables for system stability :

[0111] ;

[0112] (5.2) Arrival of martingale according to business needs Processing service martingale and transmission service martingale Calculate the overall service quality coefficient :

[0113] ,

[0114] in ;

[0115] (5.3) Based on the equivalent service arrival rate and equivalent processing rate Calculate the maximum number of free variables that are stable in the processing queue. :

[0116] ;

[0117] (5.4) Arrival of martingale according to business needs and processing services martingale Calculate the service quality coefficient of the processing queue :

[0118] ;

[0119] (5.5) Calculate the task timeout probability boundary based on the results of (5.1) to (5.4). Task loss probability boundary in the processing queue and the overall task loss probability boundary :

[0120] ,

[0121] in This indicates the time delay threshold.

[0122] Step 6: Calculate the upper limit of the access capacity that satisfies the system stability condition.

[0123] The stability of the system is defined as the processing queue and the cascading queue satisfying the following inequalities:

[0124] ,

[0125] This example assumes that the processing queue system remains stable and focuses on the upper bound of the access capacity under stable conditions of cascaded queues, which is calculated as follows:

[0126] (6.1) The upper bound of the access capacity under the condition of system stability is initialized as follows: ;

[0127] (6.2) The number of currently connected user satellites Set as ,in This indicates that the current number is the [number]. The next iteration; based on the efficient transmission rate in step 4. The expression calculates the number of satellites currently connected to the user. Average equivalent transmission rate And based on the probability of task generation and the number of tasks that can be generated within a single snapshot Calculate the average task generation rate :

[0128] ;

[0129] (6.3) will and Comparison:

[0130] if If the system is stable, then execute (6.4).

[0131] Otherwise, the system is unstable, and the current iteration round is recorded. Execute (6.5);

[0132] (6.4) Calculate the updated upper bound of access capacity ,in Indicates the number of high-orbit relay satellites, return (6.2);

[0133] (6.5) Output the upper limit of the current access capacity. To meet the upper limit of access capacity under stable system conditions .

[0134] Step 7: Calculate the access capacity under the user's satellite service quality assurance.

[0135] (7.1) Set the lower limit of access capacity to ;

[0136] (7.2) Determine the upper limit of access capacity Is it equal to the lower bound? :

[0137] If so, then execute (7.4);

[0138] Otherwise, proceed with (7.3);

[0139] (7.3) The number of currently connected user satellites Set as upper bound and the lower realm The intermediate value is used to determine whether the timeout probability and loss probability requirements are met. The determination rules are as follows:

[0140] (7.3.1) Calculate the number of satellites currently connected to the user using the formula in step 5. Maximum free variables for system stability Overall service quality coefficient Processing the maximum free variables of a stable queue and processing queue service quality coefficient And calculate the task timeout probability boundary based on these parameters. ;

[0141] (7.3.2) Set the task timeout probability boundary With the set timeout probability requirement threshold Comparison:

[0142] if If so, then execute (7.3.3);

[0143] Otherwise, the upper limit of the access capacity will be reached. Set to the number of satellites currently connected to users. , return (7.2);

[0144] (7.3.3) Utilize the task loss probability boundary processed in step 5 and the overall task loss probability boundary The expressions are used to calculate the percentage of storage capacity in the processing queue. The upper realm and the lower realm :

[0145] ,

[0146] in, This indicates that a threshold value is set for the probability of loss.

[0147] (7.3.4) will and Comparison:

[0148] if Then the lower limit of the access capacity will be reached. Set to the number of satellites currently connected to users. , return (7.2);

[0149] Otherwise, the upper limit of the access capacity will be reached. Set to the number of satellites currently connected to users. , return (7.2);

[0150] (7.4) Output the number of currently connected user satellites. Access capacity to ensure the quality of satellite services for users.

[0151] It should be noted that the reference numerals in the above steps and claims are only for the purpose of clearly describing the embodiments of the present invention and facilitating understanding, and their order is not limited.

[0152] Example 2: Data Relay Satellite Network Access Capacity Enhancement System with Strong Guarantee of User Satellite Service Quality

[0153] Reference Figure 3 This example includes a transmission distance calculation module 1, a transmission process construction module 2, a queue representation module 3, a quality of service boundary calculation module 4, and an access capacity calculation module 5, such as... Figure 3 As shown in (a), the access capacity calculation module 5 includes a system stability judgment submodule 51, an upper bound iteration calculation submodule 52, a service quality parameter calculation submodule 53, a task timeout probability judgment submodule 54, a task loss probability judgment submodule 55, and an access capacity iteration submodule 56, as follows. Figure 3 As shown in (b).

[0154] The working principle of the entire system is as follows:

[0155] The transmission distance calculation module 1 is used to calculate the upper and lower bounds of the transmission distance and the upper bound of the transmission distance change based on parameters such as user satellite altitude, inclination angle, and number of high-orbit relay satellites, and to transmit these parameters to the transmission process construction module 2.

[0156] The transmission process construction module 2 is used to construct a transmission capacity Markov chain describing the time-varying transmission process based on the upper and lower bounds of the transmission distance and the upper bound of the transmission distance change output by the transmission distance calculation module 1, calculate the length of the transmission capacity Markov chain based on the upper and lower bounds of the transmission distance, calculate the state of the transmission capacity Markov chain by combining the user satellite transmit power, frequency, antenna gain, link margin and noise parameters, calculate the transition probability of the transmission capacity Markov chain based on the upper bound of the transmission distance change, and transmit the transmission capacity Markov chain to the queue characterization module 3.

[0157] The queue representation module 3 is used to construct a cascaded queue system based on the transmission capacity Markov chain output by the transmission process construction module 2. It represents the constant calculation process and the transmission capacity Markov chain as the service processes of the processing queue and the transmission queue, respectively, and represents the storage resources as the storage capacity of the processing queue and the transmission queue. It combines the processing queue and the transmission queue in a cascaded form, adds corresponding multipliers, forms a cascaded queue system, and transmits this cascaded queue system to the service quality boundary calculation module 4.

[0158] The service quality boundary calculation module 4 is used to calculate the service quality boundary based on the service arrival, storage, calculation and transmission process of the cascaded queue system output by the queue characterization module 3. It maps the random process of service arrival, calculation and transmission to the martingale domain. By calculating the equivalent service arrival rate, equivalent processing rate and equivalent transmission rate, it obtains the service arrival martingale, processing service martingale and transmission service martingale, respectively. Based on these three, it calculates the task timeout probability boundary. Combined with the storage capacity, it calculates the task loss probability boundary. Then, it transmits the service quality boundary of the task timeout probability boundary and the loss probability boundary to the access capacity calculation module 5.

[0159] The access capacity calculation module 5 is used to calculate the access capacity under the user satellite service quality guarantee based on the user satellite service quality boundary output by the service quality boundary calculation module 4, through its different sub-modules, to calculate the upper limit of the access capacity that meets the system stability conditions, and to determine the access capacity under the user satellite service quality guarantee within the upper limit using a bisection method. Wherein:

[0160] The system stability judgment submodule 51 is used to calculate the average equivalent transmission rate and average task generation rate corresponding to the upper bound of the current access capacity, and to determine whether the upper bound of the current access capacity meets the system stability condition based on their relationship, and then transmits the judgment result to the upper bound iterative calculation submodule 52.

[0161] The upper bound iteration calculation submodule 52 calculates the upper bound of the access capacity that meets the system stability condition based on the system stability judgment result output by the system stability judgment submodule 51. By iteratively increasing the upper bound of the access capacity, the system stability judgment result is used to determine whether the current upper bound of the access capacity meets the system stability condition. The upper bound of the access capacity when the system stability condition is not met is obtained and transmitted to the access capacity iteration submodule 56.

[0162] The service quality parameter calculation submodule 53 is used to calculate the corresponding service quality parameters based on the number of satellites of the currently accessing user, including the maximum free variable of system stability, the overall service quality coefficient, the maximum free variable of processing queue stability, and the service quality coefficient of processing queue, and to pass these parameters to the task timeout probability judgment submodule 54 and the task loss probability judgment submodule 55.

[0163] The task timeout probability judgment submodule 54 calculates the task timeout probability boundary based on the parameters output by the service quality parameter calculation submodule 53, judges whether the task timeout probability requirement is met based on its relationship with the timeout probability requirement threshold, and transmits the judgment result to the access capacity iteration submodule 56.

[0164] The task loss probability judgment submodule 55 calculates the upper and lower bounds of the processing queue storage capacity ratio based on the parameters output by the service quality parameter calculation submodule 53, judges whether the task loss probability requirement is met based on their relationship, and transmits the judgment result to the access capacity iteration submodule 56.

[0165] The access capacity iteration submodule 56 calculates the access capacity under the user satellite service quality guarantee based on the access capacity upper bound that meets the system stability conditions output by the upper bound iteration calculation submodule 52, the timeout probability judgment result output by the task timeout probability judgment submodule 54, and the loss probability judgment result output by the task loss probability judgment submodule 55. It iteratively updates the upper and lower bounds of the access capacity using a binary search method, and sequentially uses the timeout probability judgment result and the loss probability judgment result to comprehensively judge whether the current access capacity meets the user service quality requirements. The access capacity when the upper and lower bounds of the access capacity are equal is the access capacity under the user satellite service quality guarantee.

[0166] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as program instruction products. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0167] In this embodiment, the direct coupling or communication connection between the modules can be achieved through indirect coupling or communication connection via interfaces, devices, or modules. The functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0168] The effects of this invention can be further illustrated by the following simulation experiments:

[0169] 1. Simulation conditions

[0170] The simulation parameters in this invention are shown in Table 1.

[0171] Table 1 Main Simulation Parameters

[0172]

[0173] 2. Simulation Content

[0174] Simulation 1: Under the above conditions, the user satellite service quality boundary is solved using the present invention and existing stochastic network calculus methods. The results are as follows: Figure 4 As shown, where Figure 4 (a) shows the comparison results of the two methods on the task timeout probability boundary. Figure 4 (b) shows the comparison results of the two methods on the task loss probability boundary.

[0175] from Figure 4 (a) It is evident that the task timeout probability boundary obtained by this invention is more compact than existing methods, while strictly guaranteeing the user satellite timeout probability; from Figure 4 (b) It can be seen that the mission loss probability boundary obtained by the present invention is more compact than that of the existing methods under the premise of strictly guaranteeing the user satellite mission loss probability.

[0176] Simulation 2: Under the above conditions, the access capacity under strong guarantee of user satellite service quality is solved using the present invention and existing stochastic network calculus methods. The results are as follows: Figure 5 As shown.

[0177] from Figure 5 It is evident that the method proposed in this invention can significantly improve access capacity compared to existing methods, especially for services with strict latency requirements, where the improvement effect is even more significant.

[0178] The above descriptions are merely two specific examples of the present invention and do not constitute any limitation on the present invention. Obviously, those skilled in the art, after understanding the content and principles of the present invention, may make various modifications and changes in form and detail without departing from the principles and structure of the present invention. For example, in addition to the microwave channel model used in the present invention to construct the transmission capacity Markov chain, other channel models such as laser can also be used to construct the transmission capacity Markov chain; in addition to the indirect scheme of the present invention by first solving the upper bound of the access capacity and then solving the access capacity under the guarantee of quality of service, a direct scheme can be output by not solving the upper bound of the access capacity, but sequentially increasing the access capacity until the quality of service cannot be guaranteed, thereby outputting the access capacity under the guarantee of quality of service. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A method for enhancing the access capacity of a data relay satellite network with strong guarantee of user satellite service quality, characterized in that, include: (1) Based on parameters such as user satellite altitude, inclination angle, and number of relay satellites, calculate the upper and lower bounds of transmission distance and the upper bound of transmission distance variation, and construct a Markov chain with transmission capacity as the state based on the mathematical relationship between inter-satellite transmission capacity and transmission distance, i.e., transmission capacity Markov chain. (2) Equivalent the computing resources and transmission capacity Markov chain to the service process of the queue, and equivalent the storage resources to the storage capacity of the queue, forming a cascaded queue system of processing queue-transmission queue, and mapping the random process of service arrival, computing and transmission of this system to the martingale domain, and calculating the boundary of user satellite service quality. (3) The upper limit of the access capacity under the condition of system stability is obtained by using the monotonicity of the user satellite service quality boundary, and the access capacity under the user satellite service quality guarantee is determined by the bisection method within the upper limit range.

2. The method according to claim 1, characterized in that: The calculation of the upper and lower bounds of the transmission distance and the upper bound of the change in transmission distance based on parameters such as user satellite altitude, inclination angle, and number of relay satellites in (1) includes the following: 1a) Input data relay satellite network parameters: including user satellite orbital altitude ,inclination Number of high-orbit relay satellites ; 1b) Based on snapshot Domestic user satellite With relay satellite Distance between Under the shortest distance association strategy, the transmission distance is determined as follows: ; 1c) Calculate the upper bound of the transmission distance and the lower realm : ， in, and These represent the orbital radii of the relay satellite and the user satellite, respectively. Indicates the altitude of the relay satellite; 1d) Calculate the upper bound of the change in transmission distance : ， in Indicates the duration of the snapshot. Represents the gravitational constant. That's the mass of the Earth.

3. The method according to claim 1, characterized in that: In step (1), a Markov chain with transmission capacity as its state is constructed based on the mathematical relationship between inter-satellite transmission capacity and transmission distance. This chain includes: length... ,state and transition probability The calculations are as follows: 1e) Calculate the length of the Markov chain: ,in and This indicates the upper and lower bounds of the transmission distance. The set transmission distance interval; 1f) Based on the mathematical relationship between inter-satellite transmission capacity and transmission distance, the state of the Markov chain is determined. Represented as : ， in Indicates transmission distance. Indicates the user's satellite transmission power. and These represent the antenna gains of the user satellite and the relay satellite, respectively. Represents Boltzmann's constant. Indicates system noise temperature. This represents the ratio of the required bit energy to the noise power spectral density. Indicates link margin. Indicates the transmission frequency. Represents the speed of light; 1g) Calculate the transition probability : 1g1) Calculate the maximum interval between state transitions between adjacent snapshots: ,in This represents the upper bound of the change in transmission distance; 1g2) Statistics from mainstream simulation software The proportion of occurrence: ; 1g3) Calculate the elements of the proposal probability matrix respectively and elements of the acceptance probability matrix : ， ， 1g4) The transition probability is calculated based on the result of 1g3): .

4. The method according to claim 1, characterized in that: The random process of service arrival, computation, and transmission in (2) is mapped to the martingale domain, and its implementation includes: 2a) Calculate the equivalent service arrival rate based on the service arrival model of the memoryless Markov modulation process. : ， in This indicates the number of tasks generated within any snapshot. It is a free variable; 2b) Calculate the service arrival martingale based on the equivalent service arrival rate. : ， in express The cumulative number of tasks generated within each snapshot. , Indicates snapshot The number of tasks generated internally. Indicates the number of tasks that can be generated within a single snapshot; 2c) Calculate the equivalent processing rate based on the constant computation process model. : ， in This indicates the number of tasks a user satellite can handle within a snapshot; 2d) Based on equivalent processing rate The processing service martingale is calculated. : ， in express The cumulative number of tasks processed within each snapshot; 2e) Calculate the exponential transformation matrix of the transition probability of the Markov chain based on its transmission capacity. Its elements for: ， in Indicates transmission capacity status The number of tasks that a single user satellite can transmit within the next snapshot. Indicates the task size. This indicates the compression rate of the task during the computation process; 2f) According to Calculate its spectral radius and the corresponding right eigenvector And calculate the equivalent transmission rate. : ， 2g) The transmission service martingale is calculated based on the equivalent transmission rate. : ， in, express The cumulative number of tasks transmitted within each snapshot. Indicates snapshot Number of tasks transferred internally.

5. The method according to claim 1, characterized in that: The boundary for calculating the user satellite service quality in (2) is implemented as follows: 2h) Based on equivalent service arrival rate Equivalent processing rate and equivalent transmission rate Calculate the maximum free variables for system stability : ， 2i) Arrival of martingale according to business Processing service martingale and transmission service martingale Calculate the overall service quality coefficient : ， in ; 2j) Based on equivalent service arrival rate and equivalent processing rate Calculate the maximum number of free variables that are stable in the processing queue. : ， 2k) Based on business arrival martingale and processing services martingale Calculate the service quality coefficient of the processing queue : ， 2l) Calculate the task timeout probability boundary based on the results from 2h) to 2k). Task loss probability boundary in the processing queue and the overall task loss probability boundary : ， in Indicates the delay threshold. and These represent the number of tasks that the processing queue and the transmission queue can store, respectively.

6. The method according to claim 1, characterized in that: The upper bound of access capacity that satisfies the system stability condition is calculated by utilizing the monotonicity of the service quality boundary in (3), and its implementation includes: 3a) The upper bound of the access capacity under the condition of system stability is initialized as follows: ; 3b) Number of user satellites to be connected Set as ,in This indicates that the current number is the [number]. In the next iteration, the average equivalent transmission rate is calculated. and average task generation rate : ， in, Indicates the equivalent transmission rate. This represents the probability that a task will be generated within any snapshot. Indicates the number of tasks that can be generated within a single snapshot; 3c) will and Comparison: if If the system is stable, then 3D will be executed. Otherwise, the system is unstable, and the current iteration round is recorded. (Execute 3e). 3d) Calculate the updated upper bound of the access capacity. ,in Indicates the number of high-orbit relay satellites (return 3b). 3e) Output the upper limit of the current access capacity. To meet the upper limit of access capacity under stable system conditions .

7. The method according to claim 1, characterized in that: The determination of the access capacity under the user satellite service quality guarantee using the bisection method within the upper bound range in (3) includes the following implementation: 3f) Set the lower limit of access capacity to ; 3G) Determine the upper limit of access capacity Is it equal to the lower bound? : If so, output the number of currently connected user satellites. Access capacity under quality of service assurance; Otherwise, execute for 3 hours). 3h) The number of currently connected user satellites Set as upper bound and the lower realm The intermediate value is used to determine whether the timeout probability and loss probability requirements are met. The determination rules are as follows: 3h1) Calculate the number of satellites currently connected to the user. The maximum free variables of the system stability under the following conditions Overall service quality coefficient Processing the maximum free variables of a stable queue and processing queue service quality coefficient And calculate the task timeout probability boundary based on these parameters. ; 3h2) Task timeout probability boundary With the set timeout probability requirement threshold Comparison: if Then execute 3h3). Otherwise, the upper limit of the access capacity will be reached. Set to the number of satellites currently connected to users. (return 3g); 3h3) Calculate the percentage of storage capacity in the processing queue respectively. The upper realm and the lower realm : ， ， in, This indicates that a threshold is set for the probability of loss. Indicates the storage resources of the user's satellite. Indicates the task size. This indicates the compression rate of the task during the computation process; 3h4) will and Comparison: if Then the lower limit of the access capacity will be reached. Set to the number of satellites currently connected to users. (return 3g); Otherwise, the upper limit of the access capacity will be reached. Set to the number of satellites currently connected to users. (Return to 3g).

8. A data relay satellite network access capacity enhancement system with strong user satellite service quality assurance, characterized in that, include: Transmission distance calculation module: used to calculate the upper and lower bounds of the transmission distance and the upper bound of the change in transmission distance based on parameters such as user satellite altitude, inclination angle, and number of high-orbit relay satellites; Transmission process construction module: used to construct the transmission capacity Markov chain and calculate its length, state and transition probability based on the upper and lower bounds of the transmission distance and the upper bound of the transmission distance variation, as well as the user satellite transmit power, frequency, antenna gain, link margin and noise parameters. Queue Representation Module: Used to represent storage, computing, and transmission resources as basic elements of queues, represent computing resources and transmission capacity as service processes of processing queues and transmission queues respectively using Markov chains, and represent storage resources as the storage capacity of queues; Service Quality Boundary Calculation Module: This module maps the stochastic processes of service arrival, computation, and transmission to the martingale domain, and calculates the boundary of user satellite service quality based on the service arrival martingale, processing service martingale, and transmission service martingale. Access capacity calculation module: used to calculate the access capacity under the user satellite service quality guarantee, calculate the upper limit of the access capacity under the system stability condition, and use the bisection method to determine the access capacity under the user satellite service quality guarantee within the upper limit range.

9. The system according to claim 8, characterized in that: The access capacity calculation module includes: System stability judgment submodule: used to calculate the average equivalent transmission rate and average task generation rate corresponding to the upper limit of the current access capacity, and to determine whether the upper limit of the current access capacity meets the system stability conditions based on their relationship. Upper bound iteration calculation submodule: It is used to increase the upper bound of access capacity through iteration, and successively use the results of the system stability judgment submodule to output the upper bound of access capacity that meets the system stability conditions; Service Quality Parameter Calculation Submodule: Used to calculate the service quality parameters under the current number of satellites for the accessing user, including the maximum free variable of system stability, the overall service quality coefficient, the maximum free variable of processing queue stability, and the service quality coefficient of processing queue; Task timeout probability judgment submodule: It is used to calculate the timeout probability boundary based on the results of the service quality parameter calculation submodule, and to determine whether the task timeout probability requirement is met based on its relationship with the timeout probability requirement threshold. Task loss probability judgment submodule: It is used to calculate the upper and lower bounds of the processing queue storage capacity ratio based on the results of the service quality parameter calculation submodule, and to determine whether the task loss probability requirement is met based on their relationship. Access capacity iteration submodule: It is used to iteratively update the access capacity using a binary search method. It sequentially uses the results of the task timeout probability requirement judgment submodule and the task loss probability requirement judgment submodule to output the access capacity under the user satellite service quality assurance.

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