Ground service flow generation simulation method for satellite network

Abstract

The invention discloses a satellite network-oriented ground service flow generation simulation method, and belongs to the technical field of communication. According to the method, by simulating generation factors, including geographic areas, population, time and other multi-dimensional factors, influencing ground satellite communication services, flow generation models of different levels are input according to different requirements, and finally area flow information is generated, so that the requirement of simulating satellite network input is met. Geography-population distribution information is utilized, flexible region merging is adopted, and region flow simulation models of different levels can be flexibly set according to simulation requirements, so that the simulation accuracy is ensured, and meanwhile, the ground service flow generation simulation complexity is reduced.

Description

Technical Field

[0001] The invention belongs to the field of communication technology, and in particular relates to a ground service traffic generation simulation method for a satellite network. Background Art

[0002] Low-Earth Orbit (LEO) satellite networks, with their seamless global coverage, low latency (approximately 40ms for LEO), and high reliability, have become a core component of 6G space-ground integrated networks, supporting critical scenarios such as communications in remote areas, emergency rescue, aviation and navigation. However, simulating ground satellite traffic on a global scale remains a challenge. Having a simulator for generating global ground traffic is crucial for supporting the verification of space-ground integrated network technologies and forms a crucial foundation for future satellite-to-ground air interfaces, onboard routing, and service optimization.

[0003] However, the process of simulating global ground satellite traffic generation involves a variety of factors, including geographic location, location type, population capacity, time, and random factors. The intensity of satellite service activity in different regional types (urban, desert, ocean, etc.) can vary by up to two orders of magnitude. The relationship between regional population capacity and service demand is nonlinear, and the coupling effect of time factors is particularly complex. Tidal changes in traffic volume caused by the diurnal cycle (daytime traffic intensity is 40% higher than in the early morning) must be fully considered. Furthermore, random factors (such as channel impairment caused by extreme weather and sudden user access requests) require the model to have a certain degree of dynamic response capability. The interweaving of these factors makes the generation of global low-orbit satellite ground service traffic extremely difficult. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a ground business traffic generation simulation method for low-orbit satellite networks. This method constructs a differentiated modeling mechanism by integrating multiple influencing factors to form a business traffic generation structure that can adapt to different needs, provide support for ground business traffic generation, and realize high-fidelity traffic generation and multi-region scenario modular configuration capabilities.

[0005] The technical solution adopted in the present invention is:

[0006] A method for generating terrestrial traffic simulation for satellite networks, which utilizes geographical area, population capacity, and time factors to generate global satellite traffic, includes the following steps:

[0007] Step 1: Set the simulation time information [0, T] and record the current time as t UTC , where t UTC is Universal Time;

[0008] Step 2: Divide the target area TA into M×N latitude and longitude grid cells, and obtain the target area aggregate grid cell geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}; Among them, the target area TA can support a global area at most;

[0009] Step 3: For the target area constructed in step 2, aggregate the grid cell geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, set the regional geographic information parameter rg m,n and regional population information parameter rp m,n ;

[0010] Step 4: Set the grid unit (m,n) regional model flow to generate the basic model Mod m,n , will [t m,n ,targi m,n ,rg m,n ,rp m,n ] Input to Model Mod m,n Generate regional traffic information, where t m,n is the local mean solar time of grid cell (m,n).

[0011] Furthermore, the specific method of step 2 is:

[0012] Step 2.1: Use the open source global population distribution dataset LandScan Global dataset to construct a global population distribution basic dataset GTPS = {[rgi i,j ,rti i,j ,rpi i,j ]}, where 1≤i≤I, 1≤j≤J, I and J are the number of rows and columns of the basic grid cells, rgi i,j is the regional geographic information of the basic grid cell (i, j), rti i,j The regional type information of the basic grid unit (i, j), rpi i,j The regional population information of the basic grid unit (i, j);

[0013] Step 2.2: For the target area TA, select the minimum area set TS covering TA so that

[0014]

[0015] Where G represents the set of all basic grid cells;

[0016] Step 2.3: For the region set TS in step 2.2, according to the simulation requirements, merge the adjacent d×d basic grid cells into a single aggregate grid cell, d∈{1,2,3,…D max}, where D max The upper threshold value of d;

[0017] Step 2.4, obtain the target area aggregate grid unit geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, where targi m,n is the regional geographic information of the aggregated grid cell (m,n), tarti m,n is the regional type information of the aggregated grid cell (m,n), tarpi m,n It is the regional population information of the aggregated grid unit (m,n).

[0018] Furthermore, in step 2.1:

[0019] in and They are the latitude and longitude information of the four vertices of the corresponding basic grid unit;

[0020] rti i,j ≥0, is the ratio of the number of satellite communication terminals to the population in the corresponding area, which is set according to the simulation target.

[0021] Furthermore, in step 2.3, D max The single beam size of a single satellite in the satellite system to be simulated is determined by the following steps:

[0022] Step 2.3.1: Take the single beam coverage of a single satellite in the satellite system to be simulated as the target area TA temp , get coverage TA temp The minimum basic grid unit set At this time d t =1;

[0023]

[0024] Step 2.3.2, obtain Make and With TA temp The edges of

[0025] Step 2.3.3, if then go to step 2.3.4, otherwise go to step 2.3.5;

[0026] Step 2.3.4, merge adjacent basic grid cells, adopt (d t +1)×(d t +1) adjacent basic grid cells to merge, then let d t =d t +1, obtain D go to step 2.3.2;

[0027] Step 2.3.5, if d t >1, let d t =d t -1, obtain D max =d t .

[0028] Further, the specific way of step 3 is as follows:

[0029] Step 3.1, according to tarti m,n and tarpi m,n obtained in step 2.4, let tarti m,n ={rti p,q |1≤p≤d,1≤q≤d}, tarpi m,n ={rpi p,q |1≤p≤P,1≤q≤Q}, that is, the aggregated grid cell (m, n) is composed of d×d basic grid cells;

[0030] Step 3.2, according to tarpi m,n set in step 3.1, obtain the regional population information parameter rp m,n corresponding to the target region:

[0031] rp m,n =∑ 1≤p≤d,1≤q≤d rpi p,q ;

[0032] Step 3.3, according to tarti m,n set in step 3.1, obtain the geographic information parameter rg m,n corresponding to the target region:

[0033]

[0034] Where, ∑ 1≤p≤d,1≤q≤d rpi p,q ·rti p,q is the number of satellite communication terminals corresponding to the target region.

[0035] Further, the specific way of step 4 is as follows:

[0036] In step 4.1, the traffic generation mode is selected for the aggregate grid cell (m, n), which is divided into three categories: Class I area terminal connection level, Class II area connection level, and Class III area flow level;

[0037] Step 4.2, select the basic model Mod according to the regional traffic generation mode m,n ;

[0038] Step 4.3, if it is a Class I regional terminal connection level, is used to simulate the state of each terminal in the aggregate grid cell (m,n) over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Adopt On-Off model or queuing theory model; based on Get the terminal x state f in the aggregation grid cell (m,n) x Over time t m,n change:

[0039]

[0040] Step 4.4: If it is a Class II regional connectivity level, the total number of connections LN within the simulated aggregate grid cell (m,n) is m,n and the total connection rate RN m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Use real data models, queuing theory models or AR, ARMA and ARIMA time series models based on real historical data; Get the number of connections for business y within the aggregation grid cell (m,n) Over time t m,n change:

[0041]

[0042] where r y is the service rate of service y, Y is the number of services, that is, the satellite communication system provides Y types of services in total;

[0043] Step 4.5: If it is a Class III regional connection level, the total connection rate RN used to simulate the aggregate grid unit (m,n) is m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model AR, ARMA and ARIMA time series models can be freely set or based on real historical data; based on Get the total connection rate RN within the aggregation grid unit (m,n) m,n Over time t m,n change:

[0044]

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention addresses the challenge of simulating ground satellite service traffic generation on a global scale. Ground satellite services on a global scale face difficulties such as large scale, large regional differences, and a long time span. The present invention adopts methods such as variable regional scale and hierarchical traffic generation mode to achieve traffic generation simulation on a large scale. This allows for simulation based on actual conditions and can be configured according to simulation needs, achieving flexible customization.

[0047] 2. The present invention can merge basic areas in different scenarios to reduce the complexity of traffic generation. For example, for GEO satellite coverage, the beam is usually relatively fixed, so the basic areas can be merged. However, for LEO satellites, the beam coverage will change, so the basic areas may not be merged or may be merged in a small range.

[0048] 3. The present invention can flexibly select different traffic generation requirements, such as terminal level, link level and regional traffic level, and provide simulation levels of different granularities, which can ensure the authenticity of the simulation while improving the simulation efficiency according to the simulation requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is the target area traffic generation process in an embodiment of the present invention.

[0050] Figure 2 Schematic diagram of adjacent area expansion in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the content and advantages of the present invention more clear, the present invention is described in further detail below with reference to the accompanying drawings.

[0052] Satellite network simulation is an indispensable core tool for satellite communication system design, development, deployment and operation. It overcomes the extremely high cost of physical testing and the inaccessibility of space environment, providing the ability to deeply understand complex dynamics, optimize design, verify performance, predict behavior and reduce risk in a simulated environment. Ground satellite traffic is the input of the entire satellite network simulation and the basis for overall satellite network simulation. However, due to the large coverage area of satellites, it is particularly difficult to simulate ground satellite traffic in the coverage area. The simulation process involves various factors, including geographic location, regional location type, regional population capacity, time factors, and random factors. At the same time, different simulation requirements can be simulated at different levels or granularities, such as regional terminal level, regional connection level or regional traffic level, as shown in Figure 1 .

[0053] Specifically, the method includes the following steps:

[0054] (I) Setting the basic conditions of the simulation environment

[0055] Step 1, set the simulation time information [0, T], and the current time is denoted as t UTC , where t UTC is the world standard time (UTC).

[0056] Step 2, divide the target area TA, the maximum supportable global area, and construct the target area grid geographic and population basic information, i.e. divide the target area into MxN latitude and longitude grid cells, and obtain the target area aggregated grid cell geographic-population distribution dataset TAGTPS = {[targi m,n , tarti m,n , tarpi m,n ]}.

[0057] The specific way of step 2 is:

[0058] Step 2.1, use the open source global population distribution dataset LandScan Global dataset to construct the global population distribution basic dataset GTPS = {[rgi i,j , rti i,j , rpi i,j ]}, where 1≤i≤I, 1≤j≤J, I and J are the number of rows and columns of the basic grid cells, rgi i,j is the regional geographic information of the basic grid cell (i, j), rti i,j is the regional type information of the basic grid cell (i, j), and rpi i,j is the regional population quantity information of the basic grid cell (i, j);

[0059] Step 2.2: For the target area TA, select the minimum area set TS covering TA so that

[0060]

[0061] Where G represents the set of all basic grid cells;

[0062] Step 2.3: Merge adjacent regions of the region set TS in step 2.2. Based on the simulation requirements, adjacent d×d basic grid cells can be merged into a single aggregate grid cell, d∈{1,2,3,…D max}, where D max The upper threshold of d value, if the value of d exceeds D max When d=1, the basic grid cells are not merged, and the aggregated grid cells are equal to the basic grid cells. When d>1, the adjacent basic grid cells are merged, and each aggregated grid cell is composed of d×d basic grid cells.

[0063] Step 2.4, obtain the target area aggregate grid unit geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, where targi m,n is the regional geographic information of the aggregated grid cell (m,n), tarti m,n is the regional type information of the aggregated grid cell (m,n), tarpi m,n It is the regional population information of the aggregated grid unit (m,n).

[0064] Among them, in step 2.1:

[0065] rgi i,j is the regional geographic information of the basic grid cell (i, j), in The latitude and longitude information of the four vertices of the basic grid area;

[0066] rti i,j is the regional type information of the basic grid cell (i, j), rti i,j ≥0, is the ratio of the number of satellite communication terminals to the population in the region, rti i,jIt is affected not only by natural geographical factors such as plains, deserts, oceans, and polar regions, but also by human geographical factors such as urban and rural distribution, economic factors, and administrative divisions. It is also affected by technological and economic development factors, such as mobile phone direct satellite connection technology, satellite communication terminal development, and infrastructure construction. i,j The settings can be set according to the simulation objectives. For example, in order to test the maximum communication capability of the available satellites, the rti i,j Set it up bigger;

[0067] rpi i,j The regional population information of the basic grid unit (i, j).

[0068] Among them, D in step 2.3 max The single beam size of a single satellite in the satellite system to be simulated is determined by the following steps:

[0069] Step 2.3.1: Take the single beam coverage of a single satellite in the satellite system to be simulated as the target area TA temp , get coverage TA temp The minimum basic grid unit set At this time d t =1;

[0070]

[0071] Step 2.3.2, obtain Make and With TA temp The edges intersect, such as Figure 2 As shown;

[0072] Step 2.3.3, if Then go to step 2.3.4, otherwise go to step 2.3.5;

[0073] Step 2.3.4, merge adjacent basic grid cells, using (d t +1)×(d t +1) adjacent basic grid cells are merged, and then d t =d t +1, get Go to step 2.3.2;

[0074] Step 2.3.5, if d t >1, let d t =d t -1, get D max =d t .

[0075] (2) Generate regional geographic information-population information parameters

[0076] Step 3: For the target area constructed in step 2, aggregate the grid cell geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, set the regional geographic information parameter rg m,n and regional population information parameter rp m,n .

[0077] The specific method of step 3 is:

[0078] Step 3.1, according to the tarti obtained in step 2.4 m,n and tarpi m,n , so tarti m,n ={rti p,q |1≤p≤d,1≤q≤d},tarti m,n ={rpi p,q |1≤p≤P,1≤q≤Q}, that is, the aggregate grid cell (m,n) is composed of d×d basic grid cells;

[0079] Step 3.2, according to the tarpi set in step 3.1 m,n , obtain the regional population information parameter rp corresponding to the target area m,n :

[0080] rp m,n =∑ 1≤p≤d,1≤q≤d rpi p,q ;

[0081] Step 3.3, according to the tarti set in step 3.1 m,n , get the geographic information parameter rg corresponding to the target area m,n :

[0082]

[0083] Among them, ∑ 1≤p≤d,1≤q≤d rpi p,q ·rti p,q is the number of satellite communication terminals in the corresponding target area.

[0084] (3) Generate regional traffic information based on demand

[0085] Step 4: Set the grid unit (m,n) regional model flow to generate the basic model Mod m,n , will [t m,n ,targi m,n ,rg m,n,rp m,n ] Input to Model Mod m,n Generate regional traffic information, where t m,n is the local mean solar time of grid cell (m,n).

[0086] The specific method of step 4 is:

[0087] In step 4.1, the traffic generation mode is selected for the aggregate grid cell (m, n), which is divided into three categories: Class I area terminal connection level, Class II area connection level, and Class III area flow level;

[0088] Step 4.2, select the basic model Mod according to the regional traffic generation mode m,n ;

[0089] Step 4.3, if it is a Class I regional terminal connection level, is used to simulate the state of each terminal in the aggregate grid cell (m,n) over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Adopt On-Off model or queuing theory model; based on Get the terminal x state f in the aggregation grid cell (m,n) x Over time t m,n change:

[0090]

[0091] like When the On-Off model is used, the number of terminals in the area is Each terminal switches between active (On) and silent (Off) states using (T on ,T off ,r) is described, where T on Indicates activation time, T off represents the silence duration, r represents the service traffic transmission rate, where T on and T off The setting can adopt real data model, random model such as normal distribution, Pareto distribution, constant model, etc. on and T off When using a variable normal distribution,

[0092]

[0093] where μ on (t m,n ), μ off(t m,n ), and It follows the local mean solar time The period changes, usually the time period is 24 hours;

[0094] The setting of r can select random models such as average random distribution, normal distribution, stage constant model, etc. For example, when r adopts stage constant model,

[0095]

[0096] Different services require different rates. Let p(service i) be the probability of selecting service i. The probability can be set by using a real data model, an even distribution, or other random models, as long as:

[0097]

[0098] p(business i) ≥ 0;

[0099] Step 4.4: If it is a Class II regional connectivity level, the total number of connections LN within the simulated aggregate grid cell (m,n) is m,n and the total connection rate RN m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Use real data models, queuing theory models or AR, ARMA and ARIMA time series models based on real historical data; Get the number of connections for business y within the aggregation grid cell (m,n) Over time t m,n change:

[0100]

[0101] where r y is the service rate of service u, Y is the number of services, that is, the satellite communication system provides Y types of services in total;

[0102] like Select y independent M / M / 1 queue models, where y represents a total of y independent queues, used to represent y different services. The first "M" indicates that the customer arrival time follows an exponential distribution, the second "M" indicates that the service time follows an exponential distribution, and "1" indicates that the system has only one service desk.

[0103] The inter-arrival time T of users of the i-th service follows an exponential distribution, and its probability density function is:

[0104]

[0105] That is, the number of times N reaches the user within the time length τ i (τ) is subject to the parameter λ i Poisson distribution of τ:

[0106]

[0107] Then you can get the total number of connections generated:

[0108]

[0109] The setting of Y can be set freely, y≥1, r y It means that the rate of service y is different, r y The settings can choose random models such as average random distribution, normal distribution, stage constant model, etc. For example, when r adopts stage constant model,

[0110]

[0111] Then we can get the connection rate and:

[0112]

[0113] Step 4.5: If it is a Class III regional connection level, the total connection rate RN used to simulate the aggregate grid unit (m,n) is m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model AR, ARMA and ARIMA time series models can be freely set or based on real historical data. Get the total connection rate RN within the aggregation grid unit (m,n) m,n Over time t m,n change:

[0114]

[0115] like When using the AR model,

[0116]

[0117] Where c is a constant term, α1,…,α p is the autoregressive coefficient, which indicates the weight of historical data’s impact on the current situation. is the noise term, which can be selected to follow normal distribution, uniform distribution, Pareto distribution and other random models.

Claims

1. A method for generating and simulating terrestrial traffic for satellite networks, which utilizes geographical area, population capacity, and time factors to generate global satellite traffic, characterized in that: The following steps are involved: Step 1: Set the simulation time information [0, T] and record the current time as t UTC , where t UTC is Universal Time; Step 2: Divide the target area TA into M×N latitude and longitude grid cells, and obtain the target area aggregate grid cell geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}; Among them, the target area TA can support a global area at most; Step 3: For the target area constructed in step 2, aggregate the grid cell geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, set the regional geographic information parameter rg m,n and regional population information parameter rp m,n ; Step 4: Set the grid unit (m,n) regional model flow to generate the basic model Mod m,n , will [t m,n ,targi m,n ,rg m,n ,rp m,n ] Input to Model Mod m,n Generate regional traffic information, where t m,n is the local mean solar time of grid cell (m,n).

2. The method for generating and simulating terrestrial service traffic for a satellite network according to claim 1, wherein: The specific method of step 2 is: Step 2.1: Use the open source global population distribution dataset LandScan Global dataset to construct a global population distribution basic dataset GTPS = {[rgi i,j ,rti i,j ,rpi i,j ]}, where 1≤i≤I, 1≤j≤J, I and J are the number of rows and columns of the basic grid cells, rgi i,j is the regional geographic information of the basic grid cell (i, j), rti i,j The regional type information of the basic grid unit (i, j), rpi i,j The regional population information of the basic grid unit (i, j); Step 2.2: For the target area TA, select the minimum area set TS covering TA so that Where G represents the set of all basic grid cells; Step 2.3: For the region set TS in step 2.2, according to the simulation requirements, merge the adjacent d×d basic grid cells into a single aggregate grid cell, d∈{1,2,3,…D max }, where D max The upper threshold value of d; Step 2.4, obtain the target area aggregate grid unit geographic-population distribution dataset TAGTPS = {[targi m,n ,tarti m,n ,tarpi m,n ]}, where targi m,n is the regional geographic information of the aggregated grid cell (m,n), tarti m,n is the regional type information of the aggregated grid cell (m,n), tarpi m,n It is the regional population information of the aggregated grid unit (m,n).

3. The method for generating and simulating terrestrial service traffic for a satellite network according to claim 2, wherein: In step 2.1: in and They are the latitude and longitude information of the four vertices of the corresponding basic grid unit; rti i,j ≥0, is the ratio of the number of satellite communication terminals to the population in the corresponding area, which is set according to the simulation target.

4. The method for generating and simulating terrestrial service traffic for a satellite network according to claim 2, wherein: Step 2.3 D max The single beam size of a single satellite in the satellite system to be simulated is determined by the following steps: Step 2.3.1: Take the single beam coverage of a single satellite in the satellite system to be simulated as the target area TA temp , get coverage TA temp The minimum basic grid unit set At this time d t =1; Step 2.3.2, obtain Make and With TA temp The edges of Step 2.3.3, if Then go to step 2.3.4, otherwise go to step 2.3.5; Step 2.3.4, merge adjacent basic grid cells, using (d t +1)×(d t +1) adjacent basic grid cells are merged, and then d t =d t +1, get Go to step 2.3.2; Step 2.3.5, if d t >1, let d t =d t -1, get D max =d t .

5. The method for generating and simulating terrestrial service traffic for a satellite network according to claim 2, wherein: The specific method of step 3 is: Step 3.1, according to the tarti obtained in step 2.4 m,n and tarpi m,n , so tarti m,n ={rti p,q |1≤p≤d,1≤q≤d}, tarpi m,n ={rpi p,q |1≤p≤P,1≤q≤Q}, that is, the aggregate grid cell (m,n) is composed of d×d basic grid cells; Step 3.2, according to the tarpi set in step 3.1 m,n , obtain the regional population information parameter rp corresponding to the target area m,n : Rp m,n =∑ 1≤p≤d,1≤q≤d rpi p,q ; Step 3.3, according to the tarti set in step 3.1 m,n , get the geographic information parameter rg corresponding to the target area m,n : Among them, ∑ 1≤p≤d,1≤q≤d rpi p,q ·rti p,q is the number of satellite communication terminals in the corresponding target area.

6. The method for generating and simulating terrestrial service traffic for a satellite network according to claim 1, wherein: The specific method of step 4 is: In step 4.1, the traffic generation mode is selected for the aggregate grid cell (m, n), which is divided into three categories: Class I area terminal connection level, Class II area connection level, and Class III area flow level; Step 4.2, select the basic model Mod according to the regional traffic generation mode m,n ; Step 4.3, if it is a Class I regional terminal connection level, is used to simulate the state of each terminal in the aggregate grid cell (m,n) over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Adopt On-Off model or queuing theory model; based on Get the terminal x state f in the aggregation grid cell (m,n) x Over time t m,n change: Step 4.4: If it is a Class II regional connectivity level, the total number of connections LN within the simulated aggregate grid cell (m,n) is m,n and the total connection rate RN m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model Use real data models, queuing theory models or AR, ARMA and ARIMA time series models based on real historical data; Get the number of connections for business y within the aggregation grid cell (m,n) Over time t m,n change: where r y is the service rate of service y, Y is the number of services, that is, the satellite communication system provides Y types of services in total; Step 4.5: If it is a Class III regional connection level, the total connection rate RN used to simulate the aggregate grid unit (m,n) is m,n Over time t m,n Change, that is, choose the base model Generate regional traffic information; where t m,n The local mean solar time is expressed using the aggregated grid unit (m,n). The basic model AR, ARMA and ARIMA time series models can be freely set or based on real historical data; based on Get the total connection rate RN within the aggregation grid unit (m,n) m,n Over time t m,n change:

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