Low earth orbit satellite communication service volume generation method and system based on two-dimensional kernel density estimation

Abstract

The invention provides a low-orbit satellite communication service volume generation method and system based on two-dimensional kernel density estimation, and the method comprises the steps: firstly analyzing the position distribution of a given communication terminal data set, and extracting the distribution characteristics of terminals through employing a kernel density estimation theory, so as to estimate a probability density function of terminal distribution; then, according to the estimated probability density function, an analog terminal data set similar to the original data set can be generated; then, under the condition of given system time and terminal peak service volume, calculating the time zone where each visible terminal of the satellite is located and the corresponding geographical time, and weighting the visible terminals by using a time weighting factor to obtain the communication service volume of each terminal so as to obtain the global communication service volume distribution; and according to the sub-satellite point position, obtaining the visible terminal in the data set by using the maximum scanning angle of the multi-beam antenna of the low-orbit satellite, thereby obtaining the communication service volume distribution of the satellite coverage range.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a method and system for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation. Background Art

[0002] With the widespread growth of global communication business volume and the rapid development of mobile communication technology, satellite communication services have gradually transitioned from traditional narrowband services to broadband services. In modern communications, low earth orbit (LEO) satellites are very efficient communication resource providers, and the current high data rate low orbit satellite constellations also have the ability to provide broadband communication services for communication terminals such as civil aviation aircraft. Therefore, it is necessary to consider the application of LEO satellites in the field of mobile broadband communications. The establishment of communication terminal distribution models and traffic models can provide important support for the design of LEO satellite constellations and the allocation of onboard resources.

[0003] At present, the existing research on traffic estimation and modeling mainly revolves around wireless communication networks and GEO satellite communication networks, and the main means is to predict the traffic situation at future moments based on historical traffic data. Chinese patent CN116318374 discloses a satellite communication data channel analysis system and method based on traffic analysis, which uses an information analysis mechanism to obtain the simultaneous past traffic volume of a preset communication area and uses a regional collection mechanism to obtain various communication parameters of the preset communication area, thereby predicting the satellite communication service traffic volume of the preset communication area at the set time of the day based on the simultaneous past traffic volume and various communication parameters. Chinese patent CN112020075A discloses a communication guarantee method, device, and computing equipment based on traffic prediction, which obtains the basic parameters and service data of the wireless cell and uses the service data for training to obtain the predicted value of the service data of the wireless cell. Chinese patent CN102427873 proposes a traffic modeling and flow control method based on satellite network, which applies the self-similarity theory of the service and adopts wavelet analysis and other means to analyze the self-similarity of the current satellite traffic volume, so as to achieve modeling and prediction of network traffic. The above method can effectively realize the dynamic estimation of satellite traffic within a small time range and meet the needs of resource allocation applications. However, it does not take into account the changing relationship between traffic and sub-satellite position under the high-speed and large dynamic range operation conditions of the LE0 constellation satellite, and cannot meet the needs of LEO satellite constellation design for the changing law of satellite traffic over a longer time span. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a method and system for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation.

[0005] The method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation provided by the present invention includes:

[0006] Step S1: extracting terminal distribution features of the data set based on the global communication terminal position distribution data set using a kernel density estimation algorithm, and estimating a probability density function of the terminal distribution;

[0007] Step S2: Calculate the weight of each original sample point using the estimated probability density function, and then resample the original sample points to generate a preset number of simulated communication terminals.

[0008] Step S3: Calculate the longitude and latitude of the sub-satellite point according to the orbital information of the low-orbit satellite;

[0009] Step S4: Determine the time zone and traffic time weighting factor of each terminal in the world;

[0010] Step S5: assigning peak traffic volume to each terminal using a random method according to the type, demand and number of passengers of the terminal;

[0011] Step S6: multiplying the peak traffic volume of each terminal by its corresponding traffic volume time weighting factor to obtain the total traffic volume distribution in the global scope;

[0012] Step S7: Filter the communication terminals within the satellite coverage area to obtain the communication traffic distribution within the satellite coverage area.

[0013] Preferably, the step S1 comprises:

[0014] The data set recording the communication terminal location distribution information is used as the input of the kernel density estimation algorithm to obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is:

[0015]

[0016] Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation; K represents the normal kernel function, and its calculation expression is:

[0017]

[0018] Among them, v represents the probability variable.

[0019] Preferably, the step S3 comprises:

[0020] The calculation expression of the longitude λ of the satellite sub-satellite point at time t is:

[0021] λ=α-(G0+ω e (t-t0)) (3)

[0022] Among them, ω e is the sidereal hour angle of Greenwich at the starting time, G0 is the rotation speed of the Earth, α is the right ascension of the terminal; t0 represents the reference time;

[0023] The calculation expression of latitude φ is:

[0024]

[0025] Among them, φ' is the geocentric latitude of the satellite at that moment, and f is the flattening of the earth.

[0026] Preferably, step S4 includes: determining the local time corresponding to each terminal according to the latitude and longitude coordinates of the terminal and the given standard universal coordinated time, analyzing the day and night influencing factors of human activities and social and economic development on the satellite communication business volume, and establishing a time weighted factor model for the communication business intensity; using the business volume time weighted factor with a value between 0 and 1 to characterize the relative change of the satellite communication business volume within a day.

[0027] Preferably, step S7 comprises:

[0028] Calculate the elevation angles of all communication terminals to the satellite, compare them with the given maximum elevation angle of the communication terminal to the satellite, and select the terminals within the satellite coverage from the simulated terminals;

[0029] For a certain communication terminal, the calculation expression of its elevation angle to the satellite is:

[0030]

[0031] Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R e is the radius of the earth, r is the distance from the satellite to the center of the earth;

[0032] When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible.

[0033] The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation provided by the present invention comprises:

[0034] Module M1: Based on the global communication terminal position distribution data set, the kernel density estimation algorithm is used to extract the terminal distribution characteristics of the data set and estimate the probability density function of the terminal distribution;

[0035] Module M2: Calculate the weight of each original sample point using the estimated probability density function, and then resample from the original sample points to generate a preset number of simulated communication terminals.

[0036] Module M3: Calculate the longitude and latitude of the sub-satellite point based on the orbital information of the low-orbit satellite;

[0037] Module M4: Determine the time zone and traffic time weighting factor for each terminal globally;

[0038] Module M5: assign peak traffic volume to each terminal using a random method based on the type, demand and number of passengers carried by the terminal;

[0039] Module M6: multiplying the peak traffic volume of each terminal by its corresponding traffic volume time weighting factor to obtain the total traffic volume distribution in the global scope;

[0040] Module M7: Filter the communication terminals within the satellite coverage area to obtain the communication traffic distribution within the satellite coverage area.

[0041] Preferably, the module M1 comprises:

[0042] The data set recording the communication terminal location distribution information is used as the input of the kernel density estimation algorithm to obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is:

[0043]

[0044] Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation; K represents the normal kernel function, and its calculation expression is:

[0045]

[0046] Among them, v represents the probability variable.

[0047] Preferably, the module M3 comprises:

[0048] The calculation expression of the longitude λ of the satellite sub-satellite point at time t is:

[0049] λ=α-(G0+ω e(t-t0)) (3)

[0050] Among them, ω e is the sidereal hour angle of Greenwich at the starting time, G0 is the rotation speed of the Earth, α is the right ascension of the terminal; t0 represents the reference time;

[0051] The calculation expression of latitude φ is:

[0052]

[0053] Among them, φ' is the geocentric latitude of the satellite at that moment, and f is the flattening of the earth.

[0054] Preferably, the module M4 includes: determining the local time corresponding to each terminal according to the latitude and longitude coordinates of the terminal and the given standard universal coordinated time, analyzing the day and night influencing factors of satellite communication traffic caused by human activities and social and economic development, and establishing a time weighted factor model for communication traffic intensity; using the traffic time weighted factor with a value between 0 and 1 to characterize the relative changes in satellite communication traffic within a day.

[0055] Preferably, the module M7 comprises:

[0056] Calculate the elevation angles of all communication terminals to the satellite, compare them with the given maximum elevation angle of the communication terminal to the satellite, and select the terminals within the satellite coverage from the simulated terminals;

[0057] For a certain communication terminal, the calculation expression of its elevation angle to the satellite is:

[0058]

[0059] Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R e is the radius of the earth, r is the distance from the satellite to the center of the earth;

[0060] When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] (1) The present invention is based on terminal-level traffic simulation, which fully reflects the granularity of traffic distribution and can provide a theoretical basis for satellite communication constellation configuration design, satellite communication resource allocation, satellite communication beam scheduling and other issues;

[0063] (2) The present invention constructs a traffic distribution model based on the space-time distribution of terminals and the time-weighted influence factor, which fully considers the actual distribution characteristics of satellite communication traffic and can provide a theoretical basis for satellite communication constellation configuration design, satellite communication resource allocation, satellite communication beam scheduling and other issues. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0065] Figure 1 A flow chart of a method for generating satellite communication traffic in the present invention;

[0066] Figure 2 A distribution diagram of communication terminals in the original data set in the embodiment;

[0067] Figure 3 It is a simulated terminal distribution diagram obtained after sampling by the kernel density estimation algorithm in the embodiment;

[0068] Figure 4 A schematic diagram of a traffic time weighting factor in an embodiment;

[0069] Figure 5 is a global communication traffic distribution diagram in the embodiment;

[0070] Figure 6 Schematic diagram of the distribution of communication terminals within the satellite coverage area in the embodiment. DETAILED DESCRIPTION

[0071] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0072] Example 1

[0073] like Figure 1 As shown, the present invention provides a method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation, and the flowchart of the method is shown in FIG. Figure 1 As shown, the following steps are included:

[0074] S1, based on the global communication terminal position distribution data set, extract the terminal distribution characteristics of the data set using a kernel density estimation algorithm, and estimate the probability density function of the terminal distribution;

[0075] Take the flight location data of ADS-B Exchange, the world's largest ADS-B / Mode S / MLAT data sharing organization, as an example. The data set records the information of 13,857 aircraft around the world at 2:30 p.m. on May 1, 2023, including longitude, latitude, altitude and other location data. The global communication terminals in the data set are distributed as follows: Figure 2 As shown. By using the information of this data set as the input of the kernel density estimation algorithm, we can obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is:

[0076]

[0077] Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation, both set to 0.1, x i represents the normal kernel function, which is calculated as:

[0078]

[0079] S2, using the estimated probability density function, calculates the weight of each original sample point, and resamples from the original sample points according to these weights to generate a certain number of simulated communication terminals;

[0080] The first step of resampling is to generate a probability density table: on a global scale, longitude and latitude are gridded with a step size of 1°, and the probability density value of each grid point is calculated to form a probability density table, and then the probability density is normalized. The values ​​in the probability density table are normalized so that their sum is 1 to form a discrete probability distribution. Finally, the cumulative distribution function is calculated, and the inverse transform sampling method is used to sample the two-dimensional space according to the cumulative distribution function. The resampling method is used to sample 13,000 data points of the probability density function of the kernel density estimate. The resulting simulated terminal distribution is as follows: Figure 3 As shown;

[0081] S3, calculating the longitude and latitude of the sub-satellite point according to the orbital information of the low-orbit satellite;

[0082] The calculation method of the longitude λ of the satellite sub-satellite point at time t is:

[0083] λ=α-(G0+ω e (t-t0)) (3)

[0084] Where G0 is the sidereal hour angle of Greenwich at the starting time, ω e=7.2921158×10 -5 rad / s is the Earth's rotation speed, α is the right ascension of the terminal;

[0085] The latitude φ is calculated as:

[0086]

[0087] Among them, φ' is the satellite's geocentric latitude at that moment, is the Earth's flattening;

[0088] S4, determine the time zone and traffic time weighting factor of each terminal in the world;

[0089] According to the longitude and latitude coordinates of the terminal, the local time corresponding to each terminal can be determined according to the given standard universal coordinated time (UTC), and the day and night influencing factors of human activities and social and economic development on the satellite communication traffic volume can be analyzed to establish a time weighting factor model for the communication traffic intensity; after analyzing the impact of time factors on the communication traffic volume, the traffic time weighting factor with a value between 0 and 1 can be used to characterize the relative change of satellite communication traffic volume in a day. The traffic time weighting factor has different values ​​for different local times. For a terminal, the change of its traffic time weighting factor in a day is as follows: Figure 4 As shown;

[0090] S5, determine the peak traffic volume of each terminal's time zone worldwide;

[0091] According to the type, demand and number of passengers of the terminal, a certain peak traffic volume is assigned to each terminal using a random method;

[0092] S6, multiplying the peak traffic volume of each terminal by its corresponding traffic time weighting factor can obtain the total traffic volume distribution in the world. The global satellite communication traffic volume distribution generated by the simulation terminal is as follows: Figure 5 As shown;

[0093] S7, screening communication terminals within the satellite coverage;

[0094] By calculating the elevation angles of all communication terminals to the satellite and comparing them with the maximum elevation angle of a given communication terminal to the satellite, the terminals within the satellite coverage can be screened out from the simulated terminals. For a certain communication terminal, the calculation method for its elevation angle to the satellite is:

[0095]

[0096] Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R eis the radius of the earth, and r is the distance from the satellite to the center of the earth. When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible;

[0097] According to this method, all terminals within the satellite coverage can be screened out, and then the communication traffic distribution within the satellite coverage can be obtained. The terminals within the satellite coverage obtained by this method are as follows: Figure 6 shown.

[0098] Example 2

[0099] The present invention also provides a low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation. The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation can be implemented by executing the process steps of the low-orbit satellite communication traffic generation method based on two-dimensional kernel density estimation, that is, those skilled in the art can understand the low-orbit satellite communication traffic generation method based on two-dimensional kernel density estimation as a preferred implementation of the low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation.

[0100] The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation provided by the present invention comprises: module M1: according to the global communication terminal azimuth distribution data set, extracting the terminal distribution characteristics of the data set by a kernel density estimation algorithm, and estimating the probability density function of the terminal distribution; module M2: using the estimated probability density function, calculating the weight of each original sample point, and then resampling from the original sample points to generate a preset number of simulated communication terminals; module M3: according to the orbital operation information of the low-orbit satellite, calculating the longitude and latitude position of its sub-satellite point; module M4: determining the time zone and traffic time weighting factor of each terminal in the global range; module M5: according to the type, demand and number of passengers of the terminal, using a random method to assign peak traffic to each terminal; module M6: multiplying the peak traffic of each terminal by its corresponding traffic time weighting factor to obtain the total traffic distribution in the global range; module M7: screening the communication terminals within the satellite coverage range to obtain the communication traffic distribution within the satellite coverage range.

[0101] The module M1 comprises:

[0102] The data set recording the communication terminal location distribution information is used as the input of the kernel density estimation algorithm to obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is:

[0103]

[0104] Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation; K represents the normal kernel function, and its calculation expression is:

[0105]

[0106] Among them, v represents the probability variable.

[0107] The module M3 comprises:

[0108] The calculation expression of the longitude λ of the satellite sub-satellite point at time t is:

[0109] λ=α-(G0+ω e (t-t0)) (3)

[0110] Among them, ω e is the sidereal hour angle of Greenwich at the starting time, G0 is the rotation speed of the Earth, α is the right ascension of the terminal; t0 represents the reference time;

[0111] The calculation expression of latitude φ is:

[0112]

[0113] Among them, φ' is the geocentric latitude of the satellite at that moment, and f is the flattening of the earth.

[0114] The module M4 includes: determining the local time corresponding to each terminal according to the latitude and longitude coordinates of the terminal and the given standard universal time, analyzing the day and night influencing factors of human activities and social and economic development on the satellite communication business volume, and establishing a time weighted factor model for the communication business intensity; using the business volume time weighted factor with a value between 0 and 1 to characterize the relative change of satellite communication business volume within a day.

[0115] The module M7 comprises:

[0116] Calculate the elevation angles of all communication terminals to the satellite, compare them with the given maximum elevation angle of the communication terminal to the satellite, and select the terminals within the satellite coverage from the simulated terminals;

[0117] For a certain communication terminal, the calculation expression of its elevation angle to the satellite is:

[0118]

[0119] Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R eis the radius of the earth, r is the distance from the satellite to the center of the earth;

[0120] When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible.

[0121] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0122] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation, characterized in that: include: Step S1: extracting terminal distribution features of the data set based on the global communication terminal position distribution data set using a kernel density estimation algorithm, and estimating a probability density function of the terminal distribution; Step S2: Calculate the weight of each original sample point using the estimated probability density function, and then resample the original sample points to generate a preset number of simulated communication terminals. Step S3: Calculate the longitude and latitude of the sub-satellite point according to the orbital information of the low-orbit satellite; Step S4: Determine the time zone and traffic time weighting factor of each terminal in the world; Step S5: assigning peak traffic volume to each terminal using a random method according to the type, demand and number of passengers of the terminal; Step S6: multiplying the peak traffic volume of each terminal by its corresponding traffic volume time weighting factor to obtain the total traffic volume distribution in the global scope; Step S7: Filter the communication terminals within the satellite coverage area to obtain the communication traffic distribution within the satellite coverage area.

2. The method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation according to claim 1, characterized in that: The step S1 comprises: The data set recording the communication terminal location distribution information is used as the input of the kernel density estimation algorithm to obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is: Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation; K represents the normal kernel function, and its calculation expression is: Among them, v represents the probability variable.

3. The method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation according to claim 1, characterized in that: The step S3 comprises: The calculation expression of the longitude λ of the satellite sub-satellite point at time t is: λ=α-(G0+ω e (t-t0)) (3) Among them, ω e is the sidereal hour angle of Greenwich at the starting time, G0 is the rotation speed of the Earth, α is the right ascension of the terminal; t0 represents the reference time; The calculation expression of latitude φ is: Among them, φ' is the geocentric latitude of the satellite at that moment, and f is the flattening of the earth.

4. The method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation according to claim 1, characterized in that: The step S4 includes: determining the local time corresponding to each terminal according to the latitude and longitude coordinates of the terminal and the given standard universal time, analyzing the day and night influencing factors of satellite communication traffic caused by human activities and social and economic development, and establishing a time weighted factor model for communication traffic intensity; using the traffic time weighted factor with a value between 0 and 1 to characterize the relative change of satellite communication traffic within a day.

5. The method for generating low-orbit satellite communication traffic based on two-dimensional kernel density estimation according to claim 1, characterized in that: The step S7 comprises: Calculate the elevation angles of all communication terminals to the satellite, compare them with the given maximum elevation angle of the communication terminal to the satellite, and select the terminals within the satellite coverage from the simulated terminals; For a certain communication terminal, the calculation expression of its elevation angle to the satellite is: Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R e is the radius of the earth, r is the distance from the satellite to the center of the earth; When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible.

6. A low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation, characterized in that: include: Module M1: Based on the global communication terminal position distribution data set, the kernel density estimation algorithm is used to extract the terminal distribution characteristics of the data set and estimate the probability density function of the terminal distribution; Module M2: Calculate the weight of each original sample point using the estimated probability density function, and then resample from the original sample points to generate a preset number of simulated communication terminals. Module M3: Calculate the longitude and latitude of the sub-satellite point based on the orbital information of the low-orbit satellite; Module M4: Determine the time zone and traffic time weighting factor for each terminal globally; Module M5: assign peak traffic volume to each terminal using a random method based on the type, demand and number of passengers carried by the terminal; Module M6: multiplying the peak traffic volume of each terminal by its corresponding traffic volume time weighting factor to obtain the total traffic volume distribution in the global scope; Module M7: Filter the communication terminals within the satellite coverage area to obtain the communication traffic distribution within the satellite coverage area.

7. The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation according to claim 6, characterized in that: The module M1 comprises: The data set recording the communication terminal location distribution information is used as the input of the kernel density estimation algorithm to obtain the probability density function estimate of the global communication terminal location distribution. For a given longitude x and latitude y, the estimated value of the probability density is: Where n represents the number of data points in the data set, x i and i represents the longitude and latitude of the ith data point, h x and h y represents the window width parameter in longitude and latitude estimation; K represents the normal kernel function, and its calculation expression is: Among them, v represents the probability variable.

8. The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation according to claim 6, characterized in that: The module M3 comprises: The calculation expression of the longitude λ of the satellite sub-satellite point at time t is: λ=α-(G0+ω e (t-t0)) (3) Among them, ω e is the sidereal hour angle of Greenwich at the starting time, G0 is the rotation speed of the Earth, α is the right ascension of the terminal; t0 represents the reference time; The calculation expression of latitude φ is: Among them, φ' is the geocentric latitude of the satellite at that moment, and f is the flattening of the earth.

9. The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation according to claim 6, characterized in that: The module M4 includes: determining the local time corresponding to each terminal according to the latitude and longitude coordinates of the terminal and the given standard universal time, analyzing the day and night influencing factors of human activities and social and economic development on the satellite communication business volume, and establishing a time weighted factor model for the communication business intensity; using the business volume time weighted factor with a value between 0 and 1 to characterize the relative change of satellite communication business volume within a day.

10. The low-orbit satellite communication traffic generation system based on two-dimensional kernel density estimation according to claim 6, characterized in that: The module M7 comprises: Calculate the elevation angles of all communication terminals to the satellite, compare them with the given maximum elevation angle of the communication terminal to the satellite, and select the terminals within the satellite coverage from the simulated terminals; For a certain communication terminal, the calculation expression of its elevation angle to the satellite is: Among them, the angle ψ is the geocentric angle between the satellite sub-satellite point and the terminal position, R e is the radius of the earth, r is the distance from the satellite to the center of the earth; When the satellite and the terminal are in the same hemisphere, if the elevation angle E of the terminal to the satellite is greater than the minimum elevation angle E allowed for communication min , the terminal is considered to be within the satellite coverage, otherwise it means that the satellite is invisible to the terminal and communication is impossible.

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