A method for analyzing communication performance degradation of a giant star constellation in view of coverage multiplicity sensitivity

Abstract

The application discloses a method for analyzing performance degradation of giant star constellation aiming at coverage multiplicity sensitivity, and relates to the field of orbit analysis. The method aims to solve the problem that there is no effective evaluation method for the influence of coverage multiplicity and the sensitivity of the change of coverage multiplicity performance caused by the change of the number of satellites. The method comprises the following steps: step one, counting the satellite coverage of a specified location under the condition of step-by-step calculation and the satellite coverage of the specified location under the condition of geometric solution calculation; step two, regarding the satellite set that has covered the location D in the time period T as S, arranging the different coverage time periods of the members in the satellite set S in ascending order according to the ending coverage time, and arranging the members corresponding to the same ending coverage time in ascending order according to the starting coverage time; and step three, determining the number of spliced time periods according to the average coverage multiplicity and the target average coverage multiplicity, and completing the sensitivity test. The method can be applied to the field of spacecraft system simulation.

Description

Technical Field

[0001] This invention relates to the field of orbital analysis, and more particularly to a method for analyzing the performance degradation of the Giants constellation communication system, which is sensitive to coverage multiples. Background Technology

[0002] In satellite constellation orbit prediction and coverage assessment, the first step is to parse the constellation's two-line metadata and perform orbit recursion. Specifically, this involves reading and identifying the constellation's two-line metadata and processing it according to a common format. Next, the model is initialized based on the SGP4 orbit model and the read actual variables, and the orbital state is recursively calculated based on the current moment to obtain accurate satellite orbit information.

[0003] When calculating the coverage of a constellation over a given area, the coverage area is first discretized into a sufficiently large number of sampling points. Next, it is necessary to calculate whether the line connecting the satellite to each sampling point lies within the satellite's coverage cone angle and whether the line passes through the Earth. Finally, by summing the coverage data from each sampling point, a specific numerical result is obtained.

[0004] Currently, methods for measuring constellation performance, such as coverage multiple, are primarily based on fixed constellations with completed designs, where the number, positional distribution, and relative phase relationships of satellites remain largely constant. However, for constellations undergoing dynamic changes, such as dynamic increases or decreases in the number of satellites or constellation reconfigurations, there is a lack of effective evaluation methods for their impact on performance metrics like coverage multiple. Furthermore, there is a lack of systematic methods for measuring the constellation's sensitivity to changes in coverage multiple and other performance metrics caused by variations in the number of satellites. These issues urgently need to be addressed in dynamic constellation design and performance optimization. Summary of the Invention

[0005] This invention addresses the lack of effective evaluation methods for the impact of coverage multiples and other performance characteristics, as well as the sensitivity of constellations to changes in coverage multiples and other performance characteristics caused by variations in the number of satellites. It proposes a method for analyzing the performance degradation of the Giants constellation communication network based on its sensitivity to coverage multiples. The method includes:

[0006] Step 1: Statistically analyze the satellite coverage of a specified location under both step-size calculation and geometric solution calculation.

[0007] Step 2: Denote the set of satellites that have covered location D within time period T as S. Sort the different coverage time periods corresponding to the members in satellite set S in ascending order of coverage end time, and sort the members corresponding to the same coverage end time in ascending order of coverage start time.

[0008] Step 3: Determine the number of time periods for splicing based on the average coverage weight and the target average coverage weight, analyze the curve relationship between the number of satellites after weight reduction and the reduction of coverage weight in local areas, and complete the sensitivity test.

[0009] Furthermore, a preferred method is proposed, in which step one involves statistically analyzing the satellite coverage of a specified location using step-size calculations, including:

[0010] Step 1: Create a new table Table1, with the header including satellite name, start coverage time, and end coverage time;

[0011] Steps 1 and 2: For each time i, read all satellite information for the covered location D;

[0012] Step 13: For each satellite's name Name_j, check if a corresponding row already exists in the table; if it does, update the end coverage time of that row and increment it by 1; if there is no corresponding row in the table, add a new row and set the start coverage time of that row to the current time i, and also set the end coverage time to i.

[0013] Step 14: Repeat steps 12 and 13 until all satellite information at the current time has been processed, then continue to the next time i+1, until all times in the entire time period T have been processed.

[0014] Furthermore, a preferred method is proposed, wherein the satellite coverage of a specified location in step one, calculated using geometric solutions, includes:

[0015] Geometric methods are used to obtain the start and end times of coverage of a specified location by each satellite within time period T;

[0016] Choose a fixed time step and discretize the entire time period T into multiple time points, with each time point spaced at the selected time step.

[0017] At each discrete time point, check whether all satellites cover the specified location. Record the satellite coverage information for these times.

[0018] Organize the coverage information for each discrete time point into a table or data format to display the satellite coverage at each moment.

[0019] Furthermore, a preferred embodiment is proposed, wherein step two includes:

[0020] Table1 is sorted in ascending order by the end-of-coverage time column;

[0021] For tables that have already been sorted, sort them again in ascending order by the column of the first time the data was covered.

[0022] Calculate the initial average coverage weight Ca1 of location D within time period T;

[0023] Calculate the average cover weight Ca2 at location D after weight reduction;

[0024] The required reduction in average coverage weight Cc is obtained based on the initial average coverage weight and the average coverage weight.

[0025] Cc is decomposed into an integer part CcI and a fractional part CcF, where the integer part CcI represents the number of complete time periods that need to be spliced, and the fractional part CcF represents the additional weight reduction that needs to be processed.

[0026] Create Table2 with the same header as Table1 to record the concatenated result;

[0027] Initialize the reduced weight Cn = 0 to track the weight reduction that has been processed so far;

[0028] Based on CcI, select the time period in Table1 for splicing to achieve the requirement of reducing the repetition of integer parts;

[0029] For the fractional part CcF, select an appropriate time period for partial splicing to meet the weight reduction requirements.

[0030] Furthermore, a preferred method is proposed, wherein the processing flow for the integer part includes:

[0031] Step 21: Initialize the current end time. Find the last row S1 of the initial time Tb of the coverage period T in Table1. If there is no matching row, search downwards in the time order until a suitable row is found. Push row S1 onto Table2 and delete row S1 from Table1. Set Ten to the value of the end coverage time column of row S1 and proceed to Step 22.

[0032] Step 22: If Ten is greater than or equal to the end time Te of time period T, it means that enough time periods have been processed. Increment the current reduced multiplicity Cn by 1 and proceed to Step 24; if Ten is less than Te, it means that not all time periods have been processed yet, and proceed to Step 23.

[0033] Step 23: In Table 1, search for row S2 that is equal to the current end time Ten. If no matching row is found, add or subtract from Ten to search. Take S3 as the found row, push row S3 onto Table 2, and delete row S3 from Table 1. Update Ten to the value of the "end time" column of row S3, and proceed to Step 22.

[0034] Step 24: If the current reduced multiplicity Cn is greater than or equal to the integer part CcI, exit the loop and proceed to Step 5; if Cn is less than CcI, return to Step 21 to continue processing.

[0035] Step 25: Once Cn reaches or exceeds CcI, the entire process ends.

[0036] Furthermore, a preferred method is proposed, wherein the processing flow for the decimal part includes:

[0037] Step 26: Set the current end time Ten to 0. In Table1, search for the last row S1 of the initial time Tb of the coverage period. If not found, search downwards in time order and push the found row S1 onto Table2. Then delete row S1 from Table1. Update Ten to the value of the end coverage time column of row S1.

[0038] Step 27: If Ten-Tb is greater than or equal to (Te-Tb)*CcF, then end the process and proceed to Step 29; if Ten-Tb is less than (Te-Tb)*CcF, proceed to Step 28.

[0039] Step 28: Search for row S2 in the start overwrite time column of Table1 that is equal to Ten. If not found, add or subtract from Ten to search. The last row S3 of the found row S2 will be pushed onto Table2 and removed from Table1. Update Ten to the value of the end overwrite time column of row S3. Return to step 27 and continue processing.

[0040] Step 29: If the conditions are met, the process ends.

[0041] Furthermore, an optimal method is proposed, where the average coverage multiplicity at discrete time points is:

[0042]

[0043] Among them, T b To cover the start time, T e To cover the end time, δ step C is the step size for discrete operation. i For T b +i*δ step The number of times covered.

[0044] Furthermore, a preferred embodiment is proposed, wherein the average coverage weight at consecutive time intervals is:

[0045]

[0046] Where C(t) is the coverage multiplicity at time t.

[0047] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a method for analyzing the performance degradation of the Giants constellation communication based on coverage multiple sensitivity as described in any one of the preceding claims.

[0048] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of a method for analyzing the performance degradation of the Giants constellation communication with coverage multiplicity sensitivity as described in any of the preceding claims.

[0049] The advantages of this invention are:

[0050] This invention discloses a method for analyzing the performance degradation of the Giant Squadron communication system based on coverage multiplicity sensitivity. It employs both step-by-step calculation and geometric solution to statistically analyze satellite coverage at a specified location. This dual calculation method ensures the accuracy and reliability of the evaluation results, taking into account potential errors from different calculation models, thus providing a more comprehensive coverage analysis. In step two, coverage periods within the satellite set S are arranged in ascending order of end and start times. This systematic sorting simplifies data management, facilitates subsequent analysis and processing, and improves efficiency. In step three, the number of splicing periods is determined based on the average and target average coverage multiplicity. The relationship between the number of satellites after coverage multiplicity reduction and the reduction in coverage multiplicity in local areas is analyzed to complete the sensitivity test. This method allows researchers to precisely measure and adjust the performance of the constellation design to ensure it meets specific coverage requirements and performance standards. By dynamically testing the sensitivity of satellite coverage multiplicity, this method can adapt to the impact of constellation configuration changes on performance, helping designers optimize satellite constellation layout to adapt to different needs and environmental changes. The results of the sensitivity test can guide the optimization and adjustment of the satellite constellation, improving the efficiency of constellation design. By understanding the coverage multiplicity performance under different configurations, satellite resources can be allocated more effectively to maximize coverage.

[0051] Previous evaluation methods have difficulty in effectively assessing the specific impact of constellation changes on performance such as coverage density. This invention provides a systematic method for analyzing communication performance degradation.

[0052] This invention can dynamically measure the sensitivity of a constellation to changes in coverage density caused by changes in the number of satellites, filling a gap in the evaluation of this field.

[0053] This invention is applied to the field of spacecraft system simulation. Attached Figure Description

[0054] Figure 1 Flowchart of a method for analyzing the performance degradation of the Giants constellation communication system, which is sensitive to coverage multiples;

[0055] Figure 2 This is a flowchart of the statistical calculation process described in Implementation Method 2;

[0056] Figure 3 This is a flowchart of the integer part calculation as described in Implementation Method Eleven;

[0057] Figure 4 This is a flowchart of the decimal part calculation as described in Implementation Method Eleven;

[0058] Figure 5 This is a schematic diagram of the coverage multiplicity before processing as described in Implementation Method Eleven;

[0059] Figure 6 The coverage weight-time curve after the strike is reduced based on the coverage weight as described in Implementation Method Eleven. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0061] Implementation Method 1, see [link] Figure 1 This embodiment describes a method for analyzing the performance degradation of the Giants constellation communication network, which is sensitive to coverage multiples. The method includes:

[0062] Step 1: Statistically analyze the satellite coverage of a specified location under both step-size calculation and geometric solution calculation.

[0063] Step 2: Denote the set of satellites that have covered location D within time period T as S. Sort the different coverage time periods corresponding to the members in satellite set S in ascending order of "end of coverage time". Sort the members corresponding to the same end of coverage time in ascending order of "start of coverage time".

[0064] Step 3: Determine the number of time periods for splicing based on the average coverage weight and the target average coverage weight, analyze the curve relationship between the number of satellites after weight reduction and the reduction of coverage weight in local areas, and complete the sensitivity test.

[0065] The method for analyzing the degradation of communication performance in the Giants constellation, which is sensitive to coverage multiples, as described in this embodiment also includes a satellite position acquisition step, specifically:

[0066] Step 1: Based on the format of the two-line metadata record, read the parameter values ​​under different data segments and save them to the SGP4 orbital model structure variables;

[0067] Step 2: Initialize the orbit model according to the physical meaning of the SGP4 orbit model variables, and obtain the six orbital roots under the double-row metadata orbital epoch and the position and velocity under the J2000 coordinate system;

[0068] Step 3: Combine the simulation time and simulation step size to calculate the time difference between the simulation time and the orbit epoch, substitute it into the orbit model calculation function, and perform orbit recursion and data output.

[0069] In the method for analyzing the performance degradation of the Giants constellation communication system based on coverage multiplicity sensitivity described in this embodiment, step-size calculation provides quantitative coverage data, while geometric solution calculation considers the spatial characteristics of coverage. Combining these two methods enables a more accurate assessment of satellite coverage. Sort the satellite coverage time periods to facilitate systematic analysis of coverage and to easily detect trends in coverage multiplicity changes. By adjusting the number of stitched time periods to match the target average coverage multiplicity, the sensitivity test ensures that it can simulate actual conditions and evaluate system performance.

[0070] Specifically, this embodiment describes a method for analyzing the performance degradation of the Giant Saurus constellation communication system based on coverage multiple sensitivity. It uses both step-by-step calculation and geometric solution to statistically analyze satellite coverage at a specified location. This dual calculation method ensures the accuracy and reliability of the evaluation results, taking into account potential errors from different calculation models, thus providing a more comprehensive coverage analysis. In step two, coverage time slots within the satellite set S are arranged in ascending order by end and start times. This systematic sorting simplifies data management, facilitates subsequent analysis and processing, and improves efficiency. In step three, the number of splicing time slots is determined based on the average and target average coverage multiple for sensitivity testing. This method allows researchers to precisely measure and adjust the constellation design performance to ensure it meets specific coverage requirements and performance standards. By dynamically testing the sensitivity of satellite coverage multiple, this testing method can adapt to the impact of constellation configuration changes on performance, helping designers optimize satellite constellation layout to adapt to different needs and environmental changes. The results of the sensitivity test can guide the optimization and adjustment of the satellite constellation, improving the efficiency of constellation design. By understanding the coverage multiple performance under different configurations, satellite resources can be configured more effectively to maximize coverage.

[0071] Implementation Method 2, see below Figure 2 This embodiment describes a further limitation of the method for analyzing the performance degradation of the Giant Pixnet constellation communication system sensitive to coverage multiplicity described in Embodiment 1. Step one, which involves statistically analyzing the satellite coverage at a specified location using step-size calculations, includes:

[0072] Step 1: Create a new table Table1. The table header should include the satellite name (number), start coverage time, and end coverage time.

[0073] Steps 1 and 2: For each time i, read all satellite information for the covered location D;

[0074] Step 13: For each satellite's name (number) Name_j, check if a corresponding row already exists in the table; if it does, update the end coverage time of that row and increment it by 1; if there is no corresponding row in the table, add a new row and set the start coverage time of that row to the current time i, and also set the end coverage time to i.

[0075] Step 14: Repeat steps 12 and 13 until all satellite information at the current time has been processed, then continue to the next time i+1, until all times in the entire time period T have been processed.

[0076] In step one of the methods for analyzing the performance degradation of the Giants constellation communication system sensitive to coverage overlap, as described in this embodiment, a table (Table1) is used to record satellite coverage information, making data organization and management more systematic and clear. Each row records the satellite name, start and end coverage times. This structured management helps avoid data clutter and improves processing efficiency. By reading and updating the satellite coverage information at each time point i, it is ensured that the data in the table reflects the satellite status in real time. This dynamic update mechanism makes the analysis of satellite coverage more accurate and can capture changes in coverage times. In step one-three, different processing strategies (updating or adding rows) are adopted depending on whether the satellite already exists in the table. This refined recording method ensures that the coverage time of each satellite is accurately recorded and updated, thereby improving the accuracy of the test. In step one-four, all satellite information is processed and the table is updated time-by-time, making the entire data processing process orderly. This structured processing method ensures that no data at any time is missed, improving the comprehensiveness and reliability of the test. By saving satellite coverage information in a structured table format, subsequent data analysis and sensitivity testing will be more convenient. Coverage time data can be retrieved and processed quickly, improving analysis efficiency.

[0077] Implementation Method 3: This implementation method further defines the method for analyzing the performance degradation of the Giant Pixnet constellation communication system sensitive to coverage multiplicity described in Implementation Method 1. The satellite coverage situation at a specified location calculated using geometric solutions in step one includes:

[0078] Geometric methods are used to obtain the start and end times of coverage of a specified location by each satellite within time period T;

[0079] Choose a fixed time step and discretize the entire time period T into multiple time points, with each time point spaced at the selected time step.

[0080] At each discrete point in time, check whether all satellites cover the specified location and record the satellite coverage at these times;

[0081] Organize the coverage information for each discrete time point into a table or data format to display the satellite coverage at each moment.

[0082] This embodiment describes a method for analyzing the degradation of communication performance in the Giants constellation, which is sensitive to coverage multiplicity. It calculates satellite coverage times using a geometric method, providing high accuracy. The calculation, based on satellite orbit and viewing angle, avoids errors caused by simple time stepping. The fixed-time-step discretization method allows for more detailed and continuous recording of coverage information, facilitating the analysis of dynamic changes in satellite coverage. Organizing coverage information at each time point into tables or data formats helps in the systematic management and viewing of satellite coverage, facilitating subsequent data analysis and processing. Records at each discrete time point allow for accurate monitoring of coverage multiplicity changes throughout the entire time period, providing real-time data support. The clear table or data format presents coverage information, making data analysis more intuitive and enabling rapid identification and evaluation of satellite coverage performance.

[0083] Implementation Method Four: This implementation method further defines the performance degradation analysis method for the Giant Scorpius communication system sensitive to coverage multiples described in Implementation Method One. Step two includes:

[0084] Table1 is sorted in ascending order by the end-of-coverage time column;

[0085] For tables that have already been sorted, sort them again in ascending order by the column of the first time the data was covered.

[0086] Calculate the initial average coverage weight Ca1 of location D within time period T;

[0087] Calculate the average cover weight Ca2 at location D after weight reduction;

[0088] The required reduction in average coverage weight Cc is obtained based on the initial average coverage weight and the average coverage weight.

[0089] Cc is decomposed into an integer part CcI and a fractional part CcF, where the integer part CcI represents the number of complete time periods that need to be spliced, and the fractional part CcF represents the additional weight reduction that needs to be processed.

[0090] Create Table2 with the same header as Table1 to record the concatenated result;

[0091] Initialize the reduced weight Cn = 0 to track the weight reduction that has been processed so far;

[0092] Based on CcI, select the time period in Table1 for splicing to achieve the requirement of reducing the repetition of integer parts;

[0093] For the fractional part CcF, select an appropriate time period for partial splicing to meet the weight reduction requirements.

[0094] The appropriate time period mentioned in this embodiment can be a time period with a coverage multiple close to CcF. The time period with the coverage multiple closest to CcF is selected to reduce interference to other time periods during splicing.

[0095] The appropriate time period mentioned in this embodiment can be a time period with high splicing flexibility. Priority should be given to time periods that are easy to split or adjust, so as to achieve accurate weight reduction without affecting the overall coverage.

[0096] The appropriate time period mentioned in this embodiment can be a continuous or overlapping time period. If multiple time periods overlap or are close, these time periods can be spliced ​​together to more easily achieve the desired weight reduction.

[0097] The appropriate time period mentioned in this embodiment can be the time period with the least impact. Select the time period with the least impact on other coverage conditions for partial splicing in order to avoid causing significant interference to the overall coverage quality.

[0098] Specifically, in step two of the method for analyzing the performance degradation of the Giants constellation communication system sensitive to coverage weight, as described in this embodiment, Table 1 is arranged in ascending order by the end and start times of coverage to ensure the sequentiality and logic of data processing. This sorting avoids omissions and confusion, improving the accuracy of data processing. Calculating the initial average coverage weight (Ca1) and the average coverage weight after weight reduction (Ca2) for location D provides a clear benchmark for assessing weight reduction requirements. This step ensures a clear quantitative analysis of changes in coverage weight. Decomposing the average weight reduction (Cc) to be reduced into an integer part (CcI) and a fractional part (CcF) allows for more refined weight reduction target setting. The integer part represents the complete time period to be processed, while the fractional part provides a more precise weight reduction strategy for additional weight reduction. The weight reduction requirement for the integer part is achieved by selecting and splicing time periods from Table 1, and the weight reduction for the fractional part is handled by partially splicing appropriate time periods. This method makes the weight reduction process more efficient and flexible, allowing for appropriate processing measures to be taken for different weight reduction requirements. Create Table2 with the same header as Table1 to record the concatenated results, ensuring clear recording and archiving of the processed data. This step facilitates detailed result analysis and verification after the weight reduction process. Initialize and use the reduced weight count (Cn) to track the weight reduction already processed, which helps in real-time monitoring and adjustment of the weight reduction strategy, ensuring the achievement of weight reduction goals.

[0099] Implementation Method 5: This implementation method further defines the performance degradation analysis method for the Giant Scorpius constellation communication system sensitive to coverage multiples described in Implementation Method 4. The processing flow for the integer part includes:

[0100] Step 21: Initialize the current end time. Find the last row S1 of the initial time Tb of the coverage period T in Table1. If there is no matching row, search downwards in the time order until a suitable row is found. Push row S1 onto Table2 and delete row S1 from Table1. Set Ten to the value of the end coverage time column of row S1 and proceed to Step 22.

[0101] Step 22: If Ten is greater than or equal to the end time Te of time period T, it means that enough time periods have been processed. Increment the current reduced multiplicity Cn by 1 and proceed to Step 24; if Ten is less than Te, it means that not all time periods have been processed yet, and proceed to Step 23.

[0102] Step 23: In Table 1, search for row S2 that is equal to the current end time Ten. If no matching row is found, add or subtract from Ten to search. Take S3 as the found row, push row S3 onto Table 2, and delete row S3 from Table 1. Update Ten to the value of the "end time" column of row S3, and proceed to Step 22.

[0103] Step 24: If the current reduced multiplicity Cn is greater than or equal to the integer part CcI, exit the loop and proceed to Step 5; if Cn is less than CcI, return to Step 21 to continue processing.

[0104] Step 25: Once Cn reaches or exceeds CcI, the entire process ends.

[0105] This implementation method is described in conjunction with Implementation Method 4. This implementation method ensures that each processed period accurately matches the actual coverage by dynamically searching for the start and end rows (S1, S2, S3) of the covered time period in Table1. By progressively searching for matching rows and ensuring that each covered time period is accurately processed, the possibility of missed processing is reduced. Through the progressive processing in steps two-one to two-three, the high time complexity of a full table scan is avoided, improving processing speed. Dynamically updating the data in the table during processing (e.g., deleting processed rows) reduces redundant calculations and improves efficiency.

[0106] Specifically, in the integer part processing flow described in this embodiment, steps two-one to two-three process time periods through precise matching and sorting, avoiding omissions or duplications and improving the accuracy and completeness of data processing. Furthermore, by continuously updating the end time Ten, the time periods in Table 1 can be dynamically adapted, ensuring that the processed time periods always match the current requirements, enhancing processing flexibility. By checking the relationship between Ten and the end time Te, and by real-time tracking of the reduced weight Cn, the weight reduction can be precisely controlled, avoiding excessive or insufficient weight reduction. The judgment in step two-four and the looping process returning to step two-one allow the process to quickly end after the target is met, reducing unnecessary processing steps and improving efficiency. Each step has clearly defined processing logic and data flow, ensuring the orderly progress of the entire weight reduction process and guaranteeing the reliability and repeatability of the results.

[0107] Implementation Method Six: This implementation method further defines the performance degradation analysis method for the Giant Scorpius constellation communication system sensitive to coverage multiples described in Implementation Method Four. The processing flow for the fractional part includes:

[0108] Step 26: Set the current end time Ten to 0. In Table1, search for the last row S1 of the initial time Tb of the coverage period. If not found, search downwards in time order and push the found row S1 onto Table2. Then delete row S1 from Table1. Update Ten to the value of the end coverage time column of row S1.

[0109] Step 27: If Ten-Tb is greater than or equal to (Te-Tb)*CcF, then end the process and proceed to Step 29; if Ten-Tb is less than (Te-Tb)*CcF, proceed to Step 28.

[0110] Step 28: Search for row S2 that equals Ten in the start overwrite time column of Table1. If not found, add or subtract from Ten to search. Push the last row S3 of the found row S2 onto Table2 and delete row S3 from Table1. Update Ten to the value of the end overwrite time column of row S3. Return to step 27 and continue processing.

[0111] Step 29: If the conditions are met, the process ends.

[0112] In the decimal part processing flow described in this embodiment, the accuracy of the coverage overlap sensitivity test is ensured by progressively updating Ten and comparing the initial and end times of the coverage period. The method allows adjustments based on the actual time sequence, thus maintaining the reliability of test results under different dynamic conditions. Using Tables 1 and 2 to manage the coverage period data provides a clear and structured workflow, helping to ensure the orderliness and accuracy of data processing. The conditional judgments and dynamic lookup mechanisms involved in the steps effectively handle overlaps between different coverage periods, improving processing efficiency.

[0113] By dynamically processing coverage periods (such as searching, stacking, and deleting rows), this method can flexibly handle different coverage time variations and adapt to complex constellation dynamics. Setting the end time Ten and adjusting Ten for precise coverage period filtering enables the test to effectively capture coverage information in key periods, ensuring the efficiency of coverage detection.

[0114] By progressively checking and dynamically updating the contents of Tables 1 and 2, it is possible to ensure that no important coverage periods are missed, while effectively handling the sensitivity issue of coverage overlap. The checking conditions and dynamic adjustment mechanism in the method make this testing method highly scalable and applicable to constellation dynamic change testing of different scales and complexities.

[0115] Implementation Method Seven: This implementation method further defines the performance degradation analysis method for the Giants constellation communication system sensitive to coverage multiplicity described in Implementation Method Four. The average coverage multiplicity at discrete times is:

[0116]

[0117] Among them, T b To cover the start time, T e To cover the end time, δ step C is the step size for discrete operation. i For T b +i*δ step The number of times covered.

[0118] In this embodiment, the average coverage multiplicity at discrete moments considers the start and end times of coverage, as well as the step size, making the calculation of coverage multiplicity more accurate and comprehensive. By setting the discrete running step size, the changes in coverage multiplicity at each moment can be analyzed in detail, thereby improving the sensitivity testing capability to constellation dynamic changes. This method systematically integrates coverage moment and multiplicity information, which facilitates in-depth coverage multiplicity analysis and optimizes the structure of the testing process. Considering the characteristics of constellation dynamic changes ensures that the test results can reflect the coverage situation in actual applications, improving the practical validity of the results.

[0119] Implementation Method Eight: This implementation method further defines the performance degradation analysis method for the Giants constellation communication system sensitive to coverage multiples described in Implementation Method Four. The average coverage multiple at consecutive times is:

[0120]

[0121] Where C(t) is the coverage multiplicity at time t.

[0122] Implementation Method Nine: A computer device according to this implementation method includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a method for analyzing the performance degradation of the Giants constellation communication based on coverage multiple sensitivity, as described in any one of Implementation Methods One to Eight.

[0123] Implementation Method 10: A computer-readable storage medium according to this implementation method, wherein a computer program is stored on the computer program, and the computer program is executed by a processor to perform the steps of a method for analyzing the degradation of the communication performance of the Giants constellation with coverage multiple sensitivity as described in any one of Implementation Methods 1 to 8.

[0124] Implementation Method 11, see below Figures 3 to 6This embodiment describes a specific example of the performance degradation analysis method for the Giants constellation communication system sensitive to coverage multiples, as described in Embodiment 1. It also serves to explain Embodiments 2 through 8. Specifically:

[0125] This implementation method achieves deduplication of a specified location D by statistically analyzing and stitching together the satellite coverage of the source and destination nodes within a certain time period T, thereby weakening the communication capability. Simultaneously, it performs sensitivity testing. The testing method specifically includes:

[0126] Obtain the satellite's location, including:

[0127] Step 1: Based on the format of the two-line metadata record, read the parameter values ​​under different data segments and save them to the SGP4 orbital model structure variables;

[0128] Step 2: Initialize the orbit model according to the physical meaning of the SGP4 orbit model variables, and obtain the six orbital roots under the double-row metadata orbital epoch and the position and velocity under the J2000 coordinate system;

[0129] Step 3: Combine the simulation time and simulation step size to calculate the time difference between the simulation time and the orbit epoch, substitute it into the orbit model calculation function, and perform orbit recursion and data output.

[0130] Statistical satellite coverage includes:

[0131] 1. For the step-by-step calculation method, a series of time points are obtained to show the satellite coverage of a specified location, including the number and name (number) of available satellite resources at the specified location D at each time point. The average coverage weight Ca1 of the location within the time period can be obtained. The start and end times of satellite coverage at location D are obtained in chronological order. The specific execution process is as follows: Figure 2 As shown, it includes:

[0132] The newly created header Table1 includes: satellite name (number), start coverage time, and end coverage time.

[0133] Read the name (number) of the j-th satellite at the coverage location D at time i;

[0134] If the table contains Name_j, find the Name_j row and increment the end-coverage time of that row by one step, i.e., increment the simulation step number corresponding to the end-coverage time by 1; if the table does not contain the Name_j row, add a new Name_j row, set the start-coverage time of that row to i, and set the end-coverage time of that row to i.

[0135] Continue until j reaches the coverage multiple of the current time, then proceed to the next time i+1; continue until i reaches the end time of time period T.

[0136] 2. For the geometric solution method, the start and end times of satellite coverage within time period T can be directly obtained. The time can be discretized according to a certain step size to obtain a series of times for satellite coverage of a specified location, and the above steps can be performed.

[0137] Time period splicing, including:

[0138] Considering that the weight reduction of target location D needs to be uniform within a specified time period T, the set of satellites that have covered location D within time period T is called S. This implementation method selects different coverage time periods corresponding to members in S according to certain rules to meet the requirement of uniform weight reduction. The specific steps are as follows:

[0139] For the statistically obtained Table1, sort the elements in the table in ascending order by the "End Coverage Time" column, and then sort the elements in the table in ascending order by the "Start Coverage Time" column for easy splicing and use;

[0140] Based on the weight reduction requirements, the average coverage weight Ca2 of location D within time period T after weight reduction is obtained. Then, the average weight reduction Cc = Ca1 - Ca2, which represents the number of time periods to be spliced, can be calculated. Since Cc is generally not an integer, it needs to be decomposed into an integer part CcI and a fractional part CcF, where Cc = CcI + CcF, and then spliced ​​separately.

[0141] Generate a table with the same header as Table1, Table2, and set the current reduced multiplicity Cn = 0;

[0142] The calculation process for the integer part and the calculation process for the decimal part are as follows: Figure 3 and Figure 4 As shown:

[0143] The integer part calculation process includes:

[0144]

[0145] In this embodiment, 'e' is a preset, fixed value (which can be minutes, seconds, or other units) to allow for some flexibility during the search process.

[0146] Specifically, if S2 is not found in Table1 that is exactly equal to the current end time Ten, then this range e can be added to and subtracted from Ten to find possible rows.

[0147] In this embodiment, using range e can improve the flexibility of the search, especially when the data is not accurate enough or there may be slight errors at the time period boundaries. This ensures that even with some time error, a suitable covering row can be found when processing time periods.

[0148] The decimal part calculation process includes:

[0149]

[0150] The average coverage multiplicity at discrete times is calculated using the following formula:

[0151]

[0152] The average coverage multiple over consecutive time periods is calculated using the following formula:

[0153]

[0154] In the formula, T b To cover the start time, T e To cover the end time, δ step T is the step size for discrete operation. e -T b For δ step multiples of C i For T b +i*δ step The coverage multiplicity at time t, C(t) is the coverage multiplicity at time t.

[0155] To verify the testing method proposed in this embodiment, this embodiment also imports the two-line metadata of the STARLINK constellation as of September 18, 2023, totaling 4729 satellites.

[0156] To satisfy the requirements of adjacent connectivity for co-orbital links and position-velocity constraints for inter-orbital links, this embodiment divides satellites within the same orbital plane into the same domain. Based on the current Walker configuration of the XL constellation, there are 194 orbital planes, which can be divided into 194 domains. The number of domains contained in each sub-constellation is shown in Table 1 below.

[0157] Table 1XL Communication Domain Division

[0158]

[0159]

[0160] The simulation process uses the following simplified assumptions and inferences:

[0161] Simplified Assumption 1: All nodes are permitted to connect to nodes in other domains. Since the inter-orbit forwarding settings of the XL constellation are unknown, to obtain all theoretically available links, it is assumed that there is a possibility of establishing links between inter-orbit satellites; that is, a node within each domain may establish a link with a node in an adjacent different domain. Therefore, for any given node, it is assumed that it can act as a gateway and possesses the "permission" to connect to nodes outside its domain.

[0162] Considering that there is no need for a quantitative description of the bandwidth and throughput of the XL constellation during the link degradation simulation, but rather a qualitative requirement to reduce the throughput to 1 / 10 of its original value, the bandwidth settings for the XL constellation do not use the actual uplink and downlink bandwidth of the XL. In this link simulation calculation, the bandwidth of the XL satellites is uniformly set to 10Mb / s.

[0163] Simplifying Assumption 2: Consistent Node Bandwidth. We assume all nodes have the same bandwidth, and do not consider bandwidth differences between satellites.

[0164] Corollary 2-1: All available links between the source and destination nodes intersect only at the source and destination nodes; other nodes do not overlap. To achieve the maximum transmission rate, when a link is established, the transmission rate of each node on that link is equal to its bandwidth. Therefore, when other available links between the two locations are established simultaneously, the nodes on that link are unavailable due to full-rate transmission. Ultimately, this makes the available links independent of each other, presenting as a cluster of curves starting from the source node and ending at the destination node, with no intersections between them. For ease of description, the concept of "number of available links" will be used instead of "throughput" in the following description.

[0165] Corollary 2-2: The number of available links does not exceed the minimum number of available nodes in the source and destination nodes. Based on the above assumption, since no two available links intersect, it can be further deduced that the number of available links n must be less than the number of nodes n that can connect to the source node. source The number of nodes n that can be connected to the target node dest The minimum value of ination is expressed as:

[0166] n≤min{n source n destination}

[0167] Therefore, under this assumption, the number of available links is constrained by the source and destination coverage multiples.

[0168] The simulation conditions are shown in Table 2 below:

[0169] Table 2 Simulation Condition Details

[0170]

[0171]

[0172] like Figure 5 and Figure 6As shown, under normal circumstances, the average coverage weight remains around 23. By statistically analyzing the coverage of the area during this time period, it was determined that 157 satellites cover the key areas. To maintain the coverage weight within 1 / 10 of the original coverage weight throughout the entire time period, the coverage time periods of each satellite were statistically analyzed, and the time periods were stitched together to ensure that the reduction in coverage weight remained uniform over time. After processing using this method, the same calculations yielded the processed coverage weight variation curve.

[0173] The advantages of the testing method described in this embodiment are:

[0174] 1. It can disregard object configuration, making it simple, universal, intuitive, and effective;

[0175] 2. The task requirements can be met by limiting the depth of deletion, thus reducing the impact on other links;

[0176] 3. Considering the impact of deleting satellite nodes on link connectivity throughout the entire time period, ensure that the link remains largely uninterrupted during the set time period.

[0177] Although preferred embodiments of this disclosure have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this disclosure.

[0178] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

[0179] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the published pending claims.

Claims

1. A method for analyzing the performance degradation of the Giants constellation communication system due to its coverage multiplicity sensitivity, characterized in that, The method includes: Step 1: Statistically analyze the satellite coverage of a specified location under both step-size calculation and geometric solution calculation. Step 2: Denote the set of satellites that have covered location D within time period T as S. Sort the different coverage time periods corresponding to the members in satellite set S in ascending order of coverage end time, and sort the members corresponding to the same coverage end time in ascending order of coverage start time. Step 3: Determine the number of time periods for splicing based on the average coverage weight and the target average coverage weight, analyze the curve relationship between the number of satellites after weight reduction and the reduction of coverage weight in local areas, and complete the sensitivity test; The number of time periods to be stitched is determined based on the average coverage weight and the target average coverage weight, specifically including: Calculate the initial average coverage weight Ca1 of location D within time period T; Calculate the average cover weight Ca2 at location D after weight reduction; The required reduction in average coverage weight Cc is obtained based on the initial average coverage weight and the average coverage weight. Cc is decomposed into an integer part CcI and a fractional part CcF, where CcI represents the number of complete time segments that need to be spliced, and CcF represents the additional weight reduction that needs to be processed. Create Table2 with the same header as Table1 to record the concatenated result; Initialize the reduced weight Cn=0 to track the weight reduction that has been processed so far; Based on CcI, select the time period in Table1 for splicing to achieve the requirement of reducing the repetition of integer parts; For the fractional part CcF, select an appropriate time period for partial splicing to meet the weight reduction requirements; The processing flow for the integer part includes: Step A: Initialize the current end time. Find the last row S1 of the initial time Tb of the coverage period T in Table1. If there is no matching row, search downwards according to the time order until a suitable row is found. Push row S1 onto Table2 and delete row S1 from Table1. Set Ten to the value of the end coverage time column of row S1 and proceed to step B. Step B: If Ten ≥ the end time Te of time period T, it means that enough time periods have been processed. Increase the current reduced multiplicity Cn by 1 and proceed to step D; if Ten is less than Te, it means that not all time periods have been processed yet. Proceed to step C. Step C: Search for row S2 in the start overwrite time column of Table1 that is equal to the current end time Ten. If no matching row is found, add or subtract from Ten to search. Take S3 as the found row, push row S3 onto Table2, and delete row S3 from Table1. Update Ten to the value of the end overwrite time column of row S3, and proceed to step B. Step D: If the multiplicity Cn has been reduced to ≥ the integer part CcI, exit the loop and proceed to step E; If Cn < CcI, return to step A to continue processing; Step E: Once Cn reaches or exceeds CcI, the process ends; The decimal part processing procedure includes: Step F: Set the current end time Ten to 0. In Table1, find the last row S1 of the initial time Tb of the coverage period. If not found, search downwards in time order and push the found row S1 onto Table2. Then delete row S1 from Table1. Update Ten to the value of the end coverage time column of row S1. Step G: If Ten-Tb is greater than or equal to (Te-Tb)*CcF, then the process ends and proceeds to step I; if Ten-Tb < (Te-Tb)*CcF, proceed to step H. Step H: Search for row S2 that is equal to Ten in the starting overwrite time column of Table1. If not found, add or subtract from Ten to search. The last row S3 of the found row S2 will be pushed onto Table2 and deleted from Table1. Update Ten to the value of the end overwrite time column in row S3; return to step G to continue processing; Step 1: If the conditions are met, the process will end.

2. The method for analyzing the performance degradation of the Giants constellation communication system based on coverage multiplicity sensitivity as described in claim 1, characterized in that, Step one, which involves calculating satellite coverage at a specified location using step-size increments, includes: Step 1: Create a new table Table1, with the header including satellite name, start coverage time, and end coverage time; Steps 1 and 2: For each time i, read all satellite information for the covered location D; Step 13: For each satellite's name Name_j, check if a corresponding row already exists in the table; if it does, update the end coverage time of that row and increment it by 1; if there is no corresponding row in the table, add a new row and set the start coverage time of that row to the current time i, and also set the end coverage time to i. Step 14: Repeat steps 12 and 13 until all satellite information at the current time has been processed, then continue to the next time i+1, until all times in the entire time period T have been processed.

3. The method for analyzing the performance degradation of the Giants constellation communication system based on coverage multiplicity sensitivity as described in claim 1, characterized in that, The satellite coverage of the specified location calculated using geometric solutions in step one includes: Geometric methods are used to obtain the start and end times of coverage of a specified location by each satellite within time period T; Choose a fixed time step and discretize the entire time period T into multiple time points, with each time point spaced at the selected time step. At each discrete point in time, check whether all satellites cover the specified location and record the satellite coverage at these times; Organize the coverage information for each discrete time point into a table or data format to display the satellite coverage at each moment.

4. The method for analyzing the performance degradation of the Giants constellation communication system based on coverage multiplicity sensitivity as described in claim 1, characterized in that, Step two includes: Table1 is sorted in ascending order by the end-of-coverage time column; For tables that have already been sorted, sort them again in ascending order by the time column where the coverage began.

5. The method for analyzing the performance degradation of the Giants constellation communication system based on coverage multiplicity sensitivity as described in claim 3, characterized in that, The average coverage multiplicity at discrete time points is: in, To cover the start time, To cover the end time, The step size for discrete operation. for The number of times covered.

6. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a method for analyzing the performance degradation of the Giants constellation communication based on coverage multiplicity sensitivity as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a method for analyzing the performance degradation of the Giants constellation communication system as described in any one of claims 1-5.

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