A comprehensive assessment method for collision risk of low-orbiting giant stars

Abstract

The invention belongs to the field of aerospace technology, and relates to a comprehensive assessment method for collision risk of a low-orbit supergiant constellation. The method comprises the following steps: standardizing parameters of different dimensions based on fuzzy set theory, and constructing a satellite data factor set with a unified unit; performing hierarchical decomposition on each indicator based on a hierarchical analysis method, and determining the indicator weight; performing fuzzy evaluation on each single indicator, and constructing a fuzzy evaluation matrix and an evaluation set; integrating the fuzzy evaluation results of multiple single factors into a comprehensive evaluation result, and establishing a comprehensive evaluation model; using the established comprehensive evaluation model, performing an overall assessment on the collision analysis of each satellite, and obtaining a collision warning comprehensive score; the invention combines multiple satellite collision risk parameters into a single risk measurement value through a fuzzy comprehensive evaluation method, thereby improving the scientificity and reliability of the assessment result, and providing a systematic and comprehensive assessment framework for collision warning of a low-orbit supergiant constellation.

Description

Technical Field

[0001] The present invention relates to the field of satellite control technology, and in particular to a method for comprehensively assessing collision risks of low-orbit giant stars. Background Art

[0002] With the rapid increase in the number of low-orbit giant satellites, the Earth's low-orbit space is becoming increasingly crowded, and the risk of collision has increased significantly. At present, risk assessment usually uses indicators such as minimum approach distance and collision probability to determine the risk of collision. It is difficult to fully consider multiple uncertain factors and complex system associations, resulting in limited reliability and accuracy of the results. There is an urgent need for more scientific and effective risk assessment methods for monitoring and management.

[0003] Based on this, a study was conducted on the collision risk assessment of low-orbit giant stars, and a fuzzy comprehensive evaluation method was proposed. Through quantitative indicator weights and fuzzy comprehensive evaluation, multiple parameters were combined into a single risk measurement value, thereby achieving a comprehensive and accurate assessment of satellite collision risks, providing clear and quantitative early warning information and scientific basis, and assisting decision makers in formulating effective prevention and intervention measures to ensure the safety of satellite operations and the sustainable development of the space environment. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a comprehensive assessment method for the collision risk of a low-orbit giant star.

[0005] In order to achieve the purpose of the present invention, the present invention will be implemented by adopting the following technical solutions.

[0006] A comprehensive assessment method for low-orbit giant star collision risk comprises the following steps:

[0007] S101. Standardize parameters of different dimensions based on fuzzy set theory and construct a multi-dimensional satellite data factor set with unified units.

[0008] S102, based on the hierarchical analysis method, perform hierarchical decomposition on each indicator and determine the indicator weight;

[0009] S103, perform fuzzy evaluation on each single indicator, and construct a fuzzy evaluation matrix and an evaluation set;

[0010] S104, integrating the fuzzy evaluation results of multiple single factors into a comprehensive evaluation result, and establishing a comprehensive evaluation model;

[0011] S105, using the comprehensive evaluation model, conduct an overall evaluation of the collision analysis of each satellite to obtain a comprehensive collision warning score.

[0012] As a further solution of the present invention, the process of constructing the satellite data factor set includes the following steps:

[0013] S1011. Collect relevant data affecting satellite collision risk;

[0014] S1012. Clean and preprocess the collected relevant data to remove noise and erroneous data, fill in missing values, and ensure the integrity and consistency of the relevant data;

[0015] S1013. Calculate three factors affecting satellite collision risk based on the orbit prediction model and the collision probability model: approach distance, collision probability and confidence level;

[0016] S1014. Standardize the factors according to their characteristics to eliminate the differences in dimensions between different factors so that the data can be analyzed and compared on the same scale;

[0017] A1015. Based on the standardized post-processed data, a multi-dimensional satellite data factor set is constructed, where each factor corresponds to one dimension.

[0018] As a further solution of the present invention, the relevant data includes the satellite's position, speed, attitude, forecast time and collision probability model scaling factor.

[0019] As a preferred solution of the present invention, the process of determining the indicator weights includes the following steps:

[0020] S1021, determining the comprehensive risk assessment value as the target layer, setting the three factors affecting the satellite collision risk as the criterion layer, and establishing a hierarchical model;

[0021] S1022. For each pair of factors in the criterion layer, compare them pairwise according to their importance to the target, quantify their relative importance, and construct a judgment matrix;

[0022] S1023, calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, normalizing the eigenvector, and obtaining the weight of each factor;

[0023] S1024, performing consistency check to ensure that the consistency of the judgment matrix is ​​within an acceptable range;

[0024] If the consistency test fails, the judgment matrix is ​​adjusted;

[0025] If the consistency check is passed, the weights are aggregated to form the final indicator weight set.

[0026] As a preferred embodiment of the present invention, the consistency verification process includes the following steps:

[0027] (1) Calculate the consistency index of the judgment matrix Where n is the order of the judgment matrix;

[0028] (2) According to the order of the judgment matrix, the random consistency index RI is obtained by looking up the table;

[0029] (3) Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the matrix is ​​judged to be consistent and passes the consistency test. If CR ≥ 0.1, the matrix is ​​judged to be inconsistent and fails the consistency test.

[0030] As a preferred solution of the present invention, the adjustment process of the judgment matrix includes the following steps:

[0031] (1) Perform logarithmic transformation on the judgment matrix: b ij =loga ij ;

[0032] (2) Calculate the mean of the logarithmic matrix:

[0033] (3) Perform antilogarithmic transformation based on the mean of the logarithmic matrix to restore the original scale and obtain a new judgment matrix:

[0034] As a preferred solution of the present invention, the process of constructing the fuzzy evaluation matrix and the evaluation set includes the following steps:

[0035] (1) Construct a set of all evaluation results to represent different risk levels. Each evaluation indicator is divided into three levels according to the risk level: dangerous, general, and safe;

[0036] (2) For each single indicator, a fuzzy evaluation matrix is ​​constructed according to its different value ranges and the degree of influence on the collision risk. The elements in the fuzzy evaluation matrix represent the degree of membership of each indicator at different evaluation levels.

[0037] As a preferred embodiment of the present invention, the process of establishing the comprehensive evaluation model includes the following steps:

[0038] (1) Obtain the fuzzy evaluation matrix and corresponding weight vector of each indicator;

[0039] (2) The fuzzy matrix of single factor evaluation is weighted and synthesized according to the indicator weight set;

[0040] (3) Establish a comprehensive evaluation model based on the fuzzy synthesis results.

[0041] As a further solution of the present invention, the application method of the collision warning comprehensive score comprises the following steps:

[0042] (1) Sort the comprehensive collision warning scores of all satellites and identify high-risk satellites and high-risk collision events;

[0043] (2) Based on the comprehensive collision warning score, formulate corresponding prevention and intervention measures, and focus on monitoring and adjusting high-risk satellites.

[0044] Beneficial Effects

[0045] This method introduces fuzzy set theory and hierarchical analysis method, comprehensively considers multiple influencing factors, quantifies indicator weights, and combines multiple parameters into a single risk measurement value through fuzzy comprehensive evaluation, thereby achieving a comprehensive, scientific and accurate assessment of satellite collision risks; this not only improves the accuracy of risk prediction, but also provides a solid basis for timely warning and effective management, ensuring the safety and sustainable development of space activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of monitoring the collision risk of low-orbit giant stars according to the present invention;

[0047] Figure 2 This is a flow chart of the comprehensive assessment method for the collision risk of low-orbit giant stars according to the present invention;

[0048] Figure 3 A flowchart of the process of constructing the multi-dimensional satellite data factor set of the present invention;

[0049] Figure 4 The figure is a flow chart of the indicator weight determination process of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0051] like Figure 1 As shown, due to the rapid increase in the number of low-orbit giant satellites, the congestion of the Earth's low-orbit space is becoming increasingly severe, and the risk of collision has increased significantly. It is difficult to fully consider multiple uncertain factors and complex system associations by using indicators such as minimum approach distance or collision probability to determine the collision risk, which limits the reliability and accuracy of the results. Therefore, there is an urgent need for more scientific and effective risk assessment methods for monitoring and management.

[0052] As an embodiment of the present invention, Figure 2 As shown, a comprehensive assessment method for low-orbit giant star collision risk includes the following steps:

[0053] S101. Standardize parameters of different dimensions based on fuzzy set theory and construct a multi-dimensional satellite data factor set with unified units.

[0054] S102, based on the hierarchical analysis method, perform hierarchical decomposition on each indicator and determine the indicator weight;

[0055] S103, perform fuzzy evaluation on each single indicator, and construct a fuzzy evaluation matrix and an evaluation set;

[0056] S104, integrating the fuzzy evaluation results of multiple single factors into a comprehensive evaluation result, and establishing a comprehensive evaluation model;

[0057] S105, using the comprehensive evaluation model, conduct an overall evaluation of the collision analysis of each satellite to obtain a comprehensive collision warning score.

[0058] As an embodiment of the present invention, Figure 3 As shown, the process of constructing the satellite data factor set includes the following steps:

[0059] S1011. Collect relevant data affecting satellite collision risk;

[0060] S1012. Clean and preprocess the collected relevant data to remove noise and erroneous data, fill in missing values, and ensure the integrity and consistency of the relevant data;

[0061] S1013. Calculate three factors affecting satellite collision risk based on the orbit prediction model and the collision probability model: approach distance, collision probability and confidence level;

[0062] S1014. Standardize the factors according to their characteristics to eliminate the differences in dimensions between different factors so that the data can be analyzed and compared on the same scale;

[0063] A1015. Based on the standardized post-processed data, a multi-dimensional satellite data factor set is constructed, where each factor corresponds to one dimension.

[0064] The orbit prediction model is based on a neural network model for orbit prediction, taking the orbit prediction error as the research object, and taking the error between the satellite's true orbit and the dynamics and dynamics prediction as the learning data set. The purpose of training is to use the neural network to learn the law of error data, establish an error model corresponding to the prediction model, and then use the neural network to compensate for the results of the dynamics prediction, thereby improving the accuracy of the pure dynamics prediction.

[0065] Collision probability model: In the collision coordinate system, the volumes of satellite 1 and satellite 2 are concentrated on satellite 1 to form a joint sphere R at the origin of the coordinate system, and the position uncertainty of satellite 1 and satellite 2 is concentrated on satellite 2 to form a joint error ellipsoid distribution P. The collision probability can be obtained by integrating the volume of the probability density function in the joint sphere.

[0066] As an embodiment of the present invention, Figure 4 As shown, the process of determining the indicator weight includes the following steps:

[0067] S1021, determining the comprehensive risk assessment value as the target layer, setting the three factors affecting the satellite collision risk as the criterion layer, and establishing a hierarchical model;

[0068] S1022. For each pair of factors in the criterion layer, compare them pairwise according to their importance to the target, quantify their relative importance, and construct a judgment matrix;

[0069] S1023, calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, normalizing the eigenvector, and obtaining the weight of each factor;

[0070] S1024, performing consistency check to ensure that the consistency of the judgment matrix is ​​within an acceptable range;

[0071] If the consistency test fails, the judgment matrix is ​​adjusted;

[0072] If the consistency check is passed, the weights are aggregated to form the final indicator weight set.

[0073] As an embodiment of the present invention, the consistency verification process includes the following steps:

[0074] (1) Calculate the consistency index of the judgment matrix Where n is the order of the judgment matrix;

[0075] (2) According to the order of the judgment matrix, the random consistency index RI is obtained by looking up the table;

[0076] (3) Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the matrix is ​​judged to be consistent and passes the consistency test. If CR ≥ 0.1, the matrix is ​​judged to be inconsistent and fails the consistency test.

[0077] As an embodiment of the present invention, the adjustment process of the judgment matrix includes the following steps:

[0078] (1) Perform logarithmic transformation on the judgment matrix: b ij =loga ij ;

[0079] (2) Calculate the mean of the logarithmic matrix:

[0080] (3) Perform antilogarithmic transformation based on the mean of the logarithmic matrix to restore the original scale and obtain a new judgment matrix:

[0081] As an embodiment of the present invention, the construction process of the fuzzy evaluation matrix and the evaluation set includes the following steps:

[0082] (1) Construct a set of all evaluation results to represent different risk levels. Each evaluation indicator is divided into three levels according to the risk level: dangerous, general, and safe;

[0083] (2) For each single indicator, a fuzzy evaluation matrix is ​​constructed according to its different value ranges and the degree of influence on the collision risk. The elements in the fuzzy evaluation matrix represent the degree of membership of each indicator at different evaluation levels.

[0084] As an embodiment of the present invention, the process of establishing the comprehensive evaluation model includes the following steps:

[0085] (1) Obtain the fuzzy evaluation matrix and corresponding weight vector of each indicator;

[0086] (2) The fuzzy matrix of single factor evaluation is weighted and synthesized according to the indicator weight set;

[0087] (3) Establish a comprehensive evaluation model based on the fuzzy synthesis results.

[0088] As an embodiment of the present invention, the application method of the collision warning comprehensive scoring includes the following steps:

[0089] (1) Sort the comprehensive collision warning scores of all satellites and identify high-risk satellites and high-risk collision events;

[0090] (2) Based on the comprehensive collision warning score, formulate corresponding prevention and intervention measures, and focus on monitoring and adjusting high-risk satellites.

[0091] The technical solution of the present invention is described in detail above in conjunction with the embodiments / drawings, but the present invention is not limited to the above technical solution. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A comprehensive assessment method for low-orbit giant star collision risk, characterized in that: The steps include: S101. Standardizing parameters of different dimensions based on fuzzy set theory to construct a multi-dimensional satellite data factor set with unified units. The process of constructing the satellite data factor set includes the following steps: S1011, collecting relevant data that affect satellite collision risk; wherein the relevant data includes the satellite's position, speed, attitude, forecast time, and collision probability model scaling factor; S1012. Clean and preprocess the collected relevant data to remove noise and erroneous data, fill in missing values, and ensure the integrity and consistency of the relevant data; S1013. Calculate three factors affecting satellite collision risk based on the orbit prediction model and the collision probability model: approach distance, collision probability and confidence level; S1014. Standardize the factors according to their characteristics to eliminate the differences in dimensions between different factors so that the data can be analyzed and compared on the same scale; S1015. Constructing a multi-dimensional satellite data factor set based on the standardized post-processed data, wherein each factor corresponds to one dimension; S102, performing hierarchical decomposition of each indicator based on the hierarchical analysis method to determine the indicator weight; the process of determining the indicator weight includes the following steps: S1021, determining the comprehensive risk assessment value as the target layer, setting the three factors affecting the satellite collision risk as the criterion layer, and establishing a hierarchical model; S1022. For each pair of factors in the criterion layer, compare them pairwise according to their importance to the target, quantify their relative importance, and construct a judgment matrix; S1023, calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, normalizing the eigenvector, and obtaining the weight of each factor; S1024, performing consistency check to ensure that the consistency of the judgment matrix is ​​within an acceptable range; If the consistency test fails, the judgment matrix is ​​adjusted; If the consistency check is passed, the weights are aggregated to form the final indicator weight set; S103, performing fuzzy evaluation on each single indicator, and constructing a fuzzy evaluation matrix and an evaluation set; S104, integrating the fuzzy evaluation results of multiple single factors into a comprehensive evaluation result, and establishing a comprehensive evaluation model; S105, using the comprehensive evaluation model, conduct an overall evaluation of the collision analysis of each satellite to obtain a comprehensive collision warning score.

2. The method for comprehensive assessment of low-orbit giant star collision risk according to claim 1, characterized in that: The consistency verification process includes the following steps: (1) Calculate the consistency index of the judgment matrix Where n is the order of the judgment matrix; (2) According to the order of the judgment matrix, the random consistency index RI is obtained by looking up the table; (3) Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the matrix is ​​judged to be consistent and passes the consistency test. If CR ≥ 0.1, the matrix is ​​judged to be inconsistent and fails the consistency test.

3. The method for comprehensive assessment of low-orbit giant star collision risk according to claim 1, characterized in that: The adjustment process of the judgment matrix includes the following steps: (1) Perform logarithmic transformation on the judgment matrix: b ij =loga ij ; (2) Calculate the mean of the logarithmic matrix: (3) Perform antilogarithmic transformation based on the mean of the logarithmic matrix to restore the original scale and obtain a new judgment matrix:

4. The method for comprehensive assessment of low-orbit giant star collision risk according to claim 1, characterized in that: The construction process of the fuzzy evaluation matrix and the evaluation set includes the following steps: (1) Construct a set of all evaluation results to represent different risk levels. Each evaluation indicator is divided into three levels according to the risk level: dangerous, general, and safe; (2) For each single indicator, a fuzzy evaluation matrix is ​​constructed according to its different value ranges and the degree of influence on the collision risk. The elements in the fuzzy evaluation matrix represent the degree of membership of each indicator at different evaluation levels.

5. The method for comprehensive assessment of low-orbit giant star collision risk according to claim 1, characterized in that: The process of establishing the comprehensive evaluation model includes the following steps: (1) Obtain the fuzzy evaluation matrix and corresponding weight vector of each indicator; (2) The fuzzy matrix of single factor evaluation is weighted and synthesized according to the indicator weight set; (3) Establish a comprehensive evaluation model based on the fuzzy synthesis results.

6. The method for comprehensive assessment of low-orbit giant star collision risk according to claim 1, characterized in that: The application method of the collision warning comprehensive scoring comprises the following steps: (1) Sort the comprehensive collision warning scores of all satellites and identify high-risk satellites and high-risk collision events; (2) Based on the comprehensive collision warning score, formulate corresponding prevention and intervention measures, and focus on monitoring and adjusting high-risk satellites.

Images