Evaluation method and device for spacecraft deorbit control and electronic equipment

Abstract

The invention discloses a spacecraft deorbit control evaluation method and device and electronic equipment, and relates to the technical field of spacecraft flight control, and the method comprises the steps: constructing an importance fuzzy judgment matrix of each index factor in a preset level in a preset evaluation system, different expert strategies are adopted to determine the membership degree of each index factor in the last evaluation hierarchy on each security level, so that an evaluation matrix of each index factor in the last but one evaluation hierarchy is determined according to the obtained membership degree matrix of each expert strategy, and based on the importance fuzzy judgment matrix, the evaluation degree of each index factor in the last but one evaluation hierarchy is determined. And determining a weight vector of each index factor in a preset hierarchy, and determining an evaluation result based on the evaluation matrix of each index factor under each expert strategy and the weight vector of each index factor in the preset hierarchy, so as to obtain a target security level of the target spacecraft during deorbit control. According to the invention, the technical problem of low accuracy of safety assessment of spacecraft deorbit control in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of spacecraft flight control technology, and more specifically, to an evaluation method, apparatus, and electronic equipment for spacecraft deorbit control. Background Technology

[0002] With the increasing frequency of space activities, spacecraft deorbiting and reentry have become a crucial part of the space mission's lifecycle. After completing their mission, spacecraft need to safely deorbit and re-enter the Earth's atmosphere. During reentry, spacecraft are subjected to extreme environments such as high temperatures, high pressures, and strong air currents, causing changes in their structure and performance, and even disintegration and the generation of a large amount of debris. If the impact area of ​​the debris cannot be effectively controlled, it may pose a potential threat to the safety of people and property on the ground. Spacecraft deorbiting control is a type of active deorbiting, in which the spacecraft uses its onboard propulsion system to perform orbital maneuvers, reduce its speed, leave its operational orbit, and re-enter the atmosphere to fall into a specific area. The deorbiting process is highly controllable and short in duration, ensuring that the spacecraft does not become space debris, reducing the generation of space debris, lowering its threat to spacecraft in orbit, and minimizing potential risks to people and property on the ground.

[0003] Currently, controlled deorbiting strategies for spacecraft mainly include: Orbital maneuvering strategy: Utilizing the spacecraft's onboard propulsion system, maneuvering at appropriate times to change orbital state, reduce orbital altitude, and ultimately re-enter the atmosphere; Attitude control strategy: Adjusting the spacecraft's orientation during deorbiting via the attitude control system to achieve an optimal re-entry angle into the atmosphere, allowing for stable combustion and disintegration under the high temperatures generated by atmospheric friction, thus reducing the threat of debris to the ground; Re-entry trajectory optimization strategy: Utilizing orbital dynamics models and optimization algorithms to optimize the re-entry trajectory, comprehensively considering various factors to select the optimal trajectory, minimizing orbital deviations while meeting safety constraints, and ensuring debris lands in a predetermined safe sea area. Furthermore, multiple orbital maneuvers can be used to gradually adjust the orbit to achieve the best re-entry effect. In practical applications, multiple control strategies need to be comprehensively applied based on factors such as spacecraft type, mission requirements, and orbital parameters to achieve safe deorbiting of the spacecraft.

[0004] From the perspective of space missions, safe deorbit control is a crucial guarantee for the successful completion of the mission. In related technologies, commonly used risk assessment methods for space mission safety evaluation include fuzzy evaluation, hill climbing, analytic hierarchy process (AHP), similarity method, and evaluation tree method. However, the various risk assessment indicators used to measure the evaluated object are inherently somewhat ambiguous. For example, conclusions describing safety such as "very safe" or "relatively dangerous" are natural language and difficult to quantify. Because many ambiguous factors exist in the safety assessment of spacecraft deorbit and reentry control, they are difficult to describe with precise numerical values. Furthermore, complex models have numerous qualitative safety indicators that cannot be specifically quantified, and the relative weights between evaluation indicators at the same level are difficult to determine. Applying traditional assessment methods to reentry control assessment has significant limitations, failing to accurately handle the many uncertainties during deorbiting. Qualitative assessment methods relying solely on expert experience are highly subjective, lack quantitative analytical basis, and the reliability and consistency of the assessment results are difficult to guarantee, making it difficult to accurately reflect the complex risks in the spacecraft deorbit control process.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides an evaluation method, apparatus, and electronic device for spacecraft deorbit control, to at least address the technical problem of low accuracy in safety assessment of spacecraft deorbit control in related technologies.

[0007] According to one aspect of the present invention, an evaluation method for spacecraft deorbit control is provided, comprising: constructing an importance fuzzy judgment matrix for each indicator factor in a preset level of a preset evaluation system based on spacecraft data of the target spacecraft, wherein the preset evaluation system includes: multiple evaluation levels, each evaluation level including: at least one indicator factor, and the preset levels being all other evaluation levels except the last evaluation level; employing different expert strategies to determine the membership degree of each indicator factor in the last evaluation level of the preset evaluation system at each security level in a preset security judgment set, obtaining a membership degree matrix corresponding to each expert strategy, and determining the judgment matrix for each indicator factor in the penultimate evaluation level based on the membership degree matrix; determining the weight vector of each indicator factor in the preset level based on the importance fuzzy judgment matrix; determining the judgment result based on the judgment matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level, and determining the target security level of the target spacecraft during deorbit control based on the judgment result.

[0008] Furthermore, before constructing the importance fuzzy judgment matrix of each indicator factor in the preset level of the preset evaluation system based on the spacecraft data of the target spacecraft, the process also includes: constructing a preset safety evaluation set, which includes multiple safety levels; and constructing a preset evaluation system, which is an evaluation system constructed based on the safety of the deorbit control process and the interrelationship and effects of various factors affecting safety.

[0009] Further, based on the spacecraft data of the target spacecraft, the step of constructing the importance fuzzy judgment matrix of each indicator factor in the preset evaluation system at the preset level includes: for each lower level, determining any two pairs of indicator factors in the lower level as indicator factor pairs, where the lower level is all other evaluation levels except the first evaluation level, and the same indicator factor pair is an indicator factor pair where the indicator factors are identical and the positional order of the indicator factors is the same; based on the spacecraft data of the target spacecraft, using the triangular fuzzy number algorithm to determine the triangular fuzzy number of each pair of indicator factor pairs in the lower level, where the triangular fuzzy number is a fuzzy representation of the relative importance between the two indicator factors in the indicator factor pair based on the preset indicator factor of the upper level of the lower level, and the preset indicator factor is the upper level indicator factor that the two indicator factors jointly correspond to; the triangular fuzzy number includes: three fuzzy factors; based on the triangular fuzzy number of each pair of indicator factor pairs, constructing the importance fuzzy judgment matrix of each indicator factor in each preset level.

[0010] Furthermore, the step of determining the degree of membership of each indicator factor in the last evaluation level of the preset evaluation system at each security level in the preset security judgment set by adopting different expert strategies, and obtaining the degree of membership matrix corresponding to each expert strategy, includes: for each expert strategy, using the expert strategy to determine the degree of membership of each indicator factor in the last evaluation level at each security level, wherein the sum of the degree of membership of the same indicator factor at all security levels is 1; and constructing the degree of membership matrix corresponding to the expert strategy based on all degree of membership.

[0011] Furthermore, the step of determining the evaluation matrix for each indicator factor in the penultimate evaluation level based on the membership degree matrix includes: determining the set of first indicator factors in the last evaluation level corresponding to each indicator factor in the penultimate evaluation level; selecting the membership degree vector corresponding to each first indicator factor in the set of first indicator factors from the membership degree matrix; and constructing the evaluation matrix for each indicator factor in the penultimate evaluation level based on all membership degree vectors.

[0012] Furthermore, the step of determining the weight vector of each indicator factor in the preset level based on the importance fuzzy judgment matrix includes: determining the set of second indicator factors in the next evaluation level corresponding to each indicator factor in the preset level; determining the triangular fuzzy number corresponding to each second indicator factor in the set of second indicator factors based on the importance fuzzy judgment matrix; determining the initial weight vector corresponding to the indicator factor based on all triangular fuzzy numbers; and normalizing the initial weight vector to obtain the weight vector corresponding to the indicator factor.

[0013] Furthermore, the step of determining the initial weight vector corresponding to the indicator factor based on all triangular fuzzy numbers includes: determining the importance value corresponding to each second indicator factor based on the triangular fuzzy number corresponding to each second indicator factor; for each second indicator factor, using the supremum algorithm, determining the weight value corresponding to the second indicator factor based on the importance value corresponding to the second indicator factor and the importance values ​​corresponding to other second indicator factors; and determining the initial weight vector corresponding to the indicator factor based on the weight value corresponding to each second indicator factor.

[0014] Further, the step of determining the evaluation result based on the evaluation matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level includes: determining the first comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level based on the weight vector and evaluation matrix of each indicator factor in the penultimate evaluation level; determining the second comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level based on the weight vector and the first comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level; determining the third comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level based on the weight vector and the second comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level, until the target comprehensive evaluation matrix of the indicator factor in the first evaluation level is determined, wherein the first evaluation level includes one indicator factor, and the indicator factor included in the first evaluation level is the target indicator factor; determining the final comprehensive evaluation matrix of the target indicator factor based on the target comprehensive evaluation matrix of the target indicator factor under all expert strategies; and determining the final comprehensive evaluation matrix as the evaluation result.

[0015] According to another aspect of the present invention, an evaluation device for spacecraft deorbit control is also provided, comprising: a construction unit, configured to construct an importance fuzzy judgment matrix for each indicator factor in a preset level of a preset evaluation system based on spacecraft data of the target spacecraft, wherein the preset evaluation system includes: multiple evaluation levels, each evaluation level including: at least one indicator factor, and the preset level being all other evaluation levels except the last evaluation level; a first determination unit, configured to determine the membership degree of each indicator factor in the last evaluation level of the preset evaluation system at each security level in a preset security judgment set using different expert strategies, obtaining a membership degree matrix corresponding to each expert strategy, and determining the judgment matrix of each indicator factor in the penultimate evaluation level based on the membership degree matrix; a second determination unit, configured to determine the weight vector of each indicator factor in the preset level based on the importance fuzzy judgment matrix; and a third determination unit, configured to determine the judgment result based on the judgment matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level, and determine the target security level of the target spacecraft during deorbit control based on the judgment result.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the evaluation method for spacecraft deorbit control described above.

[0017] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described spacecraft deorbit control evaluation methods.

[0018] In this invention, based on spacecraft data of the target spacecraft, an importance fuzzy judgment matrix for each indicator factor in a preset level of a preset evaluation system is constructed. Different expert strategies are employed to determine the membership degree of each indicator factor in the last evaluation level of the preset evaluation system at each security level in a preset security judgment set, resulting in a membership degree matrix corresponding to each expert strategy. Based on the membership degree matrix, the judgment matrix for each indicator factor in the penultimate evaluation level is determined. Based on the importance fuzzy judgment matrix, the weight vector for each indicator factor in the preset level is determined. Based on the judgment matrix for each indicator factor under each expert strategy and the weight vector for each indicator factor in the preset level, the judgment result is determined. Based on the judgment result, the target security level of the target spacecraft during deorbit control is determined, thereby solving the technical problem of low accuracy in safety assessment of spacecraft deorbit control in related technologies.

[0019] This invention employs a combination of fuzzy hierarchical analysis and fuzzy comprehensive evaluation. By constructing a multi-level safety assessment system encompassing target spacecraft data, it achieves the goal of quantitatively analyzing the safety of low-Earth orbit spacecraft deorbit control. This enables a comprehensive and objective evaluation of the complex risks during spacecraft deorbiting, thus solving the technical problem that traditional assessment methods struggle to accurately reflect the multi-factor risks and uncertainties during deorbit control. Specifically, a fuzzy judgment matrix is ​​first constructed for each level of indicator factors (excluding the last level). Based on expert strategies, the safety level membership of the last-level indicator factors is determined. Then, the weight vectors of each level of indicators are calculated. Finally, based on these weight vectors and the expert evaluation matrix, a comprehensive evaluation result is calculated to determine the safety level of the target spacecraft deorbit control, effectively improving the accuracy and reliability of the assessment. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0021] Figure 1 This is a flowchart of an optional evaluation method for spacecraft deorbit control according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of an optional hierarchical structure of derailment control safety assessment indicators according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of an optional spacecraft deorbit control safety assessment process according to an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of an optional spacecraft deorbit control evaluation device according to an embodiment of the present invention;

[0025] Figure 5 This is a hardware structure block diagram of an electronic device (or mobile device) for an evaluation method of spacecraft deorbit control according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It should be noted that all related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) collected and involved in this invention are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data comply with the relevant laws, regulations, and standards of the relevant regions, necessary confidentiality measures have been taken, and it does not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0029] Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Comprehensive Evaluation (FCE) are two important evaluation methods with unique advantages in handling multi-factor, fuzzy, and uncertain problems in complex systems. FAHP can decompose complex problems into multiple levels, determine the relative importance of each factor through pairwise comparisons, and thus construct a judgment matrix and calculate a weight vector. Fuzzy Comprehensive Evaluation, on the other hand, can comprehensively consider the influence of multiple factors, providing a comprehensive and objective evaluation of the evaluation object.

[0030] Based on this, this invention combines fuzzy hierarchical analysis and fuzzy comprehensive evaluation, giving full play to the advantages of both methods. Fuzzy hierarchical analysis is used to calculate the relative weights of each evaluation index factor, fully considering the fuzziness and uncertainty of expert judgment. Triangular fuzzy numbers are used, and a fuzzy judgment matrix is ​​introduced to transform the qualitative judgment of experts into quantitative analysis, so as to reasonably determine the relative importance of each factor in the evaluation process, making the weight allocation of each factor more reasonable. On this basis, fuzzy comprehensive evaluation is used to quantitatively evaluate the safety of each level of index element, and the final safety rating is obtained through fuzzy synthesis operation.

[0031] This invention overcomes the shortcomings of single methods in dealing with complex problems, and better addresses the fuzziness and uncertainty issues in the safety assessment process. It provides a safety assessment method for low-Earth orbit spacecraft deorbit control based on FAHP and FCE for the safety assessment of controlled deorbit and reentry control of spacecraft. The method includes: (1) constructing a safety judgment set including 5 evaluation levels; (2) constructing a four-level controlled deorbit control safety assessment index system; (3) constructing an importance fuzzy judgment matrix for each level of index factors using triangular fuzzy numbers; (4) comprehensively evaluating each index factor of the lowest level according to the safety judgment set; (5) calculating the relative weight vector of each level of evaluation factors according to the fuzzy judgment matrix; (6) calculating the fuzzy comprehensive evaluation results of the third level, second level indicators and first level total indicators in sequence according to the relative weight vector of the fourth level factor and the evaluation results; (7) repeating steps (5) to (6) until the comprehensive evaluation results of all expert strategies are calculated, and obtaining the final safety assessment conclusion according to the principle of maximum membership. This method improves the reliability and effectiveness of safety assessments, facilitates the quantitative evaluation of the safety of spacecraft deorbit and reentry control, and provides scientific guidance for mission planning and decision-making. Furthermore, by further analyzing the importance distribution and safety levels of indicators at each level, it can provide reference directions for subsequent spacecraft mission optimization and improvement, enhancing the safety and reliability of spacecraft reentry, and helping to reduce risks and ensure the safety of personnel and property on the ground.

[0032] The present invention will now be described in detail with reference to various embodiments.

[0033] Example 1

[0034] According to an embodiment of the present invention, an embodiment of an evaluation method for spacecraft deorbit control is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1 This is a flowchart of an optional evaluation method for spacecraft deorbit control according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0036] Step S101: Based on the spacecraft data of the target spacecraft, construct the importance fuzzy judgment matrix of each indicator factor in the preset level of the preset evaluation system. The preset evaluation system includes: multiple evaluation levels, each evaluation level includes: at least one indicator factor, and the preset level is all other evaluation levels except the last evaluation level.

[0037] In this embodiment of the invention, data such as the target spacecraft's operating status, performance parameters, and environmental conditions can be collected first. Then, an expert strategy in the aerospace field is adopted to score and judge the relative importance of each indicator factor in the preset level of the preset evaluation system. By performing pairwise comparisons and using triangular fuzzy numbers based on the 1-9 scale method, the approximate range of their relative importance is expressed, and an importance fuzzy judgment matrix is ​​constructed. This allows the qualitative judgment of the experts to be transformed into quantitative analysis when determining the relative importance weight of each indicator factor by introducing the importance fuzzy judgment matrix.

[0038] Here, the pre-defined evaluation system comprises multiple evaluation levels, each focusing on different aspects of deorbit control, such as spacecraft conditions, external environmental influences, and reentry control strategies. Each level includes at least one indicator factor, covering key risk points in the deorbit control process. The pre-defined levels are all levels except the last one, and their weights are determined by an expert fuzzy judgment matrix.

[0039] Step S102: Using different expert strategies, determine the degree of membership of each indicator factor in the last evaluation level of the preset evaluation system at each security level in the preset security judgment set, obtain the membership degree matrix corresponding to each expert strategy, and determine the judgment matrix of each indicator factor in the second to last evaluation level based on the membership degree matrix.

[0040] In this embodiment of the invention, multiple experts, including spacecraft overall experts, guidance and control experts, and flight control experts (i.e., adopting different expert strategies), can be invited to comprehensively judge the degree of membership of each indicator factor in the fourth level (i.e., the last evaluation level) at different security levels in the preset security judgment set, so as to obtain the degree of membership matrix corresponding to each expert strategy, and based on the degree of membership matrix, determine the judgment matrix of each indicator factor in the penultimate evaluation level (i.e., the third level).

[0041] Here, a set of safety assessment criteria is defined to define a set of safety levels, such as "extremely safe", "safe", "normal", "dangerous", and "extremely dangerous", which are used to describe the safety level during the spacecraft deorbit control process.

[0042] Step S103: Based on the importance fuzzy judgment matrix, determine the weight vector of each indicator factor in the preset level.

[0043] In this embodiment of the invention, an importance fuzzy judgment matrix is ​​formed by scoring and evaluating the importance of each level of indicator factors according to different expert strategies. The weight vector of each level factor relative to a certain factor in the previous level is calculated to quantify the importance of each indicator factor in the overall evaluation.

[0044] Step S104: Based on the evaluation matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level, determine the evaluation result, and based on the evaluation result, determine the target safety level of the target spacecraft during deorbit control.

[0045] In this embodiment of the invention, based on the evaluation matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level, the fuzzy comprehensive evaluation results of the third-level, second-level indicators and the first-level total indicators under each expert strategy can be calculated in sequence. Then, based on the evaluation level corresponding to the largest element value in the evaluation results, and according to the principle of maximum membership, the final conclusion of the security assessment (i.e., the target security level) is obtained.

[0046] The above steps not only take into account the ambiguity and uncertainty of expert judgments, but also achieve a precise quantitative assessment of the safety of deorbit control for low-Earth orbit spacecraft, improving the comprehensiveness and scientific rigor of the assessment. This is of great significance for optimizing spacecraft deorbit strategies and ensuring ground safety.

[0047] In summary, a combination of fuzzy hierarchical analysis and fuzzy comprehensive evaluation can be used to construct a multi-level safety assessment system encompassing target spacecraft data. This approach achieves the goal of quantitatively analyzing the safety of low-Earth orbit spacecraft deorbit control, thus enabling a comprehensive and objective evaluation of the complex risks during the deorbit process. This solves the technical problem that traditional assessment methods struggle to accurately reflect the multi-factor risks and uncertainties involved in deorbit control. Specifically, a fuzzy judgment matrix is ​​first constructed for each level of indicator factors (excluding the last level). Based on expert strategies, the safety level membership of the last-level indicator factors is determined. Then, the weight vectors of each level of indicator are calculated. Finally, based on these weight vectors and the expert evaluation matrix, a comprehensive evaluation result is calculated to determine the safety level of the target spacecraft's deorbit control, effectively improving the accuracy and reliability of the assessment.

[0048] To improve the accuracy of constructing a preset safety assessment set and a preset evaluation system, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, before constructing the importance fuzzy judgment matrix of each indicator factor in the preset level of the preset evaluation system based on the spacecraft data of the target spacecraft, a preset safety assessment set is constructed, which includes: multiple safety levels; and a preset evaluation system is constructed, which is an evaluation system constructed based on the safety of the deorbit control process and the interrelationship and effects of various factors affecting safety.

[0049] In this embodiment of the invention, a series of safety levels can be defined first as a benchmark for evaluating the safety status of the spacecraft deorbit control process. In this embodiment, the evaluation set is set to include five levels, which are represented from high to low as "very safe", "relatively safe", "moderate", "relatively dangerous" and "very dangerous". These five levels cover the full range from the most ideal state to the worst situation.

[0050] Specifically, the safety of spacecraft deorbiting and reentry control is divided into five evaluation levels, and an evaluation level set (i.e., a preset safety judgment set) is constructed. ,in, It means "very safe". Indicates "relatively safe". Indicates "generally". Indicates "relatively dangerous". The five levels, from highest to lowest safety, define different levels of safety and provide a standard for subsequent fuzzy comprehensive evaluation. A "very safe" level means that all indicators of the spacecraft are in ideal condition during the controlled deorbiting process, posing no threat to the safety of people and property on the ground; while a "relatively dangerous" level indicates that there is a significant risk during the spacecraft's reentry process, which could cause considerable harm to the ground.

[0051] For example, Table 1 is a set of security criteria.

[0052] Table 1

[0053]

[0054] In this embodiment of the invention, a multi-level evaluation framework can be constructed. This framework focuses on the safety of the deorbit control process and involves multiple aspects such as spacecraft status, environmental factors, and control strategies. The preset evaluation system is divided into four levels, with the first level being the overall safety index. The second to fourth levels are further subdivided into multiple sub-indicators, such as spacecraft structural integrity, energy subsystem safety, impact of reentry debris, and reentry monitoring and early warning capabilities. Each sub-indicator is further subdivided until a comprehensive evaluation system covering the complexity of the deorbit control process is formed.

[0055] Specifically, the safety of the deorbit control process can be used as the basic framework for overall consideration. Based on the interrelationships and effects of various factors affecting safety, a four-level safety assessment index system can be established. The highest level, or first level, is the overall safety index C. The next level, or second level, consists of sub-indicators: spacecraft health status C1, spacecraft orbital maneuvering C2, flight control mission implementation C3, and the impact of reentry debris C4. These indicators are the four main categories of factors that determine the safety of deorbit control. The third level includes 18 sub-indicators, such as C11, C12, C21, etc., which are concretizations of the second-level main indicators. Then, the third-level indicators are further subdivided to establish a fourth-level sub-indicators, such as C111, C112, etc., totaling 69.

[0056] Figure 2 This is a schematic diagram of an optional hierarchical structure for derailment control safety assessment indicators according to an embodiment of the present invention, such as... Figure 2As shown, the first-level indicator is the safety assessment of controlled deorbiting of the spacecraft (C); the second-level indicators include: spacecraft health status safety (C1), spacecraft orbital maneuver safety (C2), flight control mission implementation safety (C3), and safety due to reentry debris impact (C4); the next-level indicators (third-level indicators) after the spacecraft health status safety assessment (C1) include: spacecraft orbital parameter characteristics (C11), spacecraft structural integrity (C12), spacecraft attitude controllability (C13), GNC (Guidance, Navigation and Control) subsystem safety (C14), communication and navigation subsystem safety (C15), and energy subsystem safety (C16). These indicators are used to assess whether the current on-orbit status of the spacecraft is healthy, safe, and reliable, and whether it can affect the stable operation and successful implementation of the mission during subsequent deorbiting and reentry. Among them, ① the next-level indicators (fourth-level indicators) after the spacecraft orbital parameter characteristics (C11) include: orbital altitude (C111), orbital inclination (C112), orbital eccentricity (C113), and surface-to-mass ratio (C114). ② The next level of indicators for spacecraft structural integrity (C12) includes: structural surface integrity (C121), structural material strength (C122), material environmental adaptability (C123), and material damage propagation (C124). ③ The next level of indicators for spacecraft attitude controllability (C13) includes: attitude control accuracy (C131), attitude adjustment speed (C132), actuator reliability (C133), and attitude sensor reliability (C134). ④ The next level of indicators for the safety of the GNC control subsystem (C14) includes: real-time acquisition reliability (C141), processing and calculation accuracy (C142), control logic algorithm error (C143), and orbital parameter measurement error (C144). ⑤ The next level of indicators for the safety of the communication and navigation subsystem (C15) includes: signal receiving sensitivity, navigation and positioning accuracy (C152), and communication anti-interference capability (C153). ⑥ The next level of indicators for the safety of the energy subsystem (C16) includes: energy management and regulation capability (C161), power output stability (C162), battery health status (C163), and power supply path redundancy (C164).

[0057] The next level of indicators for spacecraft orbital maneuver safety (C2) includes: orbit control engine performance (C21), attitude and orbit coordinated control capability (C22), and on-orbit handling of orbit control faults (C23). These indicators are used to evaluate the spacecraft's propulsion system performance, attitude and orbit coordinated control accuracy, and on-orbit autonomous handling capability for orbit control faults, predicting whether the spacecraft can carry out deorbit maneuvers according to the predetermined plan and whether it will affect controlled deorbiting. Specifically: ① The next level of indicators for orbit control engine performance (C21) includes: engine thrust specific impulse (C211), thrust accuracy and stability (C212), thrust direction control accuracy (C213), and propellant remaining quantity (C214). ② The next level of indicators for attitude and orbit coordinated control capability (C22) includes: attitude and orbit mechanism coordination accuracy (C221), attitude and orbit dynamic coupling characteristics (C222), and disturbance / interference from large components (C223). ③ The next level of indicators for on-orbit handling of orbit control faults (C23) includes: redundancy backup effectiveness (C231), fault diagnosis and isolation efficiency (C232), extreme emergency orbit control success rate (C233), and fault recovery and handling time (C234).

[0058] The next level of indicators for flight control mission implementation safety (C3) includes: orbit control strategy reliability and effectiveness (C31), command generation uplink reliability (C32), mission planning, monitoring, and coordination (C33), system equipment and technical support (C34), and emergency response and risk management (C35). These indicators are used to evaluate the ground center's capabilities in orbit determination and orbit control calculations, flight control, fault diagnosis and handling, reflecting the ground center's implementation capabilities and support levels for deorbit control missions. Specifically, ① the next level of indicators for orbit control strategy reliability and effectiveness (C31) includes: target tracking and monitoring capability (C311), orbit calculation accuracy (C312), orbit prediction accuracy (C313), strategy design optimization capability (C314), and control frequency and interval (C314). ② the next level of indicators for command generation uplink reliability (C32) includes: command generation accuracy (C321), logical verification integrity (C322), command generation redundancy verification (C323), and command uplink transmission success rate (C324). ③ The next level of indicators for Task Planning, Monitoring, and Coordination (C33) includes: Task planning rationality (C331), real-time monitoring and processing capability (C332), internal and external coordination capability (C333), and task dynamic adjustment capability (C334). ④ The next level of indicators for System Equipment and Technical Support (C34) includes: Hardware equipment reliability (C341), software system stability (C342), and communication network link stability (C343). ⑤ The next level of indicators for Emergency Response and Risk Management (C35) includes: Emergency plan completeness (C351), emergency response time (C352), personnel emergency response capability (C353), and risk assessment and early warning capability (C354).

[0059] The next level of indicators for assessing the safety impact of reentry debris on Earth (C4) includes: physical characteristics of the debris (C41), accuracy of reentry trajectory prediction (C42), environmental risk of the debris landing area (C43), and reentry monitoring and early warning capabilities (C44). These indicators are primarily used to evaluate the safety risks of reentry debris to the ground and analyze its impact on the life and property of people on the ground. Specifically, ① the next level of indicators for physical characteristics of the debris (C41) includes: debris size and mass distribution (C411), structural strength and ablation resistance (C412), debris morphology and aerodynamic characteristics (C413), and hazardous substance content in the debris (C414). ② the next level of indicators for accuracy of reentry trajectory prediction (C42) includes: accuracy of the reentry point (C421), debris landing point deviation (C422), dispersion range of debris (C423), and reliability of the prediction model (C424). ③ The next level of indicators for the environmental risk of the debris landing area (C43) includes: population and facility density in the landing area (C431), ecological and environmental sensitivity (C432), impact of topography and geomorphology in the landing area (C433), and meteorological risk in the landing area (C434). ④ The next level of indicators for reentry monitoring and early warning capabilities (C44) includes: timeliness of early warning information dissemination (C441), reliability of the monitoring system (C442), and multi-source data fusion capability (C443).

[0060] In this embodiment, by constructing a preset safety evaluation set and a preset evaluation system, the safety assessment during the deorbit control process of low-Earth orbit spacecraft is standardized and systematized. The evaluation set provides clear quantitative standards for safety assessment, while the evaluation system refines the evaluation indicators through a hierarchical structure, enhancing the comprehensiveness and depth of the assessment. This not only effectively quantifies the impact of unknown and ambiguous factors on the safety of deorbit control but also provides decision-makers with clear risk assessment results, thereby guiding the optimization of spacecraft deorbit control strategies, ensuring the safe and controllable completion of deorbit missions, and reducing potential threats to ground personnel and facilities.

[0061] To improve the accuracy of determining the importance fuzzy judgment matrix of indicator factors, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, for each lower level, any two indicator factors in the lower level are determined as indicator factor pairs. The lower level refers to all other evaluation levels except the first evaluation level, and the same indicator factor pair is one where all indicator factors are identical and their positional order is the same. Based on the spacecraft data of the target spacecraft, a triangular fuzzy number algorithm is used to determine the triangular fuzzy number of each pair of indicator factor pairs in the lower level. The triangular fuzzy number is a fuzzy representation of the relative importance between two indicator factors in an indicator factor pair based on a preset indicator factor in the upper level of the lower level. The preset indicator factor is the upper-level indicator factor that both indicator factors correspond to. The triangular fuzzy number includes three fuzzy factors. Based on the triangular fuzzy number of each pair of indicator factor pairs, an importance fuzzy judgment matrix for each indicator factor in each preset level is constructed.

[0062] In this embodiment of the invention, in the constructed evaluation system, apart from the top-level overall index, each lower level is considered a "lower level". For example, the second, third, and fourth levels all belong to the lower level category. For any lower level, any two index factors within that level are selected to form an "index factor pair". Here, the same index factor pair means that these two factors appear in a fixed pair in all expert evaluations, that is, the two factors are not only the same in the lower level, but also have a fixed position order in the pair. For example, the index factor pair composed of C121 and C122 is different from the index factor pair composed of C122 and C121. Then, based on the data of the target spacecraft, the triangular fuzzy number algorithm is used to evaluate the relative importance (i.e., the triangular fuzzy number) of any pair of index factors (such as C121 and C122) relative to the preset index factor (such as C12) of the previous level. Here, the triangular fuzzy number consists of three fuzzy factors, which respectively represent the lower limit, most likely value, and upper limit of the importance of one index factor relative to another index factor in the index factor pair, that is ( ).here, This represents the minimum possible value of the importance of indicator factor Cp relative to indicator factor Cq. This represents the most likely importance value of indicator factor Cp relative to indicator factor Cq. This represents the maximum possible importance value of indicator factor Cp relative to indicator factor Cq. Then, for each indicator factor in each preset level, an importance fuzzy judgment matrix is ​​constructed based on triangular fuzzy numbers. Each element in the matrix represents the relative importance assessment value between two indicator factors, presented in the form of a triangular fuzzy number. In this way, the importance distribution of indicator factors within each level can be systematically recorded and analyzed.

[0063] Specifically, several experts with extensive experience in the aerospace field were invited to score and judge the relative importance of various factors and indicators at each level from their respective professional perspectives. By conducting pairwise comparisons and using triangular fuzzy numbers based on the 1-9 scale, the approximate range of their relative importance was expressed, thus constructing a fuzzy judgment matrix. When determining the relative importance weights of each factor, the qualitative judgments of the experts were transformed into quantitative analysis by introducing the fuzzy judgment matrix. For example, assuming that for n factor indicators at the k-th level (k=1, 2, 3, 4), an expert constructs a relative importance fuzzy judgment matrix using triangular fuzzy numbers through pairwise comparisons. The formula is as follows:

[0064] ;

[0065] Among them, the factors of the matrix Values ​​are expressed using triangular fuzzy numbers ( The expression ) represents a fuzzy representation of the relative importance of two factors Cp and Cq relative to a certain indicator at a higher level.

[0066] and The relationship between them is:

[0067] =1 / , =1 / , =1 / ;

[0068] Wherein, the triangular fuzzy number M = In this context, l and u represent the lower and upper bounds of the fuzzy number, respectively. m represents the most probable value, and the difference between l and u indicates the degree of ambiguity in the judgment. The smaller (ul) is, the clearer and more definite the judgment conclusion is; the larger it is, the more ambiguous the judgment is. It is obtained by comparing and analyzing each factor pairwise, such as the triangular fuzzy number. = This indicates that the p-th element Cp is significantly more important than the q-th element Cq, where l and u can be written as... = or = Then, correspondingly, there are = ,or = .

[0069] For example, suppose an evaluation expert constructs a fuzzy judgment matrix D for the second-level sub-indicator C12, and its four next-level, third-level sub-indicators C121, C122, C123, and C124. 12 As shown in Table 2.

[0070] Table 2

[0071]

[0072] In this embodiment, not only are the expert's subjective judgments transformed into an operational mathematical form—triangular fuzzy numbers—but a systematic importance fuzzy judgment matrix for the entire evaluation system is also constructed. This significantly enhances the scientific rigor, accuracy, and comprehensiveness of the safety assessment, especially when dealing with complex and risky tasks such as deorbit control of low-Earth orbit spacecraft. It enables more precise identification and quantification of the impact of various indicators on safety, providing solid data support for developing more effective deorbit control strategies and optimizing designs. Furthermore, by quantifying fuzziness and uncertainty, the evaluation process becomes more transparent and verifiable, which helps improve the credibility and practicality of the evaluation results.

[0073] To improve the accuracy of the membership degree matrix corresponding to each expert strategy, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, for each expert strategy, the expert strategy is used to determine the membership degree of each indicator factor in the last evaluation level at each safety level, wherein the sum of the membership degrees of the same indicator factor at all safety levels is 1; based on all membership degrees, the membership degree matrix corresponding to the expert strategy is constructed.

[0074] In this embodiment of the invention, for the last evaluation level in the preset evaluation system, that is, for each specific indicator factor in deorbit control, Y experts (including spacecraft overall experts, guidance and control experts, flight control experts, etc.) can be invited to judge the degree of membership of these indicator factors in a preset safety assessment set (e.g., "very safe", "safe", "neutral", "dangerous", "very dangerous") using their respective professional strategies. Based on their knowledge and spacecraft data, the experts assess the probability of each indicator factor belonging to different safety levels, ensuring that the sum of the membership degrees of the same indicator factor across all safety levels is 1. This restricts the experts' judgments within a standard framework, avoiding omissions or duplications in the evaluation results. Then, based on the experts' judgments on the membership degrees of all indicator factors at different safety levels, this data is organized into a membership degree matrix. The rows of the matrix represent the levels of the safety assessment set, and the columns represent the indicator factors in the last level. Each element in the matrix represents the degree of membership of a specific indicator factor to a specific safety level from the expert's perspective. For example, for expert y, the degree of membership under the "very safe" level is 0.85, the "safe" level is 0.1, the "average" level is 0.05, and the degree of membership under the "dangerous" and "very dangerous" levels is 0. This matrix reflects expert y's comprehensive judgment on the safety status of the indicator factor C121.

[0075] Specifically, Y experts, including spacecraft overall experts, guidance and control experts, and flight control experts, were invited to make a comprehensive judgment on the degree of affiliation of each evaluation factor in the fourth level with different safety levels in Table 1.

[0076] Since the assessment experts' safety judgment on a factor is not an absolute yes or no, the safety assessment results of each factor are actually fuzzy sets on the assessment set V. The fuzzy comprehensive assessment model consists of three components: assessment factors U, the assessment set V, and the single-factor fuzzy assessment matrix R. The factor set... It is a set composed of n factors. The evaluation set V is also the security evaluation set of the 5 levels in Table 1. The fuzzy evaluation matrix R is the single-factor evaluation matrix, and its matrix elements are... Used to represent factors in U Corresponding to the level in assessment V Subordinate relationship, 0 1. As shown in the following formula:

[0077] ;

[0078] Furthermore, factors The membership relationships in the corresponding evaluation V should satisfy the normalization principle, that is: =1.

[0079] In this embodiment, not only are the experts' subjective assessments of the safety status of the last-level indicator factors quantified, but a membership degree matrix under the expert strategies is also systematically constructed, enabling effective management and utilization of complex assessment information. Each expert strategy is transformed into an operable mathematical form through the membership degree matrix, which not only enhances the transparency of the assessment process but also provides a solid data foundation for subsequent fuzzy comprehensive evaluation.

[0080] To improve the accuracy of the evaluation matrix for determining the indicator factors, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, the set of first indicator factors in the last evaluation level corresponding to each indicator factor in the penultimate evaluation level is determined; the membership degree vector corresponding to each first indicator factor in the set of first indicator factors is selected from the membership degree matrix; and the evaluation matrix for each indicator factor in the penultimate evaluation level is constructed based on all membership degree vectors.

[0081] In this embodiment of the invention, for a certain indicator factor in the penultimate level, such as C31 (GNC system reliability), the indicator factors in the last evaluation level directly related to C31 are identified. These indicator factors constitute the first set of indicator factors for C31. In this example, the first set of indicator factors may include indicator factors such as C311 (guidance accuracy), C312 (navigation signal stability), and C313 (control system response speed). Then, based on the first set of indicator factors, membership degree vectors corresponding to these first indicator factors are selected from the membership degree matrix. For example, all membership degree vectors corresponding to first indicator factors such as C311, C312, and C313 are found from the membership degree matrix. These vectors contain the probability that these first indicator factors belong to different levels in the safety evaluation set. Subsequently, based on the membership degree vectors of the selected first set of indicator factors, fuzzy synthesis operation is used to construct the evaluation matrix for indicator factors such as C31 in the penultimate level. Specifically, the membership vectors of all the primary indicator factors related to C31 that have been selected are synthesized to obtain an evaluation matrix, where each row represents a security level and the elements in the matrix represent the probability that the indicator factor C31 belongs to a certain security level.

[0082] For example, regarding the evaluation factor "structural surface integrity C121", an expert, based on their experience and understanding of spacecraft structural characteristics, determined its membership level in the five rating categories "very safe, relatively safe, average, relatively dangerous, very dangerous" in Table 1 to be 0.85, 0.1, 0.05, 0.0, and 0.0, respectively. Similarly, by evaluating C122, C123, and C124, a fuzzy evaluation matrix R is obtained for the four third-level evaluation factors corresponding to C12. 12 :

[0083] ;

[0084] In this embodiment, subjective judgments of the safety status of different indicator factors during deorbit control are effectively integrated, forming a systematic evaluation matrix. This not only ensures the comprehensiveness and depth of the evaluation process but also improves the objectivity and accuracy of the safety assessment by quantifying the fuzzy judgments of experts. The construction of the evaluation matrix provides a solid data foundation for subsequent fuzzy comprehensive evaluation, enabling the calculation of the safety level of each indicator factor in the penultimate level, thus providing a scientific basis for the formulation and optimization of deorbit control strategies.

[0085] To improve the accuracy of determining the weight vector of the indicator factors, in the evaluation method for spacecraft deorbit control provided in Embodiment 1 of this application, the second set of indicator factors in the next evaluation level corresponding to each indicator factor in the preset level is determined; based on the importance fuzzy judgment matrix, the triangular fuzzy number corresponding to each second indicator factor in the second set of indicator factors is determined; based on all triangular fuzzy numbers, the initial weight vector corresponding to the indicator factor is determined; and the initial weight vector is normalized to obtain the weight vector corresponding to the indicator factor.

[0086] In this embodiment of the invention, according to the y-th expert (1) y Y) is a fuzzy judgment matrix constructed after scoring and evaluating the importance of indicators at each level. Calculate the weight vector of each level factor relative to a certain evaluation factor at the next higher level. .

[0087] Specifically, for each indicator factor in the preset hierarchy, such as "GNC system reliability C31" in the third hierarchy, it is necessary to identify all relevant indicator factors in the next evaluation hierarchy (i.e., the fourth hierarchy) directly associated with it. These factors constitute the "second indicator factor set". For example, the second indicator factor set of C31 may include, but is not limited to, C311 (guidance accuracy), C312 (navigation signal stability), C313 (control system response speed), etc. These indicator factors are sub-factors that directly affect the "GNC system reliability" of C31. Then, based on the determination of the second indicator factor set, the triangular fuzzy number algorithm is used to fuzzily quantify the importance of each second indicator factor (such as C311, C312, C313) in the set relative to its superior level (i.e., C31) indicator factor, based on each expert strategy and importance fuzzy judgment matrix. Assuming that expert y compares C311 and C312, their importance is expressed as a triangular fuzzy number ( Then, based on all the obtained triangular fuzzy numbers, specific mathematical methods, such as the geometric mean or root power method, are used to determine the initial weight vector of the indicator factors in each set of second indicator factors. Next, the initial weight vectors are normalized to obtain the weight vectors of these indicator factors with respect to the previous level indicator factors. .

[0088] For example, let X = {C1, C2, ..., C} p C n} is the set of n indicator factors at the k-th level, which collectively correspond to a certain indicator factor C at the previous level. j Triangular fuzzy number This represents the importance value of the p-th factor Cp in X relative to the q-th factor Cq. = ( Then, this nth factor relative to the previous factor C... j initial weight vector The calculation formula is:

[0089] ;

[0090] in, This represents the weight value corresponding to the indicator factor Cn calculated based on all triangular fuzzy numbers.

[0091] Through the Normalization yields these n factors (C1, C2, ...). C n Regarding the higher-level indicator factor C j weight vector As shown in the following formula:

[0092] ;

[0093] in, .

[0094] In this embodiment, the fuzzy judgments of experts regarding the relative importance of various factors in the deorbit control safety assessment system are effectively quantified. By constructing triangular fuzzy numbers and generating and normalizing weight vectors, scientific, systematic, and quantitative management of indicators at each level of the assessment system is achieved. This process not only enhances the objectivity and accuracy of the assessment results but also ensures the coherence and integrity of the assessment system. It has significant practical application value for guiding the optimization of low-Earth orbit spacecraft deorbit control strategies, improving mission success rates, and ensuring safety.

[0095] To improve the accuracy of determining the initial weight vector, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, the importance value of each second indicator factor is determined based on the triangular fuzzy number corresponding to each second indicator factor; for each second indicator factor, the supremum algorithm is used to determine the weight value of the second indicator factor based on the importance value of the second indicator factor and the importance values ​​of other second indicator factors; and the initial weight vector of the indicator factor is determined based on the weight value of each second indicator factor.

[0096] In this embodiment of the invention, the weight value corresponding to each second indicator factor can be determined based on the triangular fuzzy number corresponding to each second indicator factor. Then, based on the weight value corresponding to each second indicator factor, the initial weight vector corresponding to the indicator factor is determined. .

[0097] In this embodiment of the invention, the weight value of the indicator factor Cp , and min means to find the minimum value, supremum. It is a type of operation in fuzzy mathematics. This represents the overall importance value obtained after comparing factor Cp with all other factors in set X. This represents the overall importance value obtained after comparing factor Ck with all other factors in set X. The calculation formula is as follows:

[0098] ;

[0099] Among them, for the two triangular fuzzy numbers M1= M2= The formulas for addition, multiplication, and reciprocals are as follows:

[0100] ;

[0101] ;

[0102] ;

[0103] supremum operation The calculation formula is:

[0104] .

[0105] In this embodiment, not only are the fuzzy expert judgments transformed into specific numerical values, but a supremum algorithm is also used to ensure the rationality and objectivity of the weight allocation. The generated initial weight vector provides an important basis for the subsequent normalization processing and comprehensive evaluation of the weight vector, thus enhancing the scientific nature and accuracy of the evaluation system. Ultimately, this improves the reliability of the safety assessment of low-Earth orbit spacecraft deorbit control, provides more accurate data support for spacecraft mission planning and decision-making, helps optimize flight control strategies, enhances the safety and efficiency of the spacecraft reentry process, and reduces potential threats to personnel and property on the ground.

[0106] To improve the accuracy of the evaluation results, in the spacecraft deorbit control evaluation method provided in Embodiment 1 of this application, a first comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level is determined based on the weight vector and evaluation matrix of each indicator factor in the penultimate evaluation level; a second comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level is determined based on the weight vector and the first comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level; a third comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level is determined based on the weight vector and the second comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level, and so on, until the target comprehensive evaluation matrix of the indicator factor in the first evaluation level is determined, wherein the first evaluation level includes one indicator factor, and the indicator factor included in the first evaluation level is the target indicator factor; based on the target comprehensive evaluation matrix of the target indicator factor under all expert strategies, the final comprehensive evaluation matrix of the target indicator factor is determined; and the final comprehensive evaluation matrix is ​​determined as the evaluation result.

[0107] In this embodiment of the invention, for each indicator factor in the penultimate evaluation level, such as C31 (GNC system reliability), based on the weight vector of C31 and the evaluation matrix of the next evaluation level (i.e., the last evaluation level), the composition operation in the fuzzy comprehensive evaluation method (FCE) is used to fuse the weight vector and the evaluation matrix to calculate the first comprehensive evaluation matrix B1 (C31) of C31. The composition operation multiplies the weight vector with each column of the evaluation matrix to obtain a new matrix that integrates the weights and expert evaluation. For example, if C31 has four sub-indicators (C311, C312, C313, C314), the calculation process of B1 (C31) will involve fuzzy multiplication operations on each column of the evaluation matrix and the weight vector. Then, for each indicator factor in the penultimate evaluation level, such as C3, the second comprehensive evaluation matrix B2 (C3) of C3 is obtained using the weight vector of C3 and the first comprehensive evaluation matrix B1 (C31). For example, using fuzzy multiplication, the weight vector is multiplied by the fuzzy values ​​of the corresponding columns of all first comprehensive evaluation matrices, and finally a comprehensive evaluation matrix B2(C3) is obtained. Then, based on the weight vectors of each indicator factor in the fourth-to-last evaluation level and the second comprehensive evaluation matrix, the third comprehensive evaluation matrix for each indicator factor in the fourth-to-last evaluation level is determined, until the target comprehensive evaluation matrix for the indicator factors in the first evaluation level is determined. Here, the first evaluation level includes only one indicator factor (the target indicator factor), namely the overall safety indicator C. Finally, based on the target comprehensive evaluation matrices of the target indicator factors under all expert strategies, a final comprehensive evaluation is performed. This involves summing the target comprehensive evaluation matrices of all experts and determining a final comprehensive evaluation matrix using statistical methods such as averaging or medianing. This matrix will serve as the evaluation result for the entire evaluation process.

[0108] For example, suppose the p factors in the fourth level are related to the indicator factors in the previous level, i.e., the third level. The weight vector is The fuzzy evaluation matrix given by the y-th expert for these p factors is: Therefore, the third-level indicator factors are calculated. Fuzzy comprehensive evaluation results Its formula is: ;

[0109] After the fuzzy comprehensive evaluation results of each indicator factor in the third level are calculated, they are then combined with the indicator factors in the third level. Compared to the second level factors weight vector Calculate the second level Fuzzy comprehensive evaluation results Its formula is: ;

[0110] Based on the comprehensive evaluation results of the second-level factors and weight vector The fuzzy comprehensive evaluation result of the overall safety index (i.e., the overall safety index C) comprehensively evaluated by the y-th expert is calculated. The results are as follows:

[0111] ;

[0112] The process of repeatedly calculating the evaluation matrix of each factor in the fourth level corresponding to each expert strategy and the fuzzy comprehensive evaluation result of the total security index is repeated until the comprehensive evaluation result of all expert strategies is calculated. Based on the principle of maximum membership, the final security assessment conclusion is obtained. That is, starting from y=1, the calculation is repeated until y=Y, thus obtaining the comprehensive evaluation results of all Y experts. The total security index for this assessment is then calculated. The result is:

[0113] ;

[0114] Find the evaluation level in Table 1 corresponding to the largest element value in evaluation result B, and draw the final conclusion of the security assessment based on the principle of maximum membership.

[0115] For example, suppose the final calculated B is Since the largest element value in B is the second element 0.42, and its corresponding rating level in Table 1 is v2 (relatively safe), the final conclusion of this safety assessment is "relatively safe" based on the principle of maximum membership.

[0116] In this embodiment, a comprehensive evaluation process is constructed, progressively advancing from bottom-level indicators to top-level target indicators. This process not only quantifies the safety status of each indicator factor but also systematically integrates expert opinions and the importance of indicators through the synthesis of weight vectors and evaluation matrices, ensuring the scientific rigor and objectivity of the entire evaluation process. The final determination of the comprehensive evaluation matrix provides a comprehensive and detailed result for the safety assessment of low-Earth orbit spacecraft deorbit control. It not only considers the subjective judgment of experts but also conducts quantitative analysis based on a multi-level indicator system, significantly enhancing the practicality and decision support capabilities of the evaluation system. Thus, it can effectively guide the design and optimization of spacecraft missions, improve the safety and controllability of the spacecraft reentry process, and reduce potential risks to personnel and property on the ground.

[0117] Figure 3 This is a schematic diagram of an optional spacecraft deorbit control safety assessment process according to an embodiment of the present invention, such as... Figure 3As shown, a combination of fuzzy hierarchical analysis and fuzzy comprehensive evaluation is used to comprehensively assess the safety of spacecraft deorbit control. The evaluation process is as follows:

[0118] (1) Construct a security evaluation set including 5 evaluation levels;

[0119] (2) Through in-depth analysis of the controlled deorbiting and reentry process of spacecraft, key factors affecting safety are identified, and a four-level controlled deorbiting safety assessment index system is constructed.

[0120] (3) Invite experts to construct a fuzzy judgment matrix of the relative importance of each level of indicator factors using triangular fuzzy numbers;

[0121] (4) Experts comprehensively evaluate each indicator factor at the lowest level based on the safety assessment set;

[0122] (5) Using the fuzzy hierarchical analysis method, calculate the relative weight vector of each level of evaluation factors based on the fuzzy judgment matrix of the i-th expert;

[0123] (6) Combining the fuzzy comprehensive evaluation method, based on the relative weight vector of the fourth-level factors and the evaluation results, calculate the fuzzy comprehensive evaluation results of the expert's third, second and first level total indicators in turn;

[0124] (7) Continue to calculate the fuzzy comprehensive evaluation result of the next expert (i.e., i=i+1) until the comprehensive evaluation results of all experts are calculated. Based on the principle of maximum membership, the final security assessment conclusion is obtained.

[0125] In this embodiment of the invention, a combination of fuzzy hierarchical analysis and fuzzy comprehensive evaluation is employed. By constructing a multi-level safety assessment system encompassing target spacecraft data, the aim is to quantitatively analyze the safety of low-Earth orbit spacecraft deorbit control. This achieves a comprehensive and objective evaluation of the complex risks during spacecraft deorbiting, thus solving the technical problem that traditional assessment methods struggle to accurately reflect the multi-factor risks and uncertainties during deorbit control. Specifically, a fuzzy judgment matrix for each level of indicator factors (excluding the last level) is first constructed. Based on expert strategies, the degree of security level membership of the last level indicator factors is determined. Then, the weight vectors of each level of indicators are calculated. Finally, based on these weight vectors and the expert evaluation matrix, a comprehensive evaluation result is calculated to determine the safety level of the target spacecraft deorbit control, effectively improving the accuracy and reliability of the assessment.

[0126] The following is a detailed description with reference to another embodiment.

[0127] Example 2

[0128] The spacecraft deorbit control evaluation device provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0129] Figure 4 This is a schematic diagram of an optional spacecraft deorbit control evaluation device according to an embodiment of the present invention, such as... Figure 4 As shown, the evaluation device may include: a construction unit 40, a first determination unit 41, a second determination unit 42, and a third determination unit 43.

[0130] Among them, the construction unit 40 is used to construct the importance fuzzy judgment matrix of each indicator factor in the preset level of the preset evaluation system based on the spacecraft data of the target spacecraft. The preset evaluation system includes: multiple evaluation levels, each evaluation level includes: at least one indicator factor, and the preset level is all other evaluation levels except the last evaluation level.

[0131] The first determining unit 41 is used to determine the degree of membership of each indicator factor in the last evaluation level of the preset evaluation system at each security level in the preset security judgment set by adopting different expert strategies, so as to obtain the degree of membership matrix corresponding to each expert strategy, and determine the judgment matrix of each indicator factor in the second to last evaluation level based on the degree of membership matrix.

[0132] The second determining unit 42 is used to determine the weight vector of each indicator factor in the preset level based on the importance fuzzy judgment matrix.

[0133] The third determining unit 43 is used to determine the evaluation result based on the evaluation matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset level, and to determine the target safety level of the target spacecraft during deorbit control based on the evaluation result.

[0134] The aforementioned assessment device employs a combination of fuzzy hierarchical analysis and fuzzy comprehensive evaluation. By constructing a multi-level safety assessment system encompassing target spacecraft data, it achieves the goal of quantitatively analyzing the safety of low-Earth orbit spacecraft deorbit control. This enables a comprehensive and objective evaluation of the complex risks during spacecraft deorbiting, thus solving the technical problem that traditional assessment methods struggle to accurately reflect the multi-factor risks and uncertainties during deorbit control. Specifically, it first constructs fuzzy judgment matrices for each level of indicator factors (excluding the last level). Based on expert strategies, it determines the degree of security level membership of the last-level indicator factors. Then, it calculates the weight vectors for each level of indicators. Finally, based on these weight vectors and the expert evaluation matrix, it comprehensively calculates the evaluation result, thereby determining the safety level of the target spacecraft's deorbit control and effectively improving the accuracy and reliability of the assessment.

[0135] Optionally, the evaluation device further includes: a first construction module, used to construct a preset safety judgment set before constructing the importance fuzzy judgment matrix of each indicator factor in the preset level of the preset evaluation system based on the spacecraft data of the target spacecraft, wherein the preset safety judgment set includes: multiple safety levels; and a second construction module, used to construct the preset evaluation system, wherein the preset evaluation system is an evaluation system constructed based on the safety of the deorbit control process and the interrelationship and effects of various factors affecting safety.

[0136] Optionally, the construction unit includes: a first determining module, used to determine any two pairs of indicator factors in each lower level as indicator factor pairs, wherein the lower level is all other evaluation levels except the first evaluation level, and the same indicator factor pair is an indicator factor pair in which the indicator factors are the same and the positional order of the indicator factors are the same; a second determining module, used to determine the triangular fuzzy number of each pair of indicator factor pairs in the lower level based on the spacecraft data of the target spacecraft, using a triangular fuzzy number algorithm, wherein the triangular fuzzy number is a fuzzy representation of the relative importance between the two indicator factors in the indicator factor pair based on the preset indicator factors of the upper level of the lower level, and the preset indicator factors are the upper level indicator factors that the two indicator factors correspond to; the triangular fuzzy number includes: three fuzzy factors; and a first construction module, used to construct an importance fuzzy judgment matrix for each indicator factor in each preset level based on the triangular fuzzy number of each pair of indicator factor pairs.

[0137] Optionally, the first determining unit includes: a third determining module, used to determine the membership degree of each indicator factor in the last evaluation level at each security level for each expert strategy, wherein the sum of the membership degrees of the same indicator factor at all security levels is 1; and a third construction module, used to construct a membership degree matrix corresponding to the expert strategy based on all membership degrees.

[0138] Optionally, the first determining unit further includes: a fourth determining module, used to determine the set of first indicator factors in the last evaluation level corresponding to each indicator factor in the penultimate evaluation level; a first filtering module, used to filter the membership degree vector corresponding to each first indicator factor in the set of first indicator factors from the membership degree matrix; and a fourth constructing module, used to construct the evaluation matrix for each indicator factor in the penultimate evaluation level based on all membership degree vectors.

[0139] Optionally, the second determining unit includes: a fifth determining module, used to determine the set of second indicator factors in the next evaluation level corresponding to each indicator factor in the preset level; a sixth determining module, used to determine the triangular fuzzy number corresponding to each second indicator factor in the set of second indicator factors based on the importance fuzzy judgment matrix; a seventh determining module, used to determine the initial weight vector corresponding to the indicator factor based on all triangular fuzzy numbers; and a first processing module, used to normalize the initial weight vector to obtain the weight vector corresponding to the indicator factor.

[0140] Optionally, the seventh determining module includes: a first determining submodule, used to determine the importance value of each second indicator factor based on the triangular fuzzy number corresponding to each second indicator factor; a second determining submodule, used to determine the weight value of each second indicator factor for each second indicator factor by using the supremum algorithm based on the importance value of the second indicator factor and the importance values ​​of other second indicator factors; and a third determining submodule, used to determine the initial weight vector of the indicator factor based on the weight value of each second indicator factor.

[0141] Optionally, the third determining unit includes: an eighth determining module, used to determine a first comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level based on the weight vector and evaluation matrix of each indicator factor in the penultimate evaluation level; a ninth determining module, used to determine a second comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level based on the weight vector and the first comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level; a tenth determining module, used to determine a third comprehensive evaluation matrix for each indicator factor in the penultimate evaluation level based on the weight vector and the second comprehensive evaluation matrix of each indicator factor in the penultimate evaluation level, until the target comprehensive evaluation matrix of the indicator factor in the first evaluation level is determined, wherein the first evaluation level includes one indicator factor, and the indicator factor included in the first evaluation level is the target indicator factor; an eleventh determining module, used to determine the final comprehensive evaluation matrix of the target indicator factor based on the target comprehensive evaluation matrix of the target indicator factor under all expert strategies; and a twelfth determining module, used to determine the final comprehensive evaluation matrix as the evaluation result.

[0142] The aforementioned evaluation device may also include a processor and a memory. The aforementioned construction unit 40, first determining unit 41, second determining unit 42, third determining unit 43, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0143] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, the evaluation result is determined based on the evaluation matrix of each indicator factor under each expert strategy and the weight vector of each indicator factor in the preset hierarchy. Based on the evaluation result, the target safety level of the target spacecraft during deorbit control is determined.

[0144] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0145] This invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: based on spacecraft data of the target spacecraft, constructing an importance fuzzy judgment matrix for each indicator factor in a preset level of a preset evaluation system; employing different expert strategies to determine the membership degree of each indicator factor in the last evaluation level of the preset evaluation system at each security level in a preset security judgment set, obtaining a membership degree matrix corresponding to each expert strategy; and based on the membership degree matrix, determining the judgment matrix for each indicator factor in the penultimate evaluation level; based on the importance fuzzy judgment matrix, determining the weight vector for each indicator factor in the preset level; based on the judgment matrix for each indicator factor under each expert strategy and the weight vector for each indicator factor in the preset level, determining the judgment result; and based on the judgment result, determining the target security level of the target spacecraft during deorbit control.

[0146] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the evaluation method for spacecraft deorbit control described above.

[0147] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described evaluation method for spacecraft deorbit control.

[0148] Figure 5 This is a hardware structure block diagram of an electronic device (or mobile device) for an evaluation method of spacecraft deorbit control according to an embodiment of the present invention. Figure 5 As shown, an electronic device may include one or more processors (e.g., Figure 5The processors 502a, 502b, ..., 502n, etc., may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), and a memory 504 for storing data. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports in the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 5 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown.

[0149] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0150] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0151] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0152] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0155] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0156] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of evaluating a spacecraft de-orbit control, characterized by, The method comprises the following steps: Based on the spacecraft data of the target spacecraft, a fuzzy judgment matrix of the importance of each index factor in a preset level in a preset evaluation system is constructed, wherein the preset evaluation system comprises a plurality of evaluation levels, each evaluation level comprises at least one index factor, and the preset level is all other evaluation levels except the last evaluation level; Using different expert strategies, the membership degree of each index factor in the last evaluation level of the preset evaluation system at each safety level in the preset safety judgment set is determined, and a membership degree matrix corresponding to each expert strategy is obtained, and based on the membership degree matrix, a judgment matrix of each index factor in the second last evaluation level is determined; Based on the fuzzy judgment matrix of the importance, a weight vector of each index factor in the preset level is determined; Based on the judgment matrix of each index factor under each expert strategy and the weight vector of each index factor in the preset level, a judgment result is determined, and based on the judgment result, a target safety level of the target spacecraft during the orbit control is determined.

2. The evaluation method according to claim 1, characterized in that Before constructing a fuzzy judgment matrix of the importance of each index factor in a preset level in a preset evaluation system based on spacecraft data of a target spacecraft, the method further comprises the following steps: The preset safety judgment set is constructed, wherein the preset safety judgment set comprises a plurality of safety levels; The preset evaluation system is constructed, wherein the preset evaluation system is an evaluation system constructed based on the interrelation and action of factors affecting safety, with safety in the orbit control process as the framework.

3. The evaluation method according to claim 1, characterized in that The step of constructing a fuzzy judgment matrix of the importance of each index factor in a preset level in a preset evaluation system based on spacecraft data of a target spacecraft comprises the following steps: For each low level, any two index factors in the low level are determined as an index factor pair, wherein the low level is all other evaluation levels except the first evaluation level, and the same index factor pair is an index factor pair with the same index factors and the same position order of the index factors; Based on the spacecraft data of the target spacecraft, a triangular fuzzy number algorithm is used to determine a triangular fuzzy number of each index factor pair in the low level, wherein the triangular fuzzy number is a fuzzy representation of the relative importance between the two index factors in the index factor pair based on a preset index factor of the upper level of the low level, the preset index factor is an index factor of the upper level corresponding to both index factors, and the triangular fuzzy number comprises three fuzzy factors; Based on the triangular fuzzy number of each index factor pair, the fuzzy judgment matrix of the importance of each index factor in each preset level is constructed.

4. The evaluation method according to claim 1, characterized in that The step of determining the membership degree of each index factor in the last evaluation level of the preset evaluation system at each safety level in the preset safety judgment set using different expert strategies to obtain a membership degree matrix corresponding to each expert strategy comprises the following steps: For each of the expert strategies, the membership degrees of each of the index factors in each of the safety levels are determined by using the expert strategy, wherein the sum of the membership degrees of the same index factor in all the safety levels is 1; Based on all the membership degrees, the membership degree matrix corresponding to the expert strategy is constructed.

5. The evaluation method according to claim 1, characterized in that Based on the membership degree matrix, the step of determining the judgment matrix of each of the index factors in the second last evaluation level comprises: determining a first index factor set in the last evaluation level corresponding to each of the index factors in the second last evaluation level; screening a membership degree vector corresponding to each of the first index factors in the first index factor set from the membership degree matrix; based on all the membership degree vectors, constructing the judgment matrix of each of the index factors in the second last evaluation level.

6. The evaluation method according to claim 1, characterized in that Based on the importance fuzzy judgment matrix, the step of determining the weight vector of each of the index factors in the preset level comprises: determining a second index factor set in the next evaluation level corresponding to each of the index factors in the preset level; based on the importance fuzzy judgment matrix, determining a triangular fuzzy number corresponding to each of the second index factors in the second index factor set; based on all the triangular fuzzy numbers, determining an initial weight vector corresponding to the index factor; normalizing the initial weight vector to obtain the weight vector corresponding to the index factor.

7. The evaluation method according to claim 6, characterized in that Based on all the triangular fuzzy numbers, the step of determining the initial weight vector corresponding to the index factor comprises: based on the triangular fuzzy number corresponding to each of the second index factors, determining an importance value corresponding to each of the second index factors; for each of the second index factors, using the supremum algorithm to determine a weight value corresponding to the second index factor based on the importance value corresponding to the second index factor and the importance values corresponding to other second index factors; based on the weight value corresponding to each of the second index factors, determining the initial weight vector corresponding to the index factor.

8. The evaluation method according to claim 1, characterized in that Based on the judgment matrix of each of the index factors under each of the expert strategies and the weight vector of each of the index factors in the preset level, the step of determining the judgment result comprises: based on the weight vector and the judgment matrix of each of the index factors in the second last evaluation level, determining a first comprehensive judgment matrix of each of the index factors in the second last evaluation level; based on the weight vector and the first comprehensive judgment matrix of each of the index factors in the third last evaluation level, determining a second comprehensive judgment matrix of each of the index factors in the third last evaluation level; determining a third comprehensive evaluation matrix of each of the index factors in the fourth last evaluation level based on the weight vector of each of the index factors in the fourth last evaluation level and the second comprehensive evaluation matrix, until a target comprehensive evaluation matrix of the index factor in the first evaluation level is determined, wherein the first evaluation level includes one index factor, and the index factor in the first evaluation level is a target index factor; determining a final comprehensive evaluation matrix of the target index factor based on the target comprehensive evaluation matrix of the target index factor under all the expert strategies; determining the final comprehensive evaluation matrix as the evaluation result.

9. An evaluation device for spacecraft deorbit control, characterized in that, The method comprises the following steps: constructing an importance fuzzy judgment matrix of each index factor in a preset level in a preset evaluation system based on spacecraft data of a target spacecraft, wherein the preset evaluation system comprises a plurality of evaluation levels, each of the evaluation levels comprises at least one index factor, and the preset level is all other evaluation levels except for a last evaluation level; determining, by a first determining unit, a membership degree of each of the index factors in the last evaluation level in the preset evaluation system at each safety level in a preset safety evaluation set by using different expert strategies, obtaining a membership degree matrix corresponding to each of the expert strategies, and determining an evaluation matrix of each of the index factors in a second last evaluation level based on the membership degree matrix; determining, by a second determining unit, a weight vector of each of the index factors in the preset level based on the importance fuzzy judgment matrix; determining, by a third determining unit, an evaluation result based on the evaluation matrix of each of the index factors under each of the expert strategies and the weight vector of each of the index factors in the preset level, and determining a target safety level of the target spacecraft in orbit control based on the evaluation result.

10. An electronic device, comprising: The method comprises the following steps: one or more processors and a memory, the memory being configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method for evaluating orbit control of a spacecraft according to any one of claims 1 to 8.

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