An on-orbit spacecraft system performance evaluation method based on abnormal information

Abstract

The application discloses a kind of based on abnormal information's on-orbit spacecraft system efficiency evaluation method, comprising: the same platform system object vector of constructing on-orbit spacecraft system object;Task profile of the task executed by on-orbit spacecraft system object is constructed;The state set of on-orbit spacecraft system object is constructed;According to state set, based on the availability vector corresponding to object vector according to the historical abnormal state frequency of on-orbit spacecraft system object;According to state set, based on the average recovery time of historical abnormal state, the reliability square matrix is calculated;According to state set and task profile, based on the telemetry data of on-orbit spacecraft system, the ability vector corresponding to task profile is calculated;According to the availability vector, reliability square matrix and ability vector, system efficiency is obtained.

Description

Technical Field

[0001] This invention relates to a method for evaluating the performance of an on-orbit spacecraft system based on anomaly information, belonging to the technical field of on-orbit spacecraft systems. Background Technology

[0002] In-orbit spacecraft systems are costly and mission-critical, presenting significant challenges for on-orbit maintenance. Therefore, accurate performance evaluation to identify problems early is crucial. Effectiveness is defined as a system's ability to meet specific mission objectives under given conditions. To accurately assess the performance of in-orbit spacecraft systems and ensure they possess the capabilities necessary to accomplish their assigned missions, evaluating their effectiveness is essential.

[0003] Currently, various algorithms and models exist for system performance evaluation, including the grey relational analysis method, the TOPSIS method, and the ADC method. Among these methods, the performance evaluation method based on the ADC method is the most widely used. The ADC performance evaluation method aims to evaluate the system based on three key factors (availability, reliability, and capability), integrating these factors into a performance index that reflects the overall performance of the system. In this method, the availability vector A = [A1, A2, ..., A...]. i ,…A n This quantifies the system's state at the start of the task, representing the probability of the system being in different states, where A... i This represents the probability that the system is in state si when the task begins. The reliability of the system is represented by a square matrix D = [D...]. ii′ ]n×n represents, where D ii′ This represents the probability that the system transitions from state si to state si′ during the task. The capability vector C = [C1, C2, ..., C...]. i ,…C n ] T This represents the performance index achieved by the system in each state. The ADC method has been widely used in performance evaluation in fields such as vehicles, equipment systems, and weapon systems.

[0004] The ADC method was initially proposed in 1965 by the Weapon Systems Effectiveness Industry Advisory Committee (WSEIAC) to measure the effectiveness of military systems, including intercontinental ballistic missile (ICBM) systems, ground-controlled approach (GCA) systems, and Doppler navigation radar systems. It measures system conditions at mission start (availability), state transitions during mission (credibility), and system performance (capability) under different states by rationally designing critical states. Effectiveness metrics can be calculated by multiplying vectors (or matrices) A, D, and C. Many studies have focused on evaluating different target systems using traditional or enhanced ADC models, such as vehicles, equipment systems, and weapon systems. On-orbit spacecraft systems are high-value, mission-critical systems with extremely high repair difficulty. Therefore, several studies have proposed methods for evaluating the effectiveness of various spacecraft systems. Based on the Analytic Hierarchy Process (AHP) and the ADC method, Liu et al. evaluated the effectiveness of Earth observation satellite systems. De et al. used the Markov chain method to model the state transitions of remote sensing satellites. Shao et al. introduced birth and death processes, proposing a G-BDP-ADC model for low Earth orbit (LEO) satellite communication systems. Subsequently, they combined grey system theory with the ADC method to develop a G-Lz-ADC performance evaluation method for a single communication satellite system.

[0005] However, traditional ADC performance evaluation methods primarily focus on military weapon systems. For on-orbit spacecraft systems, traditional ADC performance evaluation methods face the following challenges: 1. The availability vector A represents the probability of the system being in various states at the start of the mission. However, for on-orbit spacecraft systems, data samples of abnormal states are very limited. Therefore, although traditional ADC methods have theoretical and research significance, their engineering applicability is weak due to the lack of sample data for each state. 2. The reliability matrix D represents the probability of the system transitioning between states during the mission. Existing research typically uses traditional reliability indicators such as mean time between failures (MTBF) and mean time to repair (MTTR) to characterize this probability. However, obtaining these design indicators in practical engineering applications of on-orbit spacecraft system performance evaluation missions in the aerospace field is difficult, significantly limiting their application.

[0006] Historical anomaly information of spacecraft, as recorded data of anomalous events during the lifecycle of an on-orbit spacecraft system, contains a large amount of information and provides valuable reference for applying the availability vector A and reliability matrix D of traditional ADC methods to the calculation of on-orbit spacecraft systems. Furthermore, given the similar design and consistent anomaly patterns of on-orbit spacecraft systems on the same platform, the dataset can be expanded by integrating anomaly data from multiple spacecraft on the same platform, providing data support for the availability and reliability calculations of the ADC method.

[0007] In summary, in order to address the problem of the weak engineering practicality of traditional methods for evaluating the performance of on-orbit spacecraft systems, it is necessary to provide a performance evaluation method that can make full use of anomaly information of on-orbit spacecraft systems, and provide a reference for evaluating the mission support capability of on-orbit spacecraft systems from the perspective of engineering feasibility. Summary of the Invention

[0008] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a method for evaluating the performance of an on-orbit spacecraft system based on anomaly information, so as to accurately evaluate the performance of the on-orbit spacecraft system.

[0009] The technical solution of this invention is:

[0010] This invention discloses an on-orbit spacecraft system performance evaluation method based on anomaly information, comprising:

[0011] Construct a platform-based system object vector for on-orbit spacecraft system objects;

[0012] Construct a mission profile of the tasks performed by the on-orbit spacecraft system object;

[0013] Construct the state set of on-orbit spacecraft system objects;

[0014] Based on the state set, and using the historical abnormal state frequency of the on-orbit spacecraft system object, calculate the availability vector corresponding to the object vector;

[0015] Based on the set of states, the reliability matrix is ​​calculated using the average recovery time of historical abnormal states.

[0016] Based on the state set and mission profile, and using telemetry data from the on-orbit spacecraft system, the capability vector corresponding to the mission profile is calculated.

[0017] The system effectiveness is obtained based on the availability vector, the reliability matrix, and the capability vector.

[0018] Furthermore, in the above method, the same-platform system object vector of the on-orbit spacecraft system object is specifically:

[0019] S = [S1,S2,…,S] k ,…S r ]

[0020] Wherein, S1 represents the on-orbit spacecraft system object, S k This represents the k-th other spacecraft system object on the same platform. The information of each object includes the platform name, the research and development unit, the subsystem information, and the unit information; r represents the number of spacecraft objects on this platform.

[0021] Furthermore, in the above method, the mission profile of the task performed by the on-orbit spacecraft system object. This includes: the task domain, task name, performance indicators related to task support capabilities, and the calculation methods for these indicators.

[0022] Furthermore, in the above method, the state set of the on-orbit spacecraft system object is specifically: [s1,s2,…,si,…,sn], where s1 is the normal state, that is, the design performance index of the mission can be achieved in this state, si is the abnormal state, and i=2,…,n.

[0023] Furthermore, in the above method, the availability vector is specifically:

[0024] A = [A1, A2, ..., A i ,…A n ]

[0025]

[0026] Where n is the number of states, A i This represents the probability that the system is in state si. Indicates the spacecraft S k The duration of the j-th occurrence of the i-th type of anomaly; Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Total time currently in orbit.

[0027] Furthermore, in the above method, the credibility matrix specifically refers to:

[0028] D = [D ii′ ] n×n ,in

[0029]

[0030] Where t is the task duration, R i Let D be the probability that the system starts from state s1 and transitions to state si within the task duration t. ii′ This represents the probability that the system transitions from state si to state si′ during the task, where n is the number of states. Indicates the spacecraft S k The duration of the j-th occurrence of the i-th type of anomaly; Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Total time currently in orbit.

[0031] Furthermore, in the above method, the capability vector corresponding to the task profile is specifically:

[0032] C = [C1,C2,…,C] i ,…C n ] T

[0033]

[0034] Among them, C i This represents the system's performance index when the spacecraft system is in state si, where i = 1, ..., n, n is the number of states, and p = [p1, p2, ...] is a vector of spacecraft system telemetry parameters. This is a function for calculating the system's performance indicators when the spacecraft system is in state si.

[0035] Furthermore, in the above method, the system performance specifically refers to:

[0036] E = A·D·C

[0037] Where E is system performance, A is availability vector, D is reliability matrix, C is capability vector, and · is matrix product.

[0038] The advantages of this invention over the prior art are as follows:

[0039] (1) This invention addresses the characteristics of on-orbit spacecraft systems in the aerospace field, such as being unrepairable during missions, not being able to automatically recover after state switching, state transitions during missions affecting overall performance, and performance indicators being calculated from telemetry parameters. It improves the traditional ADC method so that it can be used for performance evaluation of this special system of on-orbit spacecraft.

[0040] (2) By introducing historical anomaly information, this invention uses the anomaly frequency and mean recovery time in the historical anomaly information to replace the reliability MTBF and MTTR indicators in the traditional ADC performance evaluation method to calculate availability and credibility, which effectively solves the problem of weak engineering practicality and limited application of the traditional ADC method for on-orbit spacecraft system objects.

[0041] (3) In view of the similarity of the status of the same platform system in the on-orbit spacecraft system, the present invention can effectively solve the problem of lack of abnormal status data of the on-orbit spacecraft system by introducing spacecraft system data from multiple identical spacecraft platforms to expand the abnormal dataset.

[0042] (4) In view of the fact that the on-orbit spacecraft system has relatively rich real-time and historical time-series data of telemetry parameters, the present invention effectively solves the problem that the state capability indicators of the traditional ADC method are static values ​​and cannot reflect the real-time state of the system by using telemetry parameter data as input for performance capability indicators.

[0043] (5) Based on an engineering-feasible approach, the present invention can accurately assess the performance of on-orbit spacecraft systems, ensure that the systems have the capability to complete their assigned tasks, and provide a reference for the assessment of the mission support capability of on-orbit spacecraft systems. Attached Figure Description

[0044] Figure 1 This is a flowchart of an on-orbit spacecraft system performance evaluation method based on anomaly information according to the present invention. Detailed Implementation

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] like Figure 1 As shown, this invention provides a method for evaluating the performance of an on-orbit spacecraft system based on anomaly information, comprising the following steps:

[0047] Based on historical anomaly information of the target spacecraft and its similar platform spacecraft systems, the actual probability is replaced by the theoretical probability. The system availability is calculated based on the frequency of historical anomalies. At the same time, the mean recovery time of anomalies is used instead of traditional reliability indicators to evaluate the system's reliability. Finally, the system availability, reliability, and capability are combined to calculate the on-orbit spacecraft system effectiveness.

[0048] (1) Define the on-orbit spacecraft system object S1 under study and obtain its spacecraft platform information. The spacecraft platform information mainly includes: platform name (e.g., a general-purpose platform for a certain low-Earth orbit satellite), development unit, subsystem information, unit information, and information on other spacecraft on the same platform. Further, obtain other spacecraft system objects on the same platform as S1 to form a spacecraft system object vector, specifically:

[0049] S = [S1,S2,…,S] k ,…S r ]

[0050] Wherein, S1 represents the on-orbit spacecraft system object, S k (k = 2, ..., r) represents the kth other spacecraft system object on the same platform. r represents the number of spacecraft objects on this platform.

[0051] (2) Mission information profile of the mission performed by the on-orbit spacecraft system object S1 as described in definition (1) Specifically, this includes the following mission information: mission domain, mission name, performance indicators related to mission support capabilities, and calculation methods for these indicators. For example, it includes specific missions of on-orbit spacecraft in fields such as scientific measurement, communication, navigation, and remote sensing, and the performance indicators related to mission support capabilities.

[0052] (3) Define the set of states of the on-orbit spacecraft system object S1, specifically: states s1 to sn: [s1, s2, ..., si, ..., sn], which should include one normal state (state s1, in which the design performance indicators of the mission can be achieved) and one or more abnormal states (states s2 to sn, in which the design performance indicators can be partially or completely not achieved). The definition of states may include: control subsystem fault states, power supply and distribution subsystem fault states, etc.

[0053] (4) Calculate the availability vector A based on the historical abnormal state frequency of the on-orbit spacecraft system. Based on the state set defined in (3), the availability vector A = [A1, A2, ..., A...]. i ,…A n This describes the probability that an on-orbit spacecraft system will be in different states at the start of a mission. The calculation method is as follows:

[0054]

[0055] Where n is the number of states defined in (3), A i Let A represent the probability that the system is in state si. Since actual abnormal data statistics are used instead of theoretical probabilities, A is... i The formula uses approximate values; In (1), spacecraft S is defined. k The j-th occurrence of the i-th type (i=2,…,n) anomaly on (k=1,…,r) Duration, Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, where T is the total number of occurrences of spacecraft S. k Current total time in orbit.

[0056] (5) Calculate the reliability matrix D based on the average recovery time of historical abnormal states. System reliability matrix D = [D...] ii′ ] n×n Define R i The probability that the system starts from state s1 and transitions to state si within the task duration t is calculated using the following formula:

[0057]

[0058] D ii′The calculation formula is:

[0059]

[0060] Where t is the task duration, D ii′ Let n represent the probability that the system transitions from state si to state si′ during the task, where n is the number of states defined in (3). The spacecraft S described in (1) k The j-th occurrence of the i-th type (i=2,…,n) anomaly on (k=1,…,r) Duration, Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Current total time in orbit.

[0061] (6) Based on the task information profile defined in (2) Construct and calculate the capability vector C using telemetry data from the on-orbit spacecraft system. Capability vector C = [C1, C2, ..., C...] i ,…C n ] T A metric representing the performance of a system in completing a specific task under each state, where C i Let represent the system's performance capability index when the spacecraft system is in state si, where i = 1, ..., n, and n is the number of states defined in (3). For an on-orbit spacecraft system, capability can be expressed as a function of telemetry parameters: p = [p1, p2, ...] is a vector of telemetry parameters for the spacecraft system, derived from historical telemetry data records (such as...). Figure 1 Obtained from the historical telemetry database shown. This is a function for calculating the system's performance indicators when the spacecraft system is in state si, based on the mission information profile. The result is obtained, where t is the task duration, indicating that the metric calculation function is related to the task duration t.

[0062] (7) Obtain the system availability, reliability and capability from (4), (5) and (6), and calculate its effectiveness: E = A·D·C, where E is the system effectiveness, A is the availability vector defined in (4), D is the reliability matrix defined in (5), C is the capability vector defined in (6), and · is the matrix product.

[0063] Example

[0064] (1) Define the on-orbit spacecraft system object S1 under study, obtain its spacecraft platform information, and obtain the spacecraft systems S2,…,S on the same platform. rSpacecraft platform information mainly includes: platform name (e.g., a general-purpose platform for a certain low-orbit satellite), research and development unit, subsystem information, unit information, and information on other spacecraft on the same platform.

[0065] (2) Define the task information profile performed by the object For example, in specific missions of on-orbit spacecraft in fields such as scientific measurement, communication, navigation, and remote sensing, the performance indicators involved in acquiring mission support capabilities are analyzed, and the system and mission characteristics are considered in conjunction with the selected mission analysis. On-orbit spacecraft systems perform various missions throughout their lifecycle, with each mission lasting from several hours to several days. During a mission, repair or maintenance is usually not feasible (unless the mission terminates abnormally), and once the system transitions from a normal state to an abnormal state, it cannot automatically recover during the mission. Furthermore, state transitions during a mission can affect overall system performance.

[0066] (3) Define system states s1 to sn: To effectively evaluate the performance of the selected on-orbit spacecraft system for a specific mission, it is necessary to define its possible states before the evaluation. These states should include a normal state (in which the design performance indicators of the mission can be achieved) and one or more abnormal states (in which the design performance indicators are partially or completely unachievable). The definition of states may include: control subsystem failure states, power supply and distribution subsystem failure states, etc. Without loss of generality, three types of n states are defined for the on-orbit spacecraft system: state s1 represents the on-orbit spacecraft system in a normal state, state sn represents a complete failure state in which the performance indicators of the specific mission are not achieved at all, and the other states represent different abnormal states (relative to the specific mission) in which the performance indicators are partially achieved.

[0067] (4) Calculate availability. Based on the states s1 to sn defined in (3), the availability vector A = [A1, A2, ..., An] is calculated. i ,…A n Describes the probability that the on-orbit spacecraft system is in different states at the start of the mission, where A i This represents the probability of being in state si. In traditional ADC methods, A is calculated based on the reliability metrics MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) defined during the system design phase. However, for in-orbit spacecraft systems, it is often difficult to use these traditional reliability metrics to determine the availability vector for the following two reasons: 1) MTBF and MTTR metrics are designed for the entire lifecycle of a spacecraft system, subsystem, or device, while for a specific mission, only one or a few performance metrics may be relevant, thus rendering these metrics inapplicable; 2) MTBF and MTTR are not always available in performance evaluation tasks. Therefore, this invention does not rely on MTBF and MTTR metrics, but instead uses actual probabilities instead of theoretical probabilities, calculating system availability based on the frequency of historical anomalies. For a system with an operating time of T... k On-orbit spacecraft system S k Assume there are n-1 types of anomalies affecting the performance metrics related to the defined task. Historical anomaly records are shown in Table 1.

[0068] Table 1. On-orbit spacecraft S k Historical anomaly records

[0069]

[0070] …

[0071] in, Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly (i = 1 represents the normal state, and the anomaly category numbering starts from 2, i = 2, ..., n), This represents the duration of an anomaly numbered j. To alleviate the lack of anomaly state data for on-orbit spacecraft systems, this invention considers r spacecraft systems with the same spacecraft platform to expand the anomaly dataset. These r spacecraft systems have the same or similar designs, so their anomaly information can be shared. Without loss of generality, state s1 is defined as the state of normal operation of the on-orbit spacecraft system, and states s2 to sn are defined as states where the system is in anomaly type 2 to n. Based on this, the availability vector A is calculated as: A = [A1, A2, ..., A...]. i ,…A n ],in: This invention uses the ratio of the cumulative duration of each anomaly to the overall system lifetime to represent the initial probability of each state at the start of a mission. Furthermore, it utilizes anomaly data from other spacecraft systems on the same platform to compensate for data scarcity, providing a more practical and feasible method for obtaining actual measurements of the availability vector A for on-orbit spacecraft systems.

[0072] (5) Calculate reliability. The reliability of the system is determined by a square matrix D = [D ii Quantization is done using ']n×n, where D ii′This indicates that the system transitions from state si to state si during the task process. ′ The probability of [the event occurring]. Considering the difficulty of maintaining on-orbit spacecraft systems and the rarity of abnormal events, it is assumed that the system cannot transition from an abnormal state to a normal state during the mission, and that the system will not transition from one abnormal state to another. Define R. i R is the probability that the system starts from state s1 and transitions to state si within the task duration t. i The calculation formula is Where λ i It is the system failure rate associated with exception i, t is the task duration, and n is the number of states defined in (3). The spacecraft S described in (1) k The j-th occurrence of the i-th type (i=2,…,n) anomaly on (k=1,…,r) Duration, Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Current total time in orbit; D ii′ The calculation formula is: Credibility Matrix Similar to the analysis related to availability, the approximation and simplification of the reliability matrix D also uses the frequency of anomalies to represent the system failure rate λ. i Furthermore, this method incorporates anomalous data from other spacecraft systems on the same platform. Additionally, it sparsifies the reliability matrix D by considering the unique characteristics of on-orbit spacecraft systems (which can only transition from a normal state to an anomalous state during a mission).

[0073] (6) Computational ability. Ability vector C = [C1, C2, ..., C...] i ,…C n ] T Capability is a measure of the performance required to complete a specific task in each state. For other systems, capability is simply a function of state. However, on-orbit spacecraft systems are more complex, as their system state can only be obtained through on-orbit telemetry information. For on-orbit spacecraft systems, capability can be expressed as a function of telemetry: p = [p1, p2, ...] is a vector of telemetry parameters for the spacecraft system, derived from historical telemetry data records (such as...). Figure 1 Obtained from the historical telemetry database shown. This is a function for calculating the system's performance indicators when the spacecraft system is in state si, based on the mission information profile. The result is obtained, where t is the task duration, indicating that the metric calculation function is related to the task duration t.

[0074] (7) Obtain the spacecraft system availability, reliability, and capability matrix from (4), (5), and (6), and calculate its overall performance.

[0075]

[0076]

[0077] The following is an application example of this invention (all data below are hypothetical). For the target spacecraft system S1, there are 7 spacecraft systems on the same platform. We define the performance indicator as "heating time of equipment in the propulsion subsystem," with a design requirement of 380 seconds. Based on the mission and performance indicator, we define two states: 1) the target equipment is working normally; 2) the target equipment malfunctions. Based on the anomaly information in the anomaly database, historical anomaly records are as follows:

[0078] Table 2 Example of Historical Anomaly Records

[0079]

[0080]

[0081] Note: Spacecraft 2, 5, and 7 did not experience any "propulsion subsystem equipment failure" target anomalies during the statistical period, so the duration of the anomaly and the corresponding heating time are recorded as " / ".

[0082] In this example, the average heating time under abnormal conditions = total heating time under abnormal conditions / number of abnormalities = (247 + 198 + 367 + 184 + 394 + 70 + 269) seconds / 7 times = 247 seconds. Given that the heating time under normal conditions is 400 seconds and the task duration is t = 0.3 days, T... k Total = 2812 days, therefore the efficiency calculation for this example is as follows:

[0083]

[0084] Therefore, in this example, the target spacecraft system's efficiency for the propulsion subsystem heating task is 399.62 seconds, meeting the 380-second requirement. This result indicates that the system is assessed as capable of completing the mission.

[0085] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

[0086] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for evaluating the performance of an on-orbit spacecraft system based on anomaly information, characterized in that, include: Construct a platform-based system object vector for on-orbit spacecraft system objects; Construct a mission profile of the tasks performed by the on-orbit spacecraft system object; Construct the state set of on-orbit spacecraft system objects; Based on the state set, and using the historical abnormal state frequency of the on-orbit spacecraft system object, calculate the availability vector corresponding to the object vector; Based on the set of states, the reliability matrix is ​​calculated using the average recovery time of historical abnormal states. Based on the state set and mission profile, and using telemetry data from the on-orbit spacecraft system, the capability vector corresponding to the mission profile is calculated. The system effectiveness is obtained based on the availability vector, the reliability matrix, and the capability vector.

2. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The platform-based system object vector of the on-orbit spacecraft system object is specifically as follows: S=[S1,S2,…,S k ,…S r ] Wherein, S1 represents the on-orbit spacecraft system object, S k This represents the k-th other spacecraft system object on the same platform. The information of each object includes the platform name, the research and development unit, the subsystem information, and the unit information; r represents the number of spacecraft objects on this platform.

3. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, Mission profile of the tasks performed by the on-orbit spacecraft system object This includes: the task domain, task name, performance indicators related to task support capabilities, and the calculation methods for these indicators (the calculation methods are used to determine the performance indicator calculation functions in the subsequent capability vector). ).

4. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The set of states of the on-orbit spacecraft system object is specifically: [s1,s2,…,si,…,sn], where s1 is the normal state, that is, the design performance indicators of the mission can be achieved in this state, si is the abnormal state, and i=2,…,n.

5. The method for evaluating the performance of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The availability vector is specifically: A=[A1,A2,…,A i ,…A n ] Where n is the number of states, A i This represents the probability that the system is in state i. Indicates the spacecraft S k The duration of the j-th occurrence of the i-th type of anomaly; k = 1, ..., r, i = 2, ..., n. Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Total time currently in orbit.

6. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The credibility matrix is ​​specifically as follows: D = [D ii′ ] n×n ,in Where t is the task duration, R i Let D be the probability that the system starts from state s1 and transitions to state si within the task duration t. ii′ This represents the probability that the system transitions from state si to state si′ during the task, where n is the number of states. Indicates the spacecraft S k The duration of the j-th occurrence of the i-th type of anomaly; k = 1, ..., r, i = 2, ..., n. Indicates spacecraft S k The total number of occurrences of the i-th type of anomaly, T k For spacecraft S k Total time currently in orbit.

7. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The capability vector corresponding to the task profile is specifically as follows: C=[C1,C2,…,C i ,…C n ]T Among them, C i This represents the system's performance index when the spacecraft system is in state si, where i = 1, ..., n, n is the number of states, and p = [p1, p2, ...] is a vector of spacecraft system telemetry parameters; This is a function for calculating the system's performance indicators when the spacecraft system is in state si.

8. The method for evaluating the effectiveness of an on-orbit spacecraft system based on anomaly information according to claim 1, characterized in that, The system performance is specifically as follows: E = A·D·C Where E is system performance, A is availability vector, D is reliability matrix, C is capability vector, and · is matrix product.

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