Autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability

Abstract

The application discloses a kind of based on diagnosability spacecraft fault diagnosis measurement data autonomous optimization method, comprising: obtaining the state equation of spacecraft control system;According to the state equation of spacecraft control system, system dynamic expression is constructed;According to system dynamic expression, system dynamic expression determined by diagnostic measurement data is constructed;According to the system dynamic expression determined by diagnostic measurement data, the random characteristics corresponding to diagnostic measurement data combination is obtained;According to the random characteristics corresponding to diagnostic measurement data combination, the diagnosability analysis result corresponding to diagnostic measurement data combination is obtained;According to the diagnosability analysis result corresponding to diagnostic measurement data combination, diagnostic measurement data autonomous screening logic is obtained;According to diagnostic measurement data autonomous screening logic, the optimal diagnostic measurement data combination is obtained.The application effectively reduces the amount of fault diagnosis calculation by reducing the amount of data required for diagnosis, realizes the autonomous optimization of spacecraft diagnostic measurement data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of spacecraft overall, and particularly relates to a spacecraft fault diagnosis measurement data autonomous optimization method based on diagnosability. BACKGROUND

[0002] In order to ensure the safe and reliable autonomous operation of the spacecraft, the spacecraft needs to be able to realize autonomous fault diagnosis. The spacecraft has the characteristics of severe resource constraints. How to reduce the amount of diagnosis required for calculation is a core problem that needs to be solved for the rational use of limited resources of the spacecraft. Li et al. (Li Z, Zhang Y, Ai J, et al. A lightweight and explainable data-driven scheme for fault detection of aerospace sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 2023.) introduced an improved particle swarm optimization-CNN method based on advanced convolutional blocks to solve the problem of overdesign and insufficient precision in the design of CNN network in the fault diagnosis of satellite attitude control system, and provided a lightweight and high-sensitivity supervised anomaly detection method, but did not reduce the amount of diagnosis calculation from the perspective of data, which is difficult to further reduce the computing burden of the on-board computer. SUMMARY

[0003] The technical problem solved by the application is to overcome the shortcomings of the prior art and provide a spacecraft fault diagnosis measurement data autonomous optimization method based on diagnosability. By reducing the amount of data required for diagnosis, the amount of fault diagnosis calculation is effectively reduced, and the autonomous optimization of spacecraft diagnosis measurement data is realized.

[0004] The purpose of the application is achieved by the following technical scheme: a spacecraft fault diagnosis measurement data autonomous optimization method based on diagnosability, comprising: obtaining a state equation of a spacecraft control system; constructing a system dynamic expression according to the state equation of the spacecraft control system; constructing a system dynamic expression determined by diagnosis measurement data according to the system dynamic expression; obtaining a random feature corresponding to a diagnosis measurement data combination according to the system dynamic expression determined by the diagnosis measurement data; obtaining a diagnosability analysis result corresponding to the diagnosis measurement data combination according to the random feature corresponding to the diagnosis measurement data combination; obtaining a diagnosis measurement data autonomous screening logic according to the diagnosability analysis result corresponding to the diagnosis measurement data combination; and obtaining an optimal diagnosis measurement data combination according to the diagnosis measurement data autonomous screening logic.

[0005] In the above-mentioned spacecraft fault diagnosis measurement data autonomous optimization method based on diagnosability, the state equation of the spacecraft control system is obtained by the following formula:

[0006] x(k+1) = A d x(k) + B d u(k) + B d,f f(k) + B d,w w(k)

[0007] y(k) = C d x(k) + D d,f f(k) + D d,v v(k) ;

[0008] wherein y(k) is a system output vector at the kth moment, k is a moment, x(k+1) is a system state vector at the k+1th moment, x(k) is a system state vector at the kth moment, u(k) is a control input vector at the kth moment, f(k) is a fault vector at the kth moment, w(k) is a process noise vector at the kth moment, v(k) is a measurement noise vector at the kth moment, A d is a first parameter matrix of the system, B d is a second parameter matrix of the system, B d,f is a third parameter matrix of the system, B d,w is a fourth parameter matrix of the system, C d is a fifth parameter matrix of the system, D d,f is a sixth parameter matrix of the system, D d,v is a seventh parameter matrix of the system.

[0009] In the above autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression is obtained through the following formula:

[0010] d(k-s,k) - H u u(k-s,k) = H o x(k-s) + H f f(k-s,k) + H e e(k-s,k) ;

[0011] wherein,

[0012]

[0013]

[0014] wherein k represents a moment, s represents a diagnosis time window length, d(k-s,k) is a diagnosis measurement data vector of the system dynamic expression, u(k-s,k) is a control input vector of the system dynamic expression, x(k-s) is a system state vector at the k-sth moment, f(k-s,k) is a fault vector of the system dynamic expression, e(k-s,k) is a noise vector of the system dynamic expression, H uH is the first parameter matrix of the system's dynamic expression. o H is the second parameter matrix of the system's dynamic expression. f Let A be the third parameter matrix of the system dynamic expression, u(k) be the control input vector at time k, f(k) be the fault vector at time k, w(k) be the process noise vector at time k, and v(k) be the measurement noise vector at time k. d Let B be the first parameter matrix of the system. d B is the system's second parameter matrix. d,f B is the third parameter matrix of the system. d,w C is the fourth parameter matrix of the system. d D is the fifth parameter matrix of the system. d,f D is the sixth parameter matrix of the system. d,v Let be the seventh parameter matrix of the system, y(k) be the system output vector at time k, y(ks) be the system output vector at time ks, y(k-s+1) be the system output vector at time k-s+1, u(k-s+1) be the control input vector at time k-s+1, u(ks) be the control input vector at time ks, f(ks) be the fault vector at time ks, f(k-s+1) be the fault vector at time k-s+1, w(ks) be the process noise vector at time ks, and v(ks) be the measurement noise vector at time ks.

[0015] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression determined by the diagnostic measurement data is obtained through the following formula:

[0016]

[0017] Where k represents time, s represents the diagnostic time window length, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at time ks, f(ks,k) is the fault vector of the system dynamic expression, and e(ks,k) is the noise vector of the system dynamic expression. This represents the i-th combination of diagnostic measurement data. The first parameter matrix represents the system dynamic expression determined by diagnostic measurement data. The second parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The third parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The fourth parameter matrix represents the system dynamic expression determined by diagnostic measurement data. This indicates that the variables involved are determined by the combination of diagnostic measurement data of the i-th type.

[0018] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the random characteristics corresponding to the combination of diagnostic measurement data are obtained by the following formula:

[0019]

[0020] Where E() is the mean vector, D() is the covariance matrix, f(ks,k) is the fault vector in the system dynamics expression, x(ks) is the system state vector at time ks, and e(ks,k) is the noise vector in the system dynamics expression. The second parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The third parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The fourth parameter matrix represents the system dynamic expression determined by diagnostic measurement data. This indicates that the variables involved are determined by the i-th combination of diagnostic measurement data, where i is the sequence number of the diagnostic measurement data combination.

[0021] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the diagnosability analysis results corresponding to the combination of diagnostic measurement data are obtained by the following formula:

[0022]

[0023] Among them, Diagnosability Let f(ks,k) be the diagnostic capability analysis result corresponding to the i-th combination of diagnostic measurement data, f(ks,k) be the fault vector in the system dynamic expression, x(ks) be the system state vector at time ks, and e(ks,k) be the noise vector in the system dynamic expression. The second parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The third parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The fourth parameter matrix represents the system dynamic expression determined by diagnostic measurement data. To indicate that the variable involved is determined by the i-th combination of diagnostic measurement data, i is the sequence number of the diagnostic measurement data combination.

[0024] In the above-mentioned autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, the autonomous screening logic for diagnostic measurement data is obtained through the following formula:

[0025]

[0026] in, For optimal combination of diagnostic measurement data, Diagnosability This represents the diagnosticability analysis result corresponding to the i-th combination of diagnostic measurement data. This indicates that the variables involved are determined by the i-th combination of diagnostic measurement data, where i is the sequence number of the diagnostic measurement data combination.

[0027] A spacecraft fault diagnosis measurement data autonomous optimization system based on diagnosability includes: a first module for obtaining the state equation of the spacecraft control system; a second module for constructing a system dynamic expression based on the state equation of the spacecraft control system; a third module for constructing a system dynamic expression determined by diagnostic measurement data based on the system dynamic expression; a fourth module for obtaining the stochastic characteristics corresponding to the combination of diagnostic measurement data based on the system dynamic expression determined by the diagnostic measurement data; a fifth module for obtaining the diagnosability analysis result corresponding to the combination of diagnostic measurement data based on the stochastic characteristics corresponding to the combination of diagnostic measurement data; a sixth module for obtaining the autonomous selection logic of diagnostic measurement data based on the diagnosability analysis result corresponding to the combination of diagnostic measurement data; and a seventh module for obtaining the optimal combination of diagnostic measurement data based on the autonomous selection logic of diagnostic measurement data.

[0028] In the aforementioned autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, the state equation of the spacecraft control system is obtained through the following formula:

[0029] x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k)

[0030] y(k)=C d x(k)+D d,f f(k)+D d,v v(k);

[0031] Where y(k) is the system output vector at time k, k is time, x(k+1) is the system state vector at time k+1, x(k) is the system state vector at time k, u(k) is the control input vector at time k, f(k) is the fault vector at time k, w(k) is the process noise vector at time k, v(k) is the measurement noise vector at time k, and A d Let B be the first parameter matrix of the system. d B is the system's second parameter matrix. d,f B is the third parameter matrix of the system. d,w C is the fourth parameter matrix of the system. d D is the fifth parameter matrix of the system. d,f D is the sixth parameter matrix of the system. d,v This is the seventh parameter matrix of the system.

[0032] In the aforementioned autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, the system dynamic expression is obtained through the following formula:

[0033] d(ks,k)-H u u(ks,k)=H o x(ks)+H f f(ks,k)+H e e(ks,k);

[0034] in,

[0035]

[0036]

[0037] Where k represents time, s represents the diagnostic time window length, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at time ks, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, and H u H is the first parameter matrix of the system's dynamic expression. o H is the second parameter matrix of the system's dynamic expression. f Let A be the third parameter matrix of the system dynamic expression, u(k) be the control input vector at time k, f(k) be the fault vector at time k, w(k) be the process noise vector at time k, and v(k) be the measurement noise vector at time k. d Let B be the first parameter matrix of the system. d B is the system's second parameter matrix. d,f B is the third parameter matrix of the system. d,w C is the fourth parameter matrix of the system. d D is the fifth parameter matrix of the system. d,f D is the sixth parameter matrix of the system. d,v Let be the seventh parameter matrix of the system, y(k) be the system output vector at time k, y(ks) be the system output vector at time ks, y(k-s+1) be the system output vector at time k-s+1, u(k-s+1) be the control input vector at time k-s+1, u(ks) be the control input vector at time ks, f(ks) be the fault vector at time ks, f(k-s+1) be the fault vector at time k-s+1, w(ks) be the process noise vector at time ks, and v(ks) be the measurement noise vector at time ks.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] (1) This invention achieves autonomous optimization of spacecraft diagnostic measurement data by analyzing the diagnosability of spacecraft under different diagnostic information;

[0040] (2) This invention effectively reduces the amount of data to be processed for diagnosis, thus providing a solution for realizing autonomous fault diagnosis of spacecraft.

[0041] (3) The system dynamic expression determined by diagnostic measurement data proposed in this invention can be used to describe the dynamics of the system under different diagnostic measurement data and provide a basis for subsequent system feature analysis.

[0042] (4) The diagnostic measurement data autonomous selection logic proposed in this invention can be used for spacecraft to autonomously select diagnostic measurement data, reduce the computational burden of onboard computers from the data end, and has a clear principle, simple design process, and strong engineering practicality. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1 This is a flowchart of an autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, provided in an embodiment of the present invention. Detailed Implementation

[0045] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0046] To ensure the smooth implementation of future deep space exploration and other missions, it is necessary to reduce the amount of computation required for spacecraft fault diagnosis from a data perspective. This requires researching autonomous optimization methods for spacecraft diagnostic measurement data, selecting the data that maximizes diagnostic capabilities, reducing the computational burden on onboard computers from the data end, overcoming the stringent constraints of spacecraft resources, and ensuring its safe, reliable, and autonomous operation.

[0047] Figure 1 This is a flowchart of an autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability, provided in an embodiment of the present invention. For example... Figure 1 As shown, the method includes the following steps:

[0048] The state equations of the spacecraft control system are obtained;

[0049] Construct the system dynamic expression based on the state equations of the spacecraft control system;

[0050] Based on the system dynamic expression, construct the system dynamic expression determined by the diagnostic measurement data;

[0051] Based on the system dynamic expression determined by the diagnostic measurement data, the random characteristics corresponding to the combination of diagnostic measurement data are obtained;

[0052] Based on the random characteristics corresponding to the combination of diagnostic measurement data, the diagnosticability analysis results corresponding to the combination of diagnostic measurement data are obtained;

[0053] Based on the diagnosticability analysis results corresponding to the combination of diagnostic measurement data, the autonomous screening logic for diagnostic measurement data is obtained.

[0054] The optimal combination of diagnostic measurement data is calculated based on the autonomous selection logic of the diagnostic measurement data, thereby determining the measurement data used for fault diagnosis.

[0055] Specifically, the method includes the following steps:

[0056] (1) Using spacecraft design parameters, the state equations (A, B, C, D, E, F, G ... d B d B d,f B d,w C d D d,f D d,v ):

[0057] x(k+1)=A d x(k)+B d u(k)+B d,f f(k)+B d,w w(k)

[0058] y(k)=C d x(k)+D d,f f(k)+D d,v v(k)

[0059] Where y(k) is the system output vector at time k, k represents time, x(k+1) is the system state vector at time k+1, x(k) is the system state vector at time k, u(k) is the control input vector at time k, f(k) is the fault vector at time k, w(k) is the process noise vector at time k, v(k) is the measurement noise vector at time k, and A d Let B be the first parameter matrix of the system. d B is the system's second parameter matrix. d,f B is the third parameter matrix of the system. d,w C is the fourth parameter matrix of the system. d D is the fifth parameter matrix of the system. d,f D is the sixth parameter matrix of the system. d,v This is the seventh parameter matrix of the system.

[0060] (2) Based on the spacecraft control system state equations obtained in (1), construct the system dynamic expression (H). u H o H f H e ):

[0061] d(ks,k)-H u u(ks,k)=H o x(ks)+H f f(ks,k)+H e e(ks,k)

[0062] in,

[0063]

[0064] Where k represents time, s represents the diagnostic time window length, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at time ks, f(ks,k) is the fault vector of the system dynamic expression, e(ks,k) is the noise vector of the system dynamic expression, and H u H is the first parameter matrix of the system's dynamic expression. o H is the second parameter matrix of the system's dynamic expression. fLet be the third parameter matrix of the system dynamic expression, y(k) be the system output vector at time k, y(ks) be the system output vector at time ks, y(k-s+1) be the system output vector at time k-s+1, u(k-s+1) be the control input vector at time k-s+1, u(ks) be the control input vector at time ks, f(ks) be the fault vector at time ks, f(k-s+1) be the fault vector at time k-s+1, w(ks) be the process noise vector at time ks, and v(ks) be the measurement noise vector at time ks.

[0065] (3) Based on the system dynamic expression obtained in (2), construct the system dynamic expression determined by the diagnostic measurement data.

[0066]

[0067] Where k represents time, s represents the diagnostic time window length, d(ks,k) is the diagnostic measurement data vector of the system dynamic expression, u(ks,k) is the control input vector of the system dynamic expression, x(ks) is the system state vector at time ks, f(ks,k) is the fault vector of the system dynamic expression, and e(ks,k) is the noise vector of the system dynamic expression. This represents the i-th combination of diagnostic measurement data. From matrix H u Corresponding to the measurement data used in diagnosis The rows form the first parameter matrix of the system dynamic expression determined by diagnostic measurement data; From matrix H o Corresponding to the measurement data used in diagnosis The rows form the second parameter matrix, representing the system dynamic expression determined by diagnostic measurement data; From matrix H f Corresponding to the measurement data used in diagnosis The rows form the third parameter matrix, representing the system dynamic expression determined by diagnostic measurement data; From matrix H e Corresponding to the measurement data used in diagnosis The rows form the fourth parameter matrix, representing the system's dynamic expression determined by diagnostic measurement data. This indicates that the variables involved are determined by the i-th combination of diagnostic measurement data, where i is the sequence number of the diagnostic measurement data combination.

[0068] (4) Based on the system dynamic expression determined by the diagnostic measurement data obtained in (3), assume that Follows a matrix with mean 0 and variance . The normal distribution, i.e. Follows a matrix with mean 0 and variance . The normal distribution, i.e. The random characteristics corresponding to the i-th combination of diagnostic measurement data are given:

[0069]

[0070] in, and They represent The mean vector and covariance matrix.

[0071] (5) Based on the random characteristics corresponding to the i-th diagnostic measurement data combination obtained in (4), give the diagnosticability analysis results corresponding to the i-th diagnostic measurement data combination:

[0072]

[0073] Among them, Diagnosability This represents the diagnosticability analysis result corresponding to the i-th combination of diagnostic measurement data.

[0074] (6) Based on the diagnosticability analysis results corresponding to the i-th combination of diagnostic measurement data obtained in (5), the logic for autonomous selection of diagnostic measurement data is given:

[0075]

[0076] in, This is the optimal combination of diagnostic measurement data.

[0077] (7) Based on the diagnostic measurement data autonomous selection logic obtained in (6), the optimal combination of diagnostic measurement data is autonomously calculated on the spacecraft to achieve autonomous selection of spacecraft diagnostic measurement data.

[0078] This embodiment also provides an autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability. The system includes: a first module for obtaining the state equation of the spacecraft control system; a second module for constructing a system dynamic expression based on the state equation of the spacecraft control system; a third module for constructing a system dynamic expression determined by diagnostic measurement data based on the system dynamic expression; a fourth module for obtaining random characteristics corresponding to combinations of diagnostic measurement data based on the system dynamic expression determined by the diagnostic measurement data; a fifth module for obtaining diagnosability analysis results corresponding to combinations of diagnostic measurement data based on the random characteristics corresponding to combinations of diagnostic measurement data; a sixth module for obtaining autonomous selection logic for diagnostic measurement data based on the diagnosability analysis results corresponding to combinations of diagnostic measurement data; and a seventh module for obtaining the optimal combination of diagnostic measurement data based on the autonomous selection logic for diagnostic measurement data.

[0079] This embodiment analyzes the diagnosability of spacecraft under different diagnostic information to achieve autonomous optimization of spacecraft diagnostic measurement data. It effectively reduces the computational load of fault diagnosis by decreasing the amount of data processed for diagnosis, providing a solution for autonomous fault diagnosis of spacecraft. The system dynamic expression determined by the diagnostic measurement data proposed in this embodiment can be used to describe the dynamics of the system under different diagnostic measurement data and provides a foundation for subsequent system characteristic analysis. The autonomous selection logic for diagnostic measurement data proposed in this embodiment can be used for spacecraft to autonomously optimize diagnostic measurement data, reducing the computational burden on the onboard computer from the data end. Its principle is clear, its design process is simple, and it has strong engineering practicality.

[0080] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for autonomous optimization of spacecraft fault diagnosis measurement data based on diagnosability, characterized in that... include: The state equations of the spacecraft control system are obtained; Construct the system dynamic expression based on the state equations of the spacecraft control system; Based on the system dynamic expression, construct the system dynamic expression determined by the diagnostic measurement data; The system dynamic expression determined by diagnostic measurement data is obtained through the following formula: ； in, Indicates time, Indicates the length of the diagnostic time window. The control input vector for the system's dynamic expression. For the first The system state vector at any given time. The fault vector is the dynamic expression of the system. This is the noise vector in the system's dynamic expression. Indicates the first A combination of diagnostic measurement data, The first parameter matrix represents the system dynamic expression determined by diagnostic measurement data. The second parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The third parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The fourth parameter matrix represents the system dynamic expression determined by diagnostic measurement data. This indicates that the variables involved are from the first The combination of diagnostic measurement data determines the outcome; Based on the system dynamic expression determined by the diagnostic measurement data, the random characteristics corresponding to the combination of diagnostic measurement data are obtained: ； ； in, It is the mean vector. Let covariance matrix be the variance matrix. This is the sequence number of the diagnostic measurement data combination; Based on the random characteristics corresponding to the combination of diagnostic measurement data, the diagnosticability analysis results corresponding to the combination of diagnostic measurement data are obtained; The diagnostic diagnosticness analysis results corresponding to the combination of diagnostic measurement data are obtained using the following formula: ； in, For the first Diagnostic analysis results corresponding to combinations of diagnostic measurement data; Based on the diagnosticability analysis results corresponding to the combination of diagnostic measurement data, the autonomous screening logic for diagnostic measurement data is obtained. The self-selection logic for diagnostic measurement data is obtained through the following formula: ； in, The optimal combination of diagnostic measurement data; The optimal combination of diagnostic measurement data is obtained through an autonomous selection logic based on the diagnostic measurement data.

2. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1, characterized in that: The state equations of a spacecraft control system are obtained through the following formula: ； in, For the first The system output vector at time step For the first The system state vector at any given time. For the first The system state vector at any given time. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time. The first parameter matrix of the system, This is the system's second parameter matrix. The third parameter matrix of the system. This is the fourth parameter matrix of the system. This is the fifth parameter matrix of the system. This is the sixth parameter matrix of the system. This is the seventh parameter matrix of the system.

3. The autonomous optimization method for spacecraft fault diagnosis measurement data based on diagnosability according to claim 1, characterized in that: The system dynamic expression is obtained through the following formula: ； in, , , , , , , , in, The diagnostic measurement data vector is the system's dynamic expression. The first parameter matrix of the system's dynamic expression. The second parameter matrix of the system's dynamic expression. The third parameter matrix of the system's dynamic expression. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time. The first parameter matrix of the system, This is the system's second parameter matrix. The third parameter matrix of the system. This is the fourth parameter matrix of the system. This is the fifth parameter matrix of the system. This is the sixth parameter matrix of the system. This is the seventh parameter matrix of the system. For the first The system output vector at time step For the first The system output vector at time step For the first The system output vector at time step For the first Control the input vector at all times. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time.

4. An autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability, characterized in that... include: The first module is used to obtain the state equations of the spacecraft control system; The second module is used to construct the system dynamic expression based on the state equations of the spacecraft control system. The third module is used to construct the system dynamic expression determined by the diagnostic measurement data based on the system dynamic expression; The system dynamic expression determined by diagnostic measurement data is obtained through the following formula: ； in, Indicates time, Indicates the length of the diagnostic time window. The control input vector for the system's dynamic expression. For the first The system state vector at any given time. The fault vector is the dynamic expression of the system. This is the noise vector in the system's dynamic expression. Indicates the first A combination of diagnostic measurement data, The first parameter matrix represents the system dynamic expression determined by diagnostic measurement data. The second parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The third parameter matrix represents the dynamic expression of the system determined by diagnostic measurement data. The fourth parameter matrix represents the system dynamic expression determined by diagnostic measurement data. This indicates that the variables involved are from the first The combination of diagnostic measurement data determines the outcome; The fourth module is used to obtain the random characteristics corresponding to the combination of diagnostic measurement data based on the system dynamic expression determined by the diagnostic measurement data: ； ； in, It is the mean vector. Let covariance matrix be the variance matrix. This is the sequence number of the diagnostic measurement data combination; The fifth module is used to obtain the diagnosticability analysis results corresponding to the combination of diagnostic measurement data based on the random characteristics corresponding to the combination of diagnostic measurement data. The diagnostic diagnosticness analysis results corresponding to the combination of diagnostic measurement data are obtained using the following formula: ； in, For the first Diagnostic analysis results corresponding to combinations of diagnostic measurement data; The sixth module is used to obtain the autonomous screening logic for diagnostic measurement data based on the diagnosticability analysis results corresponding to the combination of diagnostic measurement data. The self-selection logic for diagnostic measurement data is obtained through the following formula: ； in, The optimal combination of diagnostic measurement data; The seventh module is used to obtain the optimal combination of diagnostic measurement data based on the autonomous selection logic of the diagnostic measurement data.

5. The autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability according to claim 4, characterized in that: The state equations of a spacecraft control system are obtained through the following formula: ； in, For the first The system output vector at time step For the first The system state vector at any given time. For the first The system state vector at any given time. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time. The first parameter matrix of the system, This is the system's second parameter matrix. The third parameter matrix of the system. This is the fourth parameter matrix of the system. This is the fifth parameter matrix of the system. This is the sixth parameter matrix of the system. This is the seventh parameter matrix of the system.

6. The autonomous optimization system for spacecraft fault diagnosis measurement data based on diagnosability according to claim 4, characterized in that: The system dynamic expression is obtained through the following formula: ； in, , , , , , , , in, The diagnostic measurement data vector is the system's dynamic expression. The first parameter matrix of the system's dynamic expression. The second parameter matrix of the system's dynamic expression. The third parameter matrix of the system's dynamic expression. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time. The first parameter matrix of the system, This is the system's second parameter matrix. The third parameter matrix of the system. This is the fourth parameter matrix of the system. This is the fifth parameter matrix of the system. This is the sixth parameter matrix of the system. This is the seventh parameter matrix of the system. For the first The system output vector at time step For the first The system output vector at time step For the first The system output vector at time step For the first Control the input vector at all times. For the first Control the input vector at all times. For the first Time-of-fault vector, For the first Time-of-fault vector, For the first Time-process noise vector, For the first Measure the noise vector at any time.

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