Spacecraft analysis redundancy autonomous mining method based on diagnostic capability quantitative characterization

Abstract

The invention discloses a spacecraft analysis redundancy autonomous mining method based on diagnostic capability quantitative characterization, and belongs to the technical field of spacecraft overall. Firstly, a system input and output expression is constructed according to a state equation of a spacecraft control system; then, constructing system input and output expressions of different measurement channel combinations and equivalent expressions of the system input and output expressions; thirdly, giving random characteristics of system equivalent input and output of different measurement channel combinations, and on the basis, giving an analysis redundancy criterion based on a system dynamic model; secondly, a test data matrix is constructed based on spacecraft test data, and an analysis redundancy criterion based on the test data is given; and finally, giving a spacecraft analysis redundancy comprehensive criterion, and realizing spacecraft analysis redundancy autonomous mining. The method can be used for autonomous mining and redundancy analysis of the spacecraft, the autonomous fault diagnosis capability of the spacecraft is improved, and a solution is provided for realizing autonomous fault diagnosis of the spacecraft.

Description

Technical Field

[0001] The present invention belongs to the technical field of spacecraft overall design, and in particular relates to a method for autonomously mining spacecraft analytical redundancy based on quantitative characterization of diagnostic capability. Background Art

[0002] Spacecraft failures can seriously affect the performance and stability of the system and threaten the security of the system. In order to respond to possible failures in a timely manner, spacecraft need to implement autonomous fault diagnosis. The essence of fault diagnosis lies in the use of redundant relationships, including hardware redundancy and analytical redundancy. Hardware redundancy refers to the use of multiple redundant hardware components in the system to improve the reliability of the system. By comparing redundant hardware components, the fault location can be quickly located and automatic recovery can be achieved. It mainly relies on the backup of the same components. For spacecraft with strong resource constraints, this method is difficult to promote and apply; analytical redundancy is a method of generating redundant signals through a system mathematical model. Corresponding to hardware redundancy, it generates redundant signals through a system mathematical model expressed in an analytical way. Therefore, for spacecraft with strong resource constraints, the core of autonomous fault diagnosis lies in how to fully exploit the analytical redundancy between different components. Li et al. (Li L, Zhang H, Ding SX, et al. Unified Solutions to Optimal Fuzzy Observer-Based Fault Detection for Discrete-Time Nonlinear Systems[J]. IEEE Transactions on Fuzzy Systems, 2023.) used Takagi-Sugeno fuzzy dynamic modeling technology to study the optimal fault detection of discrete-time nonlinear systems. By solving multi-objective optimization problems and Riccati equations, they ensured the optimal trade-off between fault detectability and robustness to unknown inputs. They also studied fuzzy fault detection methods based on piecewise fuzzy Lyapunov functions by solving linear matrix inequalities. However, they did not deeply explore the reflection mechanism of the model and data on analytical redundancy, making it difficult to further improve the fault diagnosis capability of spacecraft. Summary of the invention

[0003] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a method for autonomous mining of spacecraft analytical redundancy based on quantitative characterization of diagnostic capabilities, to enhance the ability of autonomous fault diagnosis of spacecraft, and to provide a solution for realizing autonomous fault diagnosis of spacecraft.

[0004] The object of the present invention is achieved through the following technical scheme: a method for autonomous mining of spacecraft analytical redundancy based on quantitative characterization of diagnostic capability, comprising: obtaining the state equation of the spacecraft control system; constructing the system input-output expression according to the state equation of the spacecraft control system; constructing the system input-output expression of different measurement channel combinations according to the system input-output expression; obtaining the system equivalent input-output expression of different measurement channel combinations according to the system input-output expression of different measurement channel combinations; giving the random characteristics of the system equivalent input-output of the preset measurement channel combination i according to the system equivalent input-output expression of different measurement channel combinations; giving the analytical redundancy criterion based on the system dynamics model according to the random characteristics of the system equivalent input-output of the preset measurement channel combination; constructing the test data matrix under the condition of no fault and the fault vector of the system input-output expression; giving the analytical redundancy criterion based on the test data according to the test data matrix under the condition of no fault and the fault vector of the system input-output expression; obtaining the spacecraft analytical redundancy comprehensive criterion according to the analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data.

[0005] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the state equation of the spacecraft control system is obtained by the following formula:

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

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

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

[0009] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the system input and output expressions are obtained by the following formula:

[0010] H d (k)d s =H x (k)x(k-s+1)+H f (k)f s +H e (k)e s ;

[0011] in,

[0012]

[0013] Among them, k represents the time, s represents the length of the diagnosis time window, and d s is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, H d (k) is the first parameter matrix of the system input and output expressions, H x (k) is the second parameter matrix of the system input and output expressions, H f (k) is the third parameter matrix of the system input and output expression, H e (k) is the fourth parameter matrix of the system input-output expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A(k) is the first parameter matrix of the system at the kth moment, B(k) is the second parameter matrix of the system at the kth moment, B f (k) is the third parameter matrix of the system at the kth moment, B w (k) is the fourth parameter matrix of the system at the kth moment, C(k) is the fifth parameter matrix of the system at the kth moment, D f (k) is the sixth parameter matrix of the system at the kth moment, D v(k) is the seventh parameter matrix of the system at the kth moment, y(k) is the system output vector at the kth moment, y(k-s+1) is the system output vector at the kth moment, u(k-s+1) is the control input vector at the k-s+1th moment, f(ks) is the fault vector at the ksth moment, f(k-s+1) is the fault vector at the k-s+1th moment, f(k-s+2) is the fault vector at the k-s+2th moment, w(k-s+1) is the fault vector at the k-s+1th moment process noise vector, v(k-s+1) is the measurement noise vector at the k-s+1th moment, A(k-1) is the first parameter matrix of the system at the k-1th moment, A(k-s+2) is the first parameter matrix of the system at the k-s+2th moment, B(k-s+1) is the second parameter matrix of the system at the k-s+1th moment, C(k-s+1) is the fifth parameter matrix of the system at the k-s+1th moment, A(k-s+1) is the first parameter matrix of the system at the k-s+1th moment, D f (k-s+1) is the sixth parameter matrix of the system at the k-s+1th moment, B f (k-s+1) is the third parameter matrix of the system at the k-s+1th moment.

[0014] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the system input and output expressions of different measurement channel combinations are obtained by the following formula:

[0015]

[0016] Among them, k represents the time, represents the i-th measurement channel combination, d s is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The first parameter matrix representing the system input-output expression of the i-th measurement channel combination, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

[0017] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the system equivalent input and output expressions of different measurement channel combinations are obtained by the following formula:

[0018]

[0019] Among them, k represents the time, represents the i-th measurement channel combination, d s is the system input and output vector of the system input and output expression, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The first parameter matrix representing the system input-output expression of the i-th measurement channel combination, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

[0020] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the random characteristics of the system equivalent input and output of the preset measurement channel combination are obtained by the following formula:

[0021]

[0022] Among them, E() is the mean vector, D() is the covariance matrix, k represents the time, represents the i-th measurement channel combination, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

[0023] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the analytical redundancy criterion based on the system dynamics model is obtained by the following formula:

[0024]

[0025] in, is the analytical redundancy judgment expression based on the system dynamics model, k represents the time, represents the i-th measurement channel combination, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression of the i-th measurement channel combination, It means taking all moments into account.

[0026] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the test data matrix under fault-free conditions is obtained by the following formula:

[0027]

[0028] The test data matrix in the case of the fault vector of the system input-output expression is obtained by the following formula:

[0029]

[0030] Among them, DM represents the test data matrix under the condition of no fault, Denote the fault vector as f s The test data matrix in the case of represents the i-th measurement channel combination, N y Indicates the test data length, is the system output vector at the first moment corresponding to the ith measurement channel combination under fault-free conditions, is the Nth corresponding to the i-th measurement channel combination in the absence of faults y The system output vector at time, is the system output vector at the first moment corresponding to the i-th measurement channel combination under fault conditions, is the Nth corresponding to the i-th measurement channel combination under fault conditions y The system output vector at each moment.

[0031] In the above-mentioned spacecraft analytical redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the analytical redundancy criterion based on test data is obtained by the following formula:

[0032]

[0033]

[0034] Among them, DM represents the test data matrix under the condition of no fault, Denote the fault vector as f s The test data matrix in the case of represents the i-th measurement channel combination, It is the analytical redundant judgment expression based on the test data. Indicates that all fault vectors are considered.

[0035] In the above-mentioned spacecraft analysis redundancy autonomous mining method based on quantitative characterization of diagnostic capability, the comprehensive criterion of spacecraft analysis redundancy is obtained by the following formula:

[0036]

[0037] in, It is the analytical redundant judgment expression based on the test data. is the analytical redundancy judgment expression based on the system dynamics model, represents the i-th measurement channel combination, k represents the time, and f s The fault vector is the input and output expression of the system.

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

[0039] (1) The analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data proposed in the present invention can judge whether different measurement channel combinations have analytical redundancy from the perspectives of the system dynamics model and the test data, respectively, and can provide a basis for proposing a comprehensive analytical redundancy criterion for spacecraft;

[0040] (2) The comprehensive criterion for spacecraft analytical redundancy proposed in the present invention can be used to autonomously mine analytical redundancy for spacecraft and improve the spacecraft's autonomous fault diagnosis capability. It has a clear principle, a simple design process, and strong engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0042] Figure 1 It is a flow chart of a method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capabilities provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] In order to ensure the smooth implementation of future deep space exploration missions, it is necessary to comprehensively analyze the system characteristics from two aspects: system dynamics model and test data, clarify the relationship between measurement data of different channels, so as to reflect the redundant relationship of spacecraft analysis, reduce the demand for backup of the same components in fault diagnosis, and shift the focus of fault diagnosis from hardware redundancy to analytical redundancy, thus providing a solution for realizing autonomous fault diagnosis of spacecraft.

[0045] Figure 1 1 is a flow chart of a method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capabilities provided by an embodiment of the present invention. Figure 1 As shown in FIG. 1 , the spacecraft parsing redundancy autonomous mining method based on the quantitative characterization of diagnostic capability includes:

[0046] Obtain the state equation of the spacecraft control system;

[0047] According to the state equation of the spacecraft control system, construct the system input and output expressions;

[0048] According to the system input and output expressions, construct the system input and output expressions of different measurement channel combinations;

[0049] According to the system input and output expressions of different measurement channel combinations, the system equivalent input and output expressions of different measurement channel combinations are obtained;

[0050] According to the system equivalent input and output expressions of different measurement channel combinations, the random characteristics of the system equivalent input and output of the preset measurement channel combination i are given;

[0051] According to the random characteristics of the system equivalent input and output of the preset measurement channel combination i, an analytical redundancy criterion based on the system dynamics model is given;

[0052] Construct the fault-free and fault vectors as f s The test data matrix in the case;

[0053] According to the absence of fault and the fault vector f s The test data matrix in the case of , gives the analytical redundancy criterion based on the test data;

[0054] According to the analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data, the comprehensive analytical redundancy criterion of the spacecraft is obtained;

[0055] According to the comprehensive criterion of spacecraft analytical redundancy, it is analyzed whether different measurement channel combinations have analytical redundancy.

[0056] Specifically, the method comprises the following steps:

[0057] (1) Using the spacecraft design parameters, the state equations of the spacecraft control system (A(k), B(k), B f (k),C(k),D f (k),D v (k)):

[0058] x(k+1)=A(k)x(k)+B(k)u(k)+B f (k)f(k)+B w (k)w(k)

[0059] y(k)=C(k)x(k)+D f (k)f(k)+D v (k)v(k)

[0060] Among them, y(k) is the system output vector at the kth moment, k represents the time, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A(k) is the first parameter matrix of the system at the kth moment, B(k) is the second parameter matrix of the system at the kth moment, B f (k) is the third parameter matrix of the system at the kth moment, B w (k) is the fourth parameter matrix of the system at the kth moment, C(k) is the fifth parameter matrix of the system at the kth moment, D f (k) is the sixth parameter matrix of the system at the kth moment, D v (k) is the seventh parameter matrix of the system at the kth moment.

[0061] (2) Based on the state equation of the spacecraft control system obtained in step (1), construct the system input and output expressions (H d (k),H x (k),H f (k),H e (k)):

[0062] H d (k)d s =H x(k)x(k-s+1)+H f (k)f s +H e (k)e s

[0063] in,

[0064] Among them, k represents the time, s represents the length of the diagnosis time window, and d s is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, H d (k) is the first parameter matrix of the system input and output expressions, H x (k) is the second parameter matrix of the system input and output expressions, H f (k) is the third parameter matrix of the system input and output expression, H e (k) is the fourth parameter matrix of the system input-output expression, y(k) is the system output vector at the kth moment, y(k-s+1) is the system output vector at the k-s+1th moment, u(k-s+1) is the control input vector at the k-s+1th moment, f(ks) is the fault vector at the ksth moment, f(k-s+1) is the fault vector at the k-s+1th moment, f(k-s+2) is the fault vector at the k-s+2th moment, w(k-s+1) is the fault vector at the k-s+1th moment The process noise vector at the k-s+1th moment, v(k-s+1) is the measurement noise vector at the k-s+1th moment, A(k-1) is the first parameter matrix of the system at the k-1th moment, A(k-s+2) is the first parameter matrix of the system at the k-s+2th moment, B(k-s+1) is the second parameter matrix of the system at the k-s+1th moment, C(k-s+1) is the fifth parameter matrix of the system at the k-s+1th moment, A(k-s+1) is the first parameter matrix of the system at the k-s+1th moment, D f (k-s+1) is the sixth parameter matrix of the system at the k-s+1th moment, B f (k-s+1) is the third parameter matrix of the system at the k-s+1th moment.

[0065] (3) Based on the system input and output expressions obtained in step (2), construct the system input and output expressions for different measurement channel combinations

[0066]

[0067] Among them, k represents the time, Indicates the measurement channel combination i, ds is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, From the matrix H d (k) The first parameter matrix corresponding to the row composition of the measurement channel combination i, representing the system input and output expressions of the measurement channel combination i, From the matrix H x (k) A second parameter matrix corresponding to the row composition of the measurement channel combination i, representing the system input and output expressions of the measurement channel combination i, From the matrix H f (k) corresponds to the row composition of the measurement channel combination i, representing the third parameter matrix of the system input and output expressions of the measurement channel combination i, From the matrix H e (k) The fourth parameter matrix corresponding to the row composition of the measurement channel combination i represents the system input and output expressions of the measurement channel combination i.

[0068] (4) According to the system input-output expressions of different measurement channel combinations obtained in step (3), the system equivalent input-output expressions of different measurement channel combinations are obtained:

[0069]

[0070] Among them, k represents the time, Indicates the measurement channel combination i, d s is the system input and output vector of the system input and output expression, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, and there is f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions.

[0071] (5) According to the system equivalent input and output expressions of different measurement channel combinations obtained in step (4), assume that The mean is 0 and the variance is a matrix The normal distribution of The random characteristics of the system equivalent input and output of the measurement channel combination i are given as:

[0072]

[0073] in, and Respectively The mean vector and covariance matrix of .

[0074] (6) According to the random characteristics of the system equivalent input and output of the measurement channel combination i obtained in step (5), an analytical redundancy criterion based on the quantitative characterization of diagnostic capability and the system dynamics model is given:

[0075]

[0076] in, Indicates parsing redundant judgment expressions; It means taking all moments into account.

[0077] (7) According to the general method, the spacecraft without fault and the fault vector f are collected respectively. s Test data of measurement channel combination i in case and And construct the fault-free and fault vectors as f s The test data matrix in the case is:

[0078]

[0079]

[0080] Among them, DM represents the test data matrix under the condition of no fault, Denote the fault vector as f s The test data matrix in the case of y Indicates the test data length, is the system output vector at the first moment corresponding to the ith measurement channel combination under fault-free conditions, is the Nth corresponding to the i-th measurement channel combination in the absence of faults y The system output vector at time, is the system output vector at the first moment corresponding to the i-th measurement channel combination under fault conditions, is the Nth corresponding to the i-th measurement channel combination under fault conditions y The system output vector at each moment.

[0081] (8) According to the test data matrix obtained in step (7), the analytical redundancy criterion based on the quantitative characterization of diagnostic capability and test data is given:

[0082]

[0083] in, It is the analytical redundant judgment expression based on the test data. Indicates that all fault vectors are considered.

[0084] (9) Based on the analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data obtained in steps (6) and (8), a comprehensive analytical redundancy criterion for spacecraft based on the quantitative characterization of diagnostic capability is given:

[0085]

[0086] (10) Based on the comprehensive criterion for spacecraft analytical redundancy based on the quantitative characterization of diagnostic capability obtained in step (9), the analytical redundancy is determined autonomously by analyzing on-board whether different measurement channel combinations have analytical redundancy, thereby realizing autonomous mining of spacecraft analytical redundancy.

[0087] The analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data proposed in this embodiment can judge whether different measurement channel combinations have analytical redundancy from the perspectives of the system dynamics model and the test data, respectively, and can provide a basis for proposing a comprehensive analytical redundancy criterion for spacecraft. The comprehensive analytical redundancy criterion for spacecraft proposed in this embodiment can be used for autonomously exploring analytical redundancy for spacecraft and improving the autonomous fault diagnosis capability of spacecraft. It has a clear principle, a simple design process, and strong engineering practicality.

[0088] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for autonomously mining redundancy in spacecraft analysis based on quantitative characterization of diagnostic capabilities, characterized by include: Obtain the state equation of the spacecraft control system; According to the state equation of the spacecraft control system, construct the system input and output expressions; According to the system input and output expressions, construct the system input and output expressions of different measurement channel combinations; According to the system input and output expressions of different measurement channel combinations, the system equivalent input and output expressions of different measurement channel combinations are obtained; According to the system equivalent input and output expressions of different measurement channel combinations, the random characteristics of the system equivalent input and output of the preset measurement channel combination i are given; According to the random characteristics of the system equivalent input and output of the preset measurement channel combination, an analytical redundancy criterion based on the system dynamics model is given; Construct test data matrices without faults and with fault vectors of system input and output expressions; According to the test data matrix under the condition of no fault and fault vector of system input and output expression, the analytical redundancy criterion based on test data is given; According to the analytical redundancy criterion based on the system dynamics model and the analytical redundancy criterion based on the test data, the comprehensive analytical redundancy criterion of the spacecraft is obtained.

2. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The state equation of the spacecraft control system is obtained by the following formula: x(k+1)=A(k)x(k)+B(k)u(k)+B f (k)f(k)+B w (k)w(k) y(k)=C(k)x(k)+D f (k)f(k)+D v (k)v(k); Among them, y(k) is the system output vector at the kth moment, k represents the time, x(k+1) is the system state vector at the k+1th moment, x(k) is the system state vector at the kth moment, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A(k) is the first parameter matrix of the system at the kth moment, B(k) is the second parameter matrix of the system at the kth moment, B f (k) is the third parameter matrix of the system at the kth moment, Bw(k) is the fourth parameter matrix of the system at the kth moment, C(k) is the fifth parameter matrix of the system at the kth moment, D f (k) is the sixth parameter matrix of the system at the kth moment, D v (k) is the seventh parameter matrix of the system at the kth moment.

3. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The system input and output expressions are obtained through the following formula: H d (k)d s =H x (k)x(k-s+1)+H f (k)f s +H e (k)e s ; in, Among them, k represents the time, s represents the length of the diagnosis time window, and d s is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, H d (k) is the first parameter matrix of the system input and output expressions, H x (k) is the second parameter matrix of the system input and output expressions, H f (k) is the third parameter matrix of the system input-output expression, He(k) is the fourth parameter matrix of the system input-output expression, u(k) is the control input vector at the kth moment, f(k) is the fault vector at the kth moment, w(k) is the process noise vector at the kth moment, v(k) is the measurement noise vector at the kth moment, A(k) is the first parameter matrix of the system at the kth moment, B(k) is the second parameter matrix of the system at the kth moment, B f (k) is the third parameter matrix of the system at the kth moment, B w (k) is the fourth parameter matrix of the system at the kth moment, C(k) is the fifth parameter matrix of the system at the kth moment, D f (k) is the sixth parameter matrix of the system at the kth moment, Dv(k) is the seventh parameter matrix of the system at the kth moment, y(k) is the system output vector at the kth moment, y(k-s+1) is the system output vector at the k-s+1th moment, u(k-s+1) is the control input vector at the k-s+1th moment, f(ks) is the fault vector at the ksth moment, f(k-s+1) is the fault vector at the k-s+1th moment, f(k-s+2) is the fault vector at the k-s+2th moment, w(k-s+1) is the fault vector at the k-s+1th moment is the process noise vector, v(k-s+1) is the measurement noise vector at the k-s+1th moment, A(k-1) is the first parameter matrix of the system at the k-1th moment, A(k-s+2) is the first parameter matrix of the system at the k-s+2th moment, B(k-s+1) is the second parameter matrix of the system at the k-s+1th moment, C(k-s+1) is the fifth parameter matrix of the system at the k-s+1th moment, A(k-1) is the first parameter matrix of the system at the k-1th moment, A(k-s+1) is the first parameter matrix of the system at the k-s+1th moment, D f (k-s+1) is the sixth parameter matrix of the system at the k-s+1th moment, B f (k-s+1) is the third parameter matrix of the system at the k-s+1th moment.

4. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The system input and output expressions of different measurement channel combinations are obtained by the following formula: Among them, k represents the time, represents the i-th measurement channel combination, d s is the system input and output vector of the system input and output expression, x(k-s+1) is the system state vector at the k-s+1th moment, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The first parameter matrix representing the system input-output expression of the i-th measurement channel combination, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

5. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The system equivalent input and output expressions of different measurement channel combinations are obtained by the following formula: Among them, k represents the time, represents the i-th measurement channel combination, d s is the system input and output vector of the system input and output expression, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The first parameter matrix representing the system input-output expression of the i-th measurement channel combination, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

6. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The random characteristics of the system equivalent input and output of the preset measurement channel combination are obtained by the following formula: Among them, E() is the mean vector, D() is the covariance matrix, k represents the time, represents the i-th measurement channel combination, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression for the i-th measurement channel combination.

7. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The analytical redundancy criterion based on the system dynamics model is obtained by the following formula: in, is the analytical redundancy judgment expression based on the system dynamics model, k represents the time, represents the i-th measurement channel combination, is the equivalent matrix of the system equivalent input and output expressions of different measurement channel combinations, f s is the fault vector of the system input and output expression, e s is the noise vector of the system input and output expressions, The second parameter matrix representing the system input and output expressions for the i-th measurement channel combination, The third parameter matrix representing the system input-output expression of the i-th measurement channel combination, The fourth parameter matrix representing the system input-output expression of the i-th measurement channel combination, It means taking all moments into account.

8. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The test data matrix under fault-free conditions is obtained by the following formula: The test data matrix in the case of the fault vector of the system input-output expression is obtained by the following formula: Among them, DM represents the test data matrix under the condition of no fault, Denote the fault vector as f s The test data matrix in the case of represents the i-th measurement channel combination, N y Indicates the test data length, is the system output vector at the first moment corresponding to the ith measurement channel combination under fault-free conditions, is the Nth corresponding to the i-th measurement channel combination in the absence of faults y The system output vector at time, is the system output vector at the first moment corresponding to the i-th measurement channel combination under fault conditions, is the Nth corresponding to the i-th measurement channel combination under fault conditions y The system output vector at each moment.

9. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The analytical redundancy criterion based on test data is obtained by the following formula: Among them, DM represents the test data matrix under the condition of no fault, DM fs Denote the fault vector as f s The test data matrix in the case of represents the i-th measurement channel combination, It is the analytical redundant judgment expression based on the test data. Indicates that all fault vectors are considered.

10. The method for autonomously mining spacecraft parsing redundancy based on quantitative characterization of diagnostic capability according to claim 1 is characterized by: The comprehensive criterion for spacecraft analytical redundancy is obtained by the following formula: in, It is the analytical redundant judgment expression based on the test data. is the analytical redundancy judgment expression based on the system dynamics model, represents the i-th measurement channel combination, k represents the time, and f s The fault vector is the input and output expression of the system.

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