High-precision composite measurement method and system for in-orbit deformation of satellite large-scale structure

Abstract

The invention provides a satellite large-scale structure on-orbit deformation high-precision composite measurement method and system, and the method comprises the steps: constructing a high-precision on-orbit deformation measurement system through a distributed fiber grating and a non-orthogonal laser autocollimator, and carrying out the deformation reconstruction and optimization through a motion recursion theory and a reconstruction parameter optimization model based on prior angle knowledge. And determining the fiber bragg grating compensation amount according to the fiber bragg grating measurement mapping deep learning model, thereby realizing high-precision composite measurement of on-orbit deformation of a large-scale structure.

Description

Technical Field

[0001] The present invention relates to the field of satellite technology, and in particular to a high-precision composite measurement method and system for on-orbit deformation of a large satellite structure. Background Art

[0002] The on-orbit temperature variation of large remote sensing satellites can reach 200℃~300℃. Large-area mounting plates, large-size array payloads, etc. are deformed by changes in external heat flux on-orbit, resulting in inconsistent references for multiple satellite payloads, errors in line of sight pointing or surface, and thus affecting detection performance. Therefore, it is necessary to monitor the on-orbit thermal deformation of large structures in real time and compensate for and control thermal deformation error terms.

[0003] At present, laser interferometers, laser autocollimators and other measuring equipment can detect single-point pointing changes, but a single set of equipment cannot cover multiple monitoring points efficiently and at low cost; distributed fiber Bragg gratings can monitor surface changes, but the instrument itself has spectral drift and accuracy degradation after long-term on-orbit. Therefore, a high-precision composite measurement method for on-orbit deformation that combines single-point high-precision measurement and multi-point distributed detection is needed. Summary of the invention

[0004] In view of the defects in the prior art, the purpose of the present invention is to provide a high-precision composite measurement method and system for on-orbit deformation of large satellite structures.

[0005] A high-precision composite measurement method for on-orbit deformation of a large satellite structure provided by the present invention comprises:

[0006] Step S1: constructing a high-precision on-orbit deformation measurement system using distributed fiber Bragg gratings and non-orthogonal laser autocollimator;

[0007] Step S2: Perform deformation reconstruction and optimization through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge;

[0008] Step S3: Determine the fiber Bragg grating compensation amount according to the fiber Bragg grating measurement mapping deep learning model.

[0009] Furthermore, the step S2 comprises:

[0010] Step S2.1: Establish the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basic coordinate system BCS for subsequent calculations;

[0011] Step S2.2: Using the motion recursion theory, a surface reconstruction model based on distributed multi-core fiber Bragg grating is established;

[0012] Step S2.3: Establishing a fiber Bragg grating measurement model for relative pointing of the calibration end of a non-orthogonal laser autocollimator;

[0013] Step S2.4: According to the measurement data of the non-orthogonal laser autocollimator, the pointing constraint of the calibration end is established, and based on this, the optimization model of the reconstruction parameters - rotation angle and bending angle is constructed;

[0014] Step S2.5: According to the optimized reconstruction parameters, deformation optimized reconstruction is achieved through a surface reconstruction model based on distributed multi-core fiber gratings.

[0015] Furthermore, the step S2.2 comprises:

[0016] According to the node position s of the distributed multi-core fiber Bragg grating i and the bending angle θ measured at each node i and curvature k i The reconstruction parameters of adjacent fiber Bragg grating intermediate nodes are calculated by nonlinear interpolation, the local coordinate system LCS of each node and the coordinate transformation relationship between the local coordinate systems of adjacent nodes are established, the coordinates are unified to the basic calculation coordinate system BCS, and the surface reconstruction is realized by B-spline curve / surface fitting method and Coons surface theory.

[0017] Furthermore, the step S2.3 includes:

[0018] Determine the node O closest to the calibration end in the motion path from the reference end to the calibration end m , establish the calibration end normal vector k about the reconstruction parameter θ m The measurement model, j = 1, 2, 3, ..., m, where a =

[100] T , R x , R y and R z are the rotation matrices around the x-axis, y-axis, and z-axis respectively.

[0019] Furthermore, the step S2.4 comprises:

[0020] The linear error equation is used to express the relationship between k and the reconstruction parameter and its error Δ, combined with the calibration end normal vector measured by the non-orthogonal laser autocollimator. Building the error equation Where H is the coefficient matrix, Measure the calibration end normal vector for the fiber Bragg grating.

[0021] Furthermore, the step S2.4 comprises:

[0022] Constructing a reconstruction parameter optimization model The weighted optimal solution is Among them, P is a weight matrix with zero off-diagonal values, λ is the Lagrange coefficient, and I is the unit matrix.

[0023] Furthermore, the step S3 comprises:

[0024] Step S3.1: Establish a neural network model for converting spectral signals to fiber Bragg grating strain measurement to achieve signal-to-strain demodulation;

[0025] Step S3.2: Establishing a fiber Bragg grating measurement mapping deep learning model to achieve fiber Bragg grating measurement compensation output under given input conditions;

[0026] Step S3.3: Calculate the reconstruction parameters according to the compensated measurement data, and realize deformation reconstruction through the surface reconstruction model based on distributed multi-core fiber grating.

[0027] According to the present invention, a high-precision composite measurement system for on-orbit deformation of a large satellite structure is provided, comprising:

[0028] Module M1: Use distributed fiber Bragg grating and non-orthogonal laser autocollimator to build a high-precision on-orbit deformation measurement system;

[0029] Module M2: Deformation reconstruction and optimization through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge;

[0030] Module M3: Determine the fiber Bragg grating compensation amount based on the fiber Bragg grating measurement mapping deep learning model.

[0031] Furthermore, the module M2 includes:

[0032] Module M2.1: Establishing the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basic coordinate system BCS for subsequent calculations;

[0033] Module M2.2: Use the motion recursion theory to establish a surface reconstruction model based on distributed multi-core fiber Bragg gratings;

[0034] Module M2.3: Establish a fiber Bragg grating measurement model for the relative pointing of the calibration end of a non-orthogonal laser autocollimator;

[0035] Module M2.4: According to the measurement data of the non-orthogonal laser autocollimator, establish the pointing constraints of the calibration end, and build the optimization model of the reconstruction parameters - rotation angle and bending angle based on this;

[0036] Module M2.5: According to the optimized reconstruction parameters, deformation optimization reconstruction is achieved through the surface reconstruction model based on distributed multi-core fiber Bragg gratings;

[0037] The module M2.2 includes:

[0038] According to the node position s of the distributed multi-core fiber Bragg grating i and the bending angle θ measured at each node i and curvature k i , the reconstruction parameters of adjacent fiber Bragg grating intermediate nodes are calculated by nonlinear interpolation, the local coordinate system LCS of each node and the coordinate transformation relationship of the local coordinate system of adjacent nodes are established, the coordinates are unified to the basic calculation coordinate system BCS, and the surface reconstruction is realized by B-spline curve / surface fitting method and Coons surface theory;

[0039] The module M2.3 includes:

[0040] Determine the node O closest to the calibration end in the motion path from the reference end to the calibration end m , establish the calibration end normal vector k about the reconstruction parameter θ m The measurement model, j = 1, 2, 3, ..., m, where a =

[100] T , R x , R y and R z are the rotation matrices around the x-axis, y-axis, and z-axis respectively;

[0041] The module M2.4 includes:

[0042] The linear error equation is used to express the relationship between k and the reconstruction parameter and its error Δ, combined with the calibration end normal vector measured by the non-orthogonal laser autocollimator. Building the error equation Where H is the coefficient matrix, Measure the calibration end normal vector for the fiber Bragg grating;

[0043] The module M2.4 includes:

[0044] Constructing a reconstruction parameter optimization model The weighted optimal solution is Among them, P is a weight matrix with zero off-diagonal values, λ is the Lagrange coefficient, and I is the unit matrix.

[0045] Furthermore, the module M3 includes:

[0046] Module M3.1: Establish a neural network model for the conversion between spectral signals and fiber Bragg grating strain measurement to achieve signal-to-strain demodulation;

[0047] Module M3.2: Establish a deep learning model for fiber Bragg grating measurement mapping to achieve fiber Bragg grating measurement compensation output under given input conditions;

[0048] Module M3.3: Calculate the reconstruction parameters according to the compensated measurement data, and realize deformation reconstruction through the surface reconstruction model based on distributed multi-core fiber Bragg grating.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention constructs a high-precision on-orbit deformation measurement system based on distributed fiber Bragg gratings and non-orthogonal laser autocollimator, giving full play to the respective advantages of the two measurement methods. The distributed multi-core fiber Bragg grating is used for large-scale surface detection, and the non-orthogonal laser autocollimator is used for accurate measurement of relative pointing of two points, thereby realizing high-precision detection of on-orbit deformation of large structures and having good application prospects.

[0051] 2. Based on the theory of motion recursion, B-spline curve / surface fitting and Coons surface, the present invention uses the high-precision pointing measurement data of a non-orthogonal laser autocollimator as a constraint and combines the relative pointing fiber Bragg grating measurement model to construct a reconstruction parameter optimization model based on prior angle knowledge, thereby realizing the on-orbit deformation reconstruction and optimization of large structures.

[0052] 3. The present invention comprehensively considers the problems of fiber Bragg grating spectral demodulation error, spectral drift, precision degradation, etc., constructs a fiber Bragg grating measurement mapping deep learning model, realizes high-precision measurement compensation output of fiber Bragg grating under given input conditions, and provides a new idea for the on-orbit calibration of fiber Bragg grating. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0054] Figure 1 It is a structural schematic diagram of the high-precision composite measurement system for on-orbit deformation of large satellite structures of the present invention;

[0055] Figure 2 The present invention is a flow chart of the high-precision composite measurement method for on-orbit deformation of large satellite structures. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0057] Reference Figure 1 and Figure 2 As shown, a high-precision composite measurement method for on-orbit deformation of a large satellite structure comprises the following steps:

[0058] S1: A high-precision on-orbit deformation measurement system is constructed using distributed fiber Bragg gratings and non-orthogonal laser autocollimator. The high-precision on-orbit deformation measurement system includes distributed multi-core fiber Bragg gratings, non-orthogonal laser autocollimator, target and demodulator. Distributed multi-core fiber Bragg gratings can realize large-scale surface detection, and non-orthogonal laser autocollimator can realize two-point relative pointing measurement.

[0059] S2: Establishing the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basic coordinate system BCS for subsequent calculations;

[0060] S3: Establish a surface reconstruction model based on distributed multi-core fiber Bragg grating using motion recursion theory;

[0061] According to the node position s of the distributed multi-core fiber Bragg grating i and the bending angle θ measured at each node i and curvature k i , the reconstruction parameters of adjacent fiber Bragg grating intermediate nodes are calculated by nonlinear interpolation, and the local coordinate system LCS of each node and the coordinate transformation relationship of the local coordinate system of adjacent nodes (rotation matrix Translation Vector ), unify the coordinates to the basic computational coordinate system BCS, and realize surface reconstruction through B-spline curve / surface fitting method and Coons surface theory.

[0062] S4: Establish a fiber Bragg grating measurement model for the relative pointing of the calibration end of a non-orthogonal laser autocollimator;

[0063] Determine the node O closest to the calibration end in the motion path from the reference end (the optical path origin of the non-orthogonal laser autocollimator) to the calibration end (the target mirror) m , establish the calibration end normal vector k about the reconstruction parameter θ m The measurement model, j = 1, 2, 3, ..., m, where a =

[100] T , R x , R y and R z are the rotation matrices around the x-axis, y-axis, and z-axis respectively;

[0064] S5: Establish the pointing constraint of the calibration end according to the measurement data of the non-orthogonal laser autocollimator;

[0065] The linear error equation is used to express the relationship between k and the reconstruction parameter and its error Δ, combined with the calibration end normal vector measured by the non-orthogonal laser autocollimator. Building the error equation Where H is the coefficient matrix, Measure the calibration end normal vector for the fiber Bragg grating;

[0066] S6: Based on the least squares theory, considering the possibility of ill-conditioned solutions, an optimization model for the reconstruction parameters - rotation angle and bending angle is constructed The weighted optimal solution is Among them, P is a weight matrix with zero non-diagonal values, and λ is the Lagrange coefficient.

[0067] S7: According to the optimized reconstruction parameters, deformation optimized reconstruction is achieved through a surface reconstruction model based on distributed multi-core fiber Bragg gratings.

[0068] S8: Using spectral signals, displacement fields, strain fields, environmental fields, optimization parameters and other data as input, a fiber Bragg grating measurement mapping deep learning model is constructed to achieve fiber Bragg grating measurement compensation output under given input conditions.

[0069] The present invention utilizes distributed fiber Bragg gratings and non-orthogonal laser autocollimator to construct a high-precision on-orbit deformation measurement system, performs deformation reconstruction and optimization through motion recursion theory and a reconstruction parameter optimization model based on prior angle knowledge, and determines the fiber Bragg grating compensation amount according to a fiber Bragg grating measurement mapping deep learning model, thereby achieving high-precision composite measurement of on-orbit deformation of large structures.

[0070] The above is a basic embodiment of the present invention. The technical solution of the present invention is further described below through a preferred embodiment.

[0071] Reference Figure 1 and Figure 2 As shown in the figure, a high-precision composite measurement method for on-orbit deformation of large satellite structures is proposed. The overall idea is: using distributed fiber Bragg gratings and non-orthogonal laser autocollimators to build a high-precision on-orbit deformation measurement system, the distributed fiber Bragg gratings can realize large-range and high-coverage surface detection, and the non-orthogonal laser autocollimation system can realize two-point high-precision relative pointing measurement; deformation reconstruction and optimization are performed through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge; the fiber Bragg grating compensation amount is determined according to the fiber Bragg grating measurement mapping deep learning model.

[0072] The specific steps are as follows:

[0073] Step S1: construct a high-precision on-orbit deformation measurement system based on distributed fiber Bragg grating and non-orthogonal laser autocollimator, specifically: the system consists of distributed multi-core fiber Bragg grating, non-orthogonal laser autocollimator, target and demodulator; distributed multi-core fiber Bragg grating realizes large-scale surface detection, non-orthogonal laser autocollimator realizes two-point relative pointing measurement, the measurement starting point of non-orthogonal laser autocollimator and distributed multi-core fiber Bragg grating is kept consistent as much as possible, and the target mirror is arranged in the area with large relative pointing change as much as possible.

[0074] Step S2: Establish the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basis for subsequent calculation of coordinate system BCS, that is

[0075] a. MCS NOAC To MCS FBG The rotation matrix and translation vector are and α, β and γ are the rotation angles around the z-axis, y-axis and x-axis respectively, t x ,t y and t z is the translation along the x-axis, y-axis and z-axis;

[0076] b. MCS FBG The rotation matrix to BCS is a third-order unit matrix with no relative translation;

[0077] Step S3: using motion recursion theory to establish a surface reconstruction model based on distributed multi-core fiber Bragg gratings;

[0078] a. Distributed multi-core fiber Bragg grating measures the bending angle θ of each node through the neutral layer and multiple fiber Bragg gratings evenly distributed around the circumference i and curvature k i ;

[0079] b. According to θ i , k i and the fiber Bragg grating node position s i ,Through nonlinear interpolation, the intermediate node positions, bending angles and curvatures of adjacent FBGs are calculated.

[0080] c. The local coordinate system LCS is constructed with the tangent direction of each node as the z-axis, the radial direction of the first fiber Bragg grating arranged along the circumferential direction as the x-axis, and the y-axis satisfying the right-hand screw rule. For adjacent nodes, the j+1th node O j+1 At the jth node O j The local coordinate system LCS j The coordinates below are

[0081]

[0082] in, is the rotation angle,

[0083] d. According to the local coordinate system construction rules, ignoring the fiber Bragg grating torsion effect, establish LCS j+1 To LCS j Coordinate transformation relationship: rotation around the x-axis (The rotation matrix is ), then pan Right now

[0084]

[0085] e. According to the relative position relationship of adjacent nodes and the coordinate transformation relationship of the local coordinate system, all node coordinates are transformed to BCS, that is,

[0086]

[0087] f. Based on the B-spline curve fitting method, the space curve is reconstructed, and on this basis, based on the Coons surface theory, the surface subdivision reconstruction is realized through the B-spline surface fitting method.

[0088] Step S4: record the optical path origin of the non-orthogonal laser autocollimator as the reference end and the target mirror as the calibration end, and establish a fiber Bragg grating measurement model with the relative pointing of the calibration end;

[0089] a. Using the motion path from the reference end to the calibration end as a reference, determine the node O closest to the calibration end in the path. m , the calibration end normal vector is in LCS m The following is represented by k m ;

[0090] b. According to the curve reconstruction model of the motion recursion theory, the normal vector of the calibration end under BCS is Rewrite it as about θ m Measurement model

[0091]

[0092] in, a=

[100] T ;

[0093] If the calibration end coincides with the mth node, then

[0094]

[0095] Step S5: Establish the calibration end pointing constraint through the measurement data of the non-orthogonal laser autocollimator, and build the optimization model of the reconstruction parameters - rotation angle and bending angle based on it.

[0096] a. Let the normal vector of the calibration end measured by the non-orthogonal laser autocollimator be According to the space uniqueness theorem, it should satisfy Considering the actual error factor, the two normal vectors measured by different methods are not completely equal. Let ε represent the error between the two, that is,

[0097]

[0098] b. Rewrite k as an expression including the error term, then

[0099]

[0100] c. Ignore the second-order and above small quantities, then

[0101]

[0102] Where dR is the first-order derivative of the rotation matrix.

[0103] d. Order

[0104]

[0105]

[0106]

[0107] but

[0108]

[0109] e. Based on the least squares theory, considering the possibility of ill-conditioned solutions, a reconstruction parameter optimization model is constructed

[0110]

[0111] The optimal solution of the error model is

[0112]

[0113] f. Since the strain changes at each point are inconsistent, all reconstruction parameters are not equally weighted, so the weighted optimal solution of the reconstruction parameter optimization model is

[0114]

[0115] Among them, P is a non-diagonal weight matrix with zero, which can be determined by the fiber Bragg grating measurement mapping deep learning model and the multi-core fiber Bragg grating reconstruction parameter solution model.

[0116] Step S6: According to the optimized reconstruction parameters, deformation optimized reconstruction is achieved through a surface reconstruction model based on distributed multi-core fiber gratings.

[0117] Step S7: Using spectral signals, displacement fields, strain fields, environmental fields, optimization parameters and other data as input, a fiber Bragg grating measurement mapping deep learning model is constructed to achieve fiber Bragg grating measurement compensation output under given input conditions.

[0118] a. Establish a neural network model for the conversion between spectral signal and fiber Bragg grating strain measurement, use the simulated spectral data under preset strain as input and the preset strain value as the true value for training, and use the spectral data-true strain value for model verification to achieve efficient and high-precision demodulation from signal to strain;

[0119] b. Taking the measurement data (including spectral signals, theoretical and measured strains, reconstructed parameter errors, temperature, time, etc.) at different times, different preset deformations, and different application environments as input, a neural network model combining spectral signals and FBG strain measurement conversion is established to establish a FBG measurement mapping deep learning model to achieve high-precision FBG measurement compensation output under given input conditions;

[0120] c. Using the compensated measurement data as input, the reconstruction parameters are solved and the deformation reconstruction is realized through the surface reconstruction model based on distributed multi-core fiber Bragg grating.

[0121] The present invention also provides a high-precision composite measurement system for on-orbit deformation of large satellite structures. The high-precision composite measurement system for on-orbit deformation of large satellite structures can be realized by executing the process steps of the high-precision composite measurement method for on-orbit deformation of large satellite structures. That is, those skilled in the art can understand the high-precision composite measurement method for on-orbit deformation of large satellite structures as a preferred implementation of the high-precision composite measurement system for on-orbit deformation of large satellite structures.

[0122] According to the present invention, a high-precision composite measurement system for on-orbit deformation of a large satellite structure is provided, comprising:

[0123] Module M1: Construct a high-precision on-orbit deformation measurement system using distributed fiber Bragg gratings and non-orthogonal laser autocollimator.

[0124] Module M2: Deformation reconstruction and optimization through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge.

[0125] Module M3: Determine the fiber Bragg grating compensation amount based on the fiber Bragg grating measurement mapping deep learning model.

[0126] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0127] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A high-precision composite measurement method for on-orbit deformation of large satellite structures, characterized in that: include: Step S1: constructing a high-precision on-orbit deformation measurement system using distributed fiber Bragg gratings and non-orthogonal laser autocollimator; Step S2: Perform deformation reconstruction and optimization through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge; Step S3: Determine the fiber Bragg grating compensation amount according to the fiber Bragg grating measurement mapping deep learning model.

2. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 1 is characterized in that: The step S2 comprises: Step S2.1: Establish the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basic coordinate system BCS for subsequent calculations; Step S2.2: Using the motion recursion theory, a surface reconstruction model based on distributed multi-core fiber Bragg grating is established; Step S2.3: Establishing a fiber Bragg grating measurement model for relative pointing of the calibration end of a non-orthogonal laser autocollimator; Step S2.4: According to the measurement data of the non-orthogonal laser autocollimator, the pointing constraint of the calibration end is established, and based on this, the optimization model of the reconstruction parameters - rotation angle and bending angle is constructed; Step S2.5: According to the optimized reconstruction parameters, deformation optimized reconstruction is achieved through a surface reconstruction model based on distributed multi-core fiber gratings.

3. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 2 is characterized in that: The step S2.2 comprises: According to the node position s of the distributed multi-core fiber Bragg grating i and the bending angle θ measured at each node i and curvature k i The reconstruction parameters of adjacent fiber Bragg grating intermediate nodes are calculated by nonlinear interpolation, the local coordinate system LCS of each node and the coordinate transformation relationship between the local coordinate systems of adjacent nodes are established, the coordinates are unified to the basic calculation coordinate system BCS, and the surface reconstruction is realized by B-spline curve / surface fitting method and Coons surface theory.

4. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 2 is characterized in that: The step S2.3 comprises: Determine the node O closest to the calibration end in the motion path from the reference end to the calibration end m , establish the calibration end normal vector k about the reconstruction parameter θ m The measurement model, j = 1, 2, 3, ..., m, where a=[1 0 0] T , R x , R y and R z are the rotation matrices around the x-axis, y-axis, and z-axis respectively.

5. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 4 is characterized in that: The step S2.4 comprises: The linear error equation is used to express the relationship between k and the reconstruction parameter and its error Δ, combined with the calibration end normal vector measured by the non-orthogonal laser autocollimator. Building the error equation Where H is the coefficient matrix, Measure the calibration end normal vector for the fiber Bragg grating.

6. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 5 is characterized in that: The step S2.4 comprises: Constructing a reconstruction parameter optimization model The weighted optimal solution is Among them, P is a weight matrix with zero off-diagonal values, λ is the Lagrange coefficient, and I is the unit matrix.

7. The high-precision composite measurement method for on-orbit deformation of large satellite structures according to claim 1 is characterized in that: The step S3 comprises: Step S3.1: Establish a neural network model for converting spectral signals to fiber Bragg grating strain measurement to achieve signal-to-strain demodulation; Step S3.2: Establishing a fiber Bragg grating measurement mapping deep learning model to achieve fiber Bragg grating measurement compensation output under given input conditions; Step S3.3: Calculate the reconstruction parameters according to the compensated measurement data, and realize deformation reconstruction through the surface reconstruction model based on distributed multi-core fiber grating.

8. A high-precision composite measurement system for on-orbit deformation of large satellite structures, characterized in that: include: Module M1: Use distributed fiber Bragg grating and non-orthogonal laser autocollimator to build a high-precision on-orbit deformation measurement system; Module M2: Deformation reconstruction and optimization through motion recursion theory and reconstruction parameter optimization model based on prior angle knowledge; Module M3: Determine the fiber Bragg grating compensation amount based on the fiber Bragg grating measurement mapping deep learning model.

9. The high-precision composite measurement system for on-orbit deformation of large satellite structures according to claim 8 is characterized in that: The module M2 comprises: Module M2.1: Establishing the base coordinate system MCS of the distributed fiber Bragg grating measurement system FBG Measurement Coordinate System MCS with Non-Orthogonal Laser Autocollimator NOAC The conversion relationship between them is based on MCS FBG As the basic coordinate system BCS for subsequent calculations; Module M2.2: Use the motion recursion theory to establish a surface reconstruction model based on distributed multi-core fiber Bragg gratings; Module M2.3: Establish a fiber Bragg grating measurement model for the relative pointing of the calibration end of a non-orthogonal laser autocollimator; Module M2.4: According to the measurement data of the non-orthogonal laser autocollimator, establish the pointing constraints of the calibration end, and build the optimization model of the reconstruction parameters - rotation angle and bending angle based on this; Module M2.5: According to the optimized reconstruction parameters, deformation optimization reconstruction is achieved through the surface reconstruction model based on distributed multi-core fiber Bragg gratings; The module M2.2 includes: According to the node position s of the distributed multi-core fiber Bragg grating i and the bending angle θ measured at each node i and curvature k i , the reconstruction parameters of adjacent fiber Bragg grating intermediate nodes are calculated by nonlinear interpolation, the local coordinate system LCS of each node and the coordinate transformation relationship of the local coordinate system of adjacent nodes are established, the coordinates are unified to the basic calculation coordinate system BCS, and the surface reconstruction is realized by B-spline curve / surface fitting method and Coons surface theory; The module M2.3 includes: Determine the node O closest to the calibration end in the motion path from the reference end to the calibration end m , establish the calibration end normal vector k about the reconstruction parameter θ m The measurement model, j = 1, 2, 3, ..., m, where a=[1 0 0] T , R x , R y and R z are the rotation matrices around the x-axis, y-axis, and z-axis respectively; The module M2.4 includes: The linear error equation is used to express the relationship between k and the reconstruction parameter and its error Δ, combined with the calibration end normal vector measured by the non-orthogonal laser autocollimator. Building the error equation Where H is the coefficient matrix, Measure the calibration end normal vector for the fiber Bragg grating; The module M2.4 includes: Constructing a reconstruction parameter optimization model The weighted optimal solution is Among them, P is a weight matrix with zero off-diagonal values, λ is the Lagrange coefficient, and I is the unit matrix.

10. The high-precision composite measurement system for on-orbit deformation of large satellite structures according to claim 8, characterized in that: The module M3 comprises: Module M3.1: Establish a neural network model for the conversion between spectral signals and fiber Bragg grating strain measurement to achieve signal-to-strain demodulation; Module M3.2: Establish a deep learning model for fiber Bragg grating measurement mapping to achieve fiber Bragg grating measurement compensation output under given input conditions; Module M3.3: Calculate the reconstruction parameters according to the compensated measurement data, and realize deformation reconstruction through the surface reconstruction model based on distributed multi-core fiber Bragg grating.

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