Intelligent prediction method and system for vibration of large space unfolding mechanism

Abstract

The invention relates to an intelligent vibration prediction method and system for a large space unfolding mechanism. The method comprises the following steps: acquiring grating reflection wavelength change data caused by small structural deformation caused by vibration; the change of wavelength is calculated by receiving light waves reflected by the fiber grating sensor, and then the change of wavelength is converted into space and time information of vibration through signal demodulation, and local vibration displacement data are analyzed and processed; taking the analyzed and processed local vibration displacement data as the input of a GCN-LSTM neural network; and performing prediction calculation on the overall vibration state of the large space expansion mechanism by combining a space-time coupling relationship to obtain an overall vibration displacement result of the mechanism. The overall vibration of the large space unfolding mechanism can be monitored in real time, the method has the advantages of being high in precision and reliability, meanwhile, a decision basis can be provided for control over the on-orbit state of a spacecraft, and the high-precision attitude and stability of the spacecraft in the complex space environment are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of large-scale space deployment mechanism vibration prediction, and in particular to a large-scale space deployment mechanism vibration intelligent prediction method and system. Background Art

[0002] In the design and operation of spacecraft, large space deployment mechanisms are one of the key components and are widely used in the deployment and deployment of solar sails, antennas, and scientific instruments on satellites and probes. These mechanisms are usually composed of rigid and flexible bodies. The complexity of their structure and the particularity of the on-orbit use environment make them face many challenges in the deployment and locking process. Specifically, as the mechanism is deployed and the working state changes, there are complex coupling dynamic characteristics between the rigid and flexible bodies, which can easily lead to vibration and deformation. This vibration not only affects the stability of the mechanism, but may also cause a decrease in the pointing accuracy and surface accuracy of the spacecraft, and in severe cases even affect the success of the space mission.

[0003] Traditional vibration monitoring methods mainly rely on single-point sensors, which cannot effectively capture the vibration characteristics of the entire mechanism, especially in complex space environments. Changes in the on-orbit operating environment, such as temperature, vacuum, and microgravity, bring additional complexity to the deployment process of large-scale space deployment mechanisms. How to efficiently and accurately predict the overall structural deformation information based on the local vibration displacement data collected from the sensor remains a technical challenge. In response to the above problems, the present invention proposes an intelligent vibration prediction system for large-scale space deployment mechanisms. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method and system for intelligent prediction of vibration of a large-scale space deployment mechanism, which solves the problem of how to efficiently and accurately predict the overall structural deformation information based on the local vibration displacement data collected from the sensor in a complex space environment. The method arranges the fiber grating sensor at the key position of the large-scale space deployment mechanism to monitor the local structural micro-deformation caused by vibration in real time. The fiber grating sensor collects the local vibration displacement data according to the change of the reflection wavelength caused by the vibration. During the vibration monitoring process, the fiber grating sensor accurately obtains the deformation and vibration data of the local position, and uses these local vibration displacement data as the input of the GCN-LSTM neural network. Through the neural network prediction algorithm, the local vibration displacement data obtained by the fiber optic sensor is used, combined with the time-space coupling relationship, to predict and calculate the overall vibration state of the large-scale space deployment mechanism, and obtain the overall vibration displacement data of the mechanism. The system can not only monitor the overall vibration of the large-scale space deployment mechanism in real time, and has the characteristics of high precision and high reliability, but also provide a decision-making basis for the control of the on-orbit state of the spacecraft, ensuring the high-precision attitude and stability of the spacecraft in a complex space environment.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a large-scale space deployment mechanism vibration intelligent prediction method, comprising the following steps:

[0006] S1. Acquire the grating reflection wavelength change data caused by the slight deformation of the structure due to vibration through multiple fiber grating sensors distributed in fixed positions of the large space deployment mechanism;

[0007] S2, by receiving the light waves reflected by the fiber grating sensor, calculating the change of wavelength, and then converting the wavelength change into the spatial and temporal information of vibration through signal demodulation, and analyzing and processing the local vibration displacement data;

[0008] S3. Construct a GCN-LSTM neural network combination model, and use the analyzed and processed local vibration displacement data as the input of the GCN-LSTM neural network; combine the spatiotemporal coupling relationship, use the GCN-LSTM neural network to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, predict and calculate the overall vibration state of the large-scale spatial deployment mechanism, and obtain the overall vibration displacement result of the mechanism.

[0009] Furthermore, in step S1, the specific process includes the following steps:

[0010] S11, fiber Bragg grating sensors are arranged at key structural points of the truss of the large space deployment mechanism to monitor local vibrations at different locations;

[0011] S12. Fiber Bragg grating sensor transmits light into the optical fiber through a low-coherence light source. When the light wave passes through the grating in the optical fiber, the fiber Bragg grating will reflect the light of a specific wavelength back to the sensor according to its periodic structure, and the light of other wavelengths will continue to propagate, reflecting the central wavelength λ B satisfy:

[0012] λ B =2n eff Λ;

[0013] Where n eff is the effective refractive index of the fiber material, and Λ is the period length of the grating.

[0014] Furthermore, in step S2, the specific process includes the following steps:

[0015] S21. Demodulate the optical signal of the optical fiber sensor into an electrical signal. The demodulated electrical signal represents the wavelength change caused by the structural vibration. The strain ε is calculated by detecting the change in the wavelength of the reflected light:

[0016]

[0017] Where Δλ is the change in reflection wavelength, λ0 is the initial reflection wavelength, and α is the strain sensitivity of the fiber material;

[0018] S22. The process of calculating the local vibration displacement L through the strain ε is as follows:

[0019] L = ε·L0;

[0020] Where L0 is the original length of the fiber Bragg grating.

[0021] Furthermore, in step S3, the local vibration displacement data is used as the input of the GCN network. The GCN network consists of multiple layers of graph convolution. Each layer performs a graph convolution operation, gradually aggregates the features to a higher representation layer, and outputs them through a fully connected layer for the vibration prediction task of large-scale spatial expansion mechanisms. The GCN network can be expressed by the following formula:

[0022]

[0023] Among them, H (l+1) is the node feature matrix of the l+1th layer, σ is the nonlinear activation function, yes The degree matrix of is a diagonal matrix, where the degree of each node is the diagonal element, It is the adjacency matrix A plus the identity matrix I to ensure that each node includes its own information, W (l) is the weight matrix of the lth layer, It is to normalize the adjacency matrix A to prevent the eigenvalue of a node from changing with the number of neighbors, thus causing feature deviation;

[0024] At each layer, the GCN network aggregates the features of each node and passes the information of its neighboring nodes to the current node. The new features of a node v in the lth layer It can be calculated by weighted average of neighbor node features:

[0025]

[0026] in represents the neighbor set of node v, d u and d v are the degrees of nodes u and v respectively, represents the hidden representation of node u at layer l;

[0027] In the vibration prediction of large-scale spatial unfolding mechanisms, the GCN network obtains the overall vibration displacement data from the local vibration displacement data through layer-by-layer aggregation and convolution operations, thereby effectively capturing the local and global characteristics of the nodes and providing input data for the subsequent LSTM layers, thereby realizing the time series modeling and prediction of vibration data.

[0028] Furthermore, in step S2, the input of the LSTM network consists of the overall vibration displacement data of multiple time steps. The data of each time step can be represented as a vector, which includes the displacement offset of each measurement position at the corresponding time point. In the LSTM network, the information flow is controlled by three main gating mechanisms. Based on the forget gate and the input gate, the LSTM unit state C t Will be updated, the hidden state h t The calculation combines the current unit state and the output gate. This hidden state will be used as the input of the next time step and can also be provided as an output to subsequent layers or networks.

[0029] Furthermore, the specific calculation process of the LSTM network includes the following steps:

[0030] S31, the forget gate determines which information in the current unit state needs to be forgotten, and which information should be discarded based on the current input and the previous hidden state. The gate uses the sigmoid activation function to output a value between 0 and 1, where 0 means complete discard and 1 means complete retention:

[0031] f t =σ(W f ·[h t-1 , x t ]+b f );

[0032] Among them, f t is the output of the forget gate, σ is the sigmoid activation function, W f is the weight matrix of the forget gate, h t-1 is the hidden state at the previous moment, x t is the input at the current moment, b f is the bias of the forget gate;

[0033] S32, the input gate controls the impact of the current input information on the unit state, controls the addition of the current input information, determines which information needs to be updated, and generates a new candidate value vector:

[0034] i t =σ(W i ·[h t-1 ,x t ]+b i );

[0035]

[0036] Among them, i t is the output of the input gate, is the candidate cell state, W i and W Care the weight matrices of the input gate and candidate unit states, respectively, and b i and b C is the corresponding bias;

[0037] S33, the cell state is updated through the forget gate and the input gate to maintain long-term and short-term dependency information:

[0038]

[0039] Among them, C t is the current cell state, C t-1 is the unit state at the previous moment.

[0040] S34, the output gate determines how the current unit state will affect the hidden state, combines the current input and the previous hidden state, and outputs a value to determine the next hidden state:

[0041] o t =σ(W o ·[h t-1 , x t ]+b o );

[0042] h t =o t *tanh(C t );

[0043] Among them, t is the output of the output gate, W o is the weight matrix of the output gate, b o is the bias of the output gate, h t is the hidden state at the current moment.

[0044] Furthermore, in step S3, a nonlinear relationship between the local vibration data and the overall vibration data is established using the GCN-LSTM neural network. The specific process includes the following steps:

[0045] S41. Assume that there are N sensors that collect local vibration displacement data at T time points. The data can be represented as an N×T matrix:

[0046]

[0047] where d ij represents the displacement of the i-th sensor at the j-th time point, 1≤i≤N, 1≤j≤T;

[0048] If there is a connection between sensor i and sensor j, their connection relationship can be represented by the adjacency matrix A. ij =1, otherwise A ij=0; Divide the data into multiple time windows so that LSTM can learn time series features. Select the time window size as W. Each input sample can contain data from W time points. Assuming that there are data from W time points as input samples, the constructed input data shape X is (N, W);

[0049] The constructed local vibration displacement data matrix D is input into the GCN layer together with the adjacency matrix A. The GCN layer uses the adjacency matrix to process the spatial features between sensors. After passing through the GCN layer, the overall vibration displacement data features obtained are input into the LSTM layer to learn the time series features. The GCN-LSTM network output layer directly predicts the displacement of the overall structure. Represents the combined displacement of multiple sensor locations:

[0050]

[0051] in represents the predicted displacement of the i-th sensor position.

[0052] Furthermore, the local vibration displacement data are set as training samples, verification samples and test samples respectively, where the data ratio of training samples, verification samples and test samples is 8:1:1. The training samples and test samples are used to train and test the GCN-LSTM neural network combination model to obtain a trained GCN-LSTM neural network combination model; the verification samples are input into the trained GCN-LSTM neural network combination model to output the prediction results.

[0053] Furthermore, a large-scale space deployment mechanism vibration intelligent prediction system is provided, comprising:

[0054] A data acquisition module, wherein the data acquisition module is used to obtain data on changes in grating reflection wavelength caused by slight structural deformation due to vibration through a plurality of fiber grating sensors distributed at fixed positions of the large space deployment mechanism;

[0055] A vibration monitoring module, which is used to calculate the change in wavelength by receiving the light waves reflected by the fiber grating sensor, convert the wavelength change into the spatial and temporal information of the vibration through signal demodulation, and analyze and process the local vibration signal data;

[0056] The identification and prediction module is used to construct a GCN-LSTM neural network combination model, and the analyzed and processed local vibration displacement data is used as the input of the GCN-LSTM neural network combination model. In combination with the spatiotemporal coupling relationship, the GCN-LSTM neural network is used to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, and the overall vibration state of the large-scale space deployment mechanism is predicted and calculated to obtain the overall vibration displacement result of the mechanism.

[0057] By means of the above technical solution, the present invention provides a method and system for intelligent prediction of vibration of a large-scale space deployment mechanism, which has at least the following beneficial effects:

[0058] The present invention arranges fiber grating sensors at key positions of large-scale space deployment mechanisms to monitor the local structural micro-deformations caused by vibration in real time. The fiber grating sensors collect local vibration displacement data according to the change in reflection wavelength caused by vibration. During the vibration monitoring process, the fiber grating sensor accurately obtains the deformation and vibration data of the local position, and uses these local vibration displacement data as the input of the GCN-LSTM neural network. Through the neural network prediction algorithm, the local vibration displacement data obtained by the fiber optic sensor is used, combined with the time-space coupling relationship, to predict and calculate the overall vibration state of the large-scale space deployment mechanism, and obtain the overall vibration displacement data of the mechanism. The system can not only monitor the overall vibration of the large-scale space deployment mechanism in real time, and has the characteristics of high precision and high reliability, but also provide a decision-making basis for the control of the on-orbit state of the spacecraft, ensuring the high-precision attitude and stability of the spacecraft in a complex space environment. It solves the problem of how to efficiently and accurately predict the overall structural deformation information under the premise of the local vibration displacement data collected from the sensor in a complex space environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0060] Figure 1 It is a structural schematic diagram of a large-scale space deployment mechanism vibration intelligent prediction system of the present invention;

[0061] Figure 2 It is a schematic diagram of the positions of the optical fiber sensor and the vibration monitoring module of the large-scale space deployment mechanism vibration intelligent prediction system of the present invention;

[0062] Figure 3 This is a schematic diagram of the network structure of a GCN-LSTM neural network of a large-scale space deployment mechanism vibration intelligent prediction system of the present invention.

[0063] In the figure, 1. Fiber Bragg grating sensor; 2. Vibration monitoring module. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0065] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0066] Please refer to Figure 1-Figure 3 , shows a specific implementation of this embodiment. This embodiment uses the method to arrange fiber grating sensors at key positions of large-scale space deployment mechanisms to monitor the local structural micro-deformations caused by vibration in real time. The fiber grating sensors collect local vibration displacement data based on the change in reflection wavelength caused by vibration. During the vibration monitoring process, the fiber grating sensor accurately obtains the deformation and vibration data of the local position, and uses these local vibration displacement data as the input of the GCN-LSTM neural network. Through the neural network prediction algorithm, the local vibration displacement data obtained by the fiber optic sensor is used, combined with the time-space coupling relationship, to predict and calculate the overall vibration state of the large-scale space deployment mechanism, and obtain the overall vibration displacement data of the mechanism. This system can not only monitor the overall vibration of the large-scale space deployment mechanism in real time, and has the characteristics of high precision and high reliability, but also provide a decision-making basis for the control of the on-orbit state of the spacecraft, ensuring the high-precision attitude and stability of the spacecraft in a complex space environment.

[0067] Please refer to Figure 1 This embodiment proposes a large-scale space deployment mechanism vibration intelligent prediction method and system, the method comprising the following steps:

[0068] S1. Acquire the grating reflection wavelength change data caused by the slight deformation of the structure due to vibration through multiple fiber grating sensors distributed in fixed positions of the large space deployment mechanism;

[0069] As a preferred implementation of step S1, the specific process includes the following steps:

[0070] S11, fiber Bragg grating sensors are arranged at key structural points of the truss of the large space deployment mechanism to monitor local vibrations at different locations;

[0071] S12. Fiber Bragg grating sensor transmits light into the optical fiber through a low-coherence light source. When the light wave passes through the grating in the optical fiber, the fiber Bragg grating will reflect the light of a specific wavelength back to the sensor according to its periodic structure, and the light of other wavelengths will continue to propagate, reflecting the central wavelength λ B satisfy:

[0072] λ B =2n eff Λ;

[0073] Where n eff is the effective refractive index of the fiber material, and Λ is the period length of the grating.

[0074] In this embodiment, if Figure 2 As shown, the fiber Bragg grating sensor 1 is fixed at the same position of the smallest unit of the truss, and the vibration monitoring module 2 is located inside the spacecraft; the vibration monitoring module 2 supports the synchronous data acquisition and processing of multiple fiber Bragg grating sensors, and can continuously perform vibration monitoring when the deployment mechanism is deployed on-orbit or deployed and locked. When the truss structure vibrates, the structure where the fiber Bragg grating sensor is located will undergo a slight deformation. This deformation will change the period length Λ of the fiber Bragg grating, resulting in a change in the reflection center wavelength λ B Changes occur. Since this change in wavelength is proportional to the strain, the strain ε of the next step is calculated by detecting the change in the wavelength of the reflected light. The fiber Bragg grating sensor can monitor tiny deformations of the structure in real time and is suitable for extreme conditions in the space environment. It has the ability to resist electromagnetic interference and high temperature changes.

[0075] S2, by receiving the light waves reflected by the fiber grating sensor, calculating the change of wavelength, and then converting the wavelength change into the spatial and temporal information of vibration through signal demodulation, and analyzing and processing the local vibration displacement data;

[0076] As a preferred implementation of step S2, the specific process includes the following steps:

[0077] S21. Demodulate the optical signal of the optical fiber sensor into an electrical signal. The demodulated electrical signal represents the wavelength change caused by the structural vibration. The strain ε is calculated by detecting the change in the wavelength of the reflected light:

[0078]

[0079] Where Δλ is the change in reflection wavelength, λ0 is the initial reflection wavelength, and α is the strain sensitivity of the fiber material;

[0080] S22. The process of calculating the local vibration displacement L through the strain ε is as follows:

[0081] L = ε·L0;

[0082] Where L0 is the original length of the fiber Bragg grating.

[0083] In this embodiment, the strain change of the fiber grating sensor will change with the frequency and amplitude of the vibration, and the reflection wavelength of the grating will also change periodically. The data processing module in the vibration monitoring module is responsible for demodulating the optical signal of the fiber optic sensor into an electrical signal, and the demodulated electrical signal represents the wavelength change caused by the structural vibration. The signal demodulator converts the wavelength change into displacement offset and vibration frequency information, and inputs these data into the data processing unit. In the data processing module, multiple data ports of the signal acquisition unit receive electrical signals from the demodulator, representing the amplitude and frequency of the vibration at different positions. By processing these displacement signals captured by the fiber optic sensor, the displacement caused by the vibration is calculated, thereby obtaining local vibration displacement data.

[0084] S3. Construct a GCN-LSTM neural network combination model, and use the analyzed and processed local vibration displacement data as the input of the GCN-LSTM neural network; combine the spatiotemporal coupling relationship, use the GCN-LSTM neural network to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, predict and calculate the overall vibration state of the large-scale spatial deployment mechanism, and obtain the overall vibration displacement result of the mechanism.

[0085] As a preferred implementation of step S3, the local vibration displacement data is used as the input of the GCN network. The GCN network is composed of multiple layers of graph convolution. Each layer performs a graph convolution operation, and gradually aggregates the features to a higher representation layer. The output is output through a fully connected layer for the vibration prediction task of a large-scale spatial unfolding mechanism. The GCN network can be expressed by the following formula:

[0086]

[0087] Among them, H (l+1) is the node feature matrix of the l+1th layer, σ is the nonlinear activation function, yes The degree matrix of is a diagonal matrix, where the degree of each node is the diagonal element, It is the adjacency matrix A plus the identity matrix I to ensure that each node includes its own information, W (l) is the weight matrix of the lth layer, It is to normalize the adjacency matrix A to prevent the eigenvalue of a node from changing with the number of neighbors, thus causing feature deviation;

[0088] At each layer, the GCN network aggregates the features of each node and passes the information of its neighboring nodes to the current node. The new features of a node v in the lth layer It can be calculated by weighted average of neighbor node features:

[0089]

[0090] in represents the neighbor set of node v, d u and d v are the degrees of nodes u and v respectively; represents the hidden representation of node u at layer l;

[0091] The benefit of the GCN network in the vibration prediction of large-scale spatial deployment mechanisms is that it can effectively utilize the spatial topological information of the measurement points, obtain the overall vibration displacement data from the local vibration displacement data through layer-by-layer aggregation and convolution operations, thereby effectively capturing the local and global characteristics of the nodes, and providing input data for the subsequent LSTM layer, thereby realizing the time series modeling and prediction of vibration data.

[0092] As a preferred implementation of step S3, the input of the LSTM network consists of the overall vibration displacement data of multiple time steps, and the data of each time step can be represented as a vector, which includes the displacement offset of each measurement position at the corresponding time point; in the LSTM network, the information flow is controlled by three main gating mechanisms. Based on the forget gate and the input gate, the LSTM unit state C t Will be updated, the hidden state h t The calculation combines the current cell state and the output gate. This hidden state will be used as the input of the next time step, and can also be provided as an output to subsequent layers or networks. The LSTM network controls the flow of information through a series of gating mechanisms, thereby effectively capturing the long-term and short-term dependencies of time series data.

[0093] More specifically, the specific calculation process of the LSTM network includes the following steps:

[0094] S31, the forget gate determines which information in the current unit state needs to be forgotten, and which information should be discarded based on the current input and the previous hidden state. The gate uses the sigmoid activation function to output a value between 0 and 1, where 0 means complete discard and 1 means complete retention:

[0095] f t =σ(W f ·[h t-1 , x t ]+b f );

[0096] Among them, f i is the output of the forget gate, σ is the sigmoid activation function, W f is the weight matrix of the forget gate, h t-1 is the hidden state at the previous moment, x t is the input at the current moment, b f is the bias of the forget gate;

[0097] S32, the input gate controls the impact of the current input information on the unit state, controls the addition of the current input information, determines which information needs to be updated, and generates a new candidate value vector:

[0098] i t =σ(W i ·[h t-1 ,x t ]+b i );

[0099]

[0100] Among them, i t is the output of the input gate, is the candidate cell state, W i and W C are the weight matrices of the input gate and candidate unit states, respectively, and b i and b C is the corresponding bias;

[0101] S33, the cell state is updated through the forget gate and the input gate to maintain long-term and short-term dependency information:

[0102]

[0103] Among them, C t is the current cell state, C t-1 is the unit state at the previous moment.

[0104] S34, the output gate determines how the current unit state will affect the hidden state, combines the current input and the previous hidden state, and outputs a value to determine the next hidden state:

[0105] o t =σ(W o ·[h t-1 , x t ]+b o );

[0106] h t =o t *tanh(C t );

[0107] Among them, t is the output of the output gate, W o is the weight matrix of the output gate, b o is the bias of the output gate, h t is the hidden state at the current moment.

[0108] As a preferred implementation of step S3, a nonlinear relationship between local vibration data and overall vibration data is established using a GCN-LSTM neural network, such as Figure 3 As shown, the specific process includes the following steps:

[0109] S41. Assume that there are N sensors that collect local vibration displacement data at T time points. The data can be represented as an N×T matrix:

[0110]

[0111] where d ij represents the displacement of the i-th sensor at the j-th time point, 1≤i≤N, 1≤j≤T;

[0112] If there is a connection between sensor i and sensor j, their connection relationship can be represented by the adjacency matrix A. ij =1, otherwise A ij =0; Divide the data into multiple time windows so that LSTM can learn time series features. Select the time window size as W. Each input sample can contain data from W time points. Assuming that there are data from W time points as input samples, the constructed input data shape X is (N, W);

[0113] The constructed local vibration displacement data matrix D is input into the GCN layer together with the adjacency matrix A. The GCN layer uses the adjacency matrix to process the spatial features between sensors. After passing through the GCN layer, the overall vibration displacement data features obtained are input into the LSTM layer to learn the time series features. The GCN-LSTM network output layer directly predicts the displacement of the overall structure. Represents the combined displacement of multiple sensor locations:

[0114]

[0115] in represents the predicted displacement of the i-th sensor position.

[0116] In this embodiment, since the vibration data obtained by the fiber grating sensor is only discrete local vibration displacement data information, it is impossible to fully display the overall vibration state of the entire large-scale space deployment mechanism. Therefore, in this embodiment, the identification prediction module uses the vibration intelligent prediction system to establish a nonlinear coupling relationship between local vibration and overall vibration through the GCN-LSTM neural network. The neural network learns and derives the global vibration behavior of the entire flexible structure based on the local vibration displacement and time data provided by the optical fiber sensor. The identification prediction module adopts the GCN-LSTM network algorithm to learn and derive the local vibration displacement data of limited measuring points to obtain the overall vibration results of the large-scale space deployment mechanism. The vibration intelligent prediction system uses the GCN-LSTM neural network to establish a nonlinear relationship between local vibration data and overall vibration data. The GCN-LSTM network is composed of a graph convolution network and a long short-term memory network. In the vibration intelligent prediction system, the GCN network is used to process and extract the spatial features of the local vibration displacement data, and the LSTM network is used to capture the dynamic changes in the time series data. The system can not only monitor the overall vibration of large space deployment mechanisms in real time with high precision and high reliability, but also provide a decision-making basis for the control of the spacecraft's on-orbit state, ensuring the spacecraft's high-precision attitude and stability in complex space environments.

[0117] More specifically, the local vibration displacement data are set as training samples, verification samples and test samples respectively, where the data ratio of training samples, verification samples and test samples is 8:1:1. The training samples and test samples are used to train and test the GCN-LSTM neural network combination model to obtain a trained GCN-LSTM neural network combination model; the verification samples are input into the trained GCN-LSTM neural network combination model to output the prediction results.

[0118] The present invention also provides a large-scale space deployment mechanism vibration intelligent prediction system, such as Figure 1 As shown, including:

[0119] A data acquisition module, wherein the data acquisition module is used to obtain data on changes in grating reflection wavelength caused by slight structural deformation due to vibration through a plurality of fiber grating sensors distributed at fixed positions of the large space deployment mechanism;

[0120] A vibration monitoring module, which is used to calculate the change in wavelength by receiving the light waves reflected by the fiber grating sensor, convert the wavelength change into the spatial and temporal information of the vibration through signal demodulation, and analyze and process the local vibration signal data;

[0121] The identification and prediction module is used to construct a GCN-LSTM neural network combination model, and the analyzed and processed local vibration displacement data is used as the input of the GCN-LSTM neural network combination model. In combination with the spatiotemporal coupling relationship, the GCN-LSTM neural network is used to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, and the overall vibration state of the large-scale space deployment mechanism is predicted and calculated to obtain the overall vibration displacement result of the mechanism.

[0122] More specifically, a large-scale space deployment mechanism vibration intelligent prediction device is provided, which is characterized by comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the above method.

[0123] More specifically, a storage medium is provided on which a computer program is stored, wherein the computer program implements the steps of the above method when executed by a processor.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0126] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for intelligent prediction of vibration of a large space deployment mechanism, characterized in that: The following steps are involved: S1. Acquire the grating reflection wavelength change data caused by the slight deformation of the structure due to vibration through multiple fiber grating sensors distributed in fixed positions of the large space deployment mechanism; S2, by receiving the light waves reflected by the fiber grating sensor, calculating the change of wavelength, and then converting the wavelength change into the spatial and temporal information of vibration through signal demodulation, and analyzing and processing the local vibration displacement data; S3. Construct a GCN-LSTM neural network combination model, and use the analyzed and processed local vibration displacement data as the input of the GCN-LSTM neural network; combine the spatiotemporal coupling relationship, use the GCN-LSTM neural network to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, predict and calculate the overall vibration state of the large-scale spatial deployment mechanism, and obtain the overall vibration displacement result of the mechanism.

2. According to claim 1, a large-scale space deployment mechanism vibration intelligent prediction method is characterized by: In step S1, the specific process includes the following steps: S11, fiber Bragg grating sensors are arranged at key structural points of the truss of the large space deployment mechanism to monitor local vibrations at different locations; S12. Fiber Bragg grating sensor transmits light into the optical fiber through a low-coherence light source. When the light wave passes through the grating in the optical fiber, the fiber Bragg grating will reflect the light of a specific wavelength back to the sensor according to its periodic structure, and the light of other wavelengths will continue to propagate, reflecting the central wavelength λ B satisfy: l B =2n eff L; where n eff is the effective refractive index of the fiber material, and Λ is the period length of the grating.

3. According to claim 1, a large-scale space deployment mechanism vibration intelligent prediction method is characterized by: In step S2, the specific process includes the following steps: S21. Demodulate the optical signal of the optical fiber sensor into an electrical signal. The demodulated electrical signal represents the wavelength change caused by the structural vibration. The strain ε is calculated by detecting the change in the wavelength of the reflected light: Where Δλ is the change in reflection wavelength, λ0 is the initial reflection wavelength, and α is the strain sensitivity of the fiber material; S22. The process of calculating the local vibration displacement L through the strain ε is as follows: L = ε·L0; Where L0 is the original length of the fiber Bragg grating.

4. The method for intelligent prediction of vibration of a large-scale space deployment mechanism according to claim 1, characterized in that: In step S3, the local vibration displacement data is used as the input of the GCN network. The GCN network consists of multiple layers of graph convolution. Each layer performs a graph convolution operation, gradually aggregates the features to a higher representation layer, and outputs them through a fully connected layer for the vibration prediction task of large-scale spatial expansion mechanisms. The GCN network can be expressed by the following formula: Among them, H (l+1) is the node feature matrix of the l+1th layer, σ is the nonlinear activation function, yes The degree matrix of is a diagonal matrix, where the degree of each node is the diagonal element, It is the adjacency matrix A plus the identity matrix I to ensure that each node includes its own information, W (l) is the weight matrix of the lth layer, It is to normalize the adjacency matrix A to prevent the eigenvalue of a node from changing with the number of neighbors, thus causing feature deviation; At each layer, the GCN network aggregates the features of each node and passes the information of its neighboring nodes to the current node. The new features of a node v in the lth layer It can be calculated by weighted average of neighbor node features: in represents the neighbor set of node v, d u and d v are the degrees of nodes u and v respectively; represents the hidden representation of node u at layer l; In the vibration prediction of large-scale spatial unfolding mechanisms, the GCN network obtains the overall vibration displacement data from the local vibration displacement data through layer-by-layer aggregation and convolution operations, thereby effectively capturing the local and global characteristics of the nodes and providing input data for the subsequent LSTM layers, thereby realizing the time series modeling and prediction of vibration data.

5. The method for intelligent prediction of vibration of a large-scale space deployment mechanism according to claim 1, characterized in that: In step S3, the input of the LSTM network consists of the overall vibration displacement data of multiple time steps. The data of each time step can be represented as a vector, which includes the displacement offset of each measurement position at the corresponding time point. In the LSTM network, the information flow is controlled by three main gating mechanisms. Based on the forget gate and the input gate, the LSTM unit state C t Will be updated, the hidden state h t The calculation combines the current unit state and the output gate. This hidden state will be used as the input of the next time step and can also be provided as an output to subsequent layers or networks.

6. The method for intelligent prediction of vibration of a large-scale space deployment mechanism according to claim 5, characterized in that: The specific calculation process of the LSTM network includes the following steps: S31, the forget gate determines which information in the current unit state needs to be forgotten, and which information should be discarded based on the current input and the previous hidden state. The gate uses the sigmoid activation function to output a value between 0 and 1, where 0 means complete discard and 1 means complete retention: f t =σ(W f ·[h t-1 ,x t ]+b f ); Among them, f t is the output of the forget gate, σ is the sigmoid activation function, W f is the weight matrix of the forget gate, h t-1 is the hidden state at the previous moment, x t is the input at the current moment, b f is the bias of the forget gate; S32, the input gate controls the impact of the current input information on the unit state, controls the addition of the current input information, determines which information needs to be updated, and generates a new candidate value vector: i t =σ(W i ·[h t-1 ,x t ]+b i ); Among them, i t is the output of the input gate, is the candidate cell state, W i and W C are the weight matrices of the input gate and candidate unit states, respectively, and b i and b C is the corresponding bias; S33, the cell state is updated through the forget gate and the input gate to maintain long-term and short-term dependency information: Among them, C t is the current cell state, C t-1 is the unit state at the previous moment. S34, the output gate determines how the current unit state will affect the hidden state, combines the current input and the previous hidden state, and outputs a value to determine the next hidden state: the t =σ(W o ·[h t-1 ,x t ]+b o ); h t =o t *tanh(C t ); Among them, t is the output of the output gate, W o is the weight matrix of the output gate, b o is the bias of the output gate, h t is the hidden state at the current moment.

7. The method for intelligent prediction of vibration of a large-scale space deployment mechanism according to claim 1, characterized in that: In step S3, the GCN-LSTM neural network is used to establish a nonlinear relationship between the local vibration data and the overall vibration data. The specific process includes the following steps: S41. Assume that there are N sensors that collect local vibration displacement data at T time points. The data can be represented as an N×T matrix: where d ij represents the displacement of the i-th sensor at the j-th time point, 1≤i≤N, 1≤j≤T; If there is a connection between sensor i and sensor j, their connection relationship can be represented by the adjacency matrix A. ij =1, otherwise A ij =0; Divide the data into multiple time windows so that LSTM can learn time series features. Select the time window size as W. Each input sample can contain data from W time points. Assuming that there are data from W time points as input samples, the constructed input data shape X is (N, W); The constructed local vibration displacement data matrix D is input into the GCN layer together with the adjacency matrix A. The GCN layer uses the adjacency matrix to process the spatial features between sensors. After passing through the GCN layer, the overall vibration displacement data features obtained are input into the LSTM layer to learn the time series features. The GCN-LSTM network output layer directly predicts the displacement of the overall structure. Represents the combined displacement of multiple sensor locations: in represents the predicted displacement of the i-th sensor position.

8. The method for intelligent prediction of vibration of a large-scale space deployment mechanism according to claim 1, characterized in that: The local vibration displacement data are set as training samples, verification samples and test samples respectively, where the data ratio of training samples, verification samples and test samples is 8:1:

1. The training samples and test samples are used to train and test the GCN-LSTM neural network combination model to obtain a trained GCN-LSTM neural network combination model; the verification samples are input into the trained GCN-LSTM neural network combination model to output the prediction results.

9. A large-scale space deployment mechanism vibration intelligent prediction system, characterized in that: include: A data acquisition module, wherein the data acquisition module is used to obtain data on changes in grating reflection wavelength caused by slight structural deformation due to vibration through a plurality of fiber grating sensors distributed at fixed positions of the large space deployment mechanism; A vibration monitoring module, which is used to calculate the change in wavelength by receiving the light waves reflected by the fiber grating sensor, convert the wavelength change into the spatial and temporal information of the vibration through signal demodulation, and analyze and process the local vibration signal data; The identification and prediction module is used to construct a GCN-LSTM neural network combination model, and the analyzed and processed local vibration displacement data is used as the input of the GCN-LSTM neural network combination model. In combination with the spatiotemporal coupling relationship, the GCN-LSTM neural network is used to establish a nonlinear relationship between the local vibration displacement data and the overall vibration displacement data, and the overall vibration state of the large-scale space deployment mechanism is predicted and calculated to obtain the overall vibration displacement result of the mechanism.

Images