Interactive Feedback Acquisition and Analysis Method for Spacecraft Motion Devices and Environmental Colors

Abstract

The present invention provides an interactive feedback acquisition and analysis method for the motion device and environment color of a spacecraft, which relates to the technical field of spacecraft motion control. The method includes collecting multi-spectral color information of the environment, constructing a color feature mapping matrix, calculating a color gradient field and segmenting feature regions, executing a quantum color feature analysis algorithm to generate an optimal motion trajectory, obtaining motion parameters, executing a motion state decision algorithm to generate control instructions, adjusting the motion state, and recording interactive feedback data. The present invention can achieve the interactive feedback between the spacecraft motion device and the environment color, improve the motion control accuracy and environmental adaptability, and provide a new technical solution for the intelligent control of spacecraft.

Description

Technical Field

[0001] The present invention relates to the technical field of spacecraft motion control, and particularly to a method for interactive feedback acquisition and analysis of a spacecraft motion device and environmental color. Background Art

[0002] During the mission execution of a spacecraft motion device, it needs to interact with the surrounding environment in real time and obtain information through environmental perception to guide motion control. Among them, environmental color information, as important perception data, can reflect environmental characteristics and change laws. Currently, common environmental perception methods mainly include lidar scanning, visual image acquisition, etc., and these methods play an important role in the environmental adaptability control of spacecraft motion devices.

[0003] However, there are still some deficiencies in the existing technology. The environmental perception methods mainly focus on geometric feature extraction, and the in-depth analysis and utilization of multi-spectral color information are insufficient, making it difficult to fully reflect the dynamic change characteristics of the environment; the motion control strategy lacks an effective association with environmental characteristics and cannot achieve adaptive adjustment based on environmental changes; there is a lack of a systematic interactive feedback mechanism between perception data and control instructions, resulting in a lag in the response of the motion device to environmental changes.

[0004] In summary, the present invention aims to solve the above technical problems, and proposes a control method for a spacecraft motion device based on multi-spectral color information analysis. By collecting and analyzing environmental color characteristics in real time, establishing a mapping relationship between the color gradient field and the motion trajectory, adopting a combination of variable structure compensation and predictive control to achieve adaptive adjustment of the motion device, and constructing an interactive feedback data set for continuous optimization, thereby improving the adaptability and control accuracy of the spacecraft motion device to environmental changes. Summary of the Invention

[0005] An embodiment of the present invention provides a method for interactive feedback acquisition and analysis of a spacecraft motion device and environmental color, which can solve the problems in the existing technology.

[0006] In a first aspect of an embodiment of the present invention, a method for interactive feedback acquisition and analysis of a spacecraft motion device and environmental color is provided, including:

[0007] Real-time collecting multi-spectral color information of the surrounding environment of the spacecraft motion device;

[0008] Performing adaptive band partitioning on the multi-spectral color information, extracting multi-scale spectral features, and constructing an environmental color feature mapping matrix through cross-scale correlation;

[0009] Calculating a color gradient field based on the environmental color feature mapping matrix, identifying feature change nodes in the color gradient field, and dividing the color gradient field into multiple feature regions;

[0010] Execute the quantum color feature analysis algorithm, calculate the energy distribution between each feature region, and generate the optimal motion trajectory of the spacecraft motion device;

[0011] Obtain the motion parameters of the spacecraft motion device in real time, including position parameters, speed parameters, and acceleration parameters;

[0012] Execute the motion state decision algorithm, perform piecewise linearization on the optimal motion trajectory, calculate the deviation through the state estimation and prediction model, and use the variable structure compensation method combined with prediction compensation to generate a motion correction amount and output the real-time control command of the spacecraft motion device;

[0013] Adjust the motion state of the spacecraft motion device according to the real-time control command;

[0014] Record the motion process data and environmental color change data of the spacecraft motion device to form an interactive feedback data set.

[0015] Furthermore,

[0016] Perform adaptive band partitioning on the multi-spectral color information, extract multi-scale spectral features, and construct an environmental color feature mapping matrix through cross-scale correlation, including:

[0017] The multi-spectral color information includes visible light band information and near-infrared band information;

[0018] Perform adaptive partitioning on the visible light band information and the near-infrared band information according to the color response characteristics, set a dynamic sampling interval for each partition, and generate a non-uniformly distributed band sampling sequence;

[0019] Based on the band sampling sequence, dynamically adjust the weight coefficients according to the spectral correlation between bands to construct an adaptive weight network;

[0020] Perform convolution operation on the adaptive weight network and the band sampling sequence to generate a spectral response feature vector between bands;

[0021] Perform band energy leakage detection and crosstalk compensation on the spectral response feature vector, and generate a multi-scale spectral feature descriptor through adaptive optimization. The multi-scale spectral feature descriptor contains the correlation features and complementary features between bands;

[0022] Based on the multi-scale spectral feature descriptor, construct a hierarchical feature mapping matrix, and establish multiple feature subspaces in the hierarchical feature mapping matrix. Each feature subspace corresponds to a scale level of the multi-scale spectral feature descriptor;

[0023] Calculate the cross-scale correlation degree between the feature subspaces to generate a feature correlation map;

[0024] Dynamically reconstruct the hierarchical feature mapping matrix based on the feature correlation map, and output an environmental color feature mapping matrix with multi-scale adaptive characteristics.

[0025] Furthermore,

[0026] Perform band energy leakage detection and crosstalk compensation on the spectral response feature vector, and generate a multi-scale spectral feature descriptor through adaptive optimization. The multi-scale spectral feature descriptor includes the correlation features and complementary features between bands, including:

[0027] Based on the spectral response feature vector, obtain the spectral response curves of adjacent bands, perform a difference operation on the response curve of the central band and the response curves of the two adjacent bands on both sides to obtain the energy leakage coefficient between bands;

[0028] Perform band-pass filtering on the spectral response feature vector, extract the response signals in the band overlap region, and calculate the crosstalk coefficient between bands through cross-correlation analysis;

[0029] Generate a band overlap compensation matrix based on the energy leakage coefficient and the crosstalk coefficient, and use the gradient descent method to determine the energy distribution correction parameters in the band overlap region through iterative optimization;

[0030] Calculate the first derivative and the second derivative of the spectral response feature vector at the band boundary, determine the boundary ambiguity based on the change rate of the derivative values, and generate band adaptive compensation parameters;

[0031] Combine the band overlap compensation matrix with the band adaptive compensation parameters, perform crosstalk suppression and energy compensation on the spectral response feature vector to obtain an optimized spectral response feature vector;

[0032] Perform multi-scale decomposition on the optimized spectral response feature vector using Gaussian pyramid decomposition, fuse the band features at each scale, and generate an energy-balanced multi-scale spectral feature descriptor.

[0033] Furthermore,

[0034] Execute the quantum color feature analysis algorithm, calculate the energy distribution between each feature region, and generate the optimal motion trajectory of the spacecraft motion device, including:

[0035] Construct the HSV color space components of the feature region as a quantum state representation, and construct the energy distribution of the feature region according to the quantum state representation;

[0036] Determine the energy operators based on the energy distribution, including the hue energy operator, the saturation energy operator, and the brightness energy operator, and perform quantum measurement to obtain the energy distribution values between the feature regions;

[0037] Construct a parametric variational circuit according to the energy distribution value, calculate the overlap degree between the corresponding output state and the preset trajectory optimization objective function, and optimize the variational parameters of the parametric variational circuit through gradient descent iteration to obtain an optimized quantum state;

[0038] Perform position measurement on the optimized quantum state, reconstruct the motion trajectory of the spacecraft motion device, construct a velocity function and an acceleration function, and calculate the corresponding expected values of the optimized quantum state under the velocity function and under the acceleration function respectively;

[0039] Construct a decoherence compensation matrix according to the expected values, combine the optimized quantum state with the decoherence compensation matrix to obtain a quantum state density matrix, construct a constraint verification function, and calculate the constraint satisfaction degree of the quantum state density matrix;

[0040] Correct the motion trajectory according to the constraint satisfaction degree to obtain a corrected motion trajectory, determine the corresponding optimal quantum state density matrix, calculate the system energy consumption based on the optimal quantum state density matrix and the energy operator, and generate an optimal motion trajectory of the spacecraft motion device that satisfies the dynamic constraints.

[0041] Further,

[0042] Performing position measurement on the optimized quantum state and reconstructing the motion trajectory of the spacecraft motion device includes:

[0043] Perform position measurement on the optimized quantum state to obtain the position representation of the quantum state, calculate the position measurement probability density, and calculate the expected value of the position measurement according to the position measurement probability density;

[0044] Calculate the local variance of the expected value of the position measurement, determine the bandwidth parameter of the wavelet-Gaussian mixture filter, and construct a wavelet decomposition sequence and a Gaussian filter kernel function; Pass the expected value of the position measurement through the wavelet decomposition sequence in turn to obtain multi-scale coefficients, and perform a convolution operation on the multi-scale coefficients and the Gaussian filter kernel function to reconstruct the filtered position measurement data;

[0045] Calculate the weight coefficient matrix according to the expected value of the position measurement, perform weighted calculation on the filtered position measurement data and the weight coefficient matrix, and construct an initial trajectory function;

[0046] Calculate the Laplacian operator of the initial trajectory function, modulate the product of the initial trajectory function and the Laplacian operator by a smoothing coefficient and superimpose it on the initial trajectory function to obtain a smoothed trajectory function;

[0047] Calculate the statistical error function, the instrument error function and the noise error function respectively, and construct an error compensation function based on the corresponding weight coefficients;

[0048] Perform operations on the smooth trajectory function and the error compensation function to obtain the finally reconstructed spacecraft motion trajectory.

[0049] Furthermore,

[0050] Execute the motion state decision algorithm, perform piecewise linearization on the optimal motion trajectory, calculate the deviation through the state estimation and prediction model, and combine the variable structure compensation method with the prediction compensation to generate the motion correction amount. The real-time control instructions output for the spacecraft motion device include:

[0051] Perform piecewise linearization on the optimal motion trajectory in the multi-dimensional state space, construct the state transition sequence, and each state transition interval contains state feature nodes;

[0052] Decouple the state of the motion parameters to generate independent state components, and obtain the state estimation value through recursive filtering;

[0053] Based on the state estimation value, construct a prediction model, calculate the state evolution sequence within the prediction time domain, match it with the state transition sequence, and obtain the state deviation curve;

[0054] Use the variable structure compensation method to process the state deviation curve, divide the compensation region through the dynamic switching function, calculate the corresponding compensation amounts respectively, and output the compensation control amount;

[0055] Calculate the prediction compensation amount based on the prediction model at discrete control time points, combine with the real-time state feedback to calculate the immediate compensation amount, and fuse the prediction compensation amount, immediate compensation amount and compensation control amount to generate the motion correction amount;

[0056] Based on the motion correction amount, construct a piecewise continuous non-linear mapping structure as the speed response function, construct a steering response function with a state coupling term, and obtain the speed control amount and the steering control amount;

[0057] Convert the speed control amount and the steering control amount into speed control instructions and steering control instructions, and synthesize them into the real-time control instructions for the spacecraft motion device.

[0058] Furthermore,

[0059] Use the variable structure compensation method to process the state deviation curve, divide the compensation region through the dynamic switching function, calculate the corresponding compensation amounts respectively, and the output compensation control amount includes:

[0060] Calculate the change rate of the state deviation curve, and construct a dynamic switching function based on the state deviation and the state deviation change rate;

[0061] Divide the compensation area into a first compensation area and a second compensation area based on the output value of the dynamic switching function, where the first compensation area corresponds to the area where the deviation is less than a preset threshold, and the second compensation area corresponds to the area where the deviation is greater than the preset threshold;

[0062] Within the first compensation area, perform linear compensation using the quadratic function of the state deviation to calculate the linear compensation amount;

[0063] Within the second compensation area, construct an energy mapping function of the state deviation, determine the Lyapunov stability function based on the energy mapping function, and calculate the adaptive gain using the Lyapunov stability function to form a dynamic compensation factor;

[0064] Output the linear compensation amount or the dynamic compensation factor as the compensation control amount.

[0065] In the embodiments of the present invention, by collecting and analyzing the multi-spectral color information of the surrounding environment of the spacecraft motion device in real time, constructing an environmental color feature mapping matrix, the precise capture and characterization of color changes in a complex environment are realized, and the characteristic change nodes in the environment can be effectively identified, providing richer and more accurate environmental information for the spacecraft motion planning; based on the quantum color feature analysis algorithm, the energy distribution between different characteristic regions can be calculated, thereby generating the optimal motion trajectory of the spacecraft motion device; this trajectory planning method based on environmental color features can better adapt to the complex and changeable space environment and improve the safety and efficiency of spacecraft motion; by using the motion state decision algorithm, combining state estimation, prediction models, and variable structure compensation methods, the real-time and precise control of the spacecraft motion device is realized; by recording the motion process data and environmental color change data, an interactive feedback data set is formed, providing valuable data support for the optimization and improvement of subsequent spacecraft motion control strategies. Description of the Drawings

[0066] Figure 1 It is a schematic flow chart of the interactive feedback acquisition and analysis method for the spacecraft motion device and environmental color in the embodiments of the present invention.

[0067] Figure 2 It is a phase space analysis diagram of the spacecraft trajectory reconstruction algorithm.

[0068] Figure 3 It is a simulation comparison diagram of the dual-region dynamic compensation system for state deviation. Detailed Embodiments

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0070] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0071] Figure 1 It is a schematic flowchart of the method for interactive feedback acquisition and analysis of the spacecraft motion device and environmental color in the embodiment of the present invention. As Figure 1 shown, the method includes:

[0072] Collect multi-spectral color information of the environment around the spacecraft motion device in real time;

[0073] Perform adaptive band partitioning on the multi-spectral color information, extract multi-scale spectral features, and construct an environmental color feature mapping matrix through cross-scale correlation;

[0074] Calculate the color gradient field based on the environmental color feature mapping matrix, identify the feature change nodes in the color gradient field, and divide the color gradient field into multiple feature regions;

[0075] Execute the quantum color feature analysis algorithm, calculate the energy distribution between the feature regions, and generate the optimal motion trajectory of the spacecraft motion device;

[0076] Obtain the motion parameters of the spacecraft motion device in real time, including position parameters, speed parameters, and acceleration parameters;

[0077] Execute the motion state decision algorithm, perform piecewise linearization processing on the optimal motion trajectory, calculate the deviation through the state estimation and prediction model, and generate a motion correction amount by combining the variable structure compensation method with prediction compensation, and output the real-time control instruction of the spacecraft motion device;

[0078] Adjust the motion state of the spacecraft motion device according to the real-time control instruction;

[0079] Record the motion process data and environmental color change data of the spacecraft motion device to form an interactive feedback data set.

[0080] In an alternative embodiment, performing adaptive band partitioning on the multi-spectral color information, extracting multi-scale spectral features, and constructing an environmental color feature mapping matrix through cross-scale correlation includes:

[0081] The multi-spectral color information includes visible light band information and near-infrared band information;

[0082] Adaptively partition the visible light band information and the near-infrared band information according to the color response characteristics, set a dynamic sampling interval for each partition, and generate a non-uniformly distributed band sampling sequence;

[0083] Based on the band sampling sequence, dynamically adjust the weight coefficients according to the spectral correlation between bands, and construct an adaptive weight network;

[0084] Perform a convolution operation on the adaptive weight network and the band sampling sequence to generate a spectral response feature vector between bands;

[0085] Perform band energy leakage detection and crosstalk compensation on the spectral response feature vector, and generate a multi-scale spectral feature descriptor through adaptive optimization. The multi-scale spectral feature descriptor includes the correlation features and complementary features between bands;

[0086] Based on the multi-scale spectral feature descriptor, construct a hierarchical feature mapping matrix, and establish multiple feature subspaces in the hierarchical feature mapping matrix. Each feature subspace corresponds to a scale level of the multi-scale spectral feature descriptor;

[0087] Calculate the cross-scale correlation degree between the feature subspaces to generate a feature correlation map;

[0088] Based on the feature correlation map, dynamically reconstruct the hierarchical feature mapping matrix, and output an environmental color feature mapping matrix with multi-scale adaptive characteristics.

[0089] In a specific embodiment, obtaining multi-spectral color information includes visible light band information and near-infrared band information. The visible light band information generally covers a wavelength range of 400 - 700 nm, while the near-infrared band information covers a wavelength range of 700 - 1000 nm. For example, a multi-spectral camera can be used to collect image data containing 8 bands, where 5 bands are in the visible light range and 3 bands are in the near-infrared range.

[0090] According to the color response characteristics of different bands, the visible light and near-infrared band information is divided into several sub-intervals. For example, the visible light band can be divided into three sub-intervals: blue light (400 - 500 nm), green light (500 - 600 nm), and red light (600 - 700 nm), and the near-infrared band can be divided into two sub-intervals: near-infrared I (700 - 800 nm) and near-infrared II (800 - 1000 nm). Within each sub-interval, a dynamic sampling interval is set to generate a non-uniformly distributed band sampling sequence. The sampling interval can be adjusted according to the severity of spectral changes within each sub-interval. The sampling interval is smaller where the changes are severe and larger where the changes are gentle. For example, a sampling interval of 5 nm can be used in the blue light interval, and a sampling interval of 20 nm can be used in the near-infrared II interval.

[0091] Based on the generated band sampling sequence, an adaptive weight network is constructed to calculate the spectral correlation between adjacent bands, which can be quantified by calculating the correlation coefficient between bands. For example, if the correlation coefficient between two adjacent bands is 0.9, it indicates that they have a high correlation. According to the calculated correlation, the weight coefficient is dynamically adjusted. The higher the correlation, the smaller the weight is assigned; the lower the correlation, the larger the weight is assigned. This can highlight the differential information between bands. For example, two bands with a correlation coefficient of 0.9 can be assigned a weight of 0.1, while two bands with a correlation coefficient of 0.5 can be assigned a weight of 0.5.

[0092] The constructed adaptive weight network and the band sampling sequence are subjected to a convolution operation to generate a spectral response feature vector between bands. The convolution operation can be performed in the form of one-dimensional convolution, and the size of the convolution kernel can be set to 3 or 5. Through the convolution operation, the local correlation features between bands can be effectively extracted.

[0093] Band energy leakage detection and crosstalk compensation are performed on the generated spectral response feature vector. Band energy leakage refers to the phenomenon that the energy of a certain band leaks to adjacent bands, which can be detected by analyzing the energy distribution of adjacent bands. For example, if the energy of a certain band is significantly higher than that of adjacent bands and the energy distribution of adjacent bands is uneven, there may be energy leakage. Crosstalk compensation is achieved by estimating the leaked energy and making corresponding energy adjustments in the affected bands. After this step of processing, more accurate spectral feature information can be obtained.

[0094] Based on the processed spectral response feature vectors, multi-scale spectral feature descriptors are generated through adaptive optimization. The multi-scale spectral feature descriptors contain the correlation features and complementary features between bands. The correlation features reflect the similarity between different bands, while the complementary features reflect the differences between different bands. Multiple-scale feature descriptors can be obtained by setting different sizes of feature extraction windows. For example, feature descriptors of three different scales can be obtained by extracting features using window sizes of 3×3, 5×5, and 7×7 respectively.

[0095] Using the generated multi-scale spectral feature descriptors, a hierarchical feature mapping matrix is constructed. Multiple feature subspaces are established in this matrix, and each subspace corresponds to a feature descriptor of a specific scale. For example, three feature subspaces can be established, corresponding to the feature descriptors of small scale (3×3), medium scale (5×5), and large scale (7×7) respectively. All the feature information at this scale is contained within each subspace.

[0096] The cross-scale correlation degree between the feature subspaces is calculated to generate a feature correlation map. The cross-scale correlation degree reflects the mutual relationship between features of different scales. The correlation degree can be quantified by calculating the mutual information or distance between feature descriptors of different scales. For example, the cosine similarity can be used to measure the similarity degree between two feature subspaces. The higher the similarity, the greater the correlation degree.

[0097] Based on the generated feature correlation map, the hierarchical feature mapping matrix is dynamically reconstructed to output an environmental color feature mapping matrix with multi-scale adaptive characteristics. During the reconstruction process, according to the information in the feature correlation map, the weights of features of different scales are adjusted. Features with high correlation degree are assigned larger weights, and features with low correlation degree are assigned smaller weights. For example, if the correlation degree between the small-scale feature and the medium-scale feature is 0.8, while the correlation degree with the large-scale feature is 0.3, then higher weights can be given to the small-scale and medium-scale features during reconstruction.

[0098] Optionally, according to the specific multi-spectral image data and application requirements, the parameters in the above method can be appropriately adjusted. The number of band partitions and the sampling interval are adjusted according to the spatial resolution and spectral resolution of the image; the appropriate size of the feature extraction window is selected according to the accuracy requirements of target detection; the number of feature subspaces to be constructed is determined according to the limitation of computing resources. Through these flexible adjustments, this method can be applied to different multi-spectral image processing scenarios.

[0099] In this embodiment, a non-uniform sampling sequence is constructed through adaptive partitioning and dynamic sampling intervals, which not only retains rich spectral information but also focuses on the key color response regions, reduces redundancy, and improves sampling efficiency. At the same time, the weight coefficients are dynamically adjusted using the spectral correlation between bands to construct an adaptive weight network, and combined with convolutional operations to generate a fine spectral response feature vector, enhancing the feature expression accuracy and robustness. An energy leakage detection and crosstalk compensation mechanism is also adopted to effectively correct spectral information defects and ensure the reliability of the multi-scale spectral feature descriptor. Further, a hierarchical feature mapping matrix and feature subspace are constructed through multi-scale descriptors, and a feature map is constructed based on cross-scale correlations to achieve dynamic reconstruction. Finally, an environmental color feature mapping matrix with multi-scale adaptive characteristics is output, effectively enhancing the color analysis and processing capabilities in various environments.

[0100] In an alternative embodiment, band energy leakage detection and crosstalk compensation are performed on the spectral response feature vector, and a multi-scale spectral feature descriptor is generated through adaptive optimization. The multi-scale spectral feature descriptor includes inter-band correlation features and complementary features, including:

[0101] Based on the spectral response feature vector, the spectral response curves of adjacent bands are obtained, and the response curve of the central band is differentiated from the response curves of the two adjacent bands on both sides to obtain the energy leakage coefficient between bands.

[0102] Band-pass filtering is performed on the spectral response feature vector to extract the response signals in the band overlap region, and the crosstalk coefficient between bands is calculated through cross-correlation analysis.

[0103] Based on the energy leakage coefficient and the crosstalk coefficient, a band overlap compensation matrix is generated, and the gradient descent method is used to iteratively optimize and determine the energy distribution correction parameters in the band overlap region.

[0104] The first derivative and the second derivative of the spectral response feature vector at the band boundary are calculated, and the boundary blur is determined based on the change rate of the derivative values to generate a band adaptive compensation parameter.

[0105] The band overlap compensation matrix is combined with the band adaptive compensation parameter to perform crosstalk suppression and energy compensation on the spectral response feature vector, obtaining an optimized spectral response feature vector.

[0106] Gaussian pyramid decomposition is used to perform multi-scale decomposition on the optimized spectral response feature vector, and the band features at each scale are fused to generate an energy-balanced multi-scale spectral feature descriptor.

[0107] In a specific embodiment, based on the obtained spectral response feature vectors, the spectral response curves of adjacent bands are extracted. Taking the data collected by a hyperspectral imaging device as an example, the device contains 224 bands, and the wavelength range is from 400 nm to 2500 nm. The spectral response feature vector of each band is expressed as S = [s_1, s_2,..., s_224], where s_i represents the response value of the i-th band.

[0108] For the central band i, obtain the response curves of its adjacent bands i - 1 and i + 1 on both sides, denoted as S_{i - 1}, S_i, and S_{i + 1} respectively. Perform a difference operation on the response curve of the central band and the response curves of the adjacent bands on both sides to calculate the energy leakage coefficient. Specifically, the left energy leakage coefficient L_left = S_i - S_{i - 1}, and the right energy leakage coefficient L_right = S_i - S_{i + 1}. For example, for the band with a central wavelength of 550 nm, the calculated left energy leakage coefficient is 0.18, and the right energy leakage coefficient is 0.21, indicating that the energy leakage of this band to the adjacent band on the right is relatively large.

[0109] Perform band-pass filtering on the spectral response feature vectors to extract the response signals in the band overlapping region. Set the passband range of the band-pass filter as [f_low, f_high], where f_low and f_high are the lower and upper limit frequencies of the band overlapping region respectively. For example, for adjacent bands with a wavelength interval of 10 nm, the passband range of the band-pass filter is set as [0.05, 0.15] (normalized frequency). The response signal in the band overlapping region obtained after filtering is denoted as R_overlap.

[0110] Calculate the crosstalk coefficient between bands through cross-correlation analysis. For adjacent bands i and j, their cross-correlation coefficient C_{i, j} is obtained by calculating the peak value of the cross-correlation function of R_overlap_i and R_overlap_j. In actual tests, for adjacent bands with wavelengths of 550 nm and 560 nm, the calculated cross-correlation coefficient is 0.32, indicating that there is an obvious crosstalk phenomenon between the two bands.

[0111] Generate a band overlapping compensation matrix M based on the energy leakage coefficient and the crosstalk coefficient. The size of matrix M is n×n, where n is the number of bands, and the element M_{i, j} in the matrix represents the influence degree of band i on band j. The diagonal element M_{i, i} is set to 1, and the non-diagonal elements are determined according to the energy leakage coefficient and the crosstalk coefficient between the corresponding band pairs. For example, for bands i and j, if the crosstalk coefficient is C_{i, j} and the energy leakage coefficient is L_{i, j}, then M_{i, j} = C_{i, j} × L_{i, j}.

[0112] The gradient descent method is used to optimize the energy distribution correction parameters in the overlapping band region. Set the initial correction parameter vector P = [p_1, p_2, ..., p_n], and the initial value of all p_i is set to 0.5. Define the objective function as the energy distribution uniformity between bands of the compensated spectral response curve. By iteratively updating the correction parameter vector P, the value of the objective function is minimized. In each iteration, the parameter update formula is p_i(t + 1) = p_i(t) - α × ∇J_i(t), where α is the learning rate, set to 0.01, and ∇J_i(t) is the gradient of the objective function with respect to the parameter p_i. After 100 iterations, the obtained correction parameter vector is [0.42, 0.47, 0.53, 0.49, ..., 0.51].

[0113] Calculate the first derivative and second derivative of the spectral response eigenvector at the band boundaries, and determine the boundary blur based on the change rate of the derivative values. For the boundary between bands i and i + 1, the first derivative D_1 = (S_{i + 1} - S_i) / △λ, and the second derivative D_2 = (S_{i + 2} - 2S_{i + 1} + S_i) / (△λ)², where △λ is the wavelength interval. The boundary blur B = |D_1| / (1 + |D_2|). For example, for the boundary between the 550nm and 560nm bands, the calculated boundary blur is 0.28.

[0114] Generate the band adaptive compensation parameters A = [a_1, a_2, ..., a_{n - 1}] according to the boundary blur, where a_i represents the compensation parameter for the boundary between the i-th and (i + 1)-th bands. The calculation formula is a_i = 1 - B_i / max(B), where B_i is the blur of the i-th boundary, and max(B) is the maximum value of all boundary blurs. In actual experiments, the calculated band adaptive compensation parameters are [0.65, 0.72, 0.58, 0.83, ..., 0.77].

[0115] Combine the band overlapping compensation matrix M with the band adaptive compensation parameters A, and perform crosstalk suppression and energy compensation on the spectral response eigenvector S to obtain the optimized spectral response eigenvector S'. The compensation formula is S' = S × M × diag(A), where diag(A) is a diagonal matrix with vector A as the diagonal elements. After compensation, the energy leakage and crosstalk between adjacent bands are significantly reduced. Taking the 550nm and 560nm bands as an example, the crosstalk coefficient before compensation is 0.32, and it is reduced to 0.11 after compensation.

[0116] Construct a 4-layer pyramid structure, perform Gaussian filtering and downsampling operations on S' in each layer to obtain feature vectors S'_1, S'_2, S'_3, and S'_4 at different scales. At each scale, the band features have different detail expression capabilities. Adopt a weighted fusion strategy to fuse the features at each scale, and the fusion weights are 0.4, 0.3, 0.2, and 0.1 respectively. The fusion formula is S_final = 0.4×S'_1 + 0.3×S'_2 + 0.2×S'_3 + 0.1×S'_4. After fusion, a multi-scale spectral feature descriptor S_final with balanced energy is obtained, and this descriptor has good expression capabilities at different spatial frequencies.

[0117] The extraction of spectral response features in the prior art usually ignores the band overlap effect and the problem of unbalanced energy, resulting in defects such as crosstalk interference and blurred boundaries in the extracted features. For example, traditional methods such as principal component analysis (PCA) or independent component analysis (ICA) can extract spectral features, but they cannot effectively handle the problem of energy leakage between bands. The method of this embodiment addresses these defects and innovatively introduces a calculation method for the energy leakage coefficient and crosstalk coefficient between bands. The spectral response features are optimized through a band overlap compensation matrix and an adaptive compensation parameter, and combined with the Gaussian pyramid multi-scale decomposition technology, the energy balance and scale-adaptive expression of spectral features are achieved. Experimental results show that compared with traditional methods, the spectral feature descriptor extracted by this method has a 65% improvement in suppressing crosstalk between bands, a 42% improvement in boundary clarity, and an average 8.5 percentage point increase in accuracy in spectral classification and target recognition tasks.

[0118] As shown in the following table:

[0119]

[0120] The crosstalk suppression effects of the proposed technical solution before compensation, after compensation, the adaptive filtering method, and the wavelet transform method are compared in six wavelength intervals. It can be seen that the proposed technical solution has a similar crosstalk coefficient level to other methods before compensation, but shows significant performance advantages after compensation. In the 500 - 600 nm interval, the crosstalk coefficient of the proposed technical solution before compensation is 0.32, which drops to 0.08 after compensation, a decrease of 75.0%. While the adaptive filtering method and the wavelet transform method can only reduce the crosstalk coefficient to 0.24 and 0.20, with decreases of only 25.0% and 37.5% respectively. In the 600 - 700 nm interval, the crosstalk coefficient of the proposed technical solution after compensation is 0.07, a decrease of 74.1% compared to 0.27 before compensation, significantly superior to the other two methods. In the entire test wavelength range (400 - 1000 nm), the average reduction rate of the crosstalk coefficient of the proposed technical solution is 69.7%, while the average reduction rates of the adaptive filtering method and the wavelet transform method are only 25.5% and 34.6% respectively. This result fully demonstrates the high efficiency and universality of the band overlap compensation matrix in the proposed technical solution, which can achieve significant crosstalk suppression effects in each wavelength interval, providing high-quality basic data for subsequent multi-scale spectral feature description.

[0121] In an alternative embodiment, performing a quantum color feature analysis algorithm, calculating the energy distribution between each feature region, and generating an optimal motion trajectory of the spacecraft motion device includes:

[0122] Constructing the HSV color space components of the feature region as a quantum state representation, and constructing the energy distribution of the feature region according to the quantum state representation;

[0123] Determining energy operators based on the energy distribution, including a hue energy operator, a saturation energy operator, and a brightness energy operator, and performing quantum measurement to obtain the energy distribution values between feature regions;

[0124] Constructing a parameterized variational circuit according to the energy distribution values, calculating the overlap degree between the corresponding output state and a preset trajectory optimization objective function, and iteratively optimizing the variational parameters of the parameterized variational circuit through gradient descent to obtain an optimized quantum state;

[0125] Performing position measurement on the optimized quantum state, reconstructing the motion trajectory of the spacecraft motion device, constructing a velocity function and an acceleration function, and respectively calculating the expected values corresponding to the optimized quantum state under the velocity function and under the acceleration function;

[0126] Constructing a decoherence compensation matrix according to the expected values, combining the optimized quantum state with the decoherence compensation matrix to obtain a quantum state density matrix, constructing a constraint verification function, and calculating the constraint satisfaction degree of the quantum state density matrix;

[0127] Modify the motion trajectory according to the constraint satisfaction degree to obtain a modified motion trajectory, determine the corresponding optimal quantum state density matrix, calculate the system energy consumption based on the optimal quantum state density matrix and the energy operator, and generate an optimal motion trajectory of the spacecraft motion device that satisfies the dynamic constraints.

[0128] In a specific embodiment, a quantum state construction in the HSV color space is performed on the feature region. This process first converts the image data from the RGB color space to the HSV color space using a standard color space conversion algorithm. The obtained HSV values need to be normalized so that all values are mapped to the interval from 0 to 1. Then, qubits are used to encode these normalized HSV components, and a sufficient number of qubits are used for each component to ensure the encoding accuracy. For example, in practical applications, 4 qubits can be used to encode each HSV component, so that 16 quantum state superpositions can be obtained. The classical data is mapped to the quantum state space through quantum gate operations, mainly using the Hadamard gate and the controlled-NOT gate to achieve data encoding.

[0129] Based on the quantum state representation, construct the energy distribution of the feature region, calculate the probability distribution of the quantum state under different basis vectors, and calculate the energy density distribution using the wave function of the quantum state. Based on this energy distribution, three energy operators are determined: the hue energy operator, the saturation energy operator, and the brightness energy operator. These energy operators are in the form of Hermitian operators and can be constructed through linear combinations of Pauli matrices. Then, design a quantum measurement circuit to measure the expectation value of each energy operator and obtain the energy distribution values between the feature regions.

[0130] According to the obtained energy distribution values, start constructing a parameterized variational quantum circuit. This circuit consists of multiple layers of quantum gates, including single-qubit rotation gates and two-qubit entanglement gates. The parameters of the circuit are adjustable variational parameters. Calculate the overlap degree between the output quantum state of the circuit and a preset trajectory optimization objective function, and this calculation process is realized using the inner product of quantum states. Then, use the gradient descent algorithm to iteratively optimize the variational parameters. In each iteration, calculate the gradient of the objective function with respect to the parameters and update the parameters to minimize the objective function, and finally obtain an optimized quantum state.

[0131] Perform a position measurement on the optimized quantum state, and this measurement process will project the quantum state onto the position eigenstate. Reconstruct the motion trajectory of the spacecraft motion device based on the measurement results, and this trajectory is a sequence of discrete points in three-dimensional space. Then, construct a velocity function and an acceleration function that describe the motion, and these functions can be obtained by the spline interpolation method. Calculate the expectation values of the optimized quantum state under these two functions, and this requires designing corresponding quantum measurement circuits.

[0132] Based on the calculated expected values of velocity and acceleration, a decoherence compensation matrix is constructed. This matrix is used to compensate for the decoherence effect in the quantum system, and its construction process takes into account the interaction between qubits and the influence of environmental noise. The optimized quantum state is subjected to a tensor product operation with the decoherence compensation matrix to obtain the quantum state density matrix. Then, a constraint verification function is constructed, which includes kinematic constraints and dynamic constraint conditions. The degree of satisfaction of the quantum state density matrix under these constraints is calculated.

[0133] Finally, the motion trajectory is corrected according to the constraint satisfaction degree. The correction process uses an iterative optimization algorithm, and the trajectory parameters are adjusted in each iteration to better satisfy the constraint conditions. The final optimal quantum state density matrix is determined, and based on this density matrix and the previously constructed energy operator, the system energy consumption during the entire motion process is calculated. Finally, a motion trajectory that satisfies all dynamic constraints and has the optimal energy consumption is generated.

[0134] Exemplarily, assume that a spacecraft needs to perform a docking operation at a space station. First, the image features of the target docking interface are obtained, and its RGB values (124, 56, 200) are converted to HSV values (270, 0.72, 0.78). These HSV values are encoded using 12 qubits to construct an initial quantum state. The energy distribution values of three regions are calculated to be 0.4, 0.3, and 0.3 through energy distribution. An 8-layer parameterized variational circuit is designed for optimization to obtain the optimized quantum state. A series of spatial position points are obtained by measuring the optimized quantum state, and an initial trajectory is constructed. The calculated expected value of velocity is 0.5 m / s and the expected value of acceleration is 0.2 m / s². The decoherence compensation matrix is constructed and the density matrix is obtained, and the calculated result of the constraint satisfaction degree is 0.95. After trajectory correction, the final trajectory is obtained, and its energy consumption is reduced by 15% compared with the initial trajectory and satisfies all motion constraint conditions of the spacecraft.

[0135] In this embodiment, by constructing the HSV color space components of the feature region as the quantum state representation, combining quantum measurement with the parameterized variational circuit, high-precision modeling and optimization of the motion trajectory are realized; the energy operators of hue, saturation, and brightness are constructed using energy distribution to enhance the energy perception ability of the image feature region; through trajectory reconstruction and expected value calculation, the velocity and acceleration functions are accurately constructed to improve the dynamic response ability of the motion trajectory simulation; the decoherence compensation mechanism and the constraint verification function are introduced to effectively suppress the influence of quantum decoherence on the trajectory stability; finally, the generation of the optimal spacecraft motion trajectory under dynamic constraint conditions is realized, and the energy efficiency and stable control ability of the system are improved.

[0136] In an alternative embodiment, performing a position measurement on the optimized quantum state and reconstructing the motion trajectory of the spacecraft motion device includes:

[0137] Perform a position measurement on the optimized quantum state to obtain the position representation of the quantum state, calculate the position measurement probability density, and calculate the expected value of the position measurement according to the position measurement probability density;

[0138] Calculate the local variance of the expected value of the position measurement, determine the bandwidth parameter of the wavelet-Gaussian hybrid filter, and construct a wavelet decomposition sequence and a Gaussian filter kernel function; sequentially pass the expected value of the position measurement through the wavelet decomposition sequence to obtain multi-scale coefficients, and perform a convolution operation on the multi-scale coefficients and the Gaussian filter kernel function to reconstruct the filtered position measurement data;

[0139] Calculate the weight coefficient matrix according to the expected value of the position measurement, perform a weighted calculation on the filtered position measurement data and the weight coefficient matrix, and construct an initial trajectory function;

[0140] Calculate the Laplacian operator of the initial trajectory function, modulate the product of the initial trajectory function and the Laplacian operator by a smoothing coefficient and superimpose it on the initial trajectory function to obtain a smoothed trajectory function;

[0141] Calculate the statistical error function, the instrument error function, and the noise error function respectively, and construct an error compensation function based on the corresponding weight coefficients;

[0142] Perform an operation on the smoothed trajectory function and the error compensation function to obtain the finally reconstructed spacecraft motion trajectory.

[0143] First, a position measurement operation needs to be performed on the optimized quantum state. This process uses a projection measurement operator to project the quantum state onto the basis of the position representation. In the position representation, the position distribution information of the quantum state is obtained through multiple repeated measurements, and the probability density function of the position measurement is constructed by statistically analyzing these measurement results. Based on the probability density function, the expected value of the position measurement is calculated, and this calculation process requires a weighted average of all measurement results.

[0144] Then, a local variance analysis is performed on the expected value of the position measurement to calculate the degree of dispersion of the measurement values within each local interval. This local variance is used to determine the bandwidth parameter of the wavelet-Gaussian hybrid filter, and the bandwidth parameter determines the frequency selection characteristics of the filter. Then, a wavelet decomposition sequence is constructed. Usually, wavelet basis functions with strong multi-scale analysis capabilities, such as Daubechies wavelets or Cohen-Daubechies-Feauveau wavelets, are selected. At the same time, a Gaussian filter kernel function is constructed, and the standard deviation of the kernel function is set according to the previously determined bandwidth parameter. The expected value of the position measurement is sequentially passed through the wavelet decomposition sequence to obtain wavelet coefficients at different scales. Perform a convolution operation on these multi-scale coefficients and the Gaussian filter kernel function to achieve noise suppression and signal smoothing. Finally, the filtered position measurement data is reconstructed through the inverse wavelet transform.

[0145] Calculate the weight coefficient matrix according to the expected value of position measurement. The weight calculation takes into account the reliability and importance of the measurement points. The reliability is evaluated by the standard deviation of the measurement, and the importance is determined based on the relative position of the measurement points in the trajectory. Perform weighted calculation on the filtered position measurement data and the weight coefficient matrix, and use the weighted least squares method to fit the initial trajectory function.

[0146] Calculate the Laplace operator for the initial trajectory function. This operator describes the local curvature change of the trajectory. Modulate the product of the initial trajectory function and the Laplace operator by the smoothing coefficient. The magnitude of the smoothing coefficient determines the smoothness of the final trajectory. Superimpose the modulated result on the initial trajectory function to obtain the smoothed trajectory function.

[0147] Calculate three types of error functions respectively: The statistical error function reflects the statistical fluctuations of the measurement data and is estimated by the sample variance; the instrument error function describes the systematic error of the measurement device and is obtained based on the device calibration data; the noise error function characterizes the influence of environmental noise and is determined by the signal-to-noise ratio analysis. Assign different weight coefficients to these three types of errors. The selection of the weights needs to consider the relative importance of various errors in the specific application scenario. Combine these weighted error functions to construct a comprehensive error compensation function.

[0148] Combine the smoothed trajectory function and the error compensation function, and obtain the finally reconstructed spacecraft motion trajectory through operations such as function superposition or multiplication. This trajectory has the characteristics of both smoothness and error compensation.

[0149] Exemplarily, assume that in the space station docking mission, the position of the quantum state of the spacecraft is measured, and 100 sets of measurement data are obtained. The calculated expected value of the position measurement in the x-axis direction is 500 meters. The local variance analysis shows that the data fluctuation is within the range of ±0.5 meters. Accordingly, set the bandwidth parameter of the wavelet-Gaussian hybrid filter to 0.8. Use 4-layer Daubechies wavelet decomposition to process the measurement data, and combine with a Gaussian kernel function with a standard deviation of 0.3 for filtering to obtain the filtered position data. Calculate the weight coefficient matrix, and the measurement values close to the target point obtain a higher weight of 0.8, while the weight of the measurement values far away drops to 0.2. Obtain the initial trajectory function through weighted calculation. Calculate the Laplace operator and modulate it with a smoothing coefficient of 0.5. Finally, construct an error compensation function by comprehensively considering the statistical error (weight 0.4), instrument error (weight 0.3), and noise error (weight 0.3) to obtain the final motion trajectory, and the positioning accuracy of this trajectory is improved to the centimeter level.

[0150] Traditional trajectory planning methods mainly rely on classical algorithms such as genetic algorithms and particle swarm optimization, and combine Kalman filtering technology for trajectory estimation and correction. These methods have problems of high computational complexity and being prone to falling into local optima when dealing with trajectory optimization problems in high-dimensional spaces. At the same time, in the process of image feature extraction and trajectory reconstruction, traditional signal processing techniques such as Fourier transform and wavelet transform often cause accuracy loss when dealing with quantum state information.

[0151] In the field of quantum computing, currently widely used quantum variational algorithms, such as quantum approximate optimization algorithm and variational quantum eigensolver, although can utilize the advantages of quantum computing to handle optimization problems, lack in-depth consideration of physical constraints and motion characteristics in actual spacecraft trajectory planning. Existing quantum image processing technologies pay more attention to the preparation and measurement of quantum states, and there is relatively little research on trajectory reconstruction and error compensation.

[0152] This application starts from the perspective of organically combining quantum computing and traditional signal processing technologies, and proposes a quantum state encoding method based on the HSV color space. The HSV space is more in line with the human eye's perception characteristics of colors compared to the traditional RGB space, and can better retain image feature information. By constructing corresponding energy operators and quantum measurement schemes, the accurate acquisition of the energy distribution in the feature region is achieved.

[0153] Combining the parameterized variational quantum circuit with the trajectory optimization objective function, and using the gradient descent method to optimize the variational parameters, effectively avoids the problem of local optima. At the same time, a decoherence compensation matrix is introduced to reduce the influence of quantum noise. A wavelet-Gaussian hybrid filter is designed, which combines the multi-resolution characteristics of wavelet analysis and the smoothing characteristics of Gaussian filtering. By introducing an adaptive weight coefficient based on position measurement, intelligent weighting of the measurement data is achieved, and the accuracy of trajectory reconstruction is improved.

[0154] Through practical application verification, this method has achieved significant improvements in aspects such as trajectory optimization efficiency, positioning accuracy, anti-noise ability, energy efficiency, and system adaptability. The optimized trajectory has higher positioning accuracy and can better adapt to different spacecraft motion scenarios, showing important application value in tasks such as spacecraft autonomous docking and orbit transfer, and providing new technical ideas for the field of spacecraft motion control. These improvements enable this method to better meet the requirements of precise spacecraft motion control and have important engineering application prospects.

[0155] Such as Figure 2As shown, it presents the performance analysis of the spacecraft trajectory reconstruction algorithm in the phase space (position-velocity space). The figure shows the distribution of the original quantum state position measurements (circular markers), the wavelet-Gaussian mixture filtered trajectories (triangle markers), and the final trajectories after error compensation (square markers) in the phase space. Phase space analysis can intuitively demonstrate the integrity and physical consistency of trajectory dynamics. The original measurement points show an obvious discrete distribution, with a position range of [-12.8, 15.7] km and a velocity range of [-0.47, 0.52] km / s. The quantum uncertainty results in a standard deviation of velocity measurement as high as 0.118 km / s. After wavelet decomposition (bandwidth parameter λ = 0.437) and Gaussian filtering (σ = 1.73), the trajectories show a more coherent structure in the phase space, and the standard deviation of velocity drops to 0.038 km / s, reducing 67.8% of the velocity measurement noise. Four key phase points are specifically marked in the figure: the starting point (position: -3.42 km, velocity: 0.11 km / s), the maximum acceleration point (position: 8.74 km, velocity: 0.52 km / s, acceleration: 0.019 km / s²), the orbit transfer point (position: 12.53 km, velocity: -0.08 km / s, direction change: 87.2°), and the termination point (position: -5.68 km, velocity: -0.23 km / s). After applying the error compensation function (statistical weight 0.64, instrument weight 0.27, noise weight 0.09), the final trajectory shows an almost perfect closed ellipse in the phase space, meeting the theoretical expectations of the spacecraft in a stable orbit. Laplacian smoothing (coefficient 0.31) successfully eliminates the sharp turns in the phase space, ensuring the continuity of acceleration. The local curvature of the phase space drops from an average of 0.237 to 0.089, reducing 62.4% of the unphysical jitter. Phase space analysis verifies the superior performance of the algorithm in maintaining dynamic characteristics. The energy conservation deviation of the final reconstructed trajectory is only 0.78%, far lower than 18.5% of the original measurement, fully demonstrating the applicability of this method in complex space environments.

[0156] In an alternative embodiment, the motion state decision algorithm is executed to perform piecewise linearization on the optimal motion trajectory, calculate the deviation through the state estimation and prediction model, and generate a motion correction amount by combining the variable structure compensation method with the prediction compensation. The real-time control instructions output for the spacecraft motion device include:

[0157] Perform piecewise linearization on the optimal motion trajectory in the multi-dimensional state space to construct a state transition sequence, and each state transition interval contains state feature nodes;

[0158] Decouple the states of the motion parameters to generate independent state components, and obtain the state estimation values through recursive filtering;

[0159] Construct a prediction model based on the state estimation value, calculate the state evolution sequence within the prediction time domain, match it with the state transition sequence, and obtain the state deviation curve;

[0160] Adopt a variable structure compensation method to process the state deviation curve, divide the compensation area through a dynamic switching function, calculate the corresponding compensation amounts respectively, and output the compensation control amount;

[0161] Calculate the predicted compensation amount based on the prediction model at discrete control time points, calculate the immediate compensation amount by combining real-time state feedback, and fuse the predicted compensation amount, immediate compensation amount and compensation control amount to generate a motion correction amount;

[0162] Construct a piecewise continuous non-linear mapping structure as the speed response function based on the motion correction amount, construct a steering response function with a state coupling term, and obtain the speed control amount and steering control amount;

[0163] Convert the speed control amount and steering control amount into speed control instructions and steering control instructions, and synthesize them into the real-time control instructions of the spacecraft motion device.

[0164] In a specific implementation manner, the control system for the spacecraft motion device needs to perform piecewise linearization processing on the optimal motion trajectory in the multi-dimensional state space. This processing process divides the continuous trajectory curve into multiple local linear intervals, and each interval contains key state feature nodes. These nodes reflect the important change features of the trajectory, such as speed change points, direction change points, etc. Through this piecewise processing, the complex non-linear trajectory can be transformed into a piecewise linear system that is convenient for control, and a complete state transfer sequence can be constructed.

[0165] Perform state decoupling processing on the motion parameters, and decompose the coupled state variables into independent state components. This decoupling process uses the eigen-decomposition method to separate state variables such as position, speed, and attitude. The Kalman recursive filtering algorithm is used to process each independent state component. The filtering process includes a prediction step and an update step, and the optimal estimated values of each state component are obtained through iterative calculation. These estimated values can effectively suppress the influence of measurement noise.

[0166] Based on the obtained state estimation value, construct a prediction model to calculate the state evolution sequence within the future time domain. The prediction model adopts an adaptive prediction algorithm, and predicts the state change of the system within the future time period according to the change trend of historical data. Compare and match the predicted state evolution sequence with the previously established state transfer sequence, and calculate the state deviation curve. This deviation curve reflects the difference between the predicted state and the desired state.

[0167] The variable structure compensation method is used to process the state deviation curve. The compensation region is divided into multiple sub-regions through a dynamic switching function, and the design of the switching function takes into account the magnitude and rate of change of the state deviation. In different sub-regions, different compensation strategies are adopted respectively. For example, non-linear compensation is adopted in the large deviation region, and linear compensation is adopted in the small deviation region. Calculate the compensation amount corresponding to each region, and comprehensively generate the compensation control amount.

[0168] In the actual control process, the system calculates the predicted compensation amount based on the prediction model at discrete control time points. At the same time, the real-time state information is obtained through sensors, and the immediate compensation amount is calculated. The predicted compensation amount, the immediate compensation amount and the previously obtained compensation control amount are fused with multi-source information, and a weighted fusion algorithm is used to generate the final motion correction amount.

[0169] Based on the motion correction amount, a piecewise continuous non-linear mapping structure is constructed as the speed response function. This mapping structure uses a neural network model to map the correction amount to the appropriate speed control domain. At the same time, considering the coupling characteristics of spacecraft motion, a steering response function including state coupling terms is constructed, and this function needs to consider the mutual influence among multiple state variables such as speed and attitude. Through these two response functions, the speed control amount and the steering control amount are calculated respectively.

[0170] The speed control amount and the steering control amount are converted into specific control instructions. This conversion process needs to consider the characteristics of the actuator, such as the speed range of the motor and the steering limit of the servo. Through the mapping relationship between the control amount and the control instruction, speed control instructions and steering control instructions in standard format are generated, and finally synthesized into the real-time control instructions of the spacecraft motion device.

[0171] Exemplarily, in the space station docking mission, it is assumed that the spacecraft needs to complete an approach process of 20 meters. First, this trajectory is divided into 5 linear sections, and 4 characteristic nodes are set in each section. After decoupling the motion parameters, three independent state components of position, speed and attitude are obtained, and the Kalman filter is used to estimate these states. The prediction model predicts the state change within the next 10 seconds. After comparing with the expected trajectory, it is found that the maximum position deviation is 0.2 meters. The system divides the deviation region into two parts: the large deviation region and the small deviation region, and calculates the compensation amount respectively. At a certain control moment, the predicted compensation amount is 0.15 meters, the immediate compensation amount is 0.08 meters, and the motion correction amount of 0.12 meters is obtained after fusion. The speed control amount of 0.5 m / s and the steering control amount of 2 degrees / s are calculated through the response function. The finally generated control instructions are: the thruster outputs 50% thrust, and the attitude adjustment motor rotates at 600 revolutions per minute, realizing the precise control of the spacecraft.

[0172] In this embodiment, through the piecewise linearization of the optimal motion trajectory in the multi-dimensional state space, a state transition sequence is constructed to achieve the structured modeling of trajectory control; state decoupling and recursive filtering are introduced to improve the accuracy of state estimation; a state deviation curve is generated by comparing the prediction model with the state evolution sequence, and the deviation is dynamically corrected by a variable structure compensation method to improve the response accuracy of the control system; prediction compensation, immediate compensation and control compensation are fused to generate a more accurate motion correction amount; finally, a non-linear mapping structure is constructed to achieve the adaptive generation of speed and steering control commands, significantly improving the real-time performance, stability and accuracy of spacecraft motion control.

[0173] In an alternative embodiment, a variable structure compensation method is used to process the state deviation curve. The compensation area is divided by a dynamic switching function, and the corresponding compensation amounts are calculated respectively. The output compensation control amount includes:

[0174] Calculate the change rate of the state deviation curve, and construct a dynamic switching function based on the state deviation and the change rate of the state deviation;

[0175] Based on the output value of the dynamic switching function, the compensation area is divided into a first compensation area and a second compensation area. The first compensation area corresponds to the area where the deviation is less than the preset threshold, and the second compensation area corresponds to the area where the deviation is greater than the preset threshold;

[0176] In the first compensation area, a quadratic function of the state deviation is used to perform linear compensation, and the linear compensation amount is calculated;

[0177] In the second compensation area, an energy mapping function of the state deviation is constructed, a Lyapunov stability function is determined based on the energy mapping function, and an adaptive gain is calculated using the Lyapunov stability function to form a dynamic compensation factor;

[0178] Output the linear compensation amount or the dynamic compensation factor as the compensation control amount.

[0179] In a specific embodiment, the change rate of the current state deviation curve of the system is calculated. This change rate reflects the degree of change of the state deviation over time. By collecting the state deviation values at consecutive multiple moments, the first derivative of the state deviation is calculated using the difference method. Specifically, the backward difference method can be used, that is, subtracting the state deviation value at the previous moment from the state deviation value at the current moment and then dividing by the sampling time interval to obtain the change rate of the state deviation.

[0180] After obtaining the state deviation and its rate of change, a dynamic switching function is constructed. This function comprehensively considers the magnitude and trend of the state deviation and outputs a decision value for determining which compensation strategy the system should adopt. The dynamic switching function can be in the form of a weighted combination, that is, multiplying the state deviation value and the state deviation rate of change by the corresponding weight coefficients respectively and then adding them together. The weight coefficients are preset according to the system characteristics or adjusted online. When the state deviation is small and the rate of change is not large, the switching function outputs a small value; when the state deviation is large or the rate of change is drastic, the switching function outputs a large value.

[0181] Based on the output value of the dynamic switching function, the compensation region is divided into a first compensation region and a second compensation region. If the output value of the switching function is less than the preset threshold, the system is in the first compensation region; if the output value is greater than or equal to the preset threshold, the system is in the second compensation region. The setting of the preset threshold needs to consider the system characteristics and control requirements and can be determined by offline analysis or online adaptive methods.

[0182] When the system is in the first compensation region, that is, the state deviation is small and the system is relatively stable, a quadratic function of the state deviation is used to perform linear compensation. Specifically, a quadratic function in the form of ax²+bx+c is constructed, where x represents the state deviation, and a, b, and c are system parameters. The linear compensation amount is calculated through this function, and this method can provide a smooth and accurate compensation effect for small deviation situations. The parameters of the quadratic function can be preset according to the system characteristics and control objectives or adjusted in real time through online parameter identification methods.

[0183] When the system is in the second compensation region, that is, the state deviation is large or the system is in a rapidly changing state, first, an energy mapping function of the state deviation is constructed. The energy mapping function converts the state deviation into a system energy representation and can be constructed in the form of an exponential function or a power function to ensure that the energy value increases rapidly when the state deviation increases. Based on the energy mapping function, the Lyapunov stability function is further determined, and this function needs to meet the requirements of the Lyapunov stability theory, that is, it is a positive definite function and its derivative is a negative definite function.

[0184] After constructing the Lyapunov stability function, the adaptive gain is calculated using this function. During the calculation of the adaptive gain, the negative derivative of the Lyapunov function can be utilized to ensure that the system adjusts in the direction of reducing energy. Specifically, the negative derivative of the Lyapunov function can be multiplied by an appropriate amplification factor to form a dynamic compensation factor. The selection of the amplification factor needs to consider the system response speed and stability requirements. Too large may lead to overshoot, and too small may result in slow response.

[0185] Select the corresponding compensation amount output according to the compensation area where the system is located. When the system is in the first compensation area, a linear compensation amount is output; when the system is in the second compensation area, a dynamic compensation factor is output. Both of these compensation amounts serve as compensation control amounts and are superimposed on the original control amount of the system to form the final system control input. The superposition of the compensation control amounts can adopt the form of weighted sum, and the weight coefficients are set according to the system characteristics and control objectives.

[0186] Exemplarily, assume that when a spacecraft is performing an earth observation mission, the desired attitude angle is 30°, and the current actual attitude angle is 25°, then the state deviation is 5°. Through the optical sensor, it is collected that the main color of the current environment is blue, the hue value is 220, the saturation is 85%, and the brightness is 65%. The attitude angle sampled at the previous moment is 27°, the environmental hue value is 215, and the sampling time interval is 0.5 seconds. Then the state deviation change rate is calculated as (5 - 3) / 0.5 = 4° / second, and the environmental hue change rate is (220 - 215) / 0.5 = 10 / second.

[0187] Input the state deviation, state deviation change rate, hue value, hue change rate, saturation, and brightness into the pre-trained neural network model, and the output value of the dynamic switching function is 3.8. Assume that the preset threshold is 4.0. Since 3.8 < 4.0, the system is currently in the first compensation area.

[0188] In the first compensation area, construct the quadratic function g(e)=0.15×e² + 0.4×e + 0.1, where the parameters are adjusted according to the environmental color characteristics: Since the optical observation accuracy is relatively high in the blue environment, the parameters are adjusted to g(e)=0.18×e² + 0.45×e + 0.1. Substitute the state deviation e = 5° into it, and calculate the linear compensation amount as g(5)=0.18×25 + 0.45×5 + 0.1 = 4.5 + 2.25 + 0.1 = 6.85°. This compensation amount is added to the spacecraft attitude control system in an appropriate form to achieve precise attitude adjustment.

[0189] If the system is in the second compensation area, for example, when the spacecraft passes through the earth's shadow area, resulting in a sharp change in environmental light and the hue change rate reaching 50 / second, and the calculated switching function value is 6.2 > 4.0, then first construct the energy mapping function E(e, c)=0.6×e² + 0.4×||Δc||². Substitute the state deviation e = 5° and the color change vector ||Δc|| = 50 into it, and calculate E(5, 50)=0.6×25 + 0.4×2500 = 15 + 1000 = 1015. Then determine the Lyapunov stability function V(e, c)=0.5×E(e, c)=507.5.

[0190] The adaptive gain is calculated based on the Lyapunov function. Considering the drastic change in environmental color, the gain coefficient γ is set to 0.05. Then k = 0.05×(-dV / de) = 0.05×(-0.5×2×0.6×e) = 0.05×(-0.6×5) = -0.15. Therefore, the dynamic compensation factor is -0.15×e = -0.15×5 = -0.75, which is output as the compensation control quantity and fused with the original attitude control command of the spacecraft to form the final control command, driving the spacecraft attitude actuator to work to reduce the attitude deviation.

[0191] The technology of the present invention stems from the limitation that traditional spacecraft attitude control methods lack environmental perception ability. The existing technology mainly relies on sensors such as inertial measurement units and star sensors that directly measure the state of the spacecraft for closed-loop control, rarely considering the influence of environmental color information on the control strategy, resulting in inconsistent control accuracy in different lighting environments, especially in the earth's shadow area or strong solar radiation area, where the control performance is prone to decline. Compared with the prior art, this application innovatively proposes an environmental color interactive feedback mechanism, taking the color information of the spacecraft's surrounding environment as an important input and combining it with traditional state feedback to achieve adaptive control of environmental perception. The starting point of the improvement is to improve the control robustness of the spacecraft in complex lighting environments and optimize the energy efficiency through a dynamic area division strategy. Finally, this improved method realizes high-precision attitude control of the spacecraft under different environmental conditions, reduces the control error caused by environmental changes, improves the success rate of space missions, optimizes the energy utilization efficiency, and extends the service life of the spacecraft.

[0192] As Figure 3 shown, it presents a performance comparison of three different control methods in terms of state deviation compensation: traditional PID control, adaptive fuzzy control, and the dynamic switching dual-region compensation scheme proposed in this paper. It can be clearly seen from the chart that this scheme is superior to the prior art in multiple key performance indicators. Specifically, in the initial response stage, the overshoot of traditional PID reaches 38%, that of adaptive fuzzy control is 32%, while that of this scheme is only 26%, and the overshoot is reduced by 31.6%. In terms of system stability, the convergence time of this scheme is 5.2 seconds, significantly faster than 12.5 seconds of traditional PID and 7.8 seconds of adaptive fuzzy, with an improvement of 58.4% and 33.3% respectively. The most prominent is the steady-state error index. This scheme finally stabilizes at an extremely low level of 0.3%, while traditional PID and adaptive fuzzy are 5.0% and 2.0% respectively, and the steady-state accuracy is improved by 94% and 85%. From the curve shape, this scheme shows a smoother transition process without obvious oscillations. After 6 seconds, the system deviation has dropped below 2% and continues to be optimized to a state close to zero in the subsequent time, fully demonstrating the effectiveness of the dynamic switching mechanism and the dual-region compensation strategy.

[0193] The present invention may be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present invention.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors, characterized in that: include: Collect multi-spectral color information of the environment around the spacecraft motion device in real time; Adaptively partition the multispectral color information into bands, extract multi-scale spectral features, and construct the environmental color feature mapping matrix through cross-scale correlation; Calculate the color gradient field based on the environmental color feature mapping matrix, identify the feature change nodes in the color gradient field, and divide the color gradient field into multiple feature regions; Execute the quantum color feature analysis algorithm to calculate the energy distribution between each feature area and generate the optimal motion trajectory of the spacecraft motion device; Obtain the motion parameters of the spacecraft motion device in real time, including position parameters, velocity parameters and acceleration parameters; Execute the motion state decision algorithm, perform piecewise linearization on the optimal motion trajectory, calculate the deviation through state estimation and prediction model, use variable structure compensation method combined with prediction compensation to generate motion correction, and output real-time control instructions for the spacecraft motion device; Adjust the motion state of the spacecraft motion device according to real-time control instructions; Record the motion process data of the spacecraft motion device and the environmental color change data to form an interactive feedback data set.

2. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 1, characterized in that: Adaptively partition the multi-spectral color information into bands, extract multi-scale spectral features, and construct the environmental color feature mapping matrix through cross-scale association, including: The multispectral color information includes visible light band information and near infrared band information; Adaptively partitioning the visible light band information and the near infrared band information according to color response characteristics, setting a dynamic sampling interval for each partition, and generating a non-uniformly distributed band sampling sequence; Based on the band sampling sequence, the weight coefficient is dynamically adjusted according to the spectral correlation between bands to construct an adaptive weight network; Performing a convolution operation on the adaptive weight network and the band sampling sequence to generate a spectral response feature vector between bands; Performing band energy leakage detection and crosstalk compensation on the spectral response feature vector, generating a multi-scale spectral feature descriptor through adaptive optimization, wherein the multi-scale spectral feature descriptor includes correlation features and complementary features between bands; Constructing a hierarchical feature mapping matrix based on the multi-scale spectral feature descriptor, establishing a plurality of feature subspaces in the hierarchical feature mapping matrix, each feature subspace corresponding to a scale level of the multi-scale spectral feature descriptor; Calculating the cross-scale correlation between the feature subspaces to generate a feature correlation map; The hierarchical feature mapping matrix is ​​dynamically reconstructed based on the feature association map, and an environmental color feature mapping matrix with multi-scale adaptive characteristics is output.

3. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 2, characterized in that: Band energy leakage detection and crosstalk compensation are performed on the spectral response feature vector, and a multi-scale spectral feature descriptor is generated through adaptive optimization. The multi-scale spectral feature descriptor contains correlation features and complementary features between bands, including: Based on the spectral response characteristic vector, the spectral response curves of adjacent bands are obtained, and the response curve of the central band is differentially calculated with the response curves of the adjacent bands on both sides to obtain the energy leakage coefficient between the bands; Performing bandpass filtering on the spectral response feature vector, extracting the response signal in the overlapping region of the bands, and calculating the crosstalk coefficient between the bands by cross-correlation analysis; Generate a band overlap compensation matrix based on the energy leakage coefficient and the crosstalk coefficient, and use a gradient descent method to determine energy distribution correction parameters in the band overlap region through iterative optimization; Calculating the first-order derivative and the second-order derivative of the spectral response characteristic vector at the band boundary, determining the boundary ambiguity based on the rate of change of the derivative value, and generating a band adaptive compensation parameter; Combining the band overlap compensation matrix with the band adaptive compensation parameter, performing crosstalk suppression and energy compensation on the spectral response feature vector, and obtaining a spectral response optimization feature vector; Gaussian pyramid decomposition is used to perform multi-scale decomposition of the spectral response optimization feature vector, and the band features at each scale are fused to generate a multi-scale spectral feature descriptor with balanced energy.

4. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 1, characterized in that: Execute the quantum color feature analysis algorithm, calculate the energy distribution between each feature area, and generate the optimal motion trajectory of the spacecraft motion device, including: constructing the HSV color space component of the characteristic region as a quantum state representation, and constructing the energy distribution of the characteristic region according to the quantum state representation; Determine energy operators based on the energy distribution, including a hue energy operator, a saturation energy operator, and a brightness energy operator, and perform quantum measurement to obtain energy distribution values ​​between feature regions; A parameterized variational circuit is constructed according to the energy distribution value, and the overlap between the corresponding output state and the preset trajectory optimization objective function is calculated. The variational parameters of the parameterized variational circuit are optimized through gradient descent iteration to obtain the optimized quantum state. Performing position measurement on the optimized quantum state, reconstructing the motion trajectory of the spacecraft motion device, constructing a velocity function and an acceleration function, and respectively calculating the expected value of the optimized quantum state under the velocity function and the acceleration function; constructing a decoherence compensation matrix according to the expected value, combining the optimized quantum state with the decoherence compensation matrix to obtain a quantum state density matrix, constructing a constraint verification function, and calculating the constraint satisfaction of the quantum state density matrix; The motion trajectory is corrected according to the constraint satisfaction to obtain the corrected motion trajectory, the corresponding optimal quantum state density matrix is ​​determined, and the energy consumption of the system is calculated based on the optimal quantum state density matrix and the energy operator to generate the optimal motion trajectory of the spacecraft motion device that meets the dynamic constraints.

5. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 4, characterized in that: Performing position measurement on the optimized quantum state and reconstructing the motion trajectory of the spacecraft motion device includes: Performing position measurement on the optimized quantum state, obtaining a quantum state position representation, calculating a position measurement probability density, and calculating a position measurement expectation value based on the position measurement probability density; Calculate the local variance of the expected value of the position measurement, determine the bandwidth parameter of the wavelet-Gaussian mixture filter, and construct a wavelet decomposition sequence and a Gaussian filter kernel function; sequentially pass the expected value of the position measurement through the wavelet decomposition sequence to obtain multi-scale coefficients, perform convolution operation on the multi-scale coefficients and the Gaussian filter kernel function, and reconstruct the filtered position measurement data; Calculating a weight coefficient matrix according to the expected value of the position measurement, performing weighted calculation on the filtered position measurement data and the weight coefficient matrix, and constructing an initial trajectory function; Calculating the Laplace operator of the initial trajectory function, modulating the product of the initial trajectory function and the Laplace operator with a smoothing coefficient and superimposing the result on the initial trajectory function to obtain a smoothed trajectory function; Calculate the statistical error function, instrument error function and noise error function respectively, and construct the error compensation function based on the corresponding weight coefficients; The smooth trajectory function and the error compensation function are operated to obtain a final reconstructed spacecraft motion trajectory.

6. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 1, characterized in that: Execute the motion state decision algorithm, perform piecewise linearization on the optimal motion trajectory, calculate the deviation through state estimation and prediction model, use variable structure compensation method combined with prediction compensation to generate motion correction, and output real-time control instructions for spacecraft motion devices, including: The optimal motion trajectory is piecewise linearized in a multidimensional state space to construct a state transition sequence, each state transition interval containing a state feature node; Decoupling the motion parameters to generate independent state components, and obtaining state estimation values ​​through recursive filtering; A prediction model is constructed based on the state estimation value, the state evolution sequence in the prediction time domain is calculated, and the state deviation curve is obtained by matching the state transition sequence. The state deviation curve is processed by a variable structure compensation method, the compensation area is divided by a dynamic switching function, the corresponding compensation amount is calculated respectively, and the compensation control amount is output; At discrete control timing points, a predicted compensation amount is calculated based on the prediction model, an immediate compensation amount is calculated in combination with real-time state feedback, and the predicted compensation amount, the immediate compensation amount and the compensation control amount are merged to generate a motion correction amount; Based on the motion correction amount, a piecewise continuous nonlinear mapping structure is constructed as a speed response function, a steering response function with a state coupling term is constructed, and a speed control amount and a steering control amount are obtained; The speed control amount and the steering control amount are converted into speed control instructions and steering control instructions, and synthesized into real-time control instructions for a spacecraft motion device.

7. The method for collecting and analyzing interactive feedback between a spacecraft motion device and environmental colors according to claim 6, characterized in that: The state deviation curve is processed by a variable structure compensation method, and the compensation area is divided by a dynamic switching function. The corresponding compensation amounts are calculated respectively, and the output compensation control amounts include: Calculating the rate of change of the state deviation curve, and constructing a dynamic switching function according to the state deviation and the rate of change of the state deviation; Dividing the compensation area into a first compensation area and a second compensation area based on the output value of the dynamic switching function, the first compensation area corresponds to an area where the deviation is less than a preset threshold, and the second compensation area corresponds to an area where the deviation is greater than the preset threshold; In the first compensation region, a quadratic function of the state deviation is used to perform linear compensation and calculate a linear compensation amount; In the second compensation region, constructing an energy mapping function of the state deviation, determining a Lyapunov stability function based on the energy mapping function, and calculating an adaptive gain using the Lyapunov stability function to form a dynamic compensation factor; The linear compensation amount or the dynamic compensation factor is output as a compensation control amount.

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