A self-calibration method for holographic grating

Abstract

The present invention discloses a holographic grating self-calibration method, which relates to the field of optical technology, including collecting interference image data and performing preprocessing, extracting phase data of the interference image data after preprocessing; generating a phase map based on the phase data to perform holographic grating self-calibration, and evaluating and adjusting the result of the holographic grating self-calibration; constructing a visual interface to display the holographic grating self-calibration in real time, implementing secure encryption on the phase data, and storing, collecting and analyzing the generated data. The present invention collects interference image data and performs preprocessing, extracts phase data of the interference image data after preprocessing, and generates a phase map based on the phase data to perform holographic grating self-calibration, which greatly simplifies the calibration process, improves the convenience of operation and the extraction accuracy of phase data, and significantly improves the calibration accuracy and stability of the holographic grating.

Description

Technical Field

[0001] The invention relates to the field of optical technology, in particular to a holographic grating self-calibration method. Background Art

[0002] Holographic gratings play a vital role in optical measurement and microscopy. They can achieve precise optical path control and phase modulation through the diffraction characteristics of light. Self-calibration methods of holographic gratings are particularly critical because they are directly related to the overall performance of the optical system and the breadth of its application. With the advancement of optical metrology technology, especially in high-resolution imaging and the manufacture of fine optical components, the precise calibration of holographic gratings has become particularly important. Although significant progress has been made in the design and manufacturing technology of holographic gratings in the past few decades, their self-calibration technology still faces some technical challenges.

[0003] The existing technology has some shortcomings in the self-calibration of holographic gratings. Although the existing digital calibration methods can achieve automatic calibration to a certain extent, these methods usually require complex preprocessing of the interference image and have high requirements on ambient lighting and light source stability, which limits their application flexibility in different environments. The existing technology also needs to be improved in data processing speed and evaluation accuracy after calibration. These problems may lead to error accumulation during the calibration process, thereby affecting the quality of the final image and the reliability of the system. Summary of the invention

[0004] In view of the above problems existing in the existing holographic grating self-calibration method, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that although the existing digital calibration methods can achieve automated calibration to a certain extent, these methods usually require complex preprocessing of the interference image and have high requirements on ambient lighting and light source stability, which limits their application flexibility in different environments. The existing technology also needs to be improved in data processing speed and evaluation accuracy after calibration. These problems may lead to error accumulation in the calibration process, thereby affecting the quality of the final image and the reliability of the system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a holographic grating self-calibration method, which includes collecting interference image data and preprocessing it, extracting phase data of the interference image data after preprocessing; generating a phase map based on the phase data to perform holographic grating self-calibration, and evaluating and adjusting the results of the holographic grating self-calibration; constructing a visual interface to display the holographic grating self-calibration in real time, implementing secure encryption on the phase data, and storing, collecting and analyzing the generated data.

[0007] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the collecting interference image data and preprocessing refers to fixing the holographic grating on an optical table, using broadband superluminescent diodes of different frequencies, the SLDs including red, green and blue, respectively illuminating the holographic grating vertically and using a CMOS camera to collect the interference image data of the holographic grating from the interferometer, and the time interval corresponding to the collection of the interference image data;

[0008] The interference image data are grayed out respectively, and the gray image is denoised using a Gaussian filter;

[0009] Calculate the Fourier transform of the denoised grayscale image to convert it from the spatial domain to the frequency domain, use the phase correlation formula to calculate the displacement parameters of the grayscale image, perform translation adjustment on the grayscale image based on the calculated displacement parameters, and align the grayscale image in space;

[0010] Set the top layer resolution of the pyramid to 1 / 16 of the smooth image resolution, perform Gaussian blur processing on the aligned grayscale image to obtain a smooth image, calculate the weighted average of the four adjacent smooth image pixels to obtain a new pixel value, and perform downsampling to generate the next level of Gaussian pyramid image, and stop when the set top layer is reached;

[0011] Except for the bottom layer, the Laplacian layer A is calculated based on the Gaussian pyramid image g , the formula is:

[0012] A g =G g -up(G g+1 ),

[0013] Among them G g is the g-th layer of the Gaussian pyramid, up(G g+1 ) is the size of the g+1th layer Gaussian pyramid image upsampled to the gth layer;

[0014] Use bilinear interpolation method to calculate G g+1 Upsample to get the same value as the gth layer G of the Gaussian pyramid g Same size up(G g+1 );

[0015] The bottom layer of the Gaussian pyramid image is directly used as the last layer of the Laplacian pyramid;

[0016] For each layer of the Laplacian image A g Perform quality assessment, including contrast and detail clarity, and select the one with the highest quality as the fused image A' of this layer g ;

[0017] Starting from the bottom layer, the Laplace layer A' gCompared with the reconstruction result up(G g+1 ) added together, the formula is:

[0018] G0=A' g +up(G g+1 ),

[0019] Where G0 is the fused interference image;

[0020] The fused interference image data G0 is subjected to variational image decomposition to obtain a background layer and a detail layer. The background layer is subtracted from the interference image data, and the detail layer containing interference fringes is retained. The detail layer is filtered using a Gaussian filter, and the processed detail layer is recombined to obtain the final interference image data.

[0021] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the extraction of phase data of the pre-processed interference image data refers to setting a (2n+1)×(2n+1) neighborhood window for the pixel points in the final interference image data, where n is the field radius;

[0022] Calculate the cumulative intensity of each pixel in the neighborhood of the pixel in the horizontal and vertical directions. The formula is:

[0023]

[0024] Where L x (x,y) and L y (x, y) are the accumulated intensities in the horizontal and vertical directions, respectively, and I(x, y) is the pixel intensity of the interference image;

[0025] The cumulative intensities in these two directions are combined to construct the Lissajous graph, and the formula is:

[0026] L(x,y)=L x (x,y)cos(ωt)+L y (x,y)sin(ωt),

[0027] Where L(x,y) is the Lissajous figure of the pixel point (x,y), ω is the angular frequency, and t is the time interval for collecting the interference image;

[0028] The geometric center of the Lissajous figure (x c ,y c );

[0029] The rotation angle φ is calculated using the covariance and variance of the Lissajous graph, using the formula:

[0030]

[0031] Where Cov(x,y) is the covariance of x and y, Var(x) and Var(y) are the variances of x and y respectively;

[0032] Rotate the Lissajous figure data points to align the major axis of the ellipse with the coordinate axis. The formula is:

[0033] x'=(xx c )cos(φ)+(yy c )sin(φ),

[0034] y'=-(xx c )sin(φ)+(yy c )cos(φ),

[0035] Where x' and y' are the pixel coordinates where the major axis of the ellipse is aligned with the coordinate axis;

[0036] Calculate the maximum and minimum values ​​of the pixel coordinates (x', y') after rotation to get the major and minor axis radii of the ellipse. The formula is:

[0037]

[0038] Where max(x') and max(y') are the maximum values ​​of the pixel coordinates (x', y') after rotation, min(x') and min(y') are the minimum values ​​of the pixel coordinates (x', y') after rotation, and a and b are the major and minor radii of the ellipse, respectively.

[0039] The phase value of each pixel coordinate is calculated based on the ratio of the major axis to the minor axis. The formula is:

[0040]

[0041] Where θ(u,v) is the phase value of the pixel coordinate (u,v).

[0042] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the generating a phase diagram based on phase data to perform holographic grating self-calibration refers to performing a standardization process on the phase value φ(x, y) and generating a phase diagram;

[0043] Based on the phase value θ(u,v) of the pixel coordinate (u,v) of the phase map, calculate the phase difference Δθ with the adjacent pixel coordinates xy , the formula is:

[0044] Δθ uv =θ(u,v)-θ(u+1,v),

[0045] Where θ(u+1,v) is the coordinate of the adjacent pixel;

[0046] Based on historical data, the threshold range is set to [-π,π], when the phase difference Δθ uv Greater than the threshold π or the phase difference Δθ uv When it is less than the threshold -π, it is determined as a phase jump and correction is performed. The formula is:

[0047]

[0048] where Δθ' uv is the phase difference after correction;

[0049] Based on the corrected phase difference, the correction value Δθ(u,v) is calculated using the formula:

[0050]

[0051] Where u and v are the row and column indices of the current pixel coordinates, k is a temporary variable, Δθ' kv is the corrected phase difference of the pixel coordinates in the yth column of the kth adjacent pixel;

[0052] The calibration phase value is calculated based on the correction value Δθ(u,v), and the formula is:

[0053] θ corr (u,v)=θ(u,v)+Δθ9u,v),

[0054] where θ corr (u,v) is the calibration phase value.

[0055] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the evaluation and adjustment of the result of the holographic grating self-calibration refers to taking a standard holographic grating as a reference;

[0056] The statistical error analysis is used to calculate the average error and standard deviation between the phase value of the holographic grating after calibration and the phase value of the standard grating as the evaluation result;

[0057] Collect historical calibration data to set an evaluation threshold, compare the evaluation result with the evaluation threshold, and if the evaluation result is greater than the evaluation threshold, adjust the holographic grating position; if the evaluation result is less than or equal to the evaluation threshold, keep it unchanged.

[0058] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the construction of a visualization interface to display the holographic grating self-calibration in real time refers to using the visualization tool Matplotlib to construct a visualization interface to display the collected interference image data of the holographic grating and the phase map generated by analysis in real time;

[0059] Set access to users who have passed real-name authentication.

[0060] As a preferred solution of the holographic grating self-calibration method of the present invention, wherein: the implementation of secure encryption of phase data refers to using a PKI system to distribute and manage the generation of public and private key pairs, and manage encryption keys;

[0061] The phase value θ(x,y) generated by the analysis is encrypted using AES, and the timestamp of the phase value is recorded;

[0062] Obtain the decryption key from the PKI system, use the AES decryption algorithm and key to decrypt the encrypted data, and restore the original data.

[0063] As a preferred solution of the holographic grating self-calibration method described in the present invention, the storing, collecting and analyzing the generated data refers to storing the collected interference image data of the holographic grating and the analyzed phase map in a central database, which is sorted in chronological order and marked with corresponding tags, and the collected interference image data of the holographic grating and the analyzed phase map are synchronously backed up in the cloud, and the integrity of the active data and the backup data is regularly checked.

[0064] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned holographic grating self-calibration method when executing the computer program.

[0065] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned holographic grating self-calibration method when executed by a processor.

[0066] The beneficial effects of the present invention are as follows: the present invention collects interference image data and performs preprocessing, extracts phase data of the preprocessed interference image data, and generates a phase map based on the phase data to perform self-calibration of the holographic grating, thereby greatly simplifying the calibration process, improving the convenience of operation and the extraction accuracy of phase data, and significantly improving the calibration accuracy and stability of the holographic grating. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0068] Figure 1 Schematic diagram of the process of the holographic grating self-calibration method.

[0069] Figure 2 Schematic diagram of the structure of the holographic grating self-calibration method. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0072] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0073] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and which provides a holographic grating self-calibration method. The holographic grating self-calibration method includes:

[0074] S1, collecting and preprocessing interference image data, and extracting phase data of the interference image data after preprocessing;

[0075] Specifically, collecting interference image data and preprocessing refers to fixing the holographic grating on an optical table, using broadband superluminescent diodes SLDs of different frequencies, including red, green, and blue, to vertically illuminate the holographic grating, respectively, and using a CMOS camera to collect interference image data of the holographic grating from an interferometer, as well as a time interval corresponding to the collection of the interference image data;

[0076] The interference image data are grayed out respectively, and the gray image is denoised using a Gaussian filter;

[0077] Calculate the Fourier transform of the denoised grayscale image to convert it from the spatial domain to the frequency domain, use the phase correlation formula to calculate the displacement parameters of the grayscale image, perform translation adjustment on the grayscale image based on the calculated displacement parameters, and align the grayscale image in space;

[0078] Set the top layer resolution of the pyramid to 1 / 16 of the smooth image resolution, perform Gaussian blur processing on the aligned grayscale image to obtain a smooth image, calculate the weighted average of the four adjacent smooth image pixels to obtain a new pixel value, and perform downsampling to generate the next level of Gaussian pyramid image, and stop when the set top layer is reached;

[0079] Except for the bottom layer, the Laplacian layer A is calculated based on the Gaussian pyramid image g , the formula is:

[0080] A g =G g -up(G g+1 ),

[0081] Among them G g is the g-th layer of the Gaussian pyramid, up(G g+1 ) is the size of the g+1th layer Gaussian pyramid image upsampled to the gth layer;

[0082] Use bilinear interpolation method to calculate G g+1 Upsample to get the same value as the gth layer G of the Gaussian pyramid g Same size up(G g+1 );

[0083] The bottom layer of the Gaussian pyramid image is directly used as the last layer of the Laplacian pyramid;

[0084] For each layer of the Laplacian image A g Perform quality assessment, including contrast and detail clarity, and select the one with the highest quality as the fused image A' of this layer g ;

[0085] Starting from the bottom layer, the Laplace layer A' g Compared with the reconstruction result up(G g+1 ) added together, the formula is:

[0086] G0=A' g +up(G g+1 ),

[0087] Where G0 is the fused interference image;

[0088] The fused interference image data G0 is subjected to variational image decomposition to obtain a background layer and a detail layer. The background layer is subtracted from the interference image data, and the detail layer containing interference fringes is retained. The detail layer is filtered using a Gaussian filter, and the processed detail layer is recombined to obtain the final interference image data.

[0089] The use of multi-frequency SLD and CMOS camera systems can effectively improve the quality and information content of interference images. The multi-color light source can more comprehensively evaluate the optical performance of the grating, while the high-performance CMOS camera ensures high-precision and high-resolution image capture. The noise reduction effect of these images after grayscale and Gaussian filtering provides clear and accurate input data for subsequent data analysis. The frequency properties of the image are clarified through Fourier transform, and the displacement parameters calculated using phase correlation technology can accurately adjust and align the image. Gaussian pyramid and Laplacian pyramid processing can effectively enhance the local details and contrast of the image through multi-level image decomposition and reconstruction. Selecting the highest quality Laplacian layer as the fused image can significantly improve the quality of the final image, making the interference image more suitable for precise measurement and calibration. Decomposing the interference image into background layer and detail layer can more effectively process and reconstruct the detail layer containing interference fringes. The use of this method greatly improves the visibility of fringes in the interference image and the accuracy of analysis.

[0090] Furthermore, extracting the phase data of the pre-processed interference image data means setting a (2n+1)×(2n+1) neighborhood window for the pixel points in the final interference image data, where n is the field radius;

[0091] Calculate the cumulative intensity of each pixel in the neighborhood of the pixel in the horizontal and vertical directions. The formula is:

[0092]

[0093] Where L x (x,y) and L y (x, y) are the accumulated intensities in the horizontal and vertical directions, respectively, and I(x, y) is the pixel intensity of the interference image;

[0094] The cumulative intensities in these two directions are combined to construct the Lissajous graph, and the formula is:

[0095] L(x,y)=L x (x,y)cos(ωt)+L y (x,y)sin(ωt0,

[0096] Where L(x,y) is the Lissajous figure of the pixel point (x,y), ω is the angular frequency, and t is the time interval for collecting the interference image;

[0097] The geometric center of the Lissajous figure (x c ,y c );

[0098] The rotation angle φ is calculated using the covariance and variance of the Lissajous graph, using the formula:

[0099]

[0100] Where Cov(x,y) is the covariance of x and y, Var(x) and Var(y) are the variances of x and y respectively;

[0101] Rotate the Lissajous figure data points to align the major axis of the ellipse with the coordinate axis. The formula is:

[0102] x'=(xx c )cos(φ)+(yy c )sin(φ),

[0103] y'=-(xx c )sin(φ)+(yy c )cos(φ),

[0104] Where x' and y' are the pixel coordinates where the major axis of the ellipse is aligned with the coordinate axis;

[0105] Calculate the maximum and minimum values ​​of the pixel coordinates (x', y') after rotation to get the major and minor axis radii of the ellipse. The formula is:

[0106]

[0107] Where max(x') and max(y') are the maximum values ​​of the pixel coordinates (x', y') after rotation, min(x') and min(y') are the minimum values ​​of the pixel coordinates (x', y') after rotation, and a and b are the major and minor radii of the ellipse, respectively.

[0108] The phase value of each pixel coordinate is calculated based on the ratio of the major axis to the minor axis. The formula is:

[0109]

[0110] Where θ(u,v) is the phase value of the pixel coordinate (u,v).

[0111] By calculating the cumulative intensity in the horizontal and vertical directions within the neighborhood of each pixel point, more details about the interference pattern of light waves can be obtained. This data is the key to understanding and analyzing interference images and is helpful for subsequent phase extraction and image reconstruction. Using Lissajous graphics to analyze phase information is the core innovation of this invention. Through this method, the complex wave pattern of the interference image can be converted into a computable geometric shape, so as to more accurately describe and calculate the phase change in the image. Calculating the rotation angle of the Lissajous graphics and adjusting the graphics accordingly are key steps to ensure the accuracy of subsequent analysis. By aligning the main axis and coordinate axis of the graphics, the calculation process of the phase value can be simplified and the complexity can be reduced. The major axis and minor axis radii of the ellipse are extracted from the adjusted Lissajous graphics, and then the phase value is calculated. This step shows how to extract useful quantitative information from the interference graphics. This method not only improves the accuracy of phase extraction, but also enhances the versatility of the method, and is suitable for various complex optical measurement occasions. By calculating the phase value of each pixel based on the ratio of the major axis to the minor axis, the present invention provides an efficient and accurate way to analyze and utilize interference images.

[0112] S2, generating a phase map based on the phase data to perform holographic grating self-calibration, and evaluating and adjusting the result of the holographic grating self-calibration;

[0113] Specifically, generating a phase map based on phase data to perform self-calibration of the holographic grating means standardizing the phase value θ(u,v) and generating a phase map;

[0114] Based on the phase value θ(u,v) of the pixel coordinate (u,v) of the phase map, calculate the phase difference Δθ with the adjacent pixel coordinates uv , the formula is:

[0115] Δθ uv =θ(u,v)-θ(u+1,v),

[0116] Where θ(u+1,v) is the coordinate of the adjacent pixel;

[0117] Based on historical data, the threshold range is set to [-π,π], when the phase difference Δθ uv Greater than the threshold π or the phase difference Δθ uv When it is less than the threshold -π, it is determined as a phase jump and correction is performed. The formula is:

[0118]

[0119] where Δθ' uv is the phase difference after correction;

[0120] Based on the corrected phase difference, the correction value Δθ(u,v) is calculated using the formula:

[0121]

[0122] Where u and v are the row and column indices of the current pixel coordinates, k is a temporary variable, Δθ' kv is the corrected phase difference of the pixel coordinates in the yth column of the kth adjacent pixel;

[0123] The calibration phase value is calculated based on the correction value Δθ(u,v), and the formula is:

[0124] θ corr (u,v0=θ(u,v)+Δθ(u,v),

[0125] where θ corr (u,v) is the calibration phase value.

[0126] By standardizing the phase value and generating a phase map, the present invention can effectively represent and analyze the phase distribution of the holographic grating. This step is the basis for ensuring the accuracy of subsequent calibration because it directly affects the calculation of the phase difference and the detection of the phase jump. By calculating the phase difference between adjacent pixels, the phase discontinuity area in the image can be accurately determined. This method not only improves the analysis accuracy of the phase map, but also helps to identify potential errors or anomalies in the image. A threshold range based on historical data is set to detect phase jumps. This strategy can effectively distinguish between normal phase changes and abnormal jumps caused by errors. In this way, error propagation can be reduced and the reliability of the calibration process can be improved. Correcting the phase difference is a key step in the self-calibration process of the holographic grating, which directly affects the accuracy of the final phase map. By calculating the corrected phase difference and updating the phase map, the present invention can ensure the accuracy and consistency of the phase information, thereby improving the performance and reliability of the holographic grating.

[0127] Further, evaluating and adjusting the result of the holographic grating self-calibration refers to using a standard holographic grating as a benchmark;

[0128] The statistical error analysis is used to calculate the average error and standard deviation between the phase value of the calibrated holographic grating and the phase value of the standard grating as the evaluation result;

[0129] Collect historical calibration data to set an evaluation threshold, compare the evaluation result with the evaluation threshold, and if the evaluation result is greater than the evaluation threshold, adjust the holographic grating position; if the evaluation result is less than or equal to the evaluation threshold, keep it unchanged.

[0130] By comparing the phase value of the calibrated holographic grating with the phase value of the standard grating, the accuracy of the calibration process can be intuitively evaluated. This comparison is a direct way to determine whether the holographic grating has reached the expected performance standard. These statistics provide a quantitative measure of the accuracy and consistency of the calibration results. The mean error reflects the average level of calibration deviation, while the standard deviation describes the variability of the calibration results. Low mean error and standard deviation indicate that the calibration process is highly accurate and reliable. Setting an evaluation threshold and comparing the calibration results with this threshold is an efficient quality control method. If the error of the calibration result exceeds the set threshold, the position of the holographic grating needs to be adjusted to achieve higher calibration accuracy; if the calibration result is within the threshold range, the current setting is maintained.

[0131] S3. Construct a visual interface to display the holographic grating self-calibration in real time, implement secure encryption for phase data, and store, collect and analyze the generated data;

[0132] Specifically, constructing a visualization interface to display the holographic grating self-calibration in real time refers to using a visualization tool Matplotlib to construct a visualization interface to display the collected interference image data of the holographic grating and the phase map generated by analysis in real time;

[0133] Set access to users who have passed real-name authentication.

[0134] The visualization interface built with Matplotlib is not only beautiful but also rich in functions. It can display the interference image data and phase diagram of the holographic grating in real time. This real-time display allows users to intuitively observe each step of the self-calibration process, discover possible problems in time and make adjustments, which is extremely valuable for scientific research and industrial applications. The real-time display of interference image data and phase diagrams not only improves the transparency of operations, but also enhances the user's sense of trust. Users can see the calibration effect instantly and better understand the calibration process and results, so as to make quick decisions when necessary. By setting access only to users with real-name authentication, sensitive data can be effectively protected and the flow of information can be controlled. This access control mechanism is particularly important for scenarios involving high-value or sensitive optical data to ensure the security and integrity of the data.

[0135] Furthermore, implementing secure encryption of phase data refers to using a PKI system to distribute and manage the generation of public and private key pairs and manage encryption keys;

[0136] The phase value θ(x,y) generated by the analysis is encrypted using AES, and the timestamp of the phase value is recorded;

[0137] Obtain the decryption key from the PKI system, use the AES decryption algorithm and key to decrypt the encrypted data, and restore the original data.

[0138] By using the PKI system to manage keys, we can ensure that the key distribution and use processes are safe. Such management not only reduces the risk of key leakage, but also enhances the reliability of the overall security architecture. Using AES encryption for phase data means that even if the data is intercepted during transmission or illegally accessed, third parties without appropriate keys cannot interpret the data content. This provides strong protection for holographic grating data, especially when data needs to be transmitted through an insecure network. The step of obtaining decryption keys from the PKI system ensures that only authorized users can access and decrypt sensitive data. This control mechanism improves the professionalism and effectiveness of data security management. By using the AES decryption algorithm and the corresponding key to decrypt the encrypted data, it is ensured that the data can be safely restored to its original state when needed, which allows users to flexibly process and analyze data while ensuring data security.

[0139] Furthermore, storing the data collected and analyzed means storing the collected interference image data of the holographic grating and the phase map generated by the analysis in a central database, sorting the data in chronological order and marking the corresponding tags, synchronously backing up the collected interference image data of the holographic grating and the phase map generated by the analysis in the cloud, and regularly performing integrity checks on the active data and backup data.

[0140] The interference image data and phase map of the holographic grating are stored in a central database and sorted and labeled in chronological order. This method not only enhances the data retrieval, but also improves the efficiency of data management. Appropriate labeling and sorting mechanisms make data easier to access and analyze, thereby speeding up data processing and decision-making. By backing up data to the cloud, data security and disaster recovery capabilities can be greatly improved. Cloud backup provides a protection layer for data that is physically independent of local storage, ensuring that data is not lost when a local system fails. Regular integrity testing of active data and backup data is the key to ensuring that data has not been modified without authorization. This step is an important part of maintaining data security, especially in situations involving sensitive or critical data, such as holographic grating data analysis and storage.

[0141] Embodiment 2, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0142] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0143] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0144] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A holographic grating self-calibration method, characterized in that: include, Collecting and preprocessing interference image data, and extracting phase data of the preprocessed interference image data; Generate a phase map based on the phase data to perform holographic grating self-calibration, and evaluate and adjust the results of the holographic grating self-calibration; Build a visual interface to display the holographic grating self-calibration in real time, implement secure encryption for phase data, and store, collect and analyze the generated data; The collecting and preprocessing of interference image data refers to fixing the holographic grating on an optical table, using broadband superluminescent diodes SLDs of different frequencies, including red, green and blue, to vertically illuminate the holographic grating respectively, and using a CMOS camera to collect interference image data of the holographic grating from an interferometer, as well as the time interval corresponding to the collection of the interference image data; The interference image data are grayed out respectively, and the gray image is denoised using a Gaussian filter; Calculate the Fourier transform of the denoised grayscale image to convert it from the spatial domain to the frequency domain, use the phase correlation formula to calculate the displacement parameters of the grayscale image, perform translation adjustment on the grayscale image based on the calculated displacement parameters, and align the grayscale image in space; Set the top layer resolution of the pyramid to 1 / 16 of the smooth image resolution, perform Gaussian blur processing on the aligned grayscale image to obtain a smooth image, calculate the weighted average of the four adjacent smooth image pixels to obtain a new pixel value, and perform downsampling to generate the next level of Gaussian pyramid image, and stop when the set top layer is reached; Except for the bottom layer, the Laplacian layer A is calculated based on the Gaussian pyramid image g , the formula is: A g =G g -up(G g+1 ), Among them G g is the g-th layer of the Gaussian pyramid, up(G g+1 ) is the size of the g+1th layer Gaussian pyramid image upsampled to the gth layer; Use bilinear interpolation method to calculate G g+1 Upsample to get the same value as the gth layer G of the Gaussian pyramid g Same size up(G g+1 ); The bottom layer of the Gaussian pyramid image is directly used as the last layer of the Laplacian pyramid; For each layer of the Laplacian image A g Perform quality assessment, including contrast and detail clarity, and select the one with the highest quality as the fused image A' of this layer g ; Starting from the bottom layer, the Laplace layer A' g Compared with the reconstruction result up(G g+1 ) added together, the formula is: G0=A′ g +up(G g+1 ), Where G0 is the fused interference image; The fused interference image data G0 is subjected to variational image decomposition to obtain a background layer and a detail layer. The background layer is subtracted from the interference image data, and the detail layer containing interference fringes is retained. The detail layer is filtered using a Gaussian filter, and the processed detail layer is recombined to obtain the final interference image data.

2. The holographic grating self-calibration method according to claim 1, characterized in that: Extracting the phase data of the pre-processed interference image data refers to setting a (2n+1)×(2n+1) neighborhood window for the pixel points in the final interference image data, where n is the area radius; Calculate the cumulative intensity of each pixel in the neighborhood of the pixel in the horizontal and vertical directions. The formula is: Where L x (x,y) and L y (x, y) are the accumulated intensities in the horizontal and vertical directions, respectively, and I(x, y) is the pixel intensity of the interference image; The cumulative intensities in these two directions are combined to construct the Lissajous graph, and the formula is: L(x,y)=L x (x,y)cos(ωt)+L y (x,y)sin(ωt), Where L(x,y) is the Lissajous figure of the pixel point (x,y), ω is the angular frequency, and t is the time interval for collecting the interference image; The geometric center (x) of the Lissajous figure is obtained by calculating the average position of all Lissajous figure data points. c ,y c ); The rotation angle φ is calculated using the covariance and variance of the Lissajous graph using the formula: Where Cov(x,y) is the covariance of x and y, Var(x) and Var(y) are the variances of x and y respectively; Rotate the Lissajous figure data points to align the major axis of the ellipse with the coordinate axis. The formula is: x′=(xx c )cos(φ)+(yy c )sin(φ), y′=-(xx c )sin(φ)+(yy c )cos(φ), Where x' and y' are the pixel coordinate values ​​where the major axis of the ellipse is aligned with the coordinate axis; Calculate the maximum and minimum values ​​of the pixel coordinates (x', y') after rotation to get the major and minor axis radii of the ellipse. The formula is: Where max(x') and max(y') are the maximum values ​​of the pixel coordinates (x', y') after rotation, min(x') and min(y') are the minimum values ​​of the pixel coordinates (x', y') after rotation, and a and b are the major and minor radii of the ellipse, respectively. The phase value of each pixel coordinate is calculated based on the ratio of the major axis to the minor axis. The formula is: Where θ(u,v) is the phase value of the pixel coordinate (u,v).

3. The holographic grating self-calibration method according to claim 2, characterized in that: Generating a phase diagram based on phase data to perform self-calibration of the holographic grating refers to performing standardization processing on the phase value θ(u,v) and generating a phase diagram; Based on the phase value θ(u,v) of the pixel coordinate (u,v) of the phase map, calculate the phase difference Δθ with the adjacent pixel coordinates uv , the formula is: Dth uv =θ(u,v)-θ(u+1,v), Where θ(u+1,v) is the coordinate of the adjacent pixel; Based on historical data, the threshold range is set to [-π,π], when the phase difference Δθ uv Greater than the threshold π or the phase difference Δθ uv When it is less than the threshold -π, it is determined as a phase jump and correction is performed. The formula is: where Δθ' uv is the phase difference after correction; Based on the corrected phase difference, the correction value Δθ(u,v) is calculated using the formula: Where u and v are the row and column indices of the current pixel coordinates, k is a temporary variable, Δθ' kv is the corrected phase difference of the pixel coordinates in the yth column of the kth adjacent pixel; The calibration phase value is calculated based on the correction value Δθ(u,v), and the formula is: i corr (u,v)=θ(u,v)+Δθ(u,v), where θ corr (u,v) is the calibration phase value.

4. The holographic grating self-calibration method according to claim 3, characterized in that: The evaluation and adjustment of the result of the holographic grating self-calibration refers to taking the standard holographic grating as a benchmark; The statistical error analysis is used to calculate the average error and standard deviation between the phase value of the calibrated holographic grating and the phase value of the standard grating as the evaluation result; Collect historical calibration data to set an evaluation threshold, compare the evaluation result with the evaluation threshold, and if the evaluation result is greater than the evaluation threshold, adjust the holographic grating position; if the evaluation result is less than or equal to the evaluation threshold, keep it unchanged.

5. The holographic grating self-calibration method according to claim 4, characterized in that: The said constructing a visualization interface to display the holographic grating self-calibration in real time refers to using the visualization tool Matplotlib to construct a visualization interface to display the collected interference image data of the holographic grating and the phase map generated by analysis in real time; Set access to users who have passed real-name authentication.

6. The holographic grating self-calibration method according to claim 5, characterized in that: The implementation of secure encryption of phase data refers to using a PKI system to distribute and manage the generation of public and private key pairs, and manage encryption keys; The phase value θ(x,y) generated by the analysis is encrypted using AES, and the timestamp of the phase value is recorded; Obtain the decryption key from the PKI system, use the AES decryption algorithm and key to decrypt the encrypted data, and restore the original data.

7. The holographic grating self-calibration method according to claim 6, characterized in that: The storing, collecting and analyzing the data refers to storing the collected interference image data of the holographic grating and the phase map generated by the analysis in a central database, sorting the data in chronological order and marking the corresponding tags in the central database, synchronously backing up the collected interference image data of the holographic grating and the phase map generated by the analysis in the cloud, and regularly performing integrity checks on the active data and the backup data.

8. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the holographic grating self-calibration method according to any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the holographic grating self-calibration method according to any one of claims 1 to 7 are implemented.

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