A data analysis method and system for micro-nano grating devices for optical sensing

Abstract

The present invention relates to the field of micro-nano optical technology, and in particular to a method and system for analyzing micro-nano grating device data for optical sensing. If one or more actual spectral characteristic data of a target micro-nano grating device exhibit baseline drift, the actual spectral characteristic data exhibiting baseline drift is defined as baseline drift spectral characteristic data; a correlation spectral control unit for the baseline drift spectral characteristic data is determined by querying a database based on the correlation spectral control unit; various real-time operating parameters of the correlation spectral control unit for the baseline drift spectral characteristic data are obtained, and parameter analysis and processing are performed on the various real-time operating parameters of the correlation spectral control unit for the baseline drift spectral characteristic data. This method not only improves the measurement accuracy of micro-nano grating devices in complex environments, but also enhances their stability and adaptability, and is of great significance for improving the overall performance of optical sensing systems.

Description

Technical Field

[0001] The present invention relates to the field of micro-nano optical technology, and in particular to a method and system for analyzing data of a micro-nano grating device for optical sensing. Background Art

[0002] With the rapid development of micro-nano grating technology, optical sensing technologies based on micro-nano grating structures have been widely used in fields such as biomedicine, environmental monitoring, and industrial inspection. Micro-nano grating devices, due to their high sensitivity, high resolution, and miniaturization, have become an essential component in building optical sensing systems. However, in practical applications, the optical properties of micro-nano grating devices can be affected by environmental factors, device aging, and operating parameter drift, resulting in changes in their spectral response, which in turn affects sensing accuracy and stability. For example, factors such as light source intensity fluctuations, detector noise, and temperature changes can all cause baseline drift in the spectral signal, causing the actual spectral data obtained to deviate from the preset spectral characteristics, thereby affecting the accurate extraction of target sensing information.

[0003] Currently, methods for analyzing optical sensing data mainly focus on signal processing and feature extraction, such as wavelet transform, Fourier transform, principal component analysis, etc. However, most of these methods focus on processing the spectral signal itself, and lack analysis of the reasons behind the changes in spectral characteristics. In addition, when dealing with baseline drift problems, existing methods usually use simple linear or polynomial fitting methods for correction, which makes it difficult to effectively handle complex and variable baseline drift phenomena. Therefore, there is an urgent need for a comprehensive data analysis and processing method that can combine the working status of micro-nano grating devices, changes in spectral characteristics, and the parameters of the correlation spectrum control unit to achieve accurate identification and effective compensation of baseline drift phenomena, thereby improving the stability and reliability of optical sensing systems. Summary of the Invention

[0004] The present invention overcomes the deficiencies of the prior art and provides a method and system for analyzing data of a micro-nano grating device for optical sensing.

[0005] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0006] The present invention discloses a method for analyzing data of a micro-nano grating device for optical sensing, comprising the following steps:

[0007] Acquire actual sensing conditions of the target micro-nano grating device at a plurality of preset time nodes, and determine various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period based on the actual sensing conditions and in combination with a spectral characteristic query database;

[0008] Collecting actual spectral characteristic data of the target micro-nano grating device at several preset time nodes; screening the actual spectral characteristic data to obtain various actual spectral characteristic data sets within a preset time period;

[0009] Determine whether there is a baseline drift phenomenon in each actual spectral characteristic data of the target micro-nano grating device based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period;

[0010] If one or more actual spectral characteristic data of the target micro-nano grating device have a baseline drift phenomenon, the actual spectral characteristic data with the baseline drift phenomenon is defined as baseline drift spectral characteristic data; a correlation spectral control unit query database is constructed, and a correlation spectral control unit of the baseline drift spectral characteristic data is determined according to the correlation spectral control unit query database;

[0011] Acquire various real-time working parameters of the spectrum control unit of the correlation of baseline drift spectrum characteristic data, perform parameter analysis and processing on various real-time working parameters of the spectrum control unit of the correlation of baseline drift spectrum characteristic data, obtain abnormal working parameters, and adjust the abnormal working parameters.

[0012] Preferably, the actual sensing conditions of the target micro-nano grating device at several preset time nodes are obtained, and the preset spectral characteristic data sets of the target micro-nano grating device within a preset time period are determined based on the actual sensing conditions and in combination with the spectral characteristic query database, specifically:

[0013] Obtain various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions through the big data network;

[0014] Constructing a blank database, dividing the blank database into a plurality of sub-databases, and importing various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions into corresponding sub-databases for storage, thereby obtaining a spectral characteristic query database;

[0015] Acquiring actual sensing conditions of the target micro-nano grating device at a number of preset time nodes, and importing the actual sensing conditions of each preset time node into the spectral characteristic query database;

[0016] Calculating the mutual information value between the actual sensing condition at each preset time node and the preset sensing condition in each sub-storage bank; and sorting the mutual information values ​​between the actual sensing condition at each preset time node and the preset sensing condition in each sub-storage bank to obtain a sorting result;

[0017] Obtaining a maximum mutual information value according to the sorting result, obtaining a sub-repository corresponding to the maximum mutual information value, and extracting various preset spectral characteristic data of the target micro-nano grating device when it works at a corresponding preset time node from the sub-repository corresponding to the maximum mutual information value;

[0018] And so on, until the preset spectral characteristic data of the target micro-nano grating device when it works at each preset time node are obtained, and the preset spectral characteristic data sets within the preset time period are generated based on the preset spectral characteristic data of the target micro-nano grating device when it works at each preset time node.

[0019] Preferably, the actual spectral characteristic data is screened to obtain various actual spectral characteristic data sets within a preset time period, specifically:

[0020] Acquire characteristic data corresponding to various spectral noise data and characteristic data corresponding to various spectral characteristic data through a big data network; and obtain the number of data types of the spectral noise data and the number of data types of the spectral characteristic data;

[0021] Constructing a binary tree model, dividing a corresponding number of first-class branches in the binary tree model according to the number of data types of the spectral noise data; and dividing a corresponding number of second-class branches in the binary tree model according to the number of data types of the spectral characteristic data;

[0022] Mapping the characteristic data corresponding to each type of spectral noise data to a type of branch corresponding to the binary tree model; and mapping the characteristic data corresponding to each type of spectral characteristic data to a type of branch corresponding to the binary tree model;

[0023] Obtain the collected actual spectral characteristic data, and calculate the cosine similarity between each actual spectral characteristic data and each feature data in the binary tree model;

[0024] Allocate each actual spectral characteristic data to the first or second branch with the largest cosine similarity;

[0025] After completing the initial allocation of each actual spectral characteristic data, calculating the contour coefficient of the actual spectral characteristic data in each of the first and second branches; comparing the contour coefficient of the actual spectral characteristic data in each of the first and second branches with a preset coefficient threshold;

[0026] If the silhouette coefficients of the actual spectral characteristic data in each of the first and second branches are greater than the preset coefficient threshold, the initial allocation result is output as the final allocation result;

[0027] If there is a situation where the silhouette coefficient of the actual spectral characteristic data in at least one of the first-class branches or the second-class branches is not greater than the preset coefficient threshold, the actual spectral characteristic data of the first-class branches or the second-class branches whose silhouette coefficient is not greater than the preset coefficient threshold are redistributed until the silhouette coefficients of the actual spectral characteristic data in each of the first-class branches and the second-class branches are greater than the preset coefficient threshold, and the last allocation result is output as the final allocation result;

[0028] After the final allocation result is obtained, each of the first-class branches and the second-class branches of the binary tree model is pruned to obtain an actual spectral characteristic data set and a spectral noise data set.

[0029] Preferably, whether there is a baseline drift phenomenon in each actual spectral characteristic data of the target micro-nano grating device is determined based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period, specifically:

[0030] Sorting each actual spectral characteristic data set based on the acquisition timestamp to obtain an actual spectral characteristic data set based on a time series; constructing a curve diagram of each actual spectral characteristic data change based on the actual spectral characteristic data set based on the time series;

[0031] Acquire various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period, and construct a curve diagram of the change of various preset spectral characteristic data according to the various preset spectral characteristic data sets of the target micro-nano grating device within the preset time period;

[0032] Performing a coincidence analysis on each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph to obtain a degree of coincidence between each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph;

[0033] If the degree of overlap between a certain actual spectral characteristic data change curve and a corresponding preset spectral characteristic data change curve is not greater than a preset overlap threshold, it indicates that the actual spectral characteristic data in the micro-nano grating device has a baseline drift phenomenon;

[0034] If the degree of overlap between a certain actual spectral characteristic data change curve and the corresponding preset spectral characteristic data change curve is greater than a preset overlap threshold, it means that there is no baseline drift phenomenon in the actual spectral characteristic data in the micro-nano grating device.

[0035] Preferably, a correlation spectrum control unit query database is constructed, and a correlation spectrum control unit for determining baseline drift spectrum characteristic data is determined based on the correlation spectrum control unit query database, specifically:

[0036] Obtaining a product structure specification of the target micro-nano grating device, determining each spectrum control unit of the target micro-nano grating device according to the product structure specification, and obtaining control function characteristic information of each spectrum control unit;

[0037] Perform correlation analysis on each spectrum control unit and each spectrum characteristic data according to the control function characteristic information of each spectrum control unit, obtain spectrum control units that are correlated with each spectrum characteristic data, and obtain correlation spectrum control units for each spectrum characteristic data;

[0038] Constructing a correlation spectrum control unit query database based on the correlation spectrum control unit of each spectral characteristic data;

[0039] The baseline drift spectral characteristic data in the target micro-nano grating device is obtained, and the baseline drift spectral characteristic data in the target micro-nano grating device is imported into the correlation spectrum control unit query database for query matching to obtain the correlation spectrum control unit of the baseline drift spectral characteristic data.

[0040] Preferably, various real-time working parameters of the correlation spectrum control unit of the baseline drift spectral characteristic data are obtained, parameter analysis and processing are performed on the various real-time working parameters of the correlation spectrum control unit of the baseline drift spectral characteristic data, abnormal working parameters are obtained, and the abnormal working parameters are adjusted, specifically:

[0041] Obtaining various real-time operating parameters of the spectrum control unit related to the baseline drift spectrum characteristic data, and calculating the difference between each real-time operating parameter and the corresponding preset operating parameter to obtain parameter deviation values ​​of each real-time operating parameter;

[0042] Preset the deviation value threshold of each real-time working parameter, and compare the parameter deviation value of each real-time working parameter with the corresponding deviation value threshold;

[0043] If the parameter deviation value of a certain real-time working parameter is greater than the corresponding deviation value threshold, the real-time working parameter is defined as an abnormal working parameter;

[0044] If the parameter deviation value of a certain real-time working parameter is not greater than the corresponding deviation value threshold, the real-time working parameter is defined as a normal working parameter;

[0045] A parameter deviation value defined as an abnormal operating parameter is obtained, and the abnormal operating parameter is adjusted based on the corresponding parameter deviation value to adjust the corresponding real-time operating parameter to within a normal range.

[0046] Among them, the sensing conditions include light intensity, dust concentration, temperature, humidity and air pressure; the spectral characteristic data include reflection spectrum data, transmission spectrum data, absorption spectrum data, Raman spectrum data, ellipsometric spectrum data and dispersion spectrum data; the real-time working parameters include light source wavelength, light source intensity, polarization state of light source light, incident angle, bias voltage and current.

[0047] On the other hand, the present invention discloses a micro-nano grating device data analysis system for optical sensing, wherein the micro-nano grating device data analysis system includes a memory and a processor, wherein a micro-nano grating device data analysis method program is stored in the memory, and when the micro-nano grating device data analysis method program is executed by the processor, any one of the steps of the micro-nano grating device data analysis method is implemented.

[0048] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: First, in terms of data processing, by analyzing the preset spectral characteristic data set and the actual spectral characteristic data set, it is possible to accurately determine whether there is a baseline drift phenomenon, thereby improving the measurement accuracy. Secondly, by constructing a correlation spectrum control unit query database, the control unit that affects the spectral characteristics can be accurately located, and the possible factors that cause baseline drift can be quickly found, thereby enhancing the pertinence of system maintenance. Furthermore, the analysis and processing of the real-time working parameters of the correlation spectrum control unit can accurately identify abnormal working parameters and make adjustments, effectively correct the anomalies, and improve the stability and accuracy of the system. In addition, the present method can also achieve early fault prevention, correct problems before abnormal parameters cause serious impacts, and help optimize system performance, extend the service life of micro-nano grating devices, reduce maintenance costs, and improve their adaptability to different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0050] Figure 1 This is the overall method flow chart of the data analysis method for micro-nano grating devices;

[0051] Figure 2 This is a partial flow chart of the data analysis method for micro-nano grating devices;

[0052] Figure 3 This is the system block diagram of the micro-nano grating device data analysis system. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0055] The present invention discloses a data analysis method for micro-nano grating devices used for optical sensing, such as Figure 1 As shown, the following steps are included:

[0056] S102: Acquire actual sensing conditions of the target micro-nano grating device at a plurality of preset time nodes, and determine various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period based on the actual sensing conditions and in combination with a spectral characteristic query database;

[0057] S104: collecting actual spectral characteristic data of the target micro-nano grating device at a number of preset time nodes; screening the actual spectral characteristic data to obtain various actual spectral characteristic data sets within a preset time period;

[0058] S106: determining whether there is a baseline drift phenomenon in each actual spectral characteristic data of the target micro-nano grating device based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period;

[0059] S108: If one or more actual spectral characteristic data of the target micro-nano grating device have a baseline drift phenomenon, the actual spectral characteristic data with the baseline drift phenomenon is defined as baseline drift spectral characteristic data; a correlation spectrum control unit query database is constructed, and a correlation spectrum control unit of the baseline drift spectral characteristic data is determined based on the correlation spectrum control unit query database;

[0060] S110: Acquire various real-time working parameters of the correlation spectrum control unit of the baseline drift spectrum characteristic data, perform parameter analysis on the various real-time working parameters of the correlation spectrum control unit of the baseline drift spectrum characteristic data, obtain abnormal working parameters, and adjust the abnormal working parameters.

[0061] Among them, the sensing conditions include light intensity, dust concentration, temperature, humidity and air pressure; the spectral characteristic data include reflection spectrum data, transmission spectrum data, absorption spectrum data, Raman spectrum data, ellipsometric spectrum data and dispersion spectrum data; the real-time working parameters include light source wavelength, light source intensity, polarization state of light source light, incident angle, bias voltage and current.

[0062] This method is primarily used to monitor and correct the spectral characteristics of micro-nano grating devices under different environmental conditions to ensure the accuracy and stability of measurement results. First, the actual sensing conditions of the micro-nano grating device at multiple preset time points must be obtained. These conditions include light intensity, dust concentration, temperature, humidity, and air pressure. Based on the actual sensing conditions, a spectral characteristics database is combined to determine the expected spectral characteristics dataset for the target micro-nano grating device within a preset time period. Actual spectral characteristics data of the target micro-nano grating device is collected at the preset time points, including reflectance, transmission, absorption, Raman, ellipsometry, and dispersion spectra. The collected actual spectral characteristics data is then screened to extract the actual spectral characteristics dataset for the preset time period. The expected spectral characteristics dataset is then compared with the actual spectral characteristics dataset to determine whether baseline drift occurs. Baseline drift refers to the systematic shift of spectral data over time. If baseline drift is detected in the actual spectral characteristic data, it is necessary to identify the correlation spectrum control unit and its operating parameters (such as light source wavelength, light source intensity, polarization state of light source, incident angle, bias voltage and current, etc.) that cause this phenomenon in order to eliminate the baseline drift.

[0063] By monitoring and correcting baseline drift in spectral characteristic data, the measurement accuracy of micro-nano grating devices under different environmental conditions can be significantly improved. This helps identify and correct various factors that affect spectral characteristics, thereby enhancing the long-term stability and reliability of micro-nano grating devices. By considering multiple environmental factors (such as temperature and humidity), this method has strong environmental adaptability and is suitable for a variety of application scenarios. In summary, this data analysis method not only improves the measurement accuracy of micro-nano grating devices in complex environments, but also enhances their stability and adaptability, which is of great significance for improving the overall performance of optical sensing systems.

[0064] Preferably, the actual sensing conditions of the target micro-nano grating device at several preset time nodes are obtained, and the preset spectral characteristic data sets of the target micro-nano grating device within the preset time period are determined based on the actual sensing conditions and in combination with the spectral characteristic query database, such as Figure 2 As shown, specifically:

[0065] S202: Acquire various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions through a big data network;

[0066] S204: constructing a blank database, dividing the blank database into a plurality of sub-databases, and importing various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions into corresponding sub-databases for storage, thereby obtaining a spectral characteristic query database;

[0067] S206: Acquire actual sensing conditions of the target micro-nano grating device at a plurality of preset time nodes, and import the actual sensing conditions of each preset time node into the spectral characteristic query database;

[0068] S208: Calculating the mutual information value between the actual sensing condition at each preset time node and the preset sensing condition in each sub-repository; and sorting the mutual information values ​​between the actual sensing condition at each preset time node and the preset sensing condition in each sub-repository to obtain a sorting result;

[0069] S210: Obtaining a maximum mutual information value according to the sorting result, obtaining a sub-repository corresponding to the maximum mutual information value, and extracting various preset spectral characteristic data of the target micro-nano grating device when it is working at a corresponding preset time node from the sub-repository corresponding to the maximum mutual information value;

[0070] S212: The same process is repeated until various preset spectral characteristic data of the target micro-nano grating device when it works at various preset time nodes are obtained, and various preset spectral characteristic data sets within a preset time period are generated based on the preset spectral characteristic data of the target micro-nano grating device when it works at various preset time nodes.

[0071] This method achieves efficient management and query of spectral characteristic data by constructing a database, importing data, and calculating mutual information values. Specifically, various preset spectral characteristic data of a target micro-nano grating device under various preset sensing conditions are obtained through a big data network. A blank database is constructed and divided into several sub-databases. The preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions are imported into corresponding sub-databases for storage, forming a spectral characteristic query database. The actual sensing conditions of the target micro-nano grating device at several preset time points are obtained and imported into the spectral characteristic query database. The mutual information value between the actual sensing conditions at each preset time point and the preset sensing conditions in each sub-database is calculated. The mutual information value is a statistic that measures the degree of mutual dependence between two random variables. The mutual information values ​​between the actual sensing conditions at each preset time point and the preset sensing conditions in each sub-database are sorted to obtain a sorting result. Based on the sorting result, the maximum mutual information value is obtained. The sub-database corresponding to the maximum mutual information value is found, and the preset spectral characteristic data of the target micro-nano grating device operating at the corresponding preset time point is extracted from the sub-database. Repeat the above steps until the preset spectral characteristic data of the target micro-nano grating device when it is working at each preset time node are obtained. Based on the preset spectral characteristic data of the target micro-nano grating device when it is working at each preset time node, a preset spectral characteristic data set within a preset time period is generated.

[0072] By constructing a spectral characteristic query database, the preset spectral characteristic data is classified and stored according to the preset sensing conditions, which is convenient for rapid retrieval and use. Utilizing the mutual information value calculation method, the similarity between the actual sensing conditions and the preset sensing conditions can be accurately matched, thereby improving the accuracy of data matching. By sorting and selecting the maximum mutual information value, the preset spectral characteristic data closest to the actual sensing conditions can be quickly found, reducing query time and computing resource consumption. The database design is flexible and can be expanded and adjusted according to different application scenarios and requirements, thereby improving the adaptability and flexibility of the system. By accurately matching and extracting preset spectral characteristic data, the measurement accuracy and reliability of micro-nano grating devices in practical applications can be improved. In summary, this method can not only efficiently manage and query spectral characteristic data, but also improve the accuracy of data matching through mutual information value calculation, thereby improving data quality, data analysis accuracy and system flexibility.

[0073] Preferably, the actual spectral characteristic data is subjected to screening processing to obtain various actual spectral characteristic data sets within a preset time period, specifically:

[0074] Acquire characteristic data corresponding to various spectral noise data and characteristic data corresponding to various spectral characteristic data through a big data network; and obtain the number of data types of the spectral noise data and the number of data types of the spectral characteristic data;

[0075] Constructing a binary tree model, dividing a corresponding number of first-class branches in the binary tree model according to the number of data types of the spectral noise data; and dividing a corresponding number of second-class branches in the binary tree model according to the number of data types of the spectral characteristic data;

[0076] Mapping the characteristic data corresponding to each type of spectral noise data to a type of branch corresponding to the binary tree model; and mapping the characteristic data corresponding to each type of spectral characteristic data to a type of branch corresponding to the binary tree model;

[0077] Obtain the collected actual spectral characteristic data, and calculate the cosine similarity between each actual spectral characteristic data and each feature data in the binary tree model;

[0078] Allocate each actual spectral characteristic data to the first or second branch with the largest cosine similarity;

[0079] After completing the initial allocation of each actual spectral characteristic data, calculating the contour coefficient of the actual spectral characteristic data in each of the first and second branches; comparing the contour coefficient of the actual spectral characteristic data in each of the first and second branches with a preset coefficient threshold;

[0080] If the silhouette coefficients of the actual spectral characteristic data in each of the first and second branches are greater than the preset coefficient threshold, the initial allocation result is output as the final allocation result;

[0081] If there is a situation where the silhouette coefficient of the actual spectral characteristic data in at least one of the first-class branches or the second-class branches is not greater than the preset coefficient threshold, the actual spectral characteristic data of the first-class branches or the second-class branches whose silhouette coefficient is not greater than the preset coefficient threshold are redistributed until the silhouette coefficients of the actual spectral characteristic data in each of the first-class branches and the second-class branches are greater than the preset coefficient threshold, and the last allocation result is output as the final allocation result;

[0082] After the final allocation result is obtained, each of the first-class branches and the second-class branches of the binary tree model is pruned to obtain an actual spectral characteristic data set and a spectral noise data set.

[0083] It should be noted that two types of feature data are first acquired through the big data network: feature data corresponding to various spectral noise data types and feature data corresponding to various spectral characteristic data types (such as reflectance spectra and transmittance spectra). Simultaneously, the number of data types for the spectral noise data and the number of data types for the spectral characteristic data are also acquired. This step provides the foundational data for the subsequent construction of the binary tree model. This data serves as the basis for accurate spectral characteristic classification. Different types of spectral noise and spectral characteristics have unique characteristics. By acquiring these feature data, the actual spectral characteristic data can be classified based on these characteristics in subsequent steps. The binary tree model divides the first-class branches corresponding to the number of data types for the spectral noise data, and similarly divides the second-class branches corresponding to the number of data types for the spectral characteristic data. The binary tree model is a data structure in which each node has at most two child nodes, which facilitates data classification and organization. The feature data corresponding to each type of spectral noise data is mapped to the corresponding first-class branch of the binary tree model, and the feature data corresponding to each type of spectral characteristic data is mapped to the corresponding second-class branch. In this way, the binary tree model becomes a framework for classifying and storing spectral noise and spectral characteristic feature data. After acquiring the collected actual spectral characteristic data, the cosine similarity between each actual spectral characteristic data point and each feature data point in the binary tree model is calculated. Cosine similarity is a metric that measures the similarity between two vectors. In this scenario, it is used to determine the degree of similarity between the actual spectral characteristic data point and the feature data stored in the binary tree model. Based on the cosine similarity, each actual spectral characteristic data point is assigned to the first or second branch with the greatest cosine similarity. This step performs a preliminary classification of the actual spectral characteristic data point based on similarity. After completing the initial assignment of each actual spectral characteristic data point, the silhouette coefficient of the actual spectral characteristic data point in each first and second branch is calculated. The silhouette coefficient is a metric used to evaluate clustering effectiveness. It measures the closeness of a data point to other data points in its cluster and the degree of separation from data points in other clusters. The silhouette coefficient is then compared with a preset coefficient threshold. If the silhouette coefficient of the actual spectral characteristic data point in each first and second branch exceeds the preset coefficient threshold, the initial clustering effect is good, and the initial assignment result is output as the final assignment result. If the silhouette coefficient of the actual spectral characteristic data in at least one of the first or second branches is less than the preset coefficient threshold, the clustering effect is unsatisfactory and the actual spectral characteristic data of the first or second branches with a silhouette coefficient less than the preset coefficient threshold must be reallocated. This process is repeated until the silhouette coefficient of the actual spectral characteristic data in each first or second branch is greater than the preset coefficient threshold. The last allocation result is then output as the final allocation result.After obtaining the final allocation results, the first and second branches of the binary tree model are pruned to obtain the actual spectral characteristic dataset and the spectral noise dataset. Pruning extracts the data from the branches to form the final actual spectral characteristic dataset and the spectral noise dataset.

[0084] By constructing a binary tree model and performing data allocation and adjustment based on cosine similarity and silhouette coefficient, it is possible to precisely separate actual spectral characteristic data from spectral noise data, resulting in an accurate dataset of actual spectral characteristics. This precise classification facilitates subsequent accurate analysis of spectral characteristics and avoids noise interference. By calculating the silhouette coefficient and comparing it with a preset threshold, the allocation results are repeatedly evaluated and adjusted to optimize the clustering effect. This ensures that the resulting dataset has high internal similarity and high external differences from other datasets, thus improving data quality. High-quality datasets are of great significance for subsequent spectral characteristic research and optical sensing analysis. For example, in optical sensing, the performance of micro-nano grating devices can be more accurately analyzed, improving measurement accuracy and reliability. Using a binary tree model for data mapping, allocation, and adjustment, this data structure offers efficient search and classification capabilities. When processing large amounts of spectral data, data processing can be performed rapidly, reducing computational time and resource consumption. The entire process is automated based on data features and algorithms, reducing the need for manual intervention. This not only improves data processing efficiency but also reduces the impact of human factors on data processing results, enhancing the objectivity of the results.

[0085] Preferably, whether there is a baseline drift phenomenon in each actual spectral characteristic data of the target micro-nano grating device is determined based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period, specifically:

[0086] Sorting each actual spectral characteristic data set based on the acquisition timestamp to obtain an actual spectral characteristic data set based on a time series; constructing a curve diagram of each actual spectral characteristic data change based on the actual spectral characteristic data set based on the time series;

[0087] Acquire various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period, and construct a curve diagram of the change of various preset spectral characteristic data according to the various preset spectral characteristic data sets of the target micro-nano grating device within the preset time period;

[0088] Performing a coincidence analysis on each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph to obtain a degree of coincidence between each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph;

[0089] If the degree of overlap between a certain actual spectral characteristic data change curve and a corresponding preset spectral characteristic data change curve is not greater than a preset overlap threshold, it indicates that the actual spectral characteristic data in the micro-nano grating device has a baseline drift phenomenon;

[0090] If the degree of overlap between a certain actual spectral characteristic data change curve and the corresponding preset spectral characteristic data change curve is greater than a preset overlap threshold, it means that there is no baseline drift phenomenon in the actual spectral characteristic data in the micro-nano grating device.

[0091] It should be noted that the spectral characteristic data change curve diagram intuitively shows the changing trend of the spectral characteristic data over time. The actual spectral characteristic data change curve diagram and the corresponding preset spectral characteristic data change curve diagram are analyzed for overlap to obtain the overlap between the two. The overlap can be understood as the degree of fit between the actual data and the preset data in the changing trend. The presence of baseline drift can be judged based on the overlap. By comparing the change curve diagrams of the actual spectral characteristic data and the preset spectral characteristic data, it is possible to accurately judge whether there is a baseline drift phenomenon. The baseline drift phenomenon will affect the stability of the measurement results. By timely detecting and correcting the baseline drift phenomenon, the long-term working stability of the micro-nano grating device can be enhanced. By using an automated method to detect the baseline drift phenomenon, the need for manual intervention is reduced, and the accuracy and efficiency of the detection are improved. In summary, the present method can effectively detect and judge the baseline drift phenomenon by comparing the change curve diagrams of the actual spectral characteristic data and the preset spectral characteristic data, thereby improving the measurement accuracy and system stability.

[0092] Preferably, a correlation spectrum control unit query database is constructed, and a correlation spectrum control unit for determining baseline drift spectrum characteristic data is determined based on the correlation spectrum control unit query database, specifically:

[0093] Obtaining a product structure specification of the target micro-nano grating device, determining each spectrum control unit of the target micro-nano grating device according to the product structure specification, and obtaining control function characteristic information of each spectrum control unit;

[0094] Perform correlation analysis on each spectrum control unit and each spectrum characteristic data according to the control function characteristic information of each spectrum control unit, obtain spectrum control units that are correlated with each spectrum characteristic data, and obtain correlation spectrum control units for each spectrum characteristic data;

[0095] Constructing a correlation spectrum control unit query database based on the correlation spectrum control unit of each spectral characteristic data;

[0096] The baseline drift spectral characteristic data in the target micro-nano grating device is obtained, and the baseline drift spectral characteristic data in the target micro-nano grating device is imported into the correlation spectrum control unit query database for query matching to obtain the correlation spectrum control unit of the baseline drift spectral characteristic data.

[0097] It should be noted that the product structure specification contains important information about the structural design, components, etc. of the micro-nano grating device, and is the basis for subsequent analysis. The spectral control unit is a component or functional module that can affect the spectral characteristics. In addition, the control function characteristic information of each spectral control unit is obtained. This information describes how each spectral control unit affects the spectral characteristics, such as the ability to change wavelength, intensity, etc. Using the control function characteristic information of each spectral control unit, a correlation analysis is performed on each spectral control unit and each spectral characteristic data (such as reflection spectrum, transmission spectrum, etc.). For example, it can be analyzed through gray correlation analysis or combined with manual experience. This analysis aims to find out which spectral control units will affect specific spectral characteristic data. Through correlation analysis, the correlation spectral control units of each spectral characteristic data are determined. For example, a spectral control unit may have a strong correlation with the reflection spectrum data, but a weak correlation with the transmission spectrum data.

[0098] A correlation spectrum control unit query database is constructed based on the correlation spectrum control units of each spectral characteristic data obtained previously. This database is constructed to facilitate subsequent query matching. It organizes the spectral characteristic data and the spectral control units related thereto in a manner that is easy to query. The baseline drift spectral characteristic data in the target micro-nano grating device is obtained. These data are spectral characteristic data that have been previously determined to have baseline drift phenomena. The baseline drift spectral characteristic data are imported into the correlation spectrum control unit query database for query matching. Through the associated information in the database, the correlation spectrum control units corresponding to these baseline drift spectral characteristic data are found.

[0099] In summary, by querying and matching the relevant spectral control units of the baseline drift spectral characteristic data, it is possible to quickly locate which spectral control units may be the cause of baseline drift, which is of great significance for promptly resolving performance issues of micro-nano grating devices and improving their operating stability. Once the spectral control units related to baseline drift are determined, these units can be maintained, adjusted, or optimized in a targeted manner, which can more accurately solve the baseline drift problem and thus improve the overall performance of the micro-nano grating device.

[0100] Preferably, various real-time working parameters of the correlation spectrum control unit of the baseline drift spectral characteristic data are obtained, parameter analysis and processing are performed on the various real-time working parameters of the correlation spectrum control unit of the baseline drift spectral characteristic data, abnormal working parameters are obtained, and the abnormal working parameters are adjusted, specifically:

[0101] Obtaining various real-time operating parameters of the spectrum control unit related to the baseline drift spectrum characteristic data, and calculating the difference between each real-time operating parameter and the corresponding preset operating parameter to obtain parameter deviation values ​​of each real-time operating parameter;

[0102] Preset the deviation value threshold of each real-time working parameter, and compare the parameter deviation value of each real-time working parameter with the corresponding deviation value threshold;

[0103] If the parameter deviation value of a certain real-time working parameter is greater than the corresponding deviation value threshold, the real-time working parameter is defined as an abnormal working parameter;

[0104] If the parameter deviation value of a certain real-time working parameter is not greater than the corresponding deviation value threshold, the real-time working parameter is defined as a normal working parameter;

[0105] A parameter deviation value defined as an abnormal operating parameter is obtained, and the abnormal operating parameter is adjusted based on the corresponding parameter deviation value to adjust the corresponding real-time operating parameter to within a normal range.

[0106] It should be noted that the real-time operating parameters of the correlation spectrum control unit are first acquired for baseline drift spectral characteristic data. These real-time operating parameters are key indicators reflecting the current operating status of the correlation spectrum control unit, including, for example, light source wavelength, light source intensity, light source polarization state, incident angle, bias voltage, and current. Parameter deviation values ​​for each real-time operating parameter are then calculated. The preset operating parameters are the values ​​these parameters should have under normal operating conditions. By calculating the deviation values, the degree to which the current real-time operating parameters deviate from the ideal state can be intuitively understood. A deviation threshold is pre-set for each real-time operating parameter. This threshold serves as the basis for determining whether an operating parameter is abnormal. Its setting should take into account various factors, including the system's accuracy and stability requirements, and the actual operating environment. If the parameter deviation value of a real-time operating parameter exceeds the corresponding deviation threshold, it indicates that the parameter deviates significantly from the normal range and is defined as an abnormal parameter. Conversely, if the parameter deviation value of a real-time operating parameter does not exceed the corresponding deviation threshold, the real-time operating parameter is defined as a normal operating parameter. For parameters defined as abnormal, the parameter deviation value is obtained. This deviation value contains information about the degree and direction of the abnormality and serves as an important basis for adjustment. Abnormal operating parameters are adjusted based on the corresponding parameter deviations to bring the corresponding real-time operating parameters within the normal range. The adjustment method varies depending on the specific operating parameter type. For example, light source intensity can be achieved by adjusting the power supply voltage or current, while the incident angle can be achieved by adjusting the position of the optical component.

[0107] Adjustments are made based on the deviations of abnormal operating parameters. This targeted adjustment method directly affects the abnormal parameters and adjusts them back to the normal range. This helps quickly restore the normal operation of the correlation spectroscopy control unit, thereby reducing or eliminating baseline drift. By correcting the abnormal operating parameters, the operating state of the correlation spectroscopy control unit is made more stable, thereby improving the operating stability of the entire micro-nano grating device. Stable operating conditions help improve the accuracy of spectral characteristic measurements and ensure the performance of micro-nano grating devices in applications such as optical sensing.

[0108] In addition, the method further comprises the following steps:

[0109] Obtaining fault characteristic data corresponding to various types of faults occurring in target micro-nano grating devices through a big data network; and obtaining historical operating characteristic data of target micro-nano grating devices within a preset time period before various types of faults occurred;

[0110] The maximum likelihood estimation method is introduced to estimate the historical operating characteristic data of the target micro-nano grating device in a preset time period before various types of faults occur, and the historical operating characteristic data in a preset time period before various types of faults occur are used to obtain the transition probability of the target micro-nano grating device from a normal state to a faulty state under the conditions of various historical operating characteristic data.

[0111] A transition probability matrix is ​​constructed based on the transition probability of the target micro-nano grating device transitioning from a normal state to a fault state under various historical working characteristic data conditions;

[0112] Constructing a support vector machine model, and importing the transition probability matrix into the support vector machine model for coding learning until the model parameters meet the preset requirements;

[0113] When the abnormal operating parameters are adjusted, it is further determined whether the target micro-nano grating device still has baseline drift. If so, the real-time operating characteristic data of the target micro-nano grating device at the current time node is obtained;

[0114] Importing the real-time working characteristic data of the target micro-nano grating device at the current time node into the support vector machine model for evaluation, and outputting the transition probability of the target micro-nano grating device transitioning from a normal working state to a faulty state at the current time node and several future time nodes;

[0115] If the transition probability of the target micro-nano grating device from the normal working state to the fault state at the current time node is greater than the preset transition probability value, the current time node is output as the fault warning time node;

[0116] If the transition probability of the target micro-nano grating device from the normal working state to the fault state at a certain future time node is greater than the preset transition probability value, then the future time node is output as the fault warning time node.

[0117] It should be noted that two types of data are first acquired through the big data network: first, fault signature data corresponding to various types of faults occurring in the target micro-nano grating device. This data can reflect the device's performance characteristics under different fault types, such as specific spectral changes and electrical parameter anomalies. Second, historical operating characteristic data of the target micro-nano grating device within a preset time period before each type of fault occurs. This data records various characteristics of the device under normal operating conditions immediately before the fault occurs, such as operating temperature, operating voltage, and spectral characteristics. A maximum likelihood estimation method is introduced to estimate the historical operating characteristic data of the target micro-nano grating device within a preset time period before each type of fault occurs. Here, the historical operating characteristic data before the fault occurs is used as input to estimate the transition probability of the device from the normal state to the fault state under these data conditions. The transition probability reflects the probability of the device transitioning from the normal state to the fault state under specific historical operating characteristic data conditions. It is a key indicator for quantifying the likelihood of device failure. Estimated from a large amount of historical data, it can comprehensively consider the impact of multiple operating characteristics on the occurrence of failure. The transition probability matrix is ​​an integrated representation of the relationship between historical operating characteristic data and fault transition probabilities. It provides the data foundation for the support vector machine model, enabling it to learn the probability patterns of faults occurring under different operating characteristics. The transition probability matrix is ​​imported into the support vector machine model for encoding learning. During the learning process, the model adjusts its parameters based on the data in the matrix to find the optimal classification or regression rules. Preset requirements include meeting certain performance standards for model accuracy, recall rate, and other performance indicators, or reducing the model's loss function value to a certain level. Through this learning process, the support vector machine model can learn the complex relationship between historical operating characteristic data and fault transition probabilities.

[0118] After adjusting the abnormal operating parameters, if baseline drift still occurs in the target micro-nano grating device, real-time operating characteristic data for the device at the current time point is obtained. This real-time operating characteristic data is then input into a support vector machine model for evaluation. Based on the input real-time operating characteristic data, the support vector machine model outputs the transition probability of the target micro-nano grating device transitioning from a normal operating state to a faulty state at the current time point and at several future time points. If the transition probability at the current time point is greater than a preset transition probability value, the current time point is output as the fault warning time point. If the transition probability at a future time point is greater than a preset transition probability value, the future time point is output as the fault warning time point. This step enables real-time prediction and early warning of device failures, enabling early detection of potential failures so that appropriate measures can be taken. By determining the fault transition probability at the current and future time points, real-time fault warning is achieved, allowing measures to be taken in advance to avoid failures or reduce the losses caused by failures, thereby improving the reliability and stability of the micro-nano grating device.

[0119] In addition, the method further comprises the following steps:

[0120] Obtaining structural engineering drawing information of a target micro-nano grating device, constructing a three-dimensional structural diagram of the target micro-nano grating device based on the structural engineering drawing information; and obtaining preset operating parameter range information of each component unit in the target micro-nano grating device;

[0121] Establishing a mapping relationship between each component unit and the preset operating parameter range information, and importing the preset operating parameter range information of each component unit into the three-dimensional structure diagram according to the mapping relationship between each component unit and the preset operating parameter range information;

[0122] Set the parameters of dynamic simulation, including time step, simulation duration, and boundary conditions; obtain a dynamic simulation model diagram of the target micro-nano grating device;

[0123] If the transition probability of the target micro-nano grating device from the normal working state to the fault state at the current time node is greater than a preset transition probability value, or if the transition probability of the target micro-nano grating device from the normal working state to the fault state at a future time node is greater than a preset transition probability value; then obtain the fault type of the target micro-nano grating device transitioning from the normal working state to the fault state;

[0124] According to the fault type of the target micro-nano grating device transitioning from a normal working state to a faulty state, several maintenance plans are retrieved from the big data network;

[0125] Importing each maintenance plan into the dynamic simulation model diagram to perform simulated maintenance, and obtaining predicted working characteristic data of the target micro-nano grating device after the simulated maintenance of each maintenance plan;

[0126] Calculate the Pearson correlation coefficient between the predicted working characteristic data and the preset working characteristic data of the target micro-nano grating device after the simulated maintenance of each maintenance plan;

[0127] A maintenance plan corresponding to the maximum Pearson correlation coefficient is obtained, and the maintenance plan corresponding to the maximum Pearson correlation coefficient is output as a recommended maintenance plan.

[0128] It should be noted that the structural engineering drawings contain key structural information, such as the geometry, dimensions, and relative positions of each component of the micro-nano grating device. The preset operating parameter range information for each component of the target micro-nano grating device is obtained. These parameter ranges cover the possible parameter value ranges of each component under normal operating conditions, such as electrical parameters (voltage and current ranges, etc.) and optical parameters (refractive index and reflectivity ranges, etc.). A mapping relationship is then established between each component and the preset operating parameter range information. This mapping relationship associates each component with a corresponding operating parameter range. Through this mapping relationship, the preset operating parameter range information for each component is imported into the three-dimensional structural diagram, thus containing not only structural information but also operating parameter information, laying the foundation for constructing a dynamic simulation model. If the transition probability of the target micro-nano grating device from a normal operating state to a faulty state at the current time point or a future time point is greater than the preset transition probability value, this indicates that the device may be about to fail or has already failed. At this point, it is necessary to obtain the fault type that caused the target micro-nano grating device to transition from a normal operating state to a faulty state. The big data network contains a large amount of information, including micro-nano grating device failure repair cases, empirical data, and related technical documentation. Each repair plan is imported into a dynamic simulation model diagram for repair simulation. During the simulation, adjustments are made to the micro-nano grating device in the dynamic simulation model diagram based on the repair plan (e.g., changing the operating parameters of a component unit, repairing a damaged structure, etc.). Predicted operating characteristic data for the target micro-nano grating device after each repair plan simulation is then obtained. These predicted operating characteristic data reflect the device's likely operating state after adopting different repair plans. The Pearson correlation coefficient is calculated between the predicted operating characteristic data and the preset operating characteristic data for the target micro-nano grating device after each repair plan simulation. The Pearson correlation coefficient is a statistical indicator that measures the linear correlation between two variables. It is used here to assess the degree of similarity between the predicted operating characteristic data and the preset operating characteristic data. By determining the occurrence of a fault based on transition probability, it can provide early warning of potential micro-nano grating device failures, providing a basis for timely repair measures and improving the scientific nature of repair decisions. This increases the likelihood of selecting the most appropriate repair plan, thereby enhancing repair efficiency and post-repair performance of the micro-nano grating device.

[0129] The present invention also discloses a data analysis system for micro-nano grating devices for optical sensing, such as Figure 3 As shown, the micro-nano grating device data analysis system includes a memory 20 and a processor 60. The memory 20 stores a micro-nano grating device data analysis method program. When the micro-nano grating device data analysis method program is executed by the processor 60, any one of the steps of the micro-nano grating device data analysis method is implemented.

[0130] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing data of micro-nano grating devices for optical sensing, characterized in that: The following steps are involved: Acquire actual sensing conditions of the target micro-nano grating device at a plurality of preset time nodes, and determine various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period based on the actual sensing conditions and in combination with a spectral characteristic query database; Collecting actual spectral characteristic data of the target micro-nano grating device at several preset time nodes; Performing screening processing on the actual spectral characteristic data to obtain various actual spectral characteristic data sets within a preset time period; Determine whether there is a baseline drift phenomenon in each actual spectral characteristic data of the target micro-nano grating device based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period; If one or more actual spectral characteristic data of the target micro-nano grating device have a baseline drift phenomenon, the actual spectral characteristic data with the baseline drift phenomenon is defined as baseline drift spectral characteristic data; a correlation spectral control unit query database is constructed, and a correlation spectral control unit of the baseline drift spectral characteristic data is determined according to the correlation spectral control unit query database; Acquire various real-time working parameters of the spectrum control unit of the correlation of baseline drift spectrum characteristic data, perform parameter analysis and processing on various real-time working parameters of the spectrum control unit of the correlation of baseline drift spectrum characteristic data, obtain abnormal working parameters, and adjust the abnormal working parameters.

2. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, characterized in that: The actual sensing conditions of the target micro-nano grating device at several preset time nodes are obtained, and the preset spectral characteristic data sets of the target micro-nano grating device within the preset time period are determined based on the actual sensing conditions and in combination with the spectral characteristic query database, specifically: Obtain various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions through the big data network; Constructing a blank database, dividing the blank database into a plurality of sub-databases, and importing various preset spectral characteristic data of the target micro-nano grating device under various preset sensing conditions into corresponding sub-databases for storage, thereby obtaining a spectral characteristic query database; Acquiring actual sensing conditions of the target micro-nano grating device at a number of preset time nodes, and importing the actual sensing conditions of each preset time node into the spectral characteristic query database; Calculating the mutual information value between the actual sensing condition at each preset time node and the preset sensing condition in each sub-storage bank; and sorting the mutual information values ​​between the actual sensing condition at each preset time node and the preset sensing condition in each sub-storage bank to obtain a sorting result; Obtaining a maximum mutual information value according to the sorting result, obtaining a sub-repository corresponding to the maximum mutual information value, and extracting various preset spectral characteristic data of the target micro-nano grating device when it works at a corresponding preset time node from the sub-repository corresponding to the maximum mutual information value; And so on, until the preset spectral characteristic data of the target micro-nano grating device when it works at each preset time node are obtained, and the preset spectral characteristic data sets within the preset time period are generated based on the preset spectral characteristic data of the target micro-nano grating device when it works at each preset time node.

3. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, characterized in that: The actual spectral characteristic data is screened to obtain various actual spectral characteristic data sets within a preset time period, specifically: Acquire characteristic data corresponding to various spectral noise data and characteristic data corresponding to various spectral characteristic data through a big data network; and obtain the number of data types of the spectral noise data and the number of data types of the spectral characteristic data; Constructing a binary tree model, and dividing a corresponding number of branches in the binary tree model according to the number of data types of the spectral noise data; According to the number of data types of the spectral characteristic data, a corresponding number of two-category branches are divided in the binary tree model; Mapping the characteristic data corresponding to each type of spectral noise data to a type of branch corresponding to the binary tree model; and mapping the characteristic data corresponding to each type of spectral characteristic data to a type of branch corresponding to the binary tree model; Obtain the collected actual spectral characteristic data, and calculate the cosine similarity between each actual spectral characteristic data and each feature data in the binary tree model; Allocate each actual spectral characteristic data to the first or second branch with the largest cosine similarity; After completing the initial allocation of each actual spectral characteristic data, the silhouette coefficient of the actual spectral characteristic data in each first-class branch and second-class branch is calculated; Comparing the silhouette coefficients of the actual spectral characteristic data in each of the first-class branches and the second-class branches with a preset coefficient threshold; If the silhouette coefficients of the actual spectral characteristic data in each of the first and second branches are greater than the preset coefficient threshold, the initial allocation result is output as the final allocation result; If there is a situation where the silhouette coefficient of the actual spectral characteristic data in at least one of the first-class branches or the second-class branches is not greater than the preset coefficient threshold, the actual spectral characteristic data of the first-class branches or the second-class branches whose silhouette coefficient is not greater than the preset coefficient threshold are redistributed until the silhouette coefficients of the actual spectral characteristic data in each of the first-class branches and the second-class branches are greater than the preset coefficient threshold, and the last allocation result is output as the final allocation result; After the final allocation result is obtained, each of the first-class branches and the second-class branches of the binary tree model is pruned to obtain an actual spectral characteristic data set and a spectral noise data set.

4. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, wherein: Based on the preset spectral characteristic data set and the actual spectral characteristic data set within the preset time period, it is determined whether there is a baseline drift phenomenon in the actual spectral characteristic data of the target micro-nano grating device, specifically: Sorting each actual spectral characteristic data set based on the acquisition timestamp to obtain an actual spectral characteristic data set based on a time series; constructing a curve diagram of each actual spectral characteristic data change based on the actual spectral characteristic data set based on the time series; Acquire various preset spectral characteristic data sets of the target micro-nano grating device within a preset time period, and construct a curve diagram of the change of various preset spectral characteristic data according to the various preset spectral characteristic data sets of the target micro-nano grating device within the preset time period; Performing a coincidence analysis on each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph to obtain a degree of coincidence between each actual spectral characteristic data change curve graph and the corresponding preset spectral characteristic data change curve graph; If the degree of overlap between a certain actual spectral characteristic data change curve and a corresponding preset spectral characteristic data change curve is not greater than a preset overlap threshold, it indicates that the actual spectral characteristic data in the micro-nano grating device has a baseline drift phenomenon; If the degree of overlap between a certain actual spectral characteristic data change curve and the corresponding preset spectral characteristic data change curve is greater than a preset overlap threshold, it means that there is no baseline drift phenomenon in the actual spectral characteristic data in the micro-nano grating device.

5. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, wherein: Construct a correlation spectrum control unit query database, and determine the correlation spectrum control unit of the baseline drift spectrum characteristic data according to the correlation spectrum control unit query database, specifically: Obtaining a product structure specification of the target micro-nano grating device, determining each spectrum control unit of the target micro-nano grating device according to the product structure specification, and obtaining control function characteristic information of each spectrum control unit; Perform correlation analysis on each spectrum control unit and each spectrum characteristic data according to the control function characteristic information of each spectrum control unit, obtain spectrum control units that are correlated with each spectrum characteristic data, and obtain correlation spectrum control units for each spectrum characteristic data; Constructing a correlation spectrum control unit query database based on the correlation spectrum control unit of each spectral characteristic data; The baseline drift spectral characteristic data in the target micro-nano grating device is obtained, and the baseline drift spectral characteristic data in the target micro-nano grating device is imported into the correlation spectrum control unit query database for query matching to obtain the correlation spectrum control unit of the baseline drift spectral characteristic data.

6. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, characterized in that: Acquire various real-time working parameters of the spectrum control unit related to the baseline drift spectrum characteristic data, perform parameter analysis and processing on the various real-time working parameters of the spectrum control unit related to the baseline drift spectrum characteristic data, obtain abnormal working parameters, and perform adjustment processing on the abnormal working parameters, specifically: Obtaining various real-time operating parameters of the spectrum control unit related to the baseline drift spectrum characteristic data, and calculating the difference between each real-time operating parameter and the corresponding preset operating parameter to obtain parameter deviation values ​​of each real-time operating parameter; Preset the deviation value threshold of each real-time working parameter, and compare the parameter deviation value of each real-time working parameter with the corresponding deviation value threshold; If the parameter deviation value of a certain real-time working parameter is greater than the corresponding deviation value threshold, the real-time working parameter is defined as an abnormal working parameter; If the parameter deviation value of a certain real-time working parameter is not greater than the corresponding deviation value threshold, the real-time working parameter is defined as a normal working parameter; A parameter deviation value defined as an abnormal operating parameter is obtained, and the abnormal operating parameter is adjusted based on the corresponding parameter deviation value to adjust the corresponding real-time operating parameter to within a normal range.

7. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, characterized in that: Sensing conditions include light intensity, dust concentration, temperature, humidity, and air pressure.

8. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, wherein: The spectral characteristic data includes reflection spectrum data, transmission spectrum data, absorption spectrum data, Raman spectrum data, ellipsometry spectrum data, and dispersion spectrum data.

9. The method for analyzing data of a micro-nano grating device for optical sensing according to claim 1, wherein: The real-time working parameters of the correlation spectrum control unit include the wavelength of the light source, the intensity of the light source, the polarization state of the light source, the incident angle, the bias voltage and the current.

10. A micro-nano grating device data analysis system for optical sensing, characterized in that: The micro-nano grating device data analysis system includes a memory and a processor, wherein the memory stores a micro-nano grating device data analysis method program. When the micro-nano grating device data analysis method program is executed by the processor, the micro-nano grating device data analysis method steps as described in any one of claims 1 to 9 are implemented.

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