High-resolution broadband spectrometer and implementation method thereof

Abstract

The invention relates to the technical field of spectral analysis, and discloses a high-resolution broadband spectrometer and an implementation method thereof, and the spectrometer comprises a light source which is configured to provide a continuous spectrum in a range of 300 nm to 2500 nm; a grating configured to disperse light from the light source; a cooling CCD sensor configured to receive light dispersed by the grating; a data processing unit configured to perform noise cancellation and feature extraction; wherein the data processing unit comprises a function of executing wavelet transform and principal component analysis to extract main change components of the spectral data, and a support vector machine is adopted to classify features to detect anomalies; the light source is a supercontinuum laser source. By using high resolution gratings and advanced light source adjustment mechanisms, an adaptive light source control system allows real-time adjustment of light source wavelength and intensity to accommodate the specific spectral characteristics of different samples.

Description

Technical Field

[0001] The present invention relates to the technical field of spectrum analysis, in particular to a high-resolution wide-band spectrometer and an implementation method thereof. Background Art

[0002] The spectrometer is an important scientific instrument widely used in fields such as chemistry, physics, biomedicine and environmental science. It provides information about the composition and structure of a substance by measuring its absorption, emission or scattering properties of light. The performance of a spectrometer depends largely on the spectral range and resolution it can cover, which directly affects the quality of spectral data and the breadth of its applications.

[0003] Currently, there are many types of spectrometers on the market, including spectrophotometers, Fourier transform spectrometers, and grating-based spectrometers. These devices have their own advantages and can meet different measurement needs. However, most spectrometers usually use a specific type of light source and grating, and their wavelength selection and light intensity adjustment capabilities are limited. This fixed configuration device is usually only suitable for specific applications, and its flexibility and adaptability are insufficient for experimental environments and sample analysis with changing requirements.

[0004] Although existing spectrometer technologies perform well in specific applications, they often cannot effectively adapt to the diversity of sample properties and changes in the external environment. Especially in applications that require a wide spectral range and high resolution, fixed wavelength light sources may not provide sufficient coverage or appropriate light intensity when encountering the spectral requirements of different samples, limiting the quality and reliability of spectral data. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a high-resolution wide-band spectrometer and an implementation method thereof, which solves the problem that the fixed-wavelength light source of the prior art spectrometer technology may not provide sufficient coverage or appropriate light intensity when encountering the spectral requirements of different samples, thereby limiting the quality and reliability of the spectral data.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a high-resolution wide-band spectrometer, comprising:

[0007] a light source configured to provide a continuous spectrum in the range of 300 nm to 2500 nm;

[0008] a grating configured to disperse light from the light source;

[0009] a cooled CCD sensor configured to receive light dispersed by the grating;

[0010] a data processing unit configured to perform noise removal and feature extraction;

[0011] The data processing unit includes functions for performing wavelet transform and principal component analysis to extract the main variation components of the spectral data, and classifying the features using a support vector machine to detect anomalies.

[0012] Preferably, the light source is a supercontinuum laser source, which has the ability to continuously and uniformly output light in the wavelength range of 300nm to 2500nm, so as to ensure full-band coverage and high-precision collection of spectral data.

[0013] Preferably, the grating has a resolution of at least 1000 lines per millimeter, is arranged in the optical path to efficiently disperse the light emitted from the light source, and the optimized design of the grating angle and position can maximize the resolution and spectral range of the spectral data.

[0014] Preferably, the cooled CCD sensor is configured to minimize thermal noise and electronic noise when receiving scattered light, improve the signal-to-noise ratio of spectral data, and is maintained within a certain low temperature range through cooling technology to optimize the quality and accuracy of spectral data.

[0015] Preferably, the data processing unit has the ability to perform wavelet transform to remove random noise in the data, and the wavelet transform formula used is as follows:

[0016]

[0017] Among them, c j,k represents the wavelet coefficient, ψ j,k (t) represents the wavelet basis function. This processing is intended to enhance the signal portion of the data for more accurate feature extraction and analysis.

[0018] Preferably, the data processing unit is further configured to perform principal component analysis to extract the main variation components from the preprocessed spectral data. The specific transformation formula is as follows:

[0019] T=XW

[0020] Among them, X is the centralized data matrix, and W is the eigenvector matrix of the covariance matrix. This step allows the system to identify and compress the most important information in multidimensional data to reduce the complexity of subsequent processing and improve analysis efficiency.

[0021] Preferably, the data processing unit is configured to use a support vector machine model to classify normal and abnormal spectral data, and the support vector machine model training adopts the following optimization formula and constraints:

[0022]

[0023] subjecttoy i (w Tφ(x i )+b)≥1-ξ i ,ξ i ≥0

[0024] Among them, y i represents the class label, x i represents the feature vector, w,b and,ξ,are the vector machine parameters, and C is the regularization parameter.,This model aims to distinguish the abnormal state of spectral data with high,precision to achieve real-time monitoring and rapid response.

[0025] Preferably, the anomaly detection function is based on the anomaly determination formula:

[0026] f(x)=w T φ(x)+b

[0027] If f(x)<0, the data is considered abnormal.

[0028] Preferably, the data processing unit is configured to automatically adjust the acquisition parameters of the spectrometer after identifying the anomaly, including but not limited to the intensity of the light source and the angle of the grating, so as to optimize the quality of subsequent data and reduce the probability of false alarms. This automatic adjustment function is achieved through a feedback control system, which adjusts the corresponding hardware settings based on the anomaly detection results and historical data analysis to ensure continuous optimization of spectral data and the system's adaptability.

[0029] A method for realizing a high-resolution wide-band spectrometer comprises the following steps:

[0030] Step 1, providing a light source configured to emit a continuous spectrum ranging from 300nm to 2500nm;

[0031] Step 2: using a grating to disperse the light from the light source;

[0032] Step 3, using a cooled CCD sensor to capture the light scattered by the grating;

[0033] Step 4: preprocessing the captured spectral data, including noise removal and baseline correction;

[0034] Step 5: Apply principal component analysis and support vector machine to perform feature extraction and anomaly detection on the preprocessed data;

[0035] Step 6: According to the abnormal detection results, the light source intensity and grating angle are automatically adjusted to optimize the quality of subsequent spectral data collection.

[0036] The present invention provides a high-resolution wide-band spectrometer and an implementation method thereof. It has the following beneficial effects:

[0037] 1. The present invention significantly improves the resolution and spectral coverage of the spectrometer by using high-resolution gratings and advanced light source adjustment mechanisms. The high-resolution grating ensures that each wavelength of light can be accurately dispersed, while the adaptive light source control system allows real-time adjustment of the light source wavelength and intensity to adapt to the specific spectral characteristics of different samples.

[0038] 2. The present invention combines advanced data processing algorithms, such as principal component analysis and support vector machines, to achieve automated processing of spectral data and real-time anomaly detection. These algorithms can quickly identify key features from complex spectral data and automatically distinguish between normal and abnormal spectra, greatly improving the efficiency and reliability of the monitoring process.

[0039] 3. The light source control system of the present invention can dynamically adjust the light source output according to environmental changes and sample characteristics, enhancing the adaptability and stability of the equipment under different test conditions. This adaptive adjustment ensures the consistency and repeatability of spectral data, and maintains high-quality output even when external conditions change. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is the main framework diagram of the present invention;

[0041] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1:

[0044] Please refer to the attached Figure 1 The embodiment of the present invention provides a high-resolution wide-band spectrometer, comprising:

[0045] a light source configured to provide a continuous spectrum in the range of 300 nm to 2500 nm;

[0046] a grating configured to disperse light from a light source;

[0047] a cooled CCD sensor configured to receive light dispersed by the grating;

[0048] a data processing unit configured to perform noise removal and feature extraction;

[0049] The data processing unit includes functions for performing wavelet transform and principal component analysis to extract the main variation components of the spectral data, and classifies the features using a support vector machine to detect anomalies.

[0050] In one embodiment, 1. Light source configuration

[0051] In this embodiment, a supercontinuum laser source is selected as the light source, which is specifically configured to emit a continuous spectrum in the range of 300nm to 2500nm. The light source can provide illumination with a sufficiently wide band, which is suitable for spectral analysis of a variety of samples. By accurately controlling the power and stability of the light source, the consistency and repeatability of the spectral data can be ensured, which is crucial for achieving reliable spectral measurement.

[0052] 2. Grating settings and optimization

[0053] In this embodiment, a grating with a resolution of 1000 lines per millimeter is installed in the optical path to efficiently disperse the light emitted from the light source. By precisely adjusting the angle and position of the grating, the resolution and coverage of the spectral data can be maximized. This step ensures that each wavelength of light can be effectively dispersed and accurately focused onto the cooled CCD sensor, thereby acquiring high-quality spectral images.

[0054] 3. Configuration of cooling CCD sensor

[0055] In this embodiment, a cooled CCD sensor is used to receive the light dispersed by the grating. By applying cooling technology, the sensor operates in a low temperature environment to reduce the noise caused by temperature changes. This low noise environment is key to acquiring weak spectral signals, especially when dealing with samples with low concentrations or requiring high sensitivity detection.

[0056] 4. Data preprocessing technology

[0057] In this embodiment, the preprocessing includes using wavelet transform to remove random noise in the data and performing polynomial fitting to correct the baseline of the spectral data. The wavelet transform effectively separates the noise and signal in the spectral signal by selecting a suitable mother wavelet and decomposition level. The polynomial fitting is used to correct the baseline drift that may be caused by factors such as equipment deviation and environmental changes to ensure the accuracy of the data.

[0058] 5. Principal component analysis and feature extraction

[0059] In this embodiment, principal component analysis (PCA) is applied to the preprocessed data to extract the main variation components. By calculating the covariance matrix of the data set, its main eigenvectors are extracted, which represent the most important variation directions in the data. The implementation of this step can significantly reduce the amount of data to be processed later, while retaining the most critical information for sample analysis.

[0060] 6. Application of Support Vector Machine (SVM) Model

[0061] In this embodiment, a support vector machine (SVM) model is used to classify the spectral data after preprocessing and feature extraction to identify abnormal patterns therein. SVM provides a powerful classification mechanism by building a model that maximizes the boundaries between categories in feature space. This model automatically adjusts its parameters based on the training data to best distinguish between normal and abnormal data points.

[0062] 7. Real-time monitoring and system feedback

[0063] In this embodiment, the spectrometer is equipped with an automatic feedback system for adjusting the light source intensity and grating angle in real time based on the abnormal detection results. This feedback mechanism can not only correct potential errors instantly, but also optimize the performance of the spectrometer based on the continuously collected data. The implementation of this system improves the adaptability and reliability of the overall device, enabling it to continue to operate under changing environmental conditions without loss of performance.

[0064] Furthermore, through the above-mentioned embodiments, the high-resolution wide-band spectrometer of the present invention can provide high-quality spectral data and realize real-time data monitoring and anomaly detection. The combination of these technologies not only optimizes the efficiency and accuracy of spectral analysis, but also enhances the usability and flexibility of the equipment in different application scenarios. In addition, the automatic feedback adjustment mechanism provides the equipment with a high degree of automation, reduces the need for manual intervention, and significantly improves the convenience of operation and the economic benefits of the system.

[0065] The light source is a supercontinuum laser source, which has the ability to continuously and uniformly output light in the wavelength range of 300nm to 2500nm to ensure full-band coverage and high-precision acquisition of spectral data.

[0066] The grating has a resolution of at least 1000 lines per millimeter and is arranged in the optical path to efficiently disperse the light emitted from the light source, and the optimized design of the grating angle and position can maximize the resolution and spectral range of the spectral data.

[0067] The cooled CCD sensor is configured to minimize thermal and electronic noise when receiving scattered light, improve the signal-to-noise ratio of the spectral data, and is maintained within a certain low temperature range through cooling technology to optimize the quality and accuracy of the spectral data.

[0068] The data processing unit has the ability to perform wavelet transform to remove random noise in the data. The wavelet transform formula used is as follows:

[0069]

[0070] Among them, c j,k represents the wavelet coefficient, ψ j,k (t) represents the wavelet basis function. This processing is intended to enhance the signal portion of the data for more accurate feature extraction and analysis.

[0071] The data processing unit is further configured to perform principal component analysis to extract the main variation components from the preprocessed spectral data. The specific transformation formula is as follows:

[0072] T=XW

[0073] Among them, X is the centralized data matrix, and W is the eigenvector matrix of the covariance matrix. This step allows the system to identify and compress the most important information in multidimensional data to reduce the complexity of subsequent processing and improve analysis efficiency.

[0074] The data processing unit is configured to use a support vector machine model to classify normal and abnormal spectral data. The support vector machine model training adopts the following optimization formula and constraints:

[0075]

[0076] subjecttoy i (w T φ(x i )+b)≥1-ξ i ,ξ i ≥0

[0077] Among them, y i represents the class label, x i represents the feature vector, w, b and,ξ are the vector machine parameters, and C is the regularization parameter. This model aims to distinguish the abnormal state of spectral data with high accuracy to achieve real-time monitoring and rapid response.

[0078] The anomaly detection function is based on the anomaly determination formula:

[0079] f(x)=w T φ(x)+b

[0080] If f(x) < 0, the data is considered abnormal.

[0081] The data processing unit is configured to automatically adjust the acquisition parameters of the spectrometer after identifying an anomaly, including but not limited to the intensity of the light source and the angle of the grating, so as to optimize the quality of subsequent data and reduce the probability of false alarms. This automatic adjustment function is achieved through a feedback control system, which adjusts the corresponding hardware settings based on the anomaly detection results and historical data analysis to ensure the continuous optimization of spectral data and the adaptive ability of the system.

[0082] Embodiment 2:

[0083] Please refer to the attached Figure 2 Based on the above embodiment, this embodiment provides a method for implementing a high-resolution wide-band spectrometer, comprising the following steps:

[0084] Step 1, providing a light source configured to emit a continuous spectrum ranging from 300nm to 2500nm;

[0085] Step 2: Use a grating to disperse the light from the light source;

[0086] Step 3: Use a cooled CCD sensor to capture the light scattered by the grating;

[0087] Step 4: preprocessing the captured spectral data, including noise removal and baseline correction;

[0088] Step 5: Apply principal component analysis and support vector machine to perform feature extraction and anomaly detection on the preprocessed data;

[0089] Step 6: According to the abnormal detection results, the light source intensity and grating angle are automatically adjusted to optimize the quality of subsequent spectral data collection.

[0090] In a preferred embodiment, the adaptive light source control system and the high-resolution wide-band spectrometer

[0091] Based on the original high-resolution wide-band spectrometer, the adaptive light source control system is designed to achieve more accurate spectral data acquisition, especially for those application scenarios where the spectral characteristics change with the sample type or environmental conditions. By automatically adjusting the intensity and wavelength output of the light source, the spectral signal can be optimized and the accuracy and repeatability of the analysis can be improved.

[0092] Light source control algorithm

[0093] The light source control algorithm in this embodiment dynamically adjusts the light source parameters based on the real-time monitored spectral data. The algorithm calculates the required light intensity and wavelength adjustment amount by analyzing the absorption peaks and reflection peaks in the spectral data.

[0094] Δλ=f(A current , A optimal )

[0095] ΔI=g(I current , I optimal )

[0096] Among them, Δλ and ΔI are the adjustment amounts of wavelength and light intensity respectively, A current and I current is the absorption peak and light intensity measured in the current spectral data, A optimal and I optimal It is the expected value under ideal conditions.

[0097] Light source adjustment mechanism

[0098] The light source control system includes a feedback loop that receives data from the spectrometer sensor and the data processing unit in real time. Based on this data, the system automatically adjusts the output characteristics of the light source, such as adjusting the position of the wavelength filter or changing the driving current of the LED light source.

[0099] A closed-loop control system ensures the accuracy and response speed of light source adjustment, thereby quickly adapting to changes in sample characteristics or environmental conditions.

[0100] Real-time data feedback and optimization

[0101] The data processing unit compares the real-time processed spectral data with the preset spectral model to evaluate whether the current output of the light source has reached the optimal state. If a deviation is detected, the control system adjusts the light source parameters, such as adjusting the light intensity of a specific wavelength within the spectral range.

[0102] The optimization of light source control is based on real-time data and historical trend analysis, using advanced algorithms such as gradient descent or adaptive filtering technology to fine-tune parameters to achieve the best spectral acquisition effect.

[0103] In a preferred embodiment, in a high-resolution wideband spectrometer, the stability and adjustment capability of the light source are crucial to obtaining accurate and reliable spectral data. Since changes in the absorption characteristics of the sample and environmental conditions may lead to changes in the light source output requirements, developing a mechanism that can automatically adjust the light source output characteristics to optimize the spectral signal is the key to improving the accuracy of spectral analysis.

[0104] Configuration of light source control system

[0105] The light source control system integrates a light intensity regulator and a wavelength selector, which are components that can accurately control the intensity and emission wavelength of the light source. By using a variable frequency drive LED or an adjustable laser diode, the system can accurately adjust the light intensity. At the same time, a variable wavelength filter or an electrically tuned filter (such as an acousto-optic modulator) is used to select or adjust the output wavelength of the light source.

[0106] Light intensity and wavelength adjustment strategy

[0107] The intensity and wavelength adjustment of the light source is based on real-time spectral data feedback. The system first determines the need for adjustment by analyzing the difference between the current spectrum and the predetermined target spectrum. For example, if the absorption intensity of a specific wavelength is detected to be lower than expected, the system will automatically increase the light intensity of that wavelength.

[0108] The adjustment strategy uses advanced control algorithms, such as PID control or fuzzy logic control, to fine-tune the output of the light source. These algorithms optimize the state of the light source through a continuous iterative process to achieve the best spectral acquisition conditions as quickly as possible.

[0109] Implementing feedback control

[0110] The control system receives input from the data processing unit of the spectrometer, which includes spectral data and its analysis results. Based on these data, the control system calculates the necessary adjustment parameters and adjusts the output of the light source in real time.

[0111] The feedback control mechanism includes real-time monitoring of the output state of the light source and comparing it with the predetermined spectral characteristics to ensure that the light source output is always kept in the optimal state. The system can automatically record and learn the adjustment data obtained from different spectral tests to optimize future light source adjustment strategies.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-resolution broadband spectrometer, It is characterized in that include: a light source configured to provide a continuous spectrum in the range of 300 nm to 2500 nm; a grating configured to disperse light from the light source; a cooled CCD sensor configured to receive light dispersed by the grating; a data processing unit configured to perform noise removal and feature extraction; The data processing unit includes functions for performing wavelet transform and principal component analysis to extract the main variation components of the spectral data, and classifying the features using a support vector machine to detect anomalies.

2. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The light source is a supercontinuum laser source, which has the ability to continuously and uniformly output light in the wavelength range of 300nm to 2500nm to ensure full-band coverage and high-precision collection of spectral data.

3. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The grating has a resolution of at least 1000 lines per millimeter and is arranged in the optical path to efficiently disperse the light emitted from the light source, and the optimized design of the grating angle and position can maximize the resolution and spectral range of the spectral data.

4. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The cooled CCD sensor is configured to minimize thermal noise and electronic noise when receiving scattered light, improve the signal-to-noise ratio of spectral data, and is maintained within a certain low temperature range through cooling technology to optimize the quality and accuracy of spectral data.

5. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The data processing unit has the ability to perform wavelet transform to remove random noise in the data. The wavelet transform formula used is as follows: Among them, c j,k represents the wavelet coefficient, ψ j,k (t) represents the wavelet basis function. This processing is intended to enhance the signal portion of the data for more accurate feature extraction and analysis.

6. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The data processing unit is further configured to perform principal component analysis to extract the main variation components from the preprocessed spectral data. The specific transformation formula is as follows: T=XW Among them, X is the centralized data matrix, and W is the eigenvector matrix of the covariance matrix. This step allows the system to identify and compress the most important information in multidimensional data to reduce the complexity of subsequent processing and improve analysis efficiency.

7. A high-resolution wide-band spectrometer according to claim 1, It is characterized in that The data processing unit is configured to use a support vector machine model to classify normal and abnormal spectral data. The support vector machine model training adopts the following optimization formula and constraints: subjecttoy i (w T φ(x i )+b)≥1-ξ i ,x i ≥0 Among them, y i represents the class label, x i represents the feature vector, w,b and,ξ,are the vector machine parameters, and C is the regularization parameter.,This model aims to distinguish the abnormal state of spectral data with high,precision to achieve real-time monitoring and rapid response.

8. A high-resolution wide-band spectrometer according to claim 1, It is characterized in that The anomaly detection function is based on the anomaly determination formula: f(x)=w T φ(x)+b If f(x)<0, the data is considered abnormal.

9. A high-resolution broadband spectrometer according to claim 1, It is characterized in that The data processing unit is configured to automatically adjust the acquisition parameters of the spectrometer after identifying an anomaly, including but not limited to the intensity of the light source and the angle of the grating, so as to optimize the quality of subsequent data and reduce the probability of false alarms. This automatic adjustment function is achieved through a feedback control system, which adjusts the corresponding hardware settings based on the anomaly detection results and historical data analysis to ensure continuous optimization of spectral data and the system's adaptability.

10. A method for realizing a high-resolution wide-band spectrometer, according to the high-resolution wide-band spectrometer according to any one of claims 1 to 9, It is characterized in that The following steps are involved: Step 1, providing a light source configured to emit a continuous spectrum ranging from 300nm to 2500nm; Step 2: using a grating to disperse the light from the light source; Step 3, using a cooled CCD sensor to capture the light scattered by the grating; Step 4: preprocessing the captured spectral data, including noise removal and baseline correction; Step 5: Apply principal component analysis and support vector machine to perform feature extraction and anomaly detection on the preprocessed data; Step 6: According to the abnormal detection results, the light source intensity and grating angle are automatically adjusted to optimize the quality of subsequent spectral data collection.

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