CN118549409B

CN118549409B - Raman spectrum-based rapid coal detection method

Info

Publication number: CN118549409B
Application number: CN202410736535.9A
Authority: CN
Inventors: 于玥; 韩利清; 刘阿勇嘎
Original assignee: China Inspection And Certification Group Inner Mongolia Co ltd
Current assignee: China Inspection And Certification Group Inner Mongolia Co ltd
Priority date: 2024-06-07
Filing date: 2024-06-07
Publication date: 2025-01-07
Anticipated expiration: 2044-06-07
Also published as: CN118549409A

Abstract

The application discloses a rapid coal detection method based on Raman spectrum, and relates to the field of coal detection. The method comprises the steps of 1, placing a coal sample in a multimode laser, generating quantized laser beams through multimode laser quantization technology, exciting the coal sample to generate Raman scattering, collecting Raman spectra, 2, utilizing the Raman spectra generated by the coal sample, separating characteristic peaks of different components through quantum interference effects to generate interference patterns, 3, carrying out virtual resonance processing on the interference patterns to enhance signal intensity of the interference patterns and improve signal to noise ratio to obtain enhanced interference patterns, identifying characteristic peaks in the enhanced interference patterns, and 4, calculating the content of each specific component in the coal sample according to the peak value of the characteristic peaks. The application obviously improves the accuracy and efficiency of coal detection.

Description

Raman spectrum-based rapid coal detection method

Technical Field

The application relates to the technical field of coal detection, in particular to a rapid coal detection method based on Raman spectrum.

Background

With the growing global energy demand, coal is a major fossil fuel, and plays an important role in energy production and industrial applications. However, the quality and composition of coal has a direct impact on its combustion performance, environmental impact, and economic benefits. In order to ensure efficient utilization and environmental protection of coal, rapid and accurate detection and analysis of its components are required. Traditional coal detection methods such as chemical analysis and thermogravimetric analysis, while capable of providing detailed component information, tend to be time consuming, complex to operate, and unsuitable for the need for rapid detection.

In recent years, the application of spectroscopic techniques in coal detection has been increasingly emphasized, especially infrared spectroscopy and raman spectroscopy. The infrared spectrum technology acquires component information by detecting the absorption characteristic of a sample to infrared light, and has the advantages of high sensitivity and high detection speed. However, when detecting a sample with a high carbon content, the infrared spectrum is susceptible to the scattering effect of the sample, resulting in a decrease in signal intensity and a decrease in detection accuracy. Meanwhile, the infrared spectrum has limited resolving power for multiple components in a complex sample, and accurate identification and quantitative analysis of different components are difficult to realize. In contrast, raman spectroscopy technology is a powerful tool for analyzing complex samples because it can provide detailed structural information such as molecular vibration and rotation, and is not affected by common interferents such as water and carbon dioxide. Raman spectroscopy obtains component information by detecting raman scattered light generated when a sample is irradiated with laser light. Despite the significant advantages of raman spectroscopy, its use in rapid detection of coal samples still faces some challenges. For example, raman scattering signals are inherently weak and can be easily overwhelmed by the fluorescent background signal of the sample, thereby affecting the sensitivity and accuracy of detection. Furthermore, for complex multi-component samples, the peaks of the raman spectra tend to overlap, making resolution of the different components difficult.

Disclosure of Invention

The embodiment of the application provides a quick coal detection method based on Raman spectrum, which remarkably improves the accuracy and efficiency of coal detection.

The embodiment of the application provides a rapid coal detection method based on Raman spectrum, which comprises the following steps:

step 1, placing a coal sample in a multimode laser, generating quantized laser beams through multimode laser quantization technology, exciting the coal sample to generate Raman scattering, and collecting Raman spectra;

Step 2, utilizing Raman spectrum generated by the coal sample, separating out characteristic peaks of different components through quantum interference effect, and generating interference patterns;

Step 3, performing virtual resonance processing on the interference pattern to enhance the signal intensity of the interference pattern and improve the signal to noise ratio so as to obtain an enhanced interference pattern;

and 4, mapping the characteristic peaks into a high-dimensional characteristic space, enhancing the classification effect, obtaining high-dimensional characteristics, classifying the components of the high-dimensional characteristics by using a preset multi-layer sensor, identifying each specific component in the coal sample, and calculating the content of each specific component in the coal sample according to the peak value of the characteristic peaks.

Further, step 1 specifically comprises pulverizing and sieving the coal sample to make the particle size of the coal sample uniform, enabling laser beams to uniformly irradiate each part of the coal sample, selecting a tunable multimode laser capable of generating a plurality of laser beams with different frequencies, simultaneously ensuring that the laser beams can be coherently overlapped to form quantized laser beams, obtaining the properties of the coal sample, calculating laser power based on the properties of the coal sample, ensuring that enough energy can be generated to excite raman scattering of the coal sample, applying the laser power in the multimode laser, adjusting the output frequency of the multimode laser through a frequency tuning module to enable each frequency to resonate with specific molecular vibration modes of specific cost in the coal sample, placing a quantum dot exciter in a laser beam path, enabling quantum dots to be excited at different laser frequencies, and generating quantized laser beams with high quantum efficiency through a nonlinear process, enabling the quantized laser beams with different frequencies to be overlapped through the multimode interferometer to form a comprehensive quantized laser beam, shaping the comprehensive quantized laser beams into uniform cross-sectional area through an optical lens and a collimator, ensuring that the laser energy is uniformly distributed on the coal sample and the coal sample is uniformly scattered by using the surface of the laser beam to be subjected to coal sample excitation by the raman spectrometer.

Further, properties of the coal sample include chemical bond energy, density, specific heat capacity, light absorption coefficient, heat of combustion and mass of coal molecules.

Further, based on the properties of the coal sample, the laser power P _laser was calculated using the following formula:

Wherein E _bond is chemical bond energy in coal molecules, ρ _coal is density, C _p is specific heat capacity, α _abs is light absorption coefficient, λ _i is wavelength of ith laser beam, Φ _exc is quantum dot excitation efficiency, σ _scat is Raman scattering cross section, A _beam is cross section of laser beam, ΔH _comb is combustion heat of coal, m is mass, C _v is temperature T _ambient is ambient temperature; is Planck constant, ω _i is the frequency of the ith laser beam, ΔE is the energy difference between the excited and ground states, k is the Boltzmann constant, and T is the temperature of the coal sample.

Further, the step 2 specifically includes encoding phases of the raman spectra through a phase modulator to enable characteristic peaks of different components to generate specific phase differences, decomposing the raman spectra subjected to phase modulation into interference patterns of different components through a multi-path quantum interferometer, decoding the generated interference patterns to obtain decoded spectrum intensities, and separating the characteristic peaks of different components according to the decoded spectrum intensities.

Further, let the initial spectral intensity of the Raman spectrum be I _raw (x), where x is the Raman shift, and encode the phase of the Raman spectrum by the phase modulator using the following formula:

I_mod(x)＝I_raw(x)·e^iφ(x);

wherein I _mod (x) is the spectral intensity after phase modulation, and phi (x) is the phase function, expressed using the following formula:

Wherein lambda _n is the characteristic wavelength of the nth specific component, theta _n is the phase offset of the nth specific component, alpha _nm is the phase modulation amplitude, lambda _nm is the phase modulation frequency of the mth interference path of the nth specific component, beta _nm is the phase modulation initial phase of the mth interference path of the nth specific component, N is the number of specific components in coal, and M is the number of interference paths.

Further, the formula of decomposing the raman spectrum after phase modulation into interference patterns of different components by the multipath quantum interferometer is as follows:

Where I _interf (x) is the interference pattern, d _k is the optical path difference of the kth interference path, Δφ _k is the phase difference of the kth interference path, J is the number of interferometric frequencies, gamma _kj is the interferometric amplitude of the jth interferometric frequency of the kth interference path, lambda _kj is the jth interferometric frequency of the kth interference path, and delta _kj is the jth interferometric phase of the jth interference path.

Further, the generated interference pattern is decoded by the following formula to obtain the decoded spectral intensity:

Wherein I _dec (x) is the decoded spectral intensity; Is an inverse fourier transform operator, η _p is the P-th decoded modulation coefficient, P is the number of decoded modulation coefficients, ψ _p (x) is the P-th decoded modulation phase function, defined as:

Where Q is the number of modulation frequencies contained in the decoded modulation phase function, Q is the subscript, ζ _pq is the modulation amplitude, λ _pq is the modulation frequency, and κ _pq is the modulation initial phase.

Further, according to the decoded spectrum intensity, the characteristic peak values of different components are separated by using the following formula:

Wherein I _peak,n (x) is the characteristic peak intensity of the nth specific component, sigma _n is the wavelength standard deviation of the nth specific component, ζ _nr is the peak modulation amplitude, R is the number of modulation frequencies used in the characteristic peak separation process, and lambda _nr is the peak modulation frequency; the initial phase is modulated for the peak.

According to the rapid coal detection method based on Raman spectrum, the multimode laser quantization technology is adopted, and the intensity and quality of spectrum signals are greatly improved. The conventional single-mode laser has difficulty in generating a raman scattering signal with sufficient intensity due to limited laser energy density in raman spectrum detection, and particularly when detecting low concentration components, low signal-to-noise ratio becomes a big bottleneck. The multimode laser can simultaneously generate laser beams with a plurality of frequencies, and the quantized laser beams with high energy density are formed through coherent superposition, so that molecular vibration in a sample is effectively excited, and the intensity of a Raman scattering signal is remarkably enhanced. The laser beam with high energy density ensures that a clear and high-intensity Raman spectrum signal can be obtained even in the detection of low-concentration components, and improves the sensitivity and accuracy of the detection. Secondly, the application utilizes quantum interference effect, and the Raman spectrum after phase modulation is decomposed into interference patterns with different components through a multipath quantum interferometer. The quantum interference effect is an enhancement effect based on the phase difference of light waves, and raman spectrums of different components show different interference fringes in an interference pattern by designing different optical path differences and phase differences. Thus, characteristic peaks of each component can be effectively separated, and the problem of peak value overlapping in the traditional Raman spectrum is avoided. Through the multi-path quantum interferometer, spectrum signals are better separated and enhanced, so that each component signal in a complex sample is clearer, the resolution is obviously improved, and different components in the sample can be accurately identified. Thirdly, the application introduces a phase modulation technique, and realizes further optimization of spectrum signals by carrying out accurate phase encoding and decoding on Raman spectrum. The phase modulator is used for encoding the phase of the spectrum signal so that characteristic peaks of different components generate specific phase differences. During decoding, the spectral intensities of the components can be accurately extracted and separated by inverse fourier transform and phase modulation functions. The phase modulation technology not only improves the identification degree of the spectrum signal, but also enhances the detection capability of low-concentration components, and ensures the accuracy and reliability of detection results.

Drawings

The technical solution and other advantageous effects of the present application will be made apparent by the following detailed description of the specific embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a method for rapid coal detection based on raman spectrum according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

Example 1 referring to fig. 1, a rapid coal detection method based on raman spectroscopy, the method comprising:

In the rapid detection method of the coal sample, step 1 is to place the coal sample in a multimode laser, generate quantized laser beams through multimode laser quantization technology, excite the coal sample to generate Raman scattering, and collect Raman spectra. First, the selection of multimode lasers is a critical step. The mode of a laser refers to the distribution of a laser beam across a cross section, and a multimode laser can produce multiple modes of laser beams with higher power output relative to a single mode laser. This high power output characteristic helps to excite molecules in the sample more efficiently, thereby generating a stronger raman scattering signal. Multimode lasers can cover a wider frequency range, provide rich spectral information, and facilitate subsequent spectral analysis. Next, the application of quantization techniques further improves the performance of the laser beam. The quantization technique is to quantize the energy of a laser beam so as to have the characteristic of a quantum state. During quantization, the energy of the laser beam is divided into discrete quantum units, each carrying a specific energy. This quantization process enables the laser beam to excite more precisely specific molecular vibrational modes upon interaction with the sample molecules, thereby generating raman spectra with high resolution. When the quantized laser beam irradiates on the coal sample, molecules in the sample absorb the energy of the laser and generate molecular vibration. These vibrational state changes can cause photons to undergo energy transfer during scattering, which is a raman scattering phenomenon. Raman scattering can be divided into two forms, stokes scattering, which refers to scattering photons having energies lower than the energy of the incident photons, and anti-stokes scattering, which refers to scattering photons having energies higher than the energy of the incident photons. In practical detection, the Stokes scattering signal is stronger and is easier to detect and analyze. The spectrum generated by raman scattering contains characteristic information of the sample molecules. Each molecule has a specific vibrational mode that corresponds to a characteristic peak in the raman spectrum. For coal samples, the molecular vibration modes of different components (such as carbon, hydrogen, oxygen, sulfur, etc.) are different, and thus exhibit different characteristic peaks in the raman spectrum. The presence and location of these characteristic peaks can be used to identify the composition of the sample and its content. In order to acquire and analyze raman spectra, highly sensitive spectroscopic instruments are required. These instruments are capable of collecting and separating scattered light into spectra of different wavelengths, which are recorded to form raman spectra. The high-resolution spectrum instrument can distinguish optical signals with similar wavelengths, so that characteristic peaks of different components are more clearly visible. The collected Raman spectrum provides detailed molecular information of the sample, and lays a foundation for subsequent component analysis. The whole process of step 1 underscores the importance of multimode lasers and quantization techniques in improving raman scattering signal intensity and resolution. Multimode lasers provide a high energy laser beam that effectively excites the sample molecules, while the quantization technique ensures more accurate and efficient interaction of the laser beam with the sample molecules. By the series of technical means, the generated Raman spectrum has high resolution and signal to noise ratio, and reliable basic data is provided for rapid detection of coal samples. In summary, the multimode laser quantization technique in step 1 optimizes the interaction process between the laser and the sample by enhancing the energy and quality of the laser beam, ensuring that the generated raman spectrum has high resolution and high signal-to-noise ratio. The technical improvements obviously improve the sensitivity and accuracy of detection, and provide a solid foundation for analysis and identification of the components of the coal sample in the subsequent steps.

Quantum interference is a fundamental phenomenon in quantum mechanics that describes the phase superposition effect that quantum particles (e.g., photons, electrons, etc.) produce when passing through multiple paths. When a photon can reach the detector through two or more paths, the phase differences on these paths can lead to interference phenomena. Photons will produce an increase in the detector (i.e., constructive interference) if the phase difference between the paths is an integer multiple of the wavelength, and a decrease in the detector (i.e., destructive interference) if the phase difference is an odd multiple of half the wavelength. This interference phenomenon can be used to separate and analyze complex spectral signals. In raman spectroscopy, different molecules in a sample produce raman scattering signals of their respective characteristics, which signals typically overlap to form a complex spectrum. By applying quantum interference effects, these overlapping signals can be effectively separated, making the characteristic peaks of the individual molecules clearer. Specifically, the process of separating raman spectrum signals using quantum interference effects can be divided into the following steps:

Optical path design and phase regulation, in order to realize quantum interference effect, a specific optical path system is required to be designed, so that Raman scattered light can reach a detector through a plurality of paths. In these paths, the optical path difference on each path can be precisely controlled by introducing different phase modulation devices (e.g., gratings, beam splitters, mirrors, etc.). These path differences can result in phase differences in the photons, thereby creating an interference pattern on the detector.

Interference patterns are generated in such a way that when raman scattered light passes through the optical path system, phase differences in different paths cause interference effects. Since raman scattering signals of different molecules have different wavelengths and intensities, they can form different fringes and peaks in the interference pattern. These fringes and peaks reflect characteristic information of each molecule in the sample and can be used to identify and separate different components.

Data acquisition and processing after the interference patterns are generated, high sensitivity detectors (e.g., CCD cameras, photomultiplier tubes, etc.) are required to acquire the data for these patterns. The acquired data typically includes spatial distribution of light intensity and spectral information. By analyzing these data, characteristic peaks of different molecules can be extracted. To improve the efficiency and accuracy of data processing, the interference pattern is typically further processed and optimized using computer algorithms (e.g., fourier transforms, filtering, etc.).

Separation of characteristic peaks in the interference pattern obtained by the above procedure, raman scattering signals of different molecules will appear as different peaks and fringes. By utilizing the quantum interference effect, the peaks and the fringes are effectively separated, so that the characteristic peak of each molecule is more obvious and easy to identify. By analyzing these characteristic peaks, the presence or absence of each component in the sample and its relative content can be determined.

The signal-to-noise ratio is improved, namely, the quantum interference effect can separate characteristic peaks of different molecules, and the signal-to-noise ratio (SNR) of the Raman spectrum signal can be remarkably improved. The signal intensity is enhanced through constructive interference, and the noise signal is suppressed through destructive interference, so that the useful signal is more prominent, and the detection accuracy and sensitivity are improved.

The application of the quantum interference effect in Raman spectrum detection obviously improves the resolution capability and detection efficiency of spectrum signals. Compared with the traditional spectrum separation method, the quantum interference effect can control and adjust the optical path difference more accurately, so that photons with different wavelengths generate expected interference effects, and effective separation and enhancement of overlapping signals are realized. The technology breaks through the important practical application value in rapid coal detection, and can provide higher detection precision and higher analysis speed.

Virtual resonance processing is a signal processing technique based on resonance principles, and is generally used to enhance the strength and signal-to-noise ratio of signals. Resonance refers to the response of a system that is significantly enhanced when excited at a particular frequency. In a physical system, this phenomenon can be maximized by adjusting parameters of the system to maximize its response over a certain frequency range. The virtual resonance processing is used for processing the acquired signals by simulating the resonance phenomenon, so that the purposes of enhancing the signals and suppressing noise are achieved. In raman spectroscopy, the interference pattern contains information characteristic of the sample, but this information is typically masked by noise and other interfering signals. In order to extract useful characteristic information, it is necessary to enhance the signal strength of the interference pattern and improve the signal-to-noise ratio by virtual resonance processing. Specifically, the virtual resonance process includes the steps of first, modeling the acquired interference pattern signal. The signal may be represented as a function in the frequency domain by a mathematical model (e.g., fourier transform, laplace transform, etc.). Such a frequency domain representation can reveal the spectral characteristics of the signal, helping to identify and analyze the resonant frequencies in the signal. In the frequency domain representation, finding the resonant frequency of the signal is a critical step. The resonant frequency refers to the maximum value of the response of a signal in a particular frequency range. By analyzing the spectrum of the interference pattern, the resonance frequencies corresponding to the characteristic peaks of the different components can be identified. These resonance frequencies reflect the characteristic information of the molecules in the sample. Once the resonant frequency is identified, the next step is to apply a virtual resonant filter. A virtual resonator filter is a specially designed filter whose parameters are set to enhance the signal strength in the resonant frequency range. The strength of the target signal can be remarkably improved through the frequency response curve of the filter, and noise and other interference signals can be restrained. The use of virtual resonator filters allows the useful signal in the interference pattern to be significantly enhanced. Compared with the traditional filtering method, the virtual resonance processing is more flexible and efficient, and the frequency range and the amplitude of signal enhancement can be accurately controlled, so that the signal-to-noise ratio is improved to the greatest extent. In this process, noise and other non-resonant signals are effectively suppressed, making the characteristic peaks clearer and easier to identify. Through the above processing, the resulting enhanced interference pattern contains a stronger signal and a higher signal-to-noise ratio. This enhanced interference pattern provides clearer characteristic peaks, facilitating subsequent feature analysis and component identification. In the enhanced interference pattern, characteristic peaks of different components become more obvious, and subsequent classification and quantitative analysis are greatly facilitated. On the basis of the enhanced interference pattern, each characteristic peak is identified by an algorithm (such as a peak detection algorithm, a pattern recognition algorithm, etc.). Each characteristic peak corresponds to characteristic information of one component in the sample. The identified characteristic peaks can be further used for high-dimensional characteristic mapping and multi-layer sensor classification, so that accurate identification and quantitative analysis of sample components are realized. The application of virtual resonance processing in raman spectroscopy has significant advantages. First, it can significantly enhance the signal strength and signal-to-noise ratio, making the characteristic information clearer and easier to identify. And secondly, the virtual resonance processing can flexibly adjust the resonance frequency range and the filter parameters, and adapt to different samples and detection requirements. In addition, the virtual resonance processing has higher calculation efficiency, can rapidly process and analyze a large amount of spectrum data, and meets the requirement of rapid detection.

The high-dimensional feature space refers to a space in which feature information is represented in a multi-dimensional coordinate system. In raman spectroscopy, each characteristic peak represents characteristic information of a certain component in a sample, and the characteristic peaks are mapped to a high-dimensional characteristic space, so that complex components of the sample can be described through multiple dimensions. This high-dimensional representation can capture more sample features, enhancing the ability of the classifier to distinguish sample components. In a specific operation, it is first necessary to extract characteristic peaks in the enhanced interference pattern. Each characteristic peak in the interference pattern is identified by an algorithm (e.g., peak detection algorithm, pattern recognition algorithm, etc.), and its peak position and intensity are recorded. The location and intensity of each characteristic peak reflects characteristic information of a component in the sample that will be used as an input feature for the construction of a high-dimensional feature space. Next, the extracted feature peaks are mapped into a high-dimensional feature space. In this process, each characteristic peak is considered as a dimension, and a high-dimensional characteristic vector can be obtained by mapping the peak value and intensity of different characteristic peaks onto corresponding coordinate axes in a high-dimensional space. This high-dimensional feature vector contains information of all feature peaks in the sample, which is a representation of the sample in the high-dimensional feature space. By means of the high-dimensional representation, complex characteristics of the sample can be captured, and the recognition capability of the classifier is improved. The high-dimensional features are then component classified using a preset multi-layer perceptron (MLP). The multi-layer perceptron is an artificial neural network model, has a plurality of hidden layers and nonlinear activation functions, and can learn and express complex characteristic relations. In the training process, the MLP minimizes the error between the predicted output and the actual label by adjusting the weight and the bias, so that the mapping relation between the sample characteristics and the component classification is learned. Specifically, the input layer of the MLP receives the high-dimensional feature vector, the hidden layer extracts features through nonlinear transformation, and the output layer generates a component classification result. In the application stage, the high-dimensional feature vector is input into a trained MLP model, and the model classifies the components of the sample according to the learned feature relation. Each output node corresponds to a component category, and the presence or absence and relative amounts of the components in the sample can be determined by the activation values of the output nodes. The multi-layer structure and the nonlinear activation function of the MLP enable the MLP to process complex high-dimensional characteristic data, and the MLP has higher classification precision and robustness. To further perform quantitative analysis, the content of each specific component in the sample needs to be calculated from the peak value of the characteristic peak. The intensity of the characteristic peak is in positive correlation with the concentration of the corresponding component in the sample, and the characteristic peak intensity can be converted into the component concentration through a calibration curve or a quantitative model. Specifically, a calibration curve between the characteristic peak intensities and the component concentrations is first established, and then the curve is used to convert the detected characteristic peak intensities to obtain quantitative results of the components. The combination of the high-dimensional feature mapping and the multi-layer sensor classification significantly improves the accuracy and efficiency of Raman spectrum detection. compared with the traditional spectrum analysis method, the high-dimensional feature space can capture more sample feature information, and the multilayer perceptron can process complex high-dimensional data to realize accurate component identification and quantitative analysis. The technical breakthrough not only has important application value in coal detection, but also can be popularized and applied to the field of spectrum analysis of other complex samples.

Calculating the content of each specific component in the coal sample according to the peak value of the characteristic peak is a key step in quantitative analysis of Raman spectrum. The following is a specific implementation process, including the detailed steps from feature peak extraction to quantitative calculation:

first, it is necessary to extract characteristic peaks from the enhanced interference pattern. By using a peak detection algorithm of the raman spectrum, a characteristic peak in the raman spectrum can be identified. Common peak detection algorithms include:

Zero crossing method the peak position is determined by detecting the derivative zero crossing of the signal.

Sliding window method-a sliding window is used to calculate the local maximum.

And carrying out Gaussian fitting on the signals, and extracting peak values from the fitted curve.

The specific steps are preprocessing, which is to smooth and denoise the enhanced interference pattern, such as using Gaussian or mean filtering. Peak detection-applying the algorithm described above, identifying characteristic peaks in the spectrum and recording the position (wavelength or raman shift) and intensity (peak height) of each peak.

Establishing a calibration curve is a key step in converting the characteristic peak intensities into constituent concentrations. A calibration curve is established from standard samples of known concentration reflecting the relationship between characteristic peak intensities and component concentrations. The specific process is as follows:

Preparing standard samples, namely preparing a series of standard coal samples with known concentrations.

And measuring Raman spectrum, namely carrying out Raman spectrum measurement on each standard sample, and extracting the characteristic peak intensity.

Drawing a calibration curve by plotting the known concentration of the standard sample and the corresponding characteristic peak intensity, and establishing the calibration curve by using linear regression or other fitting methods. The general form is:

I=k·C+b;

wherein I is the characteristic peak intensity, C is the component concentration, and k and b are coefficients obtained by fitting.

The components in the unknown sample are quantitatively analyzed by using a calibration curve. The method comprises the following specific steps:

And measuring the Raman spectrum of the unknown sample, namely acquiring the enhanced interference pattern of the unknown coal sample.

And extracting the characteristic peak intensity, namely extracting the characteristic peak intensity of each component in the unknown sample by applying a characteristic peak detection algorithm.

And (3) calculating the concentration, namely substituting the intensity of the extracted characteristic peak into a calibration curve, and calculating the concentration of each component. According to the calibration curve formula:

Wherein C is the concentration of the component obtained by calculation, I is the intensity of the extracted characteristic peak, and k and b are fitting coefficients of a calibration curve.

For a multicomponent sample, it is necessary to analyze multiple characteristic peaks simultaneously and calculate their respective concentrations. The method comprises the following specific steps:

and extracting all characteristic peaks, namely comprehensively analyzing the enhanced interference pattern and extracting the intensity of all relevant characteristic peaks.

The concentration is calculated by using the calibration curve and the component concentration corresponding to each characteristic peak is calculated.

And (3) integrating the results, namely, summarizing the concentration results of all the components to obtain the content distribution of each component in the sample.

During the quantitative analysis, there may be measurement errors and systematic errors, which require error analysis and correction. The method comprises the following specific steps:

Repeating the measurement, namely carrying out multiple measurements on the same unknown sample, and calculating the average value and standard deviation of the results.

Error analysis, namely analyzing the systematic error by comparing the measurement results of the standard sample and the unknown sample.

And correcting errors, namely correcting the measurement result according to the error analysis result, and ensuring the accuracy of quantitative analysis.

Assume a coal sample with intensities I _A、I_B and I _C, respectively, for characteristic peaks A, B and C detected in the Raman spectrum. We have established a corresponding calibration curve from standard samples:

for component A, I _A＝k_A·C_A+b_A;

for component B, I _B＝k_B·C_B+b_B;

For component C, I _C＝k_C·C_C+b_C;

Substituting the characteristic peak intensity into a calibration curve, and calculating to obtain:

Through the formula, the concentrations of the components A, B and C in the coal sample can be obtained, and the quantitative analysis of the sample is completed. Through the steps, quantitative analysis of each component in the coal sample can be realized. The characteristic peak extraction, the calibration curve establishment and the quantitative calculation are key links in the whole process, and the accuracy and the reliability of the detection result are ensured. The quantitative analysis method of the Raman spectrum has higher sensitivity and precision, is suitable for component analysis of complex samples, and provides important technical support for coal quality control and resource evaluation. And 4, identifying and quantitatively analyzing characteristic peaks in the enhanced interference pattern through high-dimensional characteristic mapping and multi-layer sensor classification. The construction of the high-dimensional feature space captures complex features of the sample, the distinguishing capability of the classifier is enhanced, and the multi-layer perceptron realizes accurate component classification and quantitative analysis through learning feature relation. The process remarkably improves the accuracy and reliability of Raman spectrum detection, and has remarkable technical innovation and practical value. By the method, each component in the coal sample can be rapidly and accurately identified and quantitatively analyzed, and reliable technical support is provided for quality control and resource evaluation of the coal.

Embodiment 2. Step 1 specifically comprises pulverizing and sieving coal sample to make particle diameter of coal sample uniform, laser beam irradiating each part of coal sample uniformly, selecting a tunable multimode laser capable of generating multiple laser beams with different frequencies, simultaneously ensuring that the laser beams can be coherently superimposed to form quantized laser beams, obtaining properties of coal sample, calculating laser power based on properties of coal sample, ensuring that enough energy can be generated to excite raman scattering of coal sample, applying laser power in multimode laser, adjusting output frequency of multimode laser by frequency tuning module to make each frequency resonate with specific molecular vibration mode of each specific cost in coal sample, placing quantum dot exciter in laser beam path, quantum dot being excited under different laser frequencies, generating quantized laser beams with high quantum efficiency by nonlinear process, superposing quantized laser beams with different frequencies by multimode interferometer to form an integrated quantized laser beam, shaping the integrated quantized laser beam into uniform laser beam cross-sectional area by optical lens and collimator, ensuring that the laser energy is uniformly distributed on surface of sample to coal sample, and collecting raman scattered laser beam by using Raman spectrometer to obtain uniform laser beam.

Specifically, pretreatment of coal samples is the basis of the entire detection process. The coal samples were crushed and sieved to ensure uniform particle size of the samples. The importance of this step is that it enables the laser beam to be uniformly irradiated to various portions of the coal sample, thereby producing a consistent raman scattering signal. If the sample particle size is not uniform, non-uniformity of light scattering can result, which in turn affects the accuracy of the spectrum. Therefore, the particle size of the sample is uniform through crushing and screening, and the reliability and consistency of the detection result can be effectively improved. Next, selecting a tunable multimode laser is a critical step. Multimode lasers are capable of generating a plurality of laser beams of different frequencies, and these laser beams can be coherently superimposed to form a quantized laser beam. multimode lasers are chosen based on their ability to provide laser light at multiple frequencies, which is important for the excitation of raman scattering of different molecules. The tunability of the multimode laser enables the multimode laser to adapt to the requirements of different samples, and the molecular vibration modes of various components in the samples are matched by adjusting the laser frequency. This tuning capability ensures that the laser is able to generate raman scattering signals suitable for exciting all components in the sample, thereby improving the resolution and signal-to-noise ratio of the spectrum. After the multimode lasers are selected and tuned, calculating the required laser power is the next key operation based on the properties of the coal sample (e.g., absorption characteristics, scattering characteristics, etc.). By calculating the laser power, it can be ensured that the laser beam has sufficient energy to excite raman scattering of the coal sample. By applying the calculated laser power in the multimode laser, the laser excitation process can be optimized, and enough excitation energy is provided on the premise of not damaging the sample, so that a Raman signal with enough intensity is obtained. Next, the output frequencies of the multimode lasers are adjusted by a frequency tuning module to resonate each frequency with a particular molecular vibration mode of each particular component in the coal sample. The purpose of this step is to enhance the raman signal of a particular component, thereby improving the resolution and signal-to-noise ratio of the spectrum. Resonance phenomena can significantly increase the raman scattering intensity of specific molecules, making these components more pronounced and easily identifiable in the spectrum. A quantum dot exciter is placed in the laser beam path, quantum dots are excited at different laser frequencies, and quantized laser beams with high quantum efficiency are generated through a nonlinear process. Quantum dots are nanomaterials with unique optical properties that produce intense luminescence after excitation. This high quantum efficiency luminescence helps to enhance the intensity and quality of the laser beam and to enhance the effect of stimulated raman scattering. The use of the quantum dot exciter not only improves the energy of the laser beam, but also enhances the coherence and stability of the laser beam, thereby providing a better light source for subsequent raman scattering detection. And then, overlapping the quantized laser beams with different frequencies through a multimode interferometer to form a comprehensive quantized laser beam. the multimode interferometer can coherently superimpose multiple frequencies of laser beams together to form a laser beam with higher intensity and wider frequency spectrum. The comprehensive quantized laser beam has better excitation effect, can excite a plurality of components in a sample at the same time, and improves the detection efficiency and accuracy. In this way, it is ensured that the laser beam, when it irradiates the sample, can cover all the molecular vibration modes that need to be excited, thus obtaining overall spectral information. The comprehensive quantized laser beam is shaped into uniform light spots through an optical lens and a collimator, and the sectional area of the light spots is determined so as to ensure that laser energy is uniformly distributed on the surface of a coal sample. Beam shaping is to avoid localized overheating or burning of the sample due to concentration of laser energy, thereby affecting the uniformity and accuracy of the raman scattered signal. the uniform light spots can ensure that each sample area is excited by the same laser, and the overall quality of the spectrum is improved. The design and adjustment of the optical lens and the collimator are used for ensuring that the laser beam can fully cover the surface of the sample, so that the Raman scattering signal intensity is uniform, and detection errors caused by non-uniformity of the laser beam are avoided. And finally, irradiating the uniform light spot on the coal sample to excite the coal sample to generate Raman scattering. Through the optimal design of the optical lens and the collimator, the laser beam can cover the surface of the sample completely, and a strong and uniform Raman scattering signal is excited. Raman spectra of coal samples were collected using a high sensitivity spectrometer. the spectrometer can separate scattered light into spectra of different wavelengths and record raman spectra which contain characteristic information of each component in the sample. The high sensitivity and high resolution of the spectrometer ensures that all raman scattering signals can be accurately recorded, thereby providing reliable data for subsequent spectroscopic analysis.

Example 3 properties of coal samples include chemical bond energy, density, specific heat capacity, light absorption coefficient, heat of combustion, and mass of coal molecules.

Specifically, the chemical bond energy of a coal molecule refers to the bonding energy between atoms in the molecule. These chemical bond energies directly affect the characteristic peak positions in the raman scattering spectrum. Raman scattering is a phenomenon based on the vibration of molecules whose frequency of vibration is determined by their chemical bond energy. Higher chemical bond energies correspond to higher vibrational frequencies, which are reflected in the raman spectrum as higher raman shifts. Thus, by analyzing the characteristic peaks in the raman spectrum, the presence and intensity of different chemical bonds in the coal sample can be inferred. Knowing the chemical bond energy of coal helps to select the appropriate laser wavelength to optimize the raman scattering signal, ensuring that clear spectral data is obtained. The density of coal refers to the mass of coal per unit volume. This property affects the light scattering properties of the coal sample and the intensity of the raman signal. Higher density generally means a tighter molecular arrangement in the sample, which can affect the penetration depth of the laser and the path of the raman scattered light. In order to obtain an optimal raman signal, the laser power and focal position are adjusted to ensure that the laser is able to excite the molecules in the sample sufficiently without being hindered by too high a density. The measurement and control of density is critical to improving the signal-to-noise ratio of raman spectra. Specific heat capacity is the ability of a coal sample to absorb or release heat as the temperature changes. During raman spectroscopy, irradiation of the sample with a laser beam causes local temperature increases, which may affect the thermal stability of the sample and the raman scattering signal. The coal sample with high specific heat capacity can better disperse heat under laser irradiation, and signal interference and sample damage caused by overheating are reduced. Therefore, when selecting the laser power and exposure time, the specific heat capacity of the sample needs to be taken into account to ensure that the raman scattering signal is excited without causing excessive thermal damage to the sample. The light absorption coefficient is a parameter that measures the light absorption capacity of a coal sample for a particular wavelength. The absorption coefficients of coals with different components on laser are different, so that the intensity and the distribution of Raman scattered light are affected. A higher light absorption coefficient will result in the sample absorbing more laser energy and thus enhancing the raman scattering signal. But at the same time, local overheating and pyrolysis effects of the sample may also be caused. Therefore, it is desirable to optimize the laser wavelength and power according to the light absorption coefficient of the coal sample to maximize raman scattering signal intensity without causing sample damage. Combustion heat refers to the heat released by coal when it is fully combusted, a property that is closely related to the carbon content and chemical composition of the coal. The measurement of heat of combustion may provide additional chemical composition information for raman spectroscopy analysis. For example, high heat of combustion generally corresponds to a coal sample with high carbon content and low impurity content. In raman spectroscopy, by combining combustion heat data, characteristic peaks in a spectrum can be more accurately interpreted and identified, so that the accuracy of quantitative analysis is improved. Finally, the quality of coal is the most fundamental physical quantity, but is also important in raman spectroscopy detection. The uniformity and magnitude of the sample mass directly affects the quality of the spectrum and the signal to noise ratio. A larger sample mass may provide more raman scattering signal, helping to improve the signal-to-noise ratio of the spectrum, but may also cause scattering and absorption losses of the laser. Therefore, in experimental design, it is necessary to control the sample quality to ensure that it is within a proper range to obtain optimal spectral data.

Example 4 based on the properties of the coal sample, laser power P _laser was calculated using the following formula:

Specifically, the basic concept of quantum mechanics is introduced into the formula, and the energy distribution between the excited state and the ground state is described by the Planck constant and the Boltzmann factor. The Planck constant is related to the energy difference between the excited state and the ground state and the sample temperature, and reflects the influence of the quantum effect on the laser energy requirement. Specifically, the energy difference between the excited and ground states determines the minimum energy required for excitation, while the temperature affects the distribution of particles between these energy states. The Boltzmann factor embodies the distribution rule and ensures the accuracy of the formula on the microscopic quantum level. Next, the formula considers the properties of chemical bond energy, density, specific heat capacity, light absorption coefficient, etc. of the coal sample, which together determine the absorption and scattering capabilities of the sample for laser energy. Chemical bond energy reflects the energy reserve inside the molecule, high bond energy requires higher energy laser excitation. The higher the density, the worse the laser penetration, but the more efficient the energy absorption. The specific heat capacity determines the temperature change of the sample after absorbing laser energy, and the sample with high specific heat capacity can better disperse heat under laser irradiation, so that thermal damage is reduced. The light absorption coefficient directly influences the absorption efficiency of the sample on the laser, and the sample with high absorption coefficient can absorb the laser more effectively to generate a strong Raman scattering signal. On these bases, the formula also introduces wavelength and frequency parameters of the laser beam, and the laser is made to resonate with molecular vibration modes in the sample through frequency tuning, so that the Raman scattering signal is enhanced. Laser beams of different wavelengths and frequencies are capable of exciting different molecular vibration modes in the sample, and by adjusting these parameters, the intensity and quality of the raman scattering signal can be maximized. In addition, quantum dot excitation efficiency is also an important factor, which represents the luminous efficiency of quantum dots at different laser frequencies, directly affecting the final laser power requirement. Further, the raman scattering cross-section, which represents the scattering ability of molecules to the laser, and the cross-sectional area of the laser beam, which determines the energy density of the laser at the sample surface, are considered in the formula. A larger raman scattering cross section means that the molecules are able to scatter the laser light more effectively, resulting in a stronger raman signal. The cross section area of the laser beam influences the size and energy distribution of the light spot, and reasonable beam shaping can ensure that laser energy is uniformly distributed on the surface of a sample, so that local overheating and damage are avoided. In addition, the factors of heat of combustion and sample mass reflect the macroscopic thermodynamic properties of the coal sample. The combustion heat is the energy released by coal in combustion, and a sample with high combustion heat means that the sample can release more energy after absorbing energy, so that the Raman signal is enhanced. The sample quality influences the absorption efficiency and distribution of laser energy, and a larger sample quality can provide more Raman scattering signals, so that the signal-to-noise ratio of detection is improved. The environmental temperature is used as an external factor to influence the excitation state of the sample, so that the sample is more easily excited under the high-temperature environment, and the Raman signal is enhanced.

Embodiment 5 specifically comprises the steps of encoding phases of Raman spectra through a phase modulator to enable characteristic peaks of different components to generate specific phase differences, decomposing the Raman spectra subjected to phase modulation into interference patterns of different components through a multi-path quantum interferometer, decoding the generated interference patterns to obtain decoded spectrum intensities, and separating the characteristic peaks of different components according to the decoded spectrum intensities.

In particular, the phase modulator plays a vital role in raman spectroscopy. The phase modulator can precisely control and encode the phase of the raman scattered light. Phase modulation is a technique for encoding information onto the phase of an optical wave. In raman spectroscopy, the molecular vibrational modes of different components correspond to different characteristic peaks that appear as different wavelength positions in the spectrum. Each characteristic peak can be assigned a specific phase code by a phase modulator, which produces a recognizable difference in phase. The phase coding not only enhances the identification degree of the spectrum signal, but also provides rich information for subsequent signal processing. Next, the phase modulated raman spectrum is input into a multipath quantum interferometer. The multipath quantum interferometer uses the interference phenomenon of light to decompose the spectrum after phase modulation into interference patterns of different components. Interference is a phenomenon of phase superposition after light waves meet, and by controlling the path and phase of the light waves, a series of interference fringes can be generated, and these fringes reflect the phase information of the light waves. The multi-path quantum interferometer is capable of separating a raman spectrum encoded with different phases into several interference patterns, each pattern corresponding to a particular component of the raman spectrum. This decomposition process effectively decomposes the otherwise complex raman spectrum into a plurality of simple interference patterns, each containing phase information for a particular component. After the interference patterns are generated, the next step is to decode these patterns. The decoding process is effectively an inversion of the phase information, i.e. the phase information in the interference pattern is converted back to spectral intensities by a specific algorithm. The intensity of the decoded spectrum reflects the intensity distribution of the components in the original raman spectrum. The decoding process typically requires complex computation and algorithmic support, such as fourier transform and phase recovery techniques. By these algorithms, the spectral intensities of the components can be accurately extracted from the interference pattern. And finally, separating out characteristic peaks of different components according to the decoded spectrum intensity. Since the characteristic peaks of each component are given unique phase codes during phase modulation and interference, these codes are converted into intensity signals after decoding, making the characteristic peaks of each component more pronounced in the spectrum and easily identifiable. By comparing the decoded spectrum intensities, the characteristic peak values of different components can be effectively separated, so that the accurate detection and quantitative analysis of each component in the sample are realized. The core of the step is that the phase information of the Raman spectrum is encoded, interfered, decoded and separated by utilizing the phase modulation and quantum interference technology, so that the separation and enhancement of the complex spectrum signal are realized. The method not only improves the spectral resolution and the signal-to-noise ratio, but also enhances the identification capability of different components. This technique has significant advantages over traditional spectroscopic methods in processing complex samples and multi-component analysis. The method not only can provide a clearer spectrogram, but also can identify and quantify the trace components in the sample more accurately, and has important application value for rapid and accurate detection of the coal sample.

Example 6 setting the initial spectral intensity of the raman spectrum to I _raw (x), where x is the raman shift, encoding the phase of the raman spectrum by a phase modulator using the following formula:

I_mod(x)＝I_raw(x)·e^iφ(x);

Specifically, in embodiment 6, the phases of the raman spectra are encoded by a phase modulator to achieve efficient separation and identification of characteristic peaks of different components. Specifically, the core of phase modulation is to combine the intensity of the initial raman spectrum with a phase function to generate a phase modulated spectral intensity. This phase function is designed to be able to apply a specific phase variation to the spectral signal for each raman-shifted position such that the characteristic peaks of the different components have unique phase encodings in the phase space. The phase function comprises two main parts, a base phase and a modulation phase. The fundamental phase component is related to the raman shift and the characteristic wavelength of the component, as determined by a linear relationship and phase offset, such that the characteristic peak of each component has a different origin at the fundamental phase. The modulated phase section then introduces multiple sinusoidal functions whose frequency and initial phase are modulated according to the different components and characteristics of the interference path, resulting in a complex phase modulation pattern. In this way, different interference patterns can be formed in the multipath quantum interferometer after the raman signals of different components are phase modulated. The interference pattern uses the interference phenomenon of light to decompose the modulated spectrum into a plurality of fringe patterns containing specific phase information, and the patterns reflect the phase encoding information of the raman spectrum. In the subsequent decoding process, the phase information in the interference pattern is converted back to the spectral intensity by a phase inversion technique, thereby separating out characteristic peaks of different components. Therefore, the resolution and the signal-to-noise ratio of the Raman spectrum can be effectively improved through the phase modulation and interference decoding technology, and the accurate identification and quantitative analysis of various components in a complex sample are realized. The application of the method in Raman spectrum detection not only enhances the identification degree of spectrum signals, but also remarkably improves the accuracy and efficiency of analysis, and provides powerful technical support for component analysis of complex samples.

Example 7 formula for decomposing the phase modulated raman spectrum into interference patterns of different components by means of a multipath quantum interferometer is as follows:

Where I _interf (x) is the interference pattern, d _k is the optical path difference of the kth interference path, Δφ _k is the phase difference of the kth interference path, J is the number of interferometric frequencies, gamma _kj is the interferometric amplitude of the jth interferometric frequency of the kth interference path, lambda _kj is the jth interferometric frequency of the kth interference path, and delta _kj is the jth interferometric phase of the kth interference path.

Specifically, in embodiment 7, the raman spectrum after the phase modulation is decomposed into interference patterns of different components by the multipath quantum interferometer, and a complex interference formula is used. The formula combines the phase modulated spectral signal with a plurality of interference paths to produce a composite interference pattern. The core of this process is to modulate the spectral signal by the optical path differences and phase differences of the different paths using the interference principle of light and further complicate the interference pattern by modulating frequency and phase. Specifically, the interference pattern I _interf (x) in the formula is a result of the phase-modulated raman spectrum I _mod (x) subjected to multipath interference. In the multipath quantum interferometer, the optical path difference d _k and the phase difference ΔΦ _k of each interference path k correspond to different path characteristics. These path characteristics give the optical signal on each path a unique phase change upon interference. By applying different phase differences and optical path differences to the optical signals on each path, the phase information of the respective components can be effectively separated. The formula further introduces interferometric modulation frequencies and phases that provide complex phase modulation of the optical signal on each interferometric path by modulating amplitude gamma _kj, modulating frequency lambda _kj, and modulating phase delta _kj. The modulation frequency determines the density and distribution of the interference fringes, while the modulation phase and amplitude further adjusts the morphology and intensity of the interference pattern. these modulation parameters work together so that the optical signal on each interference path exhibits unique fringes and intensity distribution in the interference pattern. In the formula, the optical signals on all paths are added to form a total interference pattern after phase modulation and interference modulation. The absolute squared component of the formula, | ², represents the calculation of the light intensity because the intensity of the interference pattern is superimposed by the amplitude and phase of the light signal of each path. In this way, the phase information of all paths is contained in the interference pattern, reflecting the phase encoding characteristics of the raman spectrum. Through these complex modulation and interference processes, the resulting interference pattern I _interf (x) contains the phase information of the components in the Raman spectrum. The phase information can be effectively extracted and separated in the decoding process, so that the identification and quantitative analysis of the spectrum signals of different components are realized. The multipath quantum interferometer can decompose and enhance the original complex Raman spectrum signal by introducing a plurality of interference paths and complex phase modulation, thereby improving the resolution and the accuracy of spectrum analysis.

Example 8 decoding the generated interference pattern to obtain decoded spectral intensities by the following formula:

Where Q is the number of modulation frequencies included in the decoded modulation phase function, Q is the subscript, _pq is the modulation amplitude, λ _pq is the modulation frequency, and k _pq is the modulation initial phase.

Specifically, the interference pattern I _interf (x) is generated by a multi-path quantum interferometer, which contains optical path differences and phase difference information for different paths. In order to extract the original spectral intensities from these interference patterns, an inverse fourier transform operation is required. Inverse fourier transformThe frequency domain signal may be converted back to a time domain signal to help recover the intensity information in the spectral signal. By inverse fourier transformation we can initially get the fundamental part of the decoded spectral intensity. In order to further extract and enhance the spectral signal, complex phase information due to phase modulation needs to be taken into account. This is achieved by adding a series of modulation terms, each comprising a decoded modulation factor and a phase modulation function. The decoded modulation factor η _p is used to adjust the contribution weight of each modulation term, and the phase modulation function ψ _p (x) modulates the signal according to different frequencies and initial phases. The phase modulation function ψ _p (x) is defined by the superposition of a plurality of sinusoidal functions whose frequency and initial phase are determined by the parameters λ _pq and k _pq, respectively, while the modulation amplitude is controlled by the parameter ζ _pq. In this way, the modulation phase function can perform fine phase adjustment on the signal in the process of inverse Fourier transformation, and phase information of specific components can be extracted. These modulation terms and the base portion are weighted and summed to obtain the final decoded spectral intensity I _dec (x). This process can be seen as multiple frequency and time domain modulation and de-modulation operations on the original interference pattern to maximize the extraction and enhancement of the individual component information in the spectral signal. Each modulation term provides additional phase information so that the final decoding result more accurately reflects the intensity distribution of the original spectral signal.

Through this decoding process, the otherwise complex interference pattern is converted to a clear spectral intensity, revealing characteristic peaks of the components in the sample. The method utilizes Fourier transformation and phase modulation technology to effectively extract useful spectrum information from complex interference patterns, and improves the accuracy and sensitivity of spectrum analysis. In practical application, the decoding process can remarkably improve the resolution and the signal-to-noise ratio of Raman spectrum detection, so that the component analysis of a complex sample is more accurate and reliable.

Example 9 based on the decoded spectral intensities, the characteristic peaks of the different components were separated using the following formula:

Specifically, the decoded spectrum intensity contains the integrated information of all components in the sample, and further weighting of the spectrum is required to extract the characteristic peak value of each component. A gaussian function is introduced into the formula that weights the optical signal according to the characteristic wavelength and the standard deviation of the wavelength of the particular component such that signals near the characteristic wavelength are enhanced and signals far from the wavelength are attenuated. Next, the formula also introduces a series of sinusoidal modulation functions that further modulate the spectral signal according to a specific modulation frequency and initial phase, ensuring that the value of the modulation function is maximized at the characteristic peak of a specific component, thereby further enhancing the signal of that component. These sinusoidal modulation functions control their contribution to the overall modulation effect by modulating the amplitude. Combining these weighting and modulation factors, a comprehensive weighted modulation function is constructed that can effectively separate the characteristic peak intensities of each specific component by multiplying the decoded spectral intensities. This process is actually to perform multiple frequency domain and time domain filtering and enhancing operations on the decoded spectrum signal, and accurately extract the characteristic information of each component from the complex spectrum signal. By the method combining Gaussian weighting and sinusoidal modulation, signals of specific components can be enhanced, interference of other components can be restrained, and separated characteristic peak signals are clearer and more accurate, so that resolution and signal-to-noise ratio of Raman spectrum analysis are remarkably improved, and stronger identification and quantification capability is provided for component analysis of complex samples. The invention obviously improves the sensitivity, resolution and signal-to-noise ratio of Raman spectrum detection by organically combining a multimode laser quantization technology, a quantum interference effect, a phase modulation technology and a Gaussian weighting and sine modulation method. Compared with the prior art, the invention not only can effectively enhance the spectrum signal intensity and improve the detection capability of low-concentration components, but also can realize the accurate separation and identification of each component signal in a complex sample through the multi-path quantum interference and phase modulation technology, thereby avoiding the interference of peak value overlapping and background noise.

The foregoing describes the intelligent data storage system with the unmanned aerial vehicle charging and battery-replacing device in detail, and the specific examples are applied to illustrate the principle and implementation of the invention, and the description of the above examples is only used for helping to understand the method and core idea of the invention, and meanwhile, the content of the present specification should not be construed as limiting the invention, since the technical personnel in the field can change the specific implementation and application scope according to the idea of the invention.

Claims

1. A rapid coal detection method based on Raman spectroscopy, characterized in that the method comprises:

Step 1: Place the coal sample in a multi-mode laser, generate a quantized laser beam through multi-mode laser quantization technology, excite the coal sample to produce Raman scattering, and collect the Raman spectrum;

Step 2: Using the Raman spectrum generated by the coal sample, the characteristic peaks of different components are separated through the quantum interference effect to generate an interference pattern;

Step 3: Perform virtual resonance processing on the interference pattern to enhance the signal strength of the interference pattern and improve the signal-to-noise ratio to obtain an enhanced interference pattern; identify characteristic peaks in the enhanced interference pattern;

Step 4: Map the characteristic peaks to the high-dimensional feature space to enhance the classification effect and obtain high-dimensional features; use the preset multi-layer perceptron to classify the high-dimensional features and identify the specific components in the coal sample; and calculate the content of each specific component in the coal sample based on the peak value of the characteristic peak;

Step 2 specifically includes: encoding the phase of the Raman spectrum through a phase modulator so that the characteristic peaks of different components produce a specific phase difference; decomposing the phase-modulated Raman spectrum into interference patterns of different components through a multi-path quantum interferometer; decoding the generated interference pattern to obtain the decoded spectral intensity; and separating the characteristic peaks of different components according to the decoded spectral intensity.

2. The rapid coal detection method based on Raman spectroscopy as described in claim 1 is characterized in that step 1 specifically includes: crushing and screening the coal sample to make the particle size of the coal sample uniform, and the laser beam can evenly irradiate all parts of the coal sample; selecting a tunable multi-mode laser, which can generate multiple laser beams of different frequencies, and at the same time ensure that these laser beams can be coherently superimposed to form a quantized laser beam, obtain the properties of the coal sample, calculate the laser power based on the properties of the coal sample, ensure that sufficient energy can be generated to excite Raman scattering of the coal sample, and apply the laser power in the multi-mode laser; adjust the output frequency of the multi-mode laser through the frequency tuning module so that each frequency is consistent with the coal sample. The specific molecular vibration modes of each specific cost in the product are resonated; a quantum dot exciter is placed in the laser beam path, the quantum dots are excited at different laser frequencies, and a quantized laser beam with high quantum efficiency is generated through a nonlinear process; the quantized laser beams of different frequencies are superimposed through a multi-mode interferometer to form a comprehensive quantized laser beam; the comprehensive quantized laser beam is shaped into a uniform light spot through an optical lens and a collimator, and its cross-sectional area is determined to ensure that the laser energy is evenly distributed on the surface of the coal sample; the comprehensive quantized laser beam is shaped into a uniform light spot using an optical lens and a collimator, and irradiated onto the coal sample to stimulate the coal sample to produce Raman scattering; the spectrometer collects the Raman spectrum of the coal sample.

3. The rapid coal detection method based on Raman spectroscopy as described in claim 2 is characterized in that the properties of the coal sample include: chemical bond energy, density, specific heat capacity, light absorption coefficient, combustion heat and mass of the coal molecules.

4. The rapid coal detection method based on Raman spectroscopy according to claim 3 is characterized in that the laser power P _laser is calculated based on the properties of the coal sample using the following formula:

Where, E _bond is the chemical bond energy in coal molecules; ρ _coal is the density; _cp is the specific heat capacity; α _abs is the light absorption coefficient; λ _i is the wavelength of the i-th laser beam; Φ _exc is the quantum dot excitation efficiency; σ _scat is the Raman scattering cross section; A _beam is the cross-sectional area of the laser beam; ΔH _comb is the combustion heat of coal; m is the mass; C _v is; T _ambient is the ambient temperature; is Planck's constant; ω _i is the frequency of the i-th laser beam; ΔE is the energy difference between the excited state and the ground state; k is the Boltzmann constant; T is the temperature of the coal sample.

5. The rapid coal detection method based on Raman spectroscopy according to claim 4 is characterized in that the initial spectral intensity of the Raman spectrum is set to I _raw (x); wherein x is the Raman shift; and the phase of the Raman spectrum is encoded by a phase modulator using the following formula:

I _mod (x)=I _raw (x)·e ^iφ(x) ;

Where I _mod (x) is the spectral intensity after phase modulation; φ(x) is the phase function, which is expressed by the following formula:

Among them, λ _n is the characteristic wavelength of the nth specific component; θ _n is the phase offset of the nth specific component; α _nm is the phase modulation amplitude; λ _nm is the phase modulation frequency of the mth interference path of the nth specific component; β _nm is the initial phase of the phase modulation of the mth interference path of the nth specific component; N is the number of specific components in coal; M is the number of interference paths.

6. The rapid coal detection method based on Raman spectroscopy according to claim 5 is characterized in that the formula for decomposing the phase-modulated Raman spectrum into interference patterns of different components by a multipath quantum interferometer is as follows:

Wherein, I _interf (x) is the interference pattern; d _k is the optical path difference of the k-th interference path; ΔΦ _k is the phase difference of the k-th interference path; J is the number of interference modulation frequencies; γ _kj is the interference modulation amplitude of the j-th interference modulation frequency of the k-th interference path; λ _kj is the j-th interference modulation frequency of the k-th interference path; δ _kj is the j-th interference modulation phase of the k-th interference path.

7. The rapid coal detection method based on Raman spectroscopy according to claim 6 is characterized in that the generated interference pattern is decoded by the following formula to obtain the decoded spectral intensity:

Where, I _dec (x) is the decoded spectral intensity; is the inverse Fourier transform operator; η _p is the p-th decoded modulation coefficient; P is the number of decoded modulation coefficients; ψ _p (x) is the p-th decoded modulation phase function, defined as:

Wherein, Q is the number of modulation frequencies contained in the decoded modulation phase function; q is a subscript; ζ _pq is the modulation amplitude; λ _pq is the modulation frequency; κ _pq is the modulation initial phase.

8. The rapid coal detection method based on Raman spectroscopy according to claim 7 is characterized in that the characteristic peaks of different components are separated using the following formula according to the decoded spectral intensity:

Wherein, I _peak,n (x) is the characteristic peak intensity of the nth specific component; σ _n is the wavelength standard deviation of the nth specific component; ξ _nr is the peak modulation amplitude; R is the number of modulation frequencies used in the characteristic peak separation process; λ _nr is the peak modulation frequency; is the peak modulation initial phase.