Rapid coal detection method based on Raman spectroscopy

Abstract

The present application discloses a rapid coal detection method based on Raman spectroscopy, which relates to the field of coal detection. The method comprises: step 1: placing a coal sample in a multimode laser, generating a quantized laser beam through multimode laser quantization technology, exciting the coal sample to generate Raman scattering, and collecting the Raman spectrum; step 2: using the Raman spectrum generated by the coal sample, separating the characteristic peaks of different components through the quantum interference effect, and generating an interference pattern; 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, and obtaining an enhanced interference pattern; identifying the characteristic peaks in the enhanced interference pattern; step 4: calculating the content of each specific component in the coal sample according to the peak value of the characteristic peak. The present invention significantly improves the accuracy and efficiency of coal detection.

Description

Technical Field

[0001] The present application relates to the technical field of coal detection, and in particular to a rapid coal detection method based on Raman spectroscopy. Background Art

[0002] As global energy demand continues to grow, coal, as a major fossil fuel, occupies an important position in energy production and industrial applications. However, the quality and composition of coal have a direct impact on its combustion performance, environmental impact and economic benefits. In order to ensure the efficient use of coal and environmental protection, its composition needs to be quickly and accurately detected and analyzed. Although traditional coal detection methods such as chemical analysis and thermogravimetric analysis can provide detailed composition information, they are often time-consuming and complicated to operate, and are not suitable for rapid detection needs.

[0003] In recent years, the application of spectroscopy in coal detection has gradually attracted attention, especially infrared spectroscopy and Raman spectroscopy. Infrared spectroscopy obtains composition information by detecting the absorption characteristics of samples to infrared light. Its advantages are high sensitivity and fast detection speed. However, when detecting samples with high carbon content, infrared spectroscopy is easily affected by the scattering effect of samples, resulting in decreased signal intensity and reduced detection accuracy. At the same time, infrared spectroscopy has limited resolution for multiple components in complex samples, making it difficult to accurately identify and quantitatively analyze different components. In contrast, Raman spectroscopy has become 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 interferences such as water and carbon dioxide. Raman spectroscopy obtains composition information by detecting Raman scattered light generated by samples when irradiated by laser. Although Raman spectroscopy has significant advantages, its application in rapid detection of coal samples still faces some challenges. For example, the Raman scattering signal is inherently weak and easily submerged by the fluorescent background signal of the sample, thus affecting the sensitivity and accuracy of the detection. In addition, for complex multi-component samples, the peaks of the Raman spectrum often overlap, making it difficult to distinguish different components. Summary of the invention

[0004] The embodiments of the present application provide a rapid coal detection method based on Raman spectroscopy, which significantly improves the accuracy and efficiency of coal detection.

[0005] The present application embodiment provides a rapid coal detection method based on Raman spectroscopy, the method comprising:

[0006] 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;

[0007] 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;

[0008] 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;

[0009] 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.

[0010] Furthermore, step 1 specifically includes: crushing and screening the coal sample to make the particle size of the coal sample uniform so that the laser beam can evenly irradiate all parts of the coal sample; selecting a tunable multi-mode laser that can generate multiple laser beams of different frequencies while ensuring that these laser beams can be coherently superimposed to form a quantized laser beam, obtaining the properties of the coal sample, calculating the laser power based on the properties of the coal sample, ensuring that sufficient energy can be generated to excite Raman scattering of the coal sample, and applying the laser power in the multi-mode laser; adjusting the output frequency of the multi-mode laser through a frequency tuning module so that each frequency is consistent with the specific molecules of each specific cost in the coal sample. Vibration mode resonance; placing a quantum dot exciter 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.

[0011] Furthermore, 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.

[0012] Furthermore, based on the properties of the coal sample, the laser power P is calculated using the following formula: laser :

[0013]

[0014] Among them, E bond is the chemical bond energy in coal molecules; ρ coal is the density; c p is the specific heat capacity; αabs is the light absorption coefficient; 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 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.

[0015] Furthermore, 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.

[0016] Furthermore, the initial spectral intensity of the Raman spectrum is set to I raw (x); where x is the Raman shift; the phase of the Raman spectrum is encoded by the phase modulator using the following formula:

[0017] I mod (x) = I raw (x)·e iφ(x) ;

[0018] Among them, I mod (x) is the spectral intensity after phase modulation; φ(x) is the phase function, which is expressed by the following formula:

[0019]

[0020] 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; nm is the phase modulation frequency of the mth interference path of the nth specific component; β nm is the initial phase of 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.

[0021] Furthermore, the formula for decomposing the phase modulated Raman spectrum into interference patterns of different components through a multipath quantum interferometer is as follows:

[0022]

[0023] Among them, 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 interference modulation frequency; γ kj is the interference modulation amplitude of the jth interference modulation frequency of the kth interference path; kj is the jth interference modulation frequency of the kth interference path; δ kj is the jth interference modulation phase of the jth interference path.

[0024] Furthermore, the generated interference pattern is decoded by the following formula to obtain the decoded spectral intensity:

[0025]

[0026] Among them, I dec (x) is the decoded spectral intensity; is the inverse Fourier transform operator; η p is the pth decoded modulation coefficient; P is the number of decoded modulation coefficients; ψ p (x) is the pth decoded modulation phase function, defined as:

[0027]

[0028] Where Q is the number of modulation frequencies contained in the decoded modulation phase function; q is a subscript; ζ pq is the modulation amplitude; pq is the modulation frequency; κ pq is the initial phase of modulation.

[0029] Furthermore, according to the decoded spectral intensity, the characteristic peaks of different components are separated using the following formula:

[0030]

[0031] Among them, 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.

[0032] The rapid coal detection method based on Raman spectroscopy provided by the present application adopts multi-mode laser quantization technology, which greatly improves the intensity and quality of the spectral signal. Traditional single-mode lasers are difficult to generate Raman scattering signals of sufficient intensity in Raman spectroscopy detection due to limited laser energy density, especially when detecting low-concentration components, the low signal-to-noise ratio becomes a major bottleneck. Multi-mode lasers can simultaneously generate laser beams of multiple frequencies, form a quantized laser beam with high energy density through coherent superposition, effectively excite molecular vibrations in the sample, and thus significantly enhance the intensity of the Raman scattering signal. This high-energy-density laser beam ensures that even in the detection of low-concentration components, a clear and high-intensity Raman spectral signal can still be obtained, thereby improving the sensitivity and accuracy of the detection. Secondly, the present invention utilizes the quantum interference effect, and decomposes the phase-modulated Raman spectrum into interference patterns of different components through a multi-path quantum interferometer. The quantum interference effect is an enhancement effect based on the phase difference of light waves. By designing different optical path differences and phase differences, the Raman spectra of different components show different interference fringes in the interference pattern. In this way, the characteristic peaks of each component can be effectively separated, avoiding the problem of peak overlap in traditional Raman spectra. Through the multi-path quantum interferometer, the spectral signal is better separated and enhanced, making the signals of each component in the complex sample clearer, the resolution is significantly improved, and the different components in the sample can be identified more accurately. Third, the present invention introduces phase modulation technology, and further optimizes the spectral signal by accurately phase encoding and decoding the Raman spectrum. The phase modulator is used to encode the phase of the spectral signal so that the characteristic peaks of different components produce a specific phase difference. During the decoding process, the spectral intensity of each component can be accurately extracted and separated through the inverse Fourier transform and phase modulation function. Phase modulation technology not only improves the recognition of spectral signals, but also enhances the detection ability of low-concentration components, ensuring the accuracy and reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0034] Figure 1 A schematic diagram of the method flow of a rapid coal detection method based on Raman spectroscopy provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0036] Example 1: Reference Figure 1 , a rapid coal detection method based on Raman spectroscopy, the method comprising:

[0037] 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;

[0038] In the rapid detection method of coal samples, step 1 is to place the coal sample in a multimode laser, generate a quantized laser beam through multimode laser quantization technology, excite the coal sample to produce Raman scattering, and collect Raman spectra. First, the selection of a multimode laser is a key step. The mode of the laser refers to the distribution of the laser beam in the cross section. The multimode laser can generate laser beams with multiple modes. Compared with single-mode lasers, multimode lasers have higher power output. This high-power output characteristic helps to more effectively excite the molecules in the sample, thereby generating stronger Raman scattering signals. Multimode lasers can cover a wider frequency range and provide rich spectral information, which is conducive to subsequent spectral analysis. Next, the application of quantization technology further improves the performance of the laser beam. Quantization technology refers to the quantization of the energy of the laser beam to give it the characteristics of a quantum state. During the quantization process, the energy of the laser beam is divided into discrete quantum units, each of which carries a specific energy. This quantization process enables the laser beam to more accurately excite specific molecular vibration modes when interacting with the sample molecules, thereby generating a Raman spectrum with high resolution. When a quantized laser beam is irradiated onto a coal sample, the molecules in the sample absorb the energy of the laser and produce molecular vibrations. These changes in vibration states cause energy transfer of photons during the scattering process, which is the Raman scattering phenomenon. Raman scattering can be divided into two forms: Stokes scattering and anti-Stokes scattering. Stokes scattering refers to the energy of the scattered photons being lower than the energy of the incident photons, while anti-Stokes scattering refers to the energy of the scattered photons being higher than the energy of the incident photons. In actual detection, the Stokes scattering signal is stronger and easier to detect and analyze. The spectrum generated by Raman scattering contains the characteristic information of the sample molecules. Each molecule has a specific vibration mode, which corresponds to the characteristic peaks in the Raman spectrum. For coal samples, different components (such as carbon, hydrogen, oxygen, sulfur, etc.) have different molecular vibration modes, and therefore show different characteristic peaks in the Raman spectrum. The presence and position of these characteristic peaks can be used to identify the composition and content of the sample. In order to collect and analyze Raman spectra, highly sensitive spectrometers are required. These instruments can collect and separate the scattered light into spectra of different wavelengths, and record them to form Raman spectra. High-resolution spectrometers can distinguish light signals of similar wavelengths, making the characteristic peaks of different components more clearly visible. The collected Raman spectra provide detailed molecular information of the sample, laying the foundation for subsequent component analysis. The entire process of step 1 emphasizes the importance of multi-mode lasers and quantization technology in improving the intensity and resolution of Raman scattering signals. Multi-mode lasers provide high-energy laser beams to effectively excite sample molecules, while quantization technology ensures that the interaction between the laser beam and sample molecules is more precise and efficient.Through this series of technical means, the generated Raman spectrum has a high resolution and signal-to-noise ratio, providing reliable basic data for the rapid detection of coal samples. In short, the multi-mode laser quantization technology 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. These technical improvements significantly improve the sensitivity and accuracy of detection, providing a solid foundation for the analysis and identification of coal sample components in subsequent steps.

[0039] 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;

[0040] Quantum interference is a fundamental phenomenon in quantum mechanics that describes the phase superposition effect of quantum particles (such as photons, electrons, etc.) when they pass through multiple paths. When a photon can reach the detector through two or more paths, the phase difference on these paths will cause interference. If the phase difference between the paths is an integer multiple of the wavelength, the photon will be enhanced on the detector (i.e., constructive interference); if the phase difference is an odd multiple of half the wavelength, the photon will be weakened on the detector (i.e., destructive interference). This interference phenomenon can be used to separate and analyze complex spectral signals. In Raman spectroscopy detection, different molecules in the sample will generate their own characteristic Raman scattering signals, which usually overlap to form a complex spectrum. By applying the quantum interference effect, these overlapping signals can be effectively separated, making the characteristic peaks of each molecule clearer. Specifically, the process of separating Raman spectroscopy signals using the quantum interference effect can be divided into the following steps:

[0041] Optical path design and phase control: In order to achieve the quantum interference effect, a specific optical path system needs to be designed so that the Raman scattered light can reach the detector through multiple paths. In these paths, by introducing different phase control devices (such as gratings, beam splitters, mirrors, etc.), the optical path difference on each path can be precisely controlled. These optical path differences will cause phase differences of photons, thereby generating interference patterns on the detector.

[0042] Generation of interference patterns: When Raman scattered light passes through the above optical path system, the phase difference on different paths will lead to the generation of interference effects. Since the Raman scattering signals of different molecules have different wavelengths and intensities, they will form different fringes and peaks in the interference pattern. These interference fringes and peaks reflect the characteristic information of each molecule in the sample and can be used to identify and separate different components.

[0043] Data acquisition and processing: After the interference pattern is generated, a highly sensitive detector (such as a CCD camera, photomultiplier tube, etc.) is needed to collect the data of these patterns. The collected data usually includes the spatial distribution of light intensity and spectral information. By analyzing these data, the characteristic peaks of different molecules can be extracted. In order to improve the efficiency and accuracy of data processing, computer algorithms (such as Fourier transform, filtering, etc.) are usually used to further process and optimize the interference pattern.

[0044] Separation of characteristic peaks: In the interference pattern obtained through the above steps, the Raman scattering signals of different molecules will show different peaks and stripes. Using the quantum interference effect, these peaks and stripes are effectively separated, making the characteristic peaks of each molecule 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.

[0045] Improvement of signal-to-noise ratio: The quantum interference effect can not only separate the characteristic peaks of different molecules, but also significantly improve the signal-to-noise ratio (SNR) of Raman spectroscopy signals. By enhancing the signal intensity through constructive interference and suppressing the noise signal through destructive interference, the useful signal can be made more prominent, thereby improving the accuracy and sensitivity of detection.

[0046] The application of quantum interference effect in Raman spectroscopy detection has significantly improved the resolution and detection efficiency of spectral signals. Compared with traditional spectral separation methods, quantum interference effect can more accurately control and adjust the optical path difference, so that photons of different wavelengths produce the expected interference effect, thereby achieving effective separation and enhancement of overlapping signals. This technological breakthrough has important practical application value in rapid coal detection, and can provide higher detection accuracy and faster analysis speed.

[0047] 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;

[0048] Virtual resonance processing is a signal processing technology based on the principle of resonance, which is usually used to enhance the strength and signal-to-noise ratio of signals. The resonance phenomenon refers to the fact that when a system is excited at a specific frequency, its response will be significantly enhanced. In physical systems, this phenomenon can be achieved by adjusting the parameters of the system to maximize its response within a certain frequency range. Virtual resonance processing simulates this resonance phenomenon and processes the collected signal to achieve the purpose of enhancing the signal and suppressing noise. In Raman spectroscopy detection, the interference pattern contains the characteristic information of the sample, but this information is usually masked by noise and other interference 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 through virtual resonance processing. Specifically, virtual resonance processing includes the following steps: First, the collected interference pattern signal needs to be modeled. Through mathematical models (such as Fourier transform, Laplace transform, etc.), the signal can be represented as a function in the frequency domain. This frequency domain representation can reveal the spectral characteristics of the signal and help identify and analyze the resonant frequency in the signal. In the frequency domain representation, finding the resonant frequency of the signal is a key step. The resonant frequency refers to the maximum value of the signal response within a specific frequency range. By analyzing the spectrum of the interference pattern, the resonant frequencies corresponding to the characteristic peaks of different components can be identified. These resonant frequencies reflect the characteristic information of the molecules in the sample. Once the resonant frequencies are identified, the next step is to apply a virtual resonance filter. A virtual resonance filter is a specially designed filter whose parameters are set to enhance the signal strength within the resonant frequency range. Through the frequency response curve of the filter, the intensity of the target signal can be significantly increased while suppressing noise and other interfering signals. The application of the virtual resonance filter has led to a significant enhancement of the useful signal in the interference pattern. Compared with traditional filtering methods, virtual resonance processing is more flexible and efficient, and the frequency range and amplitude of signal enhancement can be precisely controlled to maximize the signal-to-noise ratio. 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 enhanced interference pattern obtained contains a stronger signal and a higher signal-to-noise ratio. This enhanced interference pattern provides clearer characteristic peaks, which is helpful for subsequent feature analysis and component identification. In the enhanced interference pattern, the characteristic peaks of different components become more obvious, which greatly facilitates the subsequent classification and quantitative analysis. Based on the enhanced interference pattern, the individual characteristic peaks are identified by algorithms (such as peak detection algorithms, pattern recognition algorithms, etc.). Each characteristic peak corresponds to the characteristic information of a component in the sample. The identified characteristic peaks can be further used for high-dimensional feature mapping and multi-layer perceptron classification, thereby achieving accurate identification and quantitative analysis of sample components. The application of virtual resonance processing in Raman spectroscopy detection has significant advantages. First, it can significantly enhance the signal intensity and signal-to-noise ratio, making the characteristic information clearer and easier to identify.Secondly, virtual resonance processing can flexibly adjust the resonance frequency range and filter parameters to adapt to different samples and detection requirements. In addition, virtual resonance processing also has high computational efficiency and can quickly process and analyze large amounts of spectral data to meet the needs of rapid detection.

[0049] 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.

[0050] High-dimensional feature space refers to the space that represents feature information in a multidimensional coordinate system. In Raman spectroscopy detection, each characteristic peak represents the characteristic information of a certain component in the sample. By mapping these characteristic peaks to a high-dimensional feature space, the complex components of the sample can be described through multiple dimensions. This high-dimensional representation can capture more sample features and enhance the classifier's ability to distinguish sample components. In specific operations, it is first necessary to extract the characteristic peaks in the enhanced interference pattern. The various characteristic peaks in the interference pattern are identified by algorithms (such as peak detection algorithms, pattern recognition algorithms, etc.), and their peak positions and intensities are recorded. The position and intensity of each characteristic peak reflect the characteristic information of a certain component in the sample, and this information will be used as input features for the construction of a high-dimensional feature space. Next, the extracted characteristic peaks are mapped to the high-dimensional feature space. In this process, each characteristic peak is regarded as a dimension. By mapping the peaks and intensities of different characteristic peaks to the corresponding coordinate axes in the high-dimensional space, a high-dimensional feature vector can be obtained. This high-dimensional feature vector contains the information of all characteristic peaks in the sample and is the representation of the sample in the high-dimensional feature space. Through this high-dimensional representation, the complex characteristics of the sample can be captured and the recognition ability of the classifier can be improved. Then, the high-dimensional features are classified into components using a preset multilayer perceptron (MLP). The multilayer perceptron is an artificial neural network model with multiple hidden layers and nonlinear activation functions, which can learn and express complex feature relationships. During the training process, the MLP adjusts weights and biases to minimize the error between the predicted output and the actual label, thereby learning the mapping relationship between sample features and component classification. Specifically, the input layer of the MLP receives high-dimensional feature vectors, the hidden layer extracts features through nonlinear transformation, and the output layer generates component classification results. In the application stage, the high-dimensional feature vectors are input into the trained MLP model, and the model will classify the samples into components based on the learned feature relationships. Each output node corresponds to a component category, and the presence or absence of each component in the sample and its relative content can be determined by the activation value of the output node. The multilayer structure and nonlinear activation function of the MLP enable it to process complex high-dimensional feature data with high classification accuracy and robustness. In order to further achieve quantitative analysis, it is necessary to calculate the content of each specific component in the sample based on the peak value of the characteristic peak. The intensity of the characteristic peak is positively correlated with the concentration of the corresponding component in the sample. The characteristic peak intensity can be converted into component concentration through a calibration curve or a quantitative model. Specifically, a calibration curve between the characteristic peak intensity and the component concentration is first established, and then the curve is used to convert the detected characteristic peak intensity to obtain the quantitative results of each component. The combination of high-dimensional feature mapping and multi-layer perceptron classification significantly improves the accuracy and efficiency of Raman spectroscopy detection. Compared with traditional spectral analysis methods, high-dimensional feature space can capture more sample feature information, and multi-layer perceptrons can process complex high-dimensional data to achieve accurate component identification and quantitative analysis.This technological breakthrough not only has important application value in coal detection, but can also be extended to the field of spectral analysis of other complex samples.

[0051] Calculating the content of each specific component in the coal sample based on the peak value of the characteristic peak is a key step in the quantitative analysis of Raman spectroscopy. The following is the specific implementation process, including detailed steps from characteristic peak extraction to quantitative calculation:

[0052] First, it is necessary to extract characteristic peaks from the enhanced interference pattern. By using the peak detection algorithm of Raman spectroscopy, the characteristic peaks in the Raman spectrum can be identified. Commonly used peak detection algorithms include:

[0053] Zero-crossing method: The peak position is determined by detecting the zero-crossing point of the derivative of the signal.

[0054] Sliding window method: Use a sliding window to calculate the local maximum.

[0055] Gaussian fitting method: Perform Gaussian fitting on the signal and extract the peak value from the fitting curve.

[0056] The specific steps are as follows: Preprocessing: Smooth and denoise the enhanced interference pattern, such as using Gaussian filtering or mean filtering. Peak detection: Apply the above algorithm to identify the characteristic peaks in the spectrum and record the position (wavelength or Raman shift) and intensity (peak height) of each peak.

[0057] Establishing a calibration curve is a key step in converting characteristic peak intensity to component concentration. The calibration curve is established using standard samples of known concentrations, reflecting the relationship between characteristic peak intensity and component concentration. The specific process is as follows:

[0058] Preparation of standard samples: Prepare a series of standard coal samples with known concentrations.

[0059] Measure Raman spectrum: Measure the Raman spectrum of each standard sample and extract its characteristic peak intensity.

[0060] Draw a calibration curve: Draw a graph of the known concentration of the standard sample and the corresponding characteristic peak intensity, and use linear regression or other fitting methods to establish a calibration curve. The general form is:

[0061] I = k·C + b;

[0062] Where I is the characteristic peak intensity, C is the component concentration, and k and b are the fitting coefficients.

[0063] Use the calibration curve to quantitatively analyze the components in unknown samples. The specific steps are as follows:

[0064] Measuring the Raman spectrum of an unknown sample: Obtaining the enhanced interference pattern of an unknown coal sample.

[0065] Extract characteristic peak intensity: Apply characteristic peak detection algorithm to extract the characteristic peak intensity of each component in the unknown sample.

[0066] Concentration calculation: Substitute the extracted characteristic peak intensity into the calibration curve to calculate the concentration of each component. According to the calibration curve formula:

[0067]

[0068] Wherein, C is the calculated component concentration, I is the extracted characteristic peak intensity, and k and b are the fitting coefficients of the calibration curve.

[0069] For multi-component samples, it is necessary to analyze multiple characteristic peaks simultaneously and calculate their respective concentrations. The specific steps are as follows:

[0070] Extract all characteristic peaks: Perform a comprehensive analysis of the enhanced interference pattern and extract all relevant characteristic peak intensities.

[0071] Calculate concentrations separately: Calculate the concentration of components corresponding to each characteristic peak separately using their respective calibration curves.

[0072] Result synthesis: The concentration results of all components are summarized to obtain the content distribution of each component in the sample.

[0073] During the quantitative analysis process, there may be measurement errors and system errors, which require error analysis and correction. The specific steps are as follows:

[0074] Repeated measurement: Perform multiple measurements on the same unknown sample and calculate the mean and standard deviation of the results.

[0075] Error Analysis: Analyze the system error by comparing the measurement results of standard samples and unknown samples.

[0076] Error correction: According to the error analysis results, the measurement results are corrected to ensure the accuracy of quantitative analysis.

[0077] Assume that there is a coal sample, and the intensities of characteristic peaks A, B, and C are detected in its Raman spectrum. A ,I B and I C We have established the corresponding calibration curve using standard samples:

[0078] For ingredient A: I A =k A ·C A +b A ;

[0079] For ingredient B: I B =k B ·C B+b B ;

[0080] For ingredient C: I C =k C ·C C +b C ;

[0081] Substituting the characteristic peak intensity into the calibration curve, we can calculate:

[0082]

[0083]

[0084]

[0085] Through the above formula, the concentrations of components A, B and C in the coal sample can be obtained, and the quantitative analysis of the sample can be completed. Through the above steps, the quantitative analysis of each component in the coal sample can be achieved. Feature peak extraction, calibration curve establishment and quantitative calculation are the key links in the whole process, ensuring the accuracy and reliability of the test results. This quantitative analysis method of Raman spectroscopy has high sensitivity and accuracy, is suitable for component analysis of complex samples, and provides important technical support for coal quality control and resource assessment. Step 4 identifies and quantitatively analyzes the characteristic peaks in the enhanced interference pattern through high-dimensional feature mapping and multi-layer perceptron classification. The construction of high-dimensional feature space captures the complex characteristics of the sample and enhances the distinguishing ability of the classifier, while the multi-layer perceptron achieves accurate component classification and quantitative analysis by learning feature relationships. This process significantly improves the accuracy and reliability of Raman spectroscopy detection, and has significant technical innovation and practical value. Through this method, the various components in the coal sample can be quickly and accurately identified and quantitatively analyzed, providing reliable technical support for coal quality control and resource assessment.

[0086] Embodiment 2: 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 specific molecules of each specific cost in the coal sample. Vibration mode resonance; placing a quantum dot exciter 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.

[0087] Specifically, the pretreatment of coal samples is the basis of the entire detection process. The coal samples are crushed and sieved to ensure that the particle size of the samples is uniform. The importance of this step is that it enables the laser beam to evenly irradiate all parts of the coal sample, thereby generating a consistent Raman scattering signal. If the sample particle size is uneven, it will lead to uneven light scattering, which will affect the accuracy of the spectrum. Therefore, making the sample particle size uniform by crushing and sieving can effectively improve the reliability and consistency of the detection results. Next, selecting a tunable multimode laser is a key step. Multimode lasers are able to generate multiple laser beams of different frequencies, and these laser beams can be coherently superimposed to form a quantized laser beam. The selection of multimode lasers is based on their ability to provide lasers at multiple frequencies, which is very important for exciting Raman scattering of different molecules. The tunability of multimode lasers enables them to adapt to the needs of different samples by adjusting the laser frequency to match the molecular vibration mode of each component in the sample. This tuning capability ensures that the laser can 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 selecting and adjusting the multimode laser, the next key operation is to calculate the required laser power based on the properties of the coal sample (such as absorption characteristics, scattering characteristics, etc.). By calculating the laser power, it is possible to ensure that the laser beam has enough energy to excite the Raman scattering of the coal sample. Applying this calculated laser power in the multimode laser can optimize the laser excitation process and ensure that sufficient excitation energy is provided without damaging the sample, thereby obtaining a Raman signal with sufficient intensity. Next, the output frequency of the multimode laser is adjusted through the frequency tuning module so that each frequency resonates with the specific molecular vibration mode of each specific component in the coal sample. The purpose of this step is to enhance the Raman signal of a specific component, thereby improving the resolution and signal-to-noise ratio of the spectrum. The resonance phenomenon can significantly increase the Raman scattering intensity of specific molecules, making these components more obvious and easy to identify in the spectrum. A quantum dot exciter is placed in the laser beam path, and the quantum dots are excited at different laser frequencies and produce a quantized laser beam with high quantum efficiency through a nonlinear process. Quantum dots are a nanomaterial with unique optical properties that can produce strong luminescence after excitation. This high quantum efficiency luminescence helps to enhance the intensity and quality of the laser beam and improve the effect of stimulating Raman scattering. The use of quantum dot exciters not only increases 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. Then, the quantized laser beams of different frequencies are superimposed through a multi-mode interferometer to form a comprehensive quantized laser beam. The multi-mode interferometer can coherently superimpose laser beams of multiple frequencies to form a laser beam with higher intensity and wider spectrum. This comprehensive quantized laser beam has a better excitation effect and can simultaneously excite multiple components in the sample, improving the efficiency and accuracy of detection.In this way, it can be ensured that when the laser beam is irradiated to the sample, it can cover all the molecular vibration modes that need to be excited, so as to obtain comprehensive spectral information. The integrated quantized laser beam is shaped into a uniform spot through the optical lens and collimator, and its cross-sectional area is determined to ensure that the laser energy is evenly distributed on the surface of the coal sample. Beam shaping is to avoid the concentration of laser energy causing local overheating or burning of the sample, thereby affecting the uniformity and accuracy of the Raman scattering signal. The uniform spot can ensure that each sample area is subjected to the same laser excitation, improving the overall quality of the spectrum. The design and adjustment of the optical lens and collimator are to ensure that the laser beam can fully cover the sample surface, so that the Raman scattering signal intensity is uniform, and avoid detection errors caused by uneven laser beams. Finally, the uniform spot is irradiated onto the coal sample to stimulate the coal sample to produce Raman scattering. Through the optimized design of the optical lens and collimator, it is ensured that the laser beam can fully cover the sample surface and stimulate strong and uniform Raman scattering signals. The Raman spectrum of the coal sample is collected using a high-sensitivity spectrometer. The spectrometer can separate the 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 ensure that all Raman scattering signals can be accurately recorded, thus providing reliable data for subsequent spectral analysis.

[0088] Example 3: 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.

[0089] Specifically, the chemical bond energy of coal molecules refers to the bonding energy between atoms in the molecule. These chemical bond energies directly affect the position of characteristic peaks in the Raman scattering spectrum. Raman scattering is a phenomenon based on molecular vibration, and the vibration frequency of a molecule is determined by its chemical bond energy. Higher chemical bond energy corresponds to higher vibration frequency, which is reflected in the Raman spectrum as a higher Raman shift. Therefore, by analyzing the characteristic peaks in the Raman spectrum, the presence and strength of different chemical bonds in the coal sample can be inferred. Understanding the chemical bond energy of coal helps to select the appropriate laser wavelength to optimize the Raman scattering signal and ensure clear spectral data. The density of coal refers to the mass of coal per unit volume. This property affects the light scattering characteristics of the coal sample and the intensity of the Raman signal. Higher density usually means that the molecules in the sample are more closely arranged, which affects the penetration depth of the laser and the path of the Raman scattered light. In order to obtain the best Raman signal, the laser power and focus position need to be adjusted to ensure that the laser can fully excite the molecules in the sample without being hindered by excessive density. The measurement and control of density is crucial to improving the signal-to-noise ratio of Raman spectroscopy. Specific heat capacity is the ability of a coal sample to absorb or release heat when the temperature changes. During Raman spectroscopy, laser beam irradiation of the sample will cause local temperature rise, which may affect the thermal stability and Raman scattering signal of the sample. Coal samples with high specific heat capacity can better disperse heat under laser irradiation, reducing signal interference and sample damage caused by overheating. Therefore, when selecting laser power and exposure time, the specific heat capacity of the sample needs to be considered to ensure that while stimulating Raman scattering signals, no excessive thermal damage is caused to the sample. The optical absorption coefficient is a parameter that measures the ability of coal samples to absorb light of a specific wavelength. Coal with different components has different absorption coefficients for lasers, which affects the intensity and distribution of Raman scattered light. A higher optical absorption coefficient causes the sample to absorb more laser energy, thereby enhancing the Raman scattering signal. But at the same time, it may also cause local overheating and pyrolysis effects of the sample. Therefore, it is necessary to optimize the laser wavelength and power according to the optical absorption coefficient of the coal sample to maximize the intensity of the Raman scattering signal without causing sample damage. The heat of combustion refers to the heat released by coal when it is completely burned. This property is closely related to the carbon content and chemical composition of coal. The determination of the heat of combustion can provide additional chemical composition information for Raman spectroscopy analysis. For example, high heat of combustion usually corresponds to coal samples with high carbon content and low impurity content. In Raman spectroscopy analysis, by combining the heat of combustion data, the characteristic peaks in the spectrum can be more accurately interpreted and identified, thereby improving the accuracy of quantitative analysis. Finally, the quality of coal is the most basic physical quantity, but it is equally important in Raman spectroscopy detection. The uniformity and magnitude of the sample quality directly affect the quality and signal-to-noise ratio of the spectrum. A larger sample mass can provide more Raman scattering signals, which helps to improve the signal-to-noise ratio of the spectrum, but it may also cause scattering and absorption losses of the laser. Therefore, in the experimental design, it is necessary to control the sample quality and ensure that it is within the appropriate range to obtain the best spectral data.

[0090] Example 4: Based on the properties of the coal sample, the laser power P is calculated using the following formula: laser :

[0091]

[0092] Among them, E bond is the chemical bond energy in coal molecules; ρ coal is the density; c p is the specific heat capacity; α abs is the light absorption coefficient; 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 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.

[0093] Specifically, the formula introduces the basic concepts of quantum mechanics, and describes the energy distribution between the excited state and the ground state through Planck's constant and Boltzmann factor. Planck's constant is related to the energy difference between the excited state and the ground state and the sample temperature, reflecting the influence of quantum effects on the laser energy demand. Specifically, the energy difference between the excited state and the ground state determines the minimum energy required for excitation, while the temperature affects the distribution of particles between these energy states. The Boltzmann factor reflects this distribution law and ensures the accuracy of the formula at the microscopic quantum level. Next, the formula takes into account the chemical bond energy, density, specific heat capacity, light absorption coefficient and other properties of the coal sample, which together determine the sample's ability to absorb and scatter laser energy. Chemical bond energy reflects the energy reserve inside the molecule, and high bond energy requires higher energy laser excitation. Density affects the penetration depth and energy absorption of the laser in the sample. The higher the density, the worse the laser penetration, but the more effective the energy absorption. Specific heat capacity determines the temperature change of the sample after absorbing laser energy. Samples with high specific heat capacity can better disperse heat and reduce thermal damage under laser irradiation. The light absorption coefficient directly affects the absorption efficiency of the sample to the laser. Samples with high absorption coefficients can absorb the laser more effectively and produce strong Raman scattering signals. On this basis, the formula also introduces the wavelength and frequency parameters of the laser beam. Through frequency tuning, the laser resonates with the molecular vibration mode in the sample, thereby enhancing the Raman scattering signal. Laser beams of different wavelengths and frequencies can excite different molecular vibration modes in the sample. By adjusting these parameters, the intensity and quality of the Raman scattering signal can be maximized. In addition, the quantum dot excitation efficiency is also an important factor. It represents the luminous efficiency of quantum dots at different laser frequencies and directly affects the final laser power requirement. Further, the Raman scattering cross section and laser beam cross-sectional area are taken into account in the formula. The former represents the scattering ability of molecules to laser, and the latter determines the energy density of the laser on the sample surface. A larger Raman scattering cross section means that the molecules can scatter the laser more effectively, thereby producing a stronger Raman signal. The laser beam cross-sectional area affects the size and energy distribution of the spot. Reasonable beam shaping can ensure that the laser energy is evenly distributed on the sample surface to avoid local overheating and damage. In addition, the factors of combustion heat and sample quality reflect the macroscopic thermodynamic properties of coal samples. Combustion heat is the energy released when coal burns. A sample with high combustion heat means that it can release more energy after absorbing energy, which enhances the Raman signal. The sample quality affects the absorption efficiency and distribution of laser energy. A larger sample quality can provide more Raman scattering signals and improve the signal-to-noise ratio of detection. Ambient temperature, as an external factor, affects the excitation state of the sample. The sample is more easily excited in a high temperature environment, which enhances the Raman signal.

[0094] Embodiment 5: Step 2 specifically includes: encoding the phase of the Raman spectrum by 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 by 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.

[0095] Specifically, phase modulators play a vital role in Raman spectroscopy analysis. Through the phase modulator, the phase of Raman scattered light can be precisely controlled and encoded. Phase modulation is a technology that encodes information into the phase of light waves. In Raman spectroscopy, the molecular vibration modes of different components correspond to different characteristic peaks, which appear as different wavelength positions in the spectrum. Through the phase modulator, a specific phase encoding can be assigned to each characteristic peak, so that it produces a recognizable difference in phase. This phase encoding not only enhances the recognition of spectral signals, 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 phase-modulated spectrum into interference patterns of different components. Interference is the phenomenon of phase superposition after light waves meet. By controlling the path and phase of the light waves, a series of interference fringes can be generated, which reflect the phase information of the light waves. The multipath quantum interferometer can separate Raman spectra with different phase encodings into several interference patterns, each of which corresponds to a specific component in the Raman spectrum. This decomposition process effectively decomposes the originally complex Raman spectrum into multiple simple interference patterns, each of which contains the phase information of a specific component. After the interference patterns are generated, the next step is to decode these patterns. The decoding process is actually the inversion of phase information, that is, the phase information in the interference pattern is converted back to spectral intensity through a specific algorithm. The decoded spectral intensity reflects the intensity distribution of each component in the original Raman spectrum. The decoding process usually requires complex calculations and algorithm support, such as Fourier transform and phase recovery technology. Through these algorithms, the spectral intensity of each component can be accurately extracted from the interference pattern. Finally, according to the decoded spectral intensity, the characteristic peaks of different components are separated. Since the characteristic peaks of each component are given unique phase codes during the phase modulation and interference process, these codes are converted into intensity signals after decoding, making the characteristic peaks of each component more obvious and easy to identify in the spectrum. By comparing the decoded spectral intensities, the characteristic peaks of different components can be effectively separated, thereby achieving accurate detection and quantitative analysis of each component in the sample. The core of this step is to use phase modulation and quantum interference technology to encode, interfere, decode and separate the phase information of Raman spectrum, so as to achieve the separation and enhancement of complex spectral signals. This method not only improves the spectral resolution and signal-to-noise ratio, but also enhances the ability to identify different components. Compared with traditional spectral analysis methods, this technology has significant advantages in dealing with complex samples and multi-component analysis. It can not only provide clearer spectra, but also more accurately identify and quantify trace components in samples, which has important application value for the rapid and accurate detection of coal samples.

[0096] Example 6: Assume that the initial spectrum intensity of the Raman spectrum is 1 raw(x); where x is the Raman shift; the phase of the Raman spectrum is encoded by the phase modulator using the following formula:

[0097] I mod (x) = I raw (x)·e iφ(x) ;

[0098] Among them, I mod (x) is the spectral intensity after phase modulation; φ(x) is the phase function, which is expressed by the following formula:

[0099]

[0100] 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; nm is the phase modulation frequency of the mth interference path of the nth specific component; β nm is the initial phase of 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.

[0101] Specifically, in Example 6, the phase of the Raman spectrum is encoded by a phase modulator to achieve effective 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 change to the spectral signal at each Raman shift position, so that the characteristic peaks of different components have unique phase encoding in the phase space. The phase function includes two main parts: the base phase and the modulation phase. The base phase part is related to the Raman shift and the characteristic wavelength of the component, and is determined by a linear relationship and a phase offset, which makes the characteristic peak of each component have different starting points on the base phase. The modulation phase part introduces multiple sinusoidal functions, and the frequencies and initial phases of these sinusoidal functions are modulated according to the characteristics of different components and interference paths to produce complex phase modulation patterns. In this way, the Raman signals of different components can form different interference patterns in a multipath quantum interferometer after phase modulation. Interference patterns use the interference phenomenon of light to decompose the modulated spectrum into multiple fringe patterns containing specific phase information. These 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 spectral intensity through phase inversion technology, thereby separating the characteristic peaks of different components. In this way, through phase modulation and interference decoding technology, the resolution and signal-to-noise ratio of Raman spectroscopy can be effectively improved, and accurate identification and quantitative analysis of multiple components in complex samples can be achieved. The application of this method in Raman spectroscopy detection not only enhances the recognition of spectral signals, but also significantly improves the accuracy and efficiency of analysis, providing strong technical support for the component analysis of complex samples.

[0102] Embodiment 7: The formula for decomposing the phase modulated Raman spectrum into interference patterns of different components by a multi-path quantum interferometer is as follows:

[0103]

[0104] Among them, 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 interference modulation frequency; γ kj is the interference modulation amplitude of the jth interference modulation frequency of the kth interference path; kj is the jth interference modulation frequency of the kth interference path; δ kj is the jth interference modulation phase of the kth interference path.

[0105] Specifically, in Example 7, the phase-modulated Raman spectrum is decomposed into interference patterns of different components by a multi-path quantum interference instrument, using a complex interference formula. The formula combines the phase-modulated spectral signal with multiple interference paths to generate a comprehensive interference pattern. The core of this process is to use the principle of light interference to modulate the spectral signal through the optical path difference and phase difference of different paths, and further complicate the interference pattern by modulating the frequency and phase. Specifically, the interference pattern I in the formula interf (x) is the Raman spectrum after phase modulation I mod (x) The result after multipath interference. In a multipath quantum interferometer, the optical path difference d of each interference path k is k and phase difference Δφ k Corresponding to different path characteristics. These path characteristics make the optical signal on each path have a unique phase change when interfering. By applying different phase differences and optical path differences to the optical signal on each path, the phase information of each component can be effectively separated. The formula further introduces interference modulation frequency and phase, and modulates the amplitude γ kj , modulation frequency λ kj and modulation phase δ kj , complex phase modulation is performed on the optical signal on each interference path. The modulation frequency determines the density and distribution of the interference fringes, while the modulation phase and amplitude further adjust the shape and intensity of the interference pattern. These modulation parameters work together to make the optical signal on each interference path present a unique fringes and intensity distribution in the interference pattern. In the formula, the optical signals on all paths are added together to form the total interference pattern after phase modulation and interference modulation. The absolute value square part in the formula |·| 2 represents the calculation of light intensity, because the intensity of the interference pattern is the sum of the amplitude and phase of the light signal of each path. In this way, the interference pattern contains the phase information of all paths, reflecting the phase encoding characteristics of Raman spectroscopy. Through these complex modulation and interference processes, the interference pattern I interf (x) contains the phase information of each component in the Raman spectrum. This phase information can be effectively extracted and separated during the decoding process, thereby realizing the identification and quantitative analysis of the spectral signals of different components. The multi-path quantum interferometer decomposes and enhances the originally complex Raman spectral signal by introducing multiple interference paths and complex phase modulation, thereby improving the resolution and accuracy of spectral analysis.

[0106] Embodiment 8: The generated interference pattern is decoded by the following formula to obtain the decoded spectral intensity:

[0107]

[0108] Among them, Idec (x) is the decoded spectral intensity; is the inverse Fourier transform operator; η p is the pth decoded modulation coefficient; P is the number of decoded modulation coefficients; ψ p (x) is the pth decoded modulation phase function, defined as:

[0109]

[0110] Where Q is the number of modulation frequencies contained in the decoded modulation phase function; q is a subscript; ∫ pq is the modulation amplitude; pq is the modulation frequency; k pq is the initial phase of modulation.

[0111] Specifically, the interference pattern I interf (x) is generated by a multipath quantum interferometer, which contains the optical path difference and phase difference information of different paths. In order to extract the original spectral intensity from these interference patterns, an inverse Fourier transform operation is required. Inverse Fourier transform The frequency domain signal can be converted back to the time domain signal to help recover the intensity information in the spectral signal. Through the inverse Fourier transform, we can preliminarily obtain the basic part of the decoded spectral intensity. In order to further extract and enhance the spectral signal, it is necessary to take into account the complex phase information brought by phase modulation. This step is achieved by adding a series of modulation terms, each of which contains a decoded modulation coefficient and a phase modulation function. The decoded modulation coefficient η p It 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. Phase modulation function ψ p (x) is defined by the superposition of multiple sinusoidal functions, whose frequencies and initial phases are determined by the parameters λ pq and k pq The modulation amplitude is determined by the parameter ζ pq In this way, the modulation phase function can finely adjust the phase of the signal during the inverse Fourier transform process and extract the phase information of a specific component. The final decoded spectral intensity I is obtained by weighted summing these modulation terms and the basic part. dec (x). This process can be viewed as performing multiple frequency and time domain modulation and demodulation operations on the original interference pattern, thereby maximizing the extraction and enhancement of each component information in the spectral signal. Each modulation term provides additional phase information, so that the final decoding result can more accurately reflect the intensity distribution of the original spectral signal.

[0112] Through this decoding process, the originally complex interference pattern is converted into a clear spectral intensity, revealing the characteristic peaks of each component in the sample. This method uses Fourier transform and phase modulation technology to effectively extract useful spectral information from complex interference patterns, improving the accuracy and sensitivity of spectral analysis. In practical applications, this decoding process can significantly improve the resolution and signal-to-noise ratio of Raman spectroscopy detection, making the component analysis of complex samples more accurate and reliable.

[0113] Embodiment 9: According to the decoded spectral intensity, the characteristic peaks of different components are separated using the following formula:

[0114]

[0115] Among them, 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.

[0116] Specifically, the decoded spectral intensity contains the comprehensive information of all components in the sample. In order to extract the characteristic peak of each component, the spectrum needs to be further weighted. A Gaussian function is introduced in the formula. This Gaussian function weights the spectral signal according to the characteristic wavelength and wavelength standard deviation of a specific component, so that the signal close to the characteristic wavelength is enhanced, while the signal far from the wavelength is attenuated. Next, the formula also introduces a series of sinusoidal modulation functions, which further modulate the spectral signal according to a specific modulation frequency and initial phase to ensure that the value of the modulation function reaches the maximum at the characteristic peak of a specific component, thereby further enhancing the signal of the component. These sinusoidal modulation functions control their contribution to the total modulation effect by modulating the amplitude. Combining these weighted and modulated factors, a comprehensive weighted modulation function is constructed, which can effectively separate the characteristic peak intensity of each specific component by multiplying it with the decoded spectral intensity. This process is actually a multiple frequency domain and time domain filtering and enhancement operation on the decoded spectral signal, accurately extracting the characteristic information of each component from the complex spectral signal. Through this method of combining Gaussian weighting and sinusoidal modulation, not only can the signal of a specific component be enhanced, but also the interference of other components can be suppressed, so that the characteristic peak signal after separation is clearer and more accurate, thereby significantly improving the resolution and signal-to-noise ratio of Raman spectroscopy analysis, and providing stronger identification and quantitative capabilities for component analysis of complex samples. The present invention significantly improves the sensitivity, resolution and signal-to-noise ratio of Raman spectroscopy detection through the organic combination of multi-mode laser quantization technology, quantum interference effect, phase modulation technology, and Gaussian weighting and sinusoidal modulation methods. Compared with the prior art, the present invention can not only effectively enhance the intensity of spectral signals and improve the detection capability of low-concentration components, but also can realize the accurate separation and identification of the signals of each component in complex samples through multi-path quantum interference and phase modulation technology, avoiding the interference of peak overlap and background noise.

[0117] The above is a detailed introduction to an intelligent data storage system with a drone charging and battery replacement device provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present 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 according to claim 1, characterized in that: Step 1 specifically includes: crushing and screening the coal sample to make the particle size of the coal sample uniform so that the laser beam can evenly irradiate all parts of the coal sample; selecting a tunable multi-mode laser that can generate multiple laser beams of different frequencies and ensure that these laser beams can be coherently superimposed to form a quantized laser beam to obtain the properties of the coal sample, calculating the laser power based on the properties of the coal sample to ensure that sufficient energy can be generated to excite Raman scattering of the coal sample, and applying the laser power in the multi-mode laser; adjusting the output frequency of the multi-mode laser through a frequency tuning module so that each frequency is consistent with the specific molecular vibration of each specific cost in the coal sample. mode resonance; placing a quantum dot exciter 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 according to claim 2, characterized in that: The properties of the coal sample include: chemical bond energy, density, specific heat capacity, light absorption coefficient, heat of combustion and mass of the coal molecules.

4. The rapid coal detection method based on Raman spectroscopy according to claim 3 is characterized in that: Based on the properties of the coal sample, the laser power P is calculated using the following formula: laser : Among them, E bond is the chemical bond energy in coal molecules; ρ coal is the density; c p is the specific heat capacity; α abs is the light absorption coefficient; 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 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, characterized in that: Assume the initial spectral intensity of Raman spectrum is I raw (x); where x is the Raman shift; the phase of the Raman spectrum is encoded by the phase modulator using the following formula: I mod (x)=I raw (x)·e iφ(x) ; Among them, 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; nm is the phase modulation frequency of the mth interference path of the nth specific component; β nm is the initial phase of 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, characterized in that: The formula for decomposing the phase-modulated Raman spectrum into interference patterns of different components through a multipath quantum interferometer is as follows: Among them, 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 interference modulation frequency; γ kj is the interference modulation amplitude of the jth interference modulation frequency of the kth interference path; kj is the jth interference modulation frequency of the kth interference path; δ kj is the jth interference modulation phase of the kth interference path.

7. The rapid coal detection method based on Raman spectroscopy according to claim 6, characterized in that: The generated interference pattern is decoded by the following formula to obtain the decoded spectral intensity: Among them, I dec (x) is the decoded spectral intensity; is the inverse Fourier transform operator; η p is the pth decoded modulation coefficient; P is the number of decoded modulation coefficients; ψ p (x) is the pth decoded modulation phase function, defined as: Where Q is the number of modulation frequencies contained in the decoded modulation phase function; q is a subscript; ζ pq is the modulation amplitude; pq is the modulation frequency; κ pq is the initial phase of modulation.

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

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