CN119269479B - A fitting frequency-locking method, product, medium and device for cavity-enhanced Raman spectroscopy - Google Patents
A fitting frequency-locking method, product, medium and device for cavity-enhanced Raman spectroscopy Download PDFInfo
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Abstract
本申请公开了一种腔增强拉曼光谱的拟合锁频方法、产品、介质及设备，涉及半导体激光拉曼光谱检测技术领域，该方法包括：获取腔增强拉曼光谱检测系统中谐振腔的误差信号和透射功率；基于误差信号和透射功率确定出有效特征；将有效特征输入至训练好的反向传播神经网络中，得到拟合误差信号；根据拟合误差信号进行腔增强拉曼光谱的拟合锁频。本申请腔增强拉曼光谱的拟合锁频方法、产品、介质及设备可在确保误差信号高灵敏度的同时有效提高锁频系统的频率稳定性和抗干扰能力。
The present application discloses a fitting frequency locking method, product, medium and device for cavity enhanced Raman spectroscopy, which relates to the field of semiconductor laser Raman spectroscopy detection technology. The method comprises: obtaining the error signal and transmission power of the resonant cavity in the cavity enhanced Raman spectroscopy detection system; determining effective features based on the error signal and transmission power; inputting the effective features into a trained back-propagation neural network to obtain a fitting error signal; and fitting frequency locking the cavity enhanced Raman spectroscopy according to the fitting error signal. The fitting frequency locking method, product, medium and device for cavity enhanced Raman spectroscopy of the present application can effectively improve the frequency stability and anti-interference ability of the frequency locking system while ensuring the high sensitivity of the error signal.
Description
Technical Field
The application relates to the technical field of semiconductor laser Raman spectrum detection, in particular to a fitting frequency locking method, a product, a medium and equipment of a cavity enhanced Raman spectrum.
Background
The gas detection has important significance in the fields of environmental monitoring, industrial process, safety, medical diagnosis and the like, and with the progress and continuous promotion of scientific technology, the cavity-enhanced Raman spectroscopy technology is gradually and widely focused as an advanced and powerful gas detection technology, and has important roles in the field of gas detection at present. The cavity enhancement Raman spectroscopy greatly enhances the Raman signal of the gas by the resonance enhancement effect of the optical resonant cavity. Compared with the traditional common Raman spectrum technology, the cavity enhanced Raman spectrum technology has higher sensitivity and specificity, and can realize accurate detection and analysis of low-concentration gas. Whereas cavity enhancement requires stabilization between the laser frequency and the cavity resonant frequency to ensure that the resonance enhancement effect in the optical resonant cavity is maximized. However, in the process of realizing optimal resonance enhancement, namely, maximizing the resonance enhancement effect, disturbance and mechanical vibration of the external environment are extremely easy to cause unlocking of the optical resonant cavity. Since the optical resonant cavity plays a crucial role in cavity enhancement, ensuring the stability and anti-interference capability of the optical resonant cavity has a crucial meaning for the cavity enhancement Raman spectrum technology.
Researchers have conducted intensive research into different methods of laser frequency locking for many years. The PDH (Pound-Drever-Hall) technology becomes a standard method for stabilizing an optical resonant cavity due to the advantages of high precision, automatic control, quick response, real-time monitoring and the like, and is widely applied to the technical fields of space laser interference gravitational wave detection, optical communication, optical atomic clocks, laser frequency locking and the like. While PDH technology is currently the most widely used method of laser frequency locking, it still suffers from a number of drawbacks. Firstly, the linear dynamic range area of the error signal of the PDH technology is very small for a F-P (Fabry-Perot) resonant cavity with high fineness, so that the control servo system performs frequency locking to become a challenging work and is extremely easy to be influenced by external interference or environmental change to cause unlocking, and secondly, on the frequency outside the locking frequency of the optical resonant cavity, an area with zero error and linear dynamic exists, so that the frequency is easy to be locked to the adjacent low resonant frequency after being disturbed, the resonance in the optical resonant cavity is lower or is not resonant, and finally the loss of a Raman scattering signal is caused.
In view of the above, scholars have proposed various effective methods to improve the anti-interference capability of PDH frequency locking technology. Aiming at the control of the F-P resonant cavity 8, the Japanese Tokyo university Shinji Miyoki and the like, two methods of near Q-phase demodulation and multi-group in-phase odd harmonic modulation frequency demodulation are proposed to expand the linear dynamic range of an error signal of the PDH technology, but the method cannot guarantee the sensitivity of the error signal, and the transmission power of the F-P resonant cavity 8 is reduced. The Shanghai university Han Sen, li Chan etc. redefine a PDH error signal by dividing the error signal of the conventional PDH technique by the transmitted power, and increase the linear dynamic range of the frequency locking system by 50 times under the condition that the length of the resonant cavity is 10m, but the method has poor effect of increasing the linear dynamic range when the resonant cavity is shorter and greatly reduces the sensitivity of the error signal. The new error signal constructed under the large modulation depth is utilized to perform pre-locking by Zhejiang university Yan Liping and the like, and then the traditional error signal corresponding to the small modulation depth is adopted to realize accurate locking, so that the linear dynamic range of the frequency locking system is enlarged by 3.4 times, the sensitivity of the error signal during locking is ensured, and the influence on the sensitivity of the error signal during enlarging the dynamic range is avoided by the method, but the nonlinear distortion of the signal becomes obvious due to the large modulation depth, and the linear dynamic range is enlarged by a limited multiple. The German institute of particle physics and celestial physics Manuel Zeyen, et al, by phase modulating the seed laser to a modulation frequency that is half the free spectral range (FREE SPECTRAL RANGE, FSR) of the F-P resonator 8, eliminates a stable but non-resonant lock point in the error signal of conventional PDH techniques, also known as the Trojan horse operating point (Trojan operation points). In this way, the F-P resonator 8 is correctly locked to the seed frequency even after a large disturbance has occurred. Although the method cannot be locked at the Trojan horse operation point, the frequency locking system is more easily affected by external interference due to the narrow frequency range of locking, so that locking is unstable or frequency hopping is frequent.
In summary, in the existing related frequency locking method for solving the defect of the PDH technology, the frequency stability and the anti-interference capability of the frequency locking system still cannot be improved while the high sensitivity of the error signal is ensured.
Disclosure of Invention
The application aims to provide a fitting frequency locking method, a product, a medium and equipment of a cavity enhanced Raman spectrum, which can ensure high sensitivity of an error signal and effectively improve the frequency stability and the anti-interference capability of a frequency locking system.
The application provides a fitting frequency locking method of a cavity enhanced Raman spectrum, which comprises the steps of obtaining an error signal and transmission power of a resonant cavity in a cavity enhanced Raman spectrum detection system, determining effective characteristics based on the error signal and the transmission power, inputting the effective characteristics into a trained counter-propagation neural network to obtain a fitting error signal, and performing fitting frequency locking of the cavity enhanced Raman spectrum according to the fitting error signal.
Optionally, the method for acquiring the error signal and the transmission power of the resonant cavity in the cavity enhanced Raman spectrum detection system specifically comprises the steps of performing piezoelectric scanning on the resonant cavity in the cavity enhanced Raman spectrum detection system, acquiring the error signal of the resonant cavity in each scanning period by adopting a high-sensitivity sensor, and synchronously measuring the transmission power of the resonant cavity in each scanning period by adopting a photoelectric detector.
Optionally, the determining the effective characteristic based on the error signal and the transmission power comprisesAnd transmitted powerError signalAmplitude and slope of (a) and transmitted powerAmplitude to slope and signal ratio of (v)/Is characterized by the amplitude and the slope of (a) and (b)Regularization method filters features and signals errorsAmplitude, transmitted power of (2)Amplitude and signal ratio of (2)/Amplitude and slope of (a) as an effective feature.
Optionally, the trained back propagation neural network is trained based on Adam optimization algorithm with the goal of minimizing the loss function.
Optionally, the Loss function trained by the back propagation neural network is a mean square error MSE: wherein n is the total number of groups of samples, i is the ith group of samples; An actual error signal for the i-th set of samples; fitting error signal for the i-th set of samples.
Optionally, the back propagation neural network comprises an input layer, three hidden layers and an output layer, wherein the three hidden layers respectively select 10, 8 and 5 nodes.
Optionally, the performing fitting frequency locking of the cavity enhanced Raman spectrum according to the fitting error signal specifically comprises the following steps of when the fitting error signal is fitIs smaller than a first preset error thresholdWhen the cavity enhanced Raman spectrum detection system is automatically switched to the locked state and uses the fitting error signalWhen fitting error signalIs smaller than a second preset error thresholdAt the same time, a first preset error threshold valueGreater than a second predetermined error thresholdThe cavity enhanced raman spectrum detection system automatically switches to a locked state and uses an error signalIf external interference exists, the fitting error signal is recovered。
In a second aspect, the application provides a computer program product comprising a computer program which, when executed by a processor, implements a fitted frequency-locking method of the cavity enhanced raman spectrum.
In a third aspect, the application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a fitted frequency-locking method of the cavity enhanced raman spectrum.
In a fourth aspect, the application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement a fitted frequency locking method of the cavity enhanced raman spectrum.
According to the specific embodiment of the application, the application provides a fitting frequency locking method, a product, a medium and equipment of a cavity enhanced Raman spectrum, and mainly provides a wide-range fitting frequency locking (WIDE RANGE FITTING frequency locking, WRFFL) method, namely a fitting frequency locking method of the cavity enhanced Raman spectrum, which is used for detecting error signals and transmission power of the resonant cavity and combining a counter-propagation neural network (Back Propagation Neural Network, BPNN) to model the resonant cavity by a BP neural network for short, so that fitting error signals of a wide-range Trojan-free operation point are obtained, the linear dynamic range of the error signals is expanded, and finally the optical resonant cavity with high stability and strong anti-interference capability is realized. In addition, the frequency locking performance test is carried out on the proposed WRFFL method by combining the cavity enhancement Raman spectrum detection system, and the WRFFL method not only improves the anti-interference capability of the frequency locking system, but also greatly improves the frequency stability of the frequency locking system, expands the linear dynamic range of an error signal, and prolongs the frequency locking time of the resonant cavity while retaining the high sensitivity characteristic of the error signal. Therefore, the fitting frequency locking method, the product, the medium and the equipment of the cavity enhanced Raman spectrum can ensure high sensitivity of error signals and effectively improve the frequency stability and the anti-interference capability of a frequency locking system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a fitting frequency locking method of a cavity enhanced raman spectrum.
Fig. 2 is a schematic structural diagram of a cavity enhanced raman spectrum detection system provided by the application.
Fig. 3 is a schematic diagram of a fitting frequency locking method of a cavity enhanced raman spectrum provided by the application.
Fig. 4 is a schematic diagram of a theoretical-based error signal simulation provided by the present application.
FIG. 5 is a schematic diagram of the multi-scan cycle error signal and the Trojan operation point according to the present application.
Fig. 6 is a schematic diagram of a training flow of a back propagation neural network based on Adam optimization algorithm provided by the application.
Fig. 7 is a schematic diagram of a fitting error signal curve and a preset error threshold according to the present application.
Fig. 8 is a schematic diagram of a fitted error signal curve and a preset linear signal obtained through a trained back propagation neural network according to the present application.
Fig. 9 is a schematic diagram of an anti-interference test result provided by the present application.
FIG. 10 is a diagram showing the error signal and reflected power over time.
The reference numerals comprise a spectrometer-5, a dichroic mirror DM-6, a polarization beam splitter PBS 3-7,F-P resonant cavity-8, a piezoelectric ceramic PZT-9, a photoelectric detector PD 1-10, a mode matching lens PML-11, a quarter wave plate-12, a polarization beam splitter PBS 2-13, a piezoelectric ceramic controller-14, a laser-15, a current controller-16, a digital-analog converter DAC-17, a Faraday isolator FI-18, a half wave plate-19, a polarization beam splitter PBS 1-20, an electro-optical modulator EOM-21, a radio frequency end RF-22, a photoelectric detector PD 2-23, a Mixer Mixer-24, a low-pass filter LPF-25, a phase shifter-26, a local oscillator LO-27, an upper computer-28 and a PID proportional integral differential feedback amplifier-29.
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 be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
The fitting frequency locking method of the cavity enhanced Raman spectrum is suitable for a Raman spectrum detection technology with high stability and strong anti-interference capability, and the transmission power and the modulation depth of a resonant cavity, namely an F-P cavity are detected through the built cavity enhanced Raman spectrum detection systemThe error signal at 1.08 has the narrowest linear dynamic range and highest sensitivity at the modulation depth. And then, through a three-layer counter-propagation neural network, according to a plurality of groups of characteristic value data of effective characteristics determined by the error signal and the transmission power, realizing the wide-range linear fitting of the error signal, providing a fitting error signal with a wide range and without Trojan horse operation points for a PDH frequency locking system, and finally realizing a cavity enhanced Raman spectrum detection system with high frequency stability and strong anti-interference capability.
As shown in fig. 2, the application utilizes optical elements and electronic components to build optical paths and electrical detection structural units, and performs debugging of the optical paths and the photoelectric detection system, thus forming the main structure of the cavity enhanced Raman spectrum detection system with wide fitting frequency locking range. The cavity enhanced Raman spectrum detection system mainly comprises an optical path structural unit and a circuit structural unit.
In the optical path structural unit, the 532nm solid state single longitudinal mode laser 15 is used as a pumping light source, the spectral linewidth of emergent light emitted by the light source is smaller than 1MHz, and the Faraday isolator FI 18 can reduce reflection efficiency through the Faraday isolator FI 18, so that the laser 15 is prevented from being damaged due to laser reflection. The beam waist position of the light emitted by the laser 15 is measured by a knife edge method and is 25cm away from the exit of the laser 15, and the beam waist size is 0.72mm. Since the polarization direction required for the incident light of the electro-optic modulator EOM 21 is horizontal polarization, a polarizer is added between the laser 15 and the electro-optic modulator EOM 21, i.e. the outgoing laser light is converted as much as possible into p-light, also called TM-polarized light, through a half wave plate HWP (half wave plate 19), and then the remaining s-light is passed through a polarizing beam splitter PBS 120, letting the p-light enter the electro-optic modulator EOM 21. The electro-optic modulator EOM 21 is capable of providing an adjustable phase difference for linearly polarized incident light. The electro-optic crystal material inside the electro-optic modulator EOM 21 acts in raman spectroscopy to cause the light to be out of phase, thereby enhancing the detection effect of the raman signal. Therefore, the electro-optical modulator EOM 21 changes the phase difference of the modulated light beam by changing the phase of the light beam, so as to improve the signal-to-noise ratio and the detection sensitivity of the raman signal. The radio frequency end RF 22 of the electro-optic modulator EOM 21 is used for applying voltage, so that the extraordinary refractive index of the electro-optic crystal in the electro-optic modulator EOM 21 is changed, the optical signals generate phase differences, and the phase differences can help to separate the Raman signals from background noise, so that the contrast ratio of the signals is improved, and the detection effect of the Raman signals is enhanced. The input signal to the radio frequency terminal RF 22 is a 20MHz signal generated by the local oscillator LO 27. The output signal of the electro-optical modulator EOM 21 then passes through the polarizing beam splitter PBS2 13, converting the linearly polarized light into circularly polarized light via the quarter wave plate 12. The laser is accurately coupled into the F-P resonant cavity 8 through the mode matching lens PML 11, the F-P resonant cavity 8 is composed of two lenses with high reflectivity, so that the laser entering the F-P resonant cavity 8 is divided into three parts, one part of the laser is transmitted through the polarization beam splitter PBS 37 and enters the photoelectric detector PD 110, the photoelectric detector PD 110 sends the acquired transmission power of the F-P resonant cavity 8 into the upper computer 28, the other part of the laser enters the spectrometer 5 through the polarization beam splitter PBS 37 and the dichroic mirror DM 6, the last part of the laser returns to the quarter wave plate 12 to be converted into linear polarized light through the circularly polarized light, and the polarization state at the moment is rotated by 90 degrees relative to the incident laser, so that the laser enters the photoelectric detector PD2 23 after passing through the polarization beam splitter PBS2 13 and then enters the circuit structural unit.
In the circuit structure unit, the signal obtained by the photodetector PD 223 is demodulated first, so that the signal is passed through a Mixer 24, the other signal input in the Mixer 24 is the same 20MHz signal generated by the local oscillator LO 27 before and is shifted by 90 DEG by the phase shifter 26, the output signal of the photodetector PD2 and the phase-shifted signal of the local oscillator LO 27 enter the Mixer 24 and are filtered by the low-pass filter LPF 25 to obtain the error signal of the PDH frequency locking systemSimultaneously, the transmission power of the F-P resonant cavity 8 is measured by the photoelectric detector PD 110And is synchronized with the error signalIs a collection of (1). For error signalFiltering and detecting each sampling pointAmplitude of (1),Amplitude and signal ratio of (2)/The four characteristic values of the amplitude and the slope of the (a) are used as input data of the back propagation neural network to train to obtain a fitting error signal, the fitting error signal is input into a PID proportional integral differential feedback amplifier 29, and compared with an error signal obtained by a low-pass filter LPF 25 to output a feedback signal. After the feedback signal of the PID proportional-integral-differential feedback amplifier 29 is processed by the DAC 17, one path of fast feedback signal is output to the port of the current controller 16 of the laser 15, the other path of slow feedback signal is output to the modulation port of the piezoelectric ceramic controller 14 on the high-reflection mirror of the F-P resonant cavity 8, the piezoelectric ceramic PZT 9 is controlled to adjust the cavity length of the F-P resonant cavity 8 according to the input slow feedback signal, so that the cavity length is matched with the wavelength of the laser 15, and the mode locking of the F-P resonant cavity 8 and the laser 15 is realized.
Well-known elements and circuits have not been described in detail in order not to obscure the application. For a better understanding of the technical solution of the present application, the following will be further described in detail in connection with theoretical calculations of the present application.
The laser 15 emits an optical signalCan be expressed asWhereinFor the intensity of the light field,Is the angular frequency, t is the time, a frequency is applied to the electrodes of the electro-optic modulator EOM 21 through the input of the radio frequency terminal RF 22Is modulated with a modulation depth ofModulated laserThe formula was changed to the following one.
(1)。
When (when)In the case of 1, the number of the times of the process is reduced,Can be converted into the following formula.
(2)。
Wherein, 、Representing the zero-order and first-order bessel functions, respectively.
At this time, the laser comprises a carrier wave and two angular frequencies respectivelyIs provided. The signal then enters the F-P resonant cavity 8, and since the magnitude of the error signal is determined by detecting the reflected signal, it is necessary to obtain the transfer characteristic of the F-P resonant cavity 8, provided thatIs the angular frequency of incident lightAngular frequency of resonance with cavityFrequency difference function between the two cavities, assuming that the F-P resonant cavity 8 is called a symmetrical lossless cavity for short, the frequency difference functionThe formula is as follows.
(3)。
Wherein, Is a reflected optical signal; the reflectivity of the high-reflection mirror of the resonant cavity; is the free spectral range of the resonant cavity.
The reflected light signal can be obtained from the formulas (2) and (3)The complex amplitude electric field representation of (a) is as follows.
(4)。
Wherein, To apply an angular frequency ofIs a modulus of a frequency difference function after modulating the signal.
Further resulting reflected light intensityAs shown below.
(5)。
Wherein, ,Is the effective power coefficient.Representing performing a conjugate operation.
Removing the DC signal and the high frequency signal in the formula (5)The remainder being intermediate frequency signals, the amplitude of which is modulatedAs shown below.
(6)。
The direct current and high frequency components are filtered out by the formula (5), and the following formula (7) is obtained by combining the formula (6).
(7)。
At this time, in the formula (7)The application derives and calculates the needed error signal based on theory principle.
It can be seen that the error signal in equation (7) has two terms, but only one term is valid for different modulation frequencies, the modulation mode is divided into high frequency modulation and low frequency modulation, and when the modulation frequency is far smaller than the line width of the resonant cavity, the low frequency modulation is the low frequency modulation, and conversely the high frequency modulation is the high frequency modulation. In practical application, high-frequency modulation is generally adopted, as shown in fig. 4 (a), and fig. 4 (a) is an error signal diagram based on theoretical high-frequency modulation, and frequency stability in optical frequency transmission is effectively ensured due to higher frequency response of the high-frequency modulation, high signal-to-noise ratio, high measurement precision and better anti-interference capability.
The two most important indicators in the error signal are the linear dynamic range and sensitivity, which directly affect the frequency locking effect and accuracy. The linear dynamic range refers to the frequency axis range between the maximum amplitude and the minimum amplitude of the error signal near zero. The slope of the curve in the linear dynamic range is defined as the sensitivity of the error signalSince equation (7) is continuously differentiable, sensitivity can be determined by deriving the error signal with respect to frequency errorAs shown in fig. 4 (b), which is a differential plot of error signal versus frequency in fig. 4. The sensitivity has important influence on the stability of the frequency locking system, the frequency noise and the noise floor of the frequency locking system. When the frequency offset is less than the full width half maximum of the etalon spectral line,The following can be simplified.
(8)。
Wherein the figure of meritSensitivity is thenThe following is provided.
(9)。
From equation (9), it can be seen that the sensitivity is constant with the incident power, the cavity quality factor and the free spectral rangeOnly with modulation depthIn relation, according to the one shown in (c) of FIG. 4Along with itThe variation graph of (2) can be obtainedTime of dayMaximum value is reached at which time the sensitivity of the error signalMaximum.
FIG. 5 is a schematic diagram of a multi-scan period error signal and a Trojan horse operating point, in which the linear dynamic range of the error signal center is relative to the free spectral range of a resonant cavity, which is one periodIs very narrow andThe frequency locking is to lock the error signal at the position of zero amplitude in the linear signal, and the transmission power of the resonant cavity is maximum at the moment, namely the generated Raman signal is strongest. However, a small disturbance from the outside may cause the system to lose lock, resulting in huge attenuation of the cavity transmission signal, and no raman signal can be detected. In the process of relocking after losing lock, besides the error signal point position with the maximum transmission power, two points with lower transmission power and two Trojan horse operation points without transmission power exist in one period. Therefore, expanding the linear dynamic range of the error signal and eliminating the Trojan horse operating point are key to ensuring the high stability and the strong anti-interference capability of the cavity enhanced Raman spectrum detection system.
In order to solve the problems of narrow linear dynamic range and Trojan operation points of the error signal, the application utilizes the back propagation neural network to perform linear fitting on the error signal, and the back propagation neural network is used for performing multistage and wide-range linear fitting in order to ensure three conditions of high sensitivity, wide dynamic range and no Trojan operation points.
For a back propagation neural network, a back propagation neural network structure is first established, the network structure comprises an input layer, three hidden layers and an output layer, and the three hidden layers respectively select 10, 8 and 5 nodes, and as linear fitting is relatively simple, the hidden layers are kept small so as to avoid overfitting. To be used forThe function is an activation function of the back propagation neural network,Is thatCan retain a small slope in the negative portion, helping to solve the "neuronal death" problem.Where x is the eigenvalue of the effective eigenvalue of the back propagation neural network input. Specifically, the input layer is a first layer of a back propagation neural network for receiving externally input data. Each node of the hidden layer typically represents a feature, the number of nodes being determined by the feature dimension of the input data. The output layer is the last layer of the back propagation neural network for generating the prediction result, i.e. the fit error signal.
As shown in fig. 1 and 3, the fitting frequency locking method of the cavity enhanced raman spectrum disclosed by the application specifically comprises the following steps.
And step 1, obtaining error signals and transmission power of a resonant cavity in the cavity enhanced Raman spectrum detection system.
Because the fitting frequency locking method of the cavity enhanced Raman spectrum needs to obtain a trained back propagation neural network, in order to acquire training and verification data sets required by the back propagation neural network, the piezoelectric scanning is performed on the resonant cavity in the cavity enhanced Raman spectrum detection system by combining with FIG. 2. Wherein the emergent power of the laser 15 is fixed to be 50mW, the reflectivity of the two high-reflectivity mirrors is 99.9%, the phase shift is fixed to be 90 degrees, and the modulation frequency is 20MHz. Firstly, triangular wave scanning signals are applied to a laser 15 or piezoelectric ceramics in a resonant cavity, linear scanning is carried out on the length or laser frequency of the resonant cavity, the scanning distance is adjusted to enable only a single fundamental mode signal to exist in a scanning period, and after matching, a high-sensitivity photoelectric sensor, namely a photoelectric detector PD2 23 is used for recording error signals in each scanning period to obtain modulation depthError signal at 1.08Simultaneously, the transmission power of the resonant cavity is measured by using the photoelectric detector PD1 10And synchronizing with the acquisition of the error signals, thereby acquiring a plurality of groups of error signals and transmission power and forming a sample set for training and verifying the subsequent counter-propagating neural network.
And 2, determining effective characteristics based on the error signal and the transmission power.
In order to select the most efficient feature points input to the back propagation neural network, the present application is based on an error signalAnd transmitted powerError signalAmplitude and slope of (a) and transmitted powerAmplitude to slope and signal ratio of (v)/Is characterized by the amplitude and slope of (c). However, the application adopts the following problems that the calculation complexity is increased, the gradient disappears or the model becomes complex and the like due to excessive characteristicsRegularization method filters features and signals errorsAmplitude, transmitted power of (2)Amplitude and signal ratio of (2)/Amplitude and slope of (a) as an effective feature.
The regularized result contains a fitted coefficient matrixFitting informationTwo important parameters. Wherein the coefficient matrixThe degree of contribution of the features in the model can be provided, the absolute value of the degree can be used as a basis for feature selection, and a screening threshold can be set to select important features.
By usingThe specific steps of the regularization method for screening the above features are as follows.
1. A dataset containing features is prepared and the data is subjected to the necessary digital filtering process.
2. Using constructed back propagation neural networksThe loss function of the regularization term builds a data training model to train the data.
3. Acquiring coefficient matrix of data training model after training。
4. According to coefficient matrixThe coefficient size of the effective characteristic is selected.
As a specific embodiment, the application is realized byRegularization screening, selecting error signalsAmplitude, transmitted power of (2)Amplitude and signal ratio of (2)/Is input to as effective feature. Specifically, the application filters the error signal and then detects the error signal obtained at each sampling pointTransmitted powerAnd the ratio thereof/The characteristic values of the four characteristics of amplitude and slope are used as input data of a group of back propagation neural networks, a target linear signal generated in real time through theoretical simulation at the sampling point is used as an ideal preset linear signal, as shown in fig. 8, the signal is used as response data to train and verify the back propagation neural networks, and the mean square error between a predicted value and a true value is used as a trained loss function.
And 3, inputting the effective characteristics into a trained back propagation neural network to obtain a fitting error signal.
The application uses the mean square error) As a loss function of the back propagation neural network training, i.e., the average of the square differences between the predicted and actual values of the back propagation neural network, the following equation is used.
(10)。
Wherein n is the total number of groups of samples, i is the ith group of samples; An actual error signal (true value) for the i-th set of samples; The error signal (predicted value) is fitted for the i-th set of samples.
The method inputs the effective characteristics into the trained back propagation neural network, and the trained back propagation neural network carries out linear fitting to generate a fitting error curve, as shown by oblique lines in fig. 7, and fitting error points can be obtained by the fitted oblique lines.
In addition, the back propagation neural network has strong generalization capability, approximation capability, self-adaptation capability and the like, but is prone to problems such as local optimal solutions. Therefore, the application also usesThe optimization algorithm performs parameter optimization on weights and thresholds in the back propagation neural network. Adopts the base ofThe self-adaptive moment estimation optimized back propagation neural network solves the problem of local optimal solution, and has high convergence speed and good robustness. After the back propagation neural network is constructed, the back propagation neural network is constructedThe optimizer performs the definition of the algorithm. Finally based onThe back-propagation neural network of the optimization algorithm models between the error signal and the target error signal, wherein,The algorithm flow chart is shown in fig. 6.
For a pair ofThe optimizer performs the algorithm definition, the algorithm steps of which are as follows.
1) First, initializing parameters, learning rateSet to 0.001, which determines the step size, momentum of each parameter updateTo set to 0.99, in order to accumulate historical gradient information, help to accelerate convergence, better guide the updating direction of parameters and estimate the secondary momentSetting to 1, selecting 1 enables the second moment estimate term to adapt quickly to the changing conditions of the gradient, thereby better adjusting the learning rate.
2) Calculating gradient of loss function using current parameters, updating momentumAn exponentially weighted average of the current gradient is accumulated into the momentum.
3) Updating a second moment estimateAn exponentially weighted average of the squares of the current gradient is accumulated into the second moment estimate.
4) Correction of、Using corrected deviations、To update parameters。
5) The above iteration is repeated.
Wherein the mean square error threshold is set toThe maximum number of iterations is set to 50.
And 4, performing fitting frequency locking of the cavity enhanced Raman spectrum according to the fitting error signal.
Obtaining a fitting error signal through a trained back propagation neural network (ADAM-BP)As shown in FIG. 8, the fitting error signal is obtained from the fitting error curveA first preset error thresholdAnd a second preset error thresholdThe values of the parameters are shown in fig. 7. When fitting error signalsIs smaller than a first preset error thresholdWhen the cavity enhanced Raman spectrum detection system is automatically switched to the locked state and uses the fitting error signalWhen fitting error signalIs smaller than a second preset error thresholdAt the same time, a first preset error threshold valueGreater than a second predetermined error thresholdCavity enhanced raman spectroscopy detection systems automatically switch to a locked state and use a more sensitive error signal. If there is external interference, the transmitted power is reduced to a second power thresholdThe fitting error signal is recovered as follows. In addition, if the transmitted power is lower than the first power thresholdFitting error signalExceeding a first preset error thresholdThe cavity enhanced raman spectroscopy detection system will initiate a rescan.Fitting the error signal expands the interval and range of the error signal. A first preset error threshold valueFirst power thresholdAnd obtaining the corresponding value obtained by repeated experiments when the target linear signal under the corresponding sampling point responds by averaging. As shown in fig. 7, each piezoelectric displacement corresponds to a transmission power line composed of different transmission power values, and since the piezoelectric displacement is selected more densely, a plurality of transmission power lines are densely arranged to form a region shown in the lower half of fig. 7.
In summary, as shown in fig. 3, the principle of the fitting frequency locking method of the cavity enhanced raman spectrum provided by the application mainly includes an error signal generating module, a back propagation neural network training and outputting module, and an error signal selecting and controlling outputting module. The locking process of the application comprises two links of scanning and locking, firstly, triangular wave scanning signals are applied to the cavity of the resonant cavity or the piezoelectric ceramics in the laser 15, linear scanning is carried out on the length of the cavity or the laser frequency, and error signals are obtained after matchingAnd according to the transmitted powerObtaining/Amplitude and slope thereof, obtaining fitting error signals through a trained back propagation neural networkAnd performing fitting frequency locking of the cavity enhanced Raman spectrum according to the fitting error signal. The DATA is additionally a large DATA set of stored error signals and transmitted power collected by the cavity enhanced raman spectroscopy detection system as input to train the counter-propagating neural network.
In addition, in order to characterize the anti-interference capability of the cavity enhanced Raman spectrum detection system based on the fitting frequency locking method of the cavity enhanced Raman spectrum, which is provided by the application, interference is respectively applied to the traditional cavity enhanced Raman spectrum detection system and the cavity enhanced Raman spectrum detection system of the application, and the result is shown in fig. 9, in which (a) in fig. 9 is a change diagram of an error signal and a piezoelectric ceramic control voltage after the traditional cavity enhanced Raman spectrum detection system is interfered, the original cavity enhanced Raman spectrum system cannot be locked again after the interference is applied,Will remain atWhile the error signal is almost 0. FIG. 9 (b) is a diagram showing the variation of the error signal and the control voltage of the piezoelectric ceramic after the interference of the cavity-enhanced Raman spectrum detection system according to the present applicationReturning toNearby, the state before the error signal is recovered after fluctuation, namely the system returns to the locking state after transient detuning, so that the cavity enhanced Raman spectrum detection system has better anti-interference capability.
In order to characterize the stability of the cavity enhanced Raman spectrum detection system based on the fitting frequency locking method of the cavity enhanced Raman spectrum, frequency locking experiments are respectively carried out under the same conditions by using error signals and fitting error signals, the locking time of the traditional cavity enhanced Raman spectrum detection system is extremely easy to lose lock within 15-20 minutes through multiple experiments, the transmission power is 0 after losing lock, and the reflection power is greatly enhanced. FIG. 10 is a schematic diagram showing the variation of the error signal and reflected power of the cavity-enhanced Raman spectrum detection system according to the present application, affected by the ambient temperature fluctuation and the ambient disturbance, the amplitude of the error signal always fluctuates around 0, and the maximum fluctuation is aboutThe reflected power amplitude also fluctuates around its minimum, with the maximum fluctuation being aboutThe frequency locking time is far longer than that of the traditional cavity enhanced Raman spectrum detection system and is stabilized for more than 200 minutes, so that the cavity enhanced Raman spectrum detection system has good long-term stability.
In some embodiments, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements the fitted frequency-locking method of cavity-enhanced raman spectroscopy.
In some embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the fitted frequency-locking method of cavity enhanced raman spectroscopy.
In some embodiments, the present application further provides a computer device, including a processor, a memory, an Input/Output interface (I/O for short), a communication interface, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method of fitting frequency locking of cavity-enhanced raman spectra.
The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store the pending transactions. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a fitted frequency-locking method of the cavity enhanced raman spectrum.
It should be noted that, the object information (including, but not limited to, object device information, object personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the object or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The principles and embodiments of the present application have been described herein with reference to specific examples, which are intended to facilitate an understanding of the principles and concepts of the application and are to be varied in scope and detail by persons of ordinary skill in the art based on the teachings herein. In view of the foregoing, this description should not be construed as limiting the application.
Claims (9)
1. A method for fitting and frequency locking of a cavity enhanced raman spectrum, comprising:
acquiring error signals and transmission power of a resonant cavity in the cavity enhancement Raman spectrum detection system;
determining an effective feature based on the error signal and the transmitted power;
Inputting the effective characteristics into a trained back propagation neural network to obtain a fitting error signal;
Performing fitting frequency locking of the cavity enhanced Raman spectrum according to the fitting error signal;
The method for determining the effective characteristics based on the error signal and the transmission power specifically comprises the following steps:
Based on error signals And transmitted powerError signalAmplitude and slope of (a) and transmitted powerAmplitude to slope and signal ratio of (v)/Is characterized by the magnitude and slope of (a);
By using Regularization method filters features and signals errorsAmplitude, transmitted power of (2)Amplitude and signal ratio of (2)/Amplitude and slope of (a) as an effective feature.
2. The method for fitting and locking frequencies of cavity-enhanced raman spectroscopy according to claim 1, wherein the step of obtaining the error signal and the transmission power of the resonant cavity in the cavity-enhanced raman spectroscopy detection system comprises:
and carrying out piezoelectric scanning on the resonant cavity in the cavity enhanced Raman spectrum detection system, acquiring error signals of the resonant cavity in each scanning period by adopting a high-sensitivity sensor, and synchronously measuring the transmission power of the resonant cavity in each scanning period by adopting a photoelectric detector.
3. The method of claim 1, wherein the trained counter-propagating neural network is trained based on Adam optimization algorithm with the goal of minimizing a loss function.
4. A method of fitting frequency locking of cavity enhanced raman spectra according to claim 3 wherein the Loss function of training by the back propagation neural network is the mean square error MSE: wherein n is the total number of groups of samples, i is the ith group of samples; An actual error signal for the i-th set of samples; fitting error signal for the i-th set of samples.
5. The method of claim 1, wherein the back propagation neural network comprises an input layer, three hidden layers and an output layer, wherein the three hidden layers are selected from 10, 8 and 5 nodes, respectively.
6. The method for fitting frequency-locking of the cavity-enhanced raman spectrum according to claim 1, wherein the performing fitting frequency-locking of the cavity-enhanced raman spectrum according to the fitting error signal specifically comprises:
When fitting error signals Is smaller than a first preset error thresholdWhen the cavity enhanced Raman spectrum detection system is automatically switched to the locked state and uses the fitting error signalWhen fitting error signalIs smaller than a second preset error thresholdAt the same time, a first preset error threshold valueGreater than a second predetermined error thresholdThe cavity enhanced raman spectrum detection system automatically switches to a locked state and uses an error signalIf external interference exists, the fitting error signal is recovered。
7. A computer program product comprising a computer program which, when executed by a processor, implements a method of fitting frequency locking of cavity enhanced raman spectra according to any one of claims 1 to 6.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the fitted frequency locking method of cavity enhanced raman spectroscopy according to any one of claims 1 to 6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the fitted frequency locking method of cavity enhanced raman spectroscopy of any one of claims 1 to 6.
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