A gas temperature measurement system and method based on laser optical technology

Abstract

The present invention relates to the field of gas temperature measurement technology, and more specifically, to a gas temperature measurement system and method based on laser optical technology. It includes: a transmitting unit for emitting laser light and generating an interference area, so that the light intensity of the signal light fluctuates; a receiving unit is placed on the optical path of the reflected signal light, and is used to receive the light intensity change of the signal light; an analog-to-digital unit converts the voltage signal output by the receiving unit and stores it; a signal processing unit performs fast Fourier transform on the data signal sequence, obtains the fluctuation change frequency of the signal and generates a calibration coefficient, and calculates the gas temperature based on the calibration coefficient and the fluctuation frequency. The present invention is designed to convert the time domain signal into a frequency domain representation through fast Fourier transform, revealing the different frequency components in the signal; by analyzing the frequency components, the main frequency and secondary frequency in the signal can be identified, which helps to extract the key features of the signal, including the frequency peak.

Description

Technical Field

[0001] The present invention relates to the technical field of gas temperature measurement, and in particular to a gas temperature measurement system and method based on laser optical technology. Background Art

[0002] Existing gas temperature measurement technologies, such as thermal resistors or thermocouples, require the sensor to be inserted into the airflow to be measured. This technology cannot measure the airflow temperature between rotating blades; due to the low response frequency and long response time, the existing technology cannot measure the airflow temperature that fluctuates at high speed; traditional contact temperature measurement methods (such as thermocouples, thermal resistors, etc.) may not be safe to use in high temperature, high pressure or dangerous environments. Contact sensors may interfere with the object being measured, change its physical state, and are easily affected by factors such as electromagnetic interference and vibration, resulting in inaccurate measurement results. Therefore, a gas temperature measurement system and method based on laser optical technology is provided. Summary of the invention

[0003] The object of the present invention is to provide a gas temperature measurement system and method based on laser optical technology to solve the problems of slow response speed, contact measurement limitations, and limited measurement accuracy proposed in the above background technology.

[0004] To achieve the above object, on the one hand, the present invention aims to provide a gas temperature measurement system based on laser optical technology, comprising:

[0005] An emitting unit, the emitting unit is used to emit laser light and generate an interference area, so that the intensity of the signal light fluctuates;

[0006] A receiving unit, the receiving unit is placed on the optical path of the reflected signal light and is used to receive the intensity change of the signal light;

[0007] An analog-to-digital unit, which converts and stores the voltage signal output by the receiving unit;

[0008] A signal processing unit performs a fast Fourier transform on a data signal sequence, obtains the fluctuation frequency of the signal and generates a calibration coefficient, and introduces the concentration of particulate matter in the air and the airflow disturbance parameters in the process of generating the calibration coefficient for optimization, so as to reduce the influence of the external environment on the signal, and calculates the gas temperature based on the calibration coefficient and the fluctuation frequency.

[0009] As a further improvement of the technical solution, the transmitting unit includes a pump laser transmitting module, a reflection module and a signal light laser transmitting module;

[0010] Among them, the pump laser emission module divides the laser into two beams. After passing through the reflection module, the two laser beams enter the focusing module in parallel and converge to form interference fringes; the signal light laser emission module provides a continuous laser signal, which is used to interact with the interference fringes formed by the pump laser emission module to modulate the intensity of the signal light.

[0011] As a further improvement of the technical solution, the reflection module is used to ensure that the two laser beams remain parallel before entering the focusing module by controlling the direction of the laser beam and the optical path difference between the two laser beams.

[0012] As a further improvement of the technical solution, the analog-to-digital unit includes a conversion module and a storage module;

[0013] Wherein, the conversion module is used to convert the voltage signal output by the receiving unit into a digital signal;

[0014] The storage module is used to store the converted digital signal.

[0015] As a further improvement of the technical solution, the signal processing unit performs fast Fourier transform on the data signal sequence to obtain the fluctuation change frequency of the signal, including the following steps:

[0016] S1.1. Calculate the frequency domain signal and construct a model including the signal's attenuation characteristics and nonlinear effect parameters to describe the signal's characteristics, which is used to optimize the fast Fourier transform process for signal processing:

[0017]

[0018] Where k represents the index of the frequency component, k = 0, 1, ..., N-1; N represents the signal length; X[k] represents the frequency domain signal; I(t n ) represents the constructed model including the attenuation characteristics and nonlinear effect parameters of the signal; n represents the index in the signal sequence; j represents the imaginary unit;

[0019] S1.2, after obtaining the frequency domain signal X[k], use the sampling frequency f s Calculate the frequency f corresponding to each frequency component k k :

[0020]

[0021] S1.3, select the frequency component with a larger amplitude in the frequency domain signal X[k] as the fluctuation change frequency f osc .

[0022] As a further improvement of the technical solution, in S1.1, the model including the attenuation characteristics of the signal and the nonlinear effect parameters is constructed as follows:

[0023]

[0024] Where, I(t) represents the function of the intensity of the signal light changing with time; A represents the initial intensity of the signal light; α represents the attenuation coefficient; β represents the amplitude of the nonlinear effect; ω represents the angular frequency of the nonlinear effect; represents the phase of the nonlinear effect; t represents time.

[0025] As a further improvement of the technical solution, the generation of the calibration coefficient comprises the following steps:

[0026] S2.1. Select a gas at a known temperature d as a calibration reference and record the signal sequence generated by the gas at this temperature;

[0027] S2.2, collecting signal sequences under different temperature conditions, each temperature condition corresponds to a set of signal sequences;

[0028] S2.3, perform fast Fourier transform on each group of signal sequences to obtain the corresponding frequency domain signal X[k];

[0029] S2.4. Analyze the signal frequency components under different temperature conditions, find the relationship between the frequency components and temperature, and establish a mathematical model to describe this relationship;

[0030] S2.5. Use the least squares method to fit the model parameters, and adjust the model parameters to make the predicted temperature as close to the actual temperature as possible;

[0031] S2.6. Obtain a set of calibration coefficients for mapping frequency components to actual temperature values.

[0032] As a further improvement of the technical solution, in S2.4, the mathematical model established is specifically:

[0033]

[0034] Where T is the temperature of the gas; f osc represents the amplitude of the frequency component; b represents the coefficient of the intercept term; c represents the frequency component f osc Influence coefficient on temperature T;

[0035] A mathematical model based on the optimization of the concentration of particulate matter in the air and the airflow disturbance parameters is introduced to reduce the impact of the external environment on the signal, specifically:

[0036]

[0037] Where T1 represents the gas temperature after the particle concentration and airflow disturbance parameters are introduced into the air; P represents the particle concentration; D represents the airflow disturbance; k PIndicates the influence coefficient of the concentration of particulate matter in the air; k D Represents the influence coefficient of airflow disturbance.

[0038] As a further improvement of the technical solution, in S2.5, the least squares method is used to fit the model parameters as follows:

[0039] S3.1. Define the residual function r i , which represents the difference between the model prediction and the actual value:

[0040] r i =T i -T pr (b,c,k P , k D , f o，i )

[0041] Among them, T i represents the actual temperature of the ith data point, T pr (b,c,k P , k D , f o，i ) represents the temperature value predicted by the model; f o，i represents the fluctuation frequency of the i-th data point;

[0042] S3.2. Define the objective function E, which represents the sum of all residual squares:

[0043]

[0044] S3.3, select the initial value b0 of the intercept term coefficient and the frequency component f osc The initial value of the coefficient of influence on temperature T c0, the initial value of the coefficient of influence on the concentration of particulate matter in the air k P0 , initial value k of the influence coefficient of airflow disturbance D0 ;

[0045] S3.4. Use the Gauss-Newton method to gradually adjust the intercept coefficient b and frequency component f osc The influence coefficient c on temperature T and the influence coefficient k on the concentration of particulate matter in the air P , the influence coefficient of airflow disturbance k D until the objective function E reaches its minimum value;

[0046] S3.5. When the change of the objective function E is less than the threshold u, stop the iteration and check whether the difference between the temperature predicted by the model and the actual temperature is within an acceptable range.

[0047] On the other hand, the present invention provides a gas temperature measurement method based on laser optical technology, based on the above-mentioned gas temperature measurement system based on laser optical technology, comprising the following steps:

[0048] S4.1, by emitting laser light and generating an interference region, the intensity of the signal light fluctuates;

[0049] S4.2, receiving the intensity change of the signal light by the reflected signal light;

[0050] S4.3, converting the light intensity change into a voltage signal and then into a data signal and storing it;

[0051] S4.4. Perform fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal and generate a calibration coefficient, and calculate the gas temperature based on the calibration coefficient and the fluctuation frequency.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. In the gas temperature measurement system and method based on laser optical technology, the fast Fourier transform can convert the time domain signal into a frequency domain representation, revealing the different frequency components in the signal; by analyzing the frequency components, the main frequency and secondary frequency in the signal can be identified, which helps to extract the key features of the signal, including the frequency peak, which is very important for the gas temperature measurement system. The fast Fourier transform result can be used to establish a mathematical model to describe the relationship between the frequency component and the temperature; the measurement system can be optimized by comparing the frequency components at different temperatures.

[0054] 2. In the gas temperature measurement system and method based on laser optical technology, by generating the relationship between the fluctuation frequency of the signal and the gas flow temperature, the calibration coefficient can be obtained through simple calibration. The calibration process is relatively simple and usually only requires data from a few known temperature points. Once the calibration is completed, the system can stably and reliably monitor temperature changes. This stability reduces the need for maintenance and calibration. It is applicable to various gases as long as the relationship between frequency and temperature is known. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is the overall flow chart of the present invention.

[0056] The meaning of each number in the figure is:

[0057] 1. Transmitting unit; 2. Receiving unit; 3. Analog-to-digital unit; 4. Signal processing unit. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Embodiment 1:

[0060] See also Figure 1 As shown, a gas temperature measurement system based on laser optical technology is provided, comprising:

[0061] The transmitting unit 1 is used to transmit laser light and generate an interference area, so that the intensity of the signal light fluctuates;

[0062] In this embodiment, the transmitting unit 1 includes a pump laser transmitting module, a reflection module and a signal light laser transmitting module;

[0063] Among them, the pump laser emission module divides the laser into two beams. After passing through the reflection module, the two laser beams enter the focusing module in parallel and converge to form interference fringes; the signal light laser emission module provides a continuous laser signal, which is used to interact with the interference fringes formed by the pump laser emission module to modulate the intensity of the signal light.

[0064] Furthermore, the reflection module and the focusing module are specifically:

[0065] The reflection module controls the direction of the laser beam and the optical path difference between the two laser beams to ensure that the two laser beams remain parallel before entering the focusing module;

[0066] The focusing module (convex lens) is used to converge parallel light beams into the same area to form interference fringes.

[0067] The receiving unit 2 is placed on the optical path of the reflected signal light and is used to receive the intensity change of the signal light;

[0068] The analog-to-digital unit 3 converts the voltage signal output by the receiving unit 2 and stores it;

[0069] In this embodiment, the analog-to-digital unit 3 includes a conversion module and a storage module;

[0070] Wherein, the conversion module is used to convert the voltage signal output by the receiving unit 2 into a digital signal;

[0071] The storage module is used to store the converted digital signal.

[0072] The signal processing unit 4 performs fast Fourier transform on the data signal sequence, obtains the fluctuation frequency of the signal and generates a calibration coefficient, and calculates the gas temperature based on the calibration coefficient and the fluctuation frequency;

[0073] In this embodiment, the signal processing unit 4 performs a fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal. The result after the fast Fourier transform conversion can intuitively display the energy distribution of the signal, that is, which frequency components contribute more to the signal, which is very useful for identifying specific patterns in the signal. The fast Fourier transform result can be used to establish a mathematical model to describe the relationship between the frequency component and the temperature, and can also be used for further data analysis. By comparing the frequency components at different temperatures to optimize the measurement system, the following steps are included:

[0074] S1.1. Calculate the frequency domain signal and construct a model including the signal's attenuation characteristics and nonlinear effect parameters to describe the signal's characteristics, which is used to optimize the fast Fourier transform process for signal processing:

[0075]

[0076] Where k represents the index of the frequency component, k = 0, 1, ..., N-1; N represents the signal length; X[k] represents the frequency domain signal; x[n] represents the nth sample value in the time domain signal sequence; I(t n ) represents the constructed model including the attenuation characteristics of the signal and the nonlinear effect parameters. The model represents the n The signal strength value on the signal sequence; n represents the index in the signal sequence; j represents the imaginary unit, indicating that j 2 =--number of 1;

[0077] Furthermore, many actual signals experience nonlinear effects during propagation, which may affect the frequency components of the signal. By establishing a mathematical model of nonlinear effects, the effects of these effects can be better predicted and compensated. If nonlinear effects exist in the signal and are not modeled correctly, artifacts may be generated. By considering these effects in advance, these artifacts can be reduced or eliminated, making the results more accurate. Both attenuation and nonlinear effects may cause the frequency components of the signal to be unclear. By using a model to describe these effects, a clearer frequency spectrum can be obtained after fast Fourier transform processing, thereby improving the frequency resolution. The model that includes the attenuation characteristics of the signal and the nonlinear effect parameters is specifically constructed as follows:

[0078]

[0079] Among them, I(t) represents the function of the intensity of the signal light changing with time; A represents the initial intensity of the signal light; α represents the attenuation coefficient, that is, the speed at which the signal intensity decays with time, which can be determined according to the properties of the gas and the propagation distance; β represents the amplitude of the nonlinear effect, that is, the nonlinear part of the signal intensity changing with time; ω represents the angular frequency of the nonlinear effect; represents the phase of the nonlinear effect; t represents time;

[0080] S1.2, after obtaining the frequency domain signal X[k], use the sampling frequency f s Calculate the frequency f corresponding to each frequency component k k :

[0081]

[0082] S1.3, select the frequency component with a larger amplitude in the frequency domain signal X[k] as the fluctuation change frequency f osc .

[0083] The generation of the calibration coefficient includes the following steps:

[0084] S2.1. Select a gas at a known temperature d as a calibration reference and record the signal sequence generated by the gas at this temperature;

[0085] S2.2, collecting signal sequences under different temperature conditions, each temperature condition corresponds to a set of signal sequences;

[0086] S2.3, perform fast Fourier transform on each group of signal sequences to obtain the corresponding frequency domain signal X[k];

[0087] S2.4. Analyze the signal frequency components under different temperature conditions, find the relationship between the frequency components and temperature, and establish a mathematical model to describe this relationship;

[0088] The mathematical model established is as follows:

[0089]

[0090] Where T is the temperature of the gas; f osc represents the amplitude of the frequency component, that is, the frequency of the fluctuation of the signal; b represents the coefficient of the intercept term, which determines the basic value of temperature prediction; c represents the frequency component fo sc Influence coefficient on temperature T;

[0091] The mathematical model established by introducing the optimization of the particle concentration in the air and the airflow disturbance parameters is used to reduce the impact of the external environment on the signal. The particle concentration in the air and the airflow disturbance may affect the propagation and detection of the laser signal. By introducing these parameters, the impact of these external factors on the measurement results can be more accurately reflected, thereby improving the measurement accuracy; changes in particle concentration may affect the intensity and frequency components of the laser signal; airflow disturbances may affect the temperature distribution, thereby affecting the measurement results; by introducing these parameters, the impact of these environmental factors can be compensated to a certain extent. Through these improvements, the model can better adapt to various environmental conditions and provide more accurate temperature measurements in practical applications, specifically:

[0092]

[0093] Where T1 represents the gas temperature after the particle concentration and airflow disturbance parameters are introduced into the air; P represents the particle concentration; D represents the airflow disturbance; k P Indicates the influence coefficient of the concentration of particulate matter in the air; k D Represents the influence coefficient of airflow disturbance.

[0094] S2.5. Use the least squares method to fit the model parameters. Adjust the model parameters to make the predicted temperature as close to the actual temperature as possible. Model parameters refer to the values ​​that can characterize the relationship between the frequency component and the temperature, including the model intercept and slope.

[0095] Among them, the concept of least squares method is simple and easy to understand. Its purpose is to minimize the residual sum of squares between the observed value and the model predicted value. This intuitive goal makes this method easy to understand and implement. It can be easily extended to nonlinear models and other more complex models, including solving nonlinear least squares problems by using gradient descent or other iterative optimization algorithms. It provides an intuitive way to evaluate the degree of fit of the model. The least squares method is used to fit the model parameters as follows:

[0096] S3.1. Define the residual function r i , which represents the difference between the model prediction and the actual value:

[0097] r i =T i -T pr (b,c,k P , k D , f o，i )

[0098] Among them, T i represents the actual temperature of the ith data point, T pr (b,c,k P , k D , fo，i ) represents the temperature value predicted by the model; f o，i represents the fluctuation frequency of the i-th data point;

[0099] S3.2. Define the objective function E, which represents the sum of all residual squares:

[0100]

[0101] The objective function is to find the intercept coefficient b and frequency component f that minimize E. osc The influence coefficient c on temperature T and the influence coefficient k on the concentration of particulate matter in the air P , the influence coefficient of airflow disturbance k D The value of

[0102] S3.3, select the initial value b0 of the intercept term coefficient and the frequency component f osc The initial value of the coefficient of influence on temperature T c0, the initial value of the coefficient of influence on the concentration of particulate matter in the air k P0 , initial value k of the influence coefficient of airflow disturbance D0 ;

[0103] S3.4. Use the Gauss-Newton method to gradually adjust the intercept coefficient b and frequency component f osc The influence coefficient c on temperature T and the influence coefficient k on the concentration of particulate matter in the air P , the influence coefficient of airflow disturbance k D until the objective function E reaches its minimum value;

[0104] S3.5, when the change of the objective function E is less than the threshold u, stop the iteration and check whether the difference between the temperature predicted by the model and the actual temperature is within an acceptable range;

[0105] S2.6. Obtain a set of calibration coefficients for mapping frequency components to actual temperature values.

[0106] Embodiment 2:

[0107] The difference between Example 2 of the present invention and Example 1 is that this example introduces a gas temperature measurement method used in a gas temperature measurement system based on laser optical technology.

[0108] A gas temperature measurement method based on laser optical technology, based on the above-mentioned gas temperature measurement system based on laser optical technology, comprises the following steps:

[0109] S4.1, by emitting laser light and generating an interference region, the intensity of the signal light fluctuates;

[0110] S4.2, receiving the intensity change of the signal light by the reflected signal light;

[0111] S4.3, converting the light intensity change into a voltage signal and then into a data signal and storing it;

[0112] S4.4. Perform fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal and generate a calibration coefficient, and calculate the gas temperature based on the calibration coefficient and the fluctuation frequency.

[0113] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A gas temperature measurement system based on laser optical technology, characterized in that: include: An emitting unit (1), the emitting unit (1) being used to emit laser light and generate an interference region, causing the intensity of the signal light to fluctuate; A receiving unit (2), the receiving unit (2) being placed on the optical path of the reflected signal light and used for receiving changes in the intensity of the signal light; An analog-to-digital unit (3), wherein the analog-to-digital unit (3) converts and stores the voltage signal output by the receiving unit (2); A signal processing unit (4), wherein the signal processing unit (4) performs a fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal and generate a calibration coefficient, and introduces the concentration of particulate matter in the air and the airflow disturbance parameter for optimization in the process of generating the calibration coefficient, so as to reduce the influence of the external environment on the signal, and calculates the gas temperature based on the calibration coefficient and the fluctuation frequency; The generation of the calibration coefficient comprises the following steps: S2.

1. Select a gas at a known temperature d as a calibration reference and record the signal sequence generated by the gas at this temperature; S2.2, collecting signal sequences under different temperature conditions, each temperature condition corresponds to a set of signal sequences; S2.3, perform fast Fourier transform on each group of signal sequences to obtain the corresponding frequency domain signal ; S2.

4. Analyze the signal frequency components under different temperature conditions, find the relationship between the frequency components and temperature, and establish a mathematical model to describe this relationship; S2.

5. Use the least squares method to fit the model parameters, and adjust the model parameters to make the predicted temperature as close to the actual temperature as possible; S2.6, obtaining a set of calibration coefficients for mapping frequency components to actual temperature values; In S2.4, the mathematical model established is specifically: in, Indicates the temperature of the gas; Indicates the amplitude of the frequency component; represents the coefficient of the intercept term; Indicates frequency components Temperature The influence coefficient of A mathematical model based on the optimization of the concentration of particulate matter in the air and the airflow disturbance parameters is introduced to reduce the impact of the external environment on the signal, specifically: in, Indicates the gas temperature after the particle concentration and airflow disturbance parameters in the air are introduced; Indicates the concentration of particulate matter; Indicates air flow disturbance; Indicates the influence coefficient of the concentration of particulate matter in the air; represents the influence coefficient of airflow disturbance; In S2.5, the least squares method is used to fit the model parameters as follows: S3.

1. Define the residual function , which represents the difference between the model prediction and the actual value: in, Indicates The actual temperature of the data point, represents the temperature value predicted by the model; Indicates The fluctuation frequency of each data point; S3.

2. Define the objective function , which represents the sum of all residual sums of squares: S3.

3. Select the initial value of the intercept term coefficient , frequency components Initial value of the coefficient of influence on temperature T , Initial value of the influence coefficient of particulate matter concentration in the air , initial value of the influence coefficient of airflow disturbance ; S3.

4. Use the Gauss-Newton method to gradually adjust the intercept coefficient , frequency components Influence coefficient on temperature T , the influence coefficient of the concentration of particulate matter in the air , the influence coefficient of air flow disturbance until the objective function Reach a minimum value; S3.

5. When the objective function When the change in is less than the threshold u, stop the iteration and check whether the difference between the temperature predicted by the model and the actual temperature is within an acceptable range.

2. The gas temperature measurement system based on laser optical technology according to claim 1, characterized in that: The transmitting unit (1) comprises a pump laser transmitting module, a reflection module and a signal light laser transmitting module; Among them, the pump laser emission module divides the laser into two beams. After passing through the reflection module, the two laser beams enter the focusing module in parallel and converge to form interference fringes; the signal light laser emission module provides a continuous laser signal, which is used to interact with the interference fringes formed by the pump laser emission module to modulate the intensity of the signal light.

3. The gas temperature measurement system based on laser optical technology according to claim 2, characterized in that: The reflection module is used to ensure that the two laser beams remain parallel before entering the focusing module by controlling the direction of the laser beams and the optical path difference between the two laser beams.

4. The gas temperature measurement system based on laser optical technology according to claim 1, characterized in that: The analog-to-digital unit (3) comprises a conversion module and a storage module; The conversion module is used to convert the voltage signal output by the receiving unit (2) into a digital signal; The storage module is used to store the converted digital signal.

5. The gas temperature measurement system based on laser optical technology according to claim 1, characterized in that: The signal processing unit (4) performs fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal, including the following steps: S1.

1. Calculate the frequency domain signal and construct a model including the signal's attenuation characteristics and nonlinear effect parameters to describe the signal's characteristics, which is used to optimize the fast Fourier transform process for signal processing: in, represents the index of the frequency component, ; Indicates the signal length; represents a frequency domain signal; It represents the constructed model including the attenuation characteristics of the signal and the nonlinear effect parameters; represents the index in the signal sequence; represents an imaginary unit; S1.

2. Obtain frequency domain signal Then, use the sampling frequency Calculate each frequency component The corresponding frequency : S1.

3. Select frequency domain signal The frequency component with a larger amplitude is used as the fluctuation frequency .

6. The gas temperature measurement system based on laser optical technology according to claim 5, characterized in that: In S1.1, the model including the attenuation characteristics of the signal and the nonlinear effect parameters is constructed as follows: in, A function representing the intensity of the signal light changing with time; Indicates the initial intensity of the signal light; represents the attenuation coefficient; represents the magnitude of the nonlinear effect; represents the angular frequency of nonlinear effects; represents the phase of nonlinear effects; Indicates time.

7. A gas temperature measurement method based on laser optical technology, based on the gas temperature measurement system based on laser optical technology as claimed in any one of claims 1 to 6, characterized in that: The steps include: S4.1, by emitting laser light and generating an interference region, the intensity of the signal light fluctuates; S4.2, receiving the intensity change of the signal light by the reflected signal light; S4.3, converting the light intensity change into a voltage signal and then into a data signal and storing it; S4.

4. Perform fast Fourier transform on the data signal sequence to obtain the fluctuation frequency of the signal and generate a calibration coefficient, and calculate the gas temperature based on the calibration coefficient and the fluctuation frequency.

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