A self-diagnosis qualification detection method for an optical fiber sensor temperature measurement system

Abstract

The application provides a self-diagnosis qualified detection method for an optical fiber sensor temperature measurement system, and has the characteristics that it comprises the steps of: optical spectrum data stability detection; optical fiber sensor access detection; optical spectrum self-adaptive promotion adjustment; optical fiber sensor channel quality parameter determination; and optical fiber sensor connector quality parameter determination.The beneficial effects of the application are that the integrity of the optical fiber sensor reflection spectrum data can be detected, stable spectrum data can be ensured to be obtained by the calculation platform, whether the optical fiber sensor has been accessed into the optical fiber sensor temperature measurement system can be detected, the influence of light source fluctuation, circuit voltage fluctuation and the like on the reflection spectrum can be shielded, whether the spectrum data is qualified can be detected, unqualified spectrum data can be promoted and repaired, the optical fiber sensor channel quality parameter and the optical fiber sensor connector quality parameter can be determined, and quality scores can be provided for subsequent temperature demodulation algorithms, so that the temperature calculation result is more accurate and stable.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of optical fiber sensor temperature measurement, and particularly relates to a self-diagnosis qualified detection method for an optical fiber sensor temperature measurement system. BACKGROUND

[0002] The optical fiber sensor temperature measurement system is used for calculating the measured temperature through a temperature demodulation algorithm on an embedded computing platform by using the spectral data of the reflected light after the light source is absorbed by the optical fiber temperature sensor.

[0003] The optical fiber sensor temperature measurement system is not afraid of electromagnetic interference, can be easily received by various optical detection devices, can be easily converted between photoelectricity and electricity, can be easily matched with highly developed modern electronic devices and computers, and is particularly suitable for use in harsh environments such as flammable, explosive, strictly limited space and strong electromagnetic interference, such as online monitoring of internal "hot spots" of switches and transformers in power systems, online measurement of the temperature of heated substances in processes such as microwave heating, microwave chemical auxiliary instruments, microwave food processing, online safety monitoring of medical devices such as radio frequency, microwave hyperthermia instruments and magnetic resonance imaging instruments, and monitoring of the temperature of key parts such as stators and bearing shells of large motors. However, the temperature demodulation algorithm of the mainstream traditional optical fiber sensor temperature measurement system on the market generally lacks a self-diagnosis detection method.

[0004] For example, the patent CN201410471389.8 does not have a self-diagnosis detection method, so it cannot perform self-diagnosis detection protection on the spectral data at the entrance of the data processing method, which makes the stability and anti-interference performance of the data processing method poor. In addition, the temperature demodulation algorithm in the traditional optical fiber sensor temperature measurement system also does not have a self-diagnosis detection algorithm for shielding the light path and the fluctuation of the light source, which can easily adversely affect the temperature calculation result. In addition, the temperature demodulation algorithm in the traditional optical fiber sensor temperature measurement system does not determine the quality parameters of the optical fiber sensor channel and the quality parameters of the optical fiber sensor connector before calculation and analysis, and cannot provide reliable, stable and qualified data for subsequent temperature calculation and demodulation. SUMMARY

[0005] The present application aims to solve the following problems in the current optical fiber sensor temperature demodulation algorithm: the entrance of the optical fiber sensor temperature demodulation algorithm does not have a self-diagnosis detection method protection, the spectral data is not detected for its eligibility, the light path quality parameters are not detected, the optical fiber connector quality parameters are not detected, the fluctuation of the light source and the fluctuation of the circuit voltage interfere with the process of collecting spectral data and affect the temperature calculation result, and a self-diagnosis qualified detection method for an optical fiber sensor temperature measurement system is provided.

[0006] This invention is achieved through the following technical solution: a self-diagnostic qualification detection method for a fiber optic sensor temperature measurement system, characterized by comprising the steps of: spectral data stability detection; fiber optic sensor access detection; spectral adaptive recommendation adjustment; determination of fiber optic sensor channel quality parameters; and determination of fiber optic sensor connection quality parameters.

[0007] Furthermore, the specific method for detecting the stability of the spectral data includes:

[0008] The embedded computing platform of the single-channel light source system cyclically collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and waits for the spectral data to stabilize. The embedded computing platform of the multi-channel light source system continuously collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and compares the stability of the edge spectral values ​​and peak spectral values ​​of two adjacent spectral data. If the change value of the two spectra reaches below the pre-configured threshold, the spectral data stability test is deemed qualified, and the spectral data enters the fiber optic sensor access detection process.

[0009] The change values ​​of the two spectra are calculated using the following formula 1.

[0010] [Formula 1](IR-IRO) / MAXMEN=(∑((IRT[i]-IROT[i]) / IRT[i])) / MAXMEN

[0011] Wherein, IR represents the last reflectance spectral value, IRO represents the penultimate reflectance spectral value, MAXMEN represents the number of elements in the reflectance spectrum, IRT represents the last reflectance spectral element group, IROT represents the penultimate reflectance spectral element group, and i represents the index value of the reflectance spectrum.

[0012] When the light source component is turned on or switched, the reflection spectrum data of the fiber optic sensor needs a period of stabilization from the time the light source is lit until it is fully collected. Furthermore, when the light source system is a single channel, the collected spectral data may not be intact as the temperature changes. Therefore, it is necessary to test the stability of the spectral data.

[0013] Furthermore, the specific method for detecting the access of the fiber optic sensor includes:

[0014] First, find the highest peak spectral value IRmax of the optical fiber sensor's reflectance spectrum, and then normalize the optical fiber sensor's reflectance spectrum data.

[0015] The spectral data is normalized using the following formula 2.

[0016] [Equation 2] R = IR[i] / IRmax

[0017] Where R represents the normalized spectral value, IR represents the original spectral value, IRmax is the maximum peak value of the original spectrum, and i represents the index value of the original spectrum.

[0018] Next, the wavelength values ​​at the peak of the reflected spectrum of the fiber optic sensor and at half the peak height were found, and the wavelength characteristic values ​​were calculated.

[0019] The wavelength characteristic value is calculated using the following formula 3.

[0020] [Equation 3] λfax=λmax-λmidl

[0021] Where λfax represents the wavelength characteristic value, λmax represents the peak wavelength value, and λmidl represents the wavelength value at half the peak height.

[0022] Finally, the wavelength characteristic value λfax is compared with ΔMAX, which represents the maximum value of λfax when the fiber optic sensor is connected. When λfax < ΔMAX, it is considered that the steep peak characteristic of the reflection spectrum of the fiber optic sensor exists and the fiber optic sensor has been connected to the fiber optic sensor temperature measurement system; otherwise, it is considered that the fiber optic sensor has not been connected.

[0023] When a fiber optic sensor is connected to a fiber optic temperature measurement system, its reflection spectrum will exhibit a steep peak characteristic. Therefore, the presence of a fiber optic sensor can be detected by identifying this steep peak characteristic.

[0024] Furthermore, the specific method for the spectral adaptive recommendation adjustment includes:

[0025] First, find the current light intensity parameter Dac and the current integration parameter Intg, and calculate the relative positions of these two dependent variable parameters within their respective configuration ranges;

[0026] The relative position of the light source intensity is calculated using the following formula 4.

[0027] [Equation 4] Dacp = (Dac - Dacmin) / (Dacmax - Dacmin)

[0028] Where Dacp represents the relative position of the light source intensity, Dac represents the current light source intensity, Dacmax represents the maximum adjustable light source intensity, and Dacmin represents the minimum adjustable light source intensity.

[0029] The relative positions of the integration parameters are calculated using Equation 5 below.

[0030] [Equation 5] Intgp = (Intg - Intgmin) / (Intgmax - Intgmin)

[0031] Where Intgp represents the relative position of the integration parameter, Intg represents the current integration parameter, Intgmax represents the maximum adjustable integration parameter, and Intgmin represents the minimum adjustable integration parameter.

[0032] Then, find the maximum peak value of the reflection spectrum of the fiber optic sensor and compare it with the configured peak value;

[0033] If the maximum peak value of the reflected spectrum of the fiber optic sensor is less than the configured peak value, the peak value needs to be increased. When Intgp <= Dacp, increase the integration parameter; when Intgp > Dacp, increase the light intensity of the light source.

[0034] If the maximum peak value of the reflected spectrum of the fiber optic sensor is greater than the configured peak value, the peak value needs to be lowered. When Intgp <= Dacp, the light intensity of the light source should be lowered; when Intgp > Dacp, the integration parameter should be lowered.

[0035] Among them, when pushing the peak value, a step-by-step feedback adjustment method is used.

[0036] Upon detecting the presence of a fiber optic sensor, adaptive adjustment is performed on the spectral data. This step aims to mitigate the impact of fluctuations in the light source and circuit voltage on the light source, ultimately affecting the reflectance spectrum. This step influences the peak values ​​by varying the control variables to achieve the desired uniform spectral pattern. The two dependent variables affecting the spectral peak values ​​are the DAC light source intensity parameter and the spectrometer's integral parameter Intg. Increasing either parameter increases the peak value, while decreasing it decreases it. By controlling these two dependent variables, a uniform spectral standard is achieved, and this standard is a configurable parameter.

[0037] Furthermore, the specific method for the step-by-step feedback adjustment includes:

[0038] Before starting the initial adjustment, you need to select the initial adjustment step size. The algorithm system has a default three-level peak difference adjustment step size. This parameter is the default configuration parameter and can be configured based on the empirical values ​​generated by the recommendation data. If the first level is less than istep1, select Dacsetup1 and Intgsetup1 for the adjustment step size. If the second level isetp2 is located in the interval [istep1, istep2], select Dacsetup2 and Intgsetup2 for the adjustment step size. If the third level is greater than isetp2, select Dacsetup3 and Intgsetup3 for the adjustment step size.

[0039] Then, the spectral data stability test and fiber optic sensor access test are performed again. After obtaining a stable spectral waveform, the adaptive recommendation and adjustment process is entered again. The maximum peak value is found and compared with the previous peak value to calculate the difference. Finally, based on the feedback of the difference, the influence of the unit integral parameter on the peak and the influence of the unit light intensity of the light source on the peak are calculated.

[0040] The peak feedback value of the integral parameter is calculated using the following formula 6.

[0041] [Equation 6] △dIntg=(IRmax-IRmaxold) / (Intgadd)

[0042] Where △dIntg represents the feedback value of the integral parameter peak, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Intgadd represents the change value of the integral parameter.

[0043] The peak feedback value of the light intensity of the light source is calculated using the following formula 7.

[0044] [Equation 7] △dDac=(IRmax-IRmaxold) / (Dacadd)

[0045] Where △dDac represents the peak feedback value of the light source intensity, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Dacadd represents the change value of the light source intensity.

[0046] If the phase difference between the reflection spectrum peak and the configured peak is within the preset threshold range, the adjustment ends; if the phase difference between the reflection spectrum peak and the configured peak is not within the preset threshold range, the split-half approximation method is used to set the adjustment parameters.

[0047] Furthermore, the specific method for setting the adjustment parameters of the split-half approximation method includes:

[0048] The change in light intensity can be calculated using the following formula 8.

[0049] [Equation 8] Dacadd=((IRcfg-IRmax)*△dDac) / Δcol

[0050] Where Dacadd represents the change in light intensity, IRcfg represents the configured peak value, IRmax represents the current peak value, ΔdDac represents the light intensity peak feedback value, and Δcol represents the number of times the configured peak value is reached by adjusting the light intensity of the light source.

[0051] The change in the integral parameter is calculated using the following formula 9.

[0052] [Equation 9] Intgadd=((IRcfg-IRmax)*△dintg) / Δcol

[0053] Where Intgadd represents the change in integral parameters, IRcfg represents the configured peak value, IRmax represents the current peak value, Δdintg represents the feedback value of the integral parameter peak, and Δcol represents the number of times the configured peak value is reached by adjusting the integral parameters.

[0054] Then, based on the above calculation results, the integral parameter adjustment is designed. If the adjustment exceeds the limit, it is adjusted downwards until it reaches the configuration peak range. The adjustment ends, and the analysis and calculation stage begins.

[0055] Furthermore, the specific method for determining the quality parameters of the fiber optic sensor channel includes:

[0056] The quality parameters of the fiber optic sensor channel are calculated from the optical path intensity variable, the spectrometer integration parameter variable, the maximum intensity variable, and their respective weight biases.

[0057] The quality parameters of the fiber optic sensor channel are calculated according to the following formula 10.

[0058] [Formula 10] IQuality=(IRmax*PIRmax)+1 / (DAC*PDAC)+1 / (Intg*PIntg)

[0059] Where IQuality represents the fiber optic sensor channel quality parameter, IRmax represents the maximum light intensity variable, PIRmax represents the maximum light intensity weight bias, DAC represents the optical path light intensity variable, PDAC represents the optical path light intensity weight bias, Intg represents the spectrometer integration parameter variable, and PIntg represents the spectrometer integration parameter weight bias.

[0060] Furthermore, the specific method for determining the fiber optic sensor splice quality parameters includes:

[0061] The fiber optic sensor splicing quality parameters are calculated by the ratio of the peak position value of the fiber optic sensor's reflection spectrum to the maximum light intensity variable.

[0062] The fiber optic splice quality parameters are calculated using the following formula 11.

[0063] [Equation 11] IPlugQuality = IFrontvalue / IRmax

[0064] Among them, IPlugQuality represents the fiber optic sensor splicing quality parameter, IFRontvalue represents the height value of the starting position of the reflection spectrum peak, and IRmax represents the maximum light intensity variable.

[0065] Then, the fiber optic sensor plug quality parameter IPlugQuality is compared with the fiber optic sensor plug quality threshold okPlugQuality. If the okPlugQuality threshold is reached, it is considered qualified.

[0066] The beneficial effects of this invention are as follows: By detecting the stability of spectral data, the integrity of the reflectance spectral data of the fiber optic sensor can be verified, ensuring that the computing platform acquires stable spectral data; by detecting fiber optic sensor access, it can be determined whether the fiber optic sensor has been connected to the fiber optic sensor temperature measurement system; by adaptive spectral recommendation adjustment, the influence of light source fluctuations, circuit voltage fluctuations, and other fluctuations on the reflectance spectrum can be shielded, and the quality of the spectral data can be detected, with unqualified spectral data being recommended and repaired; by determining the quality parameters of the fiber optic sensor channel and the quality parameters of the fiber optic sensor connection, the quality and aging status of the fiber optic sensor can be indicated, providing a quality score for the subsequent temperature demodulation algorithm, making temperature calculation more accurate and stable. [Attached Image Description]

[0067] Figure 1 This is a system flowchart of the present invention;

[0068] Figure 2 This is a diagram showing the composition of the fiber optic sensor temperature measurement system of the present invention;

[0069] Figure reference numerals: S1, Spectral data stability detection; S2, Fiber optic sensor access detection; S3, Spectral adaptive recommendation adjustment; S4, Fiber optic sensor channel quality parameter determination; S5, Fiber optic sensor connection quality parameter determination; 11, Fiber optic sensor; 12, Sensor interface; 21, Light source assembly; 22, Optical path summation assembly; 23, Spectrometer; 24, Embedded computing platform.

Detailed Implementation Methods

[0070] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0071] Example

[0072] like Figure 2 As shown, the fiber optic sensor temperature measurement system includes a fiber optic sensor component and a fiber optic sensor temperature demodulator. The fiber optic sensor component includes a fiber optic sensor 11 and a sensor interface 12. The fiber optic sensor temperature demodulator includes a light source assembly 21, an optical path combining component 22, a spectrometer 23, and an embedded computing platform 24. The embedded computing platform 24 runs the fiber optic sensor temperature demodulation algorithm and other software components. The fiber optic sensor temperature measurement system utilizes the spectral data reflected from the light source absorbed by the fiber optic temperature sensor, which is then processed by the spectrometer on the embedded computing platform using a temperature demodulation algorithm to calculate the measured temperature.

[0073] The fiber optic sensor temperature demodulation algorithm is a software method running on the embedded computing platform of a fiber optic sensor temperature demodulator. It comprises three main parts: data acquisition, data analysis, and temperature calculation. The data acquisition, analysis, and calculation process involves the embedded computing platform acquiring reflectance spectral data from the spectrometer, performing preprocessing, and then performing pre-defined analyses and calculations to obtain the measured temperature. This invention performs a series of conformity checks on the reflectance spectral data obtained from the fiber optic sensor after the embedded computing platform receives it. It adjusts unqualified spectral data to ensure conformity and determines the fiber optic sensor channel quality parameters and fiber optic splice quality parameters. This provides a reliable source of intact reflectance spectral data for the subsequent demodulation algorithm, resulting in more accurate and stable temperature calculations.

[0074] like Figure 1 As shown, a self-diagnostic qualification detection method for a fiber optic sensor temperature measurement system is characterized by the following steps: S1 spectral data stability detection; S2 fiber optic sensor access detection; S3 spectral adaptive recommendation adjustment; S4 fiber optic sensor channel quality parameter determination; S5 fiber optic sensor connection quality parameter determination.

[0075] Preferably, the specific method for detecting the stability of the S1 spectral data includes:

[0076] The embedded computing platform of the single-channel light source system continuously collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and waits for the spectral data to stabilize. The embedded computing platform of the multi-channel light source system continuously collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and compares the stability of the edge spectral values ​​and peak spectral values ​​of two adjacent spectral data. If the change of the two spectra reaches less than one percent of the pre-configured threshold, the spectral data stability test is deemed qualified, and the spectral data enters the S2 fiber optic sensor access detection and processing flow.

[0077] The change values ​​of the two spectra are calculated using the following formula 1.

[0078] [Formula 1](IR-IRO) / MAXMEN=(∑((IRT[i]-IROT[i]) / IRT[i])) / MAXMEN

[0079] Wherein, IR represents the last reflectance spectral value, IRO represents the penultimate reflectance spectral value, MAXMEN represents the number of elements in the reflectance spectrum, IRT represents the last reflectance spectral element group, IROT represents the penultimate reflectance spectral element group, and i represents the index value of the reflectance spectrum.

[0080] When the light source component is turned on or switched, the optical fiber sensor's reflected spectral data is not collected instantaneously from the moment the light source is turned on, but requires a period of stabilization. Furthermore, when the system is single-channel, the collected spectral data may not be intact as the temperature changes. Therefore, it is necessary to test the stability of the spectral data at this time.

[0081] Preferably, the specific method for detecting the S2 fiber optic sensor access includes:

[0082] First, find the highest peak spectral value IRmax of the optical fiber sensor's reflectance spectrum, and then normalize the optical fiber sensor's reflectance spectrum data.

[0083] The spectral data is normalized using the following formula 2.

[0084] [Equation 2] R = IR[i] / IRmax

[0085] Where R represents the normalized spectral value, IR represents the original spectral value, IRmax is the maximum peak value of the original spectrum, and i represents the index value of the original spectrum.

[0086] Next, the wavelength values ​​at the peak of the reflected spectrum of the fiber optic sensor and at half the peak height were found, and the wavelength characteristic values ​​were calculated.

[0087] The wavelength characteristic value is calculated using the following formula 3.

[0088] [Equation 3] λfax=λmax-λmidl

[0089] Where λfax represents the wavelength characteristic value, λmax represents the peak wavelength value, and λmidl represents the wavelength value at half the peak height.

[0090] Finally, the wavelength characteristic value λfax is compared with ΔMAX, which represents the maximum value of λfax when the fiber optic sensor is connected. When λfax < ΔMAX, it is considered that the steep peak characteristic of the reflection spectrum of the fiber optic sensor exists and the fiber optic sensor has been connected to the fiber optic sensor temperature measurement system; otherwise, it is considered that the fiber optic sensor has not been connected.

[0091] When a fiber optic sensor is connected, its reflection spectrum will show a steep peak. Therefore, the algorithm detects whether a fiber optic sensor has been connected to the fiber optic temperature measurement system by recognizing this feature.

[0092] Preferably, the specific method for the S3 spectral adaptive recommendation adjustment includes:

[0093] First, find the current light intensity parameter Dac and the current integration parameter Intg, and calculate the relative positions of these two dependent variable parameters within their respective configuration ranges;

[0094] The relative position of the light source intensity is calculated using the following formula 4.

[0095] [Equation 4] Dacp = (Dac - Dacmin) / (Dacmax - Dacmin)

[0096] Where Dacp represents the relative position of the light source intensity, Dac represents the current light source intensity, Dacmax represents the maximum adjustable light source intensity, and Dacmin represents the minimum adjustable light source intensity.

[0097] The relative positions of the integration parameters are calculated using Equation 5 below.

[0098] [Equation 5] Intgp = (Intg - Intgmin) / (Intgmax - Intgmin)

[0099] Where Intgp represents the relative position of the integration parameter, Intg represents the current integration parameter, Intgmax represents the maximum adjustable integration parameter, and Intgmin represents the minimum adjustable integration parameter.

[0100] Then, find the maximum peak value of the reflection spectrum of the fiber optic sensor and compare it with the configured peak value;

[0101] If the maximum peak value of the reflected spectrum of the fiber optic sensor is less than the configured peak value, the peak value needs to be increased. When Intgp <= Dacp, increase the integration parameter; when Intgp > Dacp, increase the light intensity of the light source.

[0102] If the maximum peak value of the reflected spectrum of the fiber optic sensor is greater than the configured peak value, the peak value needs to be lowered. When Intgp <= Dacp, the light intensity of the light source should be lowered; when Intgp > Dacp, the integration parameter should be lowered.

[0103] Among them, when pushing the peak value, a step-by-step feedback adjustment method is used.

[0104] Preferably, the specific method for the step-by-step feedback adjustment includes:

[0105] Before starting the initial adjustment, you need to select the initial adjustment step size. The algorithm system has a default three-level peak difference adjustment step size. This parameter is the default configuration parameter and can be configured based on the empirical values ​​generated by the recommendation data. If the first level is less than istep1, select Dacsetup1 and Intgsetup1 for the adjustment step size. If the second level isetp2 is located in the interval [istep1, istep2], select Dacsetup2 and Intgsetup2 for the adjustment step size. If the third level is greater than isetp2, select Dacsetup3 and Intgsetup3 for the adjustment step size.

[0106] Then, the S1 spectral data stability test and the S2 fiber optic sensor access test are performed again. After obtaining a stable spectral waveform, the S3 adaptive push adjustment process is entered again. After finding the maximum peak value, it is compared with the previous peak value and the difference is calculated. Finally, based on the feedback of the difference, the influence of the unit integral parameter on the peak generation and the influence of the unit light intensity of the light source on the peak generation are calculated.

[0107] The peak feedback value of the integral parameter is calculated using the following formula 6.

[0108] [Equation 6] △dIntg=(IRmax-IRmaxold) / (Intgadd)

[0109] Where △dIntg represents the feedback value of the integral parameter peak, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Intgadd represents the change value of the integral parameter.

[0110] The peak feedback value of the light intensity of the light source is calculated using the following formula 7.

[0111] [Equation 7] △dDac=(IRmax-IRmaxold) / (Dacadd)

[0112] Where △dDac represents the peak feedback value of the light source intensity, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Dacadd represents the change value of the light source intensity.

[0113] If the phase difference between the reflection spectrum peak and the configured peak is within one percent of the preset threshold, the adjustment ends; if the phase difference between the reflection spectrum peak and the configured peak is not within one percent of the preset threshold, the split-half approximation method is used to set the adjustment parameters.

[0114] Preferably, the specific method for setting the adjustment parameters of the split-half approximation method includes:

[0115] The change in light intensity can be calculated using the following formula 8.

[0116] [Equation 8] Dacadd=((IRcfg-IRmax)*△dDac) / Δcol

[0117] Where Dacadd represents the change in light intensity, IRcfg represents the configured peak value, IRmax represents the current peak value, ΔdDac represents the light intensity peak feedback value, and Δcol represents the number of times the configured peak value is reached by adjusting the light intensity of the light source.

[0118] The change in the integral parameter is calculated using the following formula 9.

[0119] [Equation 9] Intgadd=((IRcfg-IRmax)*△dintg) / Δcol

[0120] Where Intgadd represents the change in integral parameters, IRcfg represents the configured peak value, IRmax represents the current peak value, Δdintg represents the feedback value of the integral parameter peak, and Δcol represents the number of times the configured peak value is reached by adjusting the integral parameters.

[0121] Then, based on the above calculation results, the integral parameter adjustment is designed. If the adjustment exceeds the limit, it is adjusted downwards until it reaches the configuration peak range. The adjustment ends, and the analysis and calculation stage begins.

[0122] Preferably, the specific method for determining the quality parameters of the S4 fiber optic sensor channel includes:

[0123] The quality parameters of the fiber optic sensor channel are calculated from the optical path intensity variable, the spectrometer integration parameter variable, the maximum intensity variable, and their respective weight biases.

[0124] The quality parameters of the fiber optic sensor channel are calculated according to the following formula 10.

[0125] [Formula 10] IQuality=(IRmax*PIRmax)+1 / (DAC*PDAC)+1 / (Intg*PIntg)

[0126] Where IQuality represents the fiber optic sensor channel quality parameter, IRmax represents the maximum light intensity variable, PIRmax represents the maximum light intensity weight bias, DAC represents the optical path light intensity variable, PDAC represents the optical path light intensity weight bias, Intg represents the spectrometer integration parameter variable, and PIntg represents the spectrometer integration parameter weight bias.

[0127] Preferably, the specific method for determining the S5 fiber optic sensor splice quality parameters includes:

[0128] The fiber optic sensor splicing quality parameters are calculated by the ratio of the peak position value of the fiber optic sensor's reflection spectrum to the maximum light intensity variable.

[0129] The fiber optic sensor splice quality parameters are calculated using the following formula 11.

[0130] [Equation 11] IPlugQuality = IFrontvalue / IRmax

[0131] Among them, IPlugQuality represents the fiber optic sensor splicing quality parameter, IFRontvalue represents the height value of the starting position of the reflection spectrum peak, and IRmax represents the maximum light intensity variable.

[0132] Then, the fiber optic sensor plug quality parameter IPlugQuality is compared with the fiber optic sensor plug quality threshold okPlugQuality. If the okPlugQuality threshold is reached, it is considered qualified.

[0133] The fiber optic sensor connector quality parameters and fiber optic sensor channel quality parameters provide a quality score for the subsequent temperature demodulation algorithm processing, indicating the quality and aging status of the fiber optic sensor.

[0134] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make appropriate changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A self-diagnostic qualification detection method for a fiber optic sensor temperature measurement system, characterized in that, The steps include: spectral data stability testing; fiber optic sensor access testing; adaptive spectral recommendation adjustment; and determination of fiber optic sensor channel quality parameters. Determining the quality parameters of fiber optic sensor connectors; The specific methods for detecting the stability of the spectral data include: The embedded computing platform of the single-channel light source system cyclically collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and waits for the spectral data to stabilize. The embedded computing platform of the multi-channel light source system continuously collects spectral data of the reflected light from the fiber optic sensor from the spectrometer and compares the stability of the edge spectral values ​​and peak spectral values ​​of two adjacent spectral data. If the change value of the two spectra reaches below the pre-configured threshold, the spectral data stability test is deemed qualified, and the spectral data enters the subsequent calculation and processing flow. The change values ​​of the two spectra are calculated using the following formula 1. [Formula 1] (IR-IRO) / MAXMEN=(∑((IRT[i]-IROT[i]) / IRT[i])) / MAXMEN Where IR represents the last reflectance spectral value, IRO represents the penultimate reflectance spectral value, MAXMEN represents the number of elements in the reflectance spectrum, IRT represents the last reflectance spectral element group, IROT represents the penultimate reflectance spectral element group, and i represents the index value of the reflectance spectrum. The specific method for detecting access to the fiber optic sensor includes: First, find the highest peak spectral value IRmax of the optical fiber sensor's reflectance spectrum, and then normalize the optical fiber sensor's reflectance spectrum data. The spectral data is normalized using the following formula 2. [Equation 2] R = IR[i] / IRmax Where R represents the normalized spectral value, IR represents the original spectral value, IRmax is the maximum peak value of the original spectrum, and i represents the index value of the original spectrum. Next, the wavelength values ​​at the peak of the reflected spectrum of the fiber optic sensor and at half the peak height were found, and the wavelength characteristic values ​​were calculated. The wavelength characteristic value is calculated using the following formula 3. [Equation 3] λfax=λmax-λmidl Where λfax represents the wavelength characteristic value, λmax represents the peak wavelength value, and λmidl represents the wavelength value at half the peak height. Finally, the wavelength characteristic value λfax is compared with ΔMAX, which represents the maximum value of λfax when the fiber optic sensor is connected. When λfax < ΔMAX, it is considered that the steep peak characteristic of the reflection spectrum of the fiber optic sensor exists and the fiber optic sensor has been connected to the fiber optic sensor temperature measurement system; otherwise, it is considered that the fiber optic sensor has not been connected. The specific methods for the adaptive spectral recommendation adjustment include: First, find the current light intensity parameter Dac and the current integration parameter Intg, and calculate the relative positions of these two dependent variable parameters within their respective configuration ranges; The relative position of the light source intensity is calculated using the following formula 4. [Equation 4] Dacp = (Dac - Dacmin) / (Dacmax - Dacmin) Where Dacp represents the relative position of the light source intensity, Dac represents the current light source intensity, Dacmax represents the maximum adjustable light source intensity, and Dacmin represents the minimum adjustable light source intensity. The relative positions of the integration parameters are calculated using Equation 5 below. [Equation 5] Intgp=(Intg-Intgmin) / (Intgmax-Intgmin) Where Intgp represents the relative position of the integration parameter, Intg represents the current integration parameter, Intgmax represents the maximum adjustable integration parameter, and Intgmin represents the minimum adjustable integration parameter. Then, find the maximum peak value of the reflection spectrum of the fiber optic sensor and compare it with the configured peak value; If the maximum peak value of the reflected spectrum of the fiber optic sensor is less than the configured peak value, the peak value needs to be increased. When Intgp<=Dacp, increase the integration parameter; when Intgp>Dacp, increase the light intensity of the light source. If the maximum peak value of the reflected spectrum of the fiber optic sensor is greater than the configured peak value, the peak value needs to be lowered. When Intgp<=Dacp, the light intensity of the light source should be lowered; when Intgp>Dacp, the integration parameter should be lowered. Among them, when pushing the peak value, a step-by-step feedback adjustment method is used; The specific methods for the step-by-step feedback adjustment include: Before starting the initial adjustment, you need to select the initial adjustment step size. The algorithm system has a default three-level peak difference adjustment step size. This parameter is the default configuration parameter and can be configured based on the empirical values ​​generated by the recommendation data. If the first level is less than istep1, select Dacsetup1 and Intgsetup1 for the adjustment step size. If the second level istep2 is located in the interval [istep1, istep2], select Dacsetup2 and Intgsetup2 for the adjustment step size. If the third level is greater than istep2, select Dacsetup3 and Intgsetup3 for the adjustment step size. Then, the spectral data stability test and fiber optic sensor access test are performed again. After obtaining a stable spectral waveform, the adaptive recommendation and adjustment process is entered again. The maximum peak value is found and compared with the previous peak value to calculate the difference. Finally, based on the feedback of the difference, the influence of the unit integral parameter on the peak and the influence of the unit light intensity of the light source on the peak are calculated. The peak feedback value of the integral parameter is calculated using the following formula 6. [Equation 6] △dIntg=(IRmax-IRmaxold) / (Intgadd) Where △dIntg represents the feedback value of the integral parameter peak, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Intgadd represents the change value of the integral parameter. The peak feedback value of the light intensity of the light source is calculated using the following formula 7. [Equation 7] △dDac=(IRmax-IRmaxold) / (Dacadd) Where △dDac represents the peak feedback value of the light source intensity, IRmax represents the current peak value, IRmaxold represents the previous peak value, and Dacadd represents the change value of the light source intensity. If the phase difference between the reflection spectrum peak and the configured peak is within the preset threshold range, the adjustment ends; if the phase difference between the reflection spectrum peak and the configured peak is not within the preset threshold range, the split-half approximation method is used to set the adjustment parameters. The specific method for setting adjustment parameters in the binary approximation method includes: The change in light intensity can be calculated using the following formula 8. [Equation 8] Dacadd = ((IRcfg - IRmax) * ΔdDac) / Δcol Where Dacadd represents the change in light intensity, IRcfg represents the configured peak value, IRmax represents the current peak value, ΔdDac represents the light intensity peak feedback value, and Δcol represents the number of times the configured peak value is reached by adjusting the light intensity of the light source. The change in the integral parameter is calculated using the following formula 9. [Equation 9] Intgadd=((IRcfg-IRmax)*△dintg) / Δcol Where Intgadd represents the change in integral parameters, IRcfg represents the configured peak value, IRmax represents the current peak value, Δdintg represents the feedback value of the integral parameter peak, and Δcol represents the number of times the configured peak value is reached by adjusting the integral parameters. The specific method for determining the quality parameters of the fiber optic sensor channel includes: the quality parameters of the fiber optic sensor channel are calculated from the optical path intensity variable, the spectrometer integration parameter variable, the maximum intensity variable, and their respective weight biases; The quality parameters of the fiber optic sensor channel are calculated according to the following formula 10. [Equation 10] IQuality=(IRmax*PIRmax)+1 / (DAC*PDAC)+1 / (Intg*PIntg) Wherein, IQuality represents the fiber optic sensor channel quality parameter, IRmax represents the maximum light intensity variable, PIRmax represents the maximum light intensity weight bias, DAC represents the optical path light intensity variable, PDAC represents the optical path light intensity weight bias, Intg represents the spectrometer integration parameter variable, and PIntg represents the spectrometer integration parameter weight bias. The specific method for determining the fiber optic sensor splice quality parameters includes: the fiber optic sensor splice quality parameters are calculated by the ratio of the peak position value of the fiber optic sensor reflection spectrum to the maximum light intensity variable; The fiber optic sensor splice quality parameters are calculated using the following formula 11. [Equation 11] IPlugQuality = IFRontvalue / IRmax Among them, IPlugQuality represents the fiber optic sensor splicing quality parameter, IFRontvalue represents the height value of the starting position of the reflection spectrum peak, and IRmax represents the maximum light intensity variable. Then, the fiber optic sensor plug quality parameter IPlugQuality is compared with the fiber optic sensor plug quality threshold okPlugQuality. If the okPlugQuality threshold is reached, it is considered qualified.

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