Spatial resolution optimization algorithm based on distributed optical fiber temperature measurement system

Abstract

The invention discloses a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system. The spatial resolution optimization algorithm comprises the following steps: acquiring a pulse signal and original echo data; decomposing the pulse signal to obtain a plurality of narrow pulse signals; expanding the narrow pulse signal to the whole optical fiber to obtain data xi; obtaining the ratio of the anti-Stokes light data to the Stokes light data according to the original echo data; calculating a system response function through deconvolution; calculating the data xi through a system response function to obtain reconstructed echo data yi; performing normalization processing on the original echo data and the reconstructed echo data; carrying out variable temperature section discrimination through the normalized original echo data, and carrying out length discrimination on each variable temperature section; and debugging the obtained new normalized data, and outputting the data. In the scheme, data reconstruction cannot be affected by factors such as the length of the optical fiber, the position and the length of the variable temperature section and the like, and the method can be widely applied to various optical fiber temperature measurement conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber temperature measurement, and in particular to a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system. Background Art

[0002] In the field of optical fiber temperature measurement, the spatial resolution of the system reflects the distributed measurement capability of the system, that is, the smallest spatial unit that the sensing system can identify when measuring the temperature distribution on the optical fiber. In the prior art, for short optical fiber temperature rise, especially for a length range smaller than the spatial resolution, the single-point signal measured by the system decreases as the temperature rise range decreases, and the temperature measurement is inaccurate. Summary of the invention

[0003] In view of this, the present invention provides a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system. The spatial resolution can be reduced by adopting a pulse decomposition method according to the influence of pulse width on the spatial resolution, and the system response function can be calculated in real time for different temperature measurement data, so that factors such as optical fiber length, position and length of the variable temperature section will not affect the reconstruction of the data. The algorithm can be widely used in various optical fiber temperature measurement situations.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system includes the following steps:

[0006] S100, acquiring pulse signals and original echo data;

[0007] S200, decomposing the pulse signal to obtain a plurality of narrow pulse signals;

[0008] S300, extending the narrow pulse signal to the entire optical fiber to obtain data x i ;

[0009] S400, obtaining a ratio of anti-Stokes light data to Stokes light data according to the original echo data;

[0010] S500, calculating a system response function by deconvolution;

[0011] S600, x the data i After the system response function is calculated, the reconstructed echo data yi is obtained;

[0012] S700, performing normalization processing on the original echo data and the reconstructed echo data;

[0013] S800, determining the temperature change section by using the normalized original echo data, and determining the length of each temperature change section:

[0014] If the length of the temperature-changing section is greater than or equal to the distance resolution, retaining the portion of the normalized raw data;

[0015] If the length of the temperature-changing section is less than the distance resolution, the corresponding data in the normalized original echo data is replaced by the part of the data in the normalized reconstructed echo data;

[0016] S900: Debug the obtained new normalized data and output the data.

[0017] Preferably, the calculating the system response function by deconvolution comprises: calculating the system response function by formula

[0018] H=fft(y) / (fft(x1)+fft(x2)+......+fft(x i ))Get the system response function.

[0019] Preferably, the data xi is obtained after the system response to obtain the reconstructed echo data y i Includes: According to the formula

[0020] y i (t) = ifft(fft(x i )*H) obtains the data of each narrow pulse after the system responds.

[0021] Preferably, decomposing the pulse signal to obtain a plurality of narrow pulse signals comprises: the plurality of narrow pulses obtained by decomposition are equally spaced and infinitely narrow, and impulse signal energies of the narrow pulses are different.

[0022] Preferably, the normalization processing of the original echo data and the reconstructed echo data includes: replacing the data of the part smaller than the spatial resolution, and identifying the temperature-varying section and detecting the length by using the method of normalizing the spectrum diagram threshold of the original echo data.

[0023] Preferably, the temperature-varying segments are determined on the normalized raw echo data, and the length determination of each temperature-varying segment includes: dividing the temperature-varying segments into temperature-varying segments greater than / equal to the spatial resolution and temperature-varying segments less than the spatial resolution.

[0024] Preferably, replacing the corresponding data in the normalized original echo data with the part of the data in the reconstructed echo data after normalization includes: replacing the part of the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the original echo data with the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the reconstructed echo data.

[0025] Preferably, the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the original echo data is replaced by the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the reconstructed echo data, and then debugged according to the ratio when the original data was normalized.

[0026] Preferably, the original echo data includes echo data of a wide pulse on the entire optical fiber, or accumulation of echo data of various narrow pulses on the entire optical fiber.

[0027] It can be seen from the above technical solutions that the spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system provided by the present invention adopts a pulse decomposition method to reduce the spatial resolution by targeting the influence of pulse width on the spatial resolution, and calculates the system response function for different temperature measurement data in real time, so that factors such as optical fiber length, position and length of the temperature change section will not affect the reconstruction of the data. It can be widely used in various optical fiber temperature measurement situations, reducing the data accumulation problem caused by the width of the optical pulse. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a flow chart showing a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system according to an exemplary embodiment;

[0030] Figure 2 is a schematic diagram showing a process of optimizing a spatial resolution of a distributed optical fiber temperature measurement system according to another exemplary embodiment;

[0031] Figure 3 is a schematic diagram showing the result of acquiring pulse signals and original echo data according to an exemplary embodiment;

[0032] Figure 4 is a schematic diagram showing a result of decomposing a pulse signal to obtain multiple narrow pulse signals according to an exemplary embodiment;

[0033] Figure 5 According to an exemplary embodiment, the data x is obtained by extending the narrow pulse signal to the entire optical fiber. i Schematic diagram of the final result;

[0034] Figure 6is a schematic diagram showing the result of constructing echo data and obtaining the ratio of anti-Stokes light data to Stokes light data according to an exemplary embodiment;

[0035] Figure 7 According to an exemplary embodiment, the data x is shown as follows i After calculating the system response function, the data y i Schematic diagram of the final result;

[0036] Figure 8 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is greater than the spatial resolution according to an exemplary embodiment;

[0037] Fig. 9 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is equal to the spatial resolution according to an exemplary embodiment;

[0038] Fig.10 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is smaller than the spatial resolution according to an exemplary embodiment;

[0039] Fig.11 is a schematic diagram of original data and reconstructed data results when a plurality of temperature-changing sections of different lengths exist simultaneously in a temperature-measuring optical fiber according to an exemplary embodiment;

[0040] Fig.12 is a normalized spectrum diagram showing original data and reconstructed data of the ratio of anti-Stokes light to Stokes light according to an exemplary embodiment;

[0041] Fig.13 is a schematic diagram showing the result of replacing partial data of a temperature-varying section smaller than a spatial resolution in a normalized spectrum of original data with partial data of a temperature-varying section smaller than a spatial resolution in a normalized spectrum of reconstructed data according to an exemplary embodiment;

[0042] Fig.14 It is a schematic diagram showing the result of debugging the replaced normalized spectrum according to the ratio of the original data normalization according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] The present invention discloses a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system. The spatial resolution can be reduced by adopting a pulse decomposition method according to the influence of pulse width on the spatial resolution, and the system response function can be calculated in real time for different temperature measurement data, so that factors such as optical fiber length, temperature variable section position and length will not affect data reconstruction. The algorithm can be widely used in various optical fiber temperature measurement situations.

[0044] 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.

[0045] In an exemplary embodiment of the present disclosure, a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system is provided. Figure 1 As shown, Figure 1 is a flow chart showing a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system according to an exemplary embodiment; Figure 2 is a flowchart showing a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system according to another exemplary embodiment; Figure 3 is a schematic diagram showing the result of acquiring pulse signals and original echo data according to an exemplary embodiment; Figure 4 is a schematic diagram showing a result of decomposing a pulse signal to obtain multiple narrow pulse signals according to an exemplary embodiment; Figure 5 According to an exemplary embodiment, the data x is obtained by extending the narrow pulse signal to the entire optical fiber. i Schematic diagram of the final result; Figure 6 is a schematic diagram showing the result of constructing echo data and obtaining the ratio of anti-Stokes light data to Stokes light data according to an exemplary embodiment; Figure 7 According to an exemplary embodiment, the data x is shown as follows i After calculating the system response function, we get the data y i Schematic diagram of the final result; Figure 8 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is greater than the spatial resolution according to an exemplary embodiment; Fig. 9 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is equal to the spatial resolution according to an exemplary embodiment; Fig.10 is a schematic diagram showing original data and reconstructed data results when the length of the temperature-changing section is smaller than the spatial resolution according to an exemplary embodiment; Fig.11 is a schematic diagram of original data and reconstructed data results when a plurality of temperature-changing sections of different lengths exist simultaneously in a temperature-measuring optical fiber according to an exemplary embodiment; Fig.12 is a normalized spectrum diagram showing original data and reconstructed data of the ratio of anti-Stokes light to Stokes light according to an exemplary embodiment; Fig.13is a schematic diagram showing the result of replacing partial data of a temperature-varying section smaller than a spatial resolution in a normalized spectrum of original data with partial data of a temperature-varying section smaller than a spatial resolution in a normalized spectrum of reconstructed data according to an exemplary embodiment; Fig.14 FIG. 1 is a schematic diagram showing the result of debugging the replaced normalized spectrum according to the ratio of the original data normalization according to an exemplary embodiment. Figures 1 to 14 Provide explanation.

[0046] Some specific implementation modes described below are intended to facilitate those skilled in the art to understand the present embodiment, and the present embodiment is not limited to some specific implementation modes described below.

[0047] Reference Figure 1 An exemplary embodiment of the present disclosure provides a spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system. The spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system includes the following steps:

[0048] Step S100, acquiring pulse signals and original echo data;

[0049] Step S200, decomposing the pulse signal to obtain a plurality of narrow pulse signals;

[0050] Step S300, expanding the narrow pulse signal to the entire optical fiber to obtain data xi;

[0051] Step S400, obtaining a ratio of anti-Stokes light data to Stokes light data according to the original echo data;

[0052] Step S500, calculating the system response function by deconvolution;

[0053] Step S600, the data xi is calculated by the system response function to obtain the reconstructed echo data yi;

[0054] Step S700, normalizing the original echo data and the reconstructed echo data;

[0055] Step S800: Determine the temperature variation section by using the normalized original echo data, and determine the length of each temperature variation section:

[0056] If the length of the temperature-changing section is greater than or equal to the distance resolution, retaining the portion of the normalized raw data;

[0057] If the length of the temperature-changing section is less than the distance resolution, the corresponding data in the normalized original echo data are replaced by the data in the normalized reconstructed echo data;

[0058] S900: Debug the obtained new normalized data and output the data.

[0059] For example, refer to Figure 1 and Figure 2 Before implementing the algorithm, it is necessary to collect the light pulse data emitted by the laser. The process of the algorithm provided in this application is as follows:

[0060] Step S100, obtaining pulse signal and original echo data specifically includes: for example, constructing an ideal Gaussian pulse with an effective pulse width of 10ns, setting it as the optical pulse signal emitted by the laser, obtaining the pulse signal data and the original echo data data-adc, and the result is as follows: Figure 3 shown.

[0061] Next, step S200 is performed step by step, the pulse signal is decomposed to obtain multiple narrow pulse signals: the pulse signal is decomposed to obtain multiple narrow pulse signals data1, data2...datan. Considering that the spatial resolution is affected by the pulse width, the wider the pulse, the greater the corresponding spatial resolution. Therefore, for example, the obtained light pulse is decomposed to obtain 4 equally spaced narrow pulses. The result is as follows Figure 4 shown.

[0062] Step S300: Expand the narrow pulse signal to the entire optical fiber to obtain data x i Specifically, each narrow pulse signal obtained by decomposition is expanded to the entire optical fiber to obtain x1, x2...x n When the narrow pulse is extended to the entire optical fiber, the decomposed narrow pulse is understood as an infinitely narrow impulse signal, that is, each impulse signal has different energy, and it is extended to the entire optical fiber. According to the above example, 4 equally spaced narrow pulses are obtained, and the data x1, x2, x3, and x4 are obtained at this time. The result is as follows Figure 5 shown.

[0063] Step S400, obtaining the ratio of anti-Stokes light data to Stokes light data according to the original echo data specifically includes: constructing ideal echo data based on the 10ns light pulse constructed in step S100, for example, setting the total length of the optical fiber to 2500 meters, the temperature variation section of the optical fiber to 50 cm, and the ADC sampling frequency to 400Mhz, and obtaining the ratio of anti-Stokes light data to Stokes light data, the result is as follows: Figure 6 shown.

[0064] Step S500, calculating the system response function by deconvolution specifically includes: the echo data can be understood as the echo data of the wide pulse on the entire optical fiber, and can also be understood as the accumulation of the echo data of each narrow pulse on the entire optical fiber. Therefore, according to the above data x1, x2, x3, x4, y, the system response function can be obtained, as shown in the following formula

[0065] H=fft(y) / (fft(x1)+fft(x2)+fft(x3)+fft(x4)).

[0066] Step S600: convert data x i After the system response function is calculated, the reconstructed echo data y is obtained. i Specifically include: calculating data x1, x2...x n The data y1, y2, ... y obtained after the system responds n， The data obtained after the system response of each narrow pulse is the reconstructed echo data of the narrow pulse on the entire optical fiber.

[0067] According to the following formula

[0068] y i (t) = ifft(fft(x i )*H)

[0069] Based on the above example of obtaining four equally spaced narrow pulses, the reconstructed echo data y1, y2, y3, and y4 of each narrow pulse after the system response are obtained. The result is as follows: Figure 7 shown.

[0070] In this embodiment, when the length of the temperature change section is greater than the spatial resolution, the original data and the reconstructed data are as follows: Figure 8 As shown, the upper single chart is the original data, and the lower four combined matrices are arranged as reconstructed data. The four icons of reconstructed data correspond to the four equally spaced narrow pulses obtained in the above example from top to bottom and from left to right.

[0071] When the length of the temperature variation section is equal to the spatial resolution, the original data and the reconstructed data are as follows: Fig. 9 As shown, the upper single chart is the original data, and the lower four combined matrices are arranged as reconstructed data. The four icons of reconstructed data correspond to the four equally spaced narrow pulses obtained in the above example from top to bottom and from left to right.

[0072] When the length of the temperature change section is smaller than the spatial resolution, the original data and the reconstructed data are Fig.10 As shown, the upper single chart is the original data, and the lower four combined matrices are arranged as reconstructed data. The four icons of reconstructed data correspond to the four equally spaced narrow pulses obtained in the above example from top to bottom and from left to right.

[0073] When there are multiple temperature-varying sections of different lengths in the temperature-measuring optical fiber, the original data and the reconstructed data are Fig.11As shown, the upper single chart is the original data, and the four combined matrices arranged in the lower layer are the reconstructed data. The four icons of the reconstructed data correspond to the four equally spaced narrow pulses obtained in the above example from top to bottom and from left to right.

[0074] From the above simulation results, it can be determined without dispute that in the original data, when the length of the variable temperature section is greater than / equal to the spatial resolution, the corresponding anti-Stokes light to Stokes light ratio data is consistent; and when the length of the variable temperature section is less than the spatial resolution, the corresponding anti-Stokes light to Stokes light ratio data is relatively small, that is, when the length of the variable temperature section is less than the spatial resolution, the temperature measurement result will be affected by the temperature of the nearby optical fiber, resulting in inaccurate temperature measurement. By observing the corresponding ratios of each variable temperature section in the reconstructed data obtained by pulse decomposition, it can be found that the data of each variable temperature section are basically consistent at this time, that is, the aforementioned scheme recorded in this application effectively improves the influence of the length of the variable temperature section on the temperature measurement data, but it can also be found that the aforementioned scheme of this application introduces noise as a whole, thereby making the overall temperature measurement data signal-to-noise ratio worse. Therefore, the aforementioned scheme recorded in this application is suitable for temperature alarm scenarios of distributed optical fiber systems, and can effectively solve the problem of missed alarms caused by spatial resolution.

[0075] The present application proposes to replace data only for the part smaller than the spatial resolution to improve the problem of poor signal-to-noise ratio of temperature measurement data. Therefore, the present application also proposes to identify and detect the length of the temperature-varying section.

[0076] The algorithm described in this application optimizes the problem that the signal-to-noise ratio of the long-segment optical fiber data in the reconstructed data is worse than that of the original data, resulting in poor temperature measurement accuracy, and includes the following steps:

[0077] Take any reconstructed echo data y in the above step S600 i As the deconvolution reconstructed echo data data_Restructure, the mean values ​​of the normal temperature section of the original data and the reconstructed data are calculated and recorded as data_adc_normal and data_Restructure_normal respectively.

[0078] Step S700, normalizing the original echo data and the reconstructed echo data specifically includes: using the method of normalizing the original data spectrum diagram threshold to identify the variable temperature segment and perform length detection, for example, normalizing the original data and the reconstructed data: Data1-Normalization = Data-adc / data-dac-normal, Data2-Normalization = data-restructure / data-restructure-normal, to obtain Data1-Normalization, Data2-Normalizationg, and then using Data1-Normalizationg to identify the variable temperature segment and then determine the length of the variable temperature segment. Fig.12 The normalized spectra of the original data and the reconstructed data of the ratio of anti-Stokes light to Stokes light, from which it can be seen that the normalized spectrum of the original data is more stable at room temperature.

[0079] It should be mentioned that when there is a temperature variable section in the optical fiber, the temperature variable section is divided into a temperature variable section greater than / equal to the spatial resolution and a temperature variable section less than the spatial resolution. In order to avoid the problem of missed alarm due to the detection deviation of the temperature variable section length, the judgment threshold of whether the temperature variable section is less than the spatial resolution is set to be larger, that is, to minimize the judgment of the temperature variable section less than the spatial resolution as the temperature variable section greater than the spatial resolution.

[0080] Step S800, determining the temperature change section by using the normalized original echo data, and determining the length of each temperature change section specifically includes:

[0081] If the length of the temperature-varying section is greater than or equal to the distance resolution, then this portion of the normalized raw data is retained.

[0082] If the length of the temperature variation section is less than the distance resolution, the corresponding data in the normalized original echo data is replaced by the part of the data in the normalized reconstructed echo data, for example, the corresponding data in Data1-Normalization is replaced by the part of the data in Data2-Normalizationg.

[0083] That is, when replacing data, the data of the variable temperature section smaller than the spatial resolution in the normalized spectrum of the original data is replaced by the data of the variable temperature section smaller than the spatial resolution in the normalized spectrum of the reconstructed data. The result is as follows: Fig.13 shown.

[0084] In both cases, the replaced normalized spectra can be obtained. For further temperature demodulation, the data is debugged according to the ratio of the original data when normalized, that is, step S900, the obtained new normalized data is debugged, and the output data specifically includes:

[0085] For example: Data-result = data-adc-normal*Data1-Normalization, and finally output data Data-result.

[0086] The results are as follows Fig.14 As shown. Fig.14 It can be seen that the problem of the data of the temperature variation section smaller than the spatial resolution being affected by the surrounding point data is improved at this time, and the problem of the deterioration of the signal-to-noise ratio of the temperature measurement data caused by data reconstruction is effectively alleviated.

[0087] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A spatial resolution optimization algorithm based on a distributed optical fiber temperature measurement system, characterized in that: The following steps are involved: S100, acquiring pulse signals and original echo data; S200, decomposing the pulse signal to obtain a plurality of narrow pulse signals; S300, extending the narrow pulse signal to the entire optical fiber to obtain data x i ; S400, obtaining a ratio of anti-Stokes light data to Stokes light data according to the original echo data; S500, calculating a system response function by deconvolution; S600, x the data i After the system response function is calculated, the reconstructed echo data y is obtained. i ; S700, performing normalization processing on the original echo data and the reconstructed echo data; S800, determining the temperature change section by using the normalized original echo data, and determining the length of each temperature change section: If the length of the temperature-changing section is greater than or equal to the distance resolution, retaining the portion of the normalized raw data; If the length of the temperature-changing section is less than the distance resolution, the corresponding data in the normalized original echo data is replaced by the part of the data in the normalized reconstructed echo data; S900: Debug the obtained new normalized data and output the data.

2. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The method of calculating the system response function by deconvolution includes: H=fft(y) / (fft(x1)+fft(x2)+......+fft(x i ))Get the system response function.

3. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The data x i After the system responds, the reconstructed echo data y is obtained. i Includes: According to the formula y i (t) = ifft(fft(x i )*H) obtains the data of each narrow pulse after the system responds.

4. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: Decomposing the pulse signal to obtain a plurality of narrow pulse signals includes: the plurality of narrow pulses obtained by decomposition are equally spaced and infinitely narrow, and impulse signal energies of the narrow pulses are different.

5. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The normalization processing of the original echo data and the reconstructed echo data includes: replacing the data of the part smaller than the spatial resolution, and using the method of normalizing the spectrum diagram threshold of the original echo data to identify the variable temperature section and perform length detection.

6. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The temperature-varying segments are determined by the normalized original echo data, and the length of each temperature-varying segment is determined, including: dividing the temperature-varying segments into temperature-varying segments greater than / equal to the spatial resolution and temperature-varying segments less than the spatial resolution.

7. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The replacing of the corresponding data in the normalized original echo data with the part of the data in the normalized reconstructed echo data includes: replacing the part of the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the original echo data with the temperature-varying segment data smaller than the spatial resolution in the normalized spectrum of the reconstructed echo data.

8. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 7 is characterized in that: After replacing the variable temperature segment data smaller than the spatial resolution in the normalized spectrum of the original echo data with the variable temperature segment data smaller than the spatial resolution in the normalized spectrum of the reconstructed echo data, debugging is performed according to the ratio when the original data is normalized.

9. The spatial resolution optimization algorithm based on the distributed optical fiber temperature measurement system according to claim 1 is characterized in that: The original echo data includes the echo data of a wide pulse on the entire optical fiber, or the accumulation of echo data of various narrow pulses on the entire optical fiber.

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