Distributed temperature measurement method and system

Abstract

The present application relates to the field of distributed temperature measurement technology, and specifically to a distributed temperature measurement method and system. Among them, the distributed temperature measurement method includes: obtaining multiple groups of temperature data of a distributed temperature measurement system; constructing a first temperature table based on the order of obtaining each group of temperature data and a first preset rule; converting the first temperature table into a first temperature sequence according to a second preset rule; determining a second temperature sequence corresponding to the first temperature sequence, the second temperature sequence being obtained after denoising the first temperature sequence using a target noise reduction algorithm; extracting target temperature data in the second temperature sequence. Use an improved BEEPS algorithm to reduce the noise of temperature data, arrange data with high correlation adjacently, and set noise reduction weights for different temperature measurement points respectively, so as to improve the response speed of the distributed temperature measurement system and reduce the amount of calculation and memory usage.

Description

Technical Field

[0001] The present application relates to the field of distributed temperature measurement technology, and in particular to a distributed temperature measurement method and system. Background Art

[0002] At present, fiber-optic distributed temperature measurement is based on the demodulation of Raman scattering signal intensity. As the distance of the temperature measurement point increases, the intensity of the optical signal also decays. When the optical signal is converted into a voltage signal through a photodetector, the signal-to-noise ratio is very low because the photodetector itself brings a lot of background noise. After reading and demodulating the voltage signal through a digital acquisition device, it is difficult to obtain a stable temperature reading.

[0003] In the related art, multiple averaging algorithms are used to reduce noise and improve the signal-to-noise ratio. However, the multiple averaging algorithm is relatively time-consuming. If you want to reduce the noise by more than 10db, you need to average 100 times. Usually, the interval between two samples is 10 seconds, and a total of 1000 seconds, about 17 minutes, is required. That is, the temperature data calculated each time is the average of all data within 17 minutes. This average value cannot accurately show the temperature changes within 17 minutes, and causes a lag of about 8 minutes in temperature response. The multiple averaging algorithm has the problems of high computing resources, delayed temperature response, and long time consumption.

[0004] Therefore, it is urgent to propose a distributed temperature measurement method to improve the above problems. Summary of the invention

[0005] The present application discloses a distributed temperature measurement method and system, which uses an improved BEEPS algorithm (Bilateral Estimation-based Enhancement of Perceptual Signal-to-noise ratio) to reduce the noise of temperature data, arranges data with high correlation adjacent to each other, and sets noise reduction weights for different temperature measurement points, thereby improving the response speed of the distributed temperature measurement system and reducing the amount of calculation and memory usage.

[0006] In some embodiments, a distributed temperature measurement method is disclosed. The distributed temperature measurement method is applied to a distributed temperature measurement system. The distributed temperature measurement system includes multiple temperature measurement points. The distributed temperature measurement method includes: obtaining multiple groups of temperature data of the distributed temperature measurement system, each group of temperature data is obtained at a different time, and each group of temperature data includes the temperature value of each temperature measurement point; based on the order of obtaining each group of temperature data and a first preset rule, constructing a first temperature table, the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of temperature data, and M corresponds to the number of temperature measurement points; in the first temperature table, each row stores a group of temperature data, and each first storage bit stores a temperature value; according to a second preset rule, the first temperature table is stored in the first temperature table. A temperature table is converted into a first temperature sequence, the first temperature sequence includes a second storage bit of 1 row and N×M columns; a second temperature sequence corresponding to the first temperature sequence is determined, the second temperature sequence is obtained after the first temperature sequence is denoised using a target noise reduction algorithm; the target noise reduction algorithm adopts a target weight influence factor sequence, the target weight influence factor sequence includes multiple target weight influence factors, the target weight influence factors correspond to the temperature measurement points one by one, the target weight influence factors are used to influence the weights of the temperature values ​​of the temperature measurement points corresponding to the target weight influence factors, and each target weight influence factor is the same or different; the target temperature data in the second temperature sequence is extracted, the target temperature data includes the temperature value of each temperature measurement point with the latest acquisition time.

[0007] The distributed temperature measurement method provided by the present application is adopted to reduce noise by using the improved BEEPS algorithm, thereby reducing the problems of delayed temperature response, high memory usage, and inaccurate reflection of temperature changes within a short period of time after averaging caused by using multiple averaging algorithms for noise reduction. The acquired temperature data is constructed into a first temperature table by using the first preset rule, and the temperature data with higher correlation are arranged together, so that in the process of reducing noise for the first temperature sequence, the correlation between the data can be better considered, and the mutual influence between the data with low correlation can be reduced. In addition, based on determining the target proportion influencing factor sequence in the noise reduction algorithm, the different signal-to-noise ratios at the beginning and end of the optical fiber can be better considered, so as to achieve different noise reduction effects for the beginning and end respectively, improve the noise reduction efficiency, and reduce the amount of calculation required for noise reduction.

[0008] Optionally, based on the order of obtaining each group of temperature data and a first preset rule, a first temperature table is constructed, including: constructing a first initial temperature table, the first initial temperature table including N rows and M columns; calculating the target number of storage rows for each group of temperature data in the first initial temperature table according to the first preset rule and the order i of obtaining each group of temperature data; and storing the temperature data in a storage bit corresponding to the target number of storage rows.

[0009] Optionally, according to the first preset rule and the order i of obtaining each set of temperature data, calculating the target number of storage rows of each set of temperature data in the first initial temperature table includes:

[0010] When the order i of acquiring each set of temperature data is an odd number, the first preset rule includes the following formula:

[0011] ;

[0012] in, Store the number of rows for the target;

[0013] Alternatively, when the order i of acquiring each set of temperature data is an even number, the first preset rule includes the following formula:

[0014] .

[0015] Optionally, the second preset rule includes: starting from the first column in the first temperature table, sequentially connecting the tail of the previous column with the head of the second column to form a first temperature sequence.

[0016] Optionally, determining the second temperature sequence corresponding to the first temperature sequence includes: determining a target specific gravity influence factor sequence corresponding to the first temperature sequence based on the noise level and the basic specific gravity influence factor sequence in the distributed temperature measurement system; determining a forward sequence and a backward sequence corresponding to the first temperature sequence based on the target specific gravity influence factor sequence; and determining the second temperature sequence based on the forward sequence and the backward sequence.

[0017] Optionally, the distributed temperature measurement system includes an optical fiber, and based on the noise level and the basic gravity influence factor sequence in the distributed temperature measurement system, a target gravity influence factor sequence corresponding to the first temperature sequence is determined, including: in the distributed temperature measurement system, a first preset section and a second preset section are selected, the first preset section is located at the head of the optical fiber of the distributed temperature measurement system, and the second preset section is located at the tail of the optical fiber of the distributed temperature measurement system; the first preset section includes a plurality of first temperature measurement points, and the temperature difference between any two adjacent first temperature measurement points is less than a first preset value, and the second preset section includes a plurality of second temperature measurement points, and the temperature difference between any two adjacent second temperature measurement points is less than a second preset value; determine a first noise level in the first preset section and a second noise level in the second preset section; the noise level in the distributed temperature measurement system includes a first noise level and a second noise level; when the first noise level is greater than a first noise threshold or the second noise level is greater than a second noise threshold, adjust the basic gravity influence factor sequence according to a preset step size, and recalculate the first noise level and the second noise level; when the first noise level is greater than the first noise threshold or the second noise level is greater than the second noise threshold, determine the current gravity influence factor sequence as the target gravity influence factor sequence.

[0018] Optionally, determining the first noise level of the first preset section and the second noise level of the second preset section includes: obtaining the first noise value of each first temperature measurement point and the second noise value of each second temperature measurement point; calculating the first noise level of the plurality of first noise values ​​and the second noise level of the plurality of second noise values ​​based on the standard deviation technique; and / or the basic proportion influence factor sequence includes: the first basic proportion influence factor corresponding to the third temperature measurement point The first preset impact factor , the second basic specific gravity influence factor corresponding to the fourth temperature measurement point The second preset impact factor , ; The third temperature measurement point is the first temperature measurement point in the distributed temperature measurement system, and the fourth temperature measurement point is the last temperature measurement point in the distributed temperature measurement system; ;in, is the position sequence of the fifth temperature measurement point, The order is The third basic specific gravity influence factor corresponding to the fifth temperature measuring point, the fifth temperature measuring point is located between the third temperature measuring point and the fourth temperature measuring point, is the total number of temperature measurement points; the preset step length includes the first preset step length and the second preset step size , adjusting the basic proportion influence factor sequence according to the preset step length, including: when the first noise level is greater than the first noise threshold, adjusting the first basic proportion influence factor corresponding to the third temperature measurement point , adjust the third basic gravity influence factor corresponding to the fifth temperature measurement point ; When the second noise level is greater than the second noise threshold, adjust the first basic specific gravity influence factor corresponding to the third temperature measurement point , adjust the second basic gravity influence factor corresponding to the fourth temperature measurement point , adjust the third basic gravity influence factor corresponding to the fifth temperature measurement point .

[0019] Optionally, based on the target specific gravity influence factor sequence, determining a forward sequence and a backward sequence corresponding to the first temperature sequence includes: determining the forward sequence corresponding to the first temperature sequence according to the following formula:

[0020] ;

[0021] ;

[0022] Where k is the order of each temperature value in the first temperature sequence, is the data in the forward sequence corresponding to each temperature value in the first temperature sequence, is the target specific gravity influence factor corresponding to the temperature measurement point corresponding to the temperature value with the order k in the first temperature sequence, is the temperature value at the position k in the first temperature sequence;

[0023] ;

[0024] in, and is the intensity value corresponding to the temperature data, is the range standard deviation;

[0025] Determine the backward sequence corresponding to the first temperature sequence according to the following formula:

[0026] ;

[0027] ;

[0028] in, is the data in the backward sequence corresponding to each temperature value in the first temperature sequence;

[0029] And / or, determining a second temperature sequence according to the forward sequence and the backward sequence, comprising:

[0030] The second temperature sequence is determined according to the following formula:

[0031] .

[0032] Optionally, extracting the target temperature data in the second temperature sequence includes: converting the second temperature sequence into a second temperature table according to a third preset rule; the third preset rule includes: dividing the second temperature sequence from the beginning to the end into multiple sequences with a length equal to the number of storage bits according to the number of storage bits, and filling the multiple sequences into the second temperature table in columns; extracting a set of temperature data with the latest acquisition time in the second temperature table.

[0033] In some embodiments, a distributed temperature measurement system is provided, comprising: a light source, an optical fiber, a photodetector, and a data acquisition unit arranged in sequence along an optical axis; the light source is configured to emit a first light beam, the first light beam propagates in the optical fiber to form a second light beam; the photodetector is configured to capture the second light beam and generate an electrical signal; the data acquisition unit is configured to collect the electrical signal and process the electrical signal; the data acquisition unit is further configured to obtain multiple groups of temperature data of the distributed temperature measurement system, each group of temperature data is obtained at a different time, and each group of temperature data includes a temperature value of each temperature measurement point; based on the order of obtaining each group of temperature data and a first preset rule, a first temperature table is constructed, the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of temperature data, and M corresponds to the number of temperature measurement points; the first temperature table In the embodiment, each row stores a set of temperature data, and each first storage bit stores a temperature value; according to the second preset rule, the first temperature table is converted into a first temperature sequence, and the first temperature sequence includes 1 row of N×M columns of second storage bits; a second temperature sequence corresponding to the first temperature sequence is determined, and the second temperature sequence is obtained by denoising the first temperature sequence using a target denoising algorithm; the target denoising algorithm adopts a target proportion influence factor sequence, and the target proportion influence factor sequence includes multiple target proportion influence factors, and the target proportion influence factor corresponds to the temperature measurement point one by one, and the target proportion influence factor is used to influence the weight of the temperature value of the temperature measurement point corresponding to the target proportion influence factor, and each target proportion influence factor is the same or different; the target temperature data in the second temperature sequence is extracted, and the target temperature data includes the temperature value of each temperature measurement point with the latest acquisition time.

[0034] It can be understood that the beneficial effects that can be achieved by the technical solution provided by the above-mentioned distributed temperature measurement system can be referred to the beneficial effects in the distributed temperature measurement method and any of its optional implementations, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 Schematic diagram of temperature data provided for this application;

[0037] Figure 2 A schematic diagram of a distributed temperature measurement system provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of a distributed temperature measurement method according to an embodiment of the present application;

[0039] Figure 4A data flow diagram of temperature data provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of a first temperature meter provided in an embodiment of the present application;

[0041] Figure 6 A schematic diagram of a first preset section and a second preset section provided in an embodiment of the present application.

[0042] Reference numerals:

[0043] 10. Light source; 20. Optical fiber; 201. First temperature measuring point; 202. Second temperature measuring point; 203. Third temperature measuring point; 204. Fourth temperature measuring point; 205. Fifth temperature measuring point; 206. Sixth temperature measuring point; 30. Photoelectric detector; 40. Data acquisition unit; 401. Computer; 402. Mobile phone. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, other embodiments obtained by ordinary technicians in this field without making creative work all belong to the protection scope of the present application.

[0045] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0046] In addition, in the present application, directional terms such as "upper", "lower", "inner" and "outer" are defined relative to the orientation of the components schematically placed in the drawings. It should be understood that these directional terms are relative concepts. They are used for relative description and clarification, and they can change accordingly according to the changes in the orientation of the components placed in the drawings.

[0047] Distributed Temperature Sensing (DTS) is a temperature monitoring system based on fiber optic technology. It uses the characteristics of fiber optic sensors distributed along the entire length of the fiber to detect temperature changes along the fiber path by measuring the scattering characteristics of light in the fiber. The working principle of distributed temperature measurement is that when the laser passes through the fiber, some photons will be scattered by the molecules in the fiber to form scattered light. Scattered light includes Raman scattered light, which includes Stokes light and Anti-Stokes light. Stokes light and Anti-Stokes light are related to temperature. The intensity of Anti-Stokes light changes with temperature, and the temperature can be determined by measuring this change.

[0048] The application areas of distributed temperature measurement include power and energy, oil and gas, geological and environmental monitoring, transportation, and construction and industry. Distributed temperature measurement can achieve long-distance, high-precision and real-time monitoring of temperature.

[0049] In order to facilitate the understanding of the technical solution of the application, some background technologies involved in this application are first explained below.

[0050] The distributed temperature measurement system is based on the demodulation of the signal intensity of Raman scattered light in the optical fiber to obtain the temperature information of each temperature measurement point in the optical fiber. As the transmission distance of the optical signal becomes longer and longer, the intensity of Raman scattered light in the optical fiber decays exponentially, usually about 0.8dB / km. Therefore, in the long-distance distributed temperature measurement system, the optical signal has decayed to a very weak level after being transmitted for more than ten kilometers or dozens of kilometers. For example, after the optical signal is transmitted for 25km, it decays by 20dB, and the light intensity is only one percent of the initial intensity. In addition, since the photodetector itself will bring a lot of background noise, the attenuated optical signal is converted into a voltage signal by the photodetector. The signal-to-noise ratio is very low. After reading and demodulating the voltage signal through a digital acquisition device, it is difficult to obtain a stable temperature reading.

[0051] Usually in the long-distance temperature measurement scenario, in order to improve the signal-to-noise ratio at the end of the optical fiber, a multiple averaging algorithm is generally used to reduce noise. The multiple averaging algorithm refers to multiple sampling in the distributed temperature measurement system at a preset interval, and the average temperature of each point is calculated to achieve the purpose of noise reduction. Among them, the section of the optical fiber close to the light source is called the "head end", and the end far from the light source is called the "tail end".

[0052] However, the method of using multiple averaging algorithms to reduce noise is relatively time-consuming. For example, if you want to reduce the noise by more than 10dB, you need to average 100 times. Usually, the interval between two samples is 10 seconds, which takes a total of 1000 seconds, about 17 minutes. That is, the temperature data calculated each time is the average of all data within 17 minutes. This average cannot accurately show the temperature changes within 17 minutes, and causes a lag of about 8 minutes in temperature response. In addition, because the light intensity at the head and tail ends of the optical fiber is different, the signal-to-noise ratio is also different, and the temperature at the head end does not need so many averages. Reducing the number of averages can achieve a faster response speed, more accurately show the temperature changes in a short period of time, and occupy less computing resources.

[0053] In summary, there are three main problems with using the multiple averaging algorithm for noise reduction: the temperature response is delayed, the memory usage is high, and the temperature changes within a short period of time after averaging cannot be accurately reflected. The first two problems are because the algorithm is not efficient enough in noise reduction and does not fully utilize the relationship between the data. The third problem is that the averaging algorithm gives the same weight to each set of data and cannot distinguish between differences in temperature values ​​and time.

[0054] Therefore, it is necessary to replace the noise reduction algorithm used to solve the above problems.

[0055] Figure 1 Schematic representation of the temperature data provided for this application.

[0056] The temperature data collected by the distributed temperature measurement system each time is a one-dimensional array. Figure 1 As shown in (a), the one-dimensional array includes the temperature data of each temperature measurement point. When using the multiple averaging algorithm for noise reduction, dozens of collected temperature data need to be averaged, so the data to be processed during noise reduction is actually a two-dimensional array. Figure 1 As shown in (b), the two-dimensional array includes the temperature data collected multiple times. Figure 1 In the two-dimensional array shown in (b), the position of each temperature value is regarded as a pixel point, and each temperature value corresponds to the pixel value corresponding to the pixel point. The form of the two-dimensional array is very similar to a two-dimensional grayscale image. Therefore, it is possible to consider using image processing algorithms to reduce the noise of temperature data. The edge-preserving denoising type algorithm can more accurately reflect the temperature changes in a short time and space.

[0057] The most typical algorithm in edge-preserving denoising is the bilateral filtering algorithm. Bilateral filtering is a nonlinear filtering method. It is a compromise between the spatial proximity of the image and the similarity of pixel values. It also takes into account the spatial difference and intensity difference of pixels to achieve the purpose of edge-preserving denoising. Bilateral filtering uses a weighted average method to represent the intensity of a pixel by the weighted average of the brightness values ​​of the surrounding pixels. The weighted average used is based on Gaussian distribution. Most importantly, the weight of bilateral filtering not only considers the Euclidean distance of the pixel (such as ordinary Gaussian low-pass filtering, which only considers the influence of position on the central pixel), but also considers the radiation difference in the pixel range domain (such as the similarity between the pixel and the central pixel in the convolution kernel, color intensity, depth distance, etc.). These two weights are considered simultaneously when calculating the central pixel. It can achieve the effect of denoising while retaining the edge.

[0058] Many edge-preserving denoising methods, including bilateral filtering, have the problem of high computational complexity, mainly because they do not fully utilize the correlation between data. Considering the amount of computation and the effect of edge-preserving denoising, the BEEPS algorithm (Bilateral Estimation-based Enhancement of Perceptual Signal-to-noise ratio) in the bilateral filtering algorithm is adopted to realize the denoising of temperature data.

[0059] In the related art, the BEEPS algorithm is applied to the denoising of an image. First, the pixels of the image are connected from left to right in each column, so as to convert the two-dimensional pixel array into a one-dimensional pixel array, and then the forward recursive sequence and the backward recursive sequence of the one-dimensional pixel array are determined respectively, and the output sequence is further determined based on the forward recursive sequence and the backward recursive sequence. In this way, the denoising of the one-dimensional pixel array is completed, and then the one-dimensional pixel array is segmented and converted into a two-dimensional pixel array.

[0060] This application considers using the BEEPS algorithm to achieve noise reduction of temperature data in a distributed temperature measurement system. First, the collected temperature data is arranged into a two-dimensional array, such as Figure 1 As shown in (b). Then according to the transformation rules of the BEEPS algorithm (combined with Figure 1 As shown by the arrow in (c), the two-dimensional array is converted into a one-dimensional array (the one-dimensional array is as Figure 1 As shown in (d) in the figure). Thus, the BEEPS algorithm's forward recursive sequence, backward recursive sequence, and output sequence techniques are used to achieve noise reduction for the one-dimensional array. Then, according to the transformation rules of the inverse BEEPS algorithm, the one-dimensional array is transformed back to a two-dimensional array, and then the temperature data at the last time is extracted from the two-dimensional array.

[0061] However, combined with the above description, it can be seen that the existing BEEPS algorithm is not a noise reduction method applied to distributed temperature measurement systems, and does not take into account the correlation between temperature data and the differences in distance and time corresponding to different temperature values.

[0062] There are two obvious differences between distributed temperature measurement denoising and image edge-preserving denoising. First, image edge-preserving denoising is an algorithm for the entire two-dimensional image, which requires calculating the value of each pixel, while the temperature data is only equivalent to one row in the image, and the overall amount of calculation is significantly smaller than the entire image. Second, the signal-to-noise ratio of the image has no obvious distribution pattern on the plane, while the temperature value of distributed temperature measurement shows an obvious pattern that the signal-to-noise ratio gradually decreases with the distance between the temperature measurement point and the light source. Therefore, it is necessary to improve the BEEPS algorithm so that it can play a better role in the distributed temperature measurement system.

[0063] To sum up, in order to solve the problems of delayed temperature response, high memory usage, and inaccurate reflection of temperature changes within a short period of time after averaging caused by the use of multiple averaging algorithms for denoising, as well as the high computational complexity of the edge-preserving denoising algorithm, the present application proposes a distributed temperature measurement method and system to improve the response and reflection speed of distributed temperature measurement, reduce memory usage, and reduce computational complexity.

[0064] Specifically, the present application proposes a distributed temperature measurement method and system that uses an improved BEEPS algorithm to reduce the noise of temperature data. The improved BEEPS algorithm greatly simplifies the operation complexity, and takes into account the correlation between temperature data and the differences in distance and time corresponding to different temperature values, and is more suitable for distributed temperature measurement application scenarios.

[0065] Figure 2 A schematic diagram of a distributed temperature measurement system provided in an embodiment of the present application.

[0066] Combination Figure 2 As shown, the present application provides a distributed temperature measurement system, comprising: a light source 10, an optical fiber 20, a photodetector 30 and a data acquisition unit 40 arranged in sequence along an optical axis; the light source 10 is configured to emit a first light beam, and the first light beam propagates in the optical fiber to form a second light beam; the photodetector 30 is configured to capture the second light beam and generate an electrical signal; the data acquisition unit 40 is configured to collect the electrical signal and process and reduce the noise of the electrical signal to obtain the target temperature data after noise reduction.

[0067] Specifically, when the first light beam propagates in the light, Raman scattering occurs to form a second light beam, and after the second light beam is captured by the photodetector 30, the photodetector 30 generates an electrical signal. The electrical signal is sent to the data acquisition unit 40, which demodulates the electrical signal to obtain temperature data, and performs noise reduction on the temperature data to obtain target temperature data after noise reduction.

[0068] The distributed temperature measurement system provided in the embodiment of the present application can improve the signal-to-noise ratio of temperature data in long-distance distributed temperature measurement, especially the signal-to-noise ratio of the temperature measurement point at the tail end of the optical fiber, and can improve the response and reflection speed of distributed temperature measurement, reduce memory usage, and reduce calculation complexity.

[0069] Specifically, combined Figure 2 As shown, multiple temperature measurement points are marked on the optical fiber 20 (for example, the first temperature measurement point 201, the second temperature measurement point 202, the third temperature measurement point 203, the fourth temperature measurement point 204, the fifth temperature measurement point 205, and the sixth temperature measurement point 206). Each time temperature measurement data is obtained, the temperature measurement data includes the temperature measurement data corresponding to each temperature measurement point.

[0070] It should be understood that the number of temperature measurement points in the distributed temperature measurement system and the distance between each temperature measurement point should be determined according to the actual temperature measurement requirements. In the embodiment of the present application, only six temperature measurement points are schematically marked. In actual applications, the number of temperature measurement points can be more or less, and the present application does not impose any restrictions on this.

[0071] Specifically, the data acquisition unit 40 includes a computer 401, a mobile phone 402 or a tablet computer, etc., which can perform data processing. The data acquisition unit 40 is connected to the photoelectric detector 30 for communication ( Figure 2 The communication connection method includes wired connection or wireless connection.

[0072] Figure 3 A flow chart of a distributed temperature measurement method provided in an embodiment of the present application.

[0073] Figure 4 A data flow diagram of temperature data provided in an embodiment of the present application.

[0074] Combination Figure 2 The distributed temperature measurement system shown in the embodiment of the present application also provides a distributed temperature measurement method, which is applied to the distributed temperature measurement system. The distributed temperature measurement system includes multiple temperature measurement points. The execution subject of the method can be a data acquisition unit, such as Figure 3 As shown, the method includes steps S100 to S500, which are specifically as follows:

[0075] Step S100, obtaining multiple groups of temperature data of the distributed temperature measurement system, each group of temperature data is obtained at a different time, and each group of temperature data includes the temperature value of each temperature measurement point.

[0076] In this step, multiple sets of temperature data are acquired to facilitate processing of the multiple sets of temperature data, and target temperature data after noise reduction is obtained. The target temperature data is the temperature data with the latest acquisition time among the multiple sets of temperature data after noise reduction processed using the improved BEEPS algorithm.

[0077] Combination Figure 4 As shown, taking the multiple groups of temperature data including 6 groups of temperature data and the distributed temperature measurement system having six temperature measurement points as an example, the six temperature measurement points are Figure 2 , the first temperature measuring point 201, the second temperature measuring point 202, the third temperature measuring point 203, the fourth temperature measuring point 204, the fifth temperature measuring point 205 and the sixth temperature measuring point 206 are shown in FIG. Figure 4 As shown in (a), the 6 groups of temperature data are respectively the temperature data collected for the first time, the temperature data collected for the second time, the temperature data collected for the third time, the temperature data collected for the fourth time, the temperature data collected for the fifth time, and the temperature data collected for the sixth time in the order of the collection time.

[0078] It should be understood that the number of groups of temperature data and the number of temperature measurement points in the distributed temperature measurement method should be determined according to the actual temperature measurement requirements. In the embodiment of the present application, only six groups of temperature data and six temperature measurement points are given for illustration. In actual applications, the number of temperature measurement points and the number of groups of temperature data can be more or less, and the present application does not impose any restrictions on this.

[0079] Step S200, based on the order of obtaining each group of temperature data and the first preset rule, construct a first temperature table, the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of temperature data, and M corresponds to the number of temperature measurement points; in the first temperature table, each row stores a group of temperature data, and each first storage bit stores a temperature value.

[0080] In this step, since the closer the acquisition time is, the greater the correlation between the temperature data is, and the later the temperature data is acquired, the closer it is to the current actual temperature, that is, the later the temperature data is acquired, the greater the importance. The first temperature table constructed according to the first preset rule can arrange the temperature data acquired later together to reduce the mutual influence of data with low correlation in the subsequent noise reduction process.

[0081] Specifically, in the existing BEEPS algorithm, the two-dimensional array is arranged in the order of the acquisition of temperature data. In the distributed temperature measurement system, the last acquired temperature data should obviously be selected as the final temperature data, that is, Figure 1The data in the dashed box D in the two-dimensional array shown in (c) is shown in Figure 1. However, combined with Figure 1 In the one-dimensional array shown in (d), the temperature value An1 obtained at the first temperature measurement point for the nth time is adjacent to the temperature value A12 obtained at the second temperature measurement point for the first time. The temperature values ​​An1 and A12 are obtained in different orders and at different positions. Obviously, the correlation between the temperature values ​​An1 and A12 is low. In the process of denoising the one-dimensional array, the temperature values ​​An1 and A12 will affect each other, resulting in a large distortion of the temperature values ​​An1 and A12 after denoising.

[0082] To avoid this situation, the embodiment of the present application arranges the temperature data with high correlation together according to the correlation between the temperature data, so as to set different weights for different temperature data during the noise reduction process. In this way, the relationship between the temperature data is fully utilized to improve the noise reduction efficiency of the temperature data, and can accurately reflect the temperature changes in a short time after averaging.

[0083] In summary, the first improvement of the present application to the existing BEEPS algorithm is to change the arrangement of temperature data during data processing.

[0084] Figure 5 A schematic diagram of a first temperature meter provided in an embodiment of the present application.

[0085] For example, in combination Figure 5 As shown, when the total number of temperature data groups is N and the number of temperature measurement points is M, the first temperature table is as follows Figure 5 As shown. The total number of rows is N, each row corresponds to a set of temperature data, the total number of columns is M, and each column corresponds to a temperature measurement point. Each first storage bit stores a temperature value. For example, the temperature data stored in the first storage bit corresponding to the third temperature measurement point in the temperature data obtained for the first time is C13.

[0086] For example, Figure 4 The 6 sets of temperature data in (a) are constructed according to the first preset rule as follows: Figure 4 As shown in (b), it can be seen from the figure that since the acquisition time of the temperature data acquired for the fifth time is adjacent to the acquisition time of the temperature data acquired for the sixth time, the temperature data acquired for the fifth time is highly correlated with the temperature data acquired for the sixth time. Therefore, in the first temperature table, the temperature data acquired for the fifth time is arranged adjacent to the temperature data acquired for the sixth time to improve the correlation between the temperature data in the subsequent noise reduction processing.

[0087] For example, when the total number of groups of temperature data acquired is 6 and the number of temperature measurement points is 6, N=6 and M=6. When the total number of groups of temperature data acquired is 8 and the number of temperature measurement points is 7, N=8 and M=7.

[0088] Specifically, in the first temperature table, the temperature measurement points corresponding to the first storage bits from left to right in each row are getting farther or closer.

[0089] Exemplarily, when the number of temperature measurement points is 6 and the temperature measurement points corresponding to the first storage positions from left to right in each row are getting farther and farther away, the third temperature measurement point and the fifth temperature measurement point should be stored in the third first storage position and the fifth first storage position from left to right, respectively. When the number of temperature measurement points is 6 and the temperature measurement points corresponding to the first storage positions from left to right in each row are getting closer and closer, the third temperature measurement point and the fifth temperature measurement point should be stored in the fourth first storage position and the second first storage position from left to right, respectively.

[0090] Specifically, in the first temperature table, temperature data with a later acquisition time is stored closer to the middle row of the first temperature table.

[0091] For example, when the total number of groups of temperature data obtained is 6, the temperature data obtained for the first time is stored in the first row, the temperature data obtained for the second time is stored in the sixth row, the temperature data obtained for the third time is stored in the second row, the temperature data obtained for the fourth time is stored in the fifth row, the temperature data obtained for the fifth time is stored in the third row, and the temperature data obtained for the sixth time is stored in the fourth row. Figure 4 Middle (b).

[0092] Exemplarily, when the total number of groups of temperature data acquired is 6 groups, the temperature data acquired for the first time is stored in the first row, the temperature data acquired for the second time is stored in the second row, the temperature data acquired for the third time is stored in the fifth row, the temperature data acquired for the fourth time is stored in the sixth row, the temperature data acquired for the fifth time is stored in the third row, and the temperature data acquired for the sixth time is stored in the fourth row.

[0093] Optionally, based on the order of acquiring each set of temperature data and a first preset rule, constructing a first temperature table includes steps S201 to S203, which are specifically as follows:

[0094] Step S201: construct a first initial temperature table, which includes N rows and M columns.

[0095] Step S202 , according to the first preset rule and the order i of obtaining each set of temperature data, calculate the target number of storage rows of each set of temperature data in the first initial temperature table.

[0096] Optionally, according to the first preset rule and the order i of obtaining each set of temperature data, calculating the target number of storage rows of each set of temperature data in the first initial temperature table includes:

[0097] When the order i of acquiring each set of temperature data is an odd number, the first preset rule includes the following formula:

[0098] ;

[0099] in, Store the number of rows for the target;

[0100] Alternatively, when the order i of acquiring each set of temperature data is an even number, the first preset rule includes the following formula:

[0101] .

[0102] In this embodiment, the target number of storage rows calculated based on the formula can arrange temperature data with high correlation together, so as to reduce the mutual influence between data with low correlation in the subsequent noise reduction process.

[0103] Specifically, combined Figure 4 As shown in (a), the order of acquisition time is the "i" in the temperature data acquired for the i-th time.

[0104] Exemplarily, when i=1, that is, when determining the target number of storage rows of the temperature data acquired for the first time, , so the target storage row number of the temperature data obtained for the first time is 1. Figure 4 As shown in (b), the temperature data acquired for the first time is stored in the first row of the first initial temperature table.

[0105] When i=4, that is, when determining the target number of storage rows of the temperature data acquired for the fourth time, since the total number of groups of temperature data is 6, that is, N=6, , so the target number of rows for storing the temperature data acquired for the fourth time is 5. Figure 4 As shown in (b), the temperature data acquired for the fourth time is stored in the fifth row of the first initial temperature table.

[0106] Step S203, storing the temperature data into the storage bit corresponding to the target storage row number.

[0107] In this embodiment, the target number of storage rows for each set of temperature data in the first initial temperature table is calculated so that temperature data with high correlation are arranged together, so as to reduce the mutual influence between data with low correlation in the subsequent noise reduction process.

[0108] Step S300: converting the first temperature table into a first temperature sequence according to a second preset rule, wherein the first temperature sequence includes 1 row and N×M columns of second storage bits.

[0109] In this step, the first temperature table is segmented according to the second preset rule to obtain multiple temperature value sequences, and the temperature value sequences are spliced ​​to realize the conversion of the first temperature table into a first temperature sequence. In the first temperature sequence, data with high correlation are adjacent to each other to reduce the impact on the temperature data during the subsequent noise reduction process.

[0110] For example, according to the second preset rule (combined with Figure 4 As shown by the dotted line in (c), Figure 4 The first temperature table in (b) is converted into Figure 4 The first temperature sequence in (d).

[0111] Combination Figure 4 From the first temperature sequence in (d), it can be seen that the embodiment of the present application arranges the acquisition time of the same temperature measurement point close to the temperature value, and arranges them in sequence from near to far according to the distance of the temperature measurement point from the light source, so that data with higher correlation are adjacent, so as to fully consider the correlation between the data, improve the noise reduction efficiency of the temperature data, and improve the response speed of the temperature measurement system.

[0112] Optionally, the second preset rule includes: starting from the first column in the first temperature table, sequentially connecting the tail of the previous column with the head of the second column to form a first temperature sequence.

[0113] In this embodiment, in the first temperature sequence constructed according to the second preset rule, data with higher correlation are arranged together (e.g. Figure 4 In addition, the temperature data of each temperature measuring point is continuous, and the left and right sides of the last temperature value are both the temperature values ​​of the same temperature measuring point. In this way, the influence between the temperature data of different temperature measuring points is reduced, and the accuracy of the target temperature data finally obtained is improved.

[0114] Step S400, determine the second temperature sequence corresponding to the first temperature sequence, the second temperature sequence is obtained by denoising the first temperature sequence using a target denoising algorithm; the target denoising algorithm adopts a target weight influence factor sequence, the target weight influence factor sequence includes multiple target weight influence factors, the target weight influence factors correspond to the temperature measurement points one by one, the target weight influence factors are used to influence the weights of the temperature values ​​of the temperature measurement points corresponding to the target weight influence factors, and each target weight influence factor is the same or different.

[0115] In this step, by setting a sequence of target weight influence factors to set different target weight influence factors for each temperature measuring point, the weight of each temperature value in the noise reduction process is increased or decreased, thereby distinguishing the differences in temperature values ​​in space and time, and giving different temperature measuring points different noise reduction effects according to the different target weight influence factors corresponding to each temperature measuring point, thereby improving the accuracy of the temperature data after noise reduction, so as to accurately reflect the temperature changes in a short time after averaging.

[0116] Specifically, in a distributed temperature measurement system, the signal-to-noise ratio of the temperature data is directly related to the location of the temperature measurement point. The farther the temperature measurement point is from the light source, the lower the signal-to-noise ratio of the temperature data. The overall signal-to-noise ratio decays exponentially with the distance from the temperature measurement point. The size of the final signal-to-noise ratio or the noise level of the distributed temperature measurement system is determined according to the project requirements, and there is no fixed rule. In combination with the existing BEEPS algorithm, it can be seen that the larger the value of the specific gravity influence factor, the smoother the temperature data curve, that is, the better the noise reduction effect, but the longer the temperature response time, so the selection of the specific gravity influence factor should be determined according to the specific requirements of the application scenario. In this application, the target specific gravity influence factor is reset to find a balance between the signal-to-noise ratio and the response speed, reducing the problem of a high signal-to-noise ratio and a slow response speed, or a low signal-to-noise ratio and a fast response speed.

[0117] In summary, the second improvement of the present application to the existing BEEPS algorithm is: selecting different noise reduction weights for different temperature measurement points.

[0118] Specifically, the target noise reduction algorithm is obtained by improving the basic weight influence factor sequence in the existing BEEPS algorithm.

[0119] For example, in the target specific gravity influence factor sequence, the size of each target specific gravity influence factor is related to the distance between the temperature measurement point and the light source. By setting the target specific gravity influence factor corresponding to the temperature measurement point farther from the light source to be larger, the specific gravity of the temperature value of the temperature measurement point at the tail end of the optical fiber can be increased during the noise reduction process, thereby giving this part a better noise reduction effect during the noise reduction process.

[0120] Optionally, determining the second temperature sequence corresponding to the first temperature sequence includes steps S401 to S403, which are specifically as follows:

[0121] Step S401: determining a target specific gravity influence factor sequence corresponding to a first temperature sequence based on a noise level and a basic specific gravity influence factor sequence in a distributed temperature measurement system.

[0122] Optionally, step S401 includes steps S4011 to S4014, which are specifically as follows:

[0123] Step S4011, in the distributed temperature measurement system, select a first preset section and a second preset section, the first preset section is located at the head of the optical fiber of the distributed temperature measurement system, and the second preset section is located at the tail of the optical fiber of the distributed temperature measurement system; the first preset section includes a plurality of first temperature measurement points, and the temperature difference between any two adjacent first temperature measurement points is less than a first preset value, and the second preset section includes a plurality of second temperature measurement points, and the temperature difference between any two adjacent second temperature measurement points is less than a second preset value.

[0124] Figure 6 A schematic diagram of a first preset section and a second preset section provided in an embodiment of the present application.

[0125] Exemplarily, the process of selecting the first preset section and the second preset section is as follows:

[0126] Combination Figure 6 As shown, Figure 6 The optical fiber 20 is included, and the temperature measuring points (20, the temperature measuring points from the head end to the tail end are marked as E, F, G, ..., M, N, ..., W, X) spaced apart on the optical fiber 20. After a set of temperature data is measured, the temperatures from the temperature measuring points E to X are: 5°C, 5°C, 6°C, 5°C, 5°C, 7°C, 6°C, 7°C, 7°C, 8°C, 6°C, 5°C, 6°C, 5°C, 7°C, 5°C, 4°C, 5°C, 6°C, 5°C, the first preset value and the second preset value are both set to 1°C, the number of the first temperature points is set to 5, and the number of the second temperature points is set to 5. The temperature difference between the temperature measuring points is calculated from the temperature measuring point E to the right in sequence until the temperature difference between the five adjacent temperature measuring points is less than or equal to 1, and the five temperature measuring points are the first temperature measuring points. In this embodiment, the temperature measuring points E, F, G, H and I are the first temperature measuring points. The temperature difference between the temperature measuring points is calculated from the temperature measuring point X to the left, until the temperature difference between the five adjacent temperature measuring points is less than or equal to 1, and the five temperature measuring points are the second temperature measuring points. In this embodiment, the temperature measuring points T, U, V, W and X are the second temperature measuring points. That is, the first preset section includes the temperature measuring points E, F, G, H and I, and the second preset section includes the temperature measuring points T, U, V, W and X.

[0127] It should be understood that the first preset value and the second preset value, as well as the number of the first temperature points and the number of the second temperature points can be set as required, and this application does not impose any restrictions on this.

[0128] Specifically, after heating, the optical fiber in the pipeline trench is affected by the thermal pipeline, and the temperature distribution may experience large local fluctuations. The first preset section and the second preset section here are both intervals with uniform temperature. The first preset section and the second preset section are selected to avoid these large temperature fluctuations, statistically generate noise during instrument measurement, and reduce the interference of thermal pipelines on instrument measurement noise.

[0129] Step S4012, determining a first noise level in a first preset section and a second noise level in a second preset section; the noise level in the distributed temperature measurement system includes the first noise level and the second noise level.

[0130] Optionally, determining a first noise level of a first preset section and a second noise level of a second preset section includes step S40121 and step S40122:

[0131] Step S40121, obtaining a first noise value of each first temperature measuring point and a second noise value of each second temperature measuring point;

[0132] Step S40122: calculating the first noise level of the plurality of first noise values ​​and calculating the second noise level of the plurality of second noise values ​​based on the standard deviation technique.

[0133] In this embodiment, the standard deviation technology is used to determine the standard deviation of multiple first noise values, that is, to determine the first noise level of the first preset section, and the standard deviation of multiple second noise values, that is, to determine the second noise level of the second preset section. This is to adjust the basic weight influence factor sequence according to the first noise level and the second noise level, so as to adjust the weight of each temperature measurement point in noise reduction, so as to improve the noise reduction effect of the temperature measurement point at the tail end of the optical fiber and improve the signal-to-noise ratio of the temperature measurement point at the tail end of the optical fiber.

[0134] Exemplarily, the temperature value of the first temperature point is used as the first noise value, and the temperature value of the second temperature point is used as the second noise value.

[0135] Specifically, determining the first noise level of the first preset section includes: acquiring a first noise value of each first temperature measurement point; and calculating a standard deviation of a plurality of first noise values ​​as the first noise level.

[0136] Specifically, determining the second noise level of the second preset section includes: acquiring a second noise value of each second temperature measurement point; and calculating a standard deviation of a plurality of second noise values ​​as the second noise level.

[0137] Step S4013, when the first noise level is greater than the first noise threshold or the second noise level is greater than the second noise threshold, the basic proportion influence factor sequence is adjusted according to a preset step size, and the first noise level and the second noise level are recalculated.

[0138] In this step, if the first noise level is greater than the first noise threshold or the second noise level is greater than the second noise threshold, it indicates that the first noise level and / or the second noise level do not meet the noise level required by the distributed temperature measurement system, that is, the current noise reduction effect does not meet the requirements, and the problem of inaccurate temperature data still exists. Therefore, it is necessary to readjust the basic proportion influence factor sequence to change the current noise reduction effect.

[0139] Optionally, the basic specific gravity influence factor sequence includes: the first basic specific gravity influence factor corresponding to the third temperature measurement point The first preset impact factor , the second basic specific gravity influence factor corresponding to the fourth temperature measurement point The second preset impact factor , ; The third temperature measurement point is the first temperature measurement point in the distributed temperature measurement system, and the fourth temperature measurement point is the last temperature measurement point in the distributed temperature measurement system;

[0140] ;

[0141] in, is the position sequence of the fifth temperature measurement point, The order is The third basic specific gravity influence factor corresponding to the fifth temperature measuring point, the fifth temperature measuring point is located between the third temperature measuring point and the fourth temperature measuring point, is the total number of temperature measurement points; the preset step length includes the first preset step length and the second preset step size , adjusting the basic weight influence factor sequence according to the preset step length, including step S40131 and step S40132, as follows:

[0142] Step S40131: when the first noise level is greater than the first noise threshold, adjust the first basic specific gravity influence factor corresponding to the third temperature measurement point. , adjust the third basic specific gravity influence factor corresponding to the fifth temperature measurement point ;

[0143] Step S40132: when the second noise level is greater than the second noise threshold, adjust the first basic specific gravity influence factor corresponding to the third temperature measurement point. , adjust the second basic gravity influence factor corresponding to the fourth temperature measurement point , adjust the third basic specific gravity influence factor corresponding to the fifth temperature measurement point .

[0144] In this embodiment, based on the first preset step size and the second preset step size The influence factor of each temperature measuring point can be adjusted from the beginning or the end respectively, thereby adjusting the noise reduction weight of each temperature measuring point.

[0145] Specifically, when the first noise level is greater than the first noise threshold, after adjusting the first basic proportion influence factor corresponding to the third temperature measurement point, the third basic proportion influence factor corresponding to the fifth temperature measurement point is .

[0146] When the second noise level is greater than the second noise threshold, after adjusting the first basic specific gravity influence factor corresponding to the third temperature measuring point and adjusting the second basic specific gravity influence factor corresponding to the fourth temperature measuring point, the third basic specific gravity influence factor corresponding to the fifth temperature measuring point is .

[0147] Optionally, the first preset impact factor The value range of includes: [0.8,1).

[0148] Optionally, the second preset impact factor Greater than the first preset impact factor .

[0149] Optionally, when the temperature value of the first temperature point is used as the first noise value and the temperature value of the second temperature point is used as the second noise value, both the first noise threshold and the second noise threshold are less than or equal to 1°C.

[0150] Optionally, the first preset step length The value range includes: [0.01, 0.1], the second preset step size The value range includes: [0.001, 0.01].

[0151] Step S4014: when the first noise level is greater than the first noise threshold or the second noise level is greater than the second noise threshold, determine the current weight influence factor sequence as the target weight influence factor sequence.

[0152] In this embodiment, based on the noise levels of the first preset section and the second preset section, the size of the basic specific gravity influence factor in the basic specific gravity influence factor sequence is adjusted, so that the specific gravity influence factor in the adjusted basic specific gravity influence factor sequence affects the noise level of the first preset section and the second preset section, so that the noise level of the first preset section and the second preset section meets the requirements of the distributed temperature measurement system for the noise level, and a balance is achieved between the noise level and the response speed. Reduce the problem of high signal-to-noise ratio and slow response speed, or low signal-to-noise ratio and fast response speed.

[0153] Specifically, the basic specific gravity influence factor sequence includes a plurality of basic specific gravity influence factors, and the basic specific gravity influence factors correspond to the temperature measurement points one by one.

[0154] Step S402: determining a forward sequence and a backward sequence corresponding to the first temperature sequence based on the target specific gravity influence factor sequence.

[0155] Optionally, based on the target specific gravity influence factor sequence, determining a forward sequence and a backward sequence corresponding to the first temperature sequence includes:

[0156] Determine the forward sequence corresponding to the first temperature sequence according to the following formula:

[0157] ;

[0158] ;

[0159] Where k is the order of each temperature value in the first temperature sequence, is the data in the forward sequence corresponding to each temperature value in the first temperature sequence, is the target specific gravity influence factor corresponding to the temperature measurement point corresponding to the temperature value with the order k in the first temperature sequence, is the temperature value at the position k in the first temperature sequence;

[0160] ;

[0161] in, and is the intensity value corresponding to the temperature data, is the range standard deviation;

[0162] Determine the backward sequence corresponding to the first temperature sequence according to the following formula:

[0163] ;

[0164] ;

[0165] in, is the data in the backward sequence corresponding to each temperature value in the first temperature sequence.

[0166] In this embodiment, based on the target weighted influence factor sequence, different noise reduction weights are used for different temperature measurement points, thereby adjusting the noise reduction effect of the temperature value of each temperature measurement point and improving the temperature response speed. Step S403, determining a second temperature sequence according to the forward sequence and the backward sequence.

[0167] In this embodiment, the target specific gravity influence factor is obtained by adjusting the sequence of basic specific gravity influence factors, so as to set different target specific gravity influence factors for different temperature measurement points, thereby adjusting the weight of each temperature measurement point in noise reduction, so as to improve the noise reduction effect of the temperature measurement point at the tail end of the optical fiber, and reduce the noise reduction time of the temperature data of the temperature measurement point at the head end of the optical fiber, thereby accelerating the temperature response speed.

[0168] Optionally, determining a second temperature sequence according to the forward sequence and the backward sequence includes:

[0169] The second temperature sequence is determined according to the following formula:

[0170] .

[0171] In this embodiment, based on the target specific gravity influence factor sequence, different noise reduction weights are used for different temperature measurement points, so as to adjust the noise reduction effect of the temperature value of each temperature measurement point, improve the temperature response speed, and improve the signal-to-noise ratio of the temperature measurement point at the tail end of the optical fiber.

[0172] Step S500: extracting target temperature data in the second temperature sequence, where the target temperature data includes the latest temperature value of each temperature measurement point.

[0173] In this step, since the last temperature data obtained in sequence is the most capable of reflecting the current actual temperature, the temperature value with the latest acquisition time of each temperature measurement point in the second temperature sequence obtained after noise reduction can obtain the target temperature data that can accurately reflect the current temperature value.

[0174] Optionally, extracting target temperature data in the second temperature sequence includes step S501 and step S502, which are specifically as follows:

[0175] Step S501, converting the second temperature sequence into a second temperature table according to a third preset rule; the third preset rule includes: dividing the second temperature sequence from the beginning to the end into multiple sequences with a length equal to the number of storage bits according to the number of storage bits, and filling the multiple sequences into the second temperature table in columns.

[0176] In this step, the third preset rule may be the reverse process of the processing process of the second preset rule.

[0177] Step S502: extract a set of temperature data with the latest acquisition time from the second temperature table.

[0178] In this embodiment, since the denoising process is performed on all the acquired temperature data, and obviously in the distributed temperature measurement, only the last acquired temperature data is closest to the current temperature, it is only necessary to extract the last acquired temperature data after denoising as the target temperature data.

[0179] The distributed temperature measurement method provided by the present application is adopted to reduce noise by using the improved BEEPS algorithm, thereby reducing the problems of delayed temperature response, high memory usage, and inaccurate reflection of temperature changes within a short period of time after averaging caused by using multiple averaging algorithms for noise reduction. The acquired temperature data is constructed into a first temperature table by using the first preset rule, and the temperature data with higher correlation are arranged together, so that in the process of reducing noise for the first temperature sequence, the correlation between the data can be better considered, and the mutual influence between the data with low correlation can be reduced. In addition, based on determining the target proportion influencing factor sequence in the noise reduction algorithm, the different signal-to-noise ratios at the beginning and end of the optical fiber can be better considered, so as to achieve different noise reduction effects for the beginning and end respectively, improve the noise reduction efficiency, and reduce the amount of calculation required for noise reduction.

[0180] Corresponding to the embodiments of the aforementioned distributed temperature measurement method, the present application also provides embodiments of a distributed temperature measurement device. The distributed temperature measurement system includes: a light source, an optical fiber, a photodetector, and a data acquisition unit arranged in sequence along the optical axis; the light source is configured to emit a first light beam, and the first light beam propagates in the optical fiber to form a second light beam; the photodetector is configured to capture the second light beam and generate an electrical signal; the data acquisition unit is configured to collect the electrical signal and process the electrical signal; the data acquisition unit is also configured to obtain multiple groups of temperature data of the distributed temperature measurement system, each group of temperature data is obtained at a different time, and each group of temperature data includes the temperature value of each temperature measurement point; based on the order of obtaining each group of temperature data and the first preset rule, a first temperature table is constructed, the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of the temperature data, and M corresponds to the number of the temperature measurement points; in the first temperature table, each row stores A set of temperature data is stored, and each first storage bit stores a temperature value; according to a second preset rule, the first temperature table is converted into a first temperature sequence, and the first temperature sequence includes a second storage bit of 1 row and N×M columns; a second temperature sequence corresponding to the first temperature sequence is determined, and the second temperature sequence is obtained after the first temperature sequence is denoised by using a target noise reduction algorithm; the target noise reduction algorithm adopts a target proportion influence factor sequence, and the target proportion influence factor sequence includes multiple target proportion influence factors, and the target proportion influence factor corresponds to the temperature measurement point one by one, and the target proportion influence factor is used to influence the weight of the temperature value of the temperature measurement point corresponding to the target proportion influence factor, and each target proportion influence factor is the same or different; the target temperature data in the second temperature sequence is extracted, and the target temperature data includes the temperature value of each temperature measurement point with the latest acquisition time.

[0181] The distributed temperature measurement system provided by the present application is adopted, and the temperature data is processed by using a data acquisition unit to reduce the noise of the temperature data, and the acquired temperature data is constructed into a first temperature table using a first preset rule, and the temperature data with higher correlation are arranged together, so as to better consider the correlation between the data and reduce the mutual influence between the data with low correlation in the process of reducing the noise of the first temperature sequence. In addition, based on determining the target proportion influence factor sequence in the noise reduction algorithm, the different signal-to-noise ratios at the beginning and end of the optical fiber are better considered, so as to achieve different noise reduction effects for the beginning and end respectively, improve the noise reduction efficiency, and reduce the amount of calculation required for noise reduction.

[0182] It should be noted that those skilled in the art will easily think of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope of the present application is indicated by the claims.

[0183] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A distributed temperature measurement method, characterized in that: The distributed temperature measurement method is applied to a distributed temperature measurement system, the distributed temperature measurement system includes a plurality of temperature measurement points, and the distributed temperature measurement method includes: Acquire multiple groups of temperature data of the distributed temperature measurement system, each group of temperature data is acquired at a different time, and each group of temperature data includes a temperature value of each temperature measurement point; Based on the order of obtaining each group of the temperature data and the first preset rule, a first temperature table is constructed, wherein the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of the temperature data, and M corresponds to the number of the temperature measurement points; in the first temperature table, each row stores a group of the temperature data, and each first storage bit stores one temperature value; According to a second preset rule, the first temperature table is converted into a first temperature sequence, wherein the first temperature sequence includes a second storage bit of 1 row and N×M columns; data with high correlation in the first temperature sequence are adjacent; Determine a second temperature sequence corresponding to the first temperature sequence, where the second temperature sequence is obtained after denoising the first temperature sequence using a target denoising algorithm; the target denoising algorithm adopts a target specific gravity influence factor sequence, where the target specific gravity influence factor sequence includes a plurality of target specific gravity influence factors, where the target specific gravity influence factors correspond to the temperature measurement points one by one, and the target specific gravity influence factors are used to influence the weights of the temperature values ​​of the temperature measurement points corresponding to the target specific gravity influence factors, where each target specific gravity influence factor is the same or different; Extracting target temperature data in the second temperature sequence, the target temperature data including the latest temperature value of each temperature measurement point; The step of constructing a first temperature table based on the order of acquiring each group of the temperature data and a first preset rule comprises: Constructing a first initial temperature table, wherein the first initial temperature table includes N rows and M columns; According to a first preset rule and the order i of obtaining each group of the temperature data, calculate the target number of storage rows of each group of the temperature data in the first initial temperature table; Storing the temperature data in the storage location corresponding to the target storage row number; The step of calculating the target number of storage rows of each group of the temperature data in the first initial temperature table according to the first preset rule and the order i of obtaining each group of the temperature data comprises: In the case where the order i of obtaining each set of the temperature data is an odd number, the first preset rule includes the following formula: ； in, storing the number of rows for said target; Alternatively, when the order i of obtaining each set of temperature data is an even number, the first preset rule includes the following formula: ； The second preset rule includes: starting from the first column in the first temperature table, sequentially connecting the tail of the previous column with the head of the second column to form a first temperature sequence.

2. The distributed temperature measurement method according to claim 1, characterized in that: The determining a second temperature sequence corresponding to the first temperature sequence includes: Determine a target specific gravity influence factor sequence corresponding to the first temperature sequence based on the noise level and the basic specific gravity influence factor sequence in the distributed temperature measurement system; Based on the target specific gravity influencing factor sequence, determining a forward sequence and a backward sequence corresponding to the first temperature sequence; The second temperature sequence is determined according to the forward sequence and the backward sequence.

3. The distributed temperature measurement method according to claim 2, wherein the distributed temperature measurement system comprises an optical fiber, characterized in that: The determining, based on the noise level and the basic specific gravity influence factor sequence in the distributed temperature measurement system, a target specific gravity influence factor sequence corresponding to the first temperature sequence includes: In the distributed temperature measurement system, a first preset section and a second preset section are selected, the first preset section is located at the head of the optical fiber of the distributed temperature measurement system, and the second preset section is located at the tail of the optical fiber of the distributed temperature measurement system; the first preset section includes a plurality of first temperature measurement points, and the temperature difference between any two adjacent first temperature measurement points is less than a first preset value, and the second preset section includes a plurality of second temperature measurement points, and the temperature difference between any two adjacent second temperature measurement points is less than a second preset value; Determine a first noise level of the first preset section and a second noise level of the second preset section; the noise level in the distributed temperature measurement system includes the first noise level and the second noise level; When the first noise level is greater than a first noise threshold or the second noise level is greater than a second noise threshold, adjusting the basic proportion influence factor sequence according to a preset step size, and recalculating the first noise level and the second noise level; When the first noise level is greater than the first noise threshold or the second noise level is greater than the second noise threshold, the current weight influence factor sequence is determined to be the target weight influence factor sequence.

4. The distributed temperature measurement method according to claim 3, characterized in that: The determining a first noise level of the first preset section and a second noise level of the second preset section comprises: Acquire a first noise value of each of the first temperature measurement points and a second noise value of each of the second temperature measurement points; calculating the first noise level of a plurality of the first noise values, and calculating the second noise level of a plurality of the second noise values ​​based on a standard deviation technique; and / or, The basic specific gravity influence factor sequence includes: the first basic specific gravity influence factor corresponding to the third temperature measurement point The first preset impact factor , the second basic specific gravity influence factor corresponding to the fourth temperature measurement point The second preset impact factor , ; The third temperature measurement point is the first temperature measurement point in the distributed temperature measurement system, and the fourth temperature measurement point is the last temperature measurement point in the distributed temperature measurement system; ； in, is the position sequence of the fifth temperature measurement point, The bit sequence is The third basic specific gravity influence factor corresponding to the fifth temperature measuring point, the fifth temperature measuring point is located between the third temperature measuring point and the fourth temperature measuring point, is the total number of the temperature measuring points; The preset step length includes a first preset step length and the second preset step size , the step of adjusting the basic weight influencing factor sequence according to a preset step size includes: When the first noise level is greater than the first noise threshold, the first basic specific gravity influence factor corresponding to the third temperature measurement point is adjusted , adjust the third basic specific gravity influence factor corresponding to the fifth temperature measurement point ; When the second noise level is greater than the second noise threshold, the first basic specific gravity influence factor corresponding to the third temperature measurement point is adjusted , adjust the second basic specific gravity influence factor corresponding to the fourth temperature measurement point , adjust the third basic specific gravity influence factor corresponding to the fifth temperature measurement point .

5. The distributed temperature measurement method according to claim 2, characterized in that: The determining, based on the target specific gravity influence factor sequence, a forward sequence and a backward sequence corresponding to the first temperature sequence comprises: The forward sequence corresponding to the first temperature sequence is determined according to the following formula: ； ； Wherein, k is the order of each temperature value in the first temperature sequence, is the data in the forward sequence corresponding to each temperature value in the first temperature sequence, is the target specific gravity influence factor corresponding to the temperature measurement point corresponding to the temperature value of the order k in the first temperature sequence, is the temperature value at the position k in the first temperature sequence; ； in, and is the intensity value corresponding to the temperature data, and σ is the standard deviation of the range; The backward sequence corresponding to the first temperature sequence is determined according to the following formula: ； ； in, The data in the backward sequence corresponding to each temperature value in the first temperature sequence; And / or, determining the second temperature sequence according to the forward sequence and the backward sequence includes: The second temperature sequence is determined according to the following formula: 。 6. The distributed temperature measurement method according to claim 1, characterized in that: The extracting target temperature data in the second temperature sequence includes: According to a third preset rule, the second temperature sequence is converted into a second temperature table; the third preset rule includes: according to the number of the storage bits, the second temperature sequence is divided from the beginning to the end into a plurality of sequences with a length of the number of the storage bits, and the plurality of sequences are sequentially filled into the second temperature table by column; A set of the temperature data with the latest acquisition time is extracted from the second temperature table.

7. A distributed temperature measurement system, characterized in that: The distributed temperature measurement system includes a plurality of temperature measurement points, and the distributed temperature measurement system also includes: A light source, an optical fiber, a photodetector and a data acquisition unit are sequentially arranged along the optical axis; The light source is configured to emit a first light beam, and the first light beam propagates in the optical fiber to form a second light beam; The photodetector is configured to capture the second light beam and generate an electrical signal; The data acquisition unit is configured to acquire the electrical signal and process the electrical signal; the data acquisition unit is also configured to acquire multiple groups of temperature data of the distributed temperature measurement system, each group of the temperature data is acquired at a different time, and each group of the temperature data includes the temperature value of each temperature measurement point; based on the order of acquiring each group of the temperature data and the first preset rule, a first temperature table is constructed, the first temperature table includes N rows, each row has M first storage bits, N corresponds to the total number of groups of the temperature data, and M corresponds to the number of the temperature measurement points; in the first temperature table, each row stores a group of the temperature data, and each first storage bit stores a temperature value; according to the second preset rule, the first temperature table is converted into a first temperature sequence, the first temperature sequence includes 1 row of N×M columns of second storage bits; the data with higher correlation in the first temperature sequence are adjacent; determine the second temperature sequence corresponding to the first temperature sequence, the second temperature sequence is obtained after the first temperature sequence is denoised using a target denoising algorithm; the target denoising algorithm uses a target weight influence factor sequence, and the target weight influence factor sequence includes Contains multiple target weight influence factors, the target weight influence factors correspond to the temperature measurement points one by one, the target weight influence factors are used to influence the weight of the temperature value of the temperature measurement point corresponding to the target weight influence factor, and each target weight influence factor is the same or different; extracts the target temperature data in the second temperature sequence, the target temperature data includes the temperature value of each temperature measurement point with the latest acquisition time; based on the order of obtaining each group of the temperature data and the first preset rule, constructs a first temperature table, including: constructs a first initial temperature table, the first initial temperature table includes N rows and M columns; according to the first preset rule and the order i of obtaining each group of the temperature data, calculates the target storage row number of each group of the temperature data in the first initial temperature table; stores the temperature data in the storage bit corresponding to the target storage row number; according to the first preset rule and the order i of obtaining each group of the temperature data, calculates the target storage row number of each group of the temperature data in the first initial temperature table, including: when the order i of obtaining each group of the temperature data is an odd number, the first preset rule includes the following formula: ； in, storing the number of rows for said target; Alternatively, when the order i of obtaining each set of temperature data is an even number, the first preset rule includes the following formula: ； The second preset rule includes: starting from the first column in the first temperature table, sequentially connecting the tail of the previous column with the head of the second column to form a first temperature sequence.

Images