High-temperature anomaly detection method and system for distributed temperature measurement system

Abstract

The invention relates to the technical field of distributed optical fiber sensing detection, in particular to a high-temperature anomaly detection method and system of a distributed temperature measurement system. The distributed temperature measurement system is used for measuring the temperature of a heat distribution pipeline. The high-temperature anomaly detection method of the distributed temperature measurement system comprises the following steps: acquiring a temperature position curve graph corresponding to an optical signal output by the distributed temperature measurement system; based on a wavelet coefficient in the temperature position curve graph, decomposing the temperature position curve graph, and determining a plurality of sub-signal graphs corresponding to the temperature position curve graph; the wavelet coefficients corresponding to the sub-signal diagrams are different; based on a soft threshold technology, noise in each sub-signal graph is removed; synthesizing the de-noised sub-signal diagrams, and determining a de-noised signal diagram; and according to the de-noised sub-signal diagrams, determining abnormal temperature information in the de-noised signal diagram. The signal-to-noise ratio of the system is improved, and the measured abnormal temperature information can be determined and marked in the temperature position curve graph after noise reduction, that is, the leakage position of the heat distribution pipeline can be confirmed.

Description

Technical Field

[0001] The present application relates to the field of distributed optical fiber sensing detection technology, and in particular to a high temperature anomaly detection method and system for a distributed temperature measurement system. Background Art

[0002] Central heating is the main way to keep warm in northern my country in winter. In recent years, with the rapid advancement of my country's urbanization process, the scale of the heating pipeline network has continued to increase. By the end of 2020, the length of the national pipeline has reached 507,348 km. However, as the number of years of use of the heating network gradually increases, prominent problems such as low pipeline construction quality, serious energy waste and short pipeline life are gradually exposed. Leakage accidents in thermal pipelines occur from time to time, which brings great harm to the safety and economy of the heating network. Installing a leakage monitoring and positioning system for thermal pipelines, real-time monitoring of their operating status, early detection and elimination of leakage hazards can effectively reduce pipeline operation and maintenance costs, reduce energy loss, and increase pipeline service life. Most of the current heating pipelines in my country are not equipped with a leakage monitoring system. They basically rely on manual inspection methods such as related instruments, listening rods, and ultrasonic detectors to find leakage points. The accuracy is not high, the positioning accuracy is poor, and the leakage point cannot be found in real time. In recent years, various heating companies have tried new technologies such as resistance method, impedance method, and infrared imaging method, which also have disadvantages such as poor anti-interference ability, complex on-site construction, and inability to monitor all-weather to varying degrees.

[0003] According to the technical characteristics of optical fiber distributed temperature measurement technology, such as long monitoring distance, high positioning accuracy, and strong anti-interference ability, it can realize all-round monitoring of thermal pipelines and accurate positioning of leakage points, providing strong basic operation data support for pipeline operators. However, due to the low intensity of spontaneous Raman backscattering, this in principle leads to noisy measurements, making the signal-to-noise ratio of distributed optical fiber temperature sensing technology low, limiting the accuracy of measuring temperature. Summary of the invention

[0004] The embodiment of the present application proposes a high-temperature anomaly detection method and system for a distributed temperature measurement system, which decomposes a temperature position curve diagram based on different wavelet coefficients to reduce the noise of the signal corresponding to each wavelet coefficient, thereby improving the system signal-to-noise ratio, and can determine and mark the measured abnormal temperature information in the temperature position curve diagram after noise reduction, that is, the measured temperature corresponding to the leakage position of the thermal pipeline.

[0005] In some embodiments, a high-temperature anomaly detection method for a distributed temperature measurement system is provided. The high-temperature anomaly detection method for a distributed temperature measurement system is applied to the distributed temperature measurement system. The distributed temperature measurement system is used to measure the temperature of a thermal pipeline. The high-temperature anomaly detection method for a distributed temperature measurement system includes: obtaining a temperature position curve graph corresponding to an optical signal output by the distributed temperature measurement system; decomposing the temperature position curve graph based on the wavelet coefficients in the temperature position curve graph, and determining multiple sub-signal graphs corresponding to the temperature position curve graph; each sub-signal graph corresponds to a different wavelet coefficient; based on a soft threshold technology, removing noise from each sub-signal graph; synthesizing each sub-signal graph after noise removal to determine a denoised signal graph; and determining abnormal temperature information in the denoised signal graph based on the sub-signal graph after noise removal.

[0006] The high-temperature anomaly detection method of the distributed temperature measurement system provided in the embodiment of the present application is adopted to decompose the temperature position curve diagram based on different wavelet coefficients, so as to reduce the noise of the signal corresponding to each wavelet coefficient respectively, improve the system signal-to-noise ratio, and determine and mark the measured abnormal temperature information in the temperature position curve diagram after noise reduction, that is, the leakage location of the thermal pipeline can be confirmed.

[0007] Optionally, obtaining a temperature position curve graph corresponding to the optical signal output by the distributed temperature measurement system includes: obtaining temperature information corresponding to the optical signal output by the distributed temperature measurement system; the distributed temperature measurement system includes multiple temperature measurement points, and the temperature information includes temperature values ​​corresponding to each temperature measurement point; based on the temperature information, constructing a temperature position curve graph.

[0008] Optionally, the optical signal includes a backward Stokes optical signal and an anti-Stokes scattered light signal, and obtaining temperature information corresponding to the optical signal output by the distributed temperature measurement system includes: obtaining the backward Stokes optical signal and the anti-Stokes scattered light signal output by the distributed temperature measurement system; and demodulating to obtain temperature information based on the backward Stokes optical signal and the anti-Stokes scattered light signal.

[0009] Optionally, based on the wavelet coefficients in the temperature position curve graph, the temperature position curve graph is decomposed to determine multiple sub-signal graphs corresponding to the temperature position curve graph, including: based on multi-frequency signal decomposition technology, according to different wavelet coefficients, the temperature position curve graph is decomposed to determine multiple sub-signal graphs corresponding to the temperature position curve graph.

[0010] Optionally, based on the soft threshold technology, the noise in each sub-signal image is removed, including: comparing the target value with a preset threshold; the target value is the absolute value of the wavelet coefficient corresponding to each sub-signal image; when the target value is less than the preset threshold, the wavelet coefficient of the sub-signal image corresponding to the target value is set to a first target value, and the first target value is 0.

[0011] Optionally, after comparing the target value with the preset threshold, the method further includes: when the target value is greater than or equal to the preset threshold, setting the wavelet coefficient of the sub-signal graph corresponding to the target value to a second target value, and the second target value is half of the preset threshold.

[0012] Optionally, the wavelet coefficients include target wavelet coefficients, and the abnormal temperature information in the denoised signal graph is determined based on the sub-signal graph after noise is removed, including: determining the target sub-signal graph corresponding to the target wavelet coefficients; removing the sub-signal segments in the target sub-signal graph whose signal strength is less than a strength threshold; in the target sub-signal graph, determining the abnormal position information of the abnormal temperature information based on the signal strength difference; and marking the abnormal temperature information of the thermal pipeline in the denoised signal graph.

[0013] Optionally, sub-signal segments in the target sub-signal graph whose signal strength is less than a strength threshold are removed, including: segmenting the target sub-signal graph based on the signal strength to determine multiple signal segments with the target signal strength; calculating an expansion ratio between a first length and a second length; the first length is the sum of the lengths of multiple signal segments, and the second length is the length of the target sub-signal graph; based on the expansion ratio, expanding each signal segment to determine multiple expanded signal segments; and splicing the multiple expanded signal segments according to the temperature measurement position corresponding to each expanded signal segment.

[0014] Optionally, in the target sub-signal graph, based on the signal strength difference, the abnormal position information of the abnormal temperature information is determined, including: determining the peak points in the target sub-signal graph in sequence; determining the temperature difference between the temperature of the peak point and the temperature of the signal point within the preset range of the peak point; when the temperature difference is greater than or equal to the leakage temperature threshold, the abnormal position information includes the position information of the peak point. In some embodiments, a high temperature anomaly detection system of a distributed temperature measurement system is provided, including: a distributed temperature measurement system, the distributed temperature measurement system is configured to measure the temperature of a thermal pipeline and output an optical signal; a demodulation device, which is connected to the distributed temperature measurement system in communication; the demodulation device is configured to obtain the optical signal output by the distributed temperature measurement system and demodulate the temperature information corresponding to the optical signal; the demodulation device is also configured to obtain a temperature position curve corresponding to the optical signal output by the distributed temperature measurement system; based on the wavelet coefficients in the temperature position curve, the temperature position curve is decomposed to determine multiple sub-signal graphs corresponding to the temperature position curve; the wavelet coefficients corresponding to each sub-signal graph are different; based on the soft threshold technology, the noise in each sub-signal graph is removed; according to the sub-signal graph after the noise is removed, the abnormal temperature information of the thermal pipeline is determined.

[0015] It can be understood that the beneficial effects that can be achieved by the technical solution provided by the high temperature anomaly detection system of the distributed temperature measurement system provided above can be referred to the beneficial effects of the high temperature anomaly detection method of the distributed temperature measurement system and any of its optional implementations, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A structural block diagram of a high temperature anomaly detection system of a distributed temperature measurement system provided in an embodiment of the present application; Figure 2 A schematic diagram of a distributed temperature measurement system and a thermal pipeline provided in an embodiment of the present application; Figure 3 A flow chart of a high temperature anomaly detection method for a distributed temperature measurement system provided in an embodiment of the present application; Figure 4 A temperature position curve diagram provided for an embodiment of the present application; Figure 5 A schematic diagram of a sub-signal diagram provided in an embodiment of the present application; Figure 6 A schematic diagram of another target sub-signal graph provided in an embodiment of the present application; Figure 7 Another temperature position curve diagram provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] 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.

[0019] 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.

[0020] 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.

[0021] To facilitate understanding of the technical solution of the application, the professional terms involved in this application are first explained below.

[0022] Central heating is a heating method that uses a centralized heat source (such as a thermal power plant or a regional boiler room) to transport heat to individual buildings through a heating pipe network. Compared with decentralized heating, central heating is more efficient and less polluting, and is often used in densely populated urban areas.

[0023] The heating network refers to a facility system that uses a network of pipes to transport heat generated by heat sources (such as boiler rooms, thermal power plants, etc.) to the heating systems of various buildings, thereby achieving winter heating. The heating network generally includes the main heating network and the secondary network.

[0024] The side leakage system of heating pipes is a monitoring and alarm system used to detect leakage in heating pipe networks. It aims to detect leakage in pipes in a timely manner to prevent heat loss, pipe damage or serious heating failures. Heating pipes are usually buried underground, and it is difficult to detect water or heat leakage. Therefore, modern heating pipes are often equipped with special side leakage systems. This system can greatly improve the safety and efficiency of heating systems. Common side leakage detection methods include: acoustic detection, electromagnetic induction, laser detection or infrared thermal imaging.

[0025] Distributed Temperature Sensing (DTS) is a technology that monitors temperature changes along the line in real time. It is widely used in long-distance heating pipelines, cables, oil pipelines, tunnels and other scenarios. The DTS system can continuously monitor the temperature along the line by deploying optical fiber sensors along the entire length of the monitored equipment or pipeline. It has the advantages of high precision, strong real-time performance and strong anti-interference ability.

[0026] Raman backscattering refers to the inelastic scattering of light from the molecules of the medium when it propagates in the medium, producing scattered light with a wavelength different from that of the incident light. The uniqueness of this scattering phenomenon is that the frequency of the scattered light will shift due to changes in the molecular vibration or rotation energy level. This frequency shift is usually related to temperature, so in distributed temperature measurement systems (DTS), Raman backscattering is widely used for long-distance temperature monitoring. Raman scattering is divided into Stokes scattering and anti-Stokes scattering. Stokes scattering: The frequency of the scattered light is lower than the frequency of the incident light. Anti-Stokes scattering: The frequency of the scattered light is higher than the frequency of the incident light.

[0027] To facilitate understanding of the technical solution of the application, the relevant technologies involved in this application are explained below.

[0028] Central heating is the main way to keep warm in northern my country in winter. In recent years, with the rapid advancement of my country's urbanization process, the scale of the heating pipeline network has continued to increase. By the end of 2020, the length of the national pipeline has reached 507,348 km. However, as the number of years of use of the heating network gradually increases, prominent problems such as low pipeline construction quality, serious energy waste and short pipeline life are gradually exposed. Leakage accidents in thermal pipelines occur from time to time, which brings great harm to the safety and economy of the heating network. Installing a leakage monitoring and positioning system for thermal pipelines, real-time monitoring of their operating status, early detection and elimination of leakage hazards can effectively reduce pipeline operation and maintenance costs, reduce energy loss, and increase pipeline service life. Most of the current heating pipelines in my country are not equipped with a leakage monitoring system. They basically rely on manual inspection methods such as related instruments, listening rods, and ultrasonic detectors to find leakage points. The accuracy is not high, the positioning accuracy is poor, and the leakage point cannot be found in real time. In recent years, various heating companies have tried new technologies such as resistance method, impedance method, and infrared imaging method, which also have disadvantages such as poor anti-interference ability, complex on-site construction, and inability to monitor all-weather to varying degrees.

[0029] Among the related technologies, the fiber-optic distributed temperature measurement technology can realize all-round monitoring of thermal pipelines and accurate positioning of leakage points based on its technical characteristics such as long monitoring distance, high positioning accuracy, and strong anti-interference ability, providing strong data support for the basic operation of the pipeline network for pipeline operators. However, due to the low intensity of spontaneous Raman backscattering, this in principle leads to noisy measurements, making the signal-to-noise ratio of distributed fiber-optic temperature sensing technology low, limiting the accuracy of measuring temperature.

[0030] In order to solve the above technical problems, the embodiment of the present application proposes a high-temperature anomaly detection method and system for a distributed temperature measurement system, which decomposes a temperature position curve diagram based on different wavelet coefficients to reduce the noise of the signals corresponding to each wavelet coefficient respectively, thereby improving the system signal-to-noise ratio, and can determine and mark the measured abnormal temperature information in the temperature position curve diagram after noise reduction, that is, the measured temperature corresponding to the leakage position of the thermal pipeline.

[0031] Figure 1 This is a structural block diagram of a high temperature anomaly detection system of a distributed temperature measurement system provided in an embodiment of the present application.

[0032] Figure 2 Schematic diagram of the distributed temperature measurement system and thermal pipeline provided in the embodiment of the present application.

[0033] Combination Figure 1 and Figure 2As shown, the high temperature anomaly detection system of the distributed temperature measurement system provided in the embodiment of the present application includes: a distributed temperature measurement system 10 and a demodulation device 20. The distributed temperature measurement system 10 is configured to measure the temperature of the thermal pipeline 30 and output an optical signal. The demodulation device 20 is connected to the distributed temperature measurement system 10 for communication ( Figure 1 The dashed arrows are used to schematically indicate the transmission direction of data. The demodulation device 20 is configured to obtain the optical signal output by the distributed temperature measurement system 10 and demodulate the temperature information corresponding to the optical signal. The demodulation device 20 is also configured to construct a temperature position curve corresponding to the optical signal based on the temperature information, perform noise reduction processing on the temperature position curve, and determine and mark abnormal temperature information in the temperature position curve.

[0034] Optionally, combined Figure 2 As shown, the distributed temperature measurement system 10 includes a temperature measurement optical fiber 101, which is laid around the thermal pipeline 30. The temperature measurement optical fiber 101 is configured to measure the temperature around the thermal pipeline 30 and output a corresponding optical signal. The temperature measurement optical fiber 101 is used as a sensing medium to sense temperature changes through the Raman scattering effect.

[0035] Alternatively, if Figure 1 As shown, the demodulation device 20 includes but is not limited to one or more of a computer 201, a mobile phone 202, a tablet computer (not shown in the figure) and a mobile wearable device (not shown in the figure). It is sufficient that the demodulation device 20 can realize the above functions.

[0036] Optionally, the distributed temperature measurement system 10 also includes a light source, a detector, a signal processor, etc.

[0037] The high temperature anomaly detection system using the distributed temperature measurement system provided in the embodiment of the present application can decompose the temperature position curve corresponding to the optical signal output by the temperature measurement optical fiber 101 based on different wavelet coefficients, so as to reduce the noise of the signal corresponding to each wavelet coefficient respectively, thereby improving the system signal-to-noise ratio, and can determine and mark the measured abnormal temperature information in the temperature position curve after noise reduction, that is, the measured temperature corresponding to the leakage position of the thermal pipeline.

[0038] Specifically, different wavelet coefficients are different signal frequencies.

[0039] Figure 3 A flow chart of a high temperature anomaly detection method for a distributed temperature measurement system provided in an embodiment of the present application.

[0040] Combination Figure 1 and Figure 2The high temperature anomaly detection system of the distributed temperature measurement system shown in the figure, the embodiment of the present application also provides a high temperature anomaly detection method of the distributed temperature measurement system, the high temperature anomaly detection method of the distributed temperature measurement system is applied to the distributed temperature measurement system, the distributed temperature measurement system is used to measure the temperature of the thermal pipeline, such as Figure 3 As shown, the high temperature anomaly detection method of the distributed temperature measurement system includes steps S100 to S500, which are specifically as follows: Step S100, obtaining a temperature position curve graph corresponding to an optical signal output by a distributed temperature measurement system.

[0041] Optionally, step S100 includes step S101 and step S102, which are specifically as follows: Step S101, obtaining temperature information corresponding to an optical signal output by a distributed temperature measurement system; the distributed temperature measurement system includes a plurality of temperature measurement points, and the temperature information includes a temperature value corresponding to each temperature measurement point.

[0042] Optionally, the optical signal includes a backward Stokes optical signal and an anti-Stokes scattered optical signal, and step S101 includes step S1011 and step S1012, which are specifically as follows: Step S1011, acquiring a backward Stokes light signal and an anti-Stokes scattered light signal output by the distributed temperature measurement system.

[0043] Step S1012: Demodulate the backward Stokes light signal and the anti-Stokes scattered light signal to obtain temperature information.

[0044] In this embodiment, the temperature information of the thermal pipeline measured by the temperature measuring optical fiber is demodulated by using the back-Stokes light signal and the anti-Stokes scattered light signal, thereby facilitating the noise reduction of the temperature information and the determination of abnormal temperature information.

[0045] Exemplarily, the temperature information obtained in step S1012 is a temperature signal with noise influence at each position along the temperature measuring optical fiber. The noise influence is the noise given in the related art part, that is, the noise caused by the low spontaneous Raman backscattering intensity.

[0046] Specifically, the relationship between the ratio between the back-Stokes light signal and the anti-Stokes scattered light signal and the temperature is as follows: ; in, is the temperature at position x in the temperature measuring optical fiber, is a constant related to the system and can be obtained by calibration. is the ratio between the backward Stokes light signal and the anti-Stokes scattered light signal at the reference temperature.

[0047] Specifically, the collected backward Stokes light and anti-Stokes scattered light signals in the temperature measuring optical fiber are processed, filtered, averaged, calibrated and other pre-processing is performed to reduce the measurement error of the temperature.

[0048] Specifically, averaging is the process of reducing the impact of random noise by calculating the average value of the signal within a certain time window. The calibration process usually involves calibrating the measurement system using a standard light source with a known temperature to obtain an accurate temperature measurement coefficient.

[0049] Step S102: construct a temperature position curve diagram based on the temperature information.

[0050] In this embodiment, the temperature information corresponding to the optical signal is demodulated to construct a temperature position curve graph, so as to facilitate noise reduction processing on the temperature position curve graph, and further facilitate marking abnormal temperature information on the temperature position curve graph after noise reduction.

[0051] Exemplarily, a temperature position curve graph is constructed according to the distance between the temperature measurement position and the light source in the temperature information and in the order of distance from short to long.

[0052] Figure 4 This is a temperature position curve diagram provided for an embodiment of the present application.

[0053] For example, Figure 4 As shown, the horizontal axis represents the distance between the temperature measurement position and the light source, and the vertical axis represents the temperature value of the temperature measurement position.

[0054] Step S200 , based on the wavelet coefficients in the temperature position curve graph, decompose the temperature position curve graph to determine a plurality of sub-signal graphs corresponding to the temperature position curve graph; each sub-signal graph corresponds to a different wavelet coefficient.

[0055] Optionally, step S200 includes step S201, which is specifically as follows: Step S201 : based on the multi-frequency signal decomposition technology, decompose the temperature position curve graph according to different wavelet coefficients to determine a plurality of sub-signal graphs corresponding to the temperature position curve graph.

[0056] In this embodiment, the temperature position curve graph is decomposed into sub-signal graphs corresponding to different wavelet coefficients according to different wavelet coefficients, so as to facilitate denoising of the sub-signal graphs of each wavelet coefficient, thereby eliminating the noise caused by the low spontaneous Raman backscattering intensity and improving the signal-to-noise ratio of the system.

[0057] Figure 5 A schematic diagram of a sub-signal diagram provided in an embodiment of the present application. Figure 5 The signal frequency of the sub-signal graph given in (a) is the first wavelet coefficient, Figure 5 The signal frequency of the sub-signal given in (b) is the second wavelet coefficient, Figure 5 The signal frequency of the sub-signal given in (c) is the third wavelet coefficient, Figure 5 The signal frequency of the sub-signal given in (d) is the fourth wavelet coefficient, Figure 5 The signal frequency of the sub-signal given in (e) is the fifth wavelet coefficient. The first wavelet coefficient, the second wavelet coefficient, the third wavelet coefficient, the fourth wavelet coefficient and the fifth wavelet coefficient are different signal frequencies, and the signal frequencies increase successively from the first wavelet coefficient.

[0058] For example, in combination Figure 5 As shown, the effective signal is usually distributed in the low-frequency wavelet coefficients (such as Figure 5 (a) contains most of the effective signal and a small amount of noise), while the noise signal is usually distributed in the high-frequency wavelet coefficients (such as Figure 5 Middle (b), Figure 5 Middle (c), Figure 5 Middle (d) and Figure 5 (e) contains a small part of effective signal and most of noise). Therefore, the soft threshold technology is used to remove the small interference signal of each frequency signal to eliminate the influence of noise.

[0059] Step S300: removing noise from each sub-signal graph based on a soft thresholding technique.

[0060] Optionally, step S300 includes step S301 and step S302, which are specifically as follows: Step S301, comparing the target value with a preset threshold; the target value is the absolute value of the wavelet coefficient corresponding to each sub-signal graph.

[0061] Step S302: when the target value is less than a preset threshold, the wavelet coefficient of the sub-signal graph corresponding to the target value is set to a first target value, and the first target value is 0.

[0062] In this embodiment, by comparing the target value with the preset threshold, it is determined whether each sub-signal graph needs to be denoised. When the target value is less than the preset threshold, it indicates that denoising is required at this time, and the sub-signal graph corresponding to the target value contains more noise, so the wavelet coefficient of the sub-signal graph corresponding to the target value is set to the first target value to achieve denoising of the sub-signal graph corresponding to the target value.

[0063] For example, in combination Figure 5 As shown, Figure 5 Middle (b), Figure 5 Middle (c), Figure 5 Middle (d) and Figure 5 The absolute value of the wavelet coefficient corresponding to (e) is less than the preset threshold, that is, Figure 5 Middle (b), Figure 5 Middle (c), Figure 5 Middle (d) and Figure 5 (e) contains more noise, so Figure 5 Middle (b), Figure 5 Middle (c), Figure 5 Middle (d) and Figure 5 The wavelet coefficient corresponding to (e) is set to the first target value, that is, to 0.

[0064] Optionally, step S301 further includes step S303, which is as follows: Step S303: when the target value is greater than or equal to the preset threshold, the wavelet coefficient of the sub-signal graph corresponding to the target value is set to a second target value, and the second target value is half of the preset threshold.

[0065] In this embodiment, when the target value is greater than or equal to the preset threshold, it indicates that noise reduction is required at this time and the sub-signal graph corresponding to the target value contains less noise, so the wavelet coefficient of the sub-signal graph corresponding to the target value is set to the second target value, thereby retaining the valid signal in the sub-signal graph corresponding to the target value while reducing the impact of noise.

[0066] For example, in combination Figure 5 As shown, Figure 5 The absolute value of the wavelet coefficient corresponding to (a) is greater than or equal to the preset threshold, that is, Figure 5 (a) contains less noise, so Figure 5 The wavelet coefficient corresponding to (a) is set to the second target value, that is, to half of the preset threshold.

[0067] Step S400: synthesize the sub-signal graphs after noise removal to determine the noise reduction signal graph.

[0068] Step S500: determining abnormal temperature information in the noise reduction signal graph according to the noise-removed sub-signal graph.

[0069] Optionally, the wavelet coefficients include target wavelet coefficients, and step S500 includes steps S501 to S504, which are specifically as follows: Step S501, determining a target sub-signal graph corresponding to a target wavelet coefficient.

[0070] In this step, the sub-signal graph corresponding to the target wavelet coefficient is selected as the target sub-signal graph. Figure 5 The wavelet coefficients corresponding to the sub-signal graph given in (d) are the target wavelet coefficients. The sub-signal graph corresponding to the target wavelet coefficients is closer to the actual change signal.

[0071] Figure 6 A schematic diagram of another target sub-signal graph provided in an embodiment of the present application.

[0072] Combination Figure 6 As shown, there are some smooth straight lines in the figure, which are signal graphs without abnormal temperature information. Therefore, by removing the signal graphs of this part, it is easier to determine the abnormal temperature information.

[0073] Step S502: remove sub-signal segments in the target sub-signal graph whose signal strength is less than a strength threshold.

[0074] In this step, the sub-signal segments whose signal strength is less than the strength threshold in the target sub-signal graph are removed to amplify the effective temperature signal, so as to facilitate the determination of abnormal temperature information.

[0075] Optionally, step S502 includes steps S5021 to S5024, which are specifically as follows: Step S5021, segmenting the target sub-signal graph based on the signal strength, and determining a plurality of signal segments having the target signal strength.

[0076] Step S5022, calculating an expansion ratio between a first length and a second length; the first length is the sum of lengths of a plurality of signal segments, and the second length is the length of a target sub-signal graph.

[0077] Step S5023: Based on the expansion ratio, each signal segment is expanded to determine a plurality of expanded signal segments.

[0078] For example, the first length is 50, the second length is 100, and the expansion ratio is 0.5. When expanding each signal segment, the length of each signal segment needs to be expanded to twice the original length (ie, the reciprocal times of the expansion ratio) to construct a signal segment of the original length.

[0079] For example, the first length is 20, the second length is 100, and the expansion ratio is 0.2. When expanding each signal segment, the length of each signal segment needs to be expanded to 5 times the original length (ie, the reciprocal times of the expansion ratio) to construct a signal segment of the original length.

[0080] Step S5024, splicing the multiple extended signal segments according to the temperature measurement position corresponding to each extended signal segment.

[0081] In this step, splicing is performed according to the temperature measurement position corresponding to each extended signal segment, that is, splicing is performed according to the distance between the temperature measurement position and the light source from short to long.

[0082] In this embodiment, by segmenting the target sub-signal graph, retaining multiple signal segments with target signal strength, and splicing the retained signal segments to form sub-signal segments with signal strength less than the strength threshold, abnormal temperature information is determined. In addition, the expanded signal segments are smoother and more convenient for determining abnormal location information.

[0083] Step S503: determining abnormal position information of abnormal temperature information based on the signal strength difference in the target sub-signal graph.

[0084] Optionally, step S503 includes steps S5031 to S5033, which are specifically as follows: Step S5031, determine the peak points in the target sub-signal graph in sequence.

[0085] Exemplarily, in the target sub-signal graph, a peak point (maximum point) is determined based on the slope of the signal curve graph.

[0086] Figure 7 Another temperature position curve diagram provided in an embodiment of the present application.

[0087] For example, in combination Figure 7 As shown, Figure 7 Several peak points and signal points within the preset range of the peak points are schematically marked (such as Figure 7 (shown in the dashed box).

[0088] Step S5032, determining the temperature difference between the temperature of the peak point and the temperature of the signal point within a preset range of the peak point.

[0089] Exemplarily, the preset range is 50.

[0090] Step S5033: when the temperature difference is greater than or equal to the leakage temperature threshold, the abnormal position information includes the position information of the peak point.

[0091] Exemplarily, the leakage temperature threshold is 8°C.

[0092] In this embodiment, the temperature difference between the temperature value of the peak point in the target sub-signal graph and the temperature of the signal points around the peak point is compared in sequence to determine whether the peak point is a leak point. When the temperature difference is greater than or equal to the leak temperature threshold, the peak point is a leak point, so the peak point is included in the abnormal temperature information and marked in the noise reduction signal graph to display the leak point in real time for easy and intuitive viewing.

[0093] Step S504: marking abnormal temperature information of the thermal pipeline in the noise reduction signal graph.

[0094] In this embodiment, by determining the abnormal position information in the target sub-signal graph and marking the abnormal position information in the noise reduction signal graph, the detection of high-temperature anomalies in distributed optical fibers can be achieved, that is, the detection of leakage points in thermal pipelines can be achieved, and multiple leakage prompts can be prevented from occurring in the same leakage section.

[0095] Specifically, when there is a leak in a thermal pipeline, the temperature near the leak will increase significantly, that is, the temperature measuring optical fiber of the distributed temperature measurement system has an abnormally high temperature, so that the leak in the thermal pipeline can be detected based on the temperature of the temperature measuring optical fiber.

[0096] The high-temperature anomaly detection method of the distributed temperature measurement system provided in the embodiment of the present application decomposes the temperature position curve diagram based on different wavelet coefficients to reduce the noise of the signal corresponding to each wavelet coefficient respectively, thereby improving the system signal-to-noise ratio, and being able to determine and mark the measured abnormal temperature information in the temperature position curve diagram after noise reduction, that is, the measured temperature corresponding to the leakage position of the thermal pipeline.

[0097] Corresponding to the embodiment of the high-temperature anomaly detection method of the aforementioned distributed temperature measurement system, the present application also provides an embodiment of the high-temperature anomaly detection system of the distributed temperature measurement system. The high-temperature anomaly detection system of the distributed temperature measurement system includes: a distributed temperature measurement system and a demodulation device. The distributed temperature measurement system is configured to measure the temperature of the thermal pipeline and output an optical signal. The demodulation device is communicatively connected to the distributed temperature measurement system. The demodulation device is configured to obtain the optical signal output by the distributed temperature measurement system and demodulate the temperature information corresponding to the optical signal; the demodulation device is also configured to obtain the temperature position curve corresponding to the optical signal output by the distributed temperature measurement system; based on the wavelet coefficients in the temperature position curve, the temperature position curve is decomposed to determine multiple sub-signal graphs corresponding to the temperature position curve; the wavelet coefficients corresponding to each sub-signal graph are different; based on the soft threshold technology, the noise in each sub-signal graph is removed; according to the sub-signal graph after the noise is removed, the abnormal temperature information of the thermal pipeline is determined.

[0098] In the high-temperature anomaly detection system of the distributed temperature measurement system provided in the embodiment of the present application, the demodulation device can decompose the temperature position curve corresponding to the optical signal output by the temperature measurement optical fiber based on different wavelet coefficients, so as to reduce the noise of the signal corresponding to each wavelet coefficient respectively, thereby improving the system signal-to-noise ratio, and can determine and mark the measured abnormal temperature information in the temperature position curve after noise reduction, that is, the measured temperature corresponding to the leakage position of the thermal pipeline.

[0099] 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.

[0100] 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 high temperature anomaly detection method for a distributed temperature measurement system, characterized in that: The high temperature anomaly detection method of the distributed temperature measurement system is applied to the distributed temperature measurement system, and the distributed temperature measurement system is used to measure the temperature of the thermal pipeline. The high temperature anomaly detection method of the distributed temperature measurement system includes: Obtaining a temperature position curve graph corresponding to an optical signal output by the distributed temperature measurement system; Based on the wavelet coefficients in the temperature position curve graph, decomposing the temperature position curve graph, and determining a plurality of sub-signal graphs corresponding to the temperature position curve graph; the wavelet coefficients corresponding to each of the sub-signal graphs are different; Based on the soft threshold technology, removing the noise in each of the sub-signal graphs; synthesizing the sub-signal graphs after noise removal to determine a noise reduction signal graph; According to the sub-signal graph after noise is removed, abnormal temperature information in the noise reduction signal graph is determined.

2. The high temperature anomaly detection method of the distributed temperature measurement system according to claim 1, characterized in that: The step of obtaining a temperature position curve graph corresponding to an optical signal output by the distributed temperature measurement system includes: Acquire temperature information corresponding to the optical signal output by the distributed temperature measurement system; the distributed temperature measurement system includes a plurality of temperature measurement points, and the temperature information includes a temperature value corresponding to each of the temperature measurement points; Based on the temperature information, the temperature position curve graph is constructed.

3. The high temperature anomaly detection method of the distributed temperature measurement system according to claim 2, characterized in that: The optical signal includes a backward Stokes optical signal and an anti-Stokes scattered optical signal, and the step of obtaining temperature information corresponding to the optical signal output by the distributed temperature measurement system includes: Acquire the backward Stokes light signal and the anti-Stokes scattered light signal output by the distributed temperature measurement system; The temperature information is obtained by demodulating the backward Stokes light signal and the anti-Stokes scattered light signal.

4. The high temperature anomaly detection method of a distributed temperature measurement system according to claim 1, characterized in that: The step of decomposing the temperature position curve graph based on the wavelet coefficients in the temperature position curve graph and determining a plurality of sub-signal graphs corresponding to the temperature position curve graph comprises: Based on the multi-frequency signal decomposition technology, the temperature position curve diagram is decomposed according to the different wavelet coefficients to determine a plurality of sub-signal diagrams corresponding to the temperature position curve diagram.

5. The high temperature anomaly detection method of a distributed temperature measurement system according to claim 1, characterized in that: The removing of noise in each of the sub-signal graphs based on the soft threshold technology includes: Comparing a target value with a preset threshold; the target value is the absolute value of the wavelet coefficient corresponding to each of the sub-signal graphs; When the target value is less than a preset threshold, the wavelet coefficient of the sub-signal graph corresponding to the target value is set to a first target value, and the first target value is 0.

6. The high temperature anomaly detection method of the distributed temperature measurement system according to claim 5, characterized in that: After comparing the target value with the preset threshold, the method further includes: When the target value is greater than or equal to the preset threshold, the wavelet coefficient of the sub-signal graph corresponding to the target value is set to a second target value, and the second target value is half of the preset threshold.

7. The high temperature anomaly detection method of a distributed temperature measurement system according to claim 1, characterized in that: The wavelet coefficients include target wavelet coefficients, and determining abnormal temperature information in the noise reduction signal graph according to the sub-signal graph after noise removal includes: Determine a target sub-signal graph corresponding to the target wavelet coefficient; Remove the sub-signal segments whose signal strength is less than a strength threshold from the target sub-signal graph; In the target sub-signal graph, determining abnormal position information of the abnormal temperature information based on the signal strength difference; The abnormal temperature information of the thermal pipeline is marked in the noise reduction signal graph.

8. The high temperature anomaly detection method of the distributed temperature measurement system according to claim 7, characterized in that: The removing of the sub-signal segments whose signal strength is less than a strength threshold in the target sub-signal graph includes: Segmenting the target sub-signal graph based on the signal strength to determine a plurality of signal segments having a target signal strength; Calculating an expansion ratio between a first length and a second length; the first length is the sum of the lengths of a plurality of the signal segments, and the second length is the length of the target sub-signal graph; Based on the expansion ratio, each of the signal segments is expanded to determine a plurality of expanded signal segments; The plurality of extended signal segments are spliced ​​according to the temperature measurement position corresponding to each of the extended signal segments.

9. The high temperature anomaly detection method of a distributed temperature measurement system according to claim 7, characterized in that: Determining the abnormal position information of the abnormal temperature information based on the signal strength difference in the target sub-signal graph includes: sequentially determining peak points in the target sub-signal graph; Determine a temperature difference between the temperature of the peak point and the temperature of a signal point within a preset range of the peak point; In a case where the temperature difference is greater than or equal to a leakage temperature threshold, the abnormal position information includes position information of the peak point.

10. A high temperature anomaly detection system of a distributed temperature measurement system, characterized in that: include: A distributed temperature measurement system, wherein the distributed temperature measurement system is configured to measure the temperature of a thermal pipeline and output an optical signal; A demodulation device is communicatively connected to the distributed temperature measurement system; the demodulation device is configured to obtain the optical signal output by the distributed temperature measurement system, and demodulate the temperature information corresponding to the optical signal; the demodulation device is further configured to obtain a temperature position curve corresponding to the optical signal output by the distributed temperature measurement system; based on the wavelet coefficients in the temperature position curve, the temperature position curve is decomposed to determine a plurality of sub-signal graphs corresponding to the temperature position curve; each of the sub-signal graphs corresponds to a different wavelet coefficient; Based on the soft threshold technology, removing the noise in each of the sub-signal graphs; synthesizing the sub-signal graphs after noise removal to determine a noise reduction signal graph; According to the sub-signal graph after noise is removed, abnormal temperature information in the noise reduction signal graph is determined.

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