Distributed optical fiber heat distribution pipeline temperature anomaly detection method and system
Through distributed fiber technology and multi-frequency decomposition technology, the problem of low detection efficiency and accuracy of thermal pipelines is solved, and the comprehensive monitoring of thermal pipelines and the accurate positioning of leakage points are achieved, which improves the accuracy and efficiency of detection.
Patent Information
- Application Number
- CN202510345576.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-06-10
AI Technical Summary
The existing thermal pipeline detection methods have low detection efficiency and detection accuracy, and it is impossible to effectively detect leakage hazards and temperature abnormalities.
The distributed optical fiber technology is adopted to send detection optical signals to the optical fiber, receive and then scatter the optical signals to the Stokes and the anti-Stokes scattered optical signals, and combine multi-frequency decomposition technology and low-frequency processing to obtain the temperature mean and standard deviation, and determine the temperature threshold to identify abnormal positions in the thermal pipeline.
It realizes all-round monitoring of thermal pipelines and accurate positioning of leakage points, improves the accuracy and efficiency of detection, reduces manual judgment process, saves detection costs, and supports real-time monitoring of all sections and life cycles of thermal pipelines.
Smart Images
Figure CN120121172A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of distributed optical fiber temperature sensing, and particularly to a method and system for detecting abnormal temperatures in distributed optical fiber thermal pipelines. Background Art
[0002] As the number of years of use of the heat network gradually increases, the quality of the pipeline has become a key focus. Detecting and eliminating leakage hazards early can effectively reduce the operation and maintenance costs of the pipeline, reduce energy losses, and extend the service life of the pipeline. Relying on manual inspections using relevant instruments, listening rods, ultrasonic detectors, etc. to find leakage points has low accuracy, poor positioning accuracy, and cannot accurately detect leakage points. In recent years, various heating enterprises have tried new technologies such as the resistance method, impedance method, and infrared imaging method, but these technologies all have disadvantages such as poor anti-interference ability, complex on-site construction, and inability to monitor all-weather, resulting in low detection efficiency and detection accuracy.
[0003] Therefore, there is an urgent need for a method for detecting thermal pipelines that can simultaneously meet detection efficiency and detection accuracy. Summary of the Invention
[0004] This application provides a method and system for detecting abnormal temperatures in distributed optical fiber thermal pipelines to solve the technical problems of low detection efficiency and detection accuracy of thermal pipelines.
[0005] The method for detecting abnormal temperatures in distributed optical fiber thermal pipelines provided in the first aspect of this application includes: sending a detection optical signal into the optical fiber; wherein, the optical fiber is laid on the outer wall surface of the thermal pipeline and has the same extension direction as the thermal pipeline; receiving the backward Stokes optical signal and anti-Stokes scattered optical signal returned by the optical fiber; extracting the first data corresponding to temperature and position from the backward Stokes optical signal and anti-Stokes scattered optical signal at preset time intervals; wherein, the first data includes a plurality of original one-dimensional data; decomposing the first data using multi-frequency decomposition technology, and performing low-frequency processing on the decomposed first data to obtain second data; wherein, the second data is the low-frequency component in the first data; obtaining the temperature mean and standard deviation in the second data; determining the abnormal position in the thermal pipeline according to the temperature threshold; wherein, the temperature threshold is calculated from the temperature mean and standard deviation.
[0006] In some feasible implementation manners, after receiving the backward Stokes optical signal and anti-Stokes scattered optical signal returned by the optical fiber, the method for detecting abnormal temperatures in distributed optical fiber thermal pipelines further includes: preprocessing the backward Stokes optical signal and anti-Stokes scattered optical signal.
[0007] In some feasible implementation manners, the preprocessing includes one or more of filtering, averaging, and calibration.
[0008] In some feasible implementation manners, the preset time is 0.5 min - 3 min.
[0009] In some feasible implementation manners, low-frequency processing is performed on the decomposed first data, including: performing low-frequency processing on the first data by using a Haar wavelet function.
[0010] In some feasible implementation manners, an abnormal position in a heat pipeline is determined according to a temperature threshold, including: taking the temperature threshold as a reference point to determine a peak temperature greater than the temperature threshold in the second data; and determining the abnormal position in the heat pipeline according to the peak temperature.
[0011] The distributed optical fiber heat pipeline temperature anomaly detection method provided in the first aspect of the present application can effectively solve the technical problem that the abnormal position information of pipeline data cannot be accurately obtained from the temperature information of the distributed optical fiber heat pipeline based on Raman scattering, reduce the manual judgment process, increase the detection accuracy rate at the same time, and can quickly determine the temperature abnormal position. This detection method does not require any hardware participation, effectively saves the detection cost, and the optical fiber is laid underground, not limited by the environment, and can realize large-range and long-distance monitoring, which has very important significance for real-time monitoring of the entire section and the entire life cycle of the heat pipeline.
[0012] The distributed optical fiber heat pipeline temperature anomaly detection system provided in the second aspect of the present application adopts the distributed optical fiber heat pipeline temperature anomaly detection method provided in the first aspect. The distributed optical fiber heat pipeline temperature anomaly detection system includes: an optical fiber laid on the outer wall surface of the heat pipeline; a controller connected to the optical fiber, and the controller includes: a sending module configured to send a detection optical signal into the optical fiber; wherein, the optical fiber is laid on the outer wall surface of the heat pipeline and has the same extending direction as the heat pipeline; a receiving module configured to receive the back Stokes optical signal and the anti-Stokes scattered optical signal returned by the optical fiber; an extraction module configured to extract first data corresponding to temperature and position from the back Stokes optical signal and the anti-Stokes scattered optical signal at preset time intervals; wherein, the first data includes a plurality of original one-dimensional data; a processing module configured to decompose the first data by using a multi-frequency decomposition technique, perform low-frequency processing on the decomposed first data to obtain second data; wherein, the second data is the low-frequency component in the first data; an acquisition module configured to acquire the temperature mean value and standard deviation in the second data; a determination module configured to determine an abnormal position in the heat pipeline according to a temperature threshold; wherein, the temperature threshold is calculated from the temperature mean value and standard deviation.
[0013] In some feasible implementation manners, the controller further includes: a preprocessing module configured to preprocess the back Stokes optical signal and the anti-Stokes scattered optical signal.
[0014] In some possible implementation manners, the preprocessing includes one or more of filtering, averaging, and calibration.
[0015] The distributed optical fiber thermal pipeline temperature anomaly detection system provided in the second aspect of this application adopts the distributed optical fiber thermal pipeline temperature anomaly detection method provided in the first aspect. Therefore, its beneficial technical effects can be referred to the first aspect and will not be elaborated here. Description of the Drawings
[0016] To more clearly illustrate the technical solutions of this application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0017] Figure 1 is a schematic flowchart of a distributed optical fiber thermal pipeline temperature anomaly detection method provided by an embodiment of this application; Figure 2 is a schematic structural diagram of a distributed optical fiber thermal pipeline temperature anomaly detection system provided by an embodiment of this application; Figure 3 is a comparative analysis diagram of data processing provided by an embodiment of this application; Figure 4 is a structural block diagram of a controller provided by an embodiment of this application.
[0018] Illustration Marks: 100 - Distributed optical fiber thermal pipeline temperature anomaly detection system; 10 - Controller; 101 - Sending module; 102 - Receiving module; 103 - Extracting module; 104 - Processing module; 105 - Obtaining module; 106 - Determining module; 107 - Preprocessing module; 20 - Optical fiber. Detailed Embodiments
[0019] Next, the technical solutions in the embodiments of this application will be clearly described with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all embodiments. Based on the embodiments of this application, other embodiments obtained by those of ordinary skill in the art without creative efforts all fall within the protection scope of this application.
[0020] Hereinafter, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.
[0021] In addition, in this application, orientation terms such as "upper", "lower", "inner", and "outer" are defined relative to the orientation of the components shown in the drawings. It should be understood that these directional terms are relative concepts, which are used for relative description and clarification, and they can change accordingly with the change of the orientation of the components placed in the drawings.
[0022] To solve the technical problems of low detection efficiency and low detection accuracy of the abnormal temperature position of the thermal pipeline, an embodiment of this application provides a distributed optical fiber thermal pipeline abnormal temperature detection method. This detection method utilizes the technical characteristics of optical fiber distributed temperature measurement technology, such as long monitoring distance, high positioning accuracy, and strong anti-interference ability, and combines multi-frequency decomposition technology to achieve all-round monitoring of the thermal pipeline and accurate positioning of the leakage point, timely detect the abnormal temperature position, and provide strong support for the basic operation data of the pipeline network for the operation of the thermal pipeline.
[0023] Combined Figure 1 with Figure 2 , the distributed optical fiber thermal pipeline abnormal temperature detection method provided by the embodiment of this application can be implemented by the following steps S100 to step S600.
[0024] Step S100: Send a detection optical signal into the optical fiber.
[0025] Among them, at the beginning of the detection, a distributed optical fiber thermal pipeline abnormal temperature detection system can be built first, and the optical fiber is used as a sensor to transmit the optical signal. Specifically, the optical fiber is laid on the outer wall surface of the thermal pipeline, and the optical fiber is in the same extending direction as the thermal pipeline. A silicon core pipe can be sleeved outside the optical fiber, and the outer wall of the silicon core pipe fits on the outer wall surface of the thermal pipeline. In this way, the position on the optical fiber can correspond to the position on the thermal pipeline. By detecting the return signal in the optical fiber, the abnormal temperature position on the thermal pipeline corresponding to the optical fiber can be accurately judged. The abnormal temperature on the thermal pipeline may be caused by the leakage of the thermal pipeline and other factors. Therefore, at the abnormal temperature position of the thermal pipeline, the temperature will be relatively high due to the leakage heat, and the return signal in the optical fiber will carry the temperature and the data corresponding to its position. Therefore, by demodulating this data, the abnormal temperature detection of the thermal pipeline can be realized.
[0026] Specifically, during the laying process, it is preferably to lay the optical fiber on the side of the thermal pipeline away from the ground. Since the optical fiber is more sensitive to the outside world, with such a setting, the optical fiber is far away from the ground, avoiding the influence of factors such as ground vibration on the working state of the optical fiber, and effectively ensuring the accuracy and precision of this detection method. During the laying process, the silicon core pipe sleeved outside the optical fiber fits on the outer wall surface of the thermal pipeline, and then the backfill soil is compacted to ensure the stability of the optical fiber and the thermal pipeline.
[0027] Specifically, it is possible to control the sending of the detection light into the optical fiber, and the detection light carries the detection optical signal.
[0028] Step S200: Receive the backward Stokes optical signal and the anti-Stokes scattered optical signal returned by the optical fiber.
[0029] Specifically, the optical fiber will generate return light, which includes backward Stokes light and anti-Stokes scattered light. Among them, in the backward Stokes light, there is a backward Stokes optical signal carried. In the anti-Stokes scattered light, there is an anti-Stokes scattered optical signal carried. Temperature information is carried in the backward Stokes optical signal and the anti-Stokes scattered optical signal.
[0030] In some feasible implementation manners, after step S200, the distributed optical fiber thermal pipeline temperature anomaly detection method may further include step S201.
[0031] Step S201: Preprocess the backward Stokes optical signal and the anti-Stokes scattered optical signal.
[0032] Specifically, the influence of noise in the backward Stokes optical signal and the anti-Stokes scattered optical signal can be reduced through preprocessing methods to reduce the measurement error, thereby improving the detection accuracy.
[0033] Among them, the preprocessing methods may include one or more of filtering, averaging, and calibration.
[0034] In a specific implementation manner, the preprocessing is filtering. Filtering can remove the frequencies in specific bands in the backward Stokes optical signal and the anti-Stokes scattered optical signal, and can suppress and prevent noise interference.
[0035] In step S201, one of filtering, averaging, and calibration can be adopted, or multiple of them can be adopted. The specific preprocessing method can be determined according to the actual detection working conditions and the type of optical fiber.
[0036] Step S300: Extract the first data corresponding to temperature and position from the backward Stokes optical signal and the anti-Stokes scattered optical signal at preset time intervals.
[0037] Specifically, the preset time can be 0.5 min - 3 min. For example, the preset time can be one of 0.5 min, 1 min, 1.5 min, 2 min, 2.5 min, or 3 min.
[0038] In a specific implementation manner, the preset time can be 1 min. In this way, data is extracted every 1 min from the collected backward Stokes optical signal and anti-Stokes scattered optical signal.
[0039] Among them, the optical fiber may include a plurality of optical fiber detection segments, and the first data of each optical fiber detection segment includes a plurality of original one-dimensional data of the optical fiber detection segment. Among them, the length of each optical fiber detection segment is the same. Exemplarily, the length of each optical fiber detection segment may be 10 Km or 20 Km. Of course, the length of the optical fiber detection segment may also be other values.
[0040] Specifically, the first data includes the temperature information corresponding to different position points in each optical fiber detection segment. Thus, the temperature information containing noise effects at each position point along the optical fiber can be obtained.
[0041] Step S400: Decompose the first data using a multi-frequency decomposition technique, and perform low-frequency processing on the decomposed first data to obtain second data.
[0042] In step S400, the purpose of performing low-frequency processing on the first data is to further reduce the noise effects in the first data.
[0043] Among them, the second data is the low-frequency component in the first data.
[0044] Specifically, step S400 may be implemented by the following step S401 and step S402.
[0045] Step S401: Decompose the first data using a multi-frequency decomposition technique.
[0046] The multi-frequency decomposition technique has the advantages of high efficiency, reliability, and easy implementation, and can decompose the first data into separate frequency signals by different methods.
[0047] Step S402: Perform low-frequency processing on the decomposed first data to obtain second data.
[0048] In a specific implementation manner, the Haar wavelet function may be used to perform low-frequency processing on the first data. Of course, in other specific implementation manners, other functions may also be used to perform low-frequency processing on the first data. The embodiments of the present application do not specifically limit the specific parameters of the low-frequency processing.
[0049] Among them, the Haar wavelet function can decompose the first data into functions of different scales, and each scale corresponds to a different frequency range. Exemplarily, a coarser scale may represent the low-frequency component, while a finer scale may represent the high-frequency component. During the decomposition process, the signal is gradually decomposed into approximation coefficients and detail coefficients. The approximation coefficients may represent the low-frequency component of the signal, and the detail coefficients may represent the high-frequency component of the signal.
[0050] In step S400, the first data demodulated from each optical fiber pair can adopt a multi-frequency decomposition technique to perform low-frequency processing on the data containing noise, thereby further reducing the noise influence in the first data, and further improving the detection accuracy of the temperature abnormal position of the thermal pipeline.
[0051] Step S500: Obtain the temperature mean value and standard deviation in the second data.
[0052] In each optical fiber detection section, the temperature mean value and standard deviation value in the optical fiber detection section can be calculated according to multiple temperature data in the second data in the optical fiber detection section. Among them, each optical fiber detection section corresponds to a temperature mean value and a standard deviation.
[0053] Among them, in each optical fiber detection section, subsequently, the temperature threshold can be determined according to the temperature mean value and standard deviation in the optical fiber detection section, and then the temperature abnormal position in the thermal pipeline corresponding to the optical fiber detection section can be detected according to the temperature threshold.
[0054] Step S600: Determine the abnormal position in the thermal pipeline according to the temperature threshold.
[0055] Among them, the temperature threshold is calculated from the temperature mean value and standard deviation.
[0056] In a specific implementation manner, the temperature threshold is equal to the sum of the temperature mean value and the standard deviation. After obtaining the temperature mean value and standard deviation, the temperature threshold can be obtained. In this way, in each optical fiber detection section, the temperature abnormal position can be determined according to the temperature threshold.
[0057] Specifically, step S600 can be implemented by the following step S601 and step S602.
[0058] Step S601: Taking the temperature threshold as the reference point, determine the peak temperature in the second data that is greater than the temperature threshold.
[0059] In a specific implementation manner, refer to Figure 3 , Figure 3 In (a) of , the spectrogram of the original one-dimensional data is shown. Among them, the abscissa is the length, and at this time, the optical fiber can be an optical fiber detection section of 12 Km. In Figure 3 In (a) of , it can be seen that there are multiple positions affected by noise in the original one-dimensional data, and there are multiple peaks. Figure 3 In (b) of , the processed spectrogram is shown. It can be seen that in this implementation manner, the temperature threshold is 50 °C. Therefore, from Figure 3In (b), taking 50°C as the reference point, the data below 50°C is removed, and only the data above 50°C is retained. Among the second data obtained through low-frequency processing, there are five peak temperatures greater than the temperature threshold, which are 90°C at peak A, 61°C at peak B, 90°C at peak C, 96°C at peak D, and 63°C at peak E respectively.
[0060] Step S602: Determine the abnormal position in the heat pipeline according to the peak temperature.
[0061] In step S602, the position corresponding to the peak temperature is the temperature abnormal position. Continue to refer to Figure 3 , the positions corresponding to the five peak temperatures are L A 、L B 、L C 、L D 、L E .
[0062] Thus, the temperature abnormal position of the heat pipeline can be judged. In this way, after the temperature abnormal position is judged, technical means or manual inspection can be used to further determine the specific abnormal conditions of the temperature abnormal position of the heat pipeline. Among them, the abnormal conditions can include leakage of the heat pipeline, loose connection of the connection section of the heat pipeline, etc.
[0063] The distributed optical fiber heat pipeline temperature abnormal detection method provided by the embodiment of the present application can effectively solve the technical problem that the abnormal position information of the pipeline data cannot be accurately obtained from the temperature information of the distributed optical fiber heat pipeline based on Raman scattering, reduce the manual judgment process, and at the same time increase the detection accuracy rate, and can quickly judge the temperature abnormal position. This detection method does not require any hardware participation, effectively saves the detection cost, and the optical fiber is laid underground, not limited by the environment, and can realize large-scale and long-distance detection, which has very important significance for the real-time monitoring of the entire section and the entire life cycle of the heat pipeline.
[0064] Corresponding to the embodiment of the previous distributed optical fiber heat pipeline temperature abnormal detection method, the present application also provides an embodiment of a distributed optical fiber heat pipeline temperature abnormal detection system.
[0065] Combined with Figure 2 and Figure 4 , the distributed optical fiber heat pipeline temperature abnormal detection system 100 includes an optical fiber 20 and a controller 10. Among them, the optical fiber 20 is arranged below the ground surface, and the controller 10 is arranged above the ground surface. Specifically, the optical fiber 20 is laid on the outer wall surface of the heat pipeline; the controller 10 is connected to the optical fiber 20 and is used to receive the signal in the optical fiber 20.
[0066] Among them, the controller 10 includes: a sending module 101, a receiving module 102, an extraction module 103, a processing module 104, an acquisition module 105, a determination module 106, and a preprocessing module 107.
[0067] The sending module 101 is used to send a detection optical signal into the optical fiber 20; wherein, the optical fiber 20 is laid on the outer wall surface of the heat pipeline, and the optical fiber 20 is in the same extending direction as the heat pipeline.
[0068] That is to say, the sending module 101 is used to execute step S100 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0069] The receiving module 102 is used to receive the backscattered Stokes optical signal and the anti-Stokes scattered optical signal returned by the optical fiber 20.
[0070] That is to say, the receiving module 102 is used to execute step S200 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0071] The extraction module 103 is used to extract the first data corresponding to temperature and position from the backscattered Stokes optical signal and the anti-Stokes scattered optical signal at preset time intervals; wherein, the first data includes a plurality of original one-dimensional data.
[0072] That is to say, the extraction module 103 is used to execute step S300 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0073] The processing module 104 is used to decompose the first data by using a multi-frequency decomposition technique, perform low-frequency processing on the decomposed first data, and obtain second data; wherein, the second data is the low-frequency component in the first data.
[0074] That is to say, the processing module 104 is used to execute step S400 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0075] The acquisition module 105 is used to acquire the temperature mean and standard deviation in the second data; wherein, the optical fiber 20 may include a plurality of optical fiber detection segments, and each optical fiber detection segment corresponds to a temperature mean and a standard deviation.
[0076] That is to say, the acquisition module 105 is used to execute step S500 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0077] The determination module 106 is used to determine the abnormal position in the heat pipeline according to the temperature threshold; wherein, the temperature threshold is calculated from the temperature mean and the standard deviation.
[0078] That is to say, the determination module 106 is used to execute step S600 in the above-mentioned distributed optical fiber heat pipeline temperature anomaly detection method.
[0079] The preprocessing module 107 is used to preprocess the backward Stokes optical signal and the anti-Stokes scattered optical signal.
[0080] That is to say, the preprocessing module 107 is used to execute step S201 in the above-mentioned distributed optical fiber thermal pipeline temperature anomaly detection method. Among them, the preprocessing includes one or more of filtering, averaging, and calibration.
[0081] Compared with the traditional detection system, the distributed optical fiber thermal pipeline temperature anomaly detection system provided by the embodiments of the present application does not require any hardware improvement, can effectively save production costs, and the detection system can accurately obtain the temperature anomaly position of the thermal pipeline, improving the detection efficiency and accuracy.
[0082] It should be noted that those skilled in the art will easily think of other implementation manners of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application.
[0083] It should be understood that the present application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The true scope is indicated by the present application.
Claims
1. A distributed optical fiber thermal pipeline temperature anomaly detection method, characterized in that: include: Sending a detection light signal to an optical fiber; wherein the optical fiber is laid on the outer wall of a thermal pipeline, and the optical fiber and the thermal pipeline extend in the same direction; receiving a backward Stokes light signal and an anti-Stokes scattered light signal returned by the optical fiber; Extracting first data corresponding to temperature and position from the backward Stokes light signal and the anti-Stokes scattered light signal at preset intervals; wherein the first data includes a plurality of original one-dimensional data; Decomposing the first data by using a multi-frequency decomposition technique, and performing low-frequency processing on the decomposed first data to obtain second data; wherein the second data is a low-frequency component in the first data; Obtaining a temperature mean and a standard deviation in the second data; The abnormal position in the thermal pipeline is determined according to a temperature threshold; wherein the temperature threshold is calculated by the temperature mean and the standard deviation.
2. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 1 is characterized in that: After receiving the backward Stokes light signal and the anti-Stokes scattered light signal returned by the optical fiber, the distributed optical fiber thermal pipeline temperature anomaly detection method further includes: The backward Stokes light signal and the anti-Stokes scattered light signal are preprocessed.
3. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 2 is characterized in that: The preprocessing includes one or more of filtering, averaging, and calibration.
4. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 1 is characterized in that: The temperature threshold is equal to the sum of the temperature mean and the standard deviation.
5. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 1 is characterized in that: The preset time is 0.5min-3min.
6. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 1, characterized in that: The decomposed first data is subjected to low-frequency processing, including: The first data is subjected to low-frequency processing using a Haar wavelet function.
7. The distributed optical fiber thermal pipeline temperature anomaly detection method according to claim 1 is characterized in that: The determining the abnormal position in the thermal pipeline according to the temperature threshold comprises: Taking the temperature threshold as a reference point, determining a peak temperature in the second data that is greater than the temperature threshold; An abnormal position in the thermal pipeline is determined according to the peak temperature.
8. A distributed optical fiber thermal pipeline temperature anomaly detection system, characterized in that: The distributed optical fiber thermal pipeline temperature anomaly detection method according to any one of claims 1 to 7 is adopted, and the distributed optical fiber thermal pipeline temperature anomaly detection system comprises: An optical fiber, wherein the optical fiber is laid on the outer wall of the thermal pipeline; A controller is connected to the optical fiber, and the controller includes: A sending module is configured to send a detection optical signal to an optical fiber; wherein the optical fiber is laid on the outer wall of the thermal pipeline, and the optical fiber and the thermal pipeline extend in the same direction; A receiving module, configured to receive a backward Stokes light signal and an anti-Stokes scattered light signal returned by the optical fiber; An extraction module is configured to extract first data corresponding to temperature and position from the backward Stokes light signal and the anti-Stokes scattered light signal at a preset interval; wherein the first data includes a plurality of original one-dimensional data; a processing module configured to decompose the first data by using a multi-frequency decomposition technique, and perform low-frequency processing on the decomposed first data to obtain second data; wherein the second data is a low-frequency component in the first data; An acquisition module, configured to acquire a temperature mean and a standard deviation in the second data; A determination module is configured to determine an abnormal position in the thermal pipeline according to a temperature threshold; wherein the temperature threshold is calculated by the temperature mean and the standard deviation.
9. The distributed optical fiber thermal pipeline temperature anomaly detection system according to claim 8, characterized in that: The controller further comprises: The preprocessing module is configured to preprocess the backward Stokes light signal and the anti-Stokes scattered light signal.
10. The distributed optical fiber thermal pipeline temperature anomaly detection system according to claim 9, characterized in that: The preprocessing includes one or more of filtering, averaging, and calibration.