GIL pipe gallery short circuit fault intelligent monitoring system based on distributed optical fiber temperature measurement

Abstract

The application discloses a GIL pipe gallery short-circuit fault intelligent monitoring system based on distributed optical fiber temperature measurement, and relates to the technical field of fault prediction and health management. The system acquires full-path distributed temperature measurement data through a DTS photoelectric detection and signal demodulation module, simultaneously performs signal demodulation and determination, so as to realize synchronous output of high-precision temperature and position information; the system corrects temperature measurement deviation in combination with demodulation results and forward and backward scattering light data through a bidirectional data compensation and short-circuit fault early warning module, quantifies temperature exceeding degree and fluctuation risk, triggers corresponding grade early warning, and realizes early fault identification; the system analyzes temperature measurement data at the time of early warning to distinguish overheating and load fluctuation faults through a fault type identification and grading feedback module, generates a report in combination with fault feature database matching analysis, pushes the report according to grades, realizes accurate fault identification and efficient processing, and further ensures signal stability and operation safety of high-voltage GIL pipe galleries under high-temperature environments.

Description

Technical Field

[0001] This invention relates to the field of fault prediction and health management technology, and in particular to an intelligent monitoring system for short-circuit faults in GIL (Gas Inlet and Outer Space) tunnels based on distributed optical fiber temperature measurement. Background Technology

[0002] Underground utility tunnels housing gas-insulated metal-enclosed transmission lines (GILs) are advanced power transmission facilities. With the widespread application of GILs in high-capacity power transmission, short-circuit faults can lead to serious power accidents, creating an urgent need for real-time monitoring systems. Distributed fiber optic temperature sensing sensors (DTSs) are a key technological support. To obtain accurate temperature data, the signals received by the detectors need to be processed. After pulsed light is injected into the optical fiber, the interaction between the fiber medium and photons generates backscattered light signals, including temperature-sensitive anti-Stokes scattering, as well as relatively temperature-insensitive Rayleigh scattering, Brillouin scattering, and Stokes scattering. Due to the weak signal, DTSs require specialized detection and processing. The core of this monitoring system is based on the fiber optic backscattering Raman effect and optical time-domain reflection (OTDR) technology. The intensity of the backscattered Raman light generated during laser transmission is related to the ambient temperature, thereby enabling temperature measurement and location.

[0003] Existing technology enables intelligent monitoring of short-circuit faults in GIL (Gas Inlet Utility) tunnels. First, sensing optical fibers are laid along the GIL within the tunnel, serving as temperature sensing carriers. Next, a DTS (Digital Transmission System) detector emits a laser signal, receives and converts the backscattered light signal into an electrical signal. Then, a data processing system analyzes the electrical signal, calculating temperature and location information based on Raman scattering and OTDR (Optical Time-of-Reference) technology. Finally, temperature thresholds and rate-of-change standards are set, and monitoring data is compared in real time. Anomalies are detected, triggering an alarm immediately and simultaneously locating the fault area, thus completing intelligent monitoring.

[0004] For example, the Chinese invention patent CN115097256B discloses a method for identifying and locating short-circuit faults in transmission lines based on ground wire electromagnetic signals. This method includes: acquiring the propagation characteristics of ground wire electromagnetic signals along the transmission line under different short-circuit faults; acquiring abrupt change signal data of the ground wire electromagnetic signals obtained from monitoring based on the propagation characteristics; matching the abrupt change signal data with a pre-set database; determining the identification result of the short-circuit fault state of the transmission line based on the matching result; and locating the short-circuit point of the transmission line based on the identification result of the short-circuit fault state of the transmission line.

[0005] During the operation of electrical equipment, early faults are often accompanied by abnormal heating. In light of this, fiber optic temperature measurement technology, with its precise temperature measurement characteristics, has been widely used in equipment condition monitoring. However, in the strong electromagnetic environment and high heat load scenarios of GIL (Gas Infrared Lever) tunnels, fiber optic loss during long-distance transmission leads to attenuation of the scattered light signal. Combined with electromagnetic interference, this severely interferes with the imaging signal. Therefore, while infrared imaging can achieve non-contact temperature measurement, the interference from the strong electromagnetic environment significantly reduces the signal-to-noise ratio, making it difficult to capture the true temperature distribution. This results in decreased signal integrity in high-temperature areas, distorted temperature measurement data, and ultimately, inaccurate temperature measurement in high-pressure GIL tunnels, affecting signal stability when faults occur under high-temperature conditions. Summary of the Invention

[0006] To address the technical problem of inaccurate temperature measurement in high-voltage GIL (Gas Infrared) tunnels in existing technologies, this invention provides an intelligent monitoring system for short-circuit faults in GIL tunnels based on distributed optical fiber temperature measurement. The technical solution is as follows:

[0007] The DTS photoelectric detection and signal demodulation module is used to acquire distributed temperature measurement data along the entire path by monitoring the injection of laser pulses and the acquisition of backscattered light at designated monitoring points along the GIL tunnel. Simultaneously, it performs signal demodulation to achieve synchronous output of temperature and location information. The bidirectional data compensation and short-circuit fault early warning module compensates for attenuated signals under long-distance transmission and high heat load conditions based on the demodulation results and the acquired forward and backscattered light data, correcting temperature measurement deviations. It also triggers corresponding level early warnings based on the thermal characteristics differences in different areas of the GIL tunnel to identify and respond to short-circuit faults. The fault type identification and graded feedback module classifies the fault types in the temperature measurement data when a graded early warning is triggered, distinguishing between overheating faults and load fluctuation faults. It also performs matching analysis with a fault feature database and generates fault reports to achieve fault type identification and graded processing.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0009] 1. This invention utilizes a DTS photoelectric detection and signal demodulation module to deploy laser pulse injection and backscattered light acquisition at monitoring points along the pipeline corridor. This acquires distributed temperature measurement data along the entire path and performs demodulation judgment, achieving high-precision synchronous temperature level output. This solves the problems of low signal-to-noise ratio and difficulty in capturing true temperature distribution in traditional infrared imaging under strong electromagnetic environments. A bidirectional data compensation and early warning module combines demodulation results and forward and backward scattered light data to compensate for signal attenuation under long-distance transmission and high heat loads, correcting temperature measurement deviations. It also triggers graded early warnings based on the thermal characteristics of different areas of the pipeline corridor, avoiding signal attenuation due to fiber optic loss and reduced signal integrity in high-temperature areas. A fault type identification and feedback module classifies overheating and load fluctuation faults, generating reports based on a fault feature database for accurate processing, reducing misjudgments caused by temperature measurement distortion. The overall system improves temperature measurement accuracy and signal stability during faults, providing support for early identification and efficient handling of short-circuit faults, ensuring the safe operation of high-pressure GIL pipeline corridors.

[0010] 2. When acquiring distributed temperature measurement data along the entire path, the axial strain value and real-time temperature value along the GIL (Gas Infrared) tunnel are first obtained and drift judgment is performed: if there is no exceedance, it is marked as valid temperature measurement data; if there is exceedance, stress compensation and noise reduction filtering algorithms are used to correct it to eliminate drift. Signal demodulation judgment is based on the principle of optical time-domain reflection to calculate the distance of the scattering point to obtain position information. Temperature measurement data and position are matched to generate temperature-position correlation data. It is judged whether it meets the positioning error and signal-to-noise ratio requirements. If it meets the requirements, it is output; if it is abnormal, the signal is iteratively enhanced and recalculated. Multiple failures trigger an alarm. This process effectively solves the temperature measurement problem of GIL tunnels: drift judgment eliminates data deviations caused by laying stress and electromagnetic interference, and the correction algorithm compensates for signal attenuation caused by fiber optic loss; the iterative optimization and alarm mechanism of signal demodulation improves the problems of low signal-to-noise ratio and difficulty in capturing temperature distribution in strong electromagnetic environments, ensures signal integrity in high-temperature areas, reduces temperature measurement distortion, improves temperature measurement accuracy and signal stability during faults, and lays a data foundation for early fault identification.

[0011] 3. When compensating for signal attenuation under long-distance transmission and high heat load, the temperature change rate index is first compared with the corresponding preset value to obtain the signal attenuation deviation, and the corresponding attenuation interval is matched. The first attenuation interval uses the initial light intensity of forward and backscattered light to linearly correct the signal and compensate for temperature drift; the second attenuation interval is divided into transmission segments, and the segmented coefficients are configured according to the attenuation curve to eliminate boundary errors; the third attenuation interval calls an enhanced filtering algorithm to reduce noise, and finally the compensation results are statistically analyzed. This process specifically solves the problem of temperature measurement in GIL pipe corridors: differentiated compensation according to attenuation level avoids signal attenuation caused by fiber optic loss during long-distance transmission; enhanced filtering and temperature drift compensation improve the problem of low signal-to-noise ratio in strong electromagnetic environments, reduce temperature measurement data distortion, ensure signal integrity in high-temperature areas, improve temperature measurement accuracy, and provide stable data support for early fault identification.

[0012] 4. When calculating the temperature change rate index, firstly, forward and reverse scattered light data are collected using a DTS detector. The reverse Stokes scattered light signal is extracted to obtain forward and reverse temperature sequences. Then, the sequences are smoothed using a sliding window method and first-order difference operations are performed to obtain the temperature change rate sequence. Finally, the sequence is integrated, the cumulative value is calculated, and the harmonic average is obtained to quantify the dynamic temperature change index. The graded early warning system is based on the compensated real-time temperature and temperature change rate index, quantifying the degree of temperature exceedance and fluctuation risk for linear and curved segments respectively. After forming the early warning result, it is compared with the qualified conditions. If the conditions are met, the monitoring frequency is maintained; otherwise, the corresponding level of early warning is triggered according to the exceedance situation. This process reduces signal interference under strong electromagnetic fields and high heat loads while improving the accuracy of temperature change rate calculation. It also provides differentiated early warnings based on regional thermal characteristics, avoiding misjudgments caused by differences in heat dissipation in different areas of the pipe gallery. Furthermore, by combining attenuated signal compensation data, it reduces temperature measurement distortion and improves the stability and reliability of fault monitoring in high-temperature environments.

[0013] 5. When classifying faults based on temperature measurement data triggered by a level warning, the method first acquires the peak temperature, rate of change, and affected area of ​​the fault region, as well as the operating current, voltage, and load changes of the GIL (Gas Infrared Utility) tunnel. Then, it presets judgment thresholds for two types of faults: overheating and load fluctuation, covering multiple dimensions such as temperature change rate, affected area, and load change, and clarifies the physical meaning of each threshold. Next, it analyzes the correlation between temperature and load changes and calculates the correlation coefficient. Finally, based on the magnitude of the coefficient, it determines whether the fault is overheating, load fluctuation, or requires supplementary on-site inspection. These three conditions are mutually exclusive and cover the entire range, forming a complete judgment closed loop. This method avoids the risk of misjudgment based on a single indicator and reduces the impact of temperature measurement distortion caused by strong electromagnetic fields and high heat loads on fault identification. Simultaneously, the clear judgment logic and closed-loop design improve the accuracy and reliability of fault type identification, contributing to enhanced stability and operational efficiency of fault monitoring in high-temperature environments. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic diagram of the intelligent monitoring system for short-circuit faults in GIL pipe corridors based on distributed optical fiber temperature measurement provided in an embodiment of the present invention;

[0016] Figure 2 Logical architecture diagram of the intelligent monitoring system for short-circuit faults in GIL pipe corridor based on distributed optical fiber temperature measurement provided in this embodiment of the invention;

[0017] Figure 3 This is a schematic diagram of a temperature calibration module provided in an embodiment of the present invention.

[0018] Figure 4 This is the main operation interface of the distributed optical fiber GIL short-circuit fault monitoring system provided in this embodiment of the invention;

[0019] Figure 5 This is the user interface of the temperature query module provided in this embodiment of the invention;

[0020] Figure 6 This is a functional structure diagram of the alarm query module provided in an embodiment of the present invention;

[0021] Figure 7 This is the user interface of the alarm query module provided in this embodiment of the invention;

[0022] Figure 8 This is a functional structure diagram of the parameter setting module provided in an embodiment of the present invention;

[0023] Figure 9 The user interface for the parameter setting module provided in this embodiment of the invention. Detailed Implementation

[0024] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0025] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0026] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0027] To address the problem that traditional temperature measurement technologies are susceptible to fiber optic loss and electromagnetic interference in GIL (Gas Infrared) tunnels under strong electromagnetic environments and high heat loads, leading to inaccurate temperature measurements, poor signal stability, and difficulty in accurately identifying early faults, this invention provides an intelligent monitoring system for short-circuit faults in GIL tunnels based on distributed fiber optic temperature measurement. Figure 1 The diagram shown illustrates the structure of a GIL (Gas Infrared Lever) tunnel short-circuit fault intelligent monitoring system based on distributed fiber optic temperature measurement. The system includes:

[0028] The DTS photoelectric detection and signal demodulation module is used to monitor the laser pulse injection and backscatter light acquisition process based on DTS deployed at designated monitoring points along the GIL tunnel to obtain distributed temperature measurement data along the entire path. Simultaneously, it performs signal demodulation and judgment to achieve high-precision synchronous output of temperature and location information. The bidirectional data compensation and short-circuit fault early warning module compensates for attenuated signals under long-distance transmission and high heat load conditions based on the signal demodulation results and the acquired forward and backscatter light data, correcting temperature measurement deviations. It also triggers corresponding level early warnings based on the thermal characteristics differences in different areas of the GIL tunnel to achieve early identification and rapid response to short-circuit faults. The fault type identification and graded feedback module classifies the fault types in the temperature measurement data when a graded early warning is triggered to distinguish between overheating faults and load fluctuation faults. It also performs matching analysis with a fault feature database and generates fault reports to achieve accurate fault type identification and graded processing.

[0029] like Figure 2 The diagram shows the logical architecture of a GIL (Gas Infrared Lever) tunnel short-circuit fault intelligent monitoring system based on distributed fiber optic temperature measurement. This diagram illustrates the core workflow and component collaboration of the system. The system uses a computer as its control center, controlling a 1550nm laser via a low-noise drive circuit. This laser, combined with a pulse generator, produces probe light pulses, which are then sent to the sensing area of ​​the GIL tunnel via an isolator. The Raman scattered light generated by the short-circuit fault in the sensing area is separated by a Raman filter, received by two APDs (Avalanche Photodiodes), amplified by a 100MHz amplifier, and then subjected to 100MHz data acquisition and analog-to-digital conversion before being transmitted back to the computer for analysis. All components work collaboratively, utilizing distributed fiber optic temperature measurement technology and leveraging the temperature sensitivity of Raman scattering to achieve distributed, real-time monitoring of the GIL tunnel temperature. This provides accurate temperature data support for subsequent short-circuit fault early warning, classification, and handling, ensuring the safe operation of the GIL tunnel and timely fault detection.

[0030] The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed fiber optic temperature measurement also includes a temperature calibration module, such as... Figure 3The schematic diagram of the temperature calibration module shown is based on an STC89C52RC microcontroller as its main control core. On the hardware side, a reference fiber optic monitoring network is established using the I²C protocol and a DS18B20 high-precision temperature sensor, enabling accurate sensing of ambient temperature changes. The integrated MAX3232 chip can perform 3.3V / 5V level conversion and supports the RS-232 standard communication protocol, ensuring stable data transmission over distances exceeding 15 meters. On the software side, an innovative dynamic compensation algorithm has been developed, which uses the least squares method to fit the temperature-light intensity characteristic curve in real time. Simultaneously, sensor data is collected at 10-second intervals, and Raman scattering coefficient correction is performed every second. In terms of interface design, a dual-interface communication architecture is specifically adopted. Through Header5X2 and Header10X2 terminals, multi-channel interaction between the main control unit and the system host is achieved. Ports P1, P2, P3, and P4 respectively handle temperature data acquisition, I²C communication, and RS-232 serial transmission. The MAX3232 chip, through ports 13 and 14, enables bidirectional data exchange between the microcontroller and the host computer. Overall, this module, through the coordinated operation of physical sensing and digital compensation, significantly reduces the adverse effects of ambient temperature fluctuations on the fiber optic temperature measurement system, effectively improving the accuracy of distributed fiber optic temperature measurement and providing reliable temperature calibration assurance for the GIL (Gas Infrared) tunnel short-circuit fault intelligent monitoring system based on distributed fiber optic temperature measurement.

[0031] In a specific embodiment, for example, in a real-world application scenario of a 220kV high-voltage GIL utility tunnel, when the curved section of the tunnel experiences signal attenuation due to high summer heat load (measured attenuation reaches 0.25dB / km, within the second attenuation deviation range), the bidirectional data compensation and short-circuit fault early warning module corrects the temperature measurement deviation by using a segmented compensation algorithm combined with forward and reverse scattered light data. This reduces the original ±0.8℃ temperature measurement error to within ±0.3℃, ensuring signal integrity in the high-temperature zone. Subsequently, when the straight section of the tunnel experiences early overheating faults due to poor joint contact (temperature change rate reaches 0.6℃ / s, load change is only 5%), the DTS photoelectric detection and signal demodulation module accurately locates the fault point (error ≤2m). The fault type identification and graded feedback module quickly distinguishes overheating faults through correlation analysis (excluding load fluctuation interference) and matches similar cases in the fault feature database to generate a report containing handling solutions. This effectively improves the accuracy of temperature measurement and fault response efficiency under strong electromagnetic and high heat load scenarios, providing reliable protection for the safe operation of the GIL utility tunnel.

[0032] Furthermore, the process of acquiring distributed temperature measurement data along the entire path is as follows: Optical fibers are laid at pre-set monitoring points along the GIL tunnel to collect axial strain and temperature signals at each monitoring point during tunnel operation. After removing interference noise from a strong electromagnetic environment, the axial strain and real-time temperature values ​​at corresponding locations along the GIL tunnel are obtained. A drift judgment benchmark is preset, including preset axial strain and temperature drift reference values, while simultaneously storing the initial axial strain and temperature values ​​in the initial state after optical fiber installation. The acquired axial strain values ​​are compared with the preset axial strain values ​​and initial axial strain values ​​to obtain the real-time strain deviation and initial strain deviation. Simultaneously, the acquired real-time temperature values ​​are compared with the temperature drift reference values ​​and initial temperature values ​​to obtain the real-time temperature deviation and initial temperature deviation. If both the real-time strain deviation and initial strain deviation are not greater than the corresponding allowable strain deviation, and both the real-time temperature deviation and initial temperature deviation are not greater than the corresponding allowable temperature deviation, the results are considered accurate. If the allowable deviation is found, no drift is determined, and the axial strain value and real-time temperature value at the corresponding position along the fiber optic cable are marked as distributed temperature measurement data along the entire path. Otherwise, drift is determined, and the fiber optic stress compensation algorithm is called to correct the axial strain value. Simultaneously, wavelet denoising and adaptive filtering algorithms are called to dynamically adjust the corresponding filter coefficients according to a preset adjustment range (set by maintenance personnel based on historical operating data and on-site calibration results, and dynamically optimized according to changes in operating conditions to balance filtering effect and response speed). This further suppresses strong electromagnetic interference, improves the signal-to-noise ratio, and ensures the accuracy and stability of temperature measurement. If no drift is determined after correction, the corrected axial strain value and real-time temperature value are output as distributed temperature measurement data along the entire path. If drift is still determined after a preset number of corrections (usually set to 3), the corresponding area is marked as an interference risk segment, and a drift warning command is sent to prompt the preset personnel to check the fiber optic cable laying status on-site.

[0033] Specifically, the fiber optic stress compensation algorithm is a correction algorithm for temperature measurement deviations caused by axial strain in optical fibers due to vibration and temperature changes in GIL (Gas Infrared) tunnel environments. It collects fiber optic strain sensor data, combines the mapping relationship between fiber optic material strain and temperature measurement error, calculates the temperature deviation caused by strain, and then performs a reverse correction on the axial strain value to offset the interference of stress on temperature measurement, ensuring the accuracy of temperature data. Real-time strain deviation represents the difference between the acquired axial strain value and the preset axial strain value; initial strain deviation represents the difference between the acquired axial strain value and the initial axial strain value. Similarly, real-time temperature deviation represents the difference between the acquired real-time temperature value and the temperature drift reference value; initial temperature deviation represents the difference between the acquired real-time temperature value and the initial temperature value.

[0034] The preset axial strain and temperature drift reference values ​​are obtained through on-site calibration: After the fiber optic cable is laid and before the GIL tunnel is put into operation, axial strain and temperature data are collected at each monitoring point under no-load and rated load conditions. After statistical analysis and anomaly removal, benchmark values ​​for different locations are determined as references. Allowable strain and temperature deviations are determined through experiments and engineering experience: The measurement stability of the fiber optic cable under different stress and temperature changes is tested in a laboratory environment. Combined with long-term historical data of the GIL tunnel operation, the range of strain and temperature fluctuations under normal operating conditions is analyzed. Values ​​are set at a certain confidence level (e.g., 95%) to ensure that the deviations are within acceptable limits.

[0035] In this embodiment, the axial strain and real-time temperature values ​​at corresponding locations along the GIL (Gas Infrared) tunnel are acquired, enabling real-time monitoring of the tunnel's structural deformation and temperature distribution. This provides fundamental data for determining whether equipment may be at risk of overheating or structural abnormalities. Simultaneous consideration of real-time and initial deviations allows for data validity assessment from both dynamic (real-time strain and temperature deviations) and long-term cumulative (initial strain and temperature deviations) perspectives. This avoids overlooking long-term drift by focusing solely on real-time data or missing sudden anomalies by focusing only on initial data. It accurately identifies temperature measurement deviations caused by changes in fiber optic laying stress and strong electromagnetic interference. The entire process, through data acquisition, drift assessment, and correction optimization, ensures accurate and reliable distributed temperature measurement data along the entire path. This effectively solves the problem of temperature measurement distortion under strong electromagnetic and high heat load scenarios, providing high-quality data support for subsequent short-circuit fault warnings and fault type identification. It ensures the accuracy and stability of GIL tunnel operation status monitoring and reduces the risk of missed or misjudged faults due to inaccurate data.

[0036] Further, signal demodulation and judgment are performed. The specific process is as follows: Based on the principle of optical time-domain reflectometry, the propagation time of the laser pulse in the optical fiber is measured, and the distance of each scattering point along the GIL tube corridor is calculated to obtain the corresponding position information. The acquired temperature measurement data and position information are time-space matched to generate temperature-position correlation data, and it is determined whether they meet the signal demodulation qualification conditions. The signal demodulation qualification conditions indicate that the position positioning error does not exceed the position positioning error range, and the signal-to-noise ratio is not lower than the reference signal-to-noise ratio. If the temperature-position correlation data meets the signal demodulation qualification conditions, it is marked as a valid demodulation result and output; otherwise, it is determined as a signal demodulation failure. If demodulation fails, a threshold function is used to remove high-frequency noise and retain the valid signal based on the frequency band difference between the signal and noise. The denoised signal is then used as a reference to initiate an adaptive filtering algorithm. The filter coefficients are adjusted in real time to dynamically suppress strong electromagnetic interference. The location information is then recalculated. If the reacquired temperature-location correlation data meets the signal demodulation qualification conditions, it is marked as a valid demodulation result and output. If, after enhancement processing for a preset number of iterations (usually set to 3), the reacquired temperature-location correlation data still does not meet the signal demodulation qualification conditions, an alarm message indicating signal demodulation failure is sent to the operation and maintenance end to prompt the designated personnel to conduct on-site troubleshooting and equipment maintenance.

[0037] Specifically, when acquiring location information based on the principle of optical time-domain reflectometry, the system injects narrow-pulse lasers into the optical fibers laid along the GIL (Gas Infrared) tunnel. During transmission, the laser undergoes Rayleigh scattering due to minute fluctuations in the fiber's refractive index. The system records the time difference between the laser pulse injection and the return light from each scattering point. Combining this with the known propagation speed of laser in the fiber (approximately 2 / 3 the speed of light in a vacuum, requiring fine-tuning based on the fiber core refractive index), the distance between each scattering point and the laser injection end is calculated using the formula: distance = propagation speed × transmission time / 2 (divided by 2 for round trip). This distance is then mapped to the coordinates of the actual tunnel's laying path to obtain the specific location information of each scattering point along the GIL tunnel. The system simultaneously records the generation time of each scattering point's location information and the acquisition time of the corresponding temperature measurement data, establishing a timestamp association. Based on the coordinate parameters in the location information, the system maps the temperature values ​​at the same time and within the same tunnel coordinates to the location coordinates, forming temperature-location correlation data between a specific location within the tunnel and the corresponding temperature at that time. This ensures that each temperature data point accurately matches the actual physical location of the tunnel.

[0038] The location error range is set according to the accuracy requirements of GIL (Gas Infrared) tunnel monitoring, typically ≤2 meters. Simultaneously, considering laser pulse width, fiber dispersion characteristics, and detector time resolution, the controllability of the error is verified through multiple calibration experiments. The reference signal-to-noise ratio is set based on historical normal operating data: the average signal-to-noise ratio of scattered light signals under conditions of no electromagnetic interference and low load is statistically analyzed, and 80% of this is taken as the reference value to ensure that abnormal interference is filtered out without eliminating valid signals.

[0039] In this embodiment, the principle of optical time-domain reflectometry ensures accurate location positioning, and time-space matching ensures a one-to-one correspondence between temperature data and the actual location of the utility tunnel. Qualification criteria filter valid data, preventing distorted information from affecting monitoring. Signal enhancement processing and iterative optimization in case of anomalies improve the signal-to-noise ratio and positioning accuracy, reducing the probability of demodulation failure. Even if multiple processing attempts fail, timely alarms can quickly trigger on-site investigations. Overall, this ensures the validity and reliability of the temperature measurement data, providing accurate data support for subsequent attenuation signal compensation and fault early warning, and improving the stability of GIL utility tunnel monitoring.

[0040] Furthermore, compensation is provided for signal attenuation under long-distance transmission and high heat load conditions. Specific steps include: comparing the acquired temperature change rate index with a preset temperature change rate index to obtain the signal attenuation deviation, i.e., the difference between the acquired temperature change rate index and the preset temperature change rate index, and matching it with preset attenuation deviation intervals. The attenuation deviation intervals include a first attenuation deviation interval (≤0.1dB / km), a second attenuation deviation interval (0.1-0.3dB / km), and a third attenuation deviation interval (>0.3dB / km). The signal attenuation levels corresponding to the first, second, and third attenuation deviation intervals increase sequentially. The temperature change rate index is used to quantify the dynamic change of temperature along the GIL tunnel over time. The preset temperature change rate index is set after statistical analysis of multiple sets of temperature change data collected during laboratory simulation of different high heat load conditions of the GIL tunnel to determine the normal operating temperature fluctuation range.

[0041] If the signal attenuation deviation falls within the first attenuation deviation range, the initial light intensity values ​​of the forward and backscattered light signals are extracted. Using these initial light intensity values ​​as a benchmark, the signal intensity at each monitoring point along the path is linearly corrected. Then, temperature drift compensation is applied to the corrected signal using the temperature change rate index to obtain the compensated scattered light signal. If the signal attenuation deviation falls within the second attenuation deviation range, the entire transmission path is divided into several segments. The signal attenuation amount is extracted in each segment to form a signal attenuation curve. Based on the attenuation curve, the initial attenuation amount and attenuation change rate are extracted. Segmented compensation coefficients are configured using the initial attenuation amount and attenuation change rate. Each segment signal is compensated separately, and then a smoothing algorithm is used to eliminate the segmented compensation boundary error to obtain the compensated scattered light signal. If the signal attenuation deviation falls within the third attenuation deviation range, an enhanced filtering algorithm is called to suppress noise interference caused by the attenuation of the forward and backscattered light signals, resulting in a filtered scattered light signal. The compensation results of the attenuated signal are statistically analyzed, including the compensated scattered light signal and the filtered scattered light signal.

[0042] The specific calculation process for the temperature change rate index is as follows: First, the DTS detector host synchronously collects forward and backscattered light data, extracts the temperature-sensitive anti-Stokes scattering light signal, and converts it into forward and reverse temperature sequences based on the theoretical relationship between light intensity and temperature. This step distinguishes between forward and reverse because there are directional differences in optical fiber transmission: forward and backscattered light may be subject to different interferences (such as local fiber loss, uneven distribution of electromagnetic interference), and separate calculations are prone to deviation. At the same time, forward and reverse data can be cross-checked to reduce the impact of unilateral signal anomalies (such as signal distortion caused by local damage to the optical fiber) on the temperature sequence, ensuring the accuracy of GIL tunnel temperature monitoring. Second, within a preset monitoring period, the average temperature within the window is calculated using the sliding window method (window width 5-10 seconds) to smooth out instantaneous noise and measurement jitter, obtaining a more stable forward and reverse temperature sequence. Then, the smoothed sequence is subjected to first-order difference, that is, the temperature at adjacent time points is subtracted to obtain the forward and reverse temperature change rate sequences, reflecting the rate of temperature change at each time point and highlighting the trend of change. Finally, the temperature change rate sequence is integrally integrated over the monitoring period, and the cumulative positive and negative temperature changes are obtained to measure the total temperature change amplitude over the entire period. To balance the bias between the positive and negative data and improve stability, the two are harmonic averaged to reduce abnormal fluctuations caused by local electromagnetic interference and minor fiber optic losses, and to avoid excessive influence of single-direction data bias on the results, thus obtaining the final temperature change rate index.

[0043] Specifically, the current average light intensity is obtained by collecting the measured light intensity of forward and backscattered light at each monitoring point during the compensation period and taking the arithmetic mean of all data. The attenuation change rate is calculated by linearly or polynomially fitting the signal attenuation curve and taking the slope of the fitted curve, reflecting the rate of change of attenuation with transmission distance. The values ​​of the three attenuation deviation intervals are calibrated based on the typical attenuation characteristics of optical fiber in long-distance transmission, the accuracy requirements of pipeline monitoring, and historical fault data. In practical applications, they can be finely adjusted according to the length of optical fiber laying, changes in ambient temperature and humidity, and fluctuations in equipment load. In the first attenuation interval compensation, the initial light intensity values ​​of forward and backscattered light are collected from the unattenuated state after the initial fiber laying is completed. Linear correction is performed according to the formula: corrected light intensity = measured light intensity × (initial light intensity / current average light intensity). Then, the drift amount is calculated in combination with the temperature change rate. The reverse drift value is superimposed on the corrected signal to complete the compensation. In the second attenuation interval compensation, the entire path is divided into segments every 5-10km. The light intensity at both ends of each segment is collected to calculate the attenuation amount. The signal attenuation curve is plotted with distance as the horizontal axis and attenuation amount as the vertical axis. The initial attenuation amount and attenuation change rate are extracted by curve fitting. The compensation coefficient is configured according to the formula: compensation coefficient = 1 + (initial attenuation amount + attenuation change rate × segment distance) and multiplied by the measured signal of the segment. Finally, the segment boundary is smoothed using the moving average algorithm. In the third attenuation interval compensation, an enhanced algorithm combining wavelet threshold filtering and Kalman filtering is called. The signal is first decomposed to remove high-frequency noise. Then, low-frequency interference introduced by attenuation is suppressed by state estimation to achieve noise suppression. Finally, the processed signals of each interval are statistically analyzed as the compensation result.

[0044] In this embodiment, the dynamic temperature change is quantified using the temperature change rate index, and a differentiated compensation strategy is adopted for different attenuation intervals to avoid compensation deviations caused by a single standard. Simultaneously, the calculation of the temperature change rate index integrates forward and reverse data and undergoes multi-step processing to reduce the impact of unilateral data anomalies, providing an accurate basis for attenuation determination. Overall, efficient compensation and noise suppression of the attenuation signal are achieved, ensuring accurate temperature measurement data and providing high-quality data support for fault early warning and operational status assessment of utility tunnel equipment, thereby improving the stability and safety of utility tunnel operation monitoring.

[0045] Furthermore, based on the differences in thermal characteristics of different areas of the GIL pipe gallery, corresponding levels of early warning are triggered. The specific process is as follows: Based on the deviation ratio between the real-time temperature value obtained after attenuation signal compensation and the temperature drift reference value, the degree of temperature exceedance in different areas of the GIL pipe gallery is obtained, i.e., (real-time temperature value - temperature drift reference value) / temperature drift reference value × 100%. Simultaneously, based on the deviation ratio between the obtained temperature change rate index and the preset temperature change rate index, the temperature fluctuation risk result for different areas of the GIL pipe gallery is obtained, i.e., (real-time temperature change rate index - preset temperature change rate index) / preset temperature change rate index × 100%. Different areas of the GIL pipe gallery include straight sections and curved sections. The system summarizes the GIL (Gas Infrared Lever) tunnel early warning analysis results, including the temperature exceedance level, temperature fluctuation risk, and temperature exceedance level and temperature fluctuation risk of straight sections. It then determines whether the obtained GIL tunnel early warning analysis results meet the corresponding region's early warning eligibility criteria. These criteria indicate that the temperature exceedance level and temperature fluctuation risk of each region are not greater than preset early warning analysis results. The preset early warning analysis results include preset temperature exceedance levels and preset temperature fluctuation levels. If the GIL tunnel early warning analysis results meet the early warning eligibility criteria, the current monitoring frequency is maintained; otherwise, a corresponding level of early warning is triggered based on the exceedance area and its degree. Specifically:

[0046] If only one area shows a single GIL (Gas Infrared) tunnel warning analysis result that fails to meet the warning qualification criteria, while all indicators in another area meet the warning qualification criteria, it is determined to be a Level 1 short circuit warning. Only a temperature anomaly alert is pushed to the operation and maintenance system, and the monitoring frequency is not adjusted. If at least one GIL tunnel warning analysis result in both areas fails to meet the warning qualification criteria, it is determined to be a Level 2 short circuit warning. An audible and visual alarm is immediately triggered, an emergency notification is simultaneously sent to the preset operation and maintenance personnel, and the highest sampling frequency of the DTS detector host is used to continuously track and monitor the fault area (i.e., the GIL tunnel area corresponding to the Level 2 short circuit warning). If both areas show a single... If the GIL (Gas Infrared) tunnel early warning analysis result does not meet the early warning qualification criteria, the system will trigger an audible and visual alarm, send an emergency notification to designated personnel, and activate the highest sampling frequency. It will then prompt the designated personnel to further narrow the monitoring range of the GIL tunnel. After triggering the early warning, temperature data and temperature change rate data for the corresponding area will continue to be collected. If the corresponding GIL tunnel early warning analysis results meet the early warning qualification criteria for a consecutive set number of monitoring cycles, the early warning level will be gradually lowered until it is lifted. Otherwise, the short-circuit fault emergency response procedure will be initiated to assist in locating the fault point according to the level of the early warning trigger time. The short-circuit fault level corresponding to the Level 1 and Level 2 short-circuit warnings increases sequentially.

[0047] In this embodiment, the preset temperature exceedance level is set according to region: for the straight segment, the threshold is set to 10%-15% of the rated temperature of the reference equipment; for the curved segment, due to poor heat dissipation, the threshold is set to 8%-12%; the preset temperature fluctuation level is similarly set to 1.2 times the normal fluctuation range for the straight segment and 1.1 times for the curved segment, both determined in combination with historical faults and safety redundancy.

[0048] This example calculates the degree of temperature exceedance and fluctuation risk by region, accurately matching the thermal characteristic differences between straight and curved segments to avoid misjudgments or omissions caused by uniform standards, thus improving the targeting of warnings. It provides tiered warnings based on the region and degree of exceedance, with a lightweight first-level warning to reduce maintenance interference, and a progressively upgraded response for second-level warnings, ensuring rapid handling of emergencies while avoiding resource waste. After a warning, the monitoring frequency and range are dynamically adjusted, continuously tracking data to ensure no potential faults are overlooked, and warnings are gradually downgraded when conditions are met to avoid over-warning. It triggers emergency response procedures to assist in locating fault points, and combined with the warning time, improves fault diagnosis efficiency, reduces downtime in GIL (Gas Infrared) tunnels due to short-circuit faults, and comprehensively ensures the safe operation and efficient maintenance of the tunnel.

[0049] Furthermore, the fault types in the temperature measurement data when the level warning is triggered are classified. The specific steps are as follows: within the preset monitoring period, the correlation between temperature change and load change is analyzed to obtain the correlation coefficient used to quantify the linear dependence of temperature and load change; if the correlation coefficient meets the overheating fault judgment threshold (i.e., 0 < correlation coefficient < 0.3), it is judged as an overheating fault; if the correlation coefficient meets the load fluctuation fault judgment threshold (i.e., 0.6 < correlation coefficient < 1), it is judged as a load fluctuation; if the correlation coefficient meets the fault judgment threshold (i.e., the correlation coefficient is between 0.3 and 0.6, including the cases equal to 0.3 and 0.6), it is marked as needing to be supplemented by on-site inspection and is simultaneously pushed to the operation and maintenance terminal for further confirmation; the overheating fault judgment threshold, the load fluctuation fault judgment threshold, and the fault judgment threshold are mutually exclusive and cover the entire range of fault judgment conditions, forming a complete judgment closed loop.

[0050] The steps for obtaining the correlation coefficient are as follows: obtain the temperature change sequence and load change sequence of the GIL pipe gallery within the preset monitoring period, and perform first-order derivative operations to obtain the temperature change rate sequence and load change rate sequence to visualize the dynamic change trend of temperature and load; calculate the covariance and standard deviation of the temperature change rate sequence and the load change rate sequence, and calculate the correlation coefficient according to the Pearson correlation coefficient formula.

[0051] The fault feature database is used for matching analysis and fault report generation. Specifically, historical fault data in the fault feature database is initially classified and filtered according to the classified fault type to remove historical fault data that does not match the type. Based on the temperature measurement data within the preset monitoring period before and after the level warning is triggered, the real-time temperature peak, temperature change rate, and temperature influence range (i.e., the number of monitoring points) of the corresponding fault area are obtained through the DTS detector host. At the same time, the real-time operation data of the GIL pipe gallery, including the operating current value, operating voltage value, and load change, are obtained. The obtained temperature measurement data, real-time temperature peak, temperature change rate, temperature influence range, and real-time operation data of the GIL pipe gallery are input into the historical fault feature template stored in the fault feature database for comparison one by one to obtain the deviation coefficient of each dimension. Using the deviation coefficient of each dimension as the query key, the deviation coefficient of each dimension is cross-mapped with the deviation interval of each dimension in the preset deviation-fault mapping table to locate the unique matching historical fault data group, and perform logical verification processing to generate a structured fault report. The deviation coefficients of each dimension include the temperature peak deviation coefficient, temperature change rate deviation coefficient, temperature influence range deviation coefficient, and load change deviation coefficient.

[0052] Specifically, the load variation deviation coefficient is calculated by subtracting the difference between the real-time temperature peak and the temperature peak in the historical fault feature template, and then taking the absolute value of the ratio of the difference to the temperature peak in the historical fault feature template; the temperature change rate deviation coefficient is calculated by subtracting the difference between the real-time temperature change rate and the temperature change rate in the historical fault feature template, and then taking the absolute value of the ratio of the difference to the temperature change rate in the historical fault feature template; the temperature influence range deviation coefficient is calculated by subtracting the difference between the real-time temperature influence range and the temperature influence range in the historical fault feature template, and then taking the absolute value of the ratio of the difference to the temperature influence range in the historical fault feature template; and the load variation deviation coefficient is calculated by subtracting the difference between the real-time load variation and the load variation in the historical fault feature template, and then taking the absolute value of the ratio of the difference to the load variation in the historical fault feature template.

[0053] The specific values ​​of the preset overheating fault judgment threshold and load fluctuation fault judgment threshold are set by simulating different fault conditions of GIL pipe corridor in the laboratory, combined with the temperature resistance limit of equipment materials, power system safety operation standards and historical fault data. In actual application, when the pipe corridor laying environment (such as high temperature and high humidity) changes drastically, or when the performance of equipment degrades due to the increase in the number of years of operation, it can be fine-tuned according to real-time operation and maintenance data and equipment inspection reports to ensure that the judgment threshold is adapted to the actual working conditions and improve the accuracy of fault classification.

[0054] In this embodiment, the linear dependence of temperature and load changes is quantified by calculating the correlation coefficient, avoiding the one-sidedness of a single indicator. Specifically, in the case of overheating faults, temperature changes are mostly caused by local anomalies (such as poor contact) and have a weak correlation with the load, hence the low correlation coefficient; in the case of load fluctuation faults, temperature changes change synchronously with load adjustments, showing a strong correlation, hence the high coefficient. This distinction logic aligns with the actual fault characteristics. Secondly, setting a transition range of 0.3-0.6 and triggering on-site inspections fills the gap in fault identification between the two types, preventing misjudgments or omissions due to data ambiguity, and forming a complete judgment closed loop. Simultaneously, the correlation coefficient is calculated using the temperature and load change rate sequence, which can more sensitively capture the dynamic relationship between the two, further improving the accuracy of the judgment. The overall process provides maintenance personnel with a clear basis for fault classification, avoiding over-treatment of load fluctuations or delayed handling of overheating faults, helping to quickly develop targeted maintenance plans and reducing fault troubleshooting time.

[0055] By initially screening historical data according to fault type and eliminating mismatched data, invalid comparisons are reduced, significantly improving fault matching efficiency and avoiding matching delays caused by data redundancy. Based on multi-dimensional deviation coefficients (temperature peak, rate of change, range of influence, load change) and accurate comparison with historical templates, combined with deviation-fault mapping table cross-mapping, unique matching historical fault data groups can be accurately located, avoiding the one-sidedness of single-dimensional judgment, improving the accuracy of fault feature matching, and reducing misjudgments. The structured fault report generated by logical verification clearly presents the deviation situation and matching results of each dimension, providing operation and maintenance personnel with intuitive and comprehensive fault reference, helping to quickly grasp the core characteristics of the fault. Relying on historical fault data for auxiliary analysis, the experience of handling similar faults in the past can be learned from, shortening the time for fault cause analysis and solution formulation, improving the efficiency of GIL tunnel fault handling, reducing the impact of faults on tunnel operation, and ensuring stable power transmission.

[0056] It should be added that, such as Figure 4The main operating interface of the distributed fiber optic GIL short-circuit fault monitoring system, as shown, integrates the core system function entry and real-time monitoring data display, allowing operators to quickly grasp the GIL operating status and promptly detect abnormal short-circuit fault lights. When equipment reports a false alarm or its status becomes disordered, a reset function can restore the initial monitoring state with one click. The self-test automatically scans hardware (such as DTS detectors) and software modules (such as data transmission links), and displays the fault point in a pop-up window on the interface, significantly shortening the fault diagnosis time. The temperature curve in the main display area uses dynamic drawing technology, updating data every second. The blue real-time curve contrasts sharply with the red threshold line. When the temperature of a certain fiber optic segment approaches the 85℃ threshold, the curve will automatically flash to remind the operator, and a timestamped warning message will be pushed to the real-time message bar in the lower left corner to ensure that operators can detect anomalies immediately. In addition, the interface supports multi-channel parallel monitoring, allowing simultaneous viewing of temperature data and operating status for four channels. Each channel's status card uses color coding to intuitively distinguish the equipment's operating condition, adapting to the actual needs of simultaneous monitoring of multiple lines in GIL tunnels.

[0057] like Figure 5 The user interface of the temperature query module, as shown, focuses on the accurate retrieval and in-depth analysis of historical temperature data, compensating for the time limitations of real-time monitoring on the homepage. The query condition setting area employs a three-dimensional filtering mode based on date, time, and channel. Date selection supports cross-month queries, time selection is accurate to the second, and multiple channels can be selected for comparison, meeting complex query needs such as comparing temperatures across multiple channels within a specific time period. While the data display switching area currently only displays temperature curves, the reserved temperature list and single-point curve functions will further enrich the data presentation. Specifically, the temperature list can list temperature data every 10 seconds in chronological order, facilitating export for statistical analysis in Excel, while the single-point curve can focus on the 24-hour temperature changes at a specific monitoring point (e.g., at 160m), helping to discover periodic temperature patterns. The playback speed adjustment function in the historical temperature curve playback area supports switching between 0.01x and 0.1x speed. When it is necessary to observe the inflection point of a sudden rise or fall in temperature in detail, the speed can be reduced to analyze frame by frame, while fast playback is suitable for browsing the overall trend. The "View Details" button in the data details area will be linked to the fiber optic stress data and environmental temperature and humidity data at that time point in the future, providing multi-dimensional traceability basis for temperature anomalies.

[0058] like Figure 6The alarm query module functional structure diagram clearly presents the complete logical link of user operation, data interaction, and result display, reflecting the rigor of the system's data processing. After the user inputs query conditions, the system does not directly retrieve the raw data. Instead, it first uses a data filtering module to remove invalid alarms (such as false alarms caused by momentary electromagnetic interference), and then transmits the filtered valid data to the historical alarm database for matching to ensure the accuracy of the displayed results. The statistics panel, as an intermediate interactive link, plays a dual role in data aggregation and trend analysis. It can not only count the total number of alarms of various types within a selected time period, but also calculate the year-on-year change rate through a built-in algorithm. For example, when the number of over-temperature alarms increases by 5 times this week compared to last week, the system will automatically mark the increase and prompt the operator to pay attention to possible equipment aging, increased load, and other issues. Users can quickly locate specific alarm records from the total number of alarms on the statistics panel. This hierarchical design avoids information overload and ensures query depth. At the same time, the structure diagram reserves an alarm correlation analysis interface, which can be connected to the fault handling record database in the future to improve the traceability of fault handling.

[0059] like Figure 7 The alarm query module's user interface translates the functional structure diagram into intuitive visual operations, highlighting the core objectives of convenient querying and efficient handling. The alarm type dropdown menu in the top filtering area, in addition to options like "All Types" and "Over-temperature Alarm," also supports custom alarm level filtering (such as Level 1 Warning, Level 2 Alarm) to meet the needs of different maintenance scenarios. Specifically, routine inspections can query all types, while fault debriefing can focus on over-temperature alarms. The central statistical information card area uses color psychology design: red cards correspond to over-temperature alarms (highest urgency), yellow to fiber breakage alarms (medium urgency), and gray to equipment failures (low urgency). Weekly year-on-year comparisons on the cards are presented with prominent growth / decrease icons, allowing operators to quickly grasp alarm trends without calculation. The processing status field in the detailed alarm record list at the bottom uses a three-level classification: processed / processing / unprocessed. Records in processing are marked with the handler and progress, avoiding duplicate handling or omissions by multiple departments. The exported report button generates a PDF report containing standardized content such as alarm time, location, temperature curve, and handling suggestions, which can be directly used for maintenance meeting presentations or archiving, improving work efficiency.

[0060] like Figure 8The parameter setting module functional structure diagram shown illustrates how, after the administrator inputs configuration information on the main parameter setting interface, the system categorizes and transmits the parameters to three core processing modules: the temperature calibration module directly affects the accuracy of the real-time temperature curve. For example, adjusting the action temperature parameter will immediately update the red threshold line on the homepage to ensure that the alarm triggering standard is consistent with the configuration; the host parameter module relates to the underlying logic of data acquisition. For instance, a small adjustment to the fiber optic refractive index (from 1.46 to 1.461) will directly affect the accuracy of fault location, with the error controllable within ±1m; the meter-level configuration module corrects for physical deviations in the actual fiber optic cable installation. When the fiber optic cable length changes due to thermal expansion and contraction, adjustments by adding or subtracting values ​​ensure that the monitoring point location matches the actual physical location. The advantage of this modular design is that adjusting the parameters of one module will not affect the normal operation of other modules. Furthermore, the structure diagram clearly indicates the impact range of each parameter, helping administrators understand the chain reaction of parameter changes and avoiding system monitoring failures due to misoperation.

[0061] like Figure 9 The parameter setting module's interface features a partitioned configuration and real-time saving design. Temperature calibration, host parameters, and meter increment / decrement configuration are arranged independently in three areas. Each area's parameter input box has text prompts and reasonable numerical range limits to prevent invalid inputs. The edit / delete function in the meter increment / decrement configuration area supports real-time adjustment of configured correction points. For example, if the increment / decrement value at 1000m is found to be too large (+0.5m), it can be directly edited to +0.3m without reconfiguring all correction points. The save settings button at the bottom of the interface uses a two-confirmation mechanism; clicking it will trigger a pop-up prompt to prevent monitoring interruptions due to accidental saving. The cancel button allows for one-click restoration of the parameters before modification, ensuring the security of configuration operations. This design not only meets the refined configuration needs of professional technicians but also reduces operational risks through multiple protection mechanisms.

[0062] In addition to the aforementioned user interfaces, the system also includes a system maintenance interface, user management interface, rules engine interface, geographic view interface, report center interface, data workbench interface, and session and permission interface. The system maintenance interface serves as the central backend, ensuring data security through automatic backup (supporting multi-period settings, dual local and cloud storage) and precise data recovery (selectable by version time and data volume). System logs can be filtered for operation, login, and DTS detector operation logs to aid in fault location. The update check function indicates version impact, facilitating timely updates by administrators.

[0063] The user management interface focuses on granular permissions, differentiating between administrator (full-function operation) and operator (basic monitoring) permissions. Username uniqueness verification, secondary verification for deletion, and a reserved password reset entry ensure account security and standardized collaboration. The rules engine interface lowers the configuration threshold through visual rules, priority adjustment, and scenario simulation testing. Rule import / export functions can reuse mature solutions. The geographic view interface relies on GPS technology, supporting multi-level zoom and automatic alarm location, pop-up windows linked to temperature trend charts, and quick jump functionality for key areas, solving the challenge of locating faults in long-distance utility tunnels. The report center provides standardized daily / weekly / monthly templates containing temperature, alarm, and equipment health data. Reports are automatically named and batch exported, meeting archiving and reporting needs. The data workbench supports drag-and-drop and scroll wheel zooming to locate temperature anomalies, real-time statistical interval data characteristics, and export of CSV files for integration with professional tools for modeling, enhancing data value. The session and permission interface constructs triple security protection through password strength detection, session log saving, and permission boundary prompts, adapting to the safety requirements of the power industry.

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0065] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0066] In various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed fiber optic temperature measurement, characterized in that: The system includes: The DTS photoelectric detection and signal demodulation module is used to monitor the laser pulse injection and backscatter light acquisition process based on the DTS deployed at designated monitoring points along the GIL tunnel, so as to obtain temperature measurement data distributed throughout the entire path. At the same time, it performs signal demodulation and judgment to achieve synchronous output of temperature and location information. The bidirectional data compensation and short-circuit fault early warning module is used to compensate for the attenuation signal under long-distance transmission and high heat load conditions based on the signal demodulation results and the acquired forward and backscattered light data, so as to correct the temperature measurement deviation. At the same time, it triggers corresponding level early warning based on the thermal characteristics differences of different areas of the GIL pipe gallery, so as to realize the identification and response to short-circuit faults. The fault type identification and classification feedback module is used to classify the fault types in the temperature measurement data when the level warning is triggered, so as to distinguish between overheating faults and load fluctuation faults. At the same time, it combines the fault feature database for matching analysis and generates fault reports to realize the identification and classification of fault types. The specific steps for compensating for signal attenuation under long-distance transmission and high heat load conditions include: The obtained temperature change rate index is compared with the preset temperature change rate index to obtain the signal attenuation deviation, and matched with each preset attenuation deviation interval. The attenuation deviation interval includes a first attenuation deviation interval, a second attenuation deviation interval, and a third attenuation deviation interval. The signal attenuation level corresponding to the first attenuation deviation interval, the second attenuation deviation interval, and the third attenuation deviation interval increases sequentially. The temperature change rate index is used to quantify the dynamic change of temperature along the GIL tunnel over time. If the signal attenuation deviation belongs to the first attenuation deviation range, the initial light intensity values ​​of the forward and backward scattered light signals are extracted. Based on the initial light intensity values, the signal intensity of each monitoring point along the line is linearly corrected. Then, the temperature drift compensation is performed on the corrected signal in combination with the temperature change rate index to obtain the compensated scattered light signal. If the signal attenuation deviation belongs to the second attenuation deviation interval, the entire path transmission distance is divided into several segments on average. The signal attenuation is extracted in each segment to form a signal attenuation curve. The initial attenuation and attenuation change rate are extracted based on the attenuation curve. The segment compensation coefficient is configured with the initial attenuation and attenuation change rate. After each segment signal is compensated, the segment compensation boundary error is eliminated by a smoothing algorithm to obtain the compensated scattered light signal. If the signal attenuation deviation falls within the third attenuation deviation range, the enhanced filtering algorithm is invoked to suppress noise interference caused by the attenuation of forward and backscattered light signals, thus obtaining the filtered scattered light signal. The compensation results for the statistical attenuation signal include the compensated scattered light signal and the filtered scattered light signal.

2. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 1, characterized in that, The specific process for obtaining distributed temperature measurement data along the entire path is as follows: Obtain the axial strain and real-time temperature values ​​at corresponding locations along the GIL tunnel; The acquired axial strain value is compared with the preset axial strain value and the initial axial strain value to obtain the real-time strain deviation and the initial strain deviation. Simultaneously, the acquired real-time temperature value is compared with the temperature drift reference value and the initial temperature value to obtain the real-time temperature deviation and the initial temperature deviation. If the real-time strain deviation and the initial strain deviation are both no greater than the corresponding allowable strain deviation, and the real-time temperature deviation and the initial temperature deviation are both no greater than the corresponding allowable temperature deviation, then it is determined that there is no drift, and the axial strain value and real-time temperature value at the corresponding position along the fiber are marked as distributed temperature measurement data along the entire path. Otherwise, it is determined that there is a drift, and the fiber stress compensation algorithm is called to correct the axial strain value. At the same time, wavelet denoising and adaptive filtering algorithms are called to enhance the signal-to-noise ratio of the scattered light signal. If the correction determines that there is no drift, the corrected axial strain value and real-time temperature value will be output as distributed temperature measurement data throughout the entire path. If drift is still detected after the preset number of corrections, the corresponding area will be marked as an interference risk segment and a drift warning command will be sent to prompt the preset personnel to check the fiber optic cable laying status on-site.

3. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 2, characterized in that, The specific process for signal demodulation and determination is as follows: Based on the principle of optical time-domain reflectometry, the propagation time of the laser pulse in the optical fiber is measured, and the distance of each scattering point along the GIL tube gallery is calculated to obtain the corresponding position information. The acquired temperature measurement data is matched with the location information in a time-space manner to generate temperature-location correlation data, and it is determined whether the signal demodulation qualification conditions are met. The signal demodulation qualification condition means that the position positioning error does not exceed the position positioning error range and the signal-to-noise ratio is not lower than the reference signal-to-noise ratio; If the temperature-location correlation data meets the signal demodulation qualification criteria, it is marked as a valid demodulation result and output. Conversely, if the signal demodulation is abnormal, wavelet denoising and adaptive filtering algorithms are called to enhance the original scattered light signal, and the position information is recalculated. If the re-acquired temperature-position correlation data meets the signal demodulation qualification conditions, it is marked as a valid demodulation result and output. If, after enhancement processing for a preset number of iterations, the reacquired temperature-location correlation data still does not meet the signal demodulation qualification conditions, an alarm message indicating signal demodulation failure will be sent to the operation and maintenance end.

4. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 1, characterized in that, The specific calculation process for the temperature change rate index is as follows: The forward and reverse scattered light data are collected synchronously by the DTS detector host, the anti-Stokes scattered light signal is extracted, and the forward temperature sequence and reverse temperature sequence are calculated respectively. Within a preset monitoring period, the average temperature within the window is calculated using the sliding window method to obtain smoothed forward and reverse temperature sequences. First-order difference operations are then performed on each sequence to obtain forward and reverse temperature change rate sequences. The obtained positive and negative temperature change rate sequences are integrated to obtain the cumulative positive and negative temperature changes, and then harmonic averaged to obtain the temperature change rate index.

5. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 4, characterized in that, The process of triggering corresponding warning levels based on the thermal characteristic differences in different areas of the GIL (Gas Infrared) tunnel is as follows: Based on the deviation ratio between the real-time temperature value obtained after attenuation signal compensation and the temperature drift reference value, the degree of temperature exceedance in different areas of the GIL tunnel is obtained. At the same time, based on the deviation ratio between the obtained temperature change rate index and the preset temperature change rate index, the temperature fluctuation risk in different areas of the GIL tunnel is obtained. The different areas of the GIL tunnel include straight section areas and curved section areas. The results of the GIL (Gas Inertia Line) utility tunnel early warning analysis are summarized, including the results of temperature exceedance in straight sections, the results of temperature fluctuation risk in straight sections, the results of temperature exceedance in curved sections, and the results of temperature fluctuation risk in curved sections. Determine whether the obtained GIL tunnel early warning analysis results meet the early warning qualification conditions of the corresponding area. The early warning qualification conditions mean that the temperature exceedance result and temperature fluctuation risk result of each area are not greater than the preset early warning analysis results. The preset early warning analysis results include the preset temperature exceedance degree and the preset temperature fluctuation degree. If the GIL utility tunnel early warning analysis results meet the early warning qualification criteria, the current monitoring frequency will be maintained; otherwise, the corresponding level of early warning will be triggered based on the area and degree of exceedance.

6. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 5, characterized in that, The method of triggering corresponding level warnings based on the area and degree of exceeding the standard is as follows: If only one area has a single GIL (Gas Inlet and Outer Limit) tunnel early warning analysis result that does not meet the early warning qualification criteria, while all indicators in another area meet the early warning qualification criteria, it is judged as a Level 1 short circuit early warning. Only a temperature abnormality prompt is pushed to the operation and maintenance system, and the monitoring frequency is not adjusted. If at least one GIL (Gas Inlet and Outlet) tunnel early warning analysis result in both areas fails to meet the early warning qualification criteria, it is determined to be a Level II short circuit early warning. An emergency notification is sent to the preset maintenance personnel, and the highest sampling frequency of the DTS (Digital Transmission System) detector host is used to continuously track and monitor the fault area. If the early warning analysis results of a single GIL tunnel in both areas do not meet the early warning qualification criteria, then in addition to sending an emergency notice to the pre-set personnel and calling the highest sampling frequency, the pre-set personnel will be prompted to further narrow the monitoring range of the GIL tunnel. After an early warning is triggered, temperature data and temperature change rate data for the corresponding area will continue to be collected. If the early warning analysis results of the corresponding GIL tunnel meet the early warning qualification conditions within a preset number of monitoring cycles, the early warning level will be gradually reduced until it is lifted. Conversely, the short-circuit fault emergency response procedure will be initiated to assist in locating the fault point and triggering a level-based early warning. The short-circuit fault levels corresponding to the Level 1 short-circuit warning and the Level 2 short-circuit warning increase sequentially.

7. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) pipe racks based on distributed optical fiber temperature measurement as described in claim 1, characterized in that, The specific steps for classifying the fault types in the temperature measurement data when a level warning is triggered are as follows: Within a preset monitoring period, the correlation between temperature changes and load changes is analyzed to obtain a correlation coefficient used to quantify the linear dependence of temperature and load changes. If the correlation coefficient meets the overheating fault judgment threshold, it is judged as an overheating fault. If the correlation coefficient meets the load fluctuation fault judgment threshold, it is judged as a load fluctuation. If the correlation coefficient meets the fault judgment threshold, it is marked as needing to be supplemented by on-site inspection and judgment, and is simultaneously pushed to the operation and maintenance terminal for further confirmation. The overheating fault determination threshold represents the obtained correlation coefficient within the first correlation interval; The load fluctuation fault determination threshold indicates that the obtained correlation coefficient is within the second correlation interval. The fault determination threshold indicates that the obtained correlation coefficient is within the third correlation interval; The first correlation interval, the second correlation interval, and the third correlation interval are mutually exclusive and cover the entire range of fault judgment conditions.

8. The intelligent monitoring system for short-circuit faults in GIL pipe corridors based on distributed optical fiber temperature measurement as described in claim 7, characterized in that, The steps for obtaining the correlation coefficient are as follows: The temperature change sequence and load change sequence of the GIL pipe gallery within a preset monitoring period are obtained and processed separately to obtain the temperature change rate sequence and load change rate sequence, so as to visualize the dynamic change trend of temperature and load. Calculate the covariance and standard deviation of the temperature change rate series and the load change rate series, and obtain the correlation coefficient according to the Pearson correlation coefficient formula.

9. The intelligent monitoring system for short-circuit faults in GIL (Gas Infrared Lever) tunnels based on distributed optical fiber temperature measurement as described in claim 7, characterized in that, The process of combining the fault feature database for matching analysis and generating a fault report specifically involves: The historical fault data in the fault feature database is initially classified and filtered according to the classified fault type in order to remove historical fault data that do not match the type. The acquired temperature measurement data, real-time temperature peak, temperature change rate, temperature influence range, and real-time operation data of the GIL pipe gallery are input into the historical fault feature templates stored in the fault feature database for comparison one by one to obtain the deviation coefficients of each dimension. Using the deviation coefficients of each dimension as query keys, the deviation coefficients of each dimension are cross-mapped with the deviation intervals of each dimension in the preset deviation-fault mapping table to locate the unique matching historical fault data group, and perform logical verification processing to generate a structured fault report. The deviation coefficients for each dimension include the peak temperature deviation coefficient, the temperature change rate deviation coefficient, the temperature influence range deviation coefficient, and the load change deviation coefficient.

Images