Intelligent early warning and hierarchical linkage disposal method for hydrogen pipeline leakage

Abstract

The invention discloses an intelligent early warning and hierarchical linkage disposal method for hydrogen pipeline leakage, and relates to the technical field of hydrogen pipeline safety monitoring, and the method comprises the steps: building a pipeline health fingerprint reference library, deploying a five-dimensional cooperative sensing array, synchronously collecting, purifying and processing leakage and hydrogen embrittlement signals, and extracting core feature parameters; a dynamic algorithm is utilized to quantify hydrogen embrittlement risks, a leakage point is positioned, a comprehensive decision result containing information such as risk types and levels is output, differential grading disposal is started according to the decision result, emergency resources are deployed through a resource scheduling algorithm, a disposal plan is dynamically optimized through a closed-loop feedback mechanism, and efficient management of hydrogen pipeline safety is achieved.

Description

Technical Field

[0001] This invention relates to the field of hydrogen pipeline safety monitoring technology, specifically to a method for intelligent early warning and graded linkage response to hydrogen pipeline leaks. Background Technology

[0002] Hydrogen energy, as a clean and efficient secondary energy source, is an important component of the future energy system transformation. Its application scale in industrial production, transportation, energy storage and other fields continues to expand. In the large-scale transportation of hydrogen, pipeline transportation has become the mainstream transportation method due to its advantages such as high transportation efficiency, low energy consumption and controllable cost. However, hydrogen itself has special physicochemical properties. On the one hand, it can react with pipeline materials to cause hydrogen embrittlement, which can lead to a decrease in pipeline structural strength and cracks in the long term. On the other hand, hydrogen leakage can easily form an explosive mixture with air, which may cause combustion, explosion and other safety accidents if it encounters a source of ignition. With the rapid development of the hydrogen energy industry, the construction mileage of hydrogen pipelines is constantly increasing, and the pipeline operating environment is becoming increasingly complex, covering various scenarios such as long-distance field trunk lines and distributed pipeline networks in industrial parks. This places higher demands on the real-time monitoring, risk warning and emergency response of pipeline operation status. The industry urgently needs an integrated technical solution that can simultaneously cope with hydrogen embrittlement damage and leakage risks to ensure the safe and stable operation of hydrogen pipelines and support the large-scale advancement of the hydrogen energy industry.

[0003] Traditional hydrogen pipeline monitoring and response technologies have significant limitations and cannot meet the current high safety standards required for operation. First, traditional technologies often monitor single risks, focusing only on leakage phenomena or assessing hydrogen embrittlement damage separately, failing to simultaneously capture the correlation characteristics of two types of risks. This results in blind spots in risk identification, easily leading to safety issues due to overlooked critical hidden dangers. Second, sensor systems often suffer from asynchronous signal acquisition and weak anti-interference capabilities. Invalid signals such as environmental noise and mechanical vibration can easily mix into the monitoring data, interfering with subsequent feature extraction and risk assessment. Signal processing methods are also relatively simple, making it difficult to accurately extract the core characteristic parameters of hydrogen embrittlement and leakage from complex signals, resulting in insufficient accuracy in early warning. Third, the decision-making process lacks comprehensive consideration of multi-dimensional data such as pipeline stress, environmental factors, and operating conditions, leading to vague risk level determination and limited accuracy in leak point location, failing to provide precise basis for response. Finally, response measures often adopt a uniform operating mode, without developing differentiated plans based on risk levels, easily causing resource waste or untimely response, and lacking a closed-loop optimization mechanism, making it impossible to iteratively upgrade plans based on long-term operating data and adapt to the dynamic changes in pipeline operating conditions. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides an intelligent early warning and tiered response method for hydrogen pipeline leaks. This method constructs a pipeline health fingerprint benchmark database, deploys a five-dimensional collaborative sensor array, synchronously collects and processes leak and hydrogen embrittlement signals, extracts core feature parameters, uses dynamic algorithms to quantify hydrogen embrittlement risk and locate leak points, outputs a comprehensive decision result including risk type and level information, initiates differentiated tiered response based on the decision result, allocates emergency resources through resource scheduling algorithms, and dynamically optimizes the response plan using a closed-loop feedback mechanism, thereby achieving efficient management of hydrogen pipeline safety.

[0005] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a method for intelligent early warning and tiered coordinated response to hydrogen pipeline leaks, comprising the following steps: S1: Construct an initial health fingerprint baseline database for the pipeline and deploy a five-dimensional collaborative sensing array along the hydrogen pipeline axis and key nodes; S2: Use a five-dimensional collaborative sensing array to synchronously collect leakage-related signals and hydrogen embrittlement failure precursor signals, switch the MEMS acoustic sensor in the array to high-frequency sampling mode, preliminarily remove invalid interference signals according to preset rules and record relevant parameters; S3: An adaptive Kalman filter algorithm is used to purify the hydrogen-sensitive signal, and a multi-layer wavelet packet transform denoising algorithm is used to purify the hydrogen embrittlement-related high-frequency vibration acoustic signal and acoustic emission signal. Simultaneously, the two core characteristic parameters of leakage and hydrogen embrittlement are extracted, and the signal arrival time of adjacent effective probes is recorded. S4: Combining baseline data, GIS map coordinates and pipeline section stress information, the hydrogen embrittlement risk level is classified by dynamic hydrogen embrittlement risk quantification algorithm, the three-dimensional coordinates of the leak point are determined by hydrogen embrittlement coupled leakage three-dimensional location algorithm, and the comprehensive decision result is output. S5: Initiate differentiated and graded handling and shutdown operations based on comprehensive decision-making results, allocate emergency resources through a three-dimensional coupled resource scheduling priority algorithm, regularly update the handling plan using a closed-loop feedback plan dynamic optimization algorithm, and simultaneously update the pipeline health fingerprint benchmark database.

[0006] Furthermore, the five-dimensional collaborative sensing array is an integrated structure that combines a hydrogen-sensitive unit, a temperature sensor, a pressure sensor, a vibration sensor, and a MEMS acoustic sensor. The hydrogen-sensitive unit has a response concentration range of 0.1-1000ppm, and the MEMS acoustic sensor supports a conventional frequency of 20-2000Hz and a high-frequency sampling mode of >100kHz. The deployment method of the five-dimensional collaborative sensing array is as follows: integrated probes are arranged along the axial direction of the hydrogen pipeline at intervals of 5-10 meters. The spacing between adjacent probes at critical nodes such as flanges and welds is increased to ≤2 meters. The probes adopt a composite fixing structure of magnetic attraction and bolts. A 2-5mm high-temperature resistant silicone pad is set on the contact surface with the outer wall of the pipeline. The signal transmission adopts a dual backup method of wireless and wired transmission.

[0007] Furthermore, the process of constructing the initial health fingerprint baseline database for the pipeline includes: collecting high-frequency vibration acoustic signals in the 3-10kHz frequency band of the pipeline under undamaged and leak-free operating conditions using MEMS acoustic sensors; setting different acquisition times for the straight sections of the pipeline and key nodes such as flanges and welds; and simultaneously recording the internal pressure and medium temperature of the pipeline during the acquisition process. The collected signals are subjected to mean filtering to extract the resonance peak frequency, amplitude, phase and waveform characteristic parameters. Combined with the preset acoustic emission energy benchmark value and the critical acoustic emission event rate threshold, an initial health fingerprint benchmark database containing standardized characteristic parameters of different pipeline sections and different operating conditions is constructed.

[0008] Furthermore, when the fluctuation amplitude of the hydrogen-sensitive signal is >5% / s, the filtering gain of the adaptive Kalman filter algorithm is automatically adjusted to 0.1-0.2, and the initial value is maintained when the fluctuation amplitude is ≤5% / s; The decomposition rules of the multi-layer wavelet packet transform denoising algorithm are as follows: the first layer decomposes the original signal into a low-frequency band of 0-50kHz and a high-frequency band of 50-200kHz; the second layer decomposes the high-frequency band of 50-200kHz into three sub-bands of 50-100kHz, 100-150kHz, and 150-200kHz; and the third layer decomposes only retains the effective signal component of the 100-150kHz sub-band.

[0009] Furthermore, the core characteristic parameters of the leak include hydrogen concentration value, duration of concentration exceeding 0.1 ppm, peak amplitude of vibration signal, frequency spectrum distribution of vibration signal, characteristic frequency range of acoustic turbulence, and intensity of hydrogen-sensitive signal; The core characteristic parameters of hydrogen embrittlement include the resonant peak frequency shift, resonant peak amplitude sharpness, acoustic emission signal rise time, acoustic emission signal duration, acoustic emission event rate per unit time, and single acoustic emission signal energy value.

[0010] Furthermore, the mathematical expression of the dynamic hydrogen embrittlement risk quantification algorithm is as follows:

[0011] in, This is a hydrogen embrittlement risk index. This represents the actual drift of the high-frequency resonance peak. The resonant frequency is the benchmark frequency for healthy fingerprints. The acoustic emission event rate per unit time. The threshold for the critical event rate. The average energy of a single acoustic emission signal. As an energy reference value, , , The coupling coefficient is... This is the stress correction factor for the pipeline section. For the current monitoring duration, The time decay constant; when When the calculation result is in the interval [0, 3), it is considered risk-free; when When the calculation result is in the interval [3, 7), it is determined to be microscopic damage; when When the calculation result is in the range [7, 10], it is determined to be the initiation of macroscopic cracks.

[0012] Furthermore, the mathematical expression for the three-dimensional localization algorithm for hydrogen embrittlement coupling leakage is:

[0013] in, The three-dimensional coordinates of the leak point. Number the three adjacent sensor probes. For the first The intensity of the hydrogen-sensitive signal of each probe, For the acoustic signal from the leak point to the... The arrival time of each probe, For the first The amount of resonance peak shift monitored by each probe This is a correction factor for hydrogen embrittlement risk. For the first The three-dimensional coordinates of the probe, where 10 represents the hydrogen embrittlement risk index. The maximum value threshold.

[0014] Furthermore, the output of the comprehensive decision result specifically refers to: combining relevant parameters to predict the leakage diffusion path and speed, and outputting a comprehensive decision result that includes risk type, level, location and diffusion trend; When predicting the leakage diffusion path and speed, relevant parameters are input into an improved Gaussian diffusion model. The improved Gaussian diffusion model adapts the diffusion coefficient to the high diffusion characteristics of hydrogen by modifying the diffusion coefficient, introduces the surface roughness coefficient to adjust the horizontal diffusion rate, and combines wind direction and obstacle distribution information to correct the diffusion direction deviation. The spatial concentration distribution of leaked gas at different times is calculated with a time step of 1 second, and then the leakage diffusion path and diffusion speed at each stage are obtained by fitting.

[0015] Furthermore, the mathematical expression of the three-dimensional coupled resource scheduling priority algorithm is:

[0016] in, For resource scheduling priority, , , , 10 represents the weighting coefficient, and 10 represents the hydrogen embrittlement risk index. The maximum value threshold, The straight-line distance from the current location of the resource to the leak point. For the maximum scheduling radius, To address the error in leak location accuracy, To allow for the maximum positioning error, The current load factor of the resource. This is the threshold for resource full load.

[0017] Furthermore, the mathematical expression of the closed-loop feedback scheme dynamic optimization algorithm is:

[0018] in, To optimize the contingency plan coefficients, To improve resource scheduling and execution efficiency, As an environmental dynamic correction factor, This refers to the actual ambient wind speed. As the reference wind speed, This refers to the actual atmospheric density in the environment. Based on atmospheric density, This represents the reduction in the risk radius after the treatment. For the theoretical maximum reduction radius, This is a hydrogen embrittlement risk index. This determines the priority of resource scheduling.

[0019] The beneficial effects of this invention are: This invention integrates multi-dimensional collaborative sensing and intelligent signal processing technologies to construct a comprehensive pipeline status perception system, enabling simultaneous monitoring and accurate identification of both leakage and hydrogen embrittlement risks. Relying on an integrated sensing structure, it achieves simultaneous acquisition of multiple types of signals, and combines advanced filtering and denoising algorithms to complete signal purification and core feature extraction. Simultaneously, it captures key information related to leakage and hydrogen embrittlement, breaking through the limitations of traditional single-risk monitoring. Through a dual-risk collaborative decision-making mechanism, it integrates benchmark data, environmental factors, and pipeline operating status to accurately determine the risk type, level, and location, clarifying the diffusion trend. This solves the problems of one-sided risk identification and ambiguous location in traditional monitoring, enabling early detection of precursors to hydrogen embrittlement failure and initial leakage signals, achieving early warning of risks, allowing sufficient time for response, effectively preventing safety accidents caused by risk escalation, and improving the safety and reliability of pipeline operation.

[0020] This invention establishes a differentiated, tiered, and coordinated response mechanism and a dynamic optimization system to achieve efficient adaptation and continuous upgrading of risk response. Based on risk type and level, it formulates targeted response plans, initiates corresponding shutdown operations and resource scheduling, ensuring that response measures are scientific and reasonable, avoiding over- or under-response. Utilizing intelligent resource scheduling algorithms, it combines the urgency of risks with the priority allocation of resource status to improve emergency response efficiency and shorten response time. Through a closed-loop feedback dynamic optimization algorithm, it continuously updates response plans and the pipeline health fingerprint benchmark database, achieving closed-loop management of the entire process of monitoring, decision-making, response, and optimization. This adapts to changes in pipeline status and complex environmental impacts during long-term pipeline operation, continuously improving the adaptability and accuracy of risk prevention and control, reducing operation and maintenance costs, and ensuring the stable and safe operation of hydrogen pipelines throughout their entire lifecycle. Attached Figure Description

[0021] Figure 1 This is a flowchart of the intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 1 As shown, a method for intelligent early warning and tiered joint response to hydrogen pipeline leaks includes the following steps: S1: Construct an initial health fingerprint baseline database for the pipeline and deploy a five-dimensional collaborative sensing array along the hydrogen pipeline axis and key nodes; The five-dimensional collaborative sensing array is an integrated structure that combines a hydrogen-sensitive unit, a temperature sensor, a pressure sensor, a vibration sensor, and a MEMS acoustic sensor. The hydrogen-sensitive unit has a response concentration range of 0.1-1000ppm, and the MEMS acoustic sensor supports a conventional frequency of 20-2000Hz and a high-frequency sampling mode of >100kHz. The deployment method of the five-dimensional collaborative sensing array is as follows: integrated probes are arranged along the axial direction of the hydrogen pipeline at intervals of 5-10 meters. The spacing between adjacent probes at critical nodes such as flanges and welds is increased to ≤2 meters. The probes adopt a composite fixing structure of magnetic attraction and bolts. A 2-5mm high-temperature resistant silicone pad is set on the contact surface with the outer wall of the pipeline. The signal transmission adopts a dual backup method of wireless and wired transmission.

[0024] The process of constructing the initial health fingerprint baseline database for pipelines includes: collecting high-frequency vibration acoustic signals in the 3-10kHz frequency band of the pipeline under undamaged and leak-free operating conditions using MEMS acoustic sensors; setting different acquisition times for straight sections of the pipeline and key nodes such as flanges and welds (each 20-meter section of the straight pipeline is set as a sub-region for acquisition, and each sub-region is continuously acquired for 1 hour; each sub-region for flanges and key nodes is set as a separate sub-region for acquisition, and each sub-region is continuously acquired for 3 hours); and recording the internal pressure and medium temperature of the pipeline simultaneously during the acquisition process. The collected signals are subjected to mean filtering to extract the resonance peak frequency, amplitude, phase and waveform characteristic parameters. Combined with the preset acoustic emission energy benchmark value of 10μJ and the critical acoustic emission event rate threshold of 20 times / minute, an initial health fingerprint benchmark database containing standardized characteristic parameters of different pipeline sections and different operating conditions is constructed.

[0025] S2: Use a five-dimensional collaborative sensing array to synchronously collect leakage-related signals and hydrogen embrittlement failure precursor signals, switch the MEMS acoustic sensor in the array to high-frequency sampling mode, preliminarily remove invalid interference signals according to preset rules and record relevant parameters; In the multi-dimensional signal acquisition and screening, the preset rules are as follows: environmental noise signals with a frequency <20Hz or >20000Hz and an amplitude <0.01mV are removed; irrelevant mechanical vibration signals with an amplitude lower than 10% of the normal operating vibration amplitude and a duration <100ms are removed; and isolated hydrogen-sensitive data points with a concentration value <0.1ppm or >1000ppm and no coordinated changes in temperature and pressure signals are removed. The relevant parameters recorded include acquisition time, probe number, pipeline location, ambient wind speed, atmospheric density, ambient temperature, ambient humidity, atmospheric pressure, pipeline internal pressure, and medium temperature.

[0026] S3: An adaptive Kalman filter algorithm is used to purify the hydrogen-sensitive signal, and a multi-layer wavelet packet transform denoising algorithm is used to purify the hydrogen embrittlement-related high-frequency vibration acoustic signal and acoustic emission signal. Simultaneously, the two core characteristic parameters of leakage and hydrogen embrittlement are extracted, and the signal arrival time of adjacent effective probes is recorded. The initial value of the filter gain of the adaptive Kalman filter algorithm is set to 0.05. When the fluctuation amplitude of the hydrogen-sensitive signal is >5% / s, the filter gain of the adaptive Kalman filter algorithm is automatically adjusted to 0.1-0.2. When the fluctuation amplitude is ≤5% / s, the initial value is maintained. The decomposition rules of the multi-layer wavelet packet transform denoising algorithm are as follows: the first layer decomposes the original signal into a low-frequency band of 0-50kHz and a high-frequency band of 50-200kHz; the second layer decomposes the high-frequency band of 50-200kHz into three sub-bands of 50-100kHz, 100-150kHz, and 150-200kHz; and the third layer decomposes only retains the effective signal component of the 100-150kHz sub-band.

[0027] The core characteristic parameters of the leak include hydrogen concentration, duration of concentration exceeding 0.1 ppm, peak amplitude of vibration signal, frequency spectrum distribution of vibration signal, characteristic frequency range of acoustic turbulence, and intensity of hydrogen-sensitive signal. The core characteristic parameters of hydrogen embrittlement include the resonant peak frequency shift, resonant peak amplitude sharpness, acoustic emission signal rise time, acoustic emission signal duration, acoustic emission event rate per unit time, and single acoustic emission signal energy value.

[0028] S4: Combining baseline data, GIS map coordinates and pipeline section stress information, the hydrogen embrittlement risk level is classified by dynamic hydrogen embrittlement risk quantification algorithm, the three-dimensional coordinates of the leak point are determined by hydrogen embrittlement coupled leakage three-dimensional location algorithm, and the comprehensive decision result is output. The mathematical expression for the dynamic hydrogen embrittlement risk quantification algorithm is:

[0029] in, This is a hydrogen embrittlement risk index. This represents the actual drift of the high-frequency resonance peak. The resonant frequency is the benchmark frequency for healthy fingerprints. The acoustic emission event rate per unit time. The threshold for the critical event rate. The average energy of a single acoustic emission signal. As an energy reference value, , , The coupling coefficient is... This is the stress correction factor for the pipeline section. For the current monitoring duration, The time decay constant; when When the calculation result is in the interval [0, 3), it is considered risk-free; when When the calculation result is in the interval [3, 7), it is determined to be microscopic damage; when When the calculation result is in the range [7, 10], it is determined to be the initiation of macroscopic cracks.

[0030] The mathematical expression for the three-dimensional localization algorithm for hydrogen embrittlement coupling leakage is:

[0031] in, The three-dimensional coordinates of the leak point. Number the three adjacent sensor probes. For the first The intensity of the hydrogen-sensitive signal of each probe, For the acoustic signal from the leak point to the... The arrival time of each probe, For the first The amount of resonance peak shift monitored by each probe This is a correction factor for hydrogen embrittlement risk. For the first The three-dimensional coordinates of the probe, where 10 represents the hydrogen embrittlement risk index. The maximum value threshold.

[0032] In dual-risk collaborative decision-making, the prediction of leakage diffusion paths and speeds by combining relevant parameters yields a comprehensive decision result that includes risk type, level, location, and diffusion trend. The specific content of the combined parameters includes the actual temperature of the pipeline medium, real-time pressure inside the pipe, leak orifice diameter, hydrogen purity, real-time ambient wind speed and direction, pipeline laying type, pipeline burial depth, surface roughness coefficient, atmospheric stability level, and information on the distribution of surrounding obstacles. When predicting the leakage diffusion path and speed, the above parameters are input into an improved Gaussian diffusion model. The model adapts to the high diffusion characteristics of hydrogen by modifying the diffusion coefficient and introducing the surface roughness coefficient for adjustment. The diffusion rate is calculated by combining wind direction and obstacle distribution information to correct the diffusion direction deviation. The spatial concentration distribution of leaked gas at different times is calculated with a time step of 1 second, and then the leakage diffusion path and diffusion speed at each stage are fitted. The comprehensive decision output includes: risk type, hydrogen embrittlement risk level, leakage level, three-dimensional coordinates (x, y, z) of the leakage point and positioning accuracy error value, specific direction of diffusion path, diffusion speed value at each time node, leakage impact range within a preset time, risk warning level and boundary coordinates of key control area. All results are stored in association with timestamps and synchronously pushed to the corresponding level monitoring terminal.

[0033] S5: Initiate differentiated and graded handling and shutdown operations based on comprehensive decision-making results, allocate emergency resources through a three-dimensional coupled resource scheduling priority algorithm, regularly update the handling plan using a closed-loop feedback plan dynamic optimization algorithm, and simultaneously update the pipeline health fingerprint benchmark database.

[0034] The mathematical expression for the three-dimensional coupled resource scheduling priority algorithm is:

[0035] in, For resource scheduling priority, , , , 10 represents the weighting coefficient, and 10 represents the hydrogen embrittlement risk index. The maximum value threshold, The straight-line distance from the current location of the resource to the leak point. For the maximum scheduling radius, To address the error in leak location accuracy, To allow for the maximum positioning error, The current load factor of the resource. This is the threshold for resource full load.

[0036] The mathematical expression for the dynamic optimization algorithm of the closed-loop feedback scheme is:

[0037] in, To optimize the contingency plan coefficients, To improve resource scheduling and execution efficiency, As an environmental dynamic correction factor, This refers to the actual ambient wind speed. As the reference wind speed, This refers to the actual atmospheric density in the environment. Based on atmospheric density, This represents the reduction in the risk radius after the treatment. For the theoretical maximum reduction radius, This is a hydrogen embrittlement risk index. This determines the priority of resource scheduling.

[0038] The differentiated and graded handling procedures and the corresponding shutdown operations are initiated based on the risk type, hydrogen embrittlement risk level, and leakage level in the comprehensive decision-making results: When the risk type is hydrogen embrittlement alone: If the hydrogen embrittlement risk level is no risk, maintain normal pipeline operation and monitoring; If the hydrogen embrittlement risk level is microscopic damage, initiate the on-site inspection process, dispatch an inspection team equipped with ultrasonic non-destructive testing equipment to the corresponding pipeline section, conduct microcrack detection on the pipeline surface and inside, and implement local stress relief reinforcement operations. If the hydrogen embrittlement risk level is macroscopic crack initiation, immediately trigger the area shut-off valve of the corresponding pipeline section to isolate the pipeline section within 50 meters of the leak point, and simultaneously dispatch a repair team equipped with pipeline repair equipment to the site. When the risk type is isolated leakage: If the leakage level is Level 1, the local shut-off valve of the corresponding pipeline section is triggered to isolate the pipeline section 10 meters before and after the leakage point. If the leakage level is level two, trigger the area shut-off valve of the corresponding pipeline zone to isolate the pipeline section 50 meters before and after the leakage point. If the leakage level is level three, the main pipeline shut-off valve is triggered to isolate the entire pipeline zone to which the leakage area belongs; When there is a dual risk of hydrogen embrittlement and leakage: If the hydrogen embrittlement risk level is: microscopic damage and the leakage level is Level 1, the on-site inspection process and local shutdown operation should be initiated simultaneously. If the hydrogen embrittlement risk level is: macroscopic crack initiation and leakage level is level two or three, the corresponding shut-off valve should be triggered first, and then the emergency repair team should be dispatched to carry out hydrogen embrittlement crack repair and leakage sealing operations simultaneously. The leakage level is determined based on the hydrogen-sensitive signal concentration and leakage diffusion rate in the comprehensive decision-making results: Level 1 leakage corresponds to a hydrogen-sensitive signal concentration ≥100ppm and a leakage diffusion rate ≤0.5m / s; Level 2 leakage corresponds to a hydrogen-sensitive signal concentration ≤10ppm and a leakage diffusion rate >0.5m / s; Level 3 leakage corresponds to a hydrogen-sensitive signal concentration <10ppm and a duration ≥5 minutes, or a hydrogen-sensitive signal concentration ≥500ppm and no duration limit; the determination of the dual risks of hydrogen embrittlement and leakage is based on the simultaneous existence of the hydrogen embrittlement risk level and the leakage level obtained from the above determination in the comprehensive decision-making results, and then differentiated treatment operations are initiated according to the corresponding level combination.

[0039] Example 1: Implemented as a separate hydrogen embrittlement scenario for the factory's main hydrogen pipeline.

[0040] Inside the chemical plant, a 500-meter-long main hydrogen pipeline connects the hydrogen production unit to the reaction workshop. 80% of the pipeline is straight, with 12 flange joints and 18 weld joints along its route. The daily operating pressure is 1.2 MPa, and the medium temperature is 25-30℃. The mechanical vibration and environmental noise generated by the equipment operation within the plant necessitate real-time monitoring of the pipeline's hydrogen embrittlement risk through an intelligent early warning system to prevent hydrogen-induced damage from escalating and impacting production safety.

[0041] Implementation steps: Benchmark Construction and Sensor Deployment: When constructing the initial health fingerprint benchmark database for the pipeline, a five-dimensional collaborative sensing array integrating hydrogen sensors, temperature sensors, pressure sensors, vibration sensors, and MEMS acoustic sensors is deployed along the pipeline axis. This array can simultaneously collect multi-dimensional signals, providing comprehensive data support for subsequent risk assessment. Integrated probes are deployed at 8-meter intervals along straight pipeline sections, and at critical nodes such as flanges and welds, the spacing between adjacent probes is increased to 1.5 meters. This spacing ensures full coverage of the pipeline without blind spots, avoiding monitoring blind areas at critical nodes. The probes adopt a composite fixing structure of magnetic attraction and bolts, and a 3mm high-temperature resistant silicone pad is placed on the contact surface with the pipeline outer wall to ensure stable contact between the probes and the pipeline under factory vibration environments, reducing signal acquisition deviation. At the same time, signals are transmitted through dual backup methods of wireless and wired transmission to prevent data loss due to failure of a single transmission link. MEMS acoustic sensors were used to collect high-frequency vibration acoustic signals in the 3-10kHz frequency band of pipelines under undamaged and leak-free conditions. For straight sections, a sampling sub-region was set up every 20 meters, with continuous sampling for 1 hour in each sub-region. Separate sampling sub-regions were set up for flanges and weld joints, with continuous sampling for 3 hours in each sub-region. This long-term, targeted sampling captured stable operating signals from different sections and nodes. Pipeline internal pressure and medium temperature were recorded simultaneously during sampling to ensure the baseline data contained complete operating condition information. The collected signals were mean-filtered to extract resonance peak frequency, amplitude, phase, and waveform characteristic parameters. Combined with an acoustic emission energy baseline of 10μJ and a critical acoustic emission event rate threshold of 20 times / minute, a health fingerprint baseline database covering different pipeline sections and operating conditions was constructed. This database can serve as a reference standard for subsequent signal comparisons, ensuring accurate identification of abnormal pipeline conditions.

[0042] Multi-dimensional signal acquisition and screening: A sensor array is used to simultaneously acquire precursory signals of hydrogen embrittlement failure, as well as environmental and pipeline operation-related signals. The MEMS acoustic sensor is switched to high-frequency sampling mode. High-frequency sampling can more accurately capture subtle vibrations and acoustic emission signals caused by hydrogen embrittlement, avoiding the omission of key features by low-frequency sampling. Invalid interference signals are removed according to preset rules. This removal operation reduces the impact of environmental interference and invalid data on subsequent analysis, ensuring that the retained signals are all related to hydrogen embrittlement risk. Simultaneously, relevant parameters such as acquisition time, probe number, pipeline location, ambient wind speed, atmospheric density, ambient temperature, ambient humidity, atmospheric pressure, pipeline internal pressure, and medium temperature are recorded. These parameters provide complete background information for subsequent signal purification, feature extraction, and risk decision-making, improving the accuracy of the analysis results.

[0043] Signal purification and feature extraction: An adaptive Kalman filter algorithm is used to purify the hydrogen-sensitive signal. This algorithm automatically adjusts the filter gain according to the fluctuation amplitude of the hydrogen-sensitive signal, enhancing the filtering effect when the signal fluctuation is severe and maintaining filtering stability when the fluctuation is gentle, effectively filtering out random interference in the hydrogen-sensitive signal and ensuring the authenticity of the hydrogen-sensitive signal. A three-layer wavelet packet transform denoising algorithm is used to purify the high-frequency vibration acoustic signal and acoustic emission signal related to hydrogen embrittlement. The original signal is decomposed layer by layer according to the decomposition rules. This decomposition method can accurately separate the hydrogen embrittlement characteristic signal from noise, highlighting the high-frequency signal characteristics related to hydrogen embrittlement. Core feature parameters of hydrogen embrittlement are extracted simultaneously. These parameters can comprehensively reflect the development degree of hydrogen embrittlement in the pipeline. At the same time, the signal arrival time of adjacent effective probes is recorded to provide time-dimensional data support for subsequent risk location and analysis.

[0044] Dual-risk collaborative decision-making: Combining historical data from the health fingerprint benchmark database, GIS map coordinates, and pipeline section stress information, a dynamic hydrogen embrittlement risk quantification algorithm is used to determine the hydrogen embrittlement risk level. When the calculation result is in the range [0, 3), it is judged as risk-free. This level corresponds to no hydrogen embrittlement-related damage to the pipeline, and the deviation between its structural vibration acoustic characteristics and the healthy fingerprint benchmark parameters is within the normal fluctuation range; when When the calculation results are in the range [3, 7), it is judged as micro-damage. This level corresponds to the pipeline having hydrogen-induced microcracks but not propagating, the high-frequency resonance peak of the structure showing a slight drift, and the acoustic emission event rate per unit time not exceeding the critical threshold; when When the calculated result falls within the range of [7, 10], it is determined to be macroscopic crack initiation. This level corresponds to the hydrogen-induced microcracks in the pipeline having entered the propagation stage. The high-frequency resonance peak of the structure shows a significant drift, and the acoustic emission event rate per unit time exceeds the critical threshold. This algorithm can comprehensively quantify hydrogen embrittlement risk by integrating multi-dimensional data, avoiding the limitations of single-parameter judgment. After analysis, the hydrogen embrittlement risk index falls within the range of [3, 7), which is determined to be microscopic damage. It is clear that hydrogen-induced microcracks have occurred in the pipeline but have not yet propagated. The high-frequency resonance peak of the structure shows a slight drift, and the acoustic emission event rate per unit time does not exceed the critical threshold. At the same time, signal analysis confirms the absence of leakage-related characteristics, and the risk type is determined to be isolated hydrogen embrittlement. This decision result can accurately pinpoint the risk type and level, providing a clear basis for subsequent treatment.

[0045] Tiered Response Closed-Loop Optimization: Based on the decision-making results, the corresponding tiered response process is initiated. An inspection team equipped with ultrasonic non-destructive testing equipment is dispatched to the pipeline section. Ultrasonic non-destructive testing can accurately detect micro-cracks on the pipeline surface and internally, avoiding the omission of potential hazards by manual inspection. Simultaneously, local stress relief and reinforcement operations are implemented to alleviate localized stress concentration in the pipeline and inhibit further propagation of micro-cracks. A three-dimensional coupled resource scheduling priority algorithm is used to allocate inspection resources. This algorithm optimizes the resource scheduling order by combining factors such as risk level and resource location, ensuring that the inspection team quickly reaches the target section. Based on the inspection results and the effectiveness of the response, a closed-loop feedback dynamic optimization algorithm is used to periodically update the response plan.

[0046] In summary, this embodiment addresses the single hydrogen embrittlement scenario of a factory's main hydrogen pipeline, achieving precise risk management through a complete process. First, a comprehensive health fingerprint baseline database is constructed, with dual-backup transmission and encrypted probe deployment ensuring data reliability. Next, interference is eliminated through signal acquisition and filtering, and the signal is purified using adaptive Kalman filtering and three-layer wavelet packet transform to extract hydrogen embrittlement characteristics. Then, a dynamic hydrogen embrittlement risk quantification algorithm is used to determine the level of micro-damage. Finally, a professional inspection team is dispatched to inspect and reinforce the pipeline, and a closed-loop optimization algorithm is used to update the contingency plan and baseline. The entire process focuses on the single risk of hydrogen embrittlement, with each step closely linked, effectively suppressing microcrack propagation and ensuring the stable operation of the main pipeline.

[0047] Example 2: Implementation of a dual-risk scenario involving hydrogen embrittlement and leakage in a factory's hydrogen branch pipeline.

[0048] Inside the new energy plant, a 200-meter-long hydrogen branch pipeline diverts hydrogen from the main pipeline to multiple energy storage devices. The pipeline has 8 flange nodes and 10 weld nodes along its route, operating at a daily internal pressure of 0.8 MPa and a medium temperature of 20-28℃. Auxiliary equipment is distributed around the pipeline, posing some environmental interference. After a recent equipment maintenance, the system detected an abnormal signal, suggesting a potential dual risk of hydrogen embrittlement and leakage. A complete risk warning and handling process is required to ensure the safe hydrogen supply to the energy storage devices.

[0049] Implementation steps: Benchmark Construction and Sensor Deployment: A pipeline health fingerprint benchmark database has been completed in advance. This database provides a reliable reference for this abnormal signal analysis, avoiding risky misjudgments due to a lack of historical data. A five-dimensional collaborative sensor array is deployed along the pipeline axis at 5-meter intervals. Adjacent probes at critical nodes such as flanges and welds are spaced 1.8 meters apart. This close probe spacing allows for rapid location of abnormal signals, reducing positioning errors. The probes employ a composite fixing structure of magnetic attraction and bolts, with a 4mm high-temperature resistant silicone pad on the contact surface with the pipeline outer wall. This adapts to environmental vibrations and temperature changes after factory equipment maintenance, ensuring stable probe contact with the pipeline and preventing signal acquisition from external interference. Signal transmission is achieved through both wireless and wired dual backup methods, ensuring that abnormal signals are transmitted to the analysis terminal in real time without omission. The hydrogen-sensitive unit has a response concentration range of 0.1-1000ppm, accurately capturing hydrogen signals of different concentrations during leaks. The MEMS acoustic sensor supports conventional frequencies of 20-2000Hz and high-frequency sampling modes >100kHz, enabling it to acquire both acoustic turbulence signals generated by leaks and high-frequency vibration signals caused by hydrogen embrittlement, meeting the signal acquisition requirements for dual-risk monitoring. The database, constructed using a baseline acoustic emission energy of 10μJ and a critical acoustic emission event rate threshold of 20 times / minute, includes standardized characteristic parameters for different pipeline sections and operating conditions, providing a clear comparative standard for risk level determination.

[0050] Multi-dimensional signal acquisition and screening: Leakage-related signals and hydrogen embrittlement precursor signals are simultaneously acquired through a sensor array. Simultaneous acquisition allows for monitoring the development of both risks, avoiding fragmented risk information caused by separate acquisition. The MEMS acoustic sensor is switched to high-frequency sampling mode. High-frequency sampling accurately captures subtle acoustic turbulence signals generated by leaks and high-frequency acoustic emission signals caused by hydrogen embrittlement, avoiding the omission of key risk characteristics by low-frequency sampling. Invalid interference signals are eliminated according to preset rules. Specifically, environmental noise signals with frequencies <20Hz or >20000Hz and amplitudes <0.01mV, irrelevant mechanical vibration signals with amplitudes less than 10% of normal operating vibration amplitudes and durations <100ms (such as residual equipment vibration from maintenance), and isolated hydrogen-sensitive data points with concentrations <0.1ppm or >1000ppm and no coordinated changes in temperature or pressure signals are eliminated. This elimination process reduces the impact of environmental interference on dual-risk analysis, ensuring that the retained signals are all related to leakage or hydrogen embrittlement. The recorded parameters include acquisition time, probe number, pipeline location, ambient wind speed, wind direction, atmospheric density, ambient temperature, ambient humidity, atmospheric pressure and pipeline internal pressure, medium temperature, estimated leakage aperture data, and hydrogen purity. These parameters provide complete input information for subsequent signal purification, feature extraction, diffusion prediction, and location analysis. The hydrogen-sensitive signal concentration detection result is 60 ppm, which provides direct data support for subsequent leakage level determination.

[0051] Signal purification and feature extraction: An adaptive Kalman filter algorithm is used to purify the hydrogen-sensitive signal. This algorithm automatically adjusts the filter gain according to the fluctuation amplitude of the hydrogen-sensitive signal. When leakage causes fluctuations in the hydrogen-sensitive signal, it can quickly filter out interfering components, ensuring the accuracy of hydrogen concentration data and laying the foundation for leakage level determination. A three-layer wavelet packet transform denoising algorithm is used to purify the high-frequency vibration acoustic signal and acoustic emission signal related to hydrogen embrittlement. The signal is decomposed layer by layer according to the decomposition rules. This decomposition method can accurately separate the hydrogen embrittlement feature signal from the leakage signal and environmental noise, avoiding mutual interference between different signals and ensuring the accuracy of hydrogen embrittlement feature extraction. Two core characteristic parameters, leakage and hydrogen embrittlement, were extracted simultaneously. The core characteristic parameters for leakage included hydrogen concentration (60 ppm), duration of concentration exceeding 0.1 ppm, peak amplitude of vibration signal, frequency spectrum distribution of vibration signal, characteristic frequency range of acoustic turbulence, and intensity of hydrogen-sensitive signal, comprehensively reflecting the leakage status. The core characteristic parameters for hydrogen embrittlement included resonance peak frequency shift, resonance peak amplitude sharpness, acoustic emission signal rise time, acoustic emission signal duration, acoustic emission event rate per unit time, and energy value of a single acoustic emission signal, fully presenting the degree of hydrogen embrittlement development. At the same time, the arrival time of signals from adjacent effective probes was recorded, providing temporal data for subsequent hydrogen embrittlement coupled leak three-dimensional localization algorithms and improving the accuracy of leak point location.

[0052] Dual-risk collaborative decision-making: Combining health fingerprint benchmark database data, GIS map coordinates, and pipeline section stress information, a dynamic hydrogen embrittlement risk quantification algorithm is used to determine the hydrogen embrittlement risk level. This algorithm integrates hydrogen embrittlement characteristic parameters and pipeline stress state, calculating a hydrogen embrittlement risk index in the range of [7, 10], indicating macroscopic crack initiation. It is clear that hydrogen-induced microcracks in the pipeline have entered the propagation stage, with significant drift of the high-frequency resonance peak and an acoustic emission event rate exceeding the critical threshold per unit time. The leakage level is determined based on the hydrogen-sensitive signal concentration and leakage diffusion rate. The current hydrogen-sensitive signal concentration is 60 ppm (within the range of 10 ppm ≤ concentration < 100 ppm), and the leakage diffusion rate is 0.7 m / s (> 0.5 m / s), indicating a level two leakage. The risk type is comprehensively confirmed as a dual risk of hydrogen embrittlement and leakage. The three-dimensional coordinates of the leakage point are determined by a hydrogen embrittlement coupled leakage three-dimensional localization algorithm. This algorithm combines the arrival time of adjacent probe signals with hydrogen embrittlement risk correction to improve localization accuracy and avoid overlooking the impact of another risk on signal propagation due to single-risk localization. By combining the actual temperature of the pipeline medium, real-time pressure inside the pipe, leakage orifice diameter, hydrogen purity, real-time wind speed and direction, pipeline laying type, pipeline burial depth, surface roughness coefficient, atmospheric stability level, and surrounding obstacle distribution information, these parameters are input into an improved Gaussian diffusion model. The final output includes a comprehensive decision result containing risk type (dual risk), hydrogen embrittlement risk level (macroscopic crack initiation), leakage level (level 2), three-dimensional coordinates (x, y, z) of the leakage point and positioning accuracy error value, specific direction of the diffusion path, diffusion velocity values ​​at each time node, leakage impact range within a preset time, risk warning level, and boundary coordinates of key control areas. All results are stored and timestamped and synchronously pushed to the factory monitoring terminal, providing a comprehensive and accurate basis for subsequent graded disposal.

[0053] Tiered response closed-loop optimization: Based on comprehensive decision-making results, priority is given to triggering the regional shut-off valves of the corresponding pipeline zone, isolating pipeline sections 50 meters before and after the leak point, quickly cutting off the leak source, preventing further diffusion of hydrogen to surrounding energy storage equipment, and reducing the risk of explosion and combustion. Emergency resources are allocated through a three-dimensional coupled resource scheduling priority algorithm. This algorithm calculates resource scheduling priority by combining factors such as the hydrogen embrittlement risk index, the straight-line distance from the current resource location to the leak point, the leak location accuracy error, and the current resource load rate, ensuring that repair teams equipped with pipeline repair equipment can enter the site quickly and efficiently. Repair teams simultaneously carry out hydrogen embrittlement crack repair and leak sealing operations, addressing both risks at the same time, avoiding the development of one risk due to a single approach (e.g., only sealing the leak while ignoring hydrogen embrittlement crack propagation may lead to subsequent pipeline rupture). After the response is completed, the response plan is updated periodically using a closed-loop feedback dynamic optimization algorithm. This algorithm calculates the optimization coefficient of the plan by combining factors such as resource scheduling execution efficiency, environmental dynamic correction factors (such as the difference between actual environmental wind speed and benchmark wind speed, and the difference between actual atmospheric density and benchmark density), and the ratio of the reduction in risk radius after the response to the theoretical maximum reduction radius. The response process is adjusted according to the optimization coefficient (such as adjusting the triggering timing of shut-off valves and the priority weight of resource scheduling). At the same time, the pipeline health fingerprint benchmark database is updated to include the pipeline health status after the response in the benchmark. This enables the system to complete early warning and response more efficiently and accurately when dealing with similar dual risks in the future, thereby improving the long-term operational safety of the pipeline.

[0054] In summary, this embodiment addresses the dual-risk scenario of hydrogen branch pipelines in a factory. Relying on a prior benchmark database, it simultaneously collects and removes interference signals related to leakage and hydrogen embrittlement. Through an adaptive algorithm, it purifies the signals and extracts two types of features. A dynamic hydrogen embrittlement risk quantification algorithm is used to determine macroscopic cracks, and concentration and diffusion rate are combined to determine secondary leaks. Then, a three-dimensional localization method coupled with hydrogen embrittlement and an improved Gaussian model is used to pinpoint the location and diffusion. After priority shutdown and isolation, a three-dimensional coupling algorithm is used to schedule teams for simultaneous repairs. Finally, a closed-loop optimization plan and benchmark are implemented. This efficient and collaborative process handles both risks, quickly controls leaks and cracks, and ensures the safety of hydrogen supply to energy storage equipment.

[0055] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the invention.

Claims

1. A method for intelligent early warning and tiered linkage response to hydrogen pipeline leaks, characterized in that, Includes the following steps: S1: Construct an initial health fingerprint baseline database for the pipeline and deploy a five-dimensional collaborative sensing array along the hydrogen pipeline axis and key nodes; S2: Use a five-dimensional collaborative sensing array to synchronously collect leakage-related signals and hydrogen embrittlement failure precursor signals, switch the MEMS acoustic sensor in the array to high-frequency sampling mode, preliminarily remove invalid interference signals according to preset rules and record relevant parameters; S3: An adaptive Kalman filter algorithm is used to purify the hydrogen-sensitive signal, and a multi-layer wavelet packet transform denoising algorithm is used to purify the hydrogen embrittlement-related high-frequency vibration acoustic signal and acoustic emission signal. Simultaneously, the two core characteristic parameters of leakage and hydrogen embrittlement are extracted, and the signal arrival time of adjacent effective probes is recorded. S4: Combining baseline data, GIS map coordinates and pipeline section stress information, the hydrogen embrittlement risk level is classified by dynamic hydrogen embrittlement risk quantification algorithm, the three-dimensional coordinates of the leak point are determined by hydrogen embrittlement coupled leakage three-dimensional location algorithm, and the comprehensive decision result is output. S5: Initiate differentiated and graded handling and shutdown operations based on comprehensive decision-making results, allocate emergency resources through a three-dimensional coupled resource scheduling priority algorithm, regularly update the handling plan using a closed-loop feedback plan dynamic optimization algorithm, and simultaneously update the pipeline health fingerprint benchmark database.

2. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The five-dimensional collaborative sensing array is an integrated structure that combines a hydrogen-sensitive unit, a temperature sensor, a pressure sensor, a vibration sensor, and a MEMS acoustic sensor. The hydrogen-sensitive unit has a response concentration range of 0.1-1000ppm, and the MEMS acoustic sensor supports a conventional frequency of 20-2000Hz and a high-frequency sampling mode of >100kHz. The deployment method of the five-dimensional collaborative sensing array is as follows: integrated probes are arranged along the axial direction of the hydrogen pipeline at intervals of 5-10 meters. The spacing between adjacent probes at critical nodes such as flanges and welds is increased to ≤2 meters. The probes adopt a composite fixing structure of magnetic attraction and bolts. A 2-5mm high-temperature resistant silicone pad is set on the contact surface with the outer wall of the pipeline. The signal transmission adopts a dual backup method of wireless and wired transmission.

3. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The process of constructing the initial health fingerprint baseline database for pipelines includes: collecting high-frequency vibration acoustic signals in the 3-10kHz frequency band of the pipeline under the condition of no damage and no leakage through MEMS acoustic sensors; setting different acquisition times for the straight sections of the pipeline and key nodes such as flanges and welds; and recording the internal pressure and medium temperature of the pipeline simultaneously during the acquisition process. The collected signals are subjected to mean filtering to extract the resonance peak frequency, amplitude, phase and waveform characteristic parameters. Combined with the preset acoustic emission energy benchmark value and the critical acoustic emission event rate threshold, an initial health fingerprint benchmark database containing standardized characteristic parameters of different pipeline sections and different operating conditions is constructed.

4. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, When the fluctuation amplitude of the hydrogen-sensitive signal is > 5% / s, the filter gain of the adaptive Kalman filter algorithm is automatically adjusted to 0.1-0.2, and the initial value is maintained when the fluctuation amplitude is ≤ 5% / s. The decomposition rules of the multi-layer wavelet packet transform denoising algorithm are as follows: the first layer decomposes the original signal into a low-frequency band of 0-50kHz and a high-frequency band of 50-200kHz; the second layer decomposes the high-frequency band of 50-200kHz into three sub-bands of 50-100kHz, 100-150kHz, and 150-200kHz; and the third layer decomposes only retains the effective signal component of the 100-150kHz sub-band.

5. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The core characteristic parameters of the leak include hydrogen concentration, duration of concentration exceeding 0.1 ppm, peak amplitude of vibration signal, frequency spectrum distribution of vibration signal, characteristic frequency range of acoustic turbulence, and intensity of hydrogen-sensitive signal. The core characteristic parameters of hydrogen embrittlement include the resonant peak frequency shift, resonant peak amplitude sharpness, acoustic emission signal rise time, acoustic emission signal duration, acoustic emission event rate per unit time, and single acoustic emission signal energy value.

6. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The mathematical expression for the dynamic hydrogen embrittlement risk quantification algorithm is: in, This is a hydrogen embrittlement risk index. This represents the actual drift of the high-frequency resonance peak. The resonant frequency is the benchmark frequency for healthy fingerprints. The acoustic emission event rate per unit time. The threshold for the critical event rate. The average energy of a single acoustic emission signal. As an energy reference value, , , The coupling coefficient is... This is the stress correction factor for the pipeline section. For the current monitoring duration, The time decay constant; when When the calculation result is in the interval [0, 3), it is considered risk-free; when When the calculation result is in the interval [3, 7), it is determined to be microscopic damage; when When the calculation result is in the range [7, 10], it is determined to be the initiation of macroscopic cracks.

7. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The mathematical expression for the three-dimensional localization algorithm for hydrogen embrittlement coupling leakage is: in, The three-dimensional coordinates of the leak point. Number the three adjacent sensor probes. For the first The intensity of the hydrogen-sensitive signal of each probe, For the acoustic signal from the leak point to the... The arrival time of each probe, For the first The amount of resonance peak shift monitored by each probe This is a correction factor for hydrogen embrittlement risk. For the first The three-dimensional coordinates of the probe, where 10 represents the hydrogen embrittlement risk index. The maximum value threshold.

8. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The output of the comprehensive decision result specifically refers to: combining relevant parameters to predict the leakage diffusion path and speed, and outputting a comprehensive decision result including risk type, level, location and diffusion trend; When predicting the leakage diffusion path and speed, relevant parameters are input into an improved Gaussian diffusion model. The improved Gaussian diffusion model adapts the diffusion coefficient to the high diffusion characteristics of hydrogen by modifying the diffusion coefficient, introduces the surface roughness coefficient to adjust the horizontal diffusion rate, and combines wind direction and obstacle distribution information to correct the diffusion direction deviation. The spatial concentration distribution of leaked gas at different times is calculated with a time step of 1 second, and then the leakage diffusion path and diffusion speed at each stage are obtained by fitting.

9. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The mathematical expression for the three-dimensional coupled resource scheduling priority algorithm is: in, For resource scheduling priority, , , , 10 represents the weighting coefficient, and 10 represents the hydrogen embrittlement risk index. The maximum value threshold, The straight-line distance from the current location of the resource to the leak point. For the maximum scheduling radius, To address the error in leak location accuracy, To allow for the maximum positioning error, The current load factor of the resource. This is the threshold for resource full load.

10. The intelligent early warning and graded linkage response method for hydrogen pipeline leaks according to claim 1, characterized in that, The mathematical expression for the dynamic optimization algorithm of the closed-loop feedback scheme is: in, To optimize the contingency plan coefficients, To improve resource scheduling and execution efficiency, As an environmental dynamic correction factor, This refers to the actual ambient wind speed. As the reference wind speed, This refers to the actual atmospheric density in the environment. Based on atmospheric density, This represents the reduction in the risk radius after the treatment. For the theoretical maximum reduction radius, This is a hydrogen embrittlement risk index. This determines the priority of resource scheduling.

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