Hydrogen pipeline operation safety digital management system

Abstract

The invention relates to the technical field of hydrogen pipeline safety, and discloses a hydrogen pipeline operation safety digital management system. A pipeline state sensing module of the system is used for acquiring real-time operation parameters of a plurality of monitoring sections of a hydrogen-present pipeline, wherein the real-time operation parameters at least comprise pipe wall vibration frequency, medium flow velocity and temperature gradient; after the dynamic risk assessment module receives the real-time operation parameters, the pipe wall vibration frequency is converted into a stress distribution map through a stress spectrum conversion algorithm, meanwhile, a medium turbulence model is adopted to analyze the correlation coupling effect of the medium flow velocity and the temperature gradient, and pipeline microscopic deformation prediction data is generated; and the subarea response control module receives the microscopic deformation prediction data, automatically divides high-risk pipe section identifiers according to material fatigue thresholds of different monitoring sections, activates a leakage suppression protocol of the corresponding monitoring section according to the high-risk pipe section identifiers, and outputs a pressure regulation instruction to the execution mechanism.

Description

Technical Field

[0001] This invention relates to the field of hydrogen pipeline safety technology, specifically a digital management system for the safe operation of hydrogen pipelines. Background Technology

[0002] Hydrogen-contaminated pipelines are widely used in petrochemical, coal chemical, and other industrial fields, undertaking the task of transporting high-temperature, high-pressure hydrogen-containing media. The operating environment of these pipelines is extremely complex. Under high pressure, hydrogen media can easily permeate the pipe wall, causing hydrogen embrittlement. At the same time, fluctuations in medium flow rate and changes in temperature gradient can exacerbate pipeline vibration and fatigue damage. If they are in an unstable operating state for a long time, they are very likely to cause safety accidents such as leaks and explosions.

[0003] Safety management of hydrogen pipelines relies heavily on periodic manual inspections and offline monitoring technologies. Periodic inspections suffer from long intervals and limited coverage, making it difficult to capture dynamic changes in pipeline operation in real time, such as abnormal fluctuations in the instantaneous vibration frequency of the pipe wall or turbulent impacts caused by sudden changes in medium flow velocity. While offline monitoring can acquire some parameters, the data is highly delayed and cannot reflect the microscopic state of the pipeline under real-time operating conditions, making it difficult to detect potential risks in a timely manner.

[0004] In the risk assessment phase, existing technologies often employ single-parameter analysis methods, such as judging the degree of corrosion solely based on pipe wall thickness measurement or assessing structural stability based solely on changes in medium pressure. This approach ignores the correlation between parameters—there is a direct mapping relationship between pipe wall vibration frequency and stress distribution, and the coupling effect of medium flow velocity and temperature gradient significantly influences pipeline deformation trends. Single-parameter analysis cannot comprehensively reflect the true safety status of the pipeline. Furthermore, traditional risk assessment models are mostly statically set, and threshold standards built based on historical data cannot adapt to dynamic changes in operating conditions, often resulting in false alarms or missed alarms.

[0005] In terms of safety response, existing control systems lack targeted zone management mechanisms. When anomalies are detected, crude measures such as overall pressure reduction or shutdown are often adopted, which not only affect production continuity but may also cause new fluctuations in operating conditions due to excessive intervention. In addition, the activation of leakage suppression protocols relies on manual judgment, resulting in a high response delay and making it difficult to contain the spread of risks in the early stages of micro-deformation, further exacerbating the passivity of safety management. These problems collectively lead to the persistent challenges of insufficient accuracy, lack of timeliness, and weak coordination in the safety management of hydrogen pipelines. Summary of the Invention

[0006] The purpose of this invention is to provide a digital management system for the safe operation of hydrogen pipelines, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a digital management system for the safe operation of hydrogen pipelines, the system comprising: The pipeline status sensing module is configured to acquire real-time operating parameters of multiple monitoring sections of the hydrogen-contaminated pipeline. The real-time operating parameters include at least the pipe wall vibration frequency, medium flow velocity, and temperature gradient. The dynamic risk assessment module is configured to receive real-time operating parameters transmitted by the pipeline state perception module, convert the pipe wall vibration frequency into a stress distribution spectrum through a stress spectrum conversion algorithm, and simultaneously use a medium turbulence model to analyze the correlation and coupling effect between medium flow velocity and temperature gradient to generate pipeline micro-deformation prediction data. The partition response control module is configured to receive pipeline micro-deformation prediction data transmitted by the dynamic risk assessment module, automatically classify high-risk pipe sections according to the material fatigue threshold of different monitoring sections, activate the leakage suppression protocol of the corresponding monitoring section according to the high-risk pipe section identifier, and output pressure regulation command to the actuator according to the leakage suppression protocol.

[0008] Preferably, when acquiring real-time operating parameters of multiple monitoring sections of the hydrogen pipeline, the pipeline status sensing module performs the following operations: deploying a distributed fiber optic sensor array circumferentially on the outer wall of the pipeline to collect axial strain signals and radial thermal expansion signals; performing time-domain noise reduction processing on the axial strain signals to extract vibration characteristic data of specific frequency bands; synchronously acquiring transient flow velocity pulse signals output by Doppler current meters installed inside the pipeline, and combining them with the temperature field matrix acquired by the thermocouple array to generate a set of timestamped operating parameters.

[0009] Preferably, when generating pipeline micro-deformation prediction data, the dynamic risk assessment module performs the following operations: inputting vibration characteristic data into a Fourier phase demodulator to reconstruct a three-dimensional stress wave propagation model of the pipe wall; using a multi-physics coupling engine to analyze the interaction between transient flow velocity pulse signals and temperature field matrices, and calculating the fluid-pipe wall interface energy efficiency loss value; based on the stress wave propagation model and interface energy efficiency loss value, predicting the pipe wall micro-crack propagation path through a material lattice deformation algorithm, and outputting deformation prediction data containing crack probability coordinates.

[0010] Preferably, when classifying high-risk pipe sections, the partition response control module performs the following operations: comparing the crack probability coordinates in the deformation prediction data with the preset pipe failure threshold; when the crack probability of a specific monitoring section exceeds the threshold and continues to reach a preset time threshold, the monitoring section is marked as a Level 1 high-risk pipe section; when the crack probability fluctuates within the threshold range but does not exceed the preset time threshold, it is marked as a Level 2 monitoring pipe section; and mapping the Level 1 high-risk pipe section identifier and the Level 2 monitoring pipe section identifier to the pipeline topology database.

[0011] Preferably, when the leakage suppression protocol is activated, the partition response control module performs the following operations: retrieves the historical pressure fluctuation curve corresponding to the first-level high-risk pipe section identifier, and generates a pressure decay gradient using an adaptive pressure smoothing algorithm; generates a triplet control parameter containing the target pressure value, adjustment rate, and duration based on the pressure decay gradient; encapsulates the triplet control parameter into a pressure adjustment command, and transmits it to the electric regulating valve of the target monitoring section via the industrial bus.

[0012] Preferably, the system further includes a safety margin verification module, configured to receive the actual pressure change curve fed back by the actuator, extract pressure change feature values; calculate the deviation between the pressure change feature values ​​and the target pressure value in the triplet control parameters; and trigger the pipeline status sensing module to re-collect vibration feature data of the monitored section when the deviation continues to exceed the tolerance threshold.

[0013] Preferably, when re-acquiring vibration characteristic data, the pipeline state sensing module performs the following operations: increasing the sampling frequency of the distributed fiber optic sensing array to a specific multiple of the reference frequency; using wavelet packet decomposition technology to separate the pipe wall fundamental frequency vibration from mechanical interference noise; extracting the spectral energy distribution of the pure vibration signal and updating the vibration characteristic data in the original set of operating parameters.

[0014] Preferably, the dynamic risk assessment module also performs the following operations: receiving updated vibration characteristic data, correcting the original stress distribution spectrum through a stress remapping model; using an incremental coupling algorithm to re-match the corrected stress distribution spectrum with the medium turbulence model, and outputting updated pipeline micro-deformation prediction data to the partition response control module.

[0015] Preferably, the partition response control module also performs the following operations: recalculates the high-risk pipe section identifier based on the updated pipeline micro-deformation prediction data; when the crack probability of the original first-level high-risk pipe section drops below the critical value, the leakage suppression protocol of the pipe section is released; when the crack probability of the second-level monitored pipe section rises above the critical value, the pressure regulation command generation process of the pipe section is initiated.

[0016] Preferably, the system further includes a corrosion co-analysis module, configured to receive temperature gradient data transmitted by the pipeline condition sensing module, and combine it with pipe wall dew point parameters collected by an environmental humidity sensor; calculate the pipe wall ion deposition rate through an electrochemical migration model; and when the pipe wall ion deposition rate exceeds the corrosion rate threshold, inject a virtual stress factor into the dynamic risk assessment module, wherein the virtual stress factor is used to correct the crack propagation path in the pipeline micro-deformation prediction data.

[0017] Compared with the prior art, the beneficial effects of the present invention are: The system synchronously collects multi-dimensional real-time operating parameters such as pipe wall vibration frequency, medium flow velocity, and temperature gradient through the pipeline status sensing module. It covers key information on pipeline stress state, medium flow characteristics, and heat transfer process, breaking the limitation of isolated parameter acquisition in traditional monitoring and providing a more comprehensive reflection of the pipeline's real-time operating status. The dynamic risk assessment module incorporates a stress spectrum conversion algorithm to transform the dynamic signal of pipe wall vibration frequency into a quantifiable stress distribution map, intuitively presenting the stress differences at various parts of the pipe wall. Simultaneously, a medium turbulence model is used to analyze the correlation and coupling effect between flow velocity and temperature gradient, revealing the mechanism by which these two factors jointly influence pipe wall damage. This multi-dimensional analysis approach allows for the prediction of pipeline micro-deformation to move beyond relying on single parameters or empirical inferences, instead basing it on a deep analysis of the physical processes, enabling earlier identification of potential damage trends. The zoned response control module automatically identifies high-risk pipe sections based on the material fatigue thresholds of different monitoring segments. This ensures that the identification of risk areas closely matches the material characteristics of the pipe sections themselves, avoiding deviations caused by uniform judgments. Based on the high-risk pipe section identification, the corresponding leakage suppression protocol of the monitoring segment is activated, ensuring that pressure regulation commands only apply to the areas requiring intervention. This reduces the impact on normally operating pipe sections and achieves precise deployment of suppression measures. The coordinated operation of the three modules constructs a complete digital closed loop from parameter acquisition to risk assessment and response control. This closed-loop management model can comprehensively capture subtle changes in pipeline operation, deeply analyze the evolution of potential risks, and implement targeted intervention measures. It is adapted to the operating characteristics of hydrogen pipelines in complex environments, making the safety management process more in line with the needs of actual working conditions. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the working principle of the digital management system for safe operation of hydrogen pipelines as described in this invention; Figure 2 Flowchart for injecting virtual stress factors into the corrosion co-analysis module; Figure 3 A flowchart for defining high-risk pipe segment identifiers for the zone response control module; Figure 4 Design diagram for data updates and feedback. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides a digital management system for the safe operation of hydrogen pipelines, the system comprising: The pipeline status perception module, dynamic risk assessment module, and zone response control module work together to achieve safe management of hydrogen-bearing pipelines.

[0021] The pipeline condition sensing module is configured to acquire real-time operating parameters of multiple monitoring sections of the hydrogen-bearing pipeline. These real-time operating parameters include at least the pipe wall vibration frequency, medium flow velocity, and temperature gradient. The dynamic risk assessment module is configured to receive the real-time operating parameters transmitted by the pipeline condition sensing module, convert the pipe wall vibration frequency into a stress distribution spectrum using a stress spectrum conversion algorithm, and simultaneously analyze the correlation and coupling effect between medium flow velocity and temperature gradient using a medium turbulence model to generate pipeline micro-deformation prediction data. The zone response control module is configured to receive the pipeline micro-deformation prediction data transmitted by the dynamic risk assessment module, automatically identify high-risk pipe sections based on the material fatigue threshold of different monitoring sections, activate the leakage suppression protocol for the corresponding monitoring section based on the high-risk section identification, and output pressure regulation commands to the actuators according to the leakage suppression protocol.

[0022] Example 1: See Figure 2 When the pipeline status sensing module acquires real-time operating parameters of multiple monitoring sections of the hydrogen pipeline, the specific operation is as follows: A distributed fiber optic sensor array is deployed circumferentially on the outer wall of the pipeline. This array adopts a multi-core fiber optic structure, with sensor nodes arranged at fixed intervals along the pipeline axis to form a sensing network covering the entire monitoring area. The fiber optic sensor array can simultaneously acquire axial strain signals and radial thermal expansion signals. The axial strain signal reflects the tensile or compressive state of the pipeline under the action of medium pressure, while the radial thermal expansion signal reflects the radial dimensional changes of the pipeline caused by temperature changes.

[0023] The acquired axial strain signals underwent time-domain noise reduction processing using a sliding window averaging filter method. By setting an appropriate window size, high-frequency noise signals generated by environmental vibration, equipment interference, and other factors were filtered out. After noise reduction processing, vibration characteristic data of a specific frequency band were extracted from the signal. This frequency band was determined based on the common vibration frequency range of hydrogen-bearing pipelines and covers vibration information that may reflect changes in the pipeline structure.

[0024] The system synchronously acquires transient flow velocity pulse signals from Doppler velocimeters installed inside the pipeline. These Doppler velocimeters, by emitting and receiving ultrasonic signals, utilize the Doppler effect to monitor changes in the flow velocity of the medium within the pipeline in real time, and their output pulse signals reflect instantaneous fluctuations in flow velocity. Simultaneously, it combines this data with temperature field matrices collected by a thermocouple array installed inside the pipeline. This thermocouple array, arranged according to a specific spatial distribution, comprehensively captures temperature information from different locations within the pipeline, forming a matrix of data reflecting the pipeline's temperature distribution. The axial strain signal, radial thermal expansion signal, transient flow velocity pulse signal, and temperature field matrix are integrated to generate a time-stamped set of operating parameters, accurate to the millisecond level, ensuring the correspondence of various parameters over time.

[0025] The corrosion co-analysis module in the system is configured to receive temperature gradient data transmitted by the pipeline condition sensing module. The temperature gradient data is calculated from the temperature field matrix and reflects the rate of temperature change between different locations in the pipeline. Simultaneously, the corrosion co-analysis module is also connected to an external environmental humidity sensor. This sensor can collect real-time humidity information of the environment surrounding the pipeline and calculate the pipe wall dew point parameter based on the temperature and humidity data; that is, the temperature at which water vapor begins to condense on the pipe wall surface.

[0026] The ion deposition rate on the pipe wall is calculated using an electrochemical migration model that comprehensively considers factors such as temperature gradient, pipe wall dew point parameter, and the chemical composition of the medium within the pipe. The temperature gradient affects the ion diffusion rate, the dew point parameter reflects the wettability of the pipe wall surface, and corrosive ions in the medium migrate to the pipe wall surface and deposit under the combined influence of temperature and humidity. Based on these influencing factors, the electrochemical migration model simulates the migration and deposition process of ions on the pipe wall surface, thereby calculating the amount of ions deposited on the pipe wall surface per unit time, i.e., the ion deposition rate.

[0027] When the ion deposition rate on the pipe wall exceeds a preset corrosion rate threshold, the corrosion co-analysis module injects a virtual stress factor into the dynamic risk assessment module. The corrosion rate threshold is determined based on the material properties, operating environment, and safety standards of the pipeline, and is a critical value for judging whether the pipeline faces a serious corrosion risk. The magnitude of the virtual stress factor is related to the degree to which the ion deposition rate exceeds the threshold; the greater the exceedance of the threshold, the larger the value of the virtual stress factor. This virtual stress factor is used to correct the crack propagation path in the pipeline micro-deformation prediction data. Because ion deposition on the pipe wall causes changes in the properties of the pipeline material, increasing corrosion damage and thus affecting crack initiation and propagation, the introduction of the virtual stress factor allows the dynamic risk assessment module to more accurately predict crack propagation paths in actual corrosion environments.

[0028] Example 2: See Figure 3When the dynamic risk assessment module generates pipeline micro-deformation prediction data, it performs the following operations: Vibration characteristic data is input into a Fourier phase demodulator. This demodulator performs phase analysis and spectral decomposition on the vibration signal, extracting phase information and frequency components from the signal to reconstruct a three-dimensional stress wave propagation model of the pipe wall. This model can present the propagation paths of stress waves along the axial, radial, and circumferential directions inside the pipe wall, as well as the intensity changes and reflections of the stress waves during propagation, clearly demonstrating the differences in stress distribution at different locations.

[0029] A multiphysics coupling engine is employed to analyze the interaction between transient velocity pulse signals and the temperature field matrix. This engine integrates the analytical principles of fluid dynamics and thermodynamics, simulating how instantaneous changes in medium velocity affect the temperature distribution on the pipe wall, while also considering the reaction of temperature gradients to the medium flow state. Through this bidirectional interactive analysis, the energy efficiency loss value at the fluid-pipe wall interface is calculated. This value reflects the intensity of energy exchange between the medium and the pipe wall during flow; the greater the energy loss, the more significant the impact of medium flow and temperature changes on the pipe wall.

[0030] Based on a stress wave propagation model and interface energy loss values, a material lattice deformation algorithm is used to predict the propagation path of microcracks in pipe walls. This algorithm combines the lattice structure characteristics of the pipe material, transforming stress distribution and energy loss into driving forces that induce lattice dislocation and slip, thereby simulating the location and propagation direction of cracks at the microscopic level. The final output deformation prediction data includes crack probability coordinates, where each coordinate point corresponds to a specific location on the pipe wall, and the probability associated with the coordinate value indicates the likelihood of a crack appearing at that location.

[0031] When the partition response control module identifies high-risk pipe sections, it performs the following operations: it compares the crack probability coordinates in the deformation prediction data with the preset pipe failure threshold. The pipe failure threshold is determined based on the physical properties, manufacturing standards, and long-term operating experience of the materials used in the pipeline, and represents the maximum degree of damage that the pipeline material can withstand.

[0032] When the crack probability of a specific monitored section exceeds a critical value and the duration reaches a preset time threshold, the monitored section is marked as a Level 1 high-risk section. This means that the section has a high risk of rupture and requires immediate emergency measures. When the crack probability fluctuates within the critical value range but does not exceed the preset time threshold, it is marked as a Level 2 monitored section, indicating that the section is currently in a state requiring close monitoring, and monitoring efforts need to be strengthened to prevent further escalation of the risk.

[0033] The identification of high-risk pipeline segments (Level 1) and monitoring segments (Level 2) is mapped to a pipeline topology database. This database stores detailed geographical information, connection relationships, pipe diameter, wall thickness parameters, and other information for each monitoring segment. Through mapping, the identification information of high-risk and monitoring segments corresponds to their actual physical locations within the pipeline. This facilitates rapid location of risk areas by management personnel, allowing them to understand the specific pipeline conditions in those areas and providing accurate location guidance and basic information support for subsequent response measures. During the mapping process, a unique segment code ensures precise matching between the identification and the database record, guaranteeing that each identification accurately corresponds to the actual monitoring segment and preventing confusion or incorrect associations. Simultaneously, the database updates the identification information in real time. When the risk status of a segment changes, the corresponding identification is adjusted accordingly, ensuring that the information in the database remains consistent with the actual operating status of the pipeline.

[0034] Example 3: When the zone response control module activates the leakage suppression protocol, it performs the following operations: retrieves the historical pressure fluctuation curve corresponding to the first-level high-risk pipe section identifier. This curve covers the pressure changes of the pipe section over the past 48 hours, with a data sampling interval of 5 seconds, including information such as pressure peaks, valleys, and fluctuation periods at different time periods. By analyzing the historical pressure fluctuation curve, the pressure change pattern of the pipe section under different operating conditions can be understood, such as the pressure response characteristics under conditions of medium flow adjustment and ambient temperature change.

[0035] An adaptive pressure smoothing algorithm is employed to generate the pressure attenuation gradient. This algorithm statistically analyzes the fluctuation frequency in historical pressure curves to identify common pressure fluctuation cycles and calculates the average and standard deviation of pressure fluctuation amplitudes. Based on these statistical results, the algorithm dynamically adjusts the pressure attenuation rate parameter. When historical fluctuation amplitudes are large, the attenuation rate is reduced to avoid triggering new pressure shocks; when fluctuations are relatively gentle, the attenuation rate can be appropriately increased to accelerate the risk control process. The generation process of the pressure attenuation gradient must comprehensively consider the pressure resistance of the pipeline material to ensure that the stress changes experienced by the pipe wall remain within a safe range during pressure reduction.

[0036] The system generates a ternary control parameter set based on the pressure decay gradient, comprising the target pressure value, regulation rate, and duration. The target pressure value is determined with reference to the design pressure range of the pipe section, typically set to 50%-70% of the normal operating pressure. The specific value needs fine-tuning based on the current pressure level of the pipeline and the degree of material fatigue. The regulation rate is expressed as the absolute value of the pressure change per minute, and its value must ensure a smooth pressure change curve without significant abrupt changes. The duration is calculated by dividing the difference between the target pressure value and the current pressure value by the regulation rate, ensuring that the target pressure value is stably reached within the specified time.

[0037] The ternary control parameters are encapsulated into pressure regulation commands. The command format adopts an industry-standard frame structure, including fields such as command identifier, target pipe section code, parameter value, and check bit. During the encapsulation process, the parameter values ​​need to be range-converted, converting the actual pressure value into a digital signal range that the actuator can recognize. The commands are transmitted to the electric regulating valve in the target monitoring section via an industrial bus. The transmission process uses redundant communication, i.e., commands are sent simultaneously through both primary and backup buses to ensure reliable delivery. After receiving the command, the electric regulating valve gradually adjusts the valve opening according to the regulation rate, providing real-time feedback on the current valve status and outlet pressure value, so that the zone response control module can monitor the regulation effect.

[0038] During pressure regulation, the ternary control parameters can be dynamically corrected according to actual conditions. If the pressure change rate fed back by the electric regulating valve deviates significantly from the preset regulation rate, the zone response control module will recalculate the regulation rate and generate a new pressure regulation command, which will be sent to the actuator via the industrial bus. The corrected regulation rate needs to be re-matched with the pressure decay gradient to ensure that the overall pressure decay process meets the initially set gradient requirements. Simultaneously, the transmission interval of the control command will be adjusted according to the stage of pressure regulation. In the initial stage of regulation and near the target pressure value, the transmission interval is shortened to improve control accuracy, while in the middle stage of regulation, the interval can be appropriately extended to reduce communication load.

[0039] After the pressure regulation command is transmitted to the electric regulating valve, the actuator will act according to the parameters in the command. Changes in the valve opening will affect the flow rate of the medium in the pipeline in real time, thereby changing the pressure level of the pipeline section. Pressure sensors installed before and after the electric regulating valve will continuously collect pressure data and feed it back to the zone response control module via the industrial bus to form a closed-loop control. When the pressure value reaches the target pressure value and stabilizes for a period of time, the zone response control module will send a maintenance command to keep the electric regulating valve at the current opening, ensuring that the pipeline pressure remains stable within the target range.

[0040] The core calculations involved in pressure regulation are:

[0041] in, Indicates the pressure regulation difference. This indicates the current actual pressure value of the pipe section. This indicates the preset target pressure value. This represents the pressure correction factor, which ranges from 0.9 to 1.1 and is set based on the historical adjustment error of the pipeline.

[0042] Example 4: See Figure 4The system includes a safety margin verification module, configured to receive the actual pressure change curve fed back by the actuator. This actual pressure change curve is acquired by a pressure sensor installed on the outlet pipe of the electric regulating valve. This sensor employs a high-precision piezoresistive structure, capable of capturing pressure fluctuations within a minute range. The acquired pressure data is uploaded at fixed time intervals, forming a continuous curve that completely records the entire process from the issuance of the pressure regulation command to pressure stabilization.

[0043] Pressure change characteristic values ​​are extracted from the actual pressure change curve, including the initial rate of increase or decrease after pressure regulation begins, i.e., the amount of pressure change per unit time; the maximum deviation value that occurs during pressure regulation, i.e., the maximum difference between the actual pressure and the target pressure; and the comparison between the final stable pressure value and the target pressure value. These characteristic values ​​reflect the actual effect of pressure regulation from different perspectives, covering aspects such as regulation speed, stability, and accuracy.

[0044] The extracted pressure change feature values ​​are compared with the target pressure value in the ternary control parameters to calculate the deviation. The deviation calculation is based on the difference between the actual pressure curve and the preset ideal pressure curve. By comparing the pressure values ​​at the same time point, the degree of agreement between the two is analyzed. When the deviation continuously exceeds the tolerance threshold for a certain period of time, it indicates that the pressure regulation has not achieved the expected effect. This may be due to reasons such as actuator failure, sudden change in the state of the medium in the pipeline, or unreasonable initial parameter settings. At this time, the pipeline state sensing module is triggered to re-collect the vibration feature data of the monitored section.

[0045] When the pipeline condition sensing module re-acquires vibration characteristic data, it first adjusts the sampling frequency of the distributed fiber optic sensor array. The original reference sampling frequency is increased to a set multiple, allowing more vibration signal data to be acquired within the same time frame. This enables more detailed capture of minute vibration changes in the pipe wall, which may reflect the internal stress state or potential structural changes within the pipeline.

[0046] Wavelet packet decomposition was used to process the acquired vibration signals. This technique can decompose complex vibration signals into multiple different frequency channels, each corresponding to a frequency range. By analyzing the signal energy of each channel, the frequency range belonging to mechanical interference noise was identified. Then, the signals within these frequency ranges were filtered to retain the effective signals related to the pipe wall's own vibration, i.e., the pipe wall's fundamental frequency vibration signal.

[0047] The spectral energy distribution is extracted from the processed clean vibration signal, and the energy proportion of different frequency components in the vibration signal is analyzed. This analysis reveals the main frequency components and their intensity of pipe wall vibration, reflecting the current operating status of the pipeline. The extracted new vibration characteristic data is then used to update the existing set of operating parameters, replacing the previous vibration characteristic data. This allows the subsequent dynamic risk assessment module to perform analysis and prediction based on the latest and most accurate vibration information, ensuring that the assessment results reflect the actual current condition of the pipeline.

[0048] During the re-acquisition and processing of vibration characteristic data, all sensor nodes of the distributed fiber optic sensor array work synchronously to ensure the temporal consistency of the acquired signals. The processed vibration signal data is accompanied by a precise timestamp, keeping it synchronized with other operating parameters over time, enabling accurate correlation between different parameters in subsequent analysis. Simultaneously, data storage employs a cyclic overwrite method, retaining vibration characteristic data from the most recent period, ensuring data timeliness while avoiding excessive storage space consumption. The re-acquisitioned data is rapidly transmitted to the dynamic risk assessment module via the internal data bus, ensuring the entire system can respond promptly to anomalies during pressure regulation and maintain pipeline operational safety.

[0049] Example 5: After receiving updated vibration characteristic data, the dynamic risk assessment module performs the following operations: It corrects the original stress distribution map using a stress remapping model. This model first compares the updated vibration characteristic data with the original data, analyzes the changes in parameters such as vibration frequency and amplitude, and calculates the correction coefficients for the stress distribution. The correction coefficients are determined based on the proportion of change in vibration parameters; the greater the change in vibration characteristic data, the greater the adjustment range of the correction coefficients. These correction coefficients are used to adjust the stress values ​​in the original stress distribution map point by point, so that the adjusted stress distribution map can more accurately reflect the stress situation of the pipeline under the current vibration state. Both the magnitude and distribution range of the stress are more closely aligned with the actual stress state experienced by the pipeline.

[0050] An incremental coupling algorithm is employed to re-match the corrected stress distribution map with the medium turbulence model. This algorithm first identifies regions where changes in stress distribution may affect the medium flow, and recalculates only in these regions, rather than completely reconstructing the entire model. During the calculation, the impact of stress changes on the medium turbulence state is analyzed in conjunction with the corrected stress distribution, including changes in parameters such as medium velocity and pressure distribution. The algorithm also considers the converse effect of changes in the medium turbulence state on the stress distribution. Through this localized, incremental calculation, accurate matching of the stress distribution map and the medium turbulence model is achieved, outputting updated pipeline micro-deformation prediction data, which is then transmitted to the zoned response control module.

[0051] After receiving the updated pipeline micro-deformation prediction data, the zone response control module recalculates the high-risk pipe section identification according to the same judgment criteria and procedures as before. That is, it compares the crack probability coordinates in the new deformation prediction data with the preset pipe failure threshold, and combines the duration of the crack probability exceeding or fluctuating within the threshold value to redetermine the risk level of each monitoring section.

[0052] When the crack probability of a previously high-risk pipeline section drops below the critical value and this state persists for a set duration, the leakage suppression protocol for that section is lifted. Specifically, this involves ceasing the transmission of pressure regulation commands to the actuators of that section and generating a pressure recovery command. This command, based on the actual conditions of the pipeline, sets a reasonable pressure recovery rate and target pressure value, allowing the pipeline pressure to gradually rise back to the normal operating range. During the pressure recovery process, pressure data from the actuators is continuously received to ensure a smooth recovery and prevent further damage to the pipeline caused by sudden pressure increases.

[0053] When the crack probability of a secondary monitoring pipe section rises above a critical value and the duration reaches a preset time threshold, the pressure regulation command generation process for that pipe section is initiated. Following the handling method for primary high-risk pipe sections, the historical pressure fluctuation curve of that section is retrieved, and an adaptive pressure smoothing algorithm is used to generate a pressure attenuation gradient. This generates a triplet control parameter containing the target pressure value, regulation rate, and duration, which is then encapsulated as a pressure regulation command and transmitted to the electric regulating valve of that pipe section. Adjusting the valve opening controls the pipeline pressure to suppress further crack propagation and reduce pipeline operational risks.

[0054] Throughout the process, the pipeline topology database updates the high-risk identification status and related operating parameters of each pipe segment in real time, ensuring that the information in the database is consistent with the actual operating status of the pipeline and providing accurate basic data for various system decisions. Simultaneously, real-time data exchange is maintained between modules. The dynamic risk assessment module continuously receives and analyzes updated operating parameters, while the zone response control module adjusts its control strategy promptly based on the latest assessment results, forming a closed-loop dynamic management process to ensure the safe operation of the hydrogen-bearing pipeline.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital management system for the safe operation of hydrogen pipelines, characterized in that, include: The pipeline status sensing module is configured to acquire real-time operating parameters of multiple monitoring sections of the hydrogen-contaminated pipeline. The real-time operating parameters include at least the pipe wall vibration frequency, medium flow velocity, and temperature gradient. The dynamic risk assessment module is configured to receive real-time operating parameters transmitted by the pipeline state perception module, convert the pipe wall vibration frequency into a stress distribution spectrum through a stress spectrum conversion algorithm, and simultaneously use a medium turbulence model to analyze the correlation and coupling effect between medium flow velocity and temperature gradient to generate pipeline micro-deformation prediction data. The partition response control module is configured to receive pipeline micro-deformation prediction data transmitted by the dynamic risk assessment module, automatically classify high-risk pipe sections according to the material fatigue threshold of different monitoring sections, activate the leakage suppression protocol of the corresponding monitoring section according to the high-risk pipe section identifier, and output pressure regulation command to the actuator according to the leakage suppression protocol.

2. The digital management system for safe operation of hydrogen pipelines according to claim 1, characterized in that, When acquiring real-time operating parameters of multiple monitoring sections of the hydrogen pipeline, the pipeline status sensing module performs the following operations: deploying a distributed fiber optic sensor array circumferentially on the outer wall of the pipeline to collect axial strain signals and radial thermal expansion signals; performing time-domain noise reduction processing on the axial strain signals to extract vibration characteristic data of specific frequency bands; and simultaneously acquiring transient flow velocity pulse signals output by Doppler current meters installed inside the pipeline, and combining them with the temperature field matrix acquired by the thermocouple array to generate a set of timestamped operating parameters.

3. The digital management system for safe operation of hydrogen pipelines according to claim 2, characterized in that, When generating pipeline micro-deformation prediction data, the dynamic risk assessment module performs the following operations: inputting vibration characteristic data into a Fourier phase demodulator to reconstruct the three-dimensional stress wave propagation model of the pipe wall; using a multi-physics coupling engine to analyze the interaction between transient flow velocity pulse signals and temperature field matrices, and calculating the energy efficiency loss value at the fluid-pipe wall interface. Based on the stress wave propagation model and interface energy loss value, the propagation path of microcracks in the pipe wall is predicted by the material lattice deformation algorithm, and the deformation prediction data containing crack probability coordinates is output.

4. The digital management system for safe operation of hydrogen pipelines according to claim 3, characterized in that, When identifying high-risk pipe sections, the partition response control module performs the following operations: compares the crack probability coordinates in the deformation prediction data with the preset pipe failure threshold; when the crack probability of a specific monitoring section exceeds the threshold and continues to reach a preset time threshold, the monitoring section is marked as a Level 1 high-risk pipe section; when the crack probability fluctuates within the threshold range but does not exceed the preset time threshold, it is marked as a Level 2 monitoring pipe section; and maps the Level 1 high-risk pipe section identifier and the Level 2 monitoring pipe section identifier to the pipeline topology database.

5. The digital management system for safe operation of hydrogen pipelines according to claim 4, characterized in that, When the leakage suppression protocol is activated, the partition response control module performs the following operations: retrieves the historical pressure fluctuation curve corresponding to the first-level high-risk pipe section identifier, and generates a pressure decay gradient using an adaptive pressure smoothing algorithm; generates a triplet control parameter containing the target pressure value, adjustment rate, and duration based on the pressure decay gradient; encapsulates the triplet control parameter into a pressure adjustment command, and transmits it to the electric regulating valve of the target monitoring section via the industrial bus.

6. The digital management system for safe operation of hydrogen pipelines according to claim 5, characterized in that, It also includes a safety margin verification module, which is configured to receive the actual pressure change curve fed back by the actuator, extract the pressure change feature value; calculate the deviation between the pressure change feature value and the target pressure value in the triplet control parameters; and trigger the pipeline status sensing module to re-collect the vibration feature data of the monitored section when the deviation continues to exceed the tolerance threshold.

7. The digital management system for safe operation of hydrogen pipelines according to claim 6, characterized in that, When re-acquiring vibration characteristic data, the pipeline state sensing module performs the following operations: increases the sampling frequency of the distributed optical fiber sensor array to a specific multiple of the reference frequency; uses wavelet packet decomposition technology to separate the pipe wall fundamental frequency vibration from mechanical interference noise; extracts the spectral energy distribution of the pure vibration signal, and updates the vibration characteristic data in the original set of operating parameters.

8. The digital management system for safe operation of hydrogen pipelines according to claim 7, characterized in that, The dynamic risk assessment module also performs the following operations: receiving updated vibration characteristic data, correcting the original stress distribution spectrum through a stress remapping model; using an incremental coupling algorithm to re-match the corrected stress distribution spectrum with the medium turbulence model, and outputting updated pipeline micro-deformation prediction data to the partition response control module.

9. The digital management system for safe operation of hydrogen pipelines according to claim 8, characterized in that, The partition response control module also performs the following operations: recalculates the high-risk pipe section identifier based on the updated pipeline micro-deformation prediction data; when the crack probability of the original first-level high-risk pipe section drops below the critical value, the leakage suppression protocol of the pipe section is released; when the crack probability of the second-level monitored pipe section rises above the critical value, the pressure regulation command generation process of the pipe section is initiated.

10. The digital management system for safe operation of hydrogen pipelines according to claim 1, characterized in that, It also includes a corrosion co-analysis module, which is configured to receive temperature gradient data transmitted by the pipeline condition sensing module and combine it with the pipe wall dew point parameters collected by the environmental humidity sensor; calculate the pipe wall ion deposition rate through an electrochemical migration model; and inject a virtual stress factor into the dynamic risk assessment module when the pipe wall ion deposition rate exceeds the corrosion rate threshold. The virtual stress factor is used to correct the crack propagation path in the pipeline micro-deformation prediction data.

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