High-speed train straddle train hydrogen supply hose detection method and system

Abstract

The invention relates to the technical field of rail transit, in particular to a high-speed train straddle train hydrogen supply hose detection method and system, and the method comprises the steps: arranging multi-source sensing units on a hydrogen supply hose body connecting two carriages and joints at the two ends of the hydrogen supply hose body; the preprocessing unit is used for receiving and preprocessing the original signals acquired by the multi-source sensing unit, extracting characteristic parameters capable of representing the health state of the hose, and forming a standardized state data sequence; inputting the state data sequence into a preset hose safety evaluation model for comprehensive analysis, dynamically identifying whether the hydrogen supply hose has an abnormal state at present, and evaluating the risk level of the hydrogen supply hose; and according to the evaluation result of the abnormal state and the risk level, generating corresponding early warning information or a control strategy, and outputting the early warning information or the control strategy to a train monitoring system. Therefore, the maintenance mode is improved to predictive maintenance based on the actual health state from post-maintenance or regular maintenance, and the operation and maintenance efficiency and the safety margin are improved.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, specifically to a method and system for detecting hydrogen supply hoses across high-speed trains. Background Technology

[0002] Currently, in the field of high-pressure gas transportation, especially in stationary hydrogen refueling stations, monitoring the condition of hoses typically relies on basic parameter monitoring such as pressure sensors and flow meters. In rail vehicles, the common practice for monitoring the condition of critical components is to set simple threshold alarms, such as triggering an alarm when pressure or temperature exceeds a certain fixed limit. Furthermore, traditional maintenance methods mainly depend on periodic manual inspections after the train returns to the depot, including visual inspection of the hose exterior for wear and cracks, and conducting airtightness tests.

[0003] The following problems exist in the current monitoring of hydrogen supply hoses: 1. Changes in macroscopic parameters such as pressure and flow rate are often the result of leaks or malfunctions, making it impossible to achieve early warning and preventive maintenance.

[0004] 2. Simple threshold alarms cannot distinguish between changes in operating conditions and actual faults, resulting in a high false alarm rate and an inability to locate the fault point or assess the risk level.

[0005] 3. Under high-speed operation, hoses are subjected to complex high-frequency vibrations, alternating stresses, and dynamic bending. The deterioration of their health is a gradual process. Traditional manual periodic inspections cannot capture the accumulation of microscopic damage in this dynamic process, resulting in serious monitoring blind spots and safety risks. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and system for detecting hydrogen supply hoses across trains in high-speed trains. By constructing a detection system that integrates real-time monitoring, multi-dimensional analysis, accurate evaluation, trend prediction and intelligent decision-making, the passive situation of "unknowable and uncontrollable" hydrogen supply hoses across trains has been completely changed, providing a crucial technical guarantee for the safe and reliable operation of hydrogen power systems in high-speed trains.

[0007] To address the aforementioned technical problems, a first aspect of the present invention discloses a method for detecting hydrogen supply hoses across trains in high-speed trains, the method comprising: Multi-source sensing units are installed on the main body of the hydrogen supply hose connecting the two carriages and its two end connectors to collect multi-dimensional signals related to the physical state of the hose in real time. The system receives and preprocesses the raw signals collected by the multi-source sensing unit, extracts the characteristic parameters that can characterize the health status of the hose, and forms a standardized state data sequence. The state data sequence is input into a preset hose safety assessment model for comprehensive analysis, dynamically identifying whether there is an abnormal state in the hydrogen supply hose and assessing its risk level. Based on the assessment results of the abnormal state and risk level, corresponding early warning information or control strategies are generated and output to the train monitoring system.

[0008] As an optional implementation, in the first aspect of the present invention, the deployment of the multi-source sensing unit includes: Distributed fiber optic acoustic sensors are laid around the joint areas at both ends of the hydrogen supply hose to monitor acoustic vibration signals within a specific frequency range caused by micro-leakage of hydrogen at the joints. Along the axial direction of the hydrogen supply hose, multiple fiber optic grating sensors are spaced apart on the surface of the hose to synchronously measure the multi-directional strain distribution generated by the hose during operation. A patch-type temperature sensor is installed on the surface of a key section of the hydrogen supply hose to monitor changes in the pipe wall temperature, in order to help identify leaks or blockages. A miniature inertial measurement unit is installed near the connection between the hydrogen supply hose and the vehicle compartment to measure the three-dimensional acceleration and angular velocity of the hose end in order to analyze its dynamic motion attitude.

[0009] As an optional implementation, in the first aspect of the present invention, receiving and preprocessing the raw signal acquired by the multi-source sensing unit includes: Frequency domain features are extracted from vibration signals acquired by distributed fiber optic acoustic sensors. The characteristic frequency bands related to hydrogen leakage are retained by a bandpass filter, and their signal energy values ​​are calculated as characteristic parameters. Demodulate and temperature-compensate the wavelength offset signal of the fiber Bragg grating sensor to eliminate the influence of ambient temperature changes on strain measurement values ​​and obtain the true mechanical strain time history curve. The data from the miniature inertial measurement unit are processed by coordinate transformation and integration to obtain the displacement and angle changes at the end of the hose, and the interference components caused by the rigid motion of the train body are eliminated. The data from all sensing units are synchronized and aligned in time, and then encapsulated into a standardized state data sequence with a unified timestamp.

[0010] As an optional implementation, in the first aspect of the present invention, the abnormal state of the dynamic identification of whether the hydrogen supply hose is currently in an abnormal state includes at least leakage abnormality, stress abnormality and deformation abnormality. The method for identifying leakage anomalies is as follows: when the signal energy of the distributed fiber optic acoustic sensor in the characteristic frequency band continuously exceeds the first threshold, and the patch temperature sensor detects an abnormally low temperature at the corresponding location, a leakage anomaly is determined to exist. The method for identifying stress anomalies is to analyze the strain time history curve measured by the fiber Bragg grating sensor. If the dynamic strain amplitude exceeds the second threshold, or the number of strain cycles exceeds the third threshold within a given time, it is determined that there is overstress or fatigue risk. The method for identifying abnormal deformation is to determine whether the displacement and angle of the hose end calculated by the micro inertial measurement unit exceed the safe space envelope. If it does, it is determined that there is a risk of excessive bending or stretching.

[0011] As an optional implementation, in the first aspect of the invention, the method further includes a trend prediction step based on historical data: By using current and historical state data sequences, combined with train operation parameters, a time series prediction algorithm is used to predict the changing trends of key feature parameters over a future period. If the prediction results indicate that the feature parameter will exceed its safety threshold, a forward-looking warning will be generated to prompt the system to take preventative measures.

[0012] As an optional implementation, in the first aspect of the present invention, generating the corresponding early warning information or control strategy includes: The risk level is divided into multiple levels, and different response actions are set for each level; For low-risk levels, status information is only recorded in the maintenance interface; For medium-risk levels, visual prompts are provided on the driver's interface; For high-risk levels, trigger audible and visual alarms and recommend speed limiting or system power downgrading; For emergency risk levels, instructions are automatically sent to the train control system to request the execution of safety protection procedures, including initiating emergency shutdown procedures.

[0013] As an optional implementation, in the first aspect of the present invention, the method further includes: Establish a digital twin model of the hydrogen supply hose, which has the same geometric and physical properties as the physical hose; The real-time monitored status data is mapped onto the digital twin model for visualization, thereby intuitively displaying the stress distribution, temperature field, and risk point locations of the hose.

[0014] As an optional implementation, in the first aspect of the present invention, the method further includes a report generation step: after each driving task is completed, a health assessment report is automatically generated, which includes statistics of various status parameters of the hose during the operation, records of abnormal events, and maintenance recommendations.

[0015] A second aspect of this invention discloses a high-speed train cross-train hydrogen supply hose detection system, used to implement the high-speed train cross-train hydrogen supply hose detection method described in the above embodiments, the system comprising: The sensing module consists of several sensing units deployed on the hydrogen supply hose body and connectors, and is used to collect raw signals. A data processing module, connected to the sensing module, is used to preprocess the raw signal and extract features to generate a state data sequence; The status assessment module, connected to the data processing module, has a built-in hose safety assessment model for identifying abnormal states and assessing risk levels. The decision output module, connected to the status assessment module, is used to generate early warning information or control strategies based on the assessment results and is connected to the train monitoring system.

[0016] As an optional implementation, in a second aspect of the invention, the deployment of the multi-source sensing unit includes: Distributed fiber optic acoustic sensors are laid around the joint areas at both ends of the hydrogen supply hose to monitor acoustic vibration signals within a specific frequency range caused by micro-leakage of hydrogen at the joints. Along the axial direction of the hydrogen supply hose, multiple fiber optic grating sensors are spaced apart on the surface of the hose to synchronously measure the multi-directional strain distribution generated by the hose during operation. A patch-type temperature sensor is installed on the surface of a key section of the hydrogen supply hose to monitor changes in the pipe wall temperature, in order to help identify leaks or blockages. A miniature inertial measurement unit is installed near the connection between the hydrogen supply hose and the vehicle compartment to measure the three-dimensional acceleration and angular velocity of the hose end in order to analyze its dynamic motion attitude.

[0017] As an optional implementation, in a second aspect of the invention, receiving and preprocessing the raw signals acquired by the multi-source sensing unit includes: Frequency domain features are extracted from vibration signals acquired by distributed fiber optic acoustic sensors. The characteristic frequency bands related to hydrogen leakage are retained by a bandpass filter, and their signal energy values ​​are calculated as characteristic parameters. Demodulate and temperature-compensate the wavelength offset signal of the fiber Bragg grating sensor to eliminate the influence of ambient temperature changes on strain measurement values ​​and obtain the true mechanical strain time history curve. The data from the miniature inertial measurement unit are processed by coordinate transformation and integration to obtain the displacement and angle changes at the end of the hose, and the interference components caused by the rigid motion of the train body are eliminated. The data from all sensing units are synchronized and aligned in time, and then encapsulated into a standardized state data sequence with a unified timestamp.

[0018] As an optional implementation, in a second aspect of the invention, A third aspect of this invention discloses another high-speed train cross-vehicle hydrogen supply hose detection system, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a method for detecting hydrogen supply hoses across trains in high-speed trains, as disclosed in the first aspect of this invention.

[0019] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a processor, are used to execute a method for detecting hydrogen supply hoses across trains in high-speed trains disclosed in the first aspect of the present invention.

[0020] The above technical solution provides a method and system for detecting hydrogen supply hoses across trains in high-speed trains, which has the following beneficial effects: 1. By using a multi-source sensor array to monitor multiple physical quantities such as stress, vibration, temperature, and deformation of the hose body in real time, it can accurately capture signs of micro-level deterioration before macro-faults occur. This transforms the maintenance mode from reactive repair or periodic inspection to predictive maintenance based on actual health status, thereby improving operation and maintenance efficiency and safety margin.

[0021] 2. By employing distributed fiber optic acoustic sensors to specifically capture high-frequency leakage acoustic signals and combining them with temperature anomalies for cross-validation, leakage risks can be accurately identified and located at the micro-leakage stage (i.e. when they cannot be detected by the naked eye and traditional pressure monitoring), providing a critical time window for early intervention and prevention of accidents.

[0022] 3. By monitoring the dynamic strain history of the hose in real time through fiber optic grating sensors and using a cumulative damage model for fatigue analysis, the remaining life of the hose material can be quantitatively assessed, and potential fatigue fracture risks can be warned, thus avoiding hydrogen supply interruptions or safety accidents caused by fatigue failure.

[0023] 4. This invention can not only identify a single leakage risk, but also simultaneously assess multiple risk types such as abnormal stress and abnormal deformation, and classify them into levels, providing train operators with a panoramic and quantifiable safety status dashboard to support them in making accurate operational decisions.

[0024] 5. By combining operational data and using algorithms to predict the future trends of key hose parameters, it is possible to anticipate impending risks, thereby supporting the system or driver to take preventative measures in advance and moving the safety assurance checkpoint forward.

[0025] 6. By using digital twin technology to map abstract monitoring data onto a 3D model for visualization, maintenance personnel can intuitively and quickly grasp the overall health status and risk points of the hose, greatly improving the efficiency and accuracy of status monitoring.

[0026] 7. By establishing a multi-level risk early warning mechanism and linking it with the train control system, different levels of response measures, from prompts and alarms to downgraded operation and emergency shutdown, can be automatically triggered according to the risk level, forming a complete "perception-assessment-decision-control" closed loop, which significantly improves the automation and safety level of the entire hydrogen supply system. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of a method for detecting hydrogen supply hoses across trains in a high-speed train, as disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of a high-speed train hydrogen supply hose detection system disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of another high-speed train hydrogen supply hose detection system disclosed in an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0030] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0031] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0032] This invention discloses a method and system for detecting hydrogen supply hoses across trains in high-speed trains. By constructing a detection system that integrates real-time monitoring, multi-dimensional analysis, accurate evaluation, trend prediction, and intelligent decision-making, it completely changes the passive situation of "unknowable and uncontrollable" hydrogen supply hoses across trains, and provides crucial technical support for the safe and reliable operation of hydrogen power systems in high-speed trains. The following is a detailed description of each method. Example 1

[0033] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a method for detecting hydrogen supply hoses across trains in high-speed trains, as disclosed in an embodiment of the present invention. Figure 1 The described method for detecting hydrogen supply hoses across trains in high-speed trains is applied to data processing chips, processing terminals, or processing servers. The processing server can be a local server or a cloud server; this embodiment of the invention does not limit the specific application. Figure 1 As shown, the power feedback control method for driving the working device may include the following operations: 101. Multi-source sensing units are installed on the body of the hydrogen supply hose connecting the two carriages and its two end connectors to collect multi-dimensional signals related to the physical state of the hose in real time.

[0034] Specifically, by integrating sensing units into key components such as the hose body (sensing internal strain and deformation) and the two end connectors (sensing sealing and vibration), a "neural sensing network" covering the entire cross-vehicle connection section has been constructed for the first time. This changes the past mode of only monitoring the pressure / flow at the system endpoints, achieving full-domain, seamless state perception of the entire hose connection system, eliminating monitoring blind spots. The "multi-dimensional signals" collected by this solution are directly related to the essential physical quantities of hose health: such as strain (reflecting mechanical stress), specific frequency sound waves (reflecting microscopic leakage), and temperature field (reflecting leakage and blockage). Compared to only monitoring system-level macroscopic parameters such as pressure and flow, this can capture the early and more direct signs of degradation at the intrinsic mechanism level, such as material fatigue and seal failure, achieving a qualitative change from monitoring "system performance" to diagnosing "component health." The simultaneous collection of different physical quantities of the hose at different locations at the same time provides the possibility for subsequent data fusion analysis. For example, by performing spatiotemporal correlation analysis between vibration signals at the joint and strain signals of the pipe body, it is possible to accurately distinguish between normal vibrations and abnormal impacts of the train, which greatly improves the accuracy and reliability of fault identification and location, and significantly reduces the false alarm rate.

[0035] Furthermore, since it senses the root causes of failures (such as cumulative fatigue) rather than the consequences of failures (such as sudden pressure drops), the system can predict remaining life and risk based on state trends before functional failures occur, thereby enabling predictive maintenance. This significantly advances the timing of safety assurance from "after the fact" to "before the fact," fundamentally improving the level of safety.

[0036] It is evident that this deployment scheme provides the fundamental physical guarantee for the entire detection system to have real-time, online, accurate, and forward-looking capabilities. It transforms the originally "black box" hose into a "transparent" intelligent component that can be deeply sensed, which is a key first step in improving the safety of the entire hydrogen supply system.

[0037] 102. Receive and preprocess the raw signals collected by the multi-source sensing unit, extract the feature parameters that can characterize the health status of the hose, and form a standardized state data sequence.

[0038] Specifically, raw signals from multi-source sensors typically contain significant amounts of noise, environmental interference, and dimensional differences. Preprocessing (such as filtering, noise reduction, temperature compensation, and time synchronization) acts like a professional "data cleaner," transforming the rough "raw materials" into clean, standardized "high-quality data products." This step eliminates invalid information, ensuring the accuracy and reliability of input data for subsequent analysis. It is a fundamental prerequisite for avoiding misjudgments and false alarms; directly processing the raw sensor data stream would generate a huge computational load. By extracting "feature parameters" (such as signal energy in specific frequency bands, strain amplitude, and temperature change rate), data dimensionality reduction and information condensation are achieved. This is equivalent to quickly extracting "key action frames" from lengthy surveillance footage, greatly reducing the computational burden on the core algorithm and enabling real-time, online intelligent analysis.

[0039] This step is clearly an "intelligent bridge" connecting front-end perception and back-end decision-making. It transforms chaotic raw data into a refined, standardized, and meaningful information flow, which not only lays the foundation for the accuracy of the entire system's analytical conclusions but also enables advanced functions such as precise diagnosis and predictive maintenance based on artificial intelligence to be implemented efficiently and reliably.

[0040] 103. Input the state data sequence into the preset hose safety assessment model for comprehensive analysis, dynamically identify whether there is an abnormal state in the hydrogen supply hose, and assess its risk level.

[0041] Specifically, traditional methods rely on manually setting fixed alarm thresholds (e.g., alarming when pressure exceeds X MPa), which cannot cope with complex and ever-changing operating conditions, resulting in high false alarm and missed alarm rates. This solution uses a pre-set hose safety assessment model for comprehensive analysis, providing a far more accurate and reliable diagnosis than a single threshold judgment. "Dynamic identification" means the assessment process is continuous, capturing hose status changes in real time at the second or even millisecond level. This contrasts sharply with the static, lagging mode of traditional periodic inspections or post-event analysis. The system can depict a real-time "dynamic electrocardiogram" of hose health status, immediately capturing and assessing any abnormal signs, gaining valuable time for proactive safety intervention. The model not only binaryly judges "normal / abnormal" but also further assesses the risk level (e.g., low, medium, high, emergency). This provides crucial quantitative risk information, enabling subsequent decision-making and responses to be carried out in a hierarchical and tiered manner, avoiding operational interruptions that might result from a "one-size-fits-all" alarm, and achieving the optimal balance between safety and operational efficiency.

[0042] As can be seen, this step transforms the standardized data collected in the preceding stages into a "safety situation awareness" with clear guiding significance. It is not only the intelligent core of the entire methodology but also a key qualitative leap that elevates the technical solution from the "monitoring" level to the "early warning and proactive safety" level, greatly improving the reliability and intelligence level of the entire hydrogen supply system. 104. Based on the assessment results of the abnormal state and risk level, generate corresponding early warning information or control strategies and output them to the train monitoring system.

[0043] Specifically, generating "corresponding" instructions based on risk levels reflects a precise response strategy. For example, low-risk alerts are simply recorded, while high-risk alerts trigger audible and visual alarms and recommend downgraded operation. This hierarchical, tiered response mechanism avoids operational disruptions caused by a "one-size-fits-all" shutdown, maximizing train operational efficiency while ensuring safety. The system can directly output control strategies (such as speed limits and power reduction requests) to the train's central control system. This establishes a complete link from status awareness to final control, forming an automated safety closed loop. In emergencies, this millisecond-level automatic response speed far surpasses human judgment and operation, gaining crucial time to prevent the situation from escalating. By clearly outputting warning information to the human-machine interface, it provides highly contextualized decision support for drivers and maintenance personnel. The system handles complex data analysis and preliminary decision-making, while humans are responsible for final supervision and handling of complex situations, forming a highly efficient collaboration of "machine-assisted human judgment and human control of the overall situation," reducing the burden on personnel while ensuring ultimate human control in the loop.

[0044] It is evident that this step is a crucial link that gives the entire detection system its "soul" and "limbs." It completes the final closed loop from state perception to safety decision-making and control execution, realizing the automation, intelligence, and precision of safety management, and is the core manifestation of improving the proactive safety capabilities of the hydrogen supply system for high-speed trains.

[0045] As an optional embodiment, the step of deploying the multi-source sensing unit in the above steps includes: Distributed fiber optic acoustic sensors are laid around the joint areas at both ends of the hydrogen supply hose to monitor acoustic vibration signals within a specific frequency range caused by micro-leakage of hydrogen at the joints. Along the axial direction of the hydrogen supply hose, multiple fiber optic grating sensors are spaced apart on the surface of the hose to synchronously measure the multi-directional strain distribution generated by the hose during operation. A patch-type temperature sensor is installed on the surface of a key section of the hydrogen supply hose to monitor changes in the pipe wall temperature, in order to help identify leaks or blockages. A miniature inertial measurement unit is installed near the connection between the hydrogen supply hose and the vehicle compartment to measure the three-dimensional acceleration and angular velocity of the hose end in order to analyze its dynamic motion attitude.

[0046] In this embodiment of the invention, the solution is not simply about piling up sensors, but rather about precise deployment based on a deep understanding of the fault mechanism: Targeted monitoring of the joint area: Distributed fiber optic acoustic sensors are laid around the "joint area" where leakage is most likely to occur, specifically "listening" to the unique high-frequency sound waves of leakage, thus achieving precise targeted monitoring of the most dangerous hidden dangers.

[0047] Full-line stress monitoring of the pipe body: Fiber optic grating sensors are set at intervals along the axial direction, which is like installing "sensory nerves" on the hose. This enables synchronous measurement of stress distribution throughout the pipe body, rather than single-point sampling, and can effectively capture overall deformations such as bending and torsion.

[0048] Key point status verification: Temperature sensors are installed in key pipe sections to provide direct temperature evidence for leakage (throttling cooling) or blockage (frictional heat generation), forming a multi-dimensional cross-verification with vibration and strain signals.

[0049] This solution monitors the direct physical quantities (sound waves, strain, temperature) that cause the failure, rather than indirect parameters at the system level (such as pressure, flow rate). This "mechanism-level" monitoring allows it to detect microscopic anomalies (such as sound waves from micro-leaks, micro-strain accumulation in materials) in the early stages of a failure, before changes in macroscopic parameters occur, thus achieving true early warning and predictive maintenance.

[0050] As can be seen, this embodiment not only demonstrates how to deploy sensors, but also embodies a systematic design concept: by configuring the most suitable sensing units for different fault modes and optimizing their spatial layout, a fully functional, mutually corroborating, and insightful three-dimensional intelligent sensing system is finally constructed, providing a powerful and reliable front-end sensing capability for the safety status assessment of the entire hose. As an optional embodiment, the step of receiving and preprocessing the raw signal collected by the multi-source sensing unit in the above steps includes: Frequency domain features are extracted from vibration signals acquired by distributed fiber optic acoustic sensors. The characteristic frequency bands related to hydrogen leakage are retained by a bandpass filter, and their signal energy values ​​are calculated as characteristic parameters. Demodulate and temperature-compensate the wavelength offset signal of the fiber Bragg grating sensor to eliminate the influence of ambient temperature changes on strain measurement values ​​and obtain the true mechanical strain time history curve. The data from the miniature inertial measurement unit are processed by coordinate transformation and integration to obtain the displacement and angle changes at the end of the hose, and the interference components caused by the rigid motion of the train body are eliminated. The data from all sensing units are synchronized and aligned in time, and then encapsulated into a standardized state data sequence with a unified timestamp.

[0051] In this embodiment of the invention, the preprocessing is not a simple data cleaning process, but rather a targeted information extraction based on the physical meaning of each signal: The original vibration waveform is transformed into a "characteristic frequency band energy value" that represents leakage risk through frequency domain analysis and bandpass filtering.

[0052] The light wavelength signal, which is susceptible to temperature interference, is demodulated and temperature compensated to restore the true "mechanical strain time history curve".

[0053] The raw acceleration / angular velocity data from the IMU are transformed and integrated to calculate the intuitive "displacement and angular changes".

[0054] This step is like translating various "dialects" (raw signals) into standard "Mandarin" (feature parameters), enabling subsequent evaluation models to directly understand and process these features with clear engineering significance.

[0055] The core value of this solution lies in its strong anti-interference capability: Anti-environmental interference: Mechanical vibration noise unrelated to leakage is eliminated by using a bandpass filter; the influence of ambient temperature fluctuations on strain measurement is eliminated by temperature compensation.

[0056] Anti-system interference: Through coordinate transformation and data processing, the interference of the train body's own motion on the relative attitude measurement of the hose is eliminated.

[0057] This allows the extracted feature parameters to accurately and purely reflect the state of the hose itself, greatly reducing the false alarm rate.

[0058] As can be seen, the preprocessing workflow demonstrated in this embodiment is a highly specialized and targeted data refining technology. It successfully transforms multi-source, heterogeneous, and noisy raw signals into accurate, clean, spatiotemporally synchronized, and clearly engineering-significant standardized feature sequences. This provides high-quality, fusionable, and easily processed data fuel for the intelligent diagnostic decision-making of the entire system, and is a core link in ensuring the accuracy and reliability of the final evaluation results.

[0059] As an optional embodiment, in the above steps, the abnormal state of dynamically identifying whether the hydrogen supply hose is currently in an abnormal state includes at least leakage abnormality, stress abnormality and deformation abnormality; The method for identifying leakage anomalies is as follows: when the signal energy of the distributed fiber optic acoustic sensor in the characteristic frequency band continuously exceeds the first threshold, and the patch temperature sensor detects an abnormally low temperature at the corresponding location, a leakage anomaly is determined to exist. The method for identifying stress anomalies is to analyze the strain time history curve measured by the fiber Bragg grating sensor. If the dynamic strain amplitude exceeds the second threshold, or the number of strain cycles exceeds the third threshold within a given time, it is determined that there is overstress or fatigue risk. The method for identifying abnormal deformation is to determine whether the displacement and angle of the hose end calculated by the micro inertial measurement unit exceed the safe space envelope. If it does, it is determined that there is a risk of excessive bending or stretching.

[0060] In this embodiment of the invention, the solution abandons the simplistic logic of triggering an alarm based solely on a single parameter exceeding its limit, and instead employs a multi-sensor joint criterion. For example, diagnosing "leakage anomaly" requires not only abnormal acoustic signals but also corroboration from a temperature sensor at the corresponding location (abnormally low temperature). This cross-validation mechanism effectively distinguishes between vibrations generated during normal train operation and ambient temperature fluctuations, greatly reducing the possibility of false alarms from a single sensor and making the diagnostic results more reliable.

[0061] This diagnostic logic not only focuses on short-term, sudden risks (such as exceeding dynamic strain amplitude limits, indicating instantaneous overload), but also innovatively focuses on long-term, gradual damage (such as exceeding strain cycle limits, assessing fatigue life based on Miner's law). This enables the system to not only warn of acute failures, but also predict chronic aging failure of hoses due to material fatigue, achieving health management of hoses throughout their entire lifecycle from "birth" to "retirement".

[0062] The solution explicitly defines "leakage," "stress," and "deformation" as quantitative judgment criteria based on specific thresholds (first, second, and third thresholds) and mathematical models (safety space envelope). This makes the diagnostic process completely objective, eliminates interference from subjective human factors, and ensures the consistency of diagnostic standards across different times and vehicles, laying the foundation for standardization and automation.

[0063] By cumulatively monitoring the number of strain cycles, the system can issue an early warning (process warning) before the hose reaches its fatigue life limit, indicating the need for replacement. Simultaneously, by monitoring whether the deformation approaches the "safe space envelope," an alarm can be issued before the hose collides with or interferes with surrounding components (pre-emptive warning).

[0064] As can be seen, the diagnostic logic described in this embodiment is the "intelligent" core of the entire detection system. Through methods such as multi-source information fusion, consideration of both long and short cycle risks, and quantitative objective criteria, it achieves a unity of accuracy, foresight, and engineering practicality in fault diagnosis, truly transforming monitoring data into precise insights that have direct guiding value for operational safety.

[0065] As an optional embodiment, the method further includes a trend prediction step based on historical data: By using current and historical state data sequences, combined with train operation parameters, a time series prediction algorithm is used to predict the changing trends of key feature parameters over a future period. If the prediction results indicate that the feature parameter will exceed its safety threshold, a forward-looking warning will be generated to prompt the system to take preventative measures.

[0066] In this embodiment of the invention, the generated "proactive warning" has extremely high operational value. It is no longer an emergency alert, but rather an "action suggestion" with preparation time. For example, the system can prompt, "It is expected that the hose strain will exceed the limit in 15 minutes; it is recommended to reduce the vehicle speed to below 200 km / h before then." This allows maintenance personnel or the autonomous driving system to proactively and smoothly adjust operational strategies (such as speed limits, selecting the next station for inspection), thereby avoiding emergency braking or unplanned shutdowns, mitigating risks before they occur, and significantly improving operational efficiency and passenger experience.

[0067] As an optional embodiment, the step of generating the corresponding early warning information or control strategy in the above steps includes: The risk level is divided into multiple levels, and different response actions are set for each level; For low-risk levels, status information is only recorded in the maintenance interface; For medium-risk levels, visual prompts are provided on the driver's interface; For high-risk levels, trigger audible and visual alarms and recommend speed limiting or system power downgrading; For emergency risk levels, instructions are automatically sent to the train control system to request the execution of safety protection procedures, including initiating emergency shutdown procedures.

[0068] In this embodiment of the invention, the hierarchical strategy clearly delineates the boundary between automated system execution and human judgment and decision-making. Low- to medium-risk levels primarily involve information prompts and suggestions, leaving decision-making power to experienced drivers; while emergency risk levels authorize the system to automatically execute critical safety instructions. This allocation of human and machine functions leverages the advantages of rapid and accurate machine response while retaining the ultimate decision-making power of humans in complex situations, forming an efficient, reliable, and clearly defined human-machine collaborative model that greatly enhances the overall system's intelligence and reliability.

[0069] It is evident that this approach goes beyond simply assessing risk; more importantly, it pre-defines clear and specific response actions for each risk level. This transforms warning messages from confusing alerts into actionable guidelines (such as "recommended speed limit") that drivers or the system can immediately understand and follow. This significantly shortens the decision-making time from risk perception to action, truly putting emergency plans into practice and substantially improving the system's usability and response efficiency.

[0070] As an optional embodiment, the method further includes: Establish a digital twin model of the hydrogen supply hose, which has the same geometric and physical properties as the physical hose; The real-time monitored status data is mapped onto the digital twin model for visualization, thereby intuitively displaying the stress distribution, temperature field, and risk point locations of the hose.

[0071] In this embodiment of the invention, a digital twin model that is completely corresponding to the physical hose is created and real-time monitoring data (such as stress and temperature) is dynamically mapped into it, thereby realizing panoramic, intuitive and visual monitoring of the hose's health status. This transforms the abstract multidimensional data stream into an easy-to-understand three-dimensional dynamic image, greatly reducing the cognitive burden on maintenance personnel, enabling them to quickly and accurately locate risk points, and improving status awareness, decision-making efficiency and the accuracy of maintenance work.

[0072] As an optional embodiment, the method further includes a report generation step: after each driving task is completed, a health assessment report is automatically generated, which includes statistics on various status parameters of the hose during the operation, records of abnormal events, and maintenance recommendations.

[0073] In this embodiment of the invention, the automatic generation of structured health assessment reports throughout the entire lifecycle enables the knowledge accumulation and closed-loop management of monitoring data. This transforms transient real-time data into traceable and analyzable decision-making data, providing not only a complete safety profile and precise maintenance guidance for each trip, but also a long-term accumulated health database for the entire equipment lifecycle, providing core data support for optimizing operational strategies, predictive maintenance, and improving system reliability. Example 2

[0074] Please see Figure 2 , Figure 2 This is a schematic diagram of a high-speed train hydrogen supply hose detection system disclosed in an embodiment of the present invention. Figure 2 The described high-speed train hydrogen supply hose detection system can be applied to data processing chips, processing terminals, or processing servers. The processing server can be a local server or a cloud server; this embodiment of the invention does not limit the application. Figure 2 As shown, the power feedback control device for the driven working equipment may include the following operations: The sensing module 201 consists of several sensing units arranged on the hydrogen supply hose body and connector, and is used to collect raw signals.

[0075] Specifically, by integrating sensing units into key components such as the hose body (sensing internal strain and deformation) and the two end connectors (sensing sealing and vibration), a "neural sensing network" covering the entire cross-vehicle connection section has been constructed for the first time. This changes the past mode of only monitoring the pressure / flow at the system endpoints, achieving full-domain, seamless state perception of the entire hose connection system, eliminating monitoring blind spots. The "multi-dimensional signals" collected by this solution are directly related to the essential physical quantities of hose health: such as strain (reflecting mechanical stress), specific frequency sound waves (reflecting microscopic leakage), and temperature field (reflecting leakage and blockage). Compared to only monitoring system-level macroscopic parameters such as pressure and flow, this can capture the early and more direct signs of degradation at the intrinsic mechanism level, such as material fatigue and seal failure, achieving a qualitative change from monitoring "system performance" to diagnosing "component health." The simultaneous collection of different physical quantities of the hose at different locations at the same time provides the possibility for subsequent data fusion analysis. For example, by performing spatiotemporal correlation analysis between vibration signals at the joint and strain signals of the pipe body, it is possible to accurately distinguish between normal vibrations and abnormal impacts of the train, which greatly improves the accuracy and reliability of fault identification and location, and significantly reduces the false alarm rate.

[0076] Furthermore, since it senses the root causes of failures (such as cumulative fatigue) rather than the consequences of failures (such as sudden pressure drops), the system can predict remaining life and risk based on state trends before functional failures occur, thereby enabling predictive maintenance. This significantly advances the timing of safety assurance from "after the fact" to "before the fact," fundamentally improving the level of safety.

[0077] It is evident that this deployment scheme provides the fundamental physical guarantee for the entire detection system to have real-time, online, accurate, and forward-looking capabilities. It transforms the originally "black box" hose into a "transparent" intelligent component that can be deeply sensed, which is a key first step in improving the safety of the entire hydrogen supply system.

[0078] The data processing module 202, connected to the sensing module, is used to preprocess the original signal and extract features to generate a state data sequence.

[0079] Specifically, raw signals from multi-source sensors typically contain significant amounts of noise, environmental interference, and dimensional differences. Preprocessing (such as filtering, noise reduction, temperature compensation, and time synchronization) acts like a professional "data cleaner," transforming the rough "raw materials" into clean, standardized "high-quality data products." This step eliminates invalid information, ensuring the accuracy and reliability of input data for subsequent analysis. It is a fundamental prerequisite for avoiding misjudgments and false alarms; directly processing the raw sensor data stream would generate a huge computational load. By extracting "feature parameters" (such as signal energy in specific frequency bands, strain amplitude, and temperature change rate), data dimensionality reduction and information condensation are achieved. This is equivalent to quickly extracting "key action frames" from lengthy surveillance footage, greatly reducing the computational burden on the core algorithm and enabling real-time, online intelligent analysis.

[0080] This step is clearly an "intelligent bridge" connecting front-end perception and back-end decision-making. It transforms chaotic raw data into a refined, standardized, and meaningful information flow, which not only lays the foundation for the accuracy of the entire system's analytical conclusions but also enables advanced functions such as precise diagnosis and predictive maintenance based on artificial intelligence to be implemented efficiently and reliably.

[0081] The status assessment module 203 is connected to the data processing module and has a built-in hose safety assessment model for identifying abnormal states and assessing risk levels.

[0082] Specifically, traditional methods rely on manually setting fixed alarm thresholds (e.g., alarming when pressure exceeds X MPa), which cannot cope with complex and ever-changing operating conditions, resulting in high false alarm and missed alarm rates. This solution uses a pre-set hose safety assessment model for comprehensive analysis, providing a far more accurate and reliable diagnosis than a single threshold judgment. "Dynamic identification" means the assessment process is continuous, capturing hose status changes in real time at the second or even millisecond level. This contrasts sharply with the static, lagging mode of traditional periodic inspections or post-event analysis. The system can depict a real-time "dynamic electrocardiogram" of hose health status, immediately capturing and assessing any abnormal signs, gaining valuable time for proactive safety intervention. The model not only binaryly judges "normal / abnormal" but also further assesses the risk level (e.g., low, medium, high, emergency). This provides crucial quantitative risk information, enabling subsequent decision-making and responses to be carried out in a hierarchical and tiered manner, avoiding operational interruptions that might result from a "one-size-fits-all" alarm, and achieving the optimal balance between safety and operational efficiency.

[0083] It is evident that this step transforms the standardized data collected in the preceding stages into a "safety situation awareness" with clear guiding significance. It is not only the intelligent core of the entire methodology but also a key qualitative leap that elevates the technical solution from the "monitoring" level to the "early warning and proactive safety" level, greatly enhancing the reliability and intelligence level of the entire hydrogen supply system.

[0084] The decision output module 204 is connected to the status assessment module and is used to generate early warning information or control strategies based on the assessment results, and is connected to the train monitoring system.

[0085] Specifically, generating "corresponding" instructions based on risk levels reflects a precise response strategy. For example, low-risk alerts are simply recorded, while high-risk alerts trigger audible and visual alarms and recommend downgraded operation. This hierarchical, tiered response mechanism avoids operational disruptions caused by a "one-size-fits-all" shutdown, maximizing train operational efficiency while ensuring safety. The system can directly output control strategies (such as speed limits and power reduction requests) to the train's central control system. This establishes a complete link from status awareness to final control, forming an automated safety closed loop. In emergencies, this millisecond-level automatic response speed far surpasses human judgment and operation, gaining crucial time to prevent the situation from escalating. By clearly outputting warning information to the human-machine interface, it provides highly contextualized decision support for drivers and maintenance personnel. The system handles complex data analysis and preliminary decision-making, while humans are responsible for final supervision and handling of complex situations, forming a highly efficient collaboration of "machine-assisted human judgment and human control of the overall situation," reducing the burden on personnel while ensuring ultimate human control in the loop.

[0086] It is evident that this step is a crucial link that gives the entire detection system its "soul" and "limbs." It completes the final closed loop from state perception to safety decision-making and control execution, realizing the automation, intelligence, and precision of safety management, and is the core manifestation of improving the proactive safety capabilities of the hydrogen supply system for high-speed trains. Example 3

[0087] Please see Figure 3 , Figure 3 This is a schematic diagram of another high-speed train cross-vehicle hydrogen supply hose detection system disclosed in an embodiment of the present invention. Figure 3 As shown, the device may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the method for calculating the longitudinal deformation of a shield tunnel under ground load disclosed in Embodiment 1 of the present invention. Example 4

[0088] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute some or all of the steps in the method for calculating the longitudinal deformation of a shield tunnel under ground load disclosed in Embodiment 1 of this invention. Example 5

[0089] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the method for calculating the longitudinal deformation of a shield tunnel under ground loading as described in Embodiment 1.

[0090] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0091] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0092] Finally, it should be noted that the method and system for detecting hydrogen supply hoses across trains disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting hydrogen supply hoses across trains in high-speed trains, characterized in that, The method includes: Multi-source sensing units are installed on the main body of the hydrogen supply hose connecting the two carriages and its two end connectors to collect multi-dimensional signals related to the physical state of the hose in real time. The system receives and preprocesses the raw signals collected by the multi-source sensing unit, extracts the characteristic parameters that can characterize the health status of the hose, and forms a standardized state data sequence. The state data sequence is input into a preset hose safety assessment model for comprehensive analysis, dynamically identifying whether there is an abnormal state in the hydrogen supply hose and assessing its risk level. Based on the assessment results of the abnormal state and risk level, corresponding early warning information or control strategies are generated and output to the train monitoring system.

2. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 1, characterized in that, The multi-source sensing unit includes: Distributed fiber optic acoustic sensors are laid around the joint areas at both ends of the hydrogen supply hose to monitor acoustic vibration signals within a specific frequency range caused by micro-leakage of hydrogen at the joints. Along the axial direction of the hydrogen supply hose, multiple fiber optic grating sensors are spaced apart on the surface of the hose to synchronously measure the multi-directional strain distribution generated by the hose during operation. A patch-type temperature sensor is installed on the surface of a key section of the hydrogen supply hose to monitor changes in the pipe wall temperature, in order to help identify leaks or blockages. A miniature inertial measurement unit is installed near the connection between the hydrogen supply hose and the vehicle compartment to measure the three-dimensional acceleration and angular velocity of the hose end in order to analyze its dynamic motion attitude.

3. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 2, characterized in that, The process of receiving and preprocessing the raw signals acquired by the multi-source sensing unit includes: Frequency domain features are extracted from vibration signals acquired by distributed fiber optic acoustic sensors. The characteristic frequency bands related to hydrogen leakage are retained by a bandpass filter, and their signal energy values ​​are calculated as characteristic parameters. Demodulate and temperature-compensate the wavelength offset signal of the fiber Bragg grating sensor to eliminate the influence of ambient temperature changes on strain measurement values ​​and obtain the true mechanical strain time history curve. The data from the miniature inertial measurement unit are processed by coordinate transformation and integration to obtain the displacement and angle changes at the end of the hose, and the interference components caused by the rigid motion of the train body are eliminated. The data from all sensing units are synchronized and aligned in time, and then encapsulated into a standardized state data sequence with a unified timestamp.

4. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 3, characterized in that, The abnormal states in the dynamic identification of whether the hydrogen supply hose is currently in an abnormal state include at least leakage abnormality, stress abnormality and deformation abnormality. The method for identifying leakage anomalies is as follows: when the signal energy of the distributed fiber optic acoustic sensor in the characteristic frequency band continuously exceeds the first threshold, and the patch temperature sensor detects an abnormally low temperature at the corresponding location, a leakage anomaly is determined to exist. The method for identifying stress anomalies is to analyze the strain time history curve measured by the fiber Bragg grating sensor. If the dynamic strain amplitude exceeds the second threshold, or the number of strain cycles exceeds the third threshold within a given time, it is determined that there is overstress or fatigue risk. The method for identifying abnormal deformation is to determine whether the displacement and angle of the hose end calculated by the micro inertial measurement unit exceed the safe space envelope. If it does, it is determined that there is a risk of excessive bending or stretching.

5. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 4, characterized in that, The method also includes a trend prediction step based on historical data: By using current and historical state data sequences, combined with train operation parameters, a time series prediction algorithm is used to predict the changing trends of key feature parameters over a future period. If the prediction results indicate that the feature parameter will exceed its safety threshold, a forward-looking warning will be generated to prompt the system to take preventative measures.

6. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 5, characterized in that, The generation of corresponding early warning information or control strategies includes: The risk level is divided into multiple levels, and different response actions are set for each level; For low-risk levels, status information is only recorded in the maintenance interface; For medium-risk levels, visual prompts are provided on the driver's interface; For high-risk levels, trigger audible and visual alarms and recommend speed limiting or system power downgrading; For emergency risk levels, instructions are automatically sent to the train control system to request the execution of safety protection procedures, including initiating emergency shutdown procedures.

7. The method for detecting hydrogen supply hoses across trains in high-speed trains according to claim 6, characterized in that, The method further includes: Establish a digital twin model of the hydrogen supply hose, which has the same geometric and physical properties as the physical hose; The real-time monitored status data is mapped onto the digital twin model for visualization, thereby intuitively displaying the stress distribution, temperature field, and risk point locations of the hose.

8. A method for detecting hydrogen supply hoses across trains in high-speed trains according to any one of claims 1-7, characterized in that, The method also includes a report generation step: after each driving task is completed, a health assessment report is automatically generated, which includes statistics on various status parameters of the hose during the operation, records of abnormal events, and maintenance recommendations.

9. A high-speed train cross-vehicle hydrogen supply hose detection system, used to implement the high-speed train cross-vehicle hydrogen supply hose detection method according to any one of claims 1-8, characterized in that, The system includes: The sensing module consists of several sensing units deployed on the hydrogen supply hose body and connectors, and is used to collect raw signals. A data processing module, connected to the sensing module, is used to preprocess the raw signal and extract features to generate a state data sequence; The status assessment module, connected to the data processing module, has a built-in hose safety assessment model for identifying abnormal states and assessing risk levels. The decision output module, connected to the status assessment module, is used to generate early warning information or control strategies based on the assessment results and is connected to the train monitoring system.

10. A high-speed train cross-vehicle hydrogen supply hose detection system, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a method for detecting hydrogen supply hoses across trains as described in any one of claims 1-7.

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