Multifunctional train safety monitoring system based on fiber bragg grating

Abstract

The invention relates to the technical field of train safety monitoring, and discloses a multifunctional train safety monitoring system based on a fiber bragg grating, comprising a sensing module used for adopting a fiber bragg grating sensor technology; the integration module is used for quickly integrating and summarizing multi-source heterogeneous data from a plurality of sensors; the intelligent preprocessing module is used for carrying out primary processing on the collected original data by applying an algorithm; the deep analysis module is used for data analysis and a machine learning technology; the accurate diagnosis module is used for combining a fault diagnosis algorithm and a knowledge base; and the real-time early warning module is used for early warning according to the diagnosis result. The sensing module is reasonably arranged at key parts of a train by means of the fiber grating sensor technology, physical parameters are accurately collected, the reliability of a data source is guaranteed, the integrated module receives, converts and integrates multi-source heterogeneous data, the data are managed orderly, the integrated module and the integrated module cooperate with each other, the data quality is improved, management and calling are more efficient, and stable operation of the monitoring system is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of train safety monitoring, and in particular to a multifunctional train safety monitoring system based on optical fiber gratings. Background Art

[0002] Fiber Bragg grating is a fiber component that uses periodic refractive index changes and is widely used in fiber optic communications, sensor technology, and other optical fields. The working principle of FBG is to use the periodic structure inside the optical fiber to produce a diffraction effect on light of different wavelengths, so that a certain wavelength of light will be reflected back, while other wavelengths continue to propagate. The reflection wavelength of the fiber Bragg grating is closely related to the refractive index of the optical fiber and the grating period.

[0003] Trains refer to rail vehicles used for land transportation, usually composed of a series of carriages, which can be driven by electricity or other power. In modern train technology, the operation, monitoring and management systems of trains usually involve a large number of sensors and intelligent monitoring technologies. Fiber Bragg gratings can be used to monitor the operation status of trains. Fiber Bragg grating sensors can monitor the structural health of trains, such as real-time stress, temperature and vibration monitoring of train carriages, wheels, tracks, bridges, etc. Through fiber Bragg grating sensors, potential damage or abnormalities can be detected in time, thereby improving the safety of train operation.

[0004] The development of modern railway transportation has increased the requirements for train operation safety monitoring. Fiber Bragg grating sensors are used to collect physical parameters of key parts of trains due to their many advantages. However, there are many sensors and they are multi-source and heterogeneous. The data vary greatly in format and storage. There are problems such as uneven data quality and lack of unified management, which affects the efficiency and effectiveness of subsequent analysis and safety monitoring. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multifunctional train safety monitoring system based on fiber grating, which solves the problems of invalid and abnormal data due to various factors, affecting the accurate judgment of train status, prone to misjudgment of faults, data management problems such as different data formats, scattered storage, and difficult integrated management, which hinder real-time and efficient monitoring and analysis, and cannot effectively support train operation and maintenance.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multifunctional train safety monitoring system based on fiber grating, comprising:

[0007] Perception module, integration module, intelligent pre-processing module, in-depth analysis module, precise diagnosis module, real-time warning module and comprehensive operation and maintenance module;

[0008] The sensing module is used to adopt fiber grating sensor technology and is arranged in detail at key parts of the train to collect multiple key physical parameters;

[0009] The integration module is used to quickly integrate and aggregate multi-source heterogeneous data from numerous sensors, so that the data can be uniformly managed and stored to ensure the integrity and accuracy of the data;

[0010] The intelligent preprocessing module is used to perform preliminary processing on the collected raw data using an algorithm, and the preliminary processing is used to provide a data basis for subsequent analysis and diagnosis;

[0011] The deep analysis module is used for data analysis and machine learning technology to establish a dynamic model of the train operation status, and the dynamic model is used to evaluate the health status and performance trend of the train in real time;

[0012] The accurate diagnosis module is used to combine the fault diagnosis algorithm and knowledge base to accurately diagnose abnormal data and locate the fault source, and to determine the fault type and severity;

[0013] The real-time warning module is used to send graded warning information to train drivers, operation and maintenance personnel and dispatching centers in real time through channels according to the diagnosis results, preset risk levels and thresholds;

[0014] The comprehensive operation and maintenance module is used to perform operation and maintenance management on the monitoring system, and the operation and maintenance management is used to ensure the long-term stable operation of the system.

[0015] Preferably, the perception module includes a sensor selection unit, a position planning unit and a data acquisition unit. The sensor selection unit is responsible for monitoring the types of fiber grating sensors of various parameters. The position planning unit is used to determine the installation positions of sensors at key parts of the train. The data acquisition unit is used to acquire data detected by the sensors in real time. The detailed layout includes wheels, bearings, car connection points, key car body structures and tracks. The physical parameters include temperature, vibration, stress and deformation.

[0016] Preferably, the integrated module includes a data receiving unit, a format conversion unit and a data integration unit. The data receiving unit is used to receive data from sensors, the format conversion unit is used to convert data in different formats into a standard format, and the data integration unit is used to integrate and summarize multi-source heterogeneous data.

[0017] Preferably, the intelligent preprocessing module includes a data cleaning unit, a noise filtering unit and a normalization processing unit. The data cleaning unit is used to remove invalid and abnormal data and perform preliminary processing. The noise filtering unit is used to reduce noise interference in the data. The normalization processing unit is used to normalize the data. The preliminary processing includes cleaning, denoising and normalization processing to eliminate invalid and abnormal data.

[0018] Preferably, the deep analysis module includes a data mining unit, a model building unit and a trend prediction unit. The data mining unit is used to use data mining technology to discover hidden patterns and relationships in the data. The model building unit is used to establish a train operation status analysis model based on machine learning or statistical methods. The trend prediction unit is used to predict the development trend of the train operation status. The data mining technology includes classification algorithms, cluster analysis and sequence pattern mining.

[0019] Preferably, the precise diagnosis module includes a feature extraction unit, a fault matching unit and a severity assessment unit, wherein the feature extraction unit is used to extract key features from abnormal data, the fault matching unit is used to match features with known fault patterns, and the severity assessment unit is used to assess the severity of the fault.

[0020] Preferably, the real-time warning module includes a warning level setting unit, an alarm generating unit and a notification sending unit. The warning level setting unit is used to set the warning levels corresponding to faults of different degrees. The alarm generating unit is used to generate corresponding alarm signals according to the fault conditions. The notification sending unit is used to send the warning information to relevant personnel through channels, including sound and light alarms, SMS notifications and vehicle-mounted display screens.

[0021] Preferably, the comprehensive operation and maintenance module includes a sensor calibration unit, a system upgrade unit and a performance monitoring unit. The sensor calibration unit is used to calibrate and test the sensor. The system upgrade unit is responsible for software and hardware upgrades. The performance monitoring unit is used to monitor the performance indicators of the system in real time. The performance indicators include response time and resource utilization. The operation and maintenance management includes regular calibration and maintenance of sensors, secure backup and recovery of data, and monitoring and evaluation of system performance.

[0022] The present invention provides a multifunctional train safety monitoring system based on fiber grating, which has the following beneficial effects:

[0023] 1. The present invention relies on fiber grating sensor technology through the perception module, which is reasonably arranged at key parts of the train to accurately collect physical parameters and ensure the reliability of the data source. The integrated module receives, converts and integrates multi-source heterogeneous data to manage the data in an orderly manner. The cooperation between the two improves data quality, makes management and calling more efficient, and helps the monitoring system to operate stably.

[0024] 2. The precise diagnosis module of the present invention can accurately locate the source of the fault, clarify the type and severity of the fault, and the real-time warning module can send graded warning information in a timely manner through multiple channels based on the diagnosis results. The two work together to quickly report the fault, allowing relevant personnel to respond in time to ensure the safety of train operation.

[0025] 3. The intelligent preprocessing module of the present invention cleans, filters, normalizes and preprocesses the raw data to provide pure and standardized data for analysis. The deep analysis module mines data relationships, builds dynamic models and predicts trends. The two work together to improve the accuracy of data analysis, gain insight into changes in advance, and assist operation and maintenance personnel in responding. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a system architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0027] Figure 2 This is a perception module architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0028] Figure 3 This is an integrated module architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0029] Figure 4 This is a diagram of the architecture of an intelligent preprocessing module of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0030] Figure 5 This is a diagram of the architecture of a deep analysis module of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0031] Figure 6 This is a precise diagnosis module architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0032] Figure 7 This is a real-time warning module architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention;

[0033] Figure 8 This is a comprehensive operation and maintenance module architecture diagram of a multifunctional train safety monitoring system based on fiber grating of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example:

[0036] Please see attached Figure 1 -Attached Figure 8 The embodiment of the present invention provides a multifunctional train safety monitoring system based on fiber grating, comprising:

[0037] Perception module, integration module, intelligent pre-processing module, in-depth analysis module, precise diagnosis module, real-time warning module and comprehensive operation and maintenance module;

[0038] The sensing module is used to adopt fiber grating sensor technology and is arranged in detail at key parts of the train to collect multiple key physical parameters;

[0039] The integration module is used to quickly integrate and aggregate multi-source heterogeneous data from numerous sensors, so that the data can be managed and stored in a unified manner to ensure the integrity and accuracy of the data;

[0040] The intelligent preprocessing module is used to perform preliminary processing on the collected raw data using algorithms. The preliminary processing is used to provide a data basis for subsequent analysis and diagnosis.

[0041] The deep analysis module uses data analysis and machine learning technology to build a dynamic model of the train's operating status. The dynamic model is used to evaluate the health status and performance trends of the train in real time.

[0042] The precise diagnosis module is used to combine the fault diagnosis algorithm and knowledge base to accurately diagnose abnormal data and locate the fault source, and to determine the fault type and severity;

[0043] The real-time warning module is used to send graded warning information to train drivers, operation and maintenance personnel and dispatching centers in real time through channels according to the diagnosis results, preset risk levels and thresholds;

[0044] The comprehensive operation and maintenance module is used to perform operation and maintenance management on the monitoring system, and the operation and maintenance management is used to ensure the long-term stable operation of the system.

[0045] The perception module includes a sensor selection unit, a position planning unit and a data acquisition unit. The sensor selection unit is responsible for monitoring the types of fiber grating sensors of various parameters. The position planning unit is used to determine the installation positions of sensors at key parts of the train. The data acquisition unit is used to obtain the data detected by the sensors in real time. The detailed layout includes wheels, bearings, car connection points, key car body structures and tracks. The physical parameters include temperature, vibration, stress and deformation.

[0046] Specifically, the sensor selection unit predicts the changing trends of various physical quantities at different stages in advance based on the different operating conditions and operating condition conversion characteristics of the train, so as to specifically consider the adaptability of the fiber grating sensor under different conditions, achieve high-precision coverage of physical parameter monitoring, and enable subtle changes in various parameters to be detected in time, greatly improving the early detection capability of potential problems of the train, helping to take preventive measures in advance, ensuring the safety and stability of train operation, optimizing the integration effect of sensors and the overall train system, reducing the difficulty and cost of system integration, and improving the operating efficiency of the entire monitoring system;

[0047] The location planning unit considers the convenience of daily maintenance and inspection of trains, and reasonably plans the installation location of sensors to ensure that the efficiency of fault monitoring is significantly improved during routine maintenance, component replacement, and troubleshooting. Once an abnormality occurs in a key part, the sensor can immediately feedback accurate information to help maintenance personnel quickly identify the source of the fault, shorten maintenance time, and reduce losses caused by train shutdowns due to faults. This makes it easier for the maintenance team to work efficiently. In the long run, it can reduce maintenance costs, improve the quality and efficiency of the entire train operation and maintenance work, and enable the train to maintain a high operational availability rate;

[0048] The data acquisition unit integrates data preprocessing functions, and performs preliminary noise reduction, filtering, and outlier screening on the data while collecting it, reducing the workload of subsequent data analysis links, providing strong data support for real-time monitoring and dynamic evaluation of trains, and helping to timely grasp the actual operating status of the train.

[0049] The integrated module includes a data receiving unit, a format conversion unit and a data integration unit. The data receiving unit is used to receive data from sensors, the format conversion unit is used to convert data in different formats into a standard format, and the data integration unit is used to integrate and summarize multi-source heterogeneous data.

[0050] Specifically, the data receiving unit builds a stable and highly compatible communication link, which can adapt to the different communication protocols and transmission methods used by various sensors to avoid data loss or reception interruption due to communication differences. It realizes comprehensive and complete reception of sensor data, maximizes the integrity of data acquisition, and provides sufficient and accurate raw materials for subsequent format conversion, integration and other operations, making the starting end of the data chain of the entire monitoring system stable and reliable;

[0051] The format conversion unit deeply analyzes the characteristics of various data formats from different sensors, including differences in data structure, encoding method, storage format, etc., and develops accurate and efficient format conversion algorithms and rules based on pre-set unified standard format requirements to improve the versatility and interoperability of data and facilitate the construction of a unified train status monitoring database;

[0052] The data integration unit classifies and sorts out multi-source heterogeneous data in detail, and reasonably divides them according to dimensions such as sensors at different key parts of the data source, data types such as temperature, vibration and other physical parameter-related data, and time series. On this basis, through integration and summary, operation and maintenance personnel can conveniently view and analyze various aspects of train operation data from a holistic perspective, avoiding the tedious process of switching back and forth to find data in multiple different data sources, thereby improving work efficiency and the comprehensiveness of data analysis.

[0053] The intelligent preprocessing module includes a data cleaning unit, a noise filtering unit and a normalization processing unit. The data cleaning unit is used to remove invalid and abnormal data and perform preliminary processing. The noise filtering unit is used to reduce noise interference in the data. The normalization processing unit is used to normalize the data. The preliminary processing includes cleaning, denoising and normalization processing to eliminate invalid and abnormal data.

[0054] Specifically, the data cleaning unit carefully constructs multi-dimensional data verification rules, meticulously checks the data integrity and rationality, accurately locks invalid and abnormal data, and then uses appropriate strategies to properly handle them according to the causes, ensuring that the remaining data can accurately reflect the status of the train and provide reliable materials for subsequent analysis. The data environment has been purified, and the data quality has been greatly improved after removing "impurities", which greatly reduces analysis deviations, making subsequent noise filtering, normalization and other operations more efficient, helping to accurately control the status of various parts of the train;

[0055] The noise filtering unit uses professional algorithms such as wavelet transform and Kalman filtering to analyze the data spectrum, accurately locate the noise frequency band and amplitude characteristics, customize the filtering plan accordingly, and dynamically optimize according to different train working conditions to effectively reduce noise interference. The processed data becomes clear and can more accurately present the changes in physical parameters of key parts of the train, providing strong support for subsequent fault diagnosis, performance evaluation and other work, ensuring more scientific decision-making for train operation and maintenance;

[0056] The normalization processing unit uses a scientific normalization algorithm to carefully sort out data of different dimensions, convert them into a specific standard range, eliminate dimension differences, and allow all types of data to stand on the same starting line, facilitating subsequent comprehensive comparison and in-depth analysis. The standardization of data specifications greatly facilitates the integration of multi-source data, facilitates the application of various analysis models, effectively improves the overall data processing and analysis efficiency, and better serves the comprehensive monitoring and evaluation of train operation status.

[0057] The deep analysis module includes a data mining unit, a model building unit and a trend prediction unit. The data mining unit is used to use data mining technology to discover hidden patterns and relationships in the data. The model building unit is used to establish a train operation status analysis model based on machine learning or statistical methods. The trend prediction unit is used to predict the development trend of the train operation status. Data mining technology includes classification algorithms, cluster analysis and sequence pattern mining.

[0058] Specifically, the data mining unit uses data mining technologies such as classification algorithms, cluster analysis, and sequence pattern mining to conduct in-depth analysis of massive and complex data and sort out the relationships between data from different angles. It broadens the channels for obtaining train operation information, allowing operation and maintenance personnel to gain insight into the rules hidden behind the data, which helps to discover potential risks in advance. The hidden patterns and relationships mined can serve as an important reference for subsequent model construction, trend forecasting, and other work, making the work of the entire analysis module more targeted and forward-looking, and improving the accuracy of train operation and maintenance;

[0059] The model building unit uses machine learning or statistical methods, based on the historical data of train operation and various features and relationships discovered by the data mining unit, to gradually create tools that can accurately simulate and evaluate the different operating conditions of trains, so that they can adapt to various actual working conditions. It can quantify and present complex train operation conditions through models, quickly locate abnormal points, and also provide a reliable computing basis for trend prediction, enhancing the reliability and intelligence level of train operation and maintenance decision-making;

[0060] The trend prediction unit is based on the established train operation status analysis model and the rich past operation data, fully considers various influencing factors, and strives to make the prediction results fit the actual situation and capture potential change trends in a timely manner by continuously updating data and optimizing prediction algorithms. Provide reference information on train operation trends to operation and maintenance personnel in advance, so that they can plan ahead and formulate corresponding maintenance, scheduling and other response strategies in advance, effectively avoiding or reducing train operation failures caused by emergencies.

[0061] The precise diagnosis module includes a feature extraction unit, a fault matching unit and a severity assessment unit. The feature extraction unit is used to extract key features from abnormal data, the fault matching unit is used to match the features with known fault patterns, and the severity assessment unit is used to assess the severity of the fault.

[0062] Specifically, the feature extraction unit uses professional data processing methods and feature analysis methods to carefully identify the valuable key information in the complex abnormal data, and accurately extract the key features that can represent the nature of the abnormality and reflect the potential causes of the fault. For example, from the abnormal temperature and vibration data changes, unique fluctuation patterns, extreme value conditions and other key elements are extracted, which effectively simplifies the amount of data for subsequent analysis. Focusing on key features makes fault troubleshooting more targeted and avoids getting lost in a large amount of irrelevant data;

[0063] The fault matching unit relies on a rich fault knowledge base to compare and match the extracted key features with the pre-organized known fault modes one by one. Through rigorous algorithms and logical judgments, it quickly locks in the corresponding fault mode and accurately determines the fault type, achieving rapid and accurate fault location and reducing the blindness and time cost of manual troubleshooting. It allows operation and maintenance personnel to quickly know the root cause of the fault and take corresponding repair measures in a timely manner, thereby minimizing the impact of the fault on the train operation and ensuring that the train returns to normal operation as soon as possible;

[0064] The severity assessment unit comprehensively considers multiple relevant factors of the fault, such as the frequency of the fault, the scope of impact on key parts of the train, possible chain reactions, etc., and uses scientific and reasonable assessment models and quantitative standards to objectively and accurately judge the severity level of the fault, providing a reliable basis for subsequent maintenance priority setting and resource allocation. It helps the operation and maintenance team to reasonably allocate resources and arrange maintenance sequence, give priority to high-severity faults, and avoid further deterioration of faults and more serious consequences. At the same time, it also provides a key reference for the dynamic adjustment of the overall train operation and maintenance plan to ensure the safety and stability of train operation.

[0065] The real-time warning module includes a warning level setting unit, an alarm generating unit and a notification sending unit. The warning level setting unit is used to set the warning level corresponding to faults of different degrees. The alarm generating unit is used to generate corresponding alarm signals according to the fault conditions. The notification sending unit is used to send the warning information to relevant personnel through channels, including sound and light alarms, SMS notifications and vehicle-mounted display screens.

[0066] Specifically, the warning level setting unit deeply analyzes the different degrees of impact of various faults on train operation safety and efficiency, and scientifically and rationally divides different levels of warning levels according to factors such as the severity of the fault, the speed of development, and the size of the possible consequences, laying the foundation for subsequent accurate alarm prompts and information transmission; allowing relevant personnel to intuitively and quickly judge the severity of the fault through the warning level, avoiding unnecessary panic or insufficient attention to the fault due to warning confusion, and ensuring that the train operation and maintenance work can deal with various fault conditions in an orderly manner;

[0067] The alarm generation unit uses professional signal generation technology to generate alarm signals with corresponding characteristics based on the set warning level and the specific circumstances of the actual fault. These signals have clear identification, and different levels of alarms will have significant differences in the tone and frequency of the sound, the color of the light signal, the flashing frequency, etc., which is convenient for relevant personnel to quickly identify the meaning of the alarm in a complex environment. When a fault occurs, it can quickly attract the attention of relevant personnel, so that they will know the abnormal situation at the first time and can roughly judge the severity of the fault through the alarm signal, so as to buy precious time for timely response and taking corresponding measures, effectively improve the timeliness of responding to faults, and reduce the losses and impacts that may be caused by faults;

[0068] The notification sending unit integrates the advantages of multiple information transmission channels such as sound and light alarms, SMS notifications, and on-board display screens. According to the preset rules and processes, the warning information containing key information such as warning level and brief fault situation is accurately sent to the train driver, operation and maintenance personnel and other relevant responsible parties through the corresponding channels. It achieves all-round and no-dead-angle coverage of warning information, avoiding the omission of important warning information due to single channel failure or personnel's failure to pay attention in time. Multi-channel notification can ensure that relevant personnel can obtain information and respond in time to the greatest extent, enhance the collaborative ability of the entire train operation system to deal with sudden failures, and ensure the safety and stability of train operation.

[0069] The comprehensive operation and maintenance module includes a sensor calibration unit, a system upgrade unit and a performance monitoring unit. The sensor calibration unit is used to calibrate and test the sensor. The system upgrade unit is responsible for software and hardware upgrades. The performance monitoring unit is used to monitor the system's performance indicators in real time. The performance indicators include response time and resource utilization. Operation and maintenance management includes regular calibration and maintenance of sensors, secure backup and recovery of data, and monitoring and evaluation of system performance.

[0070] The specific sensor calibration unit carefully calibrates the sensors at key parts of the train, corrects possible measurement deviations, and comprehensively detects whether the functions of the sensors are normal and whether the performance is up to standard, ensuring that the sensors can continuously and accurately obtain physical parameter data related to train operation. It effectively improves the accuracy of sensor measurement data, reduces erroneous monitoring results caused by the sensor's own errors, and provides high-quality data support for subsequent train status analysis, fault diagnosis and other work. Regular calibration and testing can extend the service life of sensors, reduce operation and maintenance problems caused by sensor failures, and ensure the stable operation of the train monitoring system;

[0071] The system upgrade unit pays close attention to the development of new technologies in the software field and the room for performance improvement in hardware, and carefully plans software and hardware upgrade plans based on the actual needs of train operation and maintenance. For software, update functional modules, optimize algorithms to improve data processing and analysis capabilities, etc.; for hardware, replace components with better performance to improve the overall operating efficiency of the system and ensure that the system always adapts to the increasingly complex operation and maintenance requirements of the train. Software upgrades can bring a smarter and more efficient data processing and analysis experience, and improve the scientific nature of operation and maintenance decisions; hardware upgrades can enhance the stability and response speed of the system, enabling it to better cope with massive data and complex working conditions. Overall, it improves the competitiveness of the system and helps train operation and maintenance work to be carried out more smoothly and efficiently;

[0072] The performance monitoring unit tracks the performance indicators of the system in real time and continuously, focusing on the response time to ensure that the system can respond quickly when receiving data requests or fault warnings; accurately count resource utilization, reasonably allocate computing resources, storage resources, etc., to avoid resource waste or bottlenecks. At the same time, the system performance status is judged according to the set threshold to detect potential problems in a timely manner. It allows operation and maintenance personnel to grasp the system operation status at the first time, detect abnormal trends such as performance degradation in advance, so as to take optimization measures in time to avoid affecting the normal monitoring and operation and maintenance of the train. Provide data basis for the continuous optimization of the system, ensure that the entire operation and maintenance system always maintains a good performance state, and efficiently serve the train operation.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multifunctional train safety monitoring system based on fiber grating, characterized in that: include: Perception module, integration module, intelligent pre-processing module, in-depth analysis module, precise diagnosis module, real-time warning module and comprehensive operation and maintenance module; The sensing module is used to adopt fiber grating sensor technology and is arranged in detail at key parts of the train to collect multiple key physical parameters; The integration module is used to quickly integrate and aggregate multi-source heterogeneous data from numerous sensors, so that the data can be uniformly managed and stored to ensure the integrity and accuracy of the data; The intelligent preprocessing module is used to perform preliminary processing on the collected raw data using an algorithm, and the preliminary processing is used to provide a data basis for subsequent analysis and diagnosis; The deep analysis module is used for data analysis and machine learning technology to establish a dynamic model of the train operation status, and the dynamic model is used to evaluate the health status and performance trend of the train in real time; The accurate diagnosis module is used to combine the fault diagnosis algorithm and knowledge base to accurately diagnose abnormal data and locate the fault source, and to determine the fault type and severity; The real-time warning module is used to send graded warning information to train drivers, operation and maintenance personnel and dispatching centers in real time through channels according to the diagnosis results, preset risk levels and thresholds; The comprehensive operation and maintenance module is used to perform operation and maintenance management on the monitoring system, and the operation and maintenance management is used to ensure the long-term stable operation of the system.

2. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The perception module includes a sensor selection unit, a position planning unit and a data acquisition unit. The sensor selection unit is responsible for monitoring the type of fiber grating sensors of various parameters. The position planning unit is used to determine the installation position of the sensor at the key parts of the train. The data acquisition unit is used to obtain the data detected by the sensor in real time. The detailed layout includes wheels, bearings, car connection points, key car body structures and tracks. The physical parameters include temperature, vibration, stress and deformation.

3. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The integrated module includes a data receiving unit, a format conversion unit and a data integration unit. The data receiving unit is used to receive data from sensors, the format conversion unit is used to convert data in different formats into a standard format, and the data integration unit is used to integrate and summarize multi-source heterogeneous data.

4. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The intelligent preprocessing module includes a data cleaning unit, a noise filtering unit and a normalization processing unit. The data cleaning unit is used to remove invalid and abnormal data and perform preliminary processing. The noise filtering unit is used to reduce noise interference in the data. The normalization processing unit is used to normalize the data. The preliminary processing includes cleaning, denoising and normalization processing to eliminate invalid and abnormal data.

5. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The deep analysis module includes a data mining unit, a model building unit and a trend prediction unit. The data mining unit is used to use data mining technology to discover hidden patterns and relationships in the data. The model building unit is used to establish a train operation status analysis model based on machine learning or statistical methods. The trend prediction unit is used to predict the development trend of the train operation status. The data mining technology includes classification algorithms, cluster analysis and sequence pattern mining.

6. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The precise diagnosis module includes a feature extraction unit, a fault matching unit and a severity assessment unit. The feature extraction unit is used to extract key features from abnormal data, the fault matching unit is used to match features with known fault patterns, and the severity assessment unit is used to assess the severity of the fault.

7. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The real-time warning module includes a warning level setting unit, an alarm generating unit and a notification sending unit. The warning level setting unit is used to set the warning levels corresponding to faults of different degrees. The alarm generating unit is used to generate corresponding alarm signals according to the fault conditions. The notification sending unit is used to send the warning information to relevant personnel through channels, including sound and light alarms, SMS notifications and vehicle-mounted display screens.

8. The multifunctional train safety monitoring system based on fiber grating according to claim 1 is characterized in that: The comprehensive operation and maintenance module includes a sensor calibration unit, a system upgrade unit and a performance monitoring unit. The sensor calibration unit is used to calibrate and test the sensor. The system upgrade unit is responsible for the software and hardware upgrade work. The performance monitoring unit is used to monitor the performance indicators of the system in real time. The performance indicators include response time and resource utilization. The operation and maintenance management includes regular calibration and maintenance of sensors, secure backup and recovery of data, and monitoring and evaluation of system performance.

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