Rail transportation monitoring method and system

Abstract

The invention relates to the technical field of rail transportation monitoring, in particular to a rail transportation monitoring method and system, and the method comprises the following steps: S1, system initialization equipment configuration management; s2, multi-source data acquisition and real-time processing; s3, monitoring an intelligent early warning mechanism in real time; and S4, carrying out system maintenance performance optimization management. Hardware equipment such as a sensor and a camera are accurately calibrated, a redundant network communication protocol is configured, the stability and safety of data transmission are ensured, a multi-source data fusion technology and a machine learning algorithm are utilized, running data of the rail transportation system are collected, processed and analyzed in real time, abnormal conditions are detected in time, early warning is given out, and the safety of the rail transportation system is improved. The operation state of the system is displayed in real time through a visualization technology, sound-light alarm and short message notification are triggered, and an emergency plan is started according to the abnormal severity.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transportation monitoring, and in particular to a rail transportation monitoring method and system. Background Art

[0002] Rail transportation monitoring systems are a core component of modern rail transit systems and are widely used in scenarios such as high-speed rail trunk line scheduling, urban subway operations, and freight line management. Existing technologies primarily utilize distributed sensor networks combined with industrial-grade vision systems, collecting operational data through a ZigBee / 4G hybrid network, building a monitoring data warehouse based on a time-series database, and applying pattern recognition algorithms for abnormal condition detection. The current system has achieved technical indicators such as train positioning accuracy of ±5 meters and axle temperature monitoring response time of 200ms, and has developed standardized solutions for contact network condition monitoring and wheel-rail force anomaly warning. With the increasing density of rail transit networks and the increase in train operating speeds, the industry has placed higher demands on the monitoring system's data fusion capabilities, warning response speed, and system adaptive maintenance.

[0003] However, existing systems suffer from poor equipment coordination. The lack of a unified calibration standard for multi-brand sensors leads to cumulative measurement errors. Panoramic cameras experience image jitter errors of up to 3-5 pixels due to bracket vibration. Data transmission reliability is insufficient. Traditional TCP / IP protocols experience packet loss rates exceeding 15% in complex scenarios like tunnels. Transmission delays for critical status information fluctuate by as much as ±800ms. Intelligent analysis capabilities are weak, and threshold-based early warning mechanisms fail to identify complex fault signatures, resulting in over 30% missed early warnings of mechanical wear. System maintenance mechanisms are rigid, with less than 40% matching manual inspection cycles with equipment wear cycles. Software upgrades require an average of 2.3 hours of downtime. Therefore, we propose a rail transportation monitoring method and system. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, the first object of the present invention is to provide a rail transportation monitoring method and system to solve the problems in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A rail transportation monitoring method and system comprises the following steps:

[0007] S1. System initialization device configuration management;

[0008] S2, real-time processing of multi-source data collection;

[0009] S3, real-time monitoring intelligent early warning mechanism;

[0010] S4. System maintenance performance optimization management.

[0011] The present invention is further configured as follows: in the step S1, system initialization device configuration management:

[0012] S1.1. Conduct a comprehensive inspection of the sensors, cameras, communication modules and other hardware devices in the rail transportation system and accurately calibrate the sensors using professional calibration tools;

[0013] S1.2. Configure the network communication protocol within the system to ensure stable and reliable data transmission between devices, and adopt a redundant network design to prevent data loss caused by single point failures;

[0014] S1.3. Start the monitoring software system, load the necessary drivers and configuration files, and perform a system self-check to ensure that all modules are started normally and in standby mode;

[0015] S1.4. Configure access control, data encryption and other security policies according to the security requirements of the rail transportation system to ensure that the system is not attacked or interfered with externally during operation.

[0016] The present invention is further configured as follows: in the step S2, multi-source data acquisition and real-time processing:

[0017] S2.1. Use sensors and cameras to collect real-time operating data from the rail transportation system, including train position, speed, and track status, using multi-source data fusion technology;

[0018] S2.2. Perform pre-processing operations such as filtering and denoising on the collected raw data to remove outliers and noise;

[0019] S2.3. Store the processed data in a database and use distributed storage technology to ensure high availability and scalability of the data and establish a data index;

[0020] S2.4. Use machine learning algorithms to analyze collected data in real time, detect abnormal situations and issue early warnings in a timely manner.

[0021] The present invention is further configured as follows: in the step S3, real-time monitoring intelligent early warning mechanism:

[0022] S3.1. The operating status of the rail transportation system, including train position, speed, track status, and other information, will be displayed in real time on the large screen in the monitoring center. Using visualization technology, monitoring personnel can intuitively understand the system's operating status.

[0023] S3.2. When the system detects an abnormal situation, it automatically triggers the early warning mechanism, alerts the monitoring personnel through sound and light alarms, SMS notifications, etc., and records the detailed information of the abnormal event;

[0024] S3.3. Based on the severity of the abnormal event, initiate the corresponding emergency plan to ensure the safe operation of the rail transportation system;

[0025] S3.4. Conduct in-depth analysis of the data generated during the monitoring process to identify potential safety hazards and operational bottlenecks to provide data support for system optimization.

[0026] The present invention is further configured as follows: in the step S4, system maintenance performance optimization management:

[0027] S4.1. Regularly maintain and service the hardware equipment in the rail transportation system, replace aging components, and ensure the long-term stable operation of the equipment;

[0028] S4.2. Upgrade the monitoring software system based on actual operation and user feedback, fix known vulnerabilities, and add new functional modules;

[0029] S4.3. Regularly back up important data in the system to ensure rapid data recovery in the event of an emergency, and test the data recovery process;

[0030] S4.4. Analyze system operation data to identify performance bottlenecks and optimize the system.

[0031] The present invention is further configured to include: a basic security environment construction module for device detection, network configuration, and security policy deployment before system startup; a data acquisition information processing module for collecting data from multiple source devices and performing cleaning, storage, and analysis; an operation visualization response module for displaying the system operation status in real time and triggering early warnings and emergency responses in the event of anomalies; and a health maintenance efficiency improvement module for regular maintenance, software upgrades, and performance optimization.

[0032] The basic security environment construction module includes a device status diagnosis and calibration module for detecting and calibrating hardware devices in the system, a communication network architecture configuration module for configuring the system's internal network communication protocol, a software environment initialization module for starting the monitoring software system to load necessary drivers and configuration files for system self-test, and a security protection policy deployment module for configuring access control and data encryption security policies;

[0033] The data acquisition information processing module includes a multi-source data acquisition module for collecting operating data in the rail transportation system in real time through sensors and cameras, a data cleaning preprocessing module for performing preprocessing operations such as filtering and denoising on the collected raw data to remove outliers and noise, a data storage indexing module for storing the processed data in a database using distributed storage technology, and an abnormal data identification module for performing real-time analysis of the collected data using a machine learning algorithm to detect abnormal situations and issue early warnings in a timely manner;

[0034] The operation visualization response module includes an operation status visualization module for displaying the operation status of the rail transportation system in real time on a large screen in the monitoring center using visualization technology, an intelligent early warning trigger module for automatically triggering an early warning mechanism to alert monitoring personnel through sound and light alarms, text message notifications, etc. when the system detects an abnormal situation, an emergency response mechanism module for activating corresponding emergency plans based on the severity of the abnormal event, and an operation data analysis module for conducting in-depth analysis of data generated during the monitoring process to identify potential safety hazards and operation bottlenecks;

[0035] The health maintenance and efficiency improvement module includes an equipment health maintenance module for regularly maintaining and servicing hardware equipment in the rail transportation system and replacing aging components, a software function iteration module for upgrading the monitoring software system according to actual operating conditions and user feedback, repairing known vulnerabilities and adding new functional modules, a data backup and recovery mechanism module for regularly backing up important data in the system, and a system efficiency optimization module for optimizing the system by analyzing system operation data to identify performance bottlenecks.

[0036] The present invention is further configured as follows: the device status diagnosis and calibration module provides a stable hardware foundation for the communication network architecture module by detecting and calibrating hardware devices; the communication network architecture configuration module provides a reliable network environment for the software environment initialization module by configuring the network communication protocol; the software environment initialization module provides an initialized software environment for the security protection strategy deployment module by loading drivers and configuration files.

[0037] The present invention is further configured as follows: the multi-source data acquisition module collects data through sensors and cameras to provide raw data for the data cleaning and preprocessing module; the data cleaning and preprocessing module provides cleaned data for the data storage index module through filtering and denoising operations; the data storage index module provides structured data for the abnormal data identification module through distributed storage technology.

[0038] The present invention is further configured as follows: the operation status visualization module provides real-time operation status display to the intelligent early warning trigger module through visualization technology; the intelligent early warning trigger module provides abnormal warning information to the emergency response mechanism module through sound and light alarms and SMS notifications; the emergency response mechanism module provides abnormal event processing results to the operation data analysis module by initiating an emergency plan.

[0039] The present invention is further configured as follows: the equipment health maintenance module provides stable hardware support for the software function iteration module through regular maintenance and care; the software function iteration module provides an optimized software environment for the data backup and recovery mechanism module through upgrading and repairing vulnerabilities; the data backup and recovery mechanism module provides data security protection for the system performance optimization module by regularly backing up data.

[0040] Beneficial effects

[0041] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0042] The present invention ensures the stability and security of data transmission by precisely calibrating hardware devices such as sensors and cameras and configuring redundant network communication protocols. It utilizes multi-source data fusion technology and machine learning algorithms to collect, process and analyze the operating data of the rail transportation system in real time, promptly detect abnormal situations and issue early warnings, and display the system operating status in real time through visualization technology, triggering sound and light alarms and SMS notifications, and initiating emergency plans based on the severity of the abnormality. The system ensures long-term stable operation and high efficiency of the system by regularly maintaining hardware devices, upgrading software systems, backing up important data, and optimizing system performance. These functions work together to significantly improve the safety, reliability, and operating efficiency of the rail transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic flow chart of a rail transportation monitoring method according to the present invention;

[0044] Figure 2 This is a module schematic diagram of a rail transportation monitoring system of the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] The present invention will be further described below with reference to the embodiments.

[0047] Example 1

[0048] like Figure 1 As shown, a rail transportation monitoring method includes the following steps:

[0049] S1. System initialization device configuration management;

[0050] S2, real-time processing of multi-source data collection;

[0051] S3, real-time monitoring intelligent early warning mechanism;

[0052] S4. System maintenance performance optimization management;

[0053] In the step S1, system initialization device configuration management:

[0054] S1.1. Conduct a comprehensive inspection of the sensors, cameras, communication modules and other hardware devices in the rail transportation system and accurately calibrate the sensors using professional calibration tools;

[0055] S1.2. Configure the network communication protocol within the system to ensure stable and reliable data transmission between devices, and adopt a redundant network design to prevent data loss caused by single point failures;

[0056] S1.3. Start the monitoring software system, load the necessary drivers and configuration files, and perform a system self-check to ensure that all modules are started normally and in standby mode;

[0057] S1.4. Configure access control, data encryption and other security policies according to the security requirements of the rail transportation system to ensure that the system is not attacked or interfered with externally during operation;

[0058] In the step S2, multi-source data acquisition and real-time processing:

[0059] S2.1. Use sensors and cameras to collect real-time operating data from the rail transportation system, including train position, speed, and track status, using multi-source data fusion technology;

[0060] S2.2. Perform pre-processing operations such as filtering and denoising on the collected raw data to remove outliers and noise;

[0061] S2.3. Store the processed data in a database and use distributed storage technology to ensure high availability and scalability of the data and establish a data index;

[0062] S2.4. Use machine learning algorithms to analyze collected data in real time, detect anomalies, and issue early warnings;

[0063] In the step S3, real-time monitoring of the intelligent early warning mechanism:

[0064] S3.1. The operating status of the rail transportation system, including train position, speed, track status, and other information, will be displayed in real time on the large screen in the monitoring center. Using visualization technology, monitoring personnel can intuitively understand the system's operating status.

[0065] S3.2. When the system detects an abnormal situation, it automatically triggers the early warning mechanism, alerts the monitoring personnel through sound and light alarms, SMS notifications, etc., and records the detailed information of the abnormal event;

[0066] S3.3. Based on the severity of the abnormal event, initiate the corresponding emergency plan to ensure the safe operation of the rail transportation system;

[0067] S3.4. Conduct in-depth analysis of the data generated during the monitoring process to identify potential safety hazards and operational bottlenecks to provide data support for system optimization;

[0068] In the step S4, system maintenance performance optimization management:

[0069] S4.1. Regularly maintain and service the hardware equipment in the rail transportation system, replace aging components, and ensure the long-term stable operation of the equipment;

[0070] S4.2. Upgrade the monitoring software system based on actual operation and user feedback, fix known vulnerabilities, and add new functional modules;

[0071] S4.3. Regularly back up important data in the system to ensure rapid data recovery in the event of an emergency, and test the data recovery process;

[0072] S4.4. Analyze system operation data to identify performance bottlenecks and optimize the system.

[0073] In the above embodiment, the system initializes the device configuration management to detect and calibrate hardware devices such as sensors and cameras, configure the network communication protocol, and start the monitoring software system to ensure system security and stability. Multi-source data acquisition and real-time processing are used to collect operating data of the rail transportation system through sensors and cameras. After pre-processing such as filtering and denoising, the data is stored using distributed storage technology, and the data is analyzed in real time using machine learning algorithms to detect abnormal situations.

[0074] When the system detects an anomaly, the real-time monitoring intelligent early warning mechanism will display the operating status in real time on the large screen of the monitoring center, trigger an audible and visual alarm or SMS notification, and activate the emergency plan according to the severity of the anomaly. At the same time, the monitoring data is analyzed to discover potential hidden dangers. The system maintenance performance optimization management ensures the long-term stable operation and efficiency of the system by regularly maintaining hardware equipment, upgrading software systems, backing up important data, and optimizing system performance. The entire method realizes comprehensive monitoring, early warning and maintenance of the rail transportation system through the coordinated work of various steps.

[0075] Example 2

[0076] like Figure 2As shown, a rail transportation monitoring system includes a basic security environment construction module for equipment detection, network configuration and security policy deployment before system startup; a data acquisition information processing module for collecting data from multiple source devices and performing cleaning, storage and analysis; an operation visualization response module for displaying the system operation status in real time and triggering early warning and emergency response when anomalies occur; and a health maintenance efficiency improvement module for performing regular maintenance, software upgrades and performance optimization.

[0077] The basic security environment construction module includes a device status diagnosis and calibration module for detecting and calibrating hardware devices in the system, a communication network architecture configuration module for configuring the system's internal network communication protocol, a software environment initialization module for starting the monitoring software system to load necessary drivers and configuration files for system self-test, and a security protection policy deployment module for configuring access control and data encryption security policies;

[0078] The data acquisition information processing module includes a multi-source data acquisition module for collecting operating data in the rail transportation system in real time through sensors and cameras, a data cleaning preprocessing module for performing preprocessing operations such as filtering and denoising on the collected raw data to remove outliers and noise, a data storage indexing module for storing the processed data in a database using distributed storage technology, and an abnormal data identification module for performing real-time analysis of the collected data using a machine learning algorithm to detect abnormal situations and issue early warnings in a timely manner;

[0079] The operation visualization response module includes an operation status visualization module for displaying the operation status of the rail transportation system in real time on a large screen in the monitoring center using visualization technology, an intelligent early warning trigger module for automatically triggering an early warning mechanism to alert monitoring personnel through sound and light alarms, text message notifications, etc. when the system detects an abnormal situation, an emergency response mechanism module for activating corresponding emergency plans based on the severity of the abnormal event, and an operation data analysis module for conducting in-depth analysis of data generated during the monitoring process to identify potential safety hazards and operation bottlenecks;

[0080] The health maintenance and efficiency improvement module includes an equipment health maintenance module for regularly maintaining and servicing hardware equipment in the rail transportation system and replacing aging components; a software function iteration module for upgrading the monitoring software system based on actual operating conditions and user feedback, fixing known vulnerabilities and adding new functional modules; a data backup and recovery mechanism module for regularly backing up important data in the system; and a system efficiency optimization module for optimizing the system by analyzing system operating data to identify performance bottlenecks.

[0081] The device status diagnosis and calibration module provides a stable hardware foundation for the communication network architecture module by detecting and calibrating hardware devices; the communication network architecture configuration module provides a reliable network environment for the software environment initialization module by configuring the network communication protocol; the software environment initialization module provides an initialized software environment for the security protection strategy deployment module by loading drivers and configuration files;

[0082] The multi-source data acquisition module collects data through sensors and cameras and provides raw data to the data cleaning and pre-processing module; the data cleaning and pre-processing module provides cleaned data to the data storage index module through filtering and denoising operations; the data storage index module provides structured data to the abnormal data identification module through distributed storage technology;

[0083] The operation status visualization module provides real-time operation status display to the intelligent early warning trigger module through visualization technology; the intelligent early warning trigger module provides abnormal warning information to the emergency response mechanism module through sound and light alarms and SMS notifications; the emergency response mechanism module provides abnormal event processing results to the operation data analysis module by activating the emergency plan;

[0084] The equipment health maintenance module provides stable hardware support for the software function iteration module through regular maintenance and upkeep; the software function iteration module provides an optimized software environment for the data backup and recovery mechanism module through upgrading and repairing vulnerabilities; the data backup and recovery mechanism module provides data security protection for the system performance optimization module by regularly backing up data.

[0085] In the above embodiment, the hardware equipment is tested and calibrated by building a basic security environment, the network communication protocol is configured, and the software environment is initialized to ensure security and stability before the system is started. The data acquisition and information processing module collects data from the rail transportation system through sensors and cameras, cleans and stores it, and uses machine learning algorithms to analyze it in real time and detect abnormal situations;

[0086] When the system detects an anomaly, the operation visualization response module will display the operation status in real time on the large screen, trigger the early warning mechanism, and initiate the emergency plan according to the severity of the anomaly. At the same time, it will analyze the monitoring data to discover potential hidden dangers. The health maintenance and efficiency improvement module ensures the long-term stable operation and efficiency of the system by regularly maintaining hardware equipment, upgrading software systems and important data, and optimizing performance. The entire system realizes comprehensive monitoring, early warning and maintenance of the rail transportation system through the collaborative work of various modules.

[0087] Working principle: When in use, the present invention uses the basic security environment construction module to comprehensively detect and calibrate the hardware equipment, configure the network communication protocol, and initialize the software environment to ensure the security and stability before the system starts. The device status diagnosis and calibration module accurately calibrates hardware devices such as sensors and cameras. The communication network architecture configuration module sets up a redundant network design to prevent single point failures. The software environment initialization module loads drivers and configuration files. The security protection strategy deployment module configures access control and data encryption strategies to provide comprehensive security protection for the system.

[0088] During the operation of the system, the data acquisition information processing module collects the operation data of the rail transportation system in real time through the multi-source data acquisition module, including information such as train position, speed and track status. The data cleaning preprocessing module filters and denoises the original data to remove outliers and noise. The data storage indexing module uses distributed storage technology to store the processed data in the database to ensure high availability and scalability of the data. The abnormal data identification module uses machine learning algorithms to analyze the collected data in real time, detect abnormal situations and issue early warnings in a timely manner.

[0089] When the system detects an anomaly, the operation visualization response module displays the operating status of the rail transportation system in real time on the large screen of the monitoring center. The use of visualization technology enables monitoring personnel to intuitively understand the system operation status. The intelligent early warning trigger module automatically triggers the early warning mechanism, reminds the monitoring personnel through sound and light alarms, SMS notifications, etc., and records detailed information of the abnormal event. The emergency response mechanism module activates the corresponding emergency plan according to the severity of the abnormal event to ensure the safe operation of the rail transportation system. The operation data analysis module conducts in-depth analysis of the data generated during the monitoring process to identify potential safety hazards and operation bottlenecks, and provide data support for system optimization.

[0090] In order to ensure the long-term stable operation and high efficiency of the system, the health maintenance and efficiency improvement module regularly maintains and services the hardware equipment and replaces aging components to ensure the long-term stable operation of the equipment. The software function iteration module upgrades the monitoring software system according to the actual operation status and user feedback, fixes known vulnerabilities, and adds new functional modules. The data backup and recovery mechanism module regularly backs up important data in the system to ensure that data can be quickly restored in the event of an accident. The system efficiency optimization module analyzes the system operation data, identifies performance bottlenecks, and optimizes the system to ensure the efficient operation of the system. The entire method and system realize comprehensive monitoring, early warning and maintenance of the rail transportation system through the collaborative work of various modules.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A rail transportation monitoring method, characterized in that: The following steps are involved: S1. System initialization device configuration management; S2, real-time processing of multi-source data collection; S3, real-time monitoring intelligent early warning mechanism; S4. System maintenance performance optimization management.

2. A rail transportation monitoring method according to claim 1, characterized in that: In the step S1, system initialization device configuration management: S1.

1. Conduct a comprehensive inspection of the sensors, cameras, communication modules and other hardware devices in the rail transportation system and accurately calibrate the sensors using professional calibration tools; S1.

2. Configure the network communication protocol within the system to ensure stable and reliable data transmission between devices, and adopt a redundant network design to prevent data loss caused by single point failures; S1.

3. Start the monitoring software system, load the necessary drivers and configuration files, and perform a system self-check to ensure that all modules are started normally and in standby mode; S1.

4. Configure access control, data encryption and other security policies according to the security requirements of the rail transportation system to ensure that the system is not attacked or interfered with externally during operation.

3. A rail transportation monitoring method according to claim 1, characterized in that: In the step S2, multi-source data acquisition and real-time processing: S2.

1. Use sensors and cameras to collect real-time operational data from the rail transportation system, including train position, speed, track status, etc., using multi-source data fusion technology; S2.

2. Perform pre-processing operations such as filtering and denoising on the collected raw data to remove outliers and noise; S2.

3. Store the processed data in a database and use distributed storage technology to ensure high availability and scalability of the data and establish a data index; S2.

4. Use machine learning algorithms to analyze collected data in real time, detect abnormal situations and issue early warnings in a timely manner.

4. A rail transportation monitoring method according to claim 1, characterized in that: In the step S3, real-time monitoring of the intelligent early warning mechanism: S3.

1. The operating status of the rail transportation system, including train position, speed, track status, and other information, will be displayed in real time on the large screen in the monitoring center. Using visualization technology, monitoring personnel can intuitively understand the system's operating status. S3.

2. When the system detects an abnormal situation, it automatically triggers the early warning mechanism, alerts the monitoring personnel through sound and light alarms, SMS notifications, etc., and records the detailed information of the abnormal event; S3.

3. Based on the severity of the abnormal event, initiate the corresponding emergency plan to ensure the safe operation of the rail transportation system; S3.

4. Conduct in-depth analysis of the data generated during the monitoring process to identify potential safety hazards and operational bottlenecks to provide data support for system optimization.

5. A rail transportation monitoring method according to claim 1, characterized in that: In the step S4, system maintenance performance optimization management: S4.

1. Regularly maintain and service the hardware equipment in the rail transportation system, replace aging components, and ensure the long-term stable operation of the equipment; S4.

2. Upgrade the monitoring software system based on actual operation and user feedback, fix known vulnerabilities, and add new functional modules; S4.

3. Regularly back up important data in the system to ensure rapid data recovery in the event of an emergency, and test the data recovery process; S4.

4. Analyze system operation data to identify performance bottlenecks and optimize the system.

6. A rail transportation monitoring system, characterized in that: It includes a basic security environment building module responsible for device detection, network configuration, and security policy deployment before system startup; a data acquisition information processing module for collecting data from multiple source devices and performing cleaning, storage, and analysis; an operation visualization response module for displaying the system operation status in real time and triggering early warnings and emergency responses in the event of anomalies; and a health maintenance efficiency improvement module for regular maintenance, software upgrades, and performance optimization. The basic security environment construction module includes a device status diagnosis and calibration module for detecting and calibrating hardware devices in the system, a communication network architecture configuration module for configuring the system's internal network communication protocol, a software environment initialization module for starting the monitoring software system to load necessary drivers and configuration files for system self-test, and a security protection policy deployment module for configuring access control and data encryption security policies; The data acquisition information processing module includes a multi-source data acquisition module for collecting operating data in the rail transportation system in real time through sensors and cameras, a data cleaning preprocessing module for performing preprocessing operations such as filtering and denoising on the collected raw data to remove outliers and noise, a data storage indexing module for storing the processed data in a database using distributed storage technology, and an abnormal data identification module for performing real-time analysis of the collected data using a machine learning algorithm to detect abnormal situations and issue early warnings in a timely manner; The operation visualization response module includes an operation status visualization module for displaying the operation status of the rail transportation system in real time on a large screen in the monitoring center using visualization technology, an intelligent early warning trigger module for automatically triggering an early warning mechanism to alert monitoring personnel through sound and light alarms, text message notifications, etc. when the system detects an abnormal situation, an emergency response mechanism module for activating corresponding emergency plans based on the severity of the abnormal event, and an operation data analysis module for conducting in-depth analysis of data generated during the monitoring process to identify potential safety hazards and operation bottlenecks; The health maintenance and efficiency improvement module includes an equipment health maintenance module for regularly maintaining and servicing hardware equipment in the rail transportation system and replacing aging components, a software function iteration module for upgrading the monitoring software system according to actual operating conditions and user feedback, repairing known vulnerabilities and adding new functional modules, a data backup and recovery mechanism module for regularly backing up important data in the system, and a system efficiency optimization module for optimizing the system by analyzing system operation data to identify performance bottlenecks.

7. A rail transportation monitoring system according to claim 6, characterized in that: The device status diagnosis and calibration module provides a stable hardware foundation for the communication network architecture module by detecting and calibrating hardware devices; the communication network architecture configuration module provides a reliable network environment for the software environment initialization module by configuring the network communication protocol; the software environment initialization module provides an initialized software environment for the security protection strategy deployment module by loading drivers and configuration files.

8. A rail transportation monitoring system according to claim 6, characterized in that: The multi-source data acquisition module collects data through sensors and cameras and provides raw data for the data cleaning and preprocessing module; the data cleaning and preprocessing module provides cleaned data for the data storage index module through filtering and denoising operations; the data storage index module provides structured data for the abnormal data identification module through distributed storage technology.

9. A rail transportation monitoring system according to claim 6, characterized in that: The operation status visualization module provides real-time operation status display to the intelligent early warning trigger module through visualization technology; the intelligent early warning trigger module provides abnormal early warning information to the emergency response mechanism module through sound and light alarms and SMS notifications; The emergency response mechanism module provides abnormal event processing results to the operation data analysis module by initiating the emergency plan.

10. A rail transportation monitoring system according to claim 6, characterized in that: The equipment health maintenance module provides stable hardware support for the software function iteration module through regular maintenance and upkeep; the software function iteration module provides an optimized software environment for the data backup and recovery mechanism module through upgrading and repairing vulnerabilities; the data backup and recovery mechanism module provides data security protection for the system performance optimization module by regularly backing up data.

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