Safe operation domain system and method for train operation

Abstract

The embodiment of the invention provides a safe operation domain system and method for train operation, and the system comprises a database which is used for storing the calculation process data and result data of all safety parameters of virtual marshalling operation; the data service module is used for performing real-time calculation on safety parameters of virtual marshalling operation, analyzing a distribution rule of historical data of the safety parameters, performing calculation according to the distribution rule to obtain a reference value of each safety parameter, comparing a real-time calculation result of the safety parameters with the reference value to obtain a comparison result, and sending the comparison result to the virtual marshalling operation server; generating alarm information and early warning information according to the comparison result; and the front end is used for displaying the distribution rule and the historical change trend of the safety parameters as well as the alarm information and the early warning information.

Description

Technical Field

[0001] This document relates to the field of rail transit technology, and in particular to a safe operation domain system and method for train operation. Background Technology

[0002] With the rapid construction and development of urban rail transit, the jurisdiction of train control systems continues to expand, the responsibilities they bear increase, and their safety boundaries are constantly being refined. Against this backdrop, the traditional approach of relying solely on static assumptions about the system's safety boundaries or on manual safeguards is no longer sufficient to meet the needs of continuous safe system operation. Therefore, the addition of a safety operation domain monitoring system has become an inevitable trend.

[0003] In current urban rail transit train control systems, whether it's the commonly used CBTC system or the more advanced urban rail train control system, the moving block tracking principle is generally adopted. This principle is based on the assumption that "the lead car may stop momentarily" (commonly known as "hitting a hard wall"). Based on this, the tracking target point of subsequent trains is set to leave a certain protective distance behind the leading train, and the maximum permissible speed of the following train is accurately calculated based on the dynamic model of the following train's emergency braking, thus ensuring the operational safety between trains. The virtual train formation safety model (commonly known as "hitting a soft wall") exhibits superior safety and accuracy. This model accurately determines the final actual rear position of the leading train based on its real-time position, speed, and acceleration, thereby identifying potential hazards. Compared to traditional models, it can more accurately describe the emergency braking establishment process, describing the emergency braking rate during the emergency braking establishment process as a function that changes over time. Through this dynamic functional relationship, it more closely resembles actual braking scenarios. The model structure is as follows: Figure 1 As shown.

[0004] From the perspective of the characteristics of the virtual train formation safety model, while improving operational efficiency by shortening the safety protection distance, it also breaks the traditional conservative assumption that "the train in front can stop at any time". This makes the train face more adverse factors brought about by dynamic variables during operation, such as real-time parameter fluctuations and emergency braking response deviations in complex scenarios, which may pose a potential threat to train operation safety. Summary of the Invention

[0005] The purpose of this invention is to provide a safe operation domain system and method for train operation, aiming to solve the above-mentioned problems in the prior art.

[0006] This invention provides a safety operation domain system for train operation, comprising: The database is used to store the calculation process data and result data of various safety parameters for virtual grouping operations; The data service module is used to perform real-time calculation of safety parameters for virtual group operation, analyze the distribution pattern of historical data of the safety parameters, calculate the baseline value of each safety parameter according to the distribution pattern, compare the real-time calculation result of the safety parameter with the baseline value to obtain the comparison result, and generate alarm information and early warning information based on the comparison result. The front end is used to display the distribution pattern and historical trend of the safety parameters, as well as the alarm and warning information.

[0007] This invention provides a safety operation domain method for train operation, and a safety operation domain system for train operation, comprising: The safety parameters of the virtual group operation are calculated in real time, the distribution pattern of the historical data of the safety parameters is analyzed, the baseline value of each safety parameter is calculated according to the distribution pattern, the real-time calculation result of the safety parameter is compared with the baseline value to obtain the comparison result, and alarm information and early warning information are generated according to the comparison result. The calculation process data and result data of each safety parameter for virtual grouping operation are stored in the database, and the distribution pattern, historical change trend, alarm information and early warning information of the safety parameters are displayed on the front end.

[0008] This invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the above-described safe operation domain method for train operation.

[0009] This invention also provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor, implements the steps of the above-described safe operation domain method for train operation.

[0010] In summary, to address the safety challenges brought about by the virtual train formation safety model in improving operational efficiency, this invention addresses potential threats such as dynamic variables arising from shortened safety protection distances and the breaking of traditional conservative assumptions under the virtual train formation mode. By constructing a comprehensive safety monitoring system, the safe operation of virtual train formations is ensured, ultimately reducing the incidence of hazardous events and accidents in urban rail transit and significantly improving the safety assurance capabilities of the urban rail transit operation control system. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in 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 recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of a virtual marshalling security model in existing technology; Figure 2 This is a schematic diagram of a safe operation domain system for train operation according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the detailed architecture of a safety operation domain monitoring system for train operation according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the front-end interface of an embodiment of the present invention; Figure 5 This is a schematic diagram of the virtual grouping security model parameters according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the wet and slippery rail surface state according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the radiation range distribution of the transponder according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the common braking deceleration distribution in an embodiment of the present invention; Figure 9 This is a flowchart of a safe operation domain method for train operation according to an embodiment of the present invention; Figure 10 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] System Implementation Examples According to embodiments of the present invention, a safety operation domain system for train operation is provided. Figure 2 This is a schematic diagram of a safe operation domain system for train operation according to an embodiment of the present invention, such as... Figure 2As shown, the safety operation domain system for train operation according to an embodiment of the present invention specifically includes: Database 20 is used to store the calculation process data and result data of various safety parameters for virtual train formation operation. The safety parameters for virtual train formation operation specifically include: track safety parameters, signal safety parameters, and vehicle safety parameters. The track safety parameters specifically include: track environment parameters, which include: meteorological conditions within the line area. The signal safety parameters specifically include: ranging / speed measurement error, acceleration measurement error, and transponder radiation range. The vehicle safety parameters specifically include: traction acceleration, traction impact rate, emergency braking response delay, emergency braking setup time, deceleration during the emergency braking setup phase, deceleration during the emergency braking stabilization phase, deceleration during the emergency braking tailing phase, abnormal detection throughout the emergency braking process, abnormal detection of air cylinder pressure, and normal braking deceleration.

[0015] Data service module 22 is used to perform real-time calculation of safety parameters for virtual group operation, analyze the distribution patterns of historical data of the safety parameters, calculate the baseline values ​​of each safety parameter based on the distribution patterns, compare the real-time calculation results of the safety parameters with the baseline values ​​to obtain comparison results, and generate alarm information and early warning information based on the comparison results; specifically including: The real-time calculation submodule is used to acquire log data from the train in real time, clean and filter the acquired log data to obtain calculation parameters, and calculate the corresponding safety parameters in real time based on the calculation parameters. The analysis submodule is used to collect historical data of safety parameters, determine the baseline value of the safety parameter through distribution pattern analysis, compare the real-time calculated safety parameter with the baseline value, and trigger an early warning if it exceeds the baseline value. The safety parameter is also compared with a pre-set system safety boundary, and if it further exceeds the system safety boundary, an alarm is directly triggered. The analysis methods specifically include: normal distribution, isolated forest anomaly detection algorithm, mean and variance.

[0016] The real-time computing submodule is specifically used for: Acquire meteorological conditions and real-time monitoring images of the track surface within the line area, classify the real-time monitoring images of the track surface using a classification algorithm, and determine the dryness or wetness of the track surface based on the classification results and the meteorological conditions. The ranging / velocity measurement error is calculated by the cumulative distance of the rapid transmission during the response period and the actual distance between the transponders; the acceleration measurement error is calculated by the rapid transmission acceleration and the acceleration of the accelerometer; and the transponder radiation range is calculated by the speed and the time of entering and leaving the transponder.

[0017] The data service module 22 is further specifically used to generate corresponding risk mitigation measures information based on the alarm information and the early warning information.

[0018] Front end 24 is used to display the distribution pattern and historical trend of the safety parameters, as well as the alarm information and early warning information.

[0019] The safety operation domain system for train operation according to embodiments of the present invention takes the safety boundary of the train operation curve as the core monitoring object, establishes specialized monitoring models around three major categories: track, signaling, and vehicles, statistically analyzes the distribution patterns of various parameters, and determines in real time whether key safety boundary parameters meet preset safety assumptions. The system employs a dual mechanism for monitoring safety boundary parameters: on the one hand, it implements threshold monitoring based on the safety boundary requirements of the signaling system, triggering an alarm immediately when a parameter exceeds the threshold; on the other hand, it continuously collects and analyzes data to statistically analyze the normal operation patterns of parameters, issuing a warning once a deviation occurs. Data acquisition covers the entire operational cycle: before operation, basic data is collected through system self-inspection, track inspection vehicle operation, and train brake testing; during operation, it dynamically acquires operational status information based on real-time data transmission from the train and various subsystems. Through the above monitoring and management mechanisms, a comprehensive and multi-layered key guarantee is provided for the safe operation of urban rail transit.

[0020] In summary, the secure operation domain system of this invention mainly has the following beneficial effects: 1. The monitoring mechanism is comprehensive and accurate. The system establishes specialized monitoring models around three core categories: track, signal, and vehicle. It takes the safety boundary of virtual train formation operation as the core monitoring object, and judges in real time whether the safety boundary parameters meet the preset safety assumptions to ensure that the train operates within the specified parameter range. 2. Dual monitoring ensures reliability. It adopts a dual mechanism of threshold monitoring and normal range early warning. On the one hand, threshold monitoring is implemented according to the safety boundary requirements of the signal system, and an alarm is triggered immediately when the parameter exceeds the threshold. On the other hand, through data collection and statistics, the data distribution pattern is analyzed to clarify the normal distribution range and timely warning is given for deviations from the range, so as to achieve comprehensive and multi-level prevention and control of safety risks. 3. Strong full-cycle data support: Data acquisition covers the entire cycle before and during operation. Before operation, basic data is collected through system self-inspection, track inspection vehicle operation, and train brake test. During operation, dynamic information is obtained by relying on real-time data transmission from trains and subsystems, providing comprehensive and continuous data support for safety monitoring and ensuring the accuracy and timeliness of monitoring. 4. Aligns with industry development trends: It conforms to the development concept of "coordinated optimization of safety and efficiency" in the global rail transit industry, and follows the direction of refined safety management in the intelligent upgrading of rail transit, providing key safety assurance technology support for the development of urban rail transit towards high density, high reliability and high intelligence.

[0021] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] In this embodiment of the invention, the safety operation domain monitoring system for train operation mainly consists of three functional modules: a database, a data service module, and a front-end, such as... Figure 3 As shown. The database function stores the calculation process and results of each safety parameter; the data service module implements real-time calculation of each safety boundary parameter, using methods such as normal distribution, isolated forest anomaly detection algorithm, mean and variance to analyze the distribution patterns of historical data and derive the baseline values ​​of each parameter (e.g., using μ±3σ as the baseline value for the normal distribution method); the front-end interface displays the distribution patterns, historical trends, and alarm information of each parameter, such as... Figure 4 As shown.

[0023] The following explains the specific processing of the data service module.

[0024] The data service module of the safe operation domain monitoring system, based on the safety assumptions of virtual train formation operation, establishes models for monitoring according to three categories: track, signal, and vehicle, and determines whether these safety boundary parameters / factors meet the safety assumptions, such as... Figure 5 As shown. Virtual marshalling is allowed to run only if the parameters meet the safety assumptions; otherwise, the creation / maintenance of virtual marshalling is not allowed.

[0025] The safety assumption for the track is that within the train braking range, the track conditions meet the condition of "dry track". The track environment is the main factor affecting wheel-rail adhesion, with rain, snow, ice, frost, dew, and fog having the greatest impact. To address this, this system monitors the meteorological conditions within the track area, monitors track surface images in real time, and uses a classification algorithm to classify the track surface images and determine their dryness or wetness status. Figure 6 As shown.

[0026] The signal needs to monitor three parameters: ranging / velocity measurement error, acceleration measurement error, and transponder radiation range. The ranging / velocity measurement error is calculated using the cumulative distance of the transmission during the response period and the actual distance between transponders; the acceleration measurement error is calculated using the transmission acceleration and the accelerometer acceleration; and the transponder radiation range is calculated using the speed and the time it takes to enter and exit the transponder. This system analyzes historical data for these three parameters, finding that the historical data conforms to a normal distribution. Taking the transponder radiation range as an example... Figure 7 As shown, the data is relatively concentrated, with a large radiation range indicating a dangerous side and a small radiation range indicating a safe side. Therefore, μ+3σ is used as the baseline value for this parameter. If it is greater than this parameter, the system considers the train operation to be at risk. Furthermore, the safety boundary for the parameter is 95; if it exceeds this range, the parameter is considered not to meet the safety assumptions.

[0027] The vehicle needs to monitor traction acceleration, traction impact rate, emergency braking response delay, emergency braking setup time, deceleration during the emergency braking setup phase, deceleration during the emergency braking stabilization phase, deceleration during the emergency braking tail-end phase, anomaly detection throughout the emergency braking process, and anomaly detection of air cylinder pressure, as well as common braking deceleration. Taking common braking deceleration as an example, this system statistically analyzes the deceleration of trains at speeds greater than 5 km / h during the precise stopping phase in historical operation. The data conforms to a normal distribution, such as... Figure 8 As shown, the data is relatively concentrated. A large absolute value of deceleration indicates a dangerous side, while a small absolute value of deceleration indicates a safe side. Therefore, μ-3σ is taken as the benchmark value for this parameter. If it is less than this parameter, the system considers that there is a risk in the train operation.

[0028] For each safety parameter, this system sequentially performs five core operations: data acquisition and cleaning, parameter calculation, safety risk assessment, data storage, and generation of mitigation measures. Taking the transponder radiation range, a key parameter, as an example, the specific monitoring process is as follows: 1. Data Acquisition and Cleaning: The system acquires three data points from the train in real time: train speed, timestamp of leaving the transponder, and timestamp of entering the transponder, and automatically removes invalid data in terms of speed or timestamp.

[0029] 2. Parameter calculation: Calculate the time difference between the train leaving and entering the transponder, and multiply this difference by the average train speed during the corresponding time period to obtain the transponder radiation range parameter.

[0030] 3. Risk Assessment and Early Warning: The system first collects massive amounts of historical data on the radiation range of transponders, and determines the baseline value of the parameter through pattern analysis; then, the newly calculated real-time parameter is compared with the baseline value. If the threshold value is exceeded, an early warning is triggered; if it further exceeds the system's safety boundary, an alarm is directly triggered. Most parameters conform to normal distribution characteristics, while some parameters require the use of mean and variance characteristics.

[0031] 4. Data storage: The system stores the raw data, calculation results, and early warning alarm information of each calculation in the database.

[0032] 5. Mitigation measures issuance: The system automatically issues corresponding risk mitigation measures based on different alarm levels.

[0033] By monitoring various safety parameters throughout the entire process, the system can identify train operation risks in real time, effectively ensuring train operation safety.

[0034] In summary, to ensure safe operation in virtual formation mode and other modes, the safety operation domain system for train operation in this embodiment of the invention accurately controls key elements such as tracks, signals, and vehicles through full-cycle, multi-dimensional safety boundary monitoring, thereby reducing the incidence of hazardous events and accidents and improving the safety assurance capabilities of the urban rail transit operation control system.

[0035] From the perspective of the global rail transit industry, a safety operation domain system oriented towards train operation aligns with the development concept of synergistic optimization of safety and efficiency, and represents the direction of refined safety management in the intelligent upgrading of rail transit. With the global trend of urban rail transit developing towards high density, high reliability, and high intelligence, this system, through precise monitoring and proactive early warning, not only meets the demand for increased transport capacity but also ensures the safe operation of trains, providing a referable technical path for the safe and efficient development of the global rail transit industry.

[0036] Method Example 1 According to embodiments of the present invention, a safety operation domain method for train operation is provided, which is used in the aforementioned safety operation domain system for train operation. Figure 9 This is a flowchart of a safe operation domain method for train operation according to an embodiment of the present invention, such as... Figure 9 As shown, the safe operation domain method for train operation according to an embodiment of the present invention specifically includes: Step S901: Real-time calculation of safety parameters for virtual train formation operation; analysis of the distribution pattern of historical data for the safety parameters; calculation of baseline values ​​for each safety parameter based on the distribution pattern; comparison of the real-time calculation results of the safety parameters with the baseline values ​​to obtain comparison results; and generation of alarm and warning information based on the comparison results. The specific safety parameters for virtual train formation operation include: track safety parameters, signal safety parameters, and vehicle safety parameters. Specifically, the track safety parameters include: track environment parameters, including: meteorological conditions within the track area; the signal safety parameters include: ranging / speed measurement error, acceleration measurement error, and transponder radiation range; and the vehicle safety parameters include: traction acceleration, traction impact rate, emergency braking response delay, emergency braking setup time, deceleration during the emergency braking setup phase, deceleration during the emergency braking stabilization phase, deceleration during the emergency braking tail-end phase, abnormal detection throughout the emergency braking process, abnormal detection of air cylinder pressure, and normal braking deceleration.

[0037] Step S901 specifically includes: Specifically, it includes: Log data is acquired from the train in real time. The acquired log data is cleaned and filtered to obtain calculation parameters. Based on the calculation parameters, the corresponding safety parameters are calculated in real time. Historical data of safety parameters are collected, and a baseline value for the safety parameter is determined through distribution pattern analysis. The real-time calculated safety parameter is compared with the baseline value. If it exceeds the baseline value, an early warning is triggered. The safety parameter is compared with a pre-set system safety boundary. If it further exceeds the system safety boundary, an alarm is directly triggered. The analysis methods specifically include: normal distribution, isolated forest anomaly detection algorithm, mean and variance. Specifically, the process involves acquiring real-time log data from the train, cleaning and filtering the acquired log data to obtain calculation parameters, and then calculating the corresponding safety parameters in real time based on these parameters. Acquire meteorological conditions and real-time monitoring images of the track surface within the line area, classify the real-time monitoring images of the track surface using a classification algorithm, and determine the dryness or wetness of the track surface based on the classification results and the meteorological conditions. The ranging / velocity measurement error is calculated by the cumulative distance of the rapid transmission during the response period and the actual distance between the transponders; the acceleration measurement error is calculated by the rapid transmission acceleration and the accelerometer acceleration; and the transponder radiation range is calculated by the speed and the time of entering and leaving the transponder. Step S902: The calculation process data and result data of each safety parameter for virtual grouping operation are stored in the database, and the distribution pattern, historical change trend, alarm information and early warning information of the safety parameters are displayed on the front end.

[0038] Preferably, corresponding risk mitigation measures information can also be generated based on the alarm information and the early warning information.

[0039] Device Example 1 This invention provides an electronic device, such as... Figure 10 As shown, it includes: a memory 100, a processor 102, and a computer program stored in the memory 100 and executable on the processor 102, wherein the computer program, when executed by the processor 102, performs the steps as described in the method embodiment.

[0040] Device Example 2 This invention provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor 102, implements the steps described in the method embodiment.

[0041] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A safety operation domain system for train operation, characterized in that, include: The database is used to store the calculation process data and result data of various safety parameters for virtual grouping operations; The data service module is used to perform real-time calculation of safety parameters for virtual group operation, analyze the distribution pattern of historical data of the safety parameters, calculate the baseline value of each safety parameter according to the distribution pattern, compare the real-time calculation result of the safety parameter with the baseline value to obtain the comparison result, and generate alarm information and early warning information based on the comparison result. The front end is used to display the distribution pattern and historical trend of the safety parameters, as well as the alarm and warning information.

2. The system according to claim 1, characterized in that, The specific safety parameters for virtual train formation operation include: track safety parameters, signal safety parameters, and vehicle safety parameters. The track safety parameters specifically include: track environment parameters, including weather conditions within the track area; the signal safety parameters specifically include: ranging / speed measurement error, acceleration measurement error, and transponder radiation range; the vehicle safety parameters specifically include: traction acceleration, traction impact rate, emergency braking response delay, emergency braking setup time, deceleration during emergency braking setup, deceleration during emergency braking stabilization, deceleration during emergency braking tail-end, anomaly detection throughout the emergency braking process, anomaly detection of air cylinder pressure, and normal braking deceleration.

3. The system according to claim 1, characterized in that, The data service module specifically includes: The real-time calculation submodule is used to acquire log data from the train in real time, clean and filter the acquired log data to obtain calculation parameters, and calculate the corresponding safety parameters in real time based on the calculation parameters. The analysis submodule is used to collect historical data of safety parameters, determine the baseline value of the safety parameter through distribution pattern analysis, compare the real-time calculated safety parameter with the baseline value, and trigger an early warning if it exceeds the baseline value. The safety parameter is also compared with a pre-set system safety boundary, and if it further exceeds the system safety boundary, an alarm is directly triggered. The analysis methods specifically include: normal distribution, isolated forest anomaly detection algorithm, mean and variance.

4. The system according to claim 3, characterized in that, The real-time computing submodule is specifically used for: Acquire meteorological conditions and real-time monitoring images of the track surface within the line area, classify the real-time monitoring images of the track surface using a classification algorithm, and determine the dryness or wetness of the track surface based on the classification results and the meteorological conditions. The ranging / velocity measurement error is calculated by the cumulative distance of the rapid transmission during the response period and the actual distance between the transponders; the acceleration measurement error is calculated by the rapid transmission acceleration and the acceleration of the accelerometer; and the transponder radiation range is calculated by the speed and the time of entering and leaving the transponder.

5. The system according to claim 1, characterized in that, The data service module is further specifically used to generate corresponding risk mitigation measures information based on the alarm information and the early warning information.

6. A method for a safe operating domain for train operation, characterized in that, The method for the train-oriented safety operation domain system according to any one of claims 1 to 5 specifically includes: The safety parameters of the virtual group operation are calculated in real time, the distribution pattern of the historical data of the safety parameters is analyzed, the baseline value of each safety parameter is calculated according to the distribution pattern, the real-time calculation result of the safety parameter is compared with the baseline value to obtain the comparison result, and alarm information and early warning information are generated according to the comparison result. The calculation process data and result data of each safety parameter for virtual grouping operation are stored in the database, and the distribution pattern, historical change trend, alarm information and early warning information of the safety parameters are displayed on the front end.

7. The method according to claim 6, characterized in that, The specific safety parameters for virtual train formation operation include: track safety parameters, signal safety parameters, and vehicle safety parameters. The track safety parameters specifically include: track environment parameters, including weather conditions within the track area; the signal safety parameters specifically include: ranging / speed measurement error, acceleration measurement error, and transponder radiation range; the vehicle safety parameters specifically include: traction acceleration, traction impact rate, emergency braking response delay, emergency braking setup time, deceleration during emergency braking setup, deceleration during emergency braking stabilization, deceleration during emergency braking tail-end, anomaly detection throughout the emergency braking process, anomaly detection of air cylinder pressure, and normal braking deceleration.

8. The method according to claim 6, characterized in that, The system performs real-time calculations of safety parameters for virtual group operation, analyzes the distribution patterns of historical data for these safety parameters, calculates baseline values ​​for each safety parameter based on these distribution patterns, compares the real-time calculation results of the safety parameters with the baseline values ​​to obtain comparison results, and generates alarm and warning information based on these comparison results. Specifically, this includes: Log data is acquired from the train in real time. The acquired log data is cleaned and filtered to obtain calculation parameters. Based on the calculation parameters, the corresponding safety parameters are calculated in real time. Historical data of safety parameters are collected, and a baseline value for the safety parameter is determined through distribution pattern analysis. The real-time calculated safety parameter is compared with the baseline value. If it exceeds the baseline value, an early warning is triggered. The safety parameter is compared with a pre-set system safety boundary. If it further exceeds the system safety boundary, an alarm is directly triggered. The analysis methods specifically include: normal distribution, isolated forest anomaly detection algorithm, mean and variance. Specifically, the process involves acquiring real-time log data from the train, cleaning and filtering the acquired log data to obtain calculation parameters, and then calculating the corresponding safety parameters in real time based on these parameters. Acquire meteorological conditions and real-time monitoring images of the track surface within the line area, classify the real-time monitoring images of the track surface using a classification algorithm, and determine the dryness or wetness of the track surface based on the classification results and the meteorological conditions. The ranging / velocity measurement error is calculated by the cumulative distance of the rapid transmission during the response period and the actual distance between the transponders; the acceleration measurement error is calculated by the rapid transmission acceleration and the accelerometer acceleration; and the transponder radiation range is calculated by the speed and the time of entering and leaving the transponder. The method further includes: generating corresponding risk mitigation measures information based on the alarm information and the early warning information.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the safe operating domain method for train operation as described in any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an implementation program for information transmission, which, when executed by a processor, implements the steps of the safe operation domain method for train operation as described in any one of claims 6 to 8.

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