Train online monitoring system and method

Abstract

The present invention relates to the technical field of online train monitoring, and in particular to a system and method for online train monitoring, comprising: a monitoring module, comprising a plurality of data acquisition units arranged on a plurality of trains, for acquiring environmental data information of a plurality of node intervals; a data storage module, connected to the monitoring module, comprising a plurality of storage units arranged on a plurality of nodes on a single line, for storing environmental data information; a data analysis module, connected to the data storage module, for determining a data comparison strategy for a single node, and a response strategy; an early warning module, connected to the data analysis module, for issuing an early warning signal; and a control module, respectively connected to the data storage module, the data analysis module, and the early warning module, for determining a data storage strategy, and an early warning strategy. The present invention improves the automation level of the online train monitoring system and the safety level of train operation.

Description

Technical Field

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

[0002] With the rapid development of railway transportation, train safety monitoring has become a critical component in ensuring operational efficiency and safety. Traditional train monitoring systems rely primarily on manual inspections and fixed detection equipment, resulting in issues such as insufficient real-time performance and limited coverage. Furthermore, existing monitoring systems typically analyze only a single parameter independently, lacking comprehensive analysis of multi-source data, making it difficult to promptly identify potential risks in complex environments.

[0003] Chinese patent application publication number CN104828115A discloses a train monitoring system comprising an onboard monitoring system and a ground monitoring system. The onboard monitoring system includes an onboard main control module, a monitoring module, a satellite positioning module, and a first wireless communication module, with the onboard main control module electrically connected to the monitoring module, the satellite positioning module, and the first wireless communication module, respectively. The ground monitoring system includes a ground main control module, a data analysis module, a storage module, and a second wireless communication module, with the ground main control module electrically connected to the data analysis module, the storage module, and the second wireless communication module, respectively. The first wireless communication module and the second wireless communication module communicate wirelessly. Data from the onboard monitoring system can be transmitted to the ground monitoring system via a wireless network. The ground monitoring system then determines whether the train's operating status is normal based on the received data and promptly transmits instructions to the onboard monitoring system in the event of a fault, effectively monitoring the train and ensuring safe operation.

[0004] However, the existing technology has the following problems: the existing technology does not consider the impact of environmental factors on trains and lacks analysis of data differences between trains, resulting in poor adaptability of the monitoring system and low early warning accuracy. Summary of the Invention

[0005] To this end, the present invention provides a train online monitoring system and method for solving technical problems in the prior art such as poor system adaptability and low early warning accuracy caused by imperfect environmental factor monitoring and insufficient collaborative analysis of multi-train data.

[0006] To achieve the above object, the present invention provides a train online monitoring system, comprising:

[0007] A monitoring module, comprising a plurality of data acquisition units arranged on a plurality of trains, for acquiring environmental data information of a plurality of node intervals;

[0008] A data storage module connected to the monitoring module, comprising a plurality of storage units arranged on a plurality of nodes on a single line, for storing the environmental data information acquired by the monitoring module;

[0009] a data analysis module connected to the data storage module, configured to determine whether the data in the storage module belongs to the type of environmental anomaly event, and to determine a single-node data comparison strategy based on the type of data acquired by the second train, provided that the data storage module has stored the data acquired by the first train, and to determine a response strategy based on the rate of change of the single data or the data of the first train, the response strategy including performing the next comparison on the single data, initiating a level one warning and continuously comparing the data acquired by each train, and reducing the comparison frequency of the single data;

[0010] an early warning module, connected to the data analysis module, for responding to abnormal data determined by the data analysis module and issuing an early warning signal;

[0011] A control module is respectively connected to the data storage module, the data analysis module and the early warning module, and is used to determine a data storage strategy based on the type consistency of the single data of the first train and the second train, and to determine an early warning strategy based on the data anomaly index. The early warning strategy includes sending an audible and visual warning to the train cab, automatically limiting the train speed to a preset safe speed, and triggering emergency braking and linking with the dispatching center.

[0012] Furthermore, the types of environmental abnormal events include one or more of the following:

[0013] The ambient humidity is greater than the preset humidity;

[0014] Or, the ambient temperature is greater than the preset temperature;

[0015] Or, the ambient smoke concentration is greater than the preset concentration;

[0016] Or, the depth of accumulated water is greater than the preset depth;

[0017] Or, the vibration frequency is greater than the preset frequency;

[0018] Or, the noise is greater than the preset noise.

[0019] Furthermore, the data analysis module determines a single-node data comparison strategy based on the type of data acquired by the second train. If the data acquired by the second train belongs to an environmental abnormality event type, the single-node data comparison strategy is determined to be comparing the data of the second train with the data of the first train to obtain a single data change rate and determining a response strategy based on the single data change rate.

[0020] If the data of the second train does not belong to the type of environmental abnormal event, the data comparison strategy of the single node is determined to determine the response strategy according to the type of the single data of the first train.

[0021] Furthermore, the data analysis module determines a response strategy based on the change rate of the single data, wherein if the change rate of the single data is less than a preset change rate, the response strategy is determined to be to perform a next comparison on the single data;

[0022] If the change rate of the single data is greater than or equal to the preset change rate, the response strategy is determined to be to start a level one warning and continuously compare the data obtained from each train.

[0023] Furthermore, the data analysis module determines a response strategy based on the type of the single item of data of the first train, wherein if the single item of data of the first train does not belong to the type of environmental abnormality event, the response strategy is determined to be to reduce the comparison frequency of the single item of data;

[0024] If the data of the first train belongs to the type of environmental abnormality event, the response strategy is determined to perform the next comparison on the single data.

[0025] Furthermore, the control module determines a data storage strategy based on the consistency of the types of the individual data of the first train and the second train, wherein if the types of the individual data are consistent, the storage strategy is further determined based on the types of the individual data;

[0026] If the types of individual data items are inconsistent, the data storage strategy is determined to be all storage.

[0027] Furthermore, the control module further determines a storage strategy according to the type of the single data, wherein if the type of the single data belongs to the type of environmental abnormality event, the storage strategy is determined to be all storage;

[0028] If the type of the single data item does not belong to the type of environmental abnormal event, the storage strategy is determined to store the new data and delete the old data.

[0029] Furthermore, the control module determines an early warning strategy based on a data anomaly index after obtaining a preset group of data, wherein if the data anomaly index is less than a first preset anomaly index, a third-level early warning is initiated, and the third-level early warning is to send an audible and visual warning to the train cab;

[0030] If the data anomaly index is greater than or equal to the first preset anomaly index and less than the second preset anomaly index, a secondary warning is activated, and the secondary warning is to automatically limit the train speed to a preset safe speed;

[0031] If the data anomaly index is greater than or equal to the second preset anomaly index, a first-level warning is initiated, and the first-level warning triggers emergency braking and links to the dispatch center.

[0032] Furthermore, the data anomaly index is determined by the proportion of the number of environmental anomaly event types and the comprehensive proportion of the number of single data anomalies.

[0033] The present invention also provides a train online monitoring method, comprising:

[0034] When a single train passes through each node section of a single line, the environmental data information of the node section is obtained and stored through the data acquisition unit;

[0035] Determine for a single node whether the data obtained by a single train belongs to the type of environmental abnormality event;

[0036] Determining a data comparison strategy for a single node based on the type of the single data obtained by the second train, including determining a response strategy based on a rate of change of the single data obtained by comparing the data of the second train with that of the first train, or determining a response strategy based on the type of the single data of the first train;

[0037] Determining a data storage strategy based on type consistency of individual data of the first train and the second train;

[0038] The early warning strategy is determined based on the data anomaly index after obtaining the preset group data, including launching a first-level early warning, a second-level early warning, or a third-level early warning.

[0039] Compared with the existing technology, the beneficial effect of the present invention is that the monitoring module of the present invention obtains environmental data information within several node intervals by setting up data acquisition units on each train, including environmental humidity, temperature, smoke concentration, water depth, vibration frequency and noise, etc., thereby ensuring the comprehensiveness and accuracy of environmental factor monitoring, enabling the system to obtain the status of the train operation environment in real time and ensure the safety of train operation.

[0040] Furthermore, the data analysis module of the present invention determines the single-node data comparison strategy according to the type of data obtained by the second train, and performs comprehensive analysis in combination with the data of the first train, so as to more comprehensively evaluate the impact of environmental changes on train operation, improve the system's ability to cope with various complex situations, and significantly enhance the system's adaptability.

[0041] Furthermore, the present invention can accurately determine whether the data in the storage module belongs to these abnormal types through the data analysis module, providing a reliable basis for subsequent early warning judgment, and effectively avoiding false alarms or missed alarms caused by inaccurate environmental abnormality judgments.

[0042] Furthermore, the data analysis module of the present invention formulates a detailed response strategy based on the change rate of the single data and the different situations of the first train data. When the change rate of the single data is less than the preset change rate, the next comparison is performed to avoid overreaction; when it is greater than or equal to the preset change rate, the first-level warning is activated and the data is compared continuously to ensure that potential safety hazards can be captured in a timely manner.

[0043] Furthermore, the present invention adopts a response strategy of reducing the comparison frequency or conducting the next comparison according to whether the single data of the first train belongs to the type of environmental abnormality event. By setting up a hierarchical response mechanism, corresponding measures can be taken more accurately for different situations, thereby improving the accuracy and timeliness of the early warning.

[0044] Furthermore, the present invention determines the data storage strategy according to the type consistency of the single data of the first train and the second train through the control module. When the single data type is consistent, the storage strategy is further refined according to the data type; when the type is inconsistent, all the data is stored. The setting of the storage strategy not only ensures the complete storage of important data, but also reasonably manages non-abnormal data, effectively optimizes the data storage space, and improves the data storage efficiency.

[0045] Furthermore, the present invention determines the early warning strategy according to the data anomaly index through the control module, divides the early warning into three levels, and activates different levels of early warning measures according to different ranges of the data anomaly index. By setting up a multi-level early warning mechanism, resources can be allocated more reasonably and the efficiency and effectiveness of responding to emergencies can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of module connections of a train online monitoring system according to an embodiment of the present invention;

[0047] Figure 2 A flowchart illustrating a data analysis module determining a data comparison strategy and a response strategy for a single node according to an embodiment of the present invention;

[0048] Figure 3 This is a flow chart of a control module determining an early warning strategy based on a data anomaly index according to an embodiment of the present invention;

[0049] Figure 4 The figure is a flow chart of a train online monitoring method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical test data and the corresponding historical test results of the three months before this test. It can be understood by those skilled in the art that the present invention can determine the above parameters for a single item by selecting the value with the highest proportion as the preset standard parameter based on the data distribution, using weighted summation to use the obtained value as the preset standard parameter, substituting each historical data into a specific formula and using the value obtained by the formula as the preset standard parameter or other selection methods, as long as the present invention can clearly define the different specific situations in the single determination process through the obtained values.

[0053] See also Figures 1 to 4 As shown, they are respectively a module connection diagram of the train online monitoring system according to an embodiment of the present invention; a flow chart of the data analysis module according to an embodiment of the present invention determining the data comparison strategy and response strategy of a single node; a flow chart of the control module according to an embodiment of the present invention determining the early warning strategy according to the data anomaly index; and a flow chart of the train online monitoring method according to an embodiment of the present invention.

[0054] The train online monitoring system according to an embodiment of the present invention includes:

[0055] A monitoring module, comprising a plurality of data acquisition units arranged on a plurality of trains, for acquiring environmental data information of a plurality of node intervals;

[0056] A data storage module connected to the monitoring module, comprising a plurality of storage units arranged on a plurality of nodes on a single line, for storing the environmental data information acquired by the monitoring module;

[0057] a data analysis module connected to the data storage module, configured to determine whether the data in the storage module belongs to the type of environmental anomaly event, and to determine a single-node data comparison strategy based on the type of data acquired by the second train, provided that the data storage module has stored the data acquired by the first train, and to determine a response strategy based on the rate of change of the single data or the data of the first train, the response strategy including performing the next comparison on the single data, initiating a level one warning and continuously comparing the data acquired by each train, and reducing the comparison frequency of the single data;

[0058] an early warning module, connected to the data analysis module, for responding to abnormal data determined by the data analysis module and issuing an early warning signal;

[0059] A control module is respectively connected to the data storage module, the data analysis module and the early warning module, and is used to determine a data storage strategy based on the type consistency of the single data of the first train and the second train, and to determine an early warning strategy based on the data anomaly index. The early warning strategy includes sending an audible and visual warning to the train cab, automatically limiting the train speed to a preset safe speed, and triggering emergency braking and linking with the dispatching center.

[0060] Specifically, the first train and the second train refer to the first train and the second train passing on a single line, respectively.

[0061] Specifically, the data acquisition unit on each train includes a humidity sensor for monitoring ambient humidity, a temperature sensor for monitoring ambient temperature, a photoelectric smoke sensor for monitoring ambient smoke concentration, a distributed fiber optic liquid level sensor for monitoring water depth, a three-axis piezoelectric acceleration sensor for monitoring vibration frequency, and a noise sensor for monitoring noise.

[0062] Specifically, the types of environmental abnormal events include one or more of the following:

[0063] The ambient humidity is greater than the preset humidity of 85% RH;

[0064] Or, the ambient temperature is higher than the preset temperature of 60°C;

[0065] Or, the ambient smoke concentration is greater than the preset concentration of 0.8dB / m;

[0066] Or, the depth of accumulated water is greater than the preset depth of 150mm;

[0067] Or, the vibration frequency is greater than the preset frequency of 45Hz;

[0068] Or, the noise is greater than the preset noise by 105dB.

[0069] In an embodiment of the present invention, the preset humidity value is 85% RH, the preset temperature value is 60°C, the preset concentration value is 0.8dB / m, the preset depth value is 150mm, the preset frequency value is 45Hz, and the preset noise value is 105dB, but the above values ​​are not limited to these. Those skilled in the art can adjust the above values ​​according to actual needs.

[0070] Specifically, the data analysis module determines a single-node data comparison strategy based on the type of data acquired by the second train. If the data acquired by the second train belongs to an environmental abnormality event type, the single-node data comparison strategy is determined to be comparing the data of the second train with the data of the first train to obtain a single data change rate and determining a response strategy based on the single data change rate.

[0071] If the data of the second train does not belong to the type of environmental abnormal event, the data comparison strategy of the single node is determined to determine the response strategy according to the type of the single data of the first train.

[0072] Specifically, the single item of data refers to any type of environmental abnormal event. In this embodiment, the specific type is not limited.

[0073] Specifically, after obtaining the initial environmental data information through the first train, the data comparison strategy of a single node is determined according to the type of data obtained by the second train, which helps to quickly screen the data. By setting different strategies, the monitoring efficiency of the system can be improved, and abnormal situations can be responded to the early warning module in a timely manner.

[0074] Specifically, the data analysis module determines a response strategy based on the change rate of the single data, wherein if the change rate of the single data is less than a preset change rate of 5%, the response strategy is determined to be to perform a next comparison on the single data;

[0075] If the change rate of the single data is greater than or equal to the preset change rate, the response strategy is determined to be to start a level one warning and continuously compare the data obtained from each train.

[0076] In the embodiment of the present invention, the preset change rate is set to 5%, but the value is not limited thereto, and those skilled in the art may adjust the value according to actual needs.

[0077] Specifically, the continuous comparison is to compare the data obtained from each new train with the old data.

[0078] Specifically, the data analysis module determines a response strategy based on the type of the single data of the first train, wherein if the single data of the first train does not belong to the type of environmental abnormality event, the response strategy is determined to be to reduce the comparison frequency of the single data;

[0079] If the data of the first train belongs to the type of environmental abnormality event, the response strategy is determined to perform the next comparison on the single data.

[0080] Specifically, reducing the comparison frequency of a single item of data means adjusting the comparison frequency to once after every two data acquisitions.

[0081] Specifically, the control module determines a data storage strategy based on the consistency of the types of the individual data of the first train and the second train, wherein, if the types of the individual data are consistent, the storage strategy is further determined based on the types of the individual data;

[0082] If the types of individual data items are inconsistent, the data storage strategy is determined to be all storage.

[0083] Specifically, the control module further determines a storage strategy according to the type of the single data item, wherein if the type of the single data item belongs to the type of environmental abnormality event, the storage strategy is determined to be all storage;

[0084] If the type of the single data item does not belong to the type of environmental abnormal event, the storage strategy is determined to store the new data and delete the old data.

[0085] Specifically, the new data is the data obtained from the latest test, and the old data is the data before the latest test.

[0086] Specifically, if the type of a single item of data belongs to the type of environmental abnormal event, it means that the data is continuously abnormal. Storing all the data will help with subsequent early warnings and timely analysis of the cause of the abnormality based on data changes during maintenance; if the type of a single item of data does not belong to the type of environmental abnormal event, it means that the data remains normal. Replacing the old data with the new data will help save storage space.

[0087] Specifically, the control module determines an early warning strategy based on the data anomaly index after obtaining 10 sets of preset data. If the data anomaly index is less than the first preset anomaly index of 0.18, a third-level early warning is activated, which is to send an audible and visual warning to the train cab.

[0088] If the data anomaly index is greater than or equal to the first preset anomaly index and less than the second preset anomaly index of 0.32, a second-level warning is activated, and the second-level warning is to automatically limit the train speed to a preset safe speed;

[0089] If the data anomaly index is greater than or equal to the second preset anomaly index, a first-level warning is initiated, and the first-level warning triggers emergency braking and links to the dispatch center.

[0090] Specifically, all data obtained when a single train passes through several nodes along a single line from the starting point to the end point is a set of data.

[0091] In an embodiment of the present invention, the preset group value is 10 groups, the first preset abnormality index value is 0.18, and the second preset abnormality index value is 0.32, but the above values ​​are not limited thereto. Those skilled in the art can adjust the above values ​​according to actual needs.

[0092] Specifically, the data anomaly index is determined by the proportion of the number of environmental anomaly event types and the comprehensive proportion of the number of single data anomalies. The data anomaly index = the first weight coefficient × the proportion of the number of environmental anomaly event types + the second weight coefficient × the comprehensive proportion of the number of single data anomalies. The first weight coefficient is 0.6, and the second weight coefficient is 0.4. The proportion of the number of environmental anomaly event types is the ratio of the data types belonging to the environmental anomaly event type to all data types. The comprehensive proportion of the number of single data anomalies is the average value of the ratio of the number of times each data item is abnormal in the data obtained a preset number of times to the total number of times the single item is obtained.

[0093] The train online monitoring method according to an embodiment of the present invention includes:

[0094] Step S1, when a single train passes through each node section of a single line, the environmental data information of the node section is acquired and stored through a data acquisition unit;

[0095] Step S2: determining, for a single node, whether each data acquired by a single train belongs to an abnormal environmental event type;

[0096] Step S3: determining a single-node data comparison strategy based on the type of the single data item obtained by the second train, including determining a response strategy based on a rate of change of the single data item obtained by comparing the data of the second train with that of the first train, or determining a response strategy based on the type of the single data item obtained by the first train;

[0097] Step S4, determining a data storage strategy based on the type consistency of the single item data of the first train and the second train;

[0098] Step S5, determining an early warning strategy based on the data anomaly index after obtaining the preset group data, including starting a first-level early warning, a second-level early warning, or a third-level early warning.

[0099] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0100] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A train online monitoring system, characterized in that: include: A monitoring module, which includes several data acquisition units installed on each train, used to obtain environmental data information of several node intervals; A data storage module, connected to the monitoring module, comprising a plurality of storage units provided on each node on a single line, for storing environmental data information acquired by the monitoring module; a data analysis module connected to the data storage module, configured to determine whether the data in the storage module belongs to the type of environmental anomaly event, and to determine a single-node data comparison strategy based on the type of data acquired by the second train, provided that the data storage module has stored the data acquired by the first train, and to determine a response strategy based on the rate of change of the single data or the data of the first train, the response strategy including performing the next comparison on the single data, initiating a level one warning and continuously comparing the data acquired by each train, and reducing the comparison frequency of the single data; an early warning module, connected to the data analysis module, for responding to abnormal data determined by the data analysis module and issuing an early warning signal; a control module, connected to the data storage module, the data analysis module, and the warning module, respectively, for determining a data storage strategy based on the type consistency of the individual data of the first train and the second train, and determining a warning strategy based on the data anomaly index, the warning strategy including sending an audible and visual warning to the train cab, automatically limiting the train speed to a preset safe speed, and triggering emergency braking and linking with the dispatch center; The data analysis module determines the data comparison strategy of a single node according to the type of data obtained by the second train. include, If the data acquired by the second train belongs to the type of environmental abnormal event, the data comparison strategy of the single node is determined to be comparing the data of the second train with the data of the first train to obtain the single data change rate and determine the response strategy according to the single data change rate; If the data of the second train does not belong to the type of environmental abnormal event, the data comparison strategy of the single node is determined to be a response strategy determined according to the type of the single data of the first train; The control module determines a data storage strategy based on the consistency of the types of the individual data of the first train and the second train, wherein if the types of the individual data are consistent, the storage strategy is further determined based on the types of the individual data; If the types of individual data items are inconsistent, the data storage strategy is determined to be all storage; The control module further determines a storage strategy based on the type of the single data item, wherein if the type of the single data item belongs to an environmental abnormality event type, the storage strategy is determined to be all storage; If the type of the single data item does not belong to the type of environmental abnormal event, the storage strategy is determined to store the new data and delete the old data.

2. The train online monitoring system according to claim 1, characterized in that: Environmental abnormality event types include one or more of the following: The ambient humidity is greater than the preset humidity; Or, the ambient temperature is greater than the preset temperature; Or, the ambient smoke concentration is greater than the preset concentration; Or, the depth of accumulated water is greater than the preset depth; Or, the vibration frequency is greater than the preset frequency; Or, the noise is greater than the preset noise.

3. The train online monitoring system according to claim 2, characterized in that: The data analysis module determines a response strategy based on a comparison result of the change rate of the single data item with a preset change rate, wherein: If the change rate of the single item of data is less than the preset change rate, determining the response strategy to perform the next comparison on the single item of data; If the change rate of the single data is greater than or equal to the preset change rate, the response strategy is determined to be to start a level one warning and continuously compare the data obtained from each train.

4. The train online monitoring system according to claim 3, characterized in that: The data analysis module determines a response strategy based on whether the type of the single item of data of the first train meets the type of environmental abnormality event, wherein: If the single item data of the first train does not belong to the type of environmental abnormal event, the response strategy is determined to reduce the comparison frequency of the single item data; If the data of the first train belongs to the type of environmental abnormality event, the response strategy is determined to perform the next comparison on the single data.

5. The train online monitoring system according to claim 4, characterized in that: The control module determines the early warning strategy based on the data anomaly index after obtaining the preset group data, wherein: If the data abnormality index is less than the first preset abnormality index, a third-level warning is activated, wherein the third-level warning is to send an audible and visual warning to the train cab; If the data anomaly index is greater than or equal to the first preset anomaly index and less than the second preset anomaly index, a secondary warning is activated, and the secondary warning is to automatically limit the train speed to a preset safe speed; If the data anomaly index is greater than or equal to the second preset anomaly index, a first-level warning is initiated, and the first-level warning triggers emergency braking and links to the dispatch center.

6. The train online monitoring system according to claim 5, characterized in that: The data anomaly index is determined by the proportion of the number of environmental anomaly event types and the comprehensive proportion of the number of single data anomalies.

7. A train online monitoring method, applied to the train online monitoring system according to any one of claims 1 to 6, characterized in that: include: When a single train passes through each node section of a single line, the environmental data information of the node section is obtained and stored through the data acquisition unit; Determine for a single node whether the data obtained by a single train belongs to the type of environmental abnormality event; Determining a data comparison strategy for a single node based on the type of the single data obtained by the second train, including determining a response strategy based on a rate of change of the single data obtained by comparing the data of the second train with that of the first train, or determining a response strategy based on the type of the single data of the first train; Determining a data storage strategy based on type consistency of individual data of the first train and the second train; The early warning strategy is determined based on the data anomaly index after obtaining the preset group data, including launching a first-level early warning, a second-level early warning, or a third-level early warning.

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