CN114265372A - CBTC system real-time fault intelligent diagnosis method and system based on big data - Google Patents
CBTC system real-time fault intelligent diagnosis method and system based on big data Download PDFInfo
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Abstract
The invention discloses a CBTC system real-time fault intelligent diagnosis method and a system based on big data, firstly, an MSS subsystem self-defines a CBI subsystem fault event through a fault event self-defined configuration file; secondly, the MSS subsystem stores the interactive interface data of each subsystem of the CBTC and the data information generated in the processing process of each subsystem in real time; then, when the MSS subsystem generates or detects the fault of the CBI subsystem, searching the corresponding fault event according to the fault event self-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through the interface, wherein the MSS subsystem fault intelligent diagnosis module analyzes the data stored in the MSS subsystem until the MSS subsystem fault intelligent diagnosis module is positioned to an external interface; and finally, the MSS subsystem fault intelligent diagnosis module judges whether the fault root comes from the CBI subsystem. According to the invention, the fault root cause and the accurate time point are positioned through big data analysis, and the fault solving efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of rail transit, and particularly relates to a CBTC (communication based train control) system.
Background
The CBTC system is a communication-based train automatic control system, and mainly comprises a CC (vehicle-mounted controller) subsystem, a CBI (computer interlocking) subsystem, a ZC (zone controller) subsystem and an MSS (signal maintenance support subsystem) subsystem. The MSS subsystem is responsible for system maintenance support and provides system operation fault information and related maintenance records for maintenance personnel, but because the fault diagnosis logic of each system is complex, when a service fault occurs at present, the MSS can only provide fault information, the root cause of the fault cannot be specifically positioned, and the maintenance personnel of each subsystem still need to manually analyze system logs, and because the log information data volume is large, and partial faults are gradually accumulated, the time span is large, the fault positioning process is complex and difficult. When a fault occurs, because it takes a long time to analyze and position, the problem cannot be solved in real time, and the operation of rail transit may be influenced.
The current fault positioning mode has high requirements on maintenance personnel, and can not position faults quickly in real time, so that the fault diagnosis precision is improved, and the problem that the maintenance personnel need to position the faults quickly becomes urgent to solve at present.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a CBTC system real-time fault intelligent diagnosis method based on big data, which can improve the diagnosis precision of faults and provide more accurate fault alarm for maintenance personnel, thereby solving the problems of difficult fault location and long time consumption.
In order to solve the technical problems, the invention adopts the following technical scheme:
a CBTC system real-time fault intelligent diagnosis method based on big data,
firstly, the MSS subsystem self-defines the fault event of each subsystem of the CBTC through a fault event self-defining configuration file, and the event has uniqueness;
secondly, in the running process of each subsystem of the CBTC, the MSS subsystem stores the interactive interface data of each subsystem of the CBTC and the data information generated in the processing process of each subsystem in real time;
then, when the MSS subsystem generates or detects the fault of each subsystem, searching the corresponding fault event according to the fault event self-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through the interface, wherein the MSS subsystem fault intelligent diagnosis module analyzes the data stored in the MSS subsystem until the external interface is positioned;
finally, the MSS subsystem fault intelligent diagnosis module judges whether the fault root comes from one of the subsystems, and if the fault root comes from the subsystem, the MSS subsystem is reported; otherwise, the interface data of the fault judgment basis is fed back to the MSS subsystem, and the MSS subsystem forwards the information to the fault intelligent diagnosis module of the subsystem, and the fault intelligent diagnosis module of the subsystem continues to position the fault intelligent diagnosis module until the specific fault reason is positioned.
Preferably, the configuration file customized failure event in the MSS sub-system includes the following data: unique event ID, fault description, conditions to determine fault. Preferably, the number of the conditions for determining the failure is 1 or more, and the plurality of conditions are satisfied either simultaneously or one of them.
Preferably, the fault sent to the MSS subsystem fault intelligent diagnosis module has two sources: MSS self-defined fault events and faults forwarded from fault intelligent diagnosis modules of other subsystems of the CBTC.
Preferably, when the MSS sub-system generates or detects a fault, the MSS sub-system sends the centralized station equipment ID, the alarm serial number, the event ID, the fault object name, the CBI master system flag during the fault, the cycle number of the fault occurrence, and the big data stored by the MSS within a certain cycle range before the fault occurrence to the MSS sub-system fault intelligent diagnosis module, and the MSS sub-system fault intelligent diagnosis module analyzes the data sent by the MSS sub-system according to the fault condition corresponding to the event ID, searches for the cause a causing the fault condition, and if the existing data cannot locate the cause, the MSS sub-system still needs to request the big data, and when requesting the big data, the MSS sub-system should include the centralized station equipment ID, the log start cycle number, the cycle number and the system that needs the data; after receiving the big data request, the MSS subsystem needs to judge whether the system where the cycle number is located is consistent with the system needing data, if so, the MSS subsystem forwards obtains data in a certain cycle range from the specified initial cycle number and the system where the cycle number is located; otherwise, the corresponding time point is found out according to the appointed initial period number and the system of the period number, the data of a certain period is taken forward from the time point, and then the data are sent to the MSS subsystem fault intelligent diagnosis module.
Preferably, after receiving the big data sent by the MSS subsystem, the MSS subsystem fault intelligent diagnosis module continues to analyze the big data, locates the cause a causing the fault condition, and if the cause a is not a root cause, needs to search for the cycle number of the cause a, at this time, sends a request for the change cycle of the service parameter variable related to the cause a to the MSS subsystem, and the MSS subsystem feeds back the change cycle of the specified parameter variable; after receiving the cycle number with the cause A, the MSS subsystem fault intelligent diagnosis module requests big data to the MSS subsystem by taking the cycle number as the starting cycle number, and after receiving the request, the MSS subsystem sends the data to the MSS subsystem fault intelligent module, and the MSS subsystem fault intelligent diagnosis module continues to analyze the cause of the cause A until the cause A is positioned to an external interface.
The invention also provides a CBTC system real-time fault intelligent diagnosis system based on big data, which comprises an MSS subsystem and an MSS subsystem fault intelligent diagnosis module, wherein the MSS subsystem is provided with a fault self-defining module, a data storage module and a fault detection module; wherein,
the fault self-defining module self-defines the fault event of the CBI subsystem in a configuration mode to form a fault event self-defining configuration file, and the event has uniqueness;
the data storage module stores interface data interacted by each subsystem of the CBTC and custom data information generated in the processing process of each subsystem in real time;
after the fault detection module detects the fault of the CBI subsystem, searching a corresponding fault event according to a fault event self-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through an interface;
and after receiving the fault event, the MSS subsystem fault intelligent diagnosis module analyzes the fault event until the fault of the external interface is positioned.
According to the technical scheme, the MSS subsystem fault intelligent diagnosis module triggers diagnosis through fault events sent by the MSS subsystem, and fault root factors and accurate time points are located through big data analysis. Therefore, when a fault occurs, the MSS subsystem can provide more accurate fault alarm for maintenance personnel by calling the MSS subsystem fault intelligent diagnosis module, and the maintenance personnel can directly troubleshoot the corresponding fault interface through alarm information, so that the fault solving efficiency is improved.
The following detailed description of the present invention will be provided in conjunction with the accompanying drawings.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a deployment diagram of a CBTC system real-time fault intelligent diagnosis system based on big data according to the present invention;
FIG. 2 is a data flow diagram of a fault intelligent diagnosis module;
fig. 3 is an interaction diagram of an MSS and a fault intelligent diagnosis module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention is suitable for a CBTC system, wherein an MSS subsystem stores interface data interacted with each subsystem of the CBTC and custom data information generated in the processing process of each subsystem, an MSS subsystem fault intelligent diagnosis module triggers diagnosis by an event sent by an MSS, and fault root causes and accurate time points are positioned through big data analysis.
Example one
A CBTC system real-time fault intelligent diagnosis method based on big data is disclosed, as shown in figures 1 to 3,
firstly, an MSS subsystem self-defines a CBI subsystem fault event and other subsystem fault events in a CBTC system through a fault event self-defining configuration file, and the events have uniqueness;
secondly, in the running process of each subsystem of the CBTC, the MSS subsystem stores the interactive interface data of each subsystem of the CBTC and the data information generated in the processing process of each subsystem in real time;
then, after the MSS subsystem generates or detects the fault of the CBI subsystem (or other subsystems) (the existing MSS subsystem has the function of detecting self fault and the fault of each subsystem), the corresponding fault event is searched according to the fault event self-defined configuration file, and the fault event is sent to the MSS subsystem fault intelligent diagnosis module through the interface, the MSS subsystem fault intelligent diagnosis module locates the root cause of the fault occurrence (the external interface abnormality is the root cause) according to the big data and fault information stored by the MSS, if the fault root cause can not be located by the existing big data, the MSS still needs to apply the big data for continuous analysis until the external interface is located;
finally, taking the external interface as a CBI subsystem as an example, according to the analysis, the MSS subsystem fault intelligent diagnosis module judges whether the fault root is from the CBI subsystem, and if the fault root is from the CBI subsystem, the MSS subsystem is reported; otherwise, the interface data according to the judgment of the fault is fed back to the MSS subsystem, and the MSS subsystem forwards the information to the corresponding CBI subsystem fault intelligent diagnosis module and continues positioning the information until a specific fault reason is positioned (the fault intelligent diagnosis module of each subsystem of the existing CBTC has the functions of detecting and positioning the fault of the subsystem where the fault is positioned).
Referring to fig. 1, the MSS sub-system fault intelligent diagnosis module may be deployed in a distributed manner, and may be deployed at a centralized station a and a centralized station B simultaneously.
If the MSS subsystem detects that the CBI subsystem has a fault, the fault sent to the fault intelligent diagnosis module has two sources: MSS self-defined fault event and fault forwarded from CBI subsystem (including adjacent station CBI subsystem) fault intelligent diagnosis module, so there are two different interfaces to trigger intelligent diagnosis, and the self-defined fault event interface should include: event unique identifier, fault object, fault time point and MSS stored big data in a certain time range before fault generation; the interface forwarded from the CBI subsystem fault intelligent diagnosis module comprises: a failed link, a failure description, a failure time point, and big data stored by the MSS within a certain time range before the failure occurs.
Referring to the data flow diagram of the MSS subsystem fault intelligent diagnosis module shown in fig. 2, after receiving a fault event, the MSS subsystem fault intelligent diagnosis module traces back the big data stored by the MSS from the fault occurrence point according to the condition corresponding to the event in the configuration, and searches for the cause a causing the condition, if the cause a does not directly correspond to the system external interface, the time point of the cause a continues to be traced back forward, the cause B causing the a is analyzed, and the trace back is sequentially performed forward until the root cause, that is, the fault of the external interface is located.
The interaction of the MSS subsystem and the fault intelligent diagnosis module is given with reference to fig. 3. The configuration file self-defined event in the MSS subsystem consists of the following data: the method comprises the following steps of unique event ID, fault description and fault judging conditions, wherein the number of the fault judging conditions can be 1 or more, and a plurality of the fault judging conditions can be met simultaneously or one of the conditions can be met. When an MSS subsystem generates or detects a fault, transmitting a centralized station equipment ID, an alarm serial number, an event ID, a fault object name, a CBI main system mark during the fault, a cycle number of the fault and big data stored by the MSS within a certain cycle range before the fault is generated to an MSS subsystem fault intelligent diagnosis module, analyzing the data transmitted by the MSS subsystem fault intelligent diagnosis module according to the fault condition corresponding to the event ID, searching a reason A causing the fault condition, if the reason can not be located by existing data, still requesting the big data from the MSS, and when requesting the big data, including the centralized station equipment ID, a log starting cycle number, a cycle number and a system needing the data.
After receiving the big data request, the MSS needs to determine whether the system where the cycle number is located is consistent with the system where the data is needed, and if so, the MSS forwards obtains the data in a certain cycle range from the specified initial cycle number and the system where the cycle number is located; otherwise, the corresponding time point is found out according to the appointed initial period number and the system of the period number, data of a certain period is taken from the time point, and then the data are sent to the intelligent diagnosis module. The number of the MSS data fetching cycles is self-adaptively adjusted according to the request frequency of the intelligent diagnosis module, the network environment and the like.
After receiving the data sent by the MSS, the fault intelligent diagnosis module continues to analyze the data, locates the cause a causing the fault condition, and if the cause a is not the root cause, needs to search for the cycle number of the cause a, at this time, sends a request for the change cycle of the service parameter variable related to the cause a to the MSS, and the MSS feeds back the change cycle of the specified parameter variable. After receiving the cycle number with the cause A, the intelligent fault diagnosis module requests big data to the MSS by taking the cycle number as the starting cycle number, and after receiving the request, the MSS sends the data to the intelligent fault diagnosis module, and the intelligent fault diagnosis module continues to analyze the cause A until the external interface is positioned.
After the external interface is positioned, judging whether the external interface is from a CBI subsystem, if the external interface is from the CBI subsystem, sending the equipment ID of the centralized station, the fault period, the fault system and the fault reason to an MSS (maintenance station), and directly displaying the MSS to a maintainer; otherwise, the centralized station equipment ID, the failure time, the corresponding link, the link uniqueness identifier and the failure reason are sent to the MSS, the MSS forwards all the received failure related description information to the corresponding subsystem failure intelligent diagnosis module according to the corresponding link, and the MSS continues positioning and analyzing until the root reason is positioned.
The invention intelligently diagnoses the root cause which finally causes the fault according to the self-defined event and the big data stored by the MSS. The method has the following characteristics: when a CBTC (communication based train control) system has a service fault, the root cause of the fault can be diagnosed in real time, the accuracy of the fault is improved because the root cause directly corresponds to an external interface, and a maintainer does not need to manually analyze a large amount of log data and only needs to directly solve the problem according to fault description.
It is understood that the processing procedure of the MSS subsystem fault intelligent diagnosis module if receiving the fault of the other subsystem forwarded by the MSS is consistent with the above description when the MSS subsystem generates or detects the fault of the CBI subsystem.
Example two
A CBTC system real-time fault intelligent diagnosis system based on big data comprises an MSS subsystem and an MSS subsystem fault intelligent diagnosis module, wherein the MSS subsystem is provided with a fault self-defining module, a data storage module and a fault detection module; wherein,
the fault self-defining module self-defines the fault event of each subsystem in a configuration mode to form a fault event self-defining configuration file, and the event has uniqueness;
the data storage module stores interface data interacted by each subsystem of the CBTC and custom data information generated in the processing process of each subsystem in real time;
after the fault detection module detects the fault of the subsystem, searching for a corresponding fault event according to the fault event user-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through an interface;
and after receiving the fault event, the MSS subsystem fault intelligent diagnosis module analyzes the fault event until the fault of the external interface is positioned.
The specific implementation method and the flow corresponding to the diagnostic system in the embodiment refer to the first embodiment.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (7)
1. A CBTC system real-time fault intelligent diagnosis method based on big data is characterized in that:
firstly, the MSS subsystem self-defines the fault event of each subsystem of the CBTC through a fault event self-defining configuration file, and the event has uniqueness;
secondly, in the running process of each subsystem of the CBTC, the MSS subsystem stores the interactive interface data of each subsystem of the CBTC and the data information generated in the processing process of each subsystem in real time;
then, when the MSS subsystem generates or detects the fault of each subsystem, searching the corresponding fault event according to the fault event self-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through the interface, wherein the MSS subsystem fault intelligent diagnosis module analyzes the data stored in the MSS subsystem until the external interface is positioned;
finally, the MSS subsystem fault intelligent diagnosis module judges whether the fault root comes from one of the subsystems, and if the fault root comes from the subsystem, the MSS subsystem is reported; otherwise, the interface data of the fault judgment basis is fed back to the MSS subsystem, and the MSS subsystem forwards the information to the fault intelligent diagnosis module of the subsystem, and the fault intelligent diagnosis module of the subsystem continues to position the fault intelligent diagnosis module until the specific fault reason is positioned.
2. The CBTC system real-time fault intelligent diagnosis method based on big data as claimed in claim 1, wherein: the configuration file customized fault event in the MSS sub-system includes the following data: unique event ID, fault description, conditions to determine fault.
3. The CBTC system real-time fault intelligent diagnosis method based on big data as claimed in claim 2, wherein: the number of the conditions for determining the failure is 1 or more, and a plurality of conditions may be satisfied either simultaneously or one of them.
4. The CBTC system real-time fault intelligent diagnosis method based on big data as claimed in claim 3, wherein: the fault sent to the MSS subsystem fault intelligent diagnosis module has two sources: MSS self-defined fault events and faults forwarded from fault intelligent diagnosis modules of other subsystems of the CBTC.
5. The CBTC system real-time fault intelligent diagnosis method based on big data as claimed in claim 4, wherein: when an MSS subsystem generates or detects a fault, transmitting a centralized station equipment ID, an alarm serial number, an event ID, a fault object name, a CBI main system mark during the fault, a cycle number of the fault and big data stored by the MSS within a certain cycle range before the fault is generated to an MSS subsystem fault intelligent diagnosis module, analyzing the data transmitted by the MSS subsystem fault intelligent diagnosis module according to the fault condition corresponding to the event ID, searching a reason A causing the fault condition, if the existing data can not locate the reason, still requesting the MSS subsystem for big data, and when requesting the big data, including the system where the centralized station equipment ID, a log initial cycle number and the cycle number are located and the system needing the data; after receiving the big data request, the MSS subsystem needs to judge whether the system where the cycle number is located is consistent with the system needing data, if so, the MSS subsystem forwards obtains data in a certain cycle range from the specified initial cycle number and the system where the cycle number is located; otherwise, the corresponding time point is found out according to the appointed initial period number and the system of the period number, the data of a certain period is taken forward from the time point, and then the data are sent to the MSS subsystem fault intelligent diagnosis module.
6. The CBTC system real-time fault intelligent diagnosis method based on big data as claimed in claim 5, wherein: after receiving the big data sent by the MSS subsystem, the MSS subsystem fault intelligent diagnosis module continues to analyze and locates the reason A causing the fault condition, if the reason A is not the root cause, the MSS subsystem needs to search the cycle number of the reason A, at this time, a request of the change cycle of the service parameter variable related to the reason A is sent to the MSS subsystem, and the MSS subsystem feeds back the change cycle of the designated parameter variable; after receiving the cycle number with the cause A, the MSS subsystem fault intelligent diagnosis module requests big data to the MSS subsystem by taking the cycle number as the starting cycle number, and after receiving the request, the MSS subsystem sends the data to the MSS subsystem fault intelligent module, and the MSS subsystem fault intelligent diagnosis module continues to analyze the cause of the cause A until the cause A is positioned to an external interface.
7. A CBTC system real-time fault intelligent diagnosis system based on big data is characterized by comprising an MSS subsystem and an MSS subsystem fault intelligent diagnosis module, wherein the MSS subsystem is provided with a fault self-defining module, a data storage module and a fault detection module; wherein,
the fault self-defining module self-defines the fault event of each subsystem in a configuration mode to form a fault event self-defining configuration file, and the event has uniqueness;
the data storage module stores interface data interacted by each subsystem of the CBTC and custom data information generated in the processing process of each subsystem in real time;
after the fault detection module detects the fault of the subsystem, searching for a corresponding fault event according to the fault event user-defined configuration file, and sending the fault event to the MSS subsystem fault intelligent diagnosis module through an interface;
and after receiving the fault event, the MSS subsystem fault intelligent diagnosis module analyzes the fault event until the fault of the external interface is positioned.
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| 裴加富，等: "基于通信的列车控制数据通信子系统的智能诊断和预警分析", 城市轨道交通研究, vol. 23, no. 01, 31 January 2020 (2020-01-31), pages 75 - 78 * |
| 贾鹏;: "CBTC车载设备在线故障诊断系统研究", 铁道运输与经济, no. 11, 8 November 2018 (2018-11-08), pages 98 - 102 * |
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