CN108920846B - Risk coupling analysis method for complex operation scene of train control system of high-speed rail - Google Patents
Risk coupling analysis method for complex operation scene of train control system of high-speed rail Download PDFInfo
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Abstract
The invention discloses a risk coupling analysis method for a complex operation scene of a train control system of a high-speed rail, which comprises the following steps of: under the technical specification of a high-speed rail train control system, establishing a UML model of an operation scene of the high-speed rail train control system by utilizing a UML modeling technology; establishing a simulation model of the operation scene according to the UML model of the operation scene; injecting a fault combination into a simulation model of an operation scene to obtain simulation data; and (4) mining risk coupling rules from the obtained simulation data by using a decision tree algorithm. According to the risk coupling analysis method for the complex operation scene of the high-speed rail train control system, the complex operation scene of the high-speed rail train control system covering the small-probability fault combination event can be simulated by constructing the complex operation scene model and utilizing the system simulation technology, the fault injection technology and the machine learning algorithm, and potential risk coupling rules in the complex operation scene of the high-speed rail train control system can be comprehensively, systematically and efficiently excavated.
Description
Technical Field
The invention relates to the field of rail transit safety, in particular to a risk coupling analysis method for a complex operation scene of a high-speed rail train control system.
Background
The train operation control system of the high-speed railway is a core technical device and a safety demanding system for ensuring the high-speed safe operation of the train, and is an important technical means for ensuring the safe, punctual, comfortable and high-density uninterrupted operation of the high-speed train. Compared with the traditional signal system, the train control system has the advantages that the software and hardware are highly integrated, the subsystems are deeply coupled, and the system composition, the function logic, the state transition, the interaction behavior and the like are more complex. Therefore, the risk causing mechanism of the train control system is also evolving to be more complicated. Once the train control system has a safety problem, the driving safety of the high-speed railway is directly influenced, the driving is interrupted if the safety problem is small, and the vehicle is damaged and people are killed if the safety problem is large.
In the operation process of the train control system, various complex operation scenes are covered. Under different operation scenes, different interactive behaviors are provided among subsystems of the train control system, and the subsystems cooperate to complete the whole operation scene of the train control system through realizing different functions. However, due to the existence of such complex interaction behavior and different functional logics to be implemented between subsystems of the train control system, a high risk coupling problem is involved in an operation scene of the train control system, that is: the fault event generated by a certain subsystem can interact and couple with the fault event generated by other subsystems in a certain logic manner through the complex interaction behavior among the subsystems, and finally the train control system is caused to generate dangerous failure, so that the driving safety is endangered. The risk coupling problem in the train control system operation scene has strong concealment, complexity and harmfulness, and becomes a problem to be solved urgently in the field of high-speed rail train control system safety analysis.
At present, the research of train control system risk cause identification and risk analysis mainly focuses on the interior of a subsystem, a system safety analysis method based on a linear event chain, such as HAZOP and FMEA, is utilized, a semi-formalized or formalized model of the subsystem is combined to identify a hazard event, and theories and methods, such as FTA, ETA, fuzzy mathematics and Bayesian network, are comprehensively utilized to carry out risk analysis on the identified hazard event. In addition, a system safety analysis method based on a state diagram is also used, the system requirements are analyzed, the system state and state transition are defined, a state diagram model is established by using formal modeling and verification tools such as SCADE and UPPAAL, the system requirements are depicted and described, the system state diagram model is formally verified in a model checking mode, and whether potential safety hazards exist in the system is analyzed. And dividing the system into different layers and modules from top to bottom based on a layering idea by using a system safety analysis method based on failure propagation, modeling the failure propagation process of the modules in different layers from bottom to top by using failure logic, and analyzing the failure propagation process by using an automatic search algorithm. The disadvantages of these methods:
(1) the system safety analysis method based on the linear event chain mainly obtains system danger causes with linear causal relationship. However, the risk coupling problem of the train control system is often caused by the mutual influence of potential hazard sources in train control system subsystems under a series of complex interactions, and the potential hazard sources are often not simple linear causal relationships but present a emerging characteristic, so that the risk coupling problem of the train control system is difficult to analyze by a system safety analysis method based on a linear event chain;
(2) the system safety analysis based on the state diagram and the system safety analysis based on the failure propagation are relatively dependent on expert experience in the process of establishing the model and analyzing, and different results can be obtained due to different analysts. Furthermore, models established by these two modeling methods are difficult to represent continuous physical changes, and therefore, it is difficult to describe the miscibility of the train control system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a comprehensive, systematic and efficient method for analyzing the risk coupling of complex operation scenes of a high-speed rail train control system.
The purpose of the invention is realized by the following technical scheme: a risk coupling analysis method for a complex operation scene of a train control system of a high-speed rail comprises the following steps:
s1, under the technical specification of a high-speed rail train control system, a UML model of an operation scene of the high-speed rail train control system is established by utilizing a UML modeling technology, and the method comprises the following substeps:
s11, under the technical specification of a high-speed rail train control system, dividing a UML model into a system interaction layer, a system scene layer and a system logic layer according to the characteristics of an object to be described;
s12, in a system interaction layer, describing interaction behaviors among all participating bodies of a train control system operation scene by using an interaction diagram;
s13, in a system scene layer, describing the state transition of each participating subject in the train control system operation scene by using a state diagram;
and S104, in a system logic layer, describing the functional logic of each participating subject in the train control system operation scene by using a state diagram.
S2, establishing a simulation model of the operation scene according to the UML model of the operation scene, and comprising the following substeps:
s21, determining interaction behaviors among simulation model subsystems according to the description of a system interaction layer, and realizing interaction cooperation of the operation scene simulation model subsystems;
s22, determining the state transition of the simulation model subsystem according to the description of the system scene layer, and realizing the state transition of the operation scene simulation model subsystem;
and S23, determining the realization logic of the simulation model subsystem function according to the description of the system logic layer, and realizing the function logic of the operation scene simulation model subsystem.
S3, injecting a fault combination into the simulation model of the operation scene to obtain simulation data, and comprising the following substeps:
s31, identifying potential hazard sources in an operation scene by using a hazard and operability method (HAZOP), and constructing a fault mode library;
s32, faults are arranged and combined to generate a fault script containing a fault execution sequence;
and S33, performing combined injection of the faults by using a fault injection technology and taking the operation scene simulation model as a target system according to the fault script, and monitoring and recording operation data of the simulation system to obtain system simulation data.
S4, mining risk coupling rules from the simulation data obtained in the step S3 by using a decision tree algorithm, wherein the risk coupling rules comprise the following substeps:
s41, taking the dangerous state of the system to be analyzed as a decision attribute, taking the fault condition of each subsystem as a condition attribute, and constructing a decision table;
and S42, learning the decision table by using a decision tree algorithm, and mining risk coupling rules contained in the decision table and causing the dangerous state of the corresponding system to be generated.
Further, the simulation data generated in step S3 includes the fault condition of the operation scenario subsystem module and the system normal state and system dangerous state information of the simulation model.
The invention has the beneficial effects that: the method utilizes the UML method to construct the UML model of the complex operation scene and describe the participation subject, the functional logic, the state transition and the interactive behavior of the system in the complex operation scene of the high-speed railway train control system. And constructing an operation scene simulation system according to the UML model. And (3) injecting the fault combination into the operation scene simulation system by using a fault injection technology based on simulation, and mining the risk coupling rule causing the dangerous state of the system in the scene by using a decision tree algorithm. The risk coupling rule obtained by the method can comprehensively and accurately cover various dangerous situations in the complex operation scene of the train control system.
Drawings
FIG. 1 is a flow chart of a risk coupling analysis method for a complex operation scenario of a train control system of a high-speed rail;
FIG. 2 is a corresponding relationship between a UML model and an operation scenario simulation model in the present invention;
FIG. 3 is a flow chart of fault injection and simulation data acquisition in the present invention;
FIG. 4 is a flow diagram of learning risk coupling rules using a decision tree.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a risk coupling analysis method for a complex operation scenario of a train control system of a high-speed rail includes the following steps:
s1, establishing a UML model of an operation scene of a train control system of a high-speed rail: according to the general technical specification, the system requirement specification and other specifications of the high-speed rail train control system, a UML model of an operation scene is constructed by using a UML method, and the method comprises the following substeps:
and S11, dividing the hierarchy of the UML model.
The method divides the model of the operation scene into three levels, namely a system interaction layer, a system scene layer and a system logic layer, and describes the operation scene of the train control system together.
The system interaction layer model takes all subsystems of a train control system participating in the scene as research objects, and describes interaction behaviors among the participating subsystems by using an interaction diagram; the system scene layer model takes a single subsystem of the train control system as a research object, and describes the state transition process of each participating subject by using a state diagram; the system logic layer takes the specific function of a certain subsystem as a research object, and describes the logic processing process of realizing the specific function of the certain subsystem by using a state diagram.
And S12, establishing a system interaction layer model of the operation scene.
Specifically, a system interaction layer model is built by utilizing an interaction graph based on the description of the interaction behaviors of the participating subsystems in the technical specification. And taking each subsystem in the scene as an object in the interactive graph, taking the interactive behavior among the subsystems as a message in the interactive graph, and describing the information interaction among the subsystems and the time sequence relation of the information interaction.
And S13, establishing a system scene layer model of the operation scene.
Specifically, a system scene layer model is built by using a state diagram based on the description of the change of the subsystem state caused by information interaction in the technical specification. And taking a single subsystem as an object described by the state diagram, abstracting the change of the subsystem in the operation scene into a state, interactively abstracting information described in the system interaction layer model into a migration condition, and constructing the system scene layer model of the operation scene.
And S14, establishing a system logic layer model of the operation scene.
Specifically, based on the description of processing logic for implementing a certain function in the subsystem in the technical specification, a system logic layer model is constructed by using a state diagram. And taking the function to be executed by each state in the system scene layer model as an object described by the state diagram, analyzing the processing logic when the subsystem realizes the function, mapping the logic into the state and the transition condition thereof, and constructing the logic layer model of the system.
And S2, the corresponding relation between the operation scene UML model and the operation scene simulation model is shown in figure 2. Constructing a simulation model of the operation scene according to the model established in the step S1, comprising the following sub-steps:
s21, firstly, determining subsystems required to be realized in the simulation model according to objects described in the system interaction layer model. And then, determining the interaction among subsystems in the simulation model according to the interaction information described in the system interaction layer, and realizing the communication among the subsystems.
And S22, determining the state change of each subsystem in the simulation model due to information interaction according to the system scene layer model, and realizing the state transition of each subsystem in the simulation model in the whole operation scene.
And S23, determining variables and functions inside each subsystem in the simulation model according to the system logic layer model, and realizing functional operation logic of the subsystems for achieving the target.
And S3, taking the operation scene simulation model established in the step S2 as a target system to realize the combined injection of the faults, wherein the whole process is shown in FIG. 3 and comprises the following substeps:
and S31, analyzing the danger source existing in the operation scene by using the HAZOP.
Specifically, each object in an interaction layer model of a high-speed rail train control system operation scene system is taken as a node, interaction information of the object is selected as an element, according to a guide word provided in the HAZOP, the element is combined with the guide word, and possible deviation of the element is formed, so that possible danger sources in the operation scene are identified.
And S32, constructing the risk source analyzed in the S31 into a fault mode library which is easily identified by a program and used as a basis for fault injection.
Specifically, the detailed description of the fault set is determined based on a FARM four-tuple model by combining the result analyzed by the HAZOP, and mainly comprises the fault position, the fault type, the fault duration and the fault injection time. Wherein, the fault injection position can be obtained from the cause of fault generation in the HAZOP analysis result; the fault type corresponds to the type described by the HAZOP leader; the fault duration is the time that the fault lasts in one operation process; and controlling fault injection according to the state transition of each subsystem of the simulation model at the fault injection time.
S33, selecting faults from the fault mode library for permutation and combination by using a fault injection technology based on simulation, and injecting the faults into the operation scene simulation model to realize the combination injection of the faults, wherein the method specifically comprises the following steps:
and S331, generating a fault injection script containing a fault execution sequence.
And S332, reading the information of the fault script and acquiring a fault combination to be injected into the simulation model at the next moment.
And S333, reading corresponding faults from the fault mode library, analyzing fault information and determining the injection sequence of the faults. And then, acquiring the running state of each module in the simulation model, and waiting for executing fault injection.
And S334, when the condition of fault injection is monitored to be met, generating a corresponding fault injection command, and sending the fault injection command to a module appointed by the simulation model to modify the state or interaction information of the module, so as to realize fault injection.
S335, judging whether the faults needing to be injected in the simulation process are completely injected, if not, continuing to step S334, executing fault injection and recording the fault injection result to obtain system simulation data. If the combination of the faults is injected completely, S332 is continuously executed to read the information in the fault script and prepare for the next fault injection until the fault script is circulated completely.
S4, learning a risk coupling rule by using a decision tree according to the simulation data obtained in the step S3, as shown in FIG. 4, comprising the following steps:
and S41, constructing a decision table according to the simulation data obtained in the step S33.
Specifically, the obtained system simulation data is analyzed, and system dangerous state data contained in the system simulation data is extracted, for example: train overspeed, RBC handover failure and the like as decision attributes of the decision list; then, extracting faults generated by the operation scene simulation model subsystem in the system simulation data, such as: and (4) mobile authorization errors, emergency braking command delay and the like are used as condition attributes of the decision table to construct the decision table.
And S42, learning the decision table by using a C4.5 decision tree algorithm according to the decision table obtained in S41 to obtain a risk coupling rule causing the dangerous state of the system.
In summary, the present invention utilizes the UML method to construct the UML model of the complex operation scenario, and describes the participation subject, the functional logic, the state transition, and the interaction behavior of the system in the complex operation scenario of the train control system for high-speed rail. And constructing an operation scene simulation system according to the UML model. And (3) injecting the fault combination into the operation scene simulation system by using a fault injection technology based on simulation, and mining the risk coupling rule causing the dangerous state of the system in the scene by using a decision tree algorithm. The risk coupling rule obtained by the method can comprehensively and accurately cover various dangerous situations in the complex operation scene of the train control system.
It is to be understood that the embodiments described herein are for the purpose of assisting the reader in understanding the manner of practicing the invention and are not to be construed as limiting the scope of the invention to such particular statements and embodiments. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (5)
1. A risk coupling analysis method for a complex operation scene of a train control system of a high-speed rail is characterized by comprising the following steps: the method comprises the following steps:
s1, under the technical specification of a high-speed rail train control system, establishing a UML model of an operation scene of the high-speed rail train control system by utilizing a UML modeling technology;
s2, establishing a simulation model of the operation scene according to the UML model of the operation scene;
s3, injecting a fault combination into a simulation model of the operation scene to obtain simulation data;
the step S3 includes the following sub-steps:
s31, identifying potential hazard sources in an operation scene by using a hazard and operability method, and constructing a fault mode library;
s32, faults are arranged and combined to generate a fault script containing a fault execution sequence;
s33, performing combined injection of faults by using a fault injection technology and taking an operation scene simulation model as a target system according to a fault script, and monitoring and recording operation data of a simulation system to obtain system simulation data;
and S4, mining risk coupling rules from the simulation data obtained in the step S3 by using a decision tree algorithm.
2. The risk coupling analysis method for the complex operation scenario of the train control system of the high-speed rail according to claim 1, wherein: the step S1 includes the following sub-steps:
s11, under the technical specification of a high-speed rail train control system, dividing a UML model into a system interaction layer, a system scene layer and a system logic layer according to the characteristics of an object to be described;
s12, in a system interaction layer, describing interaction behaviors among all participating bodies of a train control system operation scene by using an interaction diagram;
s13, in a system scene layer, describing the state transition of each participating subject in the train control system operation scene by using a state diagram;
s14, in a system logic layer, describing the functional logic of each participating subject in the train control system operation scene by using a state diagram.
3. The risk coupling analysis method for the complex operation scenario of the train control system of the high-speed rail according to claim 1, wherein: the step S2 includes the following sub-steps:
s21, determining interaction behaviors among simulation model subsystems according to the description of a system interaction layer, and realizing interaction cooperation of the operation scene simulation model subsystems;
s22, determining the state transition of the simulation model subsystem according to the description of the system scene layer, and realizing the state transition of the operation scene simulation model subsystem;
and S23, determining the realization logic of the simulation model subsystem function according to the description of the system logic layer, and realizing the function logic of the operation scene simulation model subsystem.
4. The risk coupling analysis method for the complex operation scenario of the train control system of the high-speed rail according to claim 1, wherein: the simulation data generated in step S3 includes the fault condition of the operation scenario subsystem module and the system normal state and system dangerous state information of the simulation model.
5. The risk coupling analysis method for the complex operation scenario of the train control system of the high-speed rail according to claim 1, wherein: the step S4 includes the following sub-steps:
s41, taking the dangerous state of the system to be analyzed as a decision attribute, taking the fault condition of each subsystem as a condition attribute, and constructing a decision table;
and S42, learning the decision table by using a decision tree algorithm, and mining risk coupling rules contained in the decision table and causing the dangerous state of the corresponding system to be generated.
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