CN108920846A - A kind of risk coupling analytical method of high-speed rail train control system complexity operation scene - Google Patents
A kind of risk coupling analytical method of high-speed rail train control system complexity operation scene Download PDFInfo
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Abstract
The invention discloses a kind of risk coupling analytical methods of high-speed rail train control system complexity operation scene, include the following steps：Under the technical specification of high-speed rail train control system, the uml model of high-speed rail train control system operation scene is established using UML modeling technique；According to the uml model of operation scene, the simulation model of operation scene is established；The injection failure combination in the simulation model of operation scene, obtains emulation data；Using decision Tree algorithms, risk coupling rule is excavated from obtained emulation data.The risk coupling analytical method of high-speed rail train control system complexity operation scene provided by the invention, pass through the complicated building for runing model of place, utilize system simulation technology, Failure Injection Technique and machine learning algorithm, can emulate covering small probability failure combination event high-speed rail train control system complexity operation scene, can comprehensively, system, efficiently excavate high-speed rail train control system complexity operation scene in potential risks coupling rule.
Description
Technical field
The present invention relates to rail traffic security fields, specially a kind of risk coupling of high-speed rail train control system complexity operation scene
Close analysis method.
Background technique
High Speed Train Operation Control system, abbreviation train control system are the core skills for guaranteeing the operation of train high-speed secure
Art equipment and Safety-Critical System, be guarantee bullet train safety, on schedule, comfortably, high density run without interruption important technology
Means.Compared with classical signal system, the software and hardware of train control system is highly integrated, between subsystem depth couple, system form,
Function logic, state transition and interbehavior etc. are more complicated.Therefore, the dangerous reason mechanism of train control system also develops
It is all the more complicated.And once there is safety problem in train control system, will directly affect high-speed railway traffic safety, gently then driving in
It is disconnected, heavy then car crash.
During train control system operation, the operation scene of Various Complex is covered.Under different operation scenes, train control system
Subsystem between there is different interbehaviors, for each subsystem by realizing different function, train control system is completed in common cooperation
Entire operation scene.However, since there is the interbehaviors of this complexity and required reality between the subsystem of train control system
Existing different function logics lead to the risk coupled problem for containing height in the operation scene of train control system, i.e.,：Some subsystem
The event of failure that system generates, by the complex interaction behavior between subsystem, the event of failure that can be generated with other subsystems, with
Certain logic interacts, intercouples, and eventually leads to train control system and generates dangerous failure, jeopardizes traffic safety.Column control system
Risk coupled problem in system operation scene, has extremely strong concealment, complexity and harmfulness, it has also become high-speed rail train control system
Safety analysis field urgent problem to be solved.
Currently, the research of the identification of train control system danger reason and risk analysis, primarily focuses on subsystem internal, utilize
The System Safety Analysis method based on linear event chain such as HAZOP, FMEA, half formalization or formalized model in conjunction with subsystem
The identification of hazardous events, and comprehensive utilization FTA, ETA, fuzzy mathematics, Bayesian network scheduling theory and method are carried out to identification
Hazardous events carry out risk analysis.In addition, there are also the System Safety Analysis method based on state diagram of utilization, by being needed to system
It asks and is parsed, define system mode and state transition, using the Formal Modelings such as SCADE, UPPAAL and verification tool, establish
State graph model carries out system requirements to portray description, and carries out shape to system mode graph model by the way of model testing
Formula chemical examination card, analysis system whether there is potential security risk.And utilize the System Safety Analysis side propagated based on failure
System is divided into different levels and module based on the thought of layering from top to bottom, recycles inactive logic needle from bottom to top by method
The failure communication process of different levels module is modeled, failure communication process is divided using the searching algorithm of automation
Analysis.The shortcomings that these methods：
(1) what the System Safety Analysis method based on linear event chain obtained is mainly with linear causal system
Dangerous reason.However, the risk coupled problem of train control system, often in train control system subsystem potential danger source in a system
It arranges and interacts and generate under complicated reciprocation, be not often simple linear causality between them, but
A kind of characteristic emerged in large numbers is showed, therefore, the System Safety Analysis method based on linear event chain is difficult to the wind to train control system
Dangerous coupled problem is analyzed；
(2) System Safety Analysis based on state diagram and the System Safety Analysis propagated based on failure, are establishing model
With compare during analysis dependent on expertise, may due to analysis personnel difference and obtain different results.And
And the model that both modeling methods are established is difficult to embody continuous physical change, accordingly, it is difficult to describe blending together for train control system
Property.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of comprehensive, system, efficient high-speed rail column control systems
The complicated operation scene risk coupling analytical method of system.
The purpose of the present invention is achieved through the following technical solutions：A kind of high-speed rail train control system complexity operation scene
Risk coupling analytical method, includes the following steps：
S1. under the technical specification of high-speed rail train control system, high-speed rail train control system operation scene is established using UML modeling technique
Uml model, including following sub-step：
S11. under the technical specification of high-speed rail train control system, according to the feature of wanted description object, uml model is divided into
System interaction layer, system scenarios layer and system logic layer；
S12. it in system interaction layer, is described using interaction figure between the train control system operation each participation main body of scene
Interbehavior；
S13. in system scenarios layer, each participation main body itself of train control system operation scene is described using state diagram
State transition；
S104. in system logic layer, each participation main body in train control system operation scene is described using state diagram
Function logic.
S2. according to the uml model of operation scene, the simulation model of operation scene, including following sub-step are established：
S21. the interbehavior between simulation model subsystem is determined according to the description of system interaction layer, realizes operation scene
The interactive cooperation of simulation model subsystem；
S22. operation scene is realized in the state transition that simulation model subsystem itself is determined according to the description of system scenarios layer
The status change of simulation model subsystem；
S23. the realization logic that simulation model subsystem function is determined according to the description of system logic layer realizes operation scene
The function logic of simulation model subsystem.
S3. the injection failure combination in the simulation model of operation scene, obtains emulation data, including following sub-step：
S31. using potential danger source in dangerous and operability method (HAZOP) identification operation scene, failure is constructed
Pattern base；
S32. failure is subjected to permutation and combination, generates the failure script comprising failure execution sequence；
S33. Failure Injection Technique is utilized, executes event to run scene simulation model as goal systems according to failure script
The combination of barrier is injected, and is monitored and is recorded the operation data of analogue system, obtain system emulation data.
S4. decision Tree algorithms are utilized, risk coupling rule are excavated from the obtained emulation data of step S3, including following
Sub-step：
S41. using the system dangerous state to be analyzed as decision attribute, then using the fault condition of each subsystem as item
Part attribute constructs decision table；
S42. decision table is learnt using decision Tree algorithms, what excavation was wherein contained leads to correspondence system precarious position
The risk of generation couples rule.
Further, the emulation data that the step S3 is generated include the fault condition of operation scene subsystem module and imitate
The system normal condition and system dangerous status information of true mode.
The beneficial effects of the invention are as follows：The present invention utilizes UML method, constructs the uml model of complicated operation scene, and description is high
Iron train control system complexity runs the participation main body of system in scene, function logic, state transfer and interbehavior.According to UML
Model, building operation scene simulating system.Using the Failure Injection Technique based on emulation, failure combination is injected into operation scene
Analogue system recycles decision Tree algorithms, excavates to causing the risk of system dangerous state to couple rule in the scene.It is logical
The risk coupling rule that above method obtains is crossed, the various danger in train control system complexity operation scene comprehensive and accurate can be covered
Dangerous situation scape.
Detailed description of the invention
Fig. 1 is the risk coupling analytical method flow chart that high-speed rail train control system complexity runs scene；
Fig. 2 is the corresponding relationship in the present invention between uml model and operation scene simulation model；
Fig. 3 is direct fault location and emulation data acquisition flow chart in the present invention；
Fig. 4 is the flow chart that rule is coupled using decision tree learning risk.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing, but protection scope of the present invention is not limited to
It is as described below.
As shown in Figure 1, a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene, including following step
Suddenly：
S1. the uml model of high-speed rail train control system operation scene is established：According to high-speed rail train control system general technical specification, it is
The specification such as requirement profile of uniting, using UML method, the uml model of building operation scene, including following sub-step：
S11. the level of uml model is divided.
The model partition for runing scene is three system interaction layer, system scenarios layer and system logic layer layer by the present invention
Secondary, the common train control system that describes runs scene.
Wherein, all subsystems that system interaction layer model participates in the scene using train control system utilize friendship as research object
Mutual figure participates in the interbehavior between subsystem to describe these；System scenarios layer model is to grind with the single subsystem of train control system
Study carefully object, each state transition process for participating in main body itself is described using state diagram；System logic layer is with some subsystem
Concrete function be research object, described using state diagram some subsystem realize concrete function logical process process.
S12. the system interaction layer model of operation scene is established.
Specifically, based on the description in technical specification to the subsystem interbehavior of participation, system is constructed using interaction figure
Interaction layer model.Using the subsystems in scene as the object in interaction figure, using the interbehavior between subsystem as
Message in interaction figure describes information exchange and its sequential relationship between subsystems.
S13. the system scenarios layer model of operation scene is established.
Specifically, the description based on the variation that sub-system state is generated due to information exchange in technical specification utilizes
State diagram constructs system scenarios layer model.The object that single subsystem is described as state diagram, by subsystem in operation scene
Variation be abstracted as state, information exchange described in system interaction layer model is abstracted as transition condition, building operation scene
System scenarios layer model.
S14. the system logic layer model of operation scene is established.
Specifically, the description that the processing logic of some function is realized based on sub-system in technical specification, utilizes state diagram
Construct system logic layer model.Pair that the function that state each in system scenarios layer model needs to be implemented is described as state diagram
As analyzing subsystem realizes the processing logic when function, is state and its transition condition by this logical mappings, constructs system
Logical layer model.
S2. the corresponding relationship runing scene uml model and runing between scene simulation model is as shown in Figure 2.According to step
The model established in S1, the simulation model of building operation scene, including following sub-step：
S21. firstly, determining the son of required realization in simulation model according to object described in system interaction layer model
System.Then, the interactive information according to described in system interaction layer determines the interaction in simulation model between subsystem, realizes son
Communication between system.
S22. subsystems are generated due to information exchange in simulation model shape is determined according to system scenarios layer model
State transition of the subsystems in entirely operation scene in simulation model is realized in state variation.
S23. the variable and function of each subsystem internal in simulation model are determined according to system logic layer model, realize son
System is to reach the function arithmetic logic of its target.
S3. using the operation scene simulation model established in step S2 as goal systems, the combination injection of failure is realized, entirely
Process is as shown in figure 3, include following sub-step：
S31. danger source present in the operation scene is analyzed using HAZOP.
Specifically, each object in high-speed rail train control system operation scene system interaction layer model is treated as into a node, choosing
It takes the interactive information of object as element, according to the introducer provided in HAZOP, element is combined with introducer, composition is wanted
The issuable deviation of element, recognizes danger source that may be present in the operation scene.
S32. the danger source analyzed in S31 is configured to be easy to be infused by the fault pattern base of procedure identification as failure
The basis entered.
Specifically, in conjunction with HAZOP analyze as a result, based on FARM quaternary group model, determine the specific descriptions of fault set,
It mainly include abort situation, fault type, trouble duration, direct fault location moment.Wherein, direct fault location position can be from
It is obtained in failure Producing reason in HAZOP analysis result；Fault type is corresponding with the type that HAZOP introducer describes；Failure
Duration is failure duration during primary operation；The direct fault location moment is with the shape of simulation model subsystems
State migration is foundation, controls the injection of failure.
S33. the Failure Injection Technique based on emulation is utilized, failure is selected to carry out permutation and combination from fault pattern base, and
It is injected into operation scene simulation model, realizes the combination injection of failure, specifically include：
S331. the direct fault location script comprising failure execution sequence is generated.
S332. the information of read failure script, the failure combination of simulation model will be injected by obtaining subsequent time.
S333. corresponding failure is read from fault pattern base, parses the information of failure, determines the injection time of these failures
Sequence.Then, the operating status for obtaining modules in simulation model, waits pending direct fault location.
S334. after the condition for monitoring direct fault location meets, corresponding direct fault location order is generated, emulation mould is sent to
The specified module of type, enables it modify the state of oneself or interactive information, realizes the injection of failure.
S335. judge the failure for needing to inject in this simulation process whether all injections, if not finishing, after
Continuous step S334, execute direct fault location and record direct fault location as a result, obtaining system emulation data.If the combination of this failure
It has been injected into and finishes, then continue to execute S332, the information in read failure script prepares direct fault location next time, until event
Barrier script circulation finishes.
S4. according to the obtained emulation data of step S3, rule is coupled using decision tree learning risk, as shown in figure 4, packet
Include following steps：
S41. data configuration decision table is emulated according to obtained in step S33.
Specifically, the system emulation data analyzed extract system dangerous status data wherein included, such as：
Train hypervelocity, RBC handover failure etc., the decision attribute as decision table；Then, it extracts in system emulation data and runs scene
The failure that simulation model subsystem generates, such as：Mobile authorization mistake, emergency brake command delay etc., the item as decision table
Part attribute constructs decision table.
S42, the decision table according to obtained in S41 learn decision table using C4.5 decision Tree algorithms, are caused
The risk of system dangerous state couples rule.
To sum up, the present invention utilizes UML method, constructs the uml model of complicated operation scene, and description high-speed rail train control system is complicated
Run the participation main body of system in scene, function logic, state transfer and interbehavior.According to uml model, building operation
Scene simulating system.Using the Failure Injection Technique based on emulation, failure combination is injected into operation scene simulating system, then benefit
With decision Tree algorithms, excavated to causing the risk of system dangerous state to couple rule in the scene.It is obtained by above method
The risk coupling rule arrived comprehensive and accurate can cover the various dangerous scenes in train control system complexity operation scene.
It should be noted that those of ordinary skill in the art will understand that the embodiments described herein is to help
Reader is helped to understand implementation method of the invention, it should be understood that protection scope of the present invention is not limited to such special statement
And embodiment.Those skilled in the art disclosed the technical disclosures can make various do not depart from originally according to the present invention
Various other specific variations and combinations of essence are invented, these variations and combinations are still within the scope of the present invention.
Claims (6)
1. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene, it is characterised in that：Include the following steps：
S1. under the technical specification of high-speed rail train control system, high-speed rail train control system operation scene is established using UML modeling technique
Uml model；
S2. according to the uml model of operation scene, the simulation model of operation scene is established；
S3. the injection failure combination in the simulation model of operation scene, obtains emulation data；
S4. decision Tree algorithms are utilized, risk coupling rule is excavated from the obtained emulation data of step S3.
2. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene according to claim 1, special
Sign is：The step S1 includes following sub-step：
S11. under the technical specification of high-speed rail train control system, according to the feature of wanted description object, uml model is divided into system
Alternation of bed, system scenarios layer and system logic layer；
S12. in system interaction layer, each interaction participated between main body of train control system operation scene is described using interaction figure
Behavior；
S13. in system scenarios layer, each state for participating in main body itself of train control system operation scene is described using state diagram
Migration；
S14. in system logic layer, each function of participating in main body in train control system operation scene is described using state diagram and is patrolled
Volume.
3. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene according to claim 1, special
Sign is：The step S2 includes following sub-step：
S21. the interbehavior between simulation model subsystem is determined according to the description of system interaction layer, realizes operation scene simulation
The interactive cooperation of model subsystem；
S22. operation scene simulation is realized in the state transition that simulation model subsystem itself is determined according to the description of system scenarios layer
The status change of model subsystem；
S23. the realization logic that simulation model subsystem function is determined according to the description of system logic layer realizes operation scene simulation
The function logic of model subsystem.
4. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene according to claim 1, special
Sign is：The step S3 includes following sub-step：
S31. using potential danger source in dangerous and operability method identification operation scene, fault pattern base is constructed；
S32. failure is subjected to permutation and combination, generates the failure script comprising failure execution sequence；
S33. Failure Injection Technique is utilized, executes failure to run scene simulation model as goal systems according to failure script
Combination injection, monitors and records the operation data of analogue system, obtain system emulation data.
5. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene according to claim 1, special
Sign is：The emulation data that the step S3 is generated include the fault condition for runing scene subsystem module and simulation model is
Normal condition of uniting and system dangerous status information.
6. a kind of risk coupling analytical method of high-speed rail train control system complexity operation scene according to claim 1, special
Sign is：The step S4 includes following sub-step：
S41. using the system dangerous state to be analyzed as decision attribute, then using the fault condition of each subsystem as condition category
Property, construct decision table；
S42. decision table is learnt using decision Tree algorithms, what excavation was wherein contained causes correspondence system precarious position to generate
Risk couple rule.
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| CN111210165B (en) * | 2020-01-21 | 2023-04-07 | 哈尔滨工业大学 | Vehicle operation risk assessment system based on risk conduction coupling |
| CN112149293A (en) * | 2020-09-14 | 2020-12-29 | 南京航空航天大学 | Method for determining conditional probability in risk analysis |
| CN117993894A (en) * | 2024-04-03 | 2024-05-07 | 四川航天职业技术学院(四川航天高级技工学校) | Rail transit operation and maintenance state data processing method and system based on artificial intelligence |
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