CN108198271B - Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer - Google Patents

Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer Download PDF

Info

Publication number
CN108198271B
CN108198271B CN201711437963.8A CN201711437963A CN108198271B CN 108198271 B CN108198271 B CN 108198271B CN 201711437963 A CN201711437963 A CN 201711437963A CN 108198271 B CN108198271 B CN 108198271B
Authority
CN
China
Prior art keywords
seum
degree
operation risk
risk
control
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201711437963.8A
Other languages
Chinese (zh)
Other versions
CN108198271A (en
Inventor
周欣
徐先良
王阳鹏
陈俊
曹德宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Casco Signal Ltd
Original Assignee
Casco Signal Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Casco Signal Ltd filed Critical Casco Signal Ltd
Priority to CN201711437963.8A priority Critical patent/CN108198271B/en
Publication of CN108198271A publication Critical patent/CN108198271A/en
Application granted granted Critical
Publication of CN108198271B publication Critical patent/CN108198271B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention relates to a train operation risk dynamic analysis method based on an SEUM (sequence analysis Unit) vehicle-mounted computer, which comprises the following steps of: step 1, quantitatively evaluating the fault degree LF and the protection degree LB of the current operation stage; step 2: quantitatively evaluating the system progress LP of the system control process; and step 3: constructing a half-mode elliptic navel manifold model SEUM; and 4, step 4: and analyzing the system operation risk SR. Compared with the prior art, the method applies the semi-module oval navel manifold model SEUM to analyze the operation risk of the system, the SEUM is suitable for analyzing the dynamic process, and the requirements on the continuity of the control variable and the duality of the state variable enable the SEUM to have good adaptability to large-scale complex systems.

Description

Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer
Technical Field
The invention relates to a dynamic analysis method for train operation risks, in particular to a dynamic analysis method for train operation risks based on a SEUM (semi-module elliptic navel manifold model) by utilizing an on-board computer.
Background
Safety and efficiency are two important goals that train operation control systems need to achieve. Both conceptually and effectively, risks and security are closely related. Safety is to avoid unacceptable risk; risk is a combination of the likelihood of outcome and its severity. The dynamic monitoring of the system safety state in the train control system operation stage can be realized by the online analysis of the train operation risk. In a train control system, an automatic train protection subsystem (ATP) mainly comprises a vehicle-mounted computer, a speed sensor, a train braking unit, a human-computer interface, a transponder information receiving module, a positioning information receiving module and a train operation monitoring device. In the aspect of subsystem composition, the vehicle-mounted computer is the core of safety protection, and the working state information of each component, the running state information of the train and the interaction information of the subsystem and the driver are collected to the vehicle-mounted computer. Therefore, based on a proper mathematical model, by setting a train operation risk analysis function in the vehicle-mounted computer and carrying out real-time quantitative calculation on the safety risk of the train in the current operation state by utilizing various collected information, the dynamic monitoring of the operation safety state of the train control system and the online prediction of accidents can be efficiently realized.
Among a plurality of mathematical models, the manifold model is more suitable for information fusion of a large-scale complex system dynamic process. Manifold theory is an important branch of differential geometry theory, which has the potential to describe the dynamic formal evolution of various aspects of nature and contains a general approach suitable for describing the multiple changing behavior caused by gradual forces or stimuli. Actually, the manifold can be applied to various technical fields. In the field of structure observation, manifold can be used to analyze a variety of optical phenomena, for example, manifold knowledge can be applied to analyze the structural stability of the caustic of light to find all possible forms of caustic; the duffing equation of nonlinear oscillation can be described by applying a typical surface of a differential manifold, and the instability of an elastic structure can be researched. In the field of biological simulation, manifold theory can analyze the moving problem of population boundaries; depending on the particular manifold, some scholars also created a trusted system describing the predator-prey dynamic relationships along different respective directions of study. In the field of social science, differential manifolds are used to build medium-scale dynamic qualitative models (between micro-scale neuronal quantitative analysis models and macro-scale psychological qualitative analysis models) to study the activities of the human brain. In the technical field of engineering, the differential manifold can be used for analyzing volcanic eruption and fault movement so as to simulate the movement process of a continental shelf; specific manifolds can also be used to describe the speed-flow relationship and apply it to the analysis of traffic data.
In summary, it is a necessary problem to utilize an on-board computer to perform dynamic analysis of train operation risk based on a specific manifold model.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a dynamic analysis method for train running risks based on an SEUM (sequence analysis unit) vehicle-mounted computer.
The purpose of the invention can be realized by the following technical scheme:
a dynamic analysis method for train operation risk based on an SEUM (sequence analysis Unit) utilizing an on-board computer comprises the following steps:
step 1, quantitatively evaluating the fault degree LF and the protection degree LB of the current operation stage;
step 2: quantitatively evaluating the system progress LP of the system control process;
and step 3: constructing a half-mode elliptic navel manifold model SEUM;
and 4, step 4: and analyzing the system operation risk SR.
Preferably, the step 1 specifically comprises:
firstly, after system design is completed, identifying a hazard source of the system according to a specific control structure;
then, the component C is subjected to joint model based on the fault tree and the event treeiProbability of failure
Figure BDA0001526150810000021
And the severity of the possible consequences
Figure BDA0001526150810000022
Carrying out evaluation;
finally, the safety barrier B embedded in the control structurejDegree of confidence of
Figure BDA0001526150810000023
And degree of protection against related faults or system defects
Figure BDA0001526150810000024
To make an evaluation, then
Figure BDA0001526150810000025
And is
Figure BDA0001526150810000026
The fault degree LF is the risk degree of component faults accumulated in the system operation process, and the protection degree LB is the protection degree of a safety barrier which is cumulatively executed by a control structure in the current operation stage;
preferably, the step 2 specifically comprises:
the system is in0,tend]Operated during a time interval of t0At the moment when a certain system function is executed as start, it is expected that at tendThe system goals are accomplished at times, and to achieve the desired goals of the system, the system is accomplished in successive steps, each step corresponding to the achievement of a particular component function, at t, according to the system designendThe system runs to tmwhen m component functions have been executed at the time, the system progress LP at the current time is 100% xm/and m is less than or equal to n.
Preferably, the step 3 specifically comprises:
constructing a control space C of SEUM by using three variables of fault degree LF, protection degree LB and system progress LP; since LF, LB and LP do not take negative numbers, coordinate transformation is needed to be carried out on the control space of the potential function of SEUM, and the obtained new equilibrium surface equation is
Figure BDA0001526150810000031
Here, (u, v, w) corresponds to (LF, LB, LP), (X, Y) corresponds to (X, Y), and none of u, v, w is a negative number, where X is the set of safe states and Y is the set of dangerous states.
Preferably, the step 4 specifically includes:
based on SEUM, obtaining a bifurcation point set Q of a potential function V (X, Y), wherein the bifurcation point set Q is a cone in a control space C, and the safe state and the dangerous state of the system are represented by X and Y, and during the operation of the system, the safe state and the dangerous state determine a system operation risk SR under the action of the potential function V (X, Y), so that the change of the solution set of V (X, Y) in the control space C is equal to the change of the system operation risk, and the bifurcation point set Q reflects the change and interaction of three control variables of LF, LB and LP in the control space C, so that the step change of the solution set of V (X, Y) occurs, and the shape of the cone can reflect the system operation risk.
Preferably, if the volume of the cone is K, K is proportional to SR.
Preferably, the scaling factor is set to 1, i.e., K ═ SR.
Compared with the prior art, the invention has the following advantages:
1. the invention applies a half-mode elliptic navel manifold model (SEUM) to analyze the operation risk of a system. The SEUM is suitable for analysis of dynamic processes, and has good adaptability to large-scale complex systems due to the requirements of continuity of control variables and duality of state variables.
2. The quantitative evaluation process constructed by the invention can realize the rapid calculation of the risk index. The dynamic analysis of the system operation risk requires that the risk index can be calculated in real time, which can be realized based on the algorithm of the quantitative evaluation flow.
3. The method is applied to the train control vehicle-mounted computer, and can carry out dynamic quantitative evaluation on the train operation risk. The train control vehicle-mounted computer is an information processing core of a train vehicle-mounted safety protection system, and the method is applied to the vehicle-mounted computer, so that the safety of a train during operation can be directly guaranteed.
4. The manifold curved surface output by the invention can visually reflect the operation risk of the system. The shape (volume) of the cone curved surface corresponding to the SEUM can reflect the size of the system operation risk, and is helpful for a front-line operator and a decision maker of the system to quickly and intuitively know the current system operation risk.
Drawings
FIG. 1 is a control variable schematic of the operational risk of the system;
FIG. 2 is a schematic diagram of a half-mode elliptical umbilical manifold model (SEUM);
FIG. 3 is a flow chart of quantitative assessment of fault Level (LF) and protection Level (LB);
FIG. 4 is a diagram illustrating a quantitative assessment method of system progress (LP);
FIG. 5 is a schematic diagram of a SEUM-based train-control onboard computer dynamic risk analysis scenario;
fig. 6 is a schematic diagram of the output analysis result.
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 some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The dynamics of a dynamic process can be derived from a smooth potential, such as the potential described by a lyapunov function (which is similar to the classical potential in that its minima determine the equilibrium state, except that its gradient does not determine the trajectory), so that the generalized manifold is suitable for processes that are almost always located at the equilibrium point of the ordinary differential equation, whether its dynamics is of the gradient type or not; it can also be shown that manifolds are suitable for processes subject to a variational principle, or governed by many partial differential equations commonly encountered, even for situations where a limit cycle is present rather than an equilibrium point. It must be noted that the manifold is a smooth requirement for the potential of the process. Clearly, both discontinuities and continuity can be the subject of investigation, and if it is assumed that the discontinuities are inherent in the dynamics of the process, it is difficult to expect more insight into the nature of the discontinuities, since any discontinuities can be arranged in them as desired. The manifold has the potential to predict many qualitative aspects of the system, not even what differential equations will work, let alone how they are solved, and this is done on the basis of a few assumptions, which are not limiting.
Given a potential V describing the state of the process, the equation
Figure BDA0001526150810000042
Its equilibrium surface M is defined, where the subscript x indicates that the gradient is only in terms of the state variable x. This surface is constituted by all the critical points of V, i.e. by all the equilibrium points of the system (stable or otherwise). M is a manifold, namely a smooth curved surface with good performance. The odd-point set Q is a subset of M consisting of all the degradation critical points of V that satisfy
Figure BDA0001526150810000041
And Δ ≡ det { H (V) } ═ 0, where H (V) is the sea plug matrix of V:
Figure BDA0001526150810000051
q is projected into the control space C to obtain a set of diverging points B, i.e. a set of all points in C that change the morphology of V. The potential function of the elliptic umbilical manifold is V (x, y) ═ 1/3 x3-xy2+wx2+wy2-ux + vy. The phase space of the manifold is five-dimensional, the state space is two-dimensional, and the control space is three-dimensional. Equation for the equilibrium surface M is
Figure BDA0001526150810000052
The set of singularities Q is a subset of M, whose equation is
Figure BDA0001526150810000053
Consider that the cross-section w is constant. According to Δ ═ 4 (w)2-x2-y2) 0, x is wcos θ, and y is sin θ. Then the parametric equations for u and v with respect to θ are
u＝w2(cos 2θ+2 cosθ)
v＝w2(sin 2θ-2 sinθ)
When w is 0, BwIs point (u, v) = (0, 0). When w is not equal to 0, there is
du/d θ is 0 when θ is 0, pi, ± 2 pi/3
dv/d θ is 0 when θ is 0, ± 2 pi/3
Thus, BwIn (3 w)2,0),
Figure BDA0001526150810000054
And
Figure BDA0001526150810000055
has a pole point and is at (-w)2And 0) there is a vertical tangent. Let the parameter w always take positive real number to obtain the semi-module elliptic navel Model (semi-elliptic Umbilic Model-SEUM). The divergent point set Q of semm is a cone in its control parameter space, with three saddle points and a minimum point.
Based on SEUM, a dynamic analysis method of the system operation risk can be constructed. As shown in fig. 1, the interaction among the component failure occurring during the operation of the system, the safety barrier implemented in the control structure of the system, and the progress of the control process affects the accident evolution process and determines the probability and severity of the accident. Meanwhile, the product of the probability and the severity of the accident can directly measure the operation risk of the system under the current condition. Therefore, if the risk degree of the accumulated component faults under the current condition of the system is measured by the fault degree (LF), the protection degree of the safety barrier which is accumulatively executed in the current control structure is measured by the protection degree (LB), and the progress degree of the system control process to the current condition is measured by the system progress (LP), the LF, the LB and the LP determine the operation risk of the current system through certain interaction.
A failure is the termination of the ability of one functional unit to provide a desired function, or the performance of another function other than the desired function by one functional unit. Failure is the root cause of an accident. In the system operation stage, the occurrence of faults increases the probability and severity of accidents, and the operation risk of the system is increased.
Safety barriers are program-specific physical systems or subsystems designed to avoid or protect against events, or to control or limit the occurrence of faults. The safety barrier is implemented to prevent or cope with the occurrence of a fault. In the system operation stage, the execution of the safety barrier enables the system to be kept in a safe operation state, the system safety is maintained or improved, and the operation risk is further reduced.
The progress of the system execution is related to the design of the control process. For a system with stable structure, the probability and the severity of accidents are low in the early stage of the execution of the control process, namely, the operation risk is low under the condition that the system progress is low. When the control process is executed to an intermediate stage, the probability and severity of the occurrence of an accident start to increase, i.e., the operational risk starts to increase in the case where the system progress starts to increase. In practice, both 4.28-pack railway accidents and 7.23-shaft high-speed rail accidents occur after the control process is executed to a certain extent (i.e. the middle stage of the system process). When the control process is performed to the end, accidents easily occur during this period, since the dynamic complexity of the system external environment and internal control structure is maximized. For example, a challenger space shuttle detonates near the end of the launch lift-off phase; the columbia space shuttle disintegrates when the return phase is nearly completed. Thus, for a structurally stable system, as the system progress increases (i.e., the control process advances), the probability and severity of the incident tends to increase.
Since three factor sets of the fault degree (LF), the protection degree (LB) and the system progress (LP) are orthogonal, a Cartesian coordinate system formed by the three factors is used as the three-dimensional control space C. The set of system operation risks (SR) is jointly determined by a safe state set X and a dangerous state set Y of the system in an operation phase, and two orthogonal sets formed by the safe state set X and the dangerous state set Y of the system are used as a two-dimensional state space. Thus, as shown in fig. 2, semm is constructed with the control variables LF, LB and LP, and the state variables X and Y, and the operational risk SR of the system is analyzed by means of the set of divergence points Q of semm.
When the train control vehicle-mounted computer dynamic risk analysis method based on the SEUM is used, the following 4 steps are required to be completed in order to construct the method with a complete analysis framework and obtain an accurate output result.
Step 1, quantitatively evaluating the fault degree (LF) and the protection degree (LB) of the current operation stage.
The degree of failure (LF) is the risk of component failure that accumulates during system operation. The guard Level (LB) is the guard level of the safety barrier that the control structure cumulatively executes in the current operation stage. In order to dynamically evaluate LF and LB, firstly, after system design is completed, the system needs to be identified by a hazard source according to a specific control structure. According to the dangerous source identification quantitative evaluation flow shown in FIG. 3, the component C is subjected to a combined model (bow tie model) based on a fault tree and an event treeiProbability of failure
Figure BDA0001526150810000071
And the severity of the possible consequences
Figure BDA0001526150810000072
Carrying out evaluation; for safety barrier B embedded in control structurejDegree of confidence of
Figure BDA0001526150810000073
And degree of protection against related faults or system defects
Figure BDA0001526150810000074
Evaluation was performed. Then
Figure BDA0001526150810000075
And is
Figure BDA0001526150810000076
Step 2: the system progress (LP) of the system control process is quantitatively evaluated.
The system control process is the process by which the system achieves the design goal. A system control process is a collection of functions that a component plans to perform. The components in the control structure perform functions statically in a designed sequence. However, in practical operation, it is an uncertain event to consider whether a component can completely perform a specified function in a specified phase, which may be subject to system internal and external disturbances. As shown in FIG. 4, the system is at [ t ]0,tend]Operated during a time interval of t0Executing an item at a timeThe system function is initiated (e.g., the vehicle computer platform performs the departure test function) with an expectation of tendThe system objective (e.g., arrival of the train at the terminal) is completed at that time. To achieve the desired goals of the system, the steps are performed in sequential steps, each step corresponding to the performance of a particular component function. At t according to the system designendThe system runs to tmwhen m component functions have been executed at the time, the system progress LP at the current time is 100% × m/n, and m is less than or equal to n.
And step 3: constructing SEUM.
The control space C of the SEUM is constructed by three variables of fault degree (LF), protection degree (LB) and system progress (LP). Since LF, LB and LP do not take negative numbers, coordinate transformation is needed to be carried out on the control space of the potential function of SEUM, and the obtained new equilibrium surface equation is
Figure BDA0001526150810000077
Here, (u, v, w) corresponds to (LF, LB, LP), (X, Y) corresponds to (X, Y), and u, v, w are not all negative numbers. The manifold curves corresponding to the singularity set Q in the control space C are shown in fig. 2.
And 4, step 4: the system operational risk (SR) is analyzed.
Based on SEUM, a set of diverging points Q for the potential function V (X, Y) can be obtained. The set of bifurcation points Q appears as a cone in the control space C, as shown in FIG. 2. Since X and Y represent the set of safe and dangerous states of the system, which determine the system operational risk (SR) under the influence of the potential function V (X, Y) during the operation of the system, the change of the solution set of V (X, Y) in the control space C is equivalent to the change of the system operational risk. In the control space C, the set of bifurcation points Q reflects the change of three control variables LF, LB and LP and the step change of the solution set of V (X, Y) caused by the interaction described by V (X, Y), so the shape of the cone can reflect the operation risk of the system. Specifically, assuming the volume of the cone is K, K is proportional to SR. For the sake of analysis, the scaling factor is set to 1 without loss of generality, i.e., K ═ SR.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The method for analyzing the dynamic risk of the train control vehicle-mounted computer based on the SEUM is combined with a vehicle-mounted ATP subsystem platform in an ITCS (enhanced train operation control system) to analyze specific situations that component faults occur and safety barriers execute in the control process, and the method is taken as a typical implementation case.
Fig. 5 shows a scenario with broad meaning. In the vehicle-mounted ATP subsystem platform, a vehicle-mounted safety computer receives working state information (including fault information of components) transmitted by a speed sensor, a brake unit, a human-computer interaction interface, a transponder information receiving module, a positioning information receiving module and a train operation monitoring device. Safety barriers embedded in the system control structure protect against ATP subsystem component failures. During train operation, at t1The human-computer interaction interface has a fault at the moment CF 1; after t2At that time, the brake unit has failed CF 2; after t3At that time, the speed sensor has failed CF 3. For component failure CF1, the system implements safety barrier B1; for component failure CF2, the system implements safety barrier B2; for component failure CF3, the system implements safety barrier B3.
Step 1, quantitatively evaluating the fault degree (LF) and the protection degree (LB) of the current operation stage. The quantitative evaluation flow shown in FIG. 3 is used for evaluation, and the fault degree of the component fault CF1 is obtained
Figure BDA0001526150810000081
The protective degree of the safety barrier B1 is
Figure BDA0001526150810000082
The failure degree of the component failure CF2 is
Figure BDA0001526150810000083
The protective degree of the safety barrier B2 is
Figure BDA0001526150810000084
Failure trip of component failure CF3Degree of
Figure BDA0001526150810000085
The protective degree of the safety barrier B3 is
Figure BDA0001526150810000086
Step 2: the system progress (LP) of the system control process is quantitatively evaluated. Evaluating according to the quantitative evaluation flow shown in fig. 4 to obtain the time when the train runs to t1At time, the system progress of the control process is LP1＝m1N; when the train runs to t2At time, the system progress of the control process is LP2＝m2N; when the train runs to t3At time, the system progress of the control process is LP3＝m3And/n. Where n is the total number of component functions that the system control process needs to perform during the period from the start of train operation to the arrival of the train at the station, and m is the total number of component functionsiFor train running to tiThe number of component functions that the system control process needs to perform at the time. As shown in FIG. 5, in this exemplary scenario, m1＜m2＜m3Then there is LP1＜LP2＜LP3。
And step 3: constructing SEUM.
According to the step 1 and the step 2, when the train runs to t1At the moment, the fault degree of the system is
Figure BDA0001526150810000091
Degree of protection
Figure BDA0001526150810000092
When the train runs to t2At the moment, the fault degree of the system is
Figure BDA0001526150810000093
Figure BDA0001526150810000094
Degree of protection
Figure BDA0001526150810000095
When the train runs to t3At the moment, the fault degree of the system is
Figure BDA0001526150810000096
Degree of protection
Figure BDA0001526150810000097
According to the SEUM model, (LF, LB, LP) corresponds to (u, v, w). According to the equation of equilibrium surface
Figure BDA0001526150810000098
Obtaining the odd point set Q equation
Figure BDA0001526150810000099
And 4, step 4: the system operational risk (SR) is analyzed.
According to the equation of the singularity set Q and the control variables (u, v, w), the volume K of the cone manifold can be obtained, and the K is equal to the system operation risk at the current moment. In the exemplary scenario shown in FIG. 5, K can be determined according to the equation of the singular point set Q1＜K2＜K3. The specific analysis result is shown in FIG. 6, when the train runs to t1At time, the cone volume is K1I.e. the system operational risk SR1＝K1(ii) a When the train runs to t2At time, the cone volume is K2I.e. the system operational risk SR2＝K2(ii) a When the train runs to t3At time, the cone volume is K3I.e. the system operational risk SR3＝K3. Thus, in this typical scenario, the risk of system operation continues to increase.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A dynamic analysis method for train operation risk based on a vehicle-mounted computer used by SEUM is characterized in that the vehicle-mounted safety computer receives working state information transmitted by a speed sensor, a brake unit, a human-computer interaction interface, a transponder information receiving module, a positioning information receiving module and a train operation monitoring device, wherein the working state information comprises fault information of components, and the method comprises the following steps:
step 1, quantitatively evaluating the fault degree LF and the protection degree LB of the current operation stage;
step 2: quantitatively evaluating the system progress LP of the system control process;
and step 3: constructing a half-mode elliptic navel manifold model SEUM;
and 4, step 4: analyzing the system operation risk SR;
the step 3 specifically comprises the following steps:
constructing a control space C of SEUM by using three variables of fault degree LF, protection degree LB and system progress LP; since LF, LB and LP do not take negative numbers, coordinate transformation is needed to be carried out on the control space of the potential function of SEUM, and the obtained new equilibrium surface equation is
Figure FDA0002539225360000011
Here, (u, v, w) corresponds to (LF, LB, LP), (X, Y) corresponds to (X, Y), and none of u, v, w is a negative number, where X is the set of safe states and Y is the set of dangerous states;
the step 1 specifically comprises the following steps:
firstly, after system design is completed, identifying a hazard source of the system according to a specific control structure;
then, the component C is subjected to joint model based on the fault tree and the event treeiProbability of failure
Figure FDA0002539225360000012
And to the possible consequencesDegree of severity of
Figure FDA0002539225360000013
Carrying out evaluation;
finally, the safety barrier B embedded in the control structurejDegree of confidence of
Figure FDA0002539225360000014
And degree of protection against related faults or system defects
Figure FDA0002539225360000015
To make an evaluation, then
Figure FDA0002539225360000016
And is
Figure FDA0002539225360000017
The fault degree LF is the risk degree of component faults accumulated in the system operation process, and the protection degree LB is the protection degree of a safety barrier which is cumulatively executed by a control structure in the current operation stage;
the step 2 specifically comprises the following steps:
the system is in0,tend]Operated during a time interval of t0At the moment when a certain system function is executed as start, it is expected that at tendThe system goals are accomplished at times, and to achieve the desired goals of the system, the system is accomplished in successive steps, each step corresponding to the achievement of a particular component function, at t, according to the system designendThe system runs to tmwhen m component functions have been executed at the time, the system progress LP at the current time is 100% × m/n, and m is less than or equal to n.
2. The method according to claim 1, wherein the step 4 is specifically:
based on SEUM, obtaining a bifurcation point set Q of a potential function V (X, Y), wherein the bifurcation point set Q is a cone in a control space C, and the safe state and the dangerous state of the system are represented by X and Y, and during the operation of the system, the safe state and the dangerous state determine a system operation risk SR under the action of the potential function V (X, Y), so that the change of the solution set of V (X, Y) in the control space C is equal to the change of the system operation risk, and the bifurcation point set Q reflects the change and interaction of three control variables of LF, LB and LP in the control space C, so that the step change of the solution set of V (X, Y) occurs, and the shape of the cone can reflect the system operation risk.
3. The method of claim 2, wherein if the volume of the pyramid is K, K is proportional to SR.
4. A method according to claim 3, characterized in that the scaling factor is set to 1, i.e. K-SR.
CN201711437963.8A 2017-12-26 2017-12-26 Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer Active CN108198271B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711437963.8A CN108198271B (en) 2017-12-26 2017-12-26 Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711437963.8A CN108198271B (en) 2017-12-26 2017-12-26 Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer

Publications (2)

Publication Number Publication Date
CN108198271A CN108198271A (en) 2018-06-22
CN108198271B true CN108198271B (en) 2020-09-18

Family

ID=62584201

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711437963.8A Active CN108198271B (en) 2017-12-26 2017-12-26 Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer

Country Status (1)

Country Link
CN (1) CN108198271B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103114554A (en) * 2013-02-04 2013-05-22 河海大学 Forewarning method of concrete dam damage field evolution state

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8508353B2 (en) * 2009-01-26 2013-08-13 Drivecam, Inc. Driver risk assessment system and method having calibrating automatic event scoring
US7958034B2 (en) * 2007-09-28 2011-06-07 Aon Benfield Global, Inc. Method of providing catastrophic loss protection through a mortgage
CN101753381B (en) * 2009-12-25 2012-10-10 华中科技大学 Method for detecting network attack behaviors
CN106709613B (en) * 2015-07-16 2020-11-27 中国科学院信息工程研究所 Risk assessment method applicable to industrial control system
CN105843208B (en) * 2016-03-16 2018-04-10 北京交通大学 Train control system fault recovery method based on spring-go stress effect

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103114554A (en) * 2013-02-04 2013-05-22 河海大学 Forewarning method of concrete dam damage field evolution state

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于突变模型的泥石流危险度评价;安玉华，王清，张晨，赵传海;《吉林大学学报(地球科学版)》;20120331;第42卷;全文 *
系统安全突变与风险曲线研究;王阳鹏;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20171215(第12期);第15-17页，第20-22页，第38页，第49页和第51-52页 *

Also Published As

Publication number Publication date
CN108198271A (en) 2018-06-22

Similar Documents

Publication Publication Date Title
Li et al. A light gradient boosting machine for remainning useful life estimation of aircraft engines
CN109581871B (en) Industrial control system intrusion detection method of immune countermeasure sample
CN108196525B (en) The operational safety risk dynamic analysing method of Train Running Control System for High Speed
CN106529306B (en) Security of system appraisal procedure and device
EP3287902B1 (en) Fault-tolerant method and device of controlling an autonomous technical plant on the basis of a consolidated environmental model
CN103914353A (en) Combined test method for combining software reliability tests with hardware reliability tests
US8103490B2 (en) Method to aid the design of a system architecture
CN106055729A (en) Fault tree analysis method based on Monte Carlo simulation
CN102360333B (en) The component software reliability analysis method of troubleshooting process is considered based on HISQM queuing model
CN109918687A (en) A kind of train dynamics emulation mode and emulation platform based on machine learning
Szkoda et al. Reliability and availability assessment of diesel locomotive using fault tree analysis
CN108198271B (en) Train operation risk dynamic analysis method based on SEUM (remote intelligent management) utilization vehicle-mounted computer
Baptista et al. Prognostics in aeronautics with deep recurrent neural networks
Lu et al. Flexible truncation method for the reliability assessment of phased mission systems with repairable components
Vu et al. High-performance mesoscopic traffic simulation with GPU for large scale networks
Cerquitelli et al. Enabling predictive analytics for smart manufacturing through an IIoT platform
Gao et al. A dynamic fault tree based CBTC onboard ATP system safety analysis method
CN104808645A (en) Discrete event system fault diagnosis method
Zhang et al. Taking ideas from paper to practice: a case study of improving design processes through detailed modeling and systematic analysis
CN107860420A (en) A kind of large and complex structure health monitoring systems based on multi-main body cooperation
Feng et al. Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment
Huang et al. The Complexity of Model Checking Succinct Multiagent Systems.
Xu et al. Parallel Testing for Centralized Traffic Control Systems of Intelligent Railways
CN106909084B (en) A kind of master control system for fuel cell car Dynamic Synthesis test equipment
Ma et al. An analysis of air traffic management concepts of operation using simulation and formal verification

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant