CN111401712A - Urban rail transit risk assessment system and method - Google Patents

Abstract

The invention relates to the technical field of rail transit, in particular to a system and a method for urban rail transit risk assessment, which comprises a risk component module, a risk attribute module, a risk assessment module and a scene simulation module; the risk component module is a risk module consisting of facilities, equipment and personnel, wherein the facilities comprise a track, a station, an interval main structure, a control center, a substation and a vehicle base; the equipment comprises an equipment module consisting of a vehicle, power supply equipment, signal equipment, communication equipment and environmental control equipment; the personnel include train drivers, station attendants, dispatchers and station attendants. According to the method, the operation risk is evaluated, the risk level is judged and a targeted control measure is provided through boundary parameter simulation, risk point analogy and sorting, standard risk value setting and expert experience simulation.

Description

Urban rail transit risk assessment system and method

Technical Field

The invention relates to the technical field of traffic, in particular to a system and a method for urban rail transit risk assessment.

Background

As an important component of urban traffic in China, the safe operation of the urban rail traffic becomes a core problem of government concern, enterprise concern and social concern. How to effectively, comprehensively and accurately evaluate the safety risk state is of great importance when the operation of the short board is found. Since the last century, with the continuous development of computing science, computer technology, information technology and the like, big data analysis and data model technology are fully applied in various fields, and a good basis is provided for solving the problems of diversification of urban rail transit risk data, superposition of elements, complexity of calculation and the like.

Disclosure of Invention

In view of the above, an object of the present invention is to provide a system and a method for urban rail transit risk assessment, including a risk component module, a risk module standard component for establishing facilities such as stations, inter-section tunnels, viaducts, vehicle bases, etc., a drag function of a single component is realized through icons, attributes of each component can be edited, and names and types of fields in an attribute table can be established and edited.

Further, risk module standard components of equipment subsystems such as vehicles, automatic fare collection, platform doors, equipment monitoring systems and escalators are established, the dragging function of a single component is realized through icons, the attributes of the components can be edited, and the names and types of fields in an attribute table can be established and edited.

The method comprises the steps of establishing risk module standard components of key post personnel such as drivers, dispatchers and driving watchmen, realizing the dragging function of a single component through icons, editing the attribute of each component, and establishing and editing the name and type of each field in an attribute table.

The risk module components are shown in table 1.

TABLE 1 Primary Risk Components Module type, name and code

Further, according to the urban rail transit risk characteristics, the urban rail transit system mainly comprises direct risks covering facilities, equipment and personnel and indirect risks covering safety management, wherein a facility system mainly comprises immovable bodies such as lines, rails, stations, interval main structures, a control center, a substation and a vehicle base, an equipment system mainly comprises systems such as vehicles, power supplies, signals, communication and electromechanics, and personnel mainly comprise train drivers, driving operators, dispatchers, station service personnel and the like, and the systems jointly form an organic whole to guarantee urban rail transit operation. And collecting the component module risk types of different systems through accident incentive evolution and statistical analysis.

Further, the risk attribute module:

according to different user requirements, the risk data can be customized, relevant operations can be performed according to the contents of risk modules, risk components, running time, service life, risk points, risk sources, risk occurrence probability possibility, risk influence degree, risk level, events/accidents possibly caused by risks, measures which can be taken, inspection frequency and the like, and the risk data customization module calculates as shown in formula 1.

Formula R as L× S (formula 1)

L is the probability of risk occurrence, and is obtained by data statistical analysis and prediction;

s is the risk influence degree and is obtained through data statistical analysis;

the greater the value of R, the greater the risk of a single risk point causing a possible accident.

Further, the method is a method of ranking qualitative or semi-quantitative outcomes, and performing comprehensive analysis in combination with the possibility of developing a certain level of risk, primarily for risk ranking, to determine which risks require more detailed analysis, or which risks should be handled first and which risks do not need to be dealt with immediately.

The urban rail transit risk influence (S) is totally classified into 7 grades, namely S1-S7, S1 is particularly significant, S2 is significant, S3 is severe, S4 is severe, S5 is moderate, S6 is general, S7 is mild, and the judgment criteria are shown in the following table 2.

TABLE 2 urban rail transit risk impact (S) judgment criteria

The urban rail transit risk probability (L) is the frequency of the events, is totally 10 grades, respectively L1-L10, and the judgment criteria are shown in table 3.

TABLE 3 urban rail transit risk probability (L) decision criterion

The urban rail transit risk grade (R) is obtained by jointly analyzing two factors of risk influence (S) and risk probability (L), and is divided into 4 grades, namely R1-R4, the acceptable degree of each grade is shown in table 4, and the judgment criterion is shown in table 5.

TABLE 4 urban rail transit risk level judgment criteria

TABLE 5 urban rail transit risk matrix

Further, the risk assessment module mainly includes an accident syndrome module, a risk assessment index module, a risk assessment method module, and the like, wherein the inherent relationship takes the simulated accident syndrome as an object, the risk assessment of the simulated syndrome is realized through a risk database and expert assessment respectively, and a logic block diagram is shown in fig. 2.

Further, the accident syndrome module establishes accident syndromes and establishes corresponding relations between risks and consequences by combining common risk event types of local operation units according to relevant regulations of urban rail transit dangerous events related to the transportation department and through risk mechanism evolution and existing accident event analysis.

Further, the risk assessment index module establishes a risk index system covering contents such as personnel, management, facilities, equipment, external environment and the like by taking a line and a network as object models for each risk component, so as to realize risk assessment of various risk components.

And further, a risk assessment method module. Establishing a risk evaluation method library, and adopting a specific evaluation method aiming at different indexes based on various qualitative, quantitative and comprehensive analysis methods such as a check list method, an expert survey method, an analytic hierarchy process, a Monte Carlo method, a reliability analysis method, an accident tree method, an event tree method, a risk evaluation matrix method and the like.

Further, the scene simulation module selects accident symptom scenes with high occurrence, frequent occurrence and frequent occurrence of urban rail transit, simulates main risks causing accidents/incidents by setting boundary condition parameters according with actual real conditions, and carries out risk coefficient calculation and grade evaluation through methods such as data fitting, empirical assignment and calculation models.

Further, the urban rail transit risk assessment method is specifically executed according to the following steps:

s1, selecting scene accident symptoms to be simulated, and inputting relevant boundary condition parameters such as service life, running average time, fault period and the like according to actual conditions, wherein different scene boundary conditions are different;

s2, selecting relevant risk points which may cause accident symptoms from the risk database based on big data accumulation, and taking the boundary condition values and the R, L and S values as standard values;

s3: determining P, N, E value, wherein the P parameter coefficient value is obtained by comparing the actual boundary condition parameter with the standard value in the risk database and performing weighted average according to the ratio weight method; the N risk point coefficients take 1 risk point as a benchmark, one more risk point is increased by 1 and the risk point weight coefficient, then weighted average is carried out, and numerical value selection is carried out according to a step interval mode according to big data accumulation and association system analysis; e, taking the product of the expert evaluation result value and the weight risk point weight coefficient value as an experience coefficient value according to the empirical coefficient, and enabling E to be in an interval of 0.8-1.2 according to the big data normal distribution statistical result;

and S4, calculating by weighted average to obtain a C risk coefficient value of the accident symptom scene, fitting with the R grade of the accident symptom in the risk library, judging the coefficient which is multiplied by the standard risk grade, and judging whether the calculated product value of C, L and S can meet the requirement of entering different R grades to obtain an O comprehensive risk value.

Further, the formula of the algorithm is O- ∑ C, R- ∑ (P × N × E) in full (L× S)

P＝∑(P1×Q1+P2×Q2+P3×Q3+P4×Q4…)

N＝∑(N1×W1+N2×W2+N3×W3+N4×W4…)

E＝∑(E1×W1+E2×W2+E3×W3+E4×W4…)

In the formula: o is a comprehensive risk value and is a risk coefficient and risk grade fitting;

c is a risk coefficient which is the product of a P parameter coefficient, an N risk point coefficient and an E empirical coefficient;

r is a risk grade which is the product of L risk occurrence probability and S risk influence degree;

q is the weighted value of the parameter coefficient, and W is the weighted value of the risk point.

The invention has the beneficial effects that: the invention can successfully provide technical support and service for the administrative department, the industry and the operation enterprise, and specifically comprises the following steps:

(1) technical support is provided for administrative departments to carry out risk classification management and control and hidden danger investigation and supervision

According to the urban rail transit operation risk classification management and control, hidden danger investigation dual prevention management, urban rail transit operation dangerous event classification management and other systems, a risk knowledge base and a risk module component are used for management, dimensions such as risk event occurrence probability, influence degree, risk grade, possible event consequences and relevant measures are emphasized according to attributes such as operation time and service life, acquisition and analysis of different urban operation risk data are achieved, and whether the requirements of the management systems are met is judged.

(2) Intercommunication communication platform for industry risk hidden danger management

Based on the risk module component management data, the risk overall level is objectively judged through risk evaluation modules such as risk indexes and evaluation methods, the horizontal comparison of the same type of risks or the overall risk level of different cities is realized, the industry intercommunication reference of advanced management and control means is promoted, and the industry overall operation safety level is comprehensively improved.

(3) Providing technical guidance for operation enterprises to develop risk control

The method is characterized in that casualties and occurrence times are taken as objects, requirements of urban rail transit operation dangerous event hierarchical management and other aspects are combined, common and high-occurrence event types of urban rail transit for more than 1 century are systematically combed, and an operation scene simulation experiment platform is built. The platform takes operation practice as background, and realizes the evaluation of operation risk, judges the risk level and provides a targeted control measure through boundary parameter simulation, risk point analogy and combing, standard risk value setting and expert experience simulation.

Drawings

FIG. 1 is a block diagram of the present invention;

FIG. 2 is a block diagram of a risk assessment module of the present invention;

FIG. 3 is a graph of a simulated analysis of risk result data in accordance with the present invention.

Detailed Description

The present invention will be described in detail with reference to the drawings and specific embodiments, and it is to be understood that the described embodiments are only a few embodiments of the present invention, rather than the entire embodiments, and that all other embodiments obtained by those skilled in the art based on the embodiments in the present application without inventive work fall within the scope of the present application.

In the embodiment, the invention relates to a system and a method for urban rail transit risk assessment; the following describes an embodiment of the present invention in terms of a train derailment scenario simulation.

In the embodiment, modules related to train derailment are selected from the risk component module, and include a train body, a traveling system (bogie), a signal system and a track system in the aspect of facility equipment, and risk modules such as a traveling and train driver and the like under the condition of ATP failure;

in the embodiment, standard values and actual values of relevant parameters including service life, average running time, braking distance, train weight, full load rate, running speed, fault cycle, component wear rate, flaw detection cycle, personnel training and the like are set according to different component modules; as shown in table 6.

TABLE 6 Standard and actual values of the parameter coefficients

Calculated according to the formula P- ∑ (P1 × Q1+ P2 × Q2+ P3 × Q3+ P4 × Q4 …) as follows:

P1×Q1＝(12/12+22/20+15/10+75％/50％+3/1+4％/2％)/6×1.1＝1.85

P2×Q2＝(12/5+22/20+2/1+3％/1％+0.5/1)/4×1.1＝2.48

P3×Q3＝(12/4+22/20+15/10+15/7.5+75％/60％+1.5/1+3％/1％+1/1)/8×1.2＝2.15

P4×Q4＝(12/6+22/20+3/1+2％/1％)/4×1.2＝2.43

P5×Q5＝(12/15+2.5/1+4％/2％+0.2/1)/4×1.1＝1.5

P6×Q6＝(75/60)/1×0.8＝1

P＝(1.85+2.48+2.15+2.43+1.5+1)/6＝1.91

in the embodiment, in the risk data customization module, the attributes of risk points related to train derailment are extracted as shown in tables 7 and 8, for example, the service life of a steel rail is long, the flaw detection is not in place, a traveling system (bogie) is aged to generate cracks, and a civil tunnel structure has settlement deformation and the like;

TABLE 7 Risk points Attribute

Table 8 risk point coefficient step value table

According to the formula N- ∑ (N1 × W1+ N2 × W2+ N3 × W3+ N4 × W4 …)

N＝(1.2×1.0+1.0×1.5+1.2×1.6+1.3×1.8+1.3×1.9+1.2×1.5)/6＝1.87

In this embodiment, the simulated actual situation is combined, the standard risk values (risk probability, risk influence, and risk value) are compared to obtain the risk coefficient values, as shown in table 9, and the model fitting parameter coefficient values, the risk point coefficient values, and the empirical coefficient values are calculated to finally obtain the possible risk values, and the risk level is determined. The risk simulation results data analysis is shown in fig. 3.

TABLE 9 empirical coefficient values

Calculated according to formula E- ∑ (E1 × W1+ E2 × W2+ E3 × W3+ E4 × W4 …) as follows:

E＝(1.0×1.5+1.0×1.2+1.2×1.9+1.3×1.75+1.3×2.1+1.2×1.0)/6＝1.86

in summary, it can be obtained from the equation O- ∑ C, R- ∑ (P × N × E), in the form of (L× S):

C＝∑(P×N×E)＝1.91×1.87×1.86/3＝2.21

according to the risk matrix model and the statistical data of the risk database, the train derailment risk level R (L× S) is the level R1, so that the embodiment O is 2.21R1, which illustrates that the train derailment risk level in the embodiment is 2.21 of the standard risk level under the real condition, and belongs to a high risk.

Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.

Claims (10)

1. A risk assessment system for urban rail transit comprises a risk component module, a risk attribute module, a risk assessment module and a scene simulation module;

the risk component module is a risk module consisting of facilities, equipment and personnel, wherein the facilities comprise a track, a station, an interval main structure, a control center, a substation and a vehicle base;

the equipment comprises an equipment module consisting of a vehicle, power supply equipment, signal equipment, communication equipment and environmental control equipment;

the personnel include train drivers, station attendants, dispatchers and station attendants.

2. The urban rail transit risk assessment system according to claim 1, characterized in that: the risk attribute module customizes risk data according to different user requirements, and performs related operations aiming at contents such as risk modules, risk component modules, operation time, service life, risk points, risk sources, risk occurrence probability possibility, risk influence degree, risk grades, events/accidents possibly caused by risks, measures capable of being taken, inspection frequency and the like.

3. The urban rail transit risk assessment system according to claim 2, characterized in that: the risk calculation formula is as follows:

R-L× S (formula 1)

Wherein L is the probability of risk occurrence and is obtained through data statistical analysis and prediction, S is the risk influence degree and is obtained through data statistical analysis, and when the R value is larger, the accident risk possibly caused by a single risk point is higher.

4. The urban rail transit risk assessment system according to claim 1, characterized in that: the risk evaluation module comprises an accident symptom module, a risk evaluation index module and a risk evaluation method module, and realizes the risk evaluation of the simulated symptoms by taking the simulated accident symptoms as objects and respectively evaluating the risk of the simulated symptoms through a risk database and experts.

5. The urban rail transit risk assessment system according to claim 4, characterized in that: the accident syndrome module establishes accident syndromes and constructs corresponding relations between risks and consequences according to relevant regulations of urban rail transit dangerous events related to the department of transportation, by combining common risk event types of local operation units and through risk mechanism evolution and existing accident event analysis.

6. The urban rail transit risk assessment system according to claim 4, characterized in that: the risk assessment index module establishes a risk index system covering contents of personnel, management, facilities, equipment, external environment and the like by taking a line and a network as object models for each risk component, so as to realize risk assessment of various risk components.

7. The urban rail transit risk assessment system according to claim 4, characterized in that: the risk evaluation method module establishes a risk evaluation method library, and adopts a specific evaluation method aiming at different indexes based on various qualitative, quantitative and comprehensive analysis methods such as a check list method, an expert survey method, an analytic hierarchy process, a Monte Carlo method, a reliability analysis method, an accident tree method, an event tree method, a risk evaluation matrix method and the like.

8. The urban rail transit risk assessment system according to claim 1, characterized in that: selecting high-occurrence, frequent and frequent accident symptom scenes of urban rail transit, simulating main risks causing accidents/incidents by setting boundary condition parameters according with actual real conditions, and carrying out risk coefficient calculation and grade evaluation by methods such as data fitting, empirical assignment and calculation models.

9. A method for evaluating urban rail transit risks is characterized by comprising the following steps: the method comprises the following steps:

s1, selecting scene accident symptoms to be simulated, and inputting relevant boundary condition parameters such as service life, running average time, fault period and the like according to actual conditions, wherein different scene boundary conditions are different;

s2, selecting relevant risk points which may cause accident symptoms from the risk database based on big data accumulation, and taking the boundary condition values and the R, L and S values as standard values;

s3: determining P, N, E value, wherein the P parameter coefficient value is obtained by comparing the actual boundary condition parameter with the standard value in the risk database and performing weighted average according to the ratio weight method; the N risk point coefficients take 1 risk point as a benchmark, one more risk point is increased by 1 and the risk point weight coefficient, then weighted average is carried out, and numerical value selection is carried out according to a step interval mode according to big data accumulation and association system analysis; e, taking the product of the expert evaluation result value and the weight risk point weight coefficient value as an experience coefficient value according to the empirical coefficient, and enabling E to be in an interval of 0.8-1.2 according to the big data normal distribution statistical result;

and S4, calculating by weighted average to obtain a C risk coefficient value of the accident symptom scene, fitting with the R grade of the accident symptom in the risk library, judging the coefficient which is multiplied by the standard risk grade, and judging whether the calculated product value of C, L and S can meet the requirement of entering different R grades to obtain an O comprehensive risk value.

10. The urban rail transit risk assessment method according to claim 9, characterized in that: the algorithm formula is as follows:

O＝∑C※R＝∑(P×N×E)※(L×S)

P＝∑(P1×Q1+P2×Q2+P3×Q3+P4×Q4…)

N＝∑(N1×W1+N2×W2+N3×W3+N4×W4…)

E＝∑(E1×W1+E2×W2+E3×W3+E4×W4…)

in the formula: o is a comprehensive risk value and is a risk coefficient and risk grade fitting;

c is a risk coefficient which is the product of a P parameter coefficient, an N risk point coefficient and an E empirical coefficient;

r is a risk grade which is the product of L risk occurrence probability and S risk influence degree;

q is the weighted value of the parameter coefficient, and W is the weighted value of the risk point.

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