CN113537767B - Urban rail transit system operation post human factor risk control method - Google Patents
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Abstract
The embodiment of the invention provides a human factor risk control method for an operation post of an urban rail transit system, which comprises the following steps: constructing an urban rail transit system operation post human factor risk network according to the urban rail transit system operation safety influencing factors and the operation process; according to the probability and influence degree of risk occurrence, a risk point liveness evaluation method is provided by combining K-order structural entropy and the original occurrence probability of risk points; according to the activity degree of the risk points, a key safety barrier behavior intervention strategy generation method and a strategy effect evaluation method aiming at physical components and external environments are provided; according to the risk point liveness and the edge bets, a key operation linkage behavior risk control strategy generation method and a strategy effect evaluation method aiming at the post human factors are provided. The invention provides a corresponding risk control strategy generation method aiming at three risk points of people, machines and rings, and improves the safety of the system.
Description
Technical Field
The invention relates to the technical field of urban rail transit system risk control and safety management, in particular to a method for controlling human factor risk of an urban rail transit system operation post.
Background
Urban rail transit system is used as the main artery of urban development, and safety is the basis and premise of urban rail system operation. With the continuous expansion of the road network scale, the pressure and challenges facing urban rail operation safety management work are increasing. As a "man-machine-ring" complex system, urban rail operation safety is closely related to three kinds of elements, namely man, machine and ring, and the change of a certain element may cause the operation safety of the whole system. Therefore, the safety management work needs to permeate each link of operation, so that each element in the 'people, machines and rings' are coordinated, and the safety of the system is guaranteed together. Therefore, a method is required to be provided, and risk control is performed from the global aspect of the urban rail transit system, so that the possibility and influence degree of risk occurrence are controlled at acceptable levels, and the operation safety of the system is ensured.
The invention constructs the urban rail transit system operation post human factor risk network comprising three elements of human, machine and ring from the system global angle, and details the actual operation condition of the urban rail transit system. In order to ensure the operation safety of the urban rail transit system and reduce the operation risk, a risk point liveness evaluation method is provided, a corresponding risk control method is provided for three elements of people, machines and rings from the perspective of a safety manager, the possibility and the influence degree of risk occurrence in the system are controlled at acceptable levels, and theoretical support is provided for urban rail operation safety management workers.
Disclosure of Invention
The embodiment of the invention provides an operation risk control method for an urban rail transit system, which aims to overcome the defects of the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
An urban rail transit system operation post human factor risk control method comprises the following steps:
s1, extracting risk points related to operation safety according to operation safety influence factors and operation processes of an urban rail transit system, and constructing an urban rail transit system operation post human factor risk network according to a connection relation among the risk points, wherein the risk points comprise physical component risk points, external environment risk points and post human factor risk points, and the connection relation among the risk points comprises: physical connections, logical connections, operational linkage behavioral connections, and security barrier behavioral connections; splitting an urban rail transit system operation post human factor risk network to respectively obtain urban rail operation base risk subnetworks, wherein nodes comprise physical component risk points and external environment risk points; and a city rail operation linkage risk subnetwork, wherein nodes are post human factor risk points, and edges are post human factor risk point adjacent matrixes for representing operation linkage behaviors;
s2, calculating the risk point liveness for representing the state of the risk point by using the K-order structural entropy and the original occurrence probability of the risk point according to the possibility and the influence degree of the risk;
s3, generating a key safety barrier behavior intervention strategy according to the activity degree of the risk points by combining repeated calculation rules aiming at physical component risk points and external environment risk points in a urban rail operation basic risk sub-network, and evaluating the effect of the key safety barrier behavior intervention strategy through the maximum connected subgraph scale;
s4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnetwork according to the risk point liveness and the edge bets, combining repeated calculation rules, and evaluating the key operation linkage behavior risk control strategy effect through edge connectivity.
Preferably, the urban rail transit system operation post human factor risk network selects physical component risk points, external environment risk points and post human factor risk points closely related to operation safety in the urban rail transit system as network nodes, and takes physical connection relations, logical connection relations, operation linkage behavior connection relations and safety barrier behavior connection relations as connection sides;
the urban rail transit system operation post human factor risk network is an undirected and unauthorized network for representing urban rail operation characteristics.
Preferably, the step S2 includes the steps of:
s21, determining the original occurrence probability omega of the risk point i according to the statistical value and the empirical value accumulated in the urban rail operation safety management work i =g/365, where g is the number of times of annual average source failure occurrence for a physical component risk point; aiming at an external environment risk point, g is the annual average occurrence number of the event; for the purpose ofG is the number of times of illegal occurrence;
s22, calculating the K-order propagation number of risk point i in urban rail transit system operation post human factor risk networkThe calculation method comprises the following steps:
wherein:-K-order propagation number of risk point i;
n is the number of risk points;
i (·) as shortest path length l between risk point I and risk point j ij When the value is less than or equal to K, I (·) is=1, otherwise, I (·) is=0;
s23, calculating K-order structural entropy H of risk points in urban rail transit system operation post human factor risk network K The calculation method is as follows:
s24, calculating the risk point liveness I i (t) the calculation method is as follows:
wherein: i i (t) -the liveness of the risk point i at the time t;
——/>taking the value after normalization;
H K (t) -entropy of K-order structure, H (t) = { H 0 (t)，H 1 (t)，…，H d (t) }, H (t) is the set of all structural entropies from 0 th to d th;
c K (t) -weight coefficient.
Preferably, the step S3 includes the steps of:
s31, aiming at physical component risk points and external environment risk points in an urban rail transit system operation post human factor risk network, extracting urban rail operation basic risk subnetwork G (S, A) formed by the two types of risk points and physical connection and logical connection relation S ) Calculating activity values of all risk points in the urban rail operation base risk subnetwork, wherein S is a physical component risk point and external environment risk point set, m nodes are arranged in the set, and A is a group of physical component risk points and external environment risk points S Representing physical and logical connection for physical component risk points and external environment risk point adjacency matrix;
s32, selecting a risk point with the maximum liveness value for security barrier behavior intervention, namely deleting the maximum risk point from a urban rail operation base risk subnet, wherein A is as follows HS The system is characterized by comprising a post human factor risk point and physical component risk point and external environment risk point adjacency matrix, wherein the post human factor risk point and physical component risk point and external environment risk point adjacency matrix are used for representing safety barrier behaviors;
wherein n is the post human factor risk point number, m is the node number, namely the sum of the number of physical component risk points and the number of external environment risk points;
s33, calculating the maximum connected subgraph scale of the urban rail operation basic risk subnetwork at the moment, and taking the maximum connected subgraph scale as an effect evaluation index of the current intervention strategy;
s34, judging the urban rail operation basic risk subnetwork G (S, A S ) If all nodes are deleted, the process is ended, otherwise, the process returns to the step S31.
Preferably, the step S4 includes the steps of:
s41, extracting a city rail operation linkage risk subnetwork G (H, A) formed by the position human factor risk points and operation linkage behavior connection relations aiming at the position human factor risk points in the city rail traffic system operation position human factor risk network H ) Calculating activity values of all risk points in the urban rail operation linkage risk subnetwork, wherein H is a post human factor risk point set, and n nodes are included in total, namely post human factor risk points; a is that H The operation linkage behavior is represented by a post human factor risk point adjacency matrix;
s42, calculating importance degree I of all operation linkage behavior edges in urban rail operation linkage risk subnetworks ij (t)＝B ij (t)+I i (t)+I j (t)，I ij (t) is edge e ij Importance index of B ij (t) is edge e ij Edge betweenness of (I) i (t) and I j (t) is edge e ij The activity of the nodes at two ends;
s43, selecting the side with the largest importance value for carrying out operation linkage behavior risk control strategy, namely deleting the side in the urban rail operation linkage risk subnetwork;
s44, calculating edge connectivity of the urban rail operation linkage risk subnetwork at the moment, and taking the edge connectivity as an effect evaluation index of the current strategy;
s45, judging urban rail operation linkage risk subnetwork G (H, A) H ) Whether all nodes in the network are deleted, if soAnd ending, otherwise, returning to the step S41.
Preferably, the physical component risk point refers to a component or equipment contained in a specific entity system, is a risk point inherent to the system, and has a risk attribute which is a key physicochemical attribute of the risk point;
the external environment risk points refer to substances or factors which can influence operation and possibly generate or spread risks in the external environment, and the risk attributes of the risk points are combinations of occurrence frequency and severity level;
the post human factor risk point is a combination of the possibility that the risk attribute of the post human factor risk point is unsafe behavior and the severity of the consequences, wherein the subjective or objective factor which is related to the human, is led by the behaviors of the human and changes and affects the operation of the urban rail transit system and can generate or spread risks is defined as the post human factor risk point.
Preferably, the physical connection means that two physical components are spatially contacted, connected and related in various forms;
the logic connection refers to the change of the natural environment and the external environment to the facility state of the equipment as the logic connection;
the operation linkage behavior connection refers to the cooperative coordination behavior among different post human factor risk points for realizing an operation function;
the safety barrier behavior connection refers to a series of actions of monitoring, detecting, maintaining and processing physical group classification and external environment type risk points existing in an operation system by post risk points specified by a safety production system.
Preferably, the S3 includes:
the calculation method for evaluating the risk control strategy effect by using the edge connectivity index EP as the operation linkage behavior is as follows:
wherein: m, namely a connected subgraph set after edge removal of a network, wherein h is 0 … … M, and h is a natural number;
σ h the size of the h connected subgraph after edge removal.
According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention provides the artificial risk control method for the operation posts of the urban rail transit system, and the artificial risk network of the urban rail transit system operation posts facing the system global is constructed by taking the 'man-machine-ring' coupling characteristic in the urban rail transit system operation into consideration. The traditional risk point state evaluation method is improved, and the risk point liveness evaluation method is provided. Corresponding risk control strategy generation methods are provided for three types of risk points of people, machines and rings, and the safety of the system is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for controlling human factor risk in an operation post of an urban rail transit system;
FIG. 2 is a flow chart of critical security barrier behavior intervention strategy generation;
FIG. 3 is a flow chart of critical operation linkage behavior risk control strategy generation;
FIG. 4 is a schematic representation of an urban rail transit system operator station human factor risk network;
FIG. 5 is a city rail operation base risk subnetwork;
fig. 6 is a diagram of risk point liveness in an initial state of a urban rail operation base risk subnetwork;
FIG. 7 is a schematic diagram of the first 10 critical security barrier activity intervention strategies;
FIG. 8 is a key safety barrier behavior intervention strategy effect;
FIG. 9 is a city rail operation linkage risk subnetwork;
fig. 10 is a diagram of risk point liveness in an initial state of an operational linked risk subnetwork;
fig. 11 is a critical operation linkage behavior risk control strategy effect.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
Example 1
The embodiment of the invention provides an operation risk control method of an urban rail transit system, as shown in fig. 1, comprising the following steps:
s1, according to design and manufacturing data, historical accident data and operation standard manual, extracting risk points related to operation safety from three major elements of people, machines and environment by combining risk point definition, carding connection relations among the risk points, and finally constructing an urban rail transit system operation post human factor risk network according to the connection relations among the risk points. The constructed risk network is an unoriented heterogeneous complex network, and is specifically as follows:
the urban rail transit system operation post human factor risk network G (V, A) is to select human factors, physical components and external environment risk points closely related to operation safety in the urban rail transit system as network nodes, and takes physical connection relations, logical connection relations, operation linkage action connection relations and safety barrier action connection relations as connection sides, wherein V is a risk point set of the post human factor risk network in the urban rail transit system operation process, and A refers to an adjacent matrix of the network points. The constructed urban rail transit system operation post human factor risk network is an undirected unauthorized network for representing urban rail operation characteristics.
Definition:
physical component risk points: it refers to components or devices contained in a specific physical system of trains, signals, tracks, infrastructure, etc., which are inherent risk points of the system itself. The risk attribute is the key physicochemical attribute of the risk point.
External environmental risk point: refers to substances or factors which can influence the operation in the external environment such as natural environment, social environment and the like and possibly generate or spread risks, such as wind, rain, snow, fog, passengers and the like. The risk attribute of such risk points is a combination of frequency of occurrence and severity level.
Post human factor risk point: the subjective or objective factors related to the person, dominated by the person's behavior and changing may have an impact on the operation of the urban rail transit system, and the subjective or objective factors that can create or spread risks are defined as post human factor risk points. The risk attribute of the post human factor risk point is: a combination of the likelihood of unsafe behavior occurring and the severity of the consequences.
Physical connection: the physical connection relations of cementing, welding, riveting, threaded connection and the like exist among the physical component risk points, namely, the two physical components are in contact, connected and related in various forms in space.
Logical connection: the change of the natural environment such as frost, rain, snow, passengers and the like and the external environment to the state of the facility of the device is used as a logic connection.
Operation linkage behavior connection: in order to realize the operation function, the cooperation between the human factor risk points of different posts acts.
Safety barrier behavior connection: the post risk points specified by the safety production system carry out a series of actions such as monitoring, detecting, maintaining, processing and the like on physical components and external environment risk points existing in an operation system.
S2, risk control of the urban rail operation system aims at reducing the possibility and influence degree of risk occurrence in the system. In the current urban rail operation safety management work, the state of the risk points in the system is often evaluated based on experience, and the method has strong subjectivity and is difficult to measure the importance degree of the risk points in the system from the global angle. In order to effectively judge the state of risk points in the system, a risk point liveness evaluation method is provided. The method judges the possibility of occurrence of risk point risk states through the original occurrence probability, judges the influence degree of the risk point risk states in a network through K-order propagation numbers, and comprehensively judges the state of the risk points by combining K-order structural entropy, and comprises the following steps:
s21, determining the original occurrence probability omega of the risk point i according to the statistical value and the empirical value accumulated in the urban rail operation safety management work i =g/365, for physical component windDangerous point, g is the number of times of annual average source faults; aiming at an external environment risk point, g is the annual average occurrence number of the event; aiming at the personnel factor risk points of the posts, g is the occurrence frequency of the violations.
S22, the K-order propagation number of risk point i in urban rail transit system operation post human factor risk networkIn order to transmit the infection source i to adjacent risks, the number of risk points which can be infected when the transmission duration is K. The calculation method comprises the following steps:
wherein:-K-order propagation number of risk point i;
n is the number of risk points;
i (·) is a piecewise function, when the shortest path length l between the risk points I and j ij When the value is less than or equal to K, I (·) is=1, otherwise, I (·) is=0.
S23, calculating the entropy H of the K-order structure K So as to effectively measure the difference of the propagation numbers of each order between the risk points.
S24, according to the possibility of risk state occurrence of the risk points and the influence degree in the network, providing the risk point liveness I i (t) the calculation method is as follows:
wherein: i i (t) -the liveness of the risk point i at the time t;
——/>taking the value after normalization;
H K (t) -entropy of K-order structure, H (t) = { H 0 (t)，H 1 (t)，…，H d (t)}，H d Representing the structural entropy of k=d, i.e., d-order structural entropy, H (t) is a set of all the structural entropies from 0-order to d-order;
c K (t) -weight coefficient.
And S3, generating a key safety barrier behavior intervention strategy according to the risk point liveness by combining repeated calculation rules aiming at physical component risk points and external environment risk points in the urban rail operation basic risk subnetwork, and evaluating the effect of the key safety barrier behavior intervention strategy through the maximum connected subgraph scale.
And generating a key security barrier behavior intervention strategy by adopting repeated calculation rules in the complex network node attack strategy according to the 'deleting' action of the security barrier behavior intervention on physical components and external environment risk points in the risk network. The key safety barrier behavior intervention strategy generation thought based on the risk point liveness is as follows: and performing security barrier behavior intervention on the physical component with the maximum activity in the urban rail operation base risk subnetwork and the external environment risk point, and removing the risk point from the network. After the risk points are removed, the activity level of other risk points is affected, and the activity level of the risk points of the current network needs to be recalculated. A specific critical security barrier action intervention policy generation flow is shown in fig. 2.
Connectivity is a direct indicator of the performance of a network and may represent the sophistication of a network. The better the network connectivity, the faster the risk propagates in the network and the wider the impact range. And calculating the maximum connected subgraph scale to measure the performance of the risk network, and further judging the effect of the intervention strategy of the key safety barrier behavior.
S4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnetwork according to the risk point liveness and the edge bets, combining repeated calculation rules, and evaluating the key operation linkage behavior risk control strategy effect through edge connectivity.
The edge betweenness is an importance evaluation method of the edge in the network, but in the actual network, the nodes have heterogeneity, and two edges with the same edge betweenness have different importance due to the heterogeneity of the connecting nodes. Therefore, the edge importance I is calculated through the edge medium number and the edge two-end risk point liveness ij (t)＝B ij (t)+I i (t)+I j (t). Wherein I is ij (t) is edge e ij Importance index of B ij (t) is e ij Edge betweenness of (I) i (t) and I j (t) is edge e ij Two-end node liveness, e ij The method has the advantages that the sides between any two risk points i and j in the network are represented, the functional importance of the behavior sides in the network is considered, the activity of the risk points is considered, and risk transfer is easy to occur between the risk points with higher activity through operation linkage behaviors.
And generating a key operation linkage behavior risk control strategy by adopting repeated calculation rules in the complex network node attack strategy according to the 'deletion' effect of operation linkage behavior risk control on the connecting edges between the post human factor risk points in the risk network. The key operation linkage behavior risk control strategy generation thought based on the edge importance degree is as follows: and performing risk control on operation linkage behaviors corresponding to edges with the greatest importance in the urban rail operation linkage risk subnetwork, so that the operation linkage behaviors can be performed safely, and the behavior edges are removed from the network. After the behavioural edges are removed, the importance of other edges can be influenced, and the importance of the behavioural edges of the current network needs to be recalculated. The specific key operation linkage behavior risk control strategy generation flow is shown in fig. 3.
And a method for evaluating the risk control strategy effect of the operation linkage behavior by using the edge connectivity index EP. Edge connectivity can well reflect network connectivity. The calculation method comprises the following steps:
wherein: m, namely a connected subgraph set after edge removal of a network, wherein h is 0 … … M, and h is a natural number;
σ h the size of the h connected subgraph after edge removal.
Example two
And (3) extracting 79 physical component risk points, 79 external environment risk points and 262 sides according to the design and manufacturing data, the historical accident data and the operation standard manual of the urban rail transit system, wherein the urban rail transit system is shown in the following table 1, and an operation post human factor risk network of the urban rail transit system is shown in fig. 4.
TABLE 1
Aiming at physical component risk points and external environment risk points, urban rail operation basic risk subnetworks formed by the two types of risk points and physical and logical connection relations are extracted, 58 nodes are shared in a network, and 77 nodes are connected, as shown in fig. 5.
And calculating the original occurrence probability of the physical component risk points and the external environment risk points in the urban rail operation base risk subnetwork according to the related statistical data, wherein the calculation result is shown in the following table 2.
/>
TABLE 2
According to the risk point liveness calculation method, matlab software is used for calculation, and the physical components and the external environment risk point liveness calculation results in the urban rail operation base subnetwork in the initial state are shown in fig. 6. The system risk control is performed by adopting a critical safety barrier behavior intervention strategy (strategy 1) based on the activity degree of the risk points, and the schematic diagram of the first 10 critical safety barrier behavior intervention strategies is shown in fig. 7. The effect of the critical safety barrier behavior intervention strategy is evaluated through the maximum connected sub-graph scale and compared with the effect of the critical safety barrier behavior intervention strategy (strategy 2) and the random safety barrier behavior intervention strategy (strategy 3) based on the original occurrence probability, as shown in fig. 8.
The result shows that the method for generating the intervention strategy of the key safety barrier behavior can quickly reduce the maximum connected sub-graph scale in the network, and can reduce the maximum connected sub-graph scale of the network to 7 by only carrying out the intervention of the safety barrier behavior aiming at the key 10 (17.2% of the front) risk points, thereby reducing about 88%, destroying the network performance of an operation basic risk sub-network to the greatest extent and improving the operation safety of the system. In addition, the intervention of the safety barrier behavior on the key 10 risk points can greatly reduce the performance of the risk network, and the maximum activity of the rest risk points in the network is reduced from 0.262 to 0.09 in the initial state, so that the possibility of risk generation and the influence degree of propagation in the operation base subnetwork are greatly reduced.
For the post human factor risk points, the urban rail operation linkage risk subnetwork formed by the post human factor risk points and the operation linkage behavior connection relation is extracted, and the total nodes in the network are 21 and the number of the nodes is 62, as shown in fig. 9.
And calculating the original occurrence probability of the post human factor risk points in the urban rail operation linkage risk subnetwork according to the related statistical data, wherein the calculation result is shown in the following table 3.
TABLE 3 Table 3
And calculating the risk point liveness in the initial state through a formula, and calculating by using matlab software, wherein the calculation result is shown in fig. 10. And (3) performing system risk control by adopting a key operation linkage behavior risk control strategy (strategy 1) based on the edge importance degree. The key operation linkage behavior risk control strategy effect is evaluated through the side connectivity and is compared with the key operation linkage behavior risk control strategy (strategy 2) and the random operation linkage behavior risk control strategy (strategy 3) based on the activity degree of the risk points at the two ends of the side, as shown in fig. 11.
The result shows that the risk points of the post people are closely connected through the operation behavior edges, a large number of operation linkage behaviors need to be subjected to risk control, and the edge connectivity of the network is greatly reduced. Compared with other two strategies, the method provided by the invention can enable the connectivity of the edge of the risk network to be rapidly reduced, and when the risk control is carried out on 18 (29% before) key operation linkage behaviors, the connectivity of the edge of the risk network is reduced by 50%, so that the performance of the risk network is greatly destroyed.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. The utility model provides an urban rail transit system operation post human factor risk control method which is characterized by comprising the following steps:
s1, extracting risk points related to operation safety according to operation safety influence factors and operation processes of an urban rail transit system, and constructing an urban rail transit system operation post human factor risk network according to a connection relation among the risk points, wherein the risk points comprise physical component risk points, external environment risk points and post human factor risk points, and the connection relation among the risk points comprises: physical connections, logical connections, operational linkage behavioral connections, and security barrier behavioral connections; splitting an urban rail transit system operation post human factor risk network to respectively obtain urban rail operation base risk subnetworks, wherein nodes comprise physical component risk points and external environment risk points; and a city rail operation linkage risk subnetwork, wherein nodes are post human factor risk points, and edges are post human factor risk point adjacent matrixes for representing operation linkage behaviors;
s2, calculating the risk point liveness for representing the state of the risk point by using the K-order structural entropy and the original occurrence probability of the risk point according to the possibility and the influence degree of the risk; the method specifically comprises the following steps:
s21, determining the original occurrence probability omega of the risk point i according to the statistical value and the empirical value accumulated in the urban rail operation safety management work i =g/365, where g is the number of times of annual average source failure occurrence for a physical component risk point; aiming at external environment risk points, g is the annual average occurrence number of events; aiming at the personnel factor risk points of the posts, g is the occurrence frequency of violations;
s22, calculating the K-order propagation number of risk point i in urban rail transit system operation post human factor risk networkThe calculation method comprises the following steps:
wherein:-K-order propagation number of risk point i;
n is the number of risk points;
i (·) as shortest path length l between risk point I and risk point j ij When the value is less than or equal to K, I (·) is=1, otherwise, I (·) is=0;
s23, calculating K-order structural entropy H of risk points in urban rail transit system operation post human factor risk network K The calculation method is as follows:
s24, calculating the risk point liveness I i (t) the calculation method is as follows:
wherein: i i (t) -the liveness of the risk point i at the time t;
——/>taking the value after normalization;
H K (t) -entropy of K-order structure, H (t) = { H 0 (t),H 1 (t),…,H d (t) }, H (t) is the set of all structural entropies from 0 th to d th;
c K (t) -weight coefficients;
s3, generating a key safety barrier behavior intervention strategy according to the activity degree of the risk points by combining repeated calculation rules aiming at physical component risk points and external environment risk points in a urban rail operation basic risk sub-network, and evaluating the effect of the key safety barrier behavior intervention strategy through the maximum connected subgraph scale; the method specifically comprises the following steps:
s31, aiming at physical component risk points and external environment risk points in an urban rail transit system operation post human factor risk network, extracting urban rail operation basic risk subnetwork G (S, A) formed by the two types of risk points and physical connection and logical connection relation S ) Calculating activity values of all risk points in the urban rail operation base risk subnetwork, wherein S is a physical component risk point and external environment risk point set, m nodes are arranged in the set, and A is a group of physical component risk points and external environment risk points S Representing physical and logical connection for physical component risk points and external environment risk point adjacency matrix;
s32, selecting a risk point with the maximum liveness value for security barrier behavior intervention, namely deleting the maximum risk point from a urban rail operation base risk subnet, wherein A is as follows HS The system is characterized by comprising a post human factor risk point and physical component risk point and external environment risk point adjacency matrix, wherein the post human factor risk point and physical component risk point and external environment risk point adjacency matrix are used for representing safety barrier behaviors;
wherein n is the post human factor risk point number, m is the node number, namely the sum of the number of physical component risk points and the number of external environment risk points;
s33, calculating the maximum connected subgraph scale of the urban rail operation basic risk subnetwork at the moment, and taking the maximum connected subgraph scale as an effect evaluation index of the current intervention strategy;
s34, judging the urban rail operation basic risk subnetwork G (S, A S ) If all the nodes in the network are deleted, ending if yes, otherwise, returning to the step S31;
s4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnetwork according to the risk point liveness and the edge bets, and evaluating the key operation linkage behavior risk control strategy effect through edge connectivity; the method specifically comprises the following steps:
s41, extracting a city rail operation linkage risk subnetwork G (H, A) formed by the position human factor risk points and operation linkage behavior connection relations aiming at the position human factor risk points in the city rail traffic system operation position human factor risk network H ) Calculating activity values of all risk points in the urban rail operation linkage risk subnetwork, wherein H is a post human factor risk point set, and n nodes are included in total, namely post human factor risk points; a is that H The operation linkage behavior is represented by a post human factor risk point adjacency matrix;
s42, calculating importance degree I of all operation linkage behavior edges in urban rail operation linkage risk subnetworks ij (t)＝B ij (t)+I i (t)+I j (t)，I ij (t) is edge e ij Importance index of B ij (t) is edge e ij Edge betweenness of (I) i (t) and I j (t) is edge e ij The activity of the nodes at two ends;
s43, selecting the side with the largest importance value for carrying out operation linkage behavior risk control strategy, namely deleting the side in the urban rail operation linkage risk subnetwork;
s44, calculating edge connectivity of the urban rail operation linkage risk subnetwork at the moment, and taking the edge connectivity as an effect evaluation index of the current strategy; the method specifically comprises the following steps:
wherein: m, namely a connected subgraph set after edge removal of a network, wherein h is 0 … … M, and h is a natural number;
σ h the size of the h connected subgraph after edge removal;
s45, judging urban rail operation linkage risk subnetwork G (H, A) H ) If all nodes are deleted, the process is ended, otherwise, the process returns to the step S41.
2. The method according to claim 1, wherein the urban rail transit system operation post human factor risk network selects physical component risk points, external environment risk points and post human factor risk points closely related to operation safety in the urban rail transit system as network nodes, and takes physical connection relations, logical connection relations, operation linkage behavior connection relations and safety barrier behavior connection relations as connection edges;
the urban rail transit system operation post human factor risk network is an undirected and unauthorized network for representing urban rail operation characteristics.
3. The method according to claim 1, wherein the physical component risk point refers to a component or device included in a specific entity system, is a risk point inherent to the system itself, and the risk attribute is a key physicochemical attribute of the risk point;
the external environment risk points refer to substances or factors which can influence operation and possibly generate or spread risks in the external environment, and the risk attributes of the risk points are combinations of occurrence frequency and severity level;
the post human factor risk point is a combination of the possibility that the risk attribute of the post human factor risk point is unsafe behavior and the severity of the consequences, wherein the subjective or objective factor which is related to the human, is led by the behaviors of the human and changes and affects the operation of the urban rail transit system and can generate or spread risks is defined as the post human factor risk point.
4. The method of claim 1, wherein the physical connection means that two physical components are spatially in contact, connected and related in various forms;
the logic connection refers to the change of the natural environment and the external environment to the facility state of the equipment as the logic connection;
the operation linkage behavior connection refers to the cooperative coordination behavior among different post human factor risk points for realizing an operation function;
the safety barrier behavior connection refers to a series of actions of monitoring, detecting, maintaining and processing physical group classification and external environment type risk points existing in an operation system by post risk points specified by a safety production system.
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