阅读说明：本技术 城市轨道交通系统运营岗位人因风险控制方法 (Method for controlling human risk of operation post of urban rail transit system ) 是由 王艳辉 贾利民 赵晨阳 李曼 夏伟富 张天格 于 2021-07-14 设计创作，主要内容包括：本发明实施例提供了一种城市轨道交通系统运营岗位人因风险控制方法,该方法包括如下步骤：根据城市轨道交通系统运营安全影响要素及运营过程构建城市轨道交通系统运营岗位人因风险网络；根据风险发生的可能性和影响程度,结合K-阶结构熵和风险点原始发生概率提出了风险点活跃度评价方法；根据风险点活跃度,提出了针对物理组分和外部环境的关键安全屏障行为介入策略生成方法以及策略效果评价方法；根据风险点活跃度和边介数,提出了针对岗位人因的关键运营联动行为风险控制策略生成方法以及策略效果评价方法。本发明针对人、机、环三类风险点提供了相对应的风险控制策略生成方法,提高了系统的安全性。(The embodiment of the invention provides a method for controlling the human risk of an operation post of an urban rail transit system, which comprises the following steps: constructing an urban rail transit system operation post human risk network according to the urban rail transit system operation safety influence factors and the operation process; according to the possibility and the influence degree of risk occurrence, a risk point activity degree evaluation method is provided by combining K-order structure entropy and the original occurrence probability of risk points; according to the activity of risk points, a key safety barrier behavior intervention strategy generation method and a strategy effect evaluation method aiming at physical components and an external environment are provided; according to the risk point activity and the edge betweenness, a method for generating a key operation linkage behavior risk control strategy and a method for evaluating the strategy effect aiming at the post human factors are provided. The invention provides a corresponding risk control strategy generation method aiming at three types of risk points of people, machines and rings, and improves the safety of the system.)

1. A method for controlling human risk of an operation post of an urban rail transit system is characterized by comprising the following steps:

s1, extracting risk points related to operation safety according to the operation safety influence elements and the operation process of the urban rail transit system, and constructing an urban rail transit system operation post human factor risk network according to the connection relationship 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 relationship among the risk points comprises: physical connection, logical connection, operation linkage behavior connection and safety barrier behavior connection; splitting a human factor risk network of an operation post of an urban rail transit system to respectively obtain urban rail operation basic risk subnets, wherein nodes comprise physical component risk points and external environment risk points; the node is a post human factor risk point, and the edge is a post human factor risk point adjacent matrix which is used for expressing operation linkage behavior;

s2, calculating the activity of the risk points representing the state of the risk points by using K-order structural entropy and the original occurrence probability of the risk points according to the probability and the influence degree of the risk occurrence;

s3, generating a key safety barrier behavior intervention strategy aiming at physical component risk points and external environment risk points in an urban rail operation basic risk subnet by combining a repeated calculation rule according to the activity of the risk points, and evaluating the effect of the key safety barrier behavior intervention strategy through the maximum connected subgraph scale;

and S4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnet by combining with a repeated calculation rule according to the activity degree of the risk points and the edge betweenness, and evaluating the effect of the key operation linkage behavior risk control strategy through edge connectivity.

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 in the urban rail transit system, which are closely related to operation safety, 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 human factor risk network of the urban rail transit system operation post is an undirected and unauthorized network representing urban rail operation characteristics.

3. The method according to claim 1, wherein the S2 comprises the following steps:

s21, determining the original occurrence probability omega of the risk point i according to the statistic value and the empirical value accumulated in the urban rail operation safety management work_iG/365, wherein g is the annual average number of occurrence of failures for the physical component risk point; g is the annual average occurrence frequency of the event aiming at the external environment risk point; g is the number of violation occurrences for the post human factor risk points;

s22, calculating K-order propagation number of risk points i in human factor risk network of operation post of urban rail transit systemThe calculation method is as follows:

in the formula:——the K-order propagation number of the risk point i;

n is the number of risk points;

i (-) when the shortest path length l between risk point I and risk point j_ijWhen the ratio is less than or equal to K, I (·) is equal to 1, otherwise, I (·) is equal to 0;

s23, calculating K-order structure entropy H of risk points in human factor risk network of operation post of urban rail transit system^KThe calculation method is as follows:

s24, calculating the activity degree I of the risk points_i(t), the calculation method is as follows:

in the formula: i is_i(t) -the liveness of risk point i at time t;

value after normalization;

H^K(t) -K-order structure entropy, H (t) ═ H⁰(t)，H¹(t)，...，H^d(t), H (t) is a set of all structural entropies from 0 th order to d th order;

c^K(t) -weighting factor.

4. The method according to claim 1, wherein the S3 comprises the following steps:

s31, extracting the two types of risk points and an urban rail operation basic risk sub-network G (S, A) formed by physical connection and logical connection relation aiming at the physical component risk points and external environment risk points in the urban rail transit system operation post human factor risk network^S) Calculating the activity values of all risk points in the urban rail operation basic risk sub-network, wherein S is a physical component risk point and an external environment risk point set, m nodes are arranged in the set, and A is^SThe physical component risk points and the external environment risk points are adjacent matrixes and represent physical and logical connections;

s32, selecting the risk point with the maximum activity value for safety barrier behavior intervention, namely deleting the maximum risk point in the urban rail operation basic risk subnet, wherein A^HSThe method comprises the following steps of (1) indicating an adjacency matrix of post human factor risk points, physical component risk points and external environment risk points, and representing safety barrier behaviors;

wherein n is the number of post anthropogenic risk points, and m is the number of nodes, 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 sub-network 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 subnet G (S, A)^S) If all the nodes are deleted, the process is ended, otherwise, the process returns to step S31.

5. The method according to claim 1, wherein the S4 comprises the following steps:

s41, extracting post human factor risk points and an urban rail operation linkage risk sub-network G (H, A) formed by operation linkage behavior connection relation aiming at post human factor risk points in the urban rail transit system operation post human factor risk network^H) Calculating activity values of all risk points in the urban rail operation linkage risk subnet, wherein H is a post human factor risk point set and comprises n nodes in total, namely post human factor risk points; a. the^HRepresenting operation linkage behaviors for the post human factor risk point adjacency matrix;

s42, calculating importance I of all operation linkage behavior edges in urban rail operation linkage risk sub-network_ij(t)＝B_ij(t)+I_i(t)+I_j(t)，I_ij(t) is the side e_ijThe importance index of (B)_ij(t) is the side e_ijEdge index of (1)_i(t) and I_j(t) is the edge e_ijNode liveness at two ends;

s43, selecting the side with the maximum importance value to carry out an operation linkage behavior risk control strategy, namely deleting the side in the urban rail operation linkage risk subnet;

s44, calculating the edge connectivity of the urban rail operation linkage risk sub-network at the moment, and taking the edge connectivity as an effect evaluation index of the current strategy;

s45, judging city rail operation linkage risk subnet G (H, A)^H) If all the nodes are deleted, the process is ended, otherwise, the process returns to step S41.

6. The method according to claim 1, wherein 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 that is a key physicochemical attribute of the risk point;

the external environment risk points refer to substances or factors which can affect operation and possibly generate or transmit risks in the external environment, and the risk attributes of the risk points are the combination of occurrence frequency and severity level;

the post human factor risk points refer to the combination of the possibility of occurrence of unsafe behaviors and the severity of consequences, wherein the post human factor risk points are related to people, are dominated by and changed by the behaviors of people and possibly have influence on the operation of an urban rail transit system, subjective or objective factors capable of generating or spreading risks are defined as the post human factor risk points, and the risk attributes of the post human factor risk points are the combination of the possibility of occurrence of unsafe behaviors and the severity of consequences.

7. The method of claim 1, wherein physically linked means that two physical components are in various spatial contact, connection, and association;

the logical connection refers to the change of the natural environment and the external environment to the equipment facility state as the logical connection;

the operation linkage behavior connection refers to the cooperative behavior among the human factor risk points of different posts for realizing the 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.

8. The method according to claim 1, wherein the S3 includes:

the calculation method for using the edge connectivity index EP as the evaluation of the operation linkage behavior risk control strategy effect is as follows:

in the formula: m is a connected subgraph set after the edges of the network are removed, h is 0 … … M, and h is a natural number;

σ_hafter the edge is removed, the h is connectedSize of the via graph.

Technical Field

The invention relates to the technical field of risk control and safety management of urban rail transit systems, in particular to a method for controlling human-induced risk of an operation post of an urban rail transit system.

Background

The urban rail transit system is used as an aorta for urban development, and safety is the basis and the precondition for operation of the urban rail system. With the continuous expansion of the scale of road networks, the pressure and the challenge of urban rail operation safety management work are increasing. As a 'man-machine-ring' complex system, urban rail operation safety is closely related to three factors of man, machine and ring, and the change of one factor may cause the safety of the whole system operation. Therefore, safety management needs to penetrate into each link of operation, so that all elements in human, machine and ring are coordinated, and the safety of the system is ensured together. Therefore, a method is needed to be provided for risk control from the global perspective of the urban rail transit system, so that the possibility of risk occurrence and the influence degree are controlled at an acceptable level, and the system operation safety is guaranteed.

The invention constructs an urban rail transit system operation post human risk network comprising three factors of human, machine and ring from the overall system perspective, and particularly depicts the actual operation situation of the urban rail transit system. In order to guarantee the operation safety of the urban rail transit system and reduce the operation risk, a risk point activeness evaluation method is provided, corresponding risk control methods are provided for three factors of people, machines and rings from the perspective of a safety manager, the possibility and the influence degree of the risk in the system are controlled to be acceptable, 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 purpose, the invention adopts the following technical scheme.

A method for controlling human risk of an operation post of an urban rail transit system comprises the following steps:

s1, extracting risk points related to operation safety according to the operation safety influence elements and the operation process of the urban rail transit system, and constructing an urban rail transit system operation post human factor risk network according to the connection relationship 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 relationship among the risk points comprises: physical connection, logical connection, operation linkage behavior connection and safety barrier behavior connection; splitting a human factor risk network of an operation post of an urban rail transit system to respectively obtain urban rail operation basic risk subnets, wherein nodes comprise physical component risk points and external environment risk points; the node is a post human factor risk point, and the edge is a post human factor risk point adjacent matrix which is used for expressing operation linkage behavior;

s2, calculating the activity of the risk points representing the state of the risk points by using K-order structural entropy and the original occurrence probability of the risk points according to the probability and the influence degree of the risk occurrence;

s3, generating a key safety barrier behavior intervention strategy aiming at physical component risk points and external environment risk points in an urban rail operation basic risk subnet by combining a repeated calculation rule according to the activity of the risk points, and evaluating the effect of the key safety barrier behavior intervention strategy through the maximum connected subgraph scale;

and S4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnet by combining with a repeated calculation rule according to the activity degree of the risk points and the edge betweenness, and evaluating the effect of the key operation linkage behavior risk control strategy 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 edges;

the human factor risk network of the urban rail transit system operation post is an undirected and unauthorized network representing urban rail operation characteristics.

Preferably, the S2 includes the steps of:

s21, determining the original occurrence probability omega of the risk point i according to the statistic value and the empirical value accumulated in the urban rail operation safety management work_iG/365, wherein g is the annual average number of occurrence of failures for the physical component risk point; g is the annual average occurrence frequency of the event aiming at the external environment risk point; g is the number of violation occurrences for the post human factor risk points;

s22, calculating K-order propagation number of risk points i in human factor risk network of operation post of urban rail transit systemThe calculation method is as follows:

in the formula:the K-order propagation number of risk points i;

n is the number of risk points;

i (-) when the shortest path length l between risk point I and risk point j_ijWhen the ratio is less than or equal to K, I (·) is equal to 1, otherwise, I (·) is equal to 0;

s23, calculating K-order structure entropy H of risk points in human factor risk network of operation post of urban rail transit system^KThe calculation method is as follows:

s24, calculating the activity degree I of the risk points_i(t), the calculation method is as follows:

in the formula: i is_i(t) -the liveness of risk point i at time t;

——value after normalization;

H^K(t) -K-order structure entropy, H (t) ═ H⁰(t)，H¹(t)，…，H^d(t), H (t) is a set of all structural entropies from 0 th order to d th order;

c^K(t) -weighting factor.

Preferably, the S3 includes the steps of:

s31, extracting the two types of risk points and an urban rail operation basic risk sub-network G (S, A) formed by physical connection and logical connection relation aiming at the physical component risk points and external environment risk points in the urban rail transit system operation post human factor risk network^S) Calculating the activity values of all risk points in the urban rail operation basic risk sub-network, wherein S is a physical component risk point and an external environment risk point set, m nodes are arranged in the set, and A is^SThe physical component risk points and the external environment risk points are adjacent matrixes and represent physical and logical connections;

s32, selecting the risk point with the maximum activity value to intervene the safety barrier behavior, namely, on the basis of urban rail operationDeleting the maximum risk point in the risk subnet, wherein A^HSThe method comprises the following steps of (1) indicating an adjacency matrix of post human factor risk points, physical component risk points and external environment risk points, and representing safety barrier behaviors;

wherein n is the number of post anthropogenic risk points, and m is the number of nodes, 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 sub-network 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 subnet G (S, A)^S) If all the nodes are deleted, the process is ended, otherwise, the process returns to step S31.

Preferably, the S4 includes the steps of:

s41, extracting post human factor risk points and an urban rail operation linkage risk sub-network G (H, A) formed by operation linkage behavior connection relation aiming at post human factor risk points in the urban rail transit system operation post human factor risk network^H) Calculating activity values of all risk points in the urban rail operation linkage risk subnet, wherein H is a post human factor risk point set and comprises n nodes in total, namely post human factor risk points; a. the^HRepresenting operation linkage behaviors for the post human factor risk point adjacency matrix;

s42, calculating importance I of all operation linkage behavior edges in urban rail operation linkage risk sub-network_ij(t)＝B_ij(t)+I_i(t)+I_j(t)，I_ij(t) is the side e_ijThe importance index of (B)_ij(t) is the side e_ijEdge index of (1)_i(t) and I_j(t) is the edge e_ijNode liveness at two ends;

s43, selecting the side with the maximum importance value to carry out an operation linkage behavior risk control strategy, namely deleting the side in the urban rail operation linkage risk subnet;

s44, calculating the edge connectivity of the urban rail operation linkage risk sub-network at the moment, and taking the edge connectivity as an effect evaluation index of the current strategy;

s45, judging city rail operation linkage risk subnet G (H, A)^H) If all the nodes are deleted, the process is ended, otherwise, the process returns to step S41.

Preferably, the physical component risk point refers to a component or equipment contained in a specific entity system, and is a risk point inherent to the system, and the risk attribute of the physical component risk point is a key physicochemical attribute of the risk point;

the external environment risk points refer to substances or factors which can affect operation and possibly generate or transmit risks in the external environment, and the risk attributes of the risk points are the combination of occurrence frequency and severity level;

the post human factor risk points refer to the combination of the possibility of occurrence of unsafe behaviors and the severity of consequences, wherein the post human factor risk points are related to people, are dominated by and changed by the behaviors of people and possibly have influence on the operation of an urban rail transit system, subjective or objective factors capable of generating or spreading risks are defined as the post human factor risk points, and the risk attributes of the post human factor risk points are the combination of the possibility of occurrence of unsafe behaviors and the severity of consequences.

Preferably, the physical connection means that two physical components are in contact, connected and related in various forms in space;

the logical connection refers to the change of the natural environment and the external environment to the equipment facility state as the logical connection;

the operation linkage behavior connection refers to the cooperative behavior among the human factor risk points of different posts for realizing the 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 using the edge connectivity index EP as the evaluation of the operation linkage behavior risk control strategy effect is as follows:

in the formula: m is a connected subgraph set after the edges of the network are removed, h is 0 … … M, and h is a natural number;

σ_hthe size of the h-th connected subgraph after edge removal.

According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention provides the method for controlling the human factor risk of the operation post of the urban rail transit system, and the human factor risk network of the operation post of the urban rail transit system facing the system global situation is constructed by considering the human-machine-ring coupling characteristic in the operation of the urban rail system. The traditional risk point state evaluation method is improved, and the risk point activity degree evaluation method is provided. A corresponding risk control strategy generation method is provided for the 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 needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for controlling human risk in an operation post of an urban rail transit system;

FIG. 2 is a flow diagram of a critical security barrier behavior intervention policy generation;

FIG. 3 is a flow chart of a key operation linkage behavior risk control strategy generation;

FIG. 4 is a network of human risk for operating posts of an urban rail transit system;

FIG. 5 is an urban rail operation basic risk sub-network;

FIG. 6 shows the risk point activity in the initial state of the urban rail operation basic risk sub-network;

FIG. 7 is a schematic diagram of the intervention strategy for the first 10 critical safety barrier actions;

FIG. 8 illustrates intervention policy effects of key safety barrier behavior;

FIG. 9 is an urban rail operation linkage risk sub-network;

fig. 10 shows the risk point activity in the initial state of the operation linkage risk subnet;

fig. 11 shows the risk control strategy effect of the key operation linkage behavior.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of 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 the context clearly indicates otherwise. 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. As used herein, the term "and/or" 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 convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.

Example one

The embodiment of the invention provides an operation risk control method for an urban rail transit system, which comprises the following steps as shown in figure 1:

s1, according to design and manufacture data, historical accident data and an operation specification manual, risk points relevant to operation safety are extracted from three major factors of people, machines and environment in combination with risk point definition, connection relations among the risk points are combed, and finally, an urban rail transit system operation post human factor risk network is constructed according to the connection relations among the risk points. The constructed risk network is an unwarranted and undirected non-uniform complex network, and the details are as follows:

the urban rail transit system operation post human factor risk network G (V, A) selects human factor, 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 relation, logical connection relation, operation linkage behavior connection relation and safety barrier behavior connection relation as connection edges, V is a risk point set of the post human factor risk network in the operation process of the urban rail transit system, and A refers to an adjacent matrix of points in the network. The constructed human factor risk network of the urban rail transit system operation post is an undirected and unauthorized network representing urban rail operation characteristics.

Defining:

physical component risk point: refers to the components or equipment contained in a particular physical system, such as a train, signal, track, infrastructure, etc., which is a point of risk inherent to the system itself. The risk attribute is the key physicochemical attribute of the risk point.

External environmental risk points: refers to substances or factors which can affect the operation in external environments such as natural environment and social environment and can generate or transmit 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.

The post people are at risk: subjective or objective factors related to, dominated by and changed by human behavior, which may have an effect on the operation of the urban rail transit system, and which can create or propagate risks, are defined as post-human risk points. The risk attributes of the post human factor risk points are as follows: the combination of the likelihood of unsafe behavior and the severity of the consequences.

Physical connection: the physical connection relationship of gluing, welding, riveting, screwing and the like exists between the risk points of the physical components, namely, the two physical components are in contact, connected and related in various forms in space.

And (3) logical connection: the change of the state of the equipment facilities by the natural environment such as frost, rain, snow and passengers and the external environment is taken as a logical connection.

Operation linkage behavior connection: in order to realize the operation function, people at different posts cooperate with risk points.

Safety barrier behavior connection: and (3) monitoring, detecting, maintaining, processing and other actions of physical components and external environment risk points existing in the operation system by the post risk points specified by the safety production system.

S2, the purpose of risk control of the urban rail operation system is to reduce the possibility of risk occurrence and the influence degree 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, the method has strong subjectivity, and the importance degree of the risk points in the system is difficult to measure from the global perspective. In order to effectively judge the state of the risk points in the system, a risk point activity degree evaluation method is provided. The method judges the possibility of the risk state of the risk point according to the original occurrence probability, judges the influence degree of the risk state of the risk point in the network according to the K-order propagation number, and comprehensively judges the state of the risk point by combining the K-order structure entropy, and comprises the following steps:

s21, determining the original occurrence probability omega of the risk point i according to the statistic value and the empirical value accumulated in the urban rail operation safety management work_iG/365, wherein g is the annual average occurrence frequency of the faults aiming at the risk points of the physical components; g is the annual average occurrence frequency of the event aiming at the external environment risk point; and g is the number of violation occurrences for the post human factor risk points.

S22K-order propagation number of risk points i in human factor risk network of operation post of urban rail transit systemAnd (4) the number of risk points capable of infecting when the transmission time length is K in order to transmit i serving as an infection source to adjacent risks. The calculation method is as follows:

in the formula:the K-order propagation number of risk points i;

n is the number of risk points;

i (-) is a piecewise function with a shortest path length l between risk point I and risk point j_ijWhen K is less than or equal to K, I (·) is 1, otherwise, I (·) is 0.

S23, calculating K-order structure entropy H^KEffectively measure the difference of the propagation numbers of each order between the risk points.

S24, probability of occurrence of risk state according to risk point andthe influence degree in the network provides the activity degree I of the risk point_i(t), the calculation method is as follows:

in the formula: i is_i(t) -the liveness of risk point i at time t;

——value after normalization;

H^K(t) -K-order structure entropy, H (t) ═ H⁰(t)，H¹(t)，…，H^d(t)}，H^dThe structural entropy representing K ═ d, i.e., structural entropy of order d, h (t) is a set of all structural entropies from order 0 to order d;

c^K(t) -weighting factor.

And S3, generating a key safety barrier behavior intervention strategy aiming at the physical component risk points and the external environment risk points in the urban rail operation basic risk subnet by combining a repeated calculation rule according to the activity of the risk points, and evaluating the effect of the key safety barrier behavior intervention strategy by the maximum connected subgraph scale.

And generating a key security barrier behavior intervention strategy by adopting a repeated calculation rule in a complex network node attack strategy according to the 'deletion' 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 idea based on the risk point activity degree is as follows: and (4) carrying out safety barrier behavior intervention aiming at the physical component with the maximum activity and the external environment risk point in the urban rail operation basic risk subnet, and removing the risk point from the network. After the risk points are eliminated, the activity of other risk points is also affected, and the activity of the risk points of the current network needs to be recalculated. The specific critical safety barrier behavior intervention strategy generation flow is shown in fig. 2.

Connectivity is a direct measure of network performance and may indicate 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.

And S4, generating a key operation linkage behavior risk control strategy aiming at post human factor risk points in the urban rail operation linkage risk subnet by combining with a repeated calculation rule according to the activity degree of the risk points and the edge betweenness, and evaluating the effect of the key operation linkage behavior risk control strategy through edge connectivity.

The edge betweenness is an importance evaluation method of edges in a network, but in an actual network, nodes have heterogeneity, and two edges with the same edge betweenness have different importance due to heterogeneity of connecting nodes. Therefore, the edge importance I is calculated through the edge betweenness and the activity of the risk points at two ends of the edge_ij(t)＝B_ij(t)+I_i(t)+I_j(t) of (d). In the formula I_ij(t) is the side e_ijThe importance index of (B)_ij(t) is e_ijEdge index of (1)_i(t) and I_j(t) is the edge e_ijNode activity at both ends, e_ijThe method represents the edge between any two risk points i and the risk point j in the network, not only considers the functional importance of the behavior edge in the network, but also considers the activity of the risk points, and the risk points with higher activity are easier to generate risk through operation linkage behaviorAnd (5) transferring.

And controlling the 'deletion' action of connecting edges among the position human factor risk points in the risk network according to the operation linkage behavior risk, and generating a key operation linkage behavior risk control strategy by adopting a repeated calculation rule in a complex network node attack strategy. The key operation linkage behavior risk control strategy generation idea based on the edge importance degree is as follows: and risk control is carried out on the operation linkage behavior corresponding to the side with the maximum importance in the urban rail operation linkage risk sub-network, so that the operation linkage behavior can be safely carried out, and the behavior side is removed from the network. After the behavior edge is removed, the importance of other edges is also affected, and the importance of the behavior edge of the current network needs to be recalculated. A specific key operation linkage behavior risk control policy generation flow is shown in fig. 3.

And (3) utilizing the edge connectivity index EP as a method for evaluating the effect of the operation linkage behavior risk control strategy. Edge connectivity may well reflect network connectivity. The calculation method is as follows:

in the formula: m is a connected subgraph set after the edges of the network are removed, h is 0 … … M, and h is a natural number;

σ_hthe size of the h-th connected subgraph after edge removal.

Example two

According to the design and manufacture data, the historical accident data and the operation specification manual of the urban rail transit system, 79 physical component risk points, external environment risk points and post human factor risk points are extracted, and the number of the physical component risk points, the external environment risk points and the post human factor risk points is 262, and the operation post human factor risk network of the urban rail transit system is constructed as shown in the following table 1 and shown in fig. 4.

TABLE 1

For the physical component risk points and the external environment risk points, the two types of risk points and urban rail operation basic risk subnets formed by physical and logical connection relations are extracted, wherein the number of the nodes in the network is 58, and 77 connecting edges are 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 basic risk subnet according to the related statistical data, wherein the calculation result is shown in the following table 2.

TABLE 2

According to the risk point activity degree calculation method, matlab software is used for calculation, and the calculation results of the activity degrees of the physical components in the urban rail operation basic subnet and the external environment risk points in the initial state are shown in fig. 6. And (3) performing system risk control by adopting a key safety barrier behavior intervention strategy (strategy 1) based on the activity of the risk point, wherein a schematic diagram of the key safety barrier behavior intervention strategy in the first 10 times is shown in fig. 7. The effect of the intervention strategy of the key safety barrier behavior is evaluated through the maximum connected subgraph scale, and compared with the intervention strategy of the key safety barrier behavior (strategy 2) and the intervention strategy of the random safety barrier behavior (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 security barrier behavior can quickly reduce the maximum connected subgraph scale in the network, and the maximum connected subgraph scale of the network can be reduced to 7 only by performing the intervention of the security barrier behavior aiming at the key 10 (the first 17.2%) risk points, so that the reduction is about 88 percent, the network performance of the operation basic risk subnet is damaged to the maximum extent, and the system operation safety is improved. In addition, the intervention of the safety barrier behaviors on the 10 critical risk points can not only greatly reduce the performance of the risk network, but also reduce the maximum activity of the rest risk points in the network from 0.262 in the initial state to 0.09, thereby greatly reducing the risk generation possibility and the propagation influence degree in the operation basic subnet.

And aiming at the post human factor risk points, extracting an urban rail operation linkage risk subnet formed by the post human factor risk points and the operation linkage behavior connection relation, wherein the network has 21 nodes and 62 connecting edges, and the network is shown in fig. 9.

And calculating the original occurrence probability of the risk points of the post factors in the urban rail operation linkage risk subnet according to the related statistical data, wherein the calculation result is shown in the following table 3.

TABLE 3

The risk point activity degree in the initial state is calculated through a formula, the matlab software is used for calculation, and 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 edge importance. The effect of the key operation linkage behavior risk control strategy is evaluated through edge connectivity, and compared with a key operation linkage behavior risk control strategy (strategy 2) and a random operation linkage behavior risk control strategy (strategy 3) based on the activity of risk points at two ends of an edge, as shown in fig. 11.

The result shows that the connection between the risk points of the post people through the operation behavior is very tight, and the risk control needs to be carried out on a large number of operation linkage behaviors, so that the side connectivity of the network is greatly reduced. Compared with other two strategies, the method provided by the invention can quickly reduce the edge connectivity of the risk network, and when risk control is carried out on 18 (the first 29%) key operation linkage behaviors, the edge connectivity of the risk network is reduced by 50%, so that the performance of the risk network is greatly damaged.

Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.