CN111178764A - Large-scale activity treading accident dynamic risk assessment method - Google Patents
Large-scale activity treading accident dynamic risk assessment method Download PDFInfo
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Abstract
The invention provides a dynamic risk assessment method for a large-scale active tread accident, which comprises the following steps: collecting and researching trample accident data in multiple dimensions; analyzing the trample accident data to determine a Bayesian network model; according to the constructed pedaling accident Bayes network model, a multi-scene situation analysis method is adopted, accident consequences under different pedaling accident scenes are dynamically predicted by using the reasoning function of the Bayes network on the basis of accident causes and evolution paths, and a sensitivity analysis method is applied to identify key factors influencing the occurrence of the pedaling accident; and (5) carrying out risk management and control of trampling accidents. The invention has the beneficial effects that: by means of dynamic deduction of the consequences of the trampling accident through the Bayesian network, the evolution process of the trampling accident in large-scale activities can be clearly known, key influence factors can be scientifically identified, real-time data input and dynamic updating of the post result can be achieved, and therefore powerful support is provided for pre-prevention, in-service response and post-rescue of the trampling accident.
Description
Technical Field
The invention belongs to the field of urban public safety, and particularly relates to a dynamic risk assessment method for a large-scale active tread accident.
Background
The scale and frequency of various entertainment activities, sports events and mass public activities at home and abroad are increasing day by day, but large-scale activities face the potential threats of numerous participators, complex risk factors, wide social influence, serious accident consequences and the like, so that numerous sudden public safety hidden danger problems are brought, and in any large-scale population, the possibility of injury or even death exists, and the population safety problem becomes more and more important. The trample accidents which finally lead to the severe consequences such as group death and group injury under the common influence of various factors become main accident disasters of large-scale public gathering places.
While pedaling events are not uncommon in large activities, they are not of sufficient interest to the society and academia, resulting in a lack of insight into the mechanisms of occurrence of pedaling events and the severity of the consequences of the events. In addition, most of the risk analysis aiming at the trample accidents is based on fuzzy comprehensive evaluation, an analytic hierarchy process, an accident tree and the like, the traditional methods are locally static, uncertainty conditions can hardly be considered in the accident evolution process, and therefore state evaluation updating can not be generated along with the change of time, and the traditional large-scale activity static risk analysis method is poor in effect. In view of the fact that the occurrence of the pedaling accident is often sudden and real-time, the invention aims to provide a method which has higher practicability and accuracy and can comprehensively and dynamically identify the risk of the pedaling accident in large-scale activities.
Disclosure of Invention
In view of the above, the present invention is directed to a dynamic risk assessment method for a large-scale active pedaling accident, so as to solve the above-mentioned problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a dynamic risk assessment method for a large-scale active pedaling accident comprises the following steps:
s1, collecting, investigating and researching trampling accident data in multiple dimensions;
s2, analyzing trample accident data and determining a Bayesian network model;
s3, according to the constructed treading accident Bayes network model, by a multi-scene situation analysis method, on the basis of accident causes and evolution paths, the accident consequences under different treading accident scenes are dynamically predicted by using the inference function of the Bayes network, and key factors influencing the occurrence of treading accidents are identified by using a sensitivity analysis method;
and S4, carrying out risk control on the trampling accident.
Further, when the tread accident data is sufficient in step S2, a bayesian network is constructed by using a structure learning method and a parameter learning method.
Further, when the stepping accident data is limited in step S2, the bayesian network model is established as follows:
s21, establishing a Bow-Tie model of the trampling accident according to the reason and the consequence of the trampling accident, analyzing the cause path of the trampling accident, determining the structure of a Bayesian network, and identifying influence factors before and after the trampling accident;
and S22, determining a conditional probability table of the Bayesian network by adopting a Delphi method and a mutation theory, and constructing a pedaling accident Bayesian network model by combining the logical relationship among the influencing factors of the pedaling accident in the step S21.
Further, the cause path of the stepping accident is analyzed in the step S21 and is divided into a person factor, an activity site factor, an environmental factor and a management defect according to the system accident cause theory.
Further, the personnel factors comprise group characteristics and individual dangerous behaviors, the event site factors comprise original design defects and basic design failure of an event venue, the environmental factors comprise natural disasters, bad weather, accidents around the event venue and social security events inside and outside the event venue, and the management defects comprise inadequate daily management and inadequate emergency management.
Furthermore, in the step S21, the cause and the consequence of the stepping accident are combined into a Bow-Tie model, the stepping accident is used as an intermediate event, the identified cause causing the stepping accident is located on the left side of the Bow-Tie model, and the emergency protective measures and different accident consequences included after the stepping accident occurs are located on the right side of the Bow-Tie model.
Further, in step S21, the structure of the bayesian network is determined by the Bow-Tie model, and the transformation relationship is as follows:
the basic event causing the trample accident is converted into a father node in the Bayesian network, the middle event is converted into a child node of the Bayesian network, the trample accident is converted into a hub node of the Bayesian network, and the trample accident is converted into different child nodes according to the severity of the accident and according to four aspects of casualties, economic loss, social order and influence range.
Further, the process of determining the conditional probability table of the bayesian network by applying the delphire method and the mutation theory in step S22 is as follows:
s221, formulating a possibility risk questionnaire about influencing the occurrence of the stepping accidents in the large-scale activities;
s222, judging the probability of relative nodes in the treading accident risk questionnaire;
s223, summarizing and sorting the data in the step S222, and judging whether the probabilities of the relevant nodes in the step S222 are consistent or not according to a consistency test standard;
s224, if not, adjusting the content of the related node, and repeating the step S222 until the result is consistent.
Further, the consistency check criterion is that the clonal Bach confidence coefficient is greater than or equal to 0.8.
Further, the state probability of the father node in the Bayesian network is obtained by a Delphi method, and the state probability of the child node selects a corresponding mutation model according to the number of the father nodes in the Bayesian network to obtain a quantitative recursion calculation value;
if the father nodes have a complementary relation, taking the average value of a plurality of father nodes as the mutation level numerical value of the upper-level child node; if the complementary relationship does not exist, taking the minimum value of the multiple father nodes as the mutation level numerical value of the upper-level child node, selecting the corresponding mutation model according to the number of the child nodes and obtaining the system-level mutation level numerical value, and obtaining the conditional probability table of the trampling accident according to the probability data in the processing step S22.
Further, a Bayesian network model is constructed according to the determined Bayesian network structure and the conditional probability table.
Further, in step S3, different accident scenarios are set according to the constructed bayesian network model, the cause path leading to the occurrence of the stepping accident is analyzed, the occurrence probability of the node is changed, the accident consequence in a certain scenario is dynamically predicted by using the inference function of the bayesian network, the key factors influencing the stepping accident are identified by using sensitivity analysis, and measures are taken from the important factors to reduce the occurrence of the stepping accident and reduce the severity of the accident consequence.
Compared with the prior art, the tread accident dynamic risk assessment method has the following advantages:
the trampling accident dynamic risk assessment method provided by the invention is characterized in that trampling accident data are collected in a multi-dimensional mode, the trampling accident data are quantized, a Bayesian network model is constructed by combining a mutation theory and a Delphi method, then an accident cause and an evolution path are used as the basis through a multi-scene situation analysis method, an accident consequence under different trampling accident scenes is dynamically predicted by using a deduction function of a Bayesian network, and the most key influence factors causing the trampling accident are identified by using a sensitivity analysis method. By means of dynamic deduction of the consequences of the trampling accident through the Bayesian network, the evolution process of the trampling accident in large-scale activities can be clearly known, key influence factors can be scientifically identified, real-time data input and dynamic updating of the post result can be achieved, and therefore powerful support is provided for pre-prevention, in-service response and post-rescue of the trampling accident.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of a dynamic risk assessment method for pedaling accidents according to an embodiment of the present invention;
FIG. 2 is a simplified Bow-Tie diagram illustrating the cause and effect of a combined pedaling accident in accordance with the present invention;
FIG. 3 is a simplified diagram of the present invention using the Bow-Tie model to transform into a Bayesian network;
FIG. 4 is a block diagram of the structural steps of analyzing a pedaling accident using the Delphi method and the mutation theory according to the present invention.
FIG. 5 is an example of a Bayesian network in accordance with the present invention;
FIG. 6 is an example of dynamic evaluation performed in the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a dynamic risk assessment method for a large active pedaling accident includes the following steps:
s1, collecting, investigating and researching trampling accident data in multiple dimensions;
s2, analyzing trample accident data and determining a Bayesian network model;
s3, according to the constructed treading accident Bayes network model, by a multi-scene situation analysis method, on the basis of accident causes and evolution paths, the accident consequences under different treading accident scenes are dynamically predicted by using the inference function of the Bayes network, and key factors influencing the occurrence of treading accidents are identified by using a sensitivity analysis method;
and S4, carrying out risk control on the trampling accident.
When the stepping accident data is sufficient in step S2, a bayesian network is constructed by using a structure learning method and a parameter learning method. In this embodiment, a mixed method based on combination of score search and constraint, such as MMHC, GBRS, and other algorithms, determines the node order and the optimal network structure of the bayesian network, and the probability of the bayesian network is inferred by applying a precise inference algorithm such as a group tree propagation algorithm, an evidence correlation method, or an approximate inference algorithm such as a random sampling algorithm, a transformation method, and the like.
When the stepping accident data is limited in the step S2, the bayesian network model is established as follows:
s21, establishing a Bow-Tie model of the trampling accident according to the reason and the consequence of the trampling accident, analyzing the cause path of the trampling accident, determining the structure of a Bayesian network, and identifying influence factors before and after the trampling accident;
and S22, determining a conditional probability table of the Bayesian network by adopting a Delphi method and a mutation theory, and constructing a pedaling accident Bayesian network model by combining the logical relationship among the influencing factors of the pedaling accident in the step S21.
In the step S21, the cause path of the stepping accident is analyzed and classified into a person factor, a playing place factor, an environmental factor and a management defect according to the system accident cause theory.
The personnel factors comprise group characteristics and individual dangerous behaviors, the event site factors comprise original design defects and basic design failure of an event venue, the environmental factors comprise natural disasters, bad weather, accidents around the event venue and social security events inside and outside the event venue, and the management defects comprise inadequate daily management and inadequate emergency management. The basic events that lead to the pedaling accidents are analyzed and generalized from these four aspects, based on field research, literature research, historical data, expert experience, etc.
In step S21, analyzing the pedal accident consequences includes:
the personnel who take part in the large-scale activity are evacuated spontaneously, and the accident is prevented from further expanding. The organization party of the large-scale activity goes up and down in time, maintains the on-site order, organizes personnel to evacuate safely, and alarms to request for support, starts comprehensive emergency response, and carries out comprehensive rescue through cooperative cooperation of multiple departments of fire control, medical care, public security, education and media; after an accident occurs, actively organizing reconstruction, soothing casualties, drawing teaching and training, and doing good post-treatment work; protective measures after the occurrence of the trample accident are divided into spontaneous evacuation, organization and maintenance, alarm rescue and good after-treatment.
As shown in fig. 2, in the step S21, the cause and the consequence of the stepping accident are combined into a Bow-Tie model, the stepping accident is used as an intermediate event, the identified cause causing the stepping accident is located on the left side of the Bow-Tie model, the emergency protection measures and different accident consequences included after the stepping accident occurs are located on the right side of the Bow-Tie model, the stepping accident in the large-scale activity sports event is classified in detail, the stepping accident evolution path is systematically displayed, and the risk scene analysis of the stepping accident is facilitated and the implementation effect of the risk measure tracing is tracked.
As shown in fig. 3, in step S21, the structure of the bayesian network is determined by the Bow-Tie model, and the transformation relationship is as follows:
the basic events causing the trample accidents are converted into father nodes in the Bayes network, the middle events are converted into child nodes in the Bayes network, the top events, namely the trample accidents are converted into pivot nodes in the Bayes network, and the top events are converted into different child nodes according to the severity of the accidents and according to the casualties, economic losses, social order and influence ranges, in the embodiment, the basic events causing the trample accidents (such as too few exits and collapse of a stand) such as X1 and X2 are converted into father nodes in the Bayes network; intermediate events such as M1 and M2 (such as venue design defects, infrastructure destruction faults, management defects and the like) are converted into intermediate nodes of the Bayesian network; a represents a pivot node for converting a top event, namely a pedaling accident, into a Bayesian network; v1, V2 and other emergency measures (such as spontaneous evacuation, alarm rescue and the like) after the occurrence of the accident are also converted into intermediate nodes of the Bayesian network; c is a set of different accident consequences such as C1 and C2, namely, the C is converted into different child nodes such as casualties, economic losses, social order, influence ranges and the like according to the severity of an accident.
The process of determining the conditional probability table of the bayesian network using the delphire method and the mutation theory in step S22 is as follows:
s221, formulating a possibility risk questionnaire about influencing the occurrence of the stepping accidents in the large-scale activities;
s222, judging the probability of relative nodes in the treading accident risk questionnaire;
s223, summarizing and sorting the data in the step S222, and judging whether the probabilities of the joint points in the step B2 are consistent according to a consistency test standard;
s224, if not, adjusting the content of the related node, and repeating the step S222 until the result is consistent.
The consistency test criterion is a clonal Bach confidence coefficient greater than or equal to 0.8.
The state probability of the father node in the Bayesian network is obtained by a Delphi method, and the state probability of the child node selects a corresponding mutation model according to the number of the father nodes in the Bayesian network to obtain a quantitative recursion calculation value;
if a complementary relationship (small difference of probability values) exists between the father nodes, taking the average value of a plurality of father nodes as the mutation level value of the upper level child node; and if the complementation (the probability values are different greatly) does not exist, taking the minimum value of the father nodes as the mutation level numerical value of the upper level child node, selecting a corresponding mutation model according to the number of the child nodes and acquiring the mutation level numerical value of the system layer, and processing the probability data in the step B to obtain a conditional probability table causing the trampling accident.
And constructing a Bayesian network model according to the determined Bayesian network structure and the conditional probability table.
In the step S3, different accident scenarios are set according to the constructed bayesian network model, the cause path leading to the occurrence of the stepping accident is analyzed, the occurrence probability of the node is changed, the accident consequence in a certain scenario is dynamically predicted by using the inference function of the bayesian network, the key factors influencing the stepping according to the occurrence of the accident are identified by using sensitivity analysis, and measures are taken from the important factors to reduce the occurrence of the stepping accident and reduce the severity of the accident consequence.
The working process of the embodiment is as follows:
according to field investigation, literature investigation, historical data, expert experience and the like, risks possibly causing trampling accidents and consequences under emergency measures at different stages after accidents occur are identified and converted into a Bow-Tie diagram, so that the evolution path of the trampling accidents is qualitatively and clearly analyzed. And determining a conditional probability table of the Bayesian network by adopting a Delphi method and a mutation theory, and constructing a Bayesian network graph of the trample accident based on the conditional probability table and the logical relation among multiple influence factors of the trample accident. And analyzing accident consequences under different accident scenes by using a scene analysis method according to the constructed pedaling accident Bayesian network model. And identifying key influence factors causing the trampling accident by using a sensitivity analysis method, thereby realizing dynamic risk assessment and control of the trampling accident.
① the risk of trampling accidents in large-scale activities is mainly divided into four aspects, namely, ① the speed, ① the density, ① the number of vulnerable groups and ① the like of personnel participating in ① the large-scale activities when entering or leaving ① the large-scale activities, risk factors possibly causing trampling accidents in crowd characteristics or individual characteristics, whether key places possibly influencing emergency evacuation of ① the personnel, such as basic equipment, emergency facilities, temporarily built facilities, entrances and exits, stair openings and ① the like in ① the sites for developing ① the activities, are reasonably arranged or designed, namely, ① the risk factors possibly causing trampling accidents exist in ① the activity sites, ③ natural disasters such as earthquakes and tsunamis, bad weather such as rain, snow, ice and ① the like, accidents such as fires, explosions and ① the like inside and outside ① the activity sites, and social security incidents such as violence attack and ① the like are comprehensively analyzed, whether daily management and emergency management of safety guarantee measures, management, emergency preplan and ① the like of ① the large-scale activities are in place and are well as whether ① the management accidents are in place and are well as possible to cause trampling accidents, and relevant historical accidents caused by investigating and analyzing relevant on ① the site accidents.
According to a system accident cause theory, factors causing accidents are identified layer by layer from top to bottom from four aspects of personnel influence, activity site, environment influence and management defects, identified risks of the trampling accidents are classified and summarized according to the steps according to the personnel factors, the activity site factors, the environment factors and the management defects, the left half part of Bow-Tie is determined and constructed to comprehensively describe various factors causing the trampling accidents and logic relations thereof, and the purpose of identifying the risks causing the trampling accidents is achieved.
According to the rule of stepping accidents, the emergency management is divided into four continuous closed-loop stages of preparation, response, recovery, disaster prevention and reduction, namely, personnel participating in large-scale activities are spontaneously evacuated to prevent further expansion of accidents; an organization party of a large-scale activity starts in time, maintains the on-site order, organizes personnel to evacuate safely, gives an alarm to request for support, develops comprehensive rescue, and starts comprehensive emergency response through the cooperative cooperation of multiple departments such as fire control, medical care, public security, education, media and the like; after an accident occurs, reconstruction is organized actively, casualties are pacified, teaching and training are drawn, and the work of good after-treatment is done well. Failure of any protective measures such as spontaneous evacuation, organization and maintenance, alarm rescue, benevolence and the like can cause different accident consequences. And according to whether the emergency protection measures in each step are effective, constructing the right half part of the Bow-Tie to analyze a series of consequences after the stepping accident occurs, presuming the severity of the consequences of the accident, calculating the stepping accident occurrence probability under various evolution paths, and determining the evolution path which is most prone to the stepping accident so as to provide a safety measure with a reliable basis.
The Bayesian method is applied to realize quantitative calculation of the tread accident risk, wherein the probability of any node can be represented by the following formula:
wherein, P (A) and P (B) respectively represent the prior probability of the events A and B, and P (A | B) and P (B | A) respectively represent the probability of the event A occurring when the event B occurs and the probability of the event B occurring when the event A occurs, namely the posterior probability of the event A and the event B after the evidences B and A are given; p (X) represents the joint probability distribution of node X, I represents the set of all nodes in the Bayesian network, I represents one random nodeMechanical variable, xpa(i)Representing all the parents of node i. For any random variable, its joint probability distribution can be obtained by multiplying the respective local conditional probability distributions:
P(X1,X2,X3...,Xn)＝P(Xn|X1,X2,X3,...Xn-1)...P(X2|X1)P(X1)，
wherein P (X)n|X1,X2,X3,...Xn-1) Is node XnConditional probability of (A), P (X)2|X1) Representing child node X2Conditional probability of (A), P (X)1) Representing the prior probability of a parent node in this bayesian network. The conditional probability of the father node in the trampling accident is obtained through literature investigation, historical data, accident report, expert experience and the like, and the probability of the child node is obtained through a Delphi method and a mutation theory.
The structural step block diagram process of analyzing the trample accident by applying the Delphi method and the mutation theory is as follows: formulating a risk questionnaire about the possibility of influencing the occurrence of the stepping accidents in the large-scale activities, wherein the influence factors and the questionnaire purpose of the stepping accidents are comprehensively and specifically described in the questionnaire; the method comprises the steps of inviting known experts in the research field to establish an expert group, providing the experts with detailed background information, filling requirements and other necessary explanatory documents, judging the probability of relative nodes in the treading accident risk questionnaire under the condition that each expert does not have any communication, firstly, dividing the probability into 5 grades according to the occurrence probability, wherein the division standard is shown in table 1, and the higher the grade is, the higher the occurrence probability is.
TABLE 1 probability rating Table
| Grade | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Probability of | [0,0.2) | [0.2,0.4) | [0.4,0.6) | [0.6,0.8) | [0.8,1] |
After collecting the opinions of the experts, collecting and sorting the opinions of each expert, and judging whether the opinions of the experts tend to be consistent by using consistency test, wherein the standard for judging the consensus of the opinions of the experts in the invention is that a clone Bach's alpha is greater than or equal to 0.8, and the formula is as follows:
where alpha represents the value of the cloned Bach coefficient, sigma2Y represents the variance of the probability result given by the expert to a question in the questionnaire, σ2after the consensus of the opinions of each expert is checked in the first round, the information is fed back to the expert group for the opinions which fail to reach the consensus, the questions with different opinions are regulated to some extent, and the experts are invited again to distribute the node probability under the condition that no communication exists among the experts3-5 times until the opinions of the experts tend to be consistent.
Taking parent nodes of 'X1 (too fast crowd)', 'X2 (too large crowd density)', 'X3 (weak crowd quantity)', and 'X4 (sex proportion deviation)' as examples, the fuzzy probability of the nodes of five experts is shown in tables 2-5 on the basis of literature research, historical data, accident report and the like according to experience.
TABLE 2 "X1 (crowd speed too fast)" node probability distribution at each level
TABLE 3 "X2 (crowd density too large)" node probability distribution at each level
TABLE 4 node probability distribution at various levels "X3 (number of vulnerable groups)"
TABLE 5 "X4 (gender ratio bias)" node level probability distribution
The results of the consistency test are shown in table 6:
table 6 consistency analysis results of four nodes X1, X2, X3 and X4
| Node point | X1 | X2 | X3 | X4 |
| Cronbach's | 0.914 | 0.987 | 0.999 | 0.878 |
As can be seen from table 6, the cloned bach coefficients of the four nodes are all greater than 0.8, indicating that the expert opinion is consistent in the problem of the state probabilities of the four nodes.
And (4) counting all consistent expert opinions, and sorting the probability data of the child nodes according to a mutation theory method. For quantitative deterministic data, because the range and the unit of the data are different, whether the node is a forward index (the larger the numerical value is, the better the reverse index (the smaller the numerical value is), or a moderate index (the closer the numerical value is to a certain value, the better the value is), and all the data are subjected to dimensionless processing according to a corresponding principle; in this embodiment, the probabilities of the sub-nodes are uncertainty values, and the probability ranges from 0 to 1, so that no dimensionless process is required.
Taking the child node "M1 (group-specific dangerous behavior)" as an example, the probability results given by expert 1 are shown in Table 7 (because of space limitation, the opinions of other experts are not discussed in detail).
Table 7 judgment of M1 level probability by expert 1 under the influence of X1, X2, X3 and X4 in node M1
After the opinions are consistent through judgment of a plurality of rounds of experts, the average value of the probability result of each expert is used as the final result. The consensus expert opinions for the child node "M1 (group characteristic dangerous behavior)" are shown in table 8.
TABLE 8 probabilities of the levels of M1 nodes under the influence of X1, X2, X3, and X4
The M1 node has four control variables, so according to the normalization formula and the primary and secondary relation of the bowtie model in the mutation theory, under the first state combination
According to the complementary relation, the probability that the node state of the M1 is more is 0.620 under the first state combination of the four control variables X1, X2, X3 and X4 obtained by averaging; according to the same normalization formula
The conditional probability table of M1 nodes is shown in table 9, which is obtained by repeating the steps of obtaining a probability that the state of the M1 node is "less" of 0.205 by taking the minimum value, obtaining "more" and "less" states of 0.752 and 0.248, respectively, according to the weight since the sum of the two states of the M1 node is 1.
TABLE 9 conditional probability Table for child node M1
According to the same method, a corresponding mutation model is selected according to the number of the child node control variables (namely the number of father nodes) in the Bayesian network, a quantitative recursion calculation value is obtained, if a complementary (small numerical value difference) relationship exists between the father nodes, the average value of the father nodes is used as the mutation level numerical value of the child node at the previous stage, if the complementary (large numerical value difference) relationship does not exist, the minimum value of the father nodes is used as the mutation level numerical value of the child node at the previous stage, and finally a conditional probability table of all child nodes causing trampling accidents is obtained. All node probabilities are added to the bayesian network structure, as shown in fig. 5, taking X1, X2, X3, X4, and M1 as examples, and the prior probability or the conditional probability of each node is added to the bayesian network.
After a conditional probability table is determined by combining the Delphi method and the mutation theory and is added into a Bayesian network converted from a Bow-Tie diagram, as shown in FIG. 6, the influence of excessive crowd density and excessive number of vulnerable groups on the characteristic dangerous behaviors of the groups is shown, and the probability that the characteristic dangerous behaviors of the groups are more is dynamically displayed and increased from 53.8% to 65.7%. Therefore, by applying a scenario analysis method, different accident scenarios are set in the Bayesian network, and the inference function of the Bayesian network is used to dynamically predict the change situation of the accident consequences under a certain scenario, wherein the accident consequences include four aspects of casualties, economic loss, social order and influence range. And finally, judging important factors influencing the accident by using a sensitivity analysis method, and taking corresponding measures to reduce the occurrence of the trampling accident and reduce the severity of the accident consequence.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A dynamic risk assessment method for a large-scale active pedaling accident is characterized by comprising the following steps:
s1, collecting, investigating and researching trampling accident data in multiple dimensions;
s2, analyzing trample accident data and determining a Bayesian network model;
s3, according to the constructed treading accident Bayes network model, by a multi-scene situation analysis method, on the basis of accident causes and evolution paths, the accident consequences under different treading accident scenes are dynamically predicted by using the inference function of the Bayes network, and key factors influencing the occurrence of treading accidents are identified by using a sensitivity analysis method;
and S4, carrying out risk control on the trampling accident.
2. The dynamic risk assessment method for large active pedaling accident according to claim 1, wherein: when the stepping accident data is sufficient in step S2, a bayesian network is constructed by using a structure learning method and a parameter learning method.
3. The dynamic risk assessment method for large active pedaling accident according to claim 1, wherein in step S2, when the pedaling accident data is limited, the bayesian network model is built as follows:
s21, establishing a Bow-Tie model of the trampling accident according to the reason and the consequence of the trampling accident, analyzing the cause path of the trampling accident, determining the structure of a Bayesian network, and identifying influence factors before and after the trampling accident;
and S22, determining a conditional probability table of the Bayesian network by adopting a Delphi method and a mutation theory, and constructing a pedaling accident Bayesian network model by combining the logical relationship among the influencing factors of the pedaling accident in the step S21.
4. The dynamic risk assessment method for large active pedaling accident according to claim 3, wherein: in the step S21, the cause path of the stepping accident is analyzed and classified into a person factor, a playing place factor, an environmental factor and a management defect according to the system accident cause theory.
5. The dynamic risk assessment method for large active pedaling accident according to claim 3, wherein: in the step S21, the cause and the consequence of the stepping accident are combined into a Bow-Tie model, the stepping accident is used as an intermediate event, the identified cause causing the stepping accident is located on the left side of the Bow-Tie model, and the emergency protective measures and different accident consequences included after the stepping accident occurs are located on the right side of the Bow-Tie model.
6. The dynamic risk assessment method for large-scale active pedaling accident according to claim 3, wherein the Bow-Tie model in step S21 is used to determine the structure of the Bayesian network, and the transformation relationship is as follows:
the basic event causing the trample accident is converted into a father node in the Bayesian network, the middle event is converted into a child node of the Bayesian network, the trample accident is converted into a hub node of the Bayesian network, and the trample accident is converted into different child nodes according to the severity of the accident and according to four aspects of casualties, economic loss, social order and influence range.
7. The dynamic risk assessment method for large active stepping accident according to claim 3, wherein the step of determining the conditional probability table of Bayesian network using Delphi method and mutation theory in step S22 is as follows:
s221, formulating a possibility risk questionnaire about influencing the occurrence of the stepping accidents in the large-scale activities;
s222, judging the probability of relative nodes in the treading accident risk questionnaire;
s223, summarizing and sorting the data in the step S222, and judging whether the probabilities of the relevant nodes in the step S222 are consistent or not according to a consistency test standard;
s224, if not, adjusting the content of the related node, and repeating the step S222 until the result is consistent.
8. The dynamic risk assessment method for large active pedaling accident according to claim 7, wherein: the consistency test criterion is a clonal Bach confidence coefficient greater than or equal to 0.8.
9. The dynamic risk assessment method for large active pedaling accident according to claim 3, wherein: the state probability of the father node in the Bayesian network is obtained by a Delphi method, and the state probability of the child node selects a corresponding mutation model according to the number of the father nodes in the Bayesian network to obtain a quantitative recursion calculation value;
if the father nodes have a complementary relation, taking the average value of a plurality of father nodes as the mutation level numerical value of the upper-level child node; if the complementary relationship does not exist, taking the minimum value of the multiple father nodes as the mutation level numerical value of the upper-level child node, selecting the corresponding mutation model according to the number of the child nodes and obtaining the system-level mutation level numerical value, and obtaining the conditional probability table of the trampling accident according to the probability data in the processing step S22.
10. The dynamic risk assessment method for large active pedaling accident according to claim 1, wherein: in the step S3, different accident scenarios are set according to the constructed bayesian network model, the cause path leading to the occurrence of the stepping accident is analyzed, the occurrence probability of the node is changed, the accident consequence in a certain scenario is dynamically predicted by using the inference function of the bayesian network, the key factors influencing the stepping according to the occurrence of the accident are identified by using sensitivity analysis, and measures are taken from the important factors to reduce the occurrence of the stepping accident and reduce the severity of the accident consequence.
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