CN108062638A - Pipe gallery disaster chain methods of risk assessment - Google Patents
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Abstract
The present invention provides a kind of pipe gallery disaster chain methods of risk assessment.The described method includes：The risk assessment parameter of pipe gallery is determined according to pipe gallery O＆M monitoring risk data, the O＆M monitoring risk data is the frequency of the environmental monitoring data and generation corresponding to the alert event or Disaster Event occurred during pipe gallery O＆M；The Bayesian network model of pipe gallery disaster chain is built according to the risk assessment parameter；The risk assessment of pipe gallery disaster chain is carried out according to the Bayesian network model；Corresponding mitigating the disaster by cutting the disaster chain in the headstream measure is implemented according to assessment result.The present invention can build Bayesian network model according to the Evolution of Potential hazards in pipe gallery, the probability and the assessment piping lane extent of damage that each disaster of prediction piping lane occurs, so as to propose scientific and effective pipe gallery mitigating the disaster by cutting the disaster chain in the headstream mechanism and measure.
Description
Technical Field
The invention relates to the technical field of risk assessment, in particular to a comprehensive pipe rack disaster chain risk assessment method.
Background
The city utility tunnel (hereinafter referred to as utility tunnel) is an important infrastructure for laying various municipal utility pipelines and ensuring the safe and stable operation of the city lifeline system. Utility tunnel lays in the underground, can resist the infringement of multiple natural disasters such as earthquake, typhoon, ice to municipal pipeline, has also reduced the possibility of external factors such as environment, construction to pipeline network destruction, nevertheless concentrates in this hidden and narrow and small underground space and lays multiple municipal pipeline, because the existence of the calamity coupling relation between the inside various pipelines of piping lane makes municipal pipeline to the stability and the security of utility tunnel provide higher requirement.
The monitoring of present piping lane operation department to the piping lane adopts environment and gaseous monitoring facility as the main, and the artifical mode that detects for assisting expandes. The operator mainly judges the pipeline safety state according to the sensor value, and does not have any standard judgment at present according to whether the sensor value is abnormal or not. Due to the fact that the pipeline corridor is long in line, the pipeline is complex, the identifiability of the abnormal part on the surface of the pipeline is poor, the phenomenon of missing inspection is inevitable during manual inspection, and the accuracy rate is low. In addition, the accumulated operation experience of the pipe gallery in China is less, and the research on the pipeline gallery analysis and the pipeline and environment coupling relation is insufficient, so that the potential disaster source has the characteristics of uncertainty, diversity, linkage and the like. The disaster-causing process of the environment disaster events in the closed pipe gallery is complex, all the disaster events are not isolated, the disaster events often occur in time, the space transfer and diffusion are from point to line and plane, and from a single cabin to multiple cabins, the disaster events are in a relationship of mutual connection and mutual control, and a complex disaster chain system can be formed among the disaster events. The calamity chain in the piping lane is the great potential safety hazard that is worth paying attention to but does not arouse enough attention to, in case certain calamity accident appears in the piping lane inside, is the calamity chain that probably causes chain reaction promptly, and the harm that causes will far exceed the similar incident on ground, therefore utility tunnel's safety risk problem is more outstanding. Existing related researches mainly carry out risk analysis and evaluation from the perspective of single disasters such as gas leakage and fire of the comprehensive pipe rack, ignore the coupling effect between potential disaster events, and bring more problems to the later-stage operation and management of the pipe rack.
Disclosure of Invention
According to the comprehensive pipe gallery disaster chain risk assessment method, a Bayesian network model can be constructed according to the evolution rule of potential disasters in the comprehensive pipe gallery, the occurrence probability of each disaster of the pipe gallery is predicted, and the loss degree of the pipe gallery is assessed, so that a scientific and effective comprehensive pipe gallery pregnancy source chain breakage disaster reduction mechanism and measure are provided.
In a first aspect, the invention provides a comprehensive pipe rack disaster chain risk assessment method, which comprises the following steps:
determining risk evaluation parameters of the comprehensive pipe rack according to operation and maintenance monitoring risk data of the comprehensive pipe rack, wherein the operation and maintenance monitoring risk data comprise environment monitoring data corresponding to alarm events or disaster events occurring during the operation and maintenance of the comprehensive pipe rack and frequency of the occurrence;
constructing a Bayesian network model of the comprehensive pipe gallery disaster chain according to the risk assessment parameters;
performing risk assessment on the comprehensive pipe gallery disaster chain according to the Bayesian network model;
and implementing corresponding measures for chain scission and disaster reduction of the pregnancy source according to the evaluation result.
Optionally, determining the risk assessment parameters of the utility tunnel according to the utility tunnel operation and maintenance monitoring risk data includes:
determining a comprehensive pipe gallery disaster risk assessment unit;
collecting risk data of a risk evaluation unit, wherein the risk data comprise environmental monitoring data corresponding to alarm events or disaster events occurring during operation and maintenance of the comprehensive pipe rack and frequency of the occurrence;
and selecting disaster causing factors and corresponding disaster-bearing bodies according to the acquired risk data and the disaster causing factors.
Optionally, the constructing a bayesian network model of a utility tunnel disaster chain according to the risk assessment parameters comprises:
determining the coupling relation of each disaster according to the selected disaster-causing factors and the corresponding disaster-bearing bodies;
determining a disaster evolution rule according to the coupling relation of each disaster and forming a disaster chain of the comprehensive pipe rack;
and constructing a Bayesian network model according to the formed disaster chain.
Optionally, the performing risk assessment of the utility tunnel disaster chain according to the bayesian network model as follows comprises:
in a bayesian network model formed by n disaster nodes, all nodes i have m father nodes to influence the n disaster nodes, and then the evaluation mode of the whole disaster chain risk R can be calculated by the following formula:
wherein n is more than 0, i is more than or equal to 1 and less than or equal to n, m is more than or equal to 1 and less than i, and n, i and m are natural numbers;
L (j→i)l the disaster damage level of the child node i under the action of the father node j is shown, wherein j is more than or equal to 1 and less than or equal to m, and j is a natural number;
the disaster damage level has k (k is greater than 0, k belongs to N) level, wherein k is greater than 0 and is a natural number;
P (j→i)l the probability is corresponding to the disaster damage level of the child node i under the action of the parent node j.
Optionally, the performing risk assessment of the utility corridor disaster chain according to the bayesian network model further comprises:
in a Bayesian network model composed of n disaster nodes, the different disaster damage levels of any disaster node i under the action of different disaster-causing intensity levels of a father node j are L (j→i)l,t Corresponding probabilityIt can be calculated from the following formula:
wherein the damage level L (j→i)l Determining according to the national catastrophe strength grade division standard and the actual influence range;
n is more than 0, i is more than or equal to 1 and less than or equal to n, m is more than or equal to 1 and less than i, and n, i and m are natural numbers;
l is the disaster damage level of the node i, and t is the disaster intensity level of the father node j;
L (j→i)l,t the disaster-causing intensity level of the child node i at the parent node j is t and the intensity is H (j→i)t The disaster damage level under the action of (1);
disaster-causing intensity for father node is H (j→i)t Sub-node j disaster damage level L (j→i)l,t The corresponding probability;
the vector is formed by the nodes i under the action of different disaster-causing intensity levels of the father node j.
Optionally, the performing of the corresponding pregnancy source chain scission and disaster reduction measure according to the evaluation result includes:
identifying an evolution stage of the comprehensive pipe rack disaster chain according to the evaluation result;
and implementing corresponding pregnancy source chain breaking disaster reduction measures according to the evolution stage of the disaster chain.
According to the comprehensive pipe gallery disaster chain risk assessment method provided by the embodiment of the invention, risk data of a comprehensive pipe gallery are collected, comprehensive pipe gallery risk assessment parameters are selected, a Bayesian network model of a disaster chain is constructed, the risk level and the occurrence probability of each disaster node are assessed through the Bayesian network model, and the evolution stage of the comprehensive pipe gallery disaster chain is identified according to the assessment result; and implementing corresponding pregnancy source chain breaking disaster reduction measures according to the evolution stage of the disaster chain. Compared with the prior art, the comprehensive pipe rack disaster chain Bayesian network model can be constructed according to the evolution law of various potential disasters in the comprehensive pipe rack, the occurrence probability of each disaster of the pipe rack is predicted, and the loss degree of the pipe rack is evaluated, so that a scientific and effective comprehensive pipe rack pregnancy source chain breakage disaster reduction mechanism and measure are provided.
Drawings
Fig. 1 is a flowchart of a comprehensive pipe rack disaster chain risk assessment method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a bayesian network model of a disaster chain of the utility tunnel according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Inside utility tunnel system, its operation system and environment closed relatively for in case of disaster event, it is the disaster chain that is very likely to cause the chain reaction, and the harm that causes will far exceed single disaster event. It is particularly important to master the mutual influence between disaster events and between disasters and the environment in the comprehensive pipe rack system. Therefore, when analyzing the risk of the disaster chain of the corridor, we need to consider not only the index factors such as the occurrence probability and the loss of a single disaster event, but also fully consider the interrelation among the disaster events from the overall perspective, i.e. from the perspective of the chain effect of the disaster occurrence, so as to evaluate whether the occurrence of a disaster event affects the occurrence of other disaster events in the system and the degree thereof, and to evaluate the overall risk of the disaster chain. The invention provides a comprehensive pipe rack disaster chain risk assessment method, as shown in fig. 1, the method comprises the following steps:
s11, determining risk evaluation parameters of the comprehensive pipe rack according to operation and maintenance monitoring risk data of the comprehensive pipe rack, wherein the operation and maintenance monitoring risk data comprise environment monitoring data corresponding to alarm events or disaster events occurring during operation and maintenance of the comprehensive pipe rack and frequency of occurrence;
the parameters for evaluating the comprehensive pipe rack comprise various potential disaster events in the pipe rack and risk factors for generating the disaster events, such as gas leakage, fire, flood and the like, and risk factors for generating the environment, pipeline faults, equipment faults and the like in the pipe rack of the disaster events.
Among these risk assessment parameters, disaster events such as gas leakage, fire, flood, etc. occurring in the pipe rack are disaster-causing factors, and risk factors such as environment, pipeline failure, equipment failure, etc. occurring in the pipe rack, which cause the above-mentioned disasters, are disaster-causing factors. Different disaster-causing factors may cause different chained disasters, and it is therefore necessary to refer to disaster events that occurred during operation when selecting risk assessment parameters to determine the type of disaster potential within the utility tunnel. The steps for selecting the selected risk assessment parameters are as follows:
determining a comprehensive pipe rack disaster risk assessment unit, wherein a section of pipe rack is selected as a risk assessment unit as the comprehensive pipe racks are distributed in a strip shape;
collecting risk data of a risk evaluation unit, wherein the risk data comprise environmental monitoring data corresponding to alarm events or disaster events occurring during operation and maintenance of the comprehensive pipe rack and frequency of the occurrence;
because the serious disaster event has not taken place yet in the operation and maintenance period of the utility tunnel, according to the risk data of collecting various sensor alarm data as the utility tunnel, the data comprise the environmental monitoring data and the frequency of taking place that the alarm event or disaster event corresponding appears inside the operation of the pipe gallery till now, including temperature, oxygen concentration and H 2 S gas concentration, etc. The frequency with which an alarm event or disaster event a (e.g. a fire that would result from a high cable temperature) causes an alarm event or disaster event b is heavily documented. Collecting these data is used to calculate an alarm event or disaster event b, i.e. child node, under a parent node in an alarm event or disaster event a, i.e. a bayesian networkDisaster damage probability, i.e., the probability of connecting edges in a bayesian network.
And selecting disaster-causing factors and corresponding disaster-bearing bodies according to the acquired risk data and the disaster-causing factors.
Disaster factors, disaster-bearing bodies and disaster-pregnant environments are three factors forming a disaster chain, and the disaster-pregnant environment refers to a pipe gallery closed environment in which various pipelines coexist, namely a comprehensive pipe gallery. Disaster-causing factors in the utility tunnel environment refer to natural or artificial phenomena that may have adverse effects on inspection personnel, tunnel body structures, pipeline stability and safe operation in the pipe gallery, such as high temperature of cables, aging and bursting of water pipes, gas leakage, etc. The occurrence of the disaster chain is a process that a disaster causing factor generates a disaster event under the interaction of the disaster pregnant environment and the disaster bearing body, and the disaster event continuously generates a secondary disaster under the secondary action of the disaster pregnant environment and the disaster bearing body. For example, in a certain comprehensive pipe rack space and time range, a disaster-causing factor such as a fire caused by the abnormal change of gas leakage under the action of a pregnant disaster environment (a gas cabin closed space and gas concentration accumulation) occurs in a pipe rack system, and then a new disaster event such as explosion is further generated, so that a disaster bearing body (a rack body structure and a gas pipeline) in the system is influenced (attacked) by multiple disaster events (namely fire and explosion), and when the effect of the disaster bearing body reaches a critical value, a new disaster event (rack body collapse and further gas explosion) is generated, so that the phenomenon of the occurrence of the disaster event is further formed, and finally a disaster chain is formed. The disaster-causing factors in the comprehensive pipe gallery are selected according to the disaster-causing factors and the alarming or occurrence frequency of disaster events.
S12, constructing a Bayesian network model of the comprehensive pipe rack disaster chain according to the risk assessment parameters;
the method for constructing the Bayesian network model comprises the following steps:
determining the occurrence and evolution rules of each disaster according to the selected disaster-causing factors and disaster-bearing bodies;
forming a disaster chain of the comprehensive pipe rack according to the occurrence and evolution rules of all disasters;
and constructing a Bayesian network model according to the formed disaster chain.
Fig. 2 shows a bayesian network including n disaster nodes (n >0, n being a natural number).
S13, performing risk assessment on the comprehensive pipe gallery disaster chain according to the Bayesian network model;
the risk assessment of carrying out the utility tunnel disaster chain according to the Bayesian network model comprises: determining the risk level of each disaster node; and (2) calculating the probability of each disaster node connecting edge.
The measure of the disaster risk is represented by risk (R) = loss (L) × probability (P), if the probability of different disaster damage degrees of the child node under the action of a certain parent disaster event can be determined, the risk value of the child node under the action of the parent disaster event can be represented by the expected value of the risk (different disaster damage degrees are multiplied by the corresponding occurrence probability), the risks under the action of a plurality of multiple disaster events can be superposed, and the probability corresponding to the different disaster damage degrees of the node is the most main parameter concerned for risk decision. In summary, in a disaster chain network with n nodes, any child node i has m parent nodes to affect it, and the overall disaster chain risk evaluation mode can be represented as:
the risk expectation value of the child node i under the action of the parent node j in the disaster chain is represented, the disaster damage of each disaster event caused by a disaster source in the disaster chain is uncertain, and only the distribution probability of different disaster damage degrees can be obtained through an evaluation method, so that the risk of each node under the action of the parent node can be represented by the expectation value:
so at n nodes (R) consisting of disaster chains 1 ,R 2 823060, rn) in disaster networki has m father nodes to affect the disaster chain, the evaluation mode of the whole disaster chain risk can be expressed as:
in a disaster chain, each node, namely a disaster event, is a disaster carrier of a father node and is a disaster-causing factor of a next-level disaster event. For example, a cable (here, a pipeline disaster-bearing body) is interrupted due to a fire caused by a high temperature of the cable, and the interruption of the cable (a disaster-causing factor) naturally causes a power interruption in a local area to cause problems such as life and production paralysis. Therefore, the disaster damage of the node is the disaster-causing intensity of the parent node acting as the child node. In disaster risk assessment, disaster-causing intensity is generally called as disaster intensity, classification is mainly carried out at present according to disaster activity scale or activity intensity, and node disaster-causing intensity grade is classified by taking an actual influence range as a main judgment standard on the basis of referring to national disaster intensity grade classification standard and related research results.
The probability of connecting edges in a bayesian network refers to the amount of probability that a disaster causes another disaster. If the fire disaster causes explosion, the explosion probability refers to the probability of explosion of the pipe gallery under different fire intensity or the probability of explosion with different intensity. The probability of a disaster event at a child node in a disaster chain is the conditional probability under the influence of the disaster event at its parent node, i.e. the child node occurs under the occurrence of the parent node. And under the action of the parent disaster events with different disaster causing intensities, the disaster damage levels of the child disaster events are different. Then, the probability of a certain disaster damage level of the sub-disaster event under the action of different disaster-causing intensity levels of the parent disaster event needs to be obtained, and then the probability of each disaster damage level of the sub-disaster event is calculated.
Here, it should be noted that: a child node has m (multiple) father nodes, each father node has different disaster-causing strengths, namely t disaster-causing strengths, and each child node has k (multiple) father nodes and corresponding occurrence probabilities under the different disaster-causing strengths of the father nodes.
The following describes the utilization of shellfishAnd carrying out a process of calculating the disaster damage grade probability of the disaster event by using a leaf equation. Suppose that any node i in a disaster chain system has j parents and m parents, the node i is at a certain father node j, and the disaster causing intensity is H (j→i)t The disaster damage level under action is L (j→i)l,t Corresponding probability of beingl is the disaster damage level of i, and t is the disaster intensity level of j. By usingThe vector formed by the node i under the action of the parent node j with different disaster-causing intensity levels is represented, and the different disaster-causing loss levels of the node i under the action of the parent node j with different disaster-causing intensity levels are L (j→i)l,t The conditional probability of (c) can be obtained by statistical calculation according to the following formula:
then the node i under the action of the father node has different disaster damage levels L (j→i)l,t Probability of (2)Comprises the following steps:
and S14, implementing corresponding measures for chain scission and disaster reduction of the pregnancy source according to the evaluation result.
The implementation of the corresponding measures for chain scission and disaster reduction of the pregnancy source according to the evaluation result comprises the following steps:
identifying an evolution stage of the comprehensive pipe gallery disaster chain according to an evaluation result;
monitoring and tracking the comprehensive pipe rack disaster chain system, acquiring pipe rack operation and maintenance data such as pipe rack internal pipelines, environment and structural health, analyzing data change trend from multiple dimensions and multiple forms, and accurately identifying and predicting each stage of disaster chain evolution. By means of the Internet of things technology and an intelligent pipe rack management platform, the change characteristics of each stage of disaster chain evolution can be accurately identified through simulation experiment results.
And implementing corresponding pregnancy source chain breaking disaster reduction measures according to the evolution stage of the disaster chain.
The pipe gallery disaster chain evolution stage is a basis for judging whether a pregnancy source chain breakage disaster reduction measure is adopted or not and determining the time for implementing the disaster reduction measure. According to the comprehensive pipe gallery pregnancy source chain breakage disaster reduction method, a stable and efficient comprehensive pipe gallery pregnancy source chain breakage disaster reduction frame and an early warning mechanism are established from the angles of controlling a chain source and cutting off a key link, VR (Virtual Reality) interaction technology is adopted for practicing the provided comprehensive pipe gallery pregnancy source chain breakage disaster reduction measures, and effectiveness and operability of the effect are verified.
According to the comprehensive pipe gallery disaster chain risk assessment method provided by the embodiment of the invention, risk data of a comprehensive pipe gallery are collected, risk assessment parameters of the comprehensive pipe gallery are selected, a Bayesian network model of a disaster chain is constructed, the risk level and the occurrence probability of each disaster node are assessed through the Bayesian network model, and the evolution stage of the comprehensive pipe gallery disaster chain is identified according to the assessment result; and implementing corresponding pregnancy source chain breaking disaster reduction measures according to the evolution stage of the disaster chain. Compared with the prior art, the method can construct the Bayesian network model according to the evolution rule of potential disasters in the comprehensive pipe rack, predict the occurrence probability of each disaster of the pipe rack and evaluate the loss degree of the pipe rack, and therefore, a scientific and effective comprehensive pipe rack pregnancy source chain scission disaster reduction mechanism and measure are provided.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific 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.
Claims (6)
1. A comprehensive pipe rack disaster chain risk assessment method is characterized by comprising the following steps:
determining risk evaluation parameters of the comprehensive pipe rack according to operation and maintenance monitoring risk data of the comprehensive pipe rack, wherein the operation and maintenance monitoring risk data comprise environment monitoring data corresponding to alarm events or disaster events occurring during the operation and maintenance of the comprehensive pipe rack and frequency of the occurrence;
constructing a Bayesian network model of the comprehensive pipe rack disaster chain according to the risk assessment parameters;
performing risk assessment on the comprehensive pipe gallery disaster chain according to the Bayesian network model;
and implementing corresponding measures for chain scission and disaster reduction of the pregnancy source according to the evaluation result.
2. The method of claim 1, wherein determining the risk assessment parameters of the utility tunnel from the utility tunnel operation and maintenance monitoring risk data comprises:
determining a comprehensive pipe rack disaster risk assessment unit;
collecting risk data of a risk evaluation unit, wherein the risk data comprise environmental monitoring data corresponding to alarm events or disaster events occurring during operation and maintenance of the comprehensive pipe rack and frequency of the occurrence;
and selecting disaster-causing factors and corresponding disaster-bearing bodies according to the acquired risk data and the disaster-causing factors.
3. The method of claim 2, wherein constructing a Bayesian network model of a utility corridor disaster chain from the risk assessment parameters comprises:
determining the coupling relation of each disaster according to the selected disaster-causing factors and disaster-bearing bodies;
determining a disaster evolution rule according to the coupling relation of each disaster and forming a disaster chain of the comprehensive pipe rack;
and constructing a Bayesian network model according to the formed disaster chain.
4. The method of claim 3, wherein the risk assessment of the utility corridor disaster chain according to the Bayesian network model comprises:
in a bayesian network model formed by n disaster nodes, all nodes i have m father nodes to influence the n disaster nodes, and then the evaluation mode of the whole disaster chain risk R can be calculated by the following formula:
wherein n is more than 0, i is more than or equal to 1 and less than or equal to n, m is more than or equal to 1 and less than i, and n, i and m are natural numbers;
L (j→i)l the disaster damage level of the child node i under the action of the father node j is shown, wherein j is more than or equal to 1 and less than or equal to m, and j is a natural number;
the disaster damage level has k levels, wherein k is greater than 0 and is a natural number;
P (j→i)l the probability is corresponding to the disaster damage level of the child node i under the action of the parent node j.
5. The method of claim 3, wherein the risk assessment of a utility corridor disaster chain according to the Bayesian network model further comprises:
in a Bayesian network model consisting of n disaster nodes, different disaster damage levels L occur to any disaster node i under the action of a father node j (j→i)l Corresponding probabilityIt can be calculated from the following formula:
wherein the damage level L (j→i)l Determining according to the national catastrophe strength grade division standard and the actual influence range;
n is more than 0, i is more than or equal to 1 and less than or equal to n, m is more than or equal to 1 and less than i, and n, i and m are natural numbers;
l is the disaster damage level of the node i, and t is the disaster intensity level of the father node j;
L (j→i)l,t the disaster-causing intensity level of the child node i at the parent node j is t and the intensity is H (j→i)t The disaster damage level under the action of (1);
disaster-causing intensity for father node is H (j→i)t Sub-node j disaster damage level L (j→i)l,t The corresponding probability;
is a vector formed by the nodes i under the action of different disaster-causing intensity levels of the father node j.
6. The method according to any one of claims 1 to 5, wherein said performing a corresponding pregnancy loss disaster reduction measure based on the evaluation comprises:
identifying an evolution stage of the comprehensive pipe rack disaster chain according to the evaluation result;
and implementing corresponding pregnancy source chain breaking disaster reduction measures according to the evolution stage of the disaster chain.
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| CN109408686A (en) * | 2018-10-19 | 2019-03-01 | 中建二局安装工程有限公司 | A kind of underground pipe gallery big data visualization system and method |
| CN109685308A (en) * | 2018-11-15 | 2019-04-26 | 中国科学院大学 | A kind of complication system critical path appraisal procedure and system |
| CN110008991A (en) * | 2019-02-26 | 2019-07-12 | 阿里巴巴集团控股有限公司 | The identification of risk case, risk identification model generation method and device |
| CN110309620A (en) * | 2019-07-10 | 2019-10-08 | 河北省建筑科学研究院有限公司 | Based on the underground pipe gallery fire of Internet of Things and BIM explosion monitoring system and implementation method |
| CN111080139A (en) * | 2019-12-19 | 2020-04-28 | 石家庄铁道大学 | Hypergraph-based public security event scenario representation method |
| CN111191959A (en) * | 2020-01-08 | 2020-05-22 | 中国石油大学(华东) | Long-distance natural gas pipeline derivative disaster evaluation system and method |
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| CN109408686A (en) * | 2018-10-19 | 2019-03-01 | 中建二局安装工程有限公司 | A kind of underground pipe gallery big data visualization system and method |
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| CN110008991A (en) * | 2019-02-26 | 2019-07-12 | 阿里巴巴集团控股有限公司 | The identification of risk case, risk identification model generation method and device |
| CN111738038B (en) * | 2019-04-10 | 2024-04-09 | 洛阳城市建设勘察设计院有限公司 | Underground comprehensive pipe gallery crack water seepage prevention treatment method based on smart city |
| CN111738038A (en) * | 2019-04-10 | 2020-10-02 | 洛阳城市建设勘察设计院有限公司 | Underground comprehensive pipe gallery crack water seepage prevention treatment method based on smart city |
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| CN111080139A (en) * | 2019-12-19 | 2020-04-28 | 石家庄铁道大学 | Hypergraph-based public security event scenario representation method |
| CN111191959A (en) * | 2020-01-08 | 2020-05-22 | 中国石油大学(华东) | Long-distance natural gas pipeline derivative disaster evaluation system and method |
| CN111798112A (en) * | 2020-06-28 | 2020-10-20 | 中国矿业大学(北京) | Urban community social public safety comprehensive risk assessment method |
| CN111798112B (en) * | 2020-06-28 | 2021-04-13 | 中国矿业大学(北京) | Urban community social public safety comprehensive risk assessment method |
| CN112215458A (en) * | 2020-09-01 | 2021-01-12 | 青岛海信网络科技股份有限公司 | Disaster analysis method and electronic device |
| CN112365078A (en) * | 2020-11-23 | 2021-02-12 | 南京莱斯信息技术股份有限公司 | Multi-disaster-species coupling and secondary derivative evolution prediction system based on disaster chain |
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| CN112529265B (en) * | 2020-11-27 | 2021-10-26 | 安徽泽众安全科技有限公司 | Comprehensive risk assessment and prediction method and system for gas pipeline |
| CN112365191A (en) * | 2020-12-01 | 2021-02-12 | 中国水利水电科学研究院 | Risk measurement method based on typhoon-rainstorm-flood chained disasters |
| CN112598243A (en) * | 2020-12-15 | 2021-04-02 | 重庆电子工程职业学院 | Method for dynamically evaluating operation and maintenance safety of pipe rack |
| CN112801473A (en) * | 2021-01-15 | 2021-05-14 | 北京城市系统工程研究中心 | Disaster prediction method and system based on natural disaster chain |
| CN113592371B (en) * | 2021-10-08 | 2022-01-18 | 北京市科学技术研究院城市安全与环境科学研究所 | Comprehensive risk analysis system, method and equipment based on multi-dimensional risk matrix |
| CN113592371A (en) * | 2021-10-08 | 2021-11-02 | 北京市科学技术研究院城市安全与环境科学研究所 | Comprehensive risk analysis system, method and equipment based on multi-dimensional risk matrix |
| CN114186772A (en) * | 2021-10-22 | 2022-03-15 | 中山大学 | Method for predicting gas leakage risk under coupling action of multiple kinds of disasters |
| CN115293656A (en) * | 2022-10-08 | 2022-11-04 | 西南石油大学 | Parallel oil and gas pipeline domino effect risk analysis method based on Bayesian network |
| CN115829336A (en) * | 2023-02-17 | 2023-03-21 | 深圳市城市公共安全技术研究院有限公司 | Risk assessment method and device for gas pipeline leakage, equipment and storage medium |
| CN115829336B (en) * | 2023-02-17 | 2023-06-06 | 深圳市城市公共安全技术研究院有限公司 | Risk assessment method and device for leakage of fuel gas pipeline, equipment and storage medium |
| CN116307950A (en) * | 2023-05-25 | 2023-06-23 | 中建安装集团有限公司 | Building quality intelligent management system and method based on multivariate information |
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