Gas accident analysis system and method based on dynamic Bayesian network

Abstract

The invention relates to a gas accident analysis system and method based on a dynamic Bayesian network. The method comprises the following steps: constructing a dynamic Bayesian network model; gas equipment operation state data and environment parameters are obtained; processing data, and performing gas accident risk assessment and accident evolution path analysis by using an inference algorithm; and generating an accident analysis result and outputting the accident analysis result through a visual tool. According to the gas accident analysis system and method based on the dynamic Bayesian network, multiple risk factors of a gas accident are comprehensively analyzed through the dynamic Bayesian network technology, the possibility of accident occurrence can be effectively predicted, the severity of the accident can be effectively evaluated, and reasonable prevention and response measures are put forward. Compared with a traditional risk assessment method, the method is higher in accuracy, real-time performance and flexibility, can provide more scientific safety management decision support for gas enterprises, remarkably improves the gas accident prevention and emergency processing capability, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of gas safety technology, and in particular to a gas accident analysis system and method based on a dynamic Bayesian network. Background Art

[0002] At present, the analysis of gas accidents mainly relies on traditional statistical methods and accident tree analysis. Although these methods can describe the cause and effect relationship of accidents, it is difficult to dynamically reflect the evolution of accidents and the interaction of multiple factors in complex systems. Therefore, a gas accident analysis method based on dynamic Bayesian networks is urgently needed to improve the accuracy and dynamics of accident analysis. Summary of the invention

[0003] In order to solve the technical problems existing in the prior art, the present invention provides a gas accident analysis system and method based on a dynamic Bayesian network.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] A gas accident analysis method based on dynamic Bayesian network includes the following steps:

[0006] Step 1: construct a dynamic Bayesian network model;

[0007] Step 2: Obtain gas equipment operating status data and environmental parameters through real-time monitoring data and historical database;

[0008] Step 3: Input the acquired data into the dynamic Bayesian network model and use the inference algorithm to conduct gas accident risk assessment and accident evolution path analysis;

[0009] Step 4: Generate analysis results, including the probability of accident occurrence, scope of impact and emergency recommendations, and output them through visualization tools.

[0010] As a preferred technical solution, building a dynamic Bayesian network model includes: determining the key nodes and causal relationships of gas accidents; establishing a dynamic conditional probability table between nodes based on historical accident data and expert knowledge; and using a training algorithm to optimize and verify the parameters of the dynamic Bayesian network model.

[0011] As a preferred technical solution, key nodes include equipment status nodes, environmental parameter nodes and accident consequence nodes.

[0012] As a preferred technical solution, the inference algorithm adopts one of a forward-backward algorithm, a particle filter algorithm, and a Markov chain Monte Carlo method.

[0013] As a preferred technical solution, the visualization tool displays the risk distribution, accident probability and evolution path of each key node in the dynamic Bayesian network, and provides a dynamically updated risk level map. The visualization tool supports the real-time push of accident warning information and emergency handling recommendations.

[0014] As a preferred technical solution, an analysis system involved in a gas accident analysis method based on a dynamic Bayesian network includes:

[0015] A model building module, used to build and store dynamic Bayesian network models;

[0016] Data acquisition module, used to collect real-time operating data and environmental parameters of gas equipment;

[0017] Risk assessment module, used for probabilistic inference and evolution analysis of gas accidents based on dynamic Bayesian network model;

[0018] The output module is used to output accident analysis results and provide early warning and emergency suggestions.

[0019] As a preferred technical solution, the data acquisition module interacts with the gas monitoring sensor, the environmental monitoring equipment and the historical database in real time.

[0020] As a preferred technical solution, the output module supports real-time push of analysis results via display screen, SMS, email or mobile application.

[0021] As a preferred technical solution, the system also includes a high-performance processor and a computer-readable storage medium. The computer-readable storage medium stores an execution program. When the execution program is executed by the high-performance processor, a gas accident analysis method based on a dynamic Bayesian network is implemented.

[0022] As a preferred technical solution, the computer-readable storage medium supports the import and export of accident data and the archiving of analysis results.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The gas accident analysis system and method based on dynamic Bayesian network of the present invention can conduct a comprehensive analysis of various risk factors of gas accidents through dynamic Bayesian network technology, effectively predict the possibility of accidents, evaluate the severity of accidents, and propose reasonable prevention and response measures. Compared with traditional risk assessment methods, the present invention has higher accuracy, real-time and flexibility, can provide more scientific safety management decision support for gas companies, significantly improve gas accident prevention and emergency response capabilities, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1It is a flow chart of the gas accident analysis method based on dynamic Bayesian network of the present invention;

[0026] Figure 2 It is a structural schematic diagram of a gas accident analysis system based on a dynamic Bayesian network of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is further described below in conjunction with specific implementation methods:

[0028] like Figure 1 As shown, a gas accident analysis method based on a dynamic Bayesian network includes the following steps:

[0029] Step 1: construct a dynamic Bayesian network model;

[0030] Specifically, based on historical data and expert knowledge related to gas accidents, the node set of the dynamic Bayesian network and the causal relationship between nodes are determined. The node set includes gas equipment status (such as pipeline leakage, valve failure), environmental factors (such as temperature, humidity, pressure) and accident consequences (such as explosion, fire).

[0031] Based on the causal relationship, a conditional probability table between nodes is established, and the time series characteristics of the system are captured through the dynamic Bayesian network structure.

[0032] Step 2: Obtain accident-related data

[0033] Specifically, the operating status data of gas equipment, including parameters such as pressure and flow, is obtained through the real-time monitoring system. At the same time, the historical accident database and on-site detection data are combined to provide input data for model inference.

[0034] Step 3: Dynamic risk assessment and evolution analysis;

[0035] Specifically, the acquired real-time data is input into the dynamic Bayesian network model, the probability of the accident is calculated using the inference algorithm, and the possible evolution path of the accident is predicted in combination with the time series data. The inference algorithm uses one of the forward-backward algorithm, particle filter algorithm, and Markov chain Monte Carlo method.

[0036] The system will dynamically update the state nodes of the model based on the input data, and recalculate the probability distribution of each node based on the data input, and adjust the accident probability and risk assessment results in real time.

[0037] The risk assessment results include the possibility of the current accident, the type of accident and the scope of impact.

[0038] Step 4: Output the analysis results.

[0039] The analysis results are displayed through visualization tools, including the risk distribution of key nodes in the dynamic Bayesian network model; the risk level of each area and the possible accident types; and detailed accident warning information and emergency response suggestions.

[0040] like Figure 2 As shown, a gas accident analysis system based on a dynamic Bayesian network includes:

[0041] Model building module, used to pre-build and store dynamic Bayesian network models;

[0042] Data acquisition module, used to collect real-time operation data and environmental parameters of gas equipment, and interact with gas monitoring sensors, environmental monitoring equipment and historical database;

[0043] Risk assessment module, which performs probability inference and risk assessment of gas accidents based on dynamic Bayesian network model;

[0044] The output module pushes analysis results and warning information in real time via display screen, SMS, email or mobile application.

[0045] The system supports multi-scenario deployment, including accident monitoring and analysis of urban gas pipelines, gas stations and residential user terminals. The system achieves real-time monitoring and dynamic evaluation of accidents by connecting with sensor networks and monitoring systems.

[0046] The system also includes a high-performance processor and a computer-readable storage medium. The computer-readable storage medium stores an execution program. When the execution program is executed by the high-performance processor, the steps of implementing the gas accident analysis method based on the dynamic Bayesian network include:

[0047] Load and run the dynamic Bayesian network model;

[0048] Process the incoming real-time data and infer the probability of an accident;

[0049] Output accident analysis results and generate visual reports.

[0050] This embodiment is only a further explanation of the invention rather than a limitation of the invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as they are within the scope of the claims of the invention, they are protected by the patent law.

Claims

1. A gas accident analysis method based on dynamic Bayesian network, characterized in that: The method comprises the following steps: Step 1: construct a dynamic Bayesian network model; Step 2: Obtain gas equipment operating status data and environmental parameters through real-time monitoring data and historical database; Step 3: Input the acquired data into the dynamic Bayesian network model and use the inference algorithm to conduct gas accident risk assessment and accident evolution path analysis; Step 4: Generate analysis results, including the probability of accident occurrence, scope of impact and emergency recommendations, and output them through visualization tools.

2. The gas accident analysis method based on dynamic Bayesian network according to claim 1 is characterized in that: In the step 1, constructing a dynamic Bayesian network model includes: determining the key nodes and causal relationships of the gas accident; establishing a dynamic conditional probability table between nodes based on historical accident data and expert knowledge; and using a training algorithm to optimize and verify the parameters of the dynamic Bayesian network model.

3. The gas accident analysis method based on dynamic Bayesian network according to claim 2 is characterized in that: The key nodes include equipment status nodes, environmental parameter nodes and accident consequence nodes.

4. The gas accident analysis method based on dynamic Bayesian network according to claim 1 is characterized in that: The inference algorithm adopts one of a forward-backward algorithm, a particle filter algorithm, and a Markov chain Monte Carlo method.

5. The gas accident analysis method based on dynamic Bayesian network according to claim 1 is characterized in that: The visualization tool displays the risk distribution, accident probability and evolution path of each key node of the dynamic Bayesian network, and provides a dynamically updated risk level map. The visualization tool supports the real-time push of accident warning information and emergency handling suggestions.

6. A gas accident analysis system based on a dynamic Bayesian network involved in the gas accident analysis method based on a dynamic Bayesian network according to claim 1, characterized in that: The system comprises: A model building module, used to build and store dynamic Bayesian network models; Data acquisition module, used to collect real-time operating data and environmental parameters of gas equipment; Risk assessment module, used for probabilistic inference and evolution analysis of gas accidents based on dynamic Bayesian network model; The output module is used to output accident analysis results and provide early warning and emergency suggestions.

7. The gas accident analysis system based on dynamic Bayesian network according to claim 5 is characterized in that: The data acquisition module interacts with the gas monitoring sensor, the environmental monitoring equipment and the historical database in real time.

8. The gas accident analysis system based on dynamic Bayesian network according to claim 5, characterized in that: The output module supports real-time push of analysis results via display screen, text message, email or mobile application.

9. The gas accident analysis system based on dynamic Bayesian network according to claim 5, characterized in that: The system also includes a high-performance processor and a computer-readable storage medium, wherein the computer-readable storage medium stores an execution program, and when the execution program is executed by the high-performance processor, the gas accident analysis method based on the dynamic Bayesian network described in claim 1 is implemented.

10. The gas accident analysis system based on dynamic Bayesian network according to claim 9, characterized in that: The computer-readable storage medium supports the import and export of accident data and the archiving of analysis results.

Images