CN-121981542-A - Urban old community security risk coupling mechanism analysis method and system

CN121981542ACN 121981542 ACN121981542 ACN 121981542ACN-121981542-A

Abstract

The invention discloses a method and a system for analyzing a security risk coupling mechanism of an urban old community, and belongs to the technical field of urban community risk analysis. The method comprises the steps of S1 collecting emergency case data, constructing a structured case library, S2 constructing a perfect risk identification model, mining the case library to generate a safety risk list, S3 mining strong association rules of risk factors and events by using an Apriori algorithm, S4 constructing a risk coupling mechanism network by taking risks as nodes and the strong association rules as directed edges, S5 analyzing network identification key nodes and propagation paths, and providing a decoupling strategy.

Inventors

GU TIANTIAN
BAI XUE
LIU XU
Xu Kunxiang
WU HAO
WANG WENSHUN
NI GUODONG

Assignees

中国矿业大学

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (8)

1. A method for analyzing a security risk coupling mechanism of an urban old community is characterized by comprising the following steps: S1, acquiring urban old community emergency case data and constructing a structured urban old community emergency case library; S2, constructing and perfecting a safety risk identification model of the urban old community, and carrying out mining analysis on an emergency case library of the urban old community by utilizing the risk identification model to obtain a safety risk list of the urban old community; S3, carrying out association rule mining on risk factors and risk events in the safety risk list of the old and old communities in the city by using an Apriori algorithm, and screening out a strong association rule with causal relation; S4, constructing a security risk coupling mechanism network of the urban old community by taking all risks in the security risk list of the urban old community as nodes and taking the directed connection relation defined by the strong association rule as an edge; And S5, analyzing the security risk coupling mechanism of the urban old community based on the security risk coupling mechanism network of the urban old community, identifying key nodes and key propagation paths in the network, and providing a security risk decoupling strategy based on an analysis result.
2. The method for analyzing a security risk coupling mechanism of an urban old community according to claim 1, wherein the step S1 comprises: S1.1, collecting urban old community emergency cases from government public platforms, news media and social media by utilizing a web crawler; s1.2, cleaning and standardizing the collected case data to construct a structured case library; S1.3, dividing security risk factors into four categories of human factors, object factors, environment factors and management factors in advance.
3. The method for analyzing a security risk coupling mechanism of an urban old community according to claim 1, wherein the step S2 comprises: s2.1, constructing a security risk identification model based on a large language model, and optimizing the model through a few sample prompt and self-judging mechanism; s2.2, recognizing the text of the case library by using the optimized model, and extracting safety risk factors and safety risk events; and S2.3, carrying out normalization processing on risks expressed in the same semantics and different expressions, and forming a safety risk list containing a preset number of risk factors and risk events after manual verification.
4. The method for analyzing a security risk coupling mechanism of an urban old community according to claim 1, wherein the step S3 comprises: s3.1, using an Apriori algorithm as an association rule mining tool, and introducing a support degree, a confidence degree and a lifting degree as screening conditions of association rules; S3.2, constructing a transaction database taking security risk factors and events as items based on a structured urban old community emergency case library; S3.3, executing an Apriori algorithm flow, wherein the Apriori algorithm flow comprises the steps of mining frequent item sets, generating candidate association rules based on the frequent item sets, calculating the confidence and the lifting degree of the candidate association rules, and reserving rules meeting minimum support degree and minimum confidence threshold.
5. The method for analyzing the security risk coupling mechanism of the urban old community according to claim 4, wherein S3.3 further comprises the steps of implanting constraint conditions in an Apriori algorithm, defining a rule, wherein a front item is a risk factor category, a rear item is a risk event category, performing causal logic judgment on association rules mined by the Apriori algorithm, and screening out strong association rules with causal relations.
6. The method for analyzing a security risk coupling mechanism of an urban old community according to claim 1, wherein the step S4 comprises: S4.1, constructing an adjacent matrix representing the association relation between risks according to the screened strong association rule; S4.2, determining directed edges pointing to the risk event nodes from the risk factor nodes and the risk event nodes according to the sequence of the front and back items of the strong association rule to form a network edge set; And S4.3, constructing a directed security risk coupling mechanism network based on all the risk nodes and the network edge set, and displaying a network topology structure by utilizing the visualization tool.
7. The method for analyzing the security risk coupling mechanism of the old and useless urban communities according to claim 1, wherein in the step S5, the security risk coupling mechanism of the old and useless urban communities is analyzed based on the security risk coupling mechanism network of the old and useless urban communities, which comprises the following steps: Urban old community security risk coupling mechanism analysis based on degree distribution; Urban old community security risk coupling mechanism analysis based on clustering coefficients; Urban old community security risk coupling mechanism analysis based on average shortest path; and (5) analyzing the urban old community security risk coupling mechanism based on the betting centrality.
8. A system for analyzing a security risk coupling mechanism of an urban old community using the security risk coupling mechanism analysis method of any one of claims 1-7, comprising: The data acquisition and processing module acquires the case data of the urban old community emergency event and constructs a structured urban old community emergency event library; The risk identification module is used for constructing and perfecting a safety risk identification model of the urban old community, and carrying out mining analysis on an emergency case library of the urban old community by using the risk identification model to obtain a safety risk list of the urban old community; The association rule mining module is used for carrying out association rule mining on risk factors and risk events in the safety risk list of the urban old community by using an Apriori algorithm, and screening out strong association rules with causal relations; the network construction module takes all risks in the security risk list of the urban old community as nodes and takes the directed connection relation defined by the strong association rule as an edge to construct a security risk coupling mechanism network of the urban old community; The network analysis and strategy generation module is used for analyzing the urban old community security risk coupling mechanism based on the urban old community security risk coupling mechanism network, identifying key nodes and key propagation paths in the network, and providing a security risk decoupling strategy based on an analysis result.

Description

Urban old community security risk coupling mechanism analysis method and system Technical Field The invention relates to the technical field of urban community risk analysis, in particular to a method and a system for analyzing a safety risk coupling mechanism of an urban old community. Background With the acceleration of the urban process, community security has become a key link for urban comprehensive management. The old community has obvious vulnerability and high risk characteristics when facing various emergencies such as fire, flood, earthquake, public health event and the like due to the problems of aging of building structures, lagging of infrastructure, unsound management system and the like, and becomes a weak area for urban safe operation. In recent years, the national level highly pays attention to basic security treatment, actively promotes the construction of comprehensive disaster reduction demonstration communities, and brings old community transformation and risk prevention and control into the key work category of urban treatment. In the existing research, urban community security risk analysis focuses on single disaster species or relies on a static evaluation framework, and the analysis method generally carries out qualitative judgment based on expert experience. Although the method can identify the partially explicit risk in a specific scene, the potential coupling effect and dynamic association between different risk factors are difficult to systematically reveal. Particularly, the research on the risk coupling mechanism is still in a starting stage, and the quantitative analysis capability of complex situations such as multi-disaster superposition, risk chain evolution and the like is lacking, so that the conventional means have obvious limitations in the aspects of identifying key coupling paths and carrying out risk deduction. Therefore, how to provide a method and a system for analyzing the security risk coupling mechanism of the old and old communities in cities is a problem to be solved by those skilled in the art. Disclosure of Invention In view of the above, the invention provides a method and a system for analyzing the safety risk coupling mechanism of an urban old community, which aim to overcome subjectivity and unilateral performance of the existing method in risk identification, realize multi-source risk system and automatic identification, further quantitatively analyze the internal coupling relation and evolution rule, and provide theoretical basis and decision support for improving the safety toughness of the urban community on the whole. In order to achieve the above purpose, the present invention adopts the following technical scheme: A method for analyzing a security risk coupling mechanism of an urban old community comprises the following steps: S1, acquiring urban old community emergency case data and constructing a structured urban old community emergency case library; S2, constructing and perfecting a safety risk identification model of the urban old community, and carrying out mining analysis on an emergency case library of the urban old community by utilizing the risk identification model to obtain a safety risk list of the urban old community; S3, carrying out association rule mining on risk factors and risk events in the safety risk list of the old and old communities in the city by using an Apriori algorithm, and screening out a strong association rule with causal relation; S4, constructing a security risk coupling mechanism network of the urban old community by taking all risks in the security risk list of the urban old community as nodes and taking the directed connection relation defined by the strong association rule as an edge; And S5, analyzing the security risk coupling mechanism of the urban old community based on the security risk coupling mechanism network of the urban old community, identifying key nodes and key propagation paths in the network, and providing a security risk decoupling strategy based on an analysis result. Further, the step S1 includes: S1.1, collecting urban old community emergency cases from government public platforms, news media and social media by utilizing a web crawler; s1.2, cleaning and standardizing the collected case data to construct a structured case library; S1.3, dividing security risk factors into four categories of human factors, object factors, environment factors and management factors in advance. Further, the step S2 includes: s2.1, constructing a security risk identification model based on a large language model, and optimizing the model through a few sample prompt and self-judging mechanism; s2.2, recognizing the text of the case library by using the optimized model, and extracting safety risk factors and safety risk events; and S2.3, carrying out normalization processing on risks expressed in the same semantics and different expressions, and forming a safety risk list containing a preset number of risk fact