Urban natural disaster risk prevention and control method and system

Abstract

The invention provides an urban natural disaster risk prevention and control method and system, and belongs to the field of urban disaster risk prevention and control. The urban natural disaster risk prevention and control method comprises the following steps: acquiring monitoring data of each region in a city; analyzing and determining correlation degree data among the monitoring data according to a pre-constructed historical disaster situation database; determining a plurality of monitoring data groups according to the correlation degree data and a clustering algorithm; calculating an overall risk value of each monitoring data group; determining a disaster risk possibility score of each region in the city according to the overall risk value; based on the method, the analysis of the monitoring data of each region in the city can be realized, and the disaster risk possibility of each region is determined based on the monitoring data, so that the risk prevention and control of urban natural disasters are realized.

Description

Technical Field

[0001] The present application relates to the field of urban disaster risk prevention and control, and in particular to a method and system for urban natural disaster risk prevention and control. Background Art

[0002] Urban natural disasters include geological disasters, meteorological disasters, hydrological disasters, environmental disasters, construction safety and construction disasters, etc. In recent years, urban natural disasters have occurred frequently. As cities are gathering places with high density of human and economic activities, in order to ensure the safety of people and the stable development of social economy, the prediction and post-disaster handling of urban natural disasters need to be given priority. Compared with improving the relevant post-disaster handling system, the early prediction of urban natural disasters can more effectively reduce economic and personnel losses, ensure the safety of people and the stable development of social economy. Summary of the invention

[0003] The present application provides a method and system for urban natural disaster risk prevention and control, which can realize the prediction of urban natural disasters, enable urban areas to prepare disaster response measures in advance, and reduce the economic and human losses caused by urban natural disasters.

[0004] In a first aspect, the present application provides a method for preventing and controlling urban natural disaster risks. The method comprises: Obtain monitoring data for each area in the city; Determine the correlation data between each of the monitoring data according to the pre-built historical disaster database analysis; Determining a plurality of monitoring data groups according to the association data and the clustering algorithm; Calculate the overall risk value for each monitoring data group; A disaster risk likelihood score is determined for each area in the city based on the overall risk value.

[0005] By adopting the above technical scheme, the monitoring data of each area in the city are analyzed to determine the inherent risk value of each monitoring data; then the correlation between each monitoring data is determined based on the analysis of historical disaster data, and the monitoring data group contained in each area is determined according to the clustering algorithm, and the overall risk value of each monitoring data group is calculated, and then the disaster risk possibility score of each area is calculated based on the risk value calculation of multiple monitoring data groups contained in each urban area; based on this method, the probability of natural disasters in each urban area is determined through the analysis of monitoring data, so that corresponding prevention and control measures are taken based on the probability of natural disasters, the wind direction prevention and control of urban natural disasters is realized, the prediction of urban natural disasters is realized, the losses caused by natural disasters are reduced, and the work efficiency of post-disaster urban maintenance and social and economic recovery is improved.

[0006] Further, the calculation of the overall risk value of each monitoring data group includes: Determine the basic risk value of each monitoring data in each monitoring data group according to the monitoring data risk comparison table; Based on the overall risk value calculation model, the overall risk value is calculated according to the basic risk value.

[0007] Furthermore, the overall risk value calculation model includes: , Where O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

[0008] Furthermore, determining the disaster risk possibility score of each area in the city according to the overall risk value includes: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: , Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

[0009] Furthermore, the analysis based on the pre-built historical disaster situation database to determine the correlation data between the various monitoring data includes: The historical disaster database includes historical monitoring data corresponding to each historical disaster and characteristic information of each historical monitoring data; Determining, according to the feature information, a plurality of historical monitoring data containing the same feature; The plurality of historical monitoring data containing the same feature are analyzed to determine correlation data between the respective historical monitoring data.

[0010] Furthermore, the determining of a plurality of monitoring data groups according to the correlation data and a clustering algorithm includes, based on a clustering algorithm, dividing each of the monitoring data according to the correlation data and a preset correlation range threshold to obtain a plurality of monitoring data groups.

[0011] In a second aspect, the present application provides an urban natural disaster risk prevention and control system, the system comprising: The acquisition module is used to obtain monitoring data of each area in the city; An analysis module, used to analyze and determine the correlation data between each of the monitoring data according to a pre-built historical disaster situation database; A determination module, used to determine a plurality of monitoring data groups according to the association data and a clustering algorithm; A calculation module, used to calculate the overall risk value of each monitoring data group; The output module is used to determine the disaster risk possibility score of each area in the city according to the overall risk value.

[0012] Furthermore, the calculation module is further configured such that the calculation of the overall risk value of each monitoring data group includes: Determine and calculate the basic risk value of each monitoring data in each monitoring data group according to the monitoring data risk comparison table; Based on the overall risk value calculation model, the overall risk value is calculated according to the basic risk value.

[0013] Furthermore, the calculation module is further configured such that the overall risk value calculation model includes: , In the formula, O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

[0014] Furthermore, the output module is further configured such that determining the disaster risk possibility score of each area in the city according to the overall risk value includes: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: , Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

[0015] In summary, this application at least has the following beneficial effects: It realizes the overall analysis of multi-dimensional and multi-modal monitoring data, making the analysis of monitoring data more comprehensive and complete; Cluster analysis is performed on the monitoring data to obtain the monitoring data group. The overall risk of the monitoring data group is calculated by combining the risk value of the monitoring data itself and the associated risk value of the monitoring data, which enables further analysis of the monitoring data and improves the comprehensiveness of the monitoring data analysis. The disaster risk possibility score of the urban area is calculated based on the overall risk value of each monitoring data group in the urban area, which improves the accuracy of the prediction of disaster risks in the urban area and better realizes the risk prevention and control of urban natural disasters.

[0016] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A flow chart of a method for preventing and controlling urban natural disaster risks in an embodiment of the present application is shown; Figure 2 A block diagram of an urban natural disaster risk prevention and control system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0020] The present application provides a method and system for preventing and controlling urban natural disaster risks, which can realize risk prediction of natural disasters in various areas of the city to reduce the loss of life and economy caused by natural disasters.

[0021] In the first aspect, the present application provides a method for preventing and controlling urban natural disaster risks; Figure 1 As shown, the specific steps of a method for preventing and controlling urban natural disaster risks are as follows.

[0022] Step S110: Acquire monitoring data of each area in the city.

[0023] In an embodiment of the present application, the monitoring data is multi-dimensional and multi-modal data about urban natural disasters collected and acquired through different methods and channels; multi-dimensional data means that the monitoring data contains different types of data and corresponding location information; such as temperature data of area A, water level data of area B, wind direction and wind force level data of area C, etc.; specifically, the monitoring data may be composed of sensor monitoring data detected by different types of sensors in the urban area; multi-modal data means that the monitoring data is presented in a variety of ways, such as a pie chart, a bar chart or a separate numerical information, etc.

[0024] In the embodiment of the present application, by collecting and analyzing the monitoring data of various areas within the city, it is not only possible to predict and control the risks of various types of urban natural disasters in each area of ​​the city itself, but also possible to predict and control the risks of linked natural disasters between various areas in the city. Through more effective and accurate risk prediction, various areas in the city can prepare emergency measures and resources in advance, reducing the difficulty of post-disaster repair and recovery of the social economy, and improving personnel safety and social and economic stability.

[0025] Step S120: analyzing and determining the correlation data between each of the monitoring data according to the pre-built historical disaster situation database.

[0026] Specifically, in an embodiment of the present application, the analysis based on a pre-constructed historical disaster database to determine the correlation data between each of the monitoring data includes: the historical disaster database contains historical monitoring data corresponding to each historical disaster and characteristic information of each historical monitoring data; based on the characteristic information, multiple historical monitoring data containing the same characteristic are determined; and the multiple historical monitoring data containing the same characteristic are analyzed to determine the correlation data between each of the historical monitoring data.

[0027] In an embodiment of the present application, the historical disaster database includes the entire process of natural disasters that have occurred in history, and includes monitoring data of the entire process of disasters; each monitoring data includes the data value itself, data characteristics, and the source and location information of the data; the data characteristics are the characteristic information mentioned above, and the characteristic information includes the development trend of the data itself within the historical disaster recording time, such as the data as a whole is on an upward trend, the data as a whole is on a downward trend, or within the entire process of historical disaster recording, the data in a certain time period is on an upward trend, and the data in a certain time period is on a downward trend; by analyzing the historical monitoring data containing the same characteristics, the correlation and association between the historical monitoring data can be determined; and the correlation and association are the association data between each historical monitoring data.

[0028] Specifically, in historical disaster event A, if monitoring data b shows an upward trend and monitoring data c shows a downward trend, then monitoring data b and monitoring data c are negatively correlated; if monitoring data d shows an upward trend, then monitoring data b and monitoring data d are positively correlated; the correlation data is bound and recorded in the label information of monitoring data e and monitoring data f. By analyzing each historical disaster event in the historical disaster database, the correlation data between monitoring data e and monitoring data f can be finally determined. The correlation data includes the correlation relationship and correlation value between the monitoring data.

[0029] By analyzing historical disaster data, the correlation between various monitoring data is determined. In the embodiment of the present application, not only the risk corresponding to the numerical value of each monitoring data itself is analyzed, but also the overall risk between the related monitoring data is analyzed. With the support of multi-dimensional and multi-modal monitoring data, the risk prediction of urban natural disasters can be achieved more effectively and accurately.

[0030] Step S130: Determine multiple monitoring data groups according to the association data and the clustering algorithm.

[0031] Specifically, in an embodiment of the present application, determining a plurality of monitoring data groups according to the correlation data and a clustering algorithm includes, based on a clustering algorithm, dividing each of the monitoring data according to the correlation data and a preset correlation range threshold to obtain a plurality of monitoring data groups.

[0032] It can be understood that the clustering algorithm used in the embodiment of the present application is a conventional clustering algorithm. Based on the clustering algorithm, grouping the monitoring data according to the correlation to obtain multiple monitoring data groups is also a conventional method, which will not be repeated here.

[0033] Step S140: Calculate the overall risk value of each monitoring data group.

[0034] Specifically, in an embodiment of the present application, the calculation of the overall risk value of each monitoring data group includes determining the basic risk value of each monitoring data in each monitoring data group according to a monitoring data risk comparison table; and calculating the overall risk value according to the basic risk value based on an overall risk value calculation model.

[0035] Wherein, the overall risk value calculation model includes: , In the formula, O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

[0036] It can be understood that for different monitoring data, the risk value represented by its own characteristics can be obtained based on the pre-constructed monitoring data risk comparison table, where the monitoring data risk comparison table includes the monitoring data change rate, the basic risk value of each monitoring data, and the correspondence between the monitoring data and the basic risk value; it can be understood that the inherent characteristics of the monitoring data here are the rate at which the data rises or falls.

[0037] Step S150: Determine the disaster risk probability score of each area in the city according to the overall risk value.

[0038] Specifically, in the embodiment of the present application, determining the disaster risk possibility score of each area in the city according to the overall risk value includes: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: , Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

[0039] By adopting the solution in the embodiment of the present application, it is possible to analyze multimodal and multi-dimensional monitoring data, and to more effectively and accurately predict the risk of urban natural disasters.

[0040] In the second aspect, the present application provides a system for preventing and controlling urban natural disaster risks, referring to Figure 2 The system includes an acquisition module 210 for acquiring monitoring data of each area in the city; an analysis module 220 for determining the correlation data between each of the monitoring data based on a pre-built historical disaster database analysis; a determination module 230 for determining multiple monitoring data groups based on the correlation data and a clustering algorithm; a calculation module 240 for calculating the overall risk value of each monitoring data group; and an output module 250 for determining the disaster risk possibility score of each area in the city based on the overall risk value.

[0041] Specifically, the analysis module 220 is further configured to determine the correlation data between each of the monitoring data based on the analysis of the pre-constructed historical disaster database, including: the historical disaster database contains the historical monitoring data corresponding to each historical disaster and the characteristic information of each historical monitoring data; determine multiple historical monitoring data containing the same characteristic based on the characteristic information; analyze the multiple historical monitoring data containing the same characteristic to determine the correlation data between each of the historical monitoring data.

[0042] The determination module 230 is further configured to determine multiple monitoring data groups according to the association data and the clustering algorithm, including, based on the clustering algorithm, dividing each monitoring data according to the association data and a preset association range threshold to obtain multiple monitoring data groups.

[0043] The calculation module 240 is further configured to calculate the overall risk value of each monitoring data group, including determining the basic risk value of each monitoring data in each monitoring data group according to the monitoring data risk comparison table; and calculating the overall risk value according to the basic risk value based on the overall risk value calculation model.

[0044] Wherein, the overall risk value calculation model includes: , In the formula, O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

[0045] The output module 250 is further configured to determine the disaster risk possibility score of each area in the city according to the overall risk value, including: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: , Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

[0046] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0048] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for preventing and controlling urban natural disaster risks, characterized in that: include: Obtain monitoring data for each area in the city; Determine the correlation data between each of the monitoring data according to the pre-built historical disaster database analysis; Determining a plurality of monitoring data groups according to the association data and the clustering algorithm; Calculate the overall risk value for each monitoring data group; A disaster risk likelihood score is determined for each area in the city based on the overall risk value.

2. The method according to claim 1, characterized in that The calculation of the overall risk value of each monitoring data group includes: Determine the basic risk value of each monitoring data in each monitoring data group according to the monitoring data risk comparison table; Based on the overall risk value calculation model, the overall risk value is calculated according to the basic risk value.

3. The method according to claim 2, characterized in that The overall risk value calculation model includes: ， Where O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

4. The method according to claim 3, characterized in that Determining the disaster risk probability score of each area in the city according to the overall risk value includes: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: ， Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

5. The method according to claim 1, characterized in that The analysis and determination of the correlation data between the monitoring data based on the pre-built historical disaster situation database includes: The historical disaster database includes historical monitoring data corresponding to each historical disaster and characteristic information of each historical monitoring data; Determining, according to the feature information, a plurality of historical monitoring data containing the same feature; The plurality of historical monitoring data containing the same feature are analyzed to determine correlation data between the respective historical monitoring data.

6. The method according to claim 1, characterized in that Determining a plurality of monitoring data groups according to the association data and a clustering algorithm includes, based on a clustering algorithm, dividing each of the monitoring data according to the association data and a preset association range threshold to obtain a plurality of monitoring data groups.

7. An urban natural disaster risk prevention and control system, characterized in that: include: An acquisition module (210) is used to acquire monitoring data of each area in the city; An analysis module (220) is used to analyze and determine the correlation data between each of the monitoring data according to a pre-built historical disaster situation database; A determination module (230), configured to determine a plurality of monitoring data groups according to the association data and a clustering algorithm; A calculation module (240), used to calculate the overall risk value of each monitoring data group; The output module (250) is used to determine the disaster risk possibility score of each area in the city according to the overall risk value.

8. The system according to claim 7, characterized in that The calculation module (240) is further configured such that the calculation of the overall risk value of each monitoring data group includes: Determine and calculate the basic risk value of each monitoring data in each monitoring data group according to the monitoring data risk comparison table; Based on the overall risk value calculation model, the overall risk value is calculated according to the basic risk value.

9. The system according to claim 8, characterized in that The calculation module (240) is further configured such that the overall risk value calculation model includes: ， Where O is the overall risk value, is the ith basic risk value in the monitoring data group; n is the number of types of monitoring data in each monitoring data group obtained.

10. The system according to claim 9, characterized in that The output module (250) is further configured such that determining the disaster risk possibility score of each area in the city according to the overall risk value comprises: Based on the possibility score calculation model, the disaster risk possibility score of each area is calculated according to the overall risk value; The possibility calculation model includes: ， Where P is the disaster risk probability score, is the overall risk value of the jth monitoring data group, j ranges from 1 to m, representing the 1st to mth monitoring data groups; Indicates taking The maximum value in .

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