A mountainous town disaster prevention data analysis method based on a city information model

Abstract

The application provides a mountainous town disaster prevention and control data analysis method based on a city information model, the mountainous town is divided into multiple prevention and control regions based on a disaster zoning mechanism in the city information model, and then disaster intensity features of each prevention and control region are extracted from disaster prevention and control data; mutual information correlation is performed on each prevention and control region according to each disaster intensity feature, a disaster involvement relationship in the mountainous town is obtained, and then a disaster linkage amount of each prevention and control region in the mountainous town is determined according to the disaster involvement relationship; a core disaster area in the mountainous town is determined according to all the disaster linkage amounts, and then a linkage emergency strategy of the disaster in the mountainous town is determined through feature differences of the core disaster area and each disaster intensity feature; the disaster in the mountainous town is responded in linkage through the linkage emergency strategy. Based on the above scheme, linkage analysis of disaster features and prevention and control strategies can be realized, so that the disaster response efficiency of the mountainous town can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban supervision and management, more specifically, the present application relates to a mountain town disaster prevention data analysis method based on a city information model. BACKGROUND

[0002] Mountain towns are at risk of landslides, mudslides, floods and other natural disasters due to complex terrain and variable climate, so disaster prevention is crucial. Precise disaster risk assessment is the foundation of prevention. Through geological survey and remote sensing technology, high-risk areas are identified to provide scientific basis for disaster prevention measures. Building planning should focus on disaster-resistant design and strengthen the disaster resistance of urban infrastructure to ensure safety in the event of a disaster.

[0003] Traditional disaster prevention methods often rely on historical data or static models, lacking adaptability to dynamic changes in disasters, resulting in inaccurate disaster prediction. Linkage analysis can monitor and analyze the relationship between disaster characteristics and prevention strategies in real time, dynamically adjust prevention measures, and improve disaster prediction accuracy and response efficiency. Through in-depth analysis of disaster characteristics and linkage relationships, resource allocation can be optimized to avoid waste or uneven allocation of prevention resources. At the same time, according to the linkage effect of disasters, the rigidity of traditional prevention strategies leads to a lag in response, while linkage analysis can flexibly adjust emergency response strategies according to actual disaster characteristics, reducing disaster emergency response time and improving prevention effectiveness. Therefore, how to realize linkage analysis of disaster characteristics and prevention strategies to improve mountain town disaster response efficiency is a difficult problem in the industry. SUMMARY

[0004] The present application provides a mountain town disaster prevention data analysis method based on a city information model, which can realize linkage analysis of disaster characteristics and prevention strategies to improve mountain town disaster response efficiency.

[0005] The present application provides a mountain town disaster prevention data analysis method based on a city information model, which includes the following steps:

[0006] Obtain disaster prevention data in mountain towns within a specified time period;

[0007] Divide the mountain town into multiple prevention areas based on the disaster zoning mechanism in the city information model, and then extract the disaster intensity characteristics of each prevention area from the disaster prevention data;

[0008] According to each disaster intensity characteristic, mutual information correlation is performed on each prevention area to obtain the disaster linkage relationship in the mountain town, and then the disaster linkage amount of each prevention area in the mountain town is determined according to the disaster linkage relationship and the disaster time-frequency characteristics in the mountain town;

[0009] Obtaining an initial prevention and control strategy of the mountain town at the current moment, determining a core disaster area in the mountain town according to the initial prevention and control strategy and all disaster linkage amounts, and further determining a linkage emergency strategy of the disaster in the mountain town through the core disaster area and a feature difference of each disaster intensity feature;

[0010] Linkage response to the disaster in the mountain town through the linkage emergency strategy.

[0011] In the embodiment, the mountain town is divided into a plurality of prevention and control areas based on a disaster zoning mechanism in the city information model, which specifically includes:

[0012] Obtaining geological structure information and historical disaster distribution information in the mountain town;

[0013] Clustering geological areas in the mountain town based on the geological structure information to obtain a plurality of geological area clusters;

[0014] Extracting intra-cluster disaster features of each geological area cluster from the historical disaster distribution information;

[0015] Secondarily dividing the geological areas in the mountain town based on the disaster zoning mechanism in the city information model in combination with all intra-cluster disaster features to obtain a plurality of prevention and control areas.

[0016] In the embodiment, the disaster intensity features of each prevention and control area are extracted from the disaster prevention and control data, which specifically includes:

[0017] For each prevention and control area, extracting disaster prevention and control records of the prevention and control area from the disaster prevention and control data;

[0018] Determining prevention priorities of the prevention and control area according to the disaster prevention and control records;

[0019] Obtaining all intra-cluster disaster features in the prevention and control area;

[0020] Determining disaster intensity features of the prevention and control area through the prevention priorities and all intra-cluster disaster features, and further obtaining disaster intensity features of each prevention and control area.

[0021] In the embodiment, mutual information correlation is performed on each prevention and control area according to each disaster intensity feature to obtain disaster involvement relationships in the mountain town, which specifically includes:

[0022] For each prevention and control area, obtaining prevention and control adjacent areas of the prevention and control area, and further determining all adjacent disaster intensities corresponding to the prevention and control adjacent areas;

[0023] Performing disaster correlation between each adjacent disaster intensity and the disaster intensity features of the prevention and control area based on a mutual information mechanism to obtain a disaster involvement value of the prevention and control area, and further obtaining disaster involvement values of each prevention and control area;

[0024] determine disaster involvement relationships in the mountainous town according to all disaster involvement values.

[0025] In the embodiment, determining disaster linkage amounts of each prevention area in the mountainous town according to the disaster involvement relationships and disaster time-frequency characteristics in the mountainous town specifically comprises:

[0026] obtaining disaster time-frequency characteristics in the mountainous town;

[0027] for each prevention area in the mountainous town, obtaining a disaster involvement value of the prevention area from the disaster involvement relationships;

[0028] determining a disaster linkage amount of the prevention area according to the disaster time-frequency characteristics and the disaster involvement value, and further obtaining disaster linkage amounts of each prevention area in the mountainous town.

[0029] In the embodiment, determining a core disaster area in the mountainous town according to the initial prevention strategy and all disaster linkage amounts specifically comprises:

[0030] obtaining historical disaster prevention records in the mountainous town;

[0031] determining a core linkage threshold of disasters in the mountainous town according to the historical disaster prevention records and the initial prevention strategy;

[0032] screening out a core disaster area in the mountainous town from the mountainous town based on the core linkage threshold and all disaster linkage amounts.

[0033] In the embodiment, determining a linkage emergency strategy of disasters in the mountainous town by the core disaster area and feature differences of each disaster intensity feature specifically comprises:

[0034] obtaining all prevention areas contained in the core disaster area, and further determining feature differences of each disaster intensity feature corresponding to each prevention area;

[0035] generating a core prevention strategy of the core disaster area according to all feature differences;

[0036] performing strategy merging on the core prevention strategy and the initial prevention strategy to obtain the linkage emergency strategy of disasters in the mountainous town.

[0037] In the embodiment, responding to disasters in the mountainous town by the linkage emergency strategy is responding to disasters in the mountainous town by taking the linkage emergency strategy as a prevention strategy of disasters in the mountainous town.

[0038] In the embodiment, the city information model is a digital platform integrating various data of the mountainous town.

[0039] In this embodiment, disaster prevention data in the mountain town in a specified time period is obtained from the city information model.

[0040] The technical scheme provided by the embodiments disclosed in the present application has the following beneficial effects:

[0041] By obtaining disaster prevention data in the mountain town in a specified time period, the mountain town is divided into multiple prevention areas based on a disaster zoning mechanism in the city information model, and then disaster intensity features of each prevention area are extracted from the disaster prevention data; mutual information correlation is performed on each prevention area according to each disaster intensity feature, to obtain a disaster involvement relationship in the mountain town, and then a disaster linkage amount of each prevention area in the mountain town is determined according to the disaster involvement relationship and disaster time-frequency characteristics in the mountain town; an initial prevention strategy of the mountain town at a current time is obtained, and a core disaster area in the mountain town is determined according to the initial prevention strategy and all disaster linkage amounts, and then a linkage emergency strategy of disasters in the mountain town is determined through feature differences of the core disaster area and each disaster intensity feature; and the linkage emergency strategy is used to respond to disasters in the mountain town.

[0042] As can be seen from the present application, the core disaster area in the mountain town is determined according to the initial prevention strategy and all disaster linkage amounts, and then the linkage emergency strategy of disasters in the mountain town is determined through feature differences of the core disaster area and each disaster intensity feature, and the linkage emergency strategy is used to respond to disasters in the mountain town; first, the determination of the disaster intensity feature is a quantitative expression of the influence degree of the disaster in the mountain town, which helps to accurately assess the specific disaster risk faced by each area, and by determining the disaster intensity feature, different prevention strategies can be tailored for each area, so that the response measures can be more targeted, which helps to avoid resource waste or over-prevention in the prevention process, and ensures the scientificity and efficiency of disaster response; then, the determination of the disaster linkage amount helps to reveal the interaction and influence between disasters, especially in such a complex environment as the mountain town, multiple disasters may interact to produce composite risks, and by calculating the disaster linkage amount, it can be judged which region's disaster will trigger a chain reaction in other regions, and then the path and speed of disaster spread can be predicted, and the prevention measures can be more systematic through linkage effect analysis, which can avoid the possibility that local response fails to cover other disaster areas or ignores the chain reaction, and thus improve the coordination and efficiency of the overall disaster response.

[0043] In summary, the technical scheme adopted in the present application can realize linkage analysis of disaster characteristics and prevention strategies, thereby improving the disaster response efficiency of the mountain town. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0045] Figure 1 is an exemplary flowchart of a mountainous town disaster prevention data analysis method based on a city information model according to the present application;

[0046] Figure 2 is an exemplary flowchart of determining a prevention area according to the present application;

[0047] Figure 3 is an exemplary flowchart of determining a disaster linkage amount according to the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort are within the scope of protection of the present application.

[0049] The embodiments of the present application provide a mountainous town disaster prevention data analysis method based on a city information model. The core is to divide the mountainous town into multiple prevention areas based on the disaster zoning mechanism in the city information model, and then extract the disaster intensity features of each prevention area from the disaster prevention data. According to each disaster intensity feature, mutual information correlation is performed on each prevention area to obtain the disaster linkage relationship in the mountainous town, and then the disaster linkage amount of each prevention area in the mountainous town is determined according to the disaster linkage relationship. According to all the disaster linkage amounts, the core disaster area in the mountainous town is determined, and then the linkage emergency strategy of the disaster in the mountainous town is determined through the feature difference of the core disaster area and each disaster intensity feature. The disaster in the mountainous town is responded through the linkage emergency strategy. Based on the above scheme, the linkage analysis of disaster features and prevention strategies can be realized, so as to improve the disaster response efficiency of the mountainous town.

[0050] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the accompanying drawings and specific embodiments of the present application. Referring to Figure 1 The figure is an exemplary flowchart of a mountainous town disaster prevention data analysis method based on a city information model according to the present application, which includes the following steps:

[0051] In step S1, disaster prevention data in mountain towns within a specified time period is obtained.

[0052] It should be noted that in this application, disaster prevention data is data used to describe the conditions, processes and prevention measures of disasters. This disaster prevention data includes disaster-related environmental, meteorological, geological, infrastructure, and transportation data. In specific implementation, disaster prevention data in mountainous towns within a specified time period (default is the most recent year) can be obtained from the urban information model.

[0053] In step S2, mountain towns are divided into multiple prevention and control areas based on the disaster zoning mechanism in the urban information model, and then the disaster intensity characteristics of each prevention and control area are extracted from the disaster prevention and control data.

[0054] Preferably, in this embodiment, reference Figure 2 As shown, this diagram is an exemplary flowchart for determining prevention and control areas in an embodiment of this application. In this embodiment, the division of mountainous towns into multiple prevention and control areas based on the disaster zoning mechanism in the urban information model can be achieved through the following steps:

[0055] First, in step S21, geological structure information and historical disaster distribution information in mountain towns are obtained;

[0056] Then, in step S22, the geological regions in the mountain town are clustered based on the geological structure information to obtain multiple geological region clusters;

[0057] Secondly, in step S23, the intra-cluster disaster characteristics of each geological region cluster are extracted from the historical disaster distribution information;

[0058] Finally, in step S24, the geological regions in the mountain town are divided a second time based on the disaster zoning mechanism in the urban information model and the disaster characteristics of all clusters, resulting in multiple prevention and control areas.

[0059] It should be noted that in this application, geological structural information reflects the geological background of the town, including geological characteristic data such as the distribution of rock strata, fault zones, and soil types in mountainous towns; historical disaster distribution information refers to the spatial distribution of various disaster events that have occurred in history, including data such as the type, time of occurrence, intensity, and location of the disasters; geological region clusters represent regional clusters composed of geological regions in mountainous towns that have significant similarities in geological structure; and disaster characteristics within a cluster refer to the characteristics of historical disasters within the geological region cluster.

[0060] In a specific implementation, first, geological structure information in the mountainous town and historical disaster distribution information in a specified time period (by default, the last year) can be obtained from the city information model; second, a spatial clustering algorithm (for example, a K-means clustering algorithm) can be used to perform clustering analysis on the geological structure information, and the result of the clustering analysis can be used as a geological region cluster, that is, a plurality of geological region clusters can be obtained; then, a feature extraction algorithm (for example, a spatiotemporal feature method) can be used to extract intra-cluster disaster features of each geological region cluster from the historical disaster distribution information; finally, a disaster zoning mechanism (for example, a multi-criteria decision method) in the city information model can be used to perform weighted scoring on all intra-cluster disaster features, to obtain feature scores of the intra-cluster disaster features, and then a spatial clustering algorithm (for example, a K-means clustering algorithm) can be used to perform secondary clustering division on the geological regions in the mountainous town, and the result of the secondary clustering division can be used as a prevention and control region, that is, a plurality of prevention and control regions can be obtained.

[0061] It should be noted that the disaster zoning mechanism in the city information model is a method for scientifically dividing disaster risks and resource allocation in urban space. The disaster zoning mechanism can help decision makers to divide regions according to various disaster characteristics (such as disaster type, intensity, frequency, etc.), and to develop corresponding prevention and control strategies for each region. Common disaster zoning mechanisms include region division based on a multi-criteria decision method.

[0062] In this embodiment, the extraction of the disaster intensity feature of each prevention and control region from the disaster prevention and control data can be achieved by the following steps:

[0063] For each prevention and control region, a disaster prevention and control record of the prevention and control region is extracted from the disaster prevention and control data.

[0064] The prevention priority of the prevention and control region is determined according to the disaster prevention and control record.

[0065] All intra-cluster disaster features in the prevention and control region are obtained.

[0066] The disaster intensity feature of the prevention and control region is determined by the prevention priority and all intra-cluster disaster features, and then the disaster intensity feature of each prevention and control region is obtained.

[0067] It should be noted that in this application, the disaster intensity feature is an index for measuring the severity of a disaster; the disaster prevention and control record is a historical record of prevention measures, coping strategies, and repair conditions for the prevention and control region after the occurrence of a disaster; and the prevention priority represents the degree of priority processing of the prevention and control region.

[0068] In a specific implementation, first, for each prevention area, a disaster prevention record of the prevention area can be extracted from disaster prevention data using an existing data mining algorithm; second, a prevention response time ranking of the prevention area after each disaster can be obtained from the disaster prevention record, and an average of all prevention response time rankings can be taken as a prevention priority of the prevention area; third, all disaster features in a cluster in the prevention area can be obtained; and finally, a fuzzy logic model based on a neural network can be initialized, the prevention priority can be taken as a mapping target in the fuzzy logic model, and all disaster features in the cluster can be taken as mapping rules, a fuzzy inference on a disaster intensity of the prevention area can be performed based on the fuzzy logic model, and a result of the fuzzy inference can be taken as a disaster intensity feature of the prevention area, so that the disaster intensity feature of each prevention area can be obtained.

[0069] In step S3, mutual information correlation is performed on each prevention area according to each disaster intensity feature, and a disaster correlation relationship in the mountain town is obtained, and then the disaster correlation relationship and a disaster time-frequency characteristic in the mountain town are used to determine a disaster linkage amount of each prevention area in the mountain town.

[0070] In this embodiment, the mutual information correlation is performed on each prevention area according to each disaster intensity feature, and the disaster correlation relationship in the mountain town can be obtained by the following steps.

[0071] For each prevention area, a prevention adjacent area of the prevention area is obtained, and all adjacent disaster intensities corresponding to the prevention adjacent area are determined.

[0072] Based on a mutual information mechanism, each adjacent disaster intensity is correlated with a disaster intensity feature of the prevention area, a disaster correlation value of the prevention area is obtained, and then a disaster correlation value of each prevention area is obtained.

[0073] The disaster correlation relationship in the mountain town is determined according to all disaster correlation values.

[0074] It should be noted that in this application, the disaster correlation relationship represents a disaster propagation dependency relationship between different prevention areas; the prevention adjacent area refers to an area adjacent to or closely connected to the prevention area; the adjacent disaster intensity refers to the intensity of a disaster occurring in an area adjacent to the prevention area; and the disaster correlation value represents a disaster propagation between the prevention areas, and the greater the correlation value, the stronger the disaster propagation correlation between the areas.

[0075] In a specific implementation, first, for each prevention area, a set of other prevention areas adjacent to the prevention area is defined as a prevention adjacent area according to geographical space data and a predefined geographical area boundary in a city information model, disaster intensity features of each prevention area in the prevention adjacent area are obtained as corresponding adjacent disaster intensity, and all adjacent disaster intensity corresponding to the prevention adjacent area is obtained. Then, the disaster intensity mutual information value between each adjacent disaster intensity and the disaster intensity feature of the prevention area can be calculated as the result of disaster correlation, that is, the disaster involvement value of the prevention area, and the disaster involvement value of each prevention area can be obtained through the above method. Finally, the set of all disaster involvement values can be used as the disaster involvement relationship in the mountain town

[0076] Preferably, in the embodiment, reference is made to Figure 3 As shown in the figure, which is an exemplary flowchart for determining the disaster linkage amount in the embodiment, the disaster linkage amount of each prevention area in the mountain town can be determined according to the disaster involvement relationship and the disaster time-frequency characteristics in the mountain town by using the following steps:

[0077] First, in step S31, the disaster time-frequency characteristics in the mountain town are obtained.

[0078] Then, in step S32, for each prevention area in the mountain town, the disaster involvement value of the prevention area is obtained from the disaster involvement relationship.

[0079] Finally, in step S33, the disaster linkage amount of the prevention area is determined according to the disaster time-frequency characteristics and the disaster involvement value, and the disaster linkage amount of each prevention area in the mountain town is obtained.

[0080] It should be noted that in the present application, the disaster linkage amount represents the propagation strength and linkage risk of the disaster between different prevention areas, and the disaster linkage amount reflects the linkage effect between prevention areas due to disaster intensity and time-frequency characteristics. In a specific implementation, first, the disaster time-frequency characteristics in the mountain town can be obtained from the city information model, which is a feature describing the variation law of the disaster event in time and frequency. Then, for each prevention area in the mountain town, the disaster involvement value of the prevention area is obtained from the disaster involvement relationship. Finally, a supervised learning prediction model based on support vector machine is initialized, the disaster time-frequency characteristics are used as input features in the supervised learning prediction model, the disaster involvement value is used as a target variable in the supervised learning prediction model, and the disaster linkage amount is predicted using the supervised learning prediction model. The predicted result value can be used as the disaster linkage amount of the prevention area.

[0081] In step S4, an initial prevention strategy of the mountain town at the current time is acquired, a core disaster area in the mountain town is determined according to the initial prevention strategy and all disaster linkage amounts, and a linkage emergency strategy of the disaster in the mountain town is determined through the core disaster area and the characteristic difference of each disaster intensity characteristic.

[0082] In this embodiment, the initial prevention strategy of the mountain town at the current time is acquired, and in specific implementation, the initial prevention strategy of the mountain town at the current time can be acquired from the city information model. It should be noted that in this application, the initial prevention strategy refers to the preliminary prevention measures of the mountain town at the current time, and the initial prevention strategy includes the delimitation of the priority prevention area, resource allocation, emergency response and the like.

[0083] Preferably, in this embodiment, the core disaster area in the mountain town is determined according to the initial prevention strategy and all disaster linkage amounts by the following steps:

[0084] The historical disaster prevention records in the mountain town are acquired;

[0085] The core linkage threshold of the disaster in the mountain town is determined according to the historical disaster prevention records and the initial prevention strategy;

[0086] The core disaster area in the mountain town is screened out from the mountain town based on the core linkage threshold and all disaster linkage amounts.

[0087] It should be noted that in this application, the core disaster area represents the key area that needs to be prioritized for disaster prevention. In specific implementation, firstly, the historical disaster prevention records in the mountain town within a specified time period (by default, the last year) can be acquired from the city information model, and the historical disaster prevention records contain the disaster linkage amount at each disaster prevention time. Then, the product of the standard deviation of all disaster linkage amounts and the experience factor preset by the initial prevention strategy is taken as the threshold deviation, and the sum of the threshold deviation and the average of all disaster linkage amounts is taken as the core linkage threshold of the disaster in the mountain town. The core linkage threshold is a critical value for judging whether the prevention area needs to be prioritized for prevention. Finally, the set of each disaster linkage amount corresponding to the prevention area in the mountain town that is greater than the core linkage threshold is taken as the core disaster area in the mountain town.

[0088] In this embodiment, the linkage emergency strategy of the disaster in the mountain town is determined through the core disaster area and the characteristic difference of each disaster intensity characteristic by the following steps:

[0089] All prevention areas contained in the core disaster area are acquired, and the characteristic difference of each prevention area corresponding to the disaster intensity characteristic is determined;

[0090] generate a core prevention strategy of the core disaster area according to all feature differences;

[0091] merge the core prevention strategy and the initial prevention strategy to obtain a linkage emergency strategy of disasters in the mountain town.

[0092] It should be noted that, in the present application, the linkage emergency strategy is a global emergency response scheme for ensuring that each prevention area in the mountain town can respond quickly and in coordination; the feature difference represents the differentiated features of disaster intensity of different prevention areas; and the core prevention strategy refers to a disaster emergency prevention scheme adopted for the core disaster area.

[0093] In a specific implementation, first, all prevention areas included in the core disaster area are obtained, the mean of feature values of disaster intensity features of all prevention areas is taken as a strength mean, for each prevention area, the mean of absolute values of differences between the feature value of the disaster intensity feature of the prevention area and the feature values of disaster intensity features of other prevention areas is taken as an absolute mean, and the ratio of the absolute value of the difference between the feature value of the disaster intensity feature of the prevention area and the strength mean to the absolute mean is taken as the feature difference of the disaster intensity feature of the prevention area, so as to obtain the feature difference of the disaster intensity feature of each prevention area; then, for each prevention area in the core disaster area, specific prevention measures are formulated according to the feature difference of the disaster intensity of the prevention area, if the disaster intensity difference of the prevention area is large (such as frequent occurrence of large disasters), measures such as disaster monitoring, evacuation drills, and disaster warning can be increased, and for the area with small disaster intensity, lighter prevention measures such as strengthening warning and disaster prevention education can be selected, so as to obtain the prevention measures of each prevention area in the core disaster area as the core prevention strategy of the prevention area; finally, the initial prevention strategy is taken as a merging initial point, and the prevention measures of the same area in the core prevention strategy are overlaid on the corresponding prevention measures in the initial prevention strategy, so as to obtain the initial prevention strategy after the overlaying as the linkage emergency strategy of disasters in the mountain town.

[0094] In step S5, the linkage emergency strategy is used to respond to disasters in the mountain town.

[0095] In the present embodiment, the linkage emergency strategy is used to respond to disasters in the mountain town by taking the linkage emergency strategy as a disaster prevention strategy of disasters in the mountain town.

[0096] It can be seen that in the present application, the core disaster area in the mountain town is determined according to the initial prevention and control strategy and all disaster linkage amounts, and then the linkage emergency strategy of the disaster in the mountain town is determined through the characteristic difference of the core disaster area and the disaster intensity characteristic, and the disaster in the mountain town is responded through the linkage emergency strategy; first, the determination of the disaster intensity characteristic is the quantitative expression of the influence degree of the disaster in the mountain town, which helps to accurately evaluate the specific disaster risk faced by each region, and through the determination of the disaster intensity characteristic, different prevention and control strategies can be tailored for each region, so that the response measures can be more targeted, which helps to avoid the waste of resources or over-prevention in the prevention and control process, and ensures the scientificity and efficiency of disaster response; then, the determination of the disaster linkage amount helps to reveal the interaction and influence between disasters, especially in such a complex environment as the mountain town, multiple disasters may interact to produce composite risks, and through the calculation of the disaster linkage amount, it can be judged which region's disaster will trigger the chain reaction of other regions, and then the path and speed of disaster spread can be predicted, and through the linkage effect analysis of the prevention and control measures, the prevention and control measures can be more systematic, which can avoid the possibility that local response fails to cover other disaster areas or ignores the chain reaction, and thus improve the coordination and efficiency of the overall disaster response.

[0097] In summary, the technical solution adopted in the present application can realize the linkage analysis of disaster characteristics and prevention and control strategies, thereby improving the disaster response efficiency of the mountain town.

[0098] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The apparatus that realizes the functions specified in one flow or multiple flows and / or blocks Figure 1 The apparatus that realizes the functions specified in one flow or multiple flows and / or blocks

[0099] Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructing the relevant hardware by means of a program, and the program can be stored in a computer readable storage medium, including Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other medium that can be used to carry or store data in a computer readable manner.

[0100] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

Claims

1. A data analysis method for disaster prevention and control in mountainous towns based on urban information models, characterized in that, The data analysis method includes the following steps: Obtain disaster prevention data in mountainous towns within a specified time period; Based on the disaster zoning mechanism in the urban information model, mountainous towns are divided into multiple prevention and control zones, and then the disaster intensity characteristics of each prevention and control zone are extracted from the disaster prevention and control data. Based on the characteristics of each disaster intensity, mutual information is correlated among the prevention and control areas to obtain the disaster linkage relationship in mountain towns. Then, based on the disaster linkage relationship and the time-frequency characteristics of disasters in mountain towns, the disaster linkage amount of each prevention and control area in mountain towns is determined. The initial prevention and control strategy for mountain towns at the current moment is obtained. Based on the initial prevention and control strategy and all disaster linkage quantities, the core disaster area in the mountain town is determined. Then, the linkage emergency strategy for disasters in the mountain town is determined by the characteristic differences between the core disaster area and the intensity characteristics of each disaster. The aforementioned coordinated emergency response strategy enables coordinated responses to disasters in mountainous towns; Specifically, the disaster zoning mechanism based on the urban information model divides mountainous towns into multiple prevention and control zones, including: To obtain geological structure information and historical disaster distribution information in mountainous towns; Based on the geological structural information, the geological regions in the mountainous towns are clustered to obtain multiple geological region clusters; Extract intra-cluster disaster characteristics from the historical disaster distribution information; Based on the disaster zoning mechanism in the urban information model, combined with the disaster characteristics of all clusters, the geological regions in mountainous towns are divided into multiple prevention and control areas. Specifically, determining the disaster linkage amount of each prevention and control area in a mountain town based on the aforementioned disaster linkage relationships and the time-frequency characteristics of disasters in mountain towns includes: Obtain the time-frequency characteristics of disasters in mountainous towns; For each prevention and control area in a mountainous town, the disaster impact value of the prevention and control area is obtained from the aforementioned disaster impact relationship; Based on the time-frequency characteristics of the disaster and the disaster entanglement value, the disaster linkage amount of the prevention and control area is determined, and then the disaster linkage amount of each prevention and control area in the mountain town is obtained. Specifically, determining the coordinated emergency response strategy for disasters in mountainous towns based on the characteristic differences of the core disaster area and the intensity characteristics of each disaster includes: All prevention and control areas included in the core disaster area are obtained, and then the characteristic differences of disaster intensity characteristics corresponding to each prevention and control area are determined; Generate core prevention and control strategies for core disaster areas based on all characteristic differences; By merging the core prevention and control strategy and the initial prevention and control strategy, a coordinated emergency response strategy for disasters in mountainous towns is obtained.

2. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, Extracting disaster intensity characteristics from the disaster prevention and control data for each prevention and control area specifically includes: For each prevention and control area, extract the disaster prevention and control records of the prevention and control area from the disaster prevention and control data; The priority of disaster prevention and control in the designated areas is determined based on the disaster prevention and control records. Obtain the characteristics of all cluster-specific disasters within the prevention and control area; The disaster intensity characteristics of the prevention and control areas are determined by the prevention and control priorities and all intra-cluster disaster characteristics, thereby obtaining the disaster intensity characteristics of each prevention and control area.

3. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, Based on the characteristics of various disaster intensities, mutual information correlation was performed on various prevention and control areas to obtain the specific disaster linkage relationships in mountainous towns, including: For each prevention and control area, obtain the adjacent prevention and control areas of the prevention and control area, and then determine the intensity of all adjacent disasters corresponding to the adjacent prevention and control areas; Based on the mutual information mechanism, the intensity of each adjacent disaster is correlated with the disaster intensity characteristics of the prevention and control area to obtain the disaster entanglement value of the prevention and control area, and then the disaster entanglement value of each prevention and control area is obtained. Determine the disaster linkage relationships in mountain towns based on all disaster linkage values.

4. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, Based on the initial prevention and control strategy and all disaster linkages, the core disaster areas in mountainous towns specifically include: Obtain historical disaster prevention records in mountainous towns; The core linkage threshold of disasters in mountainous towns is determined based on the historical disaster prevention and control records and the initial prevention and control strategy. Based on the core linkage threshold and all disaster linkage quantities, the core disaster areas in mountainous towns are screened out.

5. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, The coordinated emergency response strategy for disasters in mountainous towns refers to using the coordinated emergency response strategy as a disaster prevention and control strategy for mountainous towns.

6. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, The city information model is a digital platform that integrates various types of data from mountainous towns.

7. The data analysis method for disaster prevention and control in mountainous towns based on urban information models as described in claim 1, characterized in that, Obtain disaster prevention data for mountain towns within a specified time period from the city information model.

Images