A method for constructing an urban flood disaster information model and its application

Abstract

The present invention belongs to the technical field of disaster data storage, and discloses a method and application for constructing an urban flood disaster information model, including: using urban flood disaster data to construct an urban flood disaster information model; wherein the entities and relationships in the urban flood disaster information model correspond to the entities and relationships in the urban flood disaster data; the entities in the urban flood disaster data include basic entities and attribute entities, wherein the attribute entities are attribute characteristics of the basic entities; the relationships in the urban flood disaster data include spatial relationships and semantic relationships between basic entities. The present invention also provides corresponding related applications based on the urban flood disaster information model. The present invention constructs urban flood disaster information modeling, combines time series data and spatial relationships, and fuses multi-source heterogeneous data, which can improve the comprehensiveness of urban flood disaster knowledge expression and realize multi-factor integration and analysis management of urban flood disaster data.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster data storage, and more specifically, relates to a method for constructing an urban flood disaster information model and its application. Background Art

[0002] Flood prevention and control is a comprehensive issue, involving multiple dimensions of a city, including social, natural, environmental, and management. Data elements from these dimensions interact with each other. Against this backdrop, a logical construction and storage management method for urban flood disaster data based on information models has emerged, providing new insights for urban flood prevention and response.

[0003] Compared with other urban disasters (such as earthquakes), the causative factors of urban flood disasters are mainly geographical elements and meteorological elements. Geographical elements include geographical terms, geographical nouns, geographical distribution and other knowledge that reflects the external characteristics and connections of geographical objects, as well as geographical models that describe geographical time and space transformations such as geographical evolution laws and geographical prediction laws. They are highly professional and targeted knowledge, and existing other urban disaster information models cannot be directly adopted.

[0004] Current research on information models for urban flood disaster data primarily focuses on the construction phase, typically focusing on extracting entity elements and semantic relationships from urban flood disaster event data to construct corresponding information models. However, information models constructed solely based on urban flood disaster event data are relatively simplistic and can only express limited knowledge. This also results in a coarse granularity in the results of subsequent urban flood disaster data chain deductions based on these information models, resulting in poor flood disaster impact prediction results. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method for constructing an urban flood disaster information model and its application, the purpose of which is to improve the comprehensiveness of the expression of urban flood disaster knowledge.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for constructing an urban flood disaster information model is provided, comprising:

[0007] An urban flood disaster information model is constructed using urban flood disaster data; wherein the entities and relationships in the urban flood disaster information model correspond to the entities and relationships in the urban flood disaster data;

[0008] The entities in the urban flood disaster data include basic entities and attribute entities, wherein the attribute entities are attribute characteristics of the basic entities; the basic entities include disaster factor data, meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, GDP data, disaster data and danger data in the urban flood disaster data;

[0009] The relationships in the urban flood disaster data include spatial relationships and semantic relationships between basic entities; among them, spatial relationships include hierarchical relationships, parallel relationships, connection relationships and distance relationships; there are hierarchical relationships between block data and boundary and political district data, between building and facility data and block data, between block data and land use type data, between block data and meteorological data, between block data and population data, and between block data and GDP data; there are parallel relationships between block data; there are connection relationships between block data and well point data, between well point data and pipeline data, and between building and facility data and well point data; there is a distance relationship between river system data and block data; there are semantic relationships between meteorological data and disaster factor data, between disaster factor data and dangerous situation data, between disaster situation data and disaster situation data, and between disaster situation data and well point data.

[0010] Furthermore, the urban flood disaster data includes urban flood disaster event data and urban geographic information data;

[0011] The entities involved in obtaining the urban flood disaster data include:

[0012] Extracting corresponding basic entities and attribute entities from the urban flood disaster event data and the urban geographic information data respectively;

[0013] The basic entities of the urban flood disaster event data and the urban geographic information data are integrated to obtain the basic entities in the urban flood disaster data; the attribute entities of the urban flood disaster event data and the urban geographic information data are knowledge-completed to obtain the attribute entities in the urban flood disaster data.

[0014] Furthermore, the urban flood disaster event data is integrated with the basic entities of the urban geographic information data to obtain the basic entities in the urban flood disaster data, including:

[0015] Calculate the cosine value S of the angle between the word vectors of the mth attribute of basic entity A and basic entity B Am,Bm :

[0016]

[0017] Wherein, basic entity A and basic entity B correspond to any basic entity of the urban flood disaster event data and the urban geographic information data, and the m-th attribute of basic entity A and basic entity B has n participles in total; a i 、b i The corresponding word vector is the i-th word segmentation in the m-th attribute of basic entity A and basic entity B;

[0018] Calculate the similarity S between basic entity A and basic entity B in terms of attributes A,B :

[0019]

[0020] Where k represents the total number of attributes of basic entity A and basic entity B;

[0021] The similarity S A,B The two basic entities whose values ​​are greater than a preset threshold are fused to obtain the basic entity in the urban flood disaster data.

[0022] Furthermore, the basic entities in the urban geographic information data include: meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, and GDP data;

[0023] The basic entities in the urban flood disaster event data include: disaster factor data, meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, GDP data, disaster data and dangerous situation data; among them, the disaster factor data include flood data, debris flow data, landslide data; the disaster data include weather data, economic loss data, population casualty data, house loss data, waterlogging point data and pipe burst data.

[0024] Furthermore, the semantic relationship includes: hierarchical relationship, causal relationship, correlation relationship and temporal relationship between entities;

[0025] The disaster situation has a hierarchical relationship with weather data, economic loss data, casualty data, housing loss data, waterlogging data, and pipe burst data.

[0026] There is a causal relationship between flood data, debris flow data and landslide data respectively; there is a causal relationship between flood data, debris flow data and landslide data and dangerous situation data;

[0027] There is a correlation between the waterlogging points and the well point data;

[0028] There is a time relationship between disaster factor data and between disaster factor data and its corresponding time attribute entity; among which, the time attribute entity corresponding to the disaster factor data is the occurrence time of the disaster factor.

[0029] Furthermore, the meteorological data includes historical precipitation data of the city;

[0030] The urban flood disaster information model construction method further includes:

[0031] The city's historical precipitation data is input into a trained large model to perform precipitation prediction at a fixed time resolution and spatial resolution to obtain fitted precipitation prediction data; and the fitted precipitation prediction data is used as the meteorological data.

[0032] According to a second aspect of the present invention, a method for storing urban flood disaster data is provided, comprising:

[0033] Extracting entities and relationships from urban flood disaster data;

[0034] The entities and relationships in the extracted urban flood disaster data are stored correspondingly as entities and relationships in the urban flood disaster information model to realize the storage of urban flood disaster data; wherein, the urban flood disaster information model is constructed by the urban flood disaster information model construction method described in any one of the first aspects.

[0035] According to a third aspect of the present invention, a method for spatiotemporal data chain deduction of flood disasters is provided, comprising:

[0036] Query relevant entity information of a specified flood disaster event from the urban flood disaster information model; and use the spatial and semantic relationships in the urban flood disaster information model to perform disaster chain deduction;

[0037] The urban flood disaster information model is constructed by the urban flood disaster information model construction method described in any one of the first aspects.

[0038] According to a fourth aspect of the present invention, a method for predicting the impact of urban flood disasters is provided, comprising:

[0039] Inputting the disaster chain derived by the flood disaster spatiotemporal data chain deduction method described in the third aspect and historical urban flood disaster data into the trained secondary disaster factor prediction model to predict the type of secondary disaster subsequently caused by the disaster chain, predict the time and location of the secondary disaster, and / or predict the impact range of the secondary disaster;

[0040] Among them, the secondary disasters include disasters caused by urban floods.

[0041] According to a fifth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for constructing an urban flood disaster information model as described in any one of the first aspects is implemented, the method for storing urban flood disaster data as described in the second aspect is implemented, the method for deducing a spatiotemporal data chain of flood disasters as described in the third aspect is implemented, and / or the method for predicting the impact of urban flood disasters as described in the fourth aspect is implemented.

[0042] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0043] (1) The method for constructing an urban flood disaster information model of the present invention, in addition to considering disaster factor data, meteorological data, disaster data and hazard data, also considers river system data, land use type data, boundary and political district data, building facility data, block data, well data, pipeline data, population data and GDP data when constructing the urban flood disaster information model. These data can reflect the element characteristics of flood disaster event data (such as disaster-prone environment) and the element characteristics of urban geographic information data. Based on these data that can reflect the characteristics of urban flood disasters themselves, spatial relationships including hierarchical relationships, parallel relationships, connection relationships and distance relationships are used to associate entities that can simultaneously reflect the element characteristics of flood disaster event data and urban geographic information data, and semantic relationships are used to associate meteorological data, disaster factor data, disaster data and well data. The organizational structure of the urban flood disaster information model constructed in this way is more reasonable, and can fully reflect the spatial relationship and semantic relationship between flood disaster event data and urban geographic information data, thereby improving the comprehensiveness of urban flood disaster knowledge expression.

[0044] (2) Furthermore, considering the impact of urban geographic information data on the occurrence of urban flood disasters, urban geographic information data is used as part of urban flood disaster data, and the spatiotemporal relationship of flood disaster events is combined with the spatial relationship of urban geographic information. The resulting information model integrates urban flood disaster knowledge and urban geographic information, and can fully reflect the characteristics of urban flood disaster data. Moreover, when subsequent information utilization is carried out based on this information model, urban flood disaster data resources can be fully utilized, and urban flood disaster data asset management is facilitated.

[0045] (3) Furthermore, the urban flood disaster information model constructed in the present invention includes the time attribute characteristics (time attribute entities) of the disaster factor data and the time relationship of the disaster factor data, which can fully reflect the temporal relationship of the flood disaster event data, organically combine the temporal relationship of the flood disaster event data with the spatial relationship of the urban geographic information data, and enrich the knowledge expression ability of the urban flood disaster information model.

[0046] (4) As a preference, using precipitation forecast data as meteorological data can make the meteorological data more uniform in time segments.

[0047] (5) Furthermore, the urban flood disaster information model of the present invention combines spatiotemporal relationships to fuse multi-source heterogeneous data. Based on the constructed urban flood disaster information model, the spatiotemporal disaster chain deduction of urban flood disasters is performed. This allows for in-depth analysis of the complex coupled relationships in urban flood disaster data, and further prediction of secondary disaster-causing factors, thereby enabling the multi-factor integration and analytical management of urban flood disaster data. Furthermore, the urban flood disaster data chain deduction and flood disaster impact prediction based on the urban flood disaster information model also have fine granularity and good results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of a method for constructing an urban flood disaster information model in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart for constructing an urban flood disaster information model in an embodiment of the present invention.

[0050] Figure 3 Schematic diagram of the knowledge representation structure of urban flood disaster data in an embodiment of the present invention.

[0051] Figure 4 This is an urban flood disaster information model constructed in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0053] Example 1

[0054] like Figure 1-Figure 3 As shown, an embodiment of the present invention provides a method for constructing an urban flood disaster information model, including:

[0055] An urban flood disaster information model is constructed using urban flood disaster data; wherein the entities and relationships in the urban flood disaster information model correspond to the entities and relationships in the urban flood disaster data.

[0056] The entities in urban flood disaster data include basic entities and attribute entities, among which attribute entities are the attribute characteristics of basic entities; basic entities include disaster factor data, meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, GDP data, disaster data and dangerous data in urban flood disaster data; each data is regarded as an entity.

[0057] Relationships in urban flood disaster data include spatial and semantic relationships between basic entities. Spatial relationships include hierarchical, parallel, connected, and distance relationships between basic entities. Hierarchical relationships exist between block data and boundary and administrative district data, between building and facility data and block data, between block data and land use type data, between block data and meteorological data, between block data and population data, and between block data and GDP data. Parallel relationships exist between block data and other block data. Connected relationships exist between block data and well point data, between well point data and pipeline data, and between building and facility data and well point data. Distance relationships exist between river system data and block data.

[0058] Meteorological data and disaster-causing factor data, disaster-causing factor data and dangerous situation data, disaster situation data and disaster situation data, and disaster situation data and well point data are connected through semantic relationships.

[0059] As a specific implementation method, urban flood disaster data includes urban flood disaster event data and urban geographic information data; wherein, entities extracted from urban flood disaster data include:

[0060] Entities in urban flood disaster event data and entities in urban geographic information data are extracted, and the basic entities in the extracted entities are integrated to obtain the basic entities in the urban flood disaster data; the attribute entities in the extracted entities are supplemented with knowledge to obtain the attribute entities in the urban flood disaster data.

[0061] Relationships extracted from urban flood disaster data include:

[0062] Extract the relationships between entities in flood disaster event data and the relationships between entities in urban geographic information data; align the extracted relationships to obtain the relationships in the extracted urban flood disaster data.

[0063] In this embodiment of the present invention, reference is made to relevant national standards such as regional natural disaster system theory, the Flood Control Regulations of the People's Republic of China, and natural disaster statistics, and incorporating the unique characteristics of urban flood disasters, the attributes of urban flood disaster event data are described from the perspectives of hazard factors, disaster-prone environments, disaster conditions, and hazardous conditions. Urban flood disaster hazard factors include primary hazard factors and secondary hazard factors. In this embodiment of the present invention, primary hazard factors refer to urban flood disasters, while secondary hazard factors refer to debris flows and landslides caused by urban flood disasters. Disaster-prone environmental data includes meteorological data, river system data, land use type data, boundary and administrative division data, building and facility data, neighborhood data, well point data, pipeline data, population data, and GDP data. Disaster condition data includes weather data, economic loss data, casualty data, housing loss data, waterlogging data, and piping data. In other words, in this embodiment of the present invention, the basic entities in flood disaster event data include hazard factor data, disaster-prone environmental data, disaster condition data, and hazardous condition data. Among them, disaster-causing factor data, disaster-prone environment data, disaster situation data, and risk data are first-level entities, and their corresponding second-level entities and attribute entities are shown in Table 1-3.

[0064] Table 1 Disaster factor data classification and corresponding attribute entities

[0065]

[0066] Table 2 Disaster-prone environmental data and corresponding attribute entities

[0067]

[0068] Table 3 Specific classification of disasters and dangerous situations and corresponding attribute entities

[0069]

[0070] In the embodiment of the present invention, flood disaster event data is text data, which is obtained through official and online data related to flood disaster events, as well as historical flood event-related materials. Specifically, this includes semi-structured data such as flood disaster event data released by municipal governments and other organizations (such as flood disaster accident reports) and data related to flood disaster events published on encyclopedias, as well as historical flood event-related materials, unstructured data related to flood disaster events from Weibo and web news.

[0071] In an embodiment of the present invention, the basic entities in the urban geographic information data include vector data including river system data, boundary and political district data, building facility data, block data, well point data, pipeline data, and raster data including land use type data, population data, GDP data and meteorological data.

[0072] The relationships between urban flood disaster event data and corresponding entities in urban geographic information data fall into two categories: spatial relationships and semantic relationships. Spatial relationships primarily relate to entities in urban geographic information data. Spatial relationships include hierarchical, parallel, connected, and distance relationships between basic entities. Semantic relationships include hierarchical, causal, correlated, and temporal relationships between basic entities. The relationships between first-level disaster data and its corresponding second-level basic entities are all hierarchical. For example, if disaster data includes weather data, economic loss data, casualty data, housing loss data, waterlogging data, and pipe burst data, then the disaster data has hierarchical relationships with weather data, economic loss data, casualty data, housing loss data, waterlogging data, and pipe burst data, respectively. Causal relationships exist between primary and secondary hazards in hazard factor data, between hazard factor data and hazardous situation data, and between meteorological data and hazard factor data. For example, floods cause debris flows, floods cause landslides, and meteorological conditions cause floods, debris flows, and landslides. Flood data, debris flow data, and landslide data all have causal relationships with hazardous situation data, for example, floods cause hazardous situations. Waterlogged points and well point data in disaster situation data have a correlation relationship. Temporal relationships connect hazard factor data and hazard factor data with their corresponding time attribute entities. For example, temporal relationships exist between floods, debris flows, and landslides and their corresponding time attribute entities. This is shown in Table 4 below.

[0073] Table 4 Relationship categories

[0074]

[0075] The notation in Table 4: <entity 1, relationship A, entity 2> means that entity 1 points to entity 2 and there is a relationship A between the two.

[0076] As one of the implementation methods, the ERNIE-UIE model is used to extract basic entities, attribute entities, and relationships from urban flood disaster event data, including:

[0077] Preprocessing of urban flood disaster event data includes: data format conversion, filtering stop words with Jieba, and eliminating invalid data with the regular expression library; fine-tuning the trained model provided by ERNIE-UIE; and using the fine-tuned ERNIE-UIE model to extract basic entities, attribute entities, and relationships from the processed urban flood disaster event data, thereby obtaining the basic entities, attribute entities, and relationships corresponding to the flood event data.

[0078] Preferably, for the hazard factor data in urban flood disaster event data, the corresponding attribute entities include a time attribute entity and a location attribute entity. The time attribute entity represents the time of the disaster, and the location attribute entity represents the location of the disaster. Extracting the time attribute entity includes matching strings with time expressions in the preprocessed urban flood disaster event data using regular expressions to extract the time information, standardizing the strings, and unifying time expressions in different formats using Python to obtain the time attribute entity. In this embodiment of the present invention, the unified time expression is "YYYYMMDDHH.mm."

[0079] After extracting the time attribute entity, the time relationship between the disaster factor data is obtained, including: arranging the urban flood disaster events in chronological order, that is, arranging the time expressions after unified format in chronological order, and obtaining the time series information of the urban flood disaster events, which is used to reflect the time relationship between the disaster factor entities.

[0080] The urban geographic information data is extracted using the Python programming language embedded in GIS to extract basic entities, attribute entities, and relationships, including:

[0081] First, the urban geographic information data is preprocessed, including: using GIS to read shp files (vector data of urban river systems, boundaries and administrative divisions, buildings and facilities, blocks, well points and pipelines) and tif data (raster data of population distribution, GDP, land use type, and meteorological data), obtain the number, name, longitude and latitude of meteorological and river system data, and unify the coordinates, projection transformation, and elevation benchmark of various urban geographic information data;

[0082] Then, GIS hydrological analysis and terrain analysis modules are used to analyze the geographic attributes of the pre-processed data, such as "terrain relief", "terrain wetness index", and "terrain roughness index", to obtain the basic entities and attribute entities corresponding to the urban geographic information data;

[0083] Finally, the Python programming language is used to analyze and extract the spatial relationships of the entities.

[0084] As one of the implementation methods, after extracting entities and relationships from urban flood disaster event data and urban geographic information data, the knowledge of multi-source heterogeneous data sources is integrated using entity alignment, relationship alignment, and knowledge completion. Entity alignment uses entity similarity calculation, including:

[0085] Calculate the cosine value S of the angle between the word vectors of the mth attribute of basic entity A and basic entity B Am,Bm :

[0086]

[0087] Among them, the basic entity A and the basic entity B correspond to any basic entity in the urban flood disaster event data and the urban geographic information data; and the m-th attribute of the basic entity A and the basic entity B has n word segments; a i , b i correspond to the word vectors of the i-th word segment in the m-th attribute of the basic entity A and the basic entity B; the word vector of the i-th word segment is obtained by using the GLOVE algorithm. A m and B m respectively represent the m-th attribute vectors in the basic entity A and the basic entity B; |A m | and |B m | respectively represent the absolute values of the vectors A m and B m . Here, the attribute of the basic entity is the attribute entity corresponding to the basic entity.

[0088] Calculate the similarity S of the basic entity A and the basic entity B in terms of attributes A,B :

[0089]

[0090] Among them, k represents the total number of attributes of the basic entity A and the basic entity B.

[0091] Fuse two basic entities with a similarity S A,B greater than the preset threshold to obtain the basic entity in the urban flood disaster data.

[0092] Preferably, the meteorological data includes the urban historical precipitation data, and also includes homogenizing the meteorological data. Specifically, it includes: inputting the urban historical precipitation data into a pre-trained large model (such as the Fuxi large model) to perform precipitation prediction with a fixed time resolution and spatial resolution, obtaining the precipitation prediction model with the best fitting effect, and further obtaining the precipitation prediction data with the best effect. Using the precipitation prediction data as the meteorological data can make the meteorological data more uniform in time segments. In the embodiment of the present invention, precipitation prediction is performed on the urban historical precipitation data with a time resolution of 6 hours and a spatial resolution of 0.25°.

[0093] Furthermore, the constructed urban flood disaster information model also includes the attribute characteristics of relationships. Among them, the attribute characteristics of the hierarchical relationship, the parallel relationship, the connection relationship, and the distance relationship correspond to hierarchy, parallelism, connection, and distance. The attribute characteristics of the superordinate-subordinate relationship, the causal relationship, the correlation relationship, and the time relationship correspond to superordinate-subordinate, causal, correlation, and time.

[0094] The method for constructing an urban flood disaster information model of the present invention, in addition to considering disaster factor data, meteorological data, disaster data and hazard data, also considers river system data, land use type data, boundary and political district data, building facility data, block data, well data, pipeline data, population data and GDP data when constructing the urban flood disaster information model. These data can reflect the element characteristics of flood disaster event data (such as the disaster-prone environment) and the element characteristics of urban geographic information data. Based on these data that can reflect the characteristics of urban flood disasters themselves, spatial relationships including hierarchical relationships, parallel relationships, connection relationships and distance relationships are used to associate entities that can simultaneously reflect the element characteristics of flood disaster event data and urban geographic information data. Semantic relationships are used to associate meteorological data, disaster factor data, disaster data and well data. The organizational structure of the urban flood disaster information model constructed in this way is more reasonable, and can fully reflect the spatial and semantic relationships between flood disaster event data and urban geographic information data, thereby improving the comprehensiveness of urban flood disaster knowledge expression. Moreover, the subsequent urban flood disaster data chain deduction and flood disaster impact prediction based on this urban flood disaster information model also have finer granularity and better effects.

[0095] By converting urban flood disaster data into an information model storage method, existing query statements can be used to quickly access and analyze urban flood disaster data, improving the ability to query and make real-time decisions on urban flood disaster data. In addition, the information model constructed by the present invention defines different entities according to different types of urban flood disaster data. When the storage object changes or the storage structure needs to be adjusted, the entities, the relationships between entities, and the attributes of the relationships can be directly added, deleted, or modified in the information model, such as Figure 4 shown.

[0096] The present invention constructs an urban flood disaster information model based on the inherent attributes of urban flood disaster data, and the urban flood disaster knowledge that can be expressed can be more abundant and comprehensive.

[0097] Example 2

[0098] An embodiment of the present invention provides a method for storing urban flood disaster data, comprising:

[0099] Extracting entities and relationships from urban flood disaster data; wherein, extracting entities and relationships from urban flood disaster data refers to the corresponding description in the above embodiment 1;

[0100] The entities and relationships in the extracted urban flood disaster data are stored correspondingly as the entities and relationships in the urban flood disaster information model constructed in Example 1, thereby realizing the storage of urban flood disaster data.

[0101] As a specific implementation, the storage method includes:

[0102] The meteorological data, river system data, land use type data, weather data, boundary and political district data, building facility data, block data, well point and pipeline data, disaster data, and danger data in the urban flood disaster data are stored as basic entities of the urban flood disaster information model constructed in Example 1;

[0103] Use spatial and semantic relationships to associate basic entities;

[0104] Add attribute features of basic entities and relationships to realize the storage of urban flood disaster data. For example, the attribute data such as the "occurrence time" of flood disasters, the "river name, distance to the river, and river bottom area" of river systems, the "building facility ID and building facility type" of buildings and facilities, and the "block ID, block building density, and average floor height of blocks" of blocks are batch written into the information model. For another example, the attribute data of "hourly rainfall, daily rainfall, and cumulative rainfall" of weather entities in disaster risk conditions are batch written into the information model, and the relationships between entities are batch written into the information model. In this embodiment of the present invention, basic entities, attribute entities, and the relationships between entities are written into the information model using Python.

[0105] Example 3

[0106] The embodiment of the present invention provides a method for performing spatiotemporal data chain deduction of flood disasters using the urban flood disaster information model constructed in Example 1, comprising:

[0107] Query the relevant entity information of the specified flood disaster event from the urban flood disaster information model constructed in Example 1; for example, query the disaster entity (such as weather, economic losses, casualties, house losses, waterlogging points, pipe bursts, etc.), time information (such as the occurrence time of the disaster factor), and spatial information (such as longitude and latitude, blocks, boundaries and conditions, etc.).

[0108] Disaster chain deduction is conducted by leveraging the spatial and semantic relationships within the urban flood disaster information model. For example, the causal relationships within the information model are used to analyze the causal relationships between disaster entities and identify the transmission paths and key nodes of the disaster chain, including the initiating entity that triggers the urban flood disaster and the entity that causes a sharp increase in economic losses or housing damage. The temporal relationships between entities within the information model are used to analyze the chronological order of the occurrence of disaster entities, construct a time series model of the disaster chain, and use graph algorithms to predict the development trend of the disaster. The spatial relationships between entities within the information model are used to analyze the spatial relationships between disaster-prone environmental entities, including distance relationships and hierarchical relationships, construct a spatial propagation model of the disaster chain, and use graph algorithms to predict the impact range of the disaster.

[0109] For related solutions, please refer to the corresponding description in Example 1 and will not be repeated here.

[0110] Example 4

[0111] An embodiment of the present invention provides a method for predicting the impact of urban flood disasters, including:

[0112] The spatiotemporal characteristics of the disaster chain derived from the deduction method in Example 3 and historical urban flood disaster data are input into a trained secondary hazard factor prediction model to predict the type of secondary disasters subsequently triggered by the disaster chain, such as secondary disasters (mudslides, landslides, etc.) that may be triggered by floods; the time and location of secondary disasters; and the scope of impact of secondary disasters, such as the wells and neighborhoods that may be affected by floods. In this embodiment of the present invention, a graph algorithm is used to establish the secondary hazard factor prediction model.

[0113] For related solutions, please refer to the corresponding description in Example 3, which will not be repeated here.

[0114] Example 5

[0115] Embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program implements the method for constructing an urban flood disaster information model as described in Example 1, the method for storing urban flood disaster data as described in Example 2, the method for deducing spatiotemporal data links for flood disasters as described in Example 3, and / or the method for predicting the impact of urban flood disasters as described in Example 4. For related solutions, please refer to the description of the corresponding embodiments and will not be repeated here.

[0116] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing an urban flood disaster information model, characterized in that: include: An urban flood disaster information model is constructed using urban flood disaster data; wherein the entities and relationships in the urban flood disaster information model correspond to the entities and relationships in the urban flood disaster data; The entities in the urban flood disaster data include basic entities and attribute entities, wherein the attribute entities are attribute characteristics of the basic entities; the basic entities include disaster factor data, meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, GDP data, disaster data and danger data in the urban flood disaster data; The relationships in the urban flood disaster data include spatial relationships and semantic relationships between basic entities; among them, spatial relationships include hierarchical relationships, parallel relationships, connection relationships and distance relationships; the relationships between block data and boundary and political district data, between building facility data and block data, between block data and land use type data, between block data and meteorological data, between block data and population data, and between block data and GDP data are all hierarchical relationships; the relationships between block data are parallel; the relationships between block data and well point data, between well point data and pipeline data, and between building facility data and well point data are all connection relationships; the relationships between river system data and block data are distance relationships; the relationships between meteorological data and disaster factor data, between disaster factor data and dangerous situation data, between disaster situation data and disaster situation data, and between disaster situation data and well point data are all connected through semantic relationships; The urban flood disaster data includes urban flood disaster event data and urban geographic information data; The entities involved in obtaining the urban flood disaster data include: Extracting corresponding basic entities and attribute entities from the urban flood disaster event data and the urban geographic information data respectively; The basic entities of the urban flood disaster event data and the urban geographic information data are integrated to obtain the basic entities in the urban flood disaster data; the attribute entities of the urban flood disaster event data and the urban geographic information data are knowledge-completed to obtain the attribute entities in the urban flood disaster data.

2. The method for constructing an urban flood disaster information model according to claim 1, characterized in that: The urban flood disaster event data is integrated with the basic entities of the urban geographic information data to obtain the basic entities in the urban flood disaster data, including: Calculate the cosine of the angle between the word vectors of the mth attribute of basic entity A and basic entity B : Wherein, the basic entity A and the basic entity B correspond to any basic entity of the urban flood disaster event data and the urban geographic information data, and the m-th attribute of the basic entity A and the basic entity B has n participles in total; 、 The corresponding word vector is the i-th word segmentation in the m-th attribute of basic entity A and basic entity B; Calculate the similarity between basic entity A and basic entity B in terms of attributes : in, Represents the total number of attributes of basic entity A and basic entity B; The similarity The two basic entities whose values ​​are greater than a preset threshold are fused to obtain the basic entity in the urban flood disaster data.

3. The method for constructing an urban flood disaster information model according to claim 1 or 2, characterized in that: The basic entities in the urban geographic information data include: meteorological data, river system data, land use type data, boundary and political district data, building facility data, block data, well point data, pipeline data, population data, and GDP data; The disaster factor data include flood data, debris flow data, and landslide data; the disaster situation data include weather data, economic loss data, casualty data, house loss data, waterlogging data, and pipe burst data.

4. The method for constructing an urban flood disaster information model according to claim 3, characterized in that: The semantic relationship includes: hierarchical relationship, causal relationship, correlation relationship and temporal relationship between entities; The disaster situation has a hierarchical relationship with weather data, economic loss data, casualty data, housing loss data, waterlogging data, and pipe burst data. There is a causal relationship between flood data, debris flow data and landslide data respectively; there is a causal relationship between flood data, debris flow data and landslide data and dangerous situation data; There is a correlation between the waterlogging points and the well point data; There is a time relationship between disaster factor data and between disaster factor data and its corresponding time attribute entity; among which, the time attribute entity corresponding to the disaster factor data is the occurrence time of the disaster factor.

5. The method for constructing an urban flood disaster information model according to claim 1, characterized in that: The meteorological data includes historical precipitation data of the city; The urban flood disaster information model construction method further includes: The city's historical precipitation data is input into a trained large model to perform precipitation prediction at a fixed time resolution and spatial resolution to obtain fitted precipitation prediction data; and the fitted precipitation prediction data is used as the meteorological data.

6. A method for storing urban flood disaster data, characterized in that: include: Extracting entities and relationships from urban flood disaster data; The entities and relationships in the extracted urban flood disaster data are stored correspondingly as entities and relationships in the urban flood disaster information model to realize the storage of urban flood disaster data; wherein, the urban flood disaster information model is constructed by the urban flood disaster information model construction method described in any one of claims 1-5.

7. A method for spatiotemporal data chain deduction of flood disasters, characterized in that: include: Query relevant entity information of a specified flood disaster event from the urban flood disaster information model; And use the spatial and semantic relationships in the urban flood disaster information model to deduce the disaster chain; Wherein, the urban flood disaster information model is constructed by the urban flood disaster information model construction method according to any one of claims 1-5.

8. A method for predicting the impact of urban flood disasters, characterized in that: include: Inputting the disaster chain derived by the flood disaster spatiotemporal data chain deduction method of claim 7 and historical urban flood disaster data into a trained secondary disaster factor prediction model to predict the type of secondary disaster subsequently caused by the disaster chain, predict the time and location of the secondary disaster, and / or predict the impact range of the secondary disaster; Among them, the secondary disasters include disasters caused by urban floods.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the urban flood disaster information model construction method according to any one of claims 1 to 5, implements the urban flood disaster data storage method according to claim 6, implements the flood disaster spatiotemporal data chain deduction method according to claim 7, and / or implements the urban flood disaster impact prediction method according to claim 8.

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