CN117291431B - Forest fire disaster-bearing body risk analysis method based on space-time knowledge graph - Google Patents
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Abstract
The invention provides a forest fire disaster bearing body risk analysis method based on space-time knowledge patterns, which comprises the steps of firstly, establishing a space-time knowledge pattern, and determining a forest fire burning range based on satellite and space-time knowledge pattern data; secondly, calculating fire related calculation data based on space-time knowledge patterns and calculating fire spreading speed in any direction of a plane by using an improved Wang Zhengfei forest fire spreading model; thirdly, multiplying the fire spreading speed by time to calculate the future spreading area of the fire; carrying out space-time superposition analysis on the spatial distribution of the fire spreading area and the disaster bearing body based on the space-time knowledge graph; and finally, carrying out risk reminding on disaster-bearing bodies near the fire spreading area. The method combines the vulnerability, social and economic values, damage cost and other attributes of the disaster-bearing body to perform accurate early warning.
Description
Technical Field
The invention relates to the field of geographical environment monitoring, in particular to a forest fire disaster bearing body risk analysis method based on a space-time knowledge graph.
Background
Forest fire incidents frequently occur, and serious threats are caused to disaster-bearing bodies such as residential buildings, electric power facilities, communication signal towers, ancient buildings and the like in adjacent risk areas. Disaster-bearing bodies refer to human social subjects who are directly affected and damaged by disasters. Mainly comprises various aspects of human development and social development, such as industry, agriculture, energy, construction industry, traffic, communication, education, culture, entertainment, various disaster reduction engineering facilities, production and living service facilities, various wealths accumulated by people and the like. The degree of the disaster-bearing body affected by forest fire is closely related to factors such as flammability (building materials, fireproof measures, building purposes, construction years), damage cost, economic value and the like of the disaster-bearing body.
There is a great deal of research on disaster-bearing body risk analysis. However, the focus of most disaster-bearing body risk analysis researches is to conduct static analysis of disaster-bearing body risk when disasters do not occur, and real-time risk analysis of disaster-bearing bodies in a forest fire dynamic spreading stage is not considered. When forest fires occur and spread, how to comprehensively consider factors such as flammability (building materials, fireproof measures, building purposes, construction years), damage cost, economic value and the like of the disaster-bearing body to carry out refined early warning on the disaster-bearing body needs further research. How to rapidly predict the spreading trend during the dynamic propagation of the disaster, calculate the risks of surrounding disaster-bearing bodies, and research and judge the rescue strategy of the disaster-bearing bodies has important practical significance for the emergency rescue decision support.
Therefore, the forest fire spreading trend analysis is taken as an example, the dynamic risk prediction analysis is carried out on the disaster-bearing body threatened by the disaster, the risk design is divided into extremely high risk, medium risk and low risk disaster-bearing bodies according to the forest fire spreading trend time, and the factors such as the combustibility, economic value, damage cost and the like of the disaster-bearing bodies are combined for precise early warning, so that a rescue strategy is provided. The method provided by the invention is used for accurately rescuing the forest fire spreading period.
Disclosure of Invention
Therefore, the invention provides a forest fire disaster bearing body risk analysis method based on space-time knowledge graph, which comprises the following steps:
step 1, establishing a space-time knowledge graph, and determining a forest fire combustion range based on satellite and space-time knowledge graph data;
step 2, calculating fire related calculation data based on space-time knowledge patterns and calculating fire spreading speed in any direction of a plane by using an improved Wang Zhengfei forest fire spreading model;
step 3, multiplying the fire spreading speed by time to calculate the spreading area of 15/30/45/60/90/120/180 minutes in the future;
step 4, carrying out space-time superposition analysis on the spatial distribution of the fire spreading area and the disaster bearing body based on the space-time knowledge graph;
step 5, obtaining the time of the fire spreading to the disaster-bearing body based on the space-time superposition result of the spatial distribution of the fire spreading area and the disaster-bearing body, and dividing the risk level of the disaster-bearing body;
step 6, for public facilities disaster-bearing bodies including electric facilities, communication signal towers and ancient buildings near the fire spreading area, according to the disaster-bearing body risk level calculated in the step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification to the disaster-bearing body responsible persons;
and 7, for disaster-bearing bodies of residential building near the fire spreading area, according to the disaster-bearing body risk level calculated in the step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, providing combustible risk information of the residential building disaster-bearing body stored in the space-time knowledge graph for the disaster-bearing body responsible persons, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification.
The invention has the following beneficial technical effects: a disaster spreading analysis and disaster-bearing body risk dynamic prediction method based on a space-time knowledge graph is used for constructing a disaster-bearing body risk dynamic prediction space-time knowledge graph frame in a disaster by taking forest fire spreading trend analysis as an example, and attribute characteristics such as time, space and flammability, economic value, damage cost and the like of the disaster-bearing body are considered in a conceptual layer design, so that multi-element heterogeneous data fusion and collaborative analysis in the disaster-bearing body and a disaster scene are realized. The method regularizes expert knowledge of dynamic risk analysis of disaster-bearing bodies, and divides the disaster-bearing bodies into two types of disaster-bearing bodies in residential areas and disaster-bearing bodies in public facilities. Based on the space-time relation between static and distributed disaster-bearing bodies and dynamic disaster spreading analysis results, extremely high, medium and low risk reasoning analysis of the disaster-bearing body risks in different time periods is carried out, combustible object risk information required by disaster relief and rescue is provided by combining the properties of combustibility, economic value, damage cost and the like of the disaster-bearing bodies of residential buildings, disaster relief and rescue important attention information is provided for public facilities such as electric facilities, communication signal towers and ancient buildings, and early warning information is provided for owners.
Drawings
FIG. 1 is a conceptual layer of spatiotemporal knowledge patterns;
FIG. 2 is a diagram of several exemplary attributes of a disaster recovery entity;
FIG. 3 is a diagram of dynamic and static data of a multi-element space-time forest fire;
FIG. 4 is a technical flow of forest fire spread prediction and disaster-bearing body risk intelligent analysis;
fig. 5 is a composition of rule objects.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The invention provides a forest fire disaster bearing body risk analysis method based on a space-time knowledge graph. The method comprises the following steps:
step 1, establishing a space-time knowledge graph, and determining a forest fire combustion range based on satellite and space-time knowledge graph data;
step 2, calculating fire related calculation data based on space-time knowledge patterns and calculating fire spreading speed in any direction of a plane by using an improved Wang Zhengfei forest fire spreading model;
step 3, multiplying the fire spreading speed by time to calculate the spreading area of 15/30/45/60/90/120/180 minutes in the future;
step 4, carrying out space-time superposition analysis on the spatial distribution of the fire spreading area and the disaster bearing body based on the space-time knowledge graph;
step 5, obtaining the time of the fire spreading to the disaster-bearing body based on the space-time superposition result of the spatial distribution of the fire spreading area and the disaster-bearing body, and dividing the risk level of the disaster-bearing body;
step 6, for public facilities disaster-bearing bodies including electric facilities, communication signal towers and ancient buildings near the fire spreading area, according to the disaster-bearing body risk level calculated in the step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification to the disaster-bearing body responsible persons;
and 7, for disaster-bearing bodies of residential building near the fire spreading area, according to the disaster-bearing body risk level calculated in the step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, providing combustible risk information of the residential building disaster-bearing body stored in the space-time knowledge graph for the disaster-bearing body responsible persons, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification.
The space-time knowledge graph in the step 1 is established in the following manner, and comprises the steps of constructing a concept layer of the space-time knowledge graph and constructing an instance layer of the space-time knowledge graph.
Step (1): space-time knowledge graph concept layer construction
The space-time knowledge graph concept layer comprises a concept related to disaster bearing and multiple space-time elements required by forest fire spread prediction, as shown in figure 1.
The space-time knowledge graph conceptual layer comprises disaster-bearing bodies, and typical disaster-bearing bodies related in the invention comprise public facilities such as residential building and electric power facilities, communication signal towers and ancient buildings. The space-time knowledge map conceptual layer also comprises forest fire spread prediction multiple space-time elements, namely terrains, weather and earth surface coverage types, wherein the earth surface coverage types comprise vegetation types, bare lands, water bodies, building areas, fire points and the like, and the weather comprises temperature, humidity, wind speed and wind direction; the terrain includes a grade and a slope direction. The forest fire spread prediction multi-element space-time element provides a data basis for fire spread prediction. In the invention, a protein ontology modeling tool is used for constructing a conceptual layer of the space-time knowledge graph, and in addition, time, space and attribute characteristic representation of a disaster-bearing body are considered in the conceptual layer of the space-time knowledge graph. In order to represent the spatial characteristics of the disaster-bearing body, the invention introduces a spatial body. The expression of the space ontology adopts GeoSPARQL, which is a geographical semantic query specification proposed by OGC (Open Geospatial Consortium). In order to represent the time characteristics of the disaster-bearing body, the invention introduces an SWRL time body proposed by the Stanford university to represent the general time concept of the disaster-bearing body. In the invention, the attribute characteristics of the disaster-bearing body are represented by nodes and edges of the knowledge graph. Taking ancient buildings, electric power facilities, roads and woodlands as an example, the present invention specifies several typical attributes of disaster-bearing bodies, as shown in fig. 2. Typical attributes of disaster-bearing bodies include responsible person, flammability, economic value, damage cost, central point longitude and latitude, area, importance and number of people. The flammability risk of disaster-bearing bodies varies with different building materials, fire protection measures, building uses and building years.
Step (2): the construction of the space-time knowledge graph example layer for forest fire spread prediction and the risk analysis of the disaster-bearing body often have different time characteristics. The present invention divides the multi-space-time forest fire data into dynamic data and static data according to the update frequency and accessibility, as shown in fig. 3. Dynamic data is indexed according to the hierarchical relationship of year, month, day, time, minute, and second, and the time phase of static data is defined according to the data type. For example, the collection of vegetation types should take into consideration seasonal changes, and the updating of the topographic data should take into consideration the occurrence of geological disasters such as landslide, debris flow, etc. The data may be converted between dynamic and static data according to changes in update frequency and accessibility.
Based on the classification of the multiple data, the invention constructs an example layer of the space-time knowledge graph. The invention constructs a knowledge extraction method for multi-element heterogeneous data, which converts the data into triples consisting of a subject, a predicate and an object according to the semantics of a concept layer. Driven by a computer program, the functions of which include coordinate system conversion, vector data clipping, raster calculator, vector data to raster data conversion, raster data to vector data conversion, gradient calculation, slope calculation, relative humidity calculation, etc.
Take the example of a ramp data entry instance layer. First, the present invention converts the coordinate system of elevation data from gcs_wgs_1984 to wgs_1984_utm_zone_48n using Arcpy library, and cuts the elevation data according to the spatial range of the investigation region. Next, the slope direction was calculated from the elevation using Arcpy library, and the coordinate system of the slope direction data was converted from wgs_1984_utm_zone_48n to gcs_wgs_1984. The value range of the slope direction is [0,360 ], and the slope direction is increased clockwise from the north direction. Then, the slope direction is decomposed into an east-west direction and a north-south direction, respectively. Next, the present invention uses Arcpy library to convert the numerical range of data from real numbers to integers.
The present invention provides a technique for reducing the loss of accuracy caused by data conversion using the Arcpy library. The grid value is multiplied by 10000 before conversion and the inverse calculation is performed after conversion. And saving the conversion result as a JSON file. And reading the JSON file, and writing the content in the JSON file into the space-time knowledge graph instance in a triplet format.
Expert knowledge regularization technology, the invention establishes a technical flow of forest fire spread prediction and disaster-bearing body risk intelligent analysis, as shown in figure 4.
Based on the technical flow, the invention designs a method for representing the professional knowledge of forest fire spread prediction and disaster-bearing body risk analysis as a semantic reasoning rule in a knowledge graph. First, it is necessary to determine whether the existing situation satisfies a predetermined trigger condition. If so, the system will take a corresponding response action; the response action will generate a new trigger event. For example, when a fire is detected by the system, the surface coverage type corresponding to the spatio-temporal range in which the fire is located is retrieved. If the earth's surface coverage type is water or bare land, the fire will not be considered a forest fire.
For disaster-bearing bodies of electric facilities, communication signal towers and historic building public facilities near a fire scene, the invention provides the risk level of the disaster-bearing body judged based on forest fire vintage to management and rescue workers in a highlighting and audible and visual alarm mode and adds telephone notification risk reminding to owners.
The invention stores the flammability information and risk of disaster-bearing body of residential building into space-time knowledge graph. The flammability information and risk of disaster-bearing bodies of residential buildings depend on factors such as building materials, fireproof measures, building purposes, construction years and the like.
(1) Building material: fire hazards are greater for buildings using flammable building materials. For example, wood-structured buildings are generally easier to burn in a fire, while steel-structured buildings have higher fire resistance.
(2) Fireproof measures: the fire-proof paint and heat-insulating material are used in building structure to raise the fire-resisting performance of disaster-bearing body. The fire protection isolation and partition in the building can help to slow down the spread of fire, and reduce the influence of fire on the whole building.
(3) Building use: the use of the building can also affect the flammability of its disaster-bearing body. For example, flammable chemicals may be stored with low fire resistance and high flammability risk.
(4) Construction age: older buildings typically use traditional building materials. These materials may not perform as well in fire as modern fire-resistant building materials, such as steel structures and fire-resistant insulation. Early architectural designs may not include modern fire protection requirements such as firewalls, fire protection insulation materials, and fire protection barriers. Thus, the risk of fire spread for older buildings may be high.
For disaster-bearing bodies of residential building near a fire scene, the invention provides the combustible risk information of the disaster-bearing bodies of the residential building stored by a space-time knowledge graph based on the risk level of the disaster-bearing bodies judged by forest fire tendrils, and reminds risks to management, rescue workers or owners in different forms of highlighting, audible and visual alarm and telephone notification.
The present invention formalizes the knowledge of FIG. 4 as a set of rules, each of which is described in terms of a rule object. The composition of the rule objects is shown in fig. 5. The rule object defined by the invention comprises the following three main components: (1) [ class: rule object ]: this is an identifier or name of the rule object that uniquely identifies the rule object. The role or meaning of a rule is often described by a meaningful name in order to manage and understand. (2) < attribute, trigger event >: this is one or more logical conditions that describe where the rule should be triggered or activated. Only when these conditions are met will the rules be executed. (3) < attribute: action >: an action is an action that is performed after a rule is triggered. These actions include modifying system state, generating output, triggering other rules, etc., may be implemented by functions in a programming language. An action is the core of a rule object, which defines the actual behavior of the rule. The invention extracts actions from knowledge, sequences between actions, and relationships between trigger actions. The system defines trigger events and actions for rule objects based on the information. Rules definition rules include: (1) expressivity: representing associations between different actions and different rule objects; (2) reusability: defined data attributes and object attribute design rules are used as much as possible.
Based on the technical process and rule object definition in fig. 4, a series of forest fire spread prediction and disaster-bearing body risk intelligent analysis rules can be extracted. Under the drive of a computer program, the rule can search data in the space-time knowledge graph to participate in calculation, so as to obtain the forest fire spreading condition. According to the fire spreading condition, analyzing the risks of the peripheral disaster-bearing bodies, calculating the risks of different kinds of disaster-bearing bodies around the same moment, notifying the disaster-bearing body responsibilities of the same kind of disaster-bearing bodies at different moments, and completing accurate early warning.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. A forest fire disaster bearing body risk analysis method based on space-time knowledge graph is characterized by comprising the following steps:
step 1, establishing a space-time knowledge graph, and determining a forest fire combustion range based on satellite and space-time knowledge graph data;
step 2, calculating fire related calculation data based on space-time knowledge patterns and calculating fire spreading speed in any direction of a plane by using an improved Wang Zhengfei forest fire spreading model;
step 3, multiplying the fire spreading speed by time to calculate the spreading area of 15/30/45/60/90/120/180 minutes in the future;
step 4, carrying out space-time superposition analysis on the spatial distribution of the fire spreading area and the disaster bearing body based on the space-time knowledge graph;
step 5, obtaining the time of the fire spreading to the disaster-bearing body based on the space-time superposition result of the spatial distribution of the fire spreading area and the disaster-bearing body, and dividing the risk level of the disaster-bearing body;
step 6, for public facilities disaster-bearing bodies including electric facilities, communication signal towers and ancient buildings near the fire spreading area, according to the disaster-bearing body risk level calculated in the step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification to the disaster-bearing body responsible persons;
step 7, for disaster-bearing bodies of residential building near the fire spreading area, according to the disaster-bearing body risk level calculated in step 5, deep searching disaster-bearing body responsible persons in the space-time knowledge graph, providing combustible risk information of the residential building disaster-bearing body stored in the space-time knowledge graph for the disaster-bearing body responsible persons, and carrying out risk reminding including highlighting, audible and visual alarm and telephone notification;
in the step 1, the specific steps of establishing the space-time knowledge graph are as follows:
step (1): establishing a space-time knowledge graph conceptual layer, wherein the space-time knowledge graph conceptual layer comprises disaster bearing bodies, and the disaster bearing bodies refer to residential building, electric power facilities, communication signal towers and ancient buildings; the disaster-bearing body attribute information comprises the number, the area, the combustibility, the damage cost and the longitude and latitude of a central point of responsible persons; the space-time knowledge map conceptual layer also comprises forest fire spread prediction multiple space-time elements, wherein the multiple space-time elements comprise terrain, weather and earth surface coverage types, and the earth surface coverage types comprise vegetation types, water bodies, bare lands and building areas;
step (2): establishing a space-time knowledge graph instance layer, and dividing forest fire data containing multiple space-time elements into dynamic data and static data; the dynamic data includes meteorological data; the static data comprises residential building, electric power facilities, communication signal towers, ancient building, terrains and earth surface coverage types, wherein the earth surface coverage types comprise vegetation types, water bodies, bare lands and building areas; on the basis of dynamic data and static data classification, knowledge extraction is carried out on the multiple heterogeneous dynamic data and static data, namely, the data is converted into a triplet consisting of a subject, a predicate and an object according to a semantic structure defined by a concept layer, so that an example layer of a space-time knowledge graph is constructed;
performing primary extraction of a forest fire range based on mid-infrared band data of the high-resolution fourth remote sensing satellite data, and analyzing the fire range detected by the high-resolution fourth remote sensing satellite data; and inquiring the surface coverage type in the fire range based on the surface coverage type data stored by the space-time knowledge graph, removing the space area which cannot belong to the fire combustion range, and taking the rest range as the forest fire combustion range.
2. The method according to claim 1, wherein in step 2, the forest fire combustion range obtained by the analysis in step 1 is used as an initial combustion range; inquiring forest fire spread prediction multiple space-time elements corresponding to the initial combustion range in the space-time knowledge map, and calculating an improved Wang Zhengfei forest fire spread model to obtain the forest fire spread speed in any direction of a plane.
3. The method according to claim 2, wherein in step 3, the forest fire spread speed in any direction of the plane calculated in step 2 is multiplied by 15/30/45/60/90/120/180 minutes, respectively, to calculate a future 15/30/45/60/90/120/180 minute spread area of the fire.
4. The method according to claim 3, wherein step 4, the future 15/30/45/60/90/120/180 minute spreading area calculated in step 3 is respectively analyzed in a superposition manner with the spatial distribution of the disaster-bearing body stored in the space-time knowledge graph, and whether the disaster-bearing body is within the future 15/30/45/60/90/120/180 minute spreading area is determined.
5. The method according to claim 4, wherein in step 5, disaster carriers in the future 15/30/45/60/90/120/180 minute spread are defined as extremely high risk, medium risk, low risk, respectively.
6. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 5 when the program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, realizes the steps in the method according to any one of claims 1 to 5.
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