A scenario-oriented dynamic risk assessment method for forest fires

Abstract

The present invention relates to the field of risk assessment technology, specifically a scenario-based dynamic forest fire risk assessment method. This method addresses the problem that existing models rely on existing data, have poor adaptability to sudden fires or multi-scenario conditions, and are unable to meet the requirements of real-time and precise assessment. The method comprises: constructing a forest fire behavior simulation model based on cellular automata, building a semantically driven forest fire scenario simulation knowledge graph, and constructing a comprehensive forest fire dynamic risk assessment model. By utilizing fire behavior simulation, semantic modeling, and comprehensive risk assessment model construction, the present invention constructs a dynamically updateable risk assessment framework. This method enables real-time monitoring and prediction of fire risks, enhancing disaster prevention and mitigation capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and in particular to a scenario-oriented dynamic risk assessment method for forest fires. Background Art

[0002] Forest fires are one of the most common and destructive natural disasters worldwide, causing significant damage to ecosystems and posing a serious threat to human life and property. With global warming, changes in land use, and increased human activity, the frequency and intensity of forest fires are increasing significantly. Effectively assessing and responding to forest fire risks has become a key research focus and a technical challenge in the field of forest management and disaster prevention.

[0003] Traditional forest fire risk assessment methods rely primarily on statistical and physical modeling. Statistical models are typically based on the analysis of static data. They utilize historical fire records combined with environmental factors such as vegetation type, terrain slope, and meteorological conditions to quantify risk components such as hazard, exposure, and vulnerability. These models, in turn, construct quantitative models of fire risk, reveal the primary drivers of fire occurrence and provide guidance for long-term risk forecasting. However, these models rely on existing data and are less adaptable to sudden fires or multiple scenarios, making them difficult to meet the demands of real-time and precise assessments. Physical modeling, by assuming different scenarios, constructs fire propagation models (e.g., flame propagation paths, burning rates, etc.) to simulate the dynamic changes and spread of fires, reflecting the dynamic characteristics of fire propagation. While this approach can simulate the real-time changes in fires during a forest fire, its simulation results are relatively simplistic and insufficiently reflect the multivariate factors in complex environmental conditions.

[0004] Existing risk assessment methods rely on existing data and have poor adaptability to sudden fires or multi-scenario conditions, making it difficult to meet the needs of real-time and accurate assessment under forest fire emergency conditions. Therefore, we propose a scenario-oriented dynamic forest fire risk assessment method that does not meet existing needs. Summary of the Invention

[0005] The purpose of the present invention is to provide a scenario-oriented dynamic risk assessment method for forest fires to solve the problem proposed in the above background technology that the existing model relies on existing data, has poor adaptability to sudden fires or multiple scenario conditions, and is difficult to meet the needs of real-time and accurate assessment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a scenario-oriented dynamic forest fire risk assessment method, the method comprising:

[0007] Based on the construction of a cellular automaton forest fire behavior simulation model, the forest fire prevention and control area is divided into several cells based on the needs of risk assessment. The cell states are defined according to the different fire behaviors during the forest fire burning process. The forest fire spread rate is calculated using Wang Zhengfei's forest fire spread model. Based on the states of adjacent cells and the advancement of spread time, the state transition rules of different cells are defined, thereby realizing real-time fire behavior simulation during the forest fire spread process.

[0008] The construction of a semantically driven forest fire scenario simulation knowledge graph was completed, guided by forest fire business and related theories, as well as the SSN and TIME ontology standards published by W3C. The theoretical framework of the forest fire scenario simulation knowledge graph was constructed. Semantic modeling was used to integrate and dynamically update multi-source information in the experimental area, and a cellular automation-based forest fire behavior simulation method was encapsulated to achieve the construction and update of the forest fire scenario simulation knowledge graph.

[0009] The construction of a comprehensive dynamic forest fire risk assessment model is based on the three risk factors of fire hazard, exposure and vulnerability of the hazard-bearing body. Real-time fire environmental factors such as fire spread rate, fire status and population density are introduced to assess these three risk factors. The dynamic forest fire risk assessment model is completed by constructing a comprehensive risk assessment index.

[0010] Dynamic risk assessment based on scenario simulation: The semantic prevention and control simulation scenario is set in the forest fire scenario simulation knowledge graph, and the above-mentioned cellular automaton forest fire behavior simulation model is called to realize real-time fire behavior simulation. Finally, based on the dynamic forest fire risk assessment model, real-time risk assessment indicators are introduced to realize dynamic assessment of fire risks at different times and prevention and control scenarios;

[0011] Through a comparative experiment simulating a real fire, the feasibility and prediction accuracy of the scenario-oriented dynamic risk assessment method for forest fires were evaluated.

[0012] Preferably, there are five different fire behavior cellular states in the method for constructing the cellular automaton forest fire behavior simulation model, and the fire behavior state characteristics presented by the cells are as follows:

[0013] When the cell state = 0, that is, the cell state S0: the cell is in the fire isolation area, where there is no combustible material in the fire isolation area, and the cell state remains unchanged;

[0014] When the cell state = 1, that is, the cell state S1: the combustible material in the cell is in the state of not being ignited;

[0015] When the cell state = 2, that is, the cell state S2: the combustible material in the cell has just started to burn, and the combustible material is in the internal development state and has no ability to ignite other cells outward;

[0016] When the cell state = 3, that is, the cell state S3: the combustible material in the cell is in a complete combustion state, and the combustible material begins to diffuse outward and has the ability to ignite the remaining cells;

[0017] When the cell state = 4, that is, the cell state S4: the combustible material in the cell is in a burned state.

[0018] Preferably, the rule for converting the cellular state S0 to S1 defines the non-combustible area of ​​the cell covering water and roads as S0, and the combustible area of ​​the cell covering forest as S1.

[0019] Preferably, the rule for the transition from the cell state S1 to S2 is that the combustible but unburned S1 state cell is ignited by the surrounding fully burned S3 state cells, and is ignited when the cumulative value transferred by the S1 state cell exceeds a threshold value, the threshold value being 0.65, and the unburned S1 state cell is ignited and transitions to the S2 state;

[0020]

[0021] in is the cumulative value of the ignition transfer from the adjacent cells to the current cell after the next time interval Δt; is the spreading rate of cells with spreading ability, i.e., cells in the complete combustion state S3, in the Moore neighborhood of the current position; Δt is the simulation time step and is set to 1 min, L is the horizontal distance between cells, and i and j are the coordinate indices of the current cell;

[0022] R o : initial spreading speed; K s : Combustible correction factor; K w : wind speed correction coefficient; Terrain correction factor.

[0023] Preferably, the transition rule from the cell state S2 to the S3 state defines the time process of the cell from the internal combustion state S2 to the complete combustion state S3 as Δt1, and the Δt1 is used to describe the internal forest fire spread process of the cell. When the combustion duration of the cell in the S2 state is t cost When it is greater than Δt1, the cell state is converted from S2 to S3;

[0024]

[0025] where R IN is the internal forest fire spread rate of the cell. The cells in S2 are transformed into S3 after Δt1 and have the ability to spread to the neighborhood.

[0026] Preferably, the cell state S3 to S4 transition rule defines the process of transitioning the cell from the fully burned S3 state to the extinguished S4 state as Δt2, wherein Δt2 depends on the continuous combustion of the fuel, external fire extinguishing measures and environmental conditions, and the duration of flame combustion is related to the time Δt required for complete combustion of the fuel. burn and fire extinguishing intervention time Δt inter Related;

[0027] Δt2=Δt burn +Δt inter ;

[0028]

[0029] Where W is the fuel load within the cell and is expressed in kg / m 2 , m is the mass burning rate of the combustible material, and the unit is kg / (m 2 ·min).

[0030] Preferably, the dynamic forest fire risk assessment model is based on the three risk factors of fire hazard, exposure and vulnerability of the disaster-bearing body, and constructs an index system for forest fire risk assessment. In this index system, comprehensive risk is defined as a Class A indicator, hazard, exposure and vulnerability are defined as Class B risk indicators, and fire environment factors such as real-time fire behavior, terrain, and vegetation cover are classified as Class C indicators. During the risk assessment process, dynamic assessment of forest fire risk is achieved by introducing and dynamically updating real-time data such as fire behavior and population distribution into the index system;

[0031]

[0032] where R t is the A-level indicator of comprehensive risk at time t, H t 、E t 、V t are the risk level B indicators of danger, exposure and vulnerability at time t, BI t and CI t are the B and C level indicators of risk at time t, respectively, W i is the weight of C-level indicators to B-level indicators, and n is the number of C-level indicators participating in the evaluation of B-level indicators.

[0033] Preferably, the danger level in the B-level indicators is calculated by C-level indicators such as distance from the fire scene, combustion state, and combustion rate, and their weights are set to 0.4, 0.3, and 0.3 respectively.

[0034] Preferably, the exposure in the B-level indicators is calculated by C-level indicators such as population density, distance to buildings, distance to key protection facilities, vegetation coverage, GDP, etc., and their weights are set to 0.3, 0.2, 0.25, 0.125, and 0.125 respectively.

[0035] Preferably, the B-level indicator vulnerability is calculated by C-level indicators such as the type of flammable material and the duration of ignition, and the weights thereof are set to 0.55 and 0.45 respectively.

[0036] Preferably, the stage values ​​of the A-level indicators, B-level indicators and C-level indicators are divided into five levels from I to V according to the degree of contribution to the impact of fire, and the five levels I to V are assigned values ​​of 0.2, 0.4, 0.6, 0.8 and 1 respectively. The higher the level of the risk indicator, the greater the impact of the fire.

[0037] Preferably, the fire simulation scenario setting includes the setting of emergency resources such as the fire start time, fire end time, emergency rescue time, fire point, isolation zone, firefighters, fire trucks, and number of trucks.

[0038] Preferably, the specific steps of the dynamic risk assessment method based on scenario simulation are: first, integrating multi-source data of the experimental area to complete the dynamic update of the forest fire scenario simulation knowledge graph, and setting the fire simulation scenario based on the knowledge graph; then, calling the above-mentioned cellular automaton forest fire behavior simulation model according to different fire simulation scenarios to realize real-time fire behavior simulation; finally, based on the forest fire dynamic risk assessment model, introducing real-time risk assessment indicators to evaluate the fire risk at different times and prevention and control scenarios.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. This invention breaks the limitations of traditional statistical and physical modeling by introducing the concepts of scenario simulation and dynamic data fusion. By combining multi-source data, it can more comprehensively and accurately reflect fire risk conditions, effectively improving the accuracy of fire risk assessment and its applicability in different scenarios. Based on the cellular automation method, it simulates the propagation path and speed of fire in multidimensional space, divides cellular states in detail, and clarifies their state transformation rules. It can deeply reveal the mechanism of dynamic changes in fire, helping researchers and relevant decision makers to more clearly understand the process and laws of fire development.

[0041] 2. This invention constructs a risk assessment indicator system based on fire hazard, exposure, and vulnerability of hazard-bearing structures. This system incorporates real-time information such as fire behavior and population distribution to achieve dynamic fire risk assessment and meet the real-time requirements of emergency management. By using semantically driven scenarios such as fire occurrence, environment, and emergency response, fire spread simulation and risk prediction are performed based on different prevention and control scenarios. This provides comprehensive decision-making support for fire departments and governments, enabling rapid selection of appropriate prevention and control strategies and minimizing losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the cell state matrix of the present invention;

[0043] Figure 2 This is a flow chart of the forest fire spread cell state transition of the present invention;

[0044] Figure 3 Constructing a flow chart for the forest fire scenario simulation knowledge graph of the present invention;

[0045] Figure 4 A schematic diagram of setting nodes for scenario simulation of the knowledge graph of the present invention;

[0046] Figure 5 This is a schematic diagram of the forest fire risk assessment index structure of the present invention;

[0047] Figure 6 A schematic diagram of the research location is provided for the scenario of the embodiment of the present invention;

[0048] Figure 7 Schematic diagram of simulation scenario settings for three prevention and control measures in an embodiment of the present invention;

[0049] Figure 8 The fire behavior simulation diagrams of the Yajishan 330 fire at different times in the embodiment of the present invention are (a) fire behavior simulation diagram at 13 o'clock, (b) fire spread behavior diagram at 15 o'clock, and (c) fire behavior simulation diagram at 17 o'clock;

[0050] Figure 9 17:00 fire behavior simulation diagrams for three prevention and control scenarios in the embodiment of the present invention: (a) fire behavior simulation diagram for scenario one, (b) fire behavior simulation diagram for scenario two, and (c) fire behavior simulation diagram for scenario three;

[0051] Figure 10 The risk assessment diagrams of the Yajishan 330 fire at different times in the embodiment of the present invention are (a) the risk assessment diagram at 12:00, (b) the risk assessment diagram at 15:00, and (c) the risk assessment diagram at 17:00;

[0052] Figure 11 17-minute risk assessment diagrams for three prevention and control scenarios in the embodiment of the present invention: (a) risk assessment diagram for scenario one, (b) risk assessment diagram for scenario two, and (c) risk assessment diagram for scenario three;

[0053] Figure 12 Flowchart of the entire invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] See also Figures 1 to 5 The present invention provides an embodiment of a scenario-oriented forest fire dynamic risk assessment method, the method comprising:

[0056] Based on the construction of a cellular automaton forest fire behavior simulation model, the forest fire prevention and control area is divided into several cells based on the needs of risk assessment. The cell states are defined according to the different fire behaviors during the forest fire burning process. The forest fire spread rate is calculated using Wang Zhengfei's forest fire spread model. Based on the states of adjacent cells and the advancement of spread time, the state transition rules of different cells are defined, thereby realizing real-time fire behavior simulation during the forest fire spread process.

[0057] The construction of a semantically driven forest fire scenario simulation knowledge graph was completed, guided by forest fire business and related theories, as well as the SSN and TIME ontology standards published by W3C. The theoretical framework of the forest fire scenario simulation knowledge graph was constructed. Semantic modeling was used to integrate and dynamically update multi-source information in the experimental area, and a cellular automation-based forest fire behavior simulation method was encapsulated to achieve the construction and update of the forest fire scenario simulation knowledge graph.

[0058] The construction of a comprehensive dynamic forest fire risk assessment model is based on the three risk factors of fire hazard, exposure and vulnerability of the hazard-bearing body. Real-time fire environmental factors such as fire spread rate, fire status and population density are introduced to assess these three risk factors. The dynamic forest fire risk assessment model is completed by constructing a comprehensive risk assessment index.

[0059] Dynamic risk assessment based on scenario simulation: The semantic prevention and control simulation scenario is set in the forest fire scenario simulation knowledge graph, and the above-mentioned cellular automaton forest fire behavior simulation model is called to realize real-time fire behavior simulation. Finally, based on the dynamic forest fire risk assessment model, real-time risk assessment indicators are introduced to realize dynamic assessment of fire risks at different times and prevention and control scenarios;

[0060] Through a comparative experiment simulating a real fire, the feasibility and prediction accuracy of the scenario-oriented dynamic risk assessment method for forest fires were evaluated.

[0061] See also Figure 1and Figure 2 , when the cell state = 0, that is, the cell state S0: the cell is in the fire isolation area, where there is no combustible material in the fire isolation area, and the cell state remains unchanged;

[0062] When the cell state = 1, that is, the cell state S1: the combustible material in the cell is in the state of not being ignited;

[0063] When the cell state = 2, that is, the cell state S2: the combustible material in the cell has just started to burn, and the combustible material is in the internal development state and has no ability to ignite other cells outward;

[0064] When the cell state = 3, that is, the cell state S3: the combustible material in the cell is in a complete combustion state, and the combustible material begins to diffuse outward and has the ability to ignite the remaining cells;

[0065] When the cell state = 4, that is, the cell state S4: the combustible material in the cell is in a burned state.

[0066] Among them, the transition rule of cellular state S0 to S1 defines the fire-isolated area where cells cover water areas and roads as S0, and the non-fire-isolated area where cells cover forests as S1; the transition rule of cellular state S1 to S2 defines that the combustible but unburned S1 state cells are ignited by the influence of the surrounding completely burned S3 state cells. When the cumulative value of the S1 state cells exceeds the threshold, they are ignited. The threshold is 0.65, and the unburned S1 state cells are ignited and converted to the S2 state.

[0067]

[0068] in is the cumulative value of the ignition transfer from the adjacent cells to the current cell after the next time interval Δt; is the spreading rate of cells with spreading ability, i.e., cells in the complete combustion state S3, in the Moore neighborhood of the current position; Δt is the simulation time step and is set to 1 min, L is the horizontal distance between cells, and i and j are the coordinate indices of the current cell;

[0069] R o : initial spreading speed; K s : Combustible correction factor; K w : wind speed correction coefficient; Terrain correction factor.

[0070] The transition rule from cell state S2 to S3 defines the time process of the cell from internal combustion S2 state to complete combustion S3 state as Δt1. Δt1 is used to describe the internal forest fire spread process of the cell. When the burning duration of the cell in S2 state is t cost When it is greater than Δt1, the cell state is converted from S2 to S3;

[0071]

[0072] where R IN is the internal forest fire spread rate of the cell. The cells in S2 are transformed into S3 after Δt1 and have the ability to spread to the neighborhood.

[0073] The transition rule from cell state S3 to S4 defines the process of transitioning the cell from the fully burned S3 state to the extinguished S4 state as Δt2. Δt2 depends on the continuous combustion of the fuel, external fire extinguishing measures and environmental conditions. The duration of flame combustion is related to the time Δt required for complete combustion of the fuel. burn and fire extinguishing intervention time Δt inter Related;

[0074] Δt2=Δt burn +Δt inter ;

[0075]

[0076] Where W is the fuel load within the cell and is expressed in kg / m 2 , m is the mass burning rate of the combustible material, and the unit is kg / (m 2 ·min).

[0077] See also Figure 3 Using Protégé software, the team completed ontology modeling, including a forest fire element monitoring module and forest fire knowledge elements. The Neo4j graph database was then used to integrate and formalize ontology associations, thereby constructing and shaping a knowledge graph for forest fire scenario simulation. Based on multi-source data such as remote sensing, meteorology, and ground surveys, GIS tools and Java coding were used to extract fire environment scenario characteristics such as weather conditions, terrain features, and vegetation types using geographic cells as units. Dynamic updates of surface elements and knowledge element entities in the forest fire scenario simulation knowledge graph were achieved through a Java interface, completing the construction of forest fire environment scenarios.

[0078] Refer to 4 to complete the forest fire scenario simulation. This involves setting the initial conditions for the fire, determining the fire source location and ignition time, and defining the types of control measures, including physical isolation measures and firefighting force deployment. This also includes setting the time of fire discovery and the time when emergency rescue and firefighting arrive at the scene. Emergency control measures only include isolation zones, firefighters, fire trucks, and water trucks. Isolation zones must be pre-set based on the fire's development.

[0079] See also Figure 5The dynamic forest fire risk assessment model is based on the three risk factors of fire hazard, exposure and vulnerability of the disaster-bearing body, and constructs an indicator system for forest fire risk assessment. In this indicator system, comprehensive risk is defined as a Class A indicator, hazard, exposure and vulnerability are defined as Class B risk indicators, and real-time fire behavior, terrain, vegetation cover and other fire environment factors are classified as Class C indicators. In the risk assessment process, by introducing and dynamically updating real-time data such as fire behavior and population distribution into the indicator system, a dynamic assessment of forest fire risk can be achieved;

[0080]

[0081]

[0082] where R t is the A-level indicator of comprehensive risk at time t, H t 、E t 、V t are the risk level B indicators of danger, exposure and vulnerability at time t, BI t and CI t are the B and C level indicators of risk at time t, respectively, W i is the weight of the C-level indicator to the B-level indicator, and n is the number of C-level indicators participating in the evaluation of the B-level indicators;

[0083] The B-level indicator of hazard is calculated by C-level indicators such as distance from the fire scene, combustion state, and burning rate, with weights set at 0.4, 0.3, and 0.3 respectively. The B-level indicator of exposure is calculated by C-level indicators such as population density, distance from buildings, distance from key protection facilities, vegetation coverage, and GDP, with weights set at 0.3, 0.2, 0.25, 0.125, and 0.125 respectively. The B-level indicator of vulnerability is calculated by C-level indicators such as type of flammable material and duration of fire, with weights set at 0.55 and 0.45 respectively.

[0084] The stage values ​​of A-level indicators, B-level indicators and C-level indicators are divided into five levels from I to V according to the degree of contribution to the impact of fire. The five levels I to V are assigned values ​​of 0.2, 0.4, 0.6, 0.8 and 1 respectively. The higher the level of the risk indicator, the greater the impact of fire.

[0085] The stage values ​​of A-level indicators, B-level indicators and C-level indicators are divided into five levels from I to V according to the degree of contribution to the impact of fire. The five levels I to V are assigned values ​​of 0.2, 0.4, 0.6, 0.8 and 1 respectively. The higher the level of the risk indicator, the greater the impact of fire.

[0086] The C-level indicators for evaluating risk indicators and their classification and assignment are shown in Table 1 below:

[0087] Table 1

[0088]

[0089]

[0090] The C-level indicators for evaluating exposure indicators and their classification and assignment are shown in Table 2:

[0091] Table 2

[0092]

[0093] The C-level indicators for vulnerability assessment and their classification and assignment are shown in Table 3 below:

[0094] Table 3

[0095] Flammable material type Fire duration Index level Assignment other 0-10 I 0.2 arable land 10-30 II 0.4 shrubland 30-60 III 0.6 broad-leaved forest 60-90 IV 0.8 Coniferous forest, buildings >90 V 1

[0096] Since the contribution of each C-level indicator to the B-level risk indicators of hazard, exposure, and vulnerability is different, it is necessary to set a weight for each C-level indicator. The weight setting of C-level indicators is shown in Table 4 below:

[0097] Table 4

[0098]

[0099]

[0100] Based on the evaluation of the importance of each indicator, the scores are weighted and averaged to obtain the weight of each indicator. Then, during the dynamic risk assessment process, personnel dynamically adjust the weight through real-time data feedback to make the model more accurate.

[0101] The steps of the dynamic risk assessment method based on scenario simulation are as follows: first, integrate multi-source data of the experimental area to complete the dynamic update of the forest fire scenario simulation knowledge graph, and set the fire simulation scenario based on the knowledge graph; then, call the above-mentioned cellular automaton forest fire behavior simulation model according to different fire simulation scenarios to realize real-time fire behavior simulation; finally, based on the dynamic risk assessment model of forest fire, introduce real-time risk assessment indicators to evaluate the fire risk at different times and prevention and control scenarios.

[0102] Example:

[0103] This paper takes the "3.30" fire that occurred in Yaji Mountain Scenic Area on March 30, 2019 as an example, and selects Yaji Mountain Scenic Area and its surrounding areas as the research area (such as Figure 6By implementing semantic-driven fire spread simulation to construct a real-life fire disaster scenario, and comparing the real-time fire spread simulation with the relevant reports at the time of the fire, the proposed method is verified to be effective in predicting the spread of fires in WUI, providing a scientific basis for fire management and emergency decision-making.

[0104] The simulation scenarios at 13:00, 15:00, and 17:00 after the fire broke out without emergency measures and under three emergency prevention and control measures (the three prevention and control scenarios can be seen in Table 5, and the knowledge graph prevention and control scenario settings can be seen in Figure 7 ), the simulation results are as follows Figure 8 and Figure 9 As shown in the figure, at 13:00, the fire spread to Pinggu District, and at around 17:00, the fire approached the thousand-year-old Bixia Yuanjun Temple. The simulation results are basically consistent with the fire spread trend in the news reports. It can be considered that the model can simulate the diffusion process in the study area relatively accurately.

[0105] Table 5

[0106]

[0107] By implementing the fire behavior data and other data at different times of the simulation, the fire risk at 12:00 before the fire and at 15:00 and 17:00 after the fire was evaluated (such as Figure 10 ), and the fire risk at 17:00 under the three prevention and control scenarios set above, such as Figure 11 As shown in this paper, the fire risk ranges from 0 to 1, and the higher the value, the greater the impact of fire. Figure 10 and Figure 11 Through the dynamic risk assessment under different prevention and control scenarios, it can be clearly seen that reasonable and effective prevention and control measures can significantly reduce the risk of WUI fires. In actual emergency decision-making, the dynamic risk assessment system in this article can be used to evaluate regional fire risks under different prevention and control measures scenarios and select the most appropriate prevention and control strategy to minimize the damage caused by fire.

[0108] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A scenario-oriented dynamic forest fire risk assessment method, characterized in that: The method comprises: Based on the construction of a cellular automaton forest fire behavior simulation model, the forest fire prevention and control area is divided into several cells based on the needs of risk assessment. The cell states are defined according to the different fire behaviors during the forest fire burning process. The forest fire spread rate is calculated using Wang Zhengfei's forest fire spread model. Based on the states of adjacent cells and the advancement of spread time, different cell state transition rules are defined, thereby realizing real-time fire behavior simulation during the forest fire spread process. The construction of a semantically driven forest fire scenario simulation knowledge graph was completed, guided by forest fire business and related theories, as well as the SSN and TIME ontology standards published by W3C. The theoretical framework of the forest fire scenario simulation knowledge graph was constructed. Semantic modeling was used to integrate and dynamically update multi-source information in the experimental area, and a cellular automation-based forest fire behavior simulation method was encapsulated to achieve the construction and update of the forest fire scenario simulation knowledge graph. The construction of a comprehensive dynamic forest fire risk assessment model is based on the three risk factors of fire hazard, exposure and vulnerability of the hazard-bearing body. It also incorporates real-time fire environmental factors such as fire spread rate, fire status, and population density to assess these three risk factors. The dynamic forest fire risk assessment model is completed by constructing comprehensive risk assessment indicators. Dynamic risk assessment based on scenario simulation: The semantic prevention and control simulation scenario is set in the forest fire scenario simulation knowledge graph, and the above-mentioned cellular automaton forest fire behavior simulation model is called to realize real-time fire behavior simulation. Finally, based on the dynamic forest fire risk assessment model, real-time risk assessment indicators are introduced to realize dynamic assessment of fire risks at different times and prevention and control scenarios; Through a comparative experiment simulating a real fire, the feasibility and prediction accuracy of the scenario-oriented dynamic risk assessment method for forest fires were evaluated.

2. A scenario-oriented dynamic forest fire risk assessment method according to claim 1, characterized in that: There are five different fire behavior cellular states in the method for constructing the cellular automaton forest fire behavior simulation model. The fire behavior state characteristics presented by the cells are as follows: When the cell state = 0, that is, the cell state S0: the cell is in the fire isolation area, where there is no combustible material in the fire isolation area, and the cell state remains unchanged; When the cell state = 1, that is, the cell state S1: the combustible material in the cell is in the state of not being ignited; When the cell state = 2, that is, the cell state S2: the combustible material in the cell has just started to burn, and the combustible material is in the internal development state and has no ability to ignite other cells outward; When the cell state = 3, that is, the cell state S3: the combustible material in the cell is in a complete combustion state, and the combustible material begins to diffuse outward and has the ability to ignite the remaining cells; When the cell state = 4, that is, the cell state S4: the combustible material in the cell is in a burned state.

3. A scenario-oriented dynamic forest fire risk assessment method according to claim 2, characterized in that: The rule for converting the cell state S0 to S1 defines the fire-isolated area where cells cover water areas and roads as S0, and the non-fire-isolated area where cells cover forests as S1.

4. The scenario-oriented dynamic forest fire risk assessment method according to claim 3, characterized in that: The rule for the transition from cell state S1 to S2 is that the combustible but unburned S1 state cell is ignited by the surrounding fully burned S3 state cells. When the cumulative value of the S1 state cell transmission exceeds a threshold, the cell is ignited. The threshold is 0.

65. The unburned S1 state cell is ignited and converted to S2 state. in is the cumulative value of the ignition transfer from the adjacent cells to the current cell after the next time interval Δt; is the spreading rate of cells with spreading ability, i.e., cells in the complete combustion state S3, in the Moore neighborhood of the current position; Δt is the simulation time step and is set to 1 min, L is the horizontal distance between cells, and i and j are the coordinate indices of the current cell; R o : initial spreading speed; K s : Combustible correction factor; K w : wind speed correction coefficient; Terrain correction factor.

5. The scenario-oriented dynamic forest fire risk assessment method according to claim 4, characterized in that: The cell state S2 to S3 transition rule defines the time process of the cell from the internal combustion S2 state to the complete combustion S3 state as Δt1, and the Δt1 is used to describe the internal forest fire spread process of the cell. When the combustion duration of the S3 state cell is t cost When it is greater than Δt1, the cell state is converted from S2 to S3; where R IN is the internal forest fire spread rate of the cell. The cells in S2 are transformed into S3 after Δt1 and have the ability to spread to the neighborhood.

6. The scenario-oriented dynamic forest fire risk assessment method according to claim 5, characterized in that: The cell state S3 to S4 transition rule defines the process of transitioning the cell from the fully burned S3 state to the extinguished S4 state as Δt2, which depends on the continuous combustion of the fuel, external fire extinguishing measures and environmental conditions. The flame combustion duration is related to the time Δt required for complete combustion of the fuel. burn and fire extinguishing intervention time Δt inter Related; Δt2=Δt burn +Δt inter ; Where W is the fuel load within the cell and is expressed in kg / m 2 , m is the mass burning rate of the combustible material, and the unit is kg / (m 2 ·min).

7. The scenario-oriented dynamic forest fire risk assessment method according to claim 6, characterized in that: The dynamic forest fire risk assessment model is based on three risk factors: fire hazard, exposure, and vulnerability of the hazard-bearing body. It constructs an indicator system for forest fire risk assessment. In this indicator system, comprehensive risk is defined as a Class A indicator, while hazard, exposure, and vulnerability are defined as Class B indicators. Fire environment factors, including real-time fire behavior, terrain, and vegetation cover, are classified as Class C indicators. During the risk assessment process, real-time data, including fire behavior and population distribution, is introduced and dynamically updated in the indicator system to achieve a dynamic assessment of forest fire risk. where R t is the A-level indicator of comprehensive risk at time t, H t 、E t 、V t are the risk level B indicators of danger, exposure and vulnerability at time t, BI t and CI t are the B and C level indicators of risk at time t, respectively, W i is the weight of the C-level indicator to the B-level indicator, and n is the number of C-level indicators participating in the evaluation of the B-level indicators; The danger level in the B-level index is calculated by the C-level index including the distance from the fire scene, combustion state, and combustion rate, with the weights set to 0.4, 0.3, and 0.3 respectively; The exposure in the B-level indicator is calculated by the C-level indicators including population density, distance to buildings, distance to key protected facilities, vegetation coverage, and GDP, with the weights set as 0.3, 0.2, 0.25, 0.125, and 0.125 respectively; The B-level indicator vulnerability is calculated based on the C-level indicators including the type of flammable material and the duration of ignition, with weights set at 0.55 and 0.45 respectively.

8. The scenario-oriented dynamic forest fire risk assessment method according to claim 7, characterized in that: The stage values ​​of the A-level indicators, B-level indicators and C-level indicators are divided into five levels from I to V according to the degree of contribution to the impact of fire. The five levels I to V are assigned values ​​of 0.2, 0.4, 0.6, 0.8 and 1 respectively. The higher the level of the risk indicator, the greater the impact of the fire.

9. The scenario-oriented dynamic forest fire risk assessment method according to claim 8, characterized in that: The fire simulation scenario setting includes the setting of emergency resources including the fire start time, fire end time, emergency rescue time, fire point, isolation zone, firefighters, fire trucks, and number of vehicles.

10. The scenario-oriented dynamic forest fire risk assessment method according to claim 9, characterized in that: The specific steps of the dynamic risk assessment method based on scenario simulation are as follows: first, integrate multi-source data of the experimental area to complete the dynamic update of the forest fire scenario simulation knowledge graph, and set the fire simulation scenario based on the knowledge graph; then, call the above-mentioned cellular automaton forest fire behavior simulation model according to different fire simulation scenarios to realize real-time fire behavior simulation; finally, based on the forest fire dynamic risk assessment model, introduce real-time risk assessment indicators to evaluate the fire risk at different times and prevention and control scenarios.

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