A method and system for forest fire risk assessment

Abstract

This application relates to the field of risk assessment technology, and in particular to a method and system for forest fire risk assessment. The method includes: acquiring fire information of a target forest, including hazard hazard, exposure of affected bodies, and vulnerability of affected bodies; acquiring remote sensing data of the target forest, and obtaining target information of the target forest based on the remote sensing data; correcting the exposure of affected bodies based on the target information and generating a corrected exposure; obtaining an initial risk index based on hazard hazard, corrected exposure, and vulnerability of affected bodies; obtaining a risk mitigation factor and a risk growth factor based on the fire information and target information; obtaining a comprehensive risk index based on the initial risk index, risk mitigation factor, and risk growth factor; and obtaining a risk level based on the comprehensive risk index. This application helps improve the accuracy and reliability of the assessment.

Description

Technical Field

[0001] This application relates to the field of risk assessment technology, and in particular to a method and system for forest fire risk assessment. Background Technology

[0002] Forest fires, as a high-intensity natural disturbance, are destructive not only in the instantaneous release of enormous energy that wreaks havoc on forest ecosystems, but also in triggering a chain of ecological disasters, including a sharp decline in biodiversity, soil instability, disruption of watershed hydrological functions, and massive carbon leaks, posing a serious threat to regional ecological security and sustainable development. To overcome the limitations of traditional passive response-based fire prevention models, it is essential to establish a risk assessment system based on multi-source data fusion and mechanistic models to achieve precise quantitative analysis of the spatiotemporal patterns of fire risk, fire behavior characteristics, and potential disaster chains. This work is the core foundation for building a modern forest fire management system, providing a scientific basis for precise management of combustibles, optimized layout of fire prevention infrastructure, pre-positioning of emergency resources, and the formulation of tiered response strategies. Therefore, accurately assessing forest fire risks is crucial for developing effective fire prevention measures and emergency plans.

[0003] Existing forest fire risk assessment methods often focus on single-factor analysis, such as relying solely on meteorological data (e.g., temperature, humidity, wind speed) or historical fire frequency for risk assessment. This results in limitations in assessment dimensions and insufficient dynamic adaptability. While some methods consider the characteristics of the affected bodies, they fail to effectively integrate real-time or near-real-time geospatial information, leading to discrepancies between the assessment of the exposure of affected bodies and actual objective conditions such as terrain and vegetation distribution, thus resulting in inaccurate assessment results.

[0004] Therefore, there is an urgent need for a forest fire risk assessment method and system that can comprehensively consider multiple factors and dynamically correct the assessment results in order to improve the accuracy and reliability of the assessment. Summary of the Invention

[0005] To help improve the accuracy and reliability of the assessment, this application provides a method and system for forest fire risk assessment.

[0006] Firstly, this application provides a forest fire risk assessment method, which adopts the following technical solution:

[0007] A forest fire risk assessment method includes:

[0008] Obtain fire information of the target forest, including the hazard risk, exposure of the affected areas, and vulnerability of the affected areas;

[0009] Acquire remote sensing data of the target forest, and obtain target information of the target forest based on the remote sensing data;

[0010] Based on the target information, the exposure of the disaster-bearing body is corrected, and a corrected exposure is generated;

[0011] Based on the disaster-causing hazard, the modified exposure, and the vulnerability of the disaster-bearing body, an initial risk index is obtained;

[0012] Based on the fire information and the target information, risk mitigation factors and risk growth factors are obtained;

[0013] Based on the initial risk index, the risk mitigation factor, and the risk growth factor, a comprehensive risk index is obtained;

[0014] The risk level is obtained based on the comprehensive risk index.

[0015] By employing the aforementioned technical solution, fire information such as the hazard risk, exposure level of affected bodies, and vulnerability of affected bodies in the target forest is first acquired. Simultaneously, remote sensing data of the forest is acquired, and target information is extracted from it. Next, the exposure level of affected bodies is corrected based on the target information to generate a corrected exposure level. Then, the hazard risk, corrected exposure, and vulnerability of affected bodies are combined to obtain an initial risk index. Subsequently, risk mitigation factors and risk growth factors are determined based on the fire information and target information. Then, the initial risk index and these two factors are combined to derive a comprehensive risk index. Finally, the risk level is determined based on the comprehensive risk index. By simultaneously acquiring multi-dimensional fire information such as hazard risk, exposure level of affected bodies, and vulnerability of affected bodies, a breakthrough is achieved. This approach overcomes the limitations of single-factor analysis, enabling a comprehensive assessment of forest fire risk. It incorporates target information extracted from remote sensing data to correct exposure levels of affected bodies, effectively combining real-time or near-real-time geospatial information. This makes the assessment of exposure levels more closely aligned with actual terrain, vegetation distribution, and other objective conditions, reducing assessment bias. Furthermore, by adjusting the initial risk index using risk mitigation and risk growth factors, the adaptability of the assessment results to dynamic changes is further enhanced. The resulting comprehensive risk index and risk level are more accurate and reliable, providing a more scientific and precise basis for formulating forest fire prevention and control measures and optimizing emergency plans, thus contributing to improved effectiveness and targeted measures for forest fire prevention.

[0016] Optionally, obtaining the exposure of the disaster-bearing body includes:

[0017] Obtain the resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure corresponding to the target forest;

[0018] The resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure are standardized, and the corresponding standardized values ​​are obtained.

[0019] Obtain the target weights corresponding to the resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure, respectively;

[0020] Based on the standardized numerical values ​​and the corresponding target weights, the exposure degree of the disaster-bearing body is obtained, and the exposure degree of the disaster-bearing body satisfies the following calculation formula:

[0021]

[0022] Wherein, EI is the exposure index of the disaster-bearing body, E i W is the standardized value corresponding to the i-th disaster-bearing body exposure index. i is the target weight of the i-th disaster-bearing body exposure index, and n is the number of disaster-bearing body exposure indexes.

[0023] Optionally, the step of correcting the exposure level of the disaster-bearing body based on the target information and generating a corrected exposure level includes:

[0024] Based on the target information, obtain the layout information corresponding to the target forest;

[0025] Based on the layout information, the terrain distribution information of the target forest is obtained;

[0026] Based on the terrain distribution information, the fire propagation resistance is obtained;

[0027] Based on the fire behavior propagation resistance, a corrected exposure rate is obtained, which satisfies the following calculation formula:

[0028]

[0029] Where Ex is the corrected exposure, E is the exposure of the affected body, and F is the resistance to the propagation of fire behavior.

[0030] Optionally, obtaining the fire propagation resistance based on the terrain distribution information includes:

[0031] Based on the terrain distribution information, the resistance barrier attributes are obtained;

[0032] Based on the aforementioned resistance barrier attributes, the resistance barrier type and resistance barrier distribution characteristics are obtained;

[0033] Based on the distribution characteristics of the aforementioned resistance barriers, the distribution pattern and area percentage are obtained;

[0034] Based on the type of resistance barrier, the distribution pattern, and the area ratio, the resistance to fire propagation is obtained.

[0035] Optionally, obtaining the fire propagation resistance based on the type of resistance barrier, the distribution pattern, and the area ratio includes:

[0036] Based on the aforementioned resistance barrier type, obtain the corresponding basic resistance coefficient;

[0037] Based on the aforementioned distribution pattern, obtain the distribution correction coefficient;

[0038] Based on the basic drag coefficient, the distribution correction coefficient, and the area ratio, the fire behavior propagation resistance is obtained, and the fire behavior propagation resistance satisfies the following calculation formula:

[0039]

[0040] Among them, T i Let D be the basic drag coefficient of the i-th type of resistance barrier. i S is the distribution correction factor for the i-th type of resistance barrier. i This represents the area percentage of this type of resistance barrier within the smallest unit.

[0041] Optionally, obtaining the distribution correction coefficient based on the distribution scenario includes:

[0042] Based on the distribution pattern, the target width of the resistance barrier is obtained;

[0043] Based on the target information, the target height of the target vegetation within the target area is obtained;

[0044] Obtain the target distance between the target vegetation and the resistance barrier, and obtain the overall height based on the target height and the target distance;

[0045] If the overall height is less than the target width, a first distribution correction coefficient is obtained based on the target width and the overall height, and this first distribution correction coefficient is used as the distribution correction coefficient. The first distribution correction coefficient satisfies the following calculation formula:

[0046]

[0047] If the overall height is greater than or equal to the target width, then a second distribution correction coefficient is obtained based on the target width and the overall height, and this second distribution correction coefficient is used as the distribution correction coefficient. The second distribution correction coefficient satisfies the following calculation formula:

[0048]

[0049] Where D is the basic correction coefficient, k is the width advantage influence coefficient, W is the target width of the resistance barrier, and H is the comprehensive height of the target vegetation.

[0050] Optionally, based on the fire information and the target information, obtaining the risk growth factor includes:

[0051] Based on the vulnerability of the disaster-bearing body and the target information, a basic risk coefficient is obtained;

[0052] Based on the target information, obtain the activity risk amplification coefficient and the regional correlation coefficient;

[0053] Based on the basic risk coefficient, the activity risk amplification coefficient, and the regional correlation coefficient, the risk growth factor is obtained.

[0054] Optionally, based on the target information, obtaining the activity risk amplification factor includes:

[0055] Based on the target information, obtain the regional information of the target forest;

[0056] Based on the aforementioned regional information, obtain the regional personnel information of the target region;

[0057] Based on the personnel information of the region, obtain the number of personnel, personnel structure, and personnel characteristics;

[0058] Based on the aforementioned personnel characteristics, obtain personnel interests;

[0059] Based on the number of people and their interests, obtain the percentage of people with different interests and their corresponding risk weights;

[0060] Based on the personnel structure, the proportion of the number of people, and the corresponding risk weight, the activity risk amplification coefficient is obtained.

[0061] Secondly, this application also discloses a forest fire risk assessment system, which adopts the following technical solution:

[0062] A forest fire risk assessment system includes:

[0063] The first acquisition module is used to acquire fire information of the target forest, including the hazard level, exposure of the affected body, and vulnerability of the affected body.

[0064] The second acquisition module is used to acquire remote sensing data of the target forest and acquire target information of the target forest based on the remote sensing data;

[0065] The correction module is used to correct the exposure of the disaster-bearing body based on the target information and generate a corrected exposure.

[0066] The third acquisition module is used to acquire an initial risk index based on the disaster hazard, the modified exposure, and the vulnerability of the disaster-bearing body.

[0067] The fourth acquisition module is used to acquire risk mitigation factors and risk growth factors based on the fire information and the target information;

[0068] The fifth acquisition module is used to acquire a comprehensive risk index based on the initial risk index, the risk offsetting factor, and the risk growth factor;

[0069] The sixth acquisition module is used to acquire the risk level based on the comprehensive risk index.

[0070] By employing the aforementioned technical solution, fire information such as the hazard risk, exposure level of affected bodies, and vulnerability of affected bodies in the target forest is first acquired. Simultaneously, remote sensing data of the forest is acquired, and target information is extracted from it. Next, the exposure level of affected bodies is corrected based on the target information to generate a corrected exposure level. Then, the hazard risk, corrected exposure, and vulnerability of affected bodies are combined to obtain an initial risk index. Subsequently, risk mitigation factors and risk growth factors are determined based on the fire information and target information. Then, the initial risk index and these two factors are combined to derive a comprehensive risk index. Finally, the risk level is determined based on the comprehensive risk index. By simultaneously acquiring multi-dimensional fire information such as hazard risk, exposure level of affected bodies, and vulnerability of affected bodies, a breakthrough is achieved. This approach overcomes the limitations of single-factor analysis, enabling a comprehensive assessment of forest fire risk. It incorporates target information extracted from remote sensing data to correct exposure levels of affected bodies, effectively combining real-time or near-real-time geospatial information. This makes the assessment of exposure levels more closely aligned with actual terrain, vegetation distribution, and other objective conditions, reducing assessment bias. Furthermore, by adjusting the initial risk index using risk mitigation and risk growth factors, the adaptability of the assessment results to dynamic changes is further enhanced. The resulting comprehensive risk index and risk level are more accurate and reliable, providing a more scientific and precise basis for formulating forest fire prevention and control measures and optimizing emergency plans, thus contributing to improved effectiveness and targeted measures for forest fire prevention.

[0071] In summary, this application includes the following beneficial technical effects:

[0072] By simultaneously acquiring multi-dimensional fire information, including hazard hazard, exposure of affected bodies, and vulnerability of affected bodies, this approach overcomes the limitations of single-factor analysis and achieves a comprehensive assessment of forest fire risk. Introducing target information extracted from remote sensing data and using it to correct exposure of affected bodies effectively combines real-time or near-real-time geospatial information, making the assessment of exposure more closely aligned with actual terrain, vegetation distribution, and other objective conditions, thus reducing assessment bias. Furthermore, adjusting the initial risk index through risk mitigation and risk growth factors further enhances the adaptability of the assessment results to dynamic changes. The resulting comprehensive risk index and risk level are more accurate and reliable, providing a more scientific and precise basis for formulating forest fire prevention and control measures and optimizing emergency plans, thereby improving the effectiveness and targeted nature of forest fire prevention. Attached Figure Description

[0073] Figure 1This is a flowchart of the main process of a forest fire risk assessment method according to an embodiment of this application;

[0074] Figure 2 This is a flowchart of steps S201 to S204;

[0075] Figure 3 This is a flowchart of steps S301 to S304;

[0076] Figure 4 This is a flowchart of steps S401 to S404;

[0077] Figure 5 This is a flowchart of steps S501 to S503;

[0078] Figure 6 This is a flowchart of steps S601 to S605;

[0079] Figure 7 This is a flowchart of steps S701 to S703;

[0080] Figure 8 This is a flowchart of steps S801 to S806;

[0081] Figure 9 This is a block diagram of a forest fire risk assessment system according to an embodiment of this application.

[0082] Explanation of reference numerals in the attached figures:

[0083] 1. First acquisition module; 2. Second acquisition module; 3. Correction module; 4. Third acquisition module; 5. Fourth acquisition module; 6. Fifth acquisition module; 7. Sixth acquisition module. Detailed Implementation

[0084] Firstly, this application discloses a method for assessing forest fire risk.

[0085] Reference Figure 1 A forest fire risk assessment method, comprising steps S101 to S107:

[0086] Step S101: Obtain fire information of the target forest, including the hazard level, exposure of the affected areas, and vulnerability of the affected areas.

[0087] Specifically, in this embodiment, the hazard risk refers to the potential danger of causing forest fires. Its assessment indicators include meteorological factors (such as the number of consecutive days without precipitation, average wind speed, and relative humidity) and ignition source factors (such as the density of human-caused fires and the frequency of lightning strikes). In this embodiment, meteorological data for the past 30 days can be obtained from the local meteorological department's database, and the distribution of fire sources can be statistically analyzed through forest area video monitoring systems and data reported by patrol personnel. The exposure of disaster-bearing bodies refers to the number and distribution of various disaster-bearing bodies in the target forest that may be threatened by fire. This can be determined in advance in conjunction with forestry... Resource survey data (such as tree species, quantity and distribution), property registration information of buildings in forest areas (such as building location, area and use), population census data and registration records of floating population, and economic data related to forest areas in local economic statistical yearbooks; the vulnerability of disaster-bearing bodies indicates the degree to which disaster-bearing bodies are easily damaged under the influence of fire. The fire resistance of vegetation can be determined by testing the physical characteristics of vegetation such as moisture content and ignition point. The fire resistance rating of buildings can be assessed based on the building materials and fire protection facilities. Relevant data can be obtained through test reports from professional testing institutions and building completion acceptance data.

[0088] Step S102: Obtain remote sensing data of the target forest, and obtain target information of the target forest based on the remote sensing data.

[0089] Specifically, in this embodiment, the remote sensing data is image data of the target forest area acquired through satellite remote sensing (such as Landsat series satellites and Gaofen satellites) or UAV remote sensing equipment. In this embodiment, satellite remote sensing data can be applied for from the National Remote Sensing Data Sharing Platform, or data can be collected by using UAVs for on-site aerial photography of the target forest. The target information acquired based on the remote sensing data includes topographic distribution information (such as altitude, slope, and aspect), vegetation information (such as vegetation type, vegetation coverage, and vegetation height), surface cover information (such as the location and extent of resistance barriers such as rivers, roads, and rocky areas), and traces of human activity (such as vehicle tracks on campsites and forest roads). The remote sensing images are interpreted, classified, and extracted using remote sensing image processing software (such as ENVI and ArcGIS) to obtain the aforementioned target information.

[0090] Step S103: Based on the target information, correct the exposure of the disaster-bearing body and generate the corrected exposure.

[0091] Specifically, in this embodiment, based on the terrain distribution information (such as the distribution of mountains, plains, and canyons) and resistance barrier information (such as the width and length of rivers, and the grade and direction of roads) in the target information, the propagation pattern of fire under the influence of different terrains and barriers is analyzed. For example, in mountainous terrain, fire spreads faster uphill, while resistance barriers such as rivers and roads hinder its spread. By establishing a fire behavior propagation model and inputting terrain parameters (slope, aspect) and resistance barrier parameters (type, size, distribution), the fire behavior propagation resistance is calculated. The larger the resistance value, the more difficult it is for the fire to spread in that area. The initial exposure of the affected body is then corrected based on the fire behavior propagation resistance. For areas with high fire behavior propagation resistance, even if there are many affected bodies, their actual exposure to fire risk will be reduced, thus generating a corrected exposure value that better reflects the actual situation.

[0092] Step S104: Obtain the initial risk index based on the disaster hazard, the adjusted exposure, and the vulnerability of the disaster-bearing body.

[0093] Specifically, in this embodiment, the weights of disaster hazard, modified exposure, and vulnerability of the affected body are first determined. The weights can be determined using the analytic hierarchy process (AHP), for example, by inviting experts in forestry science, fire safety, and disaster management to score the importance of each factor in forest fire risk assessment, or by using existing data from relevant technologies. Through steps such as constructing a judgment matrix and conducting consistency checks, reasonable weight values ​​are finally determined (e.g., disaster hazard weight 0.4, modified exposure weight 0.3, and vulnerability of the affected body weight 0.3). Then, the standardized values ​​of disaster hazard, modified exposure, and vulnerability of the affected body (all mapped to the [0,1] interval) are multiplied by their corresponding weights, and the products are summed to obtain the initial risk index. The initial risk index ranges from [0,1], with a higher value indicating a higher initial forest fire risk assessment.

[0094] Step S105: Based on fire information and target information, obtain risk mitigation factors and risk growth factors.

[0095] Specifically, in this embodiment, the acquisition of risk mitigation factors needs to comprehensively consider factors such as the availability of fire prevention facilities in the target forest (e.g., the width, length, and distribution density of firebreaks, the location, number, and service radius of forest fire stations), fire prevention management measures (e.g., the number of patrol personnel, patrol frequency, and the coverage and effectiveness of fire prevention publicity and education); by conducting on-site surveys and statistically analyzing relevant parameters of fire prevention facilities, combined with the work records of fire prevention management departments, the fuzzy comprehensive evaluation method is used to calculate the risk mitigation factor, with a value range of [0, 0.5]. The larger the value, the stronger the ability to mitigate risks. Obtaining risk growth factors requires analyzing high-risk factors in the target forest, such as the number of tourists entering the forest area during holidays and their activity range (through statistics from scenic area ticket sales data and forest area entrance monitoring data), the coverage and distribution of flammable vegetation (such as pine needles, dead branches and fallen leaves) (obtained through remote sensing image interpretation and on-site sampling), and the difficulty of fire source control (such as the fire-using habits of residents around the forest area and the analysis of the causes of past fire accidents). By using a weighted summation method, the degree of influence of each high-risk factor is quantified to obtain the risk growth factor, with a value range of [0, 0.5]. The larger the value, the higher the degree of increased risk.

[0096] Step S106: Obtain the comprehensive risk index based on the initial risk index, risk offsetting factor, and risk growth factor.

[0097] Specifically, in this embodiment, based on the formation mechanism of forest fire risk, the initial risk index reflects the basic risk level, the risk growth factor increases the initial risk, and the risk mitigation factor decreases the initial risk. In this embodiment, the comprehensive risk index = initial risk index × (1 + risk growth factor) × (1 - risk mitigation factor).

[0098] Step S107: Obtain the risk level based on the comprehensive risk index.

[0099] Specifically, referencing relevant national forest fire risk assessment standards and considering factors such as historical fire data, ecological value, and population density in the target forest's location, preset risk level thresholds are established: a low-risk threshold of [0, 0.3] indicates a low probability of forest fires, with minimal damage even if one occurs; a medium-risk threshold of [0.3, 0.6] indicates a certain probability of forest fires, potentially causing damage to localized areas; and a high-risk threshold of [0.6, 1.0] signifies a high probability of forest fires, which, if they occur, could cause severe ecological damage and loss of life and property. The calculated comprehensive risk index is then compared with the preset risk level thresholds to determine the corresponding risk level of the target forest, providing a clear basis for subsequent fire prevention decisions and emergency preparedness.

[0100] The forest fire risk assessment method provided in this embodiment first acquires fire information such as the hazard hazard, exposure level of affected bodies, and vulnerability of affected bodies in the target forest, while simultaneously acquiring remote sensing data of the forest and extracting target information from it. Next, the exposure level of affected bodies is corrected based on the target information to generate a corrected exposure level. Then, the hazard hazard, corrected exposure, and vulnerability of affected bodies are combined to obtain an initial risk index. Subsequently, risk mitigation factors and risk growth factors are determined based on the fire information and target information. Then, the initial risk index and these two factors are combined to derive a comprehensive risk index. Finally, the risk level is determined based on the comprehensive risk index. This method achieves its goal by simultaneously acquiring multi-dimensional fire information, including hazard hazard, exposure level of affected bodies, and vulnerability of affected bodies. This approach overcomes the limitations of single-factor analysis, enabling a comprehensive assessment of forest fire risks. It incorporates target information extracted from remote sensing data to correct exposure levels of affected bodies, effectively combining real-time or near-real-time geospatial information. This makes the assessment of exposure levels more closely aligned with actual terrain, vegetation distribution, and other objective conditions, reducing assessment bias. Furthermore, by adjusting the initial risk index using risk mitigation and risk growth factors, the adaptability of the assessment results to dynamic changes is further enhanced. The resulting comprehensive risk index and risk level are more accurate and reliable, providing a more scientific and precise basis for formulating forest fire prevention and control measures and optimizing emergency plans, thus contributing to improved effectiveness and targeted measures for forest fire prevention.

[0101] Reference Figure 2 In one embodiment of this example, obtaining the exposure level of the disaster-bearing body in step S101 includes steps S201 to S204:

[0102] Step S201: Obtain the resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure corresponding to the target forest.

[0103] Specifically, in this embodiment, resource exposure primarily targets the natural resources within the target forest, including the quantity, distribution range, and growth status of rare and endangered tree species (such as yew and dawn redwood), the stock volume of important timber forests, and the area of ​​resources with ecological conservation value such as wetlands and grasslands; relevant data are obtained through resource survey reports and field investigation records from the forestry department. Building exposure covers various buildings within the forest area, such as forest ranger workstations, forest processing plants, scenic area visitor service centers, and surrounding residential buildings (within a 1-kilometer buffer zone outside the forest area), requiring statistics on building location, building area, and building structure type (such as wooden structure or brick-concrete structure); data are obtained by reviewing building property registration files, conducting on-site measurements, and taking photographic records. Population exposure includes the number of permanent residents (such as forest rangers and residents) and the number of short-term transient residents (such as tourists and construction workers) within the forest area. Permanent resident population data can be obtained from the household registration information of the local police station, while transient population data is compiled through records from the forest area entrance access control system, scenic area ticket sales data, and personnel reporting information from construction units. The exposure of economically vulnerable bodies involves economic activities related to the target forest, such as the planting area and expected output value of economic forest crops (e.g., rubber forests and camellia oil forests), the output and market value of under-forest economic products (e.g., fungi and medicinal materials), and the annual income of the forest tourism industry; this is obtained through statistical reports from local agricultural and rural departments and forestry industry management departments, as well as the operating data of relevant enterprises.

[0104] Step S202: Standardize the resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure, and obtain the corresponding standardized values.

[0105] Specifically, in this embodiment, due to the significant differences in the units of measurement and numerical ranges of resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure (e.g., resource exposure may be measured in "trees," with values ​​ranging from tens to tens of thousands; economic disaster-bearing body exposure may be measured in "ten thousand yuan," with values ​​ranging from hundreds of thousands to hundreds of millions), it is necessary to standardize various exposure values ​​to eliminate the influence of dimensions and facilitate subsequent weighted calculations. An extreme value standardization method is adopted, with the calculation formula being "Standardized value = (Actual value - Minimum value) / (Maximum value - Minimum value)", where the minimum and maximum values ​​are reasonable ranges determined based on historical data of similar forests in the target forest area and industry standards.

[0106] Step S203: Obtain the target weights corresponding to resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure, respectively.

[0107] Specifically, in this embodiment, the target weight can be set by the user according to the actual situation and user needs, or it can be set according to relevant requirements.

[0108] Step S204: Obtain the exposure degree of the disaster-bearing body based on the standardized numerical values ​​and the corresponding target weights.

[0109] Specifically, in this embodiment, the exposure degree of the disaster-bearing body satisfies the following calculation formula:

[0110]

[0111] Wherein, EI is the exposure index of the disaster-bearing bodies. The EI value is the exposure index of the disaster-bearing bodies in the target forest, and the value ranges from [0,1]. The larger the value, the higher the degree of exposure of the disaster-bearing bodies in the target forest to fire risk. i W is the standardized value corresponding to the i-th disaster-bearing body exposure index. i is the target weight of the i-th disaster-bearing body exposure index, and n is the number of disaster-bearing body exposure indexes.

[0112] The forest fire risk assessment method provided in this embodiment first obtains the resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure of the target forest. Then, these exposure indicators are standardized to eliminate dimensional differences and obtain corresponding standardized values. Next, the target weights corresponding to each exposure indicator are determined, and finally, the disaster-bearing body exposure is calculated. By covering exposure indicators of multiple disaster-bearing bodies such as resources, buildings, population, and economy, a comprehensive consideration of the exposure status of disaster-bearing bodies is achieved. At the same time, the standardization process unifies the measurement scales of different types of indicators, and the weighted calculation combined with target weights highlights the importance of different indicators in risk assessment, making the assessment of disaster-bearing body exposure more systematic and targeted, laying a reliable foundation for the subsequent accurate calculation of forest fire risk.

[0113] Reference Figure 3 In one embodiment of this example, step S103, based on the target information, corrects the exposure degree of the disaster-bearing body and generates the corrected exposure degree, including steps S301 to S304:

[0114] Step S301: Based on the target information, obtain the layout information corresponding to the target forest.

[0115] Specifically, in this embodiment, the target information includes multi-dimensional features of the target forest. Key information related to the exposure correction of disaster-bearing bodies includes the spatial distribution of topography, vegetation, and surface cover. Based on this information, layout information can be extracted. Layout information specifically includes the distribution range and boundaries of different topographic units (such as mountains, hills, plains, and valleys) within the target forest; the distribution areas and growth status of various vegetation types (such as coniferous forests, broad-leaved forests, and shrublands); and the spatial attribute data of resistance barriers in the surface cover (such as rivers, lakes, reservoirs, highways, railways, and firebreaks), including their location, orientation, and dimensions (width, length). Spatial analysis (such as topographic analysis, vegetation classification, and target extraction) is performed on the remote sensing images using remote sensing image processing software (such as ArcGIS), combined with a small amount of field verification (such as setting sampling points at key locations to verify the accuracy of remote sensing interpretation results). Finally, a vector map of the target forest's layout information is generated, providing basic data support for subsequent topographic distribution information acquisition and exposure correction.

[0116] Step S302: Based on the layout information, obtain the terrain distribution information of the target forest.

[0117] Specifically, the layout information records in detail the spatial distribution characteristics of different terrain units in the target forest. Based on this information, digital terrain analysis technology is used to obtain terrain distribution information data.

[0118] Step S303: Based on terrain distribution information, obtain the resistance to fire propagation.

[0119] Specifically, in this embodiment, the terrain distribution data reflects the topographic undulations and spatial pattern of the target forest. Fire propagation resistance is primarily influenced by resistance barriers within the terrain, and different types of resistance barriers have varying degrees of ability to impede fire propagation. First, resistance barriers that hinder fire propagation are identified from the terrain distribution data, such as water bodies like rivers and lakes, transportation routes like highways and railways, and artificially constructed firebreaks. Then, the physical characteristics (such as the width and depth of water bodies, the width and surface material of roads, and the width, length, and vegetation type of firebreaks) and spatial distribution (such as continuous distribution, distribution density, and distance from combustibles) of various resistance barriers are analyzed. Combining this with fire dynamics principles, a fire propagation resistance calculation model is established. By inputting the parameters of various resistance barriers, the fire propagation resistance is calculated. The magnitude of this resistance value directly reflects the degree to which the resistance barrier hinders fire propagation.

[0120] Step S304: Obtain the corrected exposure based on the fire behavior propagation resistance.

[0121] Specifically, in this embodiment, the exposure level of the disaster-bearing body is the initial risk exposure level calculated based on the number and distribution of disaster-bearing bodies, but the impact of terrain resistance barriers on fire propagation is not considered. Fire propagation resistance quantifies the ability of resistance barriers to hinder fire spread; the greater the resistance, the more difficult it is for the fire to reach the area where the disaster-bearing body is located, and the lower the actual degree of exposure of the disaster-bearing body to fire risk. Therefore, the formula is used... The exposure of the affected body is corrected, where Ex is the corrected exposure, E is the initial exposure of the affected body, and F is the resistance to fire propagation (F ranges from [0,1]). This correction process makes the calculated exposure of the affected body more consistent with the actual fire propagation situation, avoiding the problem of overestimation or underestimation of exposure due to the failure to consider terrain factors, and laying the foundation for the accurate calculation of the subsequent initial risk index.

[0122] The forest fire risk assessment method provided in this embodiment first obtains the layout information of the target forest based on the target information, then obtains the topographic distribution information based on the layout information, then obtains the fire propagation resistance based on the topographic distribution information, and finally calculates and generates the corrected exposure degree through a formula. By introducing the topographic distribution information from the target information and extracting the fire propagation resistance, the influence of topography on fire spread is incorporated into the exposure degree assessment of the disaster-bearing body. This allows the corrected exposure degree to more realistically reflect the actual situation of the fire threat faced by the disaster-bearing body under different topographic conditions, effectively making up for the problem of exposure degree deviation caused by neglecting topographic factors in traditional assessments, further improving the accuracy of the exposure degree assessment of the disaster-bearing body, and providing more reliable basic data for subsequent risk index calculation.

[0123] Reference Figure 4 In one embodiment of this example, step S303: obtaining the fire propagation resistance based on terrain distribution information includes steps S401 to S404:

[0124] Step S401: Obtain the resistance barrier attributes based on terrain distribution information.

[0125] Specifically, in this embodiment, barrier attributes include physical attributes (such as material, shape, and size), spatial attributes (such as location, distribution density, and relative distance from combustibles), and functional attributes (such as whether it is naturally formed or artificially constructed, and its ability to impede fire spread). When acquiring resistance barrier attributes, the barrier type and location are first preliminarily identified through remote sensing image interpretation. Then, combined with on-site surveys (using GPS positioning to measure barrier dimensions and record material characteristics) and forestry management records (checking the construction time and design parameters of artificial barriers), a complete list of resistance barrier attributes is finally compiled, providing basic data for subsequent barrier classification and resistance calculation.

[0126] Step S402: Based on the resistance barrier attributes, obtain the resistance barrier type and resistance barrier distribution characteristics.

[0127] Specifically, based on the differences in the properties of the resistance barriers, the barriers are classified and their distribution characteristics are extracted. In this embodiment, the resistance barrier types can be divided into three categories according to their materials and functions: water bodies, artificial facilities, and natural non-combustible materials. The distribution characteristics of the resistance barriers include the distribution density, continuity, and spatial association with combustible materials.

[0128] Step S403: Based on the distribution characteristics of the resistance barrier, obtain the distribution pattern and area ratio.

[0129] Specifically, the distribution pattern refers to the spatial arrangement of the barrier. Based on distribution characteristics, it is divided into three types: continuous distribution, scattered distribution, and strip distribution. In this embodiment, the distribution pattern type can be determined by visually interpreting remote sensing images and combining this with on-site verification. Area percentage refers to the proportion of a certain type of barrier area within the smallest assessment unit. The smallest assessment unit can be a village or town. In this embodiment, the smallest assessment unit is a 1-square-kilometer grid. The area percentage = (barrier area within the unit / total area of ​​the unit) × 100%. For example, if the river area within a 1-square-kilometer grid is 0.1 square kilometers, then the river area percentage = 0.1 / 1 × 100% = 10%.

[0130] Step S404: Obtain the fire propagation resistance based on the type, distribution, and area ratio of the resistance barrier.

[0131] Specifically, in this embodiment, the calculation of fire propagation resistance needs to integrate the synergistic effects of barrier type, distribution, and area ratio. The basic resistance contribution value is determined based on the barrier type, and the basic contribution value is corrected in combination with the distribution. The corrected contribution values ​​are weighted and summed using the area ratio as the weight to obtain the fire propagation resistance within the unit.

[0132] The forest fire risk assessment method provided in this embodiment first obtains the resistance barrier attributes based on topographic distribution information, then extracts the resistance barrier type and distribution characteristics from the resistance barrier attributes, and then determines the distribution pattern and area proportion based on the resistance barrier distribution characteristics. Finally, it combines the resistance barrier type, distribution pattern, and area proportion to obtain the resistance to fire propagation. By systematically decomposing the key elements such as the attributes, type, and distribution characteristics of resistance barriers, the influence of topography on fire propagation is refined into specific parameters that can be quantified and analyzed. This makes the calculation of fire propagation resistance more scientific and targeted, accurately reflecting the actual hindering effect of different types of barriers and their distribution characteristics on fire spread. It provides a more accurate basis for subsequent correction of the exposure of disaster-bearing bodies, further improving the accuracy of the entire risk assessment system.

[0133] Reference Figure 5 In one embodiment of this example, step S404, based on the type, distribution, and area ratio of the resistance barrier, obtains the fire propagation resistance, including steps S501 to S503:

[0134] Step S501: Based on the type of resistance barrier, obtain the corresponding basic resistance coefficient.

[0135] Specifically, in this embodiment, the basic resistance coefficient is a parameter that measures the inherent ability of different types of resistance barriers to hinder the spread of fire. In this embodiment, it can be determined based on historical fire case data and laboratory simulation results.

[0136] Step S502: Based on the distribution scenario, obtain the distribution correction coefficient.

[0137] Specifically, in this embodiment, the distribution correction coefficient is a parameter used to quantify the influence of the spatial distribution characteristics of the resistance barrier on its fire-blocking effect. Essentially, it is a dynamic adjustment factor for the basic resistance coefficient. When the distribution of the resistance barrier is conducive to enhancing the fire-blocking effect, the coefficient is greater than 1; when the distribution of the resistance barrier weakens the fire-blocking effect, the coefficient is less than 1; and when the distribution has no additional effect, the coefficient is equal to 1.

[0138] Step S503: Obtain the fire propagation resistance based on the basic drag coefficient, distribution correction coefficient, and area ratio.

[0139] Specifically, in this embodiment, the propagation resistance of fire behavior satisfies the following calculation formula:

[0140]

[0141] Among them, T i Let D be the basic drag coefficient of the i-th type of resistance barrier. i S is the distribution correction factor for the i-th type of resistance barrier. i This represents the area percentage of this type of resistance barrier within the smallest unit.

[0142] The forest fire risk assessment method provided in this embodiment first determines the corresponding basic resistance coefficient based on the type of resistance barrier, then obtains the distribution correction coefficient according to the distribution situation, and finally calculates the fire propagation resistance. By quantitatively integrating the type characteristics, spatial distribution characteristics, and scale proportion of resistance barriers, a refined calculation of the fire propagation resistance is achieved. This method not only reflects the inherent differences in fire-blocking capacity of different types of barriers, but also takes into account the impact of their distribution pattern and coverage on the fire-blocking effect. This makes the assessment of fire propagation resistance more in line with the actual situation, provides reliable parameters for the accurate correction of the exposure of disaster-bearing bodies, and further improves the scientificity and accuracy of forest fire risk assessment.

[0143] Reference Figure 6In one embodiment of this example, step S502, based on the distribution situation, obtains the distribution correction coefficient, which includes steps S601 to S605:

[0144] Step S601: Based on the distribution, obtain the target width of the resistance barrier.

[0145] Specifically, in this embodiment, the target width of the resistance barrier refers to the effective blocking width of the barrier in the direction of fire spread.

[0146] Step S602: Based on the target information, obtain the target height of the target vegetation within the target area.

[0147] Specifically, in this embodiment, the target height of the target vegetation refers to the height of the vegetation around the barrier that may participate in the spread of fire.

[0148] Step S603: Obtain the target distance between the target vegetation and the resistance barrier, and obtain the comprehensive height based on the target height and the target distance.

[0149] Specifically, the target distance is the straight-line distance between the target vegetation and the edge of the barrier, and the overall height is a key parameter reflecting the relative spatial relationship between the vegetation and the barrier. In this embodiment, the overall height is equal to the target height minus the target distance.

[0150] Step S604: If the overall height is less than the target width, then obtain the first distribution correction coefficient based on the target width and the overall height, and use the first distribution correction coefficient as the distribution correction coefficient.

[0151] Specifically, in this embodiment, the first distribution correction coefficient satisfies the following calculation formula:

[0152] .

[0153] Step S605: If the overall height is greater than or equal to the target width, then obtain the second distribution correction coefficient based on the target width and the overall height, and use the second distribution correction coefficient as the distribution correction coefficient.

[0154] Specifically, in this embodiment, the second distribution correction coefficient satisfies the following calculation formula:

[0155]

[0156] Where D is the basic correction coefficient, k is the width advantage influence coefficient, W is the target width of the resistance barrier, and H is the comprehensive height of the target vegetation.

[0157] The forest fire risk assessment method provided in this embodiment first obtains the target width of the resistance barrier based on the distribution pattern, then obtains the target height of the target vegetation within the target area based on the target information, and finally obtains the target distance between the target vegetation and the resistance barrier and combines it with the target height to obtain the comprehensive height. If the comprehensive height is less than the target width, it is calculated using the first distribution correction coefficient formula and used as the distribution correction coefficient; if the comprehensive height is not less than the target width, it is calculated using the second distribution correction coefficient formula and used as the distribution correction coefficient. By introducing the comparison relationship between the resistance barrier width and the comprehensive height of the vegetation, and combining the width advantage influence coefficient to perform differentiated calculation of the distribution correction coefficient, the actual blocking effect of the resistance barrier on fire propagation under different spatial distribution patterns is accurately quantified. The method fully considers the influence of the spatial location and morphological characteristics of the vegetation and the barrier on the fire blocking effect, making the determination of the distribution correction coefficient more scientific and targeted, thereby improving the accuracy of the calculation of fire propagation resistance and providing more reliable support for subsequent exposure correction of disaster-bearing bodies and overall risk assessment.

[0158] Reference Figure 7 In one embodiment of this example, obtaining the risk growth factor based on fire information and target information in step S105 includes steps S701 to S703:

[0159] Step S701: Based on the vulnerability of the disaster-bearing body and target information, obtain the basic risk coefficient.

[0160] Specifically, the basic risk coefficient reflects the inherent risk carrying capacity of people within the target area. Its essence is the superposition and quantification result of the vulnerability of the disaster-bearing body and the static risk characteristics in the target information. In this embodiment, the basic risk base = (N×D2)×(1+D1×K1), where N is the total number of people in the target area, D2 is the second designated population density, D1 is the first designated population density, and K1 is the vulnerability amplification coefficient. The first designated population density is the density of the population with no civil capacity and limited civil capacity, and the second designated population density is the population density statistically calculated according to different classifications. The vulnerability amplification coefficient is used to quantify the first designated population, and its value ranges from 0.1 to 0.5. It represents the proportion of the overall risk that increases by 1 unit of the first designated population density (e.g., 1 person / square kilometer) on top of the basic level.

[0161] Step S702: Based on the target information, obtain the activity risk amplification coefficient and the regional correlation coefficient.

[0162] Specifically, the activity risk amplification coefficient is a parameter that quantifies the degree to which the activity characteristics (structure, behavioral preferences, etc.) of people within the target area amplify the initial risk; the regional correlation coefficient is a parameter that quantifies the degree to which the spatial correlation characteristics (land use, frequency of personnel interaction) between the target area (such as a forest) and the surrounding areas amplify the scope and frequency of risk exposure in the target area. In this embodiment, the regional correlation coefficient = 1 + (U-1) × V, where U is the land use risk coefficient, which is determined based on the correlation between the risk of the surrounding area and the forest, and its value ranges from 1.0 to 3.0; V is the regional interaction intensity, which is set based on the frequency of personnel movement between the target forest and the surrounding areas, and its value ranges from 0.3 to 0.8.

[0163] Step S703: Obtain the risk growth factor based on the basic risk coefficient, the activity risk amplification coefficient, and the regional correlation coefficient.

[0164] Specifically, in this embodiment, the risk growth factor = base risk level × activity risk amplification factor × regional correlation factor.

[0165] The forest fire risk assessment method provided in this embodiment first obtains a basic risk coefficient based on the vulnerability of the disaster-bearing body and target information. Then, it obtains an activity risk amplification coefficient and a regional correlation coefficient based on the target information. Finally, it combines the basic risk coefficient, the activity risk amplification coefficient, and the regional correlation coefficient to obtain a risk growth factor. By combining the vulnerability of the disaster-bearing body itself with activity factors and regional correlation factors in the external environment, the calculation logic of the risk growth factor is constructed in multiple dimensions. This not only reflects the sensitivity of the disaster-bearing body to fire, but also incorporates the amplification effect of human activities and inter-regional interactions on risk. This makes the assessment of the risk growth factor more comprehensive and realistic, and can accurately reflect the increasing trend of fire risk under the superposition of various factors. It provides a scientific basis for the calculation of the comprehensive risk index and further improves the accuracy and dynamic adaptability of forest fire risk assessment.

[0166] Reference Figure 8 In one embodiment of this example, obtaining the activity risk amplification factor based on the target information in step S702 includes steps S801 to S806:

[0167] Step S801: Based on the target information, obtain the regional information of the target forest.

[0168] Specifically, the regional information in the target information is the basis for locating human activity scenarios. It is used to clarify the administrative and geographical characteristics of the target forest. The regional information includes the administrative region to which the target forest belongs, the geographical zoning type, and the distribution of surrounding supporting facilities.

[0169] In this embodiment, the administrative boundaries of the target forest are determined by interpreting remote sensing images and local administrative vector maps; the geographical zoning types are divided according to geographical zoning standards; and the location and function of surrounding supporting facilities are recorded through on-site surveys, ultimately forming a structured regional information table to clarify the main areas and scenarios of human activities.

[0170] Step S802: Based on the regional information, obtain the regional personnel information of the target region.

[0171] Specifically, regional personnel information refers to the basic data set of all personnel related to forest activities within the target forest and its surrounding areas. This is core data for analyzing human activity risks, used to clarify the number, distribution, and activity objectives of personnel within the area. In this embodiment, the number of ticket purchasers can be obtained through the scenic area ticketing system, combined with the scenic area entrance access control records to calculate the actual number of visitors, simultaneously recording the entry time and expected length of stay; the number of registered residents in residential areas can be obtained from relevant local departments, and the number and length of stay of permanent and transient residents can be recorded through community grid workers' visits; the number of workers, including permanent and temporary employees, can be obtained from forestry production enterprises and understory economic cooperatives, clarifying the work area and work time, etc.

[0172] Step S803: Based on regional personnel information, obtain the number of personnel, personnel structure, and personnel characteristics.

[0173] Specifically, in this embodiment, the number of personnel, personnel structure, and personnel characteristics are key dimensions for assessing the risk of the activity.

[0174] The number of people refers to the total number of people in the target area. The personnel structure refers to the composition ratio of people in the target area according to specific dimensions, such as age, occupation, and activity type. The structural ratio can be obtained through questionnaire surveys and data correlation. The personnel characteristics refer to the individual attributes of people directly related to forest fire risk, including fire safety awareness (such as whether they have received fire prevention training), fire use habits (such as whether they have the habit of smoking or barbecuing outdoors), and emergency response capabilities (such as whether they have mastered fire escape skills). Relevant data can be obtained through on-site interviews and fire prevention knowledge tests (such as distributing simple test questionnaires). The data is recorded in categories of high-risk, medium-risk, and low-risk characteristics (such as the habit of smoking outdoors is a high-risk characteristic, while receiving fire prevention training is a low-risk characteristic).

[0175] Step S804: Obtain personnel interests based on personnel characteristics.

[0176] Specifically, in this embodiment, personnel interests refer to the interests of people in the target area, including whether they like smoking, whether they like picnicking, fishing, or camping, etc.

[0177] Step S805: Based on the number of people and their interests, obtain the percentage of people with different interests and their corresponding risk weights.

[0178] Specifically, the proportion of people refers to the ratio of the number of people with a certain type of interest to the total number of people in the target area, and the risk weight refers to the weight coefficient set according to the fire risk level of different interests. In this embodiment, the weight coefficient can be set based on the risk level of the interest, with high-risk interests having the highest weight (e.g., 0.6), medium-risk interests having a medium weight (e.g., 0.3), and low-risk interests having the lowest weight (e.g., 0.1), and the sum of all interest weights is 1.

[0179] Step S806: Based on the personnel structure, the proportion of people, and the corresponding risk weights, obtain the activity risk amplification coefficient.

[0180] Specifically, in this embodiment, the activity risk amplification factor = S × (1 + Σ(I i ×P i In this context, S represents the personnel structure coefficient, which is set based on the personnel structure and user needs. The more unfavorable the structure is to risk response, the higher the coefficient. In this embodiment, the personnel structure coefficient ranges from 1.2 to 2.0. i The risk weight of the interest of the i-th type of person; P i The percentage of people with the i-th type of interest (ΣP) i =1).

[0181] The forest fire risk assessment method provided in this embodiment first obtains regional information of the target forest based on target information, extracts regional personnel information of the target area from the regional information, and then determines the number, structure, and characteristics of personnel from the regional personnel information. Based on the personnel characteristics, personnel interests are obtained. Subsequently, the proportion of people with different interests and their corresponding risk weights are calculated by combining the number of people and their interests. Finally, the activity risk amplification coefficient is obtained based on the personnel structure, the proportion of people, and the corresponding risk weights. By breaking down the personnel-related information of the target forest layer by layer, the abstract human activity risk is transformed into quantifiable and specific parameters such as the number, structure, and interests of personnel. This method considers both the risk differences of people with different interests and the impact of personnel structure on risk, making the calculation of the activity risk amplification coefficient more targeted and accurate. It effectively makes up for the shortcomings of the general consideration of human activity risk in traditional assessments, and can more realistically reflect the amplification effect of human activities on forest fire risk, providing reliable support for the accurate calculation of risk growth factors and even comprehensive risk indices.

[0182] Secondly, this application also discloses a forest fire risk assessment system.

[0183] Reference Figure 9 A forest fire risk assessment system, comprising:

[0184] The first acquisition module is used to acquire fire information of the target forest, including the hazard level, exposure of the affected areas, and vulnerability of the affected areas.

[0185] The second acquisition module is used to acquire remote sensing data of the target forest and acquire target information of the target forest based on the remote sensing data;

[0186] The correction module is used to correct the exposure of the disaster-bearing body based on the target information and generate the corrected exposure.

[0187] The third acquisition module is used to obtain the initial risk index based on the disaster hazard, the corrected exposure, and the vulnerability of the disaster-bearing body;

[0188] The fourth acquisition module is used to acquire risk mitigation factors and risk growth factors based on fire information and target information;

[0189] The fifth acquisition module is used to obtain the comprehensive risk index based on the initial risk index, risk offsetting factor, and risk growth factor;

[0190] The sixth module is used to obtain the risk level based on the comprehensive risk index.

[0191] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A forest fire risk assessment method characterized by, The method comprises the following steps: obtaining fire information of a target forest, wherein the fire information comprises a disaster-causing risk, an exposure of a disaster-bearing body, and a vulnerability of the disaster-bearing body; obtaining remote sensing data of the target forest, and obtaining target information of the target forest based on the remote sensing data; based on the target information, correcting the exposure of the disaster-bearing body, and generating a corrected exposure; based on the disaster-causing risk, the corrected exposure, and the vulnerability of the disaster-bearing body, obtaining an initial risk index; based on the fire information and the target information, obtaining a risk offset factor and a risk growth factor; based on the initial risk index, the risk offset factor, and the risk growth factor, obtaining a comprehensive risk index; based on the comprehensive risk index, obtaining a risk level; wherein the step of correcting the exposure of the disaster-bearing body based on the target information and generating a corrected exposure comprises the following steps: based on the target information, obtaining layout information corresponding to the target forest; based on the layout information, obtaining topographic distribution information of the target forest; based on the topographic distribution information, obtaining a fire behavior propagation resistance; based on the fire behavior propagation resistance, obtaining a corrected exposure, wherein the corrected exposure satisfies the following calculation formula: wherein Ex is the corrected exposure, E is the exposure of the disaster-bearing body, and F is the fire behavior propagation resistance.

2. The forest fire risk assessment method according to claim 1, characterized in that, The method for obtaining the exposure of the disaster-bearing body comprises the following steps: obtaining resource exposure, building exposure, population exposure, and economic disaster-bearing body exposure corresponding to the target forest; standardizing the resource exposure, the building exposure, the population exposure, and the economic disaster-bearing body exposure, and obtaining corresponding standardized values; obtaining target weights corresponding to the resource exposure, the building exposure, the population exposure, and the economic disaster-bearing body exposure, respectively; based on the standardized values and the corresponding target weights, obtaining the exposure of the disaster-bearing body, wherein the exposure of the disaster-bearing body satisfies the following calculation formula: Wherein, EI is the exposure index of the hazard- bearing body, E i is the standardized value corresponding to the i th exposure index of the hazard- bearing body, W i is the target weight of the i th exposure index of the hazard- bearing body, and n is the number of exposure indexes of the hazard- bearing body.

3. The forest fire risk assessment method according to claim 1, characterized in that, The method for obtaining the fire behavior propagation resistance based on the topographic distribution information comprises the following steps: based on the topographic distribution information, obtaining resistance barrier attributes; based on the resistance barrier attributes, obtaining resistance barrier types and resistance barrier distribution characteristics; based on the resistance barrier distribution characteristics, obtaining distribution scenarios and area ratios; based on the resistance barrier types, the distribution scenarios, and the area ratios, obtaining the fire behavior propagation resistance.

4. The forest fire risk assessment method according to claim 3, wherein, The method for obtaining the fire behavior propagation resistance based on the resistance barrier types, the distribution scenarios, and the area ratios comprises the following steps: based on the resistance barrier types, obtaining corresponding basic resistance coefficients; based on the distribution scenarios, obtaining distribution correction coefficients; based on the basic resistance coefficients, the distribution correction coefficients, and the area ratios, obtaining the fire behavior propagation resistance, wherein the fire behavior propagation resistance satisfies the following calculation formula: where T i is the base drag coefficient of the i-th type of drag barrier, D i is the distribution correction coefficient of the i-th type of drag barrier, S i is the area proportion of this type of drag barrier within the minimum unit.

5. The method of forest fire risk assessment according to claim 4, characterized in that, The method for obtaining the distribution correction coefficients based on the distribution scenarios comprises the following steps: based on the distribution scenarios, obtaining target widths of the resistance barriers; based on the target information, obtaining target heights of target vegetation in a target area; acquire a target distance of the target vegetation and the resistance barrier, and acquire a comprehensive height based on the target height and the target distance; if the comprehensive height is less than the target width, acquire a first distribution correction coefficient based on the target width and the comprehensive height, and take the first distribution correction coefficient as the distribution correction coefficient, the first distribution correction coefficient satisfying the following calculation formula: if the comprehensive height is greater than or equal to the target width, acquire a second distribution correction coefficient based on the target width and the comprehensive height, and take the second distribution correction coefficient as the distribution correction coefficient, the second distribution correction coefficient satisfying the following calculation formula: wherein D is a basic correction coefficient, k is a width advantage influence coefficient, W is a target width of the resistance barrier, and H is a comprehensive height of the target vegetation.

6. The forest fire risk assessment method of claim 1, wherein, acquiring a risk growth factor based on the fire information and the target information includes: acquiring a basic risk coefficient based on the hazard-bearing body vulnerability and the target information; acquiring an activity risk amplification coefficient and a regional correlation coefficient based on the target information; acquiring a risk growth factor based on the basic risk coefficient, the activity risk amplification coefficient and the regional correlation coefficient.

7. The method of forest fire risk assessment according to claim 6, characterized in that, acquiring an activity risk amplification coefficient based on the target information includes: acquiring regional information of the target forest based on the target information; acquiring regional personnel information of a target region based on the regional information; acquiring a number of personnel, a personnel structure and a personnel characteristic based on the regional personnel information; acquiring a personnel interest based on the personnel characteristic; acquiring a number ratio of different personnel interests and a corresponding risk weight based on the number of personnel and the personnel interest; acquiring the activity risk amplification coefficient based on the personnel structure, the number ratio and the corresponding risk weight.

8. A forest fire risk assessment system for performing the method of any one of claims 1 to 7, characterized by includes: a first acquisition module configured to acquire fire information of a target forest, the fire information including a disaster-causing danger, a hazard-bearing body exposure degree and a hazard-bearing body vulnerability; a second acquisition module configured to acquire remote sensing data of the target forest, and acquire target information of the target forest based on the remote sensing data; a correction module configured to correct the hazard-bearing body exposure degree based on the target information, and generate a corrected exposure degree; a third acquisition module configured to acquire an initial risk index based on the disaster-causing danger, the corrected exposure degree and the hazard-bearing body vulnerability; a fourth acquisition module configured to acquire a risk offset factor and a risk growth factor based on the fire information and the target information; a fifth acquisition module configured to acquire a comprehensive risk index based on the initial risk index, the risk offset factor and the risk growth factor; a sixth acquisition module configured to acquire a risk level based on the comprehensive risk index.

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