Forest fire extinguishing task safety risk assessment method based on AHP-FCE
By constructing a safety risk assessment method for forest fire fighting missions based on AHP-FCE, the fire fighting risks are quantitatively assessed, solving the problem that firefighters have difficulty taking countermeasures in complex environments, and realizing scientific and systematic risk assessment and emergency management support.
Patent Information
- Application Number
- CN202511682771.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-02-24
AI Technical Summary
Existing technologies are insufficient to provide firefighters with effective safety data assessments, making it difficult for them to take accurate responses in complex environments and increasing the risk of casualties.
A forest fire fighting mission safety risk assessment method based on AHP-FCE was adopted to construct a forest fire fighting mission safety risk index system. The fire fighting risk was quantitatively assessed by using the analytic hierarchy process and fuzzy comprehensive evaluation method, and a scientific risk level assessment was provided by combining simulation verification.
It improved the ability of firefighters to respond to emergencies, reduced the risk of casualties, enhanced the scientific and systematic nature of firefighting work, and supported the establishment and improvement of the emergency management system.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention relates to the field of forest fire fighting safety risk assessment technology, specifically to a forest fire fighting mission safety risk assessment method based on AHP-FCE. Background Technology
[0002] Forests are an indispensable and precious resource in nature, occupying an extremely important position in the world's ecosystem and playing a vital role in human sustainable development. They are the natural habitat of wild animals and fertile soil for lush vegetation, maintaining the rich diversity of the world's ecological environment. However, due to the relatively fragile nature of forest ecosystems, they are frequently affected by many external influences; for example, forest fires are arguably the most destructive threat. Forest fires are extremely sudden and destructive, seriously endangering human survival and development.
[0003] Forest fire fighting is fraught with difficulties; it is a dangerous and challenging high-risk operation. Because most forest fires occur in complex terrain and harsh weather conditions, their outbreak and spread are difficult to predict accurately, greatly increasing the difficulty of firefighting. In actual firefighting operations, even a slight mistake can result in injury or death to firefighters. Forest fire prevention work in all regions is not only a necessary requirement for protecting the ecological environment but also a matter of paramount importance to the safety of life.
[0004] Forest fires cause immense damage to forest resources, the ecological environment, and the lives and property of people in the surrounding areas. Because forest fire prevention work involves a complex and ever-changing environment with numerous uncertainties, research on safety risk assessments for forest fire fighting is of great practical value. It allows for the accurate identification of various hazards in forest fire fighting, such as the uncertainty of fire direction and the risk of deflagration caused by the distribution of combustibles. This enables firefighters to develop a scientific protection and emergency evacuation plan, minimizing casualties and providing strong support for the establishment of a forest fire emergency management system. In the fire scene, firefighters can analyze emergencies, make correct judgments, and adjust their response measures accordingly. After the fire is extinguished, the entire firefighting process can be reviewed and analyzed, and the accuracy and shortcomings of the risk assessment can be studied to continuously improve emergency plans and risk assessment systems, making forest fire fighting more scientific, systematic, and efficient. This also enables fire departments to develop better response measures and safety plans.
[0005] As can be seen from the above, how to provide firefighters with a safety data assessment to facilitate their corresponding adjustments to emergency response measures? To this end, we propose a safety risk assessment method for forest fire fighting missions based on AHP-FCE. Summary of the Invention
[0006] (a) Technical problems to be solved In view of the shortcomings of the prior art, the present invention overcomes the problem in the above-mentioned background art of how to provide firefighters with a safety data assessment so that they can make corresponding adjustments to their response measures to emergencies.
[0007] (II) Technical Solution To achieve the above objectives, the technical solution adopted by the present invention is as follows: A safety risk assessment method for forest fire fighting missions based on AHP-FCE, the specific steps of which are as follows: Step S1: Design of a safety risk indicator system for forest fire fighting missions; Step S2: Construction of a risk quantification assessment model for forest fire fighting tasks; Step S3: Simulation verification.
[0008] Further defining the above technical solution, the indicator system in step S1 includes multiple sets of primary indicators, and each set of primary indicators includes multiple sets of secondary indicators. The primary indicators include topography, vegetation type, climate conditions, and tissue conditions. The secondary indicators of topography include slope factor, uphill / downhill fire factor, and slope aspect factor. The secondary indicators of meteorological conditions include wind speed factor, wind direction factor, and fire intensity factor; Secondary indicators of vegetation conditions include vegetation type factors and forest canopy density factors; The secondary indicators of organizational conditions include the path accessibility factor and the risk avoidance point coverage factor.
[0009] Further defining the above technical solution, step S2 specifically includes the following steps: Step S21: Use the analytic hierarchy process (AHP) to determine the weights of risk factors; Step S211: Construct a hierarchical model; Step S212: Construct the judgment matrix; Step S213: Calculation of the relative weights of factors at each level; Step S22: Use fuzzy comprehensive evaluation method to conduct risk quantification assessment; Step S221: Construct a comprehensive risk assessment scoring table; Step S222: Fuzzy comprehensive evaluation method.
[0010] Further defining the above technical solution, the hierarchical model constructed in step S211 includes a target layer: safety risk R of forest fire fighting mission; Criterion layer: corresponding to the first-level indicators in step 1, namely topography T, meteorological conditions M, vegetation characteristics V, and organizational support E; Indicator layer: The secondary indicators corresponding to each criterion layer.
[0011] To further define the above technical solution, step S212 involves constructing a judgment matrix to compare the relative importance of each indicator, and constructing the judgment matrices for the criterion layer and the indicator layer. The construction method is as follows: Step S2121: Design the scoring table; Step S2122: Perform pairwise comparisons of the relative importance of each indicator in the criteria layer and the indicator layer; Step S2123: Based on the scoring results of each group's scoring table, form the corresponding matrices and denote them as follows: , ; Step S2124: ... Each scoring sheet for indicators and indicators (index relative to indicators important, >1) The scores are averaged and then the lower limit is taken, i.e. This forms the final judgment matrix. The criterion layer judgment matrix is formed based on the above method. Topography and landform judgment matrix Meteorological condition judgment matrix Vegetation feature judgment matrix Organizational safeguard judgment matrix .
[0012] Further defining the above technical solution, in step S213, the weights are obtained through the eigenvector method and a consistency check is performed (CR=0.0411<0.1). The specific steps are as follows: For each judgment matrix in turn , : 1. Judgment matrix pass , thus obtaining the eigenvalue vector and eigenvector matrix .
[0013] 2: Calculate the largest eigenvalue 3: Return the eigenvector corresponding to the largest eigenvalue. 4: Normalized eigenvectors Obtain the weight vector ,in, 5. Calculate the consistency index 6. Calculate the consistency ratio (CR) , This is the expected random consistency index.
[0014] Further defining the above technical solution, the fuzzy comprehensive evaluation method in step S222 is as follows: Step S2221: Determine the factor set, evaluation set, and weight set. Factor set: that is, the set of all evaluation indicators that affect the safety risk of forest fire fighting, consistent with the hierarchical structure model of the AHP method above. The evaluation set is the collection of all levels that evaluate each factor, divided into four risk levels: low risk, medium risk, relatively high risk, and high risk. Weight set: that is, the vector of the importance of each factor relative to its superior factor, usually obtained by the AHP method above; Step S2222: Establish a membership matrix to determine the degree of membership of each single factor to each rating level. First, construct a comprehensive risk assessment scoring table for each indicator in the bottom-level indicator layer. Second, score each indicator in the indicator layer according to the scoring table. Finally, combine the risk level classification table for forest fire fighting tasks to establish a membership vector for each indicator in the indicator layer. That is, according to the score of the indicator, it corresponds to the risk level of low risk, medium risk, relatively high risk, and high risk. Set the position of the risk level in the membership vector to 1 and the rest to 0 to obtain the corresponding membership vector. Step S2223: Fuzzy synthesis, which combines the weight set and the membership matrix to obtain a comprehensive evaluation result vector. The maximum value in the vector is the final risk level result.
[0015] Further defining the above technical solution, the simulation verification in step S3 involves organizing the input and output data of the simulator to sequentially obtain the component values of the feature vector at the fire line point. Among these, wind direction, wind speed, fire intensity, slope aspect, slope, forest canopy density, combustible material type, and road conditions can be obtained by searching the environmental file of the fire simulation and its output file for the closest corresponding value to the fire line point. The safety risk of the forest fire fighting task is divided into four levels, where red indicates high risk, green indicates relatively high risk, yellow indicates medium risk, and blue indicates low risk.
[0016] (III) Beneficial Effects Compared with existing technologies, this invention provides a safety risk assessment method for forest fire fighting missions based on AHP-FCE, which has the following beneficial effects: (1) A relatively scientific and reasonable safety risk indicator system for forest fire fighting tasks was constructed.
[0017] (2) Construct a hierarchical structure model, combine expert scores, and integrate multiple expert opinions by taking the average to improve the objectivity of the score. On this basis, apply the hierarchical analysis method to transform the subjective experience of experts into quantitative weights.
[0018] (3) Apply the fuzzy comprehensive evaluation method to quantify qualitative indicators through membership functions to improve the model’s ability to handle fuzziness.
[0019] (4) In the model, each index is scored first, and then a membership vector is established through the membership function. Detailed Implementation
[0020] The present invention will now be described in further detail.
[0021] Example: This example provides a safety risk assessment method for forest fire fighting missions based on AHP-FCE, which can solve the problem of how to provide firefighters with a safety data assessment to facilitate their adjustment of response measures to emergencies. The specific steps are as follows: Step S1: Design of a safety risk indicator system for forest fire fighting missions; The indicator system includes multiple sets of primary indicators, and each set of primary indicators includes multiple sets of secondary indicators. The primary indicators include topography, vegetation type, climate conditions, and tissue conditions. The secondary indicators of topography include slope factor, uphill / downhill fire factor, and slope aspect factor. The secondary indicators of meteorological conditions include wind speed factor, wind direction factor, and fire intensity factor; Secondary indicators of vegetation conditions include vegetation type factors and forest canopy density factors; The secondary indicators of organizational conditions include the path accessibility factor and the risk avoidance point coverage factor; Step S2: Construction of a risk quantification assessment model for forest fire fighting tasks; Step S21: Use the analytic hierarchy process (AHP) to determine the weights of risk factors; Step S211: Construct a hierarchical model, including the target layer: safety risk R of forest fire fighting mission; Criterion layer: corresponding to the first-level indicators in step 1, namely topography T, meteorological conditions M, vegetation characteristics V, and organizational support E; Indicator layer: corresponding to the secondary indicators of each criterion layer; In the process of building a model using the Analytic Hierarchy Process (AHP), the first step is to determine the high-level goals or problems. Then, these goals or problems are divided into smaller goals or problems, and then the smaller problems or problems are further decomposed into the next level until the lowest level is reached. After the division is completed, the elements in each level need to be compared pairwise. Specifically, each element is paired with other elements, and their relative priority is calculated using a numerical scale. This scale is usually chosen to be a value from 1 to 9, where 1 indicates that the two elements are equally important, and 9 indicates that one element is much more important than the other. Table 2 shows the AHP scale. Step S212: Construct the judgment matrix. The judgment matrix is used to compare the relative importance of each indicator. Construct the judgment matrices for the criterion layer and the indicator layer. The construction method is as follows: Step S2121: Design a scoring table. Experts score the data on the table. Each scoring requires at least three experts to score simultaneously. When scoring, experts need to refer to existing technical literature for evaluation, such as "Forest Fire Risk Assessment in China under Multiple Climate Scenarios". The scoring table is as follows. Table 1 Scoring Table: Step S2122: Experts compare the relative importance of each indicator in the criteria layer and the indicator layer pairwise, using the 1-9 scale method, as shown in the table, and score them. Table 2 Scale of the Analytic Hierarchy Process Step S2123: Based on the scoring results of each group's scoring table, form the corresponding matrices and denote them as follows: , ; Step S2124: ... Each scoring sheet for indicators and indicators (index relative to indicators important, >1) The scores are averaged and then the lower limit is taken, i.e. This forms the final judgment matrix. ; The criterion layer judgment matrix is formed based on the above method. Topography and landform judgment matrix Meteorological condition judgment matrix Vegetation feature judgment matrix Organizational safeguard judgment matrix ; The criterion-level judgment matrix is constructed as shown in Table 3: Table 3 Criterion Layer Judgment Matrix The secondary indicator judgment matrix is shown in Table 4: Table 4(a) Matrix of Secondary Indicators for Topography and Landforms Table 4(b) Matrix of Judging Secondary Indicators of Meteorological Conditions Table 4(c) Judgment Matrix of Secondary Indicators of Vegetation Characteristics Table 4(d) Judgment Matrix of Secondary Indicators for Organizational Support Step S213: Calculate the relative weights of factors at each level, obtain the weights using the eigenvector method, and perform a consistency check (CR=0.0411<0.1). The specific steps are as follows: For each judgment matrix in turn , : 1. Judgment matrix pass , thus obtaining the eigenvalue vector and eigenvector matrix .
[0022] 2: Calculate the largest eigenvalue 3: Return the eigenvector corresponding to the largest eigenvalue. 4: Normalized eigenvectors Obtain the weight vector ,in, 5. Calculate the consistency index 6. Calculate the consistency ratio (CR) , To determine the desired random consistency index, weights are calculated using the eigenvector method, and a consistency test (CR < 0.1) is performed. An example is shown below: Criterion layer weights: climate (0.51), vegetation (0.16), topography (0.25), organization (0.07).
[0023] The weights of the secondary topographic indicators are: slope (0.53), uphill / downhill (0.29), and aspect (0.16). Table 5 Criterion Layer and Indicator Layer Step S22: Use fuzzy comprehensive evaluation method to conduct risk quantification assessment; Step S221: Construct a comprehensive risk assessment scoring table; Step S222: Fuzzy Comprehensive Evaluation Method. The fuzzy comprehensive evaluation method is specifically used to analyze and evaluate things with "fuzzy" characteristics. It adopts a hierarchical and progressive evaluation strategy: based on factor attributes, it divides factors into different categories, and conducts an independent comprehensive evaluation of each category; then, based on the single-category evaluation results, it conducts a higher-level cross-category comprehensive evaluation, thereby achieving a comprehensive and accurate evaluation of complex systems. The specific details of the fuzzy comprehensive evaluation method in step S222 are as follows: Step S2221: Determine the factor set, evaluation set, and weight set. Factor set: that is, the set of all evaluation indicators that affect the safety risk of forest fire fighting, consistent with the hierarchical structure model of the AHP method above. The evaluation set is the collection of all levels that evaluate each factor, divided into four risk levels: low risk, medium risk, relatively high risk, and high risk. Weight set: that is, the vector of the importance of each factor relative to its superior factor, usually obtained by the AHP method above; Step S2222: Establish a membership matrix to determine the degree of membership of each individual factor to each rating level. The membership degree is a number between 0 and 1, representing the degree to which a factor belongs to a certain rating level. 0 means not belonging at all, and 1 means belonging completely.
[0024] First, a comprehensive risk assessment scoring table is constructed for each indicator at the lowest level, as shown in Table 6.
[0025] Table 6(a) Scoring Table for Topographic and Geomorphological Assessment Table 6(b) Meteorological Conditions Assessment Scoring Table Table 6(c) Vegetation Characteristic Assessment Scoring Table Table 6(d) Organizational Assurance Assessment Scoring Table Secondly, based on the scoring table above, each indicator in the indicator layer is scored. Taking the slope (SG) of terrain risk as an example, if the slope SG = 40°, according to Table 6, the slope score Grade (SG) = 9. Finally, based on the risk level classification table for forest fire fighting tasks, a membership vector is established for each indicator in the indicator layer. That is, according to the score of the indicator, it corresponds to the risk level of low risk, medium risk, relatively high risk, and high risk. The position of the risk level in the membership vector is set to 1, and the rest are set to 0, thus obtaining the corresponding membership vector. Taking "slope" as an example, a membership vector is established. The fact that "slope = 9 points" belongs to the high risk of {low, medium, relatively high, high} respectively. According to Table 7, this vector is (0,0,0,1).
[0026] Table 7 Risk Level Classification of Forest Firefighting Tasks Membership function definition: Step S2223: Fuzzy synthesis, synthesizing the weight set and the membership matrix to obtain a comprehensive evaluation result vector, the maximum value in the vector is the final risk level result; Step S3: Simulation verification. By organizing the input and output data of the simulator, the component values of the feature vectors at the fire line point are obtained in sequence. Among them, wind direction, wind speed, fire intensity, slope aspect, slope, forest canopy density, combustible material type, and road conditions can be obtained by finding the corresponding values closest to the fire line point in the environmental file and its output file of the fire simulation. The safety risk of the forest fire fighting task is divided into four levels, where red indicates high risk, green indicates relatively high risk, yellow indicates medium risk, and blue indicates low risk.
[0027] By combining the analytic hierarchy process (AHP) with fuzzy comprehensive evaluation, the subjectivity of traditional experience-based judgments is avoided, while the complexity of multi-factor coupled analysis is addressed. For example, the model uses a weighted formula to transform key indicators such as terrain slope, wind speed, and vegetation type into quantifiable risk values, providing intuitive data support for fire command and offering methodological references for subsequent research.
[0028] The Analytic Hierarchy Process (AHP) was used to determine the weights of risk indicators. Through a systematic review and comprehensive comparative study of relevant data, combined with AHP and expert scoring, a scientific and reasonable selection of the weights for the factors influencing the safety risks of forest fire work was made.
[0029] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention, enabling those skilled in the art to understand and apply the invention. However, it should not be construed that the specific implementation of the invention is limited to these descriptions.
Claims
1. A method for safety risk assessment of forest fire fighting missions based on AHP-FCE, characterized in that, The specific steps are as follows: Step S1: Design of a safety risk indicator system for forest fire fighting missions; Step S2: Construction of a risk quantification assessment model for forest fire fighting tasks; Step S3: Simulation verification.
2. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 1, characterized in that, The indicator system in step S1 includes multiple sets of primary indicators, and each set of primary indicators includes multiple sets of secondary indicators. The primary indicators include topography, vegetation type, climate conditions, and tissue conditions. The secondary indicators of topography include slope factor, uphill / downhill fire factor, and slope aspect factor. The secondary indicators of meteorological conditions include wind speed factor, wind direction factor, and fire intensity factor; Secondary indicators of vegetation conditions include vegetation type factors and forest canopy density factors; The secondary indicators of organizational conditions include the path accessibility factor and the risk avoidance point coverage factor.
3. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 2, characterized in that, Step S2 specifically includes the following steps: Step S21: Use the analytic hierarchy process (AHP) to determine the weights of risk factors; Step S211: Construct a hierarchical model; Step S212: Construct the judgment matrix; Step S213: Calculation of the relative weights of factors at each level; Step S22: Use fuzzy comprehensive evaluation method to conduct risk quantification assessment; Step S221: Construct a comprehensive risk assessment scoring table; Step S222: Fuzzy comprehensive evaluation method.
4. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 3, characterized in that, Step S211 involves constructing a hierarchical model, including the target layer: safety risk R of forest fire fighting missions; Criterion layer: corresponding to the first-level indicators in step 1, namely topography T, meteorological conditions M, vegetation characteristics V, and organizational support E; Indicator layer: The secondary indicators corresponding to each criterion layer.
5. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 4, characterized in that, In step S212, a judgment matrix is constructed to compare the relative importance of each indicator. The judgment matrices for the criterion layer and the indicator layer are constructed as follows: Step S2121: Design the scoring table; Step S2122: Perform pairwise comparisons of the relative importance of each indicator in the criteria layer and the indicator layer; Step S2123: Based on the scoring results of each group's scoring table, form the corresponding matrices and denote them as follows: , ; Step S2124: ... Each scoring sheet for indicators and indicators (index relative to indicators important, >1) The scores are averaged and then the lower limit is taken, i.e. This forms the final judgment matrix. ; The criterion layer judgment matrix is formed based on the above method. Topography and landform judgment matrix Meteorological condition judgment matrix Vegetation feature judgment matrix Organizational safeguard judgment matrix .
6. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 5, characterized in that, Step S213: Obtain the weights using the eigenvector method and perform a consistency check (CR=0.0411<0.1). The specific steps are as follows: For each judgment matrix in turn , :
1. Judgment matrix pass , thus obtaining the eigenvalue vector and eigenvector matrix 2: Calculate the largest eigenvalue 3: Return the eigenvector corresponding to the largest eigenvalue. 4: Normalized eigenvectors Obtain the weight vector ,in, 5. Calculate the consistency index 6. Calculate the consistency ratio (CR) , This is the expected random consistency index.
7. The forest fire fighting mission safety risk assessment method based on AHP-FCE according to claim 6, characterized in that, The fuzzy comprehensive evaluation method in step S222 is as follows: Step S2221: Determine the factor set, evaluation set, and weight set. Factor set: that is, the set of all evaluation indicators that affect the safety risk of forest fire fighting, consistent with the hierarchical structure model of the AHP method above. The evaluation set is the collection of all levels that evaluate each factor, divided into four risk levels: low risk, medium risk, relatively high risk, and high risk. Weight set: that is, the vector of the importance of each factor relative to its superior factor, usually obtained by the AHP method above; Step S2222: Establish a membership matrix to determine the degree of membership of each single factor to each rating level. First, construct a comprehensive risk assessment scoring table for each indicator in the bottom-level indicator layer. Second, score each indicator in the indicator layer according to the scoring table. Finally, combine the risk level classification table for forest fire fighting tasks to establish a membership vector for each indicator in the indicator layer. That is, according to the score of the indicator, it corresponds to the risk level of low risk, medium risk, relatively high risk, and high risk. Set the position of the risk level in the membership vector to 1 and the rest to 0 to obtain the corresponding membership vector. Step S2223: Fuzzy synthesis, which combines the weight set and the membership matrix to obtain a comprehensive evaluation result vector. The maximum value in the vector is the final risk level result.
8. The method for assessing the safety risks of forest fire fighting missions based on AHP-FCE according to claim 7, characterized in that, In step S3, the simulation verification involves processing the input and output data of the simulator and sequentially obtaining the component values of the feature vectors at the fire line points. These components include wind direction, wind speed, fire intensity, slope aspect, slope gradient, forest canopy density, combustible material type, and road conditions. These can be obtained by searching the environmental files of the fire simulation and their output files for the corresponding values closest to the fire line points. The safety risks of forest fire fighting tasks are divided into four levels: red indicates high risk, green indicates relatively high risk, yellow indicates medium risk, and blue indicates low risk.