A forest fire hazard level assessment method based on a reliable extended confidence rule base

Abstract

The present invention belongs to the field of artificial intelligence technology and relates to a method for assessing forest fire hazard levels based on a reliable extended confidence rule base. The reliability modeling of the present invention includes constructing a set of forest fire hazard level element attributes and calculating the matching degree of the extended confidence rule attributes using a Gaussian kernel function. Reliability model reasoning includes calculating the comprehensive matching degree of the extended confidence rule base using a copula function. The reliability model also includes interpreting the optimization of extended confidence rule parameters and calculating the extended confidence rule profit and loss value to analyze the effectiveness of the confidence rules. The present invention demonstrates excellent reliability in forest fire hazard level assessment and can accurately and reliably assess forest fire hazard levels based on forest fire data.

Description

Technical Field

[0001] The invention belongs to the technical field of artificial intelligence and relates to a forest fire hazard level assessment method based on a reliable extended confidence rule base. Background Art

[0002] Forest fires have become more frequent in recent years, and responding to them has become increasingly challenging. In response to the needs of social development and security, people are increasingly aware of the dangers of forest fires. Forest fire hazard assessment is an important reference for determining the severity of fire damage and a prerequisite for organizing rescue operations and formulating resource allocation plans. Therefore, accurate and reliable assessment of fire hazard levels is crucial for forest fire response.

[0003] Thanks to the advancement of data science in the field of natural disasters, current forest fire hazard assessment methods are primarily data-driven. Compared to expert experience, data-driven methods offer significant advantages in objectivity and accuracy, particularly in forest fire emergency management. Accurately capturing forest fire hazard level information allows fire departments to issue early warnings and prepare response plans. The extended confidence rule base model, a popular expert system approach, is suitable for natural disaster hazard assessment. Its use of IF-THEN rules aligns more closely with humans' natural understanding and expression of knowledge, enabling it to describe complex nonlinear relationships between input and output information. It also utilizes fuzzy theory at the data input stage, making the modeling process more flexible. Furthermore, the confidence rule base model is a white-box approach, ensuring full visibility of the derivation process and traceability of the results, providing excellent interpretability for decision makers. The extended confidence rule base has achieved significant research results in numerous fields.

[0004] However, in forest fire hazard level assessment, traditional extended confidence rule libraries suffer from deficiencies in modeling reliability and model reasoning reliability, resulting in poorly reliable assessment results and insufficient support for fire departments' decision-making. Therefore, improving the modeling and reasoning reliability of extended confidence rule libraries has become a top priority for forest fire hazard level assessment. Summary of the Invention

[0005] This invention provides a forest fire hazard level assessment method based on a reliable extended confidence rule base (REBRB). It details the specific method flow for reliability modeling and reliability model reasoning of the REBRB in forest fire hazard level assessment. Reliability modeling includes constructing a set of forest fire hazard level element attributes and calculating the matching degree of the extended confidence rule attributes using a Gaussian kernel function. Reliability model reasoning includes calculating the comprehensive matching degree of the extended confidence rule base using a copula function. Furthermore, the method includes interpreting the optimization of extended confidence rule parameters and calculating the extended confidence rule profit and loss value to analyze the effectiveness of the confidence rule. The invention has good reliability in forest fire hazard level assessment and can accurately and reliably assess forest fire hazard levels based on forest fire data.

[0006] The technical solutions of the present invention are as follows:

[0007] A forest fire hazard level assessment method based on a reliable extended confidence rule base, the specific steps are as follows:

[0008] Step 1: Construct a forest fire hazard level feature attribute set;

[0009] Forest fire events are composed of many factors that affect fires and the relationships between these factors. The degree of forest fire damage is the result of the interaction between hazard factors and hazard-bearing bodies in a specific disaster-prone environment. Therefore, the set of fire hazard level elements can be formally expressed as

[0010] Danger Level=φ(DIF,DAB,DPE,RL) (1)

[0011] Danger Level represents the forest fire hazard level, which is a nonlinear function of DIF, DAB, DPE, and RL, and is represented by the function symbol φ. R={r d |d=1,…,D}.DIF represents the set of disaster factors, represents the attribute of the a-th hazard factor element, A represents the total number of hazard factor attributes, DAB represents the set of hazard-bearing bodies, represents the bth hazard-bearing element attribute, B represents the total number of hazard-bearing attributes, DPE represents the set of hazard-prone environments, represents the cth disaster-prone environmental factor attribute, C represents the total number of disaster-prone environmental attributes, R represents the set of association relationships, rl d represents the relationship between any two elements, and D represents the total number of relationships.

[0012] Given a set of all forest fire premise feature attributes And all attribute reference levels The initial utility value

[0013] Among them J a , J b , J c Respectively The total number of reference levels in .

[0014] In order to simplify the representation of the above set, the attribute set of the premise elements where the forest is located can be represented as V = {v1, v2, ..., v M ,in Given all attribute reference levels J m express The total number of reference levels, given the initial utility

[0015] Step 2: Use the Gaussian kernel function to calculate the matching degree of the extended confidence rule attributes;

[0016] To solve the forest fire hazard level assessment problem, K groups of forest fire data are collected from the forest fire dataset. <v k , z k >(k=1,...,K), where v represents the kth group of forest fire feature attribute input value vectors, k，m represents the mth forest fire feature attribute value in the kth group of input value vectors, z k represents the output value of the kth forest fire hazard level; then, using utility theory to transform information, the K groups of data are converted into distributed confidence of premise attributes and result attributes. The calculation formula for the distributed confidence in the mth forest fire premise factor attribute is as follows:

[0017]

[0018] in Indicates v k，m Distribution reliability

[0019] Similarly, the output value z k The output attribute distribution confidence of forest fire hazard level can be calculated

[0020]

[0021] The obtained K groups of distributed confidence about the attributes of the premise forest fire elements and the attributes of the forest fire hazard results are used to calculate the antecedent similarity AS and the posterior similarity CS of each rule in the rule base:

[0022]

[0023] in, Indicates the inconsistency between the mth attribute of the lth rule and the kth rule. σ is an adjustment parameter. The matching degree of rule attributes can be adjusted by controlling the value of σ.

[0024]

[0025] Usually σ=0.2, so that In its domain, it is consistent with AS(R l , R k ) is close to a linear relationship.

[0026] By the same token, the calculation method of the subsequent similarity CS is

[0027]

[0028] Among them, dc l，k Indicates the inconsistency between the results of the lth rule and the kth rule,

[0029]

[0030] Assume that the input value vector x of a forest fire hazard assessment problem is p =(x p，1 ,…,x p，M ), p = 1, ..., P, P is the total number of input vectors, and each input value in the input value vector is converted into a distributed representation using the formula:

[0031]

[0032] Next, input vector x p =(x p，1 …, x p，M ) and the individual matching degree of the m-th premise attribute of the k-th extension rule is calculated as follows:

[0033]

[0034] Among them, σ is a tuning parameter, which can be used to adjust the rule attribute matching degree SS by controlling the value of σ. k (x p，m , v k，m ) is adjusted, usually taking σ=0.2, so that In its domain and SS k (x p，m , v k，m ) is close to a linear relationship.

[0035] in, is the inconsistency between the input vector and the mth premise attribute of the kth extension rule

[0036]

[0037] Step 3: Use the copula function to calculate the comprehensive matching degree of the extended confidence rule;

[0038] Assume (v h , z h ) and (v m , z m ) are two sets of random variables related to forest fires. The corresponding Kendall rank correlation coefficient, τ, can be obtained. τ measures the degree of consistency in the direction of change of the two random variables. τ = 1, τ = -1, and τ → 0 indicate that the two random variables change in completely consistent, completely opposite, and completely independent directions, respectively.

[0039] The Frank copula function can be used to obtain the correlation between all forest fire element attributes.

[0040] Among them, λ∈[1,+∞], when λ→0, the variables tend to be more independent; when λ>0, it means that the variables are positively correlated; when λ<0, it means that the variables are negatively correlated.

[0041] There is a certain relationship between the parameters of the copula function and the correlation index, that is, the Kendall coefficient can be uniquely represented by the copula function. Therefore, the copula parameters can be determined based on the Kendall rank correlation coefficient τ. The copula parameter λ is the correlation coefficient between the two factors. Since the correlation coefficient between multiple factors is less than the correlation coefficient between any two factors, the minimum value of the correlation coefficient can be selected to calculate the estimated value of the multi-factor correlation coefficient.

[0042]

[0043] The cumulative attribute matching degree of the kth rule is actually the joint probability value obtained by multiplying the matching degrees of individual attributes. In other words, this is consistent with the result calculated by the copula function. Therefore, the comprehensive matching degree of the kth rule calculated according to the copula function is:

[0044]

[0045] According to the comprehensive matching degree S k , rule weight θ k and attribute weight δ m , the activation weight formula of the kth rule is γ k

[0046] Using the ER method to analyze the formula, the distributed confidence of the result attributes of forest fire hazards in all activation rules is synthesized into a new distributed confidence ρn . According to the input vector x p =(x p，1 ,…,x p，M ), the forest fire hazard level can be obtained based on REBRB reasoning as f(x p ).

[0047] Step 4: Interpretable extended confidence rule parameter optimization;

[0048] In the construction of REBRB, the candidate grade utility values ​​of forest fire premise factor attributes, the grade utility values ​​of forest fire result attributes and attribute weights are parameters to be optimized, which are usually initialized by experts based on experience. In order to make the parameter optimization process objective and interpretable, the model of the parameter optimization method is given below.

[0049] REBRB parameter optimization: When the inference evaluation result of the REBRB model for each set of data is f(x p ), the parameter optimization model of REBRB can be expressed as:

[0050]

[0051] Among them E p Indicates the number of cases where the actual hazard level is not equal to the model output result. The objective function requires E p Minimize. lb m and ub m Indicates the lower and upper bounds of the value in the mth premise attribute; lb n and ub n Indicates the lower and upper bounds of the value in the result attribute; the interpretability of parameter optimization is reflected in the parameter restriction conditions in formula (15), which means that the grade utility value is monotonically increasing or monotonically decreasing, or monotonically increasing on the left side of the maximum grade utility and monotonically decreasing on the right side, so as to ensure that there is no contradiction in the grade utility. Then the DE algorithm can be used to optimize the parameters of the above model, and finally the optimized REBRB parameters are obtained.

[0052] Step 5: Calculate the profit and loss value of the extended confidence rule to analyze the effectiveness of the rule;

[0053] The purpose of REBRB rule validity analysis is to identify invalid rules and remove them from the rule base, retaining valid rules for forest fire hazard level assessment, thereby improving the reliability of REBRB's forest fire hazard level assessment.

[0054] Rule effectiveness analysis is a multi-iterative process. Assuming there are L rules in the rule base, in each iteration, the profit and loss value of each rule needs to be calculated:

[0055]

[0056] r w Indicates the wth iteration. When the profit and loss value is -1, Recorded as an invalid rule, when the profit and loss value is 1, Recorded as a valid rule, Indicates the current rule base Contribution obtained.

[0057]

[0058] Among them, z p represents the actual forest fire hazard level in the training set, Indicates that w In the iteration of the round, the input vector is x p When the forest fire hazard level is obtained by using the l rules of REBRB to make inferences and evaluation, Indicates that w In the iteration of the round, the input vector is x p When using the l-1 rule of REBRB, that is, not including the R l The forest fire hazard level is obtained by reasoning and evaluating the rules. illustrate Its cumulative contribution is negative during the reasoning process of the dataset, lacking It will improve the results of reasoning; illustrate Its contribution is cumulatively positive during the inference process of the dataset, and it is missing It will make the reasoning result worse. w ) means in r w In the iteration of the round, the parameter set of the optimized rules in REBRB It should be noted that rule parameters are determined based on the performance of the entire rule base. Changes in the number of rules will also lead to changes in parameters. After deleting invalid rules, all remaining rules in the rule base need to be optimized and updated again to ensure the validity of the rule parameters.

[0059] Figure 2The entire process of rule validity analysis is demonstrated. First, the DE evolutionary algorithm is used to optimize the rule parameters of the rules in the initial rule base. Then, rule validity analysis is performed to calculate the profit and loss value of each rule. When the profit and loss value is not 1, the rule is recorded as an invalid rule and removed. When the profit and loss value is 1, the rule is recorded as a valid rule and placed in the rule base. The above steps are repeated until there are no invalid rules in the rule base, and finally a valid rule base consisting of valid rules is obtained. Specifically, for example, in the first round of iteration, L rules in REBRB are used for reasoning. Each rule needs to be analyzed for validity. In this iteration, a total of l1 invalid rules are deleted, and the remaining L-l1 rules are further optimized using the DE optimization algorithm. In the second round of iteration, the L-l1 rules whose parameters have been optimized in the first round are also used for reasoning. Each rule needs to be analyzed for validity. In this iteration, a total of l2 invalid rules are deleted, and the remaining L-l1-l2 rules are further optimized using the DE optimization algorithm. This process is repeated until the Wth round, when all rules are analyzed for validity and no invalid rules are found, then all remaining L1-l1-l2, ..., -l W-1 The valid rules are organized into a valid rule base, and the effective rules are used to W-1 The reasoning is performed based on the rules, and the resulting reasoning evaluation result is used as the forest fire hazard level.

[0060] Beneficial effects of the present invention:

[0061] This method enhances the reliability of forest fire hazard level assessment. It avoids rule collapse and ensures smooth implementation. It also considers the relationship between attributes and rule validity, thereby improving the accuracy and reliability of forest fire hazard level assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Schematic diagram of the steps of forest fire hazard level assessment method based on reliable confidence rule base;

[0063] Figure 2 Schematic diagram of the rule validity analysis process;

[0064] Figure 3 A geographical information heat map of forest fire hazard levels in a certain area based on a reliable confidence rule base. DETAILED DESCRIPTION

[0065] The assessment method of the present invention can reliably assess the forest fire hazard level based on forest fire element attribute information, and comprehensively assess the forest fire hazard level using forest fire data such as meteorological conditions, vegetation type, topography, fire characteristics and other factors. It can also quickly adapt to environmental changes, timely update the assessment results, and display the forest fire area in the form of intuitive charts and maps to facilitate user understanding and decision-making. It helps forest fire prevention and extinguishing technology companies to provide reliable forest fire hazard level assessment services to forestry and fire departments in a timely and accurate manner, so as to formulate fire fighting preparations and resource deployment plans. The system design adopts a B / S structure, that is, a browser / server architecture, and is built based on the Flask framework, involving web technologies such as HTML and CSS.

[0066] The system architecture can be divided into the view layer, logic layer and data layer, as shown in Table 1:

[0067] Table 1 System architecture

[0068]

[0069] 1. Upload basic forest fire data

[0070] Weather data for the fire area is obtained from meteorological authorities, along with fire spread data, geographic information, and vegetation data from forest fire infrared sensors, surveillance video, and remote sensing satellites. This real-time data is stored in the data layer. Historical forest fire data is also uploaded to the data layer, allowing searches for historical fires in the area or fires on similar terrain.

[0071] 2. The system analyzes and diagnoses the uploaded data

[0072] The experiment of this function requires the system's data layer, logic layer and view layer to work together, as follows:

[0073] Step 1: The logic layer performs modal missing detection on the forest fire data uploaded by the data layer, and can use interpolation or historical data similarity calculation to fill in the missing data.

[0074] Step 2: Use the reliable confidence rule base method to perform reliability modeling, extract forest fire characteristics, generate a forest fire feature attribute set, and convert all data into extended confidence rules. Use the Gaussian kernel function to calculate the extended confidence rule attribute matching degree.

[0075] Step 3: After modeling is complete, the reliability model is inferred using a reliable confidence rule base method. The copula function is used to calculate the comprehensive matching degree of the extended confidence rules. After the calculation is completed, the parameters of the extended confidence rules are trained using historical fire data uploaded from the data layer until the optimization stop condition is met. The optimized parameters are then generated. The profit and loss values ​​of the rules are then calculated, invalid rules are deleted, and an effective extended confidence rule base is organized. Real-time forest fire data is fed into the reliable extended confidence rule base model to determine the forest fire hazard level.

[0076] Step 4: Upload the output hazard level results to the data layer for storage, and use ArcGIS to draw a geographic heat map of the forest fire hazard level results in the data layer. Figure 3 As shown, the view layer feeds back the visualized hazard level assessment results and the geographic information heat map of the fire area to the user, and displays the real-time information of the Senli fire in the information bar.

[0077] 3. Users can visually view the forest fire hazard level

[0078] After the system completes the entire process of forest fire hazard level assessment, the assessment results are stored in the database. Users can click on the map to view the real-time forest fire hazard level results. Click on the fire area to learn more about the current fire basic information and areas with different hazard levels. Emergency plans can be activated for different areas according to the hazard level results. Finally, the system assessment results can be used as a means to assist emergency decision-making.

Claims

1. A forest fire hazard level assessment method based on a reliable confidence rule base, characterized in that: The specific steps are as follows: Step 1: Construct a forest fire hazard level element set; The set of forest fire hazard levels is expressed as Danger Level=Φ(DIF,DAB,DPE,RL) (1) Given a set of all forest fire premise feature attributes And all attribute reference levels The initial utility value The attribute set of the premise elements where the forest is located is simplified as V = {v1, v2, ..., v M ),in Given all attribute reference levels Given the initial utility Step 2: Use the Gaussian kernel function to calculate the matching degree of the extended confidence rule attributes; Collect K groups of forest fire data from the forest fire dataset <v k , z k >(k=1,...,K),v k，m It is v k The mth attribute in the mth forest fire premise factor attribute is converted into the distributed confidence of the K group of data; the distributed confidence calculation formula in the mth forest fire premise factor attribute is as follows: Among them, v k，m Distribution reliability Similarly, the output value z k The output attribute distribution confidence of forest fire hazard level can be calculated The obtained K groups of distributed confidence about the attributes of the premise forest fire elements and the attributes of the forest fire hazard results are used to calculate the antecedent similarity AS and the posterior similarity CS of each rule in the rule base: in, It represents the inconsistency between the mth attribute of the lth rule and the kth rule. v is the adjustment parameter, which can adjust the rule attribute matching degree by controlling the value of σ. By the same token, the calculation method of the subsequent similarity CS is Among them, dc l，k Indicates the inconsistency between the results of the lth rule and the kth rule, Suppose the input value vector x of a forest fire hazard assessment problem is p =(x p，1 ,…,x p，M ), converting each input value in the input value vector into a distributed representation: Next, input vector x p =(x p，1 ,…,x p，M ) and the individual matching degree of the m-th premise attribute of the k-th extension rule is: Among them, σ is the adjustment parameter, by controlling the value of σ, the rule attribute matching degree SS k (x p，m , v k，m ) to make adjustments, is the inconsistency between the input vector and the mth premise attribute of the kth extension rule Step 3: Use the copula function to calculate the comprehensive matching degree of the extended confidence rule; The Frank copula function can be used to obtain the correlation between all forest fire element attributes. According to the property that the correlation coefficient between multiple factors is less than the correlation coefficient between any two factors, the minimum value among the correlation coefficients is selected to calculate the estimated value of the multi-factor correlation coefficient. The comprehensive matching degree of the kth rule is calculated according to the copula function: Step 4: Interpretable extended confidence rule parameter optimization; Step 5: Calculate the profit and loss value of the extended confidence rule to analyze the effectiveness of the rule.

2. A forest fire hazard level assessment method based on a reliable confidence rule base as claimed in claim 1, characterized in that: The step 4 can explain the extended confidence rule parameter optimization, and the specific operations are as follows: When the inference evaluation result of the REBRB model for each set of data is f(x p ), the parameter optimization model of REBRB is expressed as: Among them E p Indicates the number of cases where the actual hazard level is not equal to the model output result. The objective function requires E p Minimize; the interpretability of parameter optimization is reflected in the parameter constraints in formula (15). Then the DE algorithm can be used to optimize the parameters of the above model, and finally the optimized REBRB parameters are obtained.

3. A forest fire hazard level assessment method based on a reliable confidence rule base as claimed in claim 1 or 2, characterized in that: Step 5 calculates the profit and loss value of the extended confidence rule to analyze the effectiveness of the rule. The specific operations are as follows: Rule effectiveness analysis is a multi-iterative process. Assume there are L rules in the rule base. In each iteration, the profit and loss value of each rule needs to be calculated: r w Indicates the wth iteration; when the profit and loss value is -1, Recorded as an invalid rule, when the profit and loss value is 1, Recorded as a valid rule, Indicates the current rule base Contributions received; Among them, z p represents the actual forest fire hazard value in the training set, Indicates that w In the iteration of the round, the forest fire hazard level obtained is Indicates that w In the iteration of round, the Rth l The forest fire hazard level obtained by reasoning and evaluating the rules; ξ(r w ) means in r w In the iteration of the round, the parameter set of the optimized rules in REBRB 4. A forest fire hazard level assessment method based on a reliable confidence rule base as claimed in claim 3, characterized in that: The illustrate Its cumulative contribution is negative during the reasoning process of the dataset, lacking It will improve the results of reasoning; illustrate Its contribution is cumulatively positive during the inference process of the dataset, and it is missing This will worsen the inference results.

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