Unmanned helicopter inspection benefit evaluation method for forest fire

Abstract

The invention discloses an unmanned helicopter patrol benefit evaluation method for forest fire, and the method comprises the steps: S1, extracting the influence factors of the patrol benefit of an unmanned helicopter through the analysis of the operation process, advantages and disadvantages of the patrol of the unmanned helicopter, and summarizing and sorting out the evaluation indexes of the patrol benefit through the patrol benefit influence factors, establishing a forest fire unmanned helicopter inspection benefit evaluation index system; s2, determining the optimal combination weight of the AHP method and the CRITIC method of each index of the inspection benefit evaluation index system through a game theory combination weighting method; and S3, the cloud model realizes quantitative evaluation of patrol benefits.

Description

Technical Field

[0001] The invention belongs to the technical field of forest fire prevention, and in particular relates to an unmanned helicopter patrol benefit evaluation method for forest fires. Background Art

[0002] In order to better prevent and control forest fires, unmanned helicopter patrol technology is widely used in forest fire monitoring and fighting.

[0003] Unmanned helicopter patrol technology refers to the use of unmanned helicopters as carriers of infrared, visible light and other sensor equipment to patrol along the expected route and transmit suspicious fire data. Its workflow can be summarized as fire detection, information analysis and command and dispatch.

[0004] The role of patrol technology in preventing and controlling forest fires is becoming increasingly prominent. However, in terms of unmanned helicopter forest fire patrols, there is still a lack of the establishment and application of evaluation index systems and assessment models. Currently, patrol technology still has the problem that the benefits of unmanned helicopter patrols are difficult to evaluate and compare. Summary of the invention

[0005] The purpose of this invention is to solve the advantages and disadvantages of unmanned helicopter patrols. Through a comprehensive evaluation model, the problem of lack of evaluation index system and establishment and application of evaluation model is solved. At present, patrol technology still has the problem that the benefits of unmanned helicopter patrols are difficult to evaluate and compare.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides an unmanned helicopter patrol benefit evaluation method for forest fires, and the evaluation method comprises the following steps:

[0008] Step S1, extracting the influencing factors of the unmanned helicopter patrol benefit by analyzing the operation process, advantages and disadvantages of the unmanned helicopter patrol, summarizing the patrol benefit influencing factors to sort out the patrol benefit evaluation indicators, and establishing the forest fire unmanned helicopter patrol benefit evaluation indicator system;

[0009] Step S2, determining the optimal combination weights of the AHP method and the CRITIC method for each indicator of the inspection benefit evaluation index system through the game theory combined weighting method;

[0010] Step S3: The cloud model realizes the quantitative evaluation of the inspection benefits.

[0011] The unmanned helicopter patrol in step S1 specifically includes:

[0012] The process of unmanned helicopter inspection for forest fire is to determine the inspection area, deploy unmanned helicopters, conduct inspections and data processing; determining the inspection area means that before the inspection work is carried out, it is necessary to determine in advance the forest area to be inspected, the tree species, tree height, crown density, etc. of the forest, so as to facilitate the subsequent unmanned helicopter inspection; deploying unmanned helicopters mainly includes selecting appropriate unmanned helicopter models, installing corresponding thermal imagers, high-definition cameras and other equipment, and inspecting and debugging them to ensure their normal operation; before the inspection, the take-off point and flight trajectory of the unmanned helicopter need to be planned; and the inspection process is that the unmanned helicopter operator takes off, flies, takes pictures and other operations on the unmanned helicopter in the unmanned helicopter control room; while the unmanned helicopter obtains real-time image information of the target area through thermal imagers, high-definition cameras and other equipment, the operator needs to make judgments and analyses based on the real-time images and data, and promptly report abnormal situations such as fire points; data processing is to process and analyze the data obtained by each unmanned helicopter, so as to provide it to the command center and relevant departments for reference, to assist in decision-making and fire fighting.

[0013] The benefit evaluation index system in step S1 specifically includes:

[0014] The three-level evaluation indicators of unmanned helicopter patrols are proposed based on the influencing factors; the three-level indicators are classified according to the internal connections and similarities between them, and the second-level indicators that can reflect the comprehensive performance of all the three-level indicators under this classification are established; the second-level indicators are further summarized to form four first-level indicators of patrol coverage, patrol quality, patrol safety and patrol cost; the indicators are organized and arranged according to the hierarchical relationship to finally form a patrol indicator system.

[0015] The evaluation index system in step S2 is optimized by combining weights using the AHP method and the CRITIC method. The specific method is as follows:

[0016] The analytic hierarchy process (AHP) is a subjective value assignment method. It requires experts to score the importance of each indicator. The judgment matrix adopts Santy's 1-9 scale method. The importance of each indicator determined by the expert score is constructed into an n-order judgment matrix T. The judgment matrix T is used to calculate the weight of each factor on the indicator:

[0017]

[0018] In the formula, a ij Represents the value of the i-th row and j-th column in the judgment matrix T;

[0019] Finally, solve the CR value. When CR < 0.1, it indicates that the consistency of the judgment matrix is ​​acceptable.

[0020]

[0021] In the formula, λ max is the maximum characteristic root of the judgment matrix T; RI is obtained from the consistency index value table;

[0022] The CRITIC method mainly uses the correlation between data to calculate weights. First, according to the relative importance of n indicators obtained by the scores of m experts, a CRITIC matrix X = (x ij ) m×n The matrix is ​​standardized, and the elements in the matrix with a value range of [0,1] are the relative importance of the indicators. Finally, the information carrying capacity C is calculated. j To reflect the weight, it can be considered that the greater the information carrying capacity, the greater the weight:

[0023] C i =σ i f i (11)

[0024]

[0025] In the formula, the standard deviation σ j Indicates the comparability of j indicators; f i Indicates the correlation between different indicators. r ij represents the Pearson correlation coefficient between indicator i and indicator j;

[0026] Game theory is used to study the coordination of subjective and objective weighting methods in conflicts, and to seek the maximum benefit of both parties. First, the weight values ​​calculated by the analytic hierarchy process and the CRITIC method are defined as follows: First-level weight vector level: {u 11 、u 12}, Secondary weight vector level: {u 21 、u 22}、Level 3 weight vector level: {u 31 、u 32 The calculated j-level weight set is: In order to minimize the deviation from "and" and "individual", the countermeasure model for indicators at all levels is:

[0027]

[0028] The linear equations corresponding to the first-order optimal derivative are:

[0029]

[0030] Normalize the linear combination weight coefficient obtained by formula (7) And solve the weight of the jth indicator of the optimal combination based on game theory, using formula uj =a1u 1j +a2u 2j , and get the game combination weights of each indicator.

[0031] Beneficial effects: The system of the present invention integrates the advantages of unmanned helicopter patrols, avoids the disadvantages of unmanned helicopter patrols, and establishes a more complete indicator system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A schematic diagram for extracting the evaluation index of the unmanned helicopter patrol benefit invented;

[0034] Figure 2 This is the unmanned helicopter patrol evaluation index system of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the invention clearer, the technical solution of the invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments in the invention, all other implementation methods obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0036] See also Figure 1-2 As shown, the unmanned helicopter patrol benefit evaluation method for forest fires provided by the present invention comprises the following steps:

[0037] Step S1, extracting the influencing factors of the unmanned helicopter patrol benefit by analyzing the operation process, advantages and disadvantages of the unmanned helicopter patrol, summarizing the patrol benefit influencing factors to sort out the patrol benefit evaluation indicators, and establishing the forest fire unmanned helicopter patrol benefit evaluation indicator system;

[0038] Step S2, determining the optimal combination weights of the AHP method and the CRITIC method for each indicator of the inspection benefit evaluation index system through the game theory combined weighting method;

[0039] Step S3: The cloud model realizes the quantitative evaluation of the inspection benefits.

[0040] The unmanned helicopter patrol in step S1 specifically includes:

[0041] The process of unmanned helicopter inspection for forest fire is to determine the inspection area, deploy unmanned helicopters, conduct inspections and data processing; determining the inspection area means that before the inspection work is carried out, it is necessary to determine in advance the forest area to be inspected, the tree species, tree height, crown density, etc. of the forest, so as to facilitate the subsequent unmanned helicopter inspection; deploying unmanned helicopters mainly includes selecting appropriate unmanned helicopter models, installing corresponding thermal imagers, high-definition cameras and other equipment, and inspecting and debugging them to ensure their normal operation; before the inspection, the take-off point and flight trajectory of the unmanned helicopter need to be planned; and the inspection process is that the unmanned helicopter operator takes off, flies, takes pictures and other operations on the unmanned helicopter in the unmanned helicopter control room; while the unmanned helicopter obtains real-time image information of the target area through thermal imagers, high-definition cameras and other equipment, the operator needs to make judgments and analyses based on the real-time images and data, and promptly report abnormal situations such as fire points; data processing is to process and analyze the data obtained by each unmanned helicopter, so as to provide it to the command center and relevant departments for reference, to assist in decision-making and fire fighting.

[0042] The benefit evaluation index system in step S1 specifically includes:

[0043] The three-level evaluation indicators of unmanned helicopter patrols are proposed based on the influencing factors; the three-level indicators are classified according to the internal connections and similarities between them, and the second-level indicators that can reflect the comprehensive performance of all the three-level indicators under this classification are established; the second-level indicators are further summarized to form four first-level indicators of patrol coverage, patrol quality, patrol safety and patrol cost; the indicators are organized and arranged according to the hierarchical relationship to finally form a patrol indicator system.

[0044] The evaluation index system in step S2 is optimized by combining weights using the AHP method and the CRITIC method. The specific method is as follows:

[0045] The analytic hierarchy process (AHP) is a subjective value assignment method. It requires experts to score the importance of each indicator. The judgment matrix adopts Santy's 1-9 scale method. The importance of each indicator determined by the expert score is constructed into an n-order judgment matrix T. The judgment matrix T is used to calculate the weight of each factor on the indicator:

[0046]

[0047] In the formula, a ij Represents the value of the i-th row and j-th column in the judgment matrix T;

[0048] Finally, solve the CR value. When CR < 0.1, it indicates that the consistency of the judgment matrix is ​​acceptable.

[0049]

[0050] In the formula, λ max is the maximum characteristic root of the judgment matrix T; RI is obtained from the consistency index value table;

[0051] The CRITIC method mainly uses the correlation between data to calculate weights. First, according to the relative importance of n indicators obtained by the scores of m experts, a CRITIC matrix X = (x ij ) m×n The matrix is ​​standardized, and the elements in the matrix with a value range of [0,1] are the relative importance of the indicators. Finally, the information carrying capacity C is calculated. j To reflect the weight, it can be considered that the greater the information carrying capacity, the greater the weight:

[0052] C i =σ i f i (18)

[0053]

[0054] In the formula, the standard deviation σ j Indicates the comparability of j indicators; f i Indicates the correlation between different indicators. r ij It represents the Pearson correlation coefficient between indicator i and indicator j;

[0055] Game theory is used to study the coordination of subjective and objective weighting methods in conflicts, and to seek the maximum benefit of both parties. First, the weight values ​​calculated by the analytic hierarchy process and the CRITIC method are defined as follows: First-level weight vector level: {u 11 、u 12}, Secondary weight vector level: {u 21 、u 22}、Level 3 weight vector level: {u 31 、u 32 The calculated j-level weight set is: In order to minimize the deviation from "and" and "individual", the countermeasure model for indicators at all levels is:

[0056]

[0057] The linear equations corresponding to the first-order optimal derivative are:

[0058]

[0059] Normalize the linear combination weight coefficient obtained by formula (7) And solve the weight of the jth indicator of the optimal combination based on game theory, using formula u j =a1u1j +a2u 2j , and get the game combination weights of each indicator.

[0060] The beneficial effect of the present invention is that the system of the present invention integrates the advantages of unmanned helicopter patrol, avoids the disadvantages of unmanned helicopter patrol, and establishes a more perfect indicator system.

[0061] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An unmanned helicopter patrol benefit evaluation method for forest fires, characterized by: The evaluation method comprises the following steps: Step S1, extracting the influencing factors of the unmanned helicopter patrol benefit by analyzing the operation process, advantages and disadvantages of the unmanned helicopter patrol, summarizing the patrol benefit influencing factors to sort out the patrol benefit evaluation indicators, and establishing the forest fire unmanned helicopter patrol benefit evaluation indicator system; Step S2, determining the optimal combination weights of the AHP method and the CRITIC method for each indicator of the inspection benefit evaluation index system through the game theory combined weighting method; Step S3: The cloud model realizes the quantitative evaluation of the inspection benefits.

2. The unmanned helicopter patrol benefit evaluation method for forest fires according to claim 1 is characterized by: The unmanned helicopter patrol in step S1 specifically includes: The process of unmanned helicopter inspection for forest fire is to determine the inspection area, deploy unmanned helicopters, conduct inspections and data processing; determining the inspection area means that before the inspection work is carried out, it is necessary to determine in advance the forest area to be inspected, the tree species, tree height, crown density, etc. of the forest, so as to facilitate the subsequent unmanned helicopter inspection; deploying unmanned helicopters mainly includes selecting appropriate unmanned helicopter models, installing corresponding thermal imagers, high-definition cameras and other equipment, and inspecting and debugging them to ensure their normal operation; before the inspection, the take-off point and flight trajectory of the unmanned helicopter need to be planned; and the inspection process is that the unmanned helicopter operator takes off, flies, takes pictures and other operations on the unmanned helicopter in the unmanned helicopter control room; while the unmanned helicopter obtains real-time image information of the target area through thermal imagers, high-definition cameras and other equipment, the operator needs to make judgments and analyses based on the real-time images and data, and promptly report abnormal situations such as fire points; data processing is to process and analyze the data obtained by each unmanned helicopter, so as to provide it to the command center and relevant departments for reference, to assist in decision-making and fire fighting.

3. The unmanned helicopter patrol benefit evaluation method for forest fires according to claim 1 is characterized in that: The benefit evaluation index system in step S1 specifically includes: The three-level evaluation indicators of unmanned helicopter patrols are proposed based on the influencing factors; the three-level indicators are classified according to the internal connections and similarities between them, and the second-level indicators that can reflect the comprehensive performance of all the three-level indicators under this classification are established; the second-level indicators are further summarized to form four first-level indicators of patrol coverage, patrol quality, patrol safety and patrol cost; the indicators are organized and arranged according to the hierarchical relationship to finally form a patrol indicator system.

4. The unmanned helicopter patrol benefit evaluation method for forest fires according to claim 1 is characterized by: The evaluation index system in step S2 is optimized by combining weights using the AHP method and the CRITIC method. The specific method is as follows: The analytic hierarchy process (AHP) is a subjective value assignment method. It requires experts to score the importance of each indicator. The judgment matrix adopts Santy's 1-9 scale method. The importance of each indicator determined by the expert score is constructed into an n-order judgment matrix T. The judgment matrix T is used to calculate the weight of each factor on the indicator: In the formula, a ij Represents the value of the i-th row and j-th column in the judgment matrix T; 最 After that, solve the CR value. When CR < 0.1, it indicates that the consistency of the judgment matrix is ​​acceptable. In the formula, λ max is the maximum characteristic root of the judgment matrix T; RI is obtained from the consistency index value table; The CRITIC method mainly uses the correlation between data to calculate weights. First, according to the relative importance of n indicators obtained by the scores of m experts, a CRITIC matrix X = (x ij ) m×n The matrix is ​​standardized, and the elements in the matrix with a value range of [0,1] are the relative importance of the indicators. Finally, the information carrying capacity C is calculated. j To reflect the weight, it can be considered that the greater the information carrying capacity, the greater the weight: C i =σ i f i (4) In the formula, the standard deviation σ j Indicates the comparability of j indicators; f i Indicates the correlation between different indicators. r ij It represents the Pearson correlation coefficient between indicator i and indicator j; Game theory is used to study the coordination of subjective and objective weighting methods in conflicts, and to seek the maximum benefit of both parties. First, the weight values ​​calculated by the analytic hierarchy process and the CRITIC method are defined as follows: First-level weight vector level: {u 11 、u 12 }, Secondary weight vector level: {u 21 、u 22 }、Level 3 weight vector level: {u 31 、u 32 The calculated j-level weight set is: In order to minimize the deviation from "and individual", the countermeasure model of indicators at all levels is: The linear equations corresponding to the first-order optimal derivative are: Normalize the linear combination weight coefficient obtained by formula (7) And solve the weight of the jth indicator of the optimal combination based on game theory, using formula u j =a1u 1j +a2u 2j , and get the game combination weights of each indicator.

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