Dam termite monitoring node deployment method based on artificial bee colony algorithm

Abstract

The invention discloses a dam termite monitoring node deployment method based on an artificial bee colony algorithm, and the method comprises the following steps: constructing a dam termite hazard risk evaluation index system, and determining a dam key termite hazard monitoring region according to a result; a coverage optimization model is established according to the to-be-monitored area, and a coverage optimization problem is defined as a monitoring node deployment problem which enables the network coverage rate in the monitored area to reach the maximum; and solving a coverage optimization problem by adopting an artificial bee colony algorithm to obtain an optimal solution, and determining a termite monitoring node deployment scheme. According to the method, through an analytic hierarchy process-fuzzy comprehensive evaluation method, the termite hazard risk of the dam area is comprehensively evaluated, and the key monitoring area Sn of the dam is determined according to the evaluation result. An artificial bee colony algorithm is introduced, a coverage model is optimized, a monitoring node deployment scheme is determined when the network coverage rate in a monitoring area reaches the maximum, the monitoring node coverage rate is increased, dam safety is guaranteed, and the monitoring deployment cost is reduced.

Description

Technical Field

[0001] The patent of this invention relates to the technical field of dam termite monitoring, and specifically to a method for deploying dam termite monitoring nodes based on an artificial bee colony algorithm. Background Art

[0002] Termites are one of the world's pests. The suitable humidity and abundant food sources of dams provide a living environment for termites. Termites often build huge nests and numerous secondary nests and extensive ant tunnels inside the dam, which damage the integrity of the dam structure and cause leakage, pipe bursts, subsidence, landslides and other serious dangers. In serious cases, it may even lead to the serious consequence of dam collapse.

[0003] Since termite damage in water conservancy projects is highly concealed, repetitive, and long-term, it is necessary to monitor and warn them, and the deployment of termite monitoring nodes is extremely important. Improper deployment will not only fail to monitor the activity signs of termites in a timely manner, but also miss the best time for prevention and control, resulting in termites harming the dam and destroying the stability of the dam, and will also cost a huge amount of money. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for deploying dam termite monitoring nodes based on an artificial bee colony algorithm, which can realize the recycling of lubricating oil of the oil-cooled motor and reducer on the low-floor wheel side.

[0005] The object of the present invention is achieved in the following manner:

[0006] A method for deploying dam termite monitoring nodes based on an artificial bee colony algorithm comprises the following steps:

[0007] S1: Construct a risk assessment index system for termite damage in dams, conduct a risk assessment of termite damage in dam areas, and determine the key monitoring areas for termites in dams based on the assessment results. n ,

[0008] The dam termite hazard risk assessment index system is constructed from three levels as the criterion layer, namely, dam vulnerability index, termite hazard source index, and hazard object index, with the dam termite hazard risk assessment as the main objective layer;

[0009] S2: According to the area to be monitored S n , build a coverage optimization model, and define the coverage optimization problem as the monitoring node deployment problem that maximizes the network coverage in the monitoring area;

[0010] S3: The improved artificial bee colony algorithm is used to solve the coverage optimization problem and obtain an approximate optimal solution to determine the termite monitoring node deployment plan.

[0011] In the above-mentioned deployment method of dam termite monitoring nodes based on artificial bee colony algorithm, the risk of dam termite damage is comprehensively evaluated based on the above-mentioned index system through hierarchical analysis-fuzzy comprehensive evaluation method, and the key monitoring area S of the dam is determined according to the evaluation results. n .

[0012] The deployment method of the dam termite monitoring node based on the artificial bee colony algorithm, the hierarchical analysis-fuzzy comprehensive evaluation method, that is, the index weight is determined by the hierarchical analysis method, the fuzzy comprehensive evaluation method is used to calculate the score, and the monitoring area S is determined. n .

[0013] The deployment method of the dam termite monitoring node based on the artificial bee colony algorithm, the hierarchical analysis method determines the indicator weights, based on the 1-9 degree scaling method, invites a number of experts in related professional fields to score each indicator under the three criterion layers, and constructs the criterion layer B n For target layer A and indicator layer C n Alignment level B n The judgment matrix is ​​normalized to obtain the eigenvector W i Determine the weight of each indicator; The maximum eigenvalue is obtained by the formula; The formula is used to obtain CI, and the average random consistency index value table is used to obtain R I The random consistency test is performed according to the following formula to determine the comprehensive weight of each evaluation index as follows:

[0014]

[0015] Where λmax is the maximum eigenvalue; Wi is the weight of the i-th indicator in the weight vector obtained after normalization; n is the order of the judgment matrix; Bw represents the product of the judgment matrix B and the weight vector W; CI is the consistency index and RI is the random consistency index.

[0016] The above-mentioned deployment method of dam termite monitoring nodes based on artificial bee colony algorithm, the fuzzy comprehensive evaluation method score calculation, firstly establishes a fuzzy comprehensive evaluation set to divide the dam termite hazard risk comprehensive evaluation results into 5 levels, each level corresponds to a different evaluation value, the comprehensive evaluation set of indicators is: V = {V1, V2, V3, V4, V5} = {no hazard, less hazard, general hazard, greater hazard, significant hazard}; the score set corresponding to the evaluation set is expressed as:

[0017] U={U1, U2, U3, U4, U5}={50, 60, 70, 80, 90};

[0018] Then the index membership is determined: the weight values ​​of each index determined by the above-mentioned analytic hierarchy process are evaluated on the basis of the research on fuzzy comprehensive evaluation method by inviting the above-mentioned experts to use the grade weight method to give a no-hazard to significant-hazard evaluation of the impact of each index on the risk of termite damage to the dam. The membership is solved according to the evaluation, and the membership evaluation matrix R is constructed. i ;

[0019] Where: Membership degree = number of experts who selected the evaluation results of a certain indicator (no influence ~ significant influence) / total number of experts;

[0020] The indicator weight set W i And the membership evaluation matrix R i The index evaluation level is obtained by multiplication, and the comprehensive evaluation score of termite damage risk is calculated. The area with a score higher than 60 is the key monitoring area for dam termites S n .

[0021] In the above-mentioned deployment method of dam termite monitoring nodes based on artificial bee colony algorithm, the S2 to be monitored area S n First, the n monitoring areas are divided into grids, and each area is discretized into M n ×N n monitoring points, for the monitoring area S n The coordinates of any point A (a in , a jn ), where 1≤i n ≤M n , 1≤j n ≤N n ; The number of sensor nodes to be deployed is D n , D n is a positive integer, and the monitoring radius of each sensor is r; assuming that sensor p in Deployed at point (x in ,y in ), the set of sensors is P n ={p 1n , p 2n ,···,p in}; then build a coverage optimization model to maximize the network coverage in the monitoring area and establish the monitoring node deployment optimization objective function:

[0022]

[0023] Where R(n) represents the monitoring area S n The coverage rate, P in Indicates monitoring area S n The total number of grid points detected by the sensor.

[0024] In the above-mentioned deployment method of dam termite monitoring nodes based on artificial bee colony algorithm, the coverage optimization model adopts a Boolean coverage model, and the coverage of the detection node to the grid point is shown in the formula:

[0025]

[0026] Among them, P in Indicates whether the grid point is covered by the monitoring node, d in is the Euclidean distance between the monitoring node and any point A in the monitoring area, and the calculation formula is as follows:

[0027]

[0028] In the above-mentioned method for deploying dam termite monitoring nodes based on artificial bee colony algorithm, S3 includes:

[0029] S31: Initialize the population and set relevant parameters, such as the number of nectar sources SN, dimension D, maximum number of iterations G, and control parameter limit, i.e., the number of times the same nectar source is mined;

[0030] The formula for initializing the population is as follows:

[0031] x in，j =x min(n)，j +rand(0,1)(x max(n)，j -x min(n)，j ) (5)

[0032] In the formula, x max(n) , j and x min(n) , j They are respectively monitoring areas S n The upper and lower limits of the boundary, Rand(0,1) is a random number uniformly distributed on the interval [0,1];

[0033] S32: Calculate the fitness value of the initial nectar source, as shown in the formula:

[0034]

[0035] Among them, fit in is the monitoring area S n The fitness of the i-th solution, f i is the objective function of the optimization problem, f i ≥0. S33: The employed bee searches for a new solution in the neighborhood of the initial solution using the following formula and calculates its fitness, and performs greedy selection. If the new solution has better fitness, it replaces the optimal solution, otherwise it remains unchanged and the number of explorations increases by 1;

[0036] NE in，j =x in，j+rand(-1,1)(x in , j -x kn , j ) (7)

[0037] Among them, x in，j It is the old nectar source of Sn in the monitoring area, NE in，j is a candidate solution generated by the neighborhood, x kn , j is a random honey source, i≠k, Rand(-1,1) is a random number uniformly distributed in the interval [-1,1];

[0038] S34: The follower bee calculates the probability p of each nectar source being selected according to fitness through the roulette method in After a follower bee chooses an employed bee, the follower bee tries to find a new solution near the nectar source of its leader bee and calculates its fitness, and makes a greedy choice. If the new solution found by the follower bee is better than the original solution of the employed bee, the new solution replaces the original solution; otherwise, the food source remains unchanged and the number of explorations increases by 1;

[0039]

[0040] S35: Scouting bee stage, count whether the number of explorations of each food source has reached the limit. If it exceeds, the employed bees exploring the current nectar source become scout bees, and randomly generate a new food source through the formula for initializing the population, and reset the number of explorations of the new food source. When the loop termination condition is met, the approximate optimal solution is output.

[0041] Compared with the prior art, the present invention has the following technical effects:

[0042] The present invention uses the hierarchical analysis-fuzzy comprehensive evaluation method to comprehensively evaluate the termite hazard risk in the dam area, and determines the key monitoring area Sn of the dam according to the evaluation results. Then the artificial bee colony algorithm is introduced to optimize the coverage model to maximize the network coverage rate in the monitoring area to determine the monitoring node deployment plan, improve the monitoring node coverage rate to ensure dam safety and reduce the monitoring deployment cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the process of the present invention.

[0044] Figure 2 It is a schematic diagram of the artificial bee colony algorithm flow of the present invention. DETAILED DESCRIPTION

[0045] Combine the following Figure 1 The deployment method of the present invention is described in detail.

[0046] A method for deploying dam termite monitoring nodes based on an artificial bee colony algorithm comprises the following steps:

[0047] S1: Construct a risk assessment index system for termite damage in dams, conduct a risk assessment of termite damage in dam areas, and determine the key monitoring areas for termites in dams based on the assessment results. n ,

[0048] The dam termite hazard risk assessment index system is constructed from three levels as the criterion layer, namely, dam vulnerability index, termite hazard source index, and hazard object index, with the dam termite hazard risk assessment as the main objective layer;

[0049] The specific indicator layer can be based on the actual situation of the dam to be studied. The indicator layer selected in this embodiment includes: ① The dam vulnerability index is the regional dam material, the distance between the monitoring point and the dam body, the thickness of the anti-seepage layer, the dam body permeability coefficient, the shear strength, the soil pH value, and the soil texture; ② The termite hazard source index is the termite survey data such as the termite species survey, the termite nest detection, and the termite activity trace survey; ③ The hazard object index selects the population density and the GDP per capita of the study area. The constructed dam termite hazard risk comprehensive evaluation index system is shown in the following table;

[0050]

[0051] S2: According to the area to be monitored S n , build a coverage optimization model, and define the coverage optimization problem as the monitoring node deployment problem that maximizes the network coverage in the monitoring area;

[0052] S3: The improved artificial bee colony algorithm is used to solve the coverage optimization problem and obtain an approximate optimal solution to determine the termite monitoring node deployment plan.

[0053] The deployment method of dam termite monitoring nodes based on artificial bee colony algorithm of the present invention, in said S1, based on the above-mentioned index system, a hierarchical analysis-fuzzy comprehensive evaluation method is used to comprehensively evaluate the dam termite hazard risk, and the key monitoring area S of the dam is determined according to the evaluation results. n .

[0054] The deployment method of the dam termite monitoring node based on the artificial bee colony algorithm of the present invention, the hierarchical analysis-fuzzy comprehensive evaluation method, that is, the index weight is determined by the hierarchical analysis method, the score is calculated by the fuzzy comprehensive evaluation method, and the monitoring area S is determined. n .

[0055] The deployment method of the dam termite monitoring node based on the artificial bee colony algorithm, the hierarchical analysis method determines the indicator weights, based on the 1-9 degree scaling method, invites a number of experts in related professional fields to score each indicator under the three criterion layers, and constructs the criterion layer Bn For target layer A and indicator layer C n Alignment level B n The judgment matrix is ​​normalized to obtain the eigenvector W i Determine the weight of each indicator; The maximum eigenvalue is obtained by the formula; The formula is used to obtain CI, and the average random consistency index value table is used to obtain R I The random consistency test is performed according to the following formula to determine the comprehensive weight of each evaluation index as follows:

[0056]

[0057] Where λmax is the maximum eigenvalue; Wi is the weight of the i-th indicator in the weight vector obtained after normalization; n is the order of the judgment matrix; Bw represents the product of the judgment matrix B and the weight vector W; CI is the consistency index and RI is the random consistency index.

[0058]

[0059]

[0060] The fuzzy comprehensive evaluation method score calculation first establishes a fuzzy comprehensive evaluation set to divide the comprehensive evaluation results of the dam termite damage risk into 5 levels, each level corresponds to a different evaluation value, and the comprehensive evaluation set of indicators is:

[0061] V = {V1, V2, V3, V4, V5} = {no harm, minor harm, moderate harm, major harm, significant harm}; the score set corresponding to the evaluation set is expressed as:

[0062] U={U1, U2, U3, U4, U5}={50, 60, 70, 80, 90};

[0063] Then the index membership is determined: the weight values ​​of each index determined by the above-mentioned analytic hierarchy process are based on the research of fuzzy comprehensive evaluation method. The above-mentioned 10 experts are invited to use the grade weight method to give an evaluation of the impact of each index on the risk of termite damage to the dam from no harm to significant harm. The membership is solved according to the evaluation, and the membership evaluation matrix R is constructed. i ;

[0064] Where: Membership degree = number of experts who selected the evaluation results of a certain indicator (no influence ~ significant influence) / total number of experts;

[0065] The indicator weight set Wi and the membership evaluation matrix Ri are multiplied to obtain the indicator evaluation level. The B1 dam vulnerability index of the criterion layer is obtained by sorting out the membership matrix and performing a composite operation with the B1 indicator layer based on the corresponding criterion layer weight to obtain the membership value L1 of the B1 subordinate index of the criterion layer. According to the above method, the B2 and B3 membership values ​​L2 and L3 can be obtained respectively to obtain the final membership evaluation matrix. According to the obtained evaluation B1, B2, B3 three system indicator weight values, multiplied by the final membership evaluation matrix, the final evaluation result of the fuzzy matrix is ​​obtained. And the score set U = {U1, U2, U3, U4, U5} = {50, 60, 70, 80, 90} corresponding to the fuzzy comprehensive evaluation index mentioned above is multiplied again to calculate the comprehensive evaluation score of termite damage risk. The area with a score higher than 60 is the key monitoring area Sn of the dam to be monitored.

[0066] The deployment method of the dam termite monitoring node based on the artificial bee colony algorithm of the present invention, the S2 to be monitored area S n First, the n monitoring areas are divided into grids, and each area is discretized into M n ×N n monitoring points, for the monitoring area S n The coordinates of any point A (a in , a jn ), where 1≤i n ≤M n , 1≤j n ≤N n ; The number of sensor nodes to be deployed is D n , D n is a positive integer, and the monitoring radius of each sensor is r; assuming that sensor p in Deployed at point (x in ,y in ), the set of sensors is P n ={p 1n , p 2n ,···,p in}; then build a coverage optimization model to maximize the network coverage in the monitoring area and establish the monitoring node deployment optimization objective function:

[0067]

[0068] Where R(n) represents the monitoring area S n The coverage rate, P in Indicates monitoring area S n The total number of grid points detected by the sensor;

[0069] The coverage optimization model adopts a Boolean coverage model, and the coverage of the detection nodes to the grid points is shown in the formula:

[0070]

[0071] Among them, P in Indicates whether the grid point is covered by the monitoring node, d in is the Euclidean distance between the monitoring node and any point A in the monitoring area, and the calculation formula is as follows:

[0072]

[0073] In the above-mentioned method for deploying dam termite monitoring nodes based on artificial bee colony algorithm, S3 includes:

[0074] S31: Initialize the population and set relevant parameters, such as the number of nectar sources SN, dimension D, maximum number of iterations G, and control parameter limit, i.e., the number of times the same nectar source is mined;

[0075] The formula for initializing the population is as follows:

[0076] x in，j =x min(n)，j +rand(0,1)(x max(n)，j -x min(n)，j ) (5)

[0077] In the formula, x max(n)，j and x min(n)，j They are respectively monitoring areas S n The upper and lower limits of the boundary, Rand(0,1) is a random number uniformly distributed on the interval [0,1];

[0078] S32: Calculate the fitness value of the initial nectar source, as shown in the formula:

[0079]

[0080] Among them, fit in is the monitoring area S n The fitness of the i-th solution, f i is the objective function of the optimization problem, f i ≥0. S33: The employed bee searches for a new solution in the neighborhood of the initial solution using the following formula and calculates its fitness, and performs greedy selection. If the new solution has better fitness, it replaces the optimal solution, otherwise it remains unchanged and the number of explorations increases by 1;

[0081] NE in，j =x in，j +rand(-1,1)(x in，j -x kn，j ) (7)

[0082] Among them, x in，jIt is the old nectar source of Sn in the monitoring area, NE in，j is a candidate solution generated by the neighborhood, x kn , j is a random honey source, i≠k, Rand(-1,1) is a random number uniformly distributed in the interval [-1,1];

[0083] S34: The follower bee calculates the probability p of each nectar source being selected according to fitness through the roulette method in After a follower bee chooses an employed bee, the follower bee tries to find a new solution near the nectar source of its leader bee and calculates its fitness, and makes a greedy choice. If the new solution found by the follower bee is better than the original solution of the employed bee, the new solution replaces the original solution; otherwise, the food source remains unchanged and the number of explorations increases by 1;

[0084]

[0085] S35: Scouting bee stage, count whether the number of explorations of each food source has reached the limit. If it exceeds, the employed bees exploring the current nectar source become scout bees, and randomly generate a new food source through the formula for initializing the population, and reset the number of explorations of the new food source. When the loop termination condition is met, the approximate optimal solution is output.

[0086] In the deployment method of the dam termite monitoring node based on the artificial bee colony algorithm described in the present invention, a square area of ​​100m×100m is selected for monitoring in the experiment, and the monitoring area is divided into grids. For the coordinates (ai, aj) of any point A in the monitoring area, 1≤i≤100, 1≤j≤100. The number of sensor nodes to be deployed is 40, and the monitoring radius of each sensor is 10. Assume that sensor pi is deployed at point (xi, yi);

[0087] Construct a coverage optimization model to maximize the network coverage in the monitoring area and establish an optimization objective function for monitoring node deployment;

[0088]

[0089] Among them, R represents the coverage rate of the monitoring area, and Pi represents the total number of grid points detected by the sensor in the monitoring area;

[0090] The coverage optimization model adopts a Boolean coverage model, and the coverage of the detection nodes to the grid points is shown in the formula;

[0091]

[0092] Among them, P i Indicates whether the grid point is covered by the monitoring node, d iis the Euclidean distance between the monitoring node and any point A in the monitoring area, and the calculation formula is as follows:

[0093]

[0094] S31: Initialize the population and set the relevant parameters as shown in the following table:

[0095]

[0096] Select 40 initial solutions according to the above initialization population formula;

[0097] S32: Calculate the fitness value of the initial nectar source, as shown in the formula;

[0098]

[0099] Among them, fit i is the fitness of the ith solution;

[0100] S33: The employed bee searches for a new solution in the neighborhood of the initial solution using the following formula and calculates its fitness, and performs greedy selection. If the new solution has better fitness, it replaces the optimal solution, otherwise it remains unchanged and the number of explorations increases by 1;

[0101] NE in，j =x in，j +rand(-1,1)(x in，j -x kn，j )

[0102] Among them, x in，j It is the old honey source of Sn in the monitoring area, NE in，j is a candidate solution generated by the neighborhood, x kn，j is a random nectar source, i≠k;

[0103] S33: The follower bee calculates the probability p of each nectar source being selected according to fitness through the roulette method (formula as follows) i After a follower bee chooses an employed bee, the follower bee tries to find a new solution near the nectar source of its leader bee and calculates its fitness, and makes a greedy choice. If the new solution found by the follower bee is better than the original solution of the employed bee, the new solution replaces the original solution; otherwise, the food source remains unchanged and the number of explorations increases by 1;

[0104]

[0105] S34: Scouting bee stage, count whether the number of explorations of each food source has reached the limit of 20. If it exceeds, the employed bees exploring the current nectar source become scout bees, and randomly generate a new food source through the formula for initializing the population, and reset the number of explorations of the new food source. When the loop termination condition is met, the output is the approximate optimal solution, which is the deployment plan for termite monitoring points in the dam area.

[0106] The above is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several changes and improvements can be made without departing from the overall concept of the present invention, which should also be regarded as the scope of protection of the present invention.

Claims

1. A method for deploying dam termite monitoring nodes based on artificial bee colony algorithm, characterized in that: The following steps are involved: S1: Construct a risk assessment index system for termite damage in dams, conduct a risk assessment of termite damage in dam areas, and determine the key monitoring areas for termites in dams based on the assessment results. n , The dam termite hazard risk assessment index system is constructed from three levels as the criterion layer, namely, dam vulnerability index, termite hazard source index, and hazard object index, with the dam termite hazard risk assessment as the main objective layer; S2: According to the area to be monitored S n , build a coverage optimization model, and define the coverage optimization problem as the monitoring node deployment problem that maximizes the network coverage in the monitoring area; S3: The improved artificial bee colony algorithm is used to solve the coverage optimization problem and obtain an approximate optimal solution to determine the termite monitoring node deployment plan.

2. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 1 is characterized in that: In S1, based on the above index system, the dam termite hazard risk is comprehensively evaluated by the hierarchical analysis-fuzzy comprehensive evaluation method, and the key monitoring area S of the dam is determined according to the evaluation results. n .

3. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 2 is characterized in that: The hierarchical analysis-fuzzy comprehensive evaluation method is to determine the index weights through the hierarchical analysis method, calculate the scores through the fuzzy comprehensive evaluation method, and determine the monitoring area S n .

4. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 3 is characterized in that: The analytic hierarchy process determines the indicator weights. Based on the 1-9 degree scale, a number of experts in related professional fields are invited to score each indicator under the three criterion layers to construct the criterion layer B. n For target layer A and indicator layer C n Alignment level B n The judgment matrix is ​​normalized to obtain the eigenvector W i Determine the weight of each indicator; The maximum eigenvalue is obtained by The formula is used to obtain CI, and the average random consistency index value table is used to obtain R I The random consistency test is performed according to the following formula to determine the comprehensive weight of each evaluation index as follows: Where λmax is the maximum eigenvalue; Wi is the weight of the i-th indicator in the weight vector obtained after normalization; n is the order of the judgment matrix; Bw represents the product of the judgment matrix B and the weight vector W; where CI is the consistency index and RI is the random consistency index.

5. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 4 is characterized in that: The fuzzy comprehensive evaluation method score calculation first establishes a fuzzy comprehensive evaluation set to divide the comprehensive evaluation results of the dam termite damage risk into 5 levels, each level corresponds to a different evaluation value, and the comprehensive evaluation set of indicators is: V = {V1, V2, V3, V4, V5} = {no harm, minor harm, moderate harm, major harm, significant harm}; the score set corresponding to the evaluation set is expressed as: U={U1, U2, U3, U4, U5}={50, 60, 70, 80, 90}; Then the index membership is determined: the weight values ​​of each index determined by the above-mentioned analytic hierarchy process are evaluated on the basis of the research on fuzzy comprehensive evaluation method by inviting the above-mentioned experts to use the grade weight method to give a no-hazard to significant-hazard evaluation of the impact of each index on the risk of termite damage to the dam. The membership is solved according to the evaluation, and the membership evaluation matrix R is constructed. i ; Where: membership degree = number of experts in selecting evaluation results for a certain indicator / total number of experts; The indicator weight set W i And the membership evaluation matrix R i The index evaluation level is obtained by multiplication, and the comprehensive evaluation score of termite damage risk is calculated. The area with a score higher than 60 is the key monitoring area for dam termites S n .

6. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 5 is characterized in that: The S2 area to be monitored S n First, the n monitoring areas are divided into grids, and each area is discretized into M n ×N n monitoring points, for the monitoring area S n The coordinates of any point A (a in , a jn ), where 1≤i n ≤M n , 1≤j n ≤N n ; The number of sensor nodes to be deployed is D n , D n is a positive integer, and the monitoring radius of each sensor is r; assuming that sensor p in Deployed at point (x in ,y in ), the set of sensors is P n ={p 1n , p 2n ,···,p in }; then build a coverage optimization model to maximize the network coverage in the monitoring area and establish the monitoring node deployment optimization objective function: Where R(n) represents the monitoring area S n The coverage rate, P in Indicates monitoring area S n The total number of grid points detected by the sensor.

7. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 4 is characterized in that: The coverage optimization model adopts a Boolean coverage model, and the coverage of the detection nodes to the grid points is shown in the formula: Among them, P in Indicates whether the grid point is covered by the monitoring node, d in is the Euclidean distance between the monitoring node and any point A in the monitoring area, and the calculation formula is as follows:

8. The method for deploying dam termite monitoring nodes based on artificial bee colony algorithm according to claim 7 is characterized in that: The S3 includes: S31: Initialize the population and set relevant parameters, such as the number of nectar sources SN, dimension D, maximum number of iterations G, and control parameter limit, i.e., the number of times the same nectar source is mined; The formula for initializing the population is as follows: x in，j =x min(n)，j +rand(0,1)(x max(n)，j -x min(n)，j ) (5) In the formula, x max(n) , j and x min(n) , j They are respectively monitoring areas S n The upper and lower limits of the boundary, Rand(0,1) is a random number uniformly distributed on the interval [0,1]; S32: Calculate the fitness value of the initial nectar source, as shown in the formula: Among them, fit in is the monitoring area S n The fitness of the i-th solution, f i is the objective function of the optimization problem, f i ≥0. S33: The employed bee searches for a new solution in the neighborhood of the initial solution using the following formula and calculates its fitness, and performs greedy selection. If the new solution has better fitness, it replaces the optimal solution, otherwise it remains unchanged and the number of explorations increases by 1; NE in，j =x in，j +rand(-1,1)(x in，j -x kn，j ) (7) Among them, x in，j It is the old honey source of Sn in the monitoring area, NE in，j is a candidate solution generated by the neighborhood, x kn，j is a random honey source, i≠k, Rand(-1,1) is a random number uniformly distributed in the interval [-1,1]; S34: The follower bee calculates the probability p of each nectar source being selected according to fitness through the roulette method in After a follower bee chooses an employed bee, the follower bee tries to find a new solution near the nectar source of its leader bee and calculates its fitness, and makes a greedy choice. If the new solution found by the follower bee is better than the original solution of the employed bee, the new solution replaces the original solution; otherwise, the food source remains unchanged and the number of explorations increases by 1; S35: Scouting bee stage, count whether the number of explorations of each food source has reached the limit. If it exceeds, the employed bees exploring the current nectar source become scout bees, and randomly generate a new food source through the formula for initializing the population, and reset the number of explorations of the new food source. When the loop termination condition is met, the approximate optimal solution is output.

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