CN110717507B - Soil moisture content sensor optimization layout method based on APDJ algorithm - Google Patents
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Abstract
The invention discloses an optimized layout method of soil moisture content sensors based on an APDJ algorithm, provides an improved Dijkstra algorithm (APDJ algorithm), and develops application of the optimized layout of the soil moisture content sensors. On the basis of ensuring the full coverage of a sensing network, soil moisture content data of each sensing node is collected in real time, an AP clustering algorithm is used for obtaining a clustering center, the clustering center is used as a starting point of a Dijkstra algorithm for searching a shortest path, a convergence point of a wireless sensing network is used as an end point, an improved Dijkstra algorithm is used for searching an optimal path with the maximum data dissimilarity and the shortest distance as targets, and finally an optimal point distribution path consisting of 6 sensor nodes is obtained.
Description
Technical Field
The invention relates to the technical field of optimal layout of soil moisture sensors, in particular to optimal layout of soil moisture sensors by adopting clustering and Dijkstra algorithm.
Background
The agricultural water-saving irrigation system collects soil moisture content information through the sensors distributed in the farmland, and reasonable sensor selection and optimized layout play an important role in accurate acquisition of soil moisture content. With respect to the problem of optimal layout of sensors, a great deal of research is carried out on the aspects of set coverage, multi-objective combination optimization and the like, and the method is widely applied to multiple fields. Considering the fault detection capability of the sensor, zhu xi Hua et al establishes a sensor layout optimization model, and realizes the layout optimization of the sensor by using the improved discrete particle swarm algorithm. Yang sail and the like research the optimal distribution of the sensors and the algorithm thereof under the condition of limited resources, establish a probability SDG model and apply the probability SDG model to a boiler system. Liulimna et al have studied the problem of sensor node distribution with the greatest coverage efficiency of wireless sensor networks and have proposed a nested grid coverage method that adapts to physical quantity distribution. At present, researches on the problem of optimizing layout of farmland soil moisture content sensors are rare. Wu Zhengyu et al combine the genetic algorithm with the weighted round set layout theory, and obtain good effect through the optimization calculation of the soil moisture content sensor under the constraint conditions of considering the coverage accuracy of the sensor, the overlap limit and the like.
At present, people focus on a stationing model and a coverage algorithm for sensor layout research, and optimization research is developed from the aspects of base station quantity, network connectivity, minimum coverage in an environment with obstacles and the like through different algorithm models. However, relevant researches on the redundancy of soil moisture content data and a sensor distribution path are still lacked. In the optimization layout process of the farmland soil moisture content sensor, not only the energy loss, the coverage precision, the signal completeness and the like of a system are considered, but also the problems of similarity and difference of soil moisture content data are considered, so that the layout of the sensor is reasonably optimized with the aims of reducing the redundancy of the data and the input cost of the sensor.
Disclosure of Invention
The invention aims to make up for the defects of the prior art, combine AP clustering with a Dijkstra algorithm, provide an improved Dijkstra algorithm and apply the Dijkstra algorithm to the optimized layout of soil moisture sensors, and the purpose of optimizing the layout of the sensors is to reduce the redundancy of data and the system cost.
The invention is realized by the following technical scheme:
the method comprises the following steps of optimizing layout of soil moisture content sensors by using AP clustering and Dijkstra algorithm:
(1) Firstly, collecting soil moisture content data: a plurality of soil moisture content sensors are arranged in an evenly distributed mode;
(2) And clustering soil moisture content data: collecting soil moisture content data on the basis of full coverage of a sensing network, and clustering the collected data by using an AP (access point) clustering algorithm to obtain a clustering center;
(3) Constructing an adjacency matrix of the Dijkstra algorithm: determining an edge weight value by the weighted combination of the dissimilarity value of the soil moisture content data and the network node path value, and constructing an adjacency matrix of the Dijkstra algorithm;
(4) And searching the optimal path by the improved Dijkstra algorithm: and (3) searching the optimal path of sensor point distribution by using the Dijkstra algorithm with the AP clustering center in the step (2) as a starting point, the convergent point of the wireless sensor network as an end point and the maximum data dissimilarity and the shortest distance as targets.
The method comprises the following steps that in the step (1), a plurality of soil moisture content sensors are arranged in an evenly distributed mode, and the method specifically comprises the following steps: 5 sensor detection points are uniformly arranged in the transverse direction and the longitudinal direction respectively, 25 detection points are arranged in total, and each detection point acquires the mass water content parameter of soil 25cm below the surface of the soil.
And (3) the AP clustering algorithm in the step (2) realizes clustering by calculating a similarity matrix of the water content of the soil nodes to obtain a clustering center. The method comprises the following specific steps:
suppose { m } 1 ,m 2 ,...,m n A data sample set, the clustering algorithm uses negative euclidean distance to represent the similarity between n data points, and the similarity is recorded as:
si,j＝-||m i -m j || 2 ,i≠j (1)
i. j represents the ith and jth data points in the data sample set respectively, n data points form an n multiplied by n similarity matrix S, and the clustering center is formed by the similarity matrix S = [ si, j =] n×n And determining the numerical value of an element s (k, k) on the diagonal, wherein the s (k, k) is called a bias parameter p, the size of the p value influences the clustering result, the larger the p is, the more the clustering number is, and the smaller the p value is, the less the clustering number is.
Constructing an adjacency matrix of the Dijkstra algorithm in the step (3), which specifically comprises the following steps:
1) Calculating the dissimilarity degree of soil moisture content of the sensor nodes:
and (3) calculating the sum of the dissimilarity values between the data of each sensor node and the data of the rest other nodes, and reflecting the dissimilarity degree of soil moisture content of each node in the whole test area from the global perspective, wherein the dissimilarity degree calculation formula is shown as a formula (2).
Wherein n is the number of sensing nodes, r i And r j Representing the soil mass water content, R, of the ith and jth sensor nodes i The larger the value is, the larger the difference between the soil moisture content of the point and the soil moisture content of the whole test area is.
2) Determining the weight value of the edge between the sensor nodes:
obtaining the dissimilarity value of each sensor node according to the formula (2) and calculating T ij ，T ij Is equal to the sum of the reciprocal of the dissimilarity value of the sensor node i and the reciprocal of the dissimilarity value of the sensor node j; d ij Denotes the planar distance, x, between 2 sensor nodes i ,y i Representing plane coordinates. Edge weight W between two sensor nodes ij From T ij And D ij And (4) obtaining the weighted combination. The calculation formula is (3), (4) and (5).
W ij ＝αT ij +βD ij (i,j＝1,2,...,n) (5)
Alpha and beta are weight factors, which represent T in the edge weight value W ij And D ij The proportion of the components is calculated; t is a unit of ij Equal to the sum of reciprocal of dissimilarity value of two nodes, the bigger the dissimilarity value of the two nodes is, T is ij The smaller; d ij Representing the planar distance between two adjacent nodes.
3) Constructing an adjacent matrix:
and (3) calculating the edge weights of 25 sensor nodes, wherein the edge weight formed by two adjacent sensors is calculated by a formula (5), the edge weight between two non-adjacent nodes is set to be infinite, which indicates that the two nodes cannot be reached, and finally the edge weight formed by 25 sensor nodes is stored by using an adjacency matrix.
Searching the best path by the improved Dijkstra algorithm in the step (4): the method comprises the following steps of taking an AP clustering center as a starting point, a convergence point of a wireless sensor network as an end point, and a Dijkstra algorithm to search an optimal path for sensor point distribution by taking the maximum data dissimilarity and the shortest distance as targets, wherein the optimal path is as follows:
applying the adjacency matrix calculated in the step (3) to a Dijkstra algorithm, and taking F as the starting point and the end point determined in the step (2) min (r i ,r j ,x i ,y i ,x j ,y j )＝αT ij (r i ,r j )+βD ij (x i ,y i ,x j ,y j ) And searching a path for the objective function to finally obtain the optimal path.
On the basis of researching the layout problem of the soil moisture sensors for water-saving irrigation, the invention combines an Affinity Propagation Clustering (AP Clustering for short) algorithm with a Dijkstra algorithm, provides an improved Dijkstra algorithm (APDJ algorithm) and applies the improved Dijkstra algorithm to the optimized layout of the soil moisture sensors. The method comprises the steps of arranging soil moisture content sensors on the basis of full coverage of a wireless sensor network, carrying out AP clustering on soil moisture content data of all nodes, determining edge weights by taking a clustering center as a starting point and a wireless sensor network convergence point as an end point and taking weighted combination of network node path values and data dissimilarity values, constructing an adjacency matrix, searching an optimal path for soil moisture content data transmission by using a Dijkstra algorithm, optimizing sensor layout, reducing the number of sensors and saving cost.
The invention has the advantages that:
(1) In the past, researches on the layout of farmland soil moisture sensors mainly focus on the aspect of optimization algorithms of network coverage rate, and related researches on redundancy problems of soil moisture data are lacked, wherein a neighbor Propagation Clustering (AP Clustering for short) algorithm is combined with a Dijkstra algorithm, and an improved Dijkstra algorithm is provided and applied to the optimization layout of the soil moisture sensors. Arranging soil moisture content sensors on the basis of full coverage of a wireless sensor network, carrying out AP clustering on soil moisture content data of each node, and taking a clustering center as a starting point and a wireless sensor network convergence point as an end point; determining edge weight values by weighted combination of network node path values and data dissimilarity values, constructing an adjacency matrix, searching an optimal path for soil moisture content data transmission by using a Dijkstra algorithm, optimizing sensor layout, reducing data redundancy and sensor quantity, and saving cost.
(2) The clustering algorithm used in the method is a deterministic algorithm, clustering results which are independently operated for multiple times are generally stable, the number and the arrangement positions of the obtained sensors are the same after the independent operation for multiple times, the reliability and the stability are realized, and the Dijkstra algorithm is simple and is convenient to realize.
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FIG. 1 is a schematic diagram of the implementation of the method of the present invention.
FIG. 2 is a distribution plot of experimental data acquisition points for the method of the invention.
FIG. 3 is a plot of the spatial distribution of field capacity for the experimental area of the method of the present invention.
Fig. 4 is a diagram of the AP clustering result of the method of the present invention.
Detailed Description
As shown in fig. 1 and 2, firstly, on the basis of ensuring the full coverage of the sensing network, collecting soil moisture content data of each node in a test area in real time, clustering the collected data by using an AP clustering algorithm to obtain a clustering center, secondly, determining an edge weight value by using a weighted combination of a dissimilarity value of the soil moisture content data and a network node path value, and constructing an adjacency matrix of a Dijkstra algorithm; and finally, with the class center as a starting point, a convergence point of the wireless sensor network as an end point, and with the maximum data dissimilarity and the shortest distance as targets, searching for an optimal path by using a Dijkstra algorithm, and arranging the soil moisture content data acquired by the sensors on the path to achieve low redundancy, so that the system cost is saved, and the problem of optimal layout of the sensors is well solved. The method specifically comprises the following steps:
step (1), collecting soil moisture content data, wherein the area of a test site is about 16000m 2 About 190m in the transverse direction and about 85m in the longitudinal direction. The sensors are uniformly distributed. In order to ensure the full coverage of the sensing network of the test area, 5 sensor detection points are uniformly arranged in the transverse direction, 5 sensor detection points are also uniformly arranged in the longitudinal direction, and 25 detection points are arranged in total. The mass water content of the soil 25cm below the soil surface was collected at each test point.
Step (2), clustering soil moisture content data: and collecting soil moisture content data on the basis of full coverage of the sensing network, and clustering the collected data by using an AP (access point) clustering algorithm to obtain a clustering center.
Step (3), constructing an adjacency matrix of the Dijkstra algorithm: and determining an edge weight value by the weighted combination of the dissimilarity value of the soil moisture content data and the network node path value, and constructing an adjacency matrix of the Dijkstra algorithm. The specific process is as follows:
(1) Calculating the dissimilarity degree of soil moisture content of the sensor nodes:
and (3) calculating the sum of the dissimilarity values between the data of each sensor node and the data of the rest other nodes, and reflecting the dissimilarity degree of soil moisture content of each node in the whole test area from the global perspective, wherein the dissimilarity degree calculation formula is shown as a formula (2).
Wherein n is the number of sensing nodes, r i And r j Representing the soil mass water content, R, of the ith and jth sensor nodes i The larger the value is, the larger the difference between the soil moisture content of the point and the soil moisture content of the whole test area is.
(2) Determining the weight value of the edge between the sensor nodes:
the AP clustering and Dijkstra algorithm soil moisture sensor optimization layout method according to claim 4, wherein the dissimilarity value of each sensing node is obtained and T is calculated according to formula (2) in step 1 ij ，T ij Is equal to the sum of the reciprocal of the dissimilarity value of the sensor node i and the reciprocal of the dissimilarity value of the sensor node j; d ij Represents the planar distance between two sensor nodes, (x) i ,y i ) Representing plane coordinates. Edge weight W between two sensor nodes ij From T ij And D ij And obtaining the result by weighted combination. The calculation formula is (3), (4) and (5).
W ij ＝αT ij +βD ij (i,j＝1,2,...,n) (5)
Alpha and beta are weight factors, which represent T in the edge weight value W ij And D ij The proportion of the active ingredients; t is ij Equal to the sum of reciprocal of dissimilarity value of two nodes, the greater the dissimilarity of two nodes, the greater T ij The smaller; d ij Representing the planar distance between two adjacent nodes.
(3) Constructing an adjacent matrix:
and (3) calculating the edge weight values of 25 sensor nodes, wherein the edge weight value formed by two adjacent sensors is calculated by formula (5), the edge weight value between the non-adjacent nodes is set to be infinite, which indicates that the two nodes cannot be reached, and finally the edge weight values formed by 25 sensor nodes are stored by using an adjacency matrix.
Step (4), searching an optimal path by using an improved Dijkstra algorithm: and (3) searching the optimal path of sensor point distribution by using the AP clustering center in the step (2) as a starting point, the convergent point of the wireless sensor network as an end point and the maximum data dissimilarity and the shortest distance as targets and by using a Dijkstra algorithm.
The method comprises the following specific implementation steps:
the method comprises the following steps: test environment and data acquisition
The area of the test site is about 16000m 2 The transverse direction is about 190m, and the longitudinal direction is about 85m. The sensors are uniformly distributed. In order to ensure the full coverage of the test area sensor network, 5 sensor detection points are transversely and uniformly arranged, 5 sensor detection points are also longitudinally and uniformly arranged, and 25 detection points are arranged in total. The distribution of the detection points of the sensors is shown in fig. 2. And each detection point acquires the mass water content parameter of the soil 25cm below the surface of the soil.
Step two: and (4) according to the acquired real-time data, making the spatial distribution of the soil mass and water content by using a Kiring algorithm.
Step three: AP clustering algorithm determination starting point
The AP clustering algorithm is a semi-supervised clustering algorithm, and the number of classes does not need to be specified in advance. The algorithm is to look each data as a node in the graph, iteratively update according to the similarity between the nodes to find a clustering center, and the similarity of two data points is calculated by using the negative number of the distance, namely the closer the distance is, the greater the similarity is. The similarity is calculated according to the formula (1).
s(i,j)＝-||r i -r j || 2 ,i≠j (1)
r i And r j The soil mass water content of the ith sensor node and the jth sensor node respectively. And taking the AP clustering center as a starting point of a Dijkstra algorithm searching path, and determining a convergent point of the wireless sensor network as an end point of the searching path.
Step four: calculating the dissimilarity degree of the soil moisture content of the sensor nodes
And (3) calculating the sum of the dissimilarity values between the data of each sensor node and the data of the rest other nodes, and reflecting the dissimilarity degree of soil moisture content of each node in the whole test area from the global perspective, wherein the dissimilarity degree calculation formula is shown as a formula (2).
Wherein n is the number of sensing nodes, r i And r j Representing the soil mass water content, R, of the ith and jth sensor nodes i The larger the value is, the larger the difference between the soil moisture content of the point and the soil moisture content of the whole test area is.
Step five: determining weight values for edges between sensor nodes
Calculating T according to the dissimilarity value of each sensing node ij ，T ij Is equal to the sum of the reciprocal of the dissimilarity value of the sensor node i and the reciprocal of the dissimilarity value of the sensor node j; d ij Represents the planar distance between two sensor nodes, (x) i ,y i ) Representing plane coordinates. Edge weight W between two sensor nodes ij From T ij And D ij And obtaining the result by weighted combination. The calculation formula is (3), (4) and (5).
W ij ＝αT ij +βD ij (i,j＝1,2,...,n) (5)
Alpha and beta are weight factors, and represent T in the edge weight W ij And D ij The proportion of the active ingredients; t is a unit of ij Equal to the sum of reciprocal of dissimilarity value of two nodes, the greater the dissimilarity of two nodes, the greater T ij The smaller; d ij Indicating the planar distance between two adjacent nodes.
Step six: constructing a contiguous matrix
And (3) calculating the edge weight values of 25 sensor nodes, wherein the edge weight value formed by two adjacent sensors is calculated by formula (5), and the edge weight value between non-adjacent nodes is set to be infinite, which indicates that the two nodes cannot be reached. And finally, storing the edge weight value formed by 25 sensor nodes by using an adjacency matrix.
Step seven: searching shortest path using Dijkstra algorithm
Applying the adjacency matrix calculated in the step six to a Dijkstra algorithm, and taking F as the reference value according to the determined starting point and the determined end point min (r i ,r j ,x i ,y i ,x j ,y j )＝αT ij (r i ,r j )+βD ij (x i ,y i ,x j ,y j ) And searching a path for the objective function to finally obtain the optimal path.
Path optimization
On an MATLAB platform, firstly using an AP clustering algorithm to obtain a clustering center, secondly, determining an edge weight value by the weighted combination of the dissimilarity value of soil moisture content data and a network node path value, and constructing an adjacency matrix of a Dijkstra algorithm; and finally, searching the optimal path by using the Dijkstra algorithm with the class center as a starting point, the convergent point of the wireless sensor network as an end point and the maximum data dissimilarity and the shortest distance as targets.
Analysis of Experimental results
The spatial distribution of the soil moisture obtained from the data collected in real time is shown in fig. 3, fig. 4 shows the AP clustering result of the soil moisture data, which is clustered into two classes, with the clustering centers being sensor nodes S13 and S14, which serve as the starting points of Dijkstra algorithm search paths.
TABLE 1 Dijkstra Algorithm Path Total cost s
Table 1 shows the test results of data acquisition 6 times in two days, and it can be known from the table that the number of AP clustering centers of 6 times is 2, i.e., S13 and S14 sensing nodes, which indicates that the test data has good consistency. And (3) taking the AP clustering center as a starting point and the convergent point S1 as an end point, and performing path search by using the Dijkstra algorithm improved by the text to obtain 6 different path results. The results of 6 tests can realize reliable data transmission only through 6 sensor nodes, namely after path optimization, the number of the sensors can be reduced from 25 to 6, and the system cost is effectively reduced.
In order to obtain the optimal path with the maximum data dissimilarity and the shortest distance, the average dissimilarity and the total path cost are used for carrying out quantitative evaluation on the test result. As can be seen from table 1, the path calculated by test No. 1 is S14-S13-S12-S11-S6-S1, the total cost of the path is 0.3613, which is the smallest among the 6 test results, and it indicates that the path is the shortest distance path, and the average dissimilarity degree of the path is 25.0652, which is the largest among the 6 test results, and the difference of data of each sensing node in the path is the largest, and the redundancy of data is the lowest.
In addition, the path calculated by the test No. 5 is S14-S13-S12-S7-S2-S1, the total path cost is 0.4274, the maximum path is the maximum path among 6 test results, the average dissimilarity degree is 20.9017, the minimum path is the minimum path among 6 test results, and the path is the longest path, the data dissimilarity degree of each sensing node is the minimum path, and the data redundancy is high.
The total cost of the paths for trials nos. 2, 3, 4 and 6 and the average dissimilarity value are between the two paths for trials nos. 1 and 5. Therefore, the results of 6 tests are compared, and the path S14-S13-S12-S11-S6-S1 calculated by the test No. 1 is the best path.
The soil temperature and humidity sensor has the model of SWR-100W, SWR-100W, and integrates the soil moisture and soil temperature sensor, thereby facilitating the measurement and research of soil moisture content and soil temperature, and having the advantages of convenient carrying, sealing, high precision and the like.
Claims (5)
1. An optimized layout method of soil moisture content sensors based on an APDJ algorithm is characterized by comprising the following steps: the method comprises the following specific steps:
(1) Firstly, collecting soil moisture content data: arranging a plurality of soil moisture content sensors in an evenly distributed mode;
(2) And clustering soil moisture content data: collecting soil moisture content data through a plurality of soil moisture content sensors on the basis of full coverage of a sensing network, and clustering the collected data by using an AP (access point) clustering algorithm to obtain a clustering center;
(3) Constructing an adjacency matrix of the Dijkstra algorithm: determining an edge weight value by the weighted combination of the dissimilarity value of the soil moisture content data and the network node path value, and constructing an adjacency matrix of the Dijkstra algorithm;
(4) And searching the optimal path by the improved Dijkstra algorithm: and (3) searching the optimal path of the soil moisture sensor distribution point by using the Dijkstra algorithm with the AP clustering center obtained in the step (2) as a starting point, the convergence point of the wireless sensor network as an end point and the maximum data dissimilarity and the shortest distance as targets.
2. The APDJ algorithm-based soil moisture content sensor optimization layout method as claimed in claim 1, characterized in that: the method comprises the following steps that in the step (1), a plurality of soil moisture content sensors are arranged in an evenly distributed mode, and the method specifically comprises the following steps: 5 sensor detection points are uniformly arranged in the transverse direction and the longitudinal direction respectively, 25 detection points are arranged in total, and each detection point acquires the mass water content parameter of soil 25cm below the surface of the soil.
3. The method for optimizing the layout of the soil moisture sensors based on the APDJ algorithm as claimed in claim 2, wherein: the step (2) is to realize clustering by calculating a similarity matrix of the water content of the soil nodes to obtain a clustering center, which comprises the following steps:
suppose { m } 1 ，m 2 ，...，m n A data sample set, the clustering algorithm uses negative euclidean distance to represent the similarity between n data points, and the similarity is recorded as:
s(i，j)＝-||m i -m j || 2 ，i≠j(1)
i. j respectively represents the ith and jth data points in the data sample set, n data points form an n multiplied by n similarity matrix S, and the clustering center is formed by the similarity matrix S = [ S (i, j)] n×n The value of the element s (k, k) on the diagonal determines, s (k,k) The value of p is called as a deviation parameter p, the clustering result is influenced by the value of p, the larger the p is, the more the clustering number is, and the smaller the p is, the less the clustering number is.
4. The APDJ algorithm-based soil moisture content sensor optimization layout method according to claim 3, characterized in that: determining an edge weight value by the weighted combination of the dissimilarity value of the soil moisture content data and the network node path value, and constructing an adjacency matrix of the Dijkstra algorithm, wherein the adjacency matrix comprises the following specific steps:
1) Calculating the dissimilarity degree of soil moisture content of the sensor nodes:
calculating the sum of dissimilarity values between the data of each sensor node and the data of the rest other nodes, reflecting the dissimilarity degree of soil moisture content of each node in the whole test area from the global perspective, wherein the dissimilarity degree calculation formula is shown as a formula (2):
wherein n is the number of sensing nodes, r i And r j Representing the soil mass water content, R, of the ith and jth sensor nodes i The larger the value is, the larger the difference between the soil moisture content of the point and the soil moisture content of the whole test area is;
2) Determining the weight value of the edge between the sensor nodes:
obtaining the dissimilarity value of each sensing node according to the formula (2) and calculating T ij ，T ij Is equal to the sum of the reciprocal of the dissimilarity value of the sensor node i and the reciprocal of the dissimilarity value of the sensor node j; d ij Represents the planar distance between two sensor nodes, (x) i ，y i ) Representing plane coordinates, the edge weight W between two sensor nodes ij From T ij And D ij The weighted combination is obtained, and the calculation formulas are (3), (4) and (5):
W ij ＝αT ij +βD ij (i，j＝1，2，...，n) (5)
alpha and beta are weight factors, which represent T in the edge weight value W ij And D ij The proportion of the active ingredients; t is a unit of ij Equal to the sum of reciprocal of dissimilarity value of two nodes, the bigger the dissimilarity value of the two nodes is, T is ij The smaller; d ij Representing the plane distance between two adjacent nodes;
3) Constructing an adjacent matrix:
and (3) calculating the edge weight values of 25 sensor nodes, wherein the edge weight value formed by two adjacent sensors is calculated by formula (5), the edge weight value between the non-adjacent nodes is set to be infinite, which indicates that the two nodes cannot be reached, and finally the edge weight values formed by 25 sensor nodes are stored by using an adjacency matrix.
5. The APDJ algorithm-based soil moisture content sensor optimization layout method according to claim 4, characterized in that: searching an optimal path by the improved Dijkstra algorithm in the step (4), wherein the optimal path is as follows: applying the adjacency matrix calculated in the step (3) to a Dijkstra algorithm, and taking F as the reference value according to the starting point and the end point determined in the step (2) min (r i ，r j ，x i ，y i ，x j ，y j )＝αT ij (r i ，r j )+βD ij (x i ，y i ，x j ，y j ) And searching a path for the target function to obtain the optimal path.
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